metadata: authors: 0: author: Lopez-Acevedo,Gladys C.
count: Mexico
docna: 0: docna: Technology and skill demand in Mexico
docty: Policy Research Working Paper
subtopic: Construction Industry,Common Carriers Industry,Food & Beverage Industry,General Manufacturing,Pulp & Paper Industry,Plastics & Rubber Industry,Skills Development and Labor Force Training,International Trade and Trade Rules,Employment and Unemployment,Business Cycles and Stabilization Policies
teratopic: Health, Nutrition and Population
abstracts: cdata!: The author investigates the effects of
            technology on the employment and wages of differently
            skilled Mexican manufacturing workers using firm panel data
            from 1992-99. She analyzes the relationship between
            technology and skill demand. Findings support the
            skill-biased technical change hypothesis. She then examines
            the temporal relationship of technology adoption to firm
            productivity and worker wages. The author finds that skilled
            labor increases after technology adoption. And wages of both
            skilled and semi-skilled workers exhibit markedly increased
            growth rates compared with the growth rate of low-skilled
            workers. The results show that investment in human capital
            improves technology-driven productivity gains.
geo_reg_key: 119228,517191
repnme: repnme: Technology and skill demand in Mexico
display_title: Technology and skill demand in Mexico
listing_relative_url: /research/2002/02/1711112/technology-skill-demand-mexico
topicv3: Manufacturing Sectors m1326521 1281,Trade m1326924 698,Skilled Workers m1504035 2732,Unskilled Workers m1504036 2733,Unemployment m1504051 2747
publishtoextweb_dt: 2010-07-01T00:00:00Z
historic_topic: Health, Nutrition and Population
origu: Poverty Reduction & Econ Mngmt (LCSPR)
no_of_pages: 26
author_keys: 0: author_key: 428192
model_extractions: I PoIIcy RESEAR H WORKING PAPER 2779

# Abstract

> L6pez-Acevedo investigates the effects of technology on

> the employment and wages of differently skilled Mexican manufacturing workers using firm panel data from 1992-99. She analyzes the relationship between technology and skill demand. Findings support the skill-

> biased technical change hypothesis. She then examines the temporal relationship of technology adoption to firm

productivity and worker wages. The author finds that skilled labor increases after technology adoption. And wages of both skilled and semi-skilled workers exhibit markedly increased growth rates compared with the growth rate of low-skilled workers. The results show that investment in human capital improves technology-driven productivity gains.

This paper-a product of the Latin America and the Caribbean Region, Poverty 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 14-046, telephone 202-458-5155, fax 202-522-2 I1, email address mgellerQu worldbank.org. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at gacevedo(a worldbank.org. February 2002. (22 pages)

> _The Policy Research Working Paper Series dissemiinates the findings of work in progress to encourage the exchange of ideas abNoit development issues. An objective of the series is to get the findings out quickly, even if the presentations are less thanT ftully polished. The papers carrv the niames of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are enttirely those of the authors. They do not niecessarily represent the view of the World Bank, its Executive Directors, or the countries thew' representt._

Produced by the Research Advisory Staff
**Mexico** - **Technology, Wages, and Employment**

# **TECHNOLOGY AND SKILL DEMAND** IN MEXICO

# **Gladys LUpez-Acevedol**

JEL Codes: L60 ; L20 ; J31 ; 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 mid-1980s, Mexico began a process of structural reform that fundamentally changed the economic environment facing productive enterprises. Liberalization began in 1984 and accelerated when Mexico joined the General Agreement on Tariffs and Trade in 1986. In 1989, the government began radical policy reforms to reduce government regulation and liberalize trade. The adoption of the North American Free Trade Agreement with the U.S. and Canada in 1994 intensified liberalization. These reforms replaced quotas with smaller tariffs, eliminated price ceilings and floors, simplified trade regulation, eased foreign direct investment restriction, and privatized state-owned enterprises.

Because of this international openness, technology now lies at the heart of Mexican economic activity. Globalization-induced competition has made firms increase the speed and efficiency of new technology adoption (TA). It has also inspired firms to increase research and development (R&D) budgets (OECD 2000).

However, liberalization has worsened Mexican wage equality (World Bank 2000). The Gini coefficient, which measures income inequality and is especially sensitive to changes around the population median, rose from 0.47 in 1984 to 0.53 in 1998 (Table 1). While the poorest quintile lost 1.3 percent of its income during this period, the richest decile increased its wealth by 7.7 percent. In relative terms, all strata except the richest lost income during this period (Figure 1).

