INFORMAL EMPLOYMENT IN MEXICO:

Transcription

1 INFORMAL EMPLOYMENT IN MEXICO: AN ANALYSIS OF RETURNS IN THE FORMAL AND INFORMAL LABOR MARKETS A Thesis submitted to the Faculty of the Graduate School of Arts and Sciences of Georgetown University in partial fulfillment of the requirements for the degree of Master of Public Policy in the Georgetown Public Policy Institute By Alfredo Gonzalez Briseno, B.S. Washington, DC April 14, 2008

2 INFORMAL EMPLOYMENT IN MEXICO: AN ANALYSIS OF RETURNS IN THE FORMAL AND INFORMAL LABOR MARKETS Alfredo Gonzalez Briseno, B.S. Thesis Advisor: Marcela Tarazona, Ph.D. ABSTRACT Some observers contend that informal employment is the only alternative that many workers have in developing countries, given the scarcity of opportunities in the competitive formal labor market. Others argue, contrary to this vision of exclusion, that workers become informal because they obtain better returns in the informal labor market. The answer to this debate might be found in the heterogeneity of informal workers group. If informal workers are separated in self-employed and salaried workers, two different visions and effects of informality arise. By using household employment surveys from Mexico, and multivariate regression methods, we analyze how wages are structured in the Mexican labor market and study whether or not the returns differ from one sector to another. Keywords: Informal employment, wages, labor market, Mexico ii

3 TABLE OF CONTENTS Section 1. Introduction... 1 Section 2. Literature Review... 4 Section 3. Research Hypothesis and Conceptual Framework... 8 Section 4. Data and Methodological Plan Section 5. Summary Statistics and Regression Results Summary Statistics...18 Regression Results...24 OLS Regression Results...24 Wage Gaps in the Labor Market...29 Sample Selection Bias...32 Section 6. Conclusions and Policy Implications References iii

4 LIST OF TABLES Table 1. Composition of the labor market in Mexico by formal and informal workers Table 2. Basic summary statistics and description of the variables involved Table 3. Percentage of formal and informal workers in Mexico Table 4. Percentage of formal and informal workers in Mexico, by division of informal workers Table 5. Percentage of formal and informal workers, by gender Table 6. Descriptive statistics of formal and informal worker s age Table 7. Percentage of formal and informal workers, by age group Table 8. Descriptive statistics of formal and informal workers education Table 9. Percentage of formal and informal workers, by level of education Table 10. Descriptive statistics of formal and informal workers wage/hour Table 11. OLS estimates of the wage equations (ln of wages per hour) in the Mexican labor market Table 12. Total wage gaps and raw differential wages between formal and informal workers Table 13. Basic summary statistics and description of the selection model Table 14. Probit estimates for the sector assignment equation in the Mexican labor market Table 15. Estimates of the wage equations (ln of wages per hour) in the Mexican labor market, corrected for sample selection bias iv

5 Section 1. Introduction Informality has become a relevant issue in the study of less developed countries in the last two decades. Evidence suggests that on average informality accounts for approximately 41 percent of GDP in developing countries (Schneider and Klinglmair, 2004). From a labor market perspective, informality represents between 30 and 70 percent of the workforce in Latin American countries (Perry et al, 2007). Beyond these significant magnitudes, there is an ongoing debate about the reasons why workers decide to be informal. It has been argued that informal employment reflects the incapacity of the state to generate sound economic conditions that are required for the creation of well-remunerated formal jobs (Packard, 2007). This reasoning implies that workers become informal because that is the best option available. A second reason considers the absence of an appropriate environment to make more attractive or at least less costly the participation in formal activities (Johnson, Kaufmann and Zoido-Lobatón, 1998; Centeno and Portes, 2006; and Loaiza and Rigolini, 2006). According to this second reason, the poor quality of public goods and services, as well as the high burden of taxes and regulations creates more incentives to be informal. Under this scenario some workers choose informal employment on a voluntary basis, because they find better opportunities and economic returns given their personal characteristics. 1

6 The debate around the sources of informality has motivated the present research project, whose aim is to analyze the returns to Mexican workers in order to understand whether they decide to be informal because they are excluded from formality, like in a segmented labor market scenario, or because they voluntarily choose informality based on the prospect of better economic returns. Although some evidence suggests that in Mexico there is a wage premium related to informality (Marcouiller, Ruiz and Woodruff, 1995), our initial expectation is that a premium is not the case for all Mexican informal workers. This assumption relies on the heterogeneity of informal workers (Günther and Launov, 2006), who can be divided in: (i) salaried workers at formal or informal businesses who do not have a contract or who do not receive all the legal benefits of a formal job (e.g., social security), and (ii) self-employed and small entrepreneurs (Djankov et al, 2002; Portes and Haller, 2003; and Packard, 2007). The dissimilar characteristics of the members of each of these groups, such as age, education or experience, seem to have different effects on the structure of their wage functions (Gindling, 1991; Marcouiller, Ruiz and Woodruff, 1995; Günther and Launov, 2006; and Packard, 2007). If different wage structures prevail in the labor market, then it might be possible to find informal workers who perform better than if they were formally employed, as well as informal workers who get worse returns than if they had a formal 2

