1 Introduction

By now, it is well known that immigrant remittances are one of the largest types of international financial flows, amounting to over $400 billion in the year 2015, and second only to foreign direct investment in terms of size. In addition, for many developing countries, remittances are quite large relative to GDP. During 2015, around 30 countries received remittance transfers worth more than five percent of GDP, and many more countries received remittances worth more than one percent of GDP.

Financial flows of this magnitude can be expected to have multiple significant impacts on the economies of remittance-receiving countries. For example, much attention has been paid to the goods market consequences of remittance receipt, such as the potential “Dutch disease” effects of remittances(Acosta et al., 2009; Barajas et al., 2011). Likewise, there are good reasons to expect that remittance inflows would have important consequences for a country’s labor market. First, remittances constitute an important source of income for millions of families around the world, lifting many of them out of poverty. To the extent that remittance receipt affects households’ consumption and investment decisions, there may be significant follow-on effects of these decisions on labor demand.

At the same time, remittances are also a non-market income transfer, and as such, can have significant impacts on the labor supply behavior of members of remittance-receiving households. Remittances are an alternative to labor income, and may therefore affect labor force participation, reservation wages, and occupational choice, among other labor supply outcomes. In addition, since households’ investment opportunities include education and training, remittances may also affect labor supply through this channel.

Because of the size of remittance flows, and the many ways that they can affect the labor market, remittance-receiving countries may exhibit wage and employment dynamics that are quite different from those in countries for which remittance receipt is trivial (or negative, as in remittance-sending countries). These differences have important implications for policymakers in remittance-receiving countries who are trying to understand trends in their labor statistics or to design policies to increase employment or improve employment opportunities for their citizens.

In addition, labor market outcomes are an important determinant of long-run growth. The quantity and quality of labor in an economy – the human capital – help determine potential GDP as well as the growth rate of actual GDP. Much research has been devoted to investigating the impact of remittance receipt on economic growth, and the findings have presented a bit of a puzzle, in the sense that remittances do not appear to increase economic growth and may in fact hinder it. Part of the explanation for these results may be found in the labor market consequences of remittances. For example, Chami et al. (2003) argue that remittances reduce work incentives and therefore decrease labor supply and economic growth.

Thus, there are many reasons why systematic and comprehensive study of the labor market effects of remittance receipt would be useful and interesting. This paper conducts a cross-country study of the labor-market effects of remittance receipt that contributes to the literature in four ways. First, nearly all previous studies of the labor market effects of migration and remittances examine a single labor market outcome, such as labor force participation, in a single-country context, using household-level data.¹ We examine the impact of remittances on unemployment, labor force participation, wage growth, and inequality across many countries, using aggregate data. In addition to estimating average effects across all countries, we also measure the variation in these effects across geographic regions, country income levels, and degree of fragility.

The second contribution of this research is that we estimate the effects of remittance receipt on sectoral employment in 14 different sectors. This exercise sheds additional light on the labor market impacts of remittances and suggests a consistent interpretation of our findings.

The third contribution of the paper regards data. This paper uses data from the ILO Global Wage Database, as well as from the ILO Global Employment Trends and Sectoral Employment databases. The latter datasets have not been used in connection with remittances before.

Finally, the paper also provides robust evidence in support of both existing and new stylized facts regarding the impact of remittances on labor markets. To begin with, we find consistently strong evidence that remittances have significant negative impacts on labor supply and positive effects on labor demand. Remittances reduce labor force participation and increase labor market informality, but also reduce unemployment. We also find, moreover, that these effects are consistently larger and more statistically significant than the effects of foreign direct investment (FDI) or official development assistance (ODA) on labor markets.

We also find strong evidence that remittances have differential effects across labor market segments such as males and females, and across industrial sectors. This evidence helps reconcile some of our findings from the aggregate data, particularly that remittances reduce both wage growth and inequality. For example, we find that remittances increase employment in the construction and real-estate sectors but reduce employment in manufacturing. This evidence in turn suggests that composition effects are important to understanding how remittances affect labor markets.

In addition, we find evidence of regional variation in labor-market impacts of remittances. For example, the impact of remittances on labor market informality is greater in regions where informality is lower. This is also one of the first papers to examine whether remittances have different effects in fragile states versus more stable states. We find that remittances actually increase wage growth and do not depress labor force participation in fragile states, which are the opposite of the effects found both on average and for more stable states.

We argue that all the labor-market effects of remittances we document in this paper tell a remarkably consistent story. For example, our findings are consistent with a Dutch Disease narrative in which remittance inflows change the relative prices of tradables, which are produced using more productive labor, and nontradables, which are produced using less productive labor, to favor nontradables at the expense of tradables. In this case, employment falls in the tradable sector but rises in the nontradable sector. These effects are consistent with a fall in overall unemployment, if employment in the nontradable sector with less-productive labor rises by more than employment in the tradable sector declines. But this outcome is also consistent with a lower rate of wage growth and a fall in measured inequality, due to the change in the composition of the employed.

This story in turn provides a deeper explanation of why previous work has found that remittances generally fail to boost economic growth in recipient economies. Remittances not only appear to decrease labor supply across the board, but they also benefit some segments of the labor market, and the overall economy, at the expense of others. The mixed effects offset each other and imply a negligible net impact on economic growth.

The evidence we present in this paper reinforces two broad messages that have emerged from the literature on the macroeconomic consequences of remittances.² First, the effects of remittances on the receiving economies are complex because of multiple pathways through which remittances affect recipients’ behavior. Second, remittances do not appear to be an unmitigated boon for any recipient economy. In particular, we find that they can have serious negative consequences for labor market outcomes, including for workers who do not receive these transfers. Therefore, countries that receive significant remittance flows need to integrate strategies for dealing with remittances into their overall development plans. We detail some suggestions for doing so in the conclusion.

The rest of the paper is organized as follows. Section 2 reviews the literature on the labor market effects of both migration and remittances. Section 3 presents the data and estimation strategies used in the paper, and presents the results. Section 4 presents an overall interpretation of the empirical results, and Section 5 concludes.

2 Literature review

When considering the impact of remittances on labor-market outcomes, it is important to take the literature on emigration and labor markets into account as well.³ Many papers in the emigration literature complement the findings in the remittances literature, while other papers on emigration utilize a “remittance channel” to explain why emigration affects labor market outcomes. In addition, emigration may affect some labor market outcomes differently than remittances do, although these effects may be difficult to disentangle empirically.⁴ We organize our review of the literature on labor market impacts of remittances and emigration by the type of labor market outcome affected by these activities. We begin with wages, because the findings in this literature can help frame, and shed light on, some of the findings regarding other labor-market impacts.

Emigration may have a significant impact on wages in the labor-sending country. Many studies — recently Elsner (2013a) for Lithuania, Elsner (2013b) for the post-Soviet era, and Dustmann et al. (2015) for Poland — find that emigration increases wages, because it reduces the size of the labor force. Mishra (2014) gives an extensive survey of this literature. It is important to note that these studies use household-level survey data to estimate the impacts of migration on wages. Docquier et al. (2013) examine the wage impacts of emigration in OECD countries, using a simulation model calibrated with parameter estimates taken from the empirical literature. This paper finds that emigration decreases the supply of highly educated workers and therefore increases their wages. And because their model includes a positive externality in which highly educated workers raise total factor productivity, emigration reduces the wages of low-skilled workers since it is mostly the highly educated who migrate.

A related paper that directly considers labor productivity is Al Mamun et al. (2015). This research finds a positive impact of remittances on labor productivity, in countries that both receive large amounts of remittances and have large labor forces. This effect does seem to decline as the amount of remittances increases above some threshold, however, and in fact the paper also finds that there is an insignificant effect of remittances on labor productivity in countries with very high remittance-GDP ratios. Although the authors propose several explanations for their findings, these results are also consistent with the findings regarding the impact of emigration on wages. Since remittances are correlated with migration, the increase in remittances can also coincide with a decline in the labor force, which increases labor productivity as firms move along the labor-demand curve and wages increase in response to the fall in labor supply.

Another labor-market issue related to the labor-supply effects of emigration is the so-called brain drain that affects many developing countries (Docquier et al., 2013). This phrase describes the fact that for many developing countries, highly skilled emigrants outnumber low-skilled emigrants. This phenomenon affects the markets for these skilled workers and potentially creates shortages of highly skilled labor, such as physician labor. In addition, governments may react to the outflow of skilled labor by adjusting the subsidies to education.