**Table 1. Inequality in Mexico, Measured by Gini Coefficient**

|**Year**|**National**|**Urban**|**Rural**|
|---|---|---|---|
|1984|0.47|0.44|0.45|
|1989|0.52|0.50|0.44|
|1992|0.53|0.50|0.43|
|1994|0.53|0.51|0.42|
|1996|0.52|0.49|0.45|
|1998|0.53|0.495|0.48|

_SouIrce:_ Author's calculations based on ENIGH.

3
**Figure 1. Wealth Distribution among Mexican Population**

<!-- Start of picture text --> 45 v .<br>40 -- * - --<br>- 35 4 g:U_ _<br>C 30-- Botom20%l<br>25 -- *-Lower 40%<br>v 25 - ~-E--Middle 30%<br>0 20 - - - As --. = --t-n --Top 10%<br>X10<br>0 - ,<br>1984 1989 1992 1994 1996 1998<br><!-- End of picture text --> _Source:_ Author's calculations based on ENIGH.

At the height of liberalization, Mexico underwent a severe currency crisis. In December 1994, Mexico sharply devalued the peso causing a deep economic recession. In 1995, real gross domestic product (GDP) fell by 6.2 percent, demand fell by 14.4 percent, and investment fell by 43 percent. This market contraction shifted trade as exports increased by almost 31 percent and imports fell by 9 percent. Nevertheless, a cheap peso bolstered exports and offered new markets to firms whose domestic sales had collapsed. Domestic demand quickly recovered, and by 1997 real GDP had returned to its pre-crisis level.

This paper investigates the skill-biased technological change (SBTC) hypothesis for Mexico, using panel data from the National Survey of Employment, Salaries, Technology, and Training (ENESTYC) and the Annual Industry Survey (EIA). The panel has observations for 1992, 1995, and 1999 (for a description of these surveys and the panel see Appendix A and B, and for a list of variables Appendix C).

Section 2 reviews Mexico-specific circumstances. Section 3 asks whether technological change is biased towards particular skill groups. Section 4 analyzes the relationship of skilled labor employment to the use of technology. Section 5 discusses productivity gains from TA and training. Section 6 presents conclusions.

4
# **2. Literature Review**

A key implication of technology diffusion is its impact on the labor market. Several studies (Davis and Haltiwanger 1991; Krueger 1993, and Mincer 1991) find that technology has raised the relative demand for more skilled workers and, consequently, reduced the demand for manual labor. Katz and Murphy (1992) use a basic supply and demand approach to show that relative labor demand shifts come from intra-industry changes (such as factor non-neutral technological change, changes in prices of non-labor inputs, and outsourcing) and inter-industry changes (such as shifts in market-wide product demand, sector differences in factor-neutral total factor productivity (TFP) growth, and trade shifts that change the domestic share of output at fixed relative wages). Aw and Batra (1999) also provide evidence that technology (measured by R&D and by worker training) has an impact on firm performance (measured by wages). This finding coheres with the World Bank (1999) "Mexican Labor Markets: New Views on Integration and Flexibility," which relates wages to technology (measured in terms of R&D and technology acquisition).

In the last decade, Mexican wage inequality has increased sharply. Three theories - the liberalization hypothesis, the labor institution hypothesis, and the SBTC hypothesis - can explain this increase in earnings inequality in Mexico. The liberalization hypothesis argues that reduced trade barriers put an economy under competitive pressure to specialize along its lines of comparative advantage. A developed country with high levels of human capital will shift to advanced industry and services as low-skilled industries feel competition from countries with abundant low-wage workers.

In a test of liberalization theory, Hanson and Harrison (1995) examine the impact of Mexican trade reform on wage structure using firm-level data. They test whether trade reform shifted employment toward skilled labor-intensive industries (the Stolper-Samuelson-Type [SST] effect). They conclude that changes within industries and firms caused the wage gap, and

> 2 See, for example, the "Symposium on Wage Inequality" (1997) and the "Symposium on How International Exchange, Technology and Institutions Affect Workers" (1997).

5
that the SST effect provides a poor explanation. Thus the increase in wage inequality was due to other factors. Burfisher and others (1993) also examine the SST effect under NAFTA.

However, the liberalization hypothesis seems ill-fitted to the Mexican experience. Mexico began lowering trade barriers after 1984, particularly for American and Canadian commerce, whose share of merchandise imports from Mexico increased from 68 percent in 1985 to 78 percent in 1996. Since Mexico has an abundant supply of low skilled labor relative to its northern neighbors, the liberalization hypothesis predicts that lowered trade barriers would raise both the wages and the demand for unskilled labor in Mexico. However, as trade barriers fell, low-skilled labor in Mexico experienced a real wage decrease while skilled labor had a real wage increase, resulting in worsened wage inequality.

