Abstract

Many households in Ghana experience high poverty levels and unreliable sources of income. As a strategy to reduce poverty level in Ghana, there are various decisions by individuals within the household to either migrate internally or engage in other activities that generate income. This study examines the use of internal migration as a livelihood strategy in Ghana using descriptive statistics and logit regression with data from the 2012/2013 Ghana Living Standards Survey (GLSS 6). In relation to most previous empirical studies, this study provides results consistent with the economic reasons for migration in Ghana. The probit regression estimates and the use of descriptive statistics support the diversification framework of livelihoods–where some internal migrants in Ghana take up multiple jobs as a means of obtaining multiple incomes. According to the findings, most migrants end up in the private informal sector at their destination places. Finally, in terms of regional distribution of internal migrants, households from the three northern regions are more likely to migrate. It is recommended that development policies of the government should be evenly distributed to ensure national development, irrespective of where one finds one’s self. Education should also be promoted to ensure that migrants have some form of learning in order to obtain employment in either the semi-formal or formal sector which requires a high level of skills.

Keywords: Households, Experience, Poverty levels, Income, Strategy, Probit regression.

JEL Classification: I30; D10; D14; D19; D61; I100.

Received: 4 January 2019 / Revised: 12 February 2019 / Accepted: 14 March 2019/ Published: 22 May 2019

Contribution/ Originality

This study contributes to the existing literature regarding the use of internal migration as a livelihood strategy. The paper’s primary contribution is finding the main reasons for internal migration in Ghana, and also involves the use of probit which has not been used in migration studies before.

1. INTRODUCTION

The improved living standard is central to the study of development economics and has been a major subject of concern for most nations and economists worldwide. The variety of livelihood strategies adopted by individuals and households in their quest to achieve good living standards are enormous. It is a livelihood framework which helps to identify poor people’s agency in developing and sustaining their livelihoods (Yeboah, 2008). The sustenance of the poor, in the present and the foreseeable future, compels them to adopt various survival strategies such as the diversification of resources and through co-insurance with co-operative groups, non-governmental organisations, and developing partners or institutions.

The idea of a livelihood strategy has become increasingly popular in development planning and has attracted attention across the world. It addresses the reduction in poverty, and also encompasses a number of important elements relevant to social, political, and economic processes in diverse geographical locations and scales (see (Beall and Kanji, 1999; Ellis, 2000; Rakodi and Lloyd, 2002; Whitehead, 2002)). Livelihood outcomes such as good health, higher incomes, and reduced vulnerability are the major focus of the poor and these trigger decisions to pursue a wide array of strategies that would assist them to achieve these outcomes. A variety of assets such as land, capital, financial resources and labour may be required as means of achieving a better living standard These help to overcoming the present vulnerabilities and the foreseeable future shocks that may make the poor more prone to deterioration in their living standards. However, in trying to improve living standards through asset creation, other unforeseen circumstances may actually increase the susceptibility of the poor to environmental factors such as natural disasters, resource unavailability and climate change.

Migration at the global level has been on the increase. Awumbila et al. (2011) concluded that in many African countries, the emigration of young people reduces the availability of labor. Generally speaking, most migrants are young and energetic individuals seriously looking for employment opportunities, but there exist differences in the various destinations of such individuals, and the seasons of migration. Some women also migrate in search of a spouse, which is usually equivalent to a form of labour migration, notwithstanding these women engage in domestic activities such as cooking, washing, domestic management, and childbirth. According to the 2012/2013 Ghana Living Standards Survey report, in terms of regional distribution of migrants, Accra has the highest proportion of migrants 60.3 per cent, followed by the rural forest with 51.6 per cent. It is against this backdrop that the study seeks to analyse the use of migration as a means of obtaining a livelihood in Ghana by finding answers to these three questions:

What are the factors influencing migration?
What are the main economic activities of internal migrants?
What are the different reasons for internal migration in Ghana based on gender?

2. REVIEW OF RELEVANT LITERATURE

Ghana's economy is principally agricultural with significant informal economic activity. The work performed by individuals and households can be formal or informal, in rural or urban regions. Rural areas usually lack the basic infrastructure and assets (material and social) that enable individuals to pursue economic activities to generate income. This increases poverty and vulnerability of the rural population in comparison to urban centres where there may exist sufficient infrastructure and credit facilities. However, as Sackey and Osei (2006) observed: “Although Ghana’s unemployment data may not be very reliable, it has been noted that the urban settlements are associated with significant unemployment, compared to the rural areas due to migration.”