Much of the literature on the labor-market impact of emigration and remittances focuses on labor-market participation. Many papers find that the labor force participation of family members who remain in the labor-sending country decreases when members emigrate or send remittances. One of the earliest papers in this literature is Funkhouser (1992), who found that remittances reduced labor force participation in Managua, Nicaragua. Other papers, including Airola (2008), Hanson (2007), Kim (2007), and Acosta et al. (2008) support these findings for different countries in Latin America. Studies performed on countries outside Latin America come to similar Conclusions, such as Kozel and Alderman (1990) for Pakistan, Rodriguez and Tiongson (2001) in the Philippines. Lokshin and Glinskaya (2009) for Nepal, or Grigorian and Melkonyan (2011) in Armenia. Abdulloev et al. (2014) find that there is a negative effect of emigration on the labor force participation among men in Tajikistan that is separate from the effect of remittances.

One study that contradicts the above findings is Posso (2012). This author examines the behavior of aggregate labor supply rather than the behavior of individuals, and performs a cross-country analysis using a 25-year panel. This paper finds that remittances increase the aggregate labor force participation of men, including those who do not receive remittances. The author argues that the increase in labor force participation is the result of non-migrant households who want to migrate after watching neighboring households receive remittances from members who emigrated, and therefore join the labor force in order to accumulate the skills and experience required to find employment abroad. This idea of a demonstration effect in migration and remittance receipt that changes the behavior of non-migrant households (that is, households with no members who emigrated) is a recurrent theme in this literature. On the other hand, given the findings discussed above that emigration increases wages in the labor-sending country, this result could simply reflect an increase in wages above many people’s reservation wage. To the extent that remittances raise their recipients’ reservation wages, it is possible that remittances may simultaneously decrease labor force participation of some households’ members but increase overall labor force participation.

Relatively few studies have considered the impact of emigration or remittances on the level of unemployment, but the ones that have typically utilize aggregate, cross-country data and hence give a perspective that differs from that of individual country studies that use household-level data. Drinkwater et al. (2009) failed to find a significant effect of remittances on unemployment across countries, but Jackmann (2014) found a positive and significant impact of remittances on unemployment for countries in Latin American and the Caribbean that have low remittance-GDP ratios. But the estimations in this paper also allowed the remittances-unemployment relationship to be nonlinear, through the use of a threshold effect, and for countries with high remittance-GDP ratios, it was found that remittances have a negative impact on unemployment.

An issue that is related to labor force participation is occupational choice. Emigration and remittances appear to have significant effects on the broad types of work that people choose to do, such as formal and informal employment, self-employment, and unpaid work such as caring for family members or contributing labor on a family farm. For example, Amuedo-Dorantes and Pozo (2006a) find that remittances reduce the amount of hours that men spend in formal work and self-employment, and increase the amount of hours spent in informal work. Similarly, Binzel and Assaad (2011) find that remittances reduce the amount of paid work outside the home that women perform in Egypt. Görlich et al. (2007) also find that remittances cause women in Moldova to decrease paid work in favor of unpaid household work. Cabegin (2006) finds that migration lowers female labor force participation and increases household work. More recently, Ivlevs (2016) finds that remittances and emigration increases the share of informal employment in a sample of six transition economies, using the Social Exclusion Survey conducted in Kazakhstan, the FYR Macedonia, Moldova, Serbia, Tajikistan and Ukraine in 2009.

The remittances literature has also focused on the choice to become self-employed. Amuedo-Dorantes and Pozo (2006b) find that remittance receipt lowers the likelihood of business ownership in the Dominican Republic, and Demirgüç-Kunt et al. (2011) obtain a similar result for Bosnia and Herzegovina. Other studies, however, find that remittances increase the likelihood of self-employment, including Funkhouser (1992) and Stanley (2015). Edwards and Rodríguez-Oreggia (2009) find that remittances increase labor force participation for some women in Mexico, and argue that this effect may be due to self-employment. The conflicting results in the literature reflect the uncertain theoretical relationship between remittances and self-employment. Remittances may loosen the financing constraints preventing people from becoming self-employed, or they may lessen the necessity of becoming self-employed in situations where employment opportunities are limited.

Remittances may not only affect the choice of work, but also the amount of effort expended on the job. This is difficult to measure for regular employment, but some studies that focus on agriculture find evidence that remittances change the effort that farmers put forth. For example, Khanal et al. (2015) find that remittance receipt among rural families in Nepal increases the amount of land that farmers abandon (that is, the amount of land left permanently fallow). Damon (2010) finds that remittance receipt increases the amount of land that farmers devote to subsistence crops and reduces the amount devoted to cash crops.

The impact of emigration and remittances on educational choices has also been studied. Several papers find that remittance receipt reduces child wage labor and increases recipient spending on education. These include Edwards and Ureta (2003), Yang (2008), Calero et al. (2009), Acosta (2011), and Alcaraz et al. (2012). Older students may be influenced by the “brain gain” phenomenon, in which the migration of highly skilled workers may give young people the incentive to obtain more education, so that they too will have the opportunity to migrate. This is yet another example of the demonstration effect of migration and remittance receipt that may have a significant impact on individuals’ labor-market behavior. Although there are some documented cases in which the “brain gain” motivation may be evident, most research suggests that this benefit is outweighed by other economic and social losses due to brain drain, which go beyond the labor-market impacts.

Overall, the evidence from the existing literature tells a remarkably consistent story about the aggregate labor-market effects of remittances. Wages and productivity tend to increase as a result of emigration and remittances, which appears to be consistent with a decrease in overall labor supply due to emigration. In addition, remittances decrease labor-force participation among recipients, which (ceteris paribus) also tends to reduce labor supply and contribute to increased wages. Remittances also tend to induce people to shift away from formal employment and toward informal and unpaid work, reducing the supply of labor in the market for formal employment, again placing upward pressure on wages. An interesting question, however, is whether remittances reduce aggregate labor force participation or, as the evidence from Posso (2012) suggests, increase it. The evidence regarding the effect of emigration and remittances on self-employment, entrepreneurship, and on the unemployment rate, on the other hand, is mixed. And while remittances appear to reduce child labor and increase educational spending, these effects probably do not have any effect on the labor market for adult workers.

Given the limited amount of research in this area that utilizes aggregate data and cross-country analysis, one important question is whether the findings from the previous literature are confirmed or contradicted by the aggregate data, and whether the aggregated data produces a set of stylized facts that is also internally consistent across different labor market outcomes. We therefore turn next to empirical exercises that aim to answer these questions.

3 Empirical assessment

As discussed in the introduction, our goal is to compile a broad set of stylized facts regarding the labor market effects of remittances in the hope that they would suggest a consistent theoretical framework for interpreting their role in the labor market and overall economy. We estimate the effects of remittances on the following measures of labor market performance: labor demand, as captured by unemployment; labor supply, as measured by labor force participation; wages, inequality, and finally, sectoral shifts in employment.

3.2 Labor demand: Unemployment and remittances

Measuring labor demand in most low- and middle-income countries is complicated by the fact that informal employment is widespread and hides the true amount of under-employment.⁸ In the absence of more precise and widely available indicators of labor demand, we chose to use ILO’s global unemployment estimates as a proxy, but also checked our results by re-running the estimations using only real observations.

In Table 1, we report the results of a dynamic Okun’s curve specification, augmented with various capital and income flows. In addition, we report results limiting our sample to only non-OECD countries in order to control for a possible upward bias of elasticities due to a higher reactivity of unemployment to labor demand in more advanced economies.⁹ In order to account for differences in unemployment rates linked to differences in the level of economic development, we also control for GDP per capita.

