Population Density and Fertility in Farm Households: A Study of the Millennium Development Authority Zones in Ghana

Population Density and Fertility in Farm Households:A Study of the Millennium Development Authority Zonesin Ghana

Clement Ahiadeke • Dominic Demr Der

Received: 9 January 2012 / Accepted: 9 November 2012 / Published online: 20 December 2012� Springer Science+Business Media Dordrecht 2012

Abstract Agriculture is mainly a rural preoccupation, and about three quarters of the

population growth in developing countries emanate from agricultural households. Some

demographers posit that the agricultural system affects birth rates; in addition, population

pressures might put stress on agricultural land in farming communities. This paper focuses

on the population to land ratio in the Millennium Development Authority (MiDA) Enu-

meration Areas (EAs) in Ghana and tackles the important question: Do households adjust

to an increasing population/land ratio by having fewer children? The authors explore this

theme in the context of rural population density and fertility in the three MiDA zones,

drawing on data collected in 23 EAs in Ghana in 2008. The results suggest that fertility in

the MiDA zones can be affected by density if nothing is done to regulate population

density. The regression estimates for the pooled data show that all the coefficients are

negative and statistically significant at the 0.05 level or better. Thus, children ever born is

inversely correlated with density, agricultural production, female literacy and the trend

variable (year). The estimates from the cross-sectional data as well as the spatial coefficient

were consistent with those of the pooled data. The results under various model specifi-

cations are stable. We find from the Cox model that areas with higher education levels and

a lower share of individuals working in agriculture, both correlated with land use. Agri-

cultural production has at most a modest independent effect on fertility. Our findings

suggest that population density has a reasonable inhibiting effect on fertility in the MiDA

Zones.

Keywords Households � Fertility � Population density � Farming � Land �Agriculture

C. Ahiadeke (&) � D. D. DerInstitute of Statistical Social and Economic Research (ISSER), University of Ghana,P.O. Box LG 74, Accra, Ghanae-mail: [email protected]

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Environ Dev Sustain (2013) 15:927–947DOI 10.1007/s10668-012-9419-8

1 Introduction

In the 1970s, demographers estimated that three quarters of the population increase in

developing countries took place within agricultural households (Yotopoulos 1978). The

significance of this estimation has not been lost on students of development from devel-

oping countries, particularly in view of the rapid scaling up of agricultural land use (Ningal

et al. 2008; Verburg et al. 2004). A growing body of work in this area then focused on the

causes and consequences of population increase and fertility in rural settings. Some of

these publications investigated the plausible notion that the agricultural system itself (type

of land tenure, type of technology, and quality and quantity of land) affected birth rates

(Carr et al. 2006).

The dynamics of population pressure on natural resources have brought about other

consequences, which in turn might strain the fragile balance between population and

environmental resources. Unfortunately, only scattered data (often for only one point in

time) are available to depict these evolutions (ISSER 2010). Among the consequences, the

most important are the fragmentation of family holdings through generational transfers and

the decline in agricultural production due to overcultivation.

Pingali et al.(1987) citing Boserup (1965) argue that population pressure does not

necessarily result in disastrous consequences, as it may lead to the evolution of farming

systems from land-using or natural resource-using systems, such as shifting cultivation to

land-saving and labor-intensive farming systems such as annual cropping systems. This

argument, however, has some shortfalls because while investment is required to establish

intensive farming systems (e.g., investment in the construction of irrigation facilities,

terracing, and tree planting), insufficient attention is paid to incentive systems which

ensure that the appropriate investment is made. It is widely recognized that investment

incentives are governed by land tenure or property rights institution, as it affects the

expected returns to investments that accrue to those who actually undertake them (Besley

1995). In sparsely populated areas of the Millennium Development Authority (MiDA)

zones, for example, the Afram Basin area, land is often owned and controlled by the family

where individual land rights are severely restricted, and benefits are shared widely among

members of extended families (Johnson 1972). If such communal ownership of land

prevails and persists, investment incentives are likely to be weak, hence stymieing

investments necessary for the intensification of farming systems (Besley 1995; Johnson

1972). Then, the extensive and natural resource-using farming systems may continue to be

practiced, contrary to the Boserupian hypothesis (Boserup 1965).

In this paper, we focus on population growth characteristics and the agricultural systems

(in particular, the ratio of population to land) in the MiDA zones in Ghana (Fig. 1) and try

to examine whether households adjust to an increasing population/land ratio by having

fewer children. By population characteristics here, we mean density (measured by land to

man ratio), fertility (defined in terms of measurable quantities like crude birth rate, child–

woman ratio, total fertility rate children ever born, etc.), family structure, inheritance

patterns, value of child labor, migration patterns, and other characteristics that could be

conducive to maintaining high fertility in a way as to depress land/man ratio in the zones.

For example, the value of child labor in traditional and transitional agricultural lands and

the potential importance of environmentally and socially determined risks as sources of

derived demand for children need not be overlooked in potentially fertile areas such as the

MiDA zones (Sutherland et al. 2004). We explore this theme in the context of rural

population density and fertility in the MiDA zones in Ghana, drawing on data collected in

23 Enumeration Areas or census districts in 2008. The relative availability of land during

928 C. Ahiadeke, D. D. Der

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the current period of agricultural modernization in the zones may not be conducive to

higher fertility if population control (or family planning) activities do not accompany the

modernization process. For example, a study suggests that farm size boosts the number of

living children not by creating a demand for more children, but by increasing the supply of

children through higher natural fertility and child survival.

2 Background

To expand the mandate of the Institute of Statistical, Social and Economic Research

(ISSER) to provide technical leadership and direction to the MiDA project impact

assessment and evaluation in Ghana, the institute conducted two key surveys: a baseline

study and a Farmer-Based Organization (FBO) study to set the agenda for future evaluation

of the MiDA project impact. In this section, we identify the various data sets employed in

doing this study. It is also important to note that the FBO survey and the baseline survey

alongside with the Ghana Demographic and Health Surveys formed the main sources of

Fig. 1 Map of Ghana showing the MiDA zones

Population Density and Fertility in Farm Households 929

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data used to establish the relationship between population density and land use in these

rural agricultural frontiers.

The first of these data sources was the Ghana Living Standards Survey Round 5 Plus

(GLSS5?) (ISSER 2009) which served as the baseline data and an extension to the

previous Ghana Living Standards Survey Round 5. A Farmer-Based Organization (FBO)

survey was also conducted in 2009. The Farmer-Based Organization (FBO) survey was

conducted essentially to provide data for the training of farmers under the agricultural

transformation project. A very important element of the FBO data was to determine the

overall living conditions of farmers and their households. The surveys were planned over a

period of 3 years during the earlier part of the life of the project interventions. Approxi-

mately 1,200 FBOs were expected to benefit from the overall project intervention. The first

round of the FBO survey started in October 2008 covering 601 randomly selected FBOs.

Survey data were collected on 10 thematic areas, including demographic and household

characteristics, education, health, activity status, migration, household transfers, infor-

mation-seeking behavior, household assets, housing, agricultural activities, and nonfarm

enterprises of households. The information collected was to serve as the tool for measuring

the impact of the interventions planned under the FBO component of the MiDA project as

a whole. As was noted above, the GLSS5? was expected to provide information on

patterns of household consumption and expenditure at a greater level of disaggregation and

to provide the baseline information on various factors including population growth and

family planning activities, to support long-term monitoring of the MiDA project.

