The importance of firm wage differentials in explaining hourly earnings variation in the large-scale sector of Guatemala

Journal of Development Economics Vol. 55 Ž1998. 115–131

The importance of firm wage differentials in explaining hourly earnings variation in the large-scale sector of Guatemala Edward Funkhouser

)

Department of Economics, UniÕersity of California, Santa Barbara, CA 93106, USA Accepted 11 September 1997

Abstract I utilize unique firm-based data to examine firm-wage differentials among private firms with ten or more workers in the capital area of Guatemala City. Approximately one-seventh of the overall hourly earnings variation in this sector is related to firm effects not captured by other observable characteristics of individuals and firms. For most variables, including observable characteristics of firms used in most previous studies, the bias from not including firm fixed effects in the estimation of log wage equations is small in magnitude, though statistically significant. q 1998 Elsevier Science B.V. JEL classification: J31; O15 Keywords: Firm-wage differentials; Determinants of earnings

1. Introduction One of the most notable findings of recent research on labor markets has been that of persistent wage differentials across industries and across firm size. 1 Though many of the possible explanations for these differentials occur at the firm )

Corresponding author. Reviews of the literature on industry wage differentials and firm differentials for the U.S. can be found in Krueger and Summers Ž1987, 1988., Brown and Medoff Ž1989. and Groshen Ž1991b.. 1

0304-3878r98r$19.00 q 1998 Elsevier Science B.V. All rights reserved. PII S 0 3 0 4 - 3 8 7 8 Ž 9 7 . 0 0 0 5 8 - 8

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level, it has been difficult to separate firm-level factors from industry or economy-wide factors affecting wage structure because of data limitations. With the exception of Abowd et al. Ž1994., these studies have most often used household survey data that do not identify workers in the same firm or establishment. Also because of data limitations, there has been very little examination of these issues, and especially of the importance of firm characteristics other than industry, for developing countries. 2 In this paper, I bridge some of these data limitations using information on individuals in 256 large firms in Guatemala. I use a unique data source compiled by the Ministry of Labor in Guatemala to examine the importance of firm wage differentials in the estimation of wage equations in the large-scale sector in that country. Moreover, because these data also include information on industry and size of the firm, the relationship between firm wage differentials and these measures that have been used in previous studies can also be made. The structure of the paper is as follows. In Section 2, I describe the 1990 establishment-level data from the large-scale sector for Guatemala. In Section 3, I explain the empirical strategy of the paper. In Section 3, the empirical strategy for estimation is described. In Section 3.2, the estimation results are reported. In Section 4, evidence on the importance of competing explanations for firm-wage differentials is evaluated for the case of Guatemala.

2. Data With a population of 9.7 million and a per capita Gross National Product of US$980 in 1992, Guatemala is a small, lower-middle income country in Central America. Approximately 60% of the population lives in rural areas and agriculture contributes approximately 25% of gross domestic product. Primary exports, principally coffee, continue to be approximately 70% of all exports. Though Guatemala had not accumulated as much foreign debt as some of its neighbors at the time of the debt crisis, it was severely affected by events of the 1980s—average annual growth from 1980 to 1992 was negative. The largest declines occurred during the period 1981–1985, when political conflict was particularly important. Mid-decade, Guatemala made a transition from a military to a democratic government. The period 1985–1986 included increases in the government deficit, unemployment, inflation, and the debt service ratio. By the 2

Little et al. Ž1987. show that for size wage differentials in India, there is a break between firms above and below 100 workers. Cortes et al. Ž1987. show the break to be at 200 workers for Colombia. Only Schaffner Ž1995. has attempted to explain the determinants of firm-size differentials for developing countries. Though there do exist studies which have examined small samples of firms in developing countries Žsee for example Knight and Sabot Ž1982., there are not any previous studies that have examined wage differentials at the firm level.