Table 1:

Unemployment dynamics and income flows

Note: Dynamic estimates using Arellano-Bond system GMM estimator, two-step estimator with robust errors. Equations 5 and 6 limit the sample to actually observed data points. Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

Table 1:

Unemployment dynamics and income flows

	(1) Baseline	(2) Investment	(3) All income flows	(4) +GDP per capita	(5) -GDP per capita Non-OECD countries	(6) Baseline: Non-OECD countries	(7) Baseline: real observations	(8) Investment: real observations
Unemployment (lagged)	0.503*** (0.137)	0.404*** (0.0397)	0.426*** (0.0474)	0.420*** (0.118)	0.494*** (0.0559)	0.464*** (0.136)	0.306*** (0.0660)	0.242*** (0.0334)
GDP growth	−0.0431*** (0.0170)		−0.0264*** (0.00915)	−0.0263** (0.0125)	−0.0238** (0.0116)	−0.0295* (0.0171)	−0.118*** (0.0201)
Remittances share (in % of GDP)	−4.114** (1.818)		−4.375*** (1.657)	−6.695** (2.773)	−6.954*** (2.554)	−4.234** (1.877)	−11.62** (5.278)	−6.866** (2.769)
Investment share (in % of GDP)		−0.133*** (0.0224)						−0.171*** (0.0304)
Remittances share (lagged, in % of GDP)		−5.355** (2.666)
FDI share (in % of GDP)			−1.233 (0.901)	−0.791 (2.396)	−0.764 (1.104)
Official aid share (lagged, in % of GDP)			0.00645 (0.00806)	−0.00976 (0.00960)	−0.0136 (0.00949)
GDP per capita				−2.23e-03 (6.71e-05)	4.10e-05 (5.22e-05)
Constant	4.632*** (1.252)	8.620*** (0.692)	5.833*** (0.586)	6.174*** (1.410)	5.327*** (0.735)	5.039*** (1.303)	7.183*** (0.633)	10.12*** (0.786)
Observations	2,284	2,153	1,826	1,804	1,725	1,929	1,361	1,331
Number of countries	139	132	120	120	115	117	124	118
Number of instruments	69	69	143	131	132	132	119	65

Note: Dynamic estimates using Arellano-Bond system GMM estimator, two-step estimator with robust errors. Equations 5 and 6 limit the sample to actually observed data points. Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

View Table

Table 1:

Unemployment dynamics and income flows

	(1) Baseline	(2) Investment	(3) All income flows	(4) +GDP per capita	(5) -GDP per capita Non-OECD countries	(6) Baseline: Non-OECD countries	(7) Baseline: real observations	(8) Investment: real observations
Unemployment (lagged)	0.503*** (0.137)	0.404*** (0.0397)	0.426*** (0.0474)	0.420*** (0.118)	0.494*** (0.0559)	0.464*** (0.136)	0.306*** (0.0660)	0.242*** (0.0334)
GDP growth	−0.0431*** (0.0170)		−0.0264*** (0.00915)	−0.0263** (0.0125)	−0.0238** (0.0116)	−0.0295* (0.0171)	−0.118*** (0.0201)
Remittances share (in % of GDP)	−4.114** (1.818)		−4.375*** (1.657)	−6.695** (2.773)	−6.954*** (2.554)	−4.234** (1.877)	−11.62** (5.278)	−6.866** (2.769)
Investment share (in % of GDP)		−0.133*** (0.0224)						−0.171*** (0.0304)
Remittances share (lagged, in % of GDP)		−5.355** (2.666)
FDI share (in % of GDP)			−1.233 (0.901)	−0.791 (2.396)	−0.764 (1.104)
Official aid share (lagged, in % of GDP)			0.00645 (0.00806)	−0.00976 (0.00960)	−0.0136 (0.00949)
GDP per capita				−2.23e-03 (6.71e-05)	4.10e-05 (5.22e-05)
Constant	4.632*** (1.252)	8.620*** (0.692)	5.833*** (0.586)	6.174*** (1.410)	5.327*** (0.735)	5.039*** (1.303)	7.183*** (0.633)	10.12*** (0.786)
Observations	2,284	2,153	1,826	1,804	1,725	1,929	1,361	1,331
Number of countries	139	132	120	120	115	117	124	118
Number of instruments	69	69	143	131	132	132	119	65

Note: Dynamic estimates using Arellano-Bond system GMM estimator, two-step estimator with robust errors. Equations 5 and 6 limit the sample to actually observed data points. Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

Results reported in Table 1 demonstrate that unemployment declines significantly and strongly across all specifications with a rise in the share of remittances, be they contemporaneous or lagged (see specification (2)). The estimated coefficient seems indeed to be smaller when the sample is limited only to non-OECD countries (around 1/3 smaller, see specification (6)), suggesting that, indeed, unemployment is less well suited to account for changes in labor demand in low-income and emerging countries with large informal labor markets. Interestingly, when limiting the sample only to real observations (specification (7)), the estimated coefficient is almost twice as large as in the other specifications, suggesting that—if anything—our results underestimate the true effect of remittances on labor demand when using a larger database that includes a significant amount of imputed data points. Also, as mentioned above, remittances have both a larger and more consistently significant impact on unemployment than either FDI or ODA, when all three are included in the estimations.

The above results can be consistent both with a fall in labor force participation and with an increase in labor demand. For example, if labor force participation falls among the unemployed by a greater proportion than among the total labor force (because of discouragement, for instance), such a fall in the participation rate could, by itself, cause total employment to decline despite a fall in the unemployment rate. Therefore, we must also examine the supply effect of remittances to get a better understanding of their effects on total labor demand, which is the purpose of the next sub-section.

3.3 Labor supply: Labor force participation and remittances

We first look at the impact of remittances on labor supply, as measured by the labor force participation rate (see Table 2). In the absence of good measures of standard control variables for labor force dynamics, only a lagged dependent variable and the size of the working-age population has been used.¹⁰ As the table demonstrates, remittances enter negatively and robustly across different specifications, including when the sample is limited to non-OECD countries (see specification (6)). Also, additional controls such as time dummies, investment ratios, the GDP per capita level relative to the United States, trade openness or real wages (either growth or levels) do not change the negative sign of remittances on labor force participation rates. In contrast to remittances, however, official aid enters positively, suggesting that the unconditional nature of remittances generates a strong income effect that depresses labor supply. FDI, on the other hand, does not seem to affect labor force participation in most specifications.

Table 2:

Dependent variable: Labor force participation rate

Note: Dynamic estimates using Arellano-Bond GMM estimator. Equations 5–9 contain time dummies. Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

Table 2:

Dependent variable: Labor force participation rate

Controls	(1) Remittances	(2) Working-age population	(3) All income flows	(4) +Liquid liabilities	(5) All income flows	(6) All income flows Non-OECD countries	(7) All controls: Wage growth	(8) All controls: Wage levels	(9) All controls: Trade openness
Labor force participation rate (lagged)	0.737*** (0.0961)	0.789*** (0.0628)	0.892*** (0.0474)	0.900*** (0.0476)	0.926*** (0.0347)	0.934*** (0.0346)	0.801*** (0.0585)	0.776*** (0.0589)	0.855*** (0.0444)
Working-age population (in % of total population)		0.00198* (0.00119)	0.00340* (0.00178)	0.00232 (0.00232)	0.00276* (0.00151)	0.00274* (0.00157)	−0.00429*** (0.00160)	0.00171 (0.00215)	−0.00165 (0.00164)
Remittances (as a share of GDP)	−6.220*** (2.171)	−4.302*** (1.374)	−3.191** (1.429)	−2.678** (1.350)	−2.358** (1.085)	−2.292** (1.164)	−6.221*** (1.393)	−7.055*** (1.792)	−3.817*** (1.232)
Official aid (as a share of GDP)			0.0426*** (0.0153)	0.0296** (0.0148)	0.0314** (0.0130)	0.0276** (0.0127)
FDI (as a share of GDP)			−1.061* (0.608)	−0.576 (0.754)	−0.902 (0.566)	(0.606)
Liquid liabilities (as a share of GDP)				−0.00951** (0.00376)
Total investment (as a share of GDP)							0.0106** (0.00415)	−0.00377 (0.00459)	0.00976** (0.00446)
Real wage; growth							0.0107*** (0.00338)		0.00657** (0.00310)
GDP per capita (relative to US)							1.66e-05*** (4.35e-06)	2.40e-05*** (6.01e-06)	1.16e-05*** (3.97e-06)
Log of real wage levels								−0.595*** (0.194)
Trade openness (in percent of GDP)									−0.00503*** (0.00189)
Constant	16.71*** (6.099)	12.00*** (3.833)	4.506 (2.967)	5.171* (3.084)	2.884 (1.932)	2.402 (1.921)	15.49*** (4.002)	18.24*** (4.879)	10.47*** (3.287)
Observations	2,284	2,278	1,820	1,450	1,820	1,741	1,484	1,528	1,480
Number of countries	139	139	120	107	120	115	92	92	92
Number of instruments	47	81	83	61	104	104	103	131	131
Time dummies	No	No	No	No	Yes	Yes	Yes	Yes	Yes
Sargan test	44.63	86.28	86.18	69.40*	75.30	68.17	78.58	120.7	118.6

Note: Dynamic estimates using Arellano-Bond GMM estimator. Equations 5–9 contain time dummies. Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

View Table

Table 2:

