Introduction
This chapter has three goals: we first present stylized facts on labor markets in Latin America from a comparative perspective to motivate and ground the subsequent work. Second, we empirically analyze the cyclical behavior of Latin American labor markets. Last, we sketch a small open-economy dynamic stochastic general equilibrium model, which can replicate the stylized facts and empirical results laid out previously.1 The importance of informality, as both a structural and cyclical feature, is stressed throughout.
Labor markets in Latin America tend to be characterized by low labor productivity, high informality, and a rigid regulatory environment, which contribute to strong duality between well-protected formal sector jobs and unprotected informal sector jobs.
To study the cyclical behavior of Latin American labor markets, we decompose changes in the unemployment rate into demand and supply factors to show how labor productivity, informality, and the participation rate adjust to limit unemployment movements during the business cycle.2 On the basis of the decompositions, this chapter argues that beyond the unemployment rate, information on formal job creation and changes in the informality rate are necessary to understand slack in Latin American labor markets.
The authors thank Bas Bakker, John Bluedorn, Valentina Flamini, Jaime Guajardo, Jorge Roldos, Antonio Spilimbergo, and Alejandro Werner for their comments. Genevieve Lindow provided outstanding research assistance.
Next, we reexamine the link between changes in unemployment and changes in output by estimating the Okun’s law relationship and explore the cross-country variation in coefficients to gain insights about how structural features of the labor market affect its cyclical behavior. Unemployment has reacted much less to changes in GDP in Latin America than it would have in advanced economies. We argue that this is the product of structural characteristics in the region’s labor markets. Although we do not find a direct link between most labor market institutions and the cyclical behavior of labor markets conditional on informality, these institutions may still influence the “structural” level of informality, thereby indirectly affecting the cyclical behavior of the labor market.
In the last part of the chapter, we draw on the empirical findings to develop a theoretical framework that allows us to study the role of labor market frictions in more detail. Specifically, we build a small open-economy dynamic stochastic general equilibrium model with two sectors, formal and informal, which can replicate the negative relationships between labor informality rate and per capita GDP, both at business cycle frequency and in a cross-section of countries, and between the Okun’s coefficient and the level of labor informality. The model is calibrated to Colombia.
The results show that labor market and tax reforms play an important role in changing the informality rate but also caution against overoptimism—with low GDP per capita, informality will remain relatively high because there is insufficient demand for formal goods. From a quantitative perspective, we find that higher productivity in the formal sector is key to explaining the difference between Colombia and countries with significantly lower informality. We use the model to study how labor informality and labor market frictions mediate the cyclical response of the economy to shocks, including commodity price shocks, which are particularly relevant in many Latin American countries. Informality is shown to play an important role as a shock absorber with the informal-formal margin limiting movements in the employed-unemployed margin.
This chapter adds to a vast literature studying the nexus of labor market institutions, informality, and unemployment in emerging market and developing economies. Notable references include the reviews of the effect of labor market institutions in emerging market and developing economies by Freeman (2010) and Betcherman (2014), as well as a recent paper by Duval and Loungani (2018) on the design of labor market institutions in emerging market and developing economies. Kugler (2019) provides a comprehensive overview of the effect of labor market institutions in Latin America by surveying the microeconomic literature. Perry and others (2007) provide a detailed study of informality in Latin America. Additional papers studying the role of informality over the business cycle include Bosch and Esteban-Pretel (2012), Castillo and Montoro (2012), Restrepo-Echavarria (2014), and Leyva and Urrutia (2020). The modeling framework presented in this chapter builds on Anand and Khera (2016) and Munkacsi and Saxegaard (2017).
Stylized Facts
Labor Productivity
The median value in 2017 for output per worker in South America was about 30 percent of the median advanced economy (20 percent in Central America). Strong employment growth in the early 2000s was accompanied by lackluster productivity growth in both Central and South America before the global financial crisis. Growth rates, however, were strong from 2008 to 2011 as commodity prices recovered quickly and many South American countries’ terms of trade peaked. Country-level data do not reveal any strong patterns, but the volatility of labor productivity growth stands out starkly, with all countries except Chile and Peru achieving positive productivity growth in only two out of the four periods (Figure 5.1).