ISSER is currently analyzing the GLSS5? and the FBO data sets in preparation for

additional work in the MiDA zones. This analytical paper emanates from the baseline

survey of 2008 (ISSER 2009), the first round survey of FBOs in 2009 (ISSER 2010) and

the Ghana Demographic and Health Survey data sets over the period 1988–2008 which

supplied the fertility variables used in the analysis (see Fig. 2, for example). It was

important to use the fertility and some other demographic data from the Ghana Demo-

graphic and Health Surveys because we felt that these semi-longitudinal data sets would

help researchers understand better the intricacies involved in population change and

environmental factors. This would, in turn, serve as a base for future work on land/man

ratios in the zones.

Fig. 2 Average crude birth rate for high- and low-density MiDA communities. Source: Note that, since1988, Ghana has been collecting Demographic and Health Survey data with the support of MacroInternational with research grant from USAID. Using these data sets, projected Crude Birth Rates (CBR) for1988, 1993, 1998, 2003, and 2008 (GSS, NMIMR, and MI, various issues) were easy to estimate. Thesevalues were mapped onto Enumeration Areas in the MiDA communities captured by the DHS surveys andthe values graphed into Fig. 1 above


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3 Data and methods

The Millennium Development Authority (MiDA) intervention zones are classified into

three categories, namely the Southern Horticultural Belt (SHB), the Afram Basin, and the

Northern Agricultural Zone (see Fig. 1). In the Southern Horticultural Belt, emphasis is

placed on pineapple production as well as other horticulture that could boost the export

potentials of farmers. For beneficiary districts around the Afram River (Afram Basin), the

focal crops are cereals, and in the districts in the Northern part of Ghana (Northern

Agricultural Zone), the focal crop is mango cultivation in addition to other tree crops. The

overarching goal for emphasis in these crops is to increase the production and productivity

of high value cash and export crops and also to enhance the competitiveness of these high

value crops in these intervention zones. The intervention also seeks to emphasize inten-

sification of the cultivation of crops in the Southern Horticultural Belt, extensification in

the Afram Basin, and diversification in the Northern Agricultural Zone.

The three project zones covering 23 census districts (or Enumeration Areas) were

selected purposively, based on the earlier nationally representative Ghana Living Standards

Survey of 2005. About 230,000 individuals are expected to benefit directly from the

t interventions, while about one million people are expected to obtain indirect benefits. The

caution here is not to increase land/man ratios through migration and fertility factors as

MiDA modernizes agricultural productivity in the zones. Rather, the findings of this study

are expected to provide the demographic agenda for further analysis and evaluation as the

MiDA program evolves.

To link the FBO data to some important population variables that could not be calcu-

lated from the MiDA data sets, we created spacial files for the FBOs data containing

similar information in the MiDA zones in the Northern Zone, Southern Zone, and the

Afram Basin. These were then linked to the 5-year Demographic and Health Surveys in

Ghana over the period 1988, 1993, 1998, 2003, and 2008 to generate a pseudo-longitudinal

data to measure total fertility rates over the last 20 years. The files contained geographic

information on the boundaries of the administrative divisions of the MiDA-FBO zones.

3.1 Sample Design and Organization of Survey

Following the Demographic and Health Surveys, a two-stage sample design was used for

the Ghana Living Standards Survey and the Farmer-based Organization design as well. For

the FBO survey, the first stage involved selecting sample points or clusters from an updated

master sampling frame constructed from the 2000 Ghana Population and Housing Census

in the second half of 2007. A total of 621 clusters (census Enumeration Areas) were

selected from the master sampling frame. The clusters were selected using systematic

sampling with probability proportional to size. A complete household listing was con-

ducted in September 2007 in all the selected clusters to provide a sampling frame for the

second stage of the selection of households. The second stage of the selection involved the

systematic sampling of 15 of the households listed in each cluster. The primary objective

of the second stage selection was to ensure adequate numbers of completed individual

interviews to provide estimates for key indicators with an acceptable precision at the

district level. Other sampling objectives were to facilitate manageable interviewer work-

load within each sample area and to reduce the effects of intra-class correlation within a

sample area on the variance of the survey estimates. Because the design was not self-

weighting so household sample weights were computed and applied to the estimation of

the survey results. This was to facilitate the estimation of the true contribution of each


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selected cluster in the sample. The main field work for the survey covered a 5-month

period (April–September 2008) in order to ensure that enough household baseline infor-

mation was taken before a significant number of MiDA interventions begun. [More

detailed information on sampling procedure for the MiDA and FBO surveys can be found

in ISSER (2009)].

3.2 Theoretical framework

It can be argued either explicitly or implicitly that almost all explanations of human

fertility have some decision-making ideas at their heart (Leibenstein 1981) not just at the

national level but also at the rural agricultural frontiers where populations tend to increase

with intensification (Carr and Pan 2002). At the very least, decision-making and population

planning play a role in situations where certain characteristics about an agricultural frontier

can mitigate incentives to reduce fertility. For example, abundant land but scarce capital,

infrastructure, and labor resources mean that investment in land is inefficient relative to

investment in labor and hence reproduction. In fact, research suggests that an exceptional

scarcity of wage-labor employment and schooling for women in remote and frontier areas

of a country may decrease the economic value of women’s time relative to that of children,

thus increasing fertility (Singh et al. 1985; Singh 1994). This is almost of necessity the case

if choice is in some way involved in fertility determination in frontier areas. The point

above suggests that a core population–environment interface in frontier environments turns

on the direct relationship between people and land. The available research on fertility–land

relationships generally supports the hypothesis that (1) where access to land is expanding

as in the MiDA zones, fertility rises; and, conversely, (2) land ownership suppresses

fertility. The literature tends to suggest that both the demand for labor on a larger farm and

the desire to expand landholdings as the family grows are considered the two primary

interpretations for the first hypothesis above (Chayanov 1986; Binswanger and McIntire

1987; Clay and Johnson 1992; Ellis 1993). The second hypothesis is explained, perhaps, by

the economic security imparted by land in lieu of children. The Philippines Rural Survey of

1952 (Hawley 1955), is, perhaps, the most striking study that positively links fertility and

farm size in which average total fertility varied from 4.8 to 7.0 per woman in her last

decade of childbearing as plot size increased from under 1 ha to over 4 ha.

Numerous researchers have examined the impact of land availability and/or farm size

on fertility behavior (Cain 1985; Clay and Johnson 1992; Easterlin 1976a, b; Hawley 1955;

Merrick 1978; Schutjer et al. 1983; Stokes 1984; Van Landingham and Hirschman 2001),

but these studies have been limited largely to historical and quantitative data. These may be

appropriate for indicating trends over time but such data do not explicitly clarify the

pathways and processes linking land to fertility decline.

Some researchers theorize that rural families deliberately produce more children

because of their need for more agricultural land and household labor (Caldwell and

Caldwell 1987; Lee 2001; Lee and Kramer 2002; Carr et al. 2006). While some theories

suggest that high fertility may be beneficial to farm families, yet the impact of different

land-use strategies or agricultural practices is not well known. When given similar access

to contraception, economic theories of fertility suggest that agriculturalists would prefer

(and achieve) greater family size than those engaged in less labor-intensive occupations.

