Income inequality within smallholder irrigation schemes in sub-

Saharan Africa 1

Ana Manero

Crawford School of Public Policy, Australian National University, Canberra, Australia

School of Commerce, University of South Australia, Adelaide, Australia

ana.manero@anu.edu.au

1 This is an Author’s Original Manuscript of an article published by Taylor & Francis in

International Journal of Water Resources Development on 02 March, 2016, available online:

http://dx.doi.org/10.1080/07900627.2016.1152461

Income inequality within smallholder irrigation schemes in sub-

Saharan Africa

Equitable income distribution is recognised as a critical element to achieve

poverty reduction, particularly in developing areas. Most of the existing literature

in this field is based on region or country-wide data, yet fewer empirical studies

exist at community levels. This paper examines income disparities within six

smallholder irrigation schemes in Zimbabwe, Tanzania and Mozambique. Gini

coefficients were used to compare local and national levels of inequality.

By-group and by-source decompositions served to identify the factors that most

contribute to income disparities. The results across the six schemes present

significant contrasts both between schemes and compared to national figures.

The results suggest that, inadvertently, some national strategies may overlook

high levels of income inequality within small communities. Rural development

strategies should recognise the importance of income inequality within the

specific areas of intervention and target growth within the activity sectors that

have the greatest potential to reduce poverty and inequality.

Keywords: income inequality, agriculture, poverty, Gini coefficient, Theil index

Introduction

It is estimated that 1.2 billion people across the world live in extreme poverty, making

this a priority area in international development (UN, 2013). Alongside with growth,

mitigating socio-economic inequalities is widely recognised as a key component of

effective poverty reduction strategies (Groll & Lambert, 2013; Kabubo-Mariara,

Mwabu, & Ndeng’e, 2012). Kuznets (1955) argued that growth may lead to an initial

rise in income inequality, but in the long-term, as the economy develops, inequality

would eventually decline. Nevertheless, without adequate redistribution interventions,

the benefits of development rarely spread spontaneously from the rich to the poor.

Instead, excessive economic disparities often entail serious, long-lasting issues such as

persistent poverty (Ravallion, 1997), violent crime (Hsieh & Pugh, 1993), corruption

(Khagram, 2005), political instability (Alesina, 1996), worsened health (Kawachi &

Kennedy, 1997) and low education levels (De Gregorio & Lee, 2002).

The interconnection between growth, poverty and inequality is particularly

crucial in rural areas, home to 70% of the developing world’s extremely poor (Ferreira,

1996; Ortiz & Cummins, 2011; Watkins, 2013). Sabates-Wheeler (2005) argues that

understanding this relationship is most critical in Sub-Saharan Africa (SSA), where

limited empirical work and data exist. Most of the existing inequality literature is based

on national or regional investigations, yet fewer studies exist within smaller areas, such

as villages or rural communities (Silva, 2013). One of the reasons for this data gap is

that broad studies are frequently based on readily-available governmental census

surveys, whereas localised research requires detailed data collection processes. Given

this gap in the literature, Ostry and Berg (2011) point to the need to further understand

the poverty-inequality nexus within small communities, in order to define more

effective and robust growth strategies.

This study investigates socio-economic inequalities within six smallholder

irrigation schemes in SSA. First, income inequality will be calculated at a local level

and then compared to national figures. Second, income inequality will be decomposed

by economic activity – solely agricultural or diversified incomes - to assess the relative

importance of the between and within-group components. Finally, an analysis by four

different sources of income will determine which are the components that most

contribute to total inequality and which ones have a (un)equalising effect. Lessons

learnt from this investigation will serve to understand how income inequalities within

smallholder agricultural communities compare to country-wide disparities and whether

commonly accepted strategies would be applicable within local contexts.

Growth, poverty and inequality in sub-Saharan Africa

Between 1995 and 2013, SSA experienced a steady increase in economic activity with

an average annual GDP growth of 4.5%, accompanied by a nine percent drop in the

poverty headcount ratio (The World Bank, 2014a). Nevertheless, the benefits have not

reached all, as the sub-continent is still home to 30% of the word’s extreme poor and

undernourished population. Following the global trend, income disparity in the region

has risen compared to 1980’s levels. Cogneau et al. (2007) indicate that SSA is the

second most unequal subcontinent in terms of income, after Latin America and the

Caribbean. Out of all SSA countries, Lesotho, South Africa and Botswana are the most

unequal, with Gini coefficients above 0.63. On the other hand, Niger and Ethiopia have

the lowest incomes disparities, with Gini coefficients below 0.35 (CIA, 2014).