The decision to migrate within Ghana is usually influenced by several factors according to Awumbila et al. (2011). These include economic necessity, social stratification, political power, and lack of access to land. In addition, migrants often move from resource-poor to resource-rich areas (see Anarfi and Kwankye (2005). Goldscheider (1983) cited by Awumbila et al. (2011) noted that the main motivation for youth migration is to seek jobs and other opportunities for improved welfare. Nonetheless, very little is known about the experiences and vulnerabilities of the many young migrants working in different occupations across Ghana.

The livelihood strategies of men and women migrants differ. Earlier studies have focused largely on female migrants from the northern part of the country moving to cities in the south. Like the Kayayo (head porters), tro-tro driver mates fit into the social category of urban poor people (see Yeboah (2008) because with little or no education, coupled with few marketable skills, they migrate from other parts of Ghana to engage in marginal economic activities (see for example, Agarwal and Prasad (1997); Opare (2003); Tanle (2003); Awumbila and Ardayfio-Schandorf (2008)).

Min-Harris (2010) observed that youth from rural areas are most often disadvantaged; with high illiteracy rates and few skills, their opportunities are confined to the cities. In consequence, most achieve at best low-wage employment, but with the risks generated by high unemployment and underemployment, poverty, drugs and crime ever present. In this context, economic migration in Ghana does more harm than good by increasing the already significant numbers of young, urban unemployed.

Nevertheless, migration has been considered as one of the more efficacious development strategies. It has led to an intensification of wealth redistribution, enhanced economic activity, the empowerment of women, increased gender equality, a reduction in the rate of infection by HIV/AIDS, malaria and tuberculosis, and new partnerships between developed and developing African countries and other stakeholders like the European Union and the World Bank.

According to Klavert (2011) "the creation of development opportunities in migratory areas would attenuate the contributing reasons for migration by people; [and] thereby also curb the problem of high levels of brain drain". The literature on migration supports the view that migration is used by poor and vulnerable households as an effective way to reduce poverty (Waddington 2003; Kanbur and Rapoport, 2005). It has been reported that the most effective way to facilitate de-concentration of economic activities and to help the poor benefit from the global economy is to remove all barriers to migration (Kanbur and Rapoport, 2005). However, though migration is a human well-being enhancement strategy, the decision to migrate is contingent on asset base and available social networks. The economics of migration as a livelihood strategy limits the extremely poor households from exploiting it as a poverty-ameliorating strategy (Waddington 2003).

Remittances from household members form a crucial source of income for investment in farm and nonfarm activities, for house construction, and for social security against unforeseen occurrences (see, for example; Scoones (1998); Waddington (2003)). According to Ellis (2000) “households who have relatives working in urban centres or abroad are usually referred to as “split families”, and the term “straddling” describes their livelihood strategies. Migration in Ghana is usually motivated by marriage, employment, conflict, nonfarm opportunities, and the desire to be free (Ghana Statistical Service, 2014). A study on north-south migration in Ghana (Geest, 2011) concluded that the amount of rainfall and crop yields were negatively correlated with migration at significance levels of one per cent and five per cent respectively, while population density was positively related to north-south migration.

With data from the 1960 population census, Beals et al. (1967) found statistically significant evidence to support the assertion that distance is a deterrent to interregional migration in Ghana. Caldwell (1968) also found strong evidence in support of the inverse relationship of distance on migration using survey data. Caldwell observed that, for all individuals aged over 20, there was a clear negative association between their propensity to migrate to the towns and the distance from the nearest large destination. Caldwell found this result to be statistically significant for both men and women. According to Beals et al. (1967) the inverse correlation of distance to migration might be linked to costs associated with information, as well as valuable cultural and social differences between the migrant’s place of origin and their destination.

The survey – conducted between 1962 and1964 – used a sample of 1,782 rural and urban households (Caldwell, 1968) It was a collaborative project between the Ghana Statistical Service and the Social Sector Policy Unit under the auspices of the Ministry of Finance and Economic Planning. The United Nations Population Fund (UNFPA) sponsored the project. Its findings suggest that favourable economic conditions at a potential destination are the principal determinants in the decision of an individual to migrate in Ghana. Ghanaian migration research suggests that the welfare level in the community or household of origin incentivises the individual to migrate.

