Chapter 6 Immigration and Employment: Substitute versus Complementary Labor in Selected African Countries

International Monetary Fund

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Keywords:: interregional immigration; effect of immigration; native worker; wage employment; immigration share; Employment; Migration; Labor markets; Self-employment; Sub-Saharan Africa; Africa; Informal employment

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Abstract

The Global Informal Workforce is a fresh look at the informal economy around the world and its impact on the macroeconomy. The book covers interactions between the informal economy, labor and product markets, gender equality, fiscal institutions and outcomes, social protection, and financial inclusion. Informality is a widespread and persistent phenomenon that affects how fast economies can grow, develop, and provide decent economic opportunities for their populations. The COVID-19 pandemic has helped to uncover the vulnerabilities of the informal workforce.

Introduction

Academic and policy circles have focused on the effect of immigration on advanced economies’ labor markets, yet this issue is particularly relevant in emerging market and developing economies because of their large informal sectors.¹ Most of the jobs in emerging market and developing economies are in the informal sector. For example, the informal economy contributes between 25 and 65 percent of GDP (IMF 2017) and accounts for 85.8 percent of total employment in sub-Saharan Africa (International Labour Organization 2018). Against this background, we ask the following questions:

How does immigration in emerging market and developing economies affect native employment, including sectoral composition (formal versus informal employment) and type of employment (self-employment versus wage employment) in each sector?
How is this effect different when foreign workers have complementary versus substitute skills compared with those of native workers?

The answers to these questions have important implications for emerging market and developing economies’ productivity, especially as African economies continue to combat the economic effect of the coronavirus disease 2019 (COVID-19) pandemic while reopening borders and open spaces (at the time of writing).² On the one hand, by importing skills where the need for human capital is high, economies could find that immigration increases labor demand, stimulates job creation, and enhances productivity in formal and informal sectors. On the other hand, by changing the sectoral composition of the labor force toward more informality, immigration could reduce productivity. As the current consensus indicates, the informal sector tends to perpetuate low-productivity jobs (Ardagna and Lusardi 2008; La Porta and Shleifer 2008; Banerjee and Duflo 2011; De Paula and Scheinkman 2011), although early studies have found the opposite (De Soto 1989, 2000).

According to the concept of demand and supply, the effect of immigration on the receiving economy’s labor market depends on whether immigrants and native workers substitute or complement one another. If immigrant and native workers have substitute skills, immigration increases labor supply, resulting in lower wages and less employment of native workers. If immigrant and native workers have skills that complement one another, immigration increases labor demand, resulting in higher wages and more employment of native workers.

To understand the effect of immigration in the context of a segmented labor market, we start with a modified version of the Rivera-Batiz (1981) model. Our modified model’s main assumptions are (1) both formal and informal sectors hire foreign workers, (2) foreign and native workers could have either substitute or complementary skill sets, (3) wages are flexible, and (4) labor markets are closed markets. Assuming foreign and native workers have substitute skills, immigration increases labor supply, reducing native employment in that sector and triggering native workers to search for jobs in the informal sector. As a result, some native workers become self-employed in the informal sector out of necessity. When foreign and native workers have complementary skills, immigration leads to an increase of labor demand in the formal sector, resulting in higher employment and economic expansion, which, in turn, stimulates further activities and job creation in the informal sector.

To empirically estimate the employment effect of immigration in Africa, we use census and household survey data from three sub-Saharan African countries—Cameroon, Ghana, and South Africa—from 2005 to 2011. We selected the countries on the basis of data available on the informal sector. A “foreign worker” is defined as a person born outside the country.³ Stylized facts reveal that immigration from outside the African continent (interregional immigration) brings workers with skills complementary to those of natives, whereas immigration within the African continent (intraregional) brings workers with skills that substitute for those of natives. We rely on those stylized facts and test the channels assumed to be operating in the theoretical framework, distinguishing between complementary and substitutive skill sets.

Results validate the theoretical framework. Whereas interregional immigration increases total native employment, intraregional immigration reduces it. Results also suggest interregional immigration tends to promote wage employment (formal and informal), whereas intraregional immigration generates more necessity-driven informal self-employment.

We make the following contributions to the existing literature. First, we estimate the effect of immigration in the context of a segmented labor market in sub-Saharan Africa, assessing the effect on total employment, sectoral allocation (employment in the formal versus the informal sector), and type of employment in each sector (self-employment versus wage employment). Second, we distinguish between two skill sets associated with foreign workers, complementary or substitute to skills of native workers, using data on immigration outside of and within sub-Saharan Africa. Third, we apply the national framework adopted by Borjas (2003) at the continental level, using cross-country data on Cameroon, Ghana, and South Africa and defining skill groups by level of education and years of experience in a particular sub-Saharan African country.

Using cross-country data allows us to consider the sub-Saharan African region as one single segmented labor market, hence accounting for the possible native move within the sub-Saharan African region that follows immigration, given that frontiers in the region are often porous. In particular, this approach implies workers of the same education and years of experience are not perfectly substitutable across countries, reflecting the diverse quality of education in the region. Because this approach asks how immigration of workers from a certain skill group affects native workers from that same skill group, it also implies that immigrants’ skills are substitutable to those of native workers. We assume this holds through migrant social networks, which reduce possible disparities in the skills required, by providing immigrants with information on employment opportunities and the labor market in destination countries (Banerjee 1984).

The rest of this chapter provides an overview of the literature, lays the theoretical framework, describes the empirical framework and stylized facts, presents the results and conducts a sensitivity analysis, and concludes with policy recommendations.

Literature Review

A common approach adopted in the literature is the area or local labor market approach. The popularity of the area approach stems from its simplicity; it relies on immigrants clustering in particular geographic locations. The typical study defines a city, state, or region as a closed labor market and correlates a measure of native economic outcome (wage, employment) on the relative quantity of immigrants in that location. Studies often focus on the United States and Europe and include Altonji and Card (1989, 1991), Schoeni (1997), Card (2001, 2007), and Card and Lewis (2007) for the US labor market; Pischke and Velling (1997) and Glitz (2012) for Germany; and Winter-Ebmer and Zweimüller (1996) for Austria. Although this approach is intuitively appealing, a well-known drawback arises from endogeneity issues, including native workers and firms responding to immigration by moving out of the specific location and immigrants selecting themselves into the specific location. As a result, the area approach could not confirm the expected results from the standard labor supply and demand model.