Zimbabwe ranks among the ten most unequal SSA countries, with a Gini

coefficient of 0.50 in 2006 (CIA, 2014). Part of Zimbabwe’s economic disparities are

derived from its agrarian socio-economic situation, which still reflects the legacy of the

colonial era, the civil war and the reforms of the late 20th century. Throughout the

1980’s and 1990’s, Zimbabweans’ livelihoods deteriorated significantly, as a result of

repetitive droughts and issues associated with its land reform (Government of

Zimbabwe, 2010; Kinsey, 2010; Mazingi & Kamidza, 2011; Schleicher, 2012).

Tanzania is one of the four most income equal countries in SSA, with a Gini

coefficient of 0.38 in 2007 (CIA, 2014). Its economy is largely dependent on rural

activities, with agriculture, hunting and forestry accounting for 27% of GDP, only

second to the service sector (48%) (NBS, 2013). During the 1980s and early 1990s,

Tanzania experienced significant economic growth, which brought poverty reduction

(14% drop in poverty headcount), but also an increase in economic inequality. In fact,

while the poor benefited from growth, the rich captured a much greater share of

economic improvement (The World Bank, 2011). Over the first decade of the 2000s, the

country experienced an average annual GPD growth of seven percent and a the national

HDI was lifted from 163rd to 151st position, on a world rank of 189 countries (UNDP,

2011). At the national level, the poverty headcount ratio is still over 28%, while in rural

areas it is considerably higher at 38% (Ministry of Planning and Economic Affairs,

2009; The World Bank, 2014b).

In Mozambique, income inequality is relatively high, with a 0.46 Gini index,

above the median of 0.43 across SSA. Between 1995 and 2003, agriculture was the

second largest contributor to GDP growth (1.7% out of 8.6%) and the main driver of

poverty reduction. Over this period, agriculture experienced an average annual growth

of 5.2%, yet this mainly represented a recovery after the 1977-1992 war, with

production returning to pre-conflict levels, rather than resulting from innovation and

investment (Virtanen & Ehrenpreis, 2007). In 1999, the Ministry of Agriculture

launched the first phase of a large-scale reform program, the Agricultural Sector Public

Expenditure Program, towards a market-based agriculture with improved public support

and more effective use of natural resources (The World Bank, 2013). Since then, the

national poverty rate has declined by one-third, although it is still high at 55%.

Worldwide, Mozambique is the tenth least developed nation, with a HDI of 0.393 in

2013 (UN, 2014) .

Research methodology

Data collection

This study is based on six smallholder irrigation schemes in Zimbabwe, Tanzania and

Mozambique. The schemes range in size from 10 to 1,200 hectares and each of them

has between 30 and 1,300 farms. Irrigation water is supplied through gravity-fed

channels and serves to produce a variety of crops, as detailed in Table 1. In addition,

many irrigators also engage in other farming activities (e.g. rainfed crops and livestock)

and non-agricultural businesses, for example through labour and self-employment.

Table 1. Characteristics of the irrigation schemes and surveys undertaken

Country Irrigation scheme, District Area

(ha)

Number of farmers Major crops

Total Surveyed

Zimbabwe

Mkoba, Vungu 10 75 68 Maize, horticulture

Silalabuhwa, Insiza 442 212 100 Maize, wheat, sugar

beans, vegetables

Tanzania Kiwere, Iringa 145 200 100

Tomato, onions,

vegetables, maize, rice

Magozi, Iringa 1,200 1,300 100 Rice

Mozambique

Associacao de 25 Setembro,

Boane 80 38 25 Vegetables

Khanimambo, Magude 16 27 9 Vegetables

Source: Rhodes, Bjornlund, and Wheeler (2014)

For the purpose of this study, the household was used as the unit of analysis.

This is justified because the irrigation schemes are subdivided into farms, each of which

is cultivated by one family, with some families having more than one farm. Given the

association between farm and household, and not farm and individual, the data

collection process was designed using households as the basic unit. The survey

consisted of 65 structured and semi-structured questions, regarding the family members

(sex, age, education, health and occupation), farm characteristics (type of crops, yield),

food security (frequency and type of foods consumed), asset ownership, revenue and

expenses, among other questions.

The surveys were conducted between May and July 2014 with some variation

from country to country. The sampling methods depended on the size of the population

in each scheme. In the three smallest schemes, those in Mozambique, and Mkoba in

Zimbabwe final samples were close to 100% of the defined population. For the three

largest schemes, those in Tanzania and Silalabuhwa in Zimbabwe, the population was

sampled using a stratified approach. Irrigators were categorised according to gender of

the household head and wealth category (poor, medium and well-resourced) and then

randomly sampled (Moyo, Moyo, & van Rooyen, 2014).

