Migration Between Ghana�s Rural and Urban Areas:

The Impact on Migrants� Welfare

Louis Boakye-Yiadom* Andrew McKay**

February 2006

* Corresponding author. E-mail: louisby@googlemail.com. Postgraduate student, Department of Economics and International Development, Univerisity of Bath, UK. ** Professor, Deparment of Economics and International Development, University of Bath, UK.

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Abstract

This paper examines the impact � on migrants� welfare � of migration between rural

and urban sectors, using data from Ghana. Employing a consumption measure of

welfare and a model that corrects for selectivity bias, the analysis also captures factors

influencing migration decisions. Our findings highlight the importance of anticipated

welfare gains and personal attributes in migration decisions. We also find support for

the positive selectivity of urban-to-rural migrants. In addition, estimates of migration

gains suggest that although some migrants incur welfare losses, migration increases �

on average � the welfare of migrants, but would reduce the mean welfare of non-

migrants if they were to migrate. Finally, the average welfare increment derived by

rural-to-urban migrants is proportionately much higher than what accrues to their

urban-to-rural counterparts.

JEL Classification: O15; O18; I31; R23

Keywords: Migration; rural-to-urban; urban-to-rural; welfare; selectivity bias

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1. Introduction

Over the years, the relevance of migration, the rationale for migrating, and the policy

response to migration patterns and magnitudes have dominated academic and policy

discussions. Ever since the seminal work of Ravenstein (1885), numerous studies

have explored various aspects of this pervasive phenomenon. The diversity of

disciplinary perspectives � e.g., demography, economics, and geography � of the

studies attests to migration�s intriguing and complex nature. Many studies address

issues relating to the rationale for migrating (see Sjaastad, 1962; Todaro, 1969; and

Lucas and Stark, 1985), migration patterns (see Ravenstein, 1885; and Lee, 1966), or

the determinants of migration (e.g., Caldwell, 1968; and Hay, 1980). Others, however,

examine the welfare impacts of these population movements (e.g., Falaris, 1987; and

Litchfield and Waddington, 2003). Migration studies can, nevertheless, be broadly

categorised into two, namely, those focusing on internal migration, and the set of

studies examining migration across national borders, with the latter body of studies

somewhat dominating in volume.

Clearly, international migration has immense significance for many countries. For

typical developing countries � especially those in sub-Saharan Africa � however,

internal migration is of equal, if not, greater importance. Given that the rural-urban

categorization is the major spatial grouping in sub-Saharan African countries, and that

urbanisation in these countries is on the rise, it is hardly surprising that rural-to-urban

migration has dominated the countries� internal migration research. The present study

augments the developing countries� migration literature by examining � for Ghana �

both rural-to-urban and urban-to-rural migration.

The main purpose of this paper is to determine the impact of Ghana�s inter-sectoral

migration1 on migrants� welfare. In pursuit of this, we also explore migration patterns

and factors that influence migration decisions. Within the context of migration

between rural and urban areas, our main research questions are as follows:

1 We use �inter-sectoral migration� to refer to migration between rural and urban sectors.

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i) What are the major influences on migration decisions?

ii) What is the impact of migration on migrants� welfare?

The study�s analysis consists of the use of both descriptive statistics and econometric

modelling. In the next section, we discuss some salient issues emerging from the

literature. The third section consists of comments relating to data, and a list of

relevant definitions. Migration patterns and a profile of migrants are presented in

section four. The empirical modelling of migration�s impact on migrants� welfare is

the focus of the fifth section, whilst section six discusses the results of the empirical

analysis. We summarise and conclude in the seventh section.

2. Relevant insights from the migration literature

General literature

A major challenge in the estimation of migrants� income (or welfare) gains relates to

the determination of what they would have earned if they had not migrated. Even

though several methods for estimating these gains have been identified in the

literature (see Lucas, 1997), two approaches seem to dominate. These methods are the

application of migration dummies in earnings (or income) functions, and the

estimation of separate income equations for migrants and non-migrants. The

application of migration dummies (in income functions) has been employed by Yap

(1976) in a study of rural-to-urban migration and urban underemployment in Brazil.

Using census data, Yap estimated income functions for three rural areas (origins of

migration) corresponding to the three major regions in Brazil. For each rural area, the

income gain to migrants (from that area) was estimated by pooling observations on

rural non-migrants, rural-to-rural migrants, and rural-to-urban migrants.

Yap�s results suggest that rural-to-urban migrants derived significant income benefits

from migration, and that no significant income gains accrued to rural-to-rural

migrants. In commenting on Yap�s methodology, Lucas (1997) raises concern about

the assumption that the effects of education and other explanatory variables are

identical in both origin and destination localities. Lucas further observes that Yap�s

approach is potentially problematic because of the possible correlation of the

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migration dummy with unobserved attributes distinguishing migrants from non-

migrants.

The issue of the counterfactual � as it relates to migrants� incomes � has often been

addressed by estimating separate income equations for migrants and non-migrants

(see, for example, Nakosteen and Zimmer, 1980; Falaris, 1987; Pessino, 1991; and

Tunali, 2000). Such methods typically incorporate an adjustment for selectivity bias

by employing techniques such as Lee�s (1978) two-step method, an approach that is

commonly known as Heckman�s (1979) two-step procedure. For example, Pessino

(1991), using data from Peru, applied Heckman�s two-step procedure to estimate

wage equations for movers and stayers in three regions; Lima, other urban, and rural.

Even though Pessino did not find evidence in support of selectivity amongst movers

in any of her samples, she found positive selectivity amongst stayers in Lima, and

negative selectivity amongst stayers in rural areas. Thus, Pessino�s evidence suggests

that Lima stayers earned more than movers would have earned if they had stayed,

whilst rural stayers earned less at their location than movers would have earned if they

had stayed. Applying similar techniques to data on Turkey, Tunali (2000) concludes

that the estimated gain from moving is negative for a large number of migrants, whilst

immense gains are reaped by a small share of migrants.

The Ghana literature

Even though migration in Ghana has attracted very few econometric analyses, the

available studies lend some support to the view that migration enhances migrants�

welfare. Using data from two waves of the Ghana Living Standards Survey (GLSS),

Litchfield and Waddington (2003) employ multivariate analyses to investigate the

impact of migration on welfare. They used standard OLS regressions (of equivalised

household consumption expenditure) and migration dummies to determine whether

migrants are better off than non-migrants. Poverty probits were also used to examine

the impact of migration on the likelihood of being poor.

Litchfield and Waddington (2003) observe, that even though the OLS regressions

suggest migrants have a higher standard of living than non-migrants, the migration

premium seemed to have halved between 1991/92 and 1998/99. The poverty probit

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for 1991/92 showed migrants having a lower probability of being poor (that is,

relative to non-migrants), but that for 1998/99 did not indicate any statistically

significant difference in the probabilities of being poor between migrants and non-

migrants. It must be noted that Litchfield and Waddington found an apparent zero

welfare-gap (that is, between migrants and non-migrants) when the analysis was

extended to non-monetary indicators of welfare. Given the paucity � in the context of

the Ghana literature � of rigorous quantitative analyses of migration, the study by

Litchfield and Waddington (2003) is an important contribution. Attention must,

however, be drawn to the fact that the reliability of the study�s results is affected by

the failure to account for selectivity bias, a shortcoming the authors acknowledge.