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time of the 1990 survey utilized in this study, there were still pressures on the government deficit and the current account, but some adjustments to control inflation had been made. During the 1990s, these pressures increased again following the attempted dissolution of the congress by then president Serrano and the accession to power of Carpio de Leon in 1993. 3 2.1. The 1990 employer file The 1990 Employer File of the Guatemalan Ministry of Labor contains information on 647 firms and 54,771 employees. The data is collected on an annual basis to monitor movements in wages and employment in the largest firms in the country. Though the data are collected to be representative of labor market conditions in large firms that are obligated by law to comply, selection of firms is not random. In addition, the Employer File does not include public employees. Job information includes three-digit occupation, type of employment status Žpermanent or contract., and shift Žday, night, mixed.. In addition limited information about the worker Žage and sex. and the firm Žfour-digit industry, representation by a union, coverage by a contract, and total number of employees in the firm. are also provided. Because the sample is nearly entirely large firms in the Department of Guatemala, I eliminate from the sample those firms with fewer than 10 workers and firms outside the capital department of Guatemala. 4 Both individuals with missing values for any variable and individuals in firms with less than 75% reporting information for all variables were also eliminated. These procedures resulted in a sample of 33,829 workers in 256 firms. 5 All these firms are subject to government regulation and differential coverage of the law should not significantly affect wage differentials in this sample, though different laws or enforcement of the laws across sectors could. Annual data on hours of work and earnings are provided. The data on earnings include payments for ordinary time, overtime, and thirteenth month pay Žaguinaldo.. In Guatemala, as in many other Latin American countries, workers receive double pay in December, resulting in a total of thirteen months of pay over the course of a year. In this paper, I exclude social insurance payments for accidents or severance pay from the total pay of each worker. Total earnings is defined to be the sum of regular pay, overtime pay, and thirteenth month. 3

For a description of these events, see Poitevin Ž1993.. The 22 administrative regions in Guatemala are called Departments. The Department of Guatemala includes the metropolitan area of the capital Guatemala City. 5 A total of 24.1% of the sample do not report hours. These workers are disproportionately younger, less educated, male, union, and mixed shift workers. There are 2485 workers in 13 firms that were eliminated because hourly earnings could be calculated for less than 75% of the persons in the firm. The largest of these firms had 865, 439, 399, 193, and 186 workers. 4

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The Employer File may be compared to a data source that is based on a random sample, the 1989 Household Survey conducted by the National Statistics Institute ŽINE.. The survey includes more detailed individual and household information, but does not identify characteristics of the firm other than industry and whether the firm of employment has more than 10 workers. This data source provides a check on the representativeness of the Employer File and further evidence on industry wage differentials. 6 To be comparable with the Employer File data, the Household Survey sample is restricted to persons in the urban areas of the Department of Guatemala in non-agricultural firms of 10 or more workers. With this restriction there are 1643 persons in the comparison sample. The data in the Employer File do not include information on education. To proxy for education, I calculate mean years of education in each two-digit occupation in the 1989 Household Survey, restricting the sample to workers in the Department of Guatemala in firms of 10 or more workers. The corresponding mean education levels within each occupation were then applied to each person in the same two-digit occupation in the 1990 Employer File Data. Those workers who do not report a two-digit occupation in the Employer file were assigned a value of one for a separate dummy variable indicating that education is missing. 2.2. Summary characteristics of the two samples The characteristics of the two samples are included in Columns Ž1. and Ž2. of Table 1. For comparison, I include information about persons employed in firms with 1 to 9 workers—calculated from the Household Survey data—in Column Ž3.. The two samples for larger firms have similar characteristics. Most workers in firms of more than 10 persons are between the ages of 20 and 39. In addition, the large scale sector is two-thirds male in both samples. The mean education— calculated from the mean level in the occupation for the Employer File sample—is also similar in the two surveys. One difference between the Employer File sample and the sample of workers in firms over 10 workers in the Household Survey is that the Employer is over-represented in financial services and under-represented in services. The similarity in the characteristics of persons employed in firms with over 10 workers in Columns Ž1. and Ž2. contrasts with the characteristics of workers in firms with fewer than 10 workers in Column Ž3.. These workers are more likely to be teenagers, are over fifty% female, and have substantially lower education than workers in firms over 10 workers. The workers in smaller firms are also disproportionately employed in services.

6

This data has been utilized to estimate wage equations by Arends Ž1992., Psacharopoulos Ž1993a,b., Psacharopolous and Ng Ž1992., Steele Ž1993. and Funkhouser Ž1996a..

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Table 1 Comparison of employer file and national household survey Employer file 1990

National household survey, 1989, Urban Guatemala City, non-agriculture

Ž1.

10 or more workers Ž2.

Fewer than 10 workers Ž3.