7 job. In contrast to previous research, the contribution of this project is to analyze the differential in wage structures and returns in Mexico, not only by considering a homogeneous group of informal workers but also by considering the effects of a heterogeneous informal labor market with two groups of workers that differ in their average personal characteristics. This paper is organized in the following way. The second section presents a literature review of the informal labor market and its policy relevance. The third section presents the hypothesis and conceptual framework that guides our research. The fourth section explains our data and methodology. The fifth section shows the results obtained from the analysis. And the final section presents the conclusions and policy implications of our research. 3

8 Section 2. Literature Review It is possible to separate the analysis of the large number of workers in the informal labor market in developing countries into two groups of thought. The first group favors the theory of a segmented labor market that consists of a lower-level segment that lacks access to good economic opportunities and an upper-level segment that provides better remuneration for workers with characteristics identical to workers in the lower level (Gindling, 1991). According to this view, informal workers select their segment because that is their only option, given the poor economic conditions and economic barriers of less developed countries (Gindling, 1991; and Pradhan, 1995). Thus, for market segmentation advocates, informal employment is the consequence of an economic exclusion of some workers from the scarce and better remunerated formal opportunities available in the upper-level labor market (Marcouiller et al, 1995; Packard, 2007; and Perry et al, 2007). Workers move to the informal sector not by choice but by force, and wait there until they find a formal job. Contrary to the first vision, a second group has shown that informality is not the outcome of an economy excluding workers because the number of formal jobs is too small to match the labor supply. Instead, according to this group, workers voluntarily select informal jobs (Maloney, 2003; Bosch and Maloney, 2006; and Perry et al, 2007) as a self-exclusion decision that is not linked to poor economic conditions or barriers 4

9 to mobility across sectors. In fact, evidence shows that informal activity has also increased during periods of economic growth (Maloney, 2003). This finding indicates that informality provides some profitable opportunities that must be as good as or better than those at the formal sector of the labor market. As a result, from time to time some workers decide to work informally because they can obtain better economic returns given their personal characteristics, like education or experience. Overall, this type of labor risk is more likely when wages and other benefits of formal employment are not attractive enough. In other words, it happens when the costs of being informal are reduced. Following the last idea, research has been conducted to compare the structure of wage functions for formal and informal workers. In countries like El Salvador, Peru and South Africa, evidence suggests that informal workers have lower wages than their formal counterparts (Marcouiller et al, 1995; Gong and Van Soest, 2001; Pradhan, 1995; and Badaoui, Strobl and Walsh, 2007). Those studies support the view of informality as a less advantaged labor segment chosen not by will but as a safeguard mechanism until formal opportunities appear. However, in countries like Mexico, evidence shows a wage premium for informal workers (Marcouiller et al, 1995), which strengthens the idea of informal employment as a self-exclusion choice that provides better returns for some workers, based on their personal characteristics. 5

10 The difference in results might be explained by the heterogeneity of the informal workers group. Some authors have analyzed them not as a homogenous group but as mixture of individuals, with different characteristics, who face dissimilar incentives to operate outside of formality (Maloney, 2003; Fields, 2005; and Günther and Launov, 2006). This feature of the informal labor market opens the door to the possibility that only some of these workers are really excluded from the economy, having in consequence less fruitful jobs than if they were formally employed. It also raises the possibility that other informal workers self-exclude because it is more profitable. The literature that describes the characteristics of informal workers helps to understand the double dynamic that the informal labor market presents. Self-employed workers compose one segment of informality. On average, these workers have less year of education than formal sector workers, but they have more experience (Packard, 2007), which they use to obtain higher returns. Overall, they can be seen as entrepreneurs that decide to take a risk because there are economic opportunities in accordance with their personal characteristics. In addition, in developing countries they also face a formal sector with elevated costs of entrance that pushes them to informality (Packard, 2007). The chance to make a profit and to avoid taxes and 6