Data used in this study includes household revenues and expenditure over the

12-month period prior to the interview. These figures were reported in each country’s

local currency, i.e. US dollar in Zimbabwe (USD), shilling in Tanzania (TSH) and

metical in Mozambique (MZN). The data was collected according to the source of

revenue and type of expenditure, and was then aggregated into on-farm and off-farm

categories, following the classification in Table 2.

Table 2. Revenue and Expenditure categories used in household survey

Revenue Expenditure

On-farm

Rainfed crops Crop inputs

Irrigated crops Harvesting/transport

Livestock sales Livestock inputs

Milk sales Hired labour

Other Irrigation

Other

Off-farm

Agricultural labour Food

Non-agricultural labour Education

Regular employment Health

Business/self-employment Social events

Remittances Housing

Seasonal work Personal transport

Other

Analytical framework

Economic inequality can be defined in many ways, but it is typically considered to be

the uneven distribution of wealth, income and/or assets among individuals of a group, or

between groups of individuals (McKay, 2002).

While the literature is consistent in affirming that there is no ideal unit of

measurement for wealth, money-metrics, i.e. income or consumption expenditure, tend

to be the preferred indicators of poverty and living standards (Sahn & Stifel, 2003).

Alternative non-monetary measures of poverty and inequality exist, such as those based

on asset ownership (Filmer & Pritchett, 2001; McKenzie, 2005) and the

Multidimensional Poverty Index, defined by a combination of education, health and

living standards indicators (Alkire & Santos, 2010; Kovacevic & Calderon, 2014). In

this paper, monetary indicators were used so as to evaluate how various income sources

contribute to total inequality.

There are a number of methods and indices to estimate economic inequality. The

section below presents a brief summary of the most common ones.

Gini coefficient

The Gini coefficient measures the extent to which the distribution of wealth

within a group deviates from a perfectly equal distribution (The World Bank, 2011). It

is the most commonly used inequality measure and is typically applied to income and

expenditure. Its advantages include being relatively easy to calculate; having a visual

representation and allowing for comparison between different size populations.

It can be estimated based on the representation of the Lorenz curve, plotting

cumulative income vs. cumulative population. The Gini coefficient can also be

mathematically calculated as follows:

𝐺 = 𝐶𝑜𝑣(𝑦, 𝐹(𝑦))2

�̅� (1)

where Cov is the covariance between income levels y and the cumulative

distribution of the same income F(y) and ȳ is average income (Bellù & Liberati, 2006b).

Lerman and Yitzhaki (1985) developed a method to decompose the Gini

coefficient as the sum of the inequality contribution of each income source. This is

obtained as a product of its own inequality, its share of total income and its correlation

with total income. Hence, the Gini coefficient for total income, G, can be formulated as

follows:

𝐺 = ∑ 𝑅𝑘𝑘𝑘=1 𝐺𝑘𝑆𝑘 (2)

where, Sk is the share of income source k in total income, Gk is the Gini

coefficient of income source k and Rk is the Gini correlation of income from source k

with the distribution of total income.

By calculating partial derivatives of the Gini coefficient with respect to a percent

change e in income source k, it is possible to estimate the percent change in total

inequality resulting from a small percent change in income source k:

𝜕𝐺𝜕𝑒⁄

𝐺=

𝑅𝑘𝐺𝑘𝑆𝑘

𝐺− 𝑆𝑘 (3)

This property is particularly useful when identifying which sources of income

have an “equalising” or “un-equalising” effect on total income (López-Feldman, 2006).

Theil Index

The Theil is a specific case of the generalised entropy indices (Bellù & Liberati, 2006a).

Its lower value is zero (perfect equality) and it has no upper limit. The index is defined

as follows:

𝑇 =1

𝑛∑ (

𝑦𝑖

�̅�)𝑖 ln (

𝑦𝑖

�̅�) (4)

where yi is the i observation and �̅� is the average income.