In general, agriculture is more labor-intensive than cattle-raising, and agriculturalists on

marginal land with few technological implements have greater labor demands (Carr et al.

2006). The limited related research has indicated that the land–fertility relationship is

complex and fails to provide a definitive response to this conundrum.


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There is empirical evidence to show that in situations where agricultural land is rela-

tively scare, thus inducing a rational decision to reduce family size and undertake steps to

curtail reproduction, the reverse rather occurs. Various theories exist regarding why the

unexpected occurs, ranging from family planning program explanations (Robinson 1992)

to those positing a change in the direction of wealth flows, making children an economic

burden rather than a benefit (Caldwell 1982).

Kenya offers an interesting scenario, in that although it remains predominantly an

agricultural country, the rapid population growth that has occurred because of declining

mortality rates has resulted in a scarcity of land (Oucho 2000; Ovuka 2000). Although

fertility is declining, the population continues to grow at a rapid pace. With the current

total fertility rate at 5 children per woman and the attendant growth rate slowed to 2.3

percent, the projection is that more than seven million people will be added to the current

population of 32.4 million over the next 15 years (Population Reference Bureau 2004). In

essence, over 90 % of Kenya’s population resides on the 18 % of land area suitable for

agriculture, and there has been a serious decline in land/man ratio over the last half century

(APPRC 1998). The land–fertility dichotomy has resulted in the Easterlin argument which

advanced the development of the supply/demand framework for analyzing reproductive

decisions when using primarily an economic perspective. This idea led to the development

of a more specific component of the supply/demand framework (Easterlin 1976a, b).

The implication of this hypothesis is that as long as children provide labor then the

desire for large family size remains unchanged given that fertility is a conscious choice,

largely impacted by the need for additional labor in the fields or in the home. More recent

evidence of land–fertility interactions have framed the relationship between fertility and

landholdings in three ways:

(1) fertility increases in direct response to labor needs which are greater when more land

is owned (land needs drive family size goals (Carr et al. 2006; Coomes et al. 2001;

Binswanger and McIntire 1987; Chayanov 1986; Clay and Johnson 1992); (2) more

land is sought to meet the needs of growing family (i.e., family size drives lands (Carr

et al. 2006; Binswanger and McIntire 1987; Chayanov 1986); and (3) larger land

holdings may be indicative of higher socioeconomic status correlated with the

increased likelihood of infant and child survival (i.e., land holdings serve as a proxy

for socioeconomic status which in some cases leads to reduced mortality and

therefore higher fertility (Clay and Johnson 1992).

These positive land–fertility correlations have been documented in other developing

country settings (Stokes et al. 1986; Cain 1984); however, as noted by Carr et al. (2006),

the majority of related land–fertility research is conducted in settled agricultural areas. The

relationship between land and fertility in the context where land is abundantly available

and where the majority of the population are rural dwellers is an issue for further research,

particularly in frontier environments such as the MiDA zones. In essence, we conceive of

fertility as a function of variables such as the age of the population, the average income

levels, educational level, the occupational distribution, the locational distribution, and the

aspects of the population’s past history. If we change the variables, we expect to get

different results; but, we must keep in mind that in general we include variables either on

the basis of experience or because general knowledge of human behavior indicates they are

likely to have influence. Decision processes could be studied in a variety of contexts. For

our purposes, however, it would seem desirable to start such studies by exploring the

MiDA communities which are rural in nature. Some reasons come to mind: (1) those living

in rural communities are likely to exhibit a considerable amount of routine behavior, and


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hence, this would appear to be a desirable context within which to study passive decision-

making (i.e., behavior ‘‘within a holding pattern’’) and (2) perhaps the most important

reason for doing rural studies is that most of the population of Ghana is rural-based and that

the population to land ratio has been decreasing because of natural population increase and

the persistent in-migration into frontier zones.

4 Demographic characteristics

Table 1 shows the summary statistics for the demographic measures at the Enumeration

Area levels for the FBO data. The selected rural settlements have a relatively youthful

population with an average age of 23 years compared to 43 years in the MiDA zones

altogether. This suggests that the FBOs are formed around the youth rather than older

people. Educational levels are low with an average of 3 years of completed schooling.

Almost 70 % of adults in the rural areas are into agriculture, and the average population

density is relatively high, particularly in the Southern and Afram Basin zones.

Among all the main sources of land ownership, family heads appear as the most

dominant source of land for use (27.6 %), followed by other male relatives (22.8 %), chiefs

(16.0 %), nonrelatives (14.8 %), and other female relatives (10.2 %). By implication, the

family as a whole (i.e., family head, male relatives, and female relatives) is considered the

most important source of land, accounting for 60.6 % of total land owned or being used by

holders at the time of the 2008 survey. Government is seen as the least significant (1.6 %)

among the major sources of land ownership considered by the study.

The average household size in the Southern zone and the Afram Basin is nearly similar,

5.3 and 5.1 persons, respectively. The average in the Northern Zone is relatively large, 7.6

persons per household. Thus, although with the smallest population size, there appears to

be the potential for a rapid population growth in the Northern sector (ISSER 2009). More

detailed analyses from the same report (ISSER 2009) suggests that the average land sizes

of holders in the various MiDA intervention zones range from 1.1 ha in the Southern Zone

to 2.1 ha in the Afram Basin. Holders in the rural Northern Zone seem to own or use larger

sizes of land than their counterparts in the urban Northern Zone. While 25.3 % of indi-

vidual land holders in the rural Northern Zone possess or use land larger than 5 acres, only

21.3 % of their urban counterparts possess or use that much land. Contrary to this pattern,

Table 1 Some demographic and land-use characteristics of the MiDA-FBO zones

Characteristics MiDA zones

Southern Afram Basin Northern

Population density (persons per sq km) 174 131 134

Number of Households 647 1259 961

Average size of operational holdings (hectares) 1.1 2.1 1.6

Number of FBOs 129.4 (647) 251.8 (1259) 192.2 (961)

Population size (persons) 13,05,172 1,203,279 947,232

Area (in sq kilometers) 1,029,603 16,350 16,061

Mean household size 5.3 5.1 7.6

Average number of educational facilities 10.8 6.9 4.6

Source: MiDA GLSS5? survey, 2008


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holders in the urban Southern Zone have bigger portions of land than their rural coun-

terparts. In the urban Southern Zone, 19.7 % of land owners or users possess land larger

than 5 acres compared to only 10.0 % of their rural counterparts who also own or use land

larger than 5 acres. However, there appears to be no clear pattern of variation in land size

between rural and urban dwellers in the Afram Basin.

4.1 Population density

Population density in the MiDA zones was calculated as the number of people per square

kilometer. The total area of each MiDA zone was derived by adding up the areas of the

districts within the zone. The population of each zone was also obtained by summing up

the populations of the districts in each zone. The district populations were extracted from

the 2000 Population and Housing Census report and then projected to 2008, the year the

FBO data were collected. The areas of each district, the projected populations, and their

respective densities are also presented in Table 1.