Age of worker 10–19 20–24 25–29 30–34 35–39 40–49 50–59 60–64 65q

7.61 26.85 21.55 14.92 10.84 11.38 4.86 1.14 0.87

10.27 20.81 18.14 15.25 11.54 13.21 7.31 1.97 1.51

25.97 19.57 15.90 10.30 7.21 10.98 5.61 2.52 1.95

Gender Male Female Mean education

69.62 30.38 8.37 a

66.32 33.68 8.78

46.34 53.66 4.88

0.12 28.85 y 5.14 18.12 4.62 18.22 24.93 0.65

0.52 31.01 1.22 6.38 15.77 6.49 6.96 31.65 0.90

0.34 17.62 6.64 16.59 3.66 2.63 52.52 y0.04

0.83

0.73

0.81

Industry Mining Industry Utilities Construction Commerce Transrcomm. Fin. serv. Services Mean log hourly wage Sd. dev. log hourly wage N

33,829

1643

a

822

a

Calculated from mean education in two-digit occupation in Household Survey sample. Hourly earnings in the 1989 survey were converted to 1990 Quetzales using the Consumer Price Index Ž1989s189.9, 1990s 268.1. reported in International Monetary Fund Ž1993.. Columns Ž2. and Ž3. are weighted by sample weights.

In the next-to-last rows of Table 1, I compare the mean and the standard deviation of the logarithm of the hourly wage between the samples. The Employer File includes firms with slightly lower mean wages and a higher standard deviation than the comparable sample of workers in the Household Survey. Workers in firms with fewer than 10 workers have substantially lower hourly earnings, but similar standard deviation in hourly earnings, than workers in firms with 10 or more workers.

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2.3. Characteristics of the employer file sample More detailed characteristics of the Employer File sample are shown in Table 2. In the top panel, Rows Ž1. – Ž8., mean characteristics by industry are shown. The overall unionization rate is 17.9% in this sample, with unionized workers being concentrated in industry and financial services ŽColumn 1.. 7 Within industry, the sectors with the highest unionization rates are: food Ž41%. and textiles Ž71%.. The industry levels range from 21% in mining to over 44% in construction. Female workers are found disproportionately in commerce and services and are under-represented in construction and mining ŽColumn 2.. The mean of total salary earnings is found in Column Ž3.. 8 In this sample, workers in finance and manufacturing have the highest annual earnings while workers in construction have the lowest annual earnings. In Columns Ž4. to Ž6., mean hours, percent with fewer than 1000 h, and percent reporting overtime hours are shown. With the exception of construction—which has lower hours, mean hours in each sector range between 1690 and 1914 per year ŽColumn 4.. In the sample, 28% of all workers that report hours worked fewer than 1000 h ŽColumn 5.. The proportion with overtime ranges from 48% in transportation to 77% in utilities ŽColumn 6.. In Column Ž7., the logarithm of hourly earnings is shown. 9 With the exception of services, industries with high hours tend also to have high hourly earnings. One of the notable findings in Table 2 is the similarity in the labor market outcomes of males and females in the large-scale sector, seen in Rows Ž9. – Ž10.. Unionization rates Ž0.178 for males and 0.180 for females., log total earnings Ž7.953 to 7.989., log ordinary earnings Ž7.737 to 7.824., hours Ž1846 to 1822. and log mean hourly earnings Ž0.770 to 0.761. of females are very similar to those of males. These data show large differences between the union and non-union sectors in Rows Ž11. – Ž12.. The log difference in total salary is 1.06 log points, including both higher hourly earnings and mean hours in the union sector. Workers represented by unions are half as likely to work fewer than 1000 h over the course of the year, average 539 more hours per year, and are 25% more likely to work overtime hours.

7

Of the 256 firms in the sample, 18 have both a union and an bargaining agreement, 43 have a bargaining agreement without a union, and three have a union without a bargaining agreement. 8 Overall, 68.8% of total compensation consists of ordinary salary, 6.9% is overtime, 6.9% is thirteenth-month, and 17.5% is social insurance payments. 9 Note that taking the mean after the calculation of hourly earnings and the logarithm can lead to inconsistent patterns across groups. For example, females have lower mean logarithm of hourly earnings and work fewer hours than males, but have a mean logarithm of total earnings that is higher than that for males.

Total Mining Industry Construction Commerce Transrcomm Fin. serv. Services Male Female Non-union Union

Percentage union

Percentage female

Log total earnings

Mean total hours

Percentage -1000 h

Percentage report overtime

Log hourly earn.