Labor Productivity Level and Growth across Latin American Regions and Countries
Sources: Feenstra, Inklaar, and Timmer 2015; International Labour Organization; World Bank; and authors.Note: Annual figures are averaged over the period indicated. Panels 2 and 4 show the median value by country grouping. AEs = advanced economies; CA + MEX = Central America and Mexico; EMs = emerging markets; EMDEs = emerging markets and developing economies; PPP = purchasing power parity; SA = South America.Labor Productivity Level and Growth across Latin American Regions and Countries
Sources: Feenstra, Inklaar, and Timmer 2015; International Labour Organization; World Bank; and authors.Note: Annual figures are averaged over the period indicated. Panels 2 and 4 show the median value by country grouping. AEs = advanced economies; CA + MEX = Central America and Mexico; EMs = emerging markets; EMDEs = emerging markets and developing economies; PPP = purchasing power parity; SA = South America.Labor Productivity Level and Growth across Latin American Regions and Countries
Sources: Feenstra, Inklaar, and Timmer 2015; International Labour Organization; World Bank; and authors.Note: Annual figures are averaged over the period indicated. Panels 2 and 4 show the median value by country grouping. AEs = advanced economies; CA + MEX = Central America and Mexico; EMs = emerging markets; EMDEs = emerging markets and developing economies; PPP = purchasing power parity; SA = South America.Informality
Informality in Latin America is high, accounting for more than 50 percent of total employment. Latin America, however, is not an outlier—the level of informality in South and Central America is broadly comparable to that in other emerging market and developing economies. Informality in advanced economies is significantly lower (Figure 5.2).3
Informal Employment as a Proportion of Total Employment, by Region and Country
Sources: International Labour Organization; and Inter-American Development Bank’s Labor Markets and Social Security Information System.Note: AEs = advanced economies; CA + MEX = Central America and Mexico; EMs = emerging markets; EMDEs = emerging markets and developing economies; PPP = purchasing power parity; SA = South America.Informal Employment as a Proportion of Total Employment, by Region and Country
Sources: International Labour Organization; and Inter-American Development Bank’s Labor Markets and Social Security Information System.Note: AEs = advanced economies; CA + MEX = Central America and Mexico; EMs = emerging markets; EMDEs = emerging markets and developing economies; PPP = purchasing power parity; SA = South America.Informal Employment as a Proportion of Total Employment, by Region and Country
Sources: International Labour Organization; and Inter-American Development Bank’s Labor Markets and Social Security Information System.Note: AEs = advanced economies; CA + MEX = Central America and Mexico; EMs = emerging markets; EMDEs = emerging markets and developing economies; PPP = purchasing power parity; SA = South America.Even within Latin America, the degree of heterogeneity is large, with labor informality ranging from around 30 to 70 percent among the largest economies. Panel 2 of Figure 5.2 presents a scatterplot of GDP per capita against the labor informality rate. It shows that, in line with standard predictions, labor informality has generally decreased as countries’ incomes have risen (except for Mexico, where, despite higher GDP per capita, informality has actually increased); yet, even for the same level of income, countries show differences in labor informality, suggesting that other factors are also at play. Mexico and Peru, but also Argentina, do worse than other countries do in their levels of development.
Latin American countries with some of the lowest and most stable unemployment rates (Mexico and Peru) are the ones with the most informality relative to their levels of development, suggesting that the second margin of adjustment (formal versus informal employment) might, to some degree, substitute for the margin of adjustment between employment and unemployment.
Figure 5.3 shows the relationship between labor informality and output informality in a broad cross-section of Latin American countries. Output informality is substantially harder to measure than labor informality; for broad country coverage, we use the estimates from Medina and Schneider (2018). The correlation between the two series is high (0.75), as expected, but the slope is rather flat. Given the informal sector’s lower productivity, even in countries with labor informality rates higher than 70 or even 80 percent, output informality tends to be around 40 percent.
Labor Informality and Output Informality in Latin America
Sources: International Labour Organization; and Medina and Schneider 2018.Labor Informality and Output Informality in Latin America
Sources: International Labour Organization; and Medina and Schneider 2018.Labor Informality and Output Informality in Latin America
Sources: International Labour Organization; and Medina and Schneider 2018.Labor Market Institutions
Labor market institutions are multidimensional and not easily described by any one set of indicators. Nevertheless, to provide an overview of the situation in Latin America, we focus on key perceptions-based indicators and indicators aimed at quantifying laws and regulations.
Panels 1 and 2 in Figure 5.4 show two key perceptions-based indicators from the World Economic Forum’s (2018) executive survey on labor markets. Whereas the flexibility of wages is similar in the whole of Latin America compared to other country groups, hiring and firing practices in South America are perceived to be substantially more rigid than nearly everywhere else.
