POLICY RESEARCH WORKING PAPER 2778 Technology and Firm Performance in Mexico Gladys L6pez-Acevedo The World Bank Latin America and the Caribbean Region Poverty Reduction and Economic Management Sector Unit February 2002 I POLICY RESEARCH WORKING PAPER 2778 Abstract L6pez-Acevedo investigates the relationship betveen a all these metrics. The effect of new technology on firm's adoption of new manufacturing technology and its performance is larger for firms located in the north and performance. A panel database that identifies in Mexico City. This marginal value significantly technological adoption and tracks firms over time allows increased after the 1994 crisis and the North American the use of different measures of firm performance- Free Trade Agreement. Furthermore, technology wages, productivity, net employment growth, job increased the wage of semi-skilled workers compared creation, and job destruction. Results show that with unskilled workers by about 11 percent over seven technology is associated with high firm performance in years. This paper-a product of the Latin America and the Caribbean Region, Povertv Reduction and Economic Management Sector Unit-is part of a larger effort in the region to reduce poverty and inequality through human capital investment. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Michael Geller, room 14-406, telephone 202-458-5155, fax 202-552-2112, email address mgellerka.worldbank.org. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at gacevedoCaoworldbank.org. February 2002. (22 pages) The Policy Research Workinig Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An ob7ective of the series is to get the,. . . out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and shozuld be cited accordingly. The; .1. . interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the vieuw of the World Bank, its Executive Directors, or the countries they represent. Produced by the Research Advisory Staff Mexico - Technology, Wages, and Employment TECHNOLOGY AND FIRM PERFORMANCE IN MEXICO Gladys L6pez-Acevedol JEL Codes: L60, L20, J3 1, J38. ' This research was completed as part of the "Mexico --- Technology Wages and Employment" study at the World Bank. We are grateful to the INEGI for providing us with the data. Joseph S. Shapiro and Erica Soler provided valuable research and editorial support. 1. Introduction In the last two decades, broad-based reforms at both the sectoral and macroeconomic levels have fundamentally restructured the economic and institutional framework in Mexico. In the mid- 1980s, Mexico began to shift from a state-interventionist system to a market-based economy. Reforms instituted a liberal trade regime, established capital-account convertibility, privatized public enterprises (including banks), and reduced government regulation of the financial, transportation, and utility sectors. At the macroeconomic level, fiscal discipline and structural reform brought about sharp decline in the fiscal deficit and inflation. The government first launched a radical program of policy reforms in 1989 aimed at reducing government regulation and liberalizing trade. Trade liberalization, which began in mid- 1985 and accelerated after Mexico joined the General Agreement on Tariffs and Trade in 1986, further intensified with the adoption of the North American Free Trade Agreement in 1994. Though the external openness of the Mexican economy has quickly expanded, internal reforms have been slower to materialize. The World Bank (1998a) indicated that the productivity difference between export and non-trade sectors reflects the difference in speed between international and internal regulatory reform. It is telling in relation to this that manufacturing, the most important trade sector, improved rapidly in the early 1990s while the service sector deteriorated. But manufacturing only accounts for 25 percent of Mexican gross domestic product, while services account for over 40 percent, which may explain the slow response of the Mexican economy to vigorous trade policy reforms (World Bank 1 998b). During this last decade of rapid development, Mexican wages have polarized. The World Bank (2000) contends that skill-biased technical change caused by trade liberalization explains best the increase in earnings inequality that Mexico has experienced. In this paper, we estimate the effect of new technology adoption (TA) on wage inequality using a rich panel database of manufacturing firms that identifies TA and tracks firms over time. Furthermore, we compare the performance of firms that adopt new technology to those that do not using three separate firm performance measures: the wages earned by workers, the productivity of a firm (output per 3 worker), and the annual growth in the number of employed workers; while other studies have tended to use a single measure of performance. Section 2 of this paper reviews relevant literature on firm performance and TA. Section 3 explains the data and our methodology. Section 4 discusses results for firm performance by time period, firm size, and firm location. Section 5 presents results of the TA determinants and wage performance joint estimation. Section 6 analyzes wage inequality. Section 7 offers conclusions. 2. Literature Review A. Performance Measures Studies measure firm performance in different ways, reflecting both the heterogeneity of the concept and the challenge of practically measuring it. In this paper we use five measures of firm performance-wages, productivity, net employment, job creation, and job destruction. These measures are proxies for a fairly amorphous concept. We want to understand how healthy a firm is, how likely it is to exist in the future, how much utility it creates for workers and consumers, and the contribution it makes to Mexico's development. Our measures by no means exhaustively cover these concepts, which collectively constitute firm performance, but a firm with high marks in these measures also has an exemplary performance. Employment growth is a prevalent measure of firm performance (Geroski 1995). Positive changes in employment represent superior performance; negative changes in employment represent inferior performance. As Caves (1998) documents in his exhaustive compilation, employment growth has been used in many types of studies as a measure of firm performance (Baldwin and Rafiqusszaman 1995; Audretsch 1995; Davis, Haltiwanger, and Schuh 1996b; Baldwin 1995). Employment growth is particularly important for policy makers who focus on job creation. As noted by Davis, Haltiwanger, and Schuh (1996a) job creation and destruction are part of a larger process deternining changes in the number and mix of jobs. In this process, new businesses enter the market, some expand, others contract, and many disappear. Additionally, capital, workers, and jobs are continually relocated between different activities. 4 The creation and destruction of jobs requires workers to decide between employment and unemployment. As a result of these processes, some workers must suffer long unemployment spells or severe declines in their earnings. Others may retire early or change residence to find work. A second measure of firm performance is the wages that the firm pays to workers. A healthy firm may pay high efficiency wages, or it may simply maintain high quality of life for its workers by paying high wages. The wages paid by firms have been used as a measure of firm performance in numerous studies, including Aw and Batra (1999), Audretsch and others (2001), Bartel and Lichtenberg (1991), Berman, Bound, and Griliches (1994), Bernard and Jensen (1995), Brown and Medoff (1989), Dunne and Schmitz (1995), Doms, Dunne, and Troske (1997), and Oosterbeek and van Praag (1995). Another firm performance used in this paper is firm productivity. This measure has also been used in numerous studies, including Baldwin and Rafiquzzaman (1995), Baldwin (1995), Bartel and Lichtenberg (1991), Aw and Batra (1999), Baily, Bartelsman, and Haltiwanger (1996), and Baily, Hulten, and Campbell (1992). Higher productivity represents superior performance; lower productivity represents inferior performance. These measures of firm performance are non-identical; in cases they may be contradictory. For example, it is certainly feasible that a firm increases productivity by reducing employment (Baily, Bartelsman, and Haltiwanger 1996). In such an instance, productivity would indicate superior performance, while employment would suggest inferior performance. We try to interpret results in cases where the firm performance measures indicate similar performance patterns. When this similarity is absent from results, we either mention each metric separately or exclude the specific results from discussion. B. Linking Technological Adoption to Firm Performance Measures Some theoretical studies argue against stating unequivocal effects of TA on a developing country's labor force. Braverman (1974) contends that the introduction of advanced technology 5 results in a reduction of the average skill of workers. In this view, technology simply replaces skilled workers. Additionally, Rush and Ferraz (1993) find that technology improvements