N

Ž1.

Ž2.

Ž3.

Ž4.

Ž5.

Ž6.

Ž7.

Ž8.

0.179 0.000 0.228 0.000 0.000 0.068 0.442 0.117 0.178 0.180 y y

0.304 0.071 0.218 0.067 0.436 0.156 0.315 0.377 y y 0.303 0.307

7.964 8.087 7.949 7.469 8.043 7.934 8.322 7.770 7.953 7.989 7.774 8.838

1838.4 1697.7 1836.8 1522.6 1832.3 1793.5 1914.5 1863.0 1845.7 1821.5 1742.1 2281.4

0.280 0.214 0.295 0.436 0.255 0.311 0.244 0.268 0.294 0.247 0.308 0.150

0.615 0.595 0.708 0.613 0.612 0.479 0.642 0.517 0.654 0.527 0.589 0.738

0.767 0.758 0.792 0.651 0.791 0.741 1.020 0.565 0.770 0.761 0.652 1.296

33,829 42 9758 1738 6130 1564 6164 8433 23,552 10,277 27,792 6037

Total earnings do not equal the product of the mean hourly wage and mean hours.

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Table 2 Characteristics of sample

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3. Estimation 3.1. Empirical strategy The wages of individual k can then be written: Wifjk s a q b X k q f i q f f q f j q e k

Ž 1.

where X k is a vector of observable characteristics of individual k; the f i , f f , f j include unobservable characteristics for the industry Ži., the firm Žf., and job Žj.; and e k is the unobservable component of wages of individual k. The strategy in this paper is to begin with a specification that includes only the individual variables, X k , and to sequentially add controls for observable firm characteristics, industry, and firm fixed effects. From these results, the importance of unobservable firm fixed effects in wage determination, once other variables have been controlled for, can be assessed. In addition, because these data include information on industry, size of firm, and unionization status that are often provided in household surveys, a unique contribution of this paper is the determination of the importance of omitting firm fixed effects in the estimation of wage equations using household data. 3.2. Results The results of the estimation of Eq. Ž1. with the Guatemalan data are presented in Table 3. 10 Variables are added sequentially from Column Ž1. to the full specification with firm effects in Columns Ž5. and Ž6.. In Column Ž1., only individual characteristics Žage, years of education, and gender. are included. Additional control variables in Column Ž2. include firm characteristics Žsize, shifts of operation, and union status. and job characteristics Žtype of contract.. In Columns Ž3. and Ž4., one-digit and four-digit industry are included. And in Columns Ž5. and Ž6., the within- and between-estimators of the firm fixed effects model are shown. Means of each variable are included in Column Ž7.. 3.3. IndiÕidual characteristics Characteristics correlated with human capital are important determinants of earnings in Guatemala. The return to an additional year of labor market experience, measured by age, has the familiar concave shape. The return to the 10 Though there have been studies of wage differentials for the smallest firms in Guatemala ŽPerez and Pablo, 1991; Bastos and Camus, 1993; Funkhouser, 1996a., previous work on Guatemala that has examined industry, size, or firm-wage differentials is limited to Sakellariou Ž1995. study which used the 1989 data to show that one-digit industry differentials are correlated with average human capital in the industry.

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constructed education measure is 0.121 per year with only individual controls in Column Ž1. and 0.089 when firm effects are included in Column Ž5.. The similarity in male–female earnings is widened when controls for education and age are included, though the female wage gap with controls is still relatively small. Females in the sample earn an hourly wage 0.196 to 0.220 log points less than males controlling for other factors. 11 To ascertain the usefulness of the constructed education measure, comparable regressions were estimated using the restricted sample of the 1989 Household Survey. The results of this estimation are reported in Appendix A. In these regressions, the actual years of education was replaced with the mean years of education in the worker’s two-digit occupation group. The coefficient on the constructed years of education variable is very similar in the regressions using both data sets Ž0.095 in the Household Survey and 0.099 in the Employer File using three-digit industry.. The use of the constructed years of education raises the coefficient on education by 1.6 log points in the Household survey data, raises the return to age, and widens the male–female wage differential substantially Žfrom y0.022 with actual years of education to y0.088 with the constructed years of education.. 12 Each of these variables is, therefore, likely to be slightly overestimated in the results using the employer file data. 3.4. Job and firm characteristics Observable firm characteristics are included in Columns Ž2. to Ž4. of Table 3. In the next rows, it can be seen that the union wage premium in these data, even with controls, is quite large. The hourly earnings of union workers is 0.36 to 0.45 log points higher than that of otherwise similar non-union workers. Because union workers have more hours, the overall difference in salary is even higher. Permanent workers earn more than either contract or withdrawn workers. 13 Patterns in the firm-size wage differential with controls can be observed in the next rows of Table 3. 14 On average, workers in firms with fewer than 50 workers earn less than workers in firms with over 50 workers. The pattern is not monotonic, though. Workers in medium sized firms Žfirms with 51–100 workers. earn significantly more than other workers. The low premium paid to workers in 11