The Theil index has certain key advantages such as being decomposable and

additive into groups, thus allowing distinction of between and within sub-group

inequality components. Assuming m groups, the Theil Index is decomposed as follows:

𝑇 = ∑ (𝑛𝑘

𝑛

𝑦𝑘̅̅ ̅̅

�̅�) 𝑇𝑘 + ∑

𝑛𝑘

𝑛(

𝑦𝑘̅̅ ̅̅

�̅�) ln (

𝑦𝑘̅̅ ̅̅

�̅�)𝑚

𝑘=1𝑚𝑘=1 (5)

where the first term of the equation is the within component, and the second

term is the between component. Similarly to by group analysis, the Theil index can be

decomposed by source of income, following the expression for m sources:

𝑇 = ∑ 1

𝑛∑ (

𝑦𝑖𝑘

�̅�) ln (

𝑦𝑖

�̅�)𝑛

𝑖=1𝑚𝑘=1 (6)

In this study, the decomposition of the Theil index in between/within sub-groups

and by income source was calculated by computing equations (5) and (6).

The Theil Index has also some drawbacks, such as, not having an intuitive

representation and not being suitable to compare populations of different sizes. Also, it

does not support non-positive values, as log 𝑥 is undefined if 𝑥 < 0. As explained by

Bellù and Liberati (2006a) and Vasilescu, Serebrenik, and van den Brand (2011), this

limitation can be overcome by replacing zeros with very small values ε > 0, such that

ITheil (x1, . . . ,xn−1, 0) ≡ ITheil (x1, . . . ,xn−1, ε). In this paper, ε was taken equal to 10-10.

The Atkinson index

Atkinson (1970) developed inequality measures based on the concept of Equally

Distributed Equivalent (EDE) income. EDE is that level of income that, if obtained by

every individual in the income distribution, would enable the society to reach the same

level of welfare as actual incomes (Bellù & Liberati, 2006c). The Atkinson index is,

hence, a welfare-based measure of inequality that can be used to address questions

around transfers of income from better-off to poorer sections of the population.

In this study, the Atkinson index will not be used as welfare considerations

specific to each of the six communities are out of the scope of this paper. Therefore,

cross-regional inequality comparisons and income decomposition analysis will be

carried out using the Gini coefficient and the Theil index.

Negative incomes and measures of inequality

In agricultural economies, two common measures of income are net cash income and

net farm income. The former compares cash receipts to cash expenses (Schnepf, 2012),

while the latter is a value of production including cash and non-cash transactions, such

as value of commodities consumed or stored by the household, depreciation or

inventory changes (Edwards, 2013). In this study, net cash income has been chosen as

the measure of household income.

Across the six irrigation schemes, 30% of the households reported higher

on-farm expenses than on-farm revenues, thus resulting in negative net (farm) cash

incomes. This poses a major constraint in the study of inequality given that the most

robust and commonly used tools, such as the Gini coefficient and Theil index, are not

defined for negative values. This issue had been discussed in the literature with different

authors adopting different approaches.

Walker and Ryan (1990) and Möllers and Buchenrieder (2011) note the

existence of negative incomes in their data, yet neither discuss the method of calculation

of household incomes or ways of dealing with non-positive incomes.

Schutz (1951) and Stich (1996) indicate that negative incomes are usually

excluded from the measurements of income inequality. This approach can be found, for

example, in Cowell (2009). Cribb, Hood, Joyce, and Phillips (2014) and Sanmartin et al.

(2003).

Nonetheless, disregarding households with negative net (farm) cash incomes is

not ideal in this study as it would ignore almost one-third of the households in the

sample. This is in line with the consideration made by Rawal, Swaminathan, and Dhar

(2008, p. 232): “Such (negative incomes) exclusion not only narrows down the database

but also misses out on a key feature of household incomes”. Moreover, Allanson (2005)

argues that negative incomes cannot be ignored in the analysis of agricultural

redistribution policies given that it is normal for farms to record losses.

A modified Gini coefficient can be calculated including zero and negative

values, based on the geometric properties of the Lorenz curve, yet its values are no

longer limited between 0 and 1. Chen, Tsaur, and Rhai (1982) reformulated the Gini

coefficient to correct this issue, yet this alternative measures still has major limitations,

such not allowing for an accurate decomposition by income source (Mishra, El-Osta, &

Gillespie, 2009).

The Australian Bureau of Statistics (ABS, 2006) notes that negative incomes are

not necessarily a good indicator of economic inequality and that it is inappropriate for

them to have a disproportionate influence on summary inequality measures. It is argued

that negative incomes often reflect the households’ business and investment

arrangements or, in other cases, incomes may be accidentally or deliberately

underreported. Thus, another common method of handling negative incomes is through

a process referred to as equivalisation, by which the individual components of market

income showing negative values are set to zero before computing the total income of

each household (OECD, 2014). The process of equivalisation has been defined by the

OECD and is used by government agencies such as the Australian Bureau of Statistics

(ABS, 2006) and the UK Department for Work & Pensions (2014).