increase skills for some groups and leave others largely unaffected. A variety of studies link TA to firm performance. One is Doms, Dunne, and Roberts (1995), who examine the impact of advanced manufacturing technology on U.S. manufacturing firms. They use data from the 1988 Survey of Manufacturing Technology to identify the adoption by establishments of 17 different advanced production technologies. These technologies include such innovations as CAD/CAM systems, robots, computers, and networks. They find evidence that firms adopting technology exhibit superior performance. Another is Audretsch and others (2001), who use wages, productivity and employment as performance measures for a panel of firms in The Netherlands. They find that investments in research and development (R&D) and skilled labor improve firm performance. Aw and Batra (1999) provide evidence that technology (measured by R&D and worker training) has an impact on firm performance (measured by wages). This is consistent with the World Bank (1999), which also relates wages to technology (measured by R&D and technology acquisition). Several studies have confirmed the relationship between TA and firm size (Mansfield 1962; Davies 1979; Romeo 1975; and Globerman 1975). This is probably one of the most robust results among surveys analyzing determinants of TA (L6pez-Acevedo 2001). Others have found that firm size determines wages. As noted by Brown and Medoff (1989), other things being equal, large employers pay more than small employers. One way to explain this wage differential is through labor quality. Under this view, larger firms employ higher quality workers due to the greater capital intensity and capital-skill complementarity of larger establishments. Revenga (1995) analyzes the impact of trade liberalization on employment and wages on Mexican manufacturing using panel data of firms for the 1984-1990 period.2 She finds that tariff 2 The data used was drawn from the plant-level Annual Manufacturing Survey. 6 reductions correlate with average wage increases. The correlation may reflect simply an increase in productivity caused by a relative increase in the portion of skilled labor. In a related vein, Tan (2000) investigates manufacturing sector data for Malaysia, and finds that information and communication technology increases total factor productivity by 4 to 6 percent annually. Sargent and Matthews (1997) conclude that installing capital intensive, computer- controlled production machinery into a formerly manual Mexican plant does not impel a firm to train low skilled workers. If the adoption of advanced manufacturing technologies causes an increase in plant size, then it also increases the firm's skill development activity. However, they also find that productivity and skill development do not correlate with compensation. 3. Data and Methodology The data used in this paper comes from a panel of manufacturing firms created with data from the National Survey of Employment, Salaries, Technology, and Training (ENESTYC) and the Annual Industry Survey (EIA). The panel includes observations for 1992, 1995, and 1999.3 Our goal is to understand, for particular types of firms, how is technology related to each firm's performance measure. For this estimation, we use a similar specification for the different performance measures: log(Pit) = ,Bo + ,i1Xt. + /2adopt,, + t (li) where: log(Pi,) = the logarithm of the performance measure; Xi, = a vector of firm characteristics; adopti, = a dummy variable indicating whether the firm adopted new technology; Ci, t= normal regression error; i = refers to the firm being considered, and t = the time period. 3 For a description of these surveys and the panel see Appendix A and B in this volume. 7 For the productivity measure, we include a continuous variable for capital assets to control for correlation between capital and TA, since both influence productivity. Within each measure, for each time period, we restrict the sample only to firms of a particular size or location to estimate situation-specific effects. We do not present results by industry, nor for microenterprises, due to insufficient observations. We measure wages in real pesos, productivity as units of output divided by the number of workers, and net employment as the difference between new hires and dismissals for a given year. Since we have detailed plant level information, we measure net job creation using firm- level employment changes, rather than worker-level changes. 4. Results Several models were estimated. Only the results from the best models are discussed here. We estimated equation (1) using a fixed effects model specification.4 As an experiment, we also estimated a random effects model specification, however, the results were broadly similar, though the fixed effects model tended to yield more robust estimates of the TA parameters of interest. Therefore, we only discuss the results of the fixed effects estimations for all the measures, organized by the sample universe (only small firms, only firms in the North, etc.), in Table 1. A. Overall On balance, firms that adopt new technology exhibit superior performance in all the metrics than those firms that did not adopt technology. Controlling for firm size, age, the skill level of workers, and firms in the maquila sector, firms that adopted new technology in the 1992- 99 sample are related with higher wages for workers of all skill levels. Controlling also for 4 The fixed effects model implements the first differencing approach that generates parameter estimates measured in terms of changes over time and, at the same time, eliminates any potential biases from unmeasured firm-level factors that may be correlated with included variables. 5 Tables Al. I-A1.38 show complete results of the fixed effects estimations for each firm performance measure. 8 capital assets, firms that adopted new technology in the same period are associated with a 26 percent higher productivity than firms that did not adopt technology. Table 1. Relation between Technology Adoption and Firm Performance Sample Measure 1992-95 ,1995-99 Diff. 1992-99 All Wages: Total 0.5058 ** 0.5594 ** 0.0536 1.2417 ** Highly skilled 0.2817 ** 0.5265 ** 0.2448 1.0614 ** Semi-skilled 0.4981 ** 0.5866 ** 0.0885 1.2722 ** Low skilled 0.2861 ** 0.4271 ** 0.1410 1.2529 ** Productivity 0.0549 ** 0.5360 ** 0.4811 0.2577 ** Net employment 0.3382 * 0.1130 --0.2252 .0.0011 Job creation 0.1846 ** 0.2189 ** 0.0343 0.0985 Job destruction 0.1040 ** -0.0277 -0.1317 -0.0438 Small size Wages: Total 0.2284 ** 0.2756 * 0.0472 1.9678 ** Highly skilled 0.1329 0.2506 0.1177 2.1315 ** Semi-skilled 0.2242 ** 0.2432 0.0190 1.9052 ** Low skilled 0.2393 ** 0.3264 * 0.0871 2.2553 ** Productivity 0.0773 ** 0.3747 0.2974 -0.0229 Net employment 0.1736 - - -0.1965 Medium size Wages: Total 0.2711 ** 0.4696 ** 0.1985 1.6908 ** Highly skilled 0.3023 ** 0.4374 ** 0.1351 1.5258 ** Semi-skilled 0.2269 ** 0.4664 ** 0.2395 1.6805 ** Low skilled 0.2145 * 0.3948 ** 0.1803 1.7769 ** Productivity 0.0839 0.3778 ** 0.2939 0.2025 ** Net employment 0.4949 -0.2620 -0.7569 :-0.0021 Large size Wages: Total 0.3797 * 0.5302 ** 0.1505 1.4971 ** Highly skilled 0.5272 0.5526 ** 0.0254 1.3165 ** Semi-skilled 0.4442 0.4974 ** 0.0532 1.5389 ** Low skilled 0.0688 0.4242 ** 0.3554 I1.6095 ** Productivity -0.5443 0.4122 ** 0.9565 0.2271 ** Net employment 0.0238 0.2741 0.2503 -0.4370 North Total wages 0.2580 ** 0.5831 ** 0.3251 0.6985 Productivity -0.0368 0.7089 ** 0.7457 0.4051 ** Net employment 0.5536 0.0501 '-0.5035 -0.0097 Center Totalwages 1.1191 ** 0.5582 ** -0.5609 .1.3955 ** Productivity 0.0947 ** 0.4634 ** 0.3687 0.2552 ** Net employment -0.0467 0.3821 0.4288 0.1822 ... ........... ....... o - - - ---- - t~~~~~~~~~~~~~~~~~....... South Totalwages 2.1293 ** 0.4658 ** -1.6635 1.5689 Productivity 0.0017 0.4959 0.4942 0.1573 Net employment -1.6972 -0.2310 1.4662 -1.2882 Mexico City Total wages 0.3618 ** 0.6487 ** 0.2869 1.5586 ** Productivity 0.0375 0.4866 ** 0.4491 0.0923 Net employment 0.5631 * -0.2403 ,-0.8034 0.1190 * Significant at 10% level; ** Significant at 5% level. Note: Figures show regression coefficients for the TA indicator variable, which in these models can be interpreted as elasticities. 