The informal sector in Nicaragua

I55AO Country Department il Latin America and Caribbean Region The World Bank Economic Notes ........... The Informal Sector in Nicaragua Carola Pessino February 1996 Nu... 4 4 Country Department II Latin America and the Caribbean Region Economic Notes NICARAGUA The Informal Sector in Nicaragua Carola Pessino February 1996 The World Bank This series presents the findings of work in progress or background material prepared as inputs to the Department's Economic and Sector Work. This Note was prepared by Carola Pessino (Consultant), as a background report for the Nicaragua Poverty Assessment (June 1995). The findings, interpretations, and conclusions are the author's own and should not be attributed to the World Bank. Questions or comments should be addressed to Ms. Pessino, email address: carola@cema.edu.ar@INTERNET. m Table of Contents 1. Introduction... 1 II. Informal Sector Employment... 1 III. Wage Differences Between Sectors... 5 IV. Segmentation or Human Capital?... 7 V. Conclusions and Policy Recommendations... 10 Statistical Appendix Tables Table 1: Evolution of the Urban Informal Sector Managua Table 2: The Informal Sector under Alternative Definitions, by Rural/Urban Location and by Gender Table 3: Main Chracteristics of Formal and Informal Sectors, 1993 Figures Figure 1: Log Wages, Males Age 25-64 Figure 2: Log Wages, Females Age 25-64  The Informal Sector in Nicaragua 1. Introduction 1. Nicaragua has a large and growing urban informal sector, accounting for more than 50% of the employed. While part of this increase can be attributed to the macroeconomic adjustment programs, a large share of this increase occurred because of formal labor market inflexibility and its increased taxation in the form of high tariffs and corporate taxes. 2. The labor market in Nicaragua is characterized by: (i) a formal sector and an informal sector either characterized by segmentation or differences in human capital; (ii) rising wages concurrent with high unemployment; (iii) low labor productivity; and (iv) excessive regulation in the formal sector of the economy. These characteristics, along with the uncertainty generated by property rights and titling issues, provide barriers to growth of the private sector and reduce the ability of the labor market to promote growth through the efficient allocation of resources and increasing labor productivity. Moreover, duality and other rigidities in the labor market affect unemployment, underemployment, and earnings, all of which have significant impacts on the levels and types of poverty. 3. Labor market flexibility is required for several reasons that range from increasing the rate of growth- in the overall economy, allowing labor to move in the face of a productive reconversion, augmenting labor productivity to decreasing unemployment, underemployment and informality. 4. This paper is complementary to Pessino (1994)', and otherwise noted the reader is referred to that work for sample and variables definitions. This study gives explicit consideration to the sectoral allocation of male and female workers. In particular, it analyses wage differences by sector, what are the determinants of choice or segmentation in each sector and whether there are differences in rates of return to human capital. It also considers explicitly the current macroeconomic environment in Nicaragua with low inflation but also relatively low real exchange rate. 5. Section II analyzes different definitions of informality and describes the evolution of informality in Nicaragua and the main differences in sociodemographic characteristics of workers in formal and informal sectors. Section III analyzes differences in wages between sectors by sex, education, industry and age cohorts. It also estimates human capital wage equations similar to Pessino (1994) with the inclusion of the dummy for informality. Section IV estimates wage equations for formal and informal workers separately. Since the allocation between sectors is presumably non-random, switching regressions techniques are used to explicitly consider the allocation of workers among sectors. It also considers the double selection involved in selecting whether to participate or not in the paid labor force and then whether to work in the formal or the informal sector. Section V presents conclusions and policy recommendations. II. Informal Sector Employment 6. The data used in this analysis are from the Living Standard Measurement Survey (LSMS) carried out in February-June 1993 by INEC, IDA and co-financed by several donors. Survey information includes socio-demographic characteristics, amounts of various sources of income including income from independent employment (type of activity, expenditure, income and inventory and capital), expenditure patterns, occupation and housing characteristics, health including anthropometric measures, and migration. 7. Work on the informal sector in Latin America has followed the ILO/PREALC approach. There are Available upon request. 2 The Informal Sector in Nicaragua three common methods used to estimate the size of the informal sector: (i) by size of establishment where they work; (ii) adding up numbers of employers, self-employed (excluding professionals), unpaid family workers and domestic workers; and (iii) counting the number of individuals earning less than the minimum wage or some measure of poverty line. Estimates from the 1970s of the size of the informal sector in Latin America generally place it at about 25 to 40% of the urban population. 8. There is evidence that the size of the informal sector rather than shrinking has been increasing at least in Managua since the stabilization plan. Table 1 shows the evolution of the urban informal sector during the last two decades at intervals where information is available. Most of the information refers to Managua. The two basic sources of data are FIDEG and INEC. FIDEG includes in the informal sector independent workers,2 employers and employees of firms with less than 5 workers (PREALC) and domestic service. INEC defines as informal all the firms, or organizations of less than 5 workers, excluding independent professionals, state firms and religious organizations. There are two distinctive periods where the rate of growth of urban informality differs significantly. From 1970 to 1989, the informal sector increased slightly from 43.6% to 47.6% of employment; a total increase of 9%. From 1989 to 1991, in only two years, we have the same 9% increase in the share of the informal sector. According to information for 1992, the share of this sector increased by 25%, that is 65.4% of those working in April 1992 belonged to the informal sector. Table 1: Evolution of the Urban Informal Sector Managua* Formal Informal Total 1970' 56.4 43.6 100.0 19822 55.0 45.0 100.0 1985' 51.8 48.2 100.0 19894 52.4 47.6 100.0 19915 47.9 52.1 100.0 19926 34.6 65.4 100.0 SOURCE: and FIDEG (1991) based on a study of UCA, percentages are calculated in relation to the labor force. ' INEC (1989) using data from ESDENIC 85, the percentage is calculated with respect to the employed in all activities except agriculture. They refer to Nicaragua. ' INEC (1990) using data from Encuestas de Coyontura. Percentages are calculated with respect to the employed in Managua, and are an average of the percentages of the 4 waves of the Survey undertaken in January, June, August and December 1989. s and ' FIDEG (July 1992) using data from the household surveys in Managua conducted in June/August 1991 and April 1992, respectively. They are calculated as a percentage of the employed. " Unless otherwise noted. See 1985 for an exception. 9. While in the first period considered, especially the second half, Nicaragua experienced the war and macroeconomic instability; from 1988 to 1992 we have periods of hyperinflation followed by stabilization plans, until the last plan started in March 1991. While the increase in the first period appears to be associated with the war and instability while there was a centralized economy with formal worker protection; the second period is more associated with hyperinflation that makes informality more profitable (given the absence of explicit contracts) and the ensuing stabilization plan. The reduction in government jobs through the "labor reconversion program" and the privatization of state-owned firms, at the time of existing collective bargaining agreements might have created excess supply of workers that had no other choice than informality, unemployment, underemployment or leaving the labor force. Another hypothesis is that the increase in informality from 1989 on coincides with the overvaluation of the Cordoba that lowered the price of inputs in the case of manufacturing and of final products in the case of street vendors or salesmen, and increased the premium of being informal. While Nicaragua is not a country that imposes high taxation on wages in the formal sector, it has high sales, corporate and foreign 2 Except those belonging to liberal professions (such as doctors, engineers. lawyers.etc) since following FIDEG. they work under similar conditions as those in the formal sector (they have balances and keep legal accounts, pay taxes,etc.). The Informal Sector in Nicaragua 3 trade taxes that creates an incentive to work in the "illegal" informal sector. 