The average population density for all the zones is 146 persons per square kilometer. It is

174 per square kilometer in the Southern Zone, 131 per square kilometer in the Afram Basin,

and 134 per square kilometer in the Northern Zone. These means, however, mask the

considerable spatial variations from 323 to 490 per square kilometer in some places. Also, the

overall average does not come close to the 790 per square kilometer population density

documented in the 2000 Population and Housing Census for parts of the Greater Accra

Region (GSS 2002). The data also represent the most complete picture of population density

and socioeconomic characteristics for most years between 1988 and 2008. Because DHS is a

repeated cross-section of individuals sampled from locations across the country including the

three MiDA zones, it was possible to compare the GIS locations for survey clusters.

4.2 Poverty levels

There was no ‘‘consumption module’’ in the MiDA-FBO data set in the 2008 study. In the

absence of comprehensive income variables of the households in the data set, it was not

possible to estimate the poverty status of the FBO member households. Thus, the poverty

level as calculated for the MiDA Ghana Living Standard Survey 5? (GLSS 5?)

descriptive report was superimposed on the districts. Thus, each district was assigned the

same poverty level as the GLSS5? report, since the data were collected in the same year

and around the same time.

5 Factors influencing disparate household sizes

Factors accounting for the differences in household size between the Northern and the

Southern zones or the rural and urban localities were examined. Also taken into consid-

eration were the desire for the large numbers of children and the different family systems in

the Northern and rural areas. These variables are considered in turn below.

5.1 Fertility patterns in the MiDA zone, 1988-2008

Figure 1 shows the average number of children ever born (CEB) for the various DHS

survey years for low and high-fertility areas for the three MiDA zones, categorized by

density (divided at the mean) to produce lower and higher density areas. The pattern


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provides two clear features. On the one hand, the high-density areas appear to have lower

average birth rate, and on the other hand, CEB declined during the period 1988– 2008 (for

the high-density areas, from about 3.8 to 2.9; for the low-density areas, from 4.3 to 3.2).

Over the same period, population density increased by 19 % in the average settlement,

from 0.70 to 0.83 persons per acre. Thus, the over-time correlation of average CEB and

average density is negative. The cross-sectional correlations are also negative. At each of

the 10 years, higher density settlements tended to have lower births from -0.08 to -0.43,

with a mean of -0.18. Note that similar analysis was done using Crude Birth rates and

similar patterns were found.

These patterns suggest that fertility in the MiDA zones is affected by density. It is

important to note, however, that these zero-order relationships could be misleading,

because density could be related to other determinants of Zonal births. Assessing the effect

of zonal density on zonal fertility requires a multivariate model which we explored below.

5.2 Density and fertility

Various cross-national studies of fertility have consistently shown inverse relationship

between density and birth rate, controlling for the level of urbanization, economic devel-

opment and others, and the results for smaller units of analysis are almost consistent (Cut-

right and Kelly 1978; Firebaugh 1982; De Sherbinin et al. 2007). In the 1970s, several inverse

density–fertility relationships were observed in various units of analysis. Nevertheless, there

have been inconclusive results for household-level data for some agricultural villages.

Explanations of density effects vary with the exception of urbanization not being a candidate,

because most studies restrict the analysis to rural areas, or include measures of urbanization

(Firebaugh 1982). Some studies attribute density effects to ‘‘population pressure’’ without

elaborating on how such pressure might operate to inhibit fertility. Other studies are more

specific, particularly when it is argued that in Taiwan, high-density facilitates the acquisition

of birth-control information. Easterlin (1967a, b) (cited in Firebaugh 1982) suggested that

inheritance was the key link between density and fertility for the nineteenth-century United

States. This bequest theory was applied to rural Brazil, and the same outcome was found.

Meanwhile, others posit that fertility tends to be lower in denser regions because children

cost more to bring up in these geographical areas. In the case of the three MiDA zones, there

could be at least two reasons for expecting a negative density effect: (a) population pressure

does not appear to affect density as large tracts of land are sparsely populated, (b) birth

control does not look imminent (as family control program in two of the zones is only about

2 % of the national average of 24 percept), and it is only in the southern horticulture zone that

land grabbing appears threatening (ISSER 2009).

5.2.1 Inheritance

Land scarcity creates a dilemma for the landholder. A surviving son is considered essential

to keep the land in the family particularly in most of the Northern settlements of the MiDA

zones. Arguably, as a result of the current high rural under-five mortality rate of 90 deaths

per 1,000 live births (GSS, GHS, and ICF Macro 2009), a large family is necessary to

insure a high survival rate. With the prevalence of the multigeniture norm, however, a large

family risks land shortage for inheritance relative to the family holdings, if many sons

survive. Once this happens, some of the children may migrate and others may be left

behind. But these activities are also a function of the current level of family planning

education programs and the general education support for the settlements. In other cases,


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couples may anticipate the density problem and try to limit fertility by having four (the

prevailing desired number of children GSS, GHS, and ICF Macro 2009) or less children.

Because the inheritance problem is more acute in higher density areas, we expect such

birth limitation to be more common in higher density communities.

5.2.2 Value of child labor

Microeconomic approaches to rural fertility stress that ‘‘the supply of and demand for

agricultural labor affect the demand for children in agricultural communities’’ (Rosen-

zweig and Evenson 1977, cited in Firebaugh 1982: 484). Studies including Firebaugh

(1982) and Carr et al. (2006) have shown that the value of child labor is very salient for

many farming households in the developing world. Child labor value in some communities

is determined largely by the availability of land. This is true even for the children of the

landless, who constitute a labor pool that can be used during peak seasons, such as harvest

times. Further, the labor pool is a community pool in that, children are most likely to work

on farms in their own communities, so it is the density of the community, and not some

aggregate, which principally determines child labor value.

5.2.3 Migration

High-density encourages migration, and migration erodes the social supports for high

fertility. Of the three MiDA zones, it is the Afram Basin that has experienced the most

migration unlike the Southern Horticultural Zone and the Northern Belt. In the Afram

Basin, for example, agricultural density (population/cultivated land) more than doubled

from 1975 to 1995. It is important to note that this paper focuses on rural-level density

effects, and not farm-level effects. This distinction is important, so the two levels of

analysis should not be confused. At the farm level, for example, the relationship between

farm size and family size could be reciprocal in that, farm size could adjust to family size

as well as the other way around. At the community level, this type of reciprocal effect is

not possible, because community land is fixed. For this reason inter alia, the results of a

community-level analysis of density and fertility do not necessarily apply to the rela-

tionship between farm size and family size within a community.

6 Other determinants of locality birth rate

Density could be correlated with other determinants of community birth rates, resulting in

inaccurate estimates of the density effect, if these other factors are not controlled for. These

determinants include agricultural production, female and male education, and some other

unknown factors.

6.1 Agricultural production

The likely effect of increased agricultural production is to lower fertility by delaying marital

cohabitation. Agricultural production in the 23 MiDA districts have been reviewed recently

and was found to be quite impressive in terms of quality and quantity (ISSER 2009).

The mean age at marriage for females is 20.8 years, while that for males is 25.3 years.

