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Report No. __ - GM
THE GAMBIA
Poverty Reduction Challenges and Opportunities
Poverty Assessment
May 29, 2009 PREM 4 Africa Region
Document of the World Bank
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TABLE OF CONTENTS
EXECUTIVE SUMMARY ............................................................................................ vii
1. POVERTY PROFILE ............................................................................................... 1
Country Background ....................................................................................................... 1
Poverty Profile ................................................................................................................ 2
Consumption Aggregates ............................................................................................ 3
Consumption-Based Poverty ...................................................................................... 9
Household Size and Composition ............................................................................. 14
Female Headed Households ...................................................................................... 14
Age Groups ............................................................................................................... 16
Education Levels ....................................................................................................... 17
Employment Status and Sector ................................................................................. 18
PPP Based Poverty Estimates ................................................................................... 20
Comparability of the 1998 and 2003 Household Surveys ........................................ 23
Qualitative Poverty Indicators ...................................................................................... 24
Conclusions ................................................................................................................... 26
2. HEALTH AND EDUCATION ............................................................................... 28
Health Sector ................................................................................................................. 28
Education Sector ........................................................................................................... 31
Conclusions ................................................................................................................... 34
3. GROWTH AND POVERTY DYNAMICS ........................................................... 35
Remittances ................................................................................................................... 42
Rural to Urban and Intersectoral Migration .................................................................. 44
Conclusions ................................................................................................................... 48
4. REGIONAL POVERTY VARIATIONS .............................................................. 50
Objective and Methodology of the Poverty Map .......................................................... 50
Reliability of the Poverty Map Estimates ..................................................................... 52
Results from the Poverty Map ...................................................................................... 54
Regional Dimensions of Poverty .................................................................................. 55
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Conclusions ................................................................................................................... 58
5. AGRICULTURE ..................................................................................................... 60
Food Security ............................................................................................................... 63
Crop Prices .................................................................................................................... 64
Crop Production Characteristics and Constraints ......................................................... 68
Determinants of Crop Production and Productivity.................................................. 70
Livestock and Horticulture ........................................................................................... 72
Nonfarm Income ........................................................................................................... 73
Groundnut Subsector .................................................................................................... 75
Groundnut Sector Marketing Arrangement .............................................................. 76
Impact of Producer Price and Fertilizer Subsidy ...................................................... 79
Conclusions ................................................................................................................... 81
Annex 1: Consumption Regressions ............................................................................. 84
Annex 2: Estimation of Poverty Line ........................................................................... 85
Annex 3: Poverty Mapping Methodology .................................................................... 87
Annex 4: Poverty Map Indicators ................................................................................ 90
Annex 5: NASS Technical Notes ................................................................................... 93
Bibliography .................................................................................................................... 97
Figures
Figure 1: Frequency Distribution of Real Per Capita Consumption ................................... 6 Figure 2: Cross-Country Price Level Indices................................................................... 21 Figure 3: Percentage of Children Stunted by Mother's Education Level ........................ 28
Figure 4: Sectoral GDP Composition ............................................................................... 35 Figure 5: Real Sectoral Growth Rates ............................................................................. 37 Figure 6: Cumulative Inflation Rates, 2004-08 ............................................................... 40 Figure 7: Poverty Headcount Accuracy, by disaggregation (administrative) level .......... 54 Figure 8: Poverty Headcount versus Accuracy, by district ............................................... 54
Figure 9: District-level Poverty Headcount ...................................................................... 54
Figure 10: Agricultural Crop Production .......................................................................... 61 Figure 11: Agricultural Crop Yields ................................................................................ 61 Figure 12: Cereal Food Balance ...................................................................................... 63
Figure 13: Cereal Demand and Supply Gap .................................................................... 64 Figure 14: Retail Crop Prices (Jan 08 to Mar 09) ............................................................ 64 Figure 15: Rice, Thailand, 5% ......................................................................................... 65 Figure 16: International and Domestic Prices of Rice ...................................................... 65
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Figure 17: International Prices of Rice and Groundnuts ................................................. 66
Figure 18: Groundnut Production and Yields, 1989 – 2008 ............................................ 77
Tables
Table 1: Mean Per Capita Expenditure in Real Terms ....................................................... 4
Table 2: Inequality in Per Capita Expenditure Distribution by Urban and Rural Areas .... 5 Table 3: Decomposition of Inequality by Regions ............................................................. 7 Table 4: Poverty Line Estimates ....................................................................................... 11 Table 5: Overall Poverty Measures (2003) ....................................................................... 12 Table 6: Poverty by Regions ............................................................................................. 13
Table 7: Sensitivity of Poverty Rate to the Choice of Poverty Line ................................. 13 Table 8: Poverty by Demographic Composition, Upper Poverty Line ............................. 14 Table 9: Poverty by Household Head's Gender ................................................................ 14
Table 10: Economically Active Population by Industry and Sex ..................................... 15 Table 11: Females Headed Households and Remittances ............................................... 16 Table 12: Poverty by Age Groups, Upper Poverty Line ................................................... 16
Table 13: Poverty by Education Level of Household Heads ............................................ 17 Table 14: Poverty by Household Head's Status of Employment ...................................... 18 Table 15: Poverty by Household Head's Industry Sector of Employment ....................... 18
Table 16: Average Annual Earnings by Industry, 2006 ................................................... 19 Table 17: Changes in the Probability of Being in Poverty ............................................... 20
Table 18: PPP Conversion Factors and Price Level Indices (2005) ................................. 22 Table 19: International PPP Poverty Headcount Ratios ................................................... 22 Table 20: Share of Births Attended by Skilled Health Staffs .......................................... 30
Table 21: Gross Enrollment Rates ................................................................................... 31 Table 22: Net Enrollment Rates, 2005/6.......................................................................... 31
Table 23: Benefit Incidence Analysis of Government Education Spending .................... 32 Table 24: Real Sectoral Growth Rates ............................................................................. 36
Table 25: Sectoral Composition of Employment (2003) ................................................. 38 Table 26: Intra-Sectoral and Rural-Urban Migration ....................................................... 39
Table 27: Real Sectoral Avg Per Capita Growth Rates, 2004-08 ..................................... 39 Table 28: Poverty Rates Basic Simulation Results .......................................................... 41 Table 29: Remittances, 2003-9 ........................................................................................ 42 Table 30: Sectoral and Urban/Rural Shares of the Working Population, 1993 - 03 ........ 44
Table 31: 2009 Projected Mean Consumption and Population Growth .......................... 46 Table 32: Descriptive Statistics on the Gambia Administrative Structure ....................... 52 Table 33: Poverty Rates based on IHS and Census 2003, by strata ................................. 53 Table 34: Poverty and Urbanization ................................................................................. 56
Table 35: Mean Distance to Education and Health Facilities .......................................... 56 Table 36: Geographical Distribution of School Facilities ............................................... 57 Table 37: Enrolment Ratios by LGAs ............................................................................. 58
Table 38: Sectoral GDP Shares......................................................................................... 60 Table 39: Groundnuts Producer Prices by Sales Channel (2006) .................................... 67 Table 40: Farm Size, Productivity and Inputs .................................................................. 68
Table 41: Estimated Determinants of the Value of Crop Production .............................. 70
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Table 42: Livestock Production ....................................................................................... 72
Table 43: Rural Households Average Nonfarm Incomes (2006) ..................................... 74 Table 44: Comparison of Nonfarm Income and Groundnuts Sold (2006) ...................... 74 Table 45: Groundnuts Farm Budget per Hectare (2008) ................................................. 80
Boxes
Box 1: Generalized Entropy Measures .............................................................................. 7 Box 2: Foster-Greer-Thorbecke (FGT) Poverty Measures ................................................. 9
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Currency Equivalents Currency Unit = Dalasi (GMD)
US$1 = 26.4 GMD (as of March 29, 2009)
Fiscal Year
January 1 – December 31
ACRONYMS AND ABBREVIATIONS
AfDB African Development Bank ASPA Agribusiness Services and Producers Association CPMS Cooperative Produce Marketing Societies DFID UK Department for International Development DOSA Department of State for Agriculture DOSFEA Department of State for Finance and Economic Affairs EA Enumeration Area ECCD Early Childhood Care and Development EFA FTI Education for All Fast Track Initiative EU European Union FOA Framework of Agreement GBOS Gambia Bureau of Statistics GFATM Global Fund to Fight AIDS, TB and Malaria GER Gross Enrolment Rate GGC Gambia Groundnut Corporation GPMB Gambia Produce Marketing Board ICP International Comparison Program IHS Integrated Household Survey IMR Infant Mortality Rate LGA Local Government Authority MDG Millennium Development Goals MMR Maternal Mortality Ratio MT Metric Ton NAS National HIV/AIDS Secretariat NASS National Agricultural Sample Survey NARI National Agricultural Research Institute NPC National Planning Commission NER Net Enrolment Rate PHC Primary Health Care PPA Participatory Poverty Assessment PPP Purchasing Power Parity PRSP Poverty Reduction Strategy Paper SPACO Strategy for Poverty Alleviation Coordinating Office SSA Sub-Saharan Africa U5MR Under-5 Mortality Rate
Vice President: Obiageli K. Ezekwesili Country Director: Habib Fetini
Sector Manager: Antonella Bassani
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Task Team Leader: Hoon S. Soh
ACKNOWLEDGMENTS
The Poverty Assessment exercise was jointly conducted by the World Bank, the Gambia Bureau
of Statistics (GBOS) and the Department of State for Agriculture (DOSA). Core members of the
Bank team and their key responsibilities consisted of: Hoon S. Soh (Senior Economist, AFTP4)
who coordinated and managed the exercise; Christophe Muller (consultant, Professor at the
University of Cergy-Pontoise, France) on the poverty simulations; Quentin Wodon (Adviser,
HDNDE) and Harold Coulombe (consultant) on the Poverty Map; Madan Gopal Singh
(consultant) on the agriculture sector; and Mariam Khanna (consultant) on the basic poverty
profiles.
The team benefited from discussions with Michael Lokshin (Senior Economist, DECRG), the
guidance provided by the peer reviewers Markus Goldstein (Senior Economist, AFTPM),
Graham Eele (Senior Statistician, DECDG) and Delfin S. Go (Lead Economist, DECVP), and
additional comments provided by McDonald Benjamin (Country Program Coordinator, AFCSN),
Barbara Weber (Senior Operations Officer, AFCCSN), Meskerem Mulatu (Senior Education
Specialist, AFTH2), Nathalie Lahire (Education Economist, AFTH2), Yi-Kyoung Lee (Health
Specialist, AFTH2) and Manievel Sene (Rural Development Specialist, AFTAR). The funding
for Madan Gopal Singh’s work was provided by AFTAR, through a Trust Fund for
Environmentally and Socially Sustainable Development (TFESSD). Overall guidance to the
exercise was provided by Antonella Bassani (Sector Manager, AFTP4). Josette Percival
(Program Assistant, AFTP4) provided administrative assistance.
The core members of the government team consisted of Alieu Ndow (Statistician General,
GBOS), Abu Camara (Director of Coordination, Dissemination and Quality, GBOS), Philippe
Gafishi (Adviser, GBOS), Malanding Jaiteh (consultant, Center for International Earth Science
Information Network, Columbia University), Sekou Omar Toure (Director of Planning and
Information, Department of State for Health and Social Welfare), F. S. Fata Jo (Director of
Planning, DOSA) and Lamin Fata Jo (Principal Planning Officer, Department of Planning,
DOSA).
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EXECUTIVE SUMMARY
1. The Poverty Assessment report for The Gambia provides an overall analysis of the
country’s poverty profile and identifies the key challenges and opportunities for reducing poverty
in the country. The poverty profile is defined in terms of both monetary and non-monetary health
and education indicators. The monetary indicators of poverty are based on individual
consumption levels, estimated from the 2003 Integrated Household Survey (IHS). Health and
education indicators were analyzed using dedicated health surveys and government monitoring
of education spending and outcomes. Given that 2003 was the latest available household survey,
the current poverty rates for 2008 and 2009 were “projected” from the 2003 poverty profile
through simulation exercises which incorporated the impact of growth, remittances and internal
migration since 2003. Next, the report examines various dimensions of regional disparity in
poverty indicators. In particular, a “poverty map” is constructed which provides more
geographically disaggregated estimates of poverty rates in a statistically robust manner. Given
the importance of rural agriculture for the country and particularly for the poor, the final chapter
of the report is devoted to the agriculture sector. It analyzes the key constraints to improved crop
production and productivity, the impact of crop price fluctuations, the importance of nonfarm
income and government support to the groundnut sector, the country’s largest cash crop and
export.
POVERTY PROFILE
2. The poverty profile of The Gambia indicates that poverty is quite pervasive. Based on the
2003 IHS, the estimated poverty headcount ratios are 58.0 percent for the upper poverty line and
51.2 percent for the lower poverty line. The minimum nutritional requirements are satisfied in
the upper poverty line together with basic non-food consumption. They are not fully satisfied in
the lower poverty line. For this lower poverty line, nonfood consumption is defined as the
amount individuals consume by reducing food consumption.1 Whichever poverty line is used,
what is clear is that a sizable share of the population is considered poor.
3. Moreover, poverty varies greatly along multiple dimensions, including household size
and composition, age and gender of the household head, education levels, employment status and
sector and geographical location. Based on the upper poverty line, the estimated poverty
headcount ratios increase from 16 percent for a household size of three to 66 percent for seven or
larger. The poverty rate for female headed households is 41 percent, compared to 61 percent for
male headed households. Household heads with secondary schooling have an estimated poverty
rate of 36 percent while the poverty rate is 64 percent for those with no schooling. Nearly 60
1 The estimate of food consumption for the poverty line assumes 2,700 kcal for the minimum daily nutritional
requirement of a young adult male. The Poverty Assessment team also plans to re-estimate the poverty rates using
2,100 kcal for the caloric requirement, which is also a widely used standard, in order to assess the impact of
changing this assumption.
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percent of the poor are under the age of 20, resulting from a bottom-heavy population pyramid
due to relatively high birth rates and rapid population growth.
Table E-1: Basic Poverty Headcount Ratios, Upper Poverty Line
(Percent)
Headcount
Rate
Distribution of
the Poor
Distribution of
the Population
Total 58.0
Urban 39.6 23.7 34.7
Rural 67.8 76.3 65.3
LGA
Banjul 7.6 0.4 3.1
Kanifing 37.6 14.8 22.8
Brikama 56.7 28.5 29.2
Mansakonko 62.6 5.1 4.7
Kerewan 69.8 18.2 15.1
Kuntaur 94.9 8.8 5.4
Janjangbureh 75.7 10.9 8.4
Basse 68.0 13.2 11.3
Number of children 0-6 years old no children 35.3 11.2 18.4
1 39.1 13.4 19.8
2 58.4 20.2 20.0
3 or more children 76.7 55.3 41.8
Household size
1 7.2 0.1 0.7
2 11.2 0.2 1.1
3 15.6 0.8 2.9
4 25.3 1.7 3.9
5 35.6 3.6 5.8
6 40.5 4.9 7.0
7 or more 65.5 88.8 78.6
Gender of household head
Male 60.5 90.8 86.9
Female 40.7 9.2 13.1
Education level of household head None 63.5 82.5 75.3
Primary 47.2 3.3 4.0
Middle 45.0 0.8 1.0
Secondary/Voc 36.0 8.0 12.8
Tertiary 31.4 1.5 2.7
Sector of employment of household head Agriculture and fishing 76.4 63.0 51.9
Manufacturing and energy 50.0 3.4 7.5
Construction 63.6 10.5 4.0
Trade, hotels and restaurants 48.8 3.8 17.7
Transport and communication 52.4 3.4 5.0
Financial management 49.2 4.2 1.5
Social and personal service 45.4 7.0 12.4
Source: 2003 IHS and authors’ calculations.
4. One of the more interesting findings is that female headed households have lower poverty
rates compared to male headed households, contrary to previous findings, and it appears that this
largely reflects remittances. Even though female heads can be less poor than male heads, the
society still retains traditional gender roles in the labor market which can constrain female
economic opportunities. The estimated labor force participation rate for females is 46 percent
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compared to 53 percent for males, but female employment is concentrated in the agriculture
sector where earnings are generally lower than the other sectors. By contrast, males work in a
much wider range of occupations.
5. The above quantitative assessments of poverty are complemented by qualitative
instruments which provide more detailed and nuanced subjective information provided directly
by communities. In The Gambia, the government conducted a series of Participatory Poverty
Assessments (PPAs) from 1999 to 2004 and a Community Scorecard Survey in 2003. These
were participatory exercises through which communities analyzed their own economic and social
challenges and constraints to reducing poverty. PPA participants in rural areas emphasized the
challenges related to agricultural production, citing in particular credit buying of groundnuts as a
cause of poverty, while urban participants focused on the lack of employment and income
generating opportunities. Both rural and urban residents cited large family size and high
dependency rates as causes of poverty, raised concerns that girls scholarship programs
discouraged boys education and expressed strong dissatisfaction with the Primary Health Care
(PHC) facilities. The Community Scorecard survey focused exclusively on health and education
facilities. For health facilities, staff capacity and availability of essential equipment received low
ranking, the latter at least partially due to only 40 percent of facilities having regular supply of
electricity. For education facilities, schools in Kanifing were rated the worst in terms of ratio of
teachers to students and the availability of core text books and functioning toilets.
HEALTH AND EDUCATION
6. The country’s health indicators are relatively good when compared to SSA standards,
although it should be noted that SSA compares poorly with other regions. The most significant
improvements have been in child mortality indicators. Up to the 1980s, The Gambia had one of
the highest Infant Mortality Rates (IMR) and Under-5 Mortality Rates (U5MR) but now has
indicators (93 and 128 per 1,000 live births, respectively) almost on par with Ghana and Senegal,
the best performers in the region. However, improvements in these indicators have stagnated in
recent years and there are significant variations across regions and income groups. Malnutrition
is a major underlying problem linked to child mortality and it is strongly associated with the
educational level of the mothers. The Maternal Mortality Rates have been more difficult to
measure accurately and it is not possible to determine whether the indicator has improved over
the years in a statistically robust manner. What is clear is that this indicator also varies greatly by
region, income and education of the mothers. Malaria continues to be the leading cause of
morbidity and mortality and it is a concern that the HIV-1 prevalence rate, although still
relatively low, has doubled from 1.4 percent in 2002 to 2.8 percent in 2006 and tripled among
those aged 15 to 24.
7. The education sector has enjoyed an extensive government program of expanded
facilities and teacher training and recruitment. Much of the expansion has been funded by
external aid, including the Education for All Fast Track Initiative (EFA FTI). As a result, there
have been significant gains in the Gross Enrolment Rates (GER) of the upper basic (40 percent in
1998/99 to 65 percent in 2005/6) and senior secondary schools (16 to 28 percent in the same
years), particularly for female students. However, enrolment rates at the lower basic schools have
essentially stagnated at 73 percent in 1998/99 to 74 percent in 2005/6 and male enrolment rates
have actually declined. In addition, there are sizable differences across regions, income groups
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and mother’s education level, particularly for senior secondary schools. Government
expenditures are found to be regressive for senior secondary schools given that richer households
have more children in these schools. However, government expenditures are progressive when
primary schools are also considered. Government expenditures for tertiary education, which has
received greater emphasis recently, are typically regressive, raising the importance of student
scholarships in providing opportunities to the poor.
GROWTH AND POVERTY DYNAMICS
8. The large sectoral disparities in the poverty rates indicate that the sectoral composition of
growth is likely to have a major impact on the extent to which growth is inclusive of the poor.
Simulations exercises were conducted in order to explore this relationship in detail by carefully
applying sectoral per capita growth patterns since 2003 to the poverty profile constructed from
the 2003 IHS.2 An interesting finding is that agriculture has grown more rapidly than industry
and services due to both higher sectoral growth rates and lower sectoral population growth rates.
Agriculture experienced higher sectoral growth rates essentially due to a relatively low starting
point in 2002, when crop production sharply declined due to low rainfalls. The relatively low
population growth rates in the sector reflect the internal migration from agriculture to industry
and services.
9. Nominal sectoral growth rates
were combined with CPI inflation
rates which were constructed
separately for urban and rural areas,
using separate consumption weights,
and also constructed separately for
food and nonfood components. These
new disaggregated CPIs revealed
significant differences in inflation
across regions and components. The
disaggregated food inflation rates in
rural areas were particularly high. This is highly relevant for the poverty analysis because the
poor are concentrated in rural areas and food dominates the consumption of Gambians, at 61
percent of total consumption.
10. The combined estimated impact of the sectoral per capita growth rates and the
disaggregated inflation rates was a reduction in the poverty headcount ratio from 2003 to 2008
by only two percentage points. Although the country enjoyed steady growth since 2003, with an
average annual per capita growth rate of 3.5 percent, this was essentially offset by the relatively
high inflation rates in rural areas, particularly for food consumption.
11. Next the simulation exercises incorporated the impact of falling remittances and the rural
to urban and intersectoral migration. The Gambia averaged 9.6 percent of GDP in international
remittances since 2003, among the highest in the world. However, remittances have steadily
2 The simulation exercises were used to provide single point estimates based on assumptions regarding the rates of
growth, decline and distribution of remittances and internal migration. The Poverty Assessment team plans to carry
out further simulations in order to analyze the impact of changing these key parameters.
Table E-2: Poverty Simulation Results, Upper Poverty Line
(Percent)
Headcount
Rate (P0)
Poverty
Gap (P1)
Squared Poverty
Gap (P2)
2003 Baseline 58.0 25.1 13.9
2008 Baseline 55.5 23.5 12.9
Adjusted for:
- Remittances 57.9 24.8 13.7
- Internal migration 43.1 16.2 8.2
- Remittance and
migration
45.5 17.4 8.9
Source: 2003 IHS and staff calculations.
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declined from 14.2 percent of GDP in 2003 to 6.4 percent in 2008, raising concerns regarding the
potential impact in terms of lower growth and higher poverty. In practice, accounting for the
decline in remittances results in only a two percentage point deterioration of the projected
poverty rate because urban residents, who are on average less poor, receive a proportionately
much greater share of remittances compared to the poorer rural residents.
12. For simulating the impact of internal migration, it was assumed that the rate of migration
since 2003 remained constant from the preceding decade when actual data were available. Based
on this assumption, internal migration was estimated to have reduced the poverty headcount ratio
by approximately 12 percentage points since 2003. This relatively large impact reflects the high
rate of migration, which predicts that the urban share of the population increased by 4.5 to 5.0
percentage points from 2003 to 2009, with a corresponding reduction in the share for the rural
population. Concurrently, the agriculture share of the population is estimated to have declined by
approximately 1 percentage point while there were corresponding increases in construction, trade,
hotels and restaurants, and transport and communication. The internal migration was in response
to higher earning opportunities and represents a reallocation of the country’s resources to sectors
with high returns.
REGIONAL POVERTY VARIATION
13. Despite its small size, the country has substantial geographical disparities in poverty
related indicators. In order to explore regional variations in poverty in a more disaggregated
manner, a poverty map was constructed from the 2003 IHS and the 2003 Population Census. The
poverty map uses econometric techniques to combine the detailed information found in the IHS
with the exhaustive coverage of the Population Census. The poverty map provides estimates of
poverty indicators at the level of districts in a statistically robust manner.
14. The poverty map indicates that in general poverty variations are greater among Local
Government Authorities (LGAs) compared to within the LGAs.3 Perhaps this should not be too
surprising given the small size of the districts, many of which have populations less than 20,000.
The LGA which has a relatively larger variation among its districts is Brikama, where poverty
estimates increase the further away from Banjul. In fact, the poverty estimates of the Foni
districts in Brikama are not statistically different from the neighboring districts in Mansakonko,
which had been considered a much poorer LGA. This is a case where the poverty map
illuminates the need for differentiated policies within an LGA. This within-LGA poverty
variation in Brikama reflects its location at the border of the urbanized western parts of the
country and the rural eastern regions and the fact that urbanization and population density are
inversely associated with poverty. To the west of Brikama lies the urbanized Greater Branjul
which has substantially lower poverty rates than the rest of the country and a population density
23 to 33 times greater than the nation’s average. By contrast, the much poorer rural LGAs to the
east has on average only 15 percent of its population in urban areas. Located at the midpoint,
Brikama has an urbanization rate of 60 percent and a population density approximately twice the
national average. Brikama has also been the largest recipient of internal migration, growing by
66 percent between the 1993 and 2003 Censuses compared to the overall population growth rate
of 31 percent.
3 LGAs are the government’s regional administrative divisions.
xii
15. The regional variations in poverty are associated with variations in access to health
facilities and upper basic and senior secondary schools, in terms of the average distance to these
facilities. By contrast, distances to lower basic schools and major roads were similar between
urban and rural areas, although there are variations among the LGAs. The findings indicate that
further expansion of schools should perhaps focus on the senior levels. Note also that there are
large regional differences in school enrolment rates, even for lower basic schools. Basse and
Kuntaur have particularly low enrolment rates. With regards to distance to major roads, access as
well as quality of the feeder roads could also be a significant determinant of access to major
roads.
AGRICULTURE
16. The performance of agriculture will continue to be a significant determinant of poverty in
The Gambia given that the sector accounts for 52 percent of the population and 63 percent of the
poor and the sector’s estimated poverty headcount ratio of 76 percent, based on the upper
poverty line, is significantly higher than the national average of 58 percent. Agriculture’s high
poverty rate explains the large urban-rural disparity in the poverty rates, 68 percent versus 40
percent respectively.
17. It would be difficult to address poverty in rural agriculture without improving crop
performance, although livestock and nonfarm activities are important sources of income. Crop
production declined continuously since 2004 before rebounding in 2008 due to relatively good
rainfalls. Crop production in The Gambia is dominated by small landholders. According to the
2007 National Agricultural Sample Survey (NASS), 85 percent of farms are smaller than one
hectare. The NASS indicates that larger farms have higher yields, use greater amounts of key
inputs per hectare, access government extension services more frequently and are more likely to
be member of producer associations. Larger farms are more diversified in their crop production
and are more likely to grow groundnuts, the country’s main cash crop and export. Some key
concerns include the fact that only 12 percent of farms access extension services and a maximum
of 14 percent use irrigation. These percentages decline to 6 percent and 10 percent respectively
for farms smaller than 0.5 hectares.
18. The key determinants of crop production and productivity were analyzed by estimating
regressions of the production functions on land, labor and production inputs. The regression
analysis allows the impact of each input to be estimated in isolation from the impact of other
inputs. The results indicate that there is a labor surplus and declining marginal productivity of
land. An additional labor input does not have a significant impact on production or productivity.
A doubling of area cultivated results in only a 40 percent increase in crop production. Hence,
there are significant limits to the current strategy focused on agricultural land expansion. Access
to credit, membership in producer associations and use of extension services are also found to be
significantly associated with higher production and productivity. Access to credit for rural
farmers appears to be better than expected, at an estimated 40 percent, and more than half of the
farmers received credit from microfinance institutions. The impact of producer associations and
extension services need to be interpreted with caution and should be further analyzed. The
xiii
causality relationship could go either way given that larger and higher productivity farmers could
for various reasons be more likely to be member of associations and receive extension services.
19. Furthermore, the fact that certain production inputs are found to have a significant impact
on crop production does not necessarily mean that their use makes economic sense. With this in
mind, the cost-effectiveness of the use of fertilizers was analyzed using a farm household budget
which outlines the costs of production, productivity and output price in an interrelated manner,
taking into account variations in rainfall. The resulting analysis reveals that the use of fertilizers
makes economic sense only if it is subsidized. Even with subsidies, the use of fertilizers results
in a significant decline in expected profits if rainfalls are low. If fertilizers without subsidies are
used, then low rainfalls result in a loss. The demand for fertilizers is thus intimately linked to the
sensitivity of crop production to rainfalls.
20. In addition to agricultural production, nonfarm income is significant for rural farmers,
particularly for smaller farmers. According to the 2007 NASS, 41 percent of farms smaller than
0.5 hectares receive nonfarm income, compared to 27 percent for farms larger than 2 hectares,
and in addition their nonfarm income is on average 80 percent larger. Remittances are the largest
source of nonfarm income. Nonfarm income was compared to groundnuts sold in order to gauge
the importance of nonfarm income. Assuming retail groundnut prices at the local markets, the
average ratio of nonfarm income to groundnuts sold is 41 percent and increases to 70 percent for
farms smaller than 0.5 hectares. If wholesale prices from the NASS are used, these percentages
increase to 118 and 234 percents respectively. Regardless of the assumptions, clearly nonfarm
sources of income are important for rural farmers, especially for the smaller landholders, and in
some cases far more important than farm activities.
21. Given the importance of groundnuts as the country’s major cash crop, the report analyzes
the history of structural changes in the sector’s production, processing and marketing
arrangements. The analysis indicates that there have been sharp fluctuations in the sector’s
performance and these fluctuations can be largely tied to sudden structural changes and policy
reversals in the sector as well as the amount of rainfall. In order to stabilize the sector and
provide a reliable and timely market for the groundnut farmers, the government developed a
sector reform Roadmap in 2007 with the objective of liberalizing the market, promoting open
competitions and protecting the farmers through a minimum producer price. Recognizing that the
well being of the sector and ultimately of the producers hinges on improving the processing and
marketing of groundnuts, a critical component of the Roadmap is the eventual divestiture of the
Gambia Groundnut Corporation (GGC). However, this will need to be complemented by
improved access to critical inputs and strengthened farmers associations. Sustained political
commitment and country ownership will be critical to continued implementation of the Roadmap,
which will be the key to attracting private investments in the sector.
22. The recent declines in production triggered discussions on the status of the country’s food
security, particularly with respect to rice which is heavily imported and accounts for on average
8 percent of total consumption. Rather than domestic supply constraints, exposure to
international price fluctuations appears to be the key issue for food security. The exposure for the
poor has been comparatively less than for the nonpoor for two reasons: (i) rural households rely
on locally produced rice for roughly half of their total rice consumption compared to less than
xiv
five percent for the urban households; and (i) while the price of imported rice increased
significantly since 2008, the price of local rice has remained mostly constant. Note that 27
percent of rural farmers are rice producers who would benefit from local rice price increases.
POLICY IMPLICATIONS
23. The major policy implications of the Poverty Analysis are grouped into four thematic
areas: (i) agricultural productivity; (ii) internal migration and regional disparity; (iii) population
policy and poverty among youths; and (iv) improving data and the M&E system.
Agricultural Productivity
24. Improving the performance of agriculture will remain a core component of the
country’s poverty reduction strategy. This will remain true as long as the majority of the poor
are rural farmers. Focusing on raising agricultural productivity is a more promising alternative to
the current government emphasis on expansion of agricultural land, given that the doubling of
area cultivated is estimated to result in only a 40 percent increase in crop production. This
percentage could in actuality be even lower if we assume that the remaining uncultivated lands
are of lower quality.
25. Understanding and addressing the low spread of irrigation systems is a priority give
that agricultural production is highly sensitive to rainfall. At a maximum, only 14 percent of
farms use irrigation. A policy priority is to analyze the key constraints to the adoption of
irrigation systems and systematically address these constraints. These could include a
combination of high start-up costs, inappropriate technology and poor extension advice.
Irrigation systems have been supported by donor projects without sustained success.
Understanding the underlying causes of this lack of success would be an important step towards
developing appropriate interventions.
26. Promoting the greater use of fertilizers will depend on changing the incentives of
farmers through complementary reforms. Fertilizer usage can be quite risky for farmers given
that crop production is highly sensitive to rainfall. This risk can be reduced by promoting greater
adoption of irrigation systems and lowering the cost of fertilizers through the greater use of
organic materials. The promotion of organic fertilizers could require a fundamental restructuring
of the extension program.
27. Revitalization of government extension services would first require understanding
the underlying causes of their low utilization. Only 12 percent of farms use extension services.
The underlying causes could be a combination of demand and supply factors. Demand for
extension services could be low if the quality of the advice is perceived to be low. Supply could
be low due to a variety of factors, including low budgetary resources, lack of qualified staffs and
poor managerial oversight. Anecdotal evidence indicates that supply constraints as simple as the
lack of fuel can have a significant impact. DOSA has undergone significant reorganizational
reforms in recent years and the process is ongoing. This would be an opportunity to prioritize the
strengthening of extension services.
xv
28. Strengthening producer associations is a credible policy for reaching the farmers.
First, membership is relatively high, even for the small farms. On average 59 percent of all
farmers and 54 percent of farms smaller than 0.5 hectares are members. Second, there is
evidence that membership has real benefits, in the form of higher groundnut producer prices.
This could be particularly true for smaller farmers who have been able to pool their outputs
through the producer associations, thus allowing them to meet the minimum volume
requirements of some buyers.
29. Restructuring the land tenure and inheritance system could address gender based
constraints to higher agricultural performance. Land is generally owned, controlled and
distributed by men. The existing system limits productive opportunities for women. However,
realistically progress could be gradual and incremental given that reforms would require
changing the country’s social and cultural norms. Therefore, complementary interventions, such
as targeted credit and extension services, should be considered that would compensate for the
barriers to productive assets for women.
30. Promotion of nonfarm activities is integral to improving the livelihood of rural
farmers. The Poverty Assessment presents evidence that nonfarm activities are an important
source of income for rural farmers, particularly for smaller farms, and can help to reduce risks by
diversifying income. However, many of the activities are relatively low value-added, such as
domestic work and petty trading, and therefore there is potential for further development and
deepening. Currently, the government lacks a cohesive rural development strategy that includes
policies to promote rural nonfarm enterprises. Such a strategy would need to address
shortcomings in property rights, credit systems and public investments in infrastructure. More
specific policies can focus on small enterprise development, agribusiness and agricultural
marketing, linkages between the urban and rural economies and regional development.
31. Sustaining commitment to implementing the groundnut sector Roadmap will
greatly benefit the rural farmers. The government adopted a Roadmap which outlines a series
of structural reforms of the sector. A key outcome of the reforms will be a stable and predictable
groundnut market for farmers, along with reduced instances of credit buying which farmers have
cited as a major cause of poverty. Addressing the shortcomings in the downstream processing
and marketing segments of this key sector of the Gambian economy will have significant
benefits for the farmers.