10. Using the LSMS information, Table 2 reports the extent of informality by urban and rural areas, and Table 3 the main socio-demographic characteristics of each group under the three definitions followed by ILO/PREALC (para. 7). According to the first definition, 30% of the population aged 12 or more work in the formal sector while 70% in the informal sector. This definition attributes the largest percentage of the working population to the informal sector: under the second definition, by type of occupation, 53% work in the informal sector, and according to the third definition based on poverty lines, 45.4% of the population are in the informal "poor" sector of the economy.' Notice that it is not surprising that this last definition has the smallest share of "informals" since as is apparent from the previous discussion, the informals are profitable especially in the services sector. Table 2: The Informal Sector under Alternative Definitions, by Rural/Urban Location and by Gender Men Women Total Urban Rural Total Urban Rural Total Urban Rural Total (1) Format 42.9 20.4 31.1 30.9 21.0 27.9 37.8 20.5 30.0 Informal 57.1 79.6 68.9 69.1 79.0 72.1 62.2 79.5 70.0 (2) Format 66.4 37.1 51.0 43.3 25.8 38.1 56.6 34.6 46.6 Informal 33.6 62.9 49.0 56.7 74.2 61.9 43.4 65.4 53.4 (3) Formal 71.3 27.9 48.5 77.1 42.8 66.8 73.8 31.2 54.6 Informal 28.7 72.1 51.5 22.9 57.2 33.2 26.2 68.8 45.4 Note: (1) Format = employees in firms with 5 or more employees + professionals; Informal = employees in firms with Less than 5 employees + domestic workers + seLf-employed workers. 20% of the observations did not match these categories, and were then classified in formal if they had INSSBI insurance. (2) Format = wage-employed + employees + self-employed (if professionals) + independent professionals. Informal = domestic workers + self-employed (not professionaLs) + unpaid family workers + employers. (3) Format = below the poverty Line of CS214.47 (USS 35.75) per month as estimated in World Bank (1993c). Informal = above the poverty line. 11. Table 3 presents the main characteristics of workers by formal/informal sector under the three alternative definitions of informality. For all definitions, informal sector workers tend to be younger; they have a higher proportion of the youth and old who are the groups with more unstable labor force attachment. The overall share of women in the labor force is of 33.3% similar to their share under definition (1) in both formal and informal sectors. However, under definition (2) they have a lower share in the formal sector and under definition (3) based on poverty, they have a higher share than the average in the formal sector. This last finding is consistent with the fact that women have a lower incidence of poverty than men in 1993 in Nicaragua.' 12. Education is a better predictor of informality under all the definitions considered. While overall, 57% of the employed did not complete primary education, between 65% and 80% (using the alternative definitions of informality) of the employed in the informal sector belong in this category, compared to only between 38% and 43% of the formal sector workers. Conversely, 5% of the employed have complete or incomplete tertiary education, between 9% and 13 % reached that level among the employed in the formal sector and only between 0.4% and 1.4% of the employed in the informal sector. The alternative definitions of informality used, although correlated, they obviously misclassify individuals into the two sectors. For example, using the first and second definition, 19% is misclassified differently (definition I classifies them as in the formal sector and definition (2) in the informal sector or viceversa). The misclassification is worst when using the third definition; compared either to definition (1) or (2), approximately 43% is misclassified. ' The World Bank (1993c) found that female-headed households were under-represented among the extremely poor and the poor. which is consistent with the under-representation of women in informality defined using poverty indexes. Moreover, Pessino (1994) found that in terms of unemployment. underemployment and wage differentials with males, females were relatively better off. 4 The Informal Sector in Nicaragua Table 3: Main Characteristics of Formal and Informal Sectors, 1993 (%) (1) (2) (3) Formal Informal Formal Informal Formal Informal Total Total 30.0 70.0 46.7 53.3 54.6 45.4 100.0 Age categories 12-14 2.6 4.9 1.7 6.4 1.8 7.1 4.2 15-24 25.5 26.1 28.2 23.9 22.6 30.1 26.0 25-54 66.6 57.4 63.9 56.9 65.8 53.4 60.2 55-64 3.8 7.0 4.5 7.3 6.2 5.8 6.0 65+ 1.5 4.6 1.7 5.4 3.6 3.8 3.7 15-64 95.9 90.5 96.6 88.2 94.5 89.2 92.1 Sex Men 69.0 65.7 72.8 61.3 59.2 75.7 66.7 Women 31.0 34.3 27.2 38.7 40.8 24.3 33.3 Household component Household head 44.8 47.1 45.9 47.0 47.1 45.6 46.4 Other 55.2 52.9 54.1 53.0 52.9 54.4 53.6 Education Never assisted 16.2 32.4 19.0 34.9 14.0 43.7 27.5 Primary incomplete 21.5 32.2 23.8 33.5 24.4 34.5 29.0 Primary complete 12.2 14.7 13.1 13.8 16.7 10.6 13.9 Secondary incomplete 21.7 15.1 21.3 13.4 23.8 9.0 17.1 Secondary complete 15.3 4.1 12.2 3.3 12.1 1.9 7.5 Sup/University incompl. 3.0 1.0 2.3 1.0 2.8 0.1 1.6 Sup/University compL. 10.2 0.6 7.3 0.1 6.2 0.2 3.5 Occupational Category Worker 31.7 14.8 42.7 0.0 15.0 25.7 19.8 Employee 62.1 10.4 55.6 0.0 36.6 13.0 25.9 Domestic Worker 0.0 8.1 0.0 10.6 4.5 7.1 5.6 Self-Employed 1.5 49.7 0.9 65.4 33.2 37.6 35.2 Independent profess. 0.2 0.4 0.8 0.0 0.5 0.2 0.4 Unpaid Family Worker 4.1 15.6 0.0 22.9 9.0 15.9 12.2 Employer 0.4 0.6 0.0 1.1 0.8 0.3 0.6 Industry Agriculture 22.8 37.3 21.6 43.1 14.3 55.3 32.9 Mining 0.1 0.1 0.2 0.1 0.1 0.2 0.1 Manufacturing 13.6 10.7 13.5 9.9 15.0 7.5 11.6 Electr.gas and water 4.0 0.1 2.7 0.1 1.7 0.8 1.3 Construction 4.5 2.6 5.6 1.1 3.2 3.1 3.2 Commerce 13.4 25.1 13.8 28.2 29.8 11.5 21.5 Transportation 4.9 3.0 5.8 1.5 4.9 2.0 3.5 Financial 3.8 0.6 3.0 0.3 2.5 0.5 1.6 Personal Services 33.1 19.8 33.5 15.4 27.9 18.9 23.8 Poverty Extreme poor 8.5 18.7 11.5 19.3 0.0 34.4 15.6 Poor 22.9 32.7 26.5 32.7 0.0 65.6 29.8 Non-poor 68.6 48.6 61.9 48.0 100.0 0.0 54.6 Underemployed Yes 8.6 21.7 12.4 22.6 9.6 27.6 17.8 No 91.4 78.3 87.6 77.4 90.4 72.4 82.2 Note: The criteria for formality/informality is defined in the note to Table 2. 13. By occupational category the classification between formality and informality is almost definitional, except that by analyzing the breakdown by definition iii) that does not use occupational categories and is based only on poverty, the self-employed are equally represented in both sectors. This is the main difference with the previous definitions (where self-employed are almost universally in the informal sector), showing moreover that the self-employed in Nicaragua are not necessarily the poorest. 14. When looking at the distribution by sector, the anomaly in the classifications by various determinants gets cleared up. Services, especially commerce is over-represented in the informal sector under the first two definitions, but being a profitable activity it is under-represented in the poverty definition of informality. However, when looking at the distribution of poverty using the first two definitions, the The Infonnal Sector in Nicaragua 5 informal sector does have a higher share of extreme poor and poor, but the main reason is their largest share in agricultural employment. Rural workers are over-represented among the poor in Nicaragua. The last classification in Table 3 shows that a higher share of the underemployed are in the informal sector, consistent with workers entering this sector while looking for a better job in the formal sector. 15. Notice that the first definition of informality accords with the one used in Nicaragua to classify urban informal workers. Comparing Tables 1 and 2, in 1992 using the FIDEG definition of the urban informal sector in Managua, 65.4% belonged to this sector. According to Table 2, for the whole country the urban informal sector in 1993 corresponds almost exactly to that definition. In most of this work then we will study informality using definition (1). III. Wage Differences Between Sectors 16. In this Section, we investigate differences in wages between workers in the formal and informal sector according to the first definition since it allows comparison with previous work of formality and informality in Nicaragua. First, we analyze raw differences in wages by education and gender. Second, we incorporate differences in age, industry and other characteristics. Finally, we control for differences in human capital endowments among workers in both sectors to analyze if differences in wages still persist. We use dummy endogenous variable techniques to control for the self-selection of workers into the formal or informal sectors. 17. Figure 1 shows log wages by educational level (complete or incomplete) for formal and informal sector male workers age 25-64 at the time of the Survey. Figure 2 presents the same information for female workers. Note that male workers have higher wage rates in the formal sector except for those with tertiary studies. This is just explained by the low (Table 3) proportion of workers in the informal sector with tertiary complete and incomplete studies (as compared to formal sector workers) and because this small number are mainly employers with less than five workers. Notwithstanding this anomaly, the figure shows that average log wages are smaller and growth in earnings by educational level is higher in the informal sector even considering only the first three educational categories. Fig.1 Log Wages Males - Age 25-64 Formal ( 2 Informal NONE PRIMARY SECONDARY TERTIARY Schooling Level The sample definition is the same used in Pessino (1994) and corresponds basically to the sample of males and females head of households or spouses of household heads that reported earnings in the seven days preceding the survey. 6 The Informal Sector in Nicaragua 18. Differences in wages for females between these sectors are much more marked and consistently higher for each educational level in the formal sector. However, contrary to males, the rate of growth of earnings is higher in the formal sector, hence we expect a higher rate of return to education there. Note also that the previous finding by Pessino (1994) about the higher (relative to males) log wage for females at the no-educational level continues to hold under this breakup of the sample of workers in the sense that both formal and informal females have higher wages than males without education. However, the relatively flat educational profile for females is more pronounced in the informal sector, where until reaching the secondary level, wages fluctuate in less than 20% for females. 