Labor Market Rigidity, by Economic Development Level and Region
Note: AEs = advanced economies; CA + MEX = Central America and Mexico; EMs = emerging markets; EMDEs = emerging markets and developing economies; OECD = Organisation for Economic Co-operation and Development;SA = South America; WEF = World Economic ForumLabor Market Rigidity, by Economic Development Level and Region
Note: AEs = advanced economies; CA + MEX = Central America and Mexico; EMs = emerging markets; EMDEs = emerging markets and developing economies; OECD = Organisation for Economic Co-operation and Development;SA = South America; WEF = World Economic ForumLabor Market Rigidity, by Economic Development Level and Region
Note: AEs = advanced economies; CA + MEX = Central America and Mexico; EMs = emerging markets; EMDEs = emerging markets and developing economies; OECD = Organisation for Economic Co-operation and Development;SA = South America; WEF = World Economic ForumLabor Market Rigidity, by Economic Development Level and Region
Note: AEs = advanced economies; CA + MEX = Central America and Mexico; EMs = emerging markets; EMDEs = emerging markets and developing economies; OECD = Organisation for Economic Co-operation and Development;SA = South America; WEF = World Economic ForumLabor Market Rigidity, by Economic Development Level and Region
Note: AEs = advanced economies; CA + MEX = Central America and Mexico; EMs = emerging markets; EMDEs = emerging markets and developing economies; OECD = Organisation for Economic Co-operation and Development;SA = South America; WEF = World Economic ForumWith this in mind, panels 3 and 4 in Figure 5.4 show summary indicators of employment protection laws and regulations constructed by the Organisation for Economic Co-operation and Development (OECD) and the Inter national Labour Organization, respectively. It is perhaps surprising that the indicators do not show that South America has stronger employment protection legislation than other countries. This raises two possibilities: (1) employment legislation is not de jure rigid in South America, but certain aspects of implementation, perhaps related to the legal system, make it de facto rigid; or (2) the aggregate indices hide more specific factors of the legislative framework, which in practice are more important for the flexibility of the labor market than other (offsetting) elements included in the index.
By correlating each subcomponent of the International Labour Organization, OECD, and World Bank Employment Protection Legislation data sets with the World Economic Forum’s 2018 perceptions of hiring and firing practices, we find that hiring and, especially, firing procedures contribute much more to the perception of rigidness than severance or redundancy pay per se (results not reported). Other factors, such as length of notice period or length of trial period (on the hiring side), do not affect perceived flexibility at all.4
Panels 5 and 6 in Figure 5.4 thus focus on hiring and firing procedures and compare Latin America to other regions. Panel 7 shows redundancy costs.5 Although this is not the case for all relevant dimensions, the three indicators shown here highlight that Latin American labor markets do exhibit noticeable rigidities in some key dimensions. Dismissal of even one worker often requires third-party approval, permanent contracts are often mandatory for permanent tasks, and redundancy costs are higher than in advanced economies or other emerging market and developing economies. These indicators suggest strong de facto job protection for formal, permanent jobs.
Panel 8 in Figure 5.4 shows the ratio of minimum wage to value added per worker, gauging how binding the minimum wage is. The cross-country comparison provides little evidence that the minimum wage is more binding in South America than in other regions, but Central America stands out as having a high ratio.
Figure 5.5 reproduces panels 5 through 8 of Figure 5.4 on hiring and firing procedures, redundancy costs, and minimum wage at the country level for the largest economies in Latin America. Cross-country comparisons suggest that those countries with persistently high informality (Mexico and Peru, as discussed) have cumbersome hiring and firing procedures. Argentina, however, has the highest redundancy costs and a high minimum wage.
Labor Market Rigidities for the Largest Economies in Latin America
Labor Market Rigidities for the Largest Economies in Latin America
Labor Market Rigidities for the Largest Economies in Latin America
As panel 4 of Figure 5.5 shows, the choice of denominator greatly determines the assessment of how binding the minimum wage is. The minimum wage is low in Brazil and Mexico, whereas it appears high in Colombia and, to a lesser degree, in Peru. The ratio of the minimum wage to the median wage is not available from the OECD for three of the six countries.
Summarizing the stylized facts, we note that (1) informality is a major feature of Latin American labor markets and (2) there is some evidence that countries with higher informality also have more rigid employment protection legislation, although de facto employment protection is a difficult concept to measure. The remainder of the chapter explores these findings through several empirical exercises.
Decomposing Unemployment Dynamics
We now use a simple approach to decompose changes in unemployment for the largest countries in Latin America into changes in labor supply and demand.6 A similar approach was implemented by Hijzen and others (2017) for OECD economies. More specifically, changes in unemployment relative to a reference period can be decomposed as follows:7
where u denotes the unemployment rate, and y, z, part, and wap are the logarithms of GDP, labor productivity, the labor force participation rate, and working-age population (* indicates the value of a variable at the beginning of the period). In this equation, changes in labor demand correspond to the sum of changes in output and changes in labor productivity, whereas changes in labor supply are captured by changes in the participation rate and the working-age population.
In broad terms, the decomposition shows that unemployment rose in the late 1990s as supply outstripped demand, then fell during the commodity boom period (2000–11) as labor demand picked up more than labor supply (Figure 5.6). The global financial crisis had only a limited effect on unemployment in the sample countries, with the trends from 2000 to 2007 similar to those from 2007 to 2011 in all countries except Mexico. In recent years, unemployment has been broadly stable, except in Argentina and Brazil, where it rose after a sharp drop in demand, even as labor supply growth slowed.