Internal Migration and Regional Disparity
32. Effective education and training programs would help migrants meet the skills
demanded in the urban sectors. The Poverty Assessment analysis indicates that internal
migration is likely to have been extensive and the impact significant on growth and poverty
reduction. Agriculture is characterized by an overall labor surplus, which allows for internal
migration to take place without a significant impact on rural production. The impact of migration
would depend on the extent to which rural migrants have the skills demanded by the growing
urban sectors, without which their options could be mostly limited to low-productivity activities.
These skills would include the basic ability to read and write, given the low literacy rates of the
population, to more specific skills required in industries and services, which even recent school
xvi
graduates might lack. It highlights the importance of effective education and vocational training
systems, based on a clear understanding of the skills demanded by the urban sectors.
Strengthening linkages between education and work can increase the relevance of training
opportunities and improve employability. The current focus on tertiary education would need to
be linked with policies to promote expanded job opportunities for the highly educated, given the
current high rates of emigration of the highly educated.
33. The poverty map and related analysis can be used to improve targeted interventions,
thus addressing the underlying causes of rural migration. The report outlines disparities in
poverty along multiple dimensions, including location, gender, household size and composition,
and education and health variables. The newly prepared poverty map can be used for finer
geographical targeting of interventions. The poverty map could also be further developed to map
non-monetary indicators, such as health and education. Government interventions should be
adapted to the differing needs and concerns of rural and urban areas.
34. Addressing the higher inflation rates in rural areas can be an effective means to
reduce poverty in these areas. Although the overall annual inflation rate was 6.8 percent in
2008, the estimated rate increases to 13.3 percent for food items in rural areas when
disaggregated weights are used. The high inflation rates in rural areas contributed to the
relatively subdued impact of growth on rural and overall poverty reduction in the simulations,
particularly given the large share of food in the consumption of rural households. Higher prices
in rural areas could reflect a combination of factors, including market segmentation and high
transportation costs. In addition to addressing these factors, a careful analysis of the CPI
components could reveal whether a select number of items are driving the price disparity.
Population Policy and Poverty among Youths
35. Reducing demographic pressure on families could significantly reduce the risk of
being poor. Larger households with more children are significantly poorer in The Gambia. In the
Participatory Poverty Assessments (PPAs), communities have identified large families and high
dependency ratios as causes of poverty. High fertility rates and rapid population growth rates
have also resulted in a large population of youths, who have high poverty rates. Urban youth
unemployment rate is very high, at 22 percent, compared to the national unemployment rate of 6
percent. These findings indicate that there is a need to revisit population related policies and
government interventions targeting the youths.
36. Managing the challenges of rapid population growth could require reviewing the
effectiveness of family planning programs. The fact that communities have identified large
families and high dependency ratios as correlates of poverty indicate that there could be a
demand for family planning. If there is widespread misconception regarding family planning and
reproductive health in general, it highlights the importance of an effective education outreach
program. An effective family planning program can contribute to a significant decline in fertility
rates. In Kenya, family planning was associated with a decline in the fertility rates4 from an
estimated 8.1 in 1977/78 to 4.7 in 1998. Improving the effectiveness of family planning could
require the strengthening of the Primary Health Care facilities, whose poor services were cited in
4 The fertility rate is defined as the average number of children born to women during her lifetime.
xvii
the PPAs. Family planning can be combined with an education campaign to emphasize the
benefits of smaller families, with regards to health and household finances. Note that improving
access to reproductive health services is also essentially for reducing maternal mortality.
37. Ensuring an effective targeting mechanism is critical to the success of government
youth programs. In the case of employment schemes, a low wage rate would ensure that only
the disadvantaged unemployed youth would participate. Age restrictions in eligibility for
participation could further sharpen targeting. Encouraging women to participate could be
considered given their relative absence in some urban sectors. Skills development and
microfinance initiatives could also be critical components of the government youth programs. In
this report, easing the school to work transition is an important policy objective.
Improving Data and the M&E System
38. The next household survey should ensure comparability with the 2003 IHS and
focus on improved data on remittances. Given that the most recent survey was conducted in
2003, it would be extremely valuable to undertake a new household expenditure survey. The
design of the next survey should ensure comparability with the 2003 IHS given the non-
comparability problems of the 2003 survey with the previous survey. The next survey should
directly collect price data and utilize standardized consumption items and quantities, the absence
of which caused significantly analytical challenges for the 2003 IHS. The field work, processing
and supervision over these activities will also need to be improved, given that deficiencies in
these areas resulted in remittances in the 2003 IHS becoming unusable. Understanding the
distribution and impact of remittances is critical given their importance to the country. GBOS is
currently conducting a small-scale Core Welfare Indicators Questionnaire (CWIQ) household
survey but this survey does not collect data on household consumption or income.
39. The National Agricultural Sample Surveys (NASS) provide valuable information on
rural farmers but quality improvements are needed. The quality of the NASS has
deteriorated over the years without regular funding and adequate supervision, even though it is
essentially the only source of data on agricultural performance. The NASS was originally
designed for projecting agricultural production and therefore lacks data on household
characteristics, limiting its use for poverty assessments. However, the NASS questionnaire can
be relatively easily augmented to incorporate questions on household characteristics.
Alternatively, the NASS could be linked with the next household survey.
40. Official statistics have undergone significant institutional reforms and capacity
building. A new Statistics Act (2005) was enacted, the previous government agency, Central
Statistics Department (CSD), was transformed into the semiautonomous GBOS, and the
Statistics Council was created to act as the Board for GBOS and also to coordinate all statistics
produced within the public sector. GBOS staffs have received significant training and the data
infrastructure has improved through the IHS (2003), population census (2003), economic census
(2006) and new CPIs.
41. However, there are several major remaining challenges to improving official
statistics. There are concerns whether the institutional reforms have been fully entrenched, in a
xviii
manner that ensures improved transparency and accountability. There are also concerns whether
GBOS has adequate capacity to maintain and continue to improve the quality, reliability, and
timeliness of economic, social, and poverty data. So far GBOS has had difficulties recruiting
new qualified staffs and many areas remain significantly understaffed, including the household
survey section which has only one staff. Timeliness in data release has been a problem, reflecting
delays in processing and analyzing data after the field works have been completed. Results from
the IHS were released in 2006 and results from the population census are still being released.
Archiving and dissemination have been particularly weak, which could be addressed through a
functioning official website and the regular publication of statistical bulletins. Surveys that are
less valued by policy-makers, such as the qualitative surveys, could be reviewed to confirm their
relevance and any reserve savings could be dedicated to the strengthening of household surveys
and dissemination.
42. The government’s Strategic Plan for the Development of Statistics (2008-11)
provides a comprehensive basis for coordinated donor support. The Statistics Strategic Plan
includes a detailed work program and proposes that GBOS enter into a contractural agreement
with the government based on performance indicators. The Strategic Plan was finalized in
January 2008 but the proposed performance agreement has so far not been prepared. Such an
agreement would allow the donors to discuss the support necessary to implement the Strategic
Plan. These discussions would take place within the context of supporting the PRSP process and
the activities of the National Planning Commission (NPC).
Table E-3: Major Policy Implications
Poverty Assessment Findings Policy Implications
Agricultural Productivity
Agricultural production is highly sensitive to
rainfall fluctuation.
Analyze the key constraints to the adoption of
irrigation systems and systematically address
these constraints
Declining marginal productivity of land (doubling
of area cultivated results in only a 40 percent
increase in crop production).
Focus on interventions on agricultural
productivity rather than the current emphasis on
expansion of agricultural land.
Only 12 percent of farms use government
extension services.
Identify and address the constraints to utilization
of extension services. Underlying causes could
range from low demand due to poor quality to
lack of supply.
Fertilizer usage might not be economically
sensible given the costs and the higher risk of loss
if rainfalls are low.
Prioritize the adoption of irrigation systems and
promote greater use of organic fertilizers which
are less costly and more sustainable.
Producer associations offer higher prices for
groundnuts compared to other buyers. Smaller
farmers are less likely to be members of producer
associations.
Strengthen producer associations and promote
membership of smaller farmers.
Nonfarm activities are an important source of
income for smaller farmers.
Increase attention to the promotion of nonfarm
activities in the country’s agricultural
development strategy.
Land tenure system and inheritance favors males,
limiting productive opportunities for females.
Review and restructure the land tenure system in
order to mitigate the gender bias.
xix
Poverty Assessment Findings Policy Implications
Sharp policy reversals in the groundnut sector
have resulted in an unstable sector with an
unreliable market for groundnut farmers, who are
among the country’s poorest group.
Sustain commitment to implementation of the
government’s groundnut sector reform Roadmap.
Internal Migration and Regional Disparity
Rural to urban and intersectoral migration is
likely to have been extensive, resulting in a
significant impact on growth and poverty
reduction.
Improve the effectiveness of education and
training programs for helping migrants meet the
skills demanded in the urban sectors.
Strengthening linkages between education and
work can increase the relevance of training
opportunities and improve employability.
Identify and address key areas where the demand
and supply for labor are not effectively matched,
including for the highly educated given their high
rate of emigration.
There are significant geographical disparities in
multiple dimensions of poverty.
Utilize geographically disaggregated information
on poverty, including education and health
indicators and the newly prepared poverty map, to
fine-tune poverty related interventions.
The concerns of the urban and rural poor are
significantly different.
Develop interventions which are specific to urban
and rural areas.
Inflation rates are significantly higher for food
items in rural areas.
Identify and address the underlying causes of
higher inflation in rural areas, which could
include market segmentation and high
transportation costs.
Population Policy and Poverty Among Youths
Larger households with high dependency ratios
are significantly poorer. High fertility rates have
resulted in rapid population growth and a large
population of youths.
In participatory surveys, communities expressed
strong dissatisfaction with Primary Health Care
(PHC) facilities and lack of electricity at health
facilities.
Review and refine policies to improve
management of population growth.
Review the effectiveness of family planning
programs and the need for an education outreach
program on the benefits of smaller families.
Improve the capacity of PHC facilities to provide
family planning services as well as services
related to reproductive health in general.
The youth population has relatively high poverty
and unemployment rates.
Review and address shortcomings in government
youth programs, paying particular attention to the
effectiveness of the targeting mechanism.
Data and the M&E System
Household expenditure surveys are central to
assessing and monitoring poverty but the latest
survey was most recently conducted in 2003.
Conduct a new household expenditure survey
The 2003 IHS is not comparable with the
previous survey due to methodological
differences.
Prioritize comparability as a key feature in the
design of the next survey.
Data collected on remittances in the 2003 IHS
were not usable due to quality deficiencies.
Focus on improving data collection and
processing of remittances in the next survey.
The quality of the data in the NASS appears to be
uneven.
Improve quality control in the design, field work
and data processing of the next NASS survey.
xx
Poverty Assessment Findings Policy Implications
The NASS, originally designed for projecting
agricultural production, lacks data on household
characteristics, limiting its use for poverty
assessments.
Incorporate questionnaires on household
characteristics in the NASS or link NASS with
the next household survey.
Despite major institutional reforms and capacity
building activities, GBOS continues to exhibit
capacity constraints and shortcomings in the
timely production and dissemination of key data.
The Statistics Strategic Plan (2008-11) proposed
that GBOS enter into a contractual agreement
with the government which outlines performance
indicators.
GBOS enters into a contractual agreement with
the government in order to improve transparency
and accountability of its operations, based on
performance indicators.
Donors discuss with the government the support
necessary to implement the Statistics Strategic
Plan.
1
1. POVERTY PROFILE
1.1 The main objectives of the Poverty Assessment report are to provide the latest analytical
information on the poverty situation of The Gambia and to identify the key challenges and
opportunities for reducing poverty in the country. The ultimate goal is to understand what would
constitute pro-poor policies. The report develops the basic poverty profile encompassing both
monetary and non-monetary well-being, including education and health indicators, using both
quantitative and qualitative surveys. Linkages between growth and poverty are explore through a
simulation exercise which “projects” the current poverty rates from the most recent household
survey conducted in 2003. Regional poverty variations are analyzed in detail, including through
the construction of a poverty map which provides geographically disaggregated estimates of the
poverty rates. Finally, the report analyzes the challenges and opportunities in improving the
livelihood of the rural farmers, who account for the largest share of the country’s poor.
1.2 The analysis presented in this report provides the basis to refine and improve
implementation of the country’s Poverty Reduction Strategy Paper (PRSP). The goal of the
PRSP is to reduce poverty by promoting inclusive growth. The findings of the report allow the
government to improve targeting of public service delivery and identify public policies which are
pro-poor. The Poverty Assessment is a core element of the Bank’s analytical support to the PRSP
process, in particular the government’s Joint General Budget Support Policy Matrix which is
being supported by budget support operations from the Bank, the EU and AfDB.
COUNTRY BACKGROUND
1.3 The Gambia is a small West African country with an estimated population of 1.6 million,
an average per capita GDP of US$320 (Atlas method, 2007) and a ranking of 155 out of 177
countries in the 2007 UN Human Development Index. The economy is relatively undiversified
and limited by a small internal market. Services account for over half of GDP, reflecting the
importance of external trade and tourism. Liberal trade policies and an efficient port
infrastructure have allowed the country to act as a regional re-export hub. Tourism is a key driver
of the economy and the country’s most significant foreign exchange earner. Agriculture accounts
for approximately a third of GDP and more than 70 percent of employment. Groundnuts are the
most important agricultural crop. They account for approximately 60 percent of domestic exports
and 55 percent of rural households engage in groundnut production. However, groundnut farmers
are among the poorest in the country and the performance of the sector has been poor in recent
years.
1.4 The country’s economic performance in recent years has been relatively strong but
growth is currently slowing down due to the global recession. The annual real GDP growth rate
has averaged 6.2 percent in the previous five years, compared to the estimated annual population
growth rate of approximately 2.8 percent. However, growth is expected to slow down to 4.6
percent in 2009 due to the impact of the global economic slowdown. Further downside risks
2
remain given the unstable global climate. The country also remains quite vulnerable to
exogenous shocks, including low rainfalls on agriculture and increased import prices. The
country’s relatively low levels of integration with the international markets could protect it to a
degree from the global economic slowdown. Certainly sectors such as hotels, restaurants and
construction will be adversely impacted by the expected fall in tourist arrivals, remittances and
FDI, but the performance of the agricultural sector is likely to be more influenced by domestic
crop production as opposed to external factors.
1.5 The country’s governance is characterized by a weak opposition and media. International
observers, including Reporters without Borders, the International Bar Association and Amnesty
International have cited the country’s shortcomings in freedom of the press and independence of
the judiciary. The Gambia is ranked 121 among 163 countries by Transparency International.
Strengthened governance could enhance the transparency and accountability of the public sector
and reduce the risk of policy slippages through greater country ownership.
1.6 The Gambia has developed a PRSP, for the years 2007 to 2011, which outlines a country
strategy for pro-poor inclusive growth. The PRSP is organized into five “pillars”: (i)
macroeconomic stability and public sector reform; (ii) promotion of pro-poor growth and
employment through private sector development; (iii) improved basic social services; (iv)
decentralization and strengthened local governance; and (v) multisectoral programs on gender,
HIV/AIDS, environment, nutrition, and population. The PRSP initiatives were chosen based on
their contribution towards the achievement of the Millenium Development Goals (MDGs). The
PRSP was developed through a participatory and consultative process. Stakeholder consultative
workshops and focus group discussions were held with representatives of the public and private
sectors and civil society. Consultations reached down to the level of local communities.
Participatory Poverty Assessments, Community Scorecards, and “budget games” were
incorporated into the preparatory process. Non-Governmental Organizations (NGOs) directly
participated in the drafting of the PRSP document.
1.7 The National Planning Commission (NPC) coordinates the preparation and
implementation of the PRSP. NPC works closely with multistakeholders, in particular the
recently established Aid Coordination Unit within the Department of State for Finance and
Economic Affairs (DOSFEA). NPC is also responsible for informing internal and external
partners of progress in implementing the PRSP.
POVERTY PROFILE
1.8 In order to measure poverty, three elements need to be defined:
(i) The relevant welfare measure.
(ii) The poverty line, the threshold below which the given household or individual will be
classified as poor.
(iii) The poverty measure (indicator), which provides a summary statistic for the population
as whole or for a population sub-group.
3
1.9 The main welfare measure in The Gambia was determined to be individual consumption
levels. Data on income are generally incomplete and unreliable. Poverty will thus be measured in
terms of the gap between actual consumption and an estimated poverty line which represents a
notional minimum level of consumption. The main sources for measuring consumption
aggregates in The Gambia have been the household expenditure surveys conducted in 1992,
1998 and 2003. This report focuses on analyzing the 2003 Integrated Household Survey (IHS). It
specifically does not compare the findings of the 2003 survey with the previous surveys because
it concludes that there are methodological problems in making such comparisons. In lieu of
comparisons with previous surveys, the analysis focuses on projecting current poverty rates from
the 2003 poverty profile, through simulation exercises that incorporate the impact of growth
dynamics, remittances and internal migration (see Chapter 2).
Consumption Aggregates
1.10 The 2003 IHS provides the latest available data on household expenditures which allows
for an estimation of the consumption-based poverty measures. The 2003 survey was based on a
sample of 4,800 households from 240 enumeration areas (EAs). However, the probably weights
were constructed for only 2,238 households. The probably weights were corrected for missing
EAs and households. The EAs were based on the 2003 Population Census stratified by urban and
rural areas for the eight Local Government Authorities (LGAs), which are the government’s
regional administrative divisions. The 14 strata were: Banjul urban, Kanifing urban, Brikama
urban and rural, Mansakonko urban and rural, Kerewan urban and rural, Kuntaur urban and rural,
Janjangbureh urban and rural, and Basse urban and rural. The survey collection started in
January 2003 and ended in May 2004. The EAs were drawn without replacement not only at
each quarter but for all subsequent quarters. Therefore, the entire country was covered at every
quarter of the survey.
1.11 The proper and consistent identification of consumption items in the survey was made
difficult due to the fact that no standardized nomenclature was used during data collection and
product names were freely entered by the enumerators. Based upon a thorough examination of
the data, GBOS was able to regroup consumption into three levels of product categories, from
the most aggregated downwards.
1.12 Food expenditures included records of food purchases, in-kind consumption and
consumed gifts collected from a daily diary administered for one month. The actual number of
days of collection for the daily diaries varied across households. Records of in-kind food
consumption and gifts were directly valued by the enumerator, who may not have known the
local prices, and it is likely that these estimates are less accurate than records for food purchases.
A total of 161 food items were recorded, organized into 15 groups: (i) bread and cereals; (ii)
meat; (iii) fish and seafood; (iv) dairy products and eggs; (v) oil and fats; (vi) fruit and nuts; (vii)
sugar and sweets; (viii) green vegetables; (ix) tubers; (x) pulses; (xi) spices; (xii) non-alcoholic
beverages; (xiii) alcoholic beverages; (xiv) tobacco and narcotics; and (xv) hotel food. The
annual food expenditures were derived by inflating food expenditures inversely proportionally to
the observation period, specific to each household.
4
1.13 Non-food expenditures were collected from a separate questionnaire based on recall
periods of 1 month, 3 months and 12 months, depending on the specific item. There were 164
items recorded, organized into 8 groups: (i) clothing; (ii) education; (iii) transportation and
communication; (iv) health; (v) housing, water and energy; (vi) recreation and culture; (vii)
household equipment; and (viii) miscellaneous, including personal care.
1.14 Food and non-food expenditures were aggregated by households, from which per capita
consumption levels were estimated after adjusting for household size and composition using
household equivalence scale. This equivalence scale was based on internal standards on
recommended dietary allowances estimated for different age and gender.5
1.15 The consumption aggregate was then adjusted for geographical and within-year price
variations using a Paasche price index. Given that a price survey was not conducted as part of the
survey, prices were derived based on the total value and quantity consumed. The Paasche price
index was derived using median prices at the EA level of these derived prices. Only ten products
were used for the price index, as they were the only products with clearly identified quantities in
the survey. These ten products are: bread, rice, sugar, magi cube, palm oil, tea bags, candles,
washing soap, salt, and sardines. The quantity information for other products was not available
due to poorly defined measurement units and therefore they were not selected.
1.16 The ten products used for the price index accounted for 28 percent of total consumption.
Given the limited coverage, products were grouped into 23 broad categories and prices of the ten
products were assigned to each category as “representative” prices. While the obtained price
index is not perfect, it addresses the data constraints in the survey. The correction for price levels
is critical as there are significant variations in prices across locations and quarters.
1.17 The resulting per capita expenditures in 2003 was 8,649 dalasis, or US$317 based on the
average exchange rate (see Table 1). This compares reasonably well with the estimate of per
capita expenditures based on aggregate macroeconomic variables. The 2003 estimated GDP is
US$353 million. However, the Economic Census conducted in 2006 indicates the actual GDP to
be 11 percent larger than the previous estimate of GDP for 2006. The same proportionate
adjustment is applied to the GDP estimate for 2003, resulting in US$392 million. The estimated
aggregate rate of consumption was 88.8 percent of GDP in 2003. The population was 1.35
million according to the 2003 Population Census. Based on these estimates, the per capita
consumption would be US$254. This seems reasonably close to the estimate of US$317 based on
IHS.6
Table 1: Mean Per Capita Expenditure in Real Terms
All 8,649.14 LGA1
Urban 11,499.84 Banjul 24,137.85
Rural 7,132.38 Kanifing 11,096.72
Brikama 8,258.68
Quintiles Mansakonko 10,263.22
1 (Lowest) 2,186.20 Kerewan 6,772.43
5 Committee on Dietary Allowances, Food and Nutrition Board, Washington, D.C., Academy of Sciences.
6 The estimates of the macroeconomic variables are from the IMF staff reports on the PRGF-supported program.
5
2 3,942.18 Kuntaur 3,700.79
3 5,899.36 Janjangbureh 5,866.51
4 8,774.46 Basse 6,753.30
5 (Highest) 22,488.06
Source: 2003 IHS and authors’ calculations.
1. Local Government Authorities.
1.18 Food largely dominates the consumption of the Gambians. Direct food purchases, own-
consumption and gifts account for 48.0 percent of total consumption. In addition, 13.2 percent of
consumption is due to expenses made at restaurants, hotels and other establishments which
provide snacks and prepared meals. Thus, the total share of food consumption is 61.2 percent.
Such a share is in line with other countries at similar levels of development. As expected, the
poorest households, in terms of nominal per capita consumption, have on average larger shares
of food consumption, consistent with Engel’s Law. The large share of food consumption
indicates that the poor will be vulnerable to large fluctuations in the price of food.
1.19 The next largest consumption groups are clothing and footwear at 9.7 percent, recreation
and culture at 7.8 percent and transport and communication at 5.1 percent. The respective shares
for all other consumption groups are less then five percent. What is notable are the small shares
spent on education, 1.1 percent, and health, 1.0 percent. The largest item in education
expenditures is school fees. Note that expenditures on school uniforms are captured under
clothing and footwear, and expenditures on school books and materials are under recreation and
culture. Expenditures on health cover medical drugs and doctor fees. The small shares spent on
health expenditures could indicate that health treatments in the country are heavily subsidized. It
certainly does not appear to reflect lack of usage or access to health services, given that 84
percent of the households incurred health expenditures compared to 49 percent for education
expenditures.
1.20 The mean per capita expenditures indicate substantial geographical variations. Mean
consumption in the urban areas is significantly larger than in rural areas. Consumption levels are
the highest in the southwest, more urbanized regions and tend to decrease moving east and north.
It would be expected that regional poverty estimates would reflect the relatively large regional
variations in average consumptions, and in fact this turns out to be the case.
Table 2: Inequality in Per Capita Expenditure Distribution by Urban and Rural Areas
Bottom Half of the
Distribution
Upper Half of the
Distribution
Inter quartile
Range Tails
p25/p10 p50/p25 p75/p50 p90/p50 p75/p25 p90/p10 Gini
Total 1.50 1.68 1.65 2.76 2.78 6.97 46.16
Urban 1.55 1.78 1.64 2.78 2.92 7.68 44.67
Rural 1.49 1.59 1.57 2.44 2.49 5.78 44.15
Source: 2003 IHS and authors’ calculations.
Note: The table presents ratios of the mean per capita expenditures of various percentiles of the distribution of per
capita expenditures.
6
1.21 The distribution of income (consumption) is far from egalitarian. The frequency
distribution of the real per capita consumption peaks at a level much lower than the mean, with
an extended tail at the upper end of the distribution (see Figure 1). The mean per capita
consumption of the highest quintile is approximately ten times the lowest quintile (see Table 1).
It is substantially larger than the other quintiles, whereas differences among the first four
quintiles are by comparison relatively small. This is consistent with the frequency distribution of
real per capita consumption. This is further corroborated by the differences in per capita
distribution by percentiles, where the differences in the upper half of the distribution are larger
than the lower half of the distribution (see Table 2). Thus, a relatively small segment of the
population has income (consumption) levels substantially higher than the rest of the population.
Figure 1: Frequency Distribution of Real Per Capita Consumption
Source: 2003 IHS and authors’ calculations.
1.22 A common measurement of inequality is the Gini coefficient. The coefficient varies
between 0, which reflects complete equality, and 1, which indicates complete inequality.
Graphically, the Gini coefficient is represented by the area between the Lorenz curve, which
maps the cumulative income shares against the distribution of the population, and a straight line
of equality.
1.23 The estimated Gini coefficient for The Gambia is 0.46. In general, poor countries have
high Gini coefficients, typically between 0.40 and 0.65, with extremes at 0.25 and 0.71. The
Gambia’s estimated Gini coefficient is within this range. Within West Africa, the Gini
coefficient for Senegal is 0.41, Ghana is 0.41 and Sierra Leonne is 0.63. Richer countries tend to
have lower Gini coefficients, typically less than 0.40. Most developed European countries and
Canada have Gini coefficients between 0.24 and 0.36. However, the United States has a Gini
coefficient of 0.46 and virtually all Latin American countries have Gini coefficients that are
higher.7
7 Data source, UNDP, Human Development Reports.
7
1.24 Although Gini Coefficients are commonly used, they are not easily decomposable to
inequality within and between sub-groups. Such decompositions help identify sources of
inequality. Alternative measures of inequality are the Generalized Entropy (GE) measures, which
have several desirable features in addition to decomposability. The range of GE is from zero to
infinity, with zero corresponding to complete inequality (see Box 1 for details).
Box 1: Generalized Entropy Measures
GE measures have the form outlined in the equation below, where n is the number of
individuals in the sample, y is the income of individual i, and α is a parameter that
determines the sensitivity of the measure to the ends of the distribution. The range of GE
is from zero to infinity, with zero corresponding to complete inequality. As the parameter
α increases, the GE measures become less sensitive to inequality at the lower end of
distribution and more sensitive to inequality at the upper end. Three cases are of special
interest, α corresponding to 0, 1, and 2. GE(0) corresponds to the mean log deviation,
G(1) to the Theil index of inequality, and G(2) to one-half the squared coefficient of
variation.
11
)1(
1)(
1
n
i
i
y
y
nGE
GE measures have the following desirable measures: (i) Transfer principle, if income is
hypothetically transferred from a poorer to a richer person, without reversing that
ranking, then the inequality measure should rise (at least not fall); (ii) Scale
independence, if all incomes change by a uniform proportion, the inequality measure
should not change; (iii) Population independence, if two identical populations are
merged, the inequality measure should not change; (iv) Anonymity, the inequality
measure must be independent of any characteristic of individuals other than their income;
and (v) Decomposability, it should be possible to decompose the measure such that the
total measure of inequality is equal to the sum of inequality within sub-groups and
between sub-groups.
1.25 The country’s estimated GE measures significantly increases as α increases from 0 to 2. It
indicates that the distribution of income (consumption) is particularly unequal at the upper end of
the distribution. Again, this is reflected in the frequency distribution of mean consumption and
the mean consumption by quintiles, which shows that highest quintile is substantially richer than
the other quintiles.
Table 3: Decomposition of Inequality by Regions
GE(0) GE(1) GE(2)
Overall 37.2 45.7 128.9
Urban 34.2 37.4 66.4
Rural 34.7 47.6 192.2
8
Within group inequality 34.5 42.9 126.0
Between group inequality 2.7 2.8 2.9
Between group inequality as % of overall inequality 7.2 6.1 2.2
Banjul 21.2 20.3 23.9
KMC 31.9 36.0 69.3
Brikama 31.9 34.4 59.4
Mansakonko 50.3 90.1 374.5
Kerewan 44.4 69.4 459.6
Kuntaur 13.8 18.4 41.4
Janjangbureh 15.8 20.5 65.5
Basse 24.6 27.2 43.6
Within group inequality 31.2 39.1 121.0
Between group inequality 6.0 6.5 7.9
Between group inequality as % of overall inequality 16.2 14.3 6.1
Source: 2003 IHS and authors’ calculations.
1.26 The GE measures were decomposed by urban and rural areas and by LGAs. inequality
within sub-groups and between sub-groups. The resulting decomposition by urban and rural
areas indicates that inequality between the two areas account for only a small proportion of
overall inequality. It also appears that the larger inequalities in the upper end of the distribution
of aggregate consumption are particularly significant for the rural areas. Consistent with the
decomposition by urban and rural areas, the decomposition by LGAs indicates that inequality
within LGAs accounts for a much greater share of the overall inequality compared to inequality
between LGAs. Mansakonko and Kerewan have significantly higher levels of inequality
compared to the rest of the LGAs. The high degree of inequality in Mansankonko is interesting
given its relatively high mean consumption levels (see Table 1). Given its location close to
Banjul, the country’s political and business capital, and its elongated shape, it could be that the
income of the residents of the LGA differs greatly depending on their proximity to Banjul. The
poverty map analysis, which provides more disaggregated estimates of the poverty rates, will
verify this theory.
1.27 Consumption regressions were estimated in order to analyze the key determinants of
consumption. For both urban and rural areas, the regressions indicate that consumption levels
significantly decrease for larger households and increase in households with more adult members
and households heads who are educated. The impact of education is much greater in urban areas
compared to rural areas. Given that rural households tend to be larger, the significant coefficient
on household size will contribute towards greater poverty in rural areas, all else being equal. The
positive impact of a greater share of adult members in the household would be consistent with
multiple earners in a household with extended family members.
1.28 Although the estimates are not significant, interestingly the coefficient estimates on
female headed households are positive, indicating that female headed households have higher
consumption levels compared to male headed households. This is consistent with the poverty
analysis presented later in this report, which indicates that female headed households have
significantly lower poverty rates compared to male headed households. Many sources have
referred to the “feminization” of poverty in The Gambia, believing that females were generally
poorer than males. The regression findings actually indicate the opposite result, where female
9
headed households have higher consumption levels than males. The estimates of the poverty
rates presented later will corroborate this result by showing that female headed households are
less poor than their male counterparts.
1.29 The coefficient estimates on the LGA dummy variables indicate that the urban area of
Mansakonko and both the urban and rural areas of Kuntaur have consumption levels which are
particularly low, controlling for other factors. Based on these results, it would be expected that
poverty in Kuntaur is severe compared to the other LGAs. Note also that the coefficient estimate
on urban Basse is similar to Kanifing and Brikama, which are regions with higher consumption
levels in proximity to Banjul. Although at the easternmost inland of the country, it appears that
Basse benefits from its role as a trade conduit into the neighboring countries.
Consumption-Based Poverty
1.30 The estimate of poverty is based on the Foster-Greer-Thorbecke (FGT) poverty
measures.8 These measures provide a summary statistic of the degree of poverty based on the
distribution of individual consumption, or any other welfare measures, in terms of their shortfall
to the poverty line. There are three major classes of FGT measures: (i) the headcount index,
which is simply the proportion of the population whose level of consumption is less than the
poverty line; (ii) the poverty gap, which is the average shortfall of the poor to the poverty line as
a proportion of the poverty line; and (iii) the squared poverty gap, which is the average of the
squared poverty gap and is a measure of the severity of poverty by providing greater weight to
the most poor (see Box 2 for details).
Box 2: Foster-Greer-Thorbecke (FGT) Poverty Measures
The FGT measures can be represented by the following generalized representation (see
equation below), where yi represents individual’s income (consumption) in increasing
order, z > 0 is the poverty line, z – yi is the shortfall of the individual’s income to the
poverty line, q is the number of poor individuals (having income no greater than z), and n
is the total number of households.
The parameter α can be viewed as a measure of poverty aversion. A larger α gives greater
emphasis to the poorest poor. P(0) is simply the headcount index, which is the proportion
of the population for whom consumption (or any other welfare measure) is less than the
poverty line. The headcount index has the advantage of being easy to construct and
understand. However, it has the disadvantage of ignoring the intensity of poverty, as it is
insensitive to the sizes of the shortfall to the poverty line.
q
i
i
z
yz
nP
1
1)(
This shortcoming of P(0) is addressed by P(1), the poverty gap measure which is the
8 Foster, J., J. Greer and E. Thorbecke, “A Class of Decomposable Poverty Measures,” Econometrica, vol. 52, No.
3, P. 761-766, May 1984. These principles were proposed by Amartya Sen.
10
average shortfall of the poor to the poverty line, as a proportion of the poverty line. It
shows how much would have to be transferred to the poor to bring their expenditures up
to the poverty line. It can be interpreted as the “minimum” cost for eliminating poverty
with transfers, assuming perfect targeting of the poor and no targeting costs or distortion
effects. However, the poverty gap measure is insensitive to difference in the severity of
poverty among the poor and therefore to transfers among the poor. In another words, it is
insensitive to inequality among the poor.
A poverty measure which is sensitive to the severity of poverty is P(2), which is the
average of the squared poverty gap. It is the weighted sum of poverty gaps (as a
proportion of the poverty line), where the weights are the proportionate poverty gaps
themselves. This index is sensitive to inequality among the poor. A transfer from a poor
to an even poorer individual would reduce the index. The disadvantage is that the index is
less intuitive and more difficult to interpret.