To answer this drawback, other studies use natural experiments or cases of unexpected migration prompted by exogenous factors (such as political events or natural disasters). For example, Card (1990) examines the influx of Cuban immigrants to Miami during the 1980 Mariel boatlift. His findings show that this migration had only small wage and employment effects on natives.

An alternative is the national labor market approach, as pioneered by Borjas (2003). Borjas (2003) exploits variations across skill groups, where skills are defined by education and experience. This approach asks how immigrants of a particular skill group affect native workers’ labor market outcomes in that skill group. Although native workers within the same skill group are perfectly substitutable, they cannot easily move to other skill groups at a certain time. In this approach, the assumption of closed labor markets in the basic textbook theory is therefore more plausible.

Borjas’ (2003) findings are in line with the standard labor supply and demand model, that is, when immigrants and natives are substitute workers, immigration is likely to harm the natives’ labor market outcomes. Since the publication of Borjas’ paper, many have followed the national labor market approach, including Bond and Gaston (2011) for Australia; Borjas and Monras (2017) for the United States, the European Union, and the former Soviet Union; and Maani and Tse (2017) for New Zealand. Some authors have also tried to account for adjustment mechanisms to immigration. Recent studies include Lewis (2011, 2013), who examines changes in technology, as well as Peri and Sparber (2009) and Ottaviano, Peri, and Wright (2013), who investigate changes in native task specialization.

Only a few publications analyze the labor market effect of immigration in emerging market and developing economies and how natives adjust to immigration. Those include Del Carpio, Ozden, and Testaverde (2015) for Malaysia; Bryant and Rukumnuaykit (2013) for Thailand; and Tumen (2016) for Turkey. Del Carpio, Ozden, and Testaverde (2015) use survey data for Malaysia and examine native responses to immigration on multiple extensive margin choices, using variation across states and over time. The authors find that natives do adapt to immigration shocks. Following the area approach, Bryant and Rukumnuaykit (2013) use survey data on Thailand and find that immigration negatively affects native wages with a magnitude stronger than in advanced economies. However, the authors did not find evidence of any effect of immigration on native employment or native migration.⁴ Using survey data on the forced immigration from Syria to Turkey, Tumen (2016) analyzes the effect of Syrian refugees in Turkey and examines labor market outcomes, including formal and informal employment, unemployment, wages, and price indices. Tumen’s paper exploits the quasi-experimental regional variation in refugee concentration before and after the inflows (and, as such, belongs to the area approach literature) and finds that the Syrian refugee influx reduced informal employment, but also prices. The author interprets those results as reflecting labor-cost advantages in informal labor-intensive sectors, which reduce the consumer prices of items produced in the informal sector relative to items produced in the formal sector.

Applying Borjas’ (2003) national approach, Sparreboom, Mertens, and Berger (2019) use census and household survey data on Ghana, Rwanda, and South Africa to estimate the employment, unemployment, and wage effects of immigration. Sparreboom, Mertens, and Berger (2019) are the first to publish a crosscountry study to examine the effect of immigration in sub-Saharan African labor markets using Borjas’ (2003) method. The authors find that the effect is likely negative for workers with less education and that the complementarity of workers helps explain the results in some countries but not in all. Their study, however, does not consider the informal sector.

Theoretical Framework

The labor market in emerging market and developing and receiving economies is often characterized by a large informal sector, which calls for a theoretical framework with a segmented labor market. To this end, our theoretical model is based on Rivera-Batiz (1981), who describes the labor market effect of immigration in the context of a two-sector segmented labor market. As in Rivera-Batiz (1981), our focus is in the short to medium term, with fixed nonlabor input (in other words, capital does not respond to immigration) and closed labor markets.

However, to apply our model to emerging market and developing economies, we made three adjustments. First, whereas the Rivera-Batiz (1981) model assumes that only the informal sector hires foreign labor, we assume both formal and informal sectors use domestic and foreign labor. There is no reason to assume only the informal sector would hire foreign workers when the need for skilled labor is high. Second, whereas the Rivera-Batiz (1981) model assumes binding wages and unemployment are characteristics of the formal sector, we assume both sectors have flexible wages and full employment. Labor unions are often weak in emerging market and developing economies, resulting in low bargaining power.⁵ Third, whereas the Rivera-Batiz (1981) model assumes domestic and foreign labor have substitute skill sets, we also consider cases in which domestic and foreign labor have complementary skill sets.

The formal sector produces an importable good X_f through a short-term production function F_f using both domestic and foreign workers, N_f and S, respectively.

X_{f} = F_{f} (N_{f} + S) F_{f}^{'} > 0, F_{f}^{"} < 0

The offer curve of foreign labor is defined as follows:

W_{S} = G (S, Z) G_{S} > 0 G_{Z} > 0,

where W_S is the wage paid to foreign labor S, and Z is the average income of the foreign worker’s origin country, assumed to be set exogenously.

Total consumption C_f is the difference between what is produced, X_f, and exported, E_f:

C_{f} = X_{f} - E_{f}

Consumption is a function of real income, Y, and the international price ratio P^r = P_f / P_i, where P_f is the price of export goods and P_i is the price of imports.

C_{f} = C_{f} (P^{t}, Y) C_{f p} > 0, C_{f y} > 0,

where C_fp is the partial derivative of C_f with respect to P^r, and C_fy is the partial derivative of C_f with respect to Y.