More insightful than the simple split into supply and demand is a look at the margins of adjustment. On the supply side, working-age population growth has been largely stable across countries and time periods; yet the labor participation rate has been an active margin of adjustment, mostly mitigating fluctuations in unemployment. In Chile, Colombia, and Peru, labor participation expanded strongly during boom years but has recently stopped growing and seen substantially weaker output growth, preventing a rise in unemployment.
Higher labor productivity reduces labor demand here, given that the same output can be produced with fewer workers. Labor productivity growth has also greatly limited fluctuations in unemployment (see, for example, Mexico during the global financial crisis, where labor productivity growth was negative, and Argentina and Brazil since 2011). One mechanism through which labor productivity can adjust to limit changes in unemployment is when firms hoard labor. Labor productivity may also fluctuate with changes in informality, considering productivity tends to be lower in the informal sector.
To show the role of labor formality and informality, the decomposition can be rewritten as follows, where l F is the logarithm of formal employment and f is the logarithm of the ratio of formal to total employment (labor formality):8
As Figure 5.7 shows, labor informality has played a crucial role in limiting movements in unemployment in Latin America. Consider Colombia, for example (panel 4). In the late 1990s, labor formality fell (as informality rose), limiting the rise in unemployment during a difficult economic time when labor demand was weak and the labor force participation rate rose. During the boom of the early 2000s, informality fell sharply, only to resume its role as a shock absorber during the global financial crisis. In the years since 2011, informality has again fallen significantly, implying that the unemployment rate did not fall as much as it would have otherwise.
Decomposing Changes in Unemployment in Latin America: The Role of Informality, 1997–2017
(Percent)
Source: Authors.Decomposing Changes in Unemployment in Latin America: The Role of Informality, 1997–2017
(Percent)
Source: Authors.Decomposing Changes in Unemployment in Latin America: The Role of Informality, 1997–2017
(Percent)
Source: Authors.Similar countercyclical properties of informality can be observed in Argentina, Chile, and Peru (Figure 5.7, panels 1, 3, and 6). Chile from 2007 to 2011 shows how a strong increase in formal labor demand was met, in roughly equal shares, with increased participation and a reduction in informality for a stable unemployment rate. The two recent periods in Brazil and Mexico (panels 2 and 5) are interesting to examine for the lack of adjustment along the informality margin: in Brazil, informality continued to fall from 2011 to 2017, even as unemployment increased strongly. Meanwhile in Mexico, informality has increased since the early 2000s, although the unemployment rate has been low and even decreasing in recent years.
Changes in informality and labor force participation have, however, overall helped limit the rise in unemployment during downturns and growth slowdowns. More generally, the decomposition highlights the limited average annual fluctuations in unemployment over the business cycle, stressing the need to look at broader labor market outcomes to assess labor market slack when studying Latin American countries. On the basis of the decompositions, we suggest using a combination of formal employment growth, the informality rate, and the unemployment rate to study the cyclical properties of labor markets in Latin America.
Revisiting Okun’s Law
Okun’s law relates changes in output to short-term changes in unemployment and is widely used to study cyclical relations between economic activity and labor markets. To compare observed fluctuations in unemployment over the business cycle in Latin America and the Caribbean with those in other emerging market and developing economies and in advanced economies, we present estimates of Okun’s law for a broad panel of countries and then explore the cross-country variation of estimated coefficients to gain insights about how key structural char-acteristics or labor market policies affect labor markets’ responsiveness to output growth.
We use a heterogeneous panel approach that allows slope coefficients to vary across countries and deals with possible cross-sectional dependency by including common factors in the estimation. The sample includes both emerging markets and advanced economies. The general empirical specification is summarized in equation (3) for i = 1, ..., N countries; and t = 1, ..., T time periods.9
where ui,t is the unemployment rate, yi,t is the log of output (real GDP), fm,t are common factors that affect all countries and change over time, and αi are country-specific fixed effects capturing country characteristics that do not change over time. These common factors are not directly observable, and their factor loadings (λi) can be country specific.
One reason accounting for such factors may be important when estimating Okun’s law is the possibility that, for example, technological changes that are common across countries could affect the relationship between unemployment and output. εi,t is the error term, which is assumed to be white noise. A caveat regarding this specification is that changes in unemployment can lead to changes in future output, posing a possible endogeneity issue.
Standard panel estimators usually treat the slope coefficients (β) as homogeneous across countries and frequently require stationarity of the variables included in the analysis, which might not be appropriate assumptions for macroeconomic panels. In addition, estimators traditionally used in panel data analysis require the assumption of cross-sectional independence throughout panel members. In the presence of cross-sectionally correlated error terms, these methods do not produce consistent estimates of the parameters of interest and can lead to incorrect inference (Kapetanios, Pesaran, and Yamagata 2011).