FGT measures are additively decomposable based on population-share weights and
satisfy the following desirable principles:
(i) Monotonicity Axiom: Given other things, a reduction in the income of a poor
household must increase the poverty measure.
(ii) Scale independence: Given other things, a pure transfer of income from a poor
household to any other household that is richer must increase the poverty measure.
1.31 Having defined the preferable poverty measure, we now turn to the poverty line, the
threshold below which the given household or individual will be classified as poor. The poverty
line was constructed using a “cost of basic needs” approach, applying methods promoted by
Ravallion (1998) for missing data on food quantities and robust extrapolation methods. We first
describe the estimation of the food component for the food poverty line. Then, we explain the
extrapolation method used to produce the final poverty line.
1.32 The food component of the poverty line is estimated by valuing a set of food items which
would provide a notional minimum amount of calories for a reference group (households). The
reference group is typically chosen such that the welfare of the households is close to the
expected poverty line. In the case of The Gambia, the reference group was defined as the set of
households belonging to the second, third and fourth quintiles, based on per capita real
consumption. The reference group was broadly defined in order to have a sample size sufficient
to get an accurate estimation of the food equation.
1.33 The minimum recommended caloric requirement was defined as 2,700 kcal per day for a
young adult male. The assumed daily nutritional requirement was chosen for two reasons. One,
this was the standard recommended by FAO/WHO/UNU (1985). Two, it was used in previous
estimates of the country’s poverty rates, based on the 1993 and 1998 household expenditure
surveys.9 Individual caloric needs were derived from the household by dividing by the average
9 However, FAO has more recently suggested 2,100 kcal for the daily caloric requirement and the World Bank
Institute (2005) also indicates that this is a widely used standard. Therefore, the Poverty Assessment team plans to
11
household size and multiplying by the average adult equivalence scale, which allows for
differential nutritional requirements by age and gender of household members. The adult
equivalence scale was based on the estimates made for the 1998 household expenditure survey.
Three domains are defined in order to account for geographical differences in consumption
habits: Banjul and Kanifing, other urban areas, and rural areas.
1.34 The food poverty line was estimated as the value of a set of food items which provide the
recommended caloric requirements, at prices prevailing during the survey. The non-food
component for the poverty line was defined in two different ways, for the lower and upper
poverty lines. For the lower poverty line, the non-food component was defined as the minimum
spending on non-food needs for a person who has a total (food and non-food) consumption level
equivalent to the value of the food items for the food poverty line. This can be considered the
minimum non-food consumption given that the person would have to reduce food consumption
below the food poverty line in order to consume non-food items. Thus the minimum caloric
requirements would not be met for the lower poverty line. For the upper poverty line, non-food
consumption was estimated such that the food share of consumption equaled the share
represented by the food poverty line. In this case, the minimum caloric requirements are just met
(see Box 3 for details on the estimation of the poverty lines).
1.35 The estimated poverty lines are presented in Table 4, for the food poverty line and the
lower and upper poverty lines, separately for each of the three domains. Several interesting
observations can be made about the poverty lines. The poverty lines for the rural areas are
relatively close to the mean consumptions in rural areas. From this alone it can be guessed that
the poverty headcount ratios are likely to be relatively high in rural areas and comparatively
higher than in urban areas. The food share of total consumption is much greater in rural areas
compared to urban areas. It implies that rural residents are more vulnerable to food price
fluctuations and therefore accounting for food and nonfood inflation separately could be
important when analyzing the impact of growth on poverty.
1.36 Finally, note that the estimated
poverty lines for rural areas are
actually higher than the corresponding
lines for urban areas. This is
essentially the result of a higher rural
food poverty line . It appears that the
food poverty line is slightly higher in
rural areas due to differences in the
weights and unit caloric prices
compared to the urban areas. The
difference in the food poverty lines
among domains is relatively small
when compared to the much larger
differences in total consumptions
across the three domains, and therefore the impact on the poverty estimates is relatively small.
re-estimate the poverty rates using this lower caloric requirement in order to analyze the impact of changing this
assumption.
Table 4: Poverty Line Estimates
Banjul and
Kanifing
Other
Urban Rural
Food Poverty Line (ZF)
Dalasis 4,488 4,337 4,615
US dollars 165 159 169
Percent of mean consumption 39.0 37.7 64.7
Lower Poverty Line (ZL)
Dalasis 5,636 5,835 6,145
US dollars 207 214 225
Percent of mean consumption 49.0 50.7 86.2
Upper Poverty Line (ZU)
Dalasis 6,388 6,771 7,009
US dollars 234 248 257 Percent of mean consumption 55.5 58.9 98.3
Source: 2003 IHS and authors’calculations.
12
1.37 The FGT poverty measures were estimated based on the poverty lines for the three
domains (see Table 5). Again the food poverty line and the lower and upper poverty lines are
presented separately for each of the three domains. According to the headcount ratios, 58.0
percent of the population is poor based on the upper poverty line and 51.2 percent is poor based
on the lower poverty line. The poverty rate is significantly greater in rural areas than in urban
areas, nearly double for the lower poverty line. In addition, rural residents account for 65 percent
of the total population. These two factors combined result in rural residents accounting for 76 to
77 percent of the poor, depending on the poverty line used.
Table 5: Overall Poverty Measures (2003)
(percent and standard errors)
Headcount
Rate (P0)
Poverty Gap
(P1)
Squared Poverty
Gap (P2)
Distribution
of the Poor
Distribution
of Population
Upper Poverty Line
Urban 39.6 (3.2) 14.8 (1.6) 7.3 (0.9) 23.7 (1.8) 34.7 (1.4)
Rural 67.8 (2.6) 30.5 (2.0) 17.4 (1.6) 76.3 (1.8) 65.3 (1.4)
Total 58.0 (2.1) 25.1 (1.4) 13.9 (1.1)
Lower Poverty Line
Urban 33.4 (3.2) 11.7 (1.4) 5.4 (0.8) 22.7 (1.9) 34.7 (1.4)
Rural 60.6 (2.9) 25.7 (2.0) 14.1 (1.5) 77.3 (1.9) 65.3 (1.4)
Total 51.2 (2.2) 20.9 (1.4) 11.1 (1.0)
Source: 2003 IHS and authors’ calculations.
1.38 The estimated poverty gap is 25.1 percent for the upper poverty line and 20.9 percent for
the lower poverty line. The poverty gap estimates are much larger in the rural areas compared to
the urban areas. Recall that the poverty gap is the population average of the shortfall of the poor
to the poverty line, as a proportion of the poverty line. Roughly based on the average of the
poverty lines, the estimated poverty gap indicates that theoretically poverty could have been
eliminated in 2003 if 2.3 billion dalasis, or US$84.1 million, were directly transferred to the
poor, assuming perfect targeting of the poor and no targeting costs or distortion effects. This
amount of transfer is equivalent to 23.8 percent of the GDP estimate in 2003. To put this in
perspective, total central government expenditures in 2003 was 22.9 percent of GDP.
1.39 The average of the squared poverty gap is estimated to be 13.9 percent for the upper
poverty line and 11.1 percent for the lower poverty line. As with the other poverty measures, the
squared poverty gap is significantly higher for rural areas compared to urban areas, indicating the
greater severity of poverty and inequality in rural regions of the country.
1.40 This urban-rural disparity is further analyzed by estimating more geographically
disaggregated estimates of the poverty measures, at the level of the LGAs (see Table 6). Brikama
accounts for the largest share of the poor, at 28 to 29 percent. Despite having the lowest poverty
rate outside of Greater Banjul, the semi-urbanized Brikama actually has the largest population of
the poor due to also having the largest total population. Kuntaur stands out for having a
particularly high poverty rate, significantly higher than the other LGAs. However, it accounts for
only 5 percent of the total population and less than 10 percent of the poor. The poverty rate is
13
particularly low in Banjul but its resident population is minimal. Although not as low, Kanifing
also has a poverty rate which is significantly lower than the other LGAs.
Table 6: Poverty by Regions
(percent and standard errors)
Poverty Headcount
Rate
Distribution of the
Poor
Distribution of
Population
Upper Poverty Line 58.0 (2.1) 100.0 100.0
Banjul 7.6 (5.0) 0.4 (0.2) 3.1 (0.4)
Kanifing 37.6 (4.2) 14.8 (1.6) 22.8 (1.2)
Brikama 56.7 (4.3) 28.5 (2.0) 29.2 (1.2)
Mansakonko 62.6 (7.1) 5.1 (0.6) 4.7 (0.5)
Kerewan 69.8 (5.0) 18.2 (2.0) 15.1 (1.1)
Kuntaur 94.9 (2.0) 8.8 (1.2) 5.4 (0.7)
Janjangbureh 75.7 (6.2) 10.9 (1.6) 8.4 (0.9)
Basse 68.0 (6.7) 13.2 (2.0) 11.3 (1.9)
Lower Poverty Line 51.2 (2.2) 100.0 100.0
Banjul 6.6 (4.1) 0.4 (0.1) 3.1 (0.4)
Kanifing 32.1 (4.2) 14.3 (1.7) 22.8 (1.2)
Brikama 49.0 (4.5) 28.0 (2.2) 29.2 (1.2)
Mansakonko 57.6 (7.4) 5.3 (0.8) 4.7 (0.5)
Kerewan 63.6 (5.6) 18.8 (2.1) 15.1 (1.1)
Kuntaur 90.0 (3.9) 9.5 (1.4) 5.4 (0.7)
Janjangbureh 62.2 (8,1) 10.2 (1.5) 8.4 (0.9)
Basse 61.1 (7.1) 13.5 (2.0) 11.3 (1.9)
Source: 2003 IHS and authors’ calculations.
1.41 Based on the poverty estimates of the LGAs, the country’s geographical distribution of
the poor can largely be viewed in two groupings: (i) the urban and peri-urban regions of Banjul,
Kanifing and Brikama which are the western regions situated below the Gambia river; and (ii)
the remaining five northern and eastern LGAs which are more rural and less populated. The first
group accounts for 55 percent of the population but only 43 to 44 percent of the poor due to
having lower poverty rates. Conversely, the second group accounts for 45 percent of the
population but 56 to 57 percent of the poor.
1.42 How sensitive are the
poverty findings to the estimated
poverty line? The analysis indicates
that changes in the poverty line result
in changes in the estimated poverty
rates in a manner which is relatively
predictable and does not result in
disproportionately large changes (see
Table 7). A percentage change in the
poverty line corresponds to a
relatively similar percentage change in the estimated poverty headcount ratio. This result
indicates that the underlying density function of per capita consumption is relatively smooth, at
least around the poverty line, which can also be seen in Figure 1.
Table 7: Sensitivity of Poverty Rate to the Choice of Poverty Line
(Percent)
Poverty
Incidence (P0)
Change from
actual (%)
Upper Poverty Line 58.0 0.00
+20% 67.5 16.39
+10% 63.3 9.13
+5% 60.9 4.97
-5% 55.5 -4.22
-10% 52.9 -8.72
-20% 46.7 -19.50
Source: 2003 IHS and authors’ calculations.
14
Household Size and Composition
1.43 The analysis of the poverty rates by household size and composition reveals that poverty
significantly increases for larger households with more children (see Table 8).
Table 8: Poverty by Demographic Composition, Upper Poverty Line
(percent)
Poverty Headcount
Rate
Distribution of the
Poor
Distribution of
Population
Upper Poverty Line 58.0 100.0 100.0
Number of children 0-6 years old
no children 35.3 11.2 18.4
1 39.1 13.4 19.8
2 58.4 20.2 20.0
3 or more children 76.7 55.3 41.8
Household size
1 7.2 0.1 0.7
2 11.2 0.2 1.1
3 15.6 0.8 2.9
4 25.3 1.7 3.9
5 35.6 3.6 5.8
6 40.5 4.9 7.0
7 or more 65.5 88.8 78.6
Source: 2003 IHS and authors’ calculations.
1.44 Gambian households are relatively large, with 79 percent of the population in households
with seven or more members and 42 percent in households with three or more children. These
households also account for a majority of the poor, nearly 90 percent and 55 percent
respectively. The estimated poverty headcount ratios continuously increase as the size of the
household and the number of children increase, resulting in a sizable difference in the poverty
rate between the largest and smallest households.
1.45 The larger households are likely to be extended family members living together as a
single household. Such household arrangements are likely to be more common in rural areas.
Hence, the higher poverty levels of larger households could simply reflect rural poverty. Larger
households could also reflect polygamous households, which again could be a more common
practice in rural areas. Based on the lower poverty line, members of polygamous households
have an estimated poverty rate of 61.4 percent compared to 43.1 percent for monogamous
households. The respective poverty rates estimated for the upper poverty line are 68.4 percent
and 49.9 percent.
Female Headed Households
Table 9: Poverty by Household Head's Gender
(percent)
15
1.46 The poverty rate differs
significantly depending on the
household head’s gender. Most of
the Gambian households are headed
by males and therefore the poverty
rates of male headed households are
similar to the average poverty
headcount rate. What is somewhat
surprising is that female headed
households have poverty rates significantly lower than male headed households. This is the
opposite result to the “feminization” of poverty that has typically been discussed in The Gambia.
1.47 There are several possible reasons
for why female headed households are
significantly less poor than male headed
households. One possible answer could be
that females are economically more active
and they tend to work in sectors such as
retail trade where earnings are relatively
higher. However, this does not appear to be
strongly supported by evidence. According
to the 2003 population census, the labor
force participation rate and the employment
rate for females are 46 percent and 41
percent respectively, compared to 53
percent and 52 percent respectively for
males. Hence, females are somewhat less economically active than males but overall the rates are
relatively similar, a pattern which can be seen in other SSA countries.
1.48 It also does not appear that females earn more than males. In fact, rough estimates
indicate that earnings of employed males are 1.6 times larger than those of employed females.10
According to the 2003 Population Census, a majority of economically active females, 62 percent,
are concentrated in the agriculture sector, where generally earnings are lower and hence the
estimated poverty rates higher (see Table 9). By comparison, only 35 percent of economically
active males work in the agriculture sector. Regarding retail trade, approximately 15 percent of
both males and females work in the sector, and more males in absolute numbers work in retail
trade given their larger overall economically active population. Males work in a wider range of
occupations, probably reflecting their educational and training background but also the
customary gender roles in the society.
1.49 Rather than differences in economic activity, female headed households could be less
poor than male headed households due to remittances. Without reliable data on remittances at the
household level, the analysis hinges on indirect evidences. First, an unusually large percentage of
female headed households are married, which would be consistent with a situation where the
husbands are away earning income and sending remittances. According to the 2003 IHS, an
10
Heintz et al (2007).
Poverty
Headcount
Distribution
of the Poor
Distribution of
Population
Upper Poverty Line 58.0 100.0 100.0
Male 60.5 90.8 86.9
Female 40.7 9.2 13.1
Lower Poverty Line 51.2 100.0 100.0
Male 53.5 91.1 86.9
Female 34.8 8.9 13.1
Source: 2003 IHS and authors’ calculations.
Table 10: Economically Active Population by Industry and Sex
(Percent)
Male Female
Total (Population) 282,440 230,970
Agriculture 34.7 62.4
Fisheries 2.0 0.5
Manufacturing 6.8 2.4
Electricity 3.9 0.2
Construction 6.6 0.2
Trade 14.9 14.1
Hotels and Restaurants 2.1 1.8
Transport 7.9 0.7
Financial 1.9 0.9
Social services 12.7 10.4 Not stated 6.4 6.5
Source: 2003 Population Census.
16
estimated 13 percent of households are headed by females and 53 percent of these female heads
of households are married. Both percentages could be considered relatively high for a traditional
Muslim country.
1.50 Second, regarding non-poor female heads, those whose employment status is inactive has
the lowest poverty rates among all possible employment statuses. By contrast, male salaried
heads have the lowest estimated poverty rates among employment statuses. Female heads who
are inactive have an estimated poverty rate of 17 to 21 percent depending on the poverty line,
whereas the average is 35 to 41 percent for all female head and 26 percent for salaried female
heads. Remittances could explain how female head who are not working could be less poor than
other female heads.
Table 11: Females Headed Households and Remittances
Mansakonko Kerewan Kuntaur Janjangbureh Basse Avg.
Percent married female heads 86.2 74.3 55.9 28.3 40.1 57.0
Percent out-migration 31.3 26.8 21.4 19.5 12.1 22.2
Source: 2003 IHS and 2003 Population Census, extracted from GBOS (2007).
Note: Out-migration is defined as the percent of population born in the LGA who were residing in other LGAs.
1.51 Third, the proportion of married female headed households is particularly high in LGAs
with high rates of out-migration (see Table 11). Mansakonko has the highest out-migration,
defined as the percent of the population born in the LGA but currently residing in other LGAs,
and also the highest share of married female headed households. The LGA with the next highest
rate of out-migration, Kerewan, also has the next highest percentage of married female heads.
This pattern would be consistent with male heads migrating for better employment opportunities,
thus leaving their spouses as heads of the households. The fact that female headed households
are less poor than male headed households could therefore be explained by remittances sent by
the migrant husbands.
Age Groups
1.52 Poverty headcount ratios estimated by age groups indicate that poverty in The Gambia
has a distinctly youthful characteristic. Nearly 60 percent of the poor in The Gambia are under
the age of 20, which reflects a bottom-heavy population pyramid resulting from relatively high
birth rates and rapid population growth. The country’s estimated birth rate is 38 per 1,000
population, higher than the world average of 21 but in line with the average of least developed
countries, 36, and the average of West Africa, 42.11
The estimated poverty rates by age groups
indicate that the very youngest, aged less than 15 years old, have relatively high poverty rates.
Those greater than 49 years old also have relatively high poverty rates but they account for a
small percentage of the population.
11
Population Reference Bureau, 2008 World Population Data Sheet.
Table 12: Poverty by Age Groups, Upper Poverty Line
(percent)
17
1.53 Higher poverty among the youths
could simply be the corollary of larger
households with more children having
significantly greater poverty rates.
However, the 2003 Population Census also
indicates that the urban youth
unemployment rate is very high, at 22
percent, compared to the national
unemployment rate of 6 percent and the
rural youth unemployment rate of 3
percent.12
Whether poverty alleviation
interventions should specifically target
youths would depend on whether the
underlying causes of poverty among youths
are factors specific to the youth population or to the households to which the youths belong. The
fact that the youth unemployment rate is high in urban areas which generally have smaller
households indicate that the former is an important factor.
1.54 In terms of the age of the household head, households with heads who are aged greater
than 49 have higher poverty rates. Households with heads aged greater than 64 account for
nearly a fifth of the population and also of the poor. These households have among the highest
poverty rates. This could simply reflect the fact that households composed of extended families
are more common in rural areas where the poverty rates are higher. Such extended households
would more typically be headed by an elder of the family.
Education Levels
1.55 The poverty rates
differ significantly by the
level of education of the
household heads (see Table
13). The vast majority of the
population and the poor have
no education. The estimated
poverty rates are almost
uniformly lower for
individuals whose household
heads have higher levels of
education. Having a
secondary or higher levels of
education reduces on average
the estimated poverty rates by 25 to 32 percent compared to having no education, depending on
the poverty line. Poverty estimates of all individuals by education levels show similar results.
Hence, education level is a critical component of the country’s poverty profile.
12
Unemployment rates extracted from Heintz et al (2007).
Age Poverty
Headcount Rate
Distribution
of the Poor
Distribution of
Population
0-5 63.8 17.5 15.9
6-14 62.1 27.5 25.7
15-19 55.2 11.7 12.3
20-24 55.5 9.6 10.0
25-29 51.3 7.7 8.8
30-34 50.2 5.6 6.5
35-39 52.1 4.5 5.1
40-44 54.3 3.8 4.0
45-49 53.1 2.8 3.0
50-54 58.6 2.4 2.3
55-59 61.2 1.6 1.5
60-64 58.6 1.7 1.7
65+ 65.0 3.5 3.2
Source: 2003 IHS and authors’ calculations
Table 13: Poverty by Education Level of Household Heads
(percent)
Poverty
Headcount Rate
Distribution
of the Poor
Distribution of
Population
Upper Poverty Line 58.0 100.0 100.0
None 63.5 82.5 75.3
Primary 47.2 3.3 4.0
Middle 45.0 0.8 1.0
Secondary/Voc 36.0 8.0 12.8
Tertiary 31.4 1.5 2.7
Lower Poverty Line 51.2 100.0 100.0
None 41.4 83.6 75.3
Primary 31.7 2.9 4.0
Middle 35.1 0.9 1.0
Secondary/Voc 16.5 7.2 12.8
Tertiary 22.6 1.4 2.7
Source: 2003 IHS and authors’ calculations.
18
1.56 In a similar vain, whether the household head is literate also results in a large difference
in the estimated poverty rate. Based on the lower poverty line, members of households whose
heads are literate have an estimated poverty rate of 28.7 percent compared to 59.1 percent for
households with non-literate heads.
Employment Status and Sector
1.57 With respect to the status of
employment of the household head,
as expected the poverty rates are
lower for the employed compared to
the unemployed (see Table 14).
However, the difference is not
statistically significant when the
standard errors are considered.
Moreover, these results need to be
interpreted with caution. The
definition of unemployed is rather
broad in The Gambia. Some survey respondents could have indicated that they were unemployed
when in fact they were engaged in self-employment. Also, in an extended household, the head of
the household could be an elder of the family who might not be the main provider for the
household. In such households, it would be more prudent to consider the employment status of
all the adult members of the household.
1.58 The poverty rates differ significantly according to the sector of employment of the heads
of the households (see Table 15). In particular, the poverty rates in the agriculture and fishing
sector are substantially higher than the other sectors, with the headcount ratio at 76 percent for
the upper poverty line and 69 percent for the lower line. Roughly half of the population works in
this sector. Hence, a combination of reducing poverty in the agriculture and facilitating labor
migration out of agriculture into the other sectors would have a sizable impact on the country’s
overall poverty rate.
1.59 The sector with the next highest poverty rate is construction and in fact agriculture,
fishing and construction are the only sectors with poverty rates above the national average. The
two sectors with the lowest poverty rates are: (i) travel, hotels and restaurants; and (ii) social and
personal service. These two sectors also have the largest shares of the population after
agriculture and fishing. Further increasing their shares could potentially have a large impact on
reducing the overall poverty rate.
Table 15: Poverty by Household Head's Industry Sector of Employment
Poverty Headcount
Rate
Distribution of the
Poor
Distribution of
Population
Upper Poverty Line 58.0 100.0 100.0
Agriculture and fishing 76.4 63.0 51.9
Table 14: Poverty by Household Head's Status of Employment
(Percent)
Poverty
Headcount
Rate
Distribution
of the Poor
Distribution of
Population
Upper Poverty Line 58.0 100.0 100.0
Employed 58.8 78.1 77.7
Unemployed 64.4 17.0 15.4
Homemaker 38.5 0.4 0.6
Retired 40.6 3.6 5.2
Student 72.7 0.4 0.3
Other 41.8 0.5 0.7
Source: 2003 IHS and authors’ calculations.
19
Groundnut producers 76.6 4.8
Manufacturing and energy 50.0 3.4 7.5
Construction 63.6 10.5 4.0
Trade, hotels and restaurants 48.8 3.8 17.7
Transport and communication 52.4 3.4 5.0
Financial management 49.2 4.2 1.5
Social and personal service 45.4 7.0 12.4
Lower Poverty Line 51.2 100.0 100.0
Agriculture and fishing 68.5 63.0 51.9
Groundnut producers 68.3 4.6
Manufacturing and energy 43.5 3.4 7.5
Construction 57.8 10.3 4.0
Trade, hotels and restaurants 41.7 3.9 17.7
Transport and communication 44.9 3.5 5.0
Financial management 45.4 4.0 1.5
Social and personal service 39.8 7.2 12.4
Source: 2003 IHS and authors’ calculations.
1.60 The 2006 Economic Census provides estimates of the average annual earnings per
employee, by industries (see Table 16). For each sector, the Economic Census provides the total
compensation to employees and the total number of employees. The estimated average annual
earnings indicate that financial services, construction and utilities have earnings substantially
higher than the other sectors. The next tier of sectors with relatively high earnings consists of
health, hotels and restaurants, telecommunication and fisheries.
1.61 What is interesting is that the estimated
poverty rates for construction are relatively high
but the average annual earnings are one of the
highest in the Economic Census. It could indicate
that the distribution of income in the sector is
highly unequal and that a large share of the
earnings is accrued to a small segment of the
sector while the majority has relatively low
earnings. This in turn would imply that there
could be limits to which the construction sector
could contribute to poverty reduction although it
has until recently been a booming sector and
considered a key driver of growth.
1.62 Based on the previously estimated consumption regressions (see Annex 1), the impact of
changes in various household characteristics on the probability of being in poverty was
estimated. Controlling for other factors, an uneducated household head becoming educated
reduces the probability of being poor in urban areas by 38 percentage points, by far the largest
impact. Education levels appear to have the largest impact on the probability of being poor.
However, the estimated impact of education is much smaller in rural areas, at 9 percent.
1.63 The household head being female as opposed to male reduces the probability of being
poor by 4 to 5 percent. This is consistent with earlier results that indicated that female headed
households had estimated poverty rates significantly lower than male headed households.
Table 16: Average Annual Earnings by Industry, 2006
(Dalasis)
Industry Avg Earnings
Fishing 25,453
Manufacturing 4,810
Utilities 57,309
Construction 57,539
Trade 5,020
Hotels, Restaurants 27,905
Transport 16,996
Telecommunications 27,015
Financial services 103,683
Real estate, business services 14,576
Education 9,056
Health and social work 34,715
Community and personal services 13,993
Source: 2006 Economic Census.
20
Increasing the number of children in the household is associated with increased probability of
poverty, with the probability increase greater for larger numbers of children. Interestingly, the
impact in increasing the probability of being poor is larger in the urban than rural areas.
Table 17: Changes in the Probability of Being in Poverty
(percent)
Urban Rural
Demographic event, child born in the family:
Change from having no children 0-6 years old to having 1 child 13.7 10.3
Change from having no children 0-6 years old to having 2 child 27.9 19.3
Change of household head:
change from Male to Female -4.8 -3.6
Education event, change in household's head education:
change from Uneducated to Educated -38.0 -9.3
Note: Based on consumption regression presented in Annex 1.
PPP Based Poverty Estimates
1.64 National poverty lines are the appropriate measure for setting national policies for
poverty reduction and for monitoring their results. However, they reflect price differences across
countries and heterogeneous local perceptions of the level of consumption or income needed to
be non-poor, given that different food bundles can provide the minimum caloric requirements
and nonfood spending considered essential can vary widely. For international poverty
measurements, there is a need for a uniform standard for comparing poverty rates. The number
of surveys in low to middle income countries has increased substantially over the last 20 years,
from 15 in 1980-82 to 118 in 2001-03. This provides a wider basis for constructing a
standardized poverty line and for international comparisons.
1.65 The international poverty line is determined from the average national poverty lines of
the poorest countries in the world, converted to U.S. dollars using Purchasing Power Parity
(PPP). PPP is the adjustment factor for differences in the purchasing power of the local
currencies, defined as the number of units of a country’s currency needed to buy the same
amount of goods and services in that country as one U.S. dollar would buy in the United States.
The PPP conversion factors take into account differences in the relative prices of goods and
services, including non-tradables, whereas the currency exchange rates are affected by BOP
flows and speculative demands. Therefore, the PPP conversion factors are a better measure of the
standard of living and for standardized cross-country comparison of real price levels
1.66 The international poverty line has long been defined as PPP US$1 dollar per day, used as
the basis for the Millennium Development Goal (MDG) indicator for halving the proportion of
poor people between 1990 and 2015. This standardized poverty line was derived originally from
studies covering 33 countries during 1980-90, whose mean poverty line adjusted to 1985 prices
was determined to be US$31 a month or US$1.02 a day. This was later updated to US$32.74 a
month or US$1.08 a day, based on the median of the lowest ten poverty lines of the original set
21
of countries, adjusted to 1993 prices using PPP conversions factors from the International
Comparison Program (ICP).
1.67 In 2008, the international poverty line was most recently updated based on the average of
the national poverty lines of the 15 poorest countries with new household surveys in 1990-05.13
These 15 countries consisted of 13 SSA countries, including The Gambia, and Tajikistan and
Nepal. The poverty lines were adjusted to PPP prices from the eighth round of the ICP conducted
in 2005, the most extensive and thorough effort to measure PPP for major components of the
GDP. The newly updated international poverty line is US$38 a month or US$1.25 a day, which
could be considered the international basis for extreme poverty.
1.68 Based on the ICP results, the 2005 PPP based GDP of The Gambia was estimated to be
US$1.1 billion, compared to the nominal GDP of US$366 million based on the average nominal
exchange rate. This large difference is reflected in the large difference between the estimated
PPP conversion factor of 7.56 and the average nominal exchange rate of 27.3 dalasis per US
dollar. It indicates that prices are quite low in The Gambia.
Figure 2: Cross-Country Price Level Indices
(GDP Price Level Indexes, World = 100)
Source: “2005 ICP Regional Summary: Sub-Saharan Africa.”
1.69 Price levels can be compared in terms of the Price Level index (PLI), which is the ratio of
a country’s PPP to the market exchange rate of the local currency. The PLI indicates the relative
price of GDP in a country, after acquiring the local currency at the prevailing nominal exchange
rate. A lower PLI would indicate that goods and services are priced relatively low in the country,
compared to the US. PLIs are generally low in poorer countries but The Gambia is exceptionally
low. The country is an outlier even accounting for its low income status.
13
World Bank (2008).
South Africa
The Gambia Ethiopia
Burundi
Namibia
Cape Verde
20%
30%
40%
50%
60%
70%
80%
90%
100%
GDP per capita
Zimbabwe (180%)
22
1.70 The country’s PPP conversion factor is particularly low in two areas: (i) housing, water,
electricity and fuel; and (ii) health. In turn, this is reflected in the low relative price levels in
these two components of the GDP. Components which have relatively high conversion factors
are food and alcoholic beverages and transportation.
1.71 Given that the country’s price levels are comparatively low, the expectation is that
poverty estimates based on the PPP adjusted poverty line are likely to be relatively low
compared to the previous estimates. This is in fact the case. For standardized estimation of the
poverty line, the international poverty line of US$1.25 per day is first converted to the local
currency using the 2005 PPP then converted to the survey year, which would be 2003 in the case
of The Gambia, using each country’s CPI. The local currency conversion is carried out using the
PPP for private consumption, which are calculated using the national average prices and
weighted GDP expenditures shares. For The Gambia, the estimated PPP conversion factor for
private consumption is 10.344.
Table 18: PPP Conversion Factors and Price Level Indices (2005)
PPP
(dalasis/US$)
Price Level Index
(World=100)
GDP 7.56 33
Individual Consumption 8.66 37
Food and nonalcoholic beverages 23.21 91
Alcoholic beverages, tobacco and narcotics 12.24 51
Clothing and footwear 10.94 39
Housing, water, electricity, gas and other fuels 2.59 12
Furnishings, household equipment and maintenance 11.01 40
Health 3.02 20
Transport 22.77 70
Communication 15.31 68
Source: World Bank, Global Purchasing Power Parities and Real Expenditures, 2005 International Comparison
Program, 2008.
1.72 For The Gambia, it has been estimated that 34.3 percent of the population is below the
PPP US$1.25 a day. This compares quite favorably with the poverty estimate for SSA of 50.9
percent and is on par with Senegal and Ghana. However, it should be noted that SSA’s poverty
rate is the highest among all regions and is considerably higher than the global estimate of 25.2
percent. In addition to US$1.25 a day, international poverty comparison can be carried out using
PPP US$2.00 a day which is the median poverty line for developing countries in 2005. The
estimated poverty rate for The Gambia based on US$2.00 a day is 56.7 percent, which again
compares favorably with the SSA regional estimate of 72.9 percent. Note also that this poverty
rate for The Gambia is essentially equivalent to the country’s national poverty estimate of 58.0
percent based on the upper poverty line.
Table 19: International PPP Poverty Headcount Ratios
23
The
Gambia Senegal Ghana
Sierra
Leone
Burkina
Faso Tanzania Uganda SSA
US$1.25/day 34.3 33.5 30.0 53.4 56.5 88.5 51.5 50.9
US$2.00/day 56.7 60.3 53.6 76.1 81.2 96.6 75.6 72.9
Source: World Bank (2008).
Comparability of the 1998 and 2003 Household Surveys
1.73 Previous to 2003, government had conducted a household expenditure survey in 1998. In
theory, these two surveys could be used to analyze poverty dynamics over time and in fact
various documents on the country by government agencies and development partners simply
compare the estimated poverty rates of the two surveys. In this section, it is argued that the 1998
and 2003 household expenditure surveys are not directly comparable due to significant
methodological differences. The two surveys differ in the survey collection period, recall period,
questionnaire design, the methodology for the construction of the poverty line, and the use of
price data. Some of these differences could be addressed to a certain extent while others would
be more difficult. Overall the required adjustments are nontrivial and substantial. In addition, the
comparison would go back to 1998 which has now become quite historical. Therefore, it was
decided that it would be more prudent to focus on poverty developments since 2003 using
simulation exercises (see Chapter 2) rather than on comparing the two previous surveys. The
main differences between the surveys are outlined below. What would be critical is that these
differences are addressed in the design of the next survey so that comparability is ensured in
future surveys.
1.74 Seasonality (survey collection period). The 1998 survey was collected from April to
May, whereas the 2003 survey was collected throughout the year through quarterly surveys.
Therefore, the former is affected by seasonal differences in income and consumption while the
latter addresses these differences by collecting data year-round. The concern is that seasonality
of consumption is an important factor in The Gambia for the large number of small subsistence
farmers who are also more likely to be poor, making it even more critical to correct for
seasonality when analyzing poverty dynamics. One possible method for adjusting for seasonality
differences between the 1998 and 2003 surveys is to only use respondents in the 2003 survey
who participated in the survey from April to May. This would correspond to the same months
when the field work for the 1998 survey was conducted. However, this would drastically reduce
the size of the 2003 survey.