A similar approach was adopted by Seidl, Pogorelskiy, and Traub (2012) who

truncated their data in a way that negative incomes were reported as zeros. Bray (2014,

p. 443) also used this technique in a comparison with four other treatment methods, thus

showing that, when negative incomes are set to zero, the resulting Gini coefficients

were consistent with the figures obtained through the alternative methods.

When it comes to adopting one method or another, Deaton (1997, p. 140) notes

that the choice between various inequality measures is sometimes made on the grounds

of practical convenience and others on the grounds of theoretical preference. Similarly,

Smeeding, O'Higgins, and Rainwater (1990, p. 11) state that “each researcher is left to

deal with zero and negative incomes as he or she sees fit”.

Given the preference to maintain all households in the sample and the choice of

Gini and Theil measures of inequality, the author deemed that the most suitable

approach to deal with negative incomes is adopt the equivalisation process described

above. Thus, negative farm incomes were converted to zero, before being added to

other income components to obtain the total.

In order to test the adequacy of the chosen method, a sensitivity analysis was

conducted (see Appendix A). The levels of income inequality calculated using the

equivalisation method are coherent with those obtained through alternative approaches,

such as excluding households with negative incomes.

The use of non-parametric tests

Non-parametric tests of statistical significance were used to analyse differences in the

distribution of incomes in different population sub-groups. This choice is justified by

the fact that commonly used parametric tests make assumptions on parameters

characterising the population’s distribution, which was not possible given the data in

this study.

The Wilcoxon rank-sum test (hereafter WRS, and also known as Wilcoxon-

Mann-Whitney) is a non-parametric test analog to the independent samples t-test that

can be used when the variables are not normally distributed (UCLA). In addition, the

two-sample Kolmogorov-Smirnov (hereafter KS) is another non-parametric test that can

be used to test the hypothesis that two populations have the same distribution. The KS

test is sensitive to any differences in the distributions (e.g. shape, spread or median) and

has more power to detect changes in the shape, whereas WRS tests on location and

shape, and has more power to detect a shift in the median (GraphPad Software, 2015).

Limitations

This study has two major limitations. First, the populations of study consist only

of members of irrigation schemes, but not the entire agricultural communities. This is

due to the fact that the data used in this study was collected as part of a research project

focusing on increasing irrigation water productivity (ACIAR, 2013). Given the nature of

this research, the population of study was limited to irrigation farmers and, hence, did

not include other members of the rural community, such as dryland farmers and non-

farmers. Studying income inequality across the entire community would not have been

possible because there is no comprehensive list of all its members that would allow

adequate probability sampling. Conversely, Irrigation Organisations keep up-to-date

lists of all their members, from which population samples were drawn. Future research

could be extended to analyse income and inequality within the entire rural community

and also investigate the potential differences between irrigators a non-irrigators.

The second limitation is the large proportion of households reporting negative

farm incomes. As previously discussed, this entails difficulties in the study of inequality

analysis, as most analysis tools are restricted to positive values. It is possible that during

the interviews, farm incomes were underreported and expenses overreported, either

accidentally or deliberately. Therefore, an improvement to this study could have been

identifying negative farm income during the interviews to then question famers about

their financial losses. This method would have improved the accuracy of the records

and also would have provided greater insight into why certain households experience

negative incomes.

Results and discussion

Income inequality at scheme and national levels

This section describes the levels of expenditure and income inequality within six

smallholder agricultural communities and compares them to their respective national

figures.

As shown in Table 3, inequalities measured by expenditure are smaller than by

income. This tendency is quite common (Aguiar & Bils, 2011; Finn, Leibbrandt, &

Woolard, 2009; Krueger & Perri, 2006) and is mainly a result of consumption

expenditure being more evenly distributed than income.

Table 3: Inequality at scheme and national levels

Scheme level National level

Consumption

expenditure

Gini

Income

Gini Income Gini

Mkoba 0.54 0.60 0.50

Silalabuhwa 0.47 0.48 0.50

Kiwere 0.54 0.60 0.38

Magozi 0.39 0.56 0.38

Ass.e 25 Setembro 0.59 0.65 0.46

Khanimambo 0.55 0.58 0.46

Source: author’s computations for scheme level and CIA (2014) for national levels.

Except for Silalabuhwa (Zimbabwe), income inequalities at scheme level are

higher than at national levels. The greatest difference is in Tanzania, where Gini income

coefficients within the agricultural communities (0.56 in Magozi and 0.60 in Kiwere)

are in the order of 50% to 60% higher than at the national scale (0.38).