This confirms other evidence for Guatemala ŽFunkhouser, 1996a,b. suggesting that much of the overall difference in earnings between males and females comes from the types of employment of each gender group and in the occupational distribution of employment within the large-scale sector. 12 When detailed occupation controls are included, in regressions not reported, the male–female difference falls below 10%. 13 Workers who no longer work at the firm are included as withdrawn workers to capture the complete payroll of the firm. The results are similar when these workers are excluded. 14 The firm-size wage pattern without controls—not shown in the Table—is similar to the firm-size wage pattern shown in Column Ž1., except that the largest firms Žthose with over 500 workers. earn less than all other firms, including the smallest firms Žthose with 10–25 workers..

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the largest firms Ž0.062 in Column Ž2.. is the result of the industry composition of these firms. When four digit industry controls are included in Column Ž4., the earnings of the largest firms, controlling for other factors, are higher than all groups except firms with 51–100 workers. Table 3 Regressions for total salary Regressions without firm effects Ž1. Constant

Ž2.

Within firm Between firm Means estimator estimator Ž3.

Ž4.

Ž5.

Ž6.

Ž7.

0.027 Ž0.420.

y3.720 Ž0.827.

0.080 0.072 0.070 0.063 0.055 Ž0.002. Ž0.002. Ž0.002. Ž0.002. Ž0.001. Age 2 r100 y0.074 y0.067 y0.064 y0.059 y0.050 Ž0.002. Ž0.002. Ž0.002. Ž0.002. Ž0.002. Years of education 0.121 0.107 0.114 0.094 0.089 Ž0.001. Ž0.001. Ž0.001. Ž0.001. Ž0.001. Education 0.967 0.863 0.878 0.683 0.612 Ž0.064. Ž0.061. Ž0.059. Ž0.054. Ž0.048. Missing Female y0.220 y0.220 y0.205 y0.216 y0.196 Ž0.008. Ž0.008. Ž0.008. Ž0.008. Ž0.007.

0.208 Ž0.040. y0.244 Ž0.052. 0.086 Ž0.022. 2.371 Ž2.673. y0.400 Ž0.162.

30.73 Ž10.55. 10.56 Ž8.02. 8.37 Ž3.36. 0.003 Ž0.057. 0.304 Ž0.460.

y0.123 Ž0.396. y0.033 Ž0.453.

0.194

y1.859 y1.635 y1.613 y0.810 Ž0.032. Ž0.037. Ž0.102. Ž0.179.

IndiÕidual characteristics Age

Job characteristics Type of employee Contract Withdrawn

Firm characteristics One-digit industry Four-digit industry Union status Labor agreement

y y0.132 y0.135 y0.117 y0.122 Ž0.009. Ž0.009. Ž0.009. Ž0.008. Ž0.148. y y0.175 y0.184 y0.123 y0.137 Ž0.008. Ž0.008. Ž0.008. Ž0.007. Ž0.151.

No No No No y y0.416 Ž0.012. Ž0.012. y y0.071 Ž0.010. y

Size of firm 26–50

y

51–100

y

101–500

y

500q

y

Yes No No Yes 0.385 0.453 Ž0.024. Ž0.149. 0.061 y0.023 Ž0.010. Ž0.010.

No No No Ž0.383. No.

y0.004. 043. 047 Ž0.025. Ž0.024. 0.300. 349 0.314 Ž0.022. Ž0.021. y0.097 0.150 0.099 Ž0.021. Ž0.020. y0.062 0.176 0.191 Ž0.022. Ž0.021.

No Ž0.024. No Ž0.021. No Ž0.020. No Ž0.022.

0.288

No Yes 0.361

y y 0.178

0.008 Ž0.066.