1.75 Recall periods. Regarding food consumptions, in the 1998 survey households were
asked to recall recent consumption items and amount but they were allowed to choose the recall
period according to days, weeks, months, quarter and year. Note that by months, the respondents
were allowed to choose any number of months from one to twelve months. By contrast, food
consumption in 2003 was based on 30 day daily diaries.
1.76 There does not appear to be a simple method to adjust for such significant differences in
the recall periods between the two surveys. The literature suggests making comparisons using
24
consumption items with common recall periods.14
In the case of the The Gambia, the problem is
that only a small share of the recall periods in the 1998 survey matched the 30 day daily diaries
in the 2003 survey. Even if the recall periods are matched with the daily diaries, it is likely that
the daily diaries will be biased upward compared to the recall periods due to more detailed
recording.
1.77 Questionnaire design. There were many more consumption items in the 2003
questionnaire compared to 1998 questionnaire. It is likely that the more detailed and expanded
list of consumption items in 2003 resulted in the recording of higher consumption levels
compared to 1998. There are basically two recommended approaches in the literature. One, the
poverty lines can be constructed first by using expenditures items common to the two surveys,
then adding the other non-common items as averages of households consuming near the common
expenditures poverty line.15
Two, consumption aggregates can be reconstructed using only items
common to the two surveys.16
The major challenge for The Gambia is that the food poverty line
for the 2003 survey was essentially constructed using only four items, which would mean
imposing similar restrictions to the estimation of a comparable poverty line for the 1998 survey.
1.78 Poverty lines. The poverty lines were constructed in both years using the standard cost of
basic needs approach. However, the basket of food items is different in the two poverty lines.
Also, non-food component of the poverty line in 2003 was estimated by using an explicit
demand for food function whereas in 1998 the average consumption shares of households near
the food poverty line was used.
1.79 Price data. The price survey was separately collected in 1998 for Banjul, urban and rural
areas. In the 2003 survey, a separate price survey was not collected and instead unit prices were
calculated for each EA form the total value and quantity of consumption of select items.
QUALITATIVE POVERTY INDICATORS
1.80 Qualitative poverty assessment can complement quantitative analysis by providing more
detailed and nuanced subjective information directly from the poor. In The Gambia, the
government carried out a series of Participatory Poverty Assessments (PPAs) from 1999 to 2004
and piloted a Community Scorecard Survey in 2003.17
These exercises were important inputs
into the country’s Poverty Reduction Strategy Paper (PRSP).
1.81 PPAs are self-assessment participatory exercises through which communities analyze
their own social and economic challenges and constraints. It provides a forum through which the
poor can express their own views within the context of their values and attitudes. In The Gambia,
various tools were utilized to facilitate the self-assessment exercises: mapping to illustrate the
location of infrastructure and housing facilities within the community; identification of
ecological zones and natural resource based, particularly important for rural communities; gender
14
Deaton (2003) and Kijima and Lanjouw (2003). 15
Lanjouw and Lanjouw (2001) and World Bank (2004). 16
Paternostro et al (2001) and World Bank (2005). 17
See SPACO (2002) and SPACO (2004). A Baseline Service Delivery survey was also conducted in 2002 but the
outputs appear to have been lost.
25
analysis of access to productive resources; analysis of seasonal variations in economic activities;
Venn diagrams of interlinked community institutions18
; wealth ranking of community members;
and focus group discussions.
1.82 Using the wealth ranking, community members were categorized according to very poor,
poor, non-poor and rich. Based on these wealth rankings, the percentage categorized as very poor
was estimated to be on average 47 percent for the dry season and 57 percent for the wet season.
If the poor are added, then the average percentage increases to 80 percent and 89 percent,
respectively. Two interesting observations can be made. First, the percentage of the very poor
appears to be in a similar range with the poverty headcount ratio estimated from the 2003 IHS. It
offers some comfort that the quantitative estimates of the poverty rate is not entirely
unreasonable. Second, there are nontrivial differences in the poverty rate between the dry and
wet seasons. However, this should be interpreted with caution as the difference and even the
ranking can change from one year to the next.
1.83 PPA participants perceived poverty to be the inadequacy of food, clothing and shelter.
Given the relative lack of information on shelter and assets in general in the previous household
survey, this is an area that could benefit from more focused attention in the next survey given its
importance to the poor. Access to education, health care and potable water was also cited as
inhibiting factors responsible for persistent poverty.
1.84 The causes of poverty cited in the PPAs differed by rural and urban areas. In the rural
areas, the causes of poverty were mainly related to agricultural production, including inadequate
rainfall, lack of farm inputs and implements, soil erosion, lack of markets for farm produce and
high interest rates on loans. Credit buying of groundnuts was also cited as a cause of poverty,
highlighting the importance of restructuring the groundnut sector. In the urban areas, the main
causes of poverty were the lack of employment and income generating opportunities, high costs
of health and education services, and the lack of capital to set up businesses. Both rural and
urban residents cited large family size and high dependency rates as causes of poverty.
1.85 PPA participants acknowledged that there is a strong gender dimension to poverty in
terms of access to, control and ownership of productive assets, including land, farm implements,
crops and credit. The land tenure system in rural areas is mostly by inheritance limited to men.
Land is generally owned, controlled and distributed by males. Females can access and work on
the land but generally do not enjoy ownership. In the urban areas, the PPAs indicated that gender
barriers to formal and informal employment are not significant.
1.86 Problems in education identified by PPA participants included inadequate numbers of
qualified teachers, high educational expenses, long walking distances to schools, lack of
boarding facilities and limited numbers of skills training centers and literacy classes. The PPA
also indicated that the scholarships for girls discourage education of boys. With respect to
challenges in health care, PPA participants cited the lack of drugs at and distance to health
facilities, high cost of drugs at pharmacies and disrespectful attitudes of health care workers.
18
Venn diagrams are diagrams that show all hypothetically possible logical relations between a finite collection of
sets (groups of things). Venn diagrams were conceived around 1880 by John Venn. They are used in many fields,
including set theory, probability, logic, statistics, and computer science.
26
There was a general sense of dissatisfaction with the performance of the Primary Health Care
(PHC) facilities, noting that some were simply not functioning. As a result, patients sought
health services outside of the PHC system despite added costs and distance.
1.87 The pilot Community Scorecard survey mostly corroborated the results of the PPAs. This
country-wide pilot covered 15 health facilities and 61 education facilities in all regions except for
Greater Banjul. These samples covered 51 percent of health facilities and 60 percent of education
facilities. Health facilities included health centers, dispensaries and hospitals but not PHCs.
Education facilities covered lower and upper basic schools but excluded senior secondary
schools. The survey consisted of assessments by the communities of the performance of health
and education facilities based on both standard indicators and indicates generated by the
communities and service providers themselves.
1.88 With regards to health facilities, the survey indicated weak staff capacity with less than
30 percent satisfactory rating of adequacy of staff at health facilities across the regions.
Availability of essential equipment received less than 15 percent satisfactory ratings except for
Kanifing. It was recognized that the lack of regular supply of electricity and water affects the
provision of essential equipment. Approximately 40 percent of health facilities did not have
regular supply of electricity. The overall rating for the availability of drugs was relatively
encouraging at 40 percent but communities felt that drugs were always in short supply expect for
essential drugs such as anti-malaria drugs during the malaria season.
1.89 With regards to education facilities, teachers received more than 70 percent approval
rating in all regions except for Kanifing, an urban area where there are more pupils per teacher.
Ratings for the adequacy of furniture and the availability of core text books and toilets were all
below 40 percent in all regions. The ratings were the worst for the urban Kanifing schools, where
none of the schools met the minimum requirements for the availability of functioning toilets (1
per 25 students) and core text books (1 per student) as well as the minimum teacher to student
ratio of 1:45.
CONCLUSIONS
1.90 The Gambia has a high rate of poverty but compares favorably in SSA. Based on the
2003 IHS, the estimated poverty headcount ratio is 58.0 percent for the upper poverty line and
51.2 percent for the lower poverty line. Based on the PPP international poverty lines, the
estimated headcount ratio is 34.3 percent for the US$1.25 a day, compared to 50.9 percent for
SSA as whole, and 56.7 percent for the US$2.00 a day, compared to 72.9 percent for SSA.
1.91 Food dominates the consumption of Gambians, accounting for 61.2 percent. Moreover, this share increases for poorer households. Hence, the country’s poverty rate will be
relatively sensitive to prices on food items. Expenditures on education and health are small, at
1.1 and 1.0 percent, respectively.
1.92 The distribution of income is far from egalitarian but is in line with similarly poor
countries. The estimated Gini coefficient is 0.46, within the range of 0.40 to 0.65 that is typical
27
for poor countries. Decomposition of inequality measures by urban and rural areas indicate that
inequality within each region is larger than across regions.
1.93 Poverty in The Gambia varies greatly along multiple dimensions. The estimated
poverty rates are significantly higher for larger households with higher dependency ratios (more
children), male headed households, households whose heads have low levels of schooling, the
agriculture sector and the LGAs east of Brikama, particularly Kuntaur. The education and health
indicators also vary greatly by region, income and in particular the education level of the
mothers.
1.94 The educational status of the household heads and the number of children have a
particularly large impact on the probability of being poor. Regression analysis indicates that
in urban areas households with heads who are educated have a 40 percent less probability of
being poor compared to households with uneducated households.
1.95 In contrast to previous results, female headed households have lower poverty rates
than male headed households. The estimated poverty headcount ratio for female headed
households is 40.7 percent, compared to 60.5 percent for male headed households. Evidences
indicate that female headed households are less poor due to remittances.
1.96 Participatory qualitative surveys identified several shortcomings in education and
health facilities: (i) strong dissatisfaction with Primary Health Care (PHC) facilities; (ii) lack of
electricity at health facilities, resulting in the unavailability of essential equipment; (iii) short
supply of drugs; and (iv) overcrowding and lack of text books and toilets in schools in urban
centers (Kanifing). However, the validity of these findings should be reconfirmed given that
these surveys were mostly conducted in the early 2000s. Participants in these surveys also cited
large family size and high dependency rates as causes of poverty.
28
2. HEALTH AND EDUCATION
HEALTH SECTOR
2.1 The Gambia has a comprehensive set of health policies, including the National Health
Policy, a National Drug Policy, and a National Nutrition Policy. Health has the third largest
budget among government ministries. Primary and secondary health care have expanded
significantly, particularly the construction of health centers through donor financing. Moreover,
investments in health centers have mostly been made in poorer regions. Physical access to basic
health services is generally good, with 85 percent of the population living within one-hour travel
time, or 7.5 km, of a health facility. Hospital beds, at 1.21 beds per 1,000, compare favorably
with other Sub-Saharan Africa (SSA) countries. The challenge now is to ensure that the health
centers are properly equipped and staffed.
2.2 The country’s health indicators are relatively good when compared to SSA standards,
particularly for child health indicators. The overall life expectancy of 59 is in line with the
average of low income countries and is better than the SSA average.19
The country’s Infant
Mortality Rate (IMR) and Under-5 Mortality Rate (U5MR) were one of the highest in the region
up to the 1980s but they have subsequently declined dramatically such that they are almost on
par with Ghana and Senegal, the best performers in West Africa. However, the declines have
slowed down in recent years. The IMR declined from 159 per 1,000 live births in 1980 to 92 in
1999 but remained at 93 in 2006. The U5MR declined from 350 per 1,000 live births in 1960 to
128 in 2000, one of the largest declines observed in the region. 20
The U5MR also did not
improve in recent year, remaining at 131 in 2006.21
2.3 In addition, there are large disparities in child mortality indicators across regions and
income groups. The estimated U5MR in rural areas is 150 compared to 96 in urban areas and 158
for the poorest quintile compared to 72 for the richest.22
Kuntaur, the LGA with the highest
estimated poverty rate, also has the highest U5MR at 195. Hence, accelerated improvements of
child mortality will likely require targeted interventions of the poorest groups. The five most
common causes of child mortality in The Gambia are malaria, upper-respiratory tract
infection/pneumonia, premature and pre-term births, birth asphysia and neonatal sepsis.
Interventions can be successful given that these common causes are preventable.
Figure 3: Percentage of Children Stunted by Mother's Education Level
19
Source is Population Reference Bureau. 20
The main source of health data in The Gambia are the MICS, most recently conducted in 2005/6. 21
Data source for other countries is the World Bank, World Development Indicators (WDIs). Note that the U5MR
estimated from the 2003 population census is 99 per thousand live births, much lower than the estimates from the
MICS. 22
The socioeconomic classes in the MICS are based on an index constructed from indicators of household wealth.
29
Source: 2003 MICS and Population Reference Bureau.
2.4 Malnutrition remains a major underlying problem for child mortality. Child malnutrition
has been associated with lower IQ and fewer years of schooling and lower productivity and
income for adults. According to available information, child malnutrition status has shown little
improvements in recent years and it could be even argued that the situation has somewhat
deteriorated. The percentage of newborns with low birth weight (below 2,500 grams) was 12
percent in 2000 and it increased to 20 percent in 2006. In 2000, 19.1 percent of children under 5
were stunted (low height for age) and the percentage actually increased to 22.4 percent in 2006.
The percentage of under 5 children who were underweight (low weight for age) also deteriorated
from 17.1 percent in 2000 to 20.3 percent in 2006. However, the percentage of children who
were wasted (low weight for height) declined from 8.2 percent in 2000 to 6.4 percent in 2006.23
There are also large disparities across regions and income groups.
2.5 Contributing factors to child malnutrition include inappropriate child feeding practices,
particularly the lack of breastfeeding, inadequate complementary feeding after 6 months age and
limited access to food of pregnant and lactating mothers. Micronutrients deficiencies (vitamin A
and iodine) are also important factors. Note also that there is a strong association across countries
between child malnutrition indicators and the education level of the mothers, including in The
Gambia (see Figure 3). Children of less educated mothers are more likely to be malnourished.
2.6 Maternal mortality ratios (MMRs) are difficult to measure and the relevant confidence
intervals are usually quite large. Based on Maternal Mortality Surveys, the estimated MMR in
1990 was 1,050 per 100,000 live births, which appears to have declined to 703 in 2002.
However, the lower and upper estimates which reflect the margins of error are 250 and 1,500,
which would mean that the decline is not statistically significant.24
The Gambia’s estimated
MMR is in line with SSA average of 900, which is significantly worse than the world’s average
of 400. The estimated MMRs of neighboring countries vary significantly: Senegal at 980, Ghana
at 560, Cote d’Ivoire at 810, Burkina Faso at 700, Mali at 970, Liberia at 1,200 and Sierra Leone
at 2,100.
2.7 Wide variations exist in MMR throughout the country. The MMR in rural areas is
estimated to be almost double that of the urban areas. Areas in the eastern regions of the country
23
Data source is the 2000 and 2003 MICS’s. The child malnutrition indictors are in comparison to a reference
population determined by WHO/CDC/NCHS. The percentage is based on two standard deviations from the median
of this reference population. 24
WHO et al (2005).
30
are estimated to have much higher MMRs than the western and central regions. Areas with an
easy access to primary health care facilities reported an MMR half that of areas with little access,
suggesting the access to services is an important determinant of MMR. Most causes of maternal
mortality in The Gambia are preventable. Among the top causes are eclampsia, sepsis and ante-
partum hemorrhage. Most of these causes can be addressed by providing adequate attention to
high risk cases, improving maternal and child health service access and providing appropriate
and timely referral and treatment of obstetric complications.
Table 20: Share of Births Attended by Skilled Health Staffs
The Gambia Senegal Ghana SSA Avg
1990
2000 2006 2002 2006 2005
Percentage of Births 44 55 57 58 49 45
Source: 2000 and 2005/6 MICS for The Gambia, and WHO for Senegal and Ghana.
2.8 Increasing the percentage of births attended by skilled health professionals trained in
antenatal care would also contribute towards reducing the relatively high MMR. Skilled health
professionals are defined to include only medical doctors, nurses, auxiliary midwives. For
comparison purposes, it does not include traditional birth attendants. This percentage increased
substantially from 44 percent in 1990 to 55 percent in 2000 but then improved minimally to 57
percent in 2006. As usual, there are significant regional and income disparities. The estimated
percentage is 83 percent for urban areas compared to 43 percent for rural areas, and 28 percent
for the poorest quintile compared to 89 percent for the richest. Kerewan has a particularly low
percentage among the LGAs, at 28 percent. The percentages of births attended by skilled health
professionals are very similar to the percentages of delivery in health facilities. This indicates
that births attended by skilled professionals almost inevitably take place in these facilities.
Hence, access to these health facilities is likely to be a critical determinant of the presence of
health professionals during birth.
2.9 For HIV/AIDS prevention and treatment, the country has a national institutional structure
based on a multisectoral approach, based on the National HIV/AIDS Council and the National
HIV/AIDS Secretariat (NAS). However, the restructuring of NAS in 2007 and the termination of
the post of divisional and municipal coordinators at the close of the IDA’s health project
virtually ended the active presence of a regional structure for NAS at that level.
2.10 The latest sero-surveillance data indicate that HIV-1 prevalence has doubled from 1.4
percent in 2002 to 2.8 percent in 2006. The prevalence of HIV among young women and men
aged 15-24 has also tripled from 0.8 percent to 2.4 percent during the same period. While the
Global Fund to Fight AIDS, TB and Malaria (GFATM) increased access to HIV/AIDS services
considerably, only 8.8 percent of people with advanced HIV infection and 14.1 percent of HIV
positive pregnant women receive antiretroviral therapy (NAS 2007). Also, there has been a
funding gap in the HIV response, especially for prevention through NGOs and CSOs, since the
closing of an IDA HIV/AIDS project in end-2006. The country is currently applying for
additional funding from the GFATM.
31
2.11 The prevention and treatment services for malaria have expanded considerably but it is
still the leading cause of morbidity and mortality, especially among pregnant women and
children under five years. Incidence of tuberculosis is estimated at 257 per 100,000 persons
(2006). Next challenges are to conduct a more thorough assessment of malaria and tuberculosis
and to strengthen disease surveillance and response capacity at all levels.
EDUCATION SECTOR
2.12 The government has a comprehensive Education Policy (2006-2015) which focuses on
expanding and improving the quality of education. Education benefits from: (i) the largest
government budget among all ministries; (ii) support from donors, including IDA, DFID, AfDB
and UNICEF; and (iii) funding from the Education for All Fast Track Initiative (EFA FTI)
catalytic grant. The education sector has benefited from an extensive government program of
expanding infrastructure, teacher training and school materials. Over 1,000 classrooms were built
under the first phase of IDA’s education project. There is a strong public-private partnership for
secondary education, thus allowing the government to leverage its own resources.
2.13 External aid has been critical for covering the government’s resource gap. Since 2003,
the country has received three US$4 million per year allocations under EFA FTI and was
approved a new multiyear allocation of US$28 million in December 2008. In comparison, the
government’s domestic budgetary allocations for education in 2008 were approximately US$18
million. In applying for the new allocation, the government committed to increasing the
education share of the domestic budget to 19 percent. The share of education in the budget
allocations, net of interest payments, increased from 14.4 percent in 2008 to 16.1 percent in the
2009 and hence would need to increase further in order to meet this commitment.
2.14 The education sector has seen some important gains
over the past five years but the challenges remain. Expansion
of access was noticeable for upper basic and senior
secondary schools but improved little for the lower basic
schools (see Table 20). However, the enrollment rates for
lower basic schools were already at a relatively high level,
compared to the SSA average of 69 percent (2000/1), and the
rates would increase by an additional 10 to 15 percentage
points if Madrassas (Islamic schools) were included. As in
other countries in the region, Madrassas vary greatly in the
instructions they offer, with some of them aligning their
curriculum with the official programs while others focus on providing religious training. Girls
lower basic GER increased substantially, thereby eliminating the gender gap, but the decline in
the corresponding boys GER has been a concern. There have been sizable increases for the upper
basic and senior secondary levels but enrollment in the senior secondary schools remains
relatively low.
Table 21: Gross Enrollment Rates
1998/99 2005/6
Lower Basic 73 74
Male 80 77
Female 67 71
Upper Basic 40 65
Male 48 66
Female 32 65
Senior Secondary 16 28
Male 21 33 Female 11 24
Source: Government of The Gambia
Table 22: Net Enrollment Rates, 2005/6
Primary Secondary
Male Female Total Male Female Total
Total 60 62 61 39 34 37
32
2.15 In line with the relatively
rapid population growth, the
school age population is also
growing at a rapid pace. The
greatest demand for school places
is in urban and peri-urban areas,
although rural areas have also
shown significant increases in the
school-age population. Access to schooling continues to show substantial regional and income
disparities. Great Banjul, Kanifing and Brikama have significantly higher Net Enrollment Rates
(NERs) compared to the rest of the country.25
Kuntaur has the lowest rates among LGAs, for
both primary (41 percent) and secondary schools (20 percent). The mother’s educational
background is strongly associated with the likelihood of the child’s schooling.
2.16 A benefit incidence analysis indicates that government spending for primary schools is
progressive but it is regressive for secondary schools (see Table 22). Overall spending on
primary and secondary schools together is progressive. It is progressive for primary schools
because poorer households have more children in school. The average number of children per
household in each level of schooling is estimated from the 2003 IHS. The government’s
budgetary allocations and the projected student population for each schooling level are for 2009.
The estimated average government spending per student is 989 dalasis (approximately US$37 at
current exchange rates) for primary schools and 1,182 dalasis (US$44) for secondary schools.
Hence, per student government budget allocations for secondary schools are higher than for
primary schools.
2.17 Assuming that the average number of children in school per household in 2009 remains
constants from 2003, the analysis indicates that the poorer households benefit from government
spending on schools to a much greater degree than richer households. Poorer households have a
lesser percentage of their children in school, but this is compensated by the fact that they have
more children than richer households.26
Richer households benefit more from government
spending on secondary schools due to having more children attending secondary schools, but the
difference in benefits is much smaller than the greater benefits poorer households receive for
spending on primary schools. This analysis does not include government spending on the tertiary
educational institutions. Per student spending at this level is significantly higher, at
approximately 9,936 dalasis (US$368) assuming roughly a student population of 5,000.
Although there are government student scholarships for the tertiary schools, most likely most of
the students are from households in the upper quintiles of the income distribution.
Table 23: Benefit Incidence Analysis of Government Education Spending
25
Net enrolment Ratio (NER) is defined as the proportion of the enrolment of the relevant age group to the
population of the relevant age group in percentage terms. Gross enrolment Ratio (GER) is defined as the proportion
of the total enrolment to the population of the relevant age group in percentage terms. 26
When the benefit incidence analysis indicates regressive government spending on schools, typically it is the result
of higher income households having more children in schools, particularly upper level (secondary) schools,
combined with higher government spending per student for upper level schools.
Regions
Urban 75 73 74 56 49 52
Rural 53 57 55 29 24 26
Quintiles
Poorest 43 46 44 21 14 18
Richest 72 79 76 61 58 59
Mother’s education
None 57 59 58 38 32 35 Secondary + 80 82 81 60 55 57
Source: 2005/6 MICS.
33
Quintiles All
1 2 3 4 5
Avg number of children 6.20 5.13 4.35 3.87 2.31 3.95
Avg percent in school 39 36 46 47 46 44
Avg number of children in school
Lower basic 1.43 1.08 1.08 0.97 0.64 0.98
Upper basic 0.96 0.85 0.92 1.02 0.66 0.86
Secondary 0.15 0.15 0.15 0.18 0.24 0.18
Total 2.54 2.09 2.15 2.18 1.55 2.03
Avg govt spending (dalasis)
Lower basic 1,416 1,070 1,067 962 637 973
Upper basic 946 844 910 1,012 656 854
Secondary 176 178 182 218 282 216
Total 2,538 2,091 2,159 2,192 1,576 2,044
Source: 2003 IHS, 2009 government budget and Third Education Project – Phase 2 PAD for student population
projections.
2.18 Education quality remains the biggest challenge. The failure rates in standardized tests
indicate that the quality of education is still poor. In particular, girls continue to under-perform
boys at all levels. Low salaries create challenges for recruiting and retaining qualified personnel
into the system. The Gambia Teacher Training College has been significantly expanding its
class size, by 72 percent from 2000 to 2005. However, the poor academic standing of the new
students has been a concern. Moreover, Gambia College lacks adequate capacity to address this
knowledge gap before the teacher-trainees are deployed to classrooms. In order to meet the
increased demand for teachers, the government has been relying on “unqualified” teachers (30
percent of lower basic school teachers are unqualified) and has been implementing accelerated
learning programs to build their capacity. The government has programs to recruit and retain
qualified staff, including hardship allowances and improved working conditions for teachers
assigned to rural areas. More than 90 percent of those sent for overseas training are back in the
civil service, an exceptionally high percentage.
2.19 According to the government’s National Literacy Policy (2005-15), The Gambia has a
low literacy rate of 46 percent. The estimated literacy rate is even lower at 28 percent for
females. However, the accuracy of these estimates is questionable, particularly since the 2005/6
MICS indicates a literacy rate of 43 percent for females. What is clear is that a large section of
the population is functionally illiterate. The MICS findings indicate that the literacy rates are
particularly low in rural areas (31 percent), the poorest quintile (16 percent) and in Basse (13
percent) and Kuntaur (16 percent). These results are for females but the qualitative conclusions
can likely be applied to the male population as well. The government has identified the
importance of increasing opportunities for adult and non-formal education, including through
large scale adult literacy courses as part of a national functional literacy program. The
government’s National Training Authority coordinates technical and vocational education
training provided by public and private agencies. Its capacity building is currently supported by
several donors.
2.20 Early Childhood Care and Development (ECCD) is an integral part of the government’s
Education Policy. The government focuses on coordination and supervision while actual services
are mostly provided by the private sector. There is a multisectoral working group, encompassing
34
the government, local communities, CSOs and international agencies, which sensitizes the public
on ECCD issues and supports ECCD centers. Since ECCD is largely provided by the private
sector, affordability becomes an issue and poses a major constraint for poorer households. In
addition, most of the ECCD facilities are concentrated in the Greater Banjul and Brikama areas,
whereas the rural areas are relatively neglected.
CONCLUSIONS
2.21 Child mortality indicators compare well in SSA after significantly improving since
the 1980s but there is little evidence that the Maternal Mortality Rates have improved.
Improvements in child mortality indicators have stagnated in recent years. Malnutrition among
children remains a major concern. Malaria is a leading cause of morbidity and mortality. The
HIV-1 prevalence rate remains relatively low but has doubled from 2002 to 2006.
2.22 Gross enrollment rates at lower basic schools have stagnated at 73-74 percent since
the late 1990s but they improved significantly for upper basic and senior secondary
schools. Government expenditures are regressive for senior secondary schools but progressive
for lower and upper basic schools. The quality of schooling remains a major concern, as
indicated by the low scores in standardized tests.
35
3. GROWTH AND POVERTY DYNAMICS
3.1 The poverty profile of The Gambia is based on the 2003 IHS, the latest available
household survey. Although it is likely that many of the basic features of the country’s overall
poverty profile have not changed significantly since 2003, it would still be worthwhile to explore
how poverty rates could have been affected by economic developments in the six years since the
previous household survey. For this purpose, this chapter describes the methodology and outlines
the results of a series of simulation exercises through which the poverty rates of 2008 and 2009
are “projected” from the 2003 IHS, by modeling the impacts of the country’s development
experience since 2003. The projections for 2008 are based on actual past developments. Various
assumptions were made where relevant data were not available. In addition to past data, the
projections for 2009 incorporate expected developments in 2009. The projection for 2009 can be
used to explore the short term impact of the ongoing global economic slowdown.
3.2 With regards to overall economic developments since 2003, the simulations focus on
growth variations among sectors and their likely distributional impact on the population. This
includes the impact of labor migrations from the slow to faster growing sectors, an
understandable response as individuals attempt to improve their livelihood. In attempting to
simulate the poverty rates, it will also be crucial to incorporate changes in international
remittances. The Gambia received international remittances at an annual average of 9.6 percent
of GDP since 2003, significantly larger than the average of 3.6 percent for developing countries
and among the top 20 in the world in terms of percentage of GDP.27
Hence, changes in
remittances could have had a significant impact on growth and poverty.
3.3 How recent economic developments could
have impacted poverty would need to be analyzed
within the context of The Gambia’s overall
economic structure. The country’s economic
structure has not changed significantly over the
years, including since 2003 (see Figure 4). The
economy remains relatively undiversified and
limited by a small internal market. Services are the
largest segment of the economy but agriculture
provides for the livelihood for a greater segment of
the population. Services account for approximately
half of GDP, reflecting the importance of trade,
communication and tourism, and provide the
livelihood for a third of working population. Agriculture accounts for approximately a third of
GDP but half of the working population and 63 percent of the poor. Hence, the performance of
the agriculture sector will have a disproportionate impact on poverty developments. The
27
World Bank, Migration and Remittances Factbook 2008.
Figure 4: Sectoral GDP Composition
(Percent)
Source: GBOS, IMF and staff calculations.
36
groundnuts subsector is particularly critical given that it is the country’s largest cash crop and
export and yet groundnut farmers are also among the poorest in the country. Industry accounts
for the remaining 15 percent of GDP, within which the largest subsector is construction followed
by manufacturing. Manufacturing, which includes groundnut and fish processing, is relatively
small at approximately 5 percent of GDP.
3.4 Since 2003 when the IHS was conducted, The Gambia has generally enjoyed steady
growth and a stable macroeconomic environment (see Table 23). Real GDP growth has averaged
6.2 percent since 2003 after a brief contraction in 2002 due to low rainfalls and poor
macroeconomic management. Growth has been supported by substantial foreign direct
investments, averaging 12.6 percent of GDP since 2003, in sectors such as tourism, construction
and mobile telephone operation. Sustained fiscal and monetary discipline has resulted in inflation
rates generally in the single digits and a relatively stable exchange rate.
Table 24: Real Sectoral Growth Rates
2004 2005 2006 2007 2008 2009 2004 -08
Cumulative
2004-08
Average
Agriculture 14.4 2.7 1.1 2.0 15.5 3.8 39.8 6.9
Groundnuts 46.0 3.7 -11.1 -7.2 25.0 3.0 56.0 9.3
Other crops 7.5 2.0 6.0 5.0 20.0 4.0 46.4 7.9
Livestock 3.2 2.3 4.5 4.5 3.0 4.0 18.7 3.5
Forestry 3.1 3.7 4.5 4.5 1.0 4.0 17.9 3.4
Fishing 10.0 5.0 5.0 4.5 3.5 4.0 31.2 5.6
Industry 3.0 6.4 13.6 8.0 0.8 3.5 35.7 6.3
Manufacturing 5.7 1.2 4.0 4.0 -2.3 4.0 12.9 2.5
Construction and mining 3.5 11.0 22.0 11.0 2.0 3.0 58.7 9.7
Electricity, water supply -13.0 5.0 5.0 5.0 9.0 5.0 9.8 1.9
Services 3.0 7.7 7.7 6.9 3.5 4.2 32.2 5.7
Trade 3.4 2.7 4.0 2.4 -4.1 2.7 8.4 1.6
Groundnuts 2.5 -2.0 3.5 -4.3 2.0 4.0 1.6 0.3
Others 3.5 3.5 4.0 3.5 -5.0 2.5 9.5 1.8
Hotels and restaurants 1.0 23.0 12.6 14.3 3.0 2.5 64.7 10.5
Transport, communications 4.8 10.7 11.6 9.2 6.7 5.2 50.8 8.6
Transport 3.0 3.0 4.0 4.0 4.0 3.5 19.3 3.6
Communications 6.0 15.7 16.0 12.0 8.0 6.0 72.1 11.5
Real estate, business services 2.2 3.0 3.0 3.0 1.0 3.0 12.8 2.4
Public administration 0.0 2.3 2.7 4.0 5.0 5.0 14.7 2.8
Other services 2.2 3.0 3.0 3.0 2.4 3.0 14.4 2.7
Real GDP 7.0 5.1 6.5 6.3 5.9 4.6 34.9 6.2
Source: IMF and authors’ calculations.
Note: Growth rates from 2004 to 2008 are estimates while the growth rate for 2009 is a projection. The average growth rates are
the average annual cumulative growth rates.
3.5 The impact of the global economic slowdown on the Gambian economy in 2008 was
mitigated by a significant rebound in crop production due to relatively good rainfall. In 2009, the
projected annual real GDP growth rate is currently 4.6 percent, which incorporates a reduction of
1.5 percentage points from the previous projection. However, projections are in constant revision
given the evolving global situation and there are already preliminary indications that
macroeconomic projections will need to be further downgraded.
37
3.6 Although growth since 2003 has been generally robust, the key question is whether the
sectoral pattern of growth has been conducive to poverty reduction and inclusive of the poor.
This would not be the case if growth was concentrated in sectors which have relatively low
poverty rates and where most of the poor do not derive their income and if barriers to mobility
prevent the poor from moving into the sectors with higher growth. In the case of The Gambia,
the concern has been that growth in fact has not been particularly pro-poor given that tourism,
construction and telecommunication have often been cited as the main drivers of growth whereas
many of the poor work in agriculture.
3.7 Since 2003, the telecommunication, hotels and restaurants, and construction sectors
registered the three highest cumulative growth rates (see Table 23). However, surprisingly the
groundnuts and other crops subsectors experienced the next highest rates of growth, in contrast
to the perception that they are low growth sectors. The high growth rates are somewhat
misleading as they were at least partially due to low initial levels of crop production in 2002.
Crop productions sharply contracted in 2002 when the country received low rainfalls. As a result,
goundnut production declined by roughly half and other crops by one third. In fact, the average
annual cumulative growth rate in 2001-08 for the groundnut sector is a negative growth rate of -
0.6 percent, compared to 9.3 percent in 2004-08. Similarly, the average annual growth rate for
other crops was 3.6 percent from 2001 to 2008 compared to 7.9 percent from 2003 to 2008.