Both are significantly higher than the national mean age at first marriage (18.4 years). The


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MiDA data indicated that 60.9 % of the population has ever been married. The indications

reveal that the rate is highest in the Northern zone (54.6 %), decreasing to 43.3 % in the

Afram Basin then to 38.7 % in the Southern zone. If these indicators are anything to go by,

then there is a high probability that agricultural production will have a negative effect on

birth rate. The expectation then is that a higher productivity relative to village fertility lay

in additional work, and not in additional income, brought by increased production. Some

might argue, however, from a microeconomic perspective, that agricultural production

should have a positive effect on fertility through its effect on income. This argument is

based on two assumptions: (1) that in these communities income has a positive effect on

fertility at the household level and (2) that there is no aggregation bias. Even if income

does affect fertility, quite possibly it is income relative to fellow community dwellers, and

not absolute income, which is critical. If so, the income effect at the community level

would be zero.

6.2 Female education

Several studies since the 1970s have suggested both at the individual and aggregate levels

that education has a negative effect on fertility (Firebaugh 1982; Coomes et al. 2001; Carr

et al. 2006). Although female education is critically important, its effects differ from

country to country. For example, in some low-literacy countries, the initial effect of

education might be to increase fertility.

Interpretations of the female education effect vary as some see female labor force

participation as the key: educated women command higher wages, so childbearing costs

more for educated women in terms of potential income foregone. However, this ‘‘income

foregone’’ argument seems irrelevant for farm households, because income opportunities

for farm village women are typically restricted to tasks which require no formal education

(clearing of land, for example). Other researchers focus on education’s probable effect on

knowledge and norms: Educated women are more likely to adopt nontraditional views on

family size, to use more effective birth-control methods and so on (Firebaugh 1982;

APPRC 1998; Van Landingham and Hirschman 2001). Other researchers also focus on

those being educated (the children) rather than on the educated and argue that education

raises the cost of children (Lee 2001; Lee and Kramer 2002). Whatever the case may be,

whether through its effects on adult norms or child costs (or both), we expect female

education to have a negative effect on fertility.

6.3 Female literacy

Female literacy was extracted from three variables in the MiDA-FBO data sets: the ‘‘age,’’

‘‘gender,’’ and the ‘‘can (name) read a phrase/sentence in English’’ variables. There is a

clear gender gap in terms of education in the zones: Only 5.8 % of women in the zones

compared to 14.1 % of men have secondary or higher levels of education. This is also

consistent with the finding of 43 % of women who have never attended school compared

with 30.2 % of men.

In terms of relationship between education and locality, the data indicate that the

proportion of adults who have never been to school is highest in the Northern zone

compared to the other two intervention zones. The figures suggest that while 61.4 % of the

adult population in the Northern zone has never been to school, only 23.8 and 25.9 % have

never been to school in the Afram Basin and Southern zones, respectively. The data also

indicate that on average, households spent GH¢122.7 annually (1.63 cedis = 1 USD at the


123

time) on each member attending school or college. However, the annual average amount

spent varies by location. The urban Southern zone recorded the highest amount

(GH¢215.9) followed by the Northern zone with an annual average expenditure of

GH¢180.5 on education.

6.4 Rural–Urban dimensions

Many studies have found an inverse relationship between fertility and the type of place of

residence. It is usually assumed that fertility is lower in urban areas than in rural com-

munities. However, the focus in this study is the community; the measure of urban–rural

composition (the absolute number of population) is differentiated by the size of the pop-

ulation. For rural areas, the population must be 500 people or less, thus making urban–rural

definition dichotomous. For the two broad MiDA communities, there is no clear-cut dis-

tinction between urban and rural. In this regard, the individual-level and community-level

relationship need not be the same.

Let uf = urban fertility rate, rf = rural fertility rate, and R = ratio of scheduled urban

population to the rural population. Suppose that urban definition affects fertility at the

individual-level, that is uf = rf, then we have two basic cases to consider.

Case I (no contextual effect). Here uf and rf for a community are not related to com-

munity R. Hence, total community fertility rate, which is a function of uf and rf, depends on

the relative sizes of the urban and rural schedules, that is, on R. For example, if uf [ rf,

then a zone with larger R will have higher fertility rate.

Case II (contextual effect). In the case of contextual effects, uf and/or rf are related to

community R. The possibilities here are numerous. For example, an urban community

within a rural setting can encourage high fertility, so when R is small, we expect uf to be

large. If rf is unaffected by R, then (total) community fertility rate is negatively related to

R. But R could also affect rf. Perhaps when R is small, the rural people feel less threatened,

thereby depressing rf. Alternatively, a small R could lead to higher rf: The rural population

often constitutes a cheap labor pool for urban dwellers, and when their labor pool is

relatively small, child labor is more likely to be important to the urban dwellers. In any

case, the basic point here is that such contextual effects are likely, and when they occur, the

community-level and individual-level relationships will differ.

6.5 The model and estimation procedure

The essence of the model employed here is to determine the relationships between land use

and demographic variables in the study areas. In this paper, the basic model estimated is a

linear regression of the form:

CEBit ¼ aþ b1DENSITYi;t�1 þ b2PRODi;t�1

þ b3%MALELITi;t�1b4%FEMLITi;t�1B5YEARþ eit ð1Þ

(i = 1, 2,…, 23; t = 1988, 1990,…, 2008)where CEBit is children ever born for each of

the 23 Enumeration Areas within the MiDA zone over time t; DENSITY is community

population/community land area; PROD is farm production per capita; %MALELIT and

%FEMLIT are male and female literacy rates, respectively; eit is error term.

Equation (1) describes ‘‘pooled’’ cross-sectional and time series data, that is, 23 Enu-

meration Areas each at 11 points in time 10, because the independent variables are lagged

1 year for a total N of 242.


123

We also include the age of the respondent to avoid biased estimates because age

structure (a) affects crude birth rate and (b) could be correlated with the explanatory

variables in the Eq. (1) above. Preliminary estimates show that the percentage of the

female population has no independent effect on CEB, and this variable was subsequently

dropped from the analysis.

Because there are missing data on key variables, our regression estimates are based on a

combination of cross-sectional (across zones) and time series (over time) covariance.

Analyses of such ‘‘pooled’’ data are not common place in demography, and two obser-

vations are in order. First, pooled data can reduce to more familiar data structure (Hannan

and Young 1977, cited in Firebaugh 1982). Among the possibilities, the best often is cross-

sectional analysis with outcome variable at time 1 as predictor. This approach, of course, is

quite inefficient, in that it ignores all the data for the intervening years. The approach is

used here to supplement and confirm the analysis of the pooled data. The chief attraction of

the cross-sectional, lagged outcome variable approach is that it enables one to use more

refined measures. For example, with the cross-sectional, lagged outcome variable, we can

use the 1988 Demographic and Health Surveys of Ghana (i.e., from 1988 to 2002), or

adjustment years, thereby minimizing the interpolation necessary for the pooled data (see

Table 2; note that community birth records are not usually kept, especially in rural areas,

so interpolation was found necessary to make adjustments). Further, a refined measure of

density is available with the cross-sectional, lagged outcome variable: household/cultivated

land area, instead of total population/total land area.