Dependent variable: Labor force participation rate

Controls	(1) Remittances	(2) Working-age population	(3) All income flows	(4) +Liquid liabilities	(5) All income flows	(6) All income flows Non-OECD countries	(7) All controls: Wage growth	(8) All controls: Wage levels	(9) All controls: Trade openness
Labor force participation rate (lagged)	0.737*** (0.0961)	0.789*** (0.0628)	0.892*** (0.0474)	0.900*** (0.0476)	0.926*** (0.0347)	0.934*** (0.0346)	0.801*** (0.0585)	0.776*** (0.0589)	0.855*** (0.0444)
Working-age population (in % of total population)		0.00198* (0.00119)	0.00340* (0.00178)	0.00232 (0.00232)	0.00276* (0.00151)	0.00274* (0.00157)	−0.00429*** (0.00160)	0.00171 (0.00215)	−0.00165 (0.00164)
Remittances (as a share of GDP)	−6.220*** (2.171)	−4.302*** (1.374)	−3.191** (1.429)	−2.678** (1.350)	−2.358** (1.085)	−2.292** (1.164)	−6.221*** (1.393)	−7.055*** (1.792)	−3.817*** (1.232)
Official aid (as a share of GDP)			0.0426*** (0.0153)	0.0296** (0.0148)	0.0314** (0.0130)	0.0276** (0.0127)
FDI (as a share of GDP)			−1.061* (0.608)	−0.576 (0.754)	−0.902 (0.566)	(0.606)
Liquid liabilities (as a share of GDP)				−0.00951** (0.00376)
Total investment (as a share of GDP)							0.0106** (0.00415)	−0.00377 (0.00459)	0.00976** (0.00446)
Real wage; growth							0.0107*** (0.00338)		0.00657** (0.00310)
GDP per capita (relative to US)							1.66e-05*** (4.35e-06)	2.40e-05*** (6.01e-06)	1.16e-05*** (3.97e-06)
Log of real wage levels								−0.595*** (0.194)
Trade openness (in percent of GDP)									−0.00503*** (0.00189)
Constant	16.71*** (6.099)	12.00*** (3.833)	4.506 (2.967)	5.171* (3.084)	2.884 (1.932)	2.402 (1.921)	15.49*** (4.002)	18.24*** (4.879)	10.47*** (3.287)
Observations	2,284	2,278	1,820	1,450	1,820	1,741	1,484	1,528	1,480
Number of countries	139	139	120	107	120	115	92	92	92
Number of instruments	47	81	83	61	104	104	103	131	131
Time dummies	No	No	No	No	Yes	Yes	Yes	Yes	Yes
Sargan test	44.63	86.28	86.18	69.40*	75.30	68.17	78.58	120.7	118.6

Note: Dynamic estimates using Arellano-Bond GMM estimator. Equations 5–9 contain time dummies. Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

Breaking down the overall effect of remittances on male versus female participation rates, the negative impact seems to be weaker for men than for women (see Tables 3 and 4). Without any further controls, remittances even seem to positively affect male participation rates. Especially when controlling for time dummies, investment, relative GDP per capita levels, trade openness and real wages, estimated elasticities of female participation rates with respect to remittances are larger than those for men and with higher significance levels (see equations (7) and (8) in Tables 3 and 4).¹¹ This is in line with findings in other labor supply studies that indicate more elastic female participation rates across a number of policy and economic variables (e.g. taxes, wages, etc.) than male rates.

Table 3:

Dependent variable: Male labor force participation rate

Note: Dynamic estimates using Arellano-Bond GMM estimator. Equations 5–9 contain time dummies. Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

Table 3:

Dependent variable: Male labor force participation rate

Controls	(1) Remittances	(2) Working-age population	(3) All income flows	(4) +Liquid liabilities	(5) All income flows	(6) All income flows Non-OECD countries	(7) All controls: Wage growth	(8) All controls: Wage levels	(9) All controls: Trade openness
Labor force participation rate (lagged)	0.895*** (0.0586)	0.754*** (0.0559)	0.681*** (0.0565)	0.694*** (0.0592)	0.906*** (0.0368)	0.897*** (0.0362)	0.848*** (0.0573)	0.838*** (0.0518)	0.954*** (0.146)
Working-age population (in % of total population)		−0.00367* (0.00191)	−0.00301 (0.00194)	−0.00295 (0.00242)	−0.00298 (0.00228)	−0.00314 (0.00226)	−0.00738** (0.00287)	−0.00613** (0.00261)	−0.00201 (0.00565)
Remittances (as a share of GDP)	0.854* (0.510)	0.535 (0.496)	−1.493*** (0.422)	−2.653*** (0.759)	−0.734* (0.424)	−0.839** (0.411)	−1.567* (0.886)	−2.013** (0.825)	−0.529 (1.020)
Official aid (as a share of GDP)			0.0127* (0.00668)	0.0106 (0.00661)	−0.00404 (0.00791)	−0.00437 (0.00772)
FDI (as a share of GDP)			−4.225*** (0.811)	−4.987*** (0.972)	−0.417 (0.434)	−0.533 (0.448)
Liquid liabilities (as a share of GDP)				0.00170 (0.00192)
Total investment (as a share of GDP)							0.00195 (0.00293)	−0.00338 (0.00476)	0.00299 (0.00536)
Real wage growth							−0.000355 (0.00332)		0.00161 (0.00754)
GDP per capita (relative to US)							2.43e-05*** (8.00e-06)	2.74e-05*** (7.93e-06)	9.78e-06 (1.69e-05)
Log of real wage levels								−0.206** (0.103)
Trade openness (in percent of GDP)									−0.00240 (0.00727)
Constant	7.739* (4.388)	20.84*** (5.348)	26.35*** (5.297)	25.64*** (5.754)	9.170** (4.237)	10.03** (4.151)	16.61*** (6.162)	18.41*** (5.966)	4.904 (15.32)
Observations	2,284	2,278	1,820	1,450	1,820	1,741	1,484	1,528	1,480
Number of countries	139	139	120	107	120	115	92	92	92
Number of instruments	47	81	83	61	104	104	103	131	27
Time dummies	No	No	No	No	Yes	Yes	Yes	Yes	Yes
Sargan test	58.93	128.1	98.54	89.76	89.52	95.18	130.3	174.2	0.167

Note: Dynamic estimates using Arellano-Bond GMM estimator. Equations 5–9 contain time dummies. Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

View Table

Table 3:

Dependent variable: Male labor force participation rate

Controls	(1) Remittances	(2) Working-age population	(3) All income flows	(4) +Liquid liabilities	(5) All income flows	(6) All income flows Non-OECD countries	(7) All controls: Wage growth	(8) All controls: Wage levels	(9) All controls: Trade openness
Labor force participation rate (lagged)	0.895*** (0.0586)	0.754*** (0.0559)	0.681*** (0.0565)	0.694*** (0.0592)	0.906*** (0.0368)	0.897*** (0.0362)	0.848*** (0.0573)	0.838*** (0.0518)	0.954*** (0.146)
Working-age population (in % of total population)		−0.00367* (0.00191)	−0.00301 (0.00194)	−0.00295 (0.00242)	−0.00298 (0.00228)	−0.00314 (0.00226)	−0.00738** (0.00287)	−0.00613** (0.00261)	−0.00201 (0.00565)
Remittances (as a share of GDP)	0.854* (0.510)	0.535 (0.496)	−1.493*** (0.422)	−2.653*** (0.759)	−0.734* (0.424)	−0.839** (0.411)	−1.567* (0.886)	−2.013** (0.825)	−0.529 (1.020)
Official aid (as a share of GDP)			0.0127* (0.00668)	0.0106 (0.00661)	−0.00404 (0.00791)	−0.00437 (0.00772)
FDI (as a share of GDP)			−4.225*** (0.811)	−4.987*** (0.972)	−0.417 (0.434)	−0.533 (0.448)
Liquid liabilities (as a share of GDP)				0.00170 (0.00192)
Total investment (as a share of GDP)							0.00195 (0.00293)	−0.00338 (0.00476)	0.00299 (0.00536)
Real wage growth							−0.000355 (0.00332)		0.00161 (0.00754)
GDP per capita (relative to US)							2.43e-05*** (8.00e-06)	2.74e-05*** (7.93e-06)	9.78e-06 (1.69e-05)
Log of real wage levels								−0.206** (0.103)
Trade openness (in percent of GDP)									−0.00240 (0.00727)
Constant	7.739* (4.388)	20.84*** (5.348)	26.35*** (5.297)	25.64*** (5.754)	9.170** (4.237)	10.03** (4.151)	16.61*** (6.162)	18.41*** (5.966)	4.904 (15.32)
Observations	2,284	2,278	1,820	1,450	1,820	1,741	1,484	1,528	1,480
Number of countries	139	139	120	107	120	115	92	92	92
Number of instruments	47	81	83	61	104	104	103	131	27
Time dummies	No	No	No	No	Yes	Yes	Yes	Yes	Yes
Sargan test	58.93	128.1	98.54	89.76	89.52	95.18	130.3	174.2	0.167

Note: Dynamic estimates using Arellano-Bond GMM estimator. Equations 5–9 contain time dummies. Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

One noteworthy finding from these estimations is that remittances tend to have both larger (in absolute value) and more consistently significant impacts on labor force participation rates than FDI and ODA have. This result suggests that remittances may also have larger impacts than FDI or ODA on other labor-market outcomes as well, and indeed we have already seen above that this is true for the unemployment rate. To the extent that remittances generally have a greater impact on labor markets than FDI or ODA, policymakers may need to adjust both the focus and the execution of both their development policies in general and their labor-market policies in particular to take remittances’ effects into account.