9 In the later period of 1995-99, firms adopting new technology are associated with 56 percent higher wages, and 54 percent higher productivity than firms that did not adopt technology. In the earlier period of 1992-95, firms that adopted new technology are related with 51 percent higher wages, 5 percent higher productivity, and employment of 34 percent more workers than firms that did not adopt technology. B. Time Period: 1992-95 versus 1995-99 For all the firm performance measures we find a marked change in the influence of technology between 1992-95 and 1995-99. Technology relation with wage and productivity performance is significantly larger in the latter period than in the earlier period. The only exceptions are for wage performance in firms located in the Center and South regions. Firms adopting technology are associated with 51 percent higher wages in the early period, and 56 percent higher wages in the later period, than firns that did not adopt technology. Although the net employment measurement for all firms appears to contradict this trend, net employment is not significant in the later period. The relation of technology with job creation, measured as the number of new hires in a given year, is higher for the 1995-99 period than for the 1992-95 period. Moreover, technology is positively associated with job destruction, measured as the numbers of dismissals in a given year, in the 1992-95 period, while there is no significant relation in the 1995-99 period. In only two statistically significant cases the relation of technology with firms' performance was higher in the early period than the latter. In the Center and South of Mexico, technology was less effective in 1995-99 than in 1992-95. In the North, the change in the wage performance between time periods was 32 percent; in the Capital, the change was 29 percent; in the Center, the change was -56 percent, and in the South it was -166 percent. We should note that in both periods technology still is associated with higher wages, but in the Center and South technology is related to wages by a smaller percentage in the later period than in the earlier period. Much of Mexico's trade-dependent industry is in the North near the U.S. border and in 10 the Capital. It may be that these industries were more affected by liberalization and the 1994 crisis, and so the increased competition they experienced added to the value of technology for them. C. Firm Size Technology is associated with higher wages in all firm sizes, but for the 1992-95 and 1995-99 periods, the relation between technology and wage performance positively correlates with firm size. However, for the overall period (1992-99), the relation of technology with wage performance is larger for smaller firms. Closer analysis of firm size paints a different picture. We ran several regressions where the dependent variable, rather than total wages, was the wages of a particular skill group. These regressions clarify the relation of technology with different types of workers. We proceeded to conduct separate analyses for small firms only, for medium firms only, and for large firms only. These analyses suggest a robust conclusion for the 1992-99 period. For a worker of any single skill group, technology negatively correlates with firm size. For highly skilled workers, small technology firms are associated to a wage increase of 213 percent, medium technology firms of 153 percent, and large technology firms of 132 percent. For low skilled workers, small technology firms are related to a wage increase of 226 percent, medium technology firms are related to a wage increase of 178 percent, and large technology firms are related to a wage increase of 161 percent. Wages for semi-skilled workers experience similar differences. It appears that for large firms relative to small ones, technology increases employment to some extent but decreases wages. In absolute terms, technology increases wages and employment in both small and large firms, but its relative effect differs between firm sizes. The relation of technology with the performance of a firm's productivity also positively correlates with firm size. For medium-size firms, technology is associated with a 20 percent effect on productivity, while for large firms it is 23 percent. 11 D. Firm Location No regional relationship exists in the first time period, but in the later period, firms located in the Capital or close to the U.S. border, present the largest effect of technology on performance. In the 1995-99 period, technology firms in the North are associated to a 58 percent wage increase over their non-technology peers; firms in the Capital are associated to a 65 percent benefit, firms in the Center are associated to a 56 percent benefit, and firms in the South are associated to a 47 percent benefit. For productivity, Northern technology firms are related to a 71 percent benefit, Capital technology firms are related to a 49 percent benefit, and Central technology firms are related to a 46 percent benefit. However, in the earlier period, this trend was reversed: Northern technology firms were associated with a 26 percent wage benefit, Capital technology firms were associated with a 36 percent benefit, Central firms were associated with a 112 percent benefit, and Southern firms were associated with a 213 percent benefit. For the complete 1992-99 period, the highest relation between productivity and technology is for the Northern firms (40 percent), and the highest relation between technology and wages is for the Capital firms (156 percent). 5. A Joint Estimation for Wage Performance and Technology Adoption In addition to the association between TA and firm performance we took into account the causality between TA and firm perfornance. Therefore, we conducted a joint estimation for the TA and worker wages equations using a three-stage least squares method. Since this paper investigates the relation of technology with firm performance rather than the determinants of TA, we only show results for the regression with worker wages as dependent variable (Table 2). These results present expected findings. Technology is related to wages by quite large amounts in all three-time periods. However, surprisingly, we find that this relation is larger for the 1992-95 period than for the 1995-99 period. Larger firns paid higher wages than smaller firms in the later period, though in the first period (1992-95) smaller firms appeared to pay higher wages than large firms. 12 Table 2. Joint Estimation for Wage Performance and Technology Adoption Dependent Variable: 1992-95 1995-99 1992-99 Log(Total Wages) Coeff. Z-St. Coeff. Z-St. Coeff. Z-St. Firm size: Small -0.7557 ** -9.840 -0.6829 -1.528 -2.3483 ** -3.540 Medium -1.3218 ** -9.249 0.0216 0.048 -1.9225 ** -2.733 Large -2.7490 ** -11.724 0.9890 ** 2.113 -0.9771 -1.345 Age 0.0119 ** 6.878 0.0063 ** 5.830 0.0075 ** 5.474 Share of labor: Semi-skilled 0.0123 ** 3.911 0.0152 ** 3.319 0.0266 ** 3.445 Low skilled 0.0077 ** 2.516 0.0091 ** 2.012 0.0226 ** 2.923 Maquila 0.0055 0.094 0.0381 0.727 -0.0192 -0.319 Technology adoption 4.2530 ** 9.416 2.3211 ** 6.723 2.6770 ** 4.523 Year: 1995 -5.6625 ** -51.945 -5.9910 ** -48.656 1999 0.2261 ** 2.462 -5.8385 ** -94.480 Constant 8.5051 ** 18.443 3.1167 ** 6.019 9.5515 ** 9.918 Number of obs. 6,425 3,388 3,141 R-sq (Technology adoption) 0.1455 0.1028 0.0770 R-sq (Wage Performance) | 0.7244 0.0771 0.8449 * Significant at 10% level; **Significant at 5% level. 6. Wage Inequality To estimate the effect of TA on wage inequality, we estimate fixed effects models where the dependent variable is the logarithm of the wages of skilled workers divided by the wages of unskilled workers. We run two regressions: one for the logarithm of the ratio of highly skilled workers' wages to unskilled workers' wages, and another for the logarithm of the ratio of semi- skilled workers' wages to unskilled workers' wages. Table 3 shows that, controlling for relevant firm characteristics; technology has exacerbated the wage gap between semi-skilled and unskilled workers by about eleven percent in the seven years of our sample. Additionally, the higher the overall skill level of a firm, the larger the wage gap between skilled and unskilled workers. We also find that smaller firms have worse wage inequality than larger firms in the 1992-95 period. Results for wage inequality between highly skilled and unskilled workers appear in Table A2.1. TA worsens wage inequality between highly skilled and unskilled workers in all three periods, but results are statistically insignificant. However, as in the first case, the higher the overall skill level of a firm, the larger the wage gap between highly skilled and unskilled workers. 13 Table 3. Wage Inequality Dependent variable: 1992-95 1995-99 1992-99 Log(semi-skilled/unskilled waves) Coeff. Z-St. Coeff. Z-St. Coeff. Z-St. Firm Characteristics Size: Small 3.3306 * 1.786 -0.8229 -1.069 -0.8450 -0.769 Medium 1.8374 1.137 -0.9877 -1.277 -0.8427 -0.762 Large -1.9115 ** -1.987 -0.9186 -1.186 -0.7914 -0.714 Age -0.0485 ** -5.125 0.0066 ** 2.516 -0.0061 ** -2.306 Employees: Highly skilled 0.0045 ** 2.197 0.0007 0.336 0.0049 ** 2.113 Semi-skilled 0.0015 ** 13.610 0.0025 ** 15.259 0.0022 ** 14.74 Unskilled -0.0016 ** -15.263 -0.0025 ** -15.607 -0.0025 ** -17.512 Maquila -0.1802 -1.428 0.0084 0.093 0.0776 0.872 Technology adoption 0.0059 0.136 0.1270 ** 2.064 0.1136 ** 2.145 Constant 0.5366 0.535 1.3566 * 1.748 1.7584 1.585 ......... ..... ............ ............ . . . .... ............ .............. .. ........... ...... ... ...... . ...... . ... . .... ....................... . . ......................... . ............... ..... ........ .... ... .- -... ............ .......... Number of obs. 5,733 3,075 2,910 R-sq: Within 0.1518 0.2985 0.2962 Between 0.0117 0.3432 0.3792 Overall 0.0127 0.3297 0.3436 Significant at 10% level; **Significant at 5% level. 