11 regulatory costs generates better levels of remuneration for workers in the informal sector, than if they comply with formal requirements. Salaried workers are the second group that compromises the informal sector. This informal group includes people that have a paid job in a formal or informal business, but do not receive all the legal benefits, beyond wages, that labor regulations establish (Packard, 2007), such as social security. Members of this group are often young people with less experience who take informal jobs because they did not find a place in the formal labor market or because they lack experience and capital, which prevents them from starting a business (Marcouiller et al, 1995; Aroca and Maloney, 1999; and Packard, 2007). The economic returns that informal salaried workers receive are lower than the compensation received by their counterparts in the formal labor market. Although both perform similar activities, only one group enjoys the full employment benefits dictated by law. 7

12 Section 3. Research Hypothesis and Conceptual Framework The present study analyzes the returns in the formal and informal labor markets in Mexico. Model (1) represents a first case of analysis, in which wages are determined in the same way for all workers. Models (2) and (3) represent a second case, in which different segments in the labor market have different equations that take account of workers personal characteristics. The models are: ln W = βx + e (1) i i i ln W = β X + e (2) Fi F i Fi ln W = β X + e (2) Ii I i Ii ln W = β X + e (3) Fi F i Fi ln W = β X + e ISEi ISE i ISEi ln W = β X + e ISALi ISAL i ISALi where i denotes individual observations, F and I differentiate formal and informal workers, and ISE and ISAL identify informal self-employed workers and informal salaried workers, respectively, lnw is the natural log of hourly wages perceived by 8

13 each worker, and X includes individual characteristics such as education, experience, gender and controls for economic sector, post-high school technical training, position at work, worked hours bracket, payment type and location where individuals work. If models (2) and (3) reflect the real labor market, then variables such as education or experience will have different effects for formal and informal workers. Thus, the analysis may help to clarify whether informal workers are the lower-level segment of the labor market that queues up waiting for an opportunity in the formal and upper-level segment; or if they voluntarily choose informality because that maximizes their returns, based on their personal characteristics; or if both cases hold for different groups within the heterogeneous group of informal workers. If we find that wages are determined separately for different groups in the labor market, it is also of interest to analyze whether workers with similar characteristics but working in different segments have wages that are significantly different from each other. This will allow us to determine if there is a wage premium or penalty for informal workers. Models (4) and (5) examine this possibility: ln W lnw > 0 (4) Fi Ii 9

14 ln W lnw > 0 (5) Fi ISEi ln W lnw > 0 (5) Fi ISALi Specifically, our goal is to study whether (i) wages are determined differently in the formal and informal sector of the labor market, and (ii) if there is a significant wage differential between sectors for workers with similar characteristics. In both cases, we will conduct two separate analyses. The first scenario will consider informal workers as a single and homogeneous group, as in models (2) and (4). The second scenario will allow for the presence of a heterogeneous informal group, composed of self-employed and salaried workers, as in models (3) and (5). For the first question, our expectation is that there is not a single wage equation in the labor market. This is supported by and consistent with studies previously conducted (Gindling, 1991; Marcouiller, Ruiz and Woodruff, 1995; Günther and Launov, 2006; and Packard, 2007), and implies, for example, that personal characteristics such as education or experience have a different effect on formal and informal workers (Packard, 2007). As previously mentioned, self-employed workers are less educated but have more experience, which appears to have more relevance for their wages than for the remuneration that formal or salaried employees obtain. 10

15 Conversely, for formal workers education seems to be more important and provides better returns than those received, on average, by informal workers. For the second question, the expectation is that formal workers earn more than informal workers when this group is analyzed as a homogenous segment. Nevertheless, if informal workers are separated into self-employed and salaried workers, it is possible that the first group has a wage premium compared to formal workers, while workers in the second group still earn lower wages. 11

16 Section 4. Data and Methodological Plan To test if there are different wage structures for formal and informal workers, as well as if there is a wage gap between observationally identical individuals of each sector, we will use information extracted from the National Urban Employment Survey, ENEU (Encuesta Nacional de Empleo Urbano) of Mexico. The National Institute of Geography, Statistic and Informatic, INEGI (Instituto Nacional de Estadística, Geografía e Informática) developed and applied this household survey until ENEU s goal was to obtain statistical information about the social, demographic and labor characteristics of the urban population in Mexico. The present study uses a sample drawn from ENEU data for the third quarter of The sample is limited to workers older than 12 years who performed an economic activity and received a monetary payment greater than zero. For people who reported having more than one job, only the main activity is considered. These restrictions exclude some informal activities done by household members that work without compensation for their family businesses. However, this exclusion is justified because the purpose of this research is to analyze differences in wage structures and compensation between different segments of the labor market. We also exclude from the sample people that reported working in the United States. The reason here is to avoid the comparison of wages determined in substantially different economic 12