The incorporation of a correction for selectivity bias is a key aspect of a recent

econometric migration study (�Migration and Household,� 2004). The study explores

� in the context of Ghana�s Volta Basin � the determinants of the migration decision,

placing particular prominence on the role of income in migration decisions. In

accounting for the fact that migrants are non-randomly selected from the population,

the study utilises Heckman�s procedure for selectivity bias correction. A major result

of the analysis is the evidence found for expected income gains in influencing

migration decisions. Furthermore, evidence was found to suggest that incomes of

migrant households are higher than those of their non-migrant colleagues. Since the

study�s geographical focus was very localised, its findings cannot be generalised for

the entire country. Notwithstanding this limitation, the results and � more importantly

� the methodology employed constitute a valuable addition to the Ghana migration

literature.

3. Data and Definitions

The 1991/92 and 1998/99 Ghana Living Standards Surveys (GLSS) constitute the

data source for the descriptive analysis, whilst the econometric analysis employs the

latter survey�s data2. The Ghana Living Standards Surveys are a series of nationally

representative household surveys, the first of which was carried out in 1987/88. The 2 Owing to a difference in questionnaire design � with respect to the migration section � between the two surveys, the 1991/92 data do not permit a precise identification of migrants (as defined in this study). We consequently employ the 1991/92 data in the descriptive analysis only, and confine the econometric analysis to the 1998/99 data.

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1991/92 and 1998/99 surveys, being the third and fourth in the series, are often

referred to as GLSS3 and GLSS4, respectively. Apart from the demographic

information collected in the surveys, the GLSS data cover various aspects of living

conditions, such as, consumption, education, health, housing, employment, and

migration. The comprehensive coverage aside, the statistical methods employed in the

surveys make the GLSS data, presumably, the most widely used survey data on living

conditions in Ghana.

At this point, it is pertinent to address issues relating to the definition of a migrant, as

used in our study. Given that our empirical analysis is mainly based on GLSS4 data, it

is instructive to identify some definitions proposed by the GLSS4 Report. The

definitions � all relating to persons aged 15 years or more � are as follows (GSS,

2000):

In-migrant: a person born outside current place of residence;

Return-migrant: a person born at current place of residence, but who had lived

elsewhere for at least one year, and returned to place of birth;

Migrant: an in-migrant or a return-migrant;

Non-migrant: a person born at current place of residence, and who has never lived

elsewhere for a period lasting, at least, one year.

Whilst the above definitions seem appropriate, it is worth noting that they do not

capture the phenomenon of seasonal migration. Consequently, there is a chance of

classifying many seasonal migrants as non-migrants. This limitation is closely related

to the data collected in the survey. In other words, the survey�s data do not permit an

examination of seasonal migration. As a result, the migration-related definitions used

in the present study similarly do not address this limitation.

A second problem with the above definitions relate to the importance placed on

birthplace. For example, the definition of an in-migrant can inappropriately classify

certain persons as in-migrants, as might occur with persons who have always lived in

a rural locality, but were born in a nearby town (possibly, the district capital). In

Ghana (and most likely, in many other developing countries), it is not uncommon for

expectant women to deliver their babies outside their localities of residence. This may

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occur in cases where rural residents give birth in nearby towns owing to inadequate

health facilities in their own localities. An expectant woman may also deliver outside

her residential locality simply because of a decision to be with the mother just before

delivery, presumably to facilitate the transfer of knowledge and skill in the nursing of

babies. In the cited and similar instances, the nursing mother typically returns (with

her child) to her usual place of residence, where the child may live permanently or

until he or she becomes an adult. Furthermore, circumstances unrelated to baby

delivery may result in children moving (along with their parents or guardians) to some

other locality and residing there permanently. Thus, a desirable feature of migrant-

related definitions is the capacity for dealing with problems posed by the strict linkage

of migrant status to place of birth.

In the light of the preceding discussion, the following definitions are proposed:

In-migrant: an �adult� (aged at least 15 years) born outside current place of residence,

and who was, at least, six years old at the time of moving to current place of

residence;

Return-migrant: an �adult� born at current place of residence (or who moved to

current place of residence before sixth birthday) and who has lived elsewhere for

more than one year and returned to current place;

Migrant: an in-migrant or a return-migrant;

Non-migrant: an �adult� who has lived at current place of residence since birth (or

before sixth birthday), and has never moved and lived elsewhere for more than one

year.

4. Migration Patterns and Profile of Migrants

The available data lend support to the prevalence of migration in Ghana, since a

sizable proportion of the population are migrants or have migrated at some point in

their lives. In 1991/92, 54 percent of Ghana�s population were migrants (that is, either

in-migrants or return-migrants), whereas the migrant share of the population in

1998/99 was 50 percent (see Table 4.1).

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Table 4.1: Extent of migration in Ghana; 1991/92 and 1998/99

Migrant status

Share (%) of population (1991/92)

Share (%) of population (1998/99)

In-migrant 37.57 34.70 Return-migrant 16.51 15.08 Non-migrant 45.91 50.22 Total 100.00 100.00 Source: Authors� computation using data from the Ghana Living Standards Survey, 1991/92 and 1998/99.

In terms of an origin-destination classification, the data suggest that rural-to-rural and

urban-to-rural forms of population movement dominate Ghana�s internal migration.

For example, in 1991/92, 17.1 percent of Ghana�s population were rural-to-rural

migrants and 15.5 percent were urban-to-rural migrants, whereas urban-to-urban and

rural-to-urban migrants constituted 12.4 percent and 4.9 percent, respectively (see

Table 4.2). The pattern of internal migration in 1998/99 was not very different from

that of 1991/92; urban-to-rural migrants accounted for 16.6 percent of the population,

whilst rural-to-rural and urban-to-urban migrants represented 14.4 percent and 10.9

percent of the population, respectively. As reflected in Table 4.2, the rural-to-urban

migrant share (4.5 percent) of the population was the lowest (that is, amongst internal

migrants) in 1998/99. On the whole, and in both survey years, the migrant category

with the lowest proportion of the population was foreign-to-urban migrants, followed

by foreign-to-rural migrants. It is important to note, however, that in both 1991/92 and

1998/99, the majority of persons migrating to Ghana from other countries were

return-migrants (see Tables A1 and A2 in the Appendix).

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Table 4.2: Distribution of types of migrants in Ghana; 1991/92 and 1998/99

Migrant Category

Proportion (%) of population; 1991/923

Proportion (%) of population; 1998/99

Urban-to-urban 12.40 10.94 Urban-to-rural 15.48 16.55 Rural-to-urban 4.90 4.47 Rural-to-rural 17.11 14.42 Foreign-to-urban 1.78 1.28 Foreign-to-rural 2.18 2.12 Non-migrant 46.15 50.22 Total 100.00 100.00 Source: Authors� computation using data from the Ghana Living Standards Survey, 1991/92 and 1998/99.

The characteristics of migrants have been fairly similar between 1991/92 and

1998/99. With regard to the gender distribution of migrants, females had a higher

share than males in each of the survey years. In 1991/92, 53.6 percent of migrants

were females, whereas the corresponding proportion in 1998/99 was 53.8 percent. It is

worth pointing out, that in 1991/92, even though female migrants outnumbered their

male colleagues, the migration rate amongst males (54.4 percent) was slightly higher

than the rate (53.8 percent) amongst females (see Table A3 in the Appendix). In

1998/99, however, the migration rate amongst females (50 percent) was higher than

that of males (49.53 percent).