The OLS regressions used by Litchfield and Waddington suggest that migrants have a higher standard of living than non-migrants, but migration benefits or premiums seemed to have declined by 50 per cent between 1991/92 and 1998/99. Ghana is still at the mid-stage of development. Even though expressions of concern as to the negative effects of migration (usually rural-to-urban) are common, it does seem that the impacts on migrants and on the households and communities of origin are generally net-beneficial, while the net effect on destination communities is unclear. Given the limitations of the studies reviewed, it is prudent to treat their findings with caution.

3. METHODOLOGY

This section discusses the theoretical framework and statistical techniques employed in determining the use of internal migration as a livelihood strategy in Ghana. It also outlines how the study was conducted: types and sources of data, model specification variables employed, prior expectations on the signs of the coefficients to be estimated, and the model estimation technique.

3.1. Data Sampling Technique

The Ghana Living Standards Survey round six (GLSS6), provides both national and regional representative indicators. It applied sampling methods and questionnaires covering broad issues of demographics, education, health, employment, and housing. To provide nationally representative statistics, the number of primary sampling units and households were increased 107 per cent over the previous GLSS5 to 18,000. The GLSS-6 employed a two-stage stratified sampling design where 1,200 enumeration areas were selected to form the primary sampling units at the first stage. These primary sampling units were allocated into the various ten regions using probability proportional to population size. The enumeration areas were further divided into urban and rural localities of residence. At the second stage, fifteen (15) households from each primary sampling unit were selected systematically giving a total sample size of 18,000 households nationwide. Of this number, 16,772 were successfully enumerated, a response rate of 93.2 per cent.

3.2. Model Specification

An examination of the household data shows that about 48.6 per cent of households in Ghana are migrants. This means that roughly 51.4 per cent of the households did not migrate. The econometric framework employed in this study with regards to the decision to migrate is explained in the next section.

3.3. The Econometric Framework of the Probit Model

The probit model is one of the methods that can be used in estimating models where the dependent variable has a binary outcome; the decision to migrate or not to migrate. It usually takes on two values, a zero (0) if a given household does not migrate, or a one (1) if a given household does migrate, as follows:

Where Equation 3.1 is a model which estimates the probability (p) that an internal migrant =1 as a function of the explanatory variables. If we represent internal migrant by “IM” then,

1 = Pr (IM = 1 | X) = F (X1β) 3.2

Where Equation 3.2 shows the probability of internal migration = 0, that is ‘no’ is derived as (1- ρ), Where F (X1β) is a cumulative distribution function of the standard normal distribution as

Where Equation 3.3 shows the probit model estimated by Maximum Likelihood Estimation, and its errors (disturbances) are assumed to follow the standard normal distribution,

Where Equation 3.4 shows the marginal effect for the probit model is derived below as:

Where Equation 3.5 illustratesthe marginal effect at the mean is estimated for the average person in the sample as:

Where Equation 3.6 shows the less likely to find individuals at the mean, the average marginal effect is estimated as the average of the individual marginal effects expressed as:

Where Equation 3.7 shows the two marginal effect yields identical result in most cases.

3.4. Estimation Technique

Two approaches will be used to analyse the data; descriptive analysis and inferential analysis. The descriptive analysis summarises the data by employing tabular presentation to make comparisons, compare and contrast households with regards to desired features; while the inferential aspect employs the use of regression analysis. With regards to migration decisions, the analysis was done in two parts. The study adds additional household variables of marital status, educational level, NHIS status, dwelling ownership, employment status, residence, land ownership and income sources that are also likely to influence a household decision to migrate internally. Thus, the probit model for this study is:

Where Equation 3.8 shows the demographic and economic variables that determine migration in Ghana.

4. EMPIRICAL RESULTS

Detailed information was collected on the demographic characteristics of households, education, health, employment, housing conditions, household agriculture, income and their components. The summary of internal migrant variables in the GLSS- 6 data presented in Table 4.1 below shows that, 52.33 per cent of the internal migrants were males' while 47.67 per cent of them were females. Table 4.1 shows that most internal migrants were within the age group of 31-54 years which shows an active age group of the population. About 43.85 per cent of the individuals fall into this age category. The proportion of internal migrants was skewed towards the non-educated with a percentage of 67.32. This underpins the economic rationale for internal migration in Ghana.