In addition, there are two basic econometric approaches to analyzing pooled data: the

‘‘error components’’ approach and the generalized least squares (GLS) approach. Each has

its advocates, and it is premature to say that one is superior to the other. In fact, GLS and

Table 2 Description of variables and data sources for pooled and cross-sectional data

Variable Component Data SourcesPooled data Cross-sectional data

CBR N = number ofbirths eachyear

Births collected from DHS1988–2008 sources

Used 5 year DHS averages tosmooth out short-term fluctuations(that is 1989 is 1988–1992 averageand 2009 is 2008—MiDA sample)

D = populationin thousands

Census, DHS sources, andMiDA

No interpolation needed for 2008.Minimal interpolation needed for1988

DENSITY N = Population See above Number of households interviewedin 2008

D = Land Total MiDA zone land. Someland area (constant overtime)

Cultivated land area 2008

PRODUCTION D = Population See above See above

%FEMLIT N = Literatefemalepopulation

Source: MiDA communitiesextrapolated from DHS dataat 5 year intervals1988–2008

Minimal interpolation needed

D = TotalfemalePopulation

Same as above Minimal interpolation needed



123

error components make fundamentally different assumptions about the structure of the

error term, so in a given analysis, one must consider which set of assumptions is more

reasonable. The error components method, for example, decomposes the error term eit into

two components: a unit-specific component li and a well-behaved component vit (Hannan

and Young 1977; StataCor 2009). A time-specific component could also be included

(StataCor 2009), but time-specific effects are unlikely in our case. Recall that Eq. (1)

includes YEAR to capture any linear time trend. Here, li represents the community-

specific effects on CEB which (a) are not captured by the explanatory variables in Eq. (1)

and (b) are constant over time. Such community-specific effects are likely here. Age

structure in particular is an important determinant of community fertility rates (measured

here by CEB); however, it does not look stable, as the FBO data appears to produce mean

younger ages than the older age segments in the MiDA communities in general. Similarly,

if some communities consistently underreport births, or if some communities have better

access to family planning programs, that would be reflected in li. In short, the error

components approach seems quite appropriate for use in this study.

Finally to verify the effects of the timing of initial FBO activity in the communities, we

estimate the Cox proportional hazard model of the form:

kcðtjGc; bÞ ¼ koðtÞ expðGc0bÞ ð2Þ

where kc (t|Gc, b) is the community-specific hazard rate; ko(t) is the baseline hazard rate;

t is the year in which the community obtained FBO coverage (here t = 2008 for all

communities); and Gc is the set of community-specific demographic and land–use-related

factors.

The Cox proportional hazards model allows us to estimate the relationships between

land use and demographic variables semi-parametrically. This model does not make

assumptions about the form of ko(t) (which is unidentified here) but does assume the hazard

rate around multiplicatively (Cameron and Trivedi 2005).

7 Results

Table 3 gives the matrix of correlation coefficients for the pooled data. Children ever born

is inversely correlated with density, agricultural production, and female literacy. CEB is

Table 3 Zero-order correlation, pooled data

CEB Density Production %FEMLIT %MALELIT

Density -0.25

Production -0.19 -0.03

%FEMLIT -0.27 0.01 0.15

%MALELIT -0.23 0.01 0.27 0.07

Year -0.38 0.19 0.08 0.62 0.67

Age -0.23 0.12 0.33 0.06 0.34

Mean

SD

N 2,231



123

also inversely correlated with density, agricultural production, female literacy, and the

trend variable (YEAR). This is consistent with Fig. 1, which pictured a decline in CEB

over the period of study. This decline is not limited to the MiDA zones only, because the

Ghana Demographic and Health survey also projected a similar decline for Ghana as a

whole. Table 2 also gives the means and standard deviations; from the means we can

characterize the ‘‘average FBO village in the average year’’ in the following way: About

one-third of the communities are in urban setting, slightly over a fourth of the females are

literate, land is not critically scarce (if distributed equally, each resident would have

0.56 ha, or about 1.4 acres), and 37 babies are born per 1,000 women.

Table 4 gives the regression estimates for the pooled data. The results are consistent

with expectations. All the coefficients are negative and statistically significant (at the 0.05

level or better). The estimated effects are really not very large, however; the largest b(standardized regression coefficient) is 0.27.

Tables 5 and 6 give the estimates for the cross-sectional data. The results closely

parallel those for the pooled data. As it were, CEB is inversely correlated with all the

independent variables (Table 5). The pattern of result for the partial coefficients (Table 6)

also tends to confirm the results from the pooled data. The coefficients for density, female

literacy, and urban–rural settlement are all negative. The coefficients for production are

also negative, but quite small. These coefficients rarely attain statistical significance at the

conventional 0.05 level. What is quite informative is the stability of the results under

various specifications. Table 6 gives the coefficients for four specifications. In each case,

denser communities tended to have lower birth rates in 2008, controlling for per capita

agricultural production, percentage female literate, proportion rural, and 1988 children ever

born. Similarly, communities with higher female literacy tended to have lower children

ever born, all things being equal; communities that are urban tended to have relatively

lower birth rates, other things, being equal.

We find from the Cox model that a number of proxies for agricultural land-use appear to

be important in driving timing. Thus communities with both higher education levels and a

lower share of individuals working in agriculture were seen to have typically correlated

with land use. The relationship between education and land use is quite strong at baseline.

Table 4 Determinants of children ever born in the MiDA zones: regression estimate for the pooled data

Explanatory variablea bb bc Cox

Population density -0.06*** -0.27 0.283

Agriculture production -0.03** -0.13 -0.492***

Female education (in years) -0.18** -0.23 0.066***

Average age -0.26** -0.22 0.098***

Average education (in years) 0.017*** 0.038*** 0.065***

Year -0.35* -0.15 0.19

R2 (adjusted) 0.77 0.39

N 2,231 2,231 2,220


* Significant at 0.05; ** significant at 0.01; *** significant at 0.001a See notes for Table 3b Metric regression coefficientc Standardized regression coefficient


123

Let us consider the findings for the other determinants of community children ever born.

We expected agricultural production to depress fertility in the high-density areas. The

results are somewhat not exactly so. Agricultural production has at most a modest

Table 5 Zero-order correlation, cross-sectional data

2008 CEB 1998 CEB Production %Femlit Density Age

1988 CEB 0.69

Production -0.32 -0.21

%Femlit -0.36 0.02 0.05

Age -0.07 0.33 0.27 -0.05 0.43

Density -0.26 -0.04 -0.22 -0.30 0.27

Mean 34.2 41.5 116.0 33.5 34.3 25.4

SD 6.8 10.9 100.2 11.3 7.4 11.3


2008 CEB average number of births, 1988–2009, divided by 2008 population * 1,000

1988 CEB average number of births, 1987–2000, divided by 1988 population * 1,000

Production farm output per capita, 2008

%FEMLIT percentage of female literate, 2008

Age age as recorded in 2008 survey

Density households per square kilometer of cultivated land, circa 2008

Table 6 Determinants of 2008 CEB: regression estimates for cross-sectional data with lagged CEB usingvarious models

Explanatory variables Model

Baselinea Male birthsb Sex ratioc Lag modeld

be bf b b b b b b

Household density -0.17 -0.24 -0.23 -0.35 -0.26 -0.24 -0.18 -0.26

Agricultural productiong -0.02 -0.07 -0.03 -0.15 -0.02 -0.09 -0.03 -0.08

% female literate -0.25** -0.35 -0.23 -0.32 -0.27** -0.36 -0.26* -0.38

Age -0.25 -0.26 -0.08 -0.07 -0.24 -0.26 -0.33 -0.25

1988 crude birth rate 0.55** 0.76 0.39* 0.54 0.56** 0.69 0.46** 0.77

R2 (adjusted) 0.67 0.20 0.70 0.66

N 2,231

Source: MiDA GLSS5? survey 2008

* Significant at 0.05; ** significant at 0.01a Uses variables as described in Table 4b Uses male births, 1987–1988, to estimate 1988 CEB, and male births, 1998–2008, to estimate 2008 CEB(in case female births are underreported)c Baseline model with community sex ratio controlled ford Lags production, female literacy, and yeare Metric regression coefficientf Standardized regression coefficientsg These results are not due to the skewed distribution of agricultural production. The results are the samewhen the skew is eliminated by taking the logarithm of production


123

independent effect on fertility. The coefficient is always negative but even in the one case

where it is significant, the b is only -0.15. This finding, however, serves as baseline

information for evaluating any impact that innovations in agricultural productivity in the

MiDA communities may have on fertility. It also serves as a challenge to family planning

activities nationwide.