17 environments. After applying these restrictions the final sample size is 154,805 workers. Once the sample is created, we identify workers in the formal segment as well as the two groups of informal workers. The group of informal self-employed workers consists of individuals who declare themselves as self-employed or owners of a business, and who also state that their activities are not registered with the government. The group of informal salaried workers includes individuals who report receiving a fixed salary or a commission for work done, but whose employer is not registered with the government or who does not give them legally required benefits besides wages, such as social security. All workers that do not fit the description of informal self-employed or informal salaried workers are formal. In addition, when the analysis assumes a homogenous informal labor market, informal self-employed and informal salaried will be combined in a single group. Table 1 shows the distribution of the 154,805 workers in the sample across employment categories. 13

18 Table 1. Composition of the labor market in Mexico by formal and informal workers Category Obs Formal workers 80,208 Informal workers 74,597 Self-employed 19,649 Salaried 54,948 Total 154,805 Once the respective sub-samples are created, it is possible to test if there is a single wage equation for all workers in the Mexican labor market, as model (1) implies, or if there are different wage functions for formal and informal workers as models (2) and (3) assume. An ordinary least square (OLS) model is used to regress the natural logarithm of wages per hour on years of education, experience, experience squared, gender, and controls for economic sector, medium and high school achievement, post-high school technical training, position at work, worked hours bracket, payment type, and location where the individual works. Initially we run separate equations for different sectors of the labor markets. Then, we use a Chow test to check whether there are statistically significant differences for the coefficients of each segment. The Chow test equation is: 14

19 SSR F = pooled m i= 1 m SSRseparate i i= 1 n m* k * (6) k SSR separate i where SSR pooled is the total sum of squared residuals of the pooled model; ΣSSR separate is the sum of the total sum of squared residuals for the separated models; n is the total sample size; k represents the number of parameters estimated in each equation; and m is the number of separate equations. To determine whether there is a wage gap for workers with similar characteristics in different segments of the labor market, we analyze the part of the gap that is not explained by the difference in the independent variables of formal and informal workers (Marcouiller, Ruiz and Woodruff, 1995). f i ( X X ) β + X ( β β ) = ( X X ) β + X ( β β ) ln W lnw = (7) f i f i f i f i i f f i If either X ( β β ) or X ( β ) i f i f β is negative, this will imply that there is a f wage differential between the formal and informal segments of the labor market. i 15

20 Finally, the literature on informal labor markets suggests that the OLS coefficients for separate wage equations can have a sample selection bias (Heckman and Hotz, 1986; Gindling, 1991; and Günther and Launov, 2006). This means that if a worker is free to choose between segments of the labor market, he/she will select the sector that provides the best returns to his/her personal characteristics. Thus, there might be some unobserved personal characteristics that make a worker more likely to choose one segment of the labor market, and that also contribute to his/her determination of wages. To analyze and solve for any problem of sample selection bias, we will use a Heckman model for sample selection (Heckman, 1986). This model is separated in two steps. The first step is a probit model that is used to estimate the segment assignment equation. This model helps to know if workers are not randomly assigned to the labor market sectors, based on their personal characteristics. The second step is a wage equation that controls for the sample selection correction term, lamda or Inverse Mills Ratio (IMR) obtained from the first step. With the help of a Heckman model for sample selection, we run a probit model (8), based on the assumption that there are separate wage equations in the labor market. The first step equation for the Heckman model is: 16

21 P j e = = α jz e ( M j) α Z (8) where j includes formal and informal workers from model (2). Formal workers are the baseline category. In addition, Z is defined by personal characteristics, such as: age, age squared, gender, dummies for the maximum level of education achieved (less than basic, basic, medium, high school, college, and graduate), a dummy for married workers, as well as controls for type of payment received and worked hours bracket. After running the probit model, the sample correction term (LAMBDA, λ) is included as an additional control variable in the original wage equation of model (2). The inclusion of the correction term helps to determine the magnitude of the bias and whether it is statistically significant. 17

22 Section 5. Summary Statistics and Regression Results Summary Statistics Before any multivariate regression is run, we present a few basic summary statistics in order to provide better insight into the motivations behind our research. Table 2 shows a brief description of the dependent and independent variables, as well as the number of observations available for each one, means, standard deviations and minimum and maximum values. Table 2. Basic summary statistics and description of the variables involved Variable Description N Mean Std Dev Min Max lnw ln of wages per hour 150, education years of education 149, experience proxy: age - education , experience squared experience squared 149, gender if female =1; if male =0 154, Note: controls for economic sector, medium and high school achievement, post-high school technical training, position at work, worked hours bracket, payment type and location are not shown, although they will be considered in the models. Some variables have missing observation because they were not reported. For the lnw, the minimum value is not zero. The minimum reported appears like zero, but it is only because no decimals are reported. The proxy for experience is age minus the number of years of education minus 6, which is considered as the number of years before education starts. 18