7. Conclusions Using a panel of firms with observations in 1992, 1995, and 1999, this paper has sought to understand how new technology correlates with the performance of Mexican manufacturing firms, measured by wages, productivity, net employment, job creation, and job destruction. We use fixed effects models to estimate firm performance and determine wage inequality. Results suggest that controlling for relevant variables, technology is positively related to firm performance. Trade liberalization and the 1994 crisis magnified this relation. The effect of new technology on firm performance also correlates positively and strongly with firm size, and proximity to the U.S. border or location in Mexico City. 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Volume Two: Technical Papers. Poverty Reduction and Economic Management Unit, Mexico Department. ---------. 2000. "Eamings Inequality after Mexico's Economic and Educational Reforms." Report No. 19945-ME (Gray Cover), May. 16 ANNEX 1: Firm Performance Fixed Effects Estimations Table Al.l. Wa e Performance of Manufacturin Firms Dependent variable: 1992-1995 1995-1999 1992-1999 log(total wages) Coeff. t-Stat. Coeff. t-Stat. Coeff. t-Stat. Firm Characteristics Size: Small -11.6052 ** -5.456 0.2905 0.672 1.9815 0.814 Medium -24.5036 ** -12.423 0.7041 1.631 2.3509 0.960 Large -50.4489 ** -31.028 1.1036 ** 2.559 2.3329 0.953 Age -2.0664 ** -143.300 0.0105 ** 6.150 -0.1000 ** -11.300 Employees: Highly skilled 0.0029 0.891 0.0054 ** 3.957 -0.0161 ** -2.005 Semi-skilled 0.0008 ** 5.068 0.0016 ** 17.057 0.0015 ** 3.870 Low skilled 0.0008 ** 5.306 0.0011 ** 11.233 0.0031 ** 6.677 Maquila -0.0773 -0.396 -0.0188 -0.315 -0.2846 -0.914 Technology adoption 0.5058 ** 7.472 0.5594 ** 13.983 1.2417 ** 6.554 Constant 75.0886 ** 59.810 4.6725 ** 10.834 8.0708 ** 3.294 Number of obs. 6,425 3,403 3,184 R-sq: within 0.8865 0.3514 0.0959 between 0.0131 0.6732 0.0441 overall 0.0162 0.5944 0.0369 * Significant at 10% level; ** Significant at 5% level. Table A1.2. Wage Performance of Highly Skilled Workers in Manufacturing Firms Dependent variable: 1992-1995 1995-1999 1992-1999 log(wages for Coeff. t-Stat. Coeff. t-Stat. Coeff. t-Stat. highly skilled workers) Firm Characteristics Size: Small -10.4433 ** -8.442 -1.4432 ** -2.349 0.6486 0.218 Medium dropped -1.0629 * -1.727 0.8157 0.274 Large -37.1335 ** -24.185 -0.8533 -1.386 0.5432 0.183 Age -2.0898 ** -133.917 0.0100 ** 3.937 -0.1030 ** -10.616 Employees: Highly skilled 0.0367 ** 9.147 0.0212 ** 10.556 0.0026 0.285 Semi-skilled 0.0007 ** 2.902 0.0015 ** 8.833 0.0007 * 1.677 Low skilled 0.0006 ** 2.908 0.0010 ** 5.858 0.0026 ** 5.033 Maquila 0.1374 0.656 -0.0482 -0.536 -0.3140 -0.905 Technology adoption 0.2817 ** 3.870 0.5265 ** 8.732 1.0614 ** 4.951 Constant 68.7051 ** 100.679 4.3511 ** 7.084 8.0569 ** 2.709 Number of obs. 5,091 2,860 2,787 R-sq: within 0.9093 0.2645 0.0890 between 0.0007 0.3563 0.0095 overall 0.0015 0.3450 0.0200 * Significant at 10% level; * Significant at 5% level. 17 Table A1.3. Wage Performance of Semi-Skilled Workers in Manufacturing Firms Dependent variable: 1992-1995 1995-1999 1992-1999 log(wages for Coeff. t-Stat. Coeff. t-Stat. Coeff. t-Stat. semi-skilled workers) Firm Characteristics Size: Small -15.7456 ** -7.043 0.8519 1.300 2.6544 0.876 Medium -30.3552 ** -14.189 1.1929 * 1.813 3.0990 1.011 Large -51.0929 ** -30.787 1.6078 ** 2.440 3.1096 1.014 Age -2.0843 ** -138.594 0.0128 ** 6.341 -0.1010 ** -11.240 Employees: Highly skilled 0.0007 0.219 0.0024 1.495 -0.0190 ** -2.324 Semi-skilled 0.0012 ** 7.515 0.0021 ** 19.023 0.0018 ** 4.647 Low skilled 0.0004 ** 2.388 0.0005 ** 3.836 0.0025 ** 5.308 Maquila -0.0358 -0.178 -0.0429 -0.605 -0.2572 -0.814 Technology adoption 0.4981 ** 7.065 0.5866 ** 12.357 1.2722 ** 6.617 Constant 77.0467 ** 58.036 3.4413 ** 5.219 6.7411 ** 2.192 Number of obs. 6,230 3,380 3,177 R-sq: within 0.8845 0.3251 0.0919 between 0.0086 0.5735 0.0266 overall 0.0125 0.5150 0.0325 * Significant at 10% level; ** Significant at 5% level. Table A1.4. Wage Perform nce of Low Skilled Workers in Manufacturing Firms Dependent variable: 1992-1995 1995-1999 1992-1999 log(wages for low Coeff. t-Stat. Coeff. t-Stat. Coeff. t-Stat. skilled workers) Firm Characteristics Size: Small -2.6941 -0.980 -0.0745 -0.121 1.8284 0.603 Medium 1.4820 0.622 0.4479 0.728 2.1268 0.700 Large -51.8257 ** -36.558 0.7639 1.241 2.1840 0.719 Age -2.1426 ** -155.520 0.0060 ** 2.539 -0.1122 ** -10.903 Employees: Highly skilled -0.0033 -1.089 0.0021 1.143 -0.0268 ** -2.954 Semi-skilled -0.0003 * -1.831 0.0001 0.375 0.0011 ** 1.957 Low skilled 0.0019 ** 12.157 0.0028 ** 19.271 0.0046 ** 8.182 Maquila 0.1674 0.900 -0.0392 -0.474 -0.3695 -1.069 Technology adoption 0.2861 ** 4.547 0.4271 ** 7.595 1.2529 ** 6.088 Constant 67.9290 ** 46.358 3.7585 ** 6.109 7.0532 ** 2.323 Number of obs. 5,896 3,095 2,916 R-sq: within 0.9131 0.2863 0.1108 between 0.0015 0.5918 0.0972 overall 0.0066 | 0.5154 0.0565 * Significant at 10% level; ** Significant at 5% level. 18 Table A1.5. Productivity Performance of Manufacturing Firms Dependent variable: 1992-1995 1995-1999 1992-1999 Log(productivity) Coeff. t-Stat. Coeff. t-Stat. Coeff. t-Stat. Firm Characteristics Size: Small dropped 3.0028 ** 2.525 3.3980 ** 3.509 Medium -3.7131 ** -5.359 2.6162 ** 2.216 3.1127 ** 3.234 Large dropped 2.6404 ** 2.243 3.0252 ** 3.150 Age 0.1150 ** 19.084 0.0130 ** 4.012 0.0183 ** 8.095 Employees: Highly skilled 0.0006 0.508 0.0021 0.693 0.0059 ** 3.117 Semi-skilled -0.0003 ** -5.388 -0.0001 -0.317 0.0001 0.831 Low skilled -0.0004 ** -5.818 0.0001 0.366 -0.0003 ** -2.753 Maquila 0.0600 0.823 -0.2120 * -1.731 -0.0323 -0.398 Technology adoption 0.0549 ** 2.051 0.5360 ** 7.022 0.2577 ** 5.355 Capital assets 0.0000 -0.595 1.4e-06 ** 2.872 1.9e-06 ** 7.450 Constant 1.0192 ** 5.208 1.0563 0.902 0.4836 0.505 Number of obs. 3,894 2,101 2,714 R-sq: within 0.2182 0.1261 0.1083 between 0.0142 0.0953 0.1 126 overall 0.0146 0.1079 0.1050 * Significant at 10% level; ** Significant at 5% level. Table A1.6. Net Employment Performance of Manufacturing Firms Dependent variable: 1992-1995 1995-1999 1992-1999 log(net employment) Coeff. t-Stat. Coeff. t-Stat. , Coeff. t-Stat. Firm Characteristics Size: Small dropped 0.8657 0.750 -0.0065 -0.017 Medium dropped 1.5397 1.325 -0.1841 -0.915 Large dropped 1.5874 1.361 dropped Age 0.0139 0.353 -0.0053 -0.567 -0.0025 -0.337 Employees: Highly skilled -0.0078 -1.369 0.0172 ** 2.014 0.0104 ** 2.088 Semi-skilled 0.0013 * 1.909 0.0013 ** 2.580 0.0004 1.324 Low skilled 0.0017 ** 4.915 0.0006 1.028 0.0006 * 1.745 Maquila -1.2054 ** -2.132 0.2843 1.085 0.0446 0.201 Technologyadoption 0.3382 * 1.838 0.1130 0.617 0.0011 0.007 Constant 1.6938 1.611 0.5863 0.494 2.3674 ** 7.164 Number of obs. 1,680 1,323 1,158 R-sq: within 0.1260 0.1016 0.0374 between 0.0742 0.1132 0.1313 overall 0.0726 0.1177 0.1117 * Significant at 10% level; ** Significant at 5% level. 19 Table A1.7. Job Creation of Manufacturing Firms Dependent variable: 1992-1995 1995-1999 1992-1999 log(new hires) Coeff. t-Stat. Coeff. t-Stat. Coeff. t-Stat. Firm Characteristics Size: Small dropped 0.9377 0.885 dropped Medium -0.2618 -0.253 1.3384 1.261 0.3246 * 2.456 Large dropped 1.5009 1.414 0.4757 ** 0.187 Age -0.0360 ** -2.639 -0.0027 -0.772 0.0006 1.191 Employees: Highly skilled 0.0004 0.161 0.0023 0.912 0.0038 8.484 Semi-skilled 0.0015 ** 8.745 0.0013 ** 6.446 0.0013 ** 7.416 Low skilled 0.0014 ** 9.386 0.0009 ** 4.890 0.0013 ** 0.486 Maquila -0.0110 -0.056 -0.1353 -1.153 0.0559 1.334 Technology adoption 0.1846 ** 2.820 0.2189 * 2.732 0.0985 11.942 Constant 3.5437 ** 8.555 1.4705 1.382 2.3608 ** 1.884 Number of obs. 4,262 2,714 2,494 R-sq: within 0.0961 0.0804 0.0814 between 0.1819 0.1506 0.1388 overall 0.1657 0.1426 0.1133 Significant at 10% level; ** Significant at 5% level. Table A1.8. Job Destruction of Manufacturing Firms Dependent variable: 1992-1995 1995-1999 1992-1999 log(laidoffs) Coeff. t-Stat. Coeff. t-Stat. Coeff. t-Stat. Firm Characteristics Size: Small 0.4891 0.499 0.5472 0.542 -1.0652 ** -6.302 Medium 1.3311 1.317 -0.3599 ** -3.933 Large -2.7041 -1.587 1.5890 1.571 dropped Age -0.0046 -0.408 -0.0021 -0.741 -0.0074 ** -2.552 Employees: Highly skilled 0.0007 0.283 0.0010 0.434 0.0016 0.551 Semi-skilled 0.0005 ** 3.092 0.0006 ** 3.349 0.0008 ** 5.329 Low skilled 0.0007 ** 5.573 0.0006 ** 3.459 0.0007 ** 4.411 Maquila -0.1039 -0.682 -0.1767 * -1.716 -0.0286 -0.275 Technology adoption 0.1040 ** 2.007 -0.0277 -0.396 -0.0438 -0.702 Constant 3.0692 ** 5.866 1.8903 * 1.867 3.6234 ** 27.782 Number of obs. 5,076 2,885 2,723 R-sq: within 0.0236 0.0594 0.0576 between 0.2641 0.1831 0.2337 overall 0.2013 0.1658 0.1710 * Significant at 10% level; ** Significant at 5% level. 