With regard to female education, most studies of fertility conclude that female edu-

cation reduces fertility. This study is no exception as the coefficient here is negative and

moderately large (the median b is -0.35) (see Table 6). The results from the rural com-

munities illustrate how relationships sometimes depend on the level of aggregation. The

rural children ever born had to be estimated, yet at the community level, the correlation

coefficients and the partial regression coefficients are negative. The weight of the evidence,

then, suggests that living in urban communities has a negative effect on fertility. A possible

explanation would be that the ready availability of labor in the urban areas drives down the

result value of child labor; however, the data do not speak directly to this point.

7.1 The effect of density

Our results show that population density has a moderate inhibiting effect on fertility in the

MiDA communities. This is particularly so in the Southern Horticultural Zone. As evident

in Tables 4 and 6, the beta coefficients for density are consistent, ranging from -0.23 to

-0.35. The estimate of b (pooled data) is -0.47. If population density increased by one

person per square kilometer, other things being consistent one would expect the CEB to

decrease by 0.047. Saying it differently, if density increased by, say 10 % in the average

community, other things holding constant, we would expect CEB in the community to

decrease by 0.84 or 2.3 %. Note that the percentage decline in CEB does not match the

percentage increase in population. This means that, at least for the short run, if a com-

munity population (and hence density) increases, we expect the number of births to

increase although birth rate declines.

8 Discussions and Conclusion

This paper’s main focus is on the characteristics of population growth and agricultural

systems in the MiDA zones in Ghana. We also wanted to find out whether households

adjust to an increasing population/land ratio by having fewer children. We did this study in

the Millennium Development Authority (MiDA) zones in Ghana using two data sources:

GLSS 5? and FBO, which were the surveys conducted by the Institute of Statistical, Social

and Economic Research (ISSER). These data sets were supplemented with the Ghana

Demographic and Health Surveys carried out over the past 20 years. A two-stage sample

design was used to select samples for data collection in the three MiDA zones covering 23

Enumeration Areas or communities.

The variables of key interest to this paper were population density, fertility patterns,

value of child labor, male, and female literacy rates. The average population density for all

the zones was found to be 146 persons per square kilometer. Fertility patterns were studied

by averaging the number of children ever born for low- and high-fertility areas for the three

MiDA zones over a number of years. This was then categorized by density to produce

lower and higher density areas. The patterns suggest that fertility in the MiDA zones is

affected by density and high-density settlements tend to have lower birth rates. Community

density and availability of land on the other hand are both determinants of child labor


123

value, but the principal determinant was found to be community density since children are

more likely to work on farms in their own community, than in other communities. Literacy

rate for males was higher than for females, and with respect to women, education was

found to have a negative effect on fertility. This could be due to the fact that educated

women have a higher tendency to adopt nontraditional views on family size, and they also

use more effective birth-control methods. Children ever born (CEB), community density,

farm production per capita (PROD), male and female literacy rates (% MALELIT,

%FEMLIT), and year were employed in a linear regression model to depict their rela-

tionship with land use.

The results show that children ever born is inversely correlated with density, agricul-

tural production, female literacy, and the trend variable year. A decline in CEB over the

study period was observed which is not limited to MiDA zones only but to Ghana as a

whole since a similar pattern was projected from the DHS survey. Denser communities,

high female literacy rate, and communities that are urban tended to have lower birth rates

compared to the other determinants. A number of proxies for agricultural land-use seem

important in driving timing. Thus communities with higher education levels and a lower

share of individuals working in agriculture were seen to have typical correlations with land

use. Agricultural production was seen to have an independent effect on fertility, which is

surprising, since it was expected to depress fertility in the high-density areas. Population

density on the other hand was seen to have a moderate inhibiting effect on fertility,

suggesting that if a community density (population) increases, the number of births is

expected to increase while birth rate declines.

This paper attempts to understand fertility behavior in relation to land use in the MiDA

zones in Ghana (an agricultural frontier area being supported by the George Bush—a

former American President’s initiative). We feel that this is an important topic for eco-

logical and population planning reasons. Population planning because the southern horti-

cultural zone appears to be experiencing high density while the rest of the zones are

relatively sparsely populated giving rise to in-migration and higher-than-average fertility.

With regard to the ecological importance of the research, we note that agricultural

expansion within the tropical world is the primary factor in deforestation in most agri-

cultural countries. Most forest conversion for agriculture is done by small farm families,

and household size has been consistently linked as a key determinant to deforestation in

such environments. The result of this study, therefore, underlies the need to accompany

frontier land development projects with population programs.

The paper has shed light on the population/land ratio interrelationships in the three

MiDA zones in Ghana. Particularly, we have critically examined one environmental

condition (density) in these zones, and the results are suggestive that increasing density

does dampen fertility. Stemming from the results of this paper, it is argued that fertility

may respond to environmental conditions in the poorer communities of the MiDA zones in

Ghana if nothing is done about planning for the future. In consequence, a factor to consider

for policy planning is the possible response of rural fertility to increasing land scarcity.

It appears that current levels of land use does not pose a threat to the population–fertility

dichotomy, and it would be good for planners to maintain or improve upon this pattern by

introducing population programs that keep land use and population density at equilibrium.

These findings lead to an acceptable conclusion that increasing density does reduce fer-

tility, and current levels of land use is not a threat to the population–fertility issue.

Acknowledgments This paper benefitted from Contract Number 4101103-01 between the MilleniumDevelopment Authorities (MiDA) and the Institute of Statistical Social and Economic Research (ISSER)


123

Farmer-Based Organisation (FBO) Survey Round 1, 2008. The original paper forms part of the 3 analyticalreports. The current version is an improvement upon the analytical report and has benefited significantlyfrom two anonymous reviewers and the editors of this journal.

References

Besley, T. (1995). Property rights and investment incentives. Journal of Political Economy, 103(5),913–937.

Binswanger, H., & McIntire, J. (1987). Behavioral and material determinants of production relations in land-abundant tropical agriculture. Economic Development and Cultural Change, 36(1), 73–99.

Boserup, E. (1965). The conditions of agricultural growth: The economics of agrarian change underpopulation pressure (4th ed.). London: George Allen and Unwin.

Cain, M. (1984). On the relationship between landholding and fertility. New York: New York Center forPolicy Study.