More than half of internal migrants (68.39%) were from rural areas. 55.51 per cent of internal migrants were married and 44.49 per cent single. A substantial (73.29 per cent) majority of internal migrants have not signed up for the National Health Insurance Scheme (NHIS). 74.63 per cent of respondents cited economic considerations in their decision to migrate. Most internal migrants engaged in economic activities other than farming as shown in Table 4.1.

Table-4.1. Profile of internal migrants in GLSS-6 survey data.

DEMOGRAPHIC VARIABLE	No. of household	Per cent of total
GENDER
Male	11,379	52.33%
Female	10,365	47.67%
AGE GROUP
7-14 years	1,779	8.38%
15-30 years	5,979	28.16%
31-54 years	9,309	43.85%
> 55 years	4,162	19.61%
EDUCATION
Educated	7,103	32.68%
Non-educated	14,635	67.32%
RESIDENCE
Urban	6,873	31.61%
Rural	14,871	68.39%
MARITAL STATUS
Married	12,070	55.51%
Single	9,674	44.49%
NHIS STATUS
NHIS registered	5,807	26.71%
NHIS unregistered	15,937	73.29%
ENGAGED IN ECONOMIC ACTIVITY
Yes	16,227	74.63%
No	5,517	25.37%
FARM OWNERSHIP
No farm	19,961	91.80%
Farm	1,783	8.20%

Table-4.2. Internal migrants engaged in economic activity by Residence.

	Engaged in economic activity
Residence	Yes	No	Total
Urban	4,442 (64.63%)	2,431 (35.37%)	6,873 (31.61%)
Rural	11,785 (79.25%)	3,086(20.75%)	14,871 (68.39%)
Total			21,744(100%)

Table 4.2 is a cross-tabulation of internal migrant employment status by location. There are more migrants in rural (79.25%) than urban. About 79.25 per cent of internal migrants in rural areas are economically active. A similar pattern exists in respect to urban migrants. Here, about 64.63 per cent were engaged in economic activities in urban areas, and 35.37 per cent unemployed.

Table-4.3. Reasons for being Non-migrant and migrant.

Reasons for Non-migration	Per cent	Reasons for migration	Per cent
Economic	19.46%	Economic	23.75%
Family matters	70.42%	Family matters	66.53%
Political or religious	0.44%	Political or religious	0.32%
Education	4.66%	Education	5.07%
Other reasons	5.02%	Other reasons	4.33%
Total	100%		100%

Table 4.3 shows economic reasons for non-migration at 19.46 per cent, while the same for migration are 23.75 per cent. This confirms a study by Gökhan and Filiztekin (2008) in Turkey that economic factors such as income differentials and unemployment rates, and social factors such as the presence of social networks, contribute positively to internal migration. Table 4.3 shows that 70.42 per cent of non-migrants attribute family matters as major determinants for their non-migration, while 66.53 per cent of the internal migrants based their decision to migrate on the same consideration. Gimba and Kumshe (2011) concluded that social factors like marriage are major determinants of non-migration. Education level has also been a major determinant. Table 4.3 shows 5.07 per cent of internal migrants base their decision on education level. Oberai et al. (1989) using descriptive statistics in a study of the reasons for internal migration in India confirmed that education, employment, technology, productivity and civic amenities were factors that led to internal migration.

Table-4.4. Binary outcome probit result: Probability that a given household will migrate.

Prob (Internal migration)= 1 yes	Probit coefficient	Standard errors
Constant	0.934***	-0.161
REGION (Greater Accra=0)
Western	-0.356***	(0.0539
Central	-0.926***	-0.0626
Volta	-0.956***	-0.0567
Eastern	-0.621***	-0.0545
Ashanti	-0.612***	-0.0538
Brong-Ahafo	-0.605***	-0.055
Northern	-0.624***	-0.0556
Upper East	-1.464***	-0.057
Upper West	-0.914***	-0.057
Sex (Female=0)
Male	-0.297***	-0.0193
Age (>55 years=0)
7-14 years	0.702***	-0.0431
15-30 years	0.320***	-0.0295
31-54 years	0.212***	-0.0256
Education (Non-educated=0)
Educated	-0.201***	0.0237
Residence (Rural=0)
Urban	-0.0351	0.0216
Marital status (Single=0)
Married	0.137***	0.0217
NHIS status (unregistered=0)
Registered	-0.0206	-0.0212
Reasons for migration (Political or religious=0)
Economic	0.159***	0.052
Family matter	0.0314	0.151
Education	0.032	0.157
Other reasons	0.0184	0.157
Farm (No-farm=0)
Farm	0.0375	-0.0344