Nevertheless, the sectoral growth rates from 2004 and beyond are the relevant growth rates for
the purposes of this analysis given that the IHS was conducted in 2003.
3.8 The groundnut subsector has continued to
experience sharp fluctuations in its growth.
Growth rates have ranged from a high of 46
percent in 2004 when the sector was still
recovering from the sharp reduction in 2002 to a
low of -11 percent in 2006. In 2008, groundnut
production grew substantially by 25 percent due to
relatively good rainfall. Even with relatively high
average growth rates, sharp annual fluctuations are
likely to have exacerbated poverty in the
subsector. Groundnut farmers would have had
difficulties maintaining consumption levels when
productions decline if savings are low, the social
safety net system is weak and they lack access to financing. These conditions are likely to exist
to some degree in the subsector.
3.9 Groundnuts and other crops together account for on average approximately a fifth of total
GDP and 62 percent of the value added in agriculture. As a result of their sizable shares and
relatively high growth rates, the agriculture sector as a whole experienced an average annual
cumulative growth rate of 6.9 percent since 2003, higher than industry and services. In industry,
the construction subsector grew at a fast pace in recent years before tapering off in 2008.
Otherwise, industry as a whole would have experienced much lower growth. The electricity and
water subsector was estimated to have grown rapidly at 9.0 percent in 2009 but it accounts for
only a small share of the economy. In services, the tourism and telecommunication subsectors
Figure 5: Real Sectoral Growth Rates
(Percent)
Source: IMF .
38
have experienced high rates of growth, benefiting from foreign direct investments in hotels and
mobile telephone operations.
3.10 Not only did agriculture grow the most rapidly since 2003, the sector accounts for the
livelihood for half of the working population and 63 percent of the poor, significantly larger than
industry and services (see Table 24 and 15). Therefore, the sector’s growth performance will
have a proportionately larger impact on overall growth and on the poor. In rural areas, four-fifths
of population works in agriculture, an overwhelming majority of the population. By contrast,
services provide nearly 70 percent of total employment in urban areas. In this sense, there is a
clear structural demarcation between the urban and rural areas.
Table 25: Sectoral Composition of Employment (2003)
Industry Urban Shares
(%)
Rural Shares
(%)
Total Shares
(%)
Growth
Rate
Agriculture 27,640 12.8 229,232 83.3 256,872 52.3 2.31
Agriculture and forestry 23,203 10.7 226,835 82.4 250,038 50.9 2.36
Fishing 4,437 2.1 2,397 0.9 6,834 1.4 0.74
Industry 41,279 19.1 14,277 5.2 55,556 11.3 3.36
Manufacturing and utilities 25,906 12.0 8,805 3.2 34,711 7.1 2.56
Construction and Mining 15,373 7.1 5,472 2.0 20,845 4.2 4.95
Services 147,405 68.1 31,634 11.5 179,039 36.4 3.08
Trade 63,112 29.2 12,836 4.7 75,948 15.5 2.94
Hotels and restaurants 9,475 4.4 866 0.3 10,341 2.1 3.68
Transport and communications 19,799 9.2 4,672 1.7 24,471 5.0 3.62
Real estate and business services 15,502 7.2 3,434 1.2 18,936 3.9 3.38
Education 9,840 4.5 4,928 1.8 14,768 3.0 3.88
Private households 12,808 5.9 1,150 0.4 13,958 2.8 1.88
Other services 16,869 7.8 3,748 1.4 20,617 4.2 2.80
Total 216,324 100.0 275,143 100.0 491,467 100.0 2.62
Source: 1991 and 2003 Population Censuses and authors’ calculations.
Note: The sectoral growth rates are the average annual cumulative growth rates of employment in each sector from 1993 to 2003.
3.11 Comparing the population censuses of 1993 and 2003 indicates that Gambians are
migrating out of the agriculture sector into industry and services (see Table 25). The average
annual cumulative growth rate of employment in agriculture was lower than the national average
growth rate of employment as well as the average annual population growth rate of 2.77 percent.
By contrast, industry and services grew more rapidly than both the employment and the
population. Industry registered the highest employment growth rates largely due to the
construction sector which grew more rapidly than any other subsector, at almost twice the
national average. Employment in almost all the subsectors in services grew more rapidly than the
national average. In particular, the subsectors education, transport and communication and hotels
and restaurants grew the fastest.28
The growth of the education sector most probably reflects
government expansion of the public school system.
28
The migration out of agriculture into industry and services between 1993 and 2003 would typically indicate a
shrinking agriculture sector and an expanding industry and services sectors. However, the available data indicate
that agriculture’s share increased from 25 percent of GDP in 1993 to 31 percent in 2003, while services declined
39
3.12 The rural to urban migration has been
even more striking than the intra-sectoral
migration (see Table 25). Between 1993 and
2003, the economically active population in urban
areas grew by an annual average rate of 4.65
percent compared to 1.29 percent in the rural
areas. As a result, the share of the working
population in urban areas increased from 35
percent in 1993 to 44 percent in 2003. The intra-
sectoral shifts in the population can basically be
traced to the urbanization phenomenon. The
migration out of agriculture is essentially the migration out of rural areas. By contrast, the
population share of agriculture in urban areas actually increased, although it remains small
compared to the size of rural agriculture. The migration into industry and services reflects the
internal migration into urban areas. For the aforementioned high population growth subsectors,
growth was urban focused. These subsectors were construction, transport and communication,
hotels and restaurants and education.
3.13 The wholesale and retail trade subsector has the second largest share of the country’s
working population, after agriculture, and the largest share by far in urban areas (see Table 24).
The urban-rural split was particularly large for this subsector. The share of the working
population in the urban trade subsector increased by approximately two percentage points. By
contrast, the rural trade subsector declined by roughly two percentage points and in fact its
working population fell in absolute numbers. It could indicate that trading activities in rural areas
are disappearing and moving to urban areas, which could detrimentally impact the performance
of other subsectors in rural areas.
3.14 Given data constraint, several fairly strong assumptions were made for the purposes of
the poverty rate simulation exercises. It is assumed that individual consumption levels grew at
the per capita growth rates of the individual’s sector of employment. Estimates of the annual
sectoral growth rates since 2003 are available (see Table 23). However, the latest available data
on the country’s population and employment are from the 2003 Population Census. Hence, the
sectoral employment growth rates since 2003 are assumed to be equal to the average annual
growth rates between 1993 and 2003. This could be considered a baseline assumption which
could be revised as part of a scenario analysis. The average annual per capita sectoral growth
rates can then be estimated based on the estimated annual sector growth rates and the assumed
sector employment growth rates (see Table 26).
from 61 percent to 55 percent (World Bank, World Development Indicators). This unexpected result could simply
be due to the poor quality of the GDP data.
Table 26: Intra-Sectoral and Rural-Urban Migration
(Annual Percent Change)
Urban Rural Total
Agriculture and fishing 7.07 1.60 2.26
Manufacturing and energy 3.90 0.11 2.51
Construction 6.69 1.86 4.85
Trade, hotels, restaurants 4.39 -0.66 2.97
Transport and communication 5.07 0.24 3.55
Other services 4.00 0.40 2.91
National 4.65 1.29 2.62
Source: 1993 and 2003 Population Censuses, based on
working population.
Table 27: Real Sectoral Avg Per Capita Growth Rates, 2004-08
(Annual Percent Change)
Sectoral Employment Per Capita
Agriculture 6.93 2.31 4.52
Industry 6.29 3.36 2.83
40
3.15 The estimated per capita sectoral
growth rates indicate that the agriculture
sector experienced by far the highest
average per capita growth rate since 2003. Although again somewhat surprising given the
general perception of agriculture as a relatively underperforming sector, the high per capita
growth rate reflects both the relatively high sectoral growth rate as well as the low employment
growth rate. The industry and services sectors grew at similar per capita rates, both lower than
the average rate of growth for overall per capita real GDP. Nevertheless, all three major sectors
experienced fairly strong per capita real growth since 2003.
3.16 Rather than apply the real per capita sectoral growth rates in the simulation exercise, the
nominal growth rates were instead applied together with the inflation rates. This allows for the
incorporation of the impact of differential inflation rates for: (i) the three domains (Greater
Banjul, other urban, rural) over which separate poverty lines were defined; (ii) food and nonfood
goods, corresponding to the food and nonfood components of the poverty lines; and (iii) the
different consumption patterns between urban and rural households, by using separate CPI
weights for the two regions. GBOS had so far been producing only a single national price index.
As part of the simulation exercise, multiple variations to the standard CPI were constructed in
order to analyze the impact of inflation dynamics on the poor.
Figure 6: Cumulative Inflation Rates, 2004-08
(Percent)
Source: GBOS and staff calculations.
3.17 The newly constructed disaggregated CPIs indicate that inflation rates can differ
significantly by all the parameters considered but the impact was particularly large when
inflation was separately estimated for urban and rural areas. Therefore, estimating rural and
urban inflation rates separately seems recommendable, particularly given the large shares of the
rural poor. In 2008, the estimated inflation rate was 5.4 percent in Banjul but 8.2 percent in rural
areas for all goods, which increases to 7.4 percent and 10.0 percent respective when only food
items are considered. When separate urban and rural consumption weights are incorporated, the
estimated overall food inflation rates increase further to 8.3 percent for Banjul and 13.3 percent
for rural areas. Such large variations can be seen in the other years as well. As a result, the
cumulative inflation rates for 2004-08 are significantly affected (see Figure 6). For example, the
estimated standard national cumulative inflation rate was 28.9 percent, whereas it was 41.2
percent for the food component in rural areas, using the consumption weights specific to rural
Services 5.74 3.08 2.58
Real GDP 6.17 2.62 3.46
Source: IMF, 1993 and 2003 Population Censuses.
41
areas. For the simulations exercise, these cumulative weighted inflation rates were applied
separately to the food and nonfood components of the poverty lines and to the three domains.
Table 28: Poverty Rates Basic Simulation Results
(Percent)
Headcount Rate
(P0)
Poverty Gap
(P1)
Squared Poverty Gap
(P2)
Upper Poverty Line
2003 Baseline 58.0 25.1 13.9
2008 Baseline 55.5 23.5 12.9
Adjusted for:
Remittances 57.9 24.8 13.7
Internal migration 43.1 16.2 8.2
Remittance and migration 45.5 17.4 8.9
2009 Baseline 55.3 23.6 13.0
Adjusted for:
Remittances 57.8 25.0 13.9
Internal migration 40.8 15.0 7.5
Remittance and migration 42.9 16.2 8.2
Lower Poverty Line
2003 Baseline 51.1 20.9 11.1
2008 Baseline 49.0 19.4 10.3
Adjusted for:
Remittances 51.3 20.6 11.0
Internal migration 36.8 12.9 6.3
Remittance and migration 39.1 13.9 6.9
2009 Baseline 49.0 19.5 10.4
Adjusted for:
Remittances 51.4 20.8 11.1
Internal migration 34.7 11.8 5.7
Remittance and migration 36.6 12.9 6.3
Source: 2003 IHS and staff calculations.
3.18 The simulated poverty rates for 2008 based on the nominal sectoral per capita growth
rates and the disaggregated weighted inflation rates indicate that there was little reduction in
poverty despite seemingly steady robust growth since 2003 (see Table 27). This result holds for
the headcount ratio, poverty gap and squared poverty gap measures. The poverty headcount ratio
declined modestly from 58.0 percent in 2003 to 55.5 percent in 2008 for the upper poverty line
and from 51.1 percent to 49.0 percent for the lower poverty line.
3.19 The poverty rates for 2009 were projected based on the projected sectoral growth rates
for 2009 (see Table 23). Only the national inflation rate for 2009 was projected and the
disaggregated inflation rates were assumed to be equal to the national rate. The results of the
simulations indicate that the poverty rates in 2009 are expected to remain virtually unchanged
from the previous year. The results are not particularly surprising given that the impact is only
over a single year and the projected slowdown in growth is somewhat manageable. However,
42
note that the current assumptions regarding growth are probably overly optimistic and it is
expected to be further revised downward in response to the rapidly changing global environment.
REMITTANCES
3.20 The downward revision of growth is likely to be accompanied by a similar adjustment to
international remittances. It will be important to account for remittances given the country’s
relatively large volume of remittances and their steady decline since 2003 (see Table 28). On
average, remittances as a share of per capita consumption declined from 10.2 percent to 4.0
percent in the same period, after adjusting for the rate of consumption and using the consumption
estimates from the 2003 IHS. In 2009, total remittances are expected to sharply decline as a
result of the global recession, by approximately 20 percent to 5.6 percent of GDP and 3.4 percent
of total per capita consumption.
3.21 International remittances are sent by overseas emigrants which account to approximately
3.7 percent of the population (2005), comparable to 4.0 percent for Senegal, 4.1 percent for
Ghana, 1.4 for Sierra Leone and 5.8 percent for Burkina Faso.29
What sets The Gambia apart
from the region is the significantly higher emigration rate of the tertiary educated, estimated at
65 percent, compared to 24 percent for Senegal, 43 percent for Ghana and 41 percent for Sierra
Leone and 3.3 percent for Burkina Faso. In fact, the emigration rate of the highly educated is
among the top 15 countries in the world. It would explain why most of the top ten destination
countries for Gambian emigrants are in Europe and the Unites States, another distinctive feature
of the country compared to its neighbors. What this implies is that much of international
remittances could be accruing to higher income households, which would affect how declining
remittances impact poverty developments.
3.22 Remittances have clearly declined since 2003 but the impact on the poverty rate will
depend on the distribution of the remittance receipts among the households. This information is
unavailable at present in The Gambia. The 2003 IHS includes remittances in its questionnaires
but the data quality appears to be poor, with many non-responses, and apparently the questions
on remittances were not uniformly asked for all households. However, data on remittances at the
household level was collected for the 2007 National Agricultural Sample Survey (NASS). The
NASS is an annual sample survey of rural farm households conducted by the Department of
Planning within the Department of State (Ministry) of Agriculture. The main purpose of the
survey is to collect data on farm production. For the 2007 NASS, the Poverty Assessment team
worked with DOSA to expand the survey to include questions on remittances received by farm
households. The survey results indicate that on average farm households received 227 dalasis per
capita in 2006, or approximately 2 percent of average per capita consumption in rural areas. The
GDP rate of consumption was then applied in order to estimate the impact on consumption.
Afterwards, the average per capita consumption for the urban areas was estimated such that the
population weighted average equaled the national average (see Table 28).
Table 29: Remittances, 2003-9
29
World Bank, Migration and Remittances Factbook 2008.
43
2003 2004 2005 2006 2007 2008 2009
Remittances (US$ mil) 52.4 52.4 47.7 51.6 52.5 50.8 40.6
Percent of GDP 14.2 13.1 10.3 10.2 8.1 6.4 5.6
Avg per capita consumption from remittance
Dalasis 879 948 799 827 725 619 586
Urban 1,738 1,875 1,580 1,635 1,433 1,224 1,158
Rural 204 220 185 192 168 144 136
Percent of total consumption 10.2 9.1 7.0 6.7 5.2 4.0 3.4
Urban 15.1 13.6 10.4 10.0 7.7 5.9 5.1
Rural 2.9 2.6 2.0 1.9 1.5 1.1 1.0
Source: 2007 NASS and IMF.
3.23 The resulting estimates of per capita remittances indicate that remittances are much more
important for urban households compared to rural households. From 2003 to 2008, on average
remittances accounted for 10.5 percent of per capita consumption in urban areas, compared to
2.0 percent in rural areas. This urban-rural disparity is consistent with the highly educated
background of Gambian emigrants and their destination countries being mostly overseas
developed countries, as opposed to neighboring countries where the cost to immigrate would be
comparably low. It is also consistent with anecdotal evidence from informal interviews
conducted in the country. The belief is that the bulk of international remittances are received by
households located in the urban areas, particularly Greater Banjul, most probably because these
households have the financial resources to arrange for the overseas migration that would
precipitate international remittances. In turn, urban households could send part of the remittances
to relatives in rural areas. Although there are significant cross-country heterogeneity regarding
the distribution of migrants and remittance receipts across socioeconomic and income segments
of the population, it is relatively common for richer households to receive more remittances. 30
3.24 Given the greater importance of international remittances for urban households, they
would be more impacted by the declining remittances in recent years. This can be seen in the
average share their remittances in total consumption, which declined from 15.1 percent in 2003
to 5.9 percent in 2008. On the other hand, income (consumption) levels of urban households are
higher than for rural households and initial poverty rates are lower. This could mitigate the
impact of declining remittances on the overall poverty rates.
3.25 The simulation exercise incorporated the impact of declining remittances by netting out
remittances from individual total consumption from the 2003 IHS before applying the per capita
sectoral growth rates. Without remittance data specific to each household, proportion netted out
was the average per capita consumption funded by remittances for urban and rural residents (see
Table 28). For 2008, the remittances are added back in again using the shares estimated from
above. The method used for incorporating remittances assumes that all households received
30
In Senegal, international migration tends to be more frequent in richer households and remittances account for
one-sixth of household consumption, while in Ghana the rich are more likely to receive international remittances and
higher amounts (Quentin Wodon). In the case of Latin America, in general remittances are a larger share of total
household income in the upper quintiles of the income distribution, mostly due to a greater share of richer
households receiving remittances (Fajnzylber and Lopez (2008)).
44
remittances at the average rate for urban and rural areas, respectively. The heterogeneous impact
of remittances within each of these areas cannot be simulated given the data constraints.
Although rather crude, the estimation technique at least allows for the probable urban-rural
disparity in the importance of remittances for households.
3.26 The simulation results indicate that declining remittances raised the projected poverty
headcount ratio for 2008 from 55.5 percent to 57.9 percent based on the upper poverty line and
from 49.0 percent to 51.3 percent based on the lower poverty line (Table 27). Essentially the
poverty reductions gained from economic growth in the past five years are wiped out by the
impact of the reduced remittances. This is also true for projected rates for 2009 and the poverty
gap and squared poverty gap measures.
3.27 How does the expected impact on poverty from the decline in remittances compare to
other countries? Cross country empirical work indicates that a ten percent decline in the
remittances to GDP ratio is associated with a rise in poverty headcount ratio by approximately
one percentage point.31
For The Gambia, the remittance to GDP ratio declined by 55 percent
from 2003 to 2008 and therefore the predicted rise in the poverty rate would be between 5 to 6
percent. The simulation exercises estimate that the headcount ratio would increase by
approximately 2 percentage points, hence smaller than what would be predicted from the cross-
country analysis. The lower predicted impact mostly likely reflects the assumption concerning
the higher receipt of remittances by urban residents compared to rural residents. If the headcount
ratio increased by 5 instead of 2 percentage points due to declining remittances, then the poverty
rate would have increased since 2003 despite steady growth of the economy. It highlights the
importance of considering the distributional impact of remittances, even though in the current
simulations only the urban-rural disparity was considered. In the next household survey,
comprehensively gathering data on remittances would allow for a more thorough understanding
of the heterogeneity of the distribution of remittances across households.
RURAL TO URBAN AND INTERSECTORAL MIGRATION
3.28 According to the comparison between the 1993 and the 2003 Population Censuses, there
have been significant rural to urban and intersectoral migrations (see Table 29). The intersectoral
migrations have been mainly from agriculture to services and the construction sector. The share
of the working population in rural areas declined by approximately 10 percentage points, from
66 percent in 1993 to 56 percent in 2003, and conversely the share for urban areas grew by 10
percentage points to 44 percent. For the entire population, the rate of urbanization was even
greater, with the population share of urban residents growing between 1993 and 2003 by 14
percentage points, from 37 to 51 percent. Clearly significant structural changes occurred in the
country’s population.
Table 30: Sectoral and Urban/Rural Shares of the Working Population, 1993 - 03
(Percent of Population and Percentage Points for the Change in Shares)
Urban Rural Total
31
Wagh et al (2007).
45
Pop.
Share
2003
Percentage
Change
1993-03
Pop.
Share
2003
Percentage
Change
1993-03
Pop.
Share
2003
Percentage
Change
1993-03
Agriculture 5.6 2.4 46.6 -5.3 52.3 -3.0
Agriculture and forestry 4.7 2.0 46.2 -4.5 50.9 -2.5
Fishing 0.9 0.4 0.5 -0.8 1.4 -0.5
Industry 8.4 1.9 2.9 -0.9 11.3 1.0
Manufacturing and utilities 5.3 0.7 1.8 -0.8 7.1 -0.1
Construction and Mining 3.1 1.2 1.1 -0.1 4.2 1.2
Services 30.0 5.2 6.4 -3.3 36.4 2.0
Trade 12.8 2.3 2.6 -1.7 15.5 0.5
Hotels and restaurants 1.9 0.4 0.2 -0.2 2.1 0.3
Transport, communications 4.0 1.0 1.0 -0.4 5.0 0.6
Real estate, business service 3.2 0.6 0.7 -0.3 3.9 0.4
Education 2.0 0.5 1.0 0.0 3.0 0.5
Private households 2.6 -0.2 0.2 -0.2 2.8 -0.4
Other services 3.4 0.6 0.8 -0.5 4.2 0.1
Total 44.0 9.5 56.0 -9.5 100.0 0.0
Source: 1993 and 2003 Population Census.
3.29 The declines in the rural areas focused on the agricultural sector, which in fact accounted
for close to 60 percent of the total decline. This decline was almost equally matched by the
increase in the share of services in urban areas. The share of the working population in urban
services grew from 25 percent in 1993 to 30 percent in 2003. In a sense, the relatively strong
rural to urban migration reflected the intrasectoral migration from agriculture to services.
Agriculture in the urban areas also experienced sizable growths in its population share, as part of
the general urbanization of the country.
3.30 The intrasectoral and rural to urban population shifts can also be viewed in terms of the
average annual cumulative growth rates for each of the subsectors by urban and rural areas (see
Table 25). The working population in urban areas grew annually by 4.7 percent, significantly
higher than the overall average growth of 2.6 percent, while rural areas grew only by 1.3 percent.
Within urban areas, agriculture, construction, transport and communication in particular
experienced strong growth.
3.31 In order to incorporate the impact of internal migration, the poverty rates for 2008 and
2009 were simulated by revising the sample population weights using the average annual growth
rates of the working population. When the population weights were not adjusted in the previous
simulations, it essentially meant that the size of the households in the survey expanded in line
with the assumed population growth of the sectors. With the reweighting incorporated, the
simulations now assume that the number of households in the urban and rural subsectors adjust
according to the average population growth rates of the sectors.
3.32 For the simulations, it is assumed that the sectoral population growth rates experienced
from 1993 to 2003 remained constant after 2003, the year when the IHS was conducted. This
could be considered a baseline assumption and scenario analyses could be conducted to test the
46
sensitivity of the findings to the assumptions regarding the intrasectoral and urban to rural
migration rates. The current set of assumptions, regarding the relatively high rate of rural to
urban and agriculture to services and construction migration, appears to be a reasonable baseline
given the erratic performance of the agriculture sector and the relatively strong performance of
construction, telecommunication and the tourism sectors since 2003. These difference in sectoral
performance would have encouraged continuation of previous internal migration patterns.
3.33 The simulation results predict that the poverty rates would have significantly declined
with the incorporation of the impact of internal migration (see Table 27). Compared to the
simulations without accounting for remittances and internal migration, incorporating the impact
of migration lowers the projected headcount ratios by approximately 12 percent in 2008 and 15
percent in 2009. Similarly strong impacts can be seen for the projected poverty gap and squared
poverty gap measures. The impact of internal migration far exceeds the predicted impact of
economic growth and reduced remittances.
3.34 Although the two migration channels are linked, the relatively strong impact of internal
migration can be characterized as mainly the result of urbanization as opposed to intersectoral
migration. This can be seen by comparing the predicted increases from 2003 to 2009 in the
average per capita real consumptions and the predicted changes in the population shares (Table
31). Essentially, urbanization involved the larger shifts in the population, an increase of 4.3
percentage points in the urban share of the population, that occurred in order to take advantage of
an initial per capita consumption disparity of approximately 61 percent.32
In the simulations, this
significant disparity is predicted to have increased to 67 percent in 2009.
Table 31: 2009 Projected Mean Consumption and Population Growth
(Dalasis and Percent)
2003
Consump.
(Dalasis)
Population
Shares
(Percent)
2009
Consump.
(Dalasis)
Population
Shares
(Percent)
Percent
Increase
Consump.
Percentage
Points
Increase
Population
Shares
All 8,649 1,36 mil 12,258 1,59 mil 41.7 0.0
Urban 11,496 34.7 16,196 39.0 40.9 4.3
Banjul and KMC 12,643 25.9 17,859 29.1 41.3 3.2
Other Urban 8,131 8.8 11,409 9.9 40.3 1.1
Rural 7,133 65.3 9,684 60.4 35.8 -4.9
Sectors
Agriculture and fishing 6,506 51.9 8,894 50.8 36.7 -1.1
Manufacturing and energy 8,873 7.5 11,248 7.5 26.8 0.0
Construction 6,941 4.0 12,146 4.6 75.0 0.6
Trade, hotels, restaurants 10,122 17.7 14,482 18.1 43.1 0.4
Transport, communication 8,123 5.0 14,252 5.3 75.5 0.3
Financial management 12,275 1.5 15,443 1.5 25.8 0.0
32
The percentage point changes in the shares of urban and rural residents do not add to one essentially due to rounding up errors
accumulated in applying the assumed annual population growth rates separately to the urban and rural populations in 2003.
47
Social and personal service 9,798 12.4 11,814 12.6 20.6 0.2
Source: 2003 IHS and authors’ calculations.
3.35 Compared to the increase in the urban share of the population, the predicted intersectoral
shifts in the population from 2003 to 2009 consists of a much smaller 1.1 percentage point
decline in the share of population in agriculture. This decline is matched by increases in the
shares of: (i) construction; (ii) trade and tourism; (iii) transport and communication; and (iv)
social and personal services. Among these four subsectors, the population shift into trade and
tourism had the greatest impact in increasing the average per capita consumption as the sector
had the second largest population share after agriculture and also the second highest average per
capita consumptions after financial services, both initially in 2003 and in terms of the projected
consumptions in 2009.
3.36 The projected improvement in the poverty rates from the impact of internal migration
should really be considered an upper bound. The simulation of the impact of internal migration
implicitly assumes that migrants immediately start earning, or to be more precise start
consuming, at the higher levels of the new sector, whether viewed in terms of urbanization or
intrasectoral migration. In reality many of the migrants are likely to work in lower paying
segments of the new sector, whether initially or on a permanent basis, given that a large part of
the population can be described as mostly unskilled labor. According to the 2003 population
census, almost half the working population has no formal schooling, 23 percent attended only
Madrassas (Islamic schools), only 3 percent had any form of post-secondary education, including
vocational training and close to half the population are illiterate. The country has been making
significant progress in bringing more children into the formal primary and secondary schooling
system, but these percentages will improve only gradually over time.
3.37 In addition to the general shortcomings in the level of formal education, the country
appears to lack the adequate infrastructure for skills development and vocational training that
would help equip workers to migrate to occupations in high demand. Private training institutions
tend to focus on IT and management but neglect technical and vocational training in areas of
relatively high demand, such as automobile maintenance, carpentry and masonry. Perhaps
reflecting this, the population census indicates that crop growers is the largest occupation and
most of the top ten occupations consist of unskilled work such as stall market seller, street
vendor, vehicle driver, domestic cleaner and security services. The top ten occupations account
for 80 percent of the working population. When the majority of the population are crop growers
and work in relatively low skilled occupation, internal migration to higher growth sectors might
result in compensations at reduced levels compared to the more skilled workers already in the
sector.
3.38 The presence of a relatively large informal sector could also indicate barriers to entry to
higher earning jobs. A large informal sector would indicate a highly segmented market where
lower skilled workers, which could perhaps be used to describe a large share of rural migrants,
earn significantly less than in the formal sector. In such a case, the simulation exercise would be
overestimating per capita income (consumption) by assuming that internal migration
immediately allows the migrant to obtain the regular earnings of the new sector.
48
3.39 There are various indications that informal employment is widespread in The Gambia.
The 2006 Economic Census indicated that 84 percent of non-agricultural enterprises were
unregistered and 73 percent of all working individuals were employed in these unregistered
enterprises. The informal sector is sometimes defined as firms with less than five workers. If
defined in this manner, the proportion of informal workers increases to 85 percent in non-
agricultural enterprises. Here the focus is on the non-agricultural sectors given that migration has
occurred from rural to urban areas. Note however that much of the agriculture sector can be
considered informal, particularly since a large share of the sector consists of small subsistence
farms.
CONCLUSIONS
3.40 Since 2003 when the IHS was conducted, it is estimated that agriculture has had a
higher per capita growth rate than industries and services, due to both higher sectoral growth
rates and lower sectoral population growth rates. However, agriculture also experienced higher
sectoral growth rates essentially due to a relatively low starting point in 2002, when crop
production sharply declined as a result of low rainfalls. In actuality, agriculture has performed
poorly in recent years and in fact the sector shrank during 2001-08. The relatively low population
growth rates in the sector reflect the high internal rural to urban migration. The rate of internal
migration and the sectoral population growth rates since 2003 are assumed to be equivalent to
the rates calculated from comparing the 1993 and 2003 population censuses, the most recently
available data. Although the actual magnitude could have differed from the assumptions used in
the simulation exercise, it seems reasonable to assume that the rate of internal migration
remained high in recent years.
3.41 Since 2003, there have been significant urban/rural and food/non-food variations in
inflation. New CPIs were constructed separately for Greater Banjul, other urban and rural areas
and for food and non-food items, using differentiated weights for urban and rural areas. These
new disaggregated CPIs revealed significantly differences in inflation across regions and
consumption items. The disaggregated food inflation rates in rural areas were particularly high,
estimated at 13.3 percent in 2003 compared to 6.8 percent for the national average. This is highly
relevant for the poverty analysis because the poor are concentrated in rural areas and food
dominates the consumption of Gambians.
3.42 The combined estimated impact of the sectoral per capita growth rates and the
disaggregated inflation rates was a reduction in the poverty headcount ratio from 2003 to
2008 by only two percentage points. Although the country enjoyed steady growth since 2003,
with an average annual per capita growth rate of 3.5 percent, its impact on poverty reduction was
offset by the relatively high rates of inflation in rural areas, particularly for food consumption.
3.43 The decline in remittances since 2003 is estimated to have resulted in only a two
percentage point deterioration of the projected poverty rate. The Gambia averaged 9.6
percent of GDP in international remittances since 2003, among the highest in the world.
However, remittances steadily declined from 14.2 percent of GDP in 2003 to 6.4 percent in
2008, but it is estimated that the resulting impact on poverty reduction was relatively small
49
because urban residents, who are on average less poor, receive a proportionately much greater
share of remittances compared to rural residents.
3.44 Internal migration was estimated to have reduced the poverty headcount ratio by as
large as 12 percentage points since 2003. This relatively large estimated impact reflects the
high assumed rate of internal migration. It is estimated that the urban share of the population
increased by 4.5 to 5.0 percentage points from 2003 to 2009, with a corresponding reduction in
the share for the rural population as Gambians migrated out of agriculture into other sectors. The
rate of internal migration since 2003 is again assumed to be equivalent to the rates between 1993
and 2003.
3.45 The Poverty Assessment team plans to conduct further scenario analyses by varying
key parameters on: (i) the sectoral and population growth rates; (ii) the rate of decline and the
urban/rural distribution of remittances; and (iii) the rate of internal migration.
50
4. REGIONAL POVERTY VARIATIONS
4.1 Despite its relatively small size, The Gambia has substantial regional disparity in poverty
related indicators. This chapter first documents the construction and presents the main results of
a poverty map of The Gambia based on the 2003 IHS and the 2003 Population Census.33
The
methodology takes advantages of detailed information found in the survey and the exhaustive
coverage of the census. It permits the calculation of poverty indicators at low levels of
desegregation, LGAs and districts in the case of The Gambia. The heterogeneity of the country
in terms of poverty across LGAs and districts should make the poverty map a useful statistical
tool in any poverty alleviating programs or projects. Following the presentation of the poverty
map, the regional dimensions of poverty related indicators are further explored, including the
distribution of schools and health facilities.
OBJECTIVE AND METHODOLOGY OF THE POVERTY MAP
4.2 Poverty profiles based on household surveys yield limited information on the geography
of poverty. In the last two decades poverty profiles have been developed into useful tools to
characterize, assess and monitor poverty. Based on information collected in household surveys,
including detailed information on expenditures and incomes, those profiles present the
characteristics of the population according to their level of monetary and non-monetary standards
of living, thus helping to assess the poverty reducing effect of some policies and compare
poverty level between regions, groups or over time.
4.3 While these household-based studies have greatly improved our knowledge of welfare
level of households in general and of the poorer ones in particular, the approach has a number of
constraints. In particular, policymakers and planners may need finely disaggregated information
in order to implement anti-poverty schemes. Typically they then need information for small
geographic units such as city neighborhoods, towns or villages. Telling a Gambian policy maker
that many among the poorest live in rural areas is not enough as this information is too vague.
Knowing which districts have the highest rate of poverty would be more useful. Even LGA-
level information often hides the existence of pockets of poverty in an otherwise relatively well-
off LGA, as well as pockets of relative wealth in a poor LGA, which could lead to poorly
targeted schemes.
4.4 In addressing this shortcoming, poverty maps combine data from household surveys and
census data in order to estimate poverty at a more geographically disaggregated level. This
chapter documents the construction of a poverty map for The Gambia and shows some results of
a poverty map based on data from the 2003/04 IHS and the 2003 Population Census. Based on a
methodology developed by Elbers, Lanjouw and Lanjouw (2002, 2003), we calculate poverty
indicators at low levels of aggregation, using the detailed information found in the survey and the
exhaustive coverage of the Census. Results at LGA and district levels are presented.
33
See Annex 2 for details on the methodology.