Cain, M. (1985). On the relationship between landholding and fertility. Population Studies, 39(1), 5–15.Caldwell, J. C. (1982). Theory of fertility decline. New York: Academic Press.Caldwell, J. C., & Caldwell, P. (1987). The cultural context of high fertility in sub-Saharan Africa. Pop-

ulation and Development Review, 13(3), 409–437.Cameron, A. C., & Trivedi, P. K. (2005). Microeconometrics: Methods and applications. Cambridge:

Cambridge University Press.Carr, D. L., & Pan, W. (2002). Fertility determinants on the frontier: Longitudinal evidence from the

Ecuadorian Amazon. Presented at the 67th annual meeting of the Population Association of America,9–11 May 2002, Atlanta, GA.

Carr, D. L., et al. (2006). Declining fertility on the frontier: The Ecuadorian Amazon. Population andEnvironment, 28(1), 17–39.

Chayanov, A. V. (1986). The theory of peasant economy. Madison: University of Wisconsin Press.Clay, D. C., & Johnson, N. E. (1992). Size of farm or size of family: Which comes first? Population Studies,

46(3), 491–505.Coomes, O., Grimard, F., & Diaz, V. (2001). Peasant farm size and family size: A causality analysis from

the Peruvian Amazon. Northeast Universities Development Consortium Conference (NEUDC).Cutright, P., & Kelly, W. R. (1978). Modernisation and other determinants of national birth, death, and

growth rates: 1958–1972. Comparative studies in Sociology, 1, 17–46.De Sherbinin, A., Carr, D., Cassels, S., & Jiang, L. (2007). Population and environment. Annual Review of

Environment and Resources, 32, 345–373.Easterlin, R. A. (1976a). Population Change and Farm Settlement in the Northern United States. Journal of

Economic History, 36(1), 45–75.Easterlin, R. A. (1976b, December). Factors in the decline of farm family fertility in the United States: Some

preliminary research results. Journal of American History, LXIII(3), 600–614.Ellis, F. (1993). Peasant economics: Farm households and agrarian development. Cambridge: Cambridge

University Press.Firebaugh, G. (1982). Population density and fertility in 22 Indian villages. Demography, 19(4), 481–494.Ghana Statistical Service. (2002). 2000 population and housing census: Summary report of the final results.

Accra: GSS.Ghana Statistical Service, Ghana Health Service, and ICF Macro. (2009). Ghana demographic and health

survey 2008. Accra, Ghana: GSS, GHS, and ICF Macro.Hannan, M. T., & Young, A. A. (1977). Estimation in panel models: Results on pooling cross-sections and

time series. In D. R. Heise (Ed.), Sociological methodology (pp. 52–83). San Francisco: Jossey-Bass.Hawley, A. H. (1955). Rural fertility in Central Luzon. American Sociological Review; International

Encyclopedia of the Social and Behavioral Sciences, 3, 1686–1689.ISSER. (2009). Impact evaluation of the Millennium Development Authority (MiDA) program report of the

baseline survey (GLSS5?). Legon: Legon Institute of Statistical, Social and Economic Research(ISSER), University of Ghana.

ISSER. (2010). Impact evaluation of MiDA programme: FBO survey, descriptive report FBO phase 1baseline data. Legon: Institute of Statistical, Social, and Economic Research (ISSER), University ofGhana.

Johnson, O. E. G. (1972). Economic analysis, the legal framework and land tenure systems. Journal of Lawand Economics, 15(1), 259–276.


123

Lee, R. (2001). Externalities to childbearing. In: N. J. Smelser & P. B. Baltes (Eds.), International ency-clopedia of the social and behavioral sciences (Vol. 3, pp. 1686–1689). Oxford, U.K: Elsevier ScienceLtd.

Lee, R., & Kramer, K. (2002). Children’s economic roles in the context of the Maya family life cycle: Cain,Caldwell, and Chayanov revisited. Population and Development Review, 28(3), 475–499.

Leibenstein, H. (1981). Economic decision theory and human fertility behaviour: A speculative essay.Population and Development Review, 7(3), 381–400.

Merrick, T. W. (1978). Fertility and land availability in Brazil. Demography, 15, 321–336.Ningal, T., Hartemink, A. E., & Bregt, A. K. (2008). Land use change and population growth in the Morobe

Province of Papua New Guinea between 1975 and 2000. Journal of Environmental Management, 87,117–124.

Oucho. (2000). Demographic implications of population distribution, density and movement within Kenya’sarable lands. In Issues in resource management and development in Kenya. Nairobi: East AfricanEducational Publishers Ltd.

Ovuka, M. (2000). Land use changes in central Kenya from the 1950 s: A possibility to generalize?GeoJournal, 51, 203–209.

Pingali, P., Bigot, Y., & Binswanger, H. P. (1987). Agricultural mechanization and the evolution of farmingsystems in sub-Saharan Africa. Baltimore, MD: Johns Hopkins University Press.

Population Reference Bureau (2004).Robinson, W. (1992). Kenya enters the fertility transition. Population Studies, 46(3), 445–457.Rosenzweig, M., & Evenson, R. E. (1977). Fertility, schooling and the economic contribution of children in

rural India: An econometric analysis, Vol. 260 of center paper. Economic Growth Centre, YaleUniversity.

Schutjer, W. A., Stokes, C. S., & Poindexter, J. R. (1983). Farm size, land ownership, and fertility in ruralEgypt. Land Economics, 59(54), 393–403.

Singh, R. D. (1994). Fertility–mortality variations across LDCs: Women’s education, labor force partici-pation, and contraceptive use. Kyklos, 47(2), 2,2209–2,2229.

Singh, S., Castelinne, J. B., et al. (1985). The proximate determinants of fertility: Sub—national variations.Population Studies, 39(1), 113–135.

StataCor. (2009). Stata release 11. Statistical software. College Station, TX: StataCorp LP.Stokes, C. S. (1984). Access to land and fertility in developing countries. In W. A. Schutjer & C. S. Stokes

(Eds.), Rural development and human fertility (pp. 195–215). New York: Macmillan.Stokes, C. S., Schutjer, W. A., & Bulatao, R. A. (1986). Is the relationship between landholding and fertility

spurious? A response to Cain. Population Studies, 40(2), 305–311.Sutherland, E. G., Carr, D. L., & Curtis, S. L. (2004). Fertility and the environment in a natural resource

dependent. The population of the Central American Isthmus in 2003. Conference papers (pp. 1–15).University of California at Los Angeles.

The African Population and Policy Research Center (APPRC). (1998). Fertility decline in Kenya: Level,trends and differentials. Nairobi: Population Council, African Population and Policy Research Center.

Van Landingham, M., & Hirschman, C. (2001). Population pressure and fertility in pre-transition Thailand.Population Studies, 55, 233–248.

Verburg, P. H., Overmars, K. P., & Witte, N. (2004). Accessibility and land use patterns at the forest fringein the North Eastern part of the Philippines. Geographical Journal, 170, 238–255.

Yotopoulos, P. A. (1978). Population and agricultural development: Selected relationships and possibleplanning uses, Vol. 2: The population problem and the development solutions. Rome: Food andAgricultural Organization.


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Population Density and Fertility in Farm Households: A Study of the Millennium Development Authority Zones in Ghana