254 Ž0.435.

y0.015 Ž0.089. 0.315 Ž0.081. 0.104 Ž0.077. 0.159 Ž0.161.

0.054 Ž0.226. 0.126 Ž0.332. 0.502 Ž0.500. 0.288 Ž0.453.

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Table 3 Žcontinued. Regressions without firm effects

Within firm estimator

Between firm estimator

Means

Ž1.

Ž2.

Ž3.

Ž4.

Ž5.

Ž6.

Ž7.

Shift Night

y

y0.248

Mixed

y 0.354 33,829

y0.423 Ž0.092. 0.041 Ž0.011. 0.549 33,829

No Ž0.086. No

R2 N

y0.082 Ž0.008. 0.419 33,829

y0.261 Ž0.095. y0.144 Ž0.009. 0.455 33,829

y0.471 Ž0.277. 0.012 Ž0.075. 0.677 33,829 Ž256 firms.

0.001 Ž0.036. 0.465 Ž0.499. 0.658

0.300 33,829

For regressions, numbers in parentheses are standard error. For means, numbers in parentheses are standard deviations.

The data on shifts reports those worked by the firm, not shifts worked by the individual. Because the estimated effect is averaged over all workers in the firms, including those who do not work undesirable shifts, the measurement of the compensating differentials for shift work is an underestimate of the true effect for individuals. 15 With the controls included, there is little evidence in favor of payment of compensating differentials for shift work in this data. In fact, workers in firms with night shifts earn significantly less than workers in firms with only day shifts. The addition of industry differentials in Column Ž3. increases the proportion of the overall variance in the hourly earnings explained by 4.4 percentage points with one-digit industry controls and by 13.0 percentage points with four-digit industry controls. 16 Similar to the findings of other authors, much of industry differentials remain after other control variables have been included. Without controls, the standard deviation in the industry differentials is 0.141 at the one-digit level, 0.344 at the two-digit level, and 0.459 at the three digit level. With controls, this standard

15 This variable could also be correlated with other unobservable characteristics of the firm, in which case the bias could be in either direction. 16 In addition, those four-digit industries which have high ordinary salary are also those with higher levels of the other components of total compensation. The correlation coefficient between the industry coefficients for ordinary salary and the industry coefficients on overtime salary is 0.48; the correlation between ordinary salary and indemnization is 0.38; and the correlation between ordinary salary and 13th month is 0.86 Žthe correlation is not one because not all workers work a full year..

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deviation increases to 0.157 at the one-digit level, decreases to 0.228 Ž33.7%. at the two-digit level, and decreases to 0.339 Ž26.1%. at the three-digit level. 17 3.5. Firm differentials The main contribution of this paper is the inclusion of firm fixed effects. The within-estimator including firm fixed effects is presented in Column Ž5.; the between estimator is presented in Column Ž6.. 18 The inclusion of firm differentials in Column Ž5. significantly improves the explanatory power of the regression, increasing the R 2 from 0.549 to 0.658. The coefficients on the individual variables, especially age, are affected slightly by the inclusion of the firm fixed effect. The positive relationship between the firm wage differentials and each of age and education, and the negative relationship between the firm wage differentials and the female variable, cause the estimates without the fixed effects to be biased upward in magnitude. The results in Column Ž6. provide evidence on the relationship between firm wage differentials and the variables found in many cross-sectional data sets including industry and size of firm. Approximately two-thirds of the variation in the firm wage differentials is accounted for by the inclusion of four digit industry and the four size variables alone. Moreover, there is little correlation between the firm-level means of individual variables Žincluded in the top rows of Column 6. and the union and size variables—the coefficients on these variables change little when the additional firm-level controls are included. 19 These calculations permit decomposition of the variance in the logarithm of hourly earnings. The 1989 Household Survey provides information on the complete sample of workers in firms of 10 or more workers in the Department of Guatemala. Noting that the three-digit industry patterns are similar for the Household Survey and the Employer File, decomposition within industry can be made with the Employer File. 17 To further compare the Employer File sample with the 1989 Household Survey sample, the industry differentials calculated from identical regressions using the two data sets were compared. Because the Household Survey data includes three-digit industry, comparison at each of the one-, two-, and three-digit level is possible. The results suggest that three- and four-digit industry controls can mitigate much of the biases resulting from industry mix in the Employer File sample. The correlation between industry differentials in the two surveys is 0.361 at the three-digit level and 0.241 at the two digit level. At the one-digit industry level, though, there is negative correlation between the industry differentials in the two surveys. For this reason, detailed industries controls at the three- and four-digit level are included in the remainder of this paper. 18 The Hausman specification test rejects the appropriateness of a random effects model. 19 To examine the importance of observable individual and job characteristics in explaining firm differentials, I calculate the variance in the firm differential with and without controls. The variance in the firm differentials across the 256 firms in the sample is reduced from 0.275 when no controls are included to 0.181 in the full specification. Sorting between firms in observable characteristics does explain some of the firm wage differentials, but substantial variation remains after these controls.