23 It is also useful to know the differences in the values of these variables for the different groups in the labor market. Table 3 presents the composition of the labor market in Mexico, in which about 48 percent of workers are informal, while 52 percent work formally. Table 4 shows a different composition that separates the large proportion of informal workers into self-employed workers and salaried workers. More than 12 percent are informal self-employed workers, while 35 percent are informal salaried workers. Table 3. Percentage of formal and informal workers in Mexico Variable % Formal workers 51.8 Informal workers 48.2 Table 4. Percentage of formal and informal workers, by division of informal workers Variable % Formal workers 51.8 Informal workers / self-employed 12.7 Informal workers / salaried 35.5 Table 5 presents the gender distribution of both segments of the labor market. As expected, the proportion of males and females in the two groups is similar to the gender distribution at the national level. However, it is important to highlight a larger 19

24 presence of females in the informal labor market, compared to the formal segment and the nation as a whole. A probable reason for a larger presence of females in informal jobs is that they might find it easier to get a job in a segment of the labor market that gives them more flexibility in terms of time and physical location. Table 5. Percentage of formal and informal workers, by gender Variable Formal Informal National Male Female Note: national values were extracted from INEGI s statistics. The mean value for age in the whole sample is 36 years. When segments are divided, informal workers are slightly older, on average, than formal workers. However, a second cut of the information shows that on average informal selfemployed workers are older than formal workers by more than seven years, while informal salaried workers are younger by a year. This finding was expected based on the idea that self-employed workers decide to work informally because they can get a higher return for their experience. In contrast, informal salaried workers are younger because informal jobs are the kind of occupation many people have before they get a position in the formal sector. At least this idea can be inferred from Table 7 that shows the distribution between formal and informal sectors for workers by age category. 20

25 Table 6. Descriptive statistics of formal and informal workers age Variable - age N Mean Std. Dev Min Max Total 154, Formal 80, Informal 74, Informal self-employed 19, Informal salaried 54, Table 7. Percentage of formal and informal workers, by age group Variable Formal Informal 24 or less more than Table 8 shows that the average years of education for the entire sample are 9.7. This number amounts to a complete elementary school education, medium school and almost a year of high school. As it happened with previous variables, there is a difference between average years of education of formal and informal workers. On average, formal workers have one more year of education than informal workers. Table 9 shows that the proportion of formal workers with medium to higher levels of education is larger than the proportion of informal workers with these education characteristics. The proportion of informal workers is larger when basic or less than basic levels of education are considered. 21

26 Table 8. Descriptive statistics of formal and informal workers education Variable - years of education N Mean Std. Dev Min Max Total 149, Formal 78, Informal 70, Informal self-employed 17, Informal salaried 53, Table 9. Percentage of formal and informal workers, by level of education Variable Formal Informal Less than basic Basic Medium High school College Graduate Across the two segments of informal workers group there is also a difference of more than two years of education. The informal self-employed workers are, on average, less educated than informal salaried workers. This is consistent with the view that self-employed choose informality because that sector s compensation is not based mainly on the years of education as the formal segment of the labor market is. Moreover, as we mentioned above, informal salaried workers are younger than informal self-employed workers. Therefore, they may still be continuing with their 22

27 education, or at least they may have more years of education and are just waiting to get a formal job where they can get higher returns. Table 10 shows that the mean wage per hour for our sample is about $119 Mexican pesos (approximately $11 USD in February of 2008). Unexpectedly, on average, wages per hour are 17.5% higher in the informal labor market than in the formal labor market. Again, there is a difference when informality is separated in two groups. Wages per hour for informal self-employed workers are significantly higher, on average, not only when compared to salaried informal workers but also when compared to formal workers. The opposite occurs in the case of informal salaried employees, who on average have the lowest wages in the labor market. Table 10. Descriptive statistics of formal and informal workers wage/hour Variable - wages/hr in MX$ N Mean Std. Dev Min Max Total 150, ,200 Formal 78, ,200 Informal 71, ,100 Informal self-employed 19, ,527 Informal salaried 52, ,100 23

28 Regression Results We now present the results of models (1), (2) and (3), and then proceed to determine whether the wage structures are statistically significantly different for each segment of the labor market or if they are reduced to a single and pooled equation for the entire labor market. OLS Regression Results Table 11 shows the estimates for the wage equations in accordance with the three models proposed. With these results we calculate a Chow-test, as shown in equation (6). This test allows us to determine whether the coefficients estimated for the separate models are statistically significantly different from each other. For model (2), which considers the presence of two different wage equations, one for formal workers and another for a homogeneous informal sector, the F statistic from the Chow test is 31.19, which implies that the coefficients from each segment are statistically significantly different from each other. For model (3), which allows for a heterogeneous informal sector, the F statistic is 59.31, which again confirms the idea that there are different segments in the Mexican labor market, each one with estimates that are statistically significantly different from the others. 24