51
4.5 The basic idea behind the methodology is rather straightforward. First, a regression
model of adult equivalent expenditure is estimated using IHS survey data, limiting the set of
explanatory variables to those which are common to both that survey and the latest Census. Next,
the coefficients from that model are applied to the Census dataset to predict the expenditure level
of every household in the Census. And finally, these predicted household expenditures are used
to construct a series of welfare indicators, for example poverty level, depth, severity and
inequality, for different geographical subgroups.
4.6 Although the idea behind the poverty map methodology is simple, its proper
implementation requires complex computations. Those complexities are due to the need to take
into account spatial autocorrelation (expenditure from households within the same cluster are
correlated) and heteroskedasticity in the development of the predictive model. Taking into
account those econometric issues ensures unbiased predictions.
4.7 A further issue making computation non-trivial is the need to compute standard errors for
each poverty measure or welfare statistics. Those standard errors are important since they tell us
how low we can disaggregate the poverty indicators. As we disaggregate results at lower and
lower levels, the number of households on which the estimates are based decreases as well and
therefore yields less and less precise estimates. At a certain point, the estimated poverty
indicators would become too imprecise to be used with confidence. The computation of standard
errors helps in deciding where to stop the disaggregation process. The methodology used is
available in a background paper.
4.8 The construction of such poverty map is demanding in terms of data. The key
requirement is to have a household survey with an expenditure module as well as a population
and housing census. In The Gambia, we rely on the IHS administrated in 2003/04. We also use
the Population and Housing Census conducted in 2003. The monetary-based poverty profile is
constructed from the survey. For the prediction of welfare levels and poverty status, apart from
household-level information, community level characteristics are also useful since differences in
geography, history, ethnicity, access to markets, public services and infrastructure, as well as
other aspects of public policy may all lead to important differences in standards of living.
4.9 As for the application of the regression model in the census data, the questionnaire of the
census is relatively detailed, which yields good predictions. At the individual level, the
questionnaire covers demography, education and economic activities. At the household level,
dwelling characteristics are also well covered. The Census database turns out around 1.34
million individuals grouped into close to 155,600 households. The Census field work grouped
households into 2,477 EAs of 63 households each on average. The administrative structure of the
country is simple. The top tier is composed of 8 LGAs which are further split into 39 districts.
Table 31 provides some descriptive statistics of the two administrative levels at which the
poverty map was constructed.
4.10 Based on past experience, around 1,000 households per administrative unit are needed to
obtain poverty statistics with a decent level of precision as measured by their coefficient of
variation, although other elements need to be taken into consideration. Only a handful of
52
districts have less than 1,000 households, and the largest district being the City of Kanifing, next
to the Capital.
Table 32: Descriptive Statistics on the Gambia Administrative Structure
Administrative # of Number of Households Number of Individuals
Units Units Median Minimum Maximum Median Minimum Maximum
LGA 8 11,198 6,776 48,408 138,039 33,602 384,434
District 39 1,647 421 48,408 15,277 2,919 316,159
Source: Authors’ calculation based on the Census 2003.
RELIABILITY OF THE POVERTY MAP ESTIMATES
4.11 The current version of The Gambia poverty map should be seen as preliminary as it has
not yet gone through a validation process in The Gambia. Despite these issues, the preliminary
poverty map presented here seems to be adequate for presenting a broad picture of the geography
of poverty and at a later stage assessing the targeting performance of selected programs where
funding is provided at the district level.
4.12 In order to maximize accuracy of poverty estimates, the regression model was estimated
at the lowest geographical level for which the IHS survey was deemed representative. A
household level expenditure model was developed for Banjul Metro Area (defined as Banjul,
Kanifing and Brikama LGAs) and separately for the rest of the country using explanatory
variables which are common to both the IHS and the Census.
4.13 The first task was to make sure the variables deemed common to both the census and the
survey were really measuring the same characteristics. For this, we first compared the questions
and modalities in both questionnaires to isolate potential variables. We then compared the means
of those (dichotomized) variables and tested whether they were equal using a 95 percent
confidence interval. Restricting ourselves to those variables should ensure that the predicted
welfare figures are consistent with survey-based poverty profile.34
That comparison exercise
was done at the strata level.
4.14 The choice of the independent variables used in the predictive model was based on a
backward stepwise selection procedure. All coefficients in the regressions were of expected sign.
The model included controls for location effects by incorporating cluster averages of some of
variables. Regressions using the base model residuals as dependant variables were estimated as
well, with the results used in the construction of the poverty map to correct for
heteroskedasticity.
4.15 The explanatory power (R2) of the regressions is 0.38 for the first strata and 0.41 for the
second one. Those R2s might seem rather low but that magnitude is not uncommon in this type
survey-based regression. More generally, the low R2s in survey-based regressions are due to four
main factors. First, in many areas households are fairly homogeneous in terms of observable
34
We also deleted or redefined dichotomic variables being less that 0.03 or larger than 0.97 to avoid serious
multicollinearity problems in our econometric models.
53
characteristics even if their consumption levels vary. Second, a large number of potential
correlates are simply not observables using standard closed-questionnaire data collection
methods. Third, some good predictors have to be discarded at first stage of the procedure when
their distributions did not appear to be identical. Fourth, many indicators do not take into
account the quality of the correlates.
4.16 The poverty estimates by strata obtained in the census are similar to those obtained in the
survey. By using the estimated parameters from the prediction model in the survey in the Census
data, we can generate poverty measures for all households in the census as well as by area. Table
32 presents estimated poverty measures for both stratum in the Census and compares them with
actual figures from the survey. For each stratum and poverty indicator, the equality of IHS-based
and Census-based indicators cannot be rejected at the 95 percent confidence level. The gap
between the survey and census estimates is small, less than one standard error. Although census-
based poverty measures can only be compared with the ones provided by the IHS survey at
stratum level, equality of those poverty measures provides a reliability test of the methodology.
Having established the reliability of the predictive models, we estimated poverty measures at the
LGA district levels (see Annex 3).
Table 33: Poverty Rates based on IHS and Census 2003, by strata
Headcount Index Poverty Gap Squared Poverty Gap
IHS
(Actual)
Census
(Predicted)
IHS
(Actual)
Census
(Predicted)
IHS
(Actual)
Census
(Predicted)
Banjul Metro 0.462 0.450 0.186 0.195 0.100 0.113
(0.031) (0.021) (0.018) (0.014) (0.013) (0.010)
Other Areas 0.738 0.749 0.335 0.377 0.189 0.231
(0.031) (0.023) (0.026) (0.020) (0.022) (0.017)
Sources: Authors’ calculation based on IHS 2003/04 and Census 2003. Robust standard errors are in parentheses.
4.17 Since the precision of poverty estimates declines as the number of households by
administrative unit decreases, one must identify at what level the map is reliable. In order to
make an “objective” judgment on the precision of those estimates, we computed coefficients of
variation for the two administrative levels described earlier and compared them with the usual
benchmark used in poverty mapping exercises. Figure 7 presents the headcount coefficients of
variation of the LGA and district level estimates and compares them with our benchmark of 0.2.
4.18 The curves in Figure 7 clearly show that the LGA and district level headcount incidence
estimates do quite well compared to our benchmark in most cases. Only a few districts seem to
have relatively high coefficients of variations and therefore a lower level of precision of their
poverty figures. Figure 8 plots coefficients of variation against poverty headcount for each
district. It shows that all the districts with higher coefficients of variation have also a poverty
headcount level well below the national level of 58.4 percent. Since one of the main applications
of the poverty map would be to target the poorest districts, we believe that the levels of precision
of the “relevant districts” are acceptable and suitable for targeting purposes. It is clear that the
poverty estimates at disaggregated levels would be good guides to policy-makers.
54
Figure 7: Poverty Headcount Accuracy, by disaggregation (administrative) level
0.1
.2.3
.4
Coeff
icie
nt
of
Variatio
n (
CV
)
0 .2 .4 .6 .8 1Proportion of households (sorted by CV)
Benchmark (0.2) LGA (Census) District (Census)
Sources: Authors’ calculation based on IHS 2003/04 and Census 2003
Figure 8: Poverty Headcount versus Accuracy, by district
020
40
60
80
100
Povert
y H
eadcount
0 .1 .2 .3 .4Coefficient of Variation (s.e./point estimate)
Nationwide Poverty Headcount (58.4%)
Coefficient of Variation Benchmark (0.2)
District
Sources: Authors’ calculation based on IHS 2003/04 and Census 2003
RESULTS FROM THE POVERTY MAP
4.19 Poverty measures for each of the 8 LGAs and 39 districts have been computed. The
estimates and standard errors are given in Annex 3. In most cases, standard errors are small so
that predicted poverty measures are reliable. The district results for poverty headcount are
reproduced in Figure 9. While the Capital Banjul and its nearer districts are clearly less poor
than the other parts of the country, poverty seems concentrated in the northern shore of the
Gambia River.
Figure 9: District-level Poverty Headcount
55
Sources: Authors’ calculation based on IHS 2003/04 and Census 2003
4.20 How could these results be used? Among others, the results could be used to design
budget allocation rules to be applied by different administrative levels toward their subdivisions:
the central government toward the LGAs, and the LGAs toward their districts. That map could
become an important tool in support of the decentralization process currently undertaken in The
Gambia or for the allocation of resources under different programs and projects. Obviously such
monetary-based target indicators could be used in conjunction with some alternative measures of
poverty based on education, health or infrastructure indicators. In particular, merging the poverty
map with education and health maps would yields powerful targeting tools. Other uses of the
poverty map could include the evaluation of locally targeted anti-poverty scheme, such as social
funds and local development schemes. And finally, researchers could use it in a multitude of
ways such as the study of relationship between poverty distribution and different socioeconomic
outcomes.
REGIONAL DIMENSIONS OF POVERTY
4.21 More than half the country’s population, 55 percent, lives in Banjul, Kanifing and
Brikama. These LGAs are heavily urbanized, with 79 percent of the population living in urban
areas. By comparison, only 15 percent of the rest of population reside in urban areas. The poor in
The Gambia can also be broadly categorized into two geographical areas, the urban poor in
Brikama and the rural poor of the central and eastern regions. Brikama has been the largest
recipient of the country’s rural migrants, resulting in its population growing by 66 percent
between the 1993 and 2003 Population Censuses. Its estimated poverty rates are higher than
Greater Banjul but relatively low compared to the other LGAs outside of Greater Banjul.
However, it accounts for close to 30 percent of the poor due to its large population. It is heavily
urbanized compared to the LGAs outside of Greater Banjul, making it distinct from the rural
poor which make up 76 percent of the country’s poor. Within Brikama, there are large variations
across districts in terms of the poverty rates. The poverty map indicates that district level poverty
estimates within Brikama generally increases the further away from Banjul. The estimates range
from 50.1 percent for Kombo North, located just south of Kanifing, to the easternmost five Foni
districts whose headcount ratios average 58 percent. In fact, the poverty estimates for the Foni
districts are not statistically different from the neighboring district in Mansakonko.
56
4.22 The poor to the east of Brikama
reside in five LGAs which are
predominantly rural. Their situation is
quite different from their counterparts in
Brikama. It is not merely coincidence that
the LGA with the highest estimated
poverty rate by far, Kuntaur, also has the
lowest rate of urbanization and one of the
lowest population density. Among the five
rural LGAs, Kerewan accounts for the
largest share of the population and of the
poor, followed by Basse, again reflecting
their relatively larger population size.
4.23 The geographical variations in
poverty are generally matched by the variations in access to key facilities. This would be
expected given that access (distance) to facilities is correlated with the degree of urbanization
and population density, which in turn are correlated with the regional poverty rates. The 2003
IHS included a questionnaire at the community level which collected information on mean
distance to health and educational facilities and major roads (see Table 34). The analysis
indicates that there are significant urban and rural differences in the mean distance to health
facilities and upper basic and senior secondary schools. The results for hospitals are not entirely
surprising given that hospitals in most countries tend to be located in urban centers for various
reasons. The large regional disparities in distances to health centers and upper basic and senior
secondary schools indicate that the government could improve the geographical distribution of
these facilities in order to ensure more equal access across locations.
4.24 Not all facilities are significantly different between urban and rural areas in terms of their
accessibility. The average distance to lower basic schools and major roads are virtually the same
between urban and rural areas. In addition, the average distance to lower basic schools is less
than for upper basic schools and senior secondary schools. This could mean that government
plans for further expansion of schools should really focus on the upper levels schools. Given the
relatively small size of the country, perhaps it is not entirely surprising that there is little
difference between the urban and rural areas in terms of the average distance to major roads.
Although the average distance is non-negligible, it should be noted that this is the distance to
major roads and it would also be important to consider access as well as quality of the feeder
roads that connect the communities to the major roads.
Table 35: Mean Distance to Education and Health Facilities
(Kilometers)
Poverty
Headcount
Ratio 1/
Health
Centers
Hospitals Lower
Basic
Schools
Upper
Basic
Schools
Senior
Secondary
Schools
Major
Road
All 58.0 6.3 25.1 2.7 5.2 14.3 11.3
Three Domains
Banjul/Kanifing 29.1 3.0 3.0 2.1 0.9 2.1 1.4
Table 34: Poverty and Urbanization
Poverty Rate Percent
Urban
Pop.
Density
2003
IHS
Poverty
Map
Banjul 7.6 9.3 100 2,922
Kanifing 37.6 39.2 100 4,247
Brikama 56.7 52.8 60.4 221
Mansakonko 62.6 62.5 18.5 45
Kerewan 69.8 79.6 20.3 77
Kuntaur 94.9 85.6 6.4 54
Janjanbureh 75.7 69.7 15.7 75
Basse 68.0 74.0 13.1 88
Total 58.0 50.5 128
Source: 2003 Population Census.
57
Other Urban 45.9 4.0 39.3 2.9 3.2 8.4 13.1
Rural 60.6 6.9 25.5 2.8 5.5 15.8 12.9
LGAs
KMC 37.6 3.0 3.0 2.1 0.9 2.1 1.4
Brikama 56.7 5.9 22.1 2.2 3.6 12.8 1.7
Mansakonko 62.6 9.7 78.2 2.4 3.6 31.5 14.4
Kerewan 69.8 7.7 29.7 4.1 6.0 17.9 4.2
Kuntaur 94.9 7.1 18.2 2.1 6.8 11.1 9.4
Janjabureh 75.7 5.5 9.1 6.7 4.9 9.0 61.4
Basse 68.0 4.4 33.9 1.2 7.8 13.6 90.9
Source: 2003 IHS, Part 3 Questionnaires for villages and communities.
Note: Villages in 34 out of 39 districts were surveyed.
1/ Poverty rates are with respect to the lower poverty line.
4.25 There are also significant variations among LGAs for the distance to facilities and major
roads. Communities in Mansankonko appear to have the least access to health facilities. Kerewan
can also be considered a priority as its averages distances to health facilities are relatively large
as well as its share of the population. Janjabureh appears to be an outlier for distance to lower
basic schools, Kuntaur and Basse for upper basic schools, and Mansankonko and Kerewan for
the senior secondary schools. Communities in Basse appear to have particularly poor access to
major roads.
4.26 The above analysis based on average distance to facilities, while useful, does not account
for geographical variations in the demand for services. For educational facilities, schools would
need to be distributed according to the local school age population. Accordingly, the actual
distribution of schools by LGAs was analyzed by the estimated school age population of each
LGA (see Table 35). Again, it can be seen that there are vast differences between the urbanized
Banjul, Kanifing and Brikama LGAs and the rest of the more rural LGAs. The coverage of the
student population in the urbanized LGA is roughly two times greater than the more rural LGAs.
For the more rural LGAs, the student coverage appears to be fairly similar across LGAs for the
primary schools but there are large variations for the senior secondary schools. Mansakonko and
Kuntaur and to a lesser extent Basse appear to particularly lack senior secondary schools
compared to their relevant school age population.
Table 36: Geographical Distribution of School Facilities
Poverty
Headcount
Ratio 1/
Lower
Basic
Schools
Upper
Basic
Schools
School age
Children
per Basic
School 2/
Senior
Secondary
Schools
School Age
Children per
Secondary
School
Total 58.0 445 154 713 68 1,679
Banjul 7.6 9 6 481 5 905
KMC 37.6 55 35 25 23 51
Brikama 56.7 91 44 475 19 1,148
Mansakonko 62.6 48 9 1,128 2 7,357
Kerewan 69.8 76 22 917 9 2,448
Kuntaur 94.9 45 10 1,084 2 7,152
Janjabureh 75.7 45 13 984 4 3,498
Basse 68.0 76 15 908 4 5,401
Source: Department of State for Health, 2003 Population Census and World Bank (2006)
1/ Poverty rates are with respect to the lower poverty line.
58
2/ The numbers of children are approximate estimates based on shares of population under 15 years old and total
number of school age children. For secondary schools, the shares of population between 15 and 24 years old were
used.
4.27 School enrollment rates can also be compared across LGAs (see Table 36). As expected,
the urbanized Greater Banjul and Brikama have higher enrolment rates compared to the other
LGAs. Unlike the previous analysis which indicated relatively even distribution of primary
schools with respect to the school age population, Basse and Kuntaur appear to have particularly
low enrolment rates. As for senior secondary schools, Basse has particularly low rates. In fact the
senior secondary school enrolment rates for all LGAs are fairly low, even for the more urbanized
areas.
Table 37: Enrolment Ratios by LGAs
Lower Basic Upper Basic Senior Combined
Upper/Senior
NER GER NER GER NER GER NER GER
Banjul 79.3 95.7 46.6 88.0 16.9 40.6 31.4 63.7
Kanifing 70.8 87.3 39.9 79.9 13.0 31.3 26.4 55.5
Brikama 73.3 90.5 38.2 80.1 8.3 23.9 23.8 53.0
Mansakonko 69.7 86.4 32.5 68.2 5.2 16.2 19.7 43.8
Kerewan 58.2 70.9 30.3 61.5 6.0 16.3 18.9 40.2
Kuntaur 42.0 51.9 21.3 41.4 4.0 11.2 13.2 27.2
Janjanbureh 51.0 61.3 26.7 53.8 4.9 12.0 16.4 34.1
Basse 43.5 53.7 17.3 33.9 2.4 7.0 10.3 21.2
The Gambia 62.5 76.8 32.5 66.2 7.9 20.7 20.6 44.3
CONCLUSIONS
4.28 A poverty map was constructed which provides estimates of poverty indicators at
the level of districts in a statistically robust manner. The poverty map uses econometric
techniques to combine the detailed information found in the 2003 IHS with the exhaustive
coverage of the 2003 Population Census.
4.29 Despite its small size, the country has substantial geographical disparities in poverty
related indicators. The poverty map indicates that in general poverty variations are greater
among LGAs compared to within the LGAs. The LGA which has a relatively larger variation
among its districts is Brikama, where poverty estimates increase the further away from Banjul. In
fact, districts in Brikama at the border with Mansakonko have estimated poverty rates which are
not statistically different from Mansakonko, which had been considered much poorer.
4.30 Poverty rates are strongly associated with the rate of urbanization and the
population density. The urbanized Greater Branjul has poverty rates substantially lower than the
rest of the country and a population density 23 to 33 times greater than the nation’s average. The
much poorer rural LGAs to the east of Brikama has on average only 15 percent of its population
in urban areas. Located at the midpoint, Brikama has an urbanization rate of 60 percent and a
population density approximately twice the national average. Brikama has also been the largest
recipient of internal migration, growing by 66 percent between the 1993 and 2003 Censuses
compared to the overall population growth rate of 31 percent.
59
4.31 The regional variations in poverty are associated with variations in access to health
facilities and upper basic and senior secondary schools, in terms of the average distance to
these facilities. By contrast, distances to lower basic schools and major roads were similar
between urban and rural areas, although there are variations among the LGAs. The findings
indicate that further expansion of schools should perhaps focus on the senior levels. Note also
that there are large regional differences in school enrolment rates, even for lower basic schools.
Basse and Kuntaur have particularly low enrolment rates. With regards to distance to major
roads, access as well as quality of the feeder roads could also be a significant determinant of
access to major roads.
60
5. AGRICULTURE
5.1 Gambian agriculture is characterized by subsistence farming comprising cereals,
groundnuts as cash crop and traditional livestock. Farmers generally practice mixed farming,
although crops account for a dominant portion of the production. Farming systems are
characterized by a wide range of production and cropping patterns. In the uplands, farming
involves crops such as groundnut, millet, sorghum, maize and horticultural crops as well as
livestock husbandry. Lowland farming traditionally is predominantly rice-based.
5.2 The poverty headcount ratio in the agriculture sector is significantly higher than the
national average, by 17 to 18 percentage points, for both the upper and lower poverty lines. The
agriculture sector accounts for approximately 30 percent of GDP but provides for the livelihood
of roughly half of the population and 63 percent of the poor. In the rural areas, the percentage of
the population engaging in agriculture increases to 82 percent, in line with the SSA average of 81
percent.35
On average, roughly four out of five households outside of greater Banjul engage in
agriculture. Hence, reducing poverty in The Gambia requires focusing on agriculture.36
5.3 Crop production is the dominant economic activity in
agriculture. It is the second largest subsector of the economy after
trade (see Table 37). The 2003 population census identifies half
of the working population as crop growers, roughly equivalent to
the percentage of the population in the agriculture sector.
However, understanding the country’s agricultural sector requires
looking beyond crop production, despite its importance.
Livestock is the third largest economic subsector. It is larger than
construction, hotels and restaurants, and the telecommunication
subsectors, some of the key drivers of growth in recent years.
Nonfarm sources of income can be substantial for rural
households, including remittances.
5.4 The Gambia has roughly 1 million hectares of total
agricultural land, of which approximately half is considered
suitable for agricultural production. The country’s key food crops
are millet, sorghum, maize and rice, and the most important cash crop is groundnut. In 2008, 353
thousand hectares was cultivated, composed of: 57 percent for coarse grain, mostly millet and
maize; 37 percent for groundnuts; 6 percent for rice; and small amounts of sesame and findo.
35
The 2003 Population Census and Haggblade, Hazell and Reardon (2007). 36
According to the 2007 National Agricultural Sample Survey (NASS), there were 92.5 thousand agricultural
holders in the six LGAs Brikama, Mansakonko, Kerewan, Kuntaur, Janjanbureh and Basse.36
The 2003 population
census indicates that there were 101.6 thousand households in these six LGAs. Assuming that the number of
households grew at an annual rate of 2.8 percent, equivalent to the average population growth rate in the decade up
to 2003, then there would have been an estimated 113.0 thousand households in 2007 and hence approximately 82
percent of the households engaged in agricultural production.
Table 38: Sectoral GDP Shares
Sector %
Agriculture 27.6
Crop Production 15.8
Livestock 9.1
Horticulture 1.3
Fishing 1.9
Forestry 0.7
Industry 16.8
Construction 6.2
Manufacturing 6.1
Services 56.5
Trade 27.6
Hotels, Restaurants 2.8
Transport 3.7
Telecommunication 8.4
Source: 2006 Economic Census.
61
Figure 10: Agricultural Crop Production
(Thousands MTs)
Figure 11: Agricultural Crop Yields (MTs per hectare)
Source: The government’s National Agricultural Sample Surveys (NASS).
5.5 Millet is the most important crop in terms of volume of production, accounting for
approximately 40 percent all crop production. Groundnut has the next largest share of total crop
production. In fact, groundnut consistently had the largest share up to 2001. Since the sharp fall
in production in 2002 due to the low rainfalls, millet has assumed the largest share of crop
production. Also since 2002, maize has gained the most in both production and areas planted.
5.6 Crop production significantly impacted by the amount of rainfall. Low rainfalls in 2002
resulted in total production falling by 40 percent, with groundnuts falling by half (see Figure 10).
Since 2002, production levels recovered up to 2004 but then steadily declined up to 2007 before
rebounding in 2008 due to relatively good rainfalls. The almost continuous decline in production
levels since 2004 was accompanied by little movement in area cultivated. Instead, the declines
were reflected in falling crop yields (see Figures 11). Crop yields have generally stagnated in the
past decade and a half. There were two periods when crop production improved, from 1998 to
2001 and from 2002 to 2004, and both of these increases can mostly be explained by the
expansion of area cultivated as opposed to improvements in yields.
5.7 The high dependence on rainfall for crop performance is of great concern given the
overall decline, shorter seasons and increased inter-annual variability of rainfalls in the country.37
Since 1965, areas with average rainfall less than 800 mm during the rainy season, July to
September, have increased from 36 to 93 percent. Yundum, Banjul, Kerewan and Jenoi located
westward of longitude 15o30’W have experienced a fall in average rainfall exceeding 200 mm,
while locations more eastward experienced smaller reductions.
5.8 Rural farmers are also exposed to seasonal “hungry periods” due to limited options for
smoothing consumption. They tend to sell the bulk of their production immediately following
harvest in order to meet immediate household needs. This seasonal spike in supply depresses
market prices, thus lowering the farmers’ income. From July to September between harvests,
37
Government of The Gambia, Gambia National Adaptation Programme of Action (NAPA) on Climate Change,
Department of State of Forestry and Environment, November 2007.
62
farmers will sometimes buy back the very produce they sold at low prices to the local
merchants. Given the relative absence of formal financing channels, farmers might have little
choice but to turn to expensive local moneylenders or sell their limited assets, thus creating a
vicious circle of deprivation and poverty.
5.9 The market for agricultural goods in The Gambia is generally free market oriented and
the supply and the demand mechanism allows for price fluctuations. One exception is the official
minimum producer price for groundnuts, currently determined by the Agribusiness Services and
Producers Association (ASPA). ASPA is an association of key stakeholders in the groundnut
sector, composed of representatives of producers, processors and traders. Although there is a
minimum producer price, it generally only applies to the formal sector. Prices in the informal
sectors, such as in the local markets, can be significantly lower than the official price.
5.10 In response to rising international prices of agricultural commodity goods in the first half
of 2008, the government and IDA, FAO, IFAD and WFP jointly conducted an analysis of The
Gambia’s agricultural sector under the Initiative on Soaring Food Prices (ISFP).38
This initiative
had been launched by the African Union (AU) and the New Partnership for Africa’s
Development (NEPAD). The analysis concluded that the country’s agriculture sector suffered
from a host of challenges. Addressing such challenges is made all the more difficult by the
absence of a credible and comprehensive sector strategy. Major constraints to improved sector
performance were identified as follows:
Agricultural Production
Limited access to inputs, such as seeds and fertilizers
Limited post-harvest processing and storage facilities
Limited access to consistent and appropriate technical field extension services
Inefficient land and water use, resulting in soil infertility and land degradation
Social Safety Nets
Acute impact of “hungry periods” among vulnerable households
Low and decreasing purchasing power, particularly among the urban poor
Limited diversification of income generating activities and assets
Trade Policy
Limited access to financing for agricultural investment
Weak market information systems, including on vulnerability, areas of acute food insecurity,
and status of production systems
Weak capacity of producer organizations
Limited coordination among public and private stakeholders
38
World Bank, FAO, IFAD and WFP, Initiative on Soaring Food Prices (ISFP), Republic of The Gambia Final
Report Situation Assessment and Country Action Plan, November 2008.
63
FOOD SECURITY
5.11 The country’s overall food security can be
considered in terms of the cereal food balance, defined
as minimum consumption requirements of cereals less
net production (see Figure 12). Cereals consist of
millet, sorghum, maize and rice. The per capita
consumption requirement of cereals was assumed to be
175 kg, of which 117 kg is rice.39
Net production,
derived from the annual NASS surveys, is defined as
gross production net of the shares attributed to post
harvest losses, seeds and livestock feed. For The
Gambia, net production was roughly assumed to be 85
percent of gross production. This appears to be
reasonably consistent with the 2007 NASS, which
estimates that post harvest losses account for 5.1 percent of total coarse grain (millet, sorghum
and maize) production and 8.4 percent is kept for seeds.
5.12 Given the assumption of a fixed per capita consumption requirement for cereal and
steadily growing population, the estimates of the cereal food deficit simply reflects fluctuations
in the country’s domestic cereal production levels. The estimated deficit increased from 66
thousand MTs (27 percent total consumption requirements) in 2005 to 150 thousand MTs (52
percent) in 2007 but then decreased to approximately 100 MTs (35 percent) in 2008 due to
improved domestic productions.
5.13 Despite what appears to be a sizable widening of the cereal food balance deficit in recent
years, in reality the country’s food security in term of the supply of food has not significantly
deteriorated compared to previous years. On average, the annual deficits in the 2000s have been
smaller than in the second half of the 1990s, whether measured in total or per capita volume or as
percent of total consumption needs. The total deficit in 2007 was historically large as a result of
the continuous decline of domestic cereal production up to that year. However, the deficit
measured as per capita or as percent of total consumption needs was no greater than in the 1990s.
In addition, the sharp increase in domestic productions in 2008 significantly reduced the deficit.
5.14 More importantly, the deficit becomes a sizable surplus when domestic cereal production
is adjusted for the underestimation of local rice production and also the imports of rice are
incorporated. The NASS only partially covers local rice production, providing data for paddy
rice but excluding irrigated rice. Comparison of the 2003 estimate of rice production from the
NASS with the total consumption of local rice from the 2003 IHS indicates that the NASS
underestimated total domestic production by 31 thousand MT. We can roughly assume that this
gap stayed constant in subsequent years.
39
This requirement follows criteria set by the Comité Permanent Inter-Etats de Lutte contre la Sécheresse dans le
Sahel (CILSS). The consumption requirement for rice of 117 kg is approximately equivalent to the actual
consumption level of 122 kg, roughly estimated from the 2003 IHS survey based on the mean per capita total
consumption inflated to 2008, the consumption shares of rice, and the average retail price of rice from the CPI
surveys.
Figure 12: Cereal Food Balance (Thousand MTs)
Source: Various annual NASS.
64
5.15 The country also imports roughly 100 MTs of
rice and 40 MTs of wheat flour. If imports of rice are
added to the domestic production of cereals, then
domestic supply consistently far exceeds
consumptions needs (see Figure 13). The excess
supply with respect to the national requirements
averaged 74 thousand MTs. This is without
considering the considerable imports of wheat flour,
which supplements the local cereal diet. This excess
supply is explained by the fact that the country re-
exports goods into neighboring countries. Imported
rice and wheat flour are generally widely available in
the country and food security risk in terms of
insufficient supply with respect to the local demand is not a particularly relevant issue for the
country. However, this is from the point of view of the country as a whole and the situation could
vary greatly from one community to the next.
CROP PRICES
5.16 Rather than supply, price fluctuations
can be a major risk for poor households in the
country. The average Gambian spends a high
proportion of their income on food, estimated
at 55 percent, and thus increasing prices could
have a substantial adverse impact on their
livelihood. Prices of major crops can vary
greatly across time, regions and marketing
channels.
5.17 Prices exhibit a strong seasonal effect,
rising during periods between the harvest
seasons when supplies are relatively low (see
Figure 14).40
For the urban poor who are net
consumers of these crops, their ability to smooth consumption during periods of higher prices,
whether through own savings or access to credit, could significant determine their overall well-
being. For the rural farmers, the impact can be either positive or negative depending on whether
the household is a net producer or consumer of the crop. Nevertheless, rural farmers are often
exposed to seasonal “hungry periods” due to limited options for smoothing consumption. They
tend to sell the bulk of their production right after harvest in order to meet immediate household
needs. This seasonal spike in supply depresses market prices, thus lowering the farmers’
potential income.
40
Note that Figure 5 covers a period when international prices sharply rose in the first half of 2008 before rapidly
declining in the second half, and therefore this could have accentuated the usual domestic seasonal pattern of price
fluctuation.
Figure 13: Cereal Demand and Supply Gap (MT)
Source: NASS, GPA, 2003 IHS
Figure 14: Retail Crop Prices (Jan 08 to Mar 09) (Dalasis per Kg)
Source: DOSA, based on prices collected at retail markets
mostly in Greater Banjul and Brikama.
65
5.18 With respect to crop prices, rice is a particularly
important food item to consider given that it accounts
for a major proportion of the local diet and is heavily
imported, thus exposing the country to international
price fluctuations. The sharp increases in international
commodity prices from the second half of 2007 to the
first half of 2008, including for rice, caused major
concerns for countries traditionally dependent on these
imports (see Figure 15). With the global economic
slowdown, international prices of rice have substantial
declined since its peak in April 2008 but current
(February 2009) prices are still 64 percent higher
nominally compared to end-2007.
5.19 The transmission of these international price increases into the domestic economy has
apparently taken place with some delays, possibly due to preexisting inventories (see Figure 16).
It could also partially reflect the government’s decision to reduce the sales tax on rice imports
from 15 percent to 5 percent in July 2007 and eliminating it altogether in May 2008.41
Regardless of the underlying reasons, it is likely that the domestic prices of imported rice will
eventually have to decline in line with the international prices, further helping the country’s poor.
5.20 In The Gambia, on average rice accounts for 8.0 percent of total consumption but the
share increases to 10.8 percent for rural households, according to the 2003 IHS. However, rural
households depend much more on the local rice for consumption. On average rural households
rely on the local variety for roughly half of their total rice consumption, compared to less than
five percent for urban households. Given that many of the poor reside in the rural areas and they
consume more of the local rice, the extent to which domestic prices of local rice track imported
rice can impact the real purchasing power of the poor.
41
It has also been proposed that the strengthening of the dalasi in the first half of 2008 contributed to the delayed
dalasi domestic price adjustment of the imported rice. However, note that Figure 7 plots the international price of
rice converted to the local currency using the monthly exchange rate. It indicates that the dalasi appreciation did not
significantly contribute to the delayed domestic price response.
Figure 15: Rice, Thailand, 5%
(US$/MT)
Source: World Bank, Global Economic Monitor
Figure 16: International and Domestic Prices of Rice
(Dalasis per Kg)
66
5.21 When the international price of rice rose
rapidly, there occurred a noticeable divergence
between the domestic prices of the imported and
locally produced rice (see Figure 16). While the
price of imported rice has significantly increased
since the middle of 2008, the local rice has
remained nearly constant.42
This would have
benefited the rural households given their greater
share of consumption of the local rice. In addition,
it is likely that rural households would expand their
consumption of the local rice as well as other food items at the expense of the imported rice, thus
further insulating them from the impact of the higher prices of imported rice.