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From the 1989 Household Survey, the overall variance in the logarithm of hourly earnings is calculated to be 0.733. Of this, 71.9% is within three-digit industry. 20 In the Employer File, 69.3% of the within three-digit industry variance is within firms. Combining these two estimates, approximately 49.8% of the total variance in the logarithm of hourly earnings is within firms. 21 Further decomposition reveals that 50.6% of within-firm variance, or 25.2% of the total variance is within-occupation within-firms and 49.4% is across occupations. Of this, differences in mean education level by occupation explain nearly one-third of the variation across occupations.

4. Explanations for firm wage differentials The results in Section 4 show that firm wage differentials are an important factor in the wage structure in the large-scale sector in Guatemala. Approximately half of wage variation is across firms and one-seventh of the explained variance in the full specification in Table 3 comes from firm effects not captured by the other independent variables. The previous literature on industry and firm wage differentials has identified several explanations for firm wage differentials that can be divided into those that operate at the level of industry interacted with size of firm Žminimum wage, union-threat, regulation, rent-sharing 22 ., the firm level Žunionization., the level of the job Žcompensating differentials and efficiency wage payments 23 ., and the level of the individual Žindividual heterogeneity 24 .. 20

This compares to 82.0% in the Employer file. This decomposition is similar to that of Davis and Haltiwanger Ž1991. for the United States, who find that approximately half of overall wage variation is across firms. Groshen Ž1991a. decomposes variance of wages within sectors of the manufacturing industry. 22 Bell Ž1995. finds that the minimum wage had a stronger effect on the wage structure in Colombia, where the minimum is closer to the average wage, than in Mexico, where the minimum is not binding. On the impact of regulation, Schaffner Ž1995. concludes that government wage regulation reduced the firm-size wage differential for blue-collar workers in Peru and Morrison Ž1994. finds that institutional measures are important determinants of industry wage differentials in Ecuador. On rents, Lai Ž1989. and Abuhadba and Romaguero Ž1993. find that measures of rents are positively rated to industry wage differentials. In contrast, Morrison Ž1994. concludes that institutional factors are more important than rents in explaining industry differentials. Perhaps the strongest evidence using developing country data is Moll Ž1993. conclusion that rents do not explain inter-industry wage differentials in South Africa, noting that even though discrimination was legalized, black workers received similar industry differentials to those of whites. 23 Efficiency wage arguments are based on the idea that marginal product is endogenous and increasing in the wage in the relevant range. Both Abuhadba and Romaguero Ž1993. for industry differentials and Schaffner Ž1995. for size differentials have found that the wage structure is similar across occupation groups, a finding which suggests that compensating differentials are not important determinants of industry of firm-size wage differentials. Schaffner Ž1995. evidence suggests that efficiency wage arguments based on turnover costs are more likely than those based on monitoring. 24 See Schaffner Ž1995.. 21