Real income Y is equal to the budget constraint:

Y = P_{f} C_{f} + P_{i} C_{i}

Profits in the formal sector, Π, is defined by the following:

Π = P^{t} . F_{f} (N_{f} + S) - W_{f} N_{f} - W_{S} S = P^{t} . F_{f} (N_{f} + S) - W_{f} N_{f} - S . G (S, Z)

The first order conditions for profit maximization with respect to N_i and S are, respectively,

\begin{matrix} P^{t}, F_{f}^{'} = W_{f} & (1) \end{matrix}

\begin{matrix} P^{r}, F_{f}^{'} = W_{S} & (2) \end{matrix}

The equilibrium conditions (1) and (2) are shown in Figure 6.1. The marginal product curve P^r. $F_{f}^{'}$ is the labor demand curve. The domestic labor supply curve is LS₀. The total labor supply curve to the sector that includes foreign as well as native workers is LS₁.

The equilibrium wage is W₁, employment of domestic labor is ON₁, and employment of foreign labor is L₁ – N₁.

The intersectoral allocation of total labor L* is determined by workers who compare the expected wage in the formal sector with the current wage in the informal sector.

Figure 6.1 shows that immigration results in lower wages, from W₀ to W₁, reducing native employment from N₀ to N₁ and forcing some native workers to become unemployed or move to the informal sector, including choosing informal self-employment for necessity reasons.

The Rivera-Batiz (1981) model assumes that all labor—foreign and domestic— is of one type, either low skilled or high skilled, yet foreign and domestic workers’ skill sets could complement one another. When skills are complementary, there would be two labor markets to consider: the low-skilled labor market and the high-skilled labor market.

Assume, for simplicity, that all foreign workers fall into the high-skilled category. In the high-skilled formal labor market, then, the arrival of high-skille immigrant workers would have a similar effect on employment as described in Figure 6.1. An increase in labor supply would reduce wages and native employment, triggering native workers to either become unemployed or search for jobs in the informal sector.

In the low-skilled formal labor market, the arrival of high-skilled immigrant workers would complement the low-skilled labor, inducing higher productivity. As a result, labor demand for low-skilled native workers would increase, raising wages and employment to W₂ and N₂ (Figure 6.2). Higher employment of low-skilled workers in the formal sector would likely support an expansion of economic activity, which, in turn, could create positive spillovers and stimulate economic activity in the informal sector, creating more jobs in that sector.⁶

Table 6.1 summarizes the expected short-term employment effects of immigration.

^{Table 6.1.}

The Expected Short-Term Employment Effects of Immigration

Source: Author.

^{Table 6.1.}

The Expected Short-Term Employment Effects of Immigration

	Complementary Labor			Substitute Labor
Total Employment	+			-
Formal Employment	+	Self-Employment Rate Wage Employment Rate	+ +	-	Self-Employment Rate Wage Employment Rate	- -
Informal Employment	+	Self-Employment Rate Wage Employment Rate	+ +	+	Self-Employment Rate Wage Employment Rate	+ +

Source: Author.

^{Table 6.1.}

The Expected Short-Term Employment Effects of Immigration

	Complementary Labor			Substitute Labor
Total Employment	+			-
Formal Employment	+	Self-Employment Rate Wage Employment Rate	+ +	-	Self-Employment Rate Wage Employment Rate	- -
Informal Employment	+	Self-Employment Rate Wage Employment Rate	+ +	+	Self-Employment Rate Wage Employment Rate	+ +

Source: Author.

Empirical Framework

We examine the effect of immigration on native total employment rate, which we further decompose into native formal employment rate and native informal employment rate. We then examine the effect of immigration on self-employment versus wage employment within each sector (formal and informal).

Stylized Facts

The sub-Saharan Africa region is an interesting case study for four reasons: (1) the region has one of the largest informal economies in the world (Figure 6.3);⁷ (2) the region needs skills and could benefit from immigration; (3) although immigration still accounts for a small proportion of the population (1 to 4 percent), immigration is both interregional and intraregional, with each type likely to bring different skills (Figure 6.4); and (4) sub-Saharan Africa’s workers are among the most entrepreneurial in the world (Figure 6.5).

Our study uses census and household survey data from the Public Use Microdata Samples (PUMS) of the Decennial Censuses and Surveys, obtained from the IPUMS⁸-International project started by the Minnesota Population Center. Because of data availability on the informal sector and employment, we focus our analysis on Cameroon, Ghana, and South Africa. Panel 1 of Annex Figures 6.1 through 6.3 captures the main stylized facts, summarized from Viseth (2020).

In all three countries, interregional immigrants are relatively more educated than intraregional immigrants, and intraregional immigrants tend to match native workers’ education profiles. These features allow us to test the hypothesis of complementarity versus substitutability between immigrant and native workers’ skills sets, as described in the theoretical framework. In all three countries, the informal sector is also large. Except for South Africa, the informal sector dominates the economies, accounting for more than 80 percent of employment in Cameroon and Ghana. In South Africa, the informal sector is smaller than the formal sector but still significant, accounting for about 12 percent of employment (excluding private households). In all three countries, informal workers tend to be less educated than formal workers.

Model Specifications

Borjas (2003) exploits variations across skill groups and time, defined by education and experience, and identifies the effect of immigration on native workers’ labor market outcomes. By conducting an analysis at the national level and not focusing on one geographic area, Borjas’ (2003) approach addresses some of the drawbacks raised by the area approach, such as natives moving out of areas where immigration is taking place. Although this approach does not consider adjustments to the capital stock, the theoretical framework’s assumption of a closed labor market, as represented by the various skill groups,⁹ becomes more plausible than previous methods. Borjas (2003) finds a significant and negative effect of immigration on native wages.

Our stylized facts show that there are variations across skills, which we use to follow Borjas’ (2003) method. Our empirical strategy applies Borjas (2003) to sub-Saharan Africa but at the regional level, using cross-sectional data and defining skill groups by education and experience in each country. With the sub-Saharan Africa region as a unit of analysis, this definition implies that workers with the same education and experience are different across countries. We assume this is the case, because within sub-Saharan Africa the quality of education is likely different from country to country.

We also assume that a foreign worker is perfectly substitutable to a native worker of the same education and experience in the considered country, because of migrants’ networks. Migrants’ networks have been shown to reduce asymmetries of information regarding labor market rules, institutions, and employment opportunities, making job search more efficient for immigrants (Waldinger 1997; Elliott 2001) and providing formal education and training required for immigrants to obtain a job in the host country (Drever and Hoffmeister 2008).

