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Demography
Published in: Demography 4/2013

01-08-2013

Remittances in the Republic of Georgia: Correlates, Economic Impact, and Social Capital Formation

Authors: Theodore P. Gerber, Karine Torosyan

Published in: Demography | Issue 4/2013

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Abstract

The economic impact of remittances on migrant-sending countries has been a subject of debate in the scholarly literature on migration. We consider the topic using a household-level approach. We use a new survey, “Georgia on the Move,” to examine migrant-level, household-level, and contextual variables associated with the probability that a household in the Republic of Georgia receives remittances. We then apply propensity score matching to estimate how remittances affect particular types of household expenditures, savings, labor supply, health, and other measures of well-being. Separate analysis of the subsample of households with a migrant currently abroad distinguishes the effects of remittances from the effects of migration as such. In Georgia, remittances improve household economic well-being without, for the most part, producing the negative consequences often suggested in the literature. We find evidence for an important aspect that has not been widely discussed in prior studies: remittances foster the formation of social capital by increasing the amount of money that households give as gifts to other households.
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Appendix
Available only for authorised users
Footnotes
1
For more detailed descriptions of Georgia’s migration patterns since 1991, see International Organization for Migration (IOM) (2008) and Tchaidze and Torosyan (2009).
 
2
The transfer data can be found online (http://​www.​nbg.​gov.​ge/​). See also Table S1 in Online Resource 1 for more details.
 
3
NBG specialists told us most remittances are transferred via the NBG because these transfers are reliable, flexible, quick, and cheap, and remittances are not taxed or questioned.
 
4
For more-detailed summaries of the debate over the economic effects of remittances, see Taylor (1999), Cohen (2005), Massey et al. (2005), Rapoport and Docquier (2005), de Haas (2010), and Garip (2012).
 
5
Precincts in heavily militarized zones, remote mountainous areas, territories occupied by Russian forces, those with fewer than 50 voters, and those with more than 50 % non-Georgian speakers were excluded at the PSU stage for practical reasons. Overall, 7.7 % of voting precincts containing 3.7 % of voters were excluded.
 
6
Because the GOTM captures remittances paid by households that left Georgia entirely, the absence of such households in the sample is not a problem for the analysis of the effects of remittances among Georgia-resident households.
 
7
Data on any remittance receipt are missing for only 5 % of households.
 
8
Frequency of contact may be endogenous if transferring remittances provides occasions for contact. However, most remittances are sent via bank transfer. Thus, our eight-category measure of frequency of contact is a reasonable proxy for the strength of the ties between the Georgia-resident household and migrant group.
 
9
This “ignorability” assumption stipulates that no unobservable variables affecting whether a household receives remittances influence the outcomes that we examine. Such unobservable variables would bias our estimates of remittance effects in one direction or the other, depending on how they affect remittance receipt and the outcomes. However, this concern is mitigated by the rich set of covariates and good fit of our remittance receipt models.
 
10
We estimate all models using Stata 11.0. For detailed introductions to propensity score matching, see Smith (1997) and Morgan (2001). For a technical treatment of kernel matching and details about estimation, see Becker and Ichino (2002).
 
11
Households may have spent money on categories not covered in the survey, so our measures of total budget and total expenditures contain errors and underestimate the true totals. The expenditure data may be error prone because of difficulties remembering or reluctance to reveal high levels of expenditures. However, we do not think that these measurement errors vary systematically by household remittance status, so we doubt that they bias our findings.
 
12
Other age ranges for school participation that we tested yielded the same results.
 
13
We adjust household earnings for household size in the standard fashion by dividing total household earnings by the square root of household size. Earnings data were missing for at least one member in 18.0 % of households. We assigned a value of zero to those individuals. Additional analyses on households with complete data revealed no differences in results.
 
14
We mean-substitute missing values on religiosity and (for absent migrants) duration abroad. We exclude six households with missing data on other variables. The GOTM sample means on some variables can be compared with official data found online (www.​geostat.​ge).
 
15
We define unemployment as not working but looking for work. Parallel analyses that included those not working and not looking among the unemployed yielded the same results.
 
16
In optimizing our specifications for this model and the model estimated on the sample of absent-migrant households, we omit most nonsignificant covariates, although we retain some to satisfy the balancing condition necessary for the estimation of propensity scores.
 
17
We tested for variation in the “elderly” effect by the presence of children and found no significant interaction.
 
18
Households with a return migrant are also significantly more likely to get remittances, but we had to remove this variable to satisfy the balancing property necessary for the propensity score analysis.
 
19
Because debt payments are a separate expenditure category, it is unlikely that transfers to other households are capturing repayment of money borrowed to pay the costs of migration.
 
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Metadata
Title
Remittances in the Republic of Georgia: Correlates, Economic Impact, and Social Capital Formation
Authors
Theodore P. Gerber
Karine Torosyan
Publication date
01-08-2013
Publisher
Springer US
Published in
Demography / Issue 4/2013
Print ISSN: 0070-3370
Electronic ISSN: 1533-7790
DOI
https://doi.org/10.1007/s13524-013-0195-3

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