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For many of these explanations, appropriate variables Žindustry dummy variables, unionization status, occupation, and observable individual characteristics. can be introduced directly as in Columns Ž2. to Ž5. of Table 3. For others, such as monitoring conditions that differentially affect distinct occupations, it is necessary to examine the structure of firm wage differentials separately by groups that are differentially affected under the proposed explanation. The results from the 1990 Employer File for Guatemala provide evidence on which of the proposed explanations for firm differentials do not appear consistent with the data. First, four-digit industry is the most important determinant of across-firm variation in earnings. Of the possible industry-level explanations—rents, minimum wages, and union threat—the significant part of the across-firm variation that is not explained by unionization make both the union-threat and rent-sharing explanations based on unionization unlikely to be the main determinants of firm-wage differentials. 25 The findings are more consistent with the institutional determinants of firm differentials, accumulation of sector-specific capital, efficiency wages, or barriers to mobility between sectors. Second, firm wage differentials are similar across occupations within firms. This finding makes the payment of compensating differentials and monitoring versions of efficiency wage theories unlikely explanations for firm-wage differentials in Guatemala. To test the hypothesis that firm differentials between different occupation groups are uncorrelated, Eq. Ž1. was estimated separately for each of nine one-digit Žstandard international. occupation groups Žmining occupations were excluded.. Correlations between the firm differentials for each pair of occupation group were calculated using all firms which had workers in the two groups. These calculations reveal a high degree of correlation between the firm-wage differentials for different occupation groups. 26 Third, additional evidence casting doubt on the importance of monitoring as a primary explanation for firm-wage differentials is that the correlation in firm wage differentials is similar for firms with above 100 workers as for firms with fewer than 100 workers. Eq. Ž1. was estimated and correlations were calculated separately for firms with more than 100 workers and firms with fewer than 100 workers. Among firms with more than 100 workers—where previous research has suggested difficulties in monitoring are more important—the correlation in the firm wage differential across pairs of one-digit occupations is above 0.66 for all

25

The unionization and collective bargaining variables alone explain only 15% of the variation in the firm wage differentials. 26 Each of the 37 resulting calculated correlations is above 0.39 and 22 are above 0.6. Some sample correlations are: professional with managers, 0.64 over 173 firms; clerical with managers 0.69 over 193 firms; artisans with laborers 0.68 over 86 firms. The categories that are least correlated with other occupations are commerce occupations and management.

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comparisons. For smaller firms—where difficulties in monitoring are less important—the correlation is lower, with the firm differentials across only one pair of occupations correlated above 0.6 and ranging down to 0.19. The finding that firm differentials are strongly correlated across occupation groups and that the correlation is stronger for large firms than for small firms suggests that neither monitoring nor compensating differentials can be the primary explanation for firm wage differentials.

5. Summary In this paper, I have utilized a unique firm-based data to examine firm-wage differentials among private firms with ten or more workers in the capital area of Guatemala City. With these data, I have estimated log hourly earnings regressions with observable firm characteristics found in many household surveys and with firm fixed effects. Approximately one-seventh of the overall hourly earnings variation in this sector is related to firm effects not captured by other observable characteristics of the firm. For most other variables, the bias from not including firm fixed effects in the estimation of log wage equations is small in magnitude, though statistically significant. Though these data are not detailed enough to examine all explanations for firm wage differentials, there is evidence against some of the proposed explanations. There is little support for explanations based on union-threat, capture of industry rents, compensating differentials, or monitoring versions of efficiency wage theory. These findings are similar to those of previous studies using data for developed countries.

Acknowledgements This paper would not have been possible without the assistance of Heidy Belice, Guillermo Flores, Carlos Gonzalez, and Erwin Diaz. Financial assistance was provided by the Social Science Research Council. In addition, I have benefitted from the comments of Juan Pablo Perez Sainz, Steve Trejo, and two anonymous referees.

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Appendix A. Comparison of determinants of hourly earnings in 1989 household survey and the 1990 Employer File 1989 Household survey Ž1. Ž2. Ž3. Ž4. Constant y1.612 y1.857 y1.332 y1.506 Ž0.099. Ž0.106. Ž0.118. Ž0.126. Age 0.064. 071 0.058 0.064 Ž0.006. Ž0.006. Ž0.005. Ž0.006. Age 2r100 y0.059 y0.072 y0.053 y0.065 Ž0.007. Ž0.007. Ž0.007. Ž0.007. Actual years 0.091 0.079 of education Ž0.003. Ž0.003. Mean edu0.115 0.095 cation in occupation Ž0.004. Ž0.005. Mean years missing Female Three-digit industry R2 N

Employer File Ž5. Ž6. y1.859 y1.497 Ž0.032. Ž0.095. 0.080 0.070 Ž0.002. Ž0.002. y0.074 y0.065 Ž0.002. Ž0.002.

0.121

0.099

Ž0.001. 0.967

Ž0.001. 0.713

Ž0.064. Ž0.057. y0.005 y0.079 y0.022 y0.088 y0.220 y0.218 Ž0.029. Ž0.031. Ž0.031. Ž0.033. Ž0.008. Ž0.008. Yes Yes Yes 0.46 1643

0.39 1643

0.51 1643

0.46 1643

0.35 33,289

0.50 33,289

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