An examination of Table 4.3 shows that the majority of Ghana�s migrants are less

than forty years old, and that little change occurred in migrants� age distribution

between 1991/92 and 1998/99. In each of the two survey years, migrants aged

between fifteen and forty-five years accounted for more than 60 percent of the

migrant population. It must be stressed though, that whilst this age distribution is not

surprising, it appears to be largely due to a generally large share of young persons in

Ghana�s population. This point is supported by the fact that amongst the non-migrant

population, the young still dominates (see Table 4.3).

3 Information on migrants� previous place of residence was missing for 0.5% of migrants.

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Table 4.3: Age distribution of migrants and non-migrants; 1991/92 and 1998/99

Age group (in years)

Share (%) amongst migrants; 1991/92

Share (%) amongst migrants; 1998/99

Share (%) amongst non-migrants; 1991/92

Share (%) amongst non-migrants; 1998/99

15 ≤ age < 25 18.64 17.38 47.95 43.12 25 ≤ age < 35 24.92 24.69 20.07 21.29 35 ≤ age < 45 21.41 22.16 11.84 14.30 45 ≤ age < 55 16.90 16.24 8.66 9.04 55 ≤ age < 65 9.83 9.52 5.11 5.61 Age ≥ 65 8.29 10.01 6.36 6.63 Total 100.00 100.00 100.00 100.00 Source: Authors� computation using data from the Ghana Living Standards Survey, 1991/92 and 1998/99.

The main reason for migrating � as indicated by migrants � are generally similar in

the two surveys. In 1991/92, �other family reasons� was the response category that

accounted for the largest share (42.8%) of reasons for migrating. This was followed

by marriage, own employment, spouse�s employment, �other�, schooling, and

drought/war in that order. In 1998/99, �other family reasons� was again the response

category accounting for the largest share (46%) of reasons for migrating. The second

most cited reason in 1998/99 was own employment, with marriage, �other�, spouse�s

employment, schooling, and drought/war following in that order.

While broad similarities were evident in the cited reasons for migrating, it is worth

highlighting some gender-related differences. For males, and in both 1991/92 and

1998/99, own employment accounted for the second largest share of reasons for

migrating. In the case of females, however, own employment ranked sixth and fourth

in 1991/92 and 1998/99, respectively. For both 1991/92 and 1998/99, males

accounted for more than 80 percent of persons who migrated because of own

employment. Furthermore, in each of the two survey years, marriage accounted for

the second highest share of reasons for female migration, whereas for males, it ranked

sixth. In each of 1991/92 and 1998/99, females again had a huge share (more than 90

percent) of persons who migrated because of marriage. Another insightful observation

relates to migration induced by spouse�s employment. In GLSS3, females constituted

about 60 percent of persons who migrated because of spouse�s employment; the

corresponding figure for GLSS4 was much higher (81.8 percent).

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On the whole, �other family reasons� was the most cited reason for migrating,

irrespective of gender and age group. This notwithstanding, it accounted for a

particularly high share of reasons amongst persons aged less than twenty-five years

(but, at least fifteen years old); the shares were 60.1 percent in 1991/92, and 73.3

percent in 1998/99. Amongst this same age group (that is, fifteen-to-twenty years) �

in both survey years � schooling was the second most stated reason for migrating, an

unsurprising result.

In order to obtain a rough measure of living standards across migrant status, we

examine � for both rural and urban sectors � the mean consumption welfare for in-

migrants, return-migrants, and non-migrants. To this end, we define an individual�s

consumption welfare as the total consumption expenditure per adult equivalent of that

individual�s household, measured in real terms. In 1991/92, return-migrants had the

highest mean consumption welfare, followed by in-migrants and non-migrants, in that

order. In 1998/99, in-migrants had the highest mean consumption welfare, with no

other clear pattern emerging (see Tables A5 and A6 in the Appendix).

A comparison of the mean consumption welfare of internal migrants indicates, that

urban-to-urban migrants had the highest consumption welfare in each of the two

survey years, with rural-to-urban, urban-to-rural, and rural-to-rural migrants following

in that order (see Table 4.4). Furthermore, in both 1991/92 and 1998/99, urban non-

migrants had, on the average, a higher level of consumption welfare than urban-to-

rural migrants, whilst rural non-migrants had a lower level of consumption welfare

than rural-to-urban migrants.

Table 4.4: Mean consumption welfare of internal migrants; 1991/92 and 1998/99

Migrant category 1991/92 1998/99 Urban-to-urban 1,845,521.8 1,994,174.1 Urban-to-rural 1,104,548.5 1,206,615.9 Rural-to-urban 1,637,388.2 1,632,757.1 Rural-to-rural 1,017,758.9 1,067,828.3 Source: Authors� computation using data from the Ghana Living Standards Survey, 1991/92 and 1998/99.

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5. Modelling of Migration�s Impact on Migrants� Welfare

This section outlines a general model for estimating the impact of migration on

migrants� welfare, since only a slight modification is required to extend the analysis to

the specific cases of rural-to-urban migration and urban-to-rural migration. The basic

modelling strategy follows very closely Lee (1978) and Nakosteen and Zimmer

(1980), and is summarised as follows:

1. A simultaneous estimation of the following three equations:

a) A migration decision equation, defined over both migrants and non-migrants;

b) A welfare equation for migrants; and

c) A welfare equation for non-migrants.

2. The use of the two welfare equations � and data on both migrants and non-migrants

� to estimate the average impact of migration on migrants� welfare.

a) Theoretical Framework

Sjaastad�s (1962) human capital framework constitutes the theoretical underpinning

for the model employed. By viewing migration as an investment in human capital,

Sjaastad suggests that prospective migrants aim to maximise the present value of the

net gains resulting from locational change. For any potential migrant, suppose the

present value of the migration generated net gain is given by:

mT

0 ntmtm C-dtpt-]eW-[W (t)PV ∫= (1)

where

Wmt represents anticipated welfare at mth prospective destination locality at time t;

Wnt represents anticipated welfare at origin locality at time t;

Cm denotes a one-time cost4 of migrating to locality m;

T: duration of migration status;

p: implicit discount rate.

4 Even though costs associated with migration are not incurred once, recurring costs of locational change are subsumed in the welfare measure.

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The individual does not migrate if PVm ≤ 0 for all m;

The individual migrates, if there exists an m for which PVm > 0, and where this

condition is satisfied by more than one prospective destination, the individual selects

the destination yielding the maximum PVm.

In order to adapt the above theoretical framework for empirical analyses, we

(following Nakosteen and Zimmer, 1980) make the following assumption:

At any given time, individual i will choose to migrate if the anticipated welfare gain

exceeds the corresponding migration costs.

This implies that at any given time, an individual will migrate if his/her proportionate

welfare gain exceeds the migration costs, as a proportion of welfare. Thus, denoting

the individual�s migration costs (as a proportion of welfare) by Qi, individual i will

migrate if:

[(Wmi � Wni)/Wni] � Qi > 0, (2)

and will not migrate if:

[(Wmi � Wni)/Wni] � Qi ≤ 0, (3)

where

Wmi denotes individual i�s welfare as a migrant; and

Wni denotes individual i�s welfare as a non-migrant.