Table 4:

Dependent variable: Female labor force participation rate

Note: Dynamic estimates using Arellano-Bond GMM estimator. Equations 5–9 contain time dummies. Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

Table 4:

Dependent variable: Female labor force participation rate

Controls	(1) Remittances	(2) Working-age population	(3) All income flows	(4) +Liquid liabilities	(5) All income flows	(6) All income flows Non-OECD countries	(7) All controls: Wage growth	(8) All controls: Wage levels	(9) All controls: Trade openness
Labor force participation rate (lagged)	0.926*** (0.0570)	0.826*** (0.0476)	0.908*** (0.0419)	0.927*** (0.0395)	0.973*** (0.0210)	0.976*** (0.0203)	0.886*** (0.0450)	0.886*** (0.0433)	0.925*** (0.0355)
Working-age population (in % of total population)		0.00490** (0.00233)	0.00376 (0.00277)	0.00155 (0.00371)	0.00376 (0.00272)	0.00315 (0.00274)	−0.00186 (0.00270)	0.00542 (0.00382)	0.000397 (0.00257)
Remittances (as a share of GDP)	−4.527 (3.019)	−7.915*** (1.995)	−5.322** (2.293)	−3.618** (1.803)	−1.607 (1.157)	−1.619 (1.209)	−7.807*** (1.918)	−6.251*** (2.253)	−4.552*** (1.768)
Official aid (as a share of GDP)			0.0548** (0.0252)	0.0296 (0.0235)	0.0275 (0.0192)	0.0225 (0.0182)
FBI (as a share of GDP)			−0.139 (0.789)	0.877 (0.955)	−0.849 (0.707)	−0.807 (0.740)
Liquid liabilities (as a share of GDP)				−0.0136** (0.00687)
Total investment (as a share of GDP)							0.0113* (0.00630)	−0.00157 (0.00632)	0.00454 (0.00585)
Real wage growth							0.0145** (0.00611)		0.00777 (0.00538)
GDP per capita (relative to US)							6.19e-06 (4.73e-06)	9.00e-06* (4.66e-06)	4.75e-06 (4.92e-06)
Log of real wage levels								−0.403* (0.228)
Trade openness (in percent of GDP)									−0.00101 (0.00173)
Constant	4.052 (2.990)	5.817*** (1.751)	2.197 (2.104)	3.295 (2.345)	−1.010 (1.334)	−0.716 (1.351)	7.224*** (2.004)	5.572** (2.463)	3.603* (1.950)
Observations	2,284	2,278	1,820	1,450	1,820	1,741	1,484	1,528	1,480
Number of countries	139	139	120	107	120	115	92	92	92
Number of instruments	47	81	83	61	104	104	103	131	131
Time dummies	No	No	No	No	Yes	Yes	Yes	Yes	Yes
Sargan test	41.15	93.19	91.74	61.47	82.78	75.71	78.44	119.2	119

Note: Dynamic estimates using Arellano-Bond GMM estimator. Equations 5–9 contain time dummies. Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

View Table

Table 4:

Dependent variable: Female labor force participation rate

Controls	(1) Remittances	(2) Working-age population	(3) All income flows	(4) +Liquid liabilities	(5) All income flows	(6) All income flows Non-OECD countries	(7) All controls: Wage growth	(8) All controls: Wage levels	(9) All controls: Trade openness
Labor force participation rate (lagged)	0.926*** (0.0570)	0.826*** (0.0476)	0.908*** (0.0419)	0.927*** (0.0395)	0.973*** (0.0210)	0.976*** (0.0203)	0.886*** (0.0450)	0.886*** (0.0433)	0.925*** (0.0355)
Working-age population (in % of total population)		0.00490** (0.00233)	0.00376 (0.00277)	0.00155 (0.00371)	0.00376 (0.00272)	0.00315 (0.00274)	−0.00186 (0.00270)	0.00542 (0.00382)	0.000397 (0.00257)
Remittances (as a share of GDP)	−4.527 (3.019)	−7.915*** (1.995)	−5.322** (2.293)	−3.618** (1.803)	−1.607 (1.157)	−1.619 (1.209)	−7.807*** (1.918)	−6.251*** (2.253)	−4.552*** (1.768)
Official aid (as a share of GDP)			0.0548** (0.0252)	0.0296 (0.0235)	0.0275 (0.0192)	0.0225 (0.0182)
FBI (as a share of GDP)			−0.139 (0.789)	0.877 (0.955)	−0.849 (0.707)	−0.807 (0.740)
Liquid liabilities (as a share of GDP)				−0.0136** (0.00687)
Total investment (as a share of GDP)							0.0113* (0.00630)	−0.00157 (0.00632)	0.00454 (0.00585)
Real wage growth							0.0145** (0.00611)		0.00777 (0.00538)
GDP per capita (relative to US)							6.19e-06 (4.73e-06)	9.00e-06* (4.66e-06)	4.75e-06 (4.92e-06)
Log of real wage levels								−0.403* (0.228)
Trade openness (in percent of GDP)									−0.00101 (0.00173)
Constant	4.052 (2.990)	5.817*** (1.751)	2.197 (2.104)	3.295 (2.345)	−1.010 (1.334)	−0.716 (1.351)	7.224*** (2.004)	5.572** (2.463)	3.603* (1.950)
Observations	2,284	2,278	1,820	1,450	1,820	1,741	1,484	1,528	1,480
Number of countries	139	139	120	107	120	115	92	92	92
Number of instruments	47	81	83	61	104	104	103	131	131
Time dummies	No	No	No	No	Yes	Yes	Yes	Yes	Yes
Sargan test	41.15	93.19	91.74	61.47	82.78	75.71	78.44	119.2	119

Note: Dynamic estimates using Arellano-Bond GMM estimator. Equations 5–9 contain time dummies. Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

In order to get a better sense of the impact of the different control variables on labor supply across our sample, we also run quantile regressions. Figure 1 depicts the marginal effects of each of the four independent control variables that have been retained in Table 2, conditional on different quantiles of the dependent variable. As the chart demonstrates, the impact of remittances on labor supply is particularly strong at very high levels of labor force participation rates as well as at lower quantiles. At both ends of the distribution of labor force participation rates, countries with high levels of remittances inflows can be found with strong regional variations: whereas most countries with high labor force participation rates can be found in Sub-Saharan Africa and South and South-East Asia (Burkina Faso, Nepal, Cambodia), those receiving countries with low participation rates are mostly found in the Middle East and North Africa (e.g. Jordan, Lebanon, Egypt). In these two groups of countries, high shares of remittances to GDP depress labor supply significantly (see Figure 1, Panel A). Interestingly, in the same group of countries, foreign direct investment leads to a significant increase in labor supply (see Figure 1, Panel B), but it is only in countries with already high levels of labor force participation rates that official aid seems to further increase them (see Figure 1, Panel C).

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Labor force participation rates – Quantile regressions

Citation: IMF Working Papers 2018, 102; 10.5089/9781484353615.001.A001

Note: The figure presents the effect of different income and capital flows on labor force participation rates for different quantiles of participation. The figure demonstrates that for both remittances and foreign direct investment, effects are largest (and significant) only for either very large or very low rates of labor force participation. In contrast, effects are more homogeneous across quantiles for both official development aid and changes in liquid liabilities.

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As labor demand increases (as demonstrated by the fall in unemployment) and labor supply declines (with a fall in labor force participation), we would a priori expect an increase in wage growth that reflects this relative shift of supply and demand in the labor market. In the next section, we will verify directly whether this is indeed the case.