19. Table Al presents log wages for males and females in each sector by age categories, educational level and industry of employment. There are several interesting features encountered in this Table. First, although Pessino (1994) finds no significant differences in average log wages between males and females, this is a product of higher average wages for females in both sectors, but their higher participation in the informal sector where wages are comparatively lower. Note that on average, wages in the formal sector are 44% higher than in the informal sector. Second, for the youngest cohort, age 25-34, we encounter the largest difference in earnings between males and females in favor of females, agreeing with the fact that female labor tends to be more favored under the current macroeconomic environment than males (if Fig.2 Log Wages Females - Age 25-64 Formal 2- 0a Informal 0 NONE PRIMARY SECONDARY TERTIARY Schooling Level younger cohorts are more representative of current labor force trends). This is true whether considering the formal or informal sector, noticing that the same favorable conditions occurred for females in both sectors. Third, by industry, wages are considerably higher in the formal agricultural sector (60% higher) and they are almost 40% higher in the formal manufacturing sector than in their informal counterpart. Notice, however, that although we still encounter higher wages for the formal sector in all industries, differences are smaller in the services and non-tradeable goods sectors. Although this reflects in part the less advantageous conditions in the informal sector, it is also an artifact of the definition of formality used where workers in firms with less than 5 employees are considered belonging to the informal sector. That is, part of the difference in wages is capturing differences in scale and technology of production. 20. Differences in (log) wages just analyzed did not control for characteristics of workers belonging to each sector. To analyze if there are still differences in wages after controlling for human capital endowments and other socio-demographic characteristics, Tables A2 and A3 show wage equations similar to those in Pessino (1994) with the addition of a dummy for informality (definition 1) for men and women The Informal Sector in Nicaragua 7 respectively. Since we are in the presence of a dummy endogenous variable, we estimated the model controlling for sectoral selection.o 21. For Nicaragua as a whole, when controlling for worker characteristics and self-selection, the dummy for informality is barely significantly greater than zero for males and less than zero for females. In Managua, there are no significant differences in wages between formal and informal sectors for males or females when controlling for nonlinearities in education. However, if we only consider education as a continuous variable (the first two columns in each region in Tables A2 and A3), women earn less in the informal sector. Most of the differences in wages are in the urban informal sector, where informal male workers tend to earn more than their formal counterpart while informal female workers tend to earn less than female workers in the formal sector. 22. These results indicate that when controlling for selecfion.into the formal or informal sector, adding different specifications for regions and the more extended is the specification of the wage equation, the lesser are the differences between wages in the formal and informal sectors of this country. With all these controls, the only significant difference is in favor of the informal sector for males in Other Urban areas (and this might be due to the fact we are aggregating across urban regions) predicting that on average males in the informal sector earn 23.5% more than workers in the formal sector. IV. Segmentation or Human Capital? 23. The last Section showed that differences in wages between formal and informal sectors for men and women are not consistently higher in the formal sector after controlling for human capital endowments in each sector. This is not a definite test of labor market segmentation or of duality in the labor market. A better approximation to test the hypothesis of duality is to estimate separate wage functions for each group and if they still differ between these groups in terms of differences in slopes' and predicted wages, then it could be argued that workers are in segmented markets. 24. Tables A4 and A5 present OLS regressions of the log wage equations in the formal and informal sectors for men and women. For men, there are substantial differences in the log wage equations. In particular, rates of return to education, experience and training are higher in the informal sector. This contradicts standard criticisms to the competitive market where workers are disadvantaged in the informal sector. However, for women (Table A5), rates of return to education and training are higher in the formal sector. Note that labor market segmentation theories hold that women are more probable to satisfy the premises of this theory than men, since their participation in the labor market is not continuous. Hence, the OLS results show in principle no segmentation for men and potential segmentation for women.8 25. Tables Al3 to A16 report the results of estimating these same OLS regressions by sector for each 6 We report only the ordinary least squares and selectivity corrected regressions, since in next section we discuss more fully the determinants of sectoral choice. These probits are available upon request. ' Note that although the labor segmentation hypothesis in one of its versions argues that pricing of human capital variables differ among sectors, this same conclusion holds without segmentation since implicit prices of skills can differ across sectors because there can be specific skills in each sector, unobservable compensating wage differentials or just the fact that skills are bundled in workers and cannot be separated. s It could be argued that women choose to enter the informal sector without any constraint or rationing since it is optimal for them to enter a market in a discontinuous way that does not put a premium on experience or job-specific experience and that it is fairly easy to enter and leave. 8 The Informal Sector in Nicaragua region: Managua, Other Urban and Other Rural areas for men and in Managua for women.9 The conclusion that rates of return to education are higher for men continues to hold in Managua and in Other Urban areas. However, rates of return to education are higher in the rural informal sector for males and mean log wages are consistently lower as reported in the last Section. 26. Separate OLS estimates of wage equations (instead of a common regression function for all sectors) for each sector are plagued by selection bias since workers can sort themselves out into each sector (see Cain, 1976). However, it is possible to correct for sample selection bias by performing joint maximum likelihood estimate (MLE) of the decision to enter each sector with the separate log wage equations. This is a standard switching regression model. However, as argued by Heckman and Hotz (1985) and Magnac (1991) this test raises delicate econometric problems so that a definite test of segmentation is not possible. They implement an alternative test based on the premise that without segmentation, conditions of entry into each sector are only determined by the wage function. There is no rationing or queuing in each sector. Hence, if one estimates the probability of entry into each sector, the slope coefficients determining earnings should be proportional (where the constant of proportionality is the standard deviation of log wages) to the slope coefficients determining the entry probability into the informal sector. 27. Since the purpose of this work is not to show academically that we proved or not duality, but rather to draw policy implications, it is important to gain some knowledge of the process of selection into each sector and if there are wide differences between them, we run the switching regression model for men and women in Nicaragua (Tables A6 and A7). 28. What are the determinants of sector selection? In the first place, one more year of education decreases the probability of entering the informal sector for males and females. For males higher experience increases this probability at low experience and decreases it at higher experience. The higher the earnings of the spouse, the higher is the probability of entering the informal sector, both for males and females. We do not have a good explanation for this fact but it might be consequence of the implicit conditioning of this sample to workers. Below we provide estimates for both the probability of participation and sector selection to analyze among other things the unconditional probability of sectoral selection. 29. Does the proportionality hypothesis against labor market segmentation as implemented by Heckman and Hotz (1987) for Panama hold in Nicaragua for men and women? Dividing the coefficients of columns 4 by those in column 1 in Table A6 for men and Table A7 for women should yield the estimated constant of proportionality; or in other words these numbers should be approximately equal for each of the slope coefficients. Although these computations yield some differences, for years of schooling, experience and earnings, there is some evidence that the hypothesis does not hold." 30. What happens with differences in slope coefficients after controlling for sectoral selection? Both for males and females the initial difference in rates of return to education survives after the selection control. One more year of education raises wages for males in the formal sector to 10.2%, while in the informal sector to 12.7%. For females, however, the rates of return are 11.1% in the formal sector and 4.8% in the informal sector. According to these estimates, rates of return to education are quite similar for men and women in the formal sector. The major difference between wage equations for males and females There are not enough observations for women in each sector to have efficient estimates of the wage equations. That is why we only reported results for Managua, showing that even there rates of return to education are higher in the formal sector. In the remainder of the study we do not separate by regions since not only we confront the problem of few observations for some groups, but the choice of rural/urban location by sex seems to be an integral part of the problem. 