It may be argued that the costs of migration depend on individual attributes (for

example, age, sex, and marital status) and community-level characteristics, such as

the cost of living and the unemployment rate. Thus, the decision to migrate � as

indicated in inequalities (2) and (3) � may be expressed as a function of (anticipated)

welfare gains, individual attributes, and community characteristics. In the tradition of

similar methodologies (see, for example, Lee 1978 and Nakosteen and Zimmer 1980),

we adopt a linear functional form for the migration decision equation as follows:

Individual i migrates if:

Ii∗ = α0 + α1[(Wmi - Wni)/Wni] + α.Gi � εi > 0, (4)

and does not migrate if

Ii∗ = α0 + α1[(Wmi - Wni)/Wni] + α.Gi � εi ≤ 0, (5)

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where

α1: Coefficient of the welfare gain variable

Gi: Vector of variables representing appropriate individual and community

characteristics

α: Vector of coefficients of the variables in Gi:

εi: An error term; and

α0: A constant term

It is also reasonable to postulate that an individual�s welfare level depends on

personal characteristics (such as educational attainment) and community attributes

(for example, the availability of socio-economic amenities). An individual�s welfare

equation can consequently be expressed as a function of variables representing

individual and community characteristics. Invoking the argument of Lee (1978) that

(LnWmi - LnWni) and (Wmi - Wni)/Wni are approximately equal, the empirical model is

specified below, with the welfare equations formulated in logarithmic form:

=∗Ii α0 + α1(LnWmi - LnWni) + α.Gi - εi

LnWmi = am + βm.Xi + εmi

LnWni = an + βn.Xi + εni

where

Ii∗ is not observed, but we rather observe

Ii = 1 if Ii∗ > 0, and Ii = 0 if Ii

∗ ≤ 0;

LnWmi: log of migrant welfare

LnWni: log of non-migrant welfare

Xi: Vector of variables representing relevant individual and community characteristics

βm: Migrant vector of coefficients of the variables in Xi

βn: Non-migrant vector of coefficients of the variables in Xi

εi, εmi, and εni are all Normally distributed error terms with zero mean and constant

variance;

All other variables retain their definitions.

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The observed variables in the model are the limited dependent variables, Wmi and Wni,

the dichotomous migration decision variable Ii, and the variables contained in vectors

Gi, and Xi. In general, the ordinary least squares (OLS) technique is inappropriate for

estimating the welfare equations due to its failure to account for selectivity bias (see

Nakosteen and Zimmer, 1980; and Lee, 1978). As a result, we employ Lee�s (1978)

proposed solution5; the welfare equations are modified by incorporating appropriate

�selectivity variables�, and adding error terms with zero means. It is worth

emphasising that even though the variables contained in Gi and Xi represent

individual and community characteristics, the two vectors need not contain identical

variables.

b) Estimation procedure

In order to estimate all the parameters of the model, the following estimation

technique is used:

i. Probit estimation of the reduced-form migration decision equation

The regressors in this equation consist of the exogenous variables in all the three

structural equations. Fitted values ( iψ� ) obtained from this (first) stage are used to

construct variables u1i and u2i, where:

u1i = -f ( iψ� ) / F( iψ� ) is the selectivity variable for the migrant welfare equation;

u2i = f ( iψ� ) / [1- F( iψ� )] is the selectivity variable for the non-migrant welfare

equation;

f: the density function of a standard normal random variable; and

F: the cumulative distribution function of a standard normal random variable.

ii. Insertion of u1i and u2i into the appropriate welfare equations and estimating the

welfare equations by OLS

Estimates obtained using the above two-step procedure are known to be consistent

(see Lee, 1978).

5 As noted earlier, this technique is often referred to as the Heckman two-step method.

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iii. Probit estimation of the structural migration decision equation

In this step, the consistent parameter estimates of the welfare equations are used to

obtain fitted values of the logarithm of welfare, which are in turn, used to compute

estimates of (LnWmi - LnWni). Together with other exogenous variables, the estimates

of (LnWmi - LnWni) are inserted into the structural decision equation to obtain the

probit estimates of the structural migration decision equation.

c) Determination of migration�s impact on migrants� welfare

In order to estimate migration�s impact on migrants� welfare, we employ simulations

of counterfactual scenarios, coupled with the estimation of an index of welfare gain

due to migration. This is accomplished by computing an index of welfare differential

between migration and non-migration scenarios as follows:

For any sub-group (δ) of the entire sample, let Nδ be the corresponding population

size;

Then, for any sub-group (δ), the average proportionate welfare increment attributable

to migration may be expressed as:

∑∈

−=δi Wni

WniWmiNδ1g

Where, Wmi and Wni �individual i�s welfare levels as a migrant, and as a non-

migrant, respectively � are proxied by their corresponding fitted values for cases

where they are unobserved.

6. Empirical Results

a) Introductory note and discussion of regressors

In applying the described model to an analysis of migration between Ghana�s rural

and urban sectors, a number of adjustments are made. These adjustments stem from

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an explicit recognition of two separate migratory movements, namely, urban-to-rural

migration and rural-to-urban migration. Two separate analyses are consequently

carried out, one for each migratory movement. It should be noted also, that a migrant

� as used in the outlined analytical model � is equivalent to an in-migrant as defined

earlier. Furthermore, we employ a consumption measure of welfare, where, an

individual�s consumption welfare is defined as the total consumption expenditure per

adult equivalent of that individual�s household, measured in real terms.

For the analysis of urban-to-rural migration, the three structural equations consist of a

migration decision equation (defined over a pooled sample of urban non-migrants and

urban-to-rural in-migrants), a welfare equation for urban-to-rural in-migrants, and a

welfare equation for urban non-migrants. Similarly, in analysing rural-to-urban

migration, the relevant structural equations comprise a migration decision equation

(defined over a pooled sample of rural non-migrants and rural-to-urban in-migrants), a

welfare equation for rural-to-urban in-migrants, and a welfare equation for rural non-

migrants.

The entire data contained 14,196 observations on persons aged 15 years or more. The

set of regressors for each of the two migration status equations includes variables for

highest educational attainment, age group, marital status6, location, and (anticipated)

welfare gain. The choice of these variables is informed by theory and by a preliminary

analysis that explored various combinations of regressors. In particular, in order to

ensure that the parameters of the structural (migration) decision equation are

identified, the welfare equations contain at least one exogenous variable that is

excluded from the structural migration equation (Nakosteen and Zimmer, 1980).

On a priori grounds, we expect education to have a positive impact on rural-to-urban

migration, and to have a negative effect on urban-to-rural migration. With regard to

age, the literature suggests that young adults tend to have a higher propensity to

migrate than the elderly. It is therefore expected that the tendency to migrate amongst

lower age groups will be higher than that of higher age groups. The impact (on

migration) of anticipated welfare gain is expected to be positive; an expectation 6 Here, what is used is a variable that places emphasis on whether (or not) a person has always been single.

19

rooted in both theoretical and intuitive considerations. Finally, apart from serving as

control variables, the locational variables can provide spatial-related insights into

population movements between rural and urban localities.