3.4 Wages and inequality

3.4.1 Do remittances lift wages?

An alternative way to measure the impact of remittances on labor outcomes is by looking at the evolution of wages. Even more than in the case of data on employment and labor force participation, the availability of wage data is patchy for low- and middle-income countries. Most people in the informal labor market do not report labor earnings and available wage data in these countries is limited to some formal sector enterprises, significantly over-estimating the average wage level. In order to remedy these biases in the available—official—wage data, Ernst et al. (2016) have recently put together a new wage database, using existing wage data with theoretical considerations in order to better take into account the size of the informal economy and the spread of working poverty to generate improved estimates of the dynamics of labor earnings.

In order to estimate the impact of remittances on (real) wage growth, we (re-) interpret equation (1) as a wage Phillips curve augmented with the (lagged) shares of remittances. Given that labor supply and demand factors are being controlled for in such a specification, remittances act as a shifter on the wage bargaining coefficient that indicates how much of the joint matching rent can be appropriated by the labor demand side.¹²

Estimating the impact of remittances on wages potentially runs into reverse causality issues as low wages might induce higher out-migration and hence a higher share of remittances flowing back into the economy. In order to (partially) remedy these issues, we lagged the remittances share by one period. Table 5 reports the results of various specifications of an otherwise standard wage inflation equation, including either investment demand or unemployment (or both) as the proxy variable for labor demand that influences wage growth. Shifts in labor supply that influence the evolution of wages are being taken into account using information on changes in the labor force participation rates.

Table 5:

Wage growth and remittances

Note: Dynamic estimates using Arellano-Bond GMM estimator, assuming weak exogeneity for income flows. Year dummies included in all regressions. Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 Source: ILO Wage projection database (see Ernst et al., 2016); own calculations.

Table 5:

Wage growth and remittances

	(1) Investment share	(2) Unemployment	(3) All basic controls	Unemployment + Remittances	(5) +Agriculture	(6) +Changes in labor force participation	(7) Full specification	(8) Full specification non-OECD countries
Real wage growth (lagged)	0.115 (0.191)	0.250 (0.309)	0.0861 (0.187)	0.223** (0.0881)	0.252*** (0.0950)	0.246*** (0.0953)	0.187* (0.0972)	0.186* (0.100)
Investment share (in % of GDP)	0.171*** (0.0393)		0.199*** (0.0323)				0.220*** (0.0309)	0.222*** (0.0365)
ΔLFPRt			−0.328** (0.164)			−0.516** (0.211)	−0.466** (0.202)	−0.348 (0.236)
Unemployment rate		−0.280** (0.141)	−0.228* (0.121)	−0.398*** (0.153)	−0.386** (0.158)	−0.381** (0.157)	−0.301** (0.147)	−0.330* (0.178)
Remittances (in % of GDP, lagged)				−30.82** (12.99)	−35.15*** (13.56)	−34.16** (13.50)	−29.74** (13.08)	−31.15** (14.55)
Agr. employment (in %. of total)					0.0399*** (0.0111)	0.0376*** (0.0110)	0.0318*** (0.0108)	0.00511 (0.0123)
Constant	−2.362*** (0.677)	3.565** (1.446)	−0.982 (1.150)	6.022*** (1.559)	4.969*** (1.595)	5.032*** (1.603)	−0.770 (1.435)	0.366 (1.977)
Observations	2.091	2,203	2,091	1,441	1,441	1,441	1,441	1,114
Number of countries	112	112	112	92	92	92	92	70
Number of instruments	41	22	43	101	101	102	91	103
Sargan test	29.57	0.195	29.61	109.4	84.95	84.55	86.06	76.75

Note: Dynamic estimates using Arellano-Bond GMM estimator, assuming weak exogeneity for income flows. Year dummies included in all regressions. Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 Source: ILO Wage projection database (see Ernst et al., 2016); own calculations.

View Table

Table 5:

Wage growth and remittances

	(1) Investment share	(2) Unemployment	(3) All basic controls	Unemployment + Remittances	(5) +Agriculture	(6) +Changes in labor force participation	(7) Full specification	(8) Full specification non-OECD countries
Real wage growth (lagged)	0.115 (0.191)	0.250 (0.309)	0.0861 (0.187)	0.223** (0.0881)	0.252*** (0.0950)	0.246*** (0.0953)	0.187* (0.0972)	0.186* (0.100)
Investment share (in % of GDP)	0.171*** (0.0393)		0.199*** (0.0323)				0.220*** (0.0309)	0.222*** (0.0365)
ΔLFPRt			−0.328** (0.164)			−0.516** (0.211)	−0.466** (0.202)	−0.348 (0.236)
Unemployment rate		−0.280** (0.141)	−0.228* (0.121)	−0.398*** (0.153)	−0.386** (0.158)	−0.381** (0.157)	−0.301** (0.147)	−0.330* (0.178)
Remittances (in % of GDP, lagged)				−30.82** (12.99)	−35.15*** (13.56)	−34.16** (13.50)	−29.74** (13.08)	−31.15** (14.55)
Agr. employment (in %. of total)					0.0399*** (0.0111)	0.0376*** (0.0110)	0.0318*** (0.0108)	0.00511 (0.0123)
Constant	−2.362*** (0.677)	3.565** (1.446)	−0.982 (1.150)	6.022*** (1.559)	4.969*** (1.595)	5.032*** (1.603)	−0.770 (1.435)	0.366 (1.977)
Observations	2.091	2,203	2,091	1,441	1,441	1,441	1,441	1,114
Number of countries	112	112	112	92	92	92	92	70
Number of instruments	41	22	43	101	101	102	91	103
Sargan test	29.57	0.195	29.61	109.4	84.95	84.55	86.06	76.75

Note: Dynamic estimates using Arellano-Bond GMM estimator, assuming weak exogeneity for income flows. Year dummies included in all regressions. Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 Source: ILO Wage projection database (see Ernst et al., 2016); own calculations.

Across all specifications, the share of remittances to GDP exerts downward pressure on aggregate wage growth, in contrast to what would have been expected from the shifts on labor demand and supply induced by remittances as analyzed in the previous section. Interpreting the results suggests that remittances might actually lower the pressure from workers asking for a higher share of the matching rent. We will see in the following sections, however, that this might not be the only interpretation and that composition effects are possibly explaining the negative estimated coefficient.

3.4.2 Remittances and the labor income share

The slowdown in wage growth may be indicating that the growth rate of labor productivity is also falling, or in other words that remittances reduce the growth of labor productivity. The most direct evidence of how remittances might affect wage growth in comparison to productivity is to look at their effect on labor’s share of income. In particular, should productivity growth fall faster than wage growth, the labor income share would increase. In addition, in those countries where governments manage to control and tax (at least parts of) the remittances flow, public sector consumption -for instance through higher public sector wages – might further contribute to a rise in the labor income share.¹³

In order to test such a relationship, Table 6 reports estimation results of the effect of remittances on changes in the labor income share in our sample, including various control variables and for the sub-sample of non-OECD countries. As the table demonstrates, remittances positively and significantly affect changes in labor income share across all specifications, indicating an increase in labor income share as the flow of remittances grows. Together with the results of the previous subsection, this suggests that remittances exercise a strong negative impact on productivity growth, over and above the one on wages.

Table 6:

Change in the labor income share and remittances

Note: Dynamic estimates using Arellano-Bond GMM estimator, assuming weak exogeneity for income flows. Year dummies included in specifications (4) and (5). Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 Source: ILO Wage projection database (see Ernst et al., 2016); own calculations.

Table 6:

Change in the labor income share and remittances

	(1) Investment share	(2) Informal employment	(3) Agricultural employment	(4) Full specification + time dummies	(5) Full specification: non-OECD countries
Change in labor income share (lagged)	−0.0268 (0.0879)	−0.0255 (0.0879)	−0.0243 (0.0880)	−0.0175 (0.0906)	−0.0320 (0.0929)
Investment share (in % of GDP)	0.0631*** (0.0239)	0.0692*** (0.0242)	0.0689*** (0.0242)	0.0805*** (0.0244)	0.0877*** (0.0279)
Informal employment (in % of GDP)		−0.0196*** (0.00711)	−0.0771*** (0.0168)	−0.0773*** (0.0168)	−0.0805*** (0.0188)
Agricultural employment (in % of total employment)			0.0727*** (0.0186)	0.0715*** (0.0184)	0.0719*** (0.0203)
Remittances (in % of GDP)	6.205** (2.956)	7.681** (3.039)	7.047** (3.027)	7.656** (3.057)	7.104** (3.395)
Constant	−1.580*** (0.596)	−1.045* (0.620)	−0.786 (0.621)	−1.862** (0.774)	−1.827* (0.994)
Observations	1,441	1,441	1,441	1,441	1,114
Number of countries	92	92	92	92	70
Number of instruments	108	109	110	128	128
Sargan test	113	113.1	113.2	103.6	96.77
Time dummies	No	No	No	Yes	Yes

Note: Dynamic estimates using Arellano-Bond GMM estimator, assuming weak exogeneity for income flows. Year dummies included in specifications (4) and (5). Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 Source: ILO Wage projection database (see Ernst et al., 2016); own calculations.