To address these potential problems, we use the common correlated effects (CCE) estimator proposed by Pesaran (2006). This estimator uses cross-sectional averages of the dependent and independent variables as proxies for unobserved common factors in the regressions (equation 3). The CCE yields consistent and efficient estimates, and its small sample properties do not seem to be affected by residual serial correlation of the error terms. Kapetanios, Pesaran, and Yamagata (2011) show that the CCE performs well when variables included in the model are nonstationary, and they advocate the use of this estimator irrespective of the order of the data’s integration. Eberhardt and Presbitero (2015) apply this approach to examine the link between debt and growth.
Baseline Results
Table 5.1 presents the results obtained when estimating different versions of Okun’s law using the CCE estimator with annual data for 127 countries from 1990 to 2017 (the panel is unbalanced, and data availability varies by country). We exclude from the sample countries with fewer than 20 years of data. The unemployment rate series (expressed as a percentage of the total labor force) comes from the World Bank World Development Indicators database, and the real GDP series in constant local currency units comes from the IMF’s World Economic Outlook database.
Okun’s Coefficient Heterogeneous Panel Estimates, 1990–2017
Okun’s Coefficient Heterogeneous Panel Estimates, 1990–2017
| ∆ Unemployment | |||||
|---|---|---|---|---|---|
| (1) | (2) | (3) | (4) | (5) | |
| ∆ GDPt | -0.125*** (0.0152) |
-0.115*** (0.0169) |
-0.124*** (0.0174) |
-0.122*** (0.0173) |
-0.126*** (0.0185) |
| ∆ GDPt-1 | -0.0330*** (0.00989) |
-0.0386*** (0.0111) |
-0.0387*** (0.0118) |
-0.0372*** (0.0122) |
|
| ∆ GDPt-2 | 0.0187* (0.0110) |
-0.00683 (0.0114) |
-0.000180 (0.0138) |
||
| ∆ GDPt-3 | 0.0341*** (0.0109) |
0.0202* (0.0110) |
|||
| ∆ GDPt-4 | 0.0282*** (0.00984) |
||||
| Constant | 0.0600 (0.0867) |
0.0239 (0.107) |
-0.0159 (0.106) |
0.0966 (0.106) |
0.205 (0.144) |
| No. of observations | 3,399 | 3,379 | 3,355 | 3,331 | 3,307 |
| No. of countries | 127 | 127 | 127 | 127 | 127 |
Okun’s Coefficient Heterogeneous Panel Estimates, 1990–2017
| ∆ Unemployment | |||||
|---|---|---|---|---|---|
| (1) | (2) | (3) | (4) | (5) | |
| ∆ GDPt | -0.125*** (0.0152) |
-0.115*** (0.0169) |
-0.124*** (0.0174) |
-0.122*** (0.0173) |
-0.126*** (0.0185) |
| ∆ GDPt-1 | -0.0330*** (0.00989) |
-0.0386*** (0.0111) |
-0.0387*** (0.0118) |
-0.0372*** (0.0122) |
|
| ∆ GDPt-2 | 0.0187* (0.0110) |
-0.00683 (0.0114) |
-0.000180 (0.0138) |
||
| ∆ GDPt-3 | 0.0341*** (0.0109) |
0.0202* (0.0110) |
|||
| ∆ GDPt-4 | 0.0282*** (0.00984) |
||||
| Constant | 0.0600 (0.0867) |
0.0239 (0.107) |
-0.0159 (0.106) |
0.0966 (0.106) |
0.205 (0.144) |
| No. of observations | 3,399 | 3,379 | 3,355 | 3,331 | 3,307 |
| No. of countries | 127 | 127 | 127 | 127 | 127 |
Specification 1 presents the results of a model with no lags of the change in real GDP; the coefficient β is around -0.12 and is statistically significant at the 1 percent level. Including up to four additional lags of the change in GDP (specifications 2 to 5) does not change the contemporaneous coefficient much. Moreover, only the first lag of the change in GDP appears to be statistically significant in a robust manner.10,11
Ball, Leigh, and Loungani (2017) obtain average estimates for β of around -0.40 for a sample of 20 advanced economies, but these authors point to significant cross-country variation in estimates.12 This suggests that unemployment responds less to output fluctuations in low-income developing countries. This conclusion is confirmed by average estimates for country income groups (Figure 5.8). We also present the sum of coefficients for changes in GDP in specification 2. Coefficient estimates are larger in absolute value for advanced economies relative to all other groupings (including South and Central America with Mexico). Coefficients for Latin America and the Caribbean are somewhat larger than for emerging markets more broadly.