The Tanzanian Ministry of Planning and Economic Affairs (2009) argues that,

given the country’s relatively low levels of inequality, redistribution of incomes is not

likely to be effective in achieving significant reductions in poverty. Instead, it suggests

that continued high rates of economic growth over the long-term will be required. By

contrast, the results of this study show that significant income inequalities exist at

smaller scales (e.g. irrigation schemes), which are currently being overlooked by

country-wide statistics.

Income dualism between agricultural and diversified sources

In rural developing areas, non-agricultural earnings represent an important part of

households’ incomes (Barrett, Reardon, & Webb, 2001; Escobal, 2001; Reardon, 1997).

While this can contribute to improved living standards, entry barriers to non-farm

activities may result in a greater divide between those who are able to diversify and

those who are not.

The aim of this section is to analyse income differences between and within two

households groups: i) those earning incomes exclusively from agriculture (Ag),

including farm incomes and agricultural labour; and ii) those having diversified income

sources (Div), including non-agricultural labour, regular, seasonal or self-employment,

business, remittances or other. Statistical significance tests were used in the

comparisons between agricultural and diversified income households.

In Zimbabwe, the vast majority of households have diversified incomes, while in

Tanzania and Mozambique, only half obtain earnings outside of agriculture. One

common characteristic to all six communities is that households who make a living

exclusively from agriculture had consistently lower mean and median incomes than

those with diversified incomes. The results of the WRS and the KS tests (Table 4)

conclude that the distribution of income is not the same in both groups and that

exclusively agricultural households rank lower in the overall income distribution. The

WRS test (p<0.10) indicated that the null hypothesis that incomes of agricultural

households are not different from diversified-income households could be rejected.

Similarly, the KS test concluded that (p<0.10) the hypothesis that both groups have the

same distribution was also rejected in all schemes, except for Magozi (Error!

Reference source not found.). Caution should be taken when interpreting the results

from the Mozambican schemes, since the power of statistical significance tests is low

when applied to such small samples (n<50).

Table 4: Income statistics by type of income

Scheme n

Mean HH

Income*

Median HH

Income*

Wilcoxon

rank-sum test

Kolmogorov-

Smirnov test

Ag Div Ag Div Ag Div Z p D p

Mkoba 6 62 179 1,098 67 475 -2.52 0.012 0.66 0.009

Silalabuhwa 20 80 411 940 180 700 -3.55 0.000 0.48 0.001

Kiwere 56 44 1,006 2,026 436 1,203 -3.29 0.001 0.43 0.000

Magozi 48 51 1,500 2,905 1,007 1,458 -1.79 0.074 0.20 0.217

As. 25

Setembro 14 11 40,634 187,707 27,930 84,000 -2.63 0.009 0.55 0.030

Khanimambo 4 5 5,250 177,610 0 173,200 -2.49 0.013 1.00 0.016

M: male-headed household; F: female-headed household

* Mkoba, Silalabuhwa in USD; Kiwere, Magozi in ‘000 TSH; As. 25 Setembro, Khanimambo in MZN,

Despite the remarkable contrast between agricultural and diversified income

households, the Theil index decomposition reveals that disparities within these two

groups are actually the main contributor to overall inequality (Table 5). The only

exception is Khanimambo, yet statistics from this scheme are subject to a very small

sample size.

Table 5. Household income analysis and decomposition by activity group

Scheme Percentage

of Ag HH

Gini Theil

Ag Div Within Between

Mkoba 9% 0.59 0.58 92% 8%

Silalabuhwa 20% 0.49 0.45 91% 9%

Kiwere 56% 0.59 0.69 90% 10%

Magozi 48% 0.55 0.59 92% 8%

As. 25 Setembro 56% 0.64 0.43 72% 28%

Khanimambo 44% 0.56 0.54 27% 73%

Ag: exclusively agricultural income household; Div: diversified income households

The results of this section suggest that promoting agricultural households to

diversify their income sources could potentially have a poverty reduction effect, as

households with a variety of incomes are consistently better-off.

Relative importance of income sources in total inequality

An extensive literature review undertaken by Senadza (2011) concluded, that to better

understand the effects of income on inequality, it is important to distinguish between the

various components of non-farm income. Hence, this section is dedicated to analysing

the effect on total inequality derived from four distinct income sources : i) Agricultural,

including on-farm income and agricultural labour; ii) Wages, including non-agricultural

labour, regular employment and seasonal work; iii) Business and self-employment and

iv) Other, including remittances and other unspecified sources.