The term “immigrant” is defined as an individual who is foreign born. To account for the two types of immigration, interregional and intraregional, we use two definitions of immigrant: (1) foreign born outside the considered country and outside the sub-Saharan Africa region (interregional immigrant) and (2) foreign born outside the considered country but within the sub-Saharan Africa region (intraregional immigrant).

The effect of immigration on the total employment rate of native-born workers is expressed as follows:

Y_{c i j} = η . Z_{c i j} + C_{c} + I_{i} + J_{j} + ε_{i j t},

where Y_cij is the employment rate of a native born in country c, with education i and experience j.

Z_cij = M_cij / N_cij is the immigrant supply shock or the immigrant share of the working-age population and measures the percentage increase in the labor supply of skill group cij caused by immigration.

The η coefficient is the parameter of interest. If η is statistically significant, the coefficient will provide information on the direction and magnitude of change in the total native employment rate from an immigration-induced labor supply shock.

We allow for linear fixed effects to control for the systematic differences in the total native employment outcome caused by differences in country characteristics, education, and experience.

C is a vector of fixed effects reflecting the characteristics of the country in consideration, which controls for total native employment differences across countries. The country fixed effects vector captures, among other factors, quality of education and the structure of the labor market (gender, labor market flexibility, labor market segmentation, and economic activity).

I and J are vectors of fixed effects indicating the group’s educational attainment and work experience, respectively, which control for differences in total native employment across education and experience groups.

The effect of immigration on the sector composition of native-born workers is expressed as follows:

Y_{c i j} = η . Z_{c i j} + C_{c} + I_{i} + J_{j} + ε_{i j t},

where Y_cij is the share of population working in either the formal sector or the informal sector.

Z_cij = M_cij / N_cij is the immigrant supply shock (that is, the immigration share of the working-age population) and measures the percentage increase in the labor supply of skill group cij from immigration.

Again, we allow for linear fixed effects.

Compared with the previous two specifications, the specification for the effect of immigration on the type of employment of native-born workers is another variable that determines employment type.

Because self-employment depends on access to capital as much as skills, we add the access to capital variable as a determinant of employment type. IPUMS categorizes individuals as owners of a dwelling if the individual has acquired his or her housing unit with a mortgage or other lending arrangement. We use this information as a proxy for access to capital, A_cij, which is calculated as the share of individuals who own a dwelling, standing for those with access to capital among the working-age population:

Y_{c i j} = η . Z_{c i j} + A_{c i j} + C c + I_{i} + J_{j} + ε_{i j t},

where Y_cij is either the share of the population working as self-employed or wage employed in the informal sector, or the share of the population working as self-employed or wage employed in the formal sector.

A_cij is proxy for access to capital (that is, the share of the working-age population of individuals who have acquired their housing unit with a mortgage or other lending arrangement).

Again, we allow for linear fixed effects.

Data

Our period of analysis is 2010. Following the literature (Borjas 2003; Mishra 2007), when 2010 was not available we proxied with the closest census and survey data: the 2010 Cameroon data are proxied by the 2005 census and survey data. The 2010 South Africa census and survey data are proxied by the 2011 census and survey data.

To define a foreign-born worker, we use data indicating the country of birth for Ghana and South Africa. Because country of birth is not available for Cameroon, we use data on Cameroonian citizenship as a proxy for country of birth. We pick the countries of birth to differentiate between foreign-born within sub-Saharan Africa and foreign-born outside sub-Saharan Africa.

Individuals are divided into seven groups of education and eight groups of experience. Educational attainment is categorized by (1) no schooling, (2) some primary school completed, (3) primary school completed, (4) lower secondary general or lower secondary technical education completed, (5) secondary general education completed, (6) some college or postsecondary technical education, and (8) college completed.

Following Borjas (2003), “work experience” is defined as the number of years that have elapsed since the person left school. We measure experience by current age minus the entry age (AT) into the labor market for the typical worker (Age – AT). Entry age is assumed to be 17 years for the first four categories, 19 years for those with secondary general completed, 21 years for people with some college or postsecondary technical education, and 23 years for college graduates. We restrict the sample to individuals with experience ranging from 1 to 40 years to focus on the individuals in the working-age and healthy life expectancy group 18 to 57 years old.¹⁰ This approach gives us eight experience groups of five-year intervals.

As specified in Borjas (2003), because women typically enter and leave employment more often than men, particularly around child-rearing, defining experience on the basis of age and entry age may not be relevant. This resulted in Borjas (2003) restricting the analysis to men, including women only as a specification test to determine the sensitivity of the results. We follow Borjas (2003) accordingly, focusing on men and including women as a specification test.

By using the sub-Saharan Africa region as our unit of analysis, our empirical strategy controls for the possibility that native workers move across countries following immigration. Because migrants’ networks may result in immigrants self-selecting into the considered country, we use past distribution of immigration as defined by the previous decade or the 1990s’ immigrant distribution (or closest to the 1990s when data are not available).¹¹ We use the 1987 Cameroonian census and survey data, the 1984 Ghanaian census and survey data, and the 1996 South African census and survey data in the construction of our immigration variable.

Results

Our empirical results largely validate the theoretical framework.

Basic Results

Table 6.2 shows that in the case of complementary skill sets or interregional immigration, immigration stimulates production and increases labor demand for native workers, resulting in higher native employment. In the case of substitute skill sets or intraregional immigration, immigrants and native workers compete for the same jobs, resulting in a decline of the native labor supply. Although some women are likely to be misclassified because of gaps in their labor experience, results are similar across genders.¹²

^{Table 6.2.}

The Effect of Immigration on Native Total Employment-to-Population Ratios

Source: Author. Note: Standard errors appear in parentheses and have been corrected for heteroskedasticity using White’s correction. There are 168 observations. The total number of country-education-experience cells is 3 × 7 × 8 = 168. Regressions are weighted by the sample size of the country-education-experience cell. Specification I defines an immigrant as foreign-born outside sub-Saharan Africa; specification II defines immigrant as foreign-born outside Country C and within sub-Saharan Africa. All specifications include fixed effects. **p < 0.05; ***p < 0.01.