10 More formal tests of this hypothesis would be required. " A similar proportionality hypothesis was implemented by Dickens and Lang (1985) but using endogenous switching regimes. Their test of segmentation establishes that the coefficients on the probability of entering the informal sector should be proportional to the differences in the coefficients of the variables in the log wage equation in each sector. The Informal Sector in Nicaragua 9 rests in the informal sector. 31. What kind of selection in unobservables operates in the sectoral choice? According to the Roy Model of self-selection, the correlation between the error term in the sectoral choice (equal to the difference in the unobservable in the wage equation for the informal sector less the unobservable in the wage equation for the formal sector) and the error term in the respective wage equation shows what the earnings of the participants in one sector would have been if they participated in the other and also what kind of (comparative or absolute) advantage workers have, if any, in each sector. Since, the correlation is positive in the formal sector and negative (and significant in both cases) in informal sector, we can conclude that the mean of log wages of those selected into the formal sector exceeds those in the whole population, while the opposite occurs in the informal sector for males and females. In this sense, the more skilled in their latent distribution go to the formal sector and the remaining to the informal sector. 32. Finally, we consider a bivariate probit model of participation decisions together with sectoral choice. We estimate a bivariate probit with selection since we observe sectoral choice once we observe participation. Wage equations are corrected for the double selection process accounting for the possible covariance between these two decision processes (Table A8). 33. Participation in the paid labor force (LFP) when estimating the bivariate probit model are similar to those reported in Pessino (1994): LFP increases with education, first increases and then decreases with experience and for women higher income of the husband and more children both affect the participation decision negatively. The decision to join the informal sector is similar to that reported in Tables A.8 and A.9 that were estimated using univariate probits. The main change, as expected, is that spouses' income does not really affect sectoral choice. 34. After the double selection correction, although rates of return to education increase (in comparison with the models without or with single selection controls) for both sectors and by gender, there are still significant differences in returns across sectors. Notice that the low return experienced by women in the informal sector when controlling only for sectoral selection increases substantially now to reach a return of more than 10%.12 35. Previous estimates of wage equations in Nicaragua by sector were conducted by Behrman and Wolfe (1984). They estimated earnings equations only for women in the formal and informal sector controlling for selection in participation (and reporting of earnings) but without considering the non-randomness of the sectoral selection. Their data was collected in 1977-1978. They estimated an overall rate of return to education for women of 11 % and found that they receive higher returns to education in the urban formal sector than in the informal sector. These are quite similar results to the ones obtained in this paper, showing that rates of return to female labor have not increased substantially since 1978. However, during 1993, females experienced higher rates of return in the informal sector than the one (almost nil) estimated by the authors. 12 The wage equations with double selectivity where estimated using the two step procedure suggested by Lee. Although estimates are consistent, the coefficients of lambda in some of the regressions where outside normal values. More experimentation is needed with a full maximum likelihood procedure to corroborate these findings. In particular, we are skeptical about the coefficient of lambda-b in the wage equation for males in the informal sector and for lambda-a in the wage equation for females in the formal sector. In both cases, we believe that the rates of return to education are biased upwards. In this case, if we stay with the estimate of the return obtained with the simple switching regression model, 12.7% in the informal sector for males, there would not be significant differences in returns across sectors. Similarly, keeping the conservative estimate of 11.1 % for the rate of return to education for females in the formal sector, would not differ significantly from the 10.1% return for the informal sector using the bivariate selection model. 10 The Informal Sector in Nicaragua V. Conclusions and Policy Recommendations 36. This paper studied differences in socio-demographic characteristics, wages and human capital returns for both male and female workers in Nicaragua. Particular attention was given to the allocation of workers into these sectors; whether there exists rationing of jobs in the formal sector that precludes workers to enter or they have to queue for accession into the sector. Finally, the macroeconomic situation in Nicaragua was explicitly taken into account in deriving explanations for the empirical results. 37. The main findings are: * There is evidence of a growing urban informal sector in Nicaragua since 1991. According to the results in this paper, there is no evidence that the increasing trend will stop, since there is a premium to work in the informal sector for male workers (after controlling for differences in human capital endowments and sectoral selection) at least in urban areas. * While women tend to earn less in the informal sector (after controlling for socioeconomic characteristics, and especially education), their interrupted work careers, less attachment to the labor force, and higher participation when husbands' wages are lower, might increase the attractiveness of the informal sector since there is less penalty for the low accumulation of job specific experience. * Human capital plays an important role in explaining sectoral selection. Both males and females with more education have higher probability of entering the formal sector. * Private rates of return to schooling for men are on average higher in the informal sector, while the reverse occurs for women, with higher returns in the formal sector. There is evidence, however, that when controlling for multiple selection rates of return are not significantly different between sectors. * Moreover, most of the differences in rates of return to education by gender are due to differences in the informal sector of Nicaragua. This is consistent with the explanation given above, since the comparatively lower return to education for women in the informal sector might be compensated for the lower penalty of discontinuity in labor force participation. * Subject to further research, it is apparent that there is no labor market segmentation in Nicaragua at least in terms of formality and informality as defined in this study. Workers sort themselves into the sector where they receive higher wages according to their specific labor market. Economic conditions (high taxation of business in the formal sector and better situation for the services sector) in Nicaragua seem to favor the growth in informality, at least for males. 38. The main policy recommendations are: More education can decrease the likelihood that a given male or female worker joins the informal sector. If one objective of policy is to reduce the informal sector, this is one way in which, at least, from the supply side of the market, the objective can be achieved. If the objective is reducing informality, there is no gain in principle in diminishing barriers to entry into the formal sector on the supply side. However, less restrictions in hiring and more labor flexibility in the formal sector, can aid in increasing demand for workers there, and hence lowering the threshold value for workers to decide to enter the formal sector. If the objective is just poverty alleviation, without considering explicitly sectoral allocation, there is no evidence that informal sector workers are poorer than their formal sector counterparts after REFERENCES Behrman, Jere R., and B. Wolfe. (1984) "Labor Force Participation and Earnings Determinants for Women in the Special Conditions of Developing Countries", Journal of Development Economics 15: 259- 288. , B. Wolfe, and D. Blau. (1985) "Human Capital and Earnings Distribution in a Developing Country. The Case of Prerevolutionary Nicaragua." Economic Development and Cultural Change, Vol. 46. Cain, G. (1976) "The Challenge of Segmented Labor Market Theories to Orthodox Theory: A Survey," Journal of Economic Literature 14: 1215-57. Dickens, W., and K. Lang. (1985) "A Test of Dual Labor Market Theory.", The American Economic Review 75:792-805. Heckman, James J.; and V. Joseph Hotz. "An Investigation of the Labor Market Earnings of Panamenian Males." Journal of Human Resources, 21:4, (Fall 1986): 507-542. Magnac, Th. (1991) "Segmented or Competitive Labor Markets?" Econometrica, Vol.59, No.1: 165-187. Mincer, Jacob. (1974) Schooling. Experience and Earnings. New York: Columbia University Press for National Bureau of Economic Research. Pessino, Carola (1992): "Nicaragua Labor Markets: Main Finding and Recommendations", The World Bank, mimeo (1994): "Labor Markets in Nicaragua after the Stabilization Plan", The World