Unemployment’s Responsiveness to GDP Changes, by Region and Country
(Percent)
Source: Authors.Note: AEs = advanced economies; CA + MEX = Central America and Mexico; EMs = emerging markets; EMDEs = emerging markets and developing economies; SA = South America.Unemployment’s Responsiveness to GDP Changes, by Region and Country
(Percent)
Source: Authors.Note: AEs = advanced economies; CA + MEX = Central America and Mexico; EMs = emerging markets; EMDEs = emerging markets and developing economies; SA = South America.Unemployment’s Responsiveness to GDP Changes, by Region and Country
(Percent)
Source: Authors.Note: AEs = advanced economies; CA + MEX = Central America and Mexico; EMs = emerging markets; EMDEs = emerging markets and developing economies; SA = South America.Okun’s Coefficients and Labor Institutions
We now examine the cross-country variation of estimated coefficients to key structural labor market characteristics and institutions (focusing on specification 2 in Table 5.1). More restrictive institutions can create distortions that would prevent the efficient allocation of labor, possibly leading to adverse effects on productivity (Freeman 2010; Duval and Loungani 2018). Restrictive institutions can also impede adjustment to shocks by reducing churning and turnover in the labor market. Nevertheless, institutions can also play a neutral or positive role by reducing information asymmetries and solving coordination problems (Freeman 2010). More precisely, we estimate the following specification, where Xj,i is a vector of control variables capturing institutional features of labor markets (including informality):
Outlier-robust regressions of Okun’s coefficients reported in Table 5.2 show that once we control for informality, most indicators capturing labor market institutions are not statistically significant, with the exception of the indicator capturing wage flexibility. To provide a sense of the variables’ relative economic importance, specification 6 demonstrates that a 1 standard deviation increase in informality increases Okun’s coefficient by 0.10 point and that a 1 standard deviation increase in wage flexibility increases the coefficient by 0.03 point.
Okun’s Coefficient, Informality, and Labor Market Institutions
Okun’s Coefficient, Informality, and Labor Market Institutions
| (1) | (2) | (3) | (4) | (5) | (6) | (7) | |
|---|---|---|---|---|---|---|---|
| Informality | 0.00368*** (0.000487) |
0.00368*** (0.000518) |
0.00366*** (0.000537) |
0.00363*** (0.000595) |
0.00355*** (0.000593) |
0.00354*** (0.000644) |
0.00353*** (0.000843) |
| Wage flexibility | 0.0419** (0.0203) |
0.0398* (0.0230) |
0.0404* (0.0234) |
0.0446* (0.0233) |
0.0442* (0.0235) |
0.0829*** (0.0276) |
|
| Hiring and firing | 0.00448 (0.0271) |
0.00575 (0.0284) |
0.0150 (0.0288) |
0.0128 (0.0291) |
-0.0118 (0.0350) |
||
| Dismissal approval | 0.00642 (0.0434) |
0.00929 (0.0433) |
0.0122 (0.0444) |
-0.000733 (0.0527) |
|||
| Fixed-term contract | 0.0604* (0.0322) |
0.0621* (0.0326) |
0.0640 (0.0409) |
||||
| Redundancy costs | 0.000456 (0.00135) |
0.000285 (0.00169) |
|||||
| Employment protection | -0.0276 (0.190) |
||||||
| Constant | -0.314*** (0.0275) |
-0.518*** (0.0984) |
-0.525*** (0.110) |
-0.533*** (0.114) |
-0.619*** (0.119) |
-0.614*** (0.120) |
-0.686*** (0.160) |
| No. of observations | 93 | 90 | 90 | 90 | 90 | 89 | 65 |
| R2 | 0.385 | 0.414 | 0.412 | 0.409 | 0.432 | 0.440 | 0.475 |
Okun’s Coefficient, Informality, and Labor Market Institutions
| (1) | (2) | (3) | (4) | (5) | (6) | (7) | |
|---|---|---|---|---|---|---|---|
| Informality | 0.00368*** (0.000487) |
0.00368*** (0.000518) |
0.00366*** (0.000537) |
0.00363*** (0.000595) |
0.00355*** (0.000593) |
0.00354*** (0.000644) |
0.00353*** (0.000843) |
| Wage flexibility | 0.0419** (0.0203) |
0.0398* (0.0230) |
0.0404* (0.0234) |
0.0446* (0.0233) |
0.0442* (0.0235) |
0.0829*** (0.0276) |
|
| Hiring and firing | 0.00448 (0.0271) |
0.00575 (0.0284) |
0.0150 (0.0288) |
0.0128 (0.0291) |
-0.0118 (0.0350) |
||
| Dismissal approval | 0.00642 (0.0434) |
0.00929 (0.0433) |
0.0122 (0.0444) |
-0.000733 (0.0527) |
|||
| Fixed-term contract | 0.0604* (0.0322) |
0.0621* (0.0326) |
0.0640 (0.0409) |
||||
| Redundancy costs | 0.000456 (0.00135) |
0.000285 (0.00169) |
|||||
| Employment protection | -0.0276 (0.190) |
||||||
| Constant | -0.314*** (0.0275) |
-0.518*** (0.0984) |
-0.525*** (0.110) |
-0.533*** (0.114) |
-0.619*** (0.119) |
-0.614*** (0.120) |
-0.686*** (0.160) |
| No. of observations | 93 | 90 | 90 | 90 | 90 | 89 | 65 |
| R2 | 0.385 | 0.414 | 0.412 | 0.409 | 0.432 | 0.440 | 0.475 |
In addition to a measure of hiring and firing practices and redundancy costs, we include in the regressions a dummy capturing whether third-party approval is required to dismiss one worker as well as a dummy indicating whether fixed-term contracts are prohibited for permanent tasks (both variables come from the World Bank’s Doing Business Indicators database). Moreover, we consider the restrictiveness of employment protection legislation as measured by the International Labour Organization’s Employment Protection Legislation Database (higher values of the indicator reflect more restrictive regulations). Country coverage for the latter indicator is somewhat more limited. Results suggest that labor market institutions are more likely to affect Okun’s coefficient indirectly, if at all (that is, to the extent that institutions affect the level of informality).