^{Table 6.2.}

The Effect of Immigration on Native Total Employment-to-Population Ratios

	Specification I: Interregional Immigration	Specification II: Intraregional Immigration
Male employment	2.626** (1.274)	-1.488*** (0.352)
Female employment	5.398*** (1.392)	-1.575*** (0.298)

^{Table 6.2.}

The Effect of Immigration on Native Total Employment-to-Population Ratios

	Specification I: Interregional Immigration	Specification II: Intraregional Immigration
Male employment	2.626** (1.274)	-1.488*** (0.352)
Female employment	5.398*** (1.392)	-1.575*** (0.298)

Tables 6.3 and 6.4 show how labor is allocated across formal and informal sectors following immigration. Interregional immigration has a positive, although not statistically significant, effect on native formal employment (Table 6.4). As immigrants enter the formal sector, production and labor demand increases, resulting in more native employment in that sector. The expansion of the formal sector induced by interregional immigration, then, has positive productivity spillovers in the informal sector, resulting in higher informal employment (Table 6.5). Intraregional immigration, conversely, decreases formal employment (Table 6.4). As immigrants enter the formal sector, they compete with native workers for the same jobs, resulting in some natives being unemployed in the formal sector or finding employment in the informal sector (Table 6.5). Results are broadly similar across gender, although the effect on female employment tends to be stronger.

^{Table 6.3.}

The Effect of Immigration on the Share of Population Working in the Formal Sector

^{Table 6.3.}

The Effect of Immigration on the Share of Population Working in the Formal Sector

	Specification I: Interregional Immigration	Specification II: Intraregional Immigration
Male employment	0.256 (1.217)	-2.638*** (0.306)
Female employment	2.745 (2.301)	-0.505 (0.452)

^{Table 6.3.}

The Effect of Immigration on the Share of Population Working in the Formal Sector

	Specification I: Interregional Immigration	Specification II: Intraregional Immigration
Male employment	0.256 (1.217)	-2.638*** (0.306)
Female employment	2.745 (2.301)	-0.505 (0.452)

^{Table 6.4.}

The Effect of Immigration on the Share of Population Working in the Informal Sector

^{Table 6.4.}

The Effect of Immigration on the Share of Population Working in the Informal Sector

	Specification I: Interregional Immigration	Specification II: Intraregional Immigration
Male employment	2.160* (1.219)	1.122*** (0.433)
Female employment	16.743 (19.875)	21.277*** (7.039)

^{Table 6.4.}

The Effect of Immigration on the Share of Population Working in the Informal Sector

	Specification I: Interregional Immigration	Specification II: Intraregional Immigration
Male employment	2.160* (1.219)	1.122*** (0.433)
Female employment	16.743 (19.875)	21.277*** (7.039)

^{Table 6.5.}

The Effect of Immigration on the Share of Population Working as Self-Employed in the Formal Sector

Source: Author. Note: Standard errors appear in parentheses and have been corrected for heteroskedasticity using White’s correction. There are 168 observations. The total number of country-education-experience cells is 3 × 7 × 8 = 168. Regressions are weighted by the sample size of the education-experience-country cell. Specification I defines an immigrant as foreign-born outside sub-Saharan Africa; specification II defines immigrant as foreign-born outside Country C and within sub-Saharan Africa. All specifications include fixed effects. ***p < 0.01.

^{Table 6.5.}

The Effect of Immigration on the Share of Population Working as Self-Employed in the Formal Sector

	Specification I: Interregional Immigration		Specification II: Intraregional Immigration
	Male	Female	Male	Female
Self-employment	-4.003*** (0.931)	-3.160*** (0.853)	-0.251 (0.457)	-0.097 (0.451)
Access to capital	0.245*** (0.025)	0.242*** (0.025)	-0.209*** (0.028)	0.216*** (0.028)

Source: Author. Note: Standard errors appear in parentheses and have been corrected for heteroskedasticity using White’s correction. There are 168 observations. The total number of country-education-experience cells is 3 × 7 × 8 = 168. Regressions are weighted by the sample size of the education-experience-country cell. Specification I defines an immigrant as foreign-born outside sub-Saharan Africa; specification II defines immigrant as foreign-born outside Country C and within sub-Saharan Africa. All specifications include fixed effects. ***p < 0.01.

^{Table 6.5.}

The Effect of Immigration on the Share of Population Working as Self-Employed in the Formal Sector

	Specification I: Interregional Immigration		Specification II: Intraregional Immigration
	Male	Female	Male	Female
Self-employment	-4.003*** (0.931)	-3.160*** (0.853)	-0.251 (0.457)	-0.097 (0.451)
Access to capital	0.245*** (0.025)	0.242*** (0.025)	-0.209*** (0.028)	0.216*** (0.028)

Source: Author. Note: Standard errors appear in parentheses and have been corrected for heteroskedasticity using White’s correction. There are 168 observations. The total number of country-education-experience cells is 3 × 7 × 8 = 168. Regressions are weighted by the sample size of the education-experience-country cell. Specification I defines an immigrant as foreign-born outside sub-Saharan Africa; specification II defines immigrant as foreign-born outside Country C and within sub-Saharan Africa. All specifications include fixed effects. ***p < 0.01.

Calculating elasticities, we estimate that (1) a 10 percent increase in interregional immigration leads to a 0.4 percent increase in informal employment, and (2) a 10 percent increase in intraregional immigration would lead to a 0.2 percent increase in informal employment.

Although both types of immigration lead to a positive effect on native employment in the informal sector, examining how the types of informal employment are affected reveals two different processes (Tables 6.5 to 6.8). The positive effect of interregional immigration on informal employment is driven by wage employment. This indicates that positive productivity spillovers from the formal sector lead to more hiring in the informal sector.

^{Table 6.6.}

The Effect of Immigration on the Share of Population Working as Wage-Employed in the Formal Sector

Source: Author. Note: Standard errors appear in parentheses and have been corrected for heteroskedasticity using White’s correction. There are 168 observations. The total number of country-education-experience cells is 3 × 7 × 8 = 168. Regressions are weighted by the sample size of the education-experience-country cell. Specification I defines an immigrant as foreign-born outside sub-Saharan Africa; specification II defines immigrant as foreign-born outside Country C and within sub-Saharan Africa. All specifications include fixed effects. ***p < 0.01.