Theory and preliminary data examination were again crucial in the selection of

regressors for the welfare equations. Most of the regressors were common to all four

welfare equations. These common regressors include variables for highest educational

attainment, employment category, selectivity, and location, as well as variables that

capture household characteristics, such as size, access to pipe-borne water for

drinking, and the use of electricity for lighting. In the analysis of urban-to-rural

migration however, a gender variable and regressors for other household

characteristics were included7 (see Table 6.1 for a list of variables used in the

analysis).

7 An initial analysis suggested the relevance of these regressors is confined to the urban-to-rural model.

20

Table 6.1: List of regressors for the econometric analysis Variable Definition

hhsize Household size agegp1 1 if 15 ≤ age (in years) < 25, 0 otherwise agegp2 1 if 25 ≤ age (in years) < 35, 0 otherwise agegp3 1 if 35 ≤ age (in years) < 45, 0 otherwise agegp4 1 if 45 ≤ age (in years) < 55, 0 otherwise agegp5 1 if 55 ≤ age (in years) < 65, 0 otherwise agegp6 (the omitted category) 1 if age (in years) ≥ 65 sex1 1 if male, 0 otherwise mar5 1 if never married, 0 otherwise hiedq1 (the omitted category) Dummy for highest educational qualification; 1 if no

educational qualification, 0 otherwise hiedq2 Dummy for highest educational qualification; 1 if

MSLC/BECE, 0 otherwise hiedq3 Dummy for highest educational qualification; 1 if

vocational, commercial, �O� or �A� level hiedq4 Dummy for highest educational qualification; 1 if T/T,

nursing, or Tech/Prof, 0 otherwise hiedq5 Dummy for highest educational qualification; 1 if

University degree holder, 0 otherwise hiedq6 Dummy for highest educational qualification; 1 if

unspecified qualification, 0 otherwise empcat1 (the omitted category) 1 if unemployed, 0 otherwise empcat2 1 if employed in agriculture, 0 otherwise empcat3 1 if employed in industry, 0 otherwise empcat4 1 if employed in services, 0 otherwise empcat5 1 if unspecified employment category, 0 otherwise farmliv1 1 if household owns/operates a farm, keeps livestock, or is

engaged in fishing, 0 otherwise foodpr1 1 if household is engaged in food processing, 0 otherwise othbus1 1 if household is engaged in some other non-farm business,

0 otherwise pbw1 1 if household drinks pipe-borne water, 0 otherwise eg1 1 if household�s main lighting source is electricity or

generator ez1 (the omitted category) Dummy for ecological zone; 1 if Coastal, 0 otherwise ez2 Dummy for ecological zone; 1 if Forest, 0 otherwise ez3 Dummy for ecological zone; 1 if Savannah, 0 otherwise reg1 Regional dummy; 1 if Western, 0 otherwise reg2 Regional dummy; 1 if Central, 0 otherwise reg3 Regional dummy; 1 if Greater Accra, 0 otherwise reg4 Regional dummy; 1 if Eastern, 0 otherwise reg5 Regional dummy; 1 if Volta, 0 otherwise reg6 Regional dummy; 1 if Ashanti, 0 otherwise reg7 Regional dummy; 1 if Brong Ahafo, 0 otherwise reg8 Regional dummy; 1 if Northern, 0 otherwise reg9 Regional dummy; 1 if Upper West, 0 otherwise reg10 (the omitted category) Regional dummy; 1 if Upper East, 0 otherwise diflnWh Estimated migrant-non-migrant gap in log of welfare sel �selectivity� variable _cons Constant term

21

b) Factors influencing urban-to-rural migration

The results of the analysis (see Table 6.2) generally conform to a priori expectations.

The significant and positive coefficient of �diflnWh� (the welfare gap variable)

suggests that the anticipated welfare gain is a major influence on urban-to-rural

migration decisions in Ghana. The results further show, that in comparison with their

colleagues who are (or have ever been) married8, urban residents who have never

been married are less likely to migrate to rural areas. This finding is contrary to what

one might expect, given the perception that persons who are less encumbered will be

more likely to migrate. This notwithstanding, it is possible that the result is a

reflection of a tendency for unmarried migrants to marry within a few years after

migrating. In this connection, it is worth noting that almost 60 percent of all in-

migrants in the entire sample have lived at their current place of residence for at least

ten years. Thus, it is plausible that most of those migrants who had never been

married at the time of migrating had experienced a change in marital status by the

time of the survey.

The role of education in urban-to-rural migration decisions is insightful. The evidence

suggests that the attainment of education beyond the vocational, commercial,

Ordinary (�O�), or Advanced (�A�) level tends to reduce the probability of migrating

from an urban area to the rural sector. This evidence is not surprising; it is common

knowledge that persons with higher levels of education tend to have a preference for

settling in urban areas, and have a better chance (relative to the less educated) of

finding employment in urban centres. Our findings further suggest, that urban

residents in the Upper East Region are more likely (relative to their counterparts in

other Regions, except the Upper West) to migrate to rural areas. Given that the Upper

East Region is one of the poorest in Ghana, this finding is reasonable.

8 Marriage, as used here, includes informal or loose unions.

22

c) Factors influencing consumption levels

With regard to living standards � that is, in terms of consumption � of urban-to-rural

in-migrants, there is strong support for a negative effect of household size on welfare.

The results show a strong negative association between household size and the

welfare of urban-to-rural in-migrants. Moreover, the magnitude of this link between

household size and welfare is the same for both urban-to-rural in-migrants and urban

non-migrants.

23

Table 6.2: Results of the urban-to-rural migration model

welf1: OLS welfare regression for urban-to-rural in-migrants

welf0: OLS welfare regression for urban non-migrants

probit: Probit regression for the pooled sample of urban non-migrants and urban-to-

rural in-migrants ----------------------------------------------------------- Variable | welf1 welf0 probit -------------+--------------------------------------------- sex1 | -0.06** -0.10*** hhsize | -0.10*** -0.09*** hiedq2 | 0.14*** 0.09*** -0.05 hiedq3 | 0.27*** 0.31*** 0.09 hiedq4 | 0.43*** 0.28*** -0.27** hiedq5 | 1.20*** 0.46** -2.13*** hiedq6 | 0.23 0.00 -0.52 empcat2 | 0.17*** 0.02 empcat3 | 0.21*** 0.07** empcat4 | 0.26*** 0.04 empcat5 | -0.07 0.10 farmliv1 | 0.00 -0.07** foodpr1 | -0.06 -0.06** othbus1 | 0.05 0.09*** pbw1 | -0.13* 0.06 eg1 | 0.14** 0.36*** ez2 | 0.18*** 0.04 0.33*** ez3 | 0.13 -0.01 -0.18 reg1 | 0.77*** 0.25** -1.22*** reg2 | 0.65*** -0.19 -2.22*** reg3 | 0.75*** 0.35*** -1.29*** reg4 | 0.45*** 0.05 -1.09*** reg5 | 0.67*** -0.01 -1.87*** reg6 | 0.69*** 0.23** -1.43*** reg7 | 0.57*** 0.18* -1.43*** reg8 | 0.25 0.12 -1.16*** reg9 | -0.02 0.10 0.56** sel | -0.33*** -0.02 agegp1 | 0.18 agegp2 | 0.19* agegp3 | 0.15 agegp4 | 0.22* agegp5 | 0.09 mar5 | -0.46*** diflnWh | 3.41*** _cons | 13.31*** 14.11*** 2.01*** -------------+--------------------------------------------- N | 1360.00 2720.00 4080.00 r2 | 0.39 0.46 r2_a | 0.38 0.45 F | 30.53 81.80 r2_p | 0.38 chi2 | 1974.61 ----------------------------------------------------------- legend: * p<.1; ** p<.05; *** p<.01