Bank, Unpublished Manuscript, Washington, D.C. Psacharopoulos, George. "Returns to education: A Further International Update and Implications." Journal of Human Resources, 20:6, (Fall 1985): 583-604. Schultz, T. Paul. "Investments in the Schooling and Health of Women and Men: Quantities and Returns". Journal of Human Resources, 28:4 (Fall 1993): 694-734. World Bank, (1993a), "Republic of Nicaragua: Review of Social Sector Issues," Report No. 10671-NI, World Bank, Washington, D.C. , (1993b), "Nicaragua: Country Economic Memorandum," Report No. 12066-NI, World Bank, Washington, D.C. , (1993c), "The Who, What and Where of Poverty in Nicaragua" Report No. 12066-NI. World Bank, Washington, D.C. The Informal Sector in Nicaragua 11 controlling for human capital endowments. In this sense, it would not be advisable to restrict entry into the informal sector in the short and medium run. However, the alleviation of poverty in the long run would be only feasible with economic growth and development and the implicit competition between formality and informality (if in this sector they pay lower or no taxation) can curtail this process. STATISTICAL APPENDIX Table Al Log Wages by Sector and Gender Table A2 Wage Equations with Dummy for Informality, Controlling for Selection, Males Age 25-64 Table A3 Wage Equations with Dummy for Informality, Controlling for Selection, Women Age 25-64 Table A4 Wage Equations for Men, Age 25-64 Table A5 Wage Equations for Women, Age 25-64 Table A6 Switching Regression Model, Formal and Informal Sectord, Men 25-64 Years Old Table A7 Switching Regression Model, Formal and Informal Sectors, Women 25-64 Years Old Table A8 Bivariate Probit for Participation and Sectoral Choice and Wage Equations, Corrected for Double Selection, Males and Females 25-64 Years Old Table A9 Means and Std. Dev. of Earnings by Criteria One (Cordobas Oro) Table A10 Means and Std. Dev. of LWAGEHBA, by Criteria One Table All Means and Std. Dev. of LWAGEHBA, by Criteria One, Other Rural Table A12 Means and Std. Dev. of LWAGEHBA, by Criteria One, Other Urban Table A13 Wage Equations for Men, Age 25-64, Household Heads Table A14 Wage Equations for Women, Age 25-64, Household Heads Table Al5 Wage Equations for Men, Age 25-64, Household Heads, Other Urban Table A16 Wage Equations for Men, Age 25-64, Household Heads, Other Rural TabLe Al: Log Wages by Sector and Gender FORMAL INFORMAL TOTAL Mate Female TotaL Mate Female Total Mate Female Total Total 1.52 1.60 1.54 1.08 1.14 1.10 1.26 1.28 1.27 (.82) (.70) (.78) (1.12) (.91) (1.04) (1.03) (.87) (.97) Age Categories 25-34 1.47 1.63 1.54 .96 1.18 1.04 1.19 1.37 1.26 (.76) (.66) (.73) (1.01) (.80) (.94) (.94) (.78) (.89) 35-44 1.69 1.57 1.66 1.17 1.17 1.17 1.39 1.28 1.35 (.85) (.72) (.82) (1.24) (.86) (1.08) (1.12) (.84) (1.02) 45-54 1.43 1.45 1.43 1.11 1.02 1.07 1.22 1.11 1.18 (.82) (.73) (.80) (1.05) (1.01) (1.03) (.98) (.97) (.98) 55-64 1.12 1.97 1.21 1.17 1.19 1.17 1.15 1.25 1.18 (.81) (.94) (.87) (1.23) (1.08) (1.17) (1.12) (1.09) (1.11) Education No Education .85 1.04 .89 .52 .89 .65 .62 .92 .71 (.59) (.56) (.59) (1.00) (.85) (.97) (.91) (.81) (.89) Primary Incomplete 1.32 1.30 1.32 1.06 1.17 1.11 1.15 1.19 1.16 (.73) (.66) (.71) (1.04) (.94) (1.00) (.96) (.91) (.94) Primary Complete 1.50 1.41 1.48 1.35 1.24 1.30 1.41 1.27 1.36 (.70) (.64) (.69) (.99) (.84) (.93) (.89) (.82) (.87) Secondary Incomplete 1.71 1.54 1.65 1.51 1.10 1.34 1.60 1.26 1.46 (.63) (.53) (.61) (1.03) (.87) (.99) (.88) (.79) (.87) Secondary Complete 1.76 1.63 1.70 1.98 1.50 1.74 1.84 1.59 1.74 (.69) (.51) (.61) (.85) (.90) (.91) (.76) (.66) (.72) Tertiary Incomplete 2.01 1.96 1.98 2.89 1.57 2.34 2.44 1.80 2.14 (.48) (.49) (.49) (1.01) (1.06) (1.22) (.90) (.80) (.91) Tertiary Complete 2.38 2.21 2.31 2.72 2.01 2.36 2.41 2.19 2.32 (.82) (.80) (.82) (.89) (.60) (.84) (.83) (.79) (.82) Industry Agriculture .90 1.09 .93 .33 -0.20 .31 .51 .59 .52 (.77) (.77) (.77) (1.10) (1.02) (1.11) (1.05) (1.08) (1.05) Manufacturing 1.71 1.77 1.72 1.42 1.31 1.37 1.57 1.40 1.50 (.76) (.87) (.78) (.81) (.89) (.86) (.80) (.91) (.84) Electricity 1.71 1.89 1.75 1.99 1.38(*) 1.87 1.73 1.86 1.76 (.76) (.47) (.72) (1.08) (.00) (1.00) (.79) (.48) (.74) Construction 1.67 1.75 1.67 1.50 - 1.50 1.58 1.75 1.58 (.68) (.27) (.67) (.59) (.59) (.64) (.27) (.64) Commerce 1.60 1.43 1.55 1.46 1.16 1.26 1.51 1.19 1.32 (.72) (.66) (.71) (1.00) (.98) (1.00) (.92) (.96) (.95) Transportation 1.76 1.99 1.79 1.56 1.86 1.57 1.65 1.96 1.67 (.69) (.34) (.67) (.99) (1.16) (.99) (.88) (.63) (.87) Financial 2.13 2.13 2.13 2.01 1.54 1.90 2.09 2.05 2.07 (.71) (.79) (.75) (1.17) (.30) (1.05) (.90) (.77) (.84) Personal Services 1.63 1.57 1.60 1.40 1.11 1.24 1.51 1.31 1.40 (.78) (.59) (.69) (.91) (.66) (.80) (.86) (.67) (.77) Note: (*) Corresponds to a single observation. Table A2 Wage Equations with Dummy for Informality, Controlling for Selection, MaLes Age 25-64 NICARAGUA MANAGUA OTHER URBAN OTHER RURAL OLS .Seec. OLS SeLec. OLS eSeec. OLS lSeec. OLS Se ec. OLS SeLec. OLS Setec. OLS Selec. Constant -0.625 -1.157 -0.519 -1.024 0.155 0.134 0.272 0.151 0.112 -0.556 0.284 -0.387 -0.047 -0.950 -0.011 -0.868 (-3.525) (-6.575) (-2.911) (-5.698) (0.578) (0.510) (0.993) ( 0.560) (0.423) (-1.935) (1.054) (-1.290) (-0.135) (-2.441) (-0.032) (-2.120) EDUCATION Yearse 0.096 0.100 0.101 0.101 0.114 0.117 0.068 0.067 (13.660) (15.354) (8.839) (8.717) (9.989) (11.413) (4.714) (4.252) Edupi 0.306 0.292 .0.209 0.223 0.355 0.323 0.335 0.321 (4.842) (4.636) (1.582) ( 1.564) (2.996) (2.749) (3.518) (3.292) Edup 0.501 0.448 0.403 0.415 0.552 0.503 0.436 0.418 (6.549) (5.965) (2.863) (2.859) (4.029) (3.770) (3.002) (2.838) Edusi 0.665 0.619 0.653 0.656 0.825 0.766 0.428 0.422 (8.052) (7.635) (4.262) ( 4.341) (5.872) (5.432) ( 2.672) (2.071) Edus 0.863 0.917 0.871 0.925 1.061 1.065 0.541 0.675 (7.581) (7.375) (4.624) ( 4.939) (5.852) (4.384) (1.404) (0.547) Edut 1.505 1.704 1.504 1.598 1.737 1.907 1.160 1.712 (12.957) (15.640) (8.061) ( 8.301) (8.783) (10.994) (2.996) (3.210) EXPERIENCE/TENURE Expern 0.041 0.035 0.036 0.031 0.015 0.014 0.014 0.010 0.042 0.038 0.039 0.037 0.049 0.041 0.045 0.040 (4.303) (3.694) (3.755) (3.138) (0.958) (0.884) (0.859) (0.560) ( 2.732) ( 2.186) ( 2.417) ( 2.054) ( 2.412) ( 1.990) ( 2.192) (1.798) Expern2 -0.00042 -0.00036 -0.00039 -0.00034 -0.00009 -0.00008 -0.00012 -0.00005 -0.00041 -0.00037 -0.00040 -0.00042 -0.00058 -0.00049 -0.00051 -0.00047 (-2.971) (-2.874) (-2.660) (-2.328) (-0.369) (-0.310) (-0.473) (-0.173) (-1.721) (-1.419) (-1.641) (-1.527) (-1.980) (-1.729) (-1.752) (-1.558) Tenure 0.00106 0.00121 0.00101 0.00110 0.043 0.043 0.045 0.045 0.0001 0.0001 0.000 0.000 -0.011 -0.015 -0.010 -0.014 (0.711) (1.325) (0.672) (1.199) (3.701) (3.548) (3.845) (3.612) (0.096) (0.062) (0.005) (-0.029) (-1.204) (-1.511) (-1.082) (-1.376) Tenure2 -0.0002 -0.0002 -0.0002 -0.0002 -0.0010 -0.0010 -0.0010 -0.0010 -0.0001 -0.0001 -0.0001 -0.0001 -0.0001 -0.00002 -0.0002 -0.00003 (-3.062) (-3.342) (-2.904) (-3.215) (-2.642) (-2.718) (-2.678) (-2.731) (-1.009) (-0.935) (-0.905) (-0.922) (-0.575) (-0.063) (-0.649) (-0.126) TRAINING D_Train 0.162 0.167 0.194 0.197 0.033 0.034 0.045 0.045 0.155 0.148 0.206 0.207 0.601 0.607 0.646 0.643 (2.642) (2.921) (3.163) (3.398) ( 0.340) ( 0.374) (0.455) ( 0.462) (1.660) (1.450) (2.180) (1.992) (4.274) (4.576) (4.520) (4.350) LOCATION DUrban 0.541 0.491 0.580 0.546 0.377 0.370 0.449 0.415 (10.741) (10.191) (11.168) (11.068) (3.778) (3.821) (4.396) ( 4.172) DReg2 0.451 0.433 0.461 0.444 (4.673) (5.253) (4.738) (5.289) D_Reg3 0.561 0.562 0.569 0.568 (6.283) (7.230) (6.335) (7.231) D_Reg4 0.485 0.481 0.503 0.501 (4.901) (5.345) (5.048) (5.530) D_Reg5 0.523 0.472 0.526 0.474 (4.858) (4.912) (4.859) (4.849) D_Reg6 0.139 0.105 0.145 0.107 (1.443) (1.176) (1.499) (1.193) D_Reg7 0.473 0.524 0.480 0.523 (4.128) (5.124) (4.167) (5.054) D_Infor1 -0.120 0.111 -0.128 0.121 0.041 0.041 0.042 0.050 -0.046 0.239 -0.064 0.235 -0.408 0.226 -0.417 0.171 (-2.598) (1.874) (-2.748) (2.075) (0.505) (0.432) (0.510) (0.519) (-0.619) (2.476) (-0.849) (2.371) (-4.876) (1.793) (-4.884) (1.331) (CONT.) Sigma 0.992 0.983 0.765 0.787 0.819 1.036 1.100 1.063 (38.673) (39.339) (31.570) (21.495) (29.590) (23.516) (20.956) (20.082) Rho 0.732 0.720 0.061 0.333 -0.263 0.740 0.812 0.773 (18.976) (18.338) ( 0.330) (2.059) (-1.490) (12.037) (15.184) (11.920) Log-Likelihood -2202 -2973 -2208 -2966 -538 -751 -538 -747 -854 -1177 -859 -1179 -790 -1050 -789 -1043 Mean of Dep.Var. 1.287 1.702 1.559 0.563 (1.075) (0.924) (0.949) (0.977) Sample Size 1616 426 624 563 Note: Asymptotic t-ratio in parentheses. Table A3 Wage Equations with Dummy for Informality, ControLLing for Selection, Women Age 25-64 NICARAGUA MANAGUA OTHER URBAN OTHER RURAL (1) (2) (1) (2) (1) (2) (1) (2) OLS Setec. OLS Selec. OLS Selec. OLS Selec. OLS Selec. OLS Selec. OLS Setec. OLS Setec. Constant 1.130 1.385 1.040 1.036 1.141 1.407 0.830 0.917 1.506 1.648 1.362 1.412 1.523 0.640 1.480 -0.066 (4.651) (5.251) (4.319) (3.508) (3.067) (3.490) (2.238) (2.004) (4.895) (4.599) (4.478) (3.509) (2.511) (0.723) (2.445) (-0.107) EDUCAT1ON Yearse 0.033 0.033 0.049 0.051 0.021 0.021 0.034 0.047 (3.661) (3.649) (3.422) (3.707) (1.570) (1.626) (1.455) (1.600) Edupi 0.138 0,138 0.211 0.214 0.109 0.107 0.113 0.136 (1.749) (1.814) (1.488) (1.424) (0.916) (0.979) (0.739) (0.834) Edup 0.192 0.192 0.050 0.054 0.058 0.054 0.841 0.826 (1.989) (2.011) (0.315) (0.316) (0.391) (0.360) (3.708) (3.603) Edusi 0.112 0.112 0.138 0.128 0.208 0.211 -0.011 0.113 (1.059) (1.027) (0.736) (0.640) (1.384) (1.516) (-0.042) (0.332) Edus 0.295 0.297 0.409 0.371 0.304 0.301 0.327 0.593 (2.288) (1.955) (1.922) (1.539) (1.725) (1.442) (0.641) (0.984) Edut 0.769 0.772 1.107 1.047 0.573 0.548 0.408 0.565 (4.990) (4.263) (4.667) (3.983) (2.478) (1.794) (0.750) (2.570) EXPERIENCE/TENURE Expern -0.015 -0.004 -0.009 -0.009 -0.007 0.004 0.016 0.016 -0.029 -0.023 -0.024 -0.022 -0.038 -0.044 -0.038 (-1.258) (-0.297) (-0.726) (-0.649) (-0.369) (0.190) (0.752) (0.683) (-1.699) (-1.142) (-1.355) (-1.050) (-1.108) (-1.102) (-1.100) Expern2 0.00026 0.00081 0.00016 0.00016 