20 Table A1.9. Wage Performance of Small Manufacturing Firms Dependent variable: 1992-1995 1995-1999 1992-1999 log(total wages) Coeff. t-Stat. Coeff. t-Stat. Coeff. t-Stat. Firm Characteristics Age -2.2342 ** -130.885 0.0115 1.345 -0.6836 ** -7.114 Employees: Highly skilled 0.0144 * 1.841 0.0509 1.305 -0.2212 -1.215 Semi-skilled 0.0035 ** 5.862 0.0230 ** 5.595 -0.0034 -0.351 Low skilled 0.0019 ** 4.538 0.0140 ** 3.107 -0.0033 -0.394 Maquila 0.0946 0.497 0.0003 0.001 0.5370 0.308 Technology adoption 0.2284 ** 2.970 0.2756 * 1.808 1.9678 ** 2.909 Constant 66.7124 ** 140.760 3.2216 ** 9.437 24.5766 ** 9.567 Number of obs. 2,187 311 206 R-sq: within 0.9526 0.4274 0.4523 between 0.0056 0.3573 0.0414 overall 0.0061 0.3508 0.0346 * Significant at 10% level; ** Significant at 5% level. Table A1.10. Wage Performance of Highly Skilled Workers in Small Manufacturing Firms Dependent variable: 1992-1995 1995-1999 1992-1999 log(wages for Coeff. t-Stat. Coeff. t-Stat. Coeff. t-Stat. highly skilled workers) _ Firm Characteristics Age -2.1817 ** -102.417 0.0158 1.172 -0.6266 ** -5.681 Employees: Highly skilled 0.0551 ** 6.100 0.2484 ** 3.904 -0.0201 -0.085 Semi-skilled 0.0015 * 1.954 0.0142 * 1.984 -0.0073 -0.686 Low skilled 0.0006 1.134 0.0060 0.835 -0.0082 -0.883 Maquila 0.2111 0.866 -0.4179 -1.204 0.5077 0.275 Technology adoption 0.1329 1.381 0.2506 0.876 2.1315 ** 2.606 Constant 64.5873 ** 107.615 1.4061 ** 2.572 21.5287 ** 7.044 Number of obs. 1,838 254 172 R-sq: within 0.9423 0.3667 0.4151 between 0.0004 0.3362 0.0326 overall 0.0032 0.3179 0.0268 * Significant at 10% level; ** Significant at 5% level. Table Al.1 1. Wage Performance of Semi-Skilled Workers in Small Manufacturing Firms Dependent variable: 1992-1995 1995-1999 1992-1999 log(wages for Coeff. t-Stat. Coeff. t-Stat. Coeff. t-Stat. semi-skilled workers) Firm Characteristics Age -2.2457 ** -127.415 0.0151 1.551 -0.6718 ** -6.664 Employees: Highly skilled -0.0084 -1.036 -0.0133 -0.296 -0.2687 -1.381 Semi-skilled 0.0076 ** 12.233 0.0361 ** 7.740 0.0036 0.364 Low skilled -0.0003 -0.628 0.0069 1.337 -0.0094 -1.098 Maquila 0.0902 0.460 0.1015 0.381 0.5639 0.313 Technology adoption 0.2242 ** 2.830 0.2432 1.406 1.9052 ** 2.726 Constant 66.3022 ** 135.379 2.2876 ** 5.882 23.6665 ** 8.832 Number of obs. 2,181 305 205 R-sq: within 0.9515 I 0.5331 0.4427 between 0.0135 0.4256 0.0427 overall 0.0038 0.4240 0.0345 * Significant at 10% level; ** Significant at 5% level. 21 Table A1.12. Wage Performance of Low Skilled Workers in Small Manufacturin Firms Dependent variable: 1992-1995 1995-1999 1992-1999 log(wages for Coeff. t-Stat. Coeff. t-Stat. Coeff. t-Stat low skilled workers) Firm Characteristics Age -2.2078 ** -108.324 0.0107 1.156 -1.1020 ** -10.732 Employees: Highly skilled 0.0284 ** 2.977 0.0556 1.083 -0.3039 -1.633 Semi-skilled -0.0032 ** -4.211 -0.0041 -0.804 -0.0051 -0.517 Low skilled 0.0053 ** 10.661 0.0303 ** 6.279 0.0112 1.094 Maquila 0.1784 0.764 -0.0177 -0.077 1.3936 0.908 Technology adoption 0.2393 ** 2.630 0.3264 * 1.846 2.2553 ** 3.838 Constant 64.9057 ** 114.613 2.1844 ** 5.847 33.0652 ** 12.470 Number of obs. 2,053 275 186 R-sq: within 0.9374 0.5372 0.6569 between 0.0085 0.2551 0.0964 overall 0.0134 0.2747 0.0647 * Significant at 10O% level; ** Significant at 5% level. Table A1.13. Productivity Performance of Small Manufacturing Firms Dependent variable: 1992-1995 1995-1999 1992-1999 log(productivity) Coeff. t-Stat. Coeff. t-Stat. Coeff. t-Stat. Firm Characteristics Age 0.0966 ** 11.531 -0.0012 -0.029 0.0907 ** 3.441 Employees: Highly skilled 0.0066 * 1.954 -0.6451 * -2.241 0.0271 0.797 Semi-skilled -0.0014 ** -5.169 0.0134 0.877 -0.0066 ** -2.751 Low skilled -0.0011 ** -4.089 0.0287 1.752 -0.0015 -0.578 Maquila -0.0112 -0.136 -0.7320 -1.038 0.4251 1.048 Technology adoption 0.0773 ** 2.226 0.3747 0.900 -0.0229 -0.155 Capital assets 0.0000 -0.829 0.0000 -0.006 0.0000 -1.047 Constant 1.2966 ** 5.281 4.2832 1.200 2.3305 ** 3.250 Number of obs. 1,605 150 132 R-sq: within 0.2302 0.6767 0.4344 between 0.0058 0.0956 0.0018 overall 0.0069 0.0753 0.0029 * Significant at 10% level; ** Significant at 5% level. Table A1.14. Net Employment Performance of Small Manufacturing Firms Dependent variable: 1992-1995 1992-1999 log(net employment) Coeff. t-Stat. Coeff. t-Stat. Firm Characteristics Age -0.1090 * -1.922 -0.0243 -0.160 Employees: Highly skilled 0.0424 1.308 -0.0788 -0.198 Semi-skilled 0.0016 0.951 0.0179 1.152 Low skilled 0.0031 ** 5.065 0.0158 1.437 Maquila 0.3343 0.523 dropped Technology adoption 0.1736 0.675 -0.1965 -0.264 Constant 4.0638 ** 2.889 1.1206 0.323 Number of obs. 585 64 R-sq: within 0.3118 0.3302 between 0.0580 0.3032 overall 0.0729 0.3490 Note: Estimation for 1995-1999 was not possible due to insufficient observations. * Significant at 10% level; ** Significant at 5% level. 22 Table A1.15. Wage Performance of Medium-size Manufacturing Firms Dependent variable: 1992-1995 1995-1999 1992-1999 log(total wages) _ Coeff. t-Stat. | Coeff. t-Stat. Coeff. t-Stat. Firm Characteristics Age -2.2438 ** -99.936 0.0152 ** 5.117 -0.2996 ** -12.433 Employees: Highlyskilled 0.0734 ** 2.698 0.0177 ** 2.749 -0.1136 ** -3.200 Semi-skilled 0.0088 ** 4.733 0.0075 ** 14.137 0.0072 ** 2.756 Low skilled I 0.0072 ** 4.647 0.0065 ** 10.995 0.0014 0.514 Maquila 0.0037 0.014 -0.0414 -0.450 -0.1122 -0.196 Technology adoption 0.2711 ** 2.981 0.4696 ** 8.295 1.6908 ** 5.547 Constant 56.1743 ** 107.432 4.2907 ** 40.263 15.4095 ** 19.297 Number of obs. I,139 1,524 1,298 R-sq: within 0.9587 0.4770 0.2217 between 0.0024 0.3347 0.0001 overall 0.0068 0.3487 0.0177 Significant at 10% level: ** Significant at 50/0 level. Table Al.16. Wage Performance of Highly Skilled Workers in Medium-sized Manufacturing Firms Dependent variable: 1992-1995 1995-1999 1992-1999 log(wages for hi wghlyskilled works Coeff. t-Stat. Coeff. t-Stat. Coeff. t-Stat. _highly skilll_ed workers) T Firm Characteristics t Age -2.2079 ** -74.638 0.0153 ** 3.291 -0.2841 ** -10.799 Employees: Highly skilled 0.2540 ** 7.447 0.0866 ** 8.225 -0.0854 ** -2.144 Semi-skilled -0.0003 -0.111 0.0045 ** 5.417 0.0048 * 1.653 Low skilled 0.0048 ** 2.187 0.0053 ** 5.590 -0.0010 -0.318 Maquila 0.2786 0.775 -0.1394 -0.967 -0.2488 -0.388 Technology adoption i0.3023 ** 2.555 0.4374 ** 4.941 1.5258 ** 4.448 Constant 55.0889 ** 77.814 2.3202 ** 13.633 13.5562 ** 15.601 Number of obs. 894 . - 1,285 1,133 R-sq: within 0.9502 0.3287 0.2003 between 0.0145 0.2144 0.0010 overall 0.0139 0.2318 0.0116 * Significant at 10% level, ** Significant at 5% level. Table A1.17. Wage Performance of Semi-Skilled Workers in Medium-sized Manufacturing Firms Dependent variable: 1992-1995 1995-1999 1992-1999 log(wages for semi-skilled workers) Coeff. t-Stat. Coeff. t-Stat Coeff. t-Stat. Firm Characteristics Age -2.2533 ** -91.120 0.0165 ** 4.701 -0.3018 ** -12.375 Employees: Highly skilled 0.0236 0.791 -0.0040 -0.534 -0.1374 ** -3.825 Semi-skilled 0.0187 ** 8.892 0.0114 ** 18.195 0.0108 ** 4.054 Low skilled -0.0007 -0.404 0.0029 ** 4.114 -0.0023 -0.811 Maquila 0.0164 0 058 -0.1558 -1.438 -0.1340 -0.231 Technology adoption 0.2269 ** 2.263 0.4664 ** 6.954 1.6805 ** 5.448 Constant 55.6548 * 96611 3.5941 ** 28.372 14.8949 ** 18.426 Number of obs. 1,131 1,516 1,297 R-sq: within 0.9522 0.4960 0.2354 between 0.0021 0.43 13 0.0004 overall 0.0063 0.4443 0.0212 * Significant at 10% level, ** Significant at 5% level. 23 Table A1.18. Wage Performance of Low Skilled Workers in Medium-size Manufacturing Firms Dependent variable: 1992-1995 1995-1999 1992-1999 log(wages for CoeM t-Stat. Coeff. t-Stat. Coeff. t-Stat. low skilled workers) Firm Characteristics Age -2.1985 ** -78.789 0.0057 1.562 -0.3750 ** -13.232 Employees: Highly skilled 0.0207 0.627 0.0101 1.152 -0.1133 ** -2.627 Semi-skilled 0.0020 0.734 0.0008 1.228 0.0058 ** 2.035 Low skilled 0.0238 ** 10.281 0.0144 ** 19.679 0.0086 ** 2.893 Maquila 0.1101 0.344 0.0070 0.063 -0.0661 -0.109 Technology adoption 0.2145 * 1.920 0.3948 ** 5.739 1.7769 ** 5.623 Constant 54.0983 ** 82.181 3.0757 ** 23.254 15.4893 ** 17.432 Number of obs. 1,042 1,404 1,196 R-sq: within 0.9443 0.5012 0.2632 between 0.0008 0.4903 0.0034 overall 0.0091 0.4860 0.0210 * Significant at 10% level; ** Significant at 5% level. Table A1.19. Productivity Performance of Medium-size Manufacturing Firms Dependent variable: 1992-1995 1995-1999 1992-1999 log(productivity) Coeff. t-Stat. Coeff. t-Stat. Coeff. t-Stat. Firm Characteristics Age 0.0725 ** 3.330 0.0200 ** 3.508 0.0385 ** 8.168 Employees: Highly skilled -0.0184 -0.834 -0.0005 -0.042 0.0127 * 1.749 Semi-skilled -0.0035 ** -2.448 0.0018 * 1.895 -0.0002 -0.426 Low skilled -0.0029 ** -2.486 0.0029 ** 2.301 -0.0003 -0.447 Maquila 0.1047 0.389 -0.2818 -1.527 -0.1210 -1.000 Technology adoption 0.0839 0.998 0.3778 ** 3.604 0.2025 ** 3.202 Capital assets -2.le-05 ** -2.111 0.0001 ** 6.928 0.0001 ** 12.179 Constant 2.3731 ** 4.468 2.7011 ** 12.591 2.5359 ** 15.750 Number of obs. 439 919 1,083 R-sq: within 0.1749 0.3774 0.3331 Between 0.0163 0.0239 0.1895 Overall 0.0047 0.0359 0.1659 * Significant at 10% level; ** Significant at 5% level. Table A1.20. Net Employment Performance of Medium-size Manufacturing Firms Dependent variable: 1992-1995 1995-1999 1992-1999 log(net employment) Coeff. t-Stat. Coeff. t-Stat. Coeff. t-Stat. Firm Characteristics Age -0.0849 -0.761 -0.0193 -0.594 0.0075 0.404 Employees: Highly skilled 0.5085 ** 2.694 -0.0041 -0.066 0.0114 0.349 Semi-skilled 0.0073 0.510 0.0077 ** 2.272 0.0023 1.027 Low skilled 0.0099 1.296 0.0078 ** 2.116 0.0031 * 1.775 Maquila -1.3010 -0.852 0.2369 0.493 0.5642 * 1.670 Technology adoption 0.4949 1.136 -0.2620 -0.800 -0.0021 -0.008 Constant 1.7751 0.666 1.4846 * 1.728 1.1655 * 1.905 Number of obs. 267 573 449 R-sq: within 0.3957 0.1136 0.0708 between 0.0477 0.1014 0.0655 overall 0.0713 0.1029 0.0775 * Significant at 10% level; ** Significant at 5% level. 