29 Table 11. OLS estimates of the wage equations (ln of wages per hour) in the Mexican labor market Model (1) Model (2) Model (3) Variable Pooled Formal Informal Formal I. Self-Employed I. Salaried education *** *** *** *** *** *** (27.73) (19.79) (20.06) (19.79) (-4.01) (25.62) experience *** *** *** *** *** (34.85) (25.01) (23.95) (25.01) (-0.79) (28.08) experience squared *** *** *** *** ** *** (-30.64) (-19.95) (-22.54) (-19.95) (-2.51) (-23.09) gender *** *** *** *** *** (-38.81) (-31.50) (-24.91) (-31.50) (-0.74) (-30.93) constant *** *** *** *** *** *** (135.03) (88.32) (94.34) (88.32) (48.26) (83.88) R F-stat N 145,657 77,713 67,944 77,713 17,062 50,882 Note: t-values reported in parenthesis. The independent variables were used as described in Table 2. Controls for economic sector, medium and high school achievement, post-high school technical training, position at work, worked hours bracket, payment type and location were included, too, although they are not reported. Even though some of the control variables are not statistically significant individually, all groups were jointly statistically significant at the level of confidence. *** Statistically significant at the level. ** Statistically significant at the 0.05 level. 25

30 Although wages are structured differently across segments of the labor market, this is not necessarily an indicator of the presence of a segmented labor market. Evidence supporting this consideration is the research previously done in Mexico (Maloney, 2003) that found mobility between segments, even in periods of economic downturn. Based on the finding of different wage equations in the Mexican labor market, it is important to analyze how the independent variables affect each group. In the case of education, the reported results were different from initial expectations. In model (2), informal workers get better returns for education than formal workers do. The ceteris paribus effect of an additional year of education for a formal worker is an increase of hourly wages of 2.4 percent, on average. In contrast, for an informal worker, the average ceteris paribus effect for each additional year of education is an increase in hourly wages of 2.8 percent. Even though the difference is not large, it deviates from the notion that formal workers get better returns for education, which is the supposed reason why people with more years of education prefer to be employed in the formal segment of the labor market. However, when informal workers are separated into two groups, as in model (3), the effect of education varies. For informal self-employed workers, the ceteris paribus effect on hourly wages of an extra year of education is -1.7 percent. For 26

31 informal salaried workers it is 3.6 percent. The negative returns to education simply reflect that informal self-employed workers are a less educated group, as Table 8 shows. These workers participate in this segment of the labor market because education is not as important there as it is in the formal segment. Instead, other things, like experience or entrepreneurship are used to get better returns. Finally, just like formal workers, informal salaried workers experience positive effects of education on wages. However, compared to the formal group, the effect is considerably larger. An explanation might be that additional years of education need to be compensated more in order to make up for the legal protections and other benefits that informal salaried workers miss (e.g., social security, or health insurance). The different effects of experience are minor for model (2). The difference between the ceteris paribus effect of an additional year of experience on hourly wages in the formal segment than in the informal segment is only two hundredths of percentage points. However, model (3) suggests that the group of self-employed experiences a very small but negative effect of experience on hourly wages. The coefficient is not statistically significant, although the quadratic term is, at the 0.05 level. This result is not expected. In fact the summary statistics and literature review suggests that the group of informal self-employed workers have more experience than 27

32 the other two groups in the labor market, and that this provides higher returns that compensate for lower levels of return for education in the informal sector. Moreover, as suggested above, informal self-employed workers are viewed as people that decide to take a labor risk by employing themselves based on their accumulated experience. In spite of the unexpected findings for informal self-employed workers, the effects of experience on hourly wages are positive and statistically significant for both formal and informal salaried workers. Between these groups, again the larger returns are obtained by the informal group. Salaried informal workers get, on average, an increase of 2.3 percent for an additional year of experience, while formal workers get a 1.8 percent increase on their hourly wages. Once more, the difference might reflect the idea that the experience of informal salaried workers needs to be compensated at higher levels, to make up for the absence of legal benefits like social security or health insurance. Finally, the effect of gender on hourly wages was expected to be negative for females. In model (2) that was the case. In both segments of the labor market there is a large negative effect of being female on hourly wages. For formal workers, holding other things constant, being female decreases hourly wages by more than 20.4 percent on average compared to being male. In the informal workers group, males earn on average 19.6 percent more than females, ceteris paribus. 28