0.00011 -0.00004 -0.00026 -0.00028 0.00052 0.00041 0.00045 0.00042 0.00051 0.00069 0.00053 (1.443) (0.401) (0.821) (0.760) (0.350) (-0.109) (-0.768) (-0.762) (2.025) (1.402) (1.699) (1.383) (1.036) (1.159) (1.070) Tenure 0.00044 0.00056 0.00044 0.00044 0.050 0.050 0.044 0.044 0.027 0.024 0.027 0.027 -0.00022 -0.0003 -0.0003 -0.00040 (0.526) (0.237) (0.531) (0.193) (3.673) (4.031) (3.306) (3.607) (2.298) (1.740) (2.267) (1.893) (-0.233) (-0.013) (-0.330) (-0.017) Tenure2 -0.0001 -0.0001 -0.0001 -0.0001 -0.0016 -0.0017 -0.0015 -0.0015 -0.0008 -0.0007 -0.0008 -0.0008 -0.0001 -0.0002 -0.0001 -0.00003 (-1.279) (-1.274) (-1.282) (-1.196) (-3.758) (-4.789) (-3.394) (-4.155) (-2.498) (-1.796) (-2.487) (-1.912) (-0.475) (-0.261) (-0.311) (-0.042) TRAINING DTrain 0.159 0.162 0.194 0.194 -0.047 -0.033 0.002 0.001 0.270 0.267 0.266 0.266 0.525 0.481 0.512 0.565 (2.256) (2.168) (2.714) (2.441) (-0.428) (-0.279) (0.021) (0.008) (2.703) (2.295) (2.607) (2.175) (2.459) (2.073) (2.420) (2.570) LOCATION 0_Urban 0.215 0.230 0.224 0.224 0.121 0.126 0.112 0.109 (3.267) (3.468) (3.386) (3.276) (0.905) (0.925) (0.837) (0.787) D_Reg2 0.109 0.132 0.107 0.107 (0.798) (0.928) (0.785) (0.759) D_Reg3 0.360 0.372 0.328 0.327 (2.782) (2.767) (2.531) (2.493) D_Reg4 0.250 0.256 0.244 0.244 (1.784) (1.782) (1.751) (1.728) D_Reg5 -0.133 -0.152 -0.141 -0.141 (-0.821) (-0.917) (-0.868) (-0.879) D_Reg6 -0.251 -0.202 -0.250 -0.250 (-1.652) (-1.385) (-1.654) (-1.764) D_Reg7 0.017 0.050 0.031 0.031 (0.095) (0.272) (0.178) (0.172) D_Infor1 -0.242 -0.229 -0.215 -0.210 -0.073 -0.405 -0.011 -0.108 -0.247 -0.357 -0.216 -0.251 -0.265 0.434 -0.282 0.449 (-3.499) (-4.859) (-3.058) (-1.574) (-0.631) (-2.452) (-0.091) (-0.554) (-2.559) (-2.274) (-2.206) (-1.400) (-1.378) ( 0.860) (-1.471) (0.818) (CONT.) sigma 0.867 0.825 0.842 0.779 0.819 0.804 0.973 0.965 (36.665) (51.384) (21.656) (27.812) (29.590) (32.557) (10.824) (8.895) Rho -0.472 -0.004 -0.536 0.071 -0.263 -0.083 0.630 0.666 (-4.715) (-0.026) (-3.372) (0.321) (-1.490) (-0.379) (2.346) (2.372) Log-Likelihood -1327 -1670 -1320 -1655 -441 -408 -433 -558 -576 -755 -574 -748 -294 -340 -287 -340 Mean of Dep.Var. 1.298 1.531 1.284 0.846 (0.913) (0.856) (0.856) (0.958) Sample Size 992 346 442 202 Note: Asymptotic t-ratio in parentheses. Table A4 Wage Equations for Men, Age 25-64 FORMAL INFORMAL (1) (2) (5) (6) (1) (2) (5) (6) Constant 0.353 0.428 -0.070 0.178 -0.628 -0.380 -0.832 -0.548 (1.841) (2.196) (-0.309) (0.719) (-2.358) (-1.410) (-3.196) (-1.785) EDUCATION Yearse 0.106 0.176 (14.417) (17.331) Edupi 0.513 0.207 0.212 0.621 0.333 0.329 (5.476) (2.161) (2.245) (7.529) (4.148) (4.082) Edup 0.677 0.394 0.378 0.987 0.484 0.464 (6.516) (3.763) (3.669) ( 9.563) (4.680) (4.464) Edusi 0.926 0.565 0.481 1.179 0.650 0.626 (8.712) (5.118) (4.324) (10.335) (5.726) (5.433) Edus 1.059 0.601 0.524 1.700 1.049 0.986 (8.154) (4.498) (3.900) ( 8.918) (5.737) (5.265) Edut 1.616 1.218 1.155 2.845 2.080 1.914 (12.831) ( 9.497) ( 8.874) (11.215) (8.664) (7.632) EXPERIENCE/TENURE Exper 0.036 0.027 0.025 0.020 0.049 0.035 0.033 0.032 (3.163) (2.293) (2.242) (1.784) (3.083) (2.157) (2.151) (2.136) Exper2 -0.00051 -0.00039 -0.00045 -0.00037 -0.00043 -0.00026 -0.00024 -0.00024 (-2.874) (-2.102) (-2.481) (-2.065) (-1.817) (-1.075) (-1.046) (-1.066) Tenure 0.023 0.020 0.024 0.023 (2.911) (2.486) (3.103) (2.882) Tenure2 -0.00050 -0.00042 -0.00094 -0.00089 (-2.176) (-1.826) (-4.255) (-4.011) DOtrain 0.150 0.111 0.315 0.337 (2.099) (1.637) (3.038) (3.506) D0train_B 0.198 0.523 (1.512) (2.307) D0train A 0.182 0.490 (1.546) (3.091) Loc2 -0.090 -0.590 (-0.646) (-2.242) Loc34 -0.109 -0.195 (-0.795) (-1.033) OTHER D_reg2 0.549 0.459 0.415 0.389 (3.615) (3.060) (3.319) (3.095) D_Reg3 0.552 0.438 0.593 0.575 (3.833) (3.065) (5.228) (5.045) D_Reg4 0.466 0.372 0.489 0.469 (2.886) (2.338) (3.940) (3.774) D_Reg5 0.371 0.382 0.487 0.481 (2.127) (2.239) (3.594) (3.560) D_Reg6 0.190 0.238 0.014 0.012 (1.208) (1.539) (0.112) (0.101) D_Reg7 0.675 0.652 0.415 0.419 (3.404) (3.353) (2.968) (3.004) D_Urban 0.400 0.387 0.662 0.670 (5.569) (5.373) (9.564) (9.643) D Sindic 0.297 -0.015 (4.087) (-0.063) D_Seguro 0.156 0.107 (2.321) (1.004) DPubLic -0.220 -0.269 (-2.461) (-1.895) Adj R2 0.341 0.337 0.420 0.445 0.230 0.218 0.348 0.350 MSE 8.43 8.45 7.90 7.74 11.57 11.66 10.65 10.63 F-Stat 101.50 38.07 25.90 21.33 103.18 36.93 33.27 25.13 D. of F 3,581 8,576 17,567 23,561 3,1026 8,1021 17,1012 23,1006 Note.- t-statistics in parentheses Table A5 Wage Equations for Women, Age 25-64 FORMAL INFORMAL (1) (2) (5) (6) (1) (2) (5) (6) Constant 1.041 0.924 0.791 0.676 1.164 1.020 0.975 1.215 (4.129) (3.694) ( 2.654) (2.161) (3.790) (3.333) (2.979) (3.267) EDUCATION Yearse 0.080 0.039 8.146) (3.233) Edupi 0.441 0.304 0.308 0.242 0.124 0.110 (3.077) (2.084) (2.137) (2.583) (1.330) (1.179) Edup 0.494 0.357 0.309 0.313 0.152 0.112 (2.948) (2.099) (1.841) (2.698) (1.323) (0.974) Edusi 0.503 0.338 0.313 0.204 0.083 0.040 (3.241) (2.016) (1.821) (1.535) (0.631) (0.299) Edus 0.711 0.527 0.544 0.469 0.208 0.122 (4.515) (3.121) (3.162) (2.507) (1.123) (0.644) Edut 1.275 1.074 1.102 0.936 0.579 0.527 7.782) ( 6.070) ( 6.115) (3.203) (2.019) (1.807) EXPERIENCE/TENURE Exper -0.020 -0.014 -0.007 -0.010 -0.010 -0.006 -0.025 -0.027 (-1.118) (-0.776) (-0.390) (-0.545) (-0.549) (-0.331) (-1.407) (-1.519) Exper2 0.00055 0.00042 0.00023 0.00032 0.00015 0.00010 0.00037 0.00039 1.646) ( 1.256) (0.653) (0.926) (0.574) (0.370) (1.435) (1.885) Tenure -0.007 -0.021 0.034 0.034 (-0.456) (-1.283) (3.510) (3.552) Tenure2 0.00023 0.00074 -0.00102 -0.00105 (0.363) (1.145) (-3.704) (-3.793) DOtrain 0.286 0.295 0.196 0.185 (3.317) (3.338) (1.978) (1.926) DOtrain_B 0.075 -0.113 (0.449) (-0.382) DOtrain_A 0.125 0.358 (0.753) (1.371) Loc2 -0.019 -0.176 (-0.094) (-0.405) Loc34 0.344 -0.118 (2.140) (-0.442) OTHER D_reg2 0.280 0.236 0.054 0.038 (1.688) (1.430) (0.296) (0.208) D_Reg3 0.248 0.215 0.358 0.344 (1.529) (1.320) (2.109) (2.019) DOReg4 0.322 0.272 0.249 0.243 (1.705) (1.439) (1.380) (1.348) D_Reg5 -0.157 -0.157 -0.113 -0.103 (-0.659) (-0.673) (-0.551) (-0.505) D_Reg6 -0.109 -0.110 -0.277 -0.256 (-0.574) (-0.586) (-1.390) (-1.287) D_Reg7 -0.087 -0.051 0.107 0.140 (-0.415) (-0.249) (0.461) (0.599) DOUrban 0.128 0.051 0.222 0.221 (1.133) (0.445) (2.787) (2.756) D Sindic 0.201 -0.540 (1.660) (-1.010) D_Seguro 0.229 0.177 (2.156) (1.566) D Public 0.057 -0.187 (0.519) (-1.318) Adi R2 0.258 0.295 0.329 0.357 0.019 0.032 0.108 0.113 MSE 6.98 6.81 6.64 6.50 11.30 11.23 10.78 10.75 F-Stat 31.62 14.79 8.60 7.37 5.72 3.97 6.17 5.00 D. of F 3,261 8,256 17,247 23,241 3,722 8,717 17,708 23,702 Note.- t-statistics in parentheses TabLe A6: Switching Regression Model, Format and Informal Sectors, Men 25-64 Years Old ALL Format Informal Switching Regressions Sectors OLS OLS OLS Switch Format Informal Constant -0.697 -0.193 -0.983 0.518 -0.874 -0.732 (-3.979) (-0.851) (-3.765) (1.765) (-3.259) (-2.512) EDUCATION Yearse 0.097 0.081 0.107 -0.073 0.102 0.127 (14.016) (10.313) (9.614) (-6.298) (11.850) (9.095) EXPERIENCE/TENURE Expern 0.038 0.037 0.045 0.037 0.026 0.036 (4.030) (3.407) (3.053) (2.394) (2.337) (2.204) Expern2 -0.00043 -0.00056 -0.00046 -0.00051 -0.00042 -0.00033 (-3.018). (-3.301) (-2.101) (-2.213) (-2.478) (-1.365) TRAINING DOtrain 0.173 0.070 0.290 -0.186 0.124 0.329 (2.814) (1.025) (3.005) (-1.905) (1.722) (3.389) LOCATION DUrban 0.555 0.372 0.620 -0.087 0.415 0.636 (10.977) (5.307) (8.975) (-1.072) (6.107) (8.527) D Reg2 0.481 0.510 0.404 -0.557 0.732 0.516 (4.973) (3.354) (3.214) (-3.608) (4.817) (4.079) D_Reg3 0.602 0.527 0.617 -0.596 0.767 0.736 (6.755) (3.648) (5.423) (-4.155) (5.245) (6.462) D_Reg4 0.505 0.436 0.486 -0.125 0.516 0.482 (5.093) (2.699) (3.912) (-0.771) (3.315) (3.740) D Reg5 0.530 0.394 0.531 -0.360 0.544 0.599 (4.912) (2.272) (3.904) (-2.145) (3.141) (4.388) D_Reg6 0.153 0.215 0.031 -0.555 0.446 0.146 (1.586) (1.367) (0.255) (-3.609) (2.659) (1.158) 0_Reg7 0.473 0.678 0.428 -0.051 0.700 0.427 (4.116) (3.403) (3.051) (-0.260) (4.170) (3.005) OTHER SaL_2 0.00015 (3.530) Hj_0006 0.019 (0.641) Log-LikeLihood -2211.0 -632.9 -1522.1 -2913.7 Sigma 0.771 1.001 (15.695) (18.995) Rho 0.702 -0.476 (8.522) (-2.993) Means of Dep.Var. 1.287 1.584 1.092 (1.075) (0.847) (1.162) Sample Size 1616 584 1030 Table A7: switching Regression Model, Format and Informal Sectors, Women 25-64 Years Old ALl Formal Informal Switching Regressions Sectors OLS OLS OLS Switch Formal Informal Constant 0.965 0.784 1.019 0.650 -0.238 1.185 (4.038) (2.678) (3.088) (1.347) (-0.557) (3.344) EDUCATION Yearse 0.040 0.065 0.016 -0.104 0.111 0.048 (4.661) (6.397) (1.296) (-6.476) (10.228) (2.911) EXPERIENCE/TENURE Expern -0.018 -0.017 -0.017 0.023 -0.023 -0.019 (-1.457) (-0.973) (-0.975) (0.908) (-0.983) (-0.993) Expern2 0.00027 0.00045 0.00024 -0.00011 0.00037 0.00026 (1.493) (1.319) (0.958) (-0.257) (0.815) (0.906) TRAINING D train 0.167 0.212 0.157 -0.089 0.314 0.175 (2.367) (2.595) (1.642) (-0.668) (2.860) (1.688) LOCATION D_Urban 0.225 0.090 0.234 -0.228 0.270 0.275 (3.417) (0.819) (2.962) (-1.774) (2.067) (3.143) D_Reg2 0.109 0.324 0.040 0.009 0.373 0.013 (0.789) (1.987) (0.222) (0.034) (1.609) (0.073) D_Reg3 0.333 0.334 0.368 0.283 0.125 0.278 (2.560) (2.118) (2.151) (1.198) (0.574) (1.578) D Reg4 0.213 0.395 0.223 0.476 0.099 0.096 (1.518) (2.144) (1.228) (1.817) (0.382) (0.510) D_Reg5 -0.167 -0.113 -0.160 0.533 -0.371 -0.277 (-1.027) (-0.476) (-0.778) (1.690) (-1.166) (-1.311) D_Reg6 -0.253 -0.049 -0.299 0.011 -0.054 -0.286 (-1.653) (-0.265) (-1.486) (0.042) (-0.211) (-1.529) D_Reg7 0.027 -0.045 0.087 -0.048 0.019 0.106 (0.154) (-0.218) (0.370) (-0.160) (0.065) (0.453) OTHER Sal 2 0.00006 (2.334) Hj_00_06 0.080 (1.568) Log-Likelihood -1334.5 -253.0 -1028.7 -1522.9 Sigma 0.820 0.969 (11.580) (23.756) Rho 0.888 -0.628 (18.174) (-4.980) Means of Dep.Var. 1.298 1.628 1.189 (0.913) (0.707) (0.947) Sample Size 992 264 726 Table A8 Bivariate Probit for Participation and Sectoral Choice and Wage Equations, Corrected for Double Selection Males and Females 25-64 Years Old, 1993 MALES FEMALES BIVARIATE PROBIT BIVARIATE PROBIT FORMAL