5.22 The analysis of the impact of price fluctuations of the other crops is complicated by the
fact many of the rural poor, which represents 76 percent of the poor, are net producers of these
crops, to an extent much greater than for rice. For these net producers, price rises would be a
benefit. This could apply to the 68 percent of rural farmers who grow coarse grain and the 55
percent who grow groundnuts (see Table 39). By contrast, only 27 percent of rural farmers grow
rice and some of these producers could still be net consumers given the large share of rice in the
local diet. Therefore, price increases in rice are more likely to have an overall adverse impact.
5.23 The retail prices of coarse grain (millet, sorghum and maize) rose by between 11 to 13
percent in 2008. It is doubtful that such an increase significantly benefited the net rural producers
of coarse grain. First, most of the coarse grant harvest is either auto-consumed or given away as
gifts and only approximately 5 percent of the harvest is sold.43
Second, price increases of coarse
grain were not significantly different from the 2008 CPI inflation rate for the rural areas,
generally estimated to be 8 percent but increasing to 14 percent when weights adjusted for the
rural consumption pattern are used. Hence, the real impact of the coarse grain price increases
was likely to have been minimal.
5.24 The situation is even more complex for the groundnut sector. In 2008, retail prices of
unshelled groundnuts increased by 12 percent nationally but only 2 percent in the rural areas.
When the annual price increase is again compared to the inflation rates, it would not appear that
the real income of groundnut farmers significantly benefited. In addition, since the peak in mid-
2008 the international price of groundnut products has fallen even faster than for rice, resulting
in prices below the levels of end-2007 (see Figure 17). Given that much of the commercialized
groundnuts are exported, this could further suppress domestic price increases.
42
This divergence between the prices of imported and local rice, apparent in the prices collected by GBOS for the
CPI, is largely absent in the prices collected by the Department of Planning of DOSA. This probably is due to the
fact that GBOS collects prices of local rice in locations throughout the country while DOSA collects their prices
mostly with Banjul and Brikama. Apparently the price of the local rice rose in the Greater Banjul area while it
remained relatively steady outside this area. Given that urban households consume very little local rice, rising local
rice prices in Banjul is not likely to have had a significant impact. 43
Gifts can be substantial given that it is highly encouraged by the local Muslim tradition.
Source: GBOS.
Figure 17: International Prices of Rice and Groundnuts
(US$ per MT)
67
5.25 Domestic groundnut prices are not
uniform. Groundnuts exhibit seasonal
variations to a much greater extent than other
crops (see Figure 14). In 2008, the difference
between prices at the peak and the trough
ranged from 50 to 250 percent, depending on
the area and whether the groundnuts are
unshelled or shelled. Hence, farmers who were
better able to time their entrance into the market
would have been able to substantially increase
their income. In addition, in 2008 on average
shelled groundnuts received prices with a
markup of 27 to 36 percent compared to unshelled groundnuts, depending on the area. Farmers
who are able to shell their groundnuts would potentially be able to capture the added-value of
processing their outputs.
5.26 The actual prices received by the farmers (producer prices) could be substantially lower
than the retail prices analyzed previously. In fact, actual prices can be even lower than the
official producer. In 2006, the official producer price was 6,500 dalasis. However, the average
actual price received by the farmers was a little above 6,000 dalasis (see Table 38). Larger
farmers reported receiving higher prices than the smaller farmers, perhaps reflecting the
preference of buyers to handle larger volumes.
Table 39: Groundnuts Producer Prices by Sales Channel (2006)
(Dalasis and Percents)
Avg
Price
Credit Sale
Trader Relative/Neighbor
Local Market
Lumuo Cooperative/Association
Private Company
Other
0 to 0.5 5,216 8 23 9 23 9 32 3 1
0.5 to 1 5,678 22 34 5 24 11 23 0 3 1 to 2 6,426 23 52 4 14 5 22 2 1 > 2 6,790 43 66 0 12 0 16 6 0 All Percent 26 45 6 11 8 25 5 2 Avg Price 6,045 5,222 3,921 5,106 5,693 6,320 5,998 5,042
Source: 2007 NASS.
5.27 Larger farmers also tend to sell more on credit. Smaller farmers cannot afford to sell on
credit given their immediate cash needs and therefore they turn to buyers, such as those in the
local market, who offer cash but at lower prices. This would at least partially explain the lower
average prices received by the smaller farmers. Government policy has always been to buy on
cash but in practice buying on credit does take place.
5.28 Prices also greatly vary across the different selling points. Traders and local markets
offered the lowest prices whereas cooperatives and producer associations offered the highest
prices. For all farmers, 45 percent or nearly half sell their groundnuts to the private traders, 25
percent to their producer association and 11 percent to local markets. The fact that producer
associations offered the highest prices is a compelling reason to support building their capacity.
Source: World Bank, Global Economic Monitor
68
Policies which help groundnut farmers to take advantage of such price differentials could
increase their income and reduce poverty in a group which is among the poorest in the country.
CROP PRODUCTION CHARACTERISTICS AND CONSTRAINTS
5.29 It has been said that much of crop production in The Gambia can be characterized as
subsistence farming on relatively small landholdings. According to the 2003 IHS, cumulatively
approximately 32 percent of farms are less than 0.5 hectare and 45 percent are less than 1
hectare. The fields smaller than 1 hectare account for only 6 percent of total landholdings. The
2007 NASS indicates an even higher proportion of small farms. Farms less than 0.5 hectare
account for 69 percent of all farms and up to 85 percent of farms are smaller than 1 hectare (see
Table 39). The average size of areas planted is less than 1 hectare.
5.30 The fact that a majority of farmers are small landholders is of concern because larger
farms tend to have significantly higher crop yields. In comparing farms larger than 2 hectares
with farms smaller than 0.5 hectares, on average the yields are higher by 60 percent for coarse
grains, 50 percent for groundnuts and 59 percent for rice. Larger farms also on average grow a
greater variety of crops. Diversification of crop production would allow for better risk
management. It also indicates the relative absence of large commercialized farms with a
specialized crop production.
5.31 Larger farms are more likely to grow groundnuts, whereas the percentage of farms
growing coarse grains and rice are relatively similar across farms of different sizes. Seventy one
percent of farms larger than 2 acres grow groundnuts, compared to 40 percent for farms smaller
than 0.5 acres. Hence, government interventions in the groundnut sector would
disproportionately benefit the larger farmers. As smaller farms are more likely to be poor, an
emphasis on targeting of these smaller farms is needed for government support to the sector to be
pro-poor.
5.32 Larger farms consistently use greater amounts of key production inputs per hectare. On
average, farms larger than 2 hectares use 28 percent more fertilizers per hectare than farms
smaller than 0.5 hectares and 102 percent more seeds. A greater percentage of the larger farms
use fertilizers, insecticides and seed dressing. Their greater use of production inputs could be
explained by a greater percentage of larger farms obtaining credit, which would finance the
purchase of inputs. Greater access to credit by the larger farms could also be due to more of them
growing groundnuts, a major cash crop and therefore more conducive for crop financing.
Table 40: Farm Size, Productivity and Inputs
Farm Size (hectares) 0 – 0.5 0.5 – 1 1 – 2 > 2 All
Percent of farms 69.1 16.2 9.2 5.6 100
Percent of area planted 23 20 23 34 100
Average area planted (Ha) 0.2 0.7 1.4 3.4 0.6
Average number of crops 1.8 2.6 2.8 3.6 2.7
Percent of female workers 58 61 65 68 62
Percent of hired labor 21 29 34 36 29
Percent of farms producing
Coarse grain 68 69 64 72 68
69
Groundnuts 40 49 63 71 55
Rice 27 25 29 34 27
Livestock 90 93 98 97 96
Tree crops 77 81 84 87 85
Crop yields (Kg/Ha)
Coarse grain 613 774 898 979 816
Groundnuts 523 615 663 782 617
Rice 512 643 763 815 686
Percent of farms
Using production inputs
Fertilizers 29.8 32.2 35.9 37.8 33.6
Fertilizers used (Kg/Ha) 65 78 82 83 77
Own seed 82 87 90 95 89
Seed dressing 8.6 12.3 17.5 19.6 14.9
Seeds used (Kg/Ha) 42 68 73 85 67
Insecticide 7.1 11.6 13.4 17.9 13.1
Accessing extension services 5.6 8.4 12.3 18.6 11.8
Owning land 81.6 84.3 82.6 83.6 82.8
Obtaining credit 34.6 34.2 44.2 44.8 39.2
Member of producer association 54.3 57.1 66.8 67.3 58.9
Using irrigation 1/ 9.8 12.5 14.6 18.9 14.1
Source: 2007 NASS.
1/ The data on the use of irrigation include also irrigation of horticultural fields as well as crop (coarse grain,
groundnuts and rice) production. It is not possible to separately identify irrigation of crop fields in the survey.
Therefore, the actual percentages of farms irrigated their crop fields will be less than the estimated percentages.
5.33 Larger farms are more likely to be members of producer (farmers) associations and use
the government’s extension services. Again, targeting of government interventions could be
critical if strengthening of the producer associations and extension services were to benefit the
smaller farmers. Particularly for groundnut farmers, membership in an association would allow
farmers to take advantage of higher producer prices (see Table 38). Although larger farms are
more likely to be members, membership in producer association appears to be relatively high
across farms of all sizes. With adequate capacity, producer associations could be an effective
instrument through which to channel support to the farmers.
5.34 By contrast, the percentage of farms accessing extension services is low across farms of
all sizes, with a high of 19 percent for farms larger than 2 acres. Hardly any of the smaller farms
use extension services. The low rate of access could be due to a combination of lack of
availability of services and low incentives to use available services if they are perceived to be of
poor quality. The current NASS does not allow for separate identification of the importance of
these possible factors but future surveys can be relatively easily modified to address this issue.
5.35 The NASS indicates that only 14 percent of farms use irrigation. The percentage declines
from a high of 19 percent for farms larger than 2 hectares to 10 percent for farms smaller than
0.5 hectares. The actual shares of farms using irrigation for crop (coarse grain, rice and
groundnut) production are likely to be even lower given that the survey identifies a farm as using
irrigation even if it is used only for horticultural fields or tree crops. However, clearly the need
exists given that crop production has historically been highly sensitive to rainfalls. Costs
associated with installing irrigation systems could be the major impediment. However, funding
to a certain degree has always been available for the agriculture sector from external donors and
NGOs. In fact, the development of irrigation systems has been a relatively prominent area of
70
support in development projects in the sector. It could be that the irrigation systems developed
were not sustainable or that initiatives focused on public project as opposed to creating the right
incentives for private uptake of irrigation systems.
Determinants of Crop Production and Productivity
5.36 Crop production remains on average the largest source of income for farm households
and therefore improving crop production and productivity would increase income and reduce
poverty in rural areas. The key determinants of crop production and productivity were analyzed
using regression analyses of production functions which link the total production and
productivities (yields) to total land and labor inputs and other production inputs, controlling for
geographical locations and average rainfall (see Table 40). The data are from the 2007 NASS,
which collected detailed information on crop farming practices.
5.37 The first three columns of Table 40 presents the coefficient estimates of the regression on
the log of the total value of production, while the last three columns presents the estimates of the
regressions on the log of crop yields.44
For each of these two sets of regressions, the basic results
are presented first followed by the results incorporating dummies for groundnuts and rice and
then the results which incorporate dummies for the LGAs. The inclusion of the LGA dummies
allows for the estimation of coefficients controlling for unobserved regional differences. It
provides reassurances that the estimated effects of different inputs do not pick up unobserved
regional effects. Alternatively, the inclusion of the log total seasonal rainfall at the district levels
in lieu of the LGA dummies further explores the effect of regional variations. The coefficient
estimates are elasticities if the variables are expressed in logs. For the dummy variables, the
coefficient estimates represent percentage changes in the value of the output or productivity.
Table 41: Estimated Determinants of the Value of Crop Production
Dependent Variable = Log Total Value of Production Log Yields
Log area planted 0.364***
0.447*** 0.383***
Log total labor 0.109 0.034 0.084
.022 .012 -.024
Log % female 0.394*** 0.51 0.377*** .149 .084 .079
Dummy fertilizers 0.139** 0.088 0.118* .173*** .161*** .119**
Dummy insecticides 0.125* 0.017 0.153** -.013 -.029 .006
Dummy seed dressing 0.070 0.098 0.116* .127*** .131*** .122**
Log quantity of seeds 0.132** -0.036 0.159*** -.065 -.094 -.063
Dummy animal/mech power 0.070 0.003 0.043 -.057 -.052 -.044
Dummy extension services 0.200** 0.224*** 0.186** .099 .104 .090
Dummy credit 0.431*** 0.464*** 0.429*** .131*** .142*** .156***
Dummy member assoc 0.403*** 0.441*** 0.417*** .284*** .294*** .272***
Log % land owned -0.046 -0.055 -0.178 .098 .100 -.025
Log total seasonal rainfall 1.049** 0.409 .482 .417
44
Additional production functions were estimated for: (i) different combinations of regressors; (ii) variations in the
definitions of the regressors, for example measuring rainfalls for different months and as percentage deviations from
the average; and (iii) coarse grain, groundnuts and rice separately. The results were largely consistent with those
presented this report.
71
Dummies for LGAs 1/
Brikama -0.032 .090
Mansakonko 0.196** .256***
Kerewan 0.085 .073
Kuntaur 0.194** .072
Basse 0.076 .197***
Dummy for groundnuts 0.028 .039
Dummy for rice 0.551*** .079
Source: The data for 2006 collected in the 2007 NASS.
Note: * significant at 10 percent, ** significant at 5 percent and *** significant at 1 percent.
1/ The LGA dummies are with respect to the LGA Janjibureh.
5.38 The results indicate that there is a labor surplus and declining marginal productivity of
land. The elasticity on labor is consistently estimated as insignificant for both total production
and productivity, implying that additional labor would not result in increased production. It
would be consistent with labor being in surplus, a situation where there would be few if any
gains on crop production from additional labor. In contrast to total labor, the coefficient estimate
on area planted is consistently significant. However, an additional percentage increase in
landholding results in only a 0.4 percent increase in overall production. In another words,
doubling agricultural land would result in only a 40 percent increase in crop production. Hence,
the current strategy which emphasizes expansion of cultivated area could have severe limitations.
5.39 Given the low marginal returns to labor and landholdings, the appropriate policy response
would be to focus on raising labor productivity through improved farming practices, including
the use of modern inputs and more effective water and land management.45
The regression
results can provide some guidance on the options available. The coefficient on the dummies for
the use of fertilizers and seed dressings are consistently significant in the regression of crop
yields. The use of these modern inputs can contribute to improved productivity of crop
production. However, note that this should not be interpreted as their cost effectiveness.
Although fertilizers and seed dressing raise crop yields, whether farmers choose to use them will
also depend on their costs. Note in particular that the estimated coefficient on fertilizers and seed
dressings are similar in magnitude, and therefore costs would be a critical determinant of the
relative attractiveness between the two inputs.
5.40 Access to credit is a significant determinant of crop production and productivity. The
estimate of the coefficient on the dummy variable on receiving credit is consistently significant
in all regressions for both production and yields. The coefficient estimates are also relatively
large, at an average of 0.44 for the log of production and 0.14 for the log of yields. These results
indicate that widening access to credit for rural farmers, including by lowering its relatively high
costs, could have a significant impact on agricultural income. In actuality, access to credit can
certainly be improved but credit appears to be available to some extent, given that approximately
40 percent of farmers had access to credit according to the 2007 NASS. Perhaps more
interestingly, the survey reveals that 56 percent of rural farmers received credit from
microfinance institutions such as the local VISACAs, 21 percent from traders and only 3 percent
45
The focus of this exposition is on improving crop production and productivity. However, surplus labor for crop
production and the associated land pressure could be reduced by rural to urban migration and also diversification of
farm labor into livestock, tree crops and nonfarm activities.
72
from the commercial banks. Despite the important role of microfinance institutions in rural
finance, their long term viability and sustainability have been questioned given their reliance on
subsidized external funding, low rates of savings mobilization, low operational self-sufficiency
and limited access by the poorest households given the need for collaterals.46
5.41 The coefficient estimates on membership in a producer association is consistently
significant and relatively large in all regressions for both crop production and productivity. The
impact of membership could be due to several reasons, including sharing of knowledge, pooling
of resources and collective bargaining for higher crop prices. However, the significant coefficient
estimates could simply be capturing a selection effect in terms of higher productivity farmers
more inclined to be active in their local associations. An instrumental variable for membership in
a producer association would allow the ordinary linear regressions to isolate the “causal”
relationship between membership and higher production and productivity.47
Given the lack of an
appropriate instrument in the current NASS dataset, future surveys would have to be used to
further explore this issue.
5.42 The coefficient estimates on extension services are consistently significant in the
regressions on total production but they are also consistently insignificant in the regression on
crop yields. The regression results indicate that extension services have not significantly raised
productivity but they have been associated with greater total production. As with the membership
in a producer association, the significant coefficients in the regressions on production do not
necessarily indicate a “causal” relationship, particularly given the insignificant results in the
regressions on productivity. The regression results could simply reflect the fact that extension
service agents tend to visit the larger farms, perhaps due to their physical accessibility or
availability. The NASS dataset does indicate that larger farms access extension services at a
higher rate than smaller farms (see Table 39).
LIVESTOCK AND HORTICULTURE
5.43 Crop production is only one
among many sources of income for farm
households. Almost all farms produce
livestock and many farms produce tree
crops as well. Ninety seven percent of
farm households undertook livestock
production, 74 percent engaged in
horticulture and 85 percent in tree crops.
5.44 On average the scale of production
per household is relatively modest (see
Table 41). Therefore, it is likely that much of the livestock is used for household consumption
and as opposed to commercialization. The country imports large volumes of meat, eggs and
46
IFAD (2005). 47
Instrumental variables are variables that are correlated with the explanatory variable but not with the dependent
variable. For the case being discussed, the explanatory variable is the dummy for membership in a producer
association and the dependent variables are the log of crop production and the log of crop yields.
Table 42: Livestock Production (Percent)
Farm
Size
(Ha)
Percent
with
Livestock
Cattle Goats Sheep Chicken
0 to 0.5 90 6.6 4.7 3.8 9.2
0.5 to 1 93 11.4 7.6 5.1 14.7
1 to 2 98 13.6 8.5 6.3 18.9
> 2 97 17.6 9.1 7.1 21.7
All 96 11.6 6.4 4.9 12.5
Source: 2007 NASS.
73
milk, perhaps as a result of this relative lack of commercialization. Livestock is also an important
store of household wealth. Based on the average market price, the value of the stock of cattle in
particular can be roughly equivalent to 65 to 85 percent of the annual rural household
consumption. Productivity and output are generally considered low due to low input utilization
and poor husbandry practices. However, larger farms have much more livestock. Farms larger
than 2 hectares own on average 13.4 cattle and 18.7 chickens compared to 3.2 and 5.8
respectively for farms smaller than 0.5 hectares. These differences indicate large income
disparities from livestock among farm households.
5.45 The horticulture subsector has typically been considered highly for its potential for
growth but reality has rarely met expectations. Much of horticultural activities appear to remain
mostly for subsistence, with farmers diversifying their production base to include vegetables and
fruits for both home consumption and marketing. Commercialized horticultural exports declined
from a peak of approximately US$5 million in the mid-1990s to US$2-3 million in recent years.
There were an estimated 30 active horticultural commercial enterprises in 1991 but currently
there are only three. A growing commercialized sector could have benefited small scale farmers
through out-grower programs and increased demand for hired labor.
5.46 Despite its recent stagnation, a large and growing European market for fresh fruits and
vegetables offers potential for future growth of the horticultural subsector. However, this is a
highly competitive market where The Gambia would need to improve its competitiveness. High
costs of water and electricity and inadequate roads and storage facilities have often been cited as
key constraints to the development of the subsector. Commercial exporters have also noted the
high costs and shortage of air freight and the lack of availability of skilled labor. The other
potential source of growth is increased local sourcing for the domestic hotel industry, an
important opportunity for small scale farmers with access to the markets. There have been
indications that the backward linkages of the tourism industry can substantially improve
household income of the participating farmers.
NONFARM INCOME
5.47 Although the previous analysis based on crop and livestock production seems to indicate
that small landholders tend to be poorer, this does not take into account differences in nonfarm
income. Forty one percent of farms smaller than 0.5 hectares receive nonfarm income, compared
to 27 percent for farms larger than 2 hectares (see Table 42).48
Among farms with nonfarm
income, the average annual nonfarm income was estimated to be 7,960 dalasis for the smallest
farms, 80 percent larger than average of 4,425 dalasis for the largest farms. In addition, on
average smaller farms have smaller households. The average household size for farms smaller
than 0.5 hectares is 6.1, while it is 12.9 for farms larger than 2 hectares.49
As result, it is likely
48
The data should be considered preliminary and interpreted with caution. The percentage of households receiving
nonfarm incomes potentially includes non-responses. The averages for each nonfarm income are estimated over
households that received that particularly nonfarm income. 49
The data on household size is from the 2006 NASS. The 2006 NASS estimates of the household size appear to be
roughly consistent with the 2003 IHS which estimated the average household size in rural areas to be 9.7.
74
that on a per capita basis nonfarm income for the smallest farms account for a significantly larger
share of total income.50
5.48 For each nonfarm activity, uniformly smaller farm households engaged in the activity to a
greater degree compared to the larger households. The single largest income source was
remittances, which also consistently had the highest percentage of households receiving income.
For those households receiving remittances, on average remittances were equivalent to
approximately 11 percent of the average total consumption based on the 2003 IHS, inflated to
2006.
Table 43: Rural Households Average Nonfarm Incomes (2006)
(Dalasis and percentage of households (in parantheses))
Total Fishing Domestic
Work
Petty
Trading
Remittances Handi-
Crafts
Other
Activities
0 to 0.5 7,960
(40.9)
7,758
(25.6)
6,585
(21.6)
6,432
(23.8)
9,832
(31.6)
4,256
(11.8)
7,856
(38.9)
0.5 to 1 5,425
(35.7)
5,696
(19.7)
4,326
(18.5)
4,521
(19.4)
8,026
(28.5)
2,136
(7.8)
6,094
(29.7)
1 to 2 4,867
(29.9)
4,752
(18.6)
3,562
(17.8)
4,423
(18.5)
7,985
(27.5)
1,986
(6.9)
5,836
(24.6)
> 2 4,425
(27.1)
4,325
(17.2)
3,021
(16.4)
3,462
(14.3)
6,932
(21.6)
1,796
(5.4)
5,123
(18.9)
All 7,960
(38.3)
7,758
(19.7)
6,585
(16.2)
6,432
(19.4)
9,832
(29.4)
4,256
(8.6)
7,856
(30.2)
Source: 2007 NASS.
5.49 Given that groundnuts are the main cash crop in The Gambia, nonfarm income was
compared to groundnuts sold in order to gauge the importance of nonfarm activities as an income
source for rural farm households (see Table 43). Data on total production and sale of groundnuts
for 2006 are from the 2007 NASS. Groundnuts sold were valued at prices at the monthly peri-
urban local markets. Total nonfarm income includes fishing, domestic work, petty trading,
remittances, handicraft and wages and salaries. For groundnut farmers, the average ratio of
nonfarm income to groundnuts sold is 41 percent. This ratio increases for smaller farms and the
ratio of 70 percent for farms smaller than 0.5 hectares is significantly larger than the larger
farms. Particularly for the smaller farms, nonfarm activities are major sources of income.
Table 44: Comparison of Nonfarm Income and Groundnuts Sold (2006)
50
It is difficult to estimate the exact nonfarm income share of total income due to the data constraint. The 2007
NASS provides the data on nonfarm income but the survey does not provide complete information on farm income.
As a rough estimate, nonfarm income can be compared to average rural household consumption based on the 2003
IHS, inflated to 2006 and controlling for the size of households. These approximate estimates indicate that nonfarm
income accounts for on average 7 percent of total household consumption, declining from 15 percent for farms
smaller than 0.5 hectares to 4 percent for farms larger than 2 hectares.
75
Farm
Size
(Ha)
Groundnuts
Production
(Kg)
Groundnuts
Production
(Dalasis)
Groundnuts
Sold
(Dalasis)
Nonfarm
Income
(Dalasis)
Ratio NF
Income to
Groundnuts
Sold
0 to 0.5 1,546 27,055 9,228 6,425 0.70
0.5 to 1 1,879 32,883 12,185 4,421 0.36
1 to 2 2,376 41,580 15,664 4,021 0.26
> 2 2,790 48,825 17,162 3,956 0.23
All 1,790 31,325 11,276 4,609 0.41
Source: Data on 2006 from the 2007 NASS and groundnut retail prices from Department of Planning, DOSA.
5.50 In fact, the importance of nonfarm income could be underestimated given that the prices
at the local markets are essentially retail prices which can be substantially higher than the
producer prices. The NASS directly asked farmers the prices received for their groundnuts. If
these prices are used, then the average ratio of nonfarm income to groundnuts sold becomes 1.18,
from a high of 2.34 for the smallest farms to 0.59 for the largest farm. For the smallest farms,
nonfarm income is more than twice the size of income from groundnuts sold. Hence, the
importance of nonfarm income to groundnuts as a source of income is substantially greater. For
the smaller farms, groundnut production appears to be only one among many economic activities
and it could even be viewed by the households as a supplementary as opposed to the main source
of income.
GROUNDNUT SUBSECTOR
5.51 Groundnuts are the country’s major cash crop and largest domestically produced export.
The 2007 NASS indicates that 41 percent of the groundnut harvest is sold compared to 5 percent
for the coarse grain harvest. According to the 2003 IHS, around half of households farm some
land and over 27 percent of households allocate some land to groundnuts. Nearly 95 percent of
groundnut farmers live in rural areas. Approximately 55 percent of total farmland is allocated to
groundnuts and 63 percent of farms allocate some land to groundnuts, increasing from 27 percent
of farms with landholdings less than 0.5 hectare to virtually all farms with landholdings
exceeding 3 hectares. Similarly, the 2007 NASS indicates that 55 percent of farmers grow
groundnuts, increasing from 40 percent for farms smaller than 0.5 hectare to 71 percent for farms
larger than 2.0 hectares.51
5.52 Groundnut farmers are on average much poorer than other households. According to the
2003 household expenditure survey, between 51-58 percent of all households in The Gambia are
classified as poor, depending on whether the lower or the upper poverty line is used. The
estimated poverty incidence for groundnut farmers is 77-83 percent, compared to 33-40 percent
for non-farmers and 57-67 percent for non-groundnut farmers. Over 51 percent of groundnut
farmers were classified as extremely poor, defined as being among the poorest 30 percent of
households, compared to 33 percent of non-groundnut farmers and 17 percent of non-farmers.
51
The 2003 IHS indicates that smaller farms allocate a higher proportion of their landholdings to groundnuts,
decreasing from 74 percent for farms smaller than 0.5 hectare to approximately 57 percent for farms larger than 2
hectares. By contrast, the 2007 NASS indicates the percentages increasing from 33 percent to 45 percent,
respectively. For the smaller farms, these are significant differences and future work could focus on confirming the
extent of their reliance on groundnuts.
76
5.53 The incidence of groundnut farming is particularly high among the poorest farmers with
around 60 percent of the poorest farmers involved in groundnut cultivation compared to less than
30 percent of the richest farmers. Groundnut farmers are still relatively well diversified in that
they allocate sizeable shares of total plots to other crops, including fruit, vegetables and rice.
Groundnut Sector Marketing Arrangement
5.54 Sustained growth and improved livelihood of groundnut farmers depend on reliable and
transparent marketing arrangements which provide competitive prices for groundnuts. This has
often not been the case in The Gambia. The marketing arrangement of the groundnut sector has
gone through several major structural changes. Before its privatization in 1993, the Gambia
Produce Marketing Board (GPMB) received government funding for pre-crop financing of
groundnut purchasing from the farmers through producer associations called Cooperative
Produce Marketing Societies (CPMS). These associations were coordinated nationally by the
Gambia Cooperative Union (GCU). The arrangement maintained the supply of inputs and
groundnuts through relatively prompt payment of producers and traders. However, the system
collapsed under the weight of non-reimbursed subsidies to farmers for output and inputs.
5.55 In 1993, GPMB was privatized and sold to the international firm Alimenta, who renamed
it the Gambia Groundnut Corporation (GGC). The GCU retained its mandate to organize the
distribution of crucial farm inputs and the delivery of groundnuts to depots but it lacked the
necessary capacity. The addition of inadequate rainfall resulted in total production falling to less
than 46 thousand MTs from over 80 thousand MTs just two years earlier (see Figure 18). A
heavily indebted GCU was unable to secure financing and was liquidated in 1998. Its groundnut
trading activities were assigned to the newly formed Federation of the Agricultural Cooperative
Societies (FACS). However, the FACS similarly lacked the capacity to fulfill its roles
effectively. Furthermore, disputes between the government and Alimenta resulted in the
renationalization of GGC in January 1999.
5.56 Eventually the dispute was settled and Alimenta was compensated by the government,
through funding from the EU. The EU also agreed to support a “Groundnut Revitalization
Program.” Under the program, the management of the sector was restructured based on a new
regulatory framework, the Framework of Agreement (FOA), agreed between the government and
the sector operators. The public and private operators, including representatives of the farmers
association, were organized into the Agribusiness Services and Producers Association (ASPA),
which would be responsible for managing the sector. The program also introduced a new seed-
plus-fertilizer credit package for farmers through the Autonomous Credit Program (ACP) and the
Seed Multiplication Program (SMP) in collaboration with the DOSA and the National
Agricultural Research Institute (NARI). By the 2000/2001 season around 8 thousand MTs of
certified seed was distributed to farmers via the ACP. Interest rates on credit received under the
ACP were charged at subsidized rates of 7-10 percent compared to market rates of 18-20 percent.
As a result, yields increased substantially and by 2001/2 outputs had increased to nearly 138
thousand MTs. In the HIPC decision point reached in December 2000, the government also
agreed to the divestiture of GGC as part of the floating completion point triggers.
77
5.57 However, reforms were not sustained. Low rainfalls during the 2002/3 planting season
resulted in a sharp decline in groundnut production. More importantly, in 2004 the government
circumvented the marketing arrangement by introducing a new licensing scheme and then
awarding a monopoly license to GAMCO, a new public-private joint venture. Furthermore,
GAMCO was able to obtain commercial bank loans based on government guarantees of the
loans. The EU withdrew its funding for the groundnut sector in response to unraveling of the
marketing arrangement. This led to the closure of the SMP and the ACP and the gradual
deterioration of the seed multiplication and distribution system and the supply of crucial inputs in
general to the farmers. When GAMCO was unable to absorb the market, exports collapsed from
US$17 million in 2004 to US$2 million in 2005. GAMCO has since gone into bankruptcy
proceedings and the government has had to assume the responsibility for paying its outstanding
loans. Meanwhile, little progress was made in divesting GGC.
Figure 18: Groundnut Production and Yields, 1989 – 2008
(Thousand MTs (left axis) and MT per hectare (right axis))
Source: DOSA, based on various NASS.
5.58 In 2007, the government prepared the “Groundnut Subsector Roadmap Implementation
Framework” order to revitalize the groundnut sector, in consultations with key public and private
stakeholders and development partners. The objective of the Roadmap is to restore sustained
growth in the groundnut sector and improve the livelihood of groundnut farmers by providing a
reliable and timely market for groundnuts based on minimum producer prices, increasing pass-
through of international prices to the farmers, and enhancing productivity at all levels of the
value chain. It would achieve this through market liberalization and open competition, based on:
(i) strengthening of the policy and regulatory framework; (ii) divestiture of GGC though options
that include full privatization and private management; (iii) maintaining a minimum producer
price to protect the farmers; (iv) establishment of a quality control system; (v) improvement of
access to agricultural production inputs; and (vi) strengthening of farmer support institutions,
including producer organizations. The Roadmap Implementation Steering Committee (RISC)
was formed to guide the implementation of the Roadmap, in particular the divestiture of GGC.
The RISC has multi-stakeholder public and private representation, including farmers
associations.
78
5.59 The government took immediate steps to implement the Roadmap, including: (i) allowing
immediate free entry of operators at all levels of the value chain; (ii) transferring management
responsibility of the sector back to ASPA; and (iii) dissolving the Board of Directors of GGC
which had been intervening in the daily operations of the company. As part of the Roadmap, a
new FOA was developed based on the principle of private sector promotion in the groundnut
sector. The FOA was signed by the government and ASPA in December 2008. ASPA on its own
determined the producer price for the 2008-09 marketing season. In order to prepare for the
divestiture of GGC, the government is currently tendering for the necessary financial and
technical audits, which will include a review of all divestiture options. The plan is for key
domestic stakeholders to discuss and select the optimal divestiture option based on the findings
of the audits, in the process building country ownership. Afterwards, the tender process can
proceed to carrying out the divestiture process. The divestiture process is expected to be finalized
by mid-2010, thus allowing the private operator of GGC adequate time to prepare for the 2010-
11 groundnut marketing season.
5.60 For the 2008/9 season, GGC was able to receive loans for crop financing from the
domestic commercial banks, made possible by government guarantees of the loans. The
groundnut harvest significantly improved to an estimated 105 MTs due to relatively good levels
of rainfall. As a result, GGC indicated that it has been able to purchase approximately 22
thousand MTs up to March 2009. As an emergency measure, the government purchased
groundnut seeds and fertilizers and distributed them to farmers as loans to be repaid in-kind. Due
to inadequate facilities, GGC utilized rented trucks and barges for the transport of its purchased
groundnuts from the depots to its processing facilities in Banjul. Meanwhile, rehabilitation work
on its facilities has started with support from the Bank’s Trade Gateway project.