24 Table A1.21. Wage Performance of Large Manufacturing Firms Dependent variable: 1992-1995 1995-1999 1992-1999 log(total wages) Coeff. t-Stat. Coeff. t-Stat. Coeff. t-Stat. Firm Characteristics Age -2.2355 ** -44.339 0.0079 ** 3.314 -0.0742 ** -6.113 Employees: Highly skilled 0.1464 1.154 0.0037 * 1.770 -0.0112 -1.244 Semi-skilled 0.0077 0.616 0.0012 ** 12.535 0.0017 ** 3.995 Low skilled 0.0298 ** 2.158 0.0009 ** 8.512 0.0035 ** 6.869 Maquila -0.3268 -0.438 0.0089 0.092 -0.0182 -0.036 Technology adoption 0.3797 * 1.808 0.5302 ** 7.590 1.4971 ** 4.837 Constant 41.6300 ** 48.372 6.1945 ** 59.762 9.4357 ** 16.680 Number of obs. 581 1,560 1,560 R-sq: within 0.9024 0.3194 0.3194 between 0.0023 0.5163 0.5163 overall 0.0037 0.4812 0.4812 Significant at 10% level; t* Significant at 5% level. Table A1.22. Wage Performance of Highly Skilled Workers in Large Manufacturing Firms Dependent variable: 1992-1995 1995-1999 1992-1999 log(wages for Coeff. t-Stat. Coeff. t-Stat. Coeff. t-Stat. highly skilled workers) Firm Characteristics Age -2.2503 ** -24.594 0.0070 ** 1.968 -0.0775 ** -5.947 Employees: Highly skilled 0.3149 1.560 0.0310 ** 8.194 0.0090 0.903 Semi-skilled 0.0011 0.078 0.0012 ** 6.963 0.0010 * 2.073 Low skilled 0.0125 0.742 0.0008 ** 4.537 0.0031 ** 5.582 Maquila -0.6012 -0.457 -0.1295 -0.855 0.0385 0.069 Technology adoption 0.5272 1.507 0.5526 5.137 1.3165 ** 3.826 Constant 43.4315 ** 27.187 3.6018 ** 23.058 7.6658 ** 12.304 Number of obs. 261 1,316 1,480 R-sq: within 0.9299 0.3222 0.1016 between 0.0008 0.2641 0.0304 overall 0.0040 0.2827 0.0421 * Significant at 10% level; ** Significant at 5% level. Table A1.23. Wage Performance of Semi-Skilled Workers in Large Manufacturing Firms Dependent variable: 1992-1995 1995-1999 1992-1999 log(wages for Coeff. t-Stat. Coeff. t-Stat. CoeM t-Stat. semi-skilled workers) Firm Characteristics Age -2.2117 ** -32.023 0.0087 ** 3.105 -0.0741 ** -6.053 Employees: Highly skilled -0.2247 -1.340 0.0002 0.085 -0.0134 -1.480 Semi-skilled 0.1459 ** 5.411 0.0016 ** 14.254 0.0019 ** 4.509 Low skilled 0.0136 0.868 0.0003 ** 2.904 0.0030 ** 5.893 Maquila 0.2785 0.283 0.0049 0.043 -0.0312 -0.061 Technology adoption 0.4442 1.626 0.4974 ** 6.044 1.5389 ** 4.925 Constant 38.1168 ** 31.355 5.6114 ** 45.972 8.8774 ** 15.551 Number of obs. 408 1,554 1,672 R-sq: within 0.8967 0.3222 0.0963 between 0.0104 0.4492 0.0284 overall 0.0155 0.4290 0.0428 * Significant at 10% level; ** Significant at 5% level. 25 Table A1.24. Wage Performance of Low Skilled Workers in Large Manufacturing Firms Dependent variable: 1992-1995 1995-1999 1992-1999 log(wages for CoefE t-Stat. Coeff. t-Stat. Coeff. t-Stat. low skilled workers) .t Firm Characteristics Age -2.2318 ** -34.646 0.0049 1.375 -0.0809 ** -5.869 Employees: Highly skilled -0.2194 -1.346 0.0036 1.085 -0.0222 ** -2.194 Semi-skilled 0.0325 0.994 0.0000 0.059 0.0015 ** 2.448 Low skilled 0.0707 ** 4.388 0.0022 ** 13.583 0.0048 ** 7.704 Maquila -1.1808 -0.994 0.0876 0.590 0.1566 0.278 Technology adoption 0.0688 0.273 0.4242 ** 3.910 1.6095 ** 4.788 Constant 40.1928 ** 38.168 4.8480 ** 30.899 7.8986 ** 12.351 Number of obs. 417 1,411 1,532 R-sq: within 0.9085 0.2958 0.1240 between 0.0018 0.5015 0.1068 overall 0.0090 0.4555 0.0791 * Significant at 10% level; ** Significant at 5% level. Table Al.25. Productivity Performance of Large Ma ufacturing Firms Dependent variable: 1992-1995 1995-1999 1992-1999 log(productivity) Coeff. t-Stat. Coeff. t-Stat. Coeff. t-Stat. Firm Characteristics Age 0.6864 0.621 0.0100 ** 2.225 0.0147 ** 5.045 Employees: Highly skilled 0.5336 0.861 0.0033 0.874 0.0053 ** 2.649 Semi-skilled 0.0895 0.378 0.0002 0.806 0.0000 -0.045 Low skilled -0.1694 -0.401 0.0002 0.828 -0.0004 ** -3.319 Maquila dropped -0.2318 -1.126 -0.0467 -0.392 Technology adoption -0.5443 -0.638 0.4122 ** 3.124 0.2271 ** 3.188 Capital assets 0.0069 0.223 9.6e-07 * 1.835 0.0000 ** 6.767 Constant -11.0303 -0.291 3.8258 ** 18.679 3.7894 ** 28.237 Number of obs. 14 1,031 1,498 R-sq: within 0.9583 0.0838 0.1216 between 0.4637 0.1333 0.0995 overall 0.3386 0.1351 0.1364 * Significant at 10% level; ** Significant at 5% level. Table A1.26. Net Employm ent Performance of Large Manufacturing Firms Dependent variable: 1992-1995 1995-1999 1992-1999 log(net employment) Coeff. t-Stat. Coeff. t-Stat. Coeff. t-Stat. Firm Characteristics Age 0.0210 0.100 -0.0030 -0.211 -0.0116 -1.015 Employees: Highly skilled 0.0361 0.067 0.0152 * 1.764 0.0122 ** 2.141 Semi-skilled 0.1342 1.205 0.0005 1.003 0.0002 0.591 Low skilled 0.1779 1.877 0.0003 0.391 0.0003 0.641 Maquila dropped 0.3862 0.881 -0.6237 -1.575 Technology adoption 0.0238 0.023 0.2741 0.889 -0.4370 -1.459 Constant -2.2565 -0.658 2.4349 ** 4.351 3.5170 ** 6.645 Number of obs. 53 647 645 R-sq: within 0.8803 0.0741 0.0783 between 0.1151 0.0437 0.0084 overall 0.1402 0.0487 0.0229 * Significant at 10% level; ** Significant at 5% level. 26 Table A1.27. Wage Performance of Manufacturing Firms in the North Region Dependent variable: 1992-1995 1995-1999 1992-1999 log(total wages) Coeff. t-Stat. Coeff. t-Stat. Coeff. t-Stat. Firm Characteristics Age -2.1823 ** -116.448 0.0086 ** 2.165 -0.1282 ** -5.388 Employees: Highly skilled -0.0041 -1.152 0.0124 ** 3.536 -0.0080 -0.559 Semi-skilled 0.0007 ** 4.172 0.0019 ** 9.730 0.0013 1.337 Low skilled 0.0005 ** 3.868 0.0008 ** 4.823 0.0022 ** 2.131 Maquila -0.0744 -0.297 -0.0636 -0.493 -0.2001 -0.307 Technology adoption 0.2580 ** 2.938 0.5831 ** 6.038 0.6985 1.567 Constant 57.7898 ** 127.590 5.5342 ** 40.187 11.8802 ** 12.658 Number of obs. 1,733 800 630 R-sq: within 0.9467 0.3195 0.0819 Between 0.0110 0.6364 0.0007 Overall 0.0007 0.5662 0.0082 * Significant at 10% level; ** Significant at 5% level. Table A1.28. Productivity Performance of Manufacturing Firms in the North Region Dependent variable: 1992-1995 1995-1999 J 1992-1999 log(productivity) Coeff. t-Stat. Coeff. t-Stat. Coeff. t-Stat. Firm Characteristics Age 0.0695 ** 4.691 0.0060 0.759 0.0118 ** 2.280 Employees: Highly skilled -0.0022 -0.496 -0.0018 -0.345 -0.0005 -0.147 Semi-skilled -0.0005 ** -2.597 0.0003 0.599 0.0000 -0.046 Low skilled -0.0003 ** -2.562 0.0002 0.365 -0.0005 ** -2.240 Maquila -0.0822 -0.458 -0.1802 -0.656 -0.1089 -0.710 Technology adoption -0.0368 -0.535 0.7089 ** 3.958 0.4051 ** 3.891 Capital assets 0.0000 0.511 2.1e-06 ** 2.267 2.4e-06 ** 5.659 Constant 2.1514 ** 4.598 3.6255 ** 11.915 3.7589 ** 17.786 Number of obs. 740 454 550 R-sq: within 0.1280 0.2178 0.1700 Between 0.0000 0.1035 0.2205 Overall 0.0002 0.1306 0.1841 * Significant at 10% level; ** Significant at 5% level. Table A1.29. Net Employment Performance of Manufacturing Firms in the North Region Dependent variable: 1992-1995 1995-1999 1 1992-1999 log(net employment) Coeff. t-Stat. Coeff. t-Stat. Coeff. t-Stat. Firm Characteristics Age 0.0777 1.106 -0.0147 -0.543 0.0033 0.144 Employees: Highly skilled -0.0087 -1.389 0.0853 ** 2.095 0.0131 1.215 Semi-skilled 0.0014 1.404 0.0011 0.923 0.0002 0.327 Low skilled 0.0014 ** 3.282 0.0010 1.006 0.0011 1.290 Maquila 1.4577 0.828 0.6246 1.201 0.6038 1.158 Technology adoption 0.5536 1.636 0.0501 0.109 -0.0097 -0.023 Constant -0.5939 -0.357 1.9476 ** 2.134 2.0121 ** 2.372 Number of obs. 521 318 211 R-sq: within 0.1967 0.2368 0.0893 between 0.2021 0.0697 0.1108 overall 0.2014 0.0766 0.1251 * Significant at 10% level; ** Significant at 5% level. 27 Table A1.30. Wage Performance of Manufacturing Firms in the Central Region l Dependent variable: 1992-1995 1995-1999 1992-1999 log(total wages) Coeff. t-Stat. Coeff. t-Stat. Coeff. t-Stat. Firm Characteristics Age -1.7333 ** -58.310 0.0169 ** 6.179 -0.1269 ** -8.762 Employees: Highly skilled 0.0126 * 1.801 0.0017 1.032 -0.0351 ** -2.446 Semi-skilled 0.0028 ** 5.976 0.0022 ** 13.508 0.0026 ** 3.517 Low skilled 0.0027 ** 5.906 0.0022 ** 12.070 0.0042 ** 6.105 Maquila 0.0003 0.001 -0.0290 -0.337 0.0076 0.017 Technology adoption 1.1191 ** 7.611 0.5582 ** 10.105 1.3955 ** 5.212 Constant 52.3282 ** 63.035 5.1170 ** 55.399 10.4436 ** 20.247 Number of obs. 2,884 1,510 1,612 R-sq: within 0.7655 0.3794 0.1230 Between 0.0667 0.5754 0.1065 overall 0.0009 0.5222 0.0565 * Significant at 10% level; ** Significant at 5% level. Table A1.31. Productivity Performance of Manufacturing Firms in the Central Region Dependent variable: 1992-1995 1995-1999 1992-1999 log(productivity) Coeff. t-Stat. Coeff. t-Stat. Coeff. t-Stat. Firm Characteristics Age 0.1274 ** 16.716 0.0127 ** 2.269 0.0230 ** 6.204 Employees: Highly skilled 0.0011 0.803 0.0035 0.409 0.0124 ** 3.390 Semi-skilled -0.0004 ** -3.944 0.0004 1.127 -0.0001 -0.431 Low skilled -0.0004 ** -4.591 0.0004 0.879 -0.0006 ** -3.161 Maquila 0.1187 1.285 -0.3592 ** -1.973 -0.2428 ** -1.987 Technology adoption 0.0947 ** 2.769 0.4634 ** 4.072 0.2552 ** 3.610 Capital assets 0.0000 -0.956 0.0000 -0.745 1.2e-06 ** 2.863 Constant 0.4943 ** 2.147 3.7676 ** 19.398 3.5785 ** 26.728 Number of obs. 1,981 951 1,353 R-sq: within 0.2862 0.0796 0.0892 Between 0.0075 0.0871 0.0616 overall 0.0099 0.0776 0.0692 Significant at 10% level; ** Significant at 5% level. Table A1.32. Net Employment Performance of Manufacturing Firms in the Central Region Dependent variable: 1992-1995 1995-1999 1992-1999 log(net employment) Coeff. t-Stat. Coeff. t-Stat. Coeff. t-Stat. Firm Characteristics Age -0.0545 -0.792 -0.0010 -0.062 -0.0023 -0.169 Employees: Highly skilled 0.0328 1.259 0.0257 1.314 0.0087 1.045 Semi-skilled 0.0011 0.672 0.0020 ** 2.776 0.0003 0.452 Low skilled 0.0019 * 1.948 0.0023 ** 2.194 0.0009 1.494 Maquila -2.2254 ** -2.763 0.2977 0.758 -0.4759 -1.400 Technology adoption -0.0467 -0.153 0.3821 1.503 0.1822 0.799 Constant 3.6011 * 1.814 1.2197 ** 2.556 2.1821 ** 4.741 Number of obs. 717 594 594 R-sq: within 0.1561 0.1382 0.1382 Between 0.0398 0.1335 0.1335 overall 0.0436 0.1429 0.1429 * Significant at 10% level; ** Significant at 5% level. 