24

As expected, strong support is found � amongst both urban-to-rural in-migrants and

urban non-migrants � for a welfare-enhancing role of education. In both welfare

equations, virtually all the education dummies have significantly positive effects on

welfare. The results further suggest that being employed (whether in agriculture,

industry, or services) enhances the welfare of urban-to-rural in-migrants, with the

services sector generating the biggest impact. In comparison with urban-to-rural in-

migrants, urban non-migrants� specific employment sector appears to be less

important in influencing welfare. Nevertheless, working in the industrial sector exerts

a positive effect on the welfare of urban non-migrants.

The results also suggest that having electricity (or a generator) for lighting enhances

the welfare of both urban-to-rural in-migrants and urban non-migrants. This finding is

tenable, given the health hazards associated with some other forms of lighting. An

intriguing finding of our analysis is the apparent higher welfare of females relative to

males. This finding holds for both welfare equations. In other words, it does appear

that female urban-to-rural in-migrants are slightly better off than their male

counterparts, and also, that the welfare of male urban non-migrants is lower (on

average) than that of their female counterparts. It must be stressed however, that this

result is not conclusive, since the welfare measure is equal for all members of a given

household, irrespective of gender.

Another notable finding of this study relates to self-selectivity. For the urban-to-rural

welfare equation, the coefficient of the selectivity variable (�sel�) is statistically

significant and negative. This provides support for the positive selectivity of urban-to-

rural in-migrants. Other results relating to urban non-migrants are worth noting; all

things being equal, individuals whose households are engaged in an agricultural or

food processing enterprise are likely to have lower levels of welfare relative to those

whose households are not engaged in these activities. On the other hand, the results

suggest that an urban non-migrant�s welfare tends to be enhanced by the household�s

ownership of some other business.

All things being equal, urban-to-rural in-migrants living in the Upper East Region

tend to have lower welfare levels than their colleagues in every other Region, with the

exception of the Upper West and Northern Regions. This finding reflects the

25

relatively low standards of living in the three northern (that is, Northern, Upper West,

and Upper East) Regions of Ghana (see GSS, 2000). Roughly similar results, but to a

much lesser degree, are found for urban non-migrants; living standards amongst urban

non-migrants residing in the Upper East Region tend to fall below those of their

counterparts in the Western, Greater Accra, Ashanti, and Brong-Ahafo Regions9.

Urban-to-rural in-migrants located in the forest ecological zone also tend to have

higher living standards than their coastal zone counterparts, a result that is apparently

linked to the concentration of cocoa farming in the rural Forest ecological zone (see

Coulombe and McKay, 2003).

d) Welfare-gain from urban-to-rural migration

The average proportionate welfare gap between in-migrant and non-migrant scenarios

is very informative. On the whole, migration has a positive impact on the welfare of

urban-to-rural in-migrants, as they incurred (on average) a 27.7 percentage welfare

gain (see Table 6.3). Further examination shows that 56.3 percent of urban-to-rural in-

migrants gained from migrating, their average gain being 72.2 percent. These findings

suggest that although gains are not guaranteed from urban-to-rural migration,

participants often benefit considerably.

A similar simulation for urban non-migrants shows that if they were to migrate to the

rural sector, they would incur a 30.5 percent welfare loss on average. As shown in

Table 6.3, migrating to the rural sector would leave the overwhelming majority (83.2

percent) of urban non-migrants un-rewarded. Indeed, amongst the three categories of

individuals shown in the Table, only urban-to-rural in-migrants gain from migration.

Whilst the findings summarised in Table 6.3 are not a direct test for selectivity bias,

they lend support to the notion that migrants � and non-migrants � are often non-

randomly selected from the population. Migrants tend to be those who have a better

chance (compared with non-migrants) of gaining from migration.

9 The coefficients of the other regional dummies were not statistically significant.

26

Table 6.3: Migration-generated welfare gains; urban-to-rural migration

(selectivity bias adjusted)

Number of persons

Mean percentage welfare gain

Percentage with welfare gain

Percentage without welfare gain

Urban-to-rural in-migrants

1,360 27.66 56.25 (Mean % gain = 72.23)

43.75 (Mean % loss = 29.64)

Urban non-migrants

2,720 -30.49 16.84 (Mean % gain = 48.68)

83.16 Mean % loss = (46.52)

Urban-to-rural in-migrants and urban non-migrants

4,080 -11.10 29.98 (Mean % gain = 63.41)

70.02 (Mean % loss = 43.00)

Since our findings are based on a correction for selectivity bias, it is worth indicating

the outcome of an analysis that does not make such an adjustment. In the absence of a

correction for selectivity bias, the analysis of welfare-gains (that is, from urban-to-

rural migration) produces considerably different results from that shown in Table 6.3

(see Table A7 in the Appendix). In failing to correct for selectivity bias, the analysis

generally shows gains from urban-to-rural migration for all categories of individuals

considered. It would be recalled � from our discussion of the welfare regression

estimates for urban-to-rural in-migrants � that there is evidence for the positive

selectivity of urban-to-rural in-migrants. Thus, failure to correct for selectivity bias

leads to an overestimation of migrant welfare, and consequently, to an overestimation

of gains from urban-to-rural migration.

e) Factors influencing rural-to-urban migration

Many of the results from the analysis of rural-to-urban migration conform to that of

the urban-to-rural analysis. The findings provide strong support for the importance �

in rural-to-urban migration decisions � of anticipated welfare gains, as reflected by

the performance of the variable �diflnWh� (see Table 6.4). The results further point to

an absence of a strong influence of age in rural-to-urban migration decisions.

Compared with rural dwellers who are (or have ever been) married, rural residents

27

who have never been married are less likely to migrate to the urban centres. As

suggested earlier, it might be misleading to draw strong conclusions from this finding,

owing to the long duration of stay at current residence of most in-migrants in our

sample.

In comparison with rural residents whose educational attainment is either lower than

MSLC/BECE10 or unspecified, rural dwellers with MSLC/BECE (that is, as their

highest educational attainment) are less likely to migrate to urban centres. Similarly,

rural dwellers with the highest educational attainment being vocational, commercial,

�O�, or �A� level, are less likely to migrate to the urban sector, that is, relative to the

reference group (those with a lower-than-MSLC/BECE or with an unspecified

educational attainment). Although the underlying reasons for these findings are

unclear, the significant and positive coefficient of �hiedq5� (the dummy for the

holding of a university degree) provides strong support for a tendency for university

graduates to settle in urban centres. Our results further show, that the Upper East

Region�s rural residents are less likely (compared to their counterparts in other

Regions) to migrate to urban centres. Considering the Upper East Region�s status as

one of the poorest in Ghana, this finding is consistent with the view that very poor

individuals or households are often unable to invest in migration.

f) Factors influencing consumption levels

One of our robust results is the strong support established for a negative relationship

between welfare and household size. This relationship exhibits similar orders of

magnitude amongst in-migrants and non-migrants. All other things being equal, rural-

to-urban in-migrants whose households drink pipe-borne water enjoy a higher level of

welfare, relative to their counterparts lacking this amenity. Furthermore, the results

for both welfare equations suggest that living standards are enhanced by the use of

electricity (or a generator) for lighting. The welfare equations for both rural non-

migrants and rural-to-urban in-migrants further reflect a high and positive link

between education and welfare. This positive association between education and

10 MSLC is the Middle School Leaving Certificate (no longer awarded), and the BECE is the Basic Education Certificate Examination. Each of these represents the highest qualification at the basic education level.