The trade theory can be adjusted to fit Mexico better: while trade liberalization pushes a country towards its comparative advantage, it also facilitates the transfer of ideas and technology. This seems to provide a better explanation of earnings inequality. Feenstra and Hanson (1996) put forward a variant of the liberalization hypothesis that involves outsourcing whereby multinational enterprises in the developed country relocate their less skill-intensive activities to the less skill-abundant developed countries. However, what is referred to as a lowskill activity in the United States may be a high-skill activity in Mexico, which could explain the similarity in earnings inequality between the two countries.

The institutional hypothesis considers broad changes such as minimum wage reductions and weakened trade unions. A binding minimum wage truncates the lower end of the wage distribution. As the real minimum wage decreases due to inflation, its effect on industry also decreases. This translates into increased wage and earnings dispersion. Since the early 1980s, institutional developments have exerted insignificant influence on earnings distribution. The distribution of real wages, for example, reveals little distortion due to the minimum wage, which suggests that the minimum wage is low enough to affect industry minimally. Similarly, union wages in Mexico are similar to nonunion wages, controlling for education differences. This data suggests that unions have little or no impact on earnings. Although we cannot entirely reject the 6
institutional hypothesis, it does not appear to be the principal explanation for Mexican earnings inequality.

The SBTC hypothesis links earnings inequality to technology that raises relative demand for skilled labor. Cragg and Epelbaum (1996) find that, given different labor supply elasticities, the primary source of Mexican wage inequality is a skill-biased demand shift rather than skilluniform demand growth. Meza (1999) also investigates the Mexican labor market, and finds that intra-industry demand shifts toward a more educated labor force explain unskilled labor wage decreases better than interindustry demand shifts. The World Bank (2000) also shows that demand increases for a more educated labor force within the economic sectors explain the increase in their premium when compared to the demand shifts for less educated workers between economic sectors. Acosta and Montes (2001) show that there is a constant increase on skill premia during the 1987-1993 period, but there is a deceleration during the 1994-99 period and a decline after 1997. These authors contend that the former may be caused by between industry differences.

Batra and Tan (1997) study the SBTC hypothesis as a plausible explanation of wage inequality using firm-level data for Colombia, Mexico, and Taiwan (China). They find that technology's impact on wages is greatest for skilled workers and lowest for unskilled workers, thereby supporting the SBTC hypothesis. In this hypothesis, skilled workers become more efficient in jobs traditionally performed by unskilled workers (Johnson 1997).

# 3. **Technology's influence on wages and employment**

To determine whether the technology of Mexican firms influences their skill demand, we estimate determinants of each skill group's share of total wages. In this fixed-effects model, we use a modified first-differencing to eliminate firm-specific error. For a cost minimizing firm, the following equation models technology's influence on wages: _Wii, = AS In VA4j, + 82 In K;,_ **+ ,63** In _Rj,_ **_+ ,B4_** _InTFPi,_ **_+ ,B5_** _InUR, + Zi, (1)_ where: 7
- **_Wi,_** _= W', - Wj,_ or the wages of firm i at time t minus the average wages of firm i over all time periods;

- _W,i_ = the wage share of skill-groupj in the total wages of firm i;

- _VA_ = value-added (calculated with INEGI's methodology, i.e. the difference between the value of the production of the firm and its expenditure in materials, water, energy and electricity) in real 1997 pesos;

- _K_ = capital assets not deflated;<sup>3</sup>

- _R_ = the relative wage of skilled workers in relation to unskilled workers; _TFP_ = total factor productivity, a measure of technological change; _UR_ = the unemployment rate, included as a control for macroeconomic shocks, and E = the normal regression error.

A positive ,6 parameter in equation (1) indicates that growing industries are more likely to increase the wages of skill groupj. A positive P2 parameter indicates that capital and skills are complementary inputs in the production process; a negative parameter indicates that capital and skills are substitutes. The /3 parameter indicates how changes in relative wages affect the wages _of_ skill group _s._ We expect this parameter to be negative, since a rise in the relative wages of one skill group leads a cost-minimizing employer to substitute towards other groups. The 34 parameter of the TFP shows the extent to which technological change is skill-biased. If TFP is skill-neutral, it should not impact skill mix. A positive **_/4_** parameter implies that technological change is skill-using or skill-biased, while a negative 64 indicates that technology is skillreplacing. Finally, /85 will indicate us how the employment of the different skill groups varies as unemployment changes.

We estimate TFP as the residual in a production function. The specification of the CobbDouglas production function is: _ln(VA),t_ = ,8jln(K)j, _+ fl21n(L)j,_ + _eit_ (2) where:

- _VA_ = value-added, in real 1997 Mexican pesos; _K_ = fixed capital assets, not deflated; _L_ = labor inputs, total hours worked per annum; = the normal regression error;

- _i_ = the individual plant being analyzed, and

3No deflator is available for fixed assets, due to this we assume that the flow of capital services is adequately reflected in its book value.