View Table

Table 6:

Change in the labor income share and remittances

	(1) Investment share	(2) Informal employment	(3) Agricultural employment	(4) Full specification + time dummies	(5) Full specification: non-OECD countries
Change in labor income share (lagged)	−0.0268 (0.0879)	−0.0255 (0.0879)	−0.0243 (0.0880)	−0.0175 (0.0906)	−0.0320 (0.0929)
Investment share (in % of GDP)	0.0631*** (0.0239)	0.0692*** (0.0242)	0.0689*** (0.0242)	0.0805*** (0.0244)	0.0877*** (0.0279)
Informal employment (in % of GDP)		−0.0196*** (0.00711)	−0.0771*** (0.0168)	−0.0773*** (0.0168)	−0.0805*** (0.0188)
Agricultural employment (in % of total employment)			0.0727*** (0.0186)	0.0715*** (0.0184)	0.0719*** (0.0203)
Remittances (in % of GDP)	6.205** (2.956)	7.681** (3.039)	7.047** (3.027)	7.656** (3.057)	7.104** (3.395)
Constant	−1.580*** (0.596)	−1.045* (0.620)	−0.786 (0.621)	−1.862** (0.774)	−1.827* (0.994)
Observations	1,441	1,441	1,441	1,441	1,114
Number of countries	92	92	92	92	70
Number of instruments	108	109	110	128	128
Sargan test	113	113.1	113.2	103.6	96.77
Time dummies	No	No	No	Yes	Yes

Note: Dynamic estimates using Arellano-Bond GMM estimator, assuming weak exogeneity for income flows. Year dummies included in specifications (4) and (5). Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 Source: ILO Wage projection database (see Ernst et al., 2016); own calculations.

3.4.3 How does inequality evolve?

A further indication of how remittances affect the distribution of incomes and, hence, the average dynamics of wages comes from a closer look at their impact on income distribution measures directly. Here, we make use of the Standardized World Income Inequality Database that contains estimates on Gini coefficients for 176 countries dating as far back as 1960. The database is making use of multiple imputation techniques as described in Solt (2016). As before, we present different specifications, including those limited to non-OECD countries (see table 7).

Table 7:

Determinants of market inequality

Note: Dynamic estimates using Arellano-Bond GMM estimator, assuming weak exogeneity for income flows. Equations 7 and 8 limit the sample to actually observed data points. Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

Table 7:

Determinants of market inequality

	(1) Basic controls	(2) Basic controls time dummies	(3) All controls	(4) All controls time dummies	(5) Sectoral controls	(6) Sectoral controls + time dummies	(7) Full specification non-OECD countries	(8) Full specification non-OFCD countries
Market inequality (lagged)	0.815*** (0.0568)	0.865*** (0.0527)	0.836*** (0.0465)	0.835*** (0.0385)	0.878*** (0.0484)	0.858*** (0.0395)	0.873*** (0.0475)	0.853*** (0.0421)
Share of working-age-to-total population (in %)			−0.0192** (0.00870)	−0.0195** (0.00773)	−0.00882 (0.00895)	−0.0114 (0.00763)	−0.0118 (0.0102)	−0.0129 (0.00881)
Export share (in %)			−6.033*** (1.472)	−6.365*** (1.488)	−3.219***(1.245)	−3.383*** (1.270)	−3.117* (1.651)	−2.419 (1.666)
GDP growth (p.a., in %)			−0.0212*** (0.00704)	−0.0240*** (0.00777)	−0.0122** (0.00616)	−0.0152** (0.00690)	−0.00537 (0.00610)	−0.00601 (0.00689)
Agriculture, forestry, hunting and fishing (%)					0.00364 (0.00358)	0.00493 (0.00343)	0.00517 (0.00384)	0.00616* (0.00373)
Mining and quarrying (%)					−0.150*** (0.0392)	−0.156*** (0.0359)	−0.159*** (0.0423)	−0.174*** (0.0398)
Manufacturing (%)					0.0104* (0.00596)	0.0105* (0.00585)	0.0160*** (0.00613)	0.0134** (0.00597)
Accommodation and restaurants (%)					−0.00617 (0.0145)	−0.00453 (0.0144)	0.0257 (0.0173)	0.0328* (0.0170)
Health and social work activities (%)					0.0221 (0.0158)	0.0280** (0.0136)	0.0104 (0.0198)	0.0192 (0.0182)
Public administration and defense; compulsory social security (%)					−0.00935 (0.00883)	−0.0117 (0.00844)	−0.00940 (0.00988)	−0.0148 (0.00946)
Other services (%)					0.108** (0.0511)	0.131*** (0.0421)	0.120** (0.0532)	0.142*** (0.0476)
Share of own-account workers, total (%.)	−0.00102 (0.00217)	−0.00307 (0.00202)	−0.0126*** (0.00305)	−0.0128*** (0.00278)	−0.00323 (0.00233)	−0.00376 (0.00231)	−0.00319 (0.00238)	−0.00322 (0.00237)
Unemployment rate (%)	0.0290*** (0.00858)	0.0195** (0.00788)	0.0227*** (0.00632)	0.0221*** (0.00583)	0.0234*** (0.00723)	0.0245*** (0.00642)	0.0165*** (0.00512)	0.0153*** (0.00481)
Remittances (share of GDP, in %)	−3.284*** (1.070)	−2.135** (0.977)	−3.452*** (1.122)	−3.394*** (0.994)	−1.824** (0.874)	−1.961** (0.786)	−1.801** (0.837)	−1.561** (0.755)
Constant	8.203*** (2.468)	6.287*** (2.285)	9.279*** (2.816)	9.579*** (2.347)	5.459** (2.433)	6.601*** (1.989)	5.674** (2.454)	6.668*** (2.136)
Observations	1,751	1,751	1,745	1,745	1,745	1,745	1,403	1,403
Number of countries	127	127	126	126	126	126	105	105
Number of instruments	95	116	98	119	105	126	117	105
Sargan test	72.21	66.13	66.87	58.38	68.60	61.97	81.74	77.91
Time dummies	No	Yes	No	Yes	No	Yes	No	Yes

Note: Dynamic estimates using Arellano-Bond GMM estimator, assuming weak exogeneity for income flows. Equations 7 and 8 limit the sample to actually observed data points. Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

View Table

Table 7:

Determinants of market inequality

	(1) Basic controls	(2) Basic controls time dummies	(3) All controls	(4) All controls time dummies	(5) Sectoral controls	(6) Sectoral controls + time dummies	(7) Full specification non-OECD countries	(8) Full specification non-OFCD countries
Market inequality (lagged)	0.815*** (0.0568)	0.865*** (0.0527)	0.836*** (0.0465)	0.835*** (0.0385)	0.878*** (0.0484)	0.858*** (0.0395)	0.873*** (0.0475)	0.853*** (0.0421)
Share of working-age-to-total population (in %)			−0.0192** (0.00870)	−0.0195** (0.00773)	−0.00882 (0.00895)	−0.0114 (0.00763)	−0.0118 (0.0102)	−0.0129 (0.00881)
Export share (in %)			−6.033*** (1.472)	−6.365*** (1.488)	−3.219***(1.245)	−3.383*** (1.270)	−3.117* (1.651)	−2.419 (1.666)
GDP growth (p.a., in %)			−0.0212*** (0.00704)	−0.0240*** (0.00777)	−0.0122** (0.00616)	−0.0152** (0.00690)	−0.00537 (0.00610)	−0.00601 (0.00689)
Agriculture, forestry, hunting and fishing (%)					0.00364 (0.00358)	0.00493 (0.00343)	0.00517 (0.00384)	0.00616* (0.00373)
Mining and quarrying (%)					−0.150*** (0.0392)	−0.156*** (0.0359)	−0.159*** (0.0423)	−0.174*** (0.0398)
Manufacturing (%)					0.0104* (0.00596)	0.0105* (0.00585)	0.0160*** (0.00613)	0.0134** (0.00597)
Accommodation and restaurants (%)					−0.00617 (0.0145)	−0.00453 (0.0144)	0.0257 (0.0173)	0.0328* (0.0170)
Health and social work activities (%)					0.0221 (0.0158)	0.0280** (0.0136)	0.0104 (0.0198)	0.0192 (0.0182)
Public administration and defense; compulsory social security (%)					−0.00935 (0.00883)	−0.0117 (0.00844)	−0.00940 (0.00988)	−0.0148 (0.00946)
Other services (%)					0.108** (0.0511)	0.131*** (0.0421)	0.120** (0.0532)	0.142*** (0.0476)
Share of own-account workers, total (%.)	−0.00102 (0.00217)	−0.00307 (0.00202)	−0.0126*** (0.00305)	−0.0128*** (0.00278)	−0.00323 (0.00233)	−0.00376 (0.00231)	−0.00319 (0.00238)	−0.00322 (0.00237)
Unemployment rate (%)	0.0290*** (0.00858)	0.0195** (0.00788)	0.0227*** (0.00632)	0.0221*** (0.00583)	0.0234*** (0.00723)	0.0245*** (0.00642)	0.0165*** (0.00512)	0.0153*** (0.00481)
Remittances (share of GDP, in %)	−3.284*** (1.070)	−2.135** (0.977)	−3.452*** (1.122)	−3.394*** (0.994)	−1.824** (0.874)	−1.961** (0.786)	−1.801** (0.837)	−1.561** (0.755)
Constant	8.203*** (2.468)	6.287*** (2.285)	9.279*** (2.816)	9.579*** (2.347)	5.459** (2.433)	6.601*** (1.989)	5.674** (2.454)	6.668*** (2.136)
Observations	1,751	1,751	1,745	1,745	1,745	1,745	1,403	1,403
Number of countries	127	127	126	126	126	126	105	105
Number of instruments	95	116	98	119	105	126	117	105
Sargan test	72.21	66.13	66.87	58.38	68.60	61.97	81.74	77.91
Time dummies	No	Yes	No	Yes	No	Yes	No	Yes