The results in Table 6 show the relative importance of each source in terms of its

contribution to the scheme’s combined income and to its total income inequality. In

Tanzania, agriculture is the most important source of income, accounting for

three-quarters of total earnings and circa 80% of inequality. Conversely, Zimbabwean

schemes rely more heavily on remittances and other sources, which also drive the

largest portion of the schemes’ income disparities. In Mozambique, incomes and

inequalities are mainly split between agriculture and wages.

Table 6: Income and inequality decomposition by source

Scheme Agriculture Wages Business and

self-employment Other

Share of

Income

Share of

Inequality

Share of

Income

Share of

Inequality

Share of

Income

Share of

Inequality

Share of

Income

Share of

Inequality

Mkoba 19% 2% 15% 23% 14% 17% 52% 58%

Silalabuhwa 34% 14% 17% 42% 5% 3% 44% 42%

Kiwere 79% 83% 7% 6% 11% 9% 3% 1%

Magozi 66% 43% 9% 15% 23% 42% 2% 0%

As. 25 Setembro 46% 10% 47% 86% 6% 4% 1% 0%

Khanimambo 52% 48% 43% 47% 5% 5% 0% 0%

As noted by Shariff and Azam (2009), ‘A key rational for studying

decompositions by source is to learn how changes in particular income source will

affect overall inequality. What impact does a marginal increase in a particular income

source have on inequality?’ In order to answer this question, a Gini decomposition

following equations (2) and (3) was carried out for each of the six irrigation schemes.

For each income source, the results summarised in Table 7 indicate the marginal impact

in total inequality due to a 1% increase in that particular source, whilst holding income

from all other sources constant. The direction and magnitude of the marginal impact are

given by the % Change. A negative sign indicates a tendency of that particular

component to reduce total inequality, while a positive sign reveals an un-equalising

effect. Logically, the larger the absolute value, the greater marginal the impact.

Table 7: Gini decomposition by income source

Scheme Agriculture Wages Business and

self-employment Other

Gini % Change Gini % Change Gini % Change Gini % Change

Mkoba 0.76 -0.07 0.93 0.02 0.92 0.01 0.76 0.04

Silalabuhwa 0.68 -0.07 0.94 0.10 0.91 -0.01 0.70 -0.01

Kiwere 0.66 0.01 0.94 0.00 0.92 -0.01 0.92 -0.01

Magozi 0.57 -0.09 0.95 0.02 0.91 0.08 0.96 -0.01

As 25 Setembro 0.54 -0.13 0.90 0.13 0.91 0.01 0.90 -0.01

Khanimambo 0.61 -0.06 0.69 0.06 0.75 -0.01 N/A N/A

In five out of six schemes, agriculture has an equalising effect. In fact, the

distribution of agricultural income is skewed towards the bottom, meaning that poorer

households rely more heavily on farming and would benefit the most from growth in

this sector. The exception to this trend is Kiwere, in Tanzania, where even the highest

income households (top two quintiles) earn a significant portion (80%) of their incomes

from agriculture.

Wage incomes have an un-equalising effect across the six schemes, while the

effect of business and self-employment is mixed, i.e. equalising three scheme (one in

each country), yet unequalising in the rest. Unequalising effects could be explained by

low wages received by poor famers and entry barriers preventing poorer households

from venturing into entrepreneurship. The fact that neighbouring schemes experience

contrasting effects reveals the existence of significant intra-country and even intra-

region differences. The policy implication is that one same strategy targeting growth in

a certain activity sector could have a positive, equalising effect in some groups, yet the

exact opposite (unequalisng) in other nearby communities.

Conclusions

This paper analyses income inequality within six smallholder irrigation schemes in

Zimbabwe, Tanzania and Mozambique using household survey data from 2014. The

Gini and Theil indices are used to measure income inequalities and decompose

inequalities by gender and source.

The results indicate that income inequalities within the irrigation communities

are considerably higher (20% to 60%) than their respective country-wide figures.

Comparisons between agricultural and diversified income households showed that those

relying exclusively on farming activities earn consistently lower incomes than their

counterparts. In five out of six schemes, agricultural income was found to have an

equalising effect, i.e. a marginal increase in agricultural income will lead to decrease in

overall inequality. Conversely, a marginal increase in wages would lead to an increase

in inequality in all schemes, while the impact of business and self-employment was

mixed across the three countries.

These findings have important policy implications. First, it is crucial to

recognise the existence of significant levels of income inequality at small scales.