8
= the time period.

t We estimate equation (1) for 1992-99 manufacturing firms panel using a fixed-effects model. Tables 2 and 3 give separate results for wages and employment shares of skill groups, so we can use both to measure skill demand.

**Table 2. Wage Determinants, by Employee Skill Group**

|**Dependent variable:**|**Hig**|**hlyski**|**lled**|**Sem**|**i-skill**|**ed**|Lo|w**skill**|**ed**|
|---|---|---|---|---|---|---|---|---|---|
|**Shareofwages**|**Coeff.**||**t-Stat.**|**Coeff.**||**t-Stat.**|**Coeff.**||t-Stat.|
|Value-added|-0.0647|**|-4.161|0.0281|**|1.966|0.0365|**|3.241|
|Capital assets|0.0479|**|5.794|-0.0401|**|-5.270|-0.0078||-1.301|
|Wageratios/us|0.0082|**|2.061|0.1204|**|32.757|-0.1286|**|-44.456|
|TFP|0.0232|**|3.864|-0.0148|**|-2.681|-0.0084|*|-1.926|
|Unemploymentrate<br>|-0.0225<br>|**<br>|-2.636<br>|0.0146<br>|*<br>|1.850<br>|0.0080<br> <br>||1.285<br>|
|OverallR<sup>2</sup>||0.0315|||0.4550|||<br>|0.5508||

* **Significant** at **10%** level; ** Significant at 5% level. _Notes: I._ Number of observations **=** 1 **,185.**

2. Regressions included indicator variables for 9 manufacturing divisions.

3. Share of wages is the share of the **firm's** total wages paid to workers of a particular skill level.

**Table 3. Employment Determinants, by Employee Skill Group**

|**Dependent variable:**|**High**|**lySkil**|**led**|**Sem**|**i-skill**|**ed**|Lo|w**skill**|**ed**<br>|
|---|---|---|---|---|---|---|---|---|---|
|**Share ofEmployment**|**Coeff.**||**t-Stat.**|**Coeff.**||**t-Stat.**|**Coeff.**||**t-Stat.**|
|**AllEmvlovees**<br>||||||||||
|Value-added|-0.8129|**|-2.487|1.1669||0.687|-0.3552||-0.210|
|Capitalassets|0.7198|**|4.140|-1.1942||-1.321|0.4785||0.531|
|Wageratios/us|0.0704||0.837|14.9349|**|34.190|-15.0057|**|-34.455|
|TFP|0.3072|**|2.428|-0.5728||-0.871|0.2653||0.404|
|Unemploymentrate<br>|-0.0458||-0.255|3.3348|**|3.566|-3.2807|**|-3.518|
|OverallR<sup>2</sup>||0.0037|||0.5869|||0.5887||
|**Male**Employees||||||||||
|Value-added|-0.0076|*|-1.895|0.0412|*|1.735|-0.0210||-0.825|
|Capitalassets|0.0077|**|2.826|-0.0277|*|-1.714|0.0088||0.510|
|Wageratios/us|0.0007||0.671|0.1335|**|20.184|-0.1392|**|-19.679|
|TFP|0.0042|**|2.946|-0.0216|**|-2.527|0.0105||1.147|
|Unemploymentrate<br>|0.0008||0.084|0.0447||0.819|-0.0713||-1.221|
|OverallR<sup>2</sup>||0.0081|||0.4319|||0.3658||
|**FemaleEmployees**||||||||||
|Value-added|-0.0013||-1.193|-0.0340|**|-2.045|0.0226||1.499|
|Capitalassets|0.0011||1.541|0.0252|**|2.234|-0.0151||-1.480<br>|
|Wageratios/us|0.0003||1.051|0.0275|**|5.945|-0.0228|**|-5.435|
|TFP|0.0007|*|1.831|0.0094||1.565|-0.0032||-0.588|
|Unemploymentrate<br>|-0.0022||-0.900<br>|0.0341<br>||0.894<br>|-0.0061<br>||-0.175<br>|
|OverallR<sup>2</sup>||0.0145|||0.0057||||0.0353||

***** Significant at **10%** level; ** Significant at 5% level. _Notes: I._ Number _of_ observations= _1,185._

2. Regressions included indicator variables for 9 manufacturing divisions.

3. Share of employment is **the** share of the fimi's total employment held by workers of a particular skill level.

The effects of output growth vary between skill groups. It appears that growing firms are less likely to hire highly skilled male employees. In contrast, for the semi-skilled group and the 9
low skilled group, the value-added paramneters are positive and significant in the wage share estimation and insignificant in the employment share specification for all workers. With reference to gender, the value-added parameter is positive for the semi-skilled male workers and negative for the semi-skilled female workers, this suggests that growing