28 Table A1.33. Wage Performance of Manufacturin Firms in the South Region Dependent variable: 1992-1995 1995-1999 1992-1999 log(total wages) Coeff. t-Stat. Coeff. t-Stat. Coeff. t-Stat. Firm Characteristics Age -1.2012 ** -15.913 0.0203 ** 2.507 -0.0773 ** -2.254 Employees: Highly skilled 0.0256 0.904 0.0116 0.783 0.0144 0.199 Semi-skilled 0.0036 ** 2.089 0.0017 ** 3.281 0.0003 0.102 Low skilled 0.0084 ** 4.103 0.0015 ** 3.579 0.0040 1.327 Maquila -1.9240 -1.324 0.5831 ** 2.402 -0.7356 -0.377 Technology adoption 2.1293 ** 4.602 0.4658 ** 2.596 1.5689 1.598 Constant 38.8099 ** 17.518 4.9897 * 16.494 9.6685 ** 5.893 Number of obs. 391 218 151 R-sq: within 0.6829 0.3296 0.1005 Between 0.0708 0.4689 0.0125 overall 0.0056 0.4395 0.0142 * Significant at 10% level; ** Significant at 5% level. Table Al.34. Productivity Performance of Manufacturing Firms in the South Region Dependent variable: 1992-1995 1995-1999 1992-1999 log(Droductivity) Coeff. t-Stat. Coeff. t-Stat. Coeff. t-Stat. Firm Characteristics Age 0.0599 ** 2.011 0.0178 * 1.858 0.0210 ** 3.265 Employees: Highly skilled 0.0009 0.123 -0.0157 -0.741 -0.0172 -1.327 Semi-skilled -0.0007 * -1.718 -0.0010 -1.079 -0.0015 ** -2.684 Low skilled -0.0016 ** -2.682 0.0001 0.102 -0.0004 -0.619 Maquila -0.1024 -0.247 0.2675 0.423 0.3057 0.799 Technology adoption 0.0017 0.011 0.4959 1.553 0.1573 0.835 Capital assets 0.0000 0.182 2.0e-05 ** 2.220 2.2e-05 ** 4.681 Constant 2.4305 ** 2.344 3.2620 ** 6.591 3.5023 ** 10.629 Number of obs. 222 112 118 R-sq: within 0.1771 0.3938 0.4045 Between 0.0012 0.0898 0.0146 Overall 0.0027 0.1260 0.0918 * Significant at 10% level; ** Significant at 5% level. Table A1.35. Net Employment Performance of Manufacturing Firms in the South Region Dependent variable: 1992-1995 1995-1999 1992-1999 log(net employment) Coeff. t-Stat. Coeff. t-Stat. Coeff. t-Stat. Firm Characteristics Age 0.0131 0.068 -0.0996 -0.782 -0.0709 -1.416 Employees: Highly skilled 0.1076 1.038 -0.0655 -0.245 0.0764 0.917 Semi-skilled 0.0029 0.337 -0.0010 -0.351 0.0010 0.445 Low skilled 0.0074 1.684 0.0002 0.025 0.0062 1.445 Maquila -1.0224 -0.731 2.5018 0.813 1.4192 0.818 Technology adoption -1.6972 -1.509 -0.2310 -0.193 -1.2882 -1.418 Constant 0.7254 0.136 5.5111 ** 2.622 3.8439 * 2.115 Number of obs. 78 81 53 R-sq: within 0.7152 0.4436 0.5148 between 0.1544 0.0102 0.1389 overall 0.1469 0.0078 0.1866 * Significant at 10% level; ** Significant at 5% level. 29 Table A1.36. Wage Performance of Manufacturing Firms in Mexico City Dependent variable: 1992-1995 1995-1999 1992-1999 log(total wages) Coeff. t-Stat. Coeff. t-Stat. Coeff. t-Stat. Firm Characteristics Age -2.2507 ** -104.274 0.0035 1.157 -0.1019 ** -5.665 Employees: Highlyskilled 0.0148 ** 2.267 0.0106 ** 3.126 -0.0175 -1.091 Semi-skilled 0.0004 ** 2.190 0.0013 ** 8.206 0.0014 ** 2.124 Low skilled 0.0006 * 1.818 0.0010 ** 4.865 0.0023 ** 2.447 Maquila 0.1028 0.381 -0.0928 -0.686 -1.1305 -1.621 Technology adoption 0.3618 ** 3.882 0.6487 ** 7.693 1.5586 ** 4.089 Constant 76.7338 ** 111.880 5.6920 ** 45.075 11.0466 ** 14.089 Number of obs. 1,417 860 748 R-sq: within 0.9509 0.2884 0.1103 between 0.2344 0.5283 0.0011 overall 0.0212 0.4698 0.0236 * Significant at 10% level; ** Significant at 5% level. Table A1.37. Productivity Performance of Manufacturing Firms in Mexico City Dependent variable: 1992-1995 1995-1999 1992-1999 log(productivity) Coeff. t-Stat. Coeff. t-Stat. Coeff. t-Stat. Firm Characteristics Age 0.1126 ** 8.402 0.0072 1.396 0.0095 ** 2.189 Employees: Highly skilled 0.0008 0.231 -0.0021 -0.474 0.0031 0.843 Semi-skilled -0.0002 * -1.756 0.0002 0.751 0.0001 0.906 Low skilled -0.0002 -1.446 0.0008 ** 2.172 0.0000 -0.229 Maquila 0.1331 0.847 0.1816 0.806 0.2152 1.231 Technology adoption 0.0375 0.678 0.4866 ** 3.718 0.0923 1.011 Capital assets 3.3e-06 0.881 3.3e-06 ** 3.152 5.5e-06 ** 5.443 Constant 0.0745 0.159 3.6537 ** 17.183 3.6820 ** 19.604 Number of obs. 951 578 652 R-sq: within 0.1915 0.1507 0.0980 Between 0.0504 0.1470 0.2049 overall 0.0476 0.1527 0.1446 * Significant at 10% level; ** Significant at 5% level. Table A1.38. Net Employment Performance of Manufacturing Firms in Mexico City Dependent variable: 1992-1995 1995-1999 1992-1999 log(net employment) Coeff. t-Stat. Coeff. t-Stat. Coeff. t-Stat. Firm Characteristics Age -0.0475 -0.667 0.0003 0.025 0.0044 0.352 Employees: Highly skilled 0.0143 0.426 0.0128 1.326 0.0088 0.909 Semi-skilled 0.0005 0.382 0.0020 * 2.002 0.0009 1.643 Low skilled 0.0043 ** 2.799 -0.0009 -0.826 0.0003 0.493 Maquila -0.9553 -0.667 -0.5789 -1.037 0.4879 1.100 Technology adoption 0.5631 * 1.824 -0.2403 -0.693 0.1190 0.388 Constant 2.7202 1.192 2.3452 ** 3.814 1.7872 ** 3.148 Number of obs. 364 320 271 R-sq: within 0.2809 | 0.1411 0.0884 between 0.0082 0.0477 0.0394 overall 0.0146 0.0564 0.0394 * Significant at 10% level; ** Significant at 5% level. 30 Annex 2: Wage Inequality Table A2.1. Wage Inequality Dependent variable: 1992-1995 1995-1999 1992-1999 Log(highly skilled/unskilled wages) Coeff. Z-St. Coeff. Z-St. Coeff. Z-St. Firm characteristics Size: Small 2.6615 1.595 -1.3684 * -1.718 -1.1810 -1.386 Medium 0.7362 0.537 -1.4635 * -1.826 -1.3873 -1.625 Large Dropped -1.5938 ** -1.984 -1.4417 * -1.689 Age 0.0313 ** 2.742 0.0045 1.464 -0.0019 -0.593 Employees: Highly skilled 0.0361 ** 12.726 0.0206 ** 8.614 0.0416 ** 14.628 Semi-skilled 0.0010 ** 5.211 0.0016 ** 7.168 0.0005 ** 2.581 Low skilled -0.0022 ** -14.093 -0.0023 ** -11.471 -0.0027 ** -15.89 Maquila -0.1107 -0.705 -0.0382 -0.354 0.1591 1.498 Technology adoption 0.0275 0.518 0.1128 1.529 0.0534 0.819 Constant -2.7354 ** -3.260 0.5792 0.724 0.7860 0.922 Number of obs. 4,744 2,616 2,564 R-sq: Within 0.1785 0.2274 0.2480 Between 0.0905 0.2676 0.3063 Overall 0.0870 0.2604 0.2952 * Significant at Io% level; **Significant at 5% level. 31 APPENDIX A INEGI has compiled the National Survey of Employment, Salaries, Technology, and Training (ENESTYC). The Ministry of Labor co-designed the questionnaire, which gathered rich information on training, technology, wages, employment, forms of labor contracting, and internal plant organization of Mexican manufacturing firms. The government conducted the survey in 1992, 1995, and 1999, but its questions on technology ask whether the firm adopted technology in the periods 1989-1992, 1994-1995, or 1997-1999, respectively. Our references to the time of technology adoption mention only the final year of the period (e.g. 1992 rather than 1989-1992). Data from the 1992 survey includes 5,071 firms, from the 1995 survey includes 5,242 firms, and from the 1999 survey includes 7,429 firms. A valuable feature of ENESTYC is that it allows us to identify the same firm in 1992, 1995, and 1999. Nonetheless, we should qualify our estimations with survivor bias. Only firms that exist in all three years can be included in the panel database. As Audretsch (1995) shows, survival likelihood is strikingly low for small and new enterprises and increases with firm size and age. Thus, the panel includes an unrepresentatively high number of large and mature firms. While random observation selection should not cause bias in our resulting estimations, surviving firms are not randomly selected. Darwinian selection of extant firms means that the firms in our sample tend to be more efficient and have better performance than an average Mexican firm. Another advantage of this database is the broad spectrum of firm sizes included by industry, shown in tables B.1-B.3. The rich information available in ENESTYC allows us to distinguish technology diffusion policies for firms of different size and character. INEGI also conducts the Annual Industrial Survey (EIA). The survey covers 6,500 manufacturing plants throughout Mexico that account for 80 percent of production in each industry group. Since the survey attempts to cover the majority of manufacturing production but not a majority of plants in all categories, our sample includes all large plants and most medium- sized scale plants, but few small-scale plants and very few microenterpise plants. 32 We link the ENESTYC panels to firms in the EIA. This allows us to combine EIA data on productivity, labor, value-added, and capital with ENESTYC variables for the plants common to both surveys. The panels also include some regional variables using the Indicators of Scientific and Technology Activity in Mexico from the National Council of Science and Technology (CONACYT). A description of the variables in the panels appears in the Appendix. The 1992-95 panel has 3,293 firms, the 1995-99 panel has 1,717 firms, and the 1992-99 panel has 1,066 firms. The information on individual establishments that INEGI gathers through its questionnaires (which law requires firms to answer) is legally confidential, and INEGI is unable to give the raw data to outside agencies. Therefore, we followed an established procedure in which most data analysis was done in INEGI's Aguascalientes headquarters with the support of INEGI personnel. Nevertheless, the reader should bear in mind the limitations on data analysis imposed by this institutional arrangement. 