28

welfare conforms to a priori expectations, and highlight the need to attach immense

importance to education in national development policies.

Table 6.4: Results of the rural-to-urban migration model

welf1: OLS welfare regression for rural-to-urban in-migrants

welf0: OLS welfare regression for rural non-migrants

probit: Probit regression for the pooled sample of rural non-migrants and rural-to-

urban in-migrants ----------------------------------------------------------- Variable | welf1 welf0 probit -------------+--------------------------------------------- hhsize | -0.09*** -0.07*** hiedq2 | 0.11** 0.04** -0.18** hiedq3 | 0.34*** 0.14*** -0.51*** hiedq4 | 0.35*** 0.20** -0.09 hiedq5 | 0.30 0.94** 1.74*** empcat2 | -0.14* 0.00 empcat3 | 0.06 0.06 empcat4 | 0.09 0.08** empcat5 | 0.02 -0.07* othbus1 | 0.07 0.12*** pbw1 | 0.34*** 0.05 eg1 | 0.38*** 0.11*** ez2 | 0.24*** 0.11*** -0.58*** ez3 | 0.05 0.21*** 0.64*** reg1 | 0.46*** 1.03*** 6.87*** reg2 | 0.00 0.81*** 7.31*** reg3 | 0.51*** 1.00*** 7.76*** reg4 | 0.20 0.76*** 7.36*** reg5 | -0.02 0.89*** 8.07*** reg6 | 0.23 0.91*** 7.70*** reg7 | 0.32* 0.74*** 6.87*** reg8 | 0.16 0.37*** 6.48*** reg9 | 0.36 0.15*** 3.78 sel | -0.03 0.12 agegp1 | -0.26* agegp2 | -0.20 agegp3 | 0.19 agegp4 | 0.18 agegp5 | 0.21 mar5 | -0.43*** diflnWh | 3.23*** _cons | 13.56*** 13.13*** -8.00*** -------------+--------------------------------------------- N | 523.00 4447.00 4970.00 r2 | 0.54 0.35 r2_a | 0.52 0.34 F | 25.63 97.58 r2_p | 0.41 chi2 | 1374.95 ----------------------------------------------------------- legend: * p<.1; ** p<.05; *** p<.01

29

With the exception of being employed in the agricultural sector (which is negatively

linked to welfare), the specific sector of one�s employment exerts no significant

impact on the welfare of rural-to-urban in-migrants. In the case of rural non-migrants,

being employed in the services sector � relative to being jobless � is positively

associated with a higher level of welfare. Rural non-migrants whose households are

engaged in business enterprises (other than agricultural or food processing ventures)

have higher welfare levels compared with those whose households do not engage in

such activities.

g) Welfare-gain from rural-to-urban migration

On the basis of our results, rural-to-urban migration is generally very rewarding for

rural-to-urban in-migrants. By migrating to urban localities, rural-to-urban in-

migrants increased their welfare by 46.3 percent, on average (see Table 6.5). This

percentage increment is considerably higher than that of their urban-to-rural

counterparts (see Table 6.3). Furthermore, the majority (68.5 percent) of rural-to-

urban in-migrants gained by migrating, the mean welfare-gain of the gainers being

80.1 percent. For the minority (31.6 percent) of rural-to-urban in-migrants who did

not gain, the average welfare-loss was 26.9 percent. Interestingly, our findings

suggest that for most rural dwellers, rural-to-urban migration is not necessarily

profitable. As shown in Table 6.5, if rural non-migrants were to migrate to urban

areas, they would incur, on average, a welfare-loss of 2 percent, and 38.4 percent of

such migrants would gain.

30

Table 6.5: Migration-generated welfare gains; rural-to-urban migration

(selectivity bias adjusted)

Number of persons

Mean percentage welfare gain

Percentage with welfare gain

Percentage without welfare gain

Rural-to-urban in-migrants

523 46.30 68.45 (Mean % gain = 80.05)

31.55 (Mean % loss = 26.92)

Rural non-migrants

4,447 -1.97 38.36 (Mean % gain = 55.52)

61.64 (Mean loss = 37.75)

Rural-to-urban in-migrants and rural non-migrants

4,970 3.11 41.53 (Mean % gain = 59.77)

58.47 (Mean % loss = 37.13)

For the purpose of comparison, Table A8 (in the Appendix) shows welfare-gains from

rural-to-urban migration when no correction is made for selectivity bias. As can be

seen, although the two sets of results are different, the disparities are not as large as

those found for the urban-to-rural analysis. This gives credence to the lack of

significance of the selectivity variables in the welfare regressions for rural-to-urban

in-migrants and rural non-migrants (see Table 6.4). The results in Table 6.5 do

suggest however, that rural residents who migrate to the urban areas tend to be those

individuals who have a better chance of reaping a welfare-gain.

h) Impact of return-migration on migrants� welfare

Our multivariate analysis has so far focused on in-migrants. Given the availability of

data on return-migrants, it is instructive to investigate the impact of migration on the

welfare of persons who have returned to their origin localities after engaging in either

urban-to-rural or rural-to-urban migration. Thus, as an ancillary exercise, welfare

regressions (with return-migrant dummies) are used to examine the impact of

migration on the welfare of those return-migrants whose previous form of migration

was urban-to-rural or rural-to-urban. We therefore estimate the following two welfare

equations:

31

i) urbwelf: OLS welfare equation for a pooled sample of urban non-migrants

and rural-to-urban return-migrants (that is, urban-to-rural-to-urban

migrants);

ii) rurwelf: OLS welfare equation for a pooled sample of rural non-migrants

and urban-to-rural return-migrants (that is, rural-to-urban-to-rural

migrants);

In each of the two equations, the sample consists of non-migrants and return-migrants

residing in the same locality. This feature � non-existent in samples for our main

model � facilitates the use of dummy variables for return-migrant status. The dummy

variables are defined as follows:

du1: 1 if urban-to-rural-to-urban migrant, 0 if urban non-migrant;

dr1: 1 if rural-to-urban-to-rural migrant, 0 if rural non-migrant.