^{Table 6.6.}

The Effect of Immigration on the Share of Population Working as Wage-Employed in the Formal Sector

	Specification I: Interregional Immigration		Specification II: Intraregional Immigration
	Male	Female	Male	Female
Wage employment	1.933*** (0.764)	4.003*** (0.871)	-0.353 (0.283)	0.151 (0.253)
Access to capital	0.088*** (0.020)	0.190*** (0.026)	0.094*** (0.023)	0.223*** (0.223)

Source: Author. Note: Standard errors appear in parentheses and have been corrected for heteroskedasticity using White’s correction. There are 168 observations. The total number of country-education-experience cells is 3 × 7 × 8 = 168. Regressions are weighted by the sample size of the education-experience-country cell. Specification I defines an immigrant as foreign-born outside sub-Saharan Africa; specification II defines immigrant as foreign-born outside Country C and within sub-Saharan Africa. All specifications include fixed effects. ***p < 0.01.

^{Table 6.6.}

The Effect of Immigration on the Share of Population Working as Wage-Employed in the Formal Sector

	Specification I: Interregional Immigration		Specification II: Intraregional Immigration
	Male	Female	Male	Female
Wage employment	1.933*** (0.764)	4.003*** (0.871)	-0.353 (0.283)	0.151 (0.253)
Access to capital	0.088*** (0.020)	0.190*** (0.026)	0.094*** (0.023)	0.223*** (0.223)

Source: Author. Note: Standard errors appear in parentheses and have been corrected for heteroskedasticity using White’s correction. There are 168 observations. The total number of country-education-experience cells is 3 × 7 × 8 = 168. Regressions are weighted by the sample size of the education-experience-country cell. Specification I defines an immigrant as foreign-born outside sub-Saharan Africa; specification II defines immigrant as foreign-born outside Country C and within sub-Saharan Africa. All specifications include fixed effects. ***p < 0.01.

^{Table 6.7.}

The Effect of Immigration on the Share of Population Working as Self-Employed in the Informal Sector

Source: Author. Note: Standard errors appear in parentheses and have been corrected for heteroskedasticity using White’s correction. There are 168 observations. The total number of country-education-experience cells is 3 × 7 × 8 = 168. Regressions are weighted by the sample size of the education-experience-country cell. Specification I defines an immigrant as foreign-born outside sub-Saharan Africa; specification II defines immigrant as foreign-born outside Country C and within sub-Saharan Africa. All specifications include fixed effects. *p < 0.10; ***p < 0.01.

^{Table 6.7.}

The Effect of Immigration on the Share of Population Working as Self-Employed in the Informal Sector

	Specification Interregional Immigration		Specification Intraregional Immigration
	Male	Female	Male	Female
Self-employment	0.206 (0.734)	1.593 (1.146)	0.847* (0.468)	0.721 (0.751)
Access to capital	0.118*** (0.028)	0.197*** (0.042)	0.140*** (0.031)	0.227*** (0.047)

Source: Author. Note: Standard errors appear in parentheses and have been corrected for heteroskedasticity using White’s correction. There are 168 observations. The total number of country-education-experience cells is 3 × 7 × 8 = 168. Regressions are weighted by the sample size of the education-experience-country cell. Specification I defines an immigrant as foreign-born outside sub-Saharan Africa; specification II defines immigrant as foreign-born outside Country C and within sub-Saharan Africa. All specifications include fixed effects. *p < 0.10; ***p < 0.01.

^{Table 6.7.}

The Effect of Immigration on the Share of Population Working as Self-Employed in the Informal Sector

	Specification Interregional Immigration		Specification Intraregional Immigration
	Male	Female	Male	Female
Self-employment	0.206 (0.734)	1.593 (1.146)	0.847* (0.468)	0.721 (0.751)
Access to capital	0.118*** (0.028)	0.197*** (0.042)	0.140*** (0.031)	0.227*** (0.047)

Source: Author. Note: Standard errors appear in parentheses and have been corrected for heteroskedasticity using White’s correction. There are 168 observations. The total number of country-education-experience cells is 3 × 7 × 8 = 168. Regressions are weighted by the sample size of the education-experience-country cell. Specification I defines an immigrant as foreign-born outside sub-Saharan Africa; specification II defines immigrant as foreign-born outside Country C and within sub-Saharan Africa. All specifications include fixed effects. *p < 0.10; ***p < 0.01.

^{Table 6.8.}

The Effect of Immigration on the Share of Population Working as Wage-Employed in the Informal Sector

Source: Author. Note: Standard errors appear in parentheses and have been corrected for heteroskedasticity using White’s correction. There are 168 observations. The total number of country-education-experience cells is 3 × 7 × 8 = 168. Regressions are weighted by the sample size of the education-experience-country cell. Specification I defines an immigrant as foreign-born outside sub-Saharan Africa; specification II defines immigrant as foreign-born outside Country C and within sub-Saharan Africa. All specifications include fixed effects. *p < 0.10; **p < 0.05; ***p < 0.01.

^{Table 6.8.}

The Effect of Immigration on the Share of Population Working as Wage-Employed in the Informal Sector

	Specification I: Interregional Immigration		Specification II: Intraregional Immigration
	Male	Female	Male	Female
Wage employment	0.305*** (0.118)	0.346*** (0.094)	-0.105** (0.054)	-0.077* (0.046)
Access to capital	-0.003 (0.003)	0.010*** (0.003)	-0.105* (0.054)	0.011*** (0.003)

Source: Author. Note: Standard errors appear in parentheses and have been corrected for heteroskedasticity using White’s correction. There are 168 observations. The total number of country-education-experience cells is 3 × 7 × 8 = 168. Regressions are weighted by the sample size of the education-experience-country cell. Specification I defines an immigrant as foreign-born outside sub-Saharan Africa; specification II defines immigrant as foreign-born outside Country C and within sub-Saharan Africa. All specifications include fixed effects. *p < 0.10; **p < 0.05; ***p < 0.01.