INFORMAL FORMAL INFORMAL LFP SECTOR LFP SECTOR Constant -0.423 -0.272 -2.251 -4.092 -1.471 0.797 -0.445 -0.055 (-1.890) (-0.853) (-2.090) (-3.701) (-6.351) (1.378) (-0.056) (-0.026) EDUCATION Yearse 0.031 -0.049 0.132 0.179 0.062 -0.116 0.197 0.101 (3.520) (-3.341) (4.866) (5.501) (7.014) (-5.843) (0.737) (2.058) EXPERIENCE/TENURE Expern 0.030 0.031 0.036 0.105 0.035 0.018 -0.044 -0.004 (2.484) (2.113) (1.398) (4.248) (3.007) (0.728) (-0.339) (-0.081) Expern2 -0.00054 -0.00043 -0.00070 -0.00156 -0.00061 0.00003 0.00025 0.00005 (-3.126) (-1.924) (-1.538) (-3.861) (-3.682) (0.074) (0.159) (0.077) Tenure 0.021 -0.00009 0.00025 (2.565) (-0.087) (0.261) Tenure2 -0.00046 -0.00028 -0.00011 (-1.884) (-3.310) (-0.924) TRAINING D_train 0.054 -0.128 0.206 0.229 0.280 0.105 (0.620) (-1.348) (2.225) (1.822) (1.372) (1.113) LOCATION D Urban 0.376 0.801 1.408 0.402 -0.276 0.462 (6.167) (3.053) (6.918) (6.791) (-0.222) (1.561) D_Reg2 0.028 0.507 0.497 0.433 -0.099 0.200 (0.289) (3.977) (3.910) (4.277) (-0.079) (0.576) D_Reg3 0.198 0.698 0.846 0.381 0.001 0.461 (2.156) (4.244) (6.160) (4.207) (0.001) (1.476) D_Reg4 0.061 0.575 0.558 0.296 0.130 0.239 (0.580) (4.024) (4.452) (2.887) (0.129) (0.817) D_Reg5 -0.018 0.304 0.439 0.062 -0.087 -0.117 (-0.188) (2.184) (3.796) (0.594) (-0.116) (-0.463) D_Reg6 0.248 0.409 0.459 0.052 -0.070 -0.233 (2.616) (1.958) (2.707) (0.470) (-0.080) (-0.862) D_Reg7 0.024 0.630 0.340 0.139 -0.102 0.068 (0.241) (4.364) (3.045) (1.313) (-0.126) (0.260) OTHER SaL 2 -0.000028 0.000034 -0.000034 0.000044 (-1.006) (0.855) (-3.639) (1.262) HJ_00_06 0.026 0.049 -0.093 0.067 (1.024) (1.615) (-3.197) (1.118) Log-Likelihood -2587.9 -581.8 -1421.2 -218.8 -947.2 Rho 0.571 -0.098 (3.055) -0.342 Lambda-A -0.366 0.329 -2.279 -0.886 (-0.511) (0.395) -0.521 -0.954 Lambda-B 1.103 3.808 -1.254 0.627 (1.169) (3.780) -0.231 0.736 Means of Dep.Var. 1.571 1.112 1.578 1.168 (0.879) (1.193) 0.723 0.954 Sample Size 585 1031 265 727 Table A9: Means and Std. Dev. of Earnings by Criteria One (Cordobas Oro) FORMAL INFORMAL TOTAL Mate Female Total Mate Female Total Mate Female Total Total 1444 1252 1382 1117 955 1047 1252 1047 1172 (1400) (1275) (1363) 2281) 1305) 1925) (1972) (1303) (1745) Age categories 25-34 1291 1230 1267 946 827 898 1102 1000 1062 (1236) (1127) (1196) 1663) 1024) 1442) (1495) (1088) (1351) 35-44 1735 1225 1585 1268 1064 1175 1470 1108 1326 (1606) (1187) (1513) 2419) 1400) 2020) (2119) 1347) (1859) 45-54 1446 1339 1416 1107 879 1005 1228 976 1127 (1446) (1887) (1585) 3047) 1026) 2369) (2600) (1273) (2173) 55-64 985 1698 1067 1275 1170 1233 1187 1211 1195 743) (1066) 819) 1900) 2074) 1972) (1643) (2019) (1778) Education No education 643 686 652 532 647 571 566 653 593 448) (403) (440) (770) (771) (772) (691) (721) (701) Primary Incomplete 1153 978 1108 1035 1009 1023 1074 1004 1045 904) 583) 838) 2301) 1411) 1924) (1955) (1319) (1715) Primary Complete 1327 1061 1277 1235 880 1074 1272 908 1136 884) 830) 880) 1452) 1005) 1282) (1259) (982) (1177) Secondary Incomplete 1576 1083 1408 1536 1148 1367 1554 1125 1384 (1277) (1138) (1254) 3197) 1704) 2659) (2523) (1529) (2194) Secondary Complete 1570 1027 1292 2057 1269 1673 1736 1105 1418 (1138) (661) 963) 2354) 1384) 1983) (1673) (960) (1397) Tertiary Incomplete 1882 1560 1723 6919 1773 4756 4328 1648 3093 886) 918) 916) 6935) 1709) 5963) (5486) (1308) (4336) Tertiary Complete 3043 2419 2790 3257 1500 2369 3057 2328 2756 (2425) (2176) (2347) 1643) 1346) 1739) (1281) (2127) (2307) Industry Agriculture 758 955 790 644 222 622 679 672 679 926) 892) 954) 2125) (235) 2074) (1846) (798) (1780) Manufacturing 1717 1480 1669 1243 927 1079 1478 1030 1299 (1359) (1403) (1371) 2984) 1273) 2268) (2336) (1316) (2003) Electricity 1702 1376 1631 2061 840(*) 1812 1724 1346 1642 (1228) (720) (1145) 1655) (.00) 1557) (1261) (710) (1174) Construction 1824 1183 1786 1074 - 1074 1431 1183 1424 (1591) (468) (1555) (689) 1555) (1262) (468) (1247) Commerce 1474 1257 1404 1692 1179 1358 1621 1188 1367 (1113) 995) (1081) 2942) 1615) 2186) (2500) (1559) (2015) Transportation 1765 1363 1715 1456 1899 1469 1587 1485 1580 (1330) (502) (1264) 1983) 1360) 1969) (1743) (820) (1693) Financial 2298 2477 2395 2385 654 1971 2328 2256 2294 (1515) (2430) (2060) 2763) ( 77) 2521) (2026) (2354) (2186) Personal Services 1457 1054 1245 1075 673 854 1251 840 1030 (1647) 1052) (1382) 1308) (523) (981) (1487) (822) (1194) Table A10: Means and Std. Dev. of LWAGEHBA, by Criteria One FORMAL INFORMAL TOTAL Mate Female Total Mate Female Total Male Female Total Total 1.75 1.81 1.77 1.56 1.35 1.45 1.66 1.49 1.59 (.75) (.75) (.75) ( .99) (.81) ( .91) ( .88) (.82) (.86) Age Category 25-34 1.66 1.87 1.73 1.48 1.34 1.41 1.58 1.57 1.58 (.72) (.68) (.71) ( .86) (.70) (.79) (.79) (.74) (.77) 35-44 1.98 1.90 1.96 1.65 1.36 1.49 1.83 1.50 1.69 (.78) (.74) (.77) (1.11) (.80) ( .96) ( .96) (.82) ( .92) 45-54 1.65 1.43 1.57 1.52 1:37 1.44 1.58 1.39 1.48 (.71) (.78) (.74) ( .79) ( .87)' .84) (.76) (.85) (.81) 55-64 1.48 2.20 1.61 1.65 1.27 1.50 1.59 1.41 1.53 (.57) (.98) (.72) (1.29) (1.05) (1.21) (1.09) (1.09) (1.09) Education Never assisted 1.23 1.28 1.24 1.03 1.16 1.09 1.11 1.19 1.14 (.36) (.39) (.37) ( .97) (.63) (.84) (.80) (.58) (.72) Primary Incomplete 1.46 1.44 1.46 1.29 1.46 1.40 1.36 1.46 1.41 (.56) (.59) (.57) ( .75) (.80) ( .78) (.68) (.77) (.73) Primary Complete 1.59 1.45 1.57 1.60 1.28 1.44 1.60 1.30 1.48 (.60) (.51) (.59) (.85) (.82) (.85) (.75) (.80) (.79) Secondary Incomplete 1.76 1.64 1.73 1.83 1.15 1.54 1.79 1.31 1.62 (.66) (.57) (.64) ( .99) (.80) (.98) (.84) (.77) (.85) Secondary Complete 1.73 1.72 1.73 2.27 1.62 1.96 1.88 1.68 1.80 (.77) (.51) (.69) (.80) (.89) (.90) (.81) (.68) (.76) Sup/University Inc. 1.97 2.05 2.00 2.95 1.56 2.20 2.34 1.81 2.09 (.49) (.56) (.52) (1.11) ( .98) (1.25) (.92) (.83) (.92) Sup/University CompL. 2.51 2.56 2.53 2.79 2.16 2.47 2.53 2.50 2.52 (.75) (.85) (.78) (.98) (.60) (.87) (.77) (.83) (.79) Industry AgricuLture 1.51 1.60(*) 1.53 1.27 .07 .99 1.38 0.79 1.25 (.93) (1.28) (1.02) (1.55) ( .50) (1.47) (1.30) (1.22) (1.31) Manufacture 1.82 2.31 1.92 1.61 1.56 1.58 1.75 1.75 1.75 (.65) (.90) (.73) (.61) (.85) (.76) (.64) (.92) (.76) Electr.gas and water 1.97 2.76(*) 2.02 3.63(*) - 3.63(*) 2.05 2.76(*) 2.10 (.93) (.00) (.92) ( .00) ( .00) (.98) (.00) (.96) Construction 1.82 1.79 1.82 1.57 - 1.57 1.71 1.79(*) 1.71 (.72) (.24) (.70) (.46) (.46) (.63) (.24) (.62) Commerce 1.62 1.62 1.62 1.46 1.38 1.41 1.53 1.42 1.47 (.74) (.54) (.69) (1.04) (.90) ( .96) (.92) (.86) (.89) Transportation 1.89 2.04 1.91 1.82 2.86 1.87 1.86 2.20 1.89 (.65) (.29) (.62) (1.07) ( .04) (1.07) (.85) (.42) (.83) Financial 2.19 2.22 2.21 2.74 2.01(*) 2.62 2.35 2.21 2.28 (.81) (.80) (.80) ( .84) (.00) ( .81) (.86) (.78) (.82) Personal Services 1.67 1.64 1.66 1.51 1.26 1.38 1.59 1.40 1.50 (.74) (.68) (.72) (.97) (.61) (.81) (.88) (.66) (.79) Note: (*) Corresponds to three or Less observations. Table All: Means and Std. Dev. of LWAGEHBA, by Criteria One, Other Rural FORMAL INFORMAL TOTAL Male Female Total Mate Female Total Mate Female Total Total .89 1.06 .92 .44 .77 .54 .58 .83 .65 (.69) (.57) (.67) (1.04) (.99) (1.03) C .97) (.92) (.96) Age Categories 25-34 .89 1.09 .95 .30 .92 .48 .49 .97 .63 (.65) (.53) (.62) ( .90) (.91) (.95) (.87) (.82) (.88) 35-44 .94 .97 .95 .60 .79 .66 .70 .82 .73 (.67) (.68) (.67) (1.23) (.96) (1.16) (1.11) (.91) (1.06) 45-54 .90 1.18 .92 .51 .38 .47 .62 .46 .59 (.73) (.06) (.71) ( .96) (1.12) (1.01) (.92) (1.09) (.96) 55-64 .58 - .58 .33 .95 .54 .39 .95 .54 (.79) (.79) ( .87) ( .86) ( .91) ( .86) ( .86) ( .89) Education Never assisted .66 .64 .66 .25 .55 .32 .37 .56 .41 (.57) (.45) (.56) ( .93) (.88) (.92) ( .86) (.83) (.86) Primary Incomplete 1.03 .78 .97 .58 .70 .63 .70 .72 .40 (.74) (.54) (.71) (1.14) (1.08) (1.12) (1.07) (1.02) (1.05) Primary Complete 1.06 1.29 1.09 .90 1.70 1.18 .96 1.63 1.15 (.78) (.50) (.75) (1.04) (.83) (1.04) (.95) (.79) (.95) Secondary Incomplete 1.33 1.35 1.33 .70 .75 .72 .91 .93 .91 (.42) (.20) (.36) (1.12) (.44) (.92) (.99) (.47) (.83) Secondary Complete 1.42(*) 1.73 1.63 1.21 1.25 1.23 1.28 1.55 1.42 (.14) (.38) (.35) (.74) (.87) (.79) (.61) (.65) (.64) Sup/University Inc. 1.83(*) - 1.83(*) 1.40(*) - 1.40(*) 1.61(*) - 1.61(*) (.00) (.00) ( .00) ( .00) (.22) (.22) Sup/University Compl. 1.53 1.39 1.46 2.46(*) - 2.46(*) 1.74 1.39 1.57 (.95) (.22) (.67) (.06) (.06) (.93) (.22) (.71) Industry Agriculture .69 .90 .72 .23 -.34 .20 .36 .40 .36 (.60) (.53) (.59) (1.02) (1.12) (1.03) ( .94) (1.02) ( .95) Manufacturing 1.29 .57(*) 1.22 1.08 .86 .95 1.15 .85 1.00 (.57) (.19) (.58) (.83) (1.00) (.94) (.75) (.98) (.89) Electr.gas and water 2.21(*) - 2.21(*) .77(*) - .77(*) 1.89 - 1.89 (.00) (.00) ( .28) ( .28) (.61) (.61) Construction 1.08 - 1.08 1.15 - 1.15 1.12 - 1.12 (.27) (.27) (.56) (.56) (.45) (.45) Commerce 1.45 .30 1.05 1.18 .87 .96 1.23 .85 .96 (.59) (.12) (.73) ( .87) (.99) ( .97) (.83) (.97) (.95) Transportation 1.82(*) 2.22(*) 1.97(*) .90 .52(*) .88 .95 1.16(*) .96 (.00) (.00) (.20) (.67) ( .00) (.65) (.68) (.83) (.69) Financial 1.61(*) - 1.61(*) .11(*) - .11(*) 1.14 - 1.14 (.13) (.13) ( .00) ( .00) (.71) (.71) Personal Services 1.12 1.35 1.24 1.11 .88 .96 1.11 1.04 1.07 (.81) (.41) (.65) (.77) (.66) (.71) (.79) (.63) (.70) Note: (*) Corresponds to three or less observations. Table A12: Means and Std. Dev. of LWAGENBA, by Crfteria One, Other Urban FORMAL INFORMAL TOTAL Mate Female Total Mate Female Total Mate Female Total Total 1.64 1.55 1.60 1.39 1.13 1.26 1.49 1.28 1.39 (.77) (.58) (.70) ( .97) (.88) (1.04) ( .90) (.82) (.87) Age Categories 25-34 1.59 1.59 1.59 1.23 1.15 1.20 1.38 1.37 1.37 (.70) (.56) (.64) ( .85) (.79) (.83) (.81) (.72) (.77) 35-44 1.80 1.48 1.68 1.48 1.18 1.33 1.63 1.28 1.47 (.76) (.56) (.71) (1.04) (.80) ( .93) ( .94) (.74) ( .87) 45-54 1.64 1.55 1.61 1.44 .93 1.20 1.51 1.05 1.31 (.81) (.71) (.78) (1.01) ( .93) (1.00) (.95) (.92) (.97) 55-64 1.06 1.32(*) 1.09 1.57 1.29 1.44 1.42 1.29 1.37 (.85) (.26) (.82) ( .99) (1.22) (1.11) ( .98) (1.18) (1.06) Education Never Assisted .90 1.10 .97 1.09 1.05 1.07 1.03 1.06 1.05 (.63) (.66) (.65) ( .86) (.84) (.85) (.81) (.82) (.81) Primary Incomplete 1.37 1.47 1.40 1.30 1.13 1.23 1.33 1.19 1.27 (.81) (.63) (.77) ( .98) (.86) ( .94) (.93) (.84) (.90) Primary Complete 1.66 1.42 1.60 1.35 1.01 1.19 1.47 1.09 1.32 (.65) (.76) (.69) (1.02) (.79) (.94) (.91) (.80) (.89) Secondary Incomplete 1.74 1.51 1.64 1.52 1.17 1.37 1.62 1.30 1.48 (.61) (.53) (.59) ( .86) (.98) (.93) (.77) (.85) (.82) Secondary Complete 1.85 1.56 1.67 1.83 1.42 1.62 1.84 1.52 1.65 (.53) (.49) (.52) (.78) (.89) (.87) (.65) (.64) (.66) Sup/University Inc. 2.20 1.80 1.94 2.87 1.63(*) 2.62 2.67 1.76 2.28 (.43) (.27) (.38) ( .86) (1.41) (1.11) (.82) (.72) (.90) Sup/University CompL. 