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Empirical Economics
Published in: Empirical Economics 4/2023

05-03-2023

Does access to credit alter migration intentions?

Authors: Marina-Selini Katsaiti, Maroula Khraiche

Published in: Empirical Economics | Issue 4/2023

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Abstract

This study investigates whether access to credit affects migration intentions. Using Gallup survey data for the years 2009 and 2010 for 17 African countries, we document a negative link between the ability to borrow and the desire to migrate. Being able to borrow reduces the likelihood of reporting wanting to migrate, especially for those with some education, those with lower income, for individuals with a bank account, and for those who feel their assets are safe. To deal with endogeneity, we assume that migration desire is driven by borrowing which in turn is determined by access to financial services. Therefore, we estimate a two-equation system for migration and borrowing, using variables describing access to financial services as instruments. We verify our findings using identification through heteroscedasticity and using a geographical instrument. Our results indicate that efforts to increase credit access in developing economies can cement residents’ attachment to their home country.
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Appendix
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Footnotes
1
Mesnard (2004) also shows that credit imperfections at home affect migration length and occupational choice of return migrants.
 
2
Rubalcava and Teruel (2005) study the effect using similar data over a shorter time horizon but finds that migration decreases.
 
3
And given that we look at migration intentions in large samples across individuals, regions, and countries, as available from the GWP, we can mitigate a potential concern that the patterns in our results stem from a systematic measurement error.
 
4
We also contribute to the literature documenting the relationship between migration and income (Dao et al. 2018; Clemens 2014; Abramitzky et al. 2013).
 
5
It is worth noting that at least 4 out of 10 respondents in Rwanda, Central African Republic, Cameroon,and Kenya say they depend at least a little on community savings groups to make a living, the highest participation in Sub-Saharan Africa.
 
6
43% of Sub-Saharan Africans reported that they have heard of microfinance institutions but they are not available in their community (Marlar 2010a).
 
7
From 5% of adult population in 2006 to 36% in 2015.
 
8
A single international dollar buys an amount of goods and services in a country similar to the amount bought by a US dollar in the USA.
 
9
Full descriptive statistics of all variables can be found in Appendix D, Table 11.
 
10
However, this correlation is statistically insignificant. The slope coefficient of the linear trend is .094 with a standard error of .107 and a p value of 0.395.
 
11
For example, a risk-taking individual may be more likely to use their own money or assets in order to invest, rather than borrow, and is more likely to engage in the risky endeavor of migration, compared to a respondent with identically measured characteristics.
 
12
With the null hypothesis of exogeneity, the Durbin \(\chi ^2\) test statistic is 4.48773, with a p value of 0.0341 and the Wu–Hausman F statistic is 4.47981 with a p value of 0.0343.
 
13
The marginal effects are presented in Table 8 and are discussed in Sect. 4.
 
14
The discussion in this section is based on Baum and Lewbel (2019).
 
15
The first-stage coefficient of the geographical IV model is negative. One explanation could be that limited funds are available for borrowing. Therefore, if many are borrowing elsewhere, few loans are left for the respondent’s region. Alternatively, if certain central regions have bank branches (which can facilitate borrowing in said regions), then neighboring (less central) regions are less likely to have bank branches and residents are less likely to borrow.
 
16
See column 1 of Tables 4, 6, and 8.
 
17
Brain drain is loosely defined as the international migration of skilled workers. Although some gains exist to the home economies from skilled emigration such as remittances, improved international networks, and expertise brought home through return migration, the extent of these gains is hard to quantify and the empirical evidence of such gains is mixed. While Beine et al. (2001) and Beine et al. (2008) document evidence of brain gain effects, Faini et al. (2003) fails to spot such an effect.
 
18
See also Amery and Anderson (1995).
 
19
We also find that a model where bank account ownership and cost of banking variables are included in both the migration and borrowing equation delivers a higher AIC and BIC than our model with an exclusion restriction (AIC = 26755.45, BIC = 27170.74).
 
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Metadata
Title
Does access to credit alter migration intentions?
Authors
Marina-Selini Katsaiti
Maroula Khraiche
Publication date
05-03-2023
Publisher
Springer Berlin Heidelberg
Published in
Empirical Economics / Issue 4/2023
Print ISSN: 0377-7332
Electronic ISSN: 1435-8921
DOI
https://doi.org/10.1007/s00181-023-02393-y

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