Note: The dependent variable is the probability of migrating internally. Figures in parenthesis are the standard errors of the estimates and ***, ** and * refer to the statistical significance of the estimates at 1%, 5%, and 10% respectively. Except for the dummies, all the other variables are in real values
Log likelihood = -12719.864 LR chi2 (22) = 1953.00 Pseudo R2= 0.0713
Number of observations = 16,772 Prob > chi2 = 0.0000

The probit regression analysis focuses on variables such as the migrant’s region, with Greater Accra as the dummy variable. Migrants’ age and age groups, as well as their gender are analysed using the binary outcome regression model.

Table 4.4 above shows the probit result of the probability of a given individual to migrate having regard to household variables. The probit coefficients are interpreted as regards significance and sign of the coefficient. The magnitude of coefficients is not considered.

As per Table 4.4, the study used Greater Accra as the reference dummy for comparison and contrast. The probit result shows that, individuals in all the other nine (9) regions are less likely to be considered internal migrants than those in Greater Accra. This relationship was strongly significant at a one per cent critical level. This show result shows that Accra been the capital city of Ghana has many business opportunities and a large population for the purchase of consumable products. This attracts many migrants to make Accra their destination points when migrating. Beals et al. (1967) in their study observed a negative relationship between distance and migration. Beals used data in the 1960 population census to establish statistically significant evidence in support of distance as a major constraint to internal migration in Ghana.

Still on the subject of location and with reference to internal migrants' residence in rural localities, it was found that those in the urban localities are less likely to migrate. This was highly significant at a 1% critical level. This is so because a resident in the urban areas has access to many things such as electricity, potable water, recreational facilities, and hospitals. This makes it less likely for an urban resident to migrate compared to a rural resident. From Table 4.4 above, urban residents are 3 percentage points less likely to migrate compared to the rural resident. In relation to gender from Table 4.4, males are less likely to migrate compared to their female counterparts. Males are 29 percentage points less likely to migrate compared to their female counterparts. This is significant at 1% critical level. This might be due to the fact that females are more likely to join their spouse at their destination point. In Ghana, most females who get married are more likely to migrate to join their spouse at their place of work compared to their male counterparts.

For the age groups, individuals within the ages of 7-14 years, 15-30 years and 31-54 years are more likely to migrate compared to individuals above 55 years. This is shown in Table 4.4, above. This is confirmed in a study by Tutu (1995) that younger individual is more likely to migrate compared to the other people. The youthful population between 7-14 years and 15-30 years are 72 and 29 percentage points more likely to migrate compared to the individuals who are 55 years and above. This is so because most of these individuals within the youthful age group are more likely to be single individuals who can migrate at any point in time compared to the older population. Also, economic reason in Ghana is one major that affects the migration decisions of the youth in Ghana. This is significant at a 1% critical level for all the age groups.

Education is one of the major determinants of internal migration in Ghana. From Table 4.4, the more educated individuals are less likely to migrate compared with an individual who is not educated. Educated individuals are 20 percentage points less likely to migrate compared to their uneducated counterparts. This usually exists in Ghana because most educated people find it easy to find jobs at their places of residence compared to uneducated individuals. Uneducated individuals who normally are unemployed migrate in Ghana in search of jobs. Lucas (2016) in a study of determinants of internal migration on Sub-Saharan African countries using descriptive statistics found that education is one of the major determinants of migration. This is confirmed in the study that; more educated individuals are less likely to migrate. This is significant at 1% critical level. Marital status is also one of the determinants of internal migration in Ghana. From Table 4.4 above, married individuals are more likely to migrate to compare to the singles. Married individuals are 13 percentage points more likely to migrate in Ghana. Most of them move to join their spouses in Ghana or some also travel in search of life partners. This makes marriage a significant factor for internal migration. This is statistically significant at 1% critical level. Tutu (1995) in his research on migration observed that the singles have higher propensity to migrate, but Caldwell (1968) research observation concerning singles was quite weak.