Therefore, broad-based strategies should be carefully examined before being applied

within local contexts, as they could overlook existing disparities and thus perpetuate, or

even worsen, economic inequalities. Policies incorporating income distribution

considerations would be more effective in achieving substantial and long-lasting

poverty reduction, rather than those targeting only economic growth.

Second, strategies aimed to lessen inequality levels within smallholder

irrigation schemes should be two-fold. On the one hand, lifting incomes from sources

having equalising effects (e.g. agriculture) could act as a poverty reduction intervention

at the same time as it would reduce income inequality. On the other hand, it is also

important to create new opportunities for poor households to diversify into more gainful

activities (e.g. skilled employment or entrepreneurship), rather than relying exclusively

on agricultural incomes.

Acknowledgments

I thank my PhD supervisors, Henning Bjornlund, Sarah Wheeler, Quentin

Grafton and Jaime Pittock for their advice and insightful comments. This study is based

on data collected through a large research project funded by the Australian Centre for

International Agricultural Research. I kindly thank my research colleagues Andre van

Rooyen, Martin Moyo, Nuru Mziray, Makarius Mdemu, Paiva Munguambe and Wilson

de Sousa for their efforts in collecting the data used in this study.

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Appendix A

This Appendix provides the results of a sensitivity analysis using various

methods of estimating income inequality. The purpose of this appendix is to verify that

results obtained through the chosen methodology are consistent with the other possible

alternatives.

Income Gini coefficients for the six communities are summarised in Table A 1

following four different methods. The first column reflects the method used in this

study, which consists of converting negative farm incomes to zero, before being added

to the rest of household income components. The second column represents HH income

Gini coefficients excluding those households with negative farm incomes. The third

column reflects HH income, excluding households with negative incomes. The fourth

column indicates the values of the Gini coefficients for household earnings, without

taking into consideration farm expenses. The last column indicates modified Gini

coefficients including negative incomes, which are calculated based on the geometric

properties of the Lorenz curve. The differences shown in this sensitivity analysis are a

result of the different calculations methods and are consistent with previous studies

(Bray, 2014) also comparing various approaches to treat negatives incomes.

Table A 1: Gini coefficient sensitivity analysis

Income Gini

Adjusting for

negative farm

incomes

Income Gini

Excluding HHs

with negative

Farm Income

Income Gini

Excluding HHs

with negative

HH income

Gini for HH

Revenue

(without

considering

farm expenses)

Modified

income Gini

including

negative

incomes

Mkoba 0.60 0.51 0.58 0.57 0.63

Silalabuhwa 0.48 0.44 0.46 0.42 0.52

Kiwere 0.60 0.52 0.52 0.53 0.93

Magozi 0.56 0.55 0.55 0.51 0.66

As. 25 Setembro 0.65 0.62 0.62 0.64 0.85

Khanimambo 0.58 0.55 0.59 0.57 0.59

As discussed in this paper, methods that exclude households with negative

incomes (Columns 2 and 3) tend to underestimate income inequalities as the bottom part

of the distribution is not taken into account. The exception is the Khanimambo scheme

where there are no households with negative incomes. Estimating economic inequality

based only on revenue (Column 4) also provides lower Gini coefficients, which

indicates that gross revenue (earnings without considering expenses) is more evenly

distributed than net income (revenue minus farm expenses). Finally, modified Gini

coefficients including negative incomes are consistently higher than those treating

negative incomes.

Similarly to the Gini coefficient sensitivity analysis, Table A 2 summarises the

results of the Theil index sensitivity analysis.

Table A 2: Theil index sensitivity analysis

Income Theil

Adjusting for

negative farm

incomes

Income Theil

Excluding

HHs with

negative and

zero Farm

Income

Income Theil

Excluding

HHs with

negative and

zero HH

income

Theil for HH

revenue

(without

considering

farm

expenses)

Mkoba 0.64 0.45 0.58 0.55

Silalabuhwa 0.41 0.31 0.35 0.27

Kiwere 0.63 0.47 0.46 0.46

Magozi 0.60 0.56 0.58 0.46

As. 25 Setembro 0.89 0.73 0.74 0.66

Khanimambo 0.66 0.17 0.31 0.36

Calculations excluding households with negative and zero incomes (Columns 2

and 3) provide lower values of the Theil index. However, it should be noted that the

Theil index does not allow direct comparisons across populations of different sizes.

Lastly, Theil indices calculated based on earnings are lower than those based on net

income, as in the case of the Gini coefficients.