These results say nothing about whether it would be desirable for unemployment to be more sensitive to the cycle. As highlighted by Ahn and others (2019), in the absence of unemployment insurance or an adequate social safety net, unemployment becoming more responsive to growth could indeed reduce, rather than increase, welfare. Yet informality has economic implications that go beyond its role in dampening the cyclicality of unemployment (Levy Algazi 2018), which makes it worth analyzing in more detail.
A Model of Labor Informality and the Business Cycle
This section briefly presents a dynamic stochastic general equilibrium model that is consistent with the empirical patterns of informality at both the business cycle and long-term frequencies. The details of the model are laid out by Lambert, Pescatori, and Toscani (2020).
Our modeling framework builds on Anand and Khera (2016) and Munkacsi and Saxegaard (2017). The model includes a representative household that consumes formal and informal goods and supplies labor, perfectly competitive intermediate goods producers, monopolistic competitive wholesale final goods producers, retailers, capital producers, and a public sector (government and a monetary authority). Formal and informal firms (which produce formal and informal goods, respectively) face different frictions in terms of entry costs, hiring costs, and payroll taxes (although informal goods can only be consumed by domestic households, formal goods can also be exported and consumed by the government).
We specify the utility function such that there is a zero-income effect on the consumption of the informal goods. This captures the so-called demand channel of informality as laid out by La Porta and Shleifer (2014), among others. Entrepreneurs who want to modernize their businesses need to generate sufficient sales to cover the fixed costs of investment. When income is low, demand for formal goods may be too low to cover fixed costs. Demand for low-quality, cheap informal goods therefore expands the informal sector at the cost of the formal sector.
This mechanism is important to allow the model to generate a decreasing, concave relationship between informality and GDP per capita. Formal sector total factor productivity (TFP) is the key model parameter that allows us to match the shape of the curve as it is in the data; however, changes in aggregate TFP create a slope that is too “flat”—informality does not fall sufficiently fast as GDP per capita increases. This result model suggests that the gap between formal and informal sector TFP (that is, the rise in formal TFP and simultaneous stagnation of informal TFP), more than the rise in aggregate TFP, is the main driver of the decline in labor market informality.
Labor informality is countercyclical in the model, as in the data, and the absolute value of the Okun’s coefficient estimated from data simulated by the model decreases with labor informality (Figure 5.9). To the best of our knowledge, these three facts (decreasing relationship between labor market informality and per capita GDP, countercyclical informality, and lower responsiveness of unemployment to GDP when informality is high) could not be jointly captured in most existing models.
The Relationship between Labor Informality and Okun’s Coefficient
Source: Authors’ calculations.The Relationship between Labor Informality and Okun’s Coefficient
Source: Authors’ calculations.The Relationship between Labor Informality and Okun’s Coefficient
Source: Authors’ calculations.We use our modeling framework to study both the effect of structural reforms on informality in the steady state and the role of informality and labor market frictions in business cycle dynamics. In particular, we calibrate the model to replicate the Colombian economy (the parameters that determine business cycle moments are estimated using a simulated method of moments) and then focus on how informality reacts to and mediates shocks over the business cycle. We find that labor market reforms are not enough to substantially reduce labor informality in the absence of an increase in formal sector productivity but are key to reduce the steady state unemployment rate.
Regarding the model dynamics, impulse response functions for both TFP and commodity price shocks display the expected reactions of macroeconomic variables (to illustrate, Figure 5.10 shows the reaction to a positive commodity price shock). Unemployment, labor informality, and output informality are shown to be countercyclical. A higher level of informality (when all else is equal, and conditional on TFP or commodity price shocks) is found to mitigate business cycle fluctuations in GDP and, especially in consumption and unemployment, confirms the role of informality as a “buffer.” This is an important consideration given that higher levels of informality are also associated with a smaller welfare state.