32

Table 6.6: Impact of inter-sectoral migration on return-migrants� welfare -------------------------------------------- Variable | urbwelf rurwelf -------------+------------------------------ du1 | -0.03 sex1 | -0.09*** -0.02 hhsize | -0.09*** -0.08*** hiedq2 | 0.09*** 0.05*** hiedq3 | 0.31*** 0.11*** hiedq4 | 0.29*** 0.23*** hiedq5 | 0.45** 0.63** hiedq6 | 0.00 0.06 empcat2 | 0.02 -0.01 empcat3 | 0.07** 0.07* empcat4 | 0.03 0.09*** empcat5 | 0.09 -0.06* farmliv1 | -0.07** -0.02 foodpr1 | -0.05** -0.03 othbus1 | 0.10*** 0.12*** pbw1 | 0.06** 0.03 eg1 | 0.36*** 0.14*** ez2 | 0.05 0.08*** ez3 | 0.01 0.17*** reg1 | 0.27** 1.03*** reg2 | -0.18 0.78*** reg3 | 0.38*** 1.12*** reg4 | 0.08 0.74*** reg5 | -0.00 0.91*** reg6 | 0.25** 0.92*** reg7 | 0.19* 0.78*** reg8 | 0.12 0.40*** reg9 | 0.11 0.18*** dr1 | 0.05** _cons | 14.07*** 13.25*** -------------+------------------------------ N | 2839.00 5437.00 r2 | 0.46 0.37 -------------------------------------------- legend: * p<.1; ** p<.05; *** p<.01

33

Our findings generally conform to those of the main model. The strong negative link

between household size and welfare is confirmed. The strength of this link is about

the same in both urban and rural areas (see Table 6.6). The positive welfare impact of

education is given further support in both rural and urban samples. In urban areas,

industrial sector workers tend to have a significantly higher level of welfare than the

unemployed, whereas in the rural areas it is the services sector workers who are

significantly better off than the unemployed. The results further highlight regional

welfare disparities, especially in rural areas; the three northern Regions (Northern,

Upper West, and Upper East) are shown to have the lowest rural welfare levels, whilst

the Greater Accra and Western Regions have the highest rural welfare levels.

We now focus attention on the coefficients of the return-migrant dummies, which are

the main variables of interest. Even though the coefficient of �du1� (the return-

migrant dummy for the urban sample) has a negative sign, it is not statistically

significant, thus providing little insight into the welfare impact of migration on return-

migrants. The coefficient of �dr1� (the return-migrant dummy for the rural sample),

on the other hand, is positive and significant, with a p-value of 0.015. This provides

evidence in support of return-migrants (that is rural-to-urban-to-rural migrants) being

better off than rural non-migrants. This finding is consistent with our earlier result

suggesting that rural-to-urban migration is, on average, very profitable for participants

(see Table 6.5).

7. Conclusion

This paper has examined the impact of migration � between rural and urban sectors �

on migrants� welfare, using data from Ghana�s 1998/99 Living Standards Survey.

Employing a consumption measure of welfare and a model that corrects for selectivity

bias, the analysis has also highlighted factors influencing migration decisions between

Ghana�s rural and urban areas.

Our findings underscore the importance of anticipated welfare gains and personal

attributes in migration decisions. We also find support for the positive selectivity of

urban-to-rural migrants. In addition, estimates of migration gains suggest that

34

although some migrants incur welfare losses, migration enhances � on average � the

welfare of migrants, but would reduce the mean welfare of non-migrants if they were

to migrate. Finally, the average welfare increment derived by rural-to-urban migrants

is proportionately much higher than what accrues to their urban-to-rural counterparts.

35

Appendix

Table A1: Distribution of migrant type by in- or return-migrant status; 1991/92

Share (%) of in-migrants

Share (%) of return-migrants

Total

Urban-to-urban 74.10 25.90 100.00 Urban-to-rural 58.75 41.25 100.00 Rural-to-urban 85.30 14.70 100.00 Rural-to-rural 77.13 22.87 100.00 Foreign-to-urban 44.68 55.32 100.00 Foreign-to-rural 44.78 55.22 100.00 Source: Authors� computation using data from the Ghana Living Standards Survey, 1991/92.

Table A2: Distribution of migrant type by in- or return-migrant status; 1998/99

Share (%) of in-migrants

Share (%) of return-migrants

Total

Urban-to-urban 81.25 18.75 100.00 Urban-to-rural 59.58 40.42 100.00 Rural-to-urban 83.02 16.98 100.00 Rural-to-rural 75.51 24.49 100.00 Foreign-to-urban 50.29 49.71 100.00 Foreign-to-rural 33.46 66.54 100.00 Source: Authors� computation using data from the Ghana Living Standards Survey, 1998/99.

Table A3: Extent of migration, by gender; 1991/9211

Sex Non-migrant share (%) of population

Migrant share (%) of population

Row Total

Male 45.60 45.79

54.40 46.38

100.00

Female 46.19 54.21

53.81 53.62

100.00

Column Total 100.00 100.00 Source: Authors� computation using data from the Ghana Living Standards Survey, 1991/92.

11 For each cell in the Table, the first value represents the row percentage, whereas the second represents the column percentage.

36

Table A4: Extent of migration, by gender; 1998/9912

Sex Non-migrant share (%) of population

Migrant share (%) of population

Row Total

Male 50.47 46.70

49.53 46.22

100.00

Female 50.00 53.30

50.00 53.78

100.00

Column Total 100.00 100.00 Source: Authors� computation using data from the Ghana Living Standards Survey, 1998/99.

Table A5: Mean consumption welfare (in constant cedis) across migrant status

and locality of residence; 1991/92

Migrant status Urban Rural In-migrant 1,779,164.7 1,049,810.4 Return-migrant 1,984,938.8 1,104,967.7 Non-migrant 1,541,151.7 870,465.35 Source: Authors� computation using data from the Ghana Living Standards Survey, 1991/92.

Table A6: Mean consumption welfare (in constant cedis) across migrant status

and locality of residence; 1998/99

Migrant status Urban Rural In-migrant 2,085,238.2 1,341,066.6 Return-migrant 1,848,170.6 1,215,428.4 Non-migrant 1,892,204.3 1,053,309.6 Source: Authors� computation using data from the Ghana Living Standards Survey, 1998/99.

12 For each cell in the Table, the first value represents the row percentage, whereas the second represents the column percentage.

37

Table A7: Migration-generated welfare gains; urban-to-rural migration

(selectivity bias unadjusted)

Number of persons

Mean percentage welfare gain

Percentage with welfare gain

Percentage without welfare gain

Urban-to-rural in-migrants

1,360 30.88 57.87 (Mean % gain = 74.56)

42.13 (Mean loss = 29.17)

Urban non-migrants

2,720 11.86 46.36 (Mean % gain = 60.84)

53.64 (Mean % loss = 30.48)

Urban-to-rural in-migrants and urban non-migrants

4,080 18.20 50.20 (Mean % gain = 66.13)

49.80 (Mean % loss = 30.11)

Table A8: Migration-generated welfare gains; rural-to-urban migration

(selectivity bias unadjusted)

Number of persons

Mean percentage welfare gain

Percentage with welfare gain

Percentage without welfare gain

Rural-to-urban in-migrants

523 36.93 63.67 (Mean % gain = 73.32)

36.33 (Mean % loss = 26.85)

Rural non-migrants

4,447 4.82 42.86 (Mean % gain = 59.37)

57.14 (Mean % loss = 36.09)

Rural-to-urban in-migrants and rural non-migrants

4,970 8.20 45.05 (Mean % gain = 61.44)

54.95 (Mean % loss = 35.45)

38

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