From Table 4.4, it can be observed that NHIS registered individuals are less likely to migrate compare to their unregistered counterpart. This means that NHIS registered individuals are more likely to have a high level of human capital stock that will enable them to engage in economic activity at a faster pace compare to unregistered counterparts. This is however not significant. The major determinant of migration is for economic reason. This is shown in Table 4.4 above. One factor that influences an individual to migrate is for economic reason. Piras (2016) observed that internal migration in Italy was determined by macroeconomic variables. According to the results of the probit analysis, it was identified that economic differences affect internal migration in Ghana compared to political or religious reasons. Additionally, Gökhan and Filiztekin (2008) reached a conclusion that income distribution is the main determinant of the internal migration. Furthermore, Cebula (2005) determined that higher unemployment rate influences migration. These are economic factors that determine internal migration. Caldwell (1968) also found that economic reasons are the major determinant of migration in Ghana. An individual is 15 age points more likely to migrate compared to religious or political reason.

Table-4.5. Reasons for migration based on gender.

Reasons for migration (Female)		Reasons for migration (Male)
Economic	14.72%	Economic	28.98%
Family matters	76.59%	Family matters	60.03%
Political or religious	0.20%	Political or religious	0.53%
Education	4.74%	Education	5.07%
Other reasons	3.75%	Other reasons	5.39%
Total	100%		100%

From Table 4.5, economic reasons account for the major reasons why men migrate compared to their female counterparts. This is shown by 28.98 for males as against 14.72 for females who attribute their migration decision to economic reasons. This confirms a study by Abdulai (2016) on reasons for internal migration in Ghana who identified economic reasons as a major determinant. Males are more likely to migrate since they normally serve as the household heads in Ghana. For females, the family matter is a major determinant of their internal migration decisions. This is represented by 76.59 from Table 4.5 compared to 60.03 for the male counterpart. Yakar (2012) identified family reasons as a major determinant of migration in Turkey. The study concluded that population growth is an important factor that affects internal migration and this is confirmed by the study as per Table 4.5. For males, education is also a major determinant of migration compared to their female counterparts. This is represented by 5.07 percent compared to 4.74 percent for females as shown in Table 4.5.

5. CONCLUSION AND POLICY RECOMMENDATIONS

Internal migration as a means of economic empowerment and poverty reduction has been an issue of international concern to individuals, governments, organisations and researchers over the years. While some studies support and attest to a positive relationship between migration and its use as a livelihood strategy in Ghana, others view migration as a break in social ties associated with cost and psychological issues such as loneliness, loss in cultural values and even death. These conflicting results coupled with the existence of only a few studies of internal migration in Ghana are the motivations for this work. In committing to rural development initiatives, the government needs to increase the speed at which policies to assist the poor and equitably redistribute investment throughout the country are implemented. The government’s agenda must also include the provision of basic service infrastructure in rural areas: electricity, potable water, community health (such as the CHPS compound) and recreation facilities. These initiatives have the potential to significantly reduce internal migration, particularly to urban areas.

In order to promote employment and agriculture, an integrated rural development policy to boost productivity would make the agricultural sector more efficient and attractive to those currently resident in rural areas. This can be done by through improved rural labour practices, modernised farming technologies, accessibility to farm inputs such as fertilizers, improved seeds for cultivation, agrochemicals, and a sufficient number of agriculture extension service providers. The Ministry Food and Agriculture (MoFA), the Local Government and Rural Development ministry and District Assemblies must collaborate to provide rural areas with access to financial credit (financial capital).Finally, in order to further promote a diversification of livelihood strategies within the Ghanaian population, it is recommended that all levels of government should implement entrepreneurship support, and move to create a favourable investment climate. These policies will expand existing sectors of the economy and generate a more diverse array of business opportunities and employment within communities, which in turn will work to reduce the migration of individuals and households.

Funding: This study received no specific financial support.

Competing Interests: The authors declare that they have no competing interests.

Contributors/Acknowledgement: Both authors contributed equally to the conception and design of the study.

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