Impulse Response Function for the Reaction to a Positive Commodity Price Shock
Source: Authors’ calculations.Impulse Response Function for the Reaction to a Positive Commodity Price Shock
Source: Authors’ calculations.Impulse Response Function for the Reaction to a Positive Commodity Price Shock
Source: Authors’ calculations.Conclusion
This chapter emphasizes the role of informality in the dynamics of labor markets in Latin America. A decomposition of changes in unemployment during several subperiods highlights the countercyclical role of informality (David, Pienknagura, and Roldos 2020). An econometric analysis of Okun’s law shows that the formal and informal adjustment margin reduce the importance of the employment and unemployment margin. This result implies that, in economies with prevalent informality, reporting only the unemployment and job creation rates (as is standard in advanced economies) may not be sufficient to capture labor market slack. To gauge the cyclical position of Latin American labor markets, reporting the informality rate is more informative.
Model simulations suggest that a country’s productivity and, notably, the productivity of its formal sector compared to that of the informal sector are key determinants of informality. Although both lower labor market frictions and higher formal sector labor productivity are important, reductions in informality will always be bounded absent productivity gains. In contrast, higher formal sector labor productivity has no direct effect on the unemployment rate, whereas labor market reform aimed at reducing frictions is key in that regard. Over the business cycle, informality acts as an important shock absorber in the model— consistent with the empirical findings discussed earlier—limiting fluctuations in unemployment and macroeconomic aggregates.
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For additional details about the empirical work, see David, Lambert, and Toscani (2019). Details of the modeling framework can be found in Lambert, Pescatori, and Toscani (2020).
In this chapter, South America includes Argentina, Bolivia, Brazil, Chile, Colombia, Ecuador, Guyana, Paraguay, Peru, Suriname, Uruguay, and Venezuela. Central America includes Belize, Costa Rica, El Salvador, Guatemala, Honduras, Nicaragua, and Panama. Although Mexico behaves similarly to South American economies in certain aspects, it is included in the Central America group because of its close integration with the US economy and lower exposure to the commodity cycle.
Cross-sectional labor informality data come from the International Labour Organization (we focus on the share of informal employment in total nonagricultural employment). Time-series labor informality data for Latin American countries come from the Inter-American Development Bank’s Labor Markets and Social Security Information System. Data are harmonized across countries. The working-age population is defined to be between 15 and 64 years old.
From the OECD indicators, specifically, stricter “notifcation procedures,” “definition of justified or unfair dismissal,” “compensation following unfair dismissal,” and “possibility of reinstatement following unfair dismissal” are significantly negatively correlated with perceptions of a more flexible labor market. From the International Labour Organization indicators, stricter rules on “valid grounds for dismissals,” “prohibited grounds for dismissals,” “procedural requirements for dismissals,” and “[more] redress [possibilities]” are significantly negatively correlated with perceptions of a more flexible labor market. Finally, from the World Bank indicators, “fxed-term contracts prohibited for permanent tasks,” “third-party notifcation if one worker is dismissed,” “third-party approval if one worker is dismissed,” “retraining or reassignment [obligations before dismissal],” “priority rules for redundancies or reemployment,” and “severance pay for redundancy dismissal (weeks of salary)” are significantly negatively correlated with perceptions of a more flexible labor market.
The World Bank data have the broadest country coverage, which makes World Bank measures preferred for the regressions in subsequent parts of this chapter.
In this section, we use labor force, working-age population, employment, and unemployment data from the International Labour Organization. Output data are from the IMF’s World Economic Out look database, and informality data are from the Inter-American Development Bank.
where u is the unemployment rate, LF is the labor force, L is the number of persons employed, LF is the number of formal employees, Y is output, ρ is the labor force participation rate defined as LF / wap, and wap is the working-age population.
The formal and informal margins operate separately from the labor force participation margin, given that the labor force comprises unemployed, formally employed, and informally employed workers.
We also consider an alternative specification in which variables are expressed as “gaps” (deviations from trend), calculated using the Hodrick-Prescott filter with a smoothing parameter of 6.25. The results obtained are quantitatively close to the ones reported in Table 5.1.
As a robustness check, we also estimate the specification using quarterly data and obtain similar esti mates. Nevertheless, the country and time-series coverage of the regressions is significantly reduced.
We undertake a similar exercise using the employment rate as the dependent variable. Results are available on request.
Ahn and others (2019) also consider the cyclical sensitivity of unemployment for a broad sample of emerging market and developing economies using more traditional panel data methods with interaction effects. They obtain results consistent and quantitatively similar to the ones discussed in this chapter.