2.25 1.89 2.09 - 1.49(*) 1.49(*) 2.25 1.87 2.07 (.84) (.49) (.72) (.12) (.12) (.84) (.49) (.72) Industry Agriculture 1.52 1.42 1.50 .74 .30 .73 1.03 1.22 1.05 (.87) (.77) (.86) (1.22) ( .86) (1.21) (1.16) ( .89) (1.14) Manufacture 1.68 1.43 1.63 1.44 1.35 1.40 1.54 1.37 1.48 (.85) (.58) (.80) (.83) (.81) (.82) (.85) (.77) (.82) Electr.gas and water 1.55 1.83 1.63 2.31(*) 1.38(*) 1.96(*) 1.58 1.81 1.64 (.69) (.43) (.64) ( .00) (.00) ( .45) (.69) (.43) (.64) Construction 1.55 1.20(*) 1.54 1.52 - 1.52 1.53 1.20(*) 1.53 (.49) (.00) (.49) (.70) (.70) (.63) (.00) (.63) Commerce 1.60 1.37 1.50 1.56 1.10 1.28 1.57 1.13 1.31 (.69) (.65) (.68) ( .98) (1.00) (1.02) (.93) (.97) (.98) Transportation 1.40 1.77 1.44 1.73 - 1.73 1.62 1.77 1.62 (.70) (.43) (.69) (.88) (.88) (.84) (.43) (.83) Financial 2.17 1.85 2.02 1.68 1.36(*) 1.58 1.97 1.71 1.86 (.56) (.69) (.64) ( .98) (.05) ( .82) (.80) (.62) (.74) Personal Services 1.72 1.55 1.61 1.30 .98 1.12 1.50 1.29 1.38 (.78) (.54) (.64) (.80) (.67) (.75) (.82) (.67) (.74) Note: (*) Corresponds to three or less observations. Table A13: Wage Equations for Men, Age 25-64, Household Heads FORMAL INFORMAL (1) (2) (5) (6) (1) (2) (5) (6) Constant 0.613 0.795 0.516 0.799 -0.034 0.343 0.368 0.492 (2.184) (2.570) ( 1.673) (2.253) (-0.066) ( 0.647) ( 0.696) ( 0.778) EDUCATION Yearse 0.096 0.149 8.223) ( 7.702) Edupi 0.257 0.148 0.171 0.364 0.254 0.218 (1.370) (0.806) (0.945) (1.835) (1.315) (1.117) Edup 0.447 0.382 0.456 0.641 0.367 0.320 (2.151) (1.847) (2.213) ( 3.186) (1.797) (1.522) Edusi 0.738 0.634 0.593 0.884 0.614 0.581 (3.580) (3.106) (2.920) ( 3.655) (2.549) (2.397) Edus 0.809 0.683 0.612 1.551 1.090 1.002 (3.481) (2.967) (2.641) ( 4.444) (3.115) (2.676) Edut 1.445 1.316 1.237 2.269 1.924 1.733 6.342) ( 5.856) ( 5.365) ( 5.708) (4.920) (4.134) EXPERIENCE/TENURE Exper 0.026 0.022 0.010 -0.004 0.038 0.025 0.005 0.006 (1.598) (1.241) (0.590) (-0.251) (1.220) (0.780) (0.155) (0.193) Exper2 -0.00028 -0.00025 -0.00012 0.00012 -0.00028 -0.00015 0.00005 0.00002 (-1.055) (-0.877) (-0.393) ( 0.399) (-0.598) (-0.300) ( 0.099) ( 0.044) Tenure 0.039 0.044 0.046 0.039 (2.915) (3.279) (2.231) (1.855) Tenure2 -0.00102 -0.00118 -0.00086 -0.00066 (-2.501) (-2.905) (-1.288) (-0.963) TRAINING D_train -0.003 0.004 0.112 0.102 (-0.028) (0.036) (0.544) (0.509) D train_B 0.317 1.192 (1.611) (1.914) D train A 0.221 0.055 (1.335) (0.156) Loc2 -0.325 -0.653 (-1.625) (-1.179) Loc34 -0.305 0.052 (-1.591) ( 0.131) LOCATION D Urban 0.440 0.467 0.411 0.481 (3.385) (3.604) (2.537) (2.776) OTHER D Sindic 0.242 0.313 (2.183) ( 0.725) D_Seguro 0.113 0.095 (0.933) (0.479) D PubLic -0.318 -0.164 (-2.601) (-0.636) Adj R2 0.293 0.266 0.316 0.347 0.219 0.179 0.248 0.252 MSE 8.83 8.93 8.63 8.43 12.54 12.86 12.31 12.27 F-Stat 29.99 10.97 10.23 7.89 20.19 6.59 7.15 5.06 D. of F 3,217 8,212 11,209 17,203 3,202 8,197 11,194 17,188 Note.- t-statistics in parentheses Table A14: Wage Equations for Women, Age 25-64, HousehoLd Heads FORMAL INFORMAL (1) (2) (5) (6) (1) (2) (5) (6) Constant 0.542 0.492 0.455 -0.044 1.227 0.982 0.995 1.501 (1.213) (1.066) ( 0.795) (-0.077) ( 2.538) ( 2.027) ( 2.030) ( 2.654) EDUCATION Yearse 0.095 0.022 4.653) ( 1.207) Edupi 0.220 0.230 0.264 0.221 0.196 0.216 (0.738) (0.742) (0.891) (1.339) (1.198) (1.336) Edup 0.289 0.296 0.068 -0.001 0.001 -0.071 (0.829) (0.824) (0.192) (-0.006) (0.008) (-0.390) Edusi 0.411 0.416 0.438 0.049 0.052 -0.022 (1.157) (1.087) (1.157) ( 0.222) (0.234) (-0.102) Edus 0.557 0.561 0.719 0.384 0.317 0.203 (1.592) (1.488) (1.944) ( 1.379) (1.148) (0.723) Edut 1.372 1.377 1.555 1.024 0.905 0.830 3.942) C 3.769) ( 4.269) ( 2.818) (2.511) (2.260) EXPERIENCE/TENURE Exper 0.027 0.048 0.049 0.040 0.011 0.026 0.005 -0.007 (0.894) (1.518) (1.447) ( 1.233) (0.372) (0.879) (0.183) (-0.232) Exper2 -0.00036 -0.00079 -0.00080 -0.00051 -0.00021 -0.00043 -0.00012 0.00004 (-0.620) (-1.273) (-1.206) (-0.787) (-0.484) (-0.984) (-0.258) ( 0.090) Tenure 0.003 -0.020 0.052 0.055 (0.077) (-0.573) (3.326) (3.561) Tenure2 -0.00018 0.00092 -0.00166 -0.00180 (-0.121) ( 0.626) (-3.416) (-3.718) TRAINING D0train 0.185 0.182 -0.102 -0.067 1.095) (1.009) (-0.708) (-0.474) D train B -0.593 -0.228 (-1.339) (-0.466) D_train_A -0.564 0.341 (-1.129) (0.787) Loc2 0.428 -0.392 ( 0.878) (-0.476) Loc34 0.953 -0.367 ( 2.116) (-0.830) LOCATION D_Urban 0.022 0.080 0.134 0.073 (0.069) (0.249) (0.882) (0.471) OTHER DSindic 0.473 -1.165 (1.866) (-1.747) DSeguro 0.346 0.286 (1.826) (1.956) D Public 0.121 -0.280 ( 0.649) (-1.510) Adj R2 0.283 0.287 0.260 0.337 0.001 0.024 0.060 0.084 MSE 8.38 8.35 8.51 8.06 11.32 11.19 10.98 10.84 F-Stat 12.84 5.52 3.88 3.69 1.07 1.77 2.48 2.39 D. of F 3,87 8,82 11,79 17,73 3,252 8,247 11,244 17,238 Note.- t-statistics in parentheses TabLe A15: Wage Equations for Men, Age 25-64, HousehoLd Heads, other Urban FORMAL INFORMAL (1) (2) (5) (6) (1) (2) (5) (6) Constant 0.308 0.465 0.163 0.142 -0.122 0.057 -0.281 0.036 0.986) (1.393) ( 0.444) ( 0.336) (-0.322) ( 0.146) (-0.697) ( 0.079) EDUCATION Yearse 0.086 0.145 6.599) ( 8.623) Edupi 0.354 0.213 0.277 0.364 0.386 0.392 (1.873) (1.120) (1.432) (2.457) (2.545) (2.580) Edup 0.536 0.424 0.432 0.582 0.565 0.547 (2.644) (2.116) (2.135) ( 3.259) (3.156) (3.054) Edusi 0.663 0.581 0.574 0.904 0.910 0.854 (3.193) (2.820) (2.682) ( 4.926) (4.879) (4.529) Edus 0.779 0.552 0.593 1.277 1.259 1.248 (3.352) (2.340) (2.427) ( 4.640) (4.535) (4.444) Edut 1.313 1.214 1.269 2.644 2.521 2.369 5.568) ( 5.184) ( 5.200) ( 6.981) (6.618) (5.948) EXPERIENCE/TENURE Exper 0.062 0.051 0.038 0.037 0.036 0.037 0.027 0.028 (3.283) (2.616) (1.936) ( 1.861) (1.597) (1.556) (1.130) (1.178) Exper2 -0.00100 -0.00089 -0.00075 -0.00072 -0.00020 -0.00028 -0.00006 -0.00007 (-3.330) (-2.826) (-2.403) (-2.259) (-0.588) (-0.776) (-0.178) (-0.195) Tenure 0.031 0.025 0.030 0.024 1.910) ( 1.514) (2.056) (1.836) Tenure2 -0.00050 -0.00039 -0.00101 -0.00095 (-0.931) (-0.705) (-2.739) (-2.561) TRAINING D_train 0.212 0.203 0.190 0.216 1.898) (1.827) (1.330) (1.510) D train A 0.046 0.210 (0.216) (0.615) D_train_B 0.305 0.603 ( 1.472) (2.574) Loc2 0.030 -0.627 ( 0.127) (-1.853) Loc34 -0.123 -0.472 (-0.483) (-1.591) LOCATION D_reg2 0.493 0.433 0.366 0.340 (2.663) (2.319) (2.061) (1.871) D_Reg4 0.482 0.424 0.408 0.382 (2.497) (2.168) (2.465) (2.288) D_Reg5 0.225 0.243 0.608 0.584 (0.920) (0.993) (3.014) (2.890) D_Reg6 0.264 0.286 0.300 0.294 (1.191) (1.284) ( 1.623) ( 1.587) D_Reg7 0.658 0.644 0.375 0.382 (2.832) (2.768) (1.848) (1.869) OTHER D Sindic 0.272 -0.152 (2.039) (-0.433) D_Seguro 0.067 0.125 (0.580) (0.753) D PubLic -0.024 -0.327 (-0.146) (-1.506) Adi R2 0.232 0.224 0.268 0.272 0.151 0.135 0.161 0.165 MSE 7.60 7.64 7.42 7.40 9.84 9.94 9.78 9.77 F-Stat 23.02 8.90 6.35 4.89 24.93 8.86 6.17 5.78 D. of F 3,216 8,211 15,204 21,198 3,400 8,395 15,388 21,382 Note.- t-statistics in parentheses Table A16: Wage Equations for Men, Age 25-64, Household Heads, Other Rural FORMAL INFORMAL (1) (2) (5) (6) (1) (2) (5) (6) Constant 0.472 0.502 0.045 -0.446 -0.830 -0.579 -0.810 -0.418 1.025) (1.059) ( 0.086) (-0.624) (-1.775) (-1.251) (-1.789) (-0.705) EDUCATION Yearse 0.068 0.096 4.040) ( 4.851) Edupi 0.383 0.353 0.331 0.361 0.360 0.325 (2.517) (2.200) (2.256) (3.013) (3.156) (2.806) Edup 0.404 0.406 0.227 0.441 0.470 0.470 (2.340) (2.295) (1.359) ( 2.078) (2.345) (2.266) Edusi 0.416 0.371 0.009 0.372 0.319 0.312 (1.831) (1.558) (0.035) ( 1.771) (1.555) (1.471) Edus 0.578 0.440 0.174 0.537 0.339 0.371 (0.641) (0.490) (0.210) ( 1.194) (0.783) (0.844) Edut 0.828 0.853 0.651 2.639 2.408 2.381 2.434) ( 2.523) ( 2.007) ( 2.278) (2.191) (2.169) EXPERIENCE/TENURE Exper 0.021 0.015 0.026 0.050 0.061 0.043 0.047 0.044 (0.766) (0.529) (0.887) ( 1.676) (2.243) (1.590) (1.811) (1.672) Exper2 -0.00039 -0.00028 -0.00044 -0.00084 -0.00076 -0.00054 -0.00050 -0.00044 (-0.962) (-0.683) (-1.041) (-1.950) (-2.016) (-1.391) (-1.344) (-1.181) Tenure -0.008 -0.014 0.002 0.003 (-0.495) (-1.004) (0.179) (0.219) Tenure2 0.00016 0.00039 -0.00053 -0.00055 0.402) ( 1.053) (-1.667) (-1.716) TRAINING Dtrain 0.419 0.314 0.852 0.618 1.921) (1.405) (4.697) (3.455) Dtrain_A 0.074 0.444 (0.145) (1.296) Dtrain_B -0.598 0.897 (-1.254) (2.891) Loc2 0.392 -1.824 ( 0.677) (-1.246) Loc34 1.516 -0.433 ( 2.520) (-1.180) LOCATION D_reg2 0.544 0.242 0.354 0.355 (2.174) (1.039) (2.131) (2.132) D_Reg4 0.285 0.231 0.614 0.621 (1.031) (0.837) (3.351) (3.326) DReg5 0.510 0.450 0.441 0.438 (2.098) (2.048) (2.629) (2.601) D_Reg6 0.255 0.265 -0.098 -0.098 (1.151) (1.328) (-0.657) (-0.656) D_Reg7 0.799 0.643 0.425 0.425 (2.305) (2.024) (2.412) (2.413) OTHER D Sindic 0.692 -1.286 (3.442) (-1.684) D_Seguro 0.477 0.110 (3.161) (0.469) DOPublic 0.135 -0.341 ( 0.284) (-0.949) Adj R2 0.142 0.141 0.169 0.331 0.049 0.091 0.194 0.195 MSE 7.72 7.72 7.60 6.81 11.17 10.92 10.28 10.27 F-Stat 8.88 3.94 2.94 4.38 8.22 6.24 7.71 5.84 D. of F 3,140 8,135 15,128 21,122 3,416 8,411 15,404 21,398 Note.- t-statistics in parentheses   LA2 Economic Notes Series No. Title Author Date LA2-1 Undernutrition in Nicaraguan Preschool Aged Children: Ellen G. Piwoz September 1995 Prevalence, Determinants, and Policy Implications LA2-2 Constraints to Private Enterprises in El Salvador: Gloria Grandolini September 1995 A Summary of Survey Results Andrew Stone Harold Bedoya Ian Bannon LA2-3 Guatemala: Constraints to Non-Traditional Exporters: A Andrew Stone January 1996 Summary of Survey Results IDC LA2-4 The Informal Sector in Nicaragua Carola Pessino February 1996