^{Table 6.8.}

The Effect of Immigration on the Share of Population Working as Wage-Employed in the Informal Sector

	Specification I: Interregional Immigration		Specification II: Intraregional Immigration
	Male	Female	Male	Female
Wage employment	0.305*** (0.118)	0.346*** (0.094)	-0.105** (0.054)	-0.077* (0.046)
Access to capital	-0.003 (0.003)	0.010*** (0.003)	-0.105* (0.054)	0.011*** (0.003)

Source: Author. Note: Standard errors appear in parentheses and have been corrected for heteroskedasticity using White’s correction. There are 168 observations. The total number of country-education-experience cells is 3 × 7 × 8 = 168. Regressions are weighted by the sample size of the education-experience-country cell. Specification I defines an immigrant as foreign-born outside sub-Saharan Africa; specification II defines immigrant as foreign-born outside Country C and within sub-Saharan Africa. All specifications include fixed effects. *p < 0.10; **p < 0.05; ***p < 0.01.

The effect of interregional immigration on self-employment is the only result that does not have the expected sign (Table 6.5). This result may indicate that expansion in the formal sector has likely increased competition, driving smaller businesses out and resulting in a negative effect on self-employment. The positive effect of intraregional immigration on informal employment is driven by self-employment (Table 6.7). As native workers are driven out of the formal sector into the informal sector, they become self-employed. Results confirm the importance of access to capital, with better access to capital shown to increase native self-employment in both formal and informal sectors.

Calculating elasticities, we find that (1) a 10 percent increase in interregional immigration raises informal wage employment by 0.5 percentage points, and (2) a 10 percent increase in intraregional immigration leads to an increase in informal self-employment by 1.9 percentage points.

Table 6.9 summarizes the expected effects of immigration on native men’s employment.

^{Table 6.9.}

Summary of Results

Source: Author. Note: Cells shaded in gray indicate statistically not significant; cells shaded in blue indicate statistically significant.

^{Table 6.9.}

Summary of Results

	Interregional Migration			Intraregional Migration
	+			-
Formal Employment Rate of Native Men	+	Self-Employment Rate	-	-	Self-Employment Rate	-
Formal Employment Rate of Native Men	+	Wage Employment Rate	+	-	Wage Employment Rate	-
Informal Employment Rate of Native Men	+	Self-Employment Rate	+	+	Self-Employment Rate	+
Informal Employment Rate of Native Men	+	Wage Employment Rate	+	+	Wage Employment Rate	-

Source: Author. Note: Cells shaded in gray indicate statistically not significant; cells shaded in blue indicate statistically significant.

^{Table 6.9.}

Summary of Results

	Interregional Migration			Intraregional Migration
	+			-
Formal Employment Rate of Native Men	+	Self-Employment Rate	-	-	Self-Employment Rate	-
Formal Employment Rate of Native Men	+	Wage Employment Rate	+	-	Wage Employment Rate	-
Informal Employment Rate of Native Men	+	Self-Employment Rate	+	+	Self-Employment Rate	+
Informal Employment Rate of Native Men	+	Wage Employment Rate	+	+	Wage Employment Rate	-

Source: Author. Note: Cells shaded in gray indicate statistically not significant; cells shaded in blue indicate statistically significant.

Our results also provide empirical evidence that necessity-driven self-employment needs to be distinguished from transformational self-employment. This distinction was identified in Schoar (2010), who argues that necessity-driven self-employment cannot automatically lead to transformational self-employment solely on the basis of, for example, greater access to capital. The author shows that to support more transformational self-employment, other factors and policy measures are needed, including product and labor market deregulation.

Specific Skill Groups Results

Because the inflows of immigrants in our sample include a large proportion of high school dropouts,¹³ we assess whether the results were driven by a specific group of high school dropouts and estimate regressions specifically for native workers with at least a high school diploma (Table 6.10).

^{Table 6.10.}

The Effect of Immigration on Employment, Sector Allocation, and Type of Employment among Individuals Who Are at Least High School Graduates

Source: Author. Note: Standard errors appear in parentheses and have been corrected for heteroskedasticity using White’s correction. There are 168 observations. The total number of country-education-experience cells is 3 × 8 × 3 = 72. Regressions are weighted by the sample size of the education-experience-country cell. The regressions include country, education, and experience effects. The regressions also include interactions between education and experience fixed effects and interactions between education and country fixed effects. *p < 0.10; **p < 0.05; ***p < 0.01.

^{Table 6.10.}

The Effect of Immigration on Employment, Sector Allocation, and Type of Employment among Individuals Who Are at Least High School Graduates

	Interregional Immigration	Intraregional Immigration
Total employment	-0.089 (0.981)	0.721 (2.928)
Formal employment	2.172 (1.535)	-1.441 (3.585)
Informal employment	-2.338** (0.911)	2.431 (1.969)
Formal self-employment	-0.974*** (0.314)	-2.296*** (0.893)
Formal wage employment	1.042 (1.209)	-2.233 (2.872)
Informal self-employment	-1.952*** (0.483)	0.503 (0.984)
Informal wage employment	-0.227* (0.135)	-0.036 (0.229)

Source: Author. Note: Standard errors appear in parentheses and have been corrected for heteroskedasticity using White’s correction. There are 168 observations. The total number of country-education-experience cells is 3 × 8 × 3 = 72. Regressions are weighted by the sample size of the education-experience-country cell. The regressions include country, education, and experience effects. The regressions also include interactions between education and experience fixed effects and interactions between education and country fixed effects. *p < 0.10; **p < 0.05; ***p < 0.01.