5.61 The eventual divestiture of GGC is but one of several critical challenges to continuing the
revitalization of the sector. Improving the groundnuts quality control system has become a
priority given the significant deterioration of the quality of groundnuts in recent years. This
includes the lack of control over the level of aflotoxin (mycotoxin), which has made it difficult to
export to the European markets. Critical activities include improving the testing capacity of GGC
and NARI, establishing proper handling and control techniques during processing of groundnuts,
installing adequate sorting mechanisms at the depot levels, and rehabilitating the river barges.
5.62 The producers associations also need to be appropriately restructured and strengthened.
The CPMS’s and its apex body, the FACS, have been largely ineffective and as a result operators
and traders became progressively reluctant to pre-finance FACS for the purchase of groundnuts.
Pilot initiatives supported by NGOs such as ActionAid have shown some success in grass roots
producer organizations purchasing and delivering groundnuts to operators and traders. However,
such initiatives would need to be significantly expanded to a comprehensive capacity building
program.
5.63 Sustained political commitment and country ownership will be critical to the success of
the groundnut sector reforms. The general acknowledgement of the failures of previous policy
actions has galvanized support for the Roadmap reforms. Country ownership of the Roadmap has
been strengthened through wide consultations and participation of both public and private
stakeholders, including farmers association and private operators. These same stakeholders are
part of ASPA and hence directly participate in the management of the sector. Private operators
79
and the farmers are considered supportive of the reforms, as they are expected to benefit from a
more stable and predictable marketing arrangement. Private investors seeking preferential
treatment from the government can potentially undermine the reforms, specifically the
divestiture of GGC. For this reason, it will be critical to ensure the orderly completion of the
GAMCO liquidation process and the prevention of future preferential treatment of any one
investor. For similar reasons, it will be critical that the divestiture options be transparently and
thoroughly discussed by all major public and private stakeholders, including private operators,
farmers associations and the commercial banks which provide the crop financing.
Impact of Producer Price and Fertilizer Subsidy
5.64 The government supports groundnut farmers by maintaining a minimum producer price
and providing subsidized fertilizers. In the past, fertilizers were distributed directly through the
government’s extension network, thus bypassing the private traders. The financing of imports of
fertilizers have relied heavily on donor support, including in-kind foreign aid. These fertilizers
are often distributed domestically at subsidized prices. The distribution of agricultural inputs at
subsidized prices is a significant disincentive for the participation of the private sector in
importing and distributing these inputs. Regression results indicate that fertilizers significantly
improve crop yields (see Table 40). However, questions remain whether fertilizers are a cost-
effective option for the groundnut farmers, particular for the smaller farms.
5.65 A basic farm budget was developed in order to assess such questions regarding the
effectiveness of government support in the groundnut sector (see Table 44). The farm budget
was developed using data provided by NARI, DOSA, private traders and medium scale farmers.
Although the farm budgets have their limitations, it provides a framework for analyzing the
impacts of various policy options within a number of climatic scenarios (drought, an average and
an above average production year). The policy scenarios include a liberalized market
environment, the current level of government support, and a situation where there is price
support but no fertilizer subsidy.
5.66 A number of assumptions have been used to develop the model. Ideally the model should
be based on differing cost structures to reflect divergences between farm sizes. However, in the
absence of such data a composite farm budget has been constructed. Additional assumptions
include the market price for fertilizer costs and the imputed value of labor. There are four
scenarios covering different combinations of the presence and absence of producer price support
and price subsidization of fertilizers. The price of groundnuts without price support was based on
the luomo market price.
5.67 The findings of the analysis can be summarized as follows:
The use of fertilizers without subsidies for rain-fed groundnut production can be value
subtracting. With normal rainfall, the expected returns decline when fertilizers are added
compared to when no fertilizers are used. Under drought conditions, fertilizer application is
expected to result in losses for the farmer.
80
Producer price support is the main benefit for groundnut farmers from the government
support to the groundnut sector. Assuming average rainfalls, the producer price support
increases returns by 25 to 35 percents, depending on whether fertilizers are subsidized.
5.68 According to the analysis of the groundnuts farm budget, fertilizer application is not
profitable within the current structure of support and the rainfed production environment. Given
the variability of rains over the past decade, the analysis indicates that fertilizer application is
value subtracting even with producer price support. However, it is important to note that the
analysis is static and over the long term the nutrient deficit within the soil might need to be
addressed if the soil is significantly degraded through continuous cultivation of groundnuts and
removal of groundnut hay for livestock feed. Without a system of burning and ploughing in crop
residues that could ensure that some nutrients are recycled into the soil, there will be a need for
some form of fertilization.
Table 45: Groundnuts Farm Budget per Hectare (2008)
(Dalasis and MTs)
No Price Support Price Support
No
Fertilizer
Fertilizer
with
Subsidies
Fertilizer
without
Subsidies
No
Fertilizer
Fertilizer
with
Subsidies
Fertilizer
without
Subsidies
Cost of Production
Land Preparation 328 328 328 328 328 328
Seed 555 555 555 555 555 555
Labor (Seeding) 164 164 164 164 164 164
Fertilizer 0 1167 1835 0 1167 1835
Labor (Weeding) 1,708 1,708 1,708 1,708 1,708 1,708
Harvesting 690 690 690 690 690 690
Labor (Threshing) 526 526 526 526 526 526
Cost to market 28 28 28 28 28 28
Total (GMD) 3,999 5,166 5,834 3,999 5,166 5,834
Yield (MT)
Drought Year 0.6 0.75 0.75 0.6 0.75 0.75
Average rainfall 0.9 1.15 1.15 0.9 1.15 1.15
Above avg rainfall 1 1.5 1.5 1 1.5 1.5
Selling Price (GMD)
Drought year 7,500 7,500 7,500 7,500 7,500 7,500
Average rainfall 6,650 6,650 6,650 7,200 7,200 7,200
Above avg rainfall 5,320 5,320 5,320 7,200 7,200 7,200
Value (GMD)
Drought Year 4,500 5,625 5,625 4,500 5,625 5,625
Average rainfall 5,985 7,648 7,648 6,480 8,280 8,280
Above avg rainfall 5,320 7,980 7,980 7,200 10,800 10,800
Profit (GMD)
Drought Year 501 459 - 209 501 459 - 209
Average rainfall 1,986 2,482 1,813 2,481 3,114 2,446
Above avg rainfall 1,321 2,814 2,146 3,201 5,634 4,966
81
5.69 The use of fertilizers could become economically sensible if the parameters of the
production environment changes. In particular, the greater adoption of irrigation could
significantly reduce the risk of major losses in years with low rainfalls. Despite the potential, the
use of irrigation systems is relatively low, reflecting a lack of capacity within both the public and
private sector in terms of financing and irrigation engineers and planners.
5.70 Lowering the costs of fertilizers through the greater use of organic materials would be
another option for creating a more favorable production environment. It points out the need to
revise the focus of agricultural research and the advice provided by extension services, which
recommend levels of fertilizer usage even higher than current actual usage. Such advice does not
seem appropriate for smallholders when the risks associated with rain-fed conditions are
considered. Significantly greater use of organic matter can be incorporated into the mixed
farming systems under which considerable amount of the country’s groundnut production is
cultivated. Better uptake pathways for the proposed agricultural research could be developed that
involve both the public and private sectors. Within the public sector, this is likely to mean
developing and strengthening of formal linkages between research and extension. NGOs can also
potentially play an important role, given that they tend to have credibility with farmers and have
a track record of effectively reaching the poor farmers.
CONCLUSIONS
5.71 The performance of agriculture is critical to poverty reduction given its importance
to the population and the poor. The agriculture sector accounts for 52 percent of the population
and 63 percent of the poor. The sector’s estimated poverty headcount ratio of 76 percent, based
on the upper poverty line, is significantly higher than the national average of 58 percent.
Agriculture’s high poverty rate explains the large urban-rural disparity in the poverty rates, 68
percent versus 40 percent respectively. Crop production is the dominant economic activity in
agriculture but livestock is also among the largest subsectors in the country.
5.72 Crop production declined continuously since 2004 before rebounding in 2008 due to
relatively good rainfalls. Crop production is highly sensitive to rainfall. Only 14 percent of
farms, at a maximum, use irrigation systems. The high dependence on rainfall for crop
performance is of great concern given the overall decline, shorter seasons and increased inter-
annual variability of rainfalls in the country.
5.73 Larger farms are more diversified, have higher yields, use greater amounts of key
inputs per hectare, access government extension services more frequently and have higher
membership in producer associations. Crop production in The Gambia is dominated by small
landholders, given that 2007 NASS indicates that 85 percent of farms are smaller than one
hectare. Larger farms are more likely to grow groundnuts, the country’s main cash crop and
export, which highlights the importance of targeting smaller farms in government support for the
sector. Only 12 percent of farms access extension services, which declines to 6 percent for farms
smaller than 0.5 hectares.
82
5.74 Regression analysis indicates that there is a farm labor surplus and declining
marginal productivity of land. An additional labor input does not have a significant impact on
production or productivity, consistent with a labor surplus in agriculture. This would mean that
migration outflow from agriculture would have a modest impact on agriculture production at the
margin. A doubling of area cultivated results in only a 40 percent increase in crop production.
Hence, there are significant limits to the current strategy focused on agricultural land expansion.
5.75 Access to credit, membership in producer associations and use of extension services
are also found to be significantly associated with higher production and productivity in the
regression analysis. Access to credit for rural farmers appears to be surprisingly reasonable, at
an estimated 40 percent, and more than half of the farmers receive credit from microfinance
institutions. The impact of producer associations and extension services need to be interpreted
with caution as the direction of causality is uncertain. Their significant impact could simply
reflect the fact that, for various reasons, larger and higher productivity farmers are more likely to
be member of associations and receive extension services.
5.76 The returns from fertilizers are intimately linked to the sensitivity of crop
production to rainfalls. The cost-effectiveness of fertilizers was analyzed using a farm
household budget which outlines the costs of production, productivity and output price in an
interrelated manner, taking into account variations in rainfall. Even with subsidies, the use of
fertilizers results in a significant decline in expected profits if rainfalls are low. Without
subsidies, fertilizers result in an expected loss under drought conditions. This is a case where the
use of production inputs found to have a significant impact on crop production does not
necessarily make economic sense.
5.77 Nonfarm income is significant for farmers, particularly for those with smaller
farms. According to the 2007 NASS, 41 percent of farms smaller than 0.5 hectares receive
nonfarm income, compared to 27 percent for farms larger than 2 hectares. In addition, nonfarm
income is on average 80 percent larger for smaller farms. Remittances are the largest source of
nonfarm income. Nonfarm income was compared to groundnuts sold in order to gauge the
importance of nonfarm income. Assuming retail groundnut prices at the local markets, the
average ratio of nonfarm income to groundnuts sold is 41 percent and increases to 70 percent for
farms smaller than 0.5 hectares. If wholesale prices from the NASS are used, these percentages
increase to 118 and 234 percents respectively.
5.78 Sharp fluctuations in the groundnut sector’s performance are largely due to sudden
structural changes and policy reversals in the sector as well as the amount of rainfall. Groundnuts are the country’s major cash crop and most important export. The sector has
undergone a series of structural changes in production, processing and marketing arrangements.
In order to stabilize the sector and provide a reliable and timely market for the groundnut
farmers, the government developed a sector reform Roadmap in 2007 with the objective of
liberalizing the market, promoting open competition and protecting the farmers through a
minimum producer price. A critical component of the Roadmap is the eventual divestiture of the
Gambia Groundnut Corporation (GGC). However, this will eventually need to be complemented
by improved access to critical inputs and strengthened farmers associations. Sustained political
83
commitment and country ownership will be critical to continued implementation of the
Roadmap, which will be critical for attracting private investments in the sector.
5.79 Rather than domestic supply constraints, exposure to international price
fluctuations appears to be the key issue for food security. Rice is the country’s major
imported food item. The supply of rice in the country typically exceeds local demand. The
exposure for the poor to international price fluctuations has been comparatively less than for the
nonpoor for two reasons: (i) rural households rely on locally produced rice for roughly half of
their total rice consumption compared to less than five percent for the urban households; and (i)
while the price of imported rice increased significantly since 2008, the price of local rice has
remained mostly constant. Twenty-seven percent of rural farmers are rice producers who would
benefit from increases in the price of local rice.
84
ANNEX 1: CONSUMPTION REGRESSIONS
Urban Rural
coef se coef se
Household characteristics
Log of household size -0.440*** 0.09 -0.380*** 0.13
Log of household size squared -0.012 0.03 -0.014 0.03
Share of children 0-6 (dropped) (dropped)
Share of children 7-16 0.461*** 0.16 0.536*** 0.18
Share of male adults 0.412*** 0.16 0.841*** 0.18
Share of female adults 0.583*** 0.18 0.717*** 0.22
Share of Elderly (>=60) 0.344 0.28 0.284 0.29
Regions
Banjul (dropped) (dropped)
KMC -0.583*** 0.07 (dropped)
Brikama -0.672*** 0.09 0.562*** 0.09
Mansakonko -0.977*** 0.13 0.607*** 0.11
Kerewan -0.720*** 0.10 0.333*** 0.09
Kuntaur -0.970*** 0.13 (dropped)
Janjangbureh -0.726*** 0.13 0.505*** 0.11
Basse -0.584*** 0.10 0.431*** 0.10
Characteristics of household head
Log of household head's age 0.207** 0.09 -0.078 0.09
Gender
Male (dropped) (dropped)
Female 0.041 0.06 0.052 0.07
Education
Uneducated (dropped) (dropped)
Educated 0.369*** 0.04 0.134** 0.06
Sector of employment
Employed (dropped) (dropped)
Unemployed 0.111 0.07 0.055 0.06
Homemaker 0.026 0.21 -0.618 0.41
Retired 0.222** 0.11 0.074 0.13
Student -0.216 0.34 -0.622** 0.27
Other -0.302 0.20 0.170 0.35
_cons 9.213*** 0.35 8.829*** 0.37
Number of observations 1,106 1,072
Adjusted R2 0.380 0.253
note: .01 - ***; .05 - **; .1 - *;
85
ANNEX 2: ESTIMATION OF POVERTY LINE
The food poverty line was constructed using the standard cost of basic needs approach, based on
the value of a set of food items which provides the recommended caloric requirements of 2,700
kcal per day for a young adult male. Only four food items were chosen for estimation of the food
poverty line: rice, bread, sugar and palm oil. They were chosen because they accounted for large
shares of the diet of the poor and their quantities were adequately observed, which allowed for
the prices to be properly estimated. These four food items were weighed according to their
budget shares. As with the estimation of the Paasche price index, the four food items were
assigned to broad food categories and thus the budget shares of these categories were used rather
than specific food items themselves.
In summary, for each domain the food poverty line, ZF, was estimated according to equation 1,
where xi is the mean value of consumption of food item i, yi is the mean calorie quantity of food
i, wi is the consumption share of food category i, HS is the mean household size and HES is the
mean household equivalence scale.
HS
HESw
y
xwZ
i
i
i
i
i
i
F
4
1
4
1
700,2
eq. (1)
In order to estimate the poverty line, the non-food component will need to be defined and
estimated in addition to the food component. The non-food component is defined for two poverty
lines, the lower and upper poverty lines. The non-food component of the lower poverty line is
defined as the minimum spending on non-food needs for a person who has a total consumption
level equivalent to the food poverty line. In another words, non-food consumption for the
poverty line, ZNF
, is defined as the difference between total consumption and food consumption
when total consumption is equivalent to the food poverty line. This would be considered the
minimum non-food consumption given that the person would have to reduce food consumption
below the food poverty line in order to consume non-food items.
i
r
i
rF
i
F
ii PPNNZxZxs lnlnlnln 413
2
210 eq. (2)
In order to estimate the minimum non-food consumption as defined above, the methodology
used was to first estimate an equation of the household demand for food consumption as a
function of total consumption. For each domain, this was achieved by estimating equation 2
based on the Quadratic Almost Ideal Demand System,52
where si is the food share (in
percentage) of total consumption of household i, xi is the per capita consumption of household i,
ZF is the food poverty line, Ni is a vector of household characteristics (household composition
and education levels), Nr is the corresponding vector for the reference group, and Pi and P
r are
the vector of prices faced by household i and the reference group respectively.
52
Banks, et al (1997).
86
Based on the estimated equation for the demand for food, non-food consumption was estimated
as ZF – (Z
F s(Z
F)), or Z
F – (Z
F ß0). Then, for each domain the poverty line is estimated as
follows.
00 2 FFFFNFFL ZZZZZZZ eq. (3)
The superscript “L” denotes lower poverty line. Note that the lower poverty line denotes a level
of consumption where the households are not meeting the minimum caloric requirements. The
upper poverty line, ZU, corresponds to households that actually meet their nutritional
requirements. This is obtained by solving for total consumption in the demand for food equation
by first setting the food share equal to the food poverty line and solving for Z using Newton’s
iterative method (see equation 4).
2210 lnln FFF ZZZZZZ
eq. (4)
87
ANNEX 3: POVERTY MAPPING METHODOLOGY
The basic idea behind the methodology developed by Elbers, Lanjouw and Lanjouw (2002,
2003) is relatively simple. At first a regression model of log of per capita expenditure is
estimated using survey data, employing a set of explanatory variables which are common to both
a survey and the census. Next, parameters from the regression are used to predict expenditure
for every household in the census. And third, a series of welfare indicators are constructed for
different geographical subgroups.
The term “welfare indicator” embrace a whole set of indicators based on household expenditures.
This note put emphasis on poverty headcount (P0) but the usual poverty and inequality indicators
can be computed (Atkinson inequality measures, generalised Entropy class inequalities index,
FGT poverty measures and Gini).
Although the idea is rather simple its proper implementation require complex computation if one
want to take into account spatial autocorrelation and heteroskedasticity in the regression model.
Furthermore, proper calculation of the different welfare indicators and its standard errors
increase tremendously its complexities.
The discussion below is divided into three parts, one for each stage necessary in the construction
of a poverty map. This discussion borrows from the original theoretical papers of Elbers,
Lanjouw and Lanjouw as well as on Mistiaen et al. (2002).
First stage
In the first instance, we need to determine a set of explanatory variables from both databases that
are meeting some criteria of comparability. In order to be able to reproduce a poverty map
consistent with the associated poverty profile, it is important to restrict ourselves to variables that
are fully comparable between the census and the survey. We start by checking the wording of
the different questions as well as the proposed answer options. From the set of selected
questions we then build a series of variables which would be tested for comparability. Although
we might want to test the comparability of the whole distributions of each variable, in practice
we restrain ourselves to test only the means. In order to maximise the predictability power of the
second-stage models all analysis would be performed at the strata level, including the
comparability of the different variables from which the definitive models would be determined.
The list of all potential variables and their equality of means test results are not presented in this
note but can be obtained on request.
88
Second stage
We first model per capita household expenditure53
using the limited sample survey. In order to
maximise accuracy we estimate the model at the lowest geographical level for which the survey
is representative. In the case of the fourth round of GLSS that level is the sampling strata: Accra,
urban costal, urban forest, urban savannah, rural coastal, rural forest and rural savannah.
Let specify a household level expenditure ( chy ) model for household h in location c, xch is a set
of explanatory variables, and chu is the residual:
chcchch uyy ]|[lnln hx ( 1 )
The locations represent clusters as defined in the first stage of typical household sampling design.
It usually also represents census enumeration areas, although it does not have to be. The
explanatory variables need to be present in both the survey and the census, and need to be
defined similarly. It also needs to have the same moments in order to properly measure the
different welfare indicators. The set of potential variables had been defined in the first stage.
If we linearise the previous equation, we model the household’s logarithmic per capita
expenditure as
chch uy βx'
chln . ( 2 )
The vector of disturbances u is distributed ),0( F . The model (2) is estimated by Generalised
Least Square (GLS). To estimate this model we need first to estimate the error variance-
covariance matrix in order to take into account possible spatial autocorrelation (expenditure
from households within a same cluster are surely correlated) and heteroskedasticity. To do so we
first specify the error terms as
chcchu ( 3 )
where c is the location effect and ch is the individual component of the error term.
In practice we first estimate equation (2) by simple OLS and use the residuals as estimate of the
overall disturbances, given by ch . We then decomposed those residuals between uncorrelated
household and location components:
chcch eu ˆ ( 4 )
53
In our study we used the welfare index constructed for the IHS poverty profile. Although that welfare index is
defined in terms of equivalent adults, the demonstration remains unchanged.
89
The location term ( c ) is estimated as cluster means of the overall residuals and therefore the
household component ( che ) is simply deducted. The heteroskedasticity in the latest error
component is modelled by the regressing its squared ( 2
che ) on a long list of all independent
variables of model (2), their squared and interactions as well as the imputed welfare. A logistic
model is used.
Both error computations are used to produce two matrices which are them sum to , the
estimated variance-covariance matrix of the original model (2). That latest matrix permits to
estimate the final set of coefficients of the main model (2).
Third stage
To complete the map we associate the estimated parameters from the second stage with the
corresponding characteristics of each household found in the census to predict the log of per
capita expenditure and the simulated disturbances.
Since the very complex disturbance structure has made the computation of the variance of the
imputed welfare index intractable, bootstrapping techniques have been used to get a measure of
the dispersion of that imputed welfare index. From the previous stage, a series of coefficients
and disturbance terms have been drawn from their corresponding distributions. We then, for
each household found in the census, simulate a value of welfare index ( r
chy ) based on the
predicted values and the disturbance terms:
)~~~exp(ˆ ' r
ch
r
c
r
ch
r
chy x (5)
That process is repeated 100 times, each time redrawing the full set of coefficients and
disturbances terms. The means of the simulated welfare index become our point estimate and the
standard deviation of our welfare index is the standard errors of these simulated estimates.
90
ANNEX 4: POVERTY MAP INDICATORS
Code Administrative
Structure Population
Poverty
Heacount
Poverty
Gap
Squared
Poverty
Gap
Number
of poor
100 Banjul 33,505 0.093 0.031 0.015 3,116
(0.022) (0.008) (0.004)
110 Banjul South 8,005 0.086 0.026 0.012 688
(0.029) (0.010) (0.005)
111 Banjul Central 8,703 0.080 0.026 0.013 696
(0.026) (0.009) (0.005)
112 Banjul North 16,797 0.104 0.035 0.017 1,747
(0.026) (0.010) (0.006)
200 Kanifing 316,060 0.392 0.166 0.095 123,896
(0.021) (0.012) (0.009)
220 Kudc 316,060 0.392 0.166 0.095 123,896
(0.021) (0.012) (0.009)
300 Brikama 383,427 0.528 0.233 0.137 202,449
(0.025) (0.017) (0.012)
330 Kombo North 163,772 0.501 0.218 0.126 82,050
(0.026) (0.017) (0.012)
331 Kombo South 60,508 0.555 0.248 0.146 33,582
(0.031) (0.021) (0.015)
332 Kombo Central 83,105 0.521 0.230 0.134 43,298
(0.028) (0.018) (0.013)
333 Kombo East 27,465 0.557 0.248 0.147 15,298
(0.041) (0.025) (0.018)
334 Foni Brefet 10,790 0.569 0.252 0.147 6,140
(0.060) (0.036) (0.025)
335 Foni Bintang 14,975 0.603 0.279 0.167 9,030
(0.051) (0.034) (0.025)
336 Foni Kansala 11,043 0.581 0.263 0.156 6,416
(0.055) (0.036) (0.027)
337 Foni Bondali 6,034 0.576 0.261 0.156 3,476
(0.069) (0.048) (0.036)
338 Foni Jarrol 5,735 0.580 0.261 0.154 3,326
(0.074) (0.047) (0.034)
400 Mansakonko 71,597 0.625 0.280 0.160 44,748
(0.065) (0.045) (0.032)
440 Kiang West 14,492 0.646 0.286 0.161 9,362
91
Code Administrative
Structure Population
Poverty
Heacount
Poverty
Gap
Squared
Poverty
Gap
Number
of poor
(0.082) (0.056) (0.040)
441 Kiang Central 7,858 0.649 0.289 0.164 5,100
(0.085) (0.060) (0.043)
442 Kiang East 6,477 0.677 0.319 0.189 4,385
(0.092) (0.065) (0.048)
443 Jarra West 23,905 0.552 0.234 0.129 13,196
(0.073) (0.046) (0.032)
444 Jarra Central 6,483 0.688 0.325 0.191 4,460
(0.092) (0.068) (0.051)
445 Jarra East 12,382 0.665 0.313 0.186 8,234
(0.073) (0.057) (0.044)
500 Kerewan 170,523 0.796 0.413 0.257 135,736
(0.033) (0.031) (0.025)
550 Lower Nuimi 43,750 0.786 0.407 0.253 34,388
(0.040) (0.038) (0.031)
551 Upper Nuimi 24,803 0.818 0.434 0.273 20,289
(0.041) (0.040) (0.033)
552 Jokadu 17,719 0.857 0.469 0.300 15,185
(0.038) (0.040) (0.034)
553 Lower Baddibu 15,277 0.778 0.393 0.240 11,886
(0.053) (0.046) (0.036)
554 Central Baddibu 14,956 0.782 0.403 0.250 11,696
(0.047) (0.041) (0.033)
555 Upper Baddibu 54,018 0.782 0.398 0.245 42,242
(0.038) (0.035) (0.028)
600 Kuntaur 78,021 0.856 0.478 0.310 66,786
(0.029) (0.033) (0.029)
660 Lower Saloum 13,498 0.820 0.438 0.277 11,068
(0.046) (0.046) (0.039)
661 Upper Saloum 15,052 0.884 0.512 0.340 13,306
(0.042) (0.050) (0.045)
662 Nianija 8,272 0.874 0.493 0.321 7,230
(0.048) (0.055) (0.049)
663 Niani 22,126 0.853 0.477 0.311 18,873
(0.038) (0.042) (0.038)
664 Sami 19,073 0.855 0.473 0.306 16,307
(0.039) (0.044) (0.039)
700 Janjanburay 105,554 0.697 0.330 0.194 73,571
(0.064) (0.049) (0.036)
770 Niamina Dankunku 5,908 0.687 0.315 0.181 4,059
92
Code Administrative
Structure Population
Poverty
Heacount
Poverty
Gap
Squared
Poverty
Gap
Number
of poor
(0.097) (0.077) (0.057)
771 Niamina West 6,538 0.722 0.350 0.209 4,720
(0.085) (0.071) (0.054)
772 Niamina East 19,129 0.690 0.320 0.186 13,199
(0.069) (0.051) (0.037)
773 Fulladu West 71,060 0.705 0.337 0.200 50,097
(0.067) (0.053) (0.039)
774 Jajanburay 2,919 0.505 0.200 0.106 1,474
(0.112) (0.061) (0.039)
800 Basse 180,381 0.740 0.366 0.221 133,482
(0.048) (0.043) (0.034)
880 Fulladu East 96,829 0.720 0.355 0.215 69,717
(0.048) (0.042) (0.033)
881 Kantora 29,639 0.770 0.383 0.231 22,822
(0.064) (0.060) (0.049)
882 Wuli 35,746 0.766 0.385 0.234 27,381
(0.056) (0.051) (0.040)
883 Sandu 18,167 0.742 0.358 0.213 13,480
(0.065) (0.058) (0.046)
Sources: Authors’ calculation based on IHS 2003/04 and Census 2003
93
ANNEX 5: NASS TECHNICAL NOTES
The Department of Planning under the Department of State for Agriculture (DOSA) conducts the
annual National Agricultural Sample Survey (NASS). The NASS is an annual nationwide sample
survey of agricultural households. The main objective of the survey is to generate up-to-date
information on crop and livestock production and productivity as well as on farming practices on
an annual basis. Information produced is used for monitoring trends in food production and
agricultural outputs in general. The annual agricultural survey covers all agricultural holders
except those residing permanently in urban areas.
The field work and processing activities typically start in May with the preparation of the survey
questionnaires and manuals. The actual field work usually takes place over one to two months.
Table A.1 outlines the detailed timeline of actual activities carried out for the 2007 NASS.
Table A.1: Key NASS Activity Milestones
Activity Commencement
Preparation of surveys instruments (Manuals, Questionnaires, etc) May Sample selection of survey units August Training of staff August Listing of households/ selection of farmers(holders) August Holding interviews September Field interviews and measurements September Yield measurements September Processing of data received from the field October
At the end of their annual training course, the enumerators and supervisors were posted to their
respective Enumeration Areas (EAs) in August and commenced filed operations. The field
operations for the survey undertaken by the enumerators consisted of five main activities:
Reconnaissance survey and canvassing of EAs. The enumerators’ first task on arrival was to
locate the area of assignment. For this purpose, they provided with an EA map showing the list
of villages in the EA together with boundaries. Senior Supervisors, together with the supervisors,
assisted the enumerators in identifying the selected EAs in the respective LGAs, to ensure
accuracy. They discussed with the village elders within the selected EAs, explaining the purpose
of the survey, acquainting them with the type of work to be done and the methods of
enumeration, as a means of soliciting full cooperation from respondents. Upon identification of
the EA, the enumerator then proceeded to canvass the EA in order to gain familiarity with the
EA and the corresponding localities.
EA listing of heads of household. The EA listing is the second assignment the enumerator
performs in the EA. The operation involves contacting all heads of households in the villages
94
within a selected EA, recording their names and asking them a few questions to determine
whether they have any agricultural operations. In the process, the enumerator completed Form 1,
the Household Listing Form. These forms are contained in a booklet referred to as the
Enumeration Area Listing Booklet.
Filling of Holding Questionnaires. On completion of the Enumeration Area Listing of heads of
households, enumerators visited the EA., interviewed a sample of holders selected from the list
of heads of households, collected information about them and their operations, and in the process
completed Form 2, the Holding Questionnaire.
Measurement of fields and filling of field questionnaires. After filling the holding
questionnaires, enumerators visited and measured all the farm fields of the selected holders for
information on area cultivated and the types of crop mix in the fields, thus completing Form 3
(Field Questionnaire) in the process. At the farm, the enumerator walks around the field and with
the help of the holder, identifies and observes the boundaries of the field. The enumerator then
makes a free-hand sketch of the field and indicates the different crops growing in the field and
afterwards measures the field using a prismatic compass and a measuring tape. Determining the
final area cultivated of the field is made with a programmable hand calculator by the supervisor.
Crop yield estimation. After the completion of field measurements, the supervisors prepare a
list of all the fields of the selected holders, with their corresponding crops, and select a sample of
two fields for each crop for yield estimation. Enumerators lay a 5m by 5m square in each
selected field and at harvest this plot is harvested and the proceeds weighed and dried. Then the
final weight is taken.
A two-tier sampling frame is used. At the first stage, the sampling frame is the list of EAs
previously used for the population census (2003) and obtained from GBOS. At the second stage,
the sampling frame is the list of agricultural households (agricultural holdings) in the country. At
the first stage, 74 EAs representing approximately 3 percent of EAs in the country were selected.
The EAs were selected with a probability proportional to its size (PPS), where the size is the
number of households in each EA with each district as a stratum.
At the second stage, 370 agricultural holders, or approximately 1 percent of holders in the
country, were selected. The selection of the sample of agricultural holdings was made with equal
probabilities and without replacement, using the systematic selection method. The sample size is
considered adequate for providing accurate estimates at the LGA level. Administratively, The
Gambia is organized into six LGAs and each LGA is divided into a number of districts. For the
NASS, the districts constitute the strata.
Regarding the field work, most of the data were obtained through direct interview in which
answers were solicited from respondents and through physical measurements of fields and
weighing of production outpus in order to obtain estimates of cultivated areas and yields. The
filled questionnaires collected from respondents are collated and entered into the computer using
the CSPro software. In order to minimize non-sampling errors, the data in the computer are
validated and the resulting dataset made ready for analysis. Initial tabulations of results from the
95
early rounds of the survey operations were produced and analyzed to check on coverage,
response rate, consistency and reliability of the information obtained.
Box A.1: NASS Estimation Methodologies
Notations:
A’h = Total number of households in the stratum h for the annual survey
A’hi = the total number of household in a sample primary unit in the stratum h
E’h= Number of sample primary units in the stratum h;
P’hi= A’hi/A’h, probability of an EA to be selected at any selection in stratum h
N’hij = Number of agricultural holders enumerated in the sample EA i stratum h,
n’hij = Number of agricultural holders selected in the sample EA i
Y’h = any variable (selected at the second stage) in the stratum h;
Y’hij= the observed variable in the holding j, enumeration area i and Stratum h
lY = estimate of any variable at Local Government Area
'Y = estimate of any variable at the national level
V ( 'ˆhY ) =estimation of the variance of hY 'ˆ
(CV) hY 'ˆ =variation coefficient of hY 'ˆ
hhi
h
EA
A
'
1
'
'Adjustment factor in stratum h
The adjusted global expansion factor for the annual survey could be written as follows:
hih
h
AE
A
. hih
h
AE
A
'.'
'
hij
hij
n
N
'
'
Estimates of Y
At District level
hijh
n
j
hij
hij
hi
hih
h
E
i hih
hh Y
n
N
AE
A
AE
AY
'
11
''
'
'.'
'
.'ˆ
At LGA level
H
h
hl YY1
'ˆ'ˆ
At National level
L
l
lYY1
'ˆ'ˆ
Sampling errors
At District level
96
The estimate of the variance is:
2'
1
)'ˆ'
''ˆ(
1'
1
'
11)'ˆ( h
hi
h
E
i hi
hhi
hhh
h YA
A
A
AY
EEEYV
h
With
hij
j
hijhi
n
YY
'
''ˆ
1
The estimate of the variation coefficient and standard deviation are:
2'ˆ
2
ˆ
)'ˆ()(
h
hY
Y
YVCV
h and
hh YhYCVY
'ˆ'ˆ ).('ˆˆ
At LGA level
H
h
hl YVYV1
)'ˆ()'ˆ(
2'ˆ
2
ˆ
)ˆ()(
l
lY
Y
YVCV
l
and ll YlY
CVY'ˆ'ˆ ).('ˆˆ
At National level
L
l
lYVYV1
)ˆ()'ˆ(
2'ˆ
2
'ˆ
)'ˆ()(
Y
YVCV Y and
'ˆ'ˆ )('ˆˆYY
CVY
97
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