Note: Dynamic estimates using Arellano-Bond GMM estimator, assuming weak exogeneity for income flows. Equations 7 and 8 limit the sample to actually observed data points. Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

As the results in Table 7 document, remittances as a share of GDP enter significantly negatively in all different specifications. The size of the estimated coefficients only changes moderately across specifications and—if anything—increases in absolute terms when more controls are added (see specification (3)).

3.4.4 Do remittances increase informality?

The above results suggest that an increased inflow of remittances increases the reservation wage, lifting labor demand and wages but leading to lower labor force participation. The rise in the reservation wage might also cause informal employment to increase. Using information on employment status in the Global Employment Trends database, we can test this hypothesis. Specifically, we test the impact of remittances on the share of vulnerable employment in total employment. Vulnerable employment is defined as the sum of own-account and contributing family workers. These categories cover very well the extent of informal employment, especially for low-income countries.

The specifications of the macro-economic determinants of informal employment follows the discussion in ILO and WTO (2009). In this literature, the single most important factor is the level of development, followed by the size and types of economic vulnerabilities an economy is facing (such as trade, inflation and public finance shocks). More detailed analysis would include tax and business regulation, which, however, is not widely available for our sample of countries.

Looking at the results in Table 8, we can indeed detect a positive impact of remittances on the share of vulnerable employment. The effect is independent of a series of control variables, with the exception of including government debt that causes the number of observations to drop significantly. In all other cases, the effect is significantly positive.

Table 8:

Remittances and informal employment

Note: Dynamic estimates using Arellano-Bond GMM estimator, assuming weak exogeneity for income flows. Year dummies included where indicated. Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1

Table 8:

Remittances and informal employment

	(1) Baseline	(2) GDP per capita + investment	(3) GDP per capita, + investment + government debt	(4) GDP per capita, + investment + inflation	(5) Full controls	(6) Baseline + time dummies	(7) Full controls + time dummies
Informal employment (lagged)	0.131 (0.0844)	0.235*** (0.0800)	0.412** (0.206)	0.235*** (0.0805)	0.276*** (0.0794)	0.120 (0.122)	0.377*** (0.0780)
GDP per capita (in logs)	−5.173*** (0.493)	−3.673*** (0.451)	−1.870*** (0.644)	−3.677*** (0.458)	−3.391*** (0.456)	−5.049*** (0.644)	−3.338*** (0.481)
Investment ratio (in % of GDP)		−0.117*** (0.0133)	−0.0895** (0.0374)	−0.116*** (0.0131)	−0.145*** (0.0175)		−0.119*** (0.0140)
Remittances (in % of GDP)	10.21*** (1.904)	11.73*** (2.183)	31.67** (15.72)	12.01*** (2.208)	10.41*** (2.327)	11.35*** (2.446)	7.352*** (2.258)
Government debt (in % of GDP)			0.0175*** (0.00521)
Inflation rate				4.25e-4*** (1.35e-4)	3.93e-4*** (1.38e-4)		3.02e-4** (1.42e-4)
Import ratio % of GDP)					2.24e-2** (8.71e-3)		1.90e-2** (8.69e-3)
Constant	82.16*** (7.620)	67.40*** (6.949)	38.13*** (10.77)	67.44*** (7.020)	62.88*** (6.896)	81.59*** (10.67)	57.44*** (7.296)
Observations	2, 257	2,176	968	2,165	2,152	2,257	2,152
Number of countries	139	132	73	132	131	139	131
Number of instruments	39	40	20	41	42	52	63
Sargan test	37.62	44.11	19.04	43.66	44.35	33.02	46.05
Time dummies	No	No	No	No	No	Yes	Yes

Note: Dynamic estimates using Arellano-Bond GMM estimator, assuming weak exogeneity for income flows. Year dummies included where indicated. Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1

View Table

Table 8:

Remittances and informal employment

	(1) Baseline	(2) GDP per capita + investment	(3) GDP per capita, + investment + government debt	(4) GDP per capita, + investment + inflation	(5) Full controls	(6) Baseline + time dummies	(7) Full controls + time dummies
Informal employment (lagged)	0.131 (0.0844)	0.235*** (0.0800)	0.412** (0.206)	0.235*** (0.0805)	0.276*** (0.0794)	0.120 (0.122)	0.377*** (0.0780)
GDP per capita (in logs)	−5.173*** (0.493)	−3.673*** (0.451)	−1.870*** (0.644)	−3.677*** (0.458)	−3.391*** (0.456)	−5.049*** (0.644)	−3.338*** (0.481)
Investment ratio (in % of GDP)		−0.117*** (0.0133)	−0.0895** (0.0374)	−0.116*** (0.0131)	−0.145*** (0.0175)		−0.119*** (0.0140)
Remittances (in % of GDP)	10.21*** (1.904)	11.73*** (2.183)	31.67** (15.72)	12.01*** (2.208)	10.41*** (2.327)	11.35*** (2.446)	7.352*** (2.258)
Government debt (in % of GDP)			0.0175*** (0.00521)
Inflation rate				4.25e-4*** (1.35e-4)	3.93e-4*** (1.38e-4)		3.02e-4** (1.42e-4)
Import ratio % of GDP)					2.24e-2** (8.71e-3)		1.90e-2** (8.69e-3)
Constant	82.16*** (7.620)	67.40*** (6.949)	38.13*** (10.77)	67.44*** (7.020)	62.88*** (6.896)	81.59*** (10.67)	57.44*** (7.296)
Observations	2, 257	2,176	968	2,165	2,152	2,257	2,152
Number of countries	139	132	73	132	131	139	131
Number of instruments	39	40	20	41	42	52	63
Sargan test	37.62	44.11	19.04	43.66	44.35	33.02	46.05
Time dummies	No	No	No	No	No	Yes	Yes

Note: Dynamic estimates using Arellano-Bond GMM estimator, assuming weak exogeneity for income flows. Year dummies included where indicated. Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1

Overall, the results of this section suggest that remittances have significant effects on the composition of the labor market in receiving countries. At first glance, the negative impacts of remittances on wage and productivity growth may seem difficult to reconcile with our finding that remittances reduce unemployment, especially if an increase in labor demand is at least partially responsible for the decline in unemployment. But these results can be reconciled by appealing to models of the labor market that are segmented by productivity or skill level and allow remittances to affect each market segment differently. For example, previous literature has found that remittances cause Dutch Disease effects in which remittances cause the less-productive nontradables sector to expand at the expense of the more-productive tradables sector. To the extent that the tradables sector is associated with high-wage and high-productivity labor (or high-skilled jobs) and the nontradables sector with low-wage and low-productivity labor (or low-skilled jobs), remittances could drive employment in the tradable sector down and employment in the nontradable sector up. If the employment gain in the latter sector is greater than the employment loss in the tradable sector, then the overall unemployment rate will decline, but measured wage and productivity growth will likely fall. All of these results are then due to composition effects. Our findings of an increase in informal employment due to an inflow in remittances support this conjecture.