^{Table 6.10.}

The Effect of Immigration on Employment, Sector Allocation, and Type of Employment among Individuals Who Are at Least High School Graduates

	Interregional Immigration	Intraregional Immigration
Total employment	-0.089 (0.981)	0.721 (2.928)
Formal employment	2.172 (1.535)	-1.441 (3.585)
Informal employment	-2.338** (0.911)	2.431 (1.969)
Formal self-employment	-0.974*** (0.314)	-2.296*** (0.893)
Formal wage employment	1.042 (1.209)	-2.233 (2.872)
Informal self-employment	-1.952*** (0.483)	0.503 (0.984)
Informal wage employment	-0.227* (0.135)	-0.036 (0.229)

Source: Author. Note: Standard errors appear in parentheses and have been corrected for heteroskedasticity using White’s correction. There are 168 observations. The total number of country-education-experience cells is 3 × 8 × 3 = 72. Regressions are weighted by the sample size of the education-experience-country cell. The regressions include country, education, and experience effects. The regressions also include interactions between education and experience fixed effects and interactions between education and country fixed effects. *p < 0.10; **p < 0.05; ***p < 0.01.

The findings indicate that our basic results may have been driven by the particular group of high school dropouts. Whereas results regarding intraregional immigration and sector allocation tend to go in the same direction as the basic results, interregional immigration is shown to have a negative effect on informal employment (both self-employment and wage employment). Interpretation of the basic results should therefore be considered within the context of sub-Saharan Africa, where immigration is largely composed of high school dropouts.

Conclusion and Policy Recommendations

We assess the effect of immigration on native employment in receiving emerging market and developing economies, using data on three sub-Saharan African countries. First, results confirm what standard textbooks predict. That is, the direction of the effect depends on the degree of substitutability or complementarity between immigrants and native workers. Should native workers be less skilled, immigration that brings higher-skilled workers increases native employment, whereas immigration that brings lower-skilled workers reduces native employment. Our results corroborate immigration studies of advanced economies such as Borjas (2003), who finds that low-skilled immigration hurts low-skilled native workers. Second, we find evidence that immigration shifts native employment between the formal and the informal sectors in receiving emerging market and developing economies.

Although both interregional and intraregional immigration positively affect informal employment, each prompts a shift to informal employment for a different reason. With interregional immigration, the informal sector is found to be where jobs are created, because the boost in native employment generated by immigration translates into more informal wage employment. With intraregional immigration, the informal sector is found to be where low-productivity jobs perpetuate.

Given our findings, receiving emerging market and developing economies should enhance efforts to increase complementarity between immigrant and native workers. Policy recommendations include (1) investing more in education and training—and ensuring the quality of the education system; (2) better targeting active labor market policies, especially in regions that receive large inflows of immigrants whose skill profiles match those of native workers; (3) reducing gender gaps to improve women’s education and labor force participation; and (4) strengthening the business environment and access to capital to help firms expand. Access to finance is important, but our findings show that it may not be enough to promote self-employment and job creation. Any policy that promotes employment in Africa should aim to yield sustained, inclusive growth, which implies an increase in the demand for formal labor. The degree of regulation of labor and product markets, as well as the political environment, could be equally important factors to enable self-employment to generate innovation and jobs for others.

Annex 6.1. Main Stylized Facts

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In the formal sector, firms are licensed, are regulated, pay taxes, and must follow specific rules and regulations governing their employees. In the informal sector, workers are not guaranteed the same protections and benefits.

The current context highlights the importance of studying how immigration would affect informal employment. In previous crises, the informal sector helped cushion the economic fallout on the for mal sector through continuous supply to the domestic economy, sustaining incomes and consumption for the majority of households. In the current crisis, however, informal workers are the most vulner able to employment and income losses.

This definition of “foreign worker” is standard in the literature (Borjas 2003; Mishra 2007).

Internal migration is the migration of native-born workers to other geographic locations as a response to immigration in a particular location.

Although labor unions are not weak in South Africa, the South African authorities have recently discussed with the IMF ways to promote a more flexible labor market (IMF 2020).

The labor market outcomes derived here would remain similar should immigration be low skilled. If immigrant workers were low skilled, the low-skilled formal labor market would see less employ ment and lower wages among native workers, triggering native workers to either become unem ployed or search for jobs in the informal sector. The high-skilled formal labor market would benefit from low-skilled immigration, supporting economic activities and creating positive spillovers in the informal sector.

Although South Africa’s informal sector is not as large as the rest of sub-Saharan Africa’s, the informal sector has been a rational response to the formal labor market’s rigidity.

IPUMS is the world’s largest collection of publicly available individual-level census data and provides census and survey data integrated across time and space.

Workers are said to be perfectly substitutable within, but not across, skill groups.

According to the World Health Organization, healthy life expectancy at birth is estimated to be 54 years old in Africa.

The use of a past instrument is common in the literature and assumes that past immigration inflows are good predictors of contemporary immigrant inflows and uncorrelated with current unobserved labor demand shocks. See, for example, Card (2001) and Mishra (2007).

On informality and gender gaps in sub-Saharan Africa, see Malta and others (2019).

The high school dropout categories are defined by those with (1) no schooling, (2) some primary school completed, (3) primary school completed, and (4) lower secondary general or lower secondary technical education completed.

Foresee

^{Table 6.10.}

The Effect of Immigration on Employment, Sector Allocation, and Type of Employment among Individuals Who Are at Least High School Graduates

	Interregional Immigration	Intraregional Immigration
Total employment	-0.089 (0.981)	0.721 (2.928)
Formal employment	2.172 (1.535)	-1.441 (3.585)
Informal employment	-2.338** (0.911)	2.431 (1.969)
Formal self-employment	-0.974*** (0.314)	-2.296*** (0.893)
Formal wage employment	1.042 (1.209)	-2.233 (2.872)
Informal self-employment	-1.952*** (0.483)	0.503 (0.984)
Informal wage employment	-0.227* (0.135)	-0.036 (0.229)

Source: Author. Note: Standard errors appear in parentheses and have been corrected for heteroskedasticity using White’s correction. There are 168 observations. The total number of country-education-experience cells is 3 × 8 × 3 = 72. Regressions are weighted by the sample size of the education-experience-country cell. The regressions include country, education, and experience effects. The regressions also include interactions between education and experience fixed effects and interactions between education and country fixed effects. *p < 0.10; **p < 0.05; ***p < 0.01.

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Specific Skill Groups Results

Conclusion and Policy Recommendations

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