33 APPENDIX B Table B.1. Manufacturing Firms in the 1992-1995 Panel by Industry and Size Size Division All Large Medium Small Micro Total 3,293 352 576 1,099 1,266 Food, beverage and tobacco 669 105 114 163 287 Textiles, clothing, leather 551 36 93 231 191 Wood and wood products 149 28 42 61 18 Paper and paper products 219 16 31 103 69 Chemical products 494 40 94 185 175 Non-metallic minerals 161 45 31 25 60 Basic metal industries 102 13 13 39 37 Metal products, machinery 897 65 147 272 413 Other manufacturing industries 51 4 11 20 16 Source: 1992-95 ENESTYC Panel. Table B.2. Manufacturing Firms in the 1995-1999 Panel by Industry and Size Size Division All Large Medium Small Micro Total 1,717 829 737 145 6 Food, beverage and tobacco 372 232 114 26 Textiles, clothing, leather 273 133 113 23 4 Wood and wood products 57 19 32 6 Paper and paper products 146 54 83 9 Chemical products 306 126 153 26 1 Non-metallic minerals 75 32 33 10 Basic metal industries 41 21 15 5 Metal products, machinery 419 198 183 37 1 Other manufacturing industries 28 14 11 3 Source: 1995-99 ENESTYC Panel. Table B.3. Manufacturing Firms in the 1992-1999 Panel by Industry and Size Size Division All Large Medium Small Micro Total 1,066 554 439 72 1 Food, beverage and tobacco 227 154 63 10 Textiles, clothing, leather 162 70 80 12 Wood and wood products 36 9 19 8 Paper and paper products 95 36 52 7 Chemical products 190 86 87 16 1 Non-metallic minerals 46 34 10 2 Basic metal industries 36 18 18 Metal products, machinery 257 138 102 17 Other manufacturing industries 17 9 8 Source: 1992-99 ENESTYC Panel. 34 APPENDIX C 1992-99 Panel Variables Description Variable Description Value From the ENESTYC Firm size according to the number of workers: Micro I - 15 Dummy for each size Firm size Small 16 - 100 1= if the firm belongs to a certain size Medium 101 -250 0= otherwise. Large 250 - more Manufacturing industries: I) Food, beverages, and tobacco 2) Textiles, clothing, and leather 3) Wood and wood products Dummy for each industry Division 4) Paper, paper products, printing, and publishing I= if the firm belongs to a certain industry 5) Chemicals, oil derivatives, and coal 0= otherwise. 6) Non-metallic mineral products 7) Basic metallic industries 8) Metallic products, machinery, and equipment 9) Other manufacturing industries Total workers Number of workers in the firm. Continuous Regions: Dummies Includes the states of Baja California, Baja California Sur, Coahuila, Chihuahua, Durango, I= if the firm is located in the North, North Nuevo Le6n, Sinaloa, Sonora, Tamaulipas, and 0= otherwise. Zacatecas. Includes the states of: Aguascalientes, Colima, Guanajuato, Hidalgo, Jalisco, Mexico, 1= if the firm is located in the Center, Center Michoacan, Morelos, Nayarit, Puebla, Queretaro, 0= otherwise. San Luis Potosi, and Tlaxcala. Includes the states of Campeche, Chiapas, I= if the firm is located in the South, South Guerrero, Oaxaca, Quintana Roo, Tabasco, 0= otherwise. Veracruz, and Yucatan. Capital Distrito Federal 1= if the firm is located in the Capital, Capital Distrito Federal0=ohrie 0= otherwise, Years Firm's age. Continuous Dummy Technology adoption Adoption of new technology. 1= if the firm adopts new technology, 0= otherwise. Highly skilled workers Number of executives and managers in the firm. Continuous Semi-skilled workers Number of production workers in the firm. Continuous Unskilled workers Number of general workers in the firm. Continuous Share of highly skilled Share of highly skilled workers from the total of Ranks between 0-100 workers workers in the firm. Share of semi-skilled Share of semi-skilled workers from the total of Ranks between 0-100 workers workers in the firm. Share of unskilled Share of unskilled workers from the total of Ranks between 0-100 workers workers in the firm. New hires New hires. Continuous Laidoffs Dismissals. Continuous Net employment New hires less dismissals. Continuous Total wages Total wages in real pesos paid in the firm. Continuous Highly skilled wages Total wages in real pesos paid to the highly Continuous skilled workers in the firm. Semi-skilled wages Total wages in real pesos paid to the semi-skilled Continuous workers in the firm. 35 Unskilled wages Total wages in real pesos paid to the unskilled Continuous workers in the firm. Share of highly skilled Share of the highly skilled workers wages from Ranks between 0-100 wages the firm's total wages. Share of semi-skilled Share of the semi-skilled workers wages from the Ranks between 0-100 wages firm's total wages. Share of unskilled Share of the unskilled workers wages from the Ranks between 0-100 wages firm's total wages. Dummy Maquila Firms dedicated to maquila activities. 1= if maquila O- otherwise. Productivity Firm's productivity measured as output per Continuous worker. From the EM Capital assets Firm's capital: fixed assets, not deflated. Continuous 36 Policy Research Working Paper Series Contact Title Author Date for paper WPS2754 Revealed Preference and Abigail Barr January 2002 T. Packard Self-Insurance: Can We Learn from Truman Packard 89078 the Self-Employed in Chile? WPS2755 A Framework for Regulating Joselito Gallardo January 2002 T. Ishibe Microfinance Institutions: The 38968 Experience in Ghana and the Philippines WPS2756 Incomeplete Enforcement of Pollution Hua Wang January 2002 H. Wang Regulation: Bargaining Power of Nlandu Mamingi 33255 Chinese Factories Benoit Laplante Susmita Dasgupta WPS2757 Strengthening the Global Trade Bernard Hoekman January 2002 P. Flewitt Architecture for Development 32724 WPS2758 Inequality, the Price of Nontradables. Hong-Ghi Min January 2002 E. Hernandez and the Real Exchange Rate: Theory 33721 and Cross-Country Evidence WPS2759 Product Quality. Productive Aart Kraay January 2002 R. Bonfield Efficiency, and International Isidro Soloaga 31248 Technology Diffusion: Evidence from James Tybout Plant-Level Panel Data WPS2760 Bank Lending to Small Businesses George R. G. Clarke January 2002 P. Sintim-Aboagye in Latin America: Does Bank Origin Robert Cull 37644 Matter? Maria Soledad Martinez Peria Susana M. Sanchez WPS2761 Precautionary Saving from Different Richard H. Adams Jr. January 2002 N. Obias Sources of Income: Evidence from 31986 Rural Pakistan WPS2762 The (Positive) Effect of Norbert R. Schady January 2002 T. Gomez Macroeconomic Crises on the 32127 Schooling and Employment Decisions Of Children in a Middle-income Country WPS2763 Capacity Building in Economics: Boris Pleskovic January 2002 B. Pleskovic Education and Research in Transition Anders Aslund 31062 Economies William Bader Robert Campbell WPS2764 What Determines the Quality Roumeen Islam January 2002 R. Islam of Institutions? Claudio E. Montenegro 32628 Policy Research Working Paper Series Contact Title Author Date for paper WPS2765 Inequality Aversion, Health Adam Wagstaff January 2002 H. Sladovich Inequalities, and Health Achievement 37698 WPS2766 Autonomy, Participation, and Learning Gunnar S. Eskeland January 2002 H. Sladovich in Argentine Schools: Findings and Deon Filmer 37698 Their Implications for Decentralization WPS2767 Child Labor: The Role of Income Rajeev H. Dehejia January 2002 A. Bonfield Variability and Access to Credit in a Roberta Gatti 31248 Cross-Section of Countries WPS2768 Trade, Foreign Exchange, and Energy Jesper Jensen January 2002 P. Flewitt Policies in the Islamic Republic of David Tarr 32724 Iran: Reform Agenda, Economic Implications, and Impact on the Poor WPS2769 Immunization in Developing Countries: Varun Gauri January 2002 H. Sladovich Its Political and Organizational Peyvand Khaleghian 37698 Determinants WPS2770 Downsizing and Productivity Gains Martin Rama January 2002 H. Sladovich In the Public and Private Sectors Constance Newman 37698 of Colombia WPS2771 Exchange Rate Appreciations, Labor Norbert M. Fiess February 2002 R. lzquierdo Market Rigidities, and Informality Marco Fugazza 84161 William Maloney WPS2772 Governance Matters Il: Updated Daniel Kaufmann February 2002 E. Farnand Indicators for 2000-01 Aart Kraay 39291 Pablo Zoido-Lobat6n WPS2773 Household Enterprises in Vietnam: Wim P. M. Vijverberg February 2002 E. Khine Survival, Growth, and Living Jonathan Haughton 37471 Standards WPS2774 Child Labor in Transition in Vietnam Eric Edmonds February 2002 R. Bonfield Carrie Turk 31248 WPS2775 Patterns of Health Care Utilization in Pravin K. Trivedi February 2002 R. Bonfield Vietnam: Analysis of 1997-98 31248 Vietnam Living Standards Survey Data WPS2776 Child Nutrition, Economic Growth, Paul Glewwe February 2002 E. Khine and the Provision of Health Care Stefanie Koch 37471 Services in Vietnam in the 1990s Bui Linh Nguyen WPS2777 Teachers' Incentives and Professional Gladys L6pez-Acevedo February 2002 M. Geller Development in Schools in Mexico 85155