33 In model (3), however, the coefficients show an interesting finding. Although, formal and informal salaried female workers earn less hourly wages, on average, than males, the returns on experience for informal salaried female workers are more than four percentage points less than the returns on experience for formal female workers. The difference seems reasonable because women might take positions as informal salaried workers due to the time flexibility they can get without loosing all their income. Finally, even when the coefficient on experience is relatively small for informal self-employed workers, it is statistically insignificant. Wage Gaps in the Labor Market Once the results from OLS models (1), (2) and (3) have been analyzed, we calculate the wage differentials across groups, in order to know what the wage gap is for workers with similar characteristics in each segment of the labor market. For this purpose, we will use the Blinder-Oaxaca decomposition technique, which allows the identification of the differential of wages between formal and informal workers in models (2) and (3). Table 12 presents the results of this analysis. 29

34 Table 12. Total wage gaps and raw differential of wages between formal and informal workers Variable Model (2) Model (3) Mean Ln Formal Wage Mean Ln Informal Wage Difference of mean Ln Wage Raw differential Note: the raw differential refers results from considering the differential attributed to endowments and coefficients, as well as the unexplained portion of the differentials. In this case, a negative value represents an advantage to the informal segment of the labor market, while a positive value means that the formal segment posses an advantage over the informal segment. The difference in the mean values of the logarithmic wage function for the formal and informal groups suggests that informal workers, as a homogenous group, have higher wages than formal workers. This is corroborated by the negative raw differential. Thus, the findings support previous research that reported the presence of a wage premium for workers in the informal labor market in Mexico (Marcouiller, Ruiz and Woodruff, 1995). However, the result is not common, and goes against the view of the informal sector as the marginalized segment of the labor market, in which workers wait until they get a position in the formal sector. Instead, the result supports the idea that there are some workers that voluntarily decide to be part of informality because they can get better returns by working in the informal segment than by working in the formal segment. 30

35 Another interesting result emerges when the informal segment is considered as a heterogeneous sector with two groups. Informal self-employed workers obtain a wage premium compared to formal workers, just as in the case where a homogeneous informal sector was considered. However, rather than showing the same result, informal salaried workers are punished by the labor market, although to a minor extent. These results coincide with our initial argument about the existence of two different groups of informal workers that experience different returns compared to formal workers. This difference shows that a disadvantaged group in the informal labor market coexists with another group of informal workers that performs better than their formal counterparts. This second group is composed of informal self-employed workers who take the risk to seize profitable opportunities that are present for them at the informal labor market, but that are absent at the same time in the formal labor market. However, there is also a disadvantaged group of informal workers with lower wages. This group of informal salaried workers has worse returns than formal workers, just as the theory of market segmentation suggests. The separate wage structures and the wage penalty are not enough evidence to conclude the presence of market segmentation. But there is enough evidence to be sure that informal salaried workers do not have a wage premium like informal self-employed workers. Although, 31

36 it is important to mention that the wage penalty with respect to formal workers is minor in magnitude. Sample Selection Bias As it was previously mentioned, the OLS coefficients estimated might be biased due sample selection problems. Some unobserved characteristics not only determine workers decisions to select one segment of the labor market, but also influence their wage function. To correct for possible bias we use a Heckman model of sample selection. The first step of this method is a probit model that plays the role of the selection equation. Table 13 presents a description and basic statistics of the variables used in this step. The model, the results of which are shown in Table 14, calculates the probability of being part of the informal segment of the labor market. The log likelihood ratio test statistic of the model is This means that workers are not randomly assigned across the formal and informal sector of the labor market based on characteristics such as age, gender, maximum level of education obtained and marital status. 32

37 Table 13. Basic summary statistics and description of the selection model Variable Description N Mean Std Dev Min Max informal if informal worker =1, else =0 154, age age 154, age squared age squared 154, gender if female =1; if male =0 154, basic if basic education was the 149, maximum level achieved =1, else =0 medium if medium education was the 149, maximum level achieved =1, else =0 high school if high school education was the 149, maximum level achieved =1, else =0 college if college education was the 149, maximum level achieved =1, else =0 graduate if graduate education was the 149, maximum level achieved =1, else =0 married if married =1, else =0 154, Note: controls for payment type and worked hours bracket are not shown, although they will be considered in the models. 33

38 Table 14. Probit estimates for the sector assignment equation in the Mexican labor market Variable P(informal=1) age (-15.09) age squared (14.66) female (9.72) basic (-17.96) medium (-39.82) high school (-43.77) college (-37.62) graduate (-11.98) married (-21.40) constant (19.40) Log-Likelihood -88, Pseudo R N Note: Chi-squared values reported in parenthesis. All variables are statistically significant at the level. 34

39 The statistical significance of the first step model results imply that a sample selection correction term, lambda, must be calculated and then be included in model (2). Table 15 shows the new coefficients estimated, controlling for the correction term. Table 15. Estimates of the wage equations (ln of wages per hour) in the Mexican labor market, corrected for sample selection bias Model (2) Variable Formal Informal education *** *** (8.18) (21.44) experience *** *** (14.02) (25.08) experience squared *** *** (-11.32) (-24.04) gender *** *** (-23.64) (-26.36) lambda *** *** (8.75) (-8.67) constant *** *** (69.12) (87.91) R F-stat N 77,713 67,944 Note: t-values reported in parenthesis. The independent variables were used as defined in Table 2. Controls for economic sector, medium and high school achievement, post-high school technical training, position at work, worked hours bracket, payment type and location were included, too, although they are not reported. Even though some of this control variables are not statistically significant different from zero, individually, all groups were jointly statistically significant at the level of confidence. *** Statistically significant at the level. 35

40 The statistical significance of both coefficients on lambda means that the sample selection was having an effect different than zero in the wage structure determination of the labor market in Mexico. With the correction, the ceteris paribus effect of education on hourly wages changes substantially for both formal and informal workers. For the first group an additional year of education, is expected to increase hourly wages by 1.4 percent, on average, holding other things constant. This value is considerably less than the 2.4 percent previously calculated in the OLS model without correction. For the second group, the ceteris paribus effect of an extra year of education on hourly wages is an increase of hourly wages by 3.7 percent, on average. This is by far a larger effect than the one reported before correcting for sample selection bias. Overall, with the correction, informal workers receive 2 percentage points more in returns to education than formal workers. Although there is no information for the scenario that considers the existence of two informal groups, it is expected that again, informal salaried workers get better results than formal workers while self-employed workers performed badly relative to all other segments of the labor market. This might be explained by the fact that self-employed workers in the informal labor market have lower levels of education than other groups. Because education is not a pre-requisite to get higher returns in the self-employed informal sector, any additional year of 36

41 education will have very low, or even negative returns as Table 11 shows. However, the informal salaried market is composed of younger workers, as Table 6 suggests. Most of them are expected to be students or recently graduated students that take informal salaried jobs as a first and most accessible step to enter the labor market. Given this situation, it is expected that they will receive higher returns for extra years of education. The corrected effect of experience on hourly wages shows a change that is similar to the corrected effect of education. In the OLS model, the returns on additional years of experience are almost equal for both segments of the labor market. However, when the sample selection bias is corrected, the group of informal workers increases its returns on experience, and the group of formal workers reduces it. In the end, the first group of workers receives one percentage point more than their formal counterparts for any additional year of education. The coefficients on gender are also corrected by the addition of lambda to the equations. In this case, the change is in the opposite direction than it was for the estimates on education and experience. Thus, the ceteris paribus effect on hourly wages of being a woman, compared to being a man, increases for formal workers while it declines for workers in the informal group. However, the magnitude of these changes is quite small, as it was in the case of experience. 37

42 We again calculate a Chow test to see whether the corrected model (2) has coefficients that are statistically significantly different from each other. The F statistic is 32.79, which confirms the results obtained with the OLS models. Thus, after correcting for sample selection bias, we can be sure that wages are structured differently for formal and informal workers in the Mexican labor market. Even though there is no information for the scenario that considers model (3), it is expected that there are also three different segments in the labor market that receive statistically significantly different returns on their personal characteristics. 38

43 Section 6. Conclusions and Policy Implications This research was motivated by the debate about the nature and composition of informality in labor markets. As initially explained, there are two groups of thought about why workers find themselves in the informal segment of the labor market. The first one is based on the assumed segmentation of the labor market. This means that workers are informal because they are excluded from the formal segment of the labor market, which systematically allocates better returns to its members. In consequence, these marginalized workers find refugee in the informal sector and wait there until they find an opportunity to enter the formal sector. The second school sees informal employment as a voluntary decision taken by workers, based on the expectation that they will obtain better returns in the informal sector than in the formal sector, given their personal characteristics. Our initial suspicion was that the two views might not be mutually exclusive, and that it was possible to find a non-homogeneous informal sector in which different groups of informal workers coexist while earning different rates of return compared to formal workers. The findings of this research support that idea. First, we find that wage functions in Mexico are structured differently for different group of workers. This implies that variables such as education, experience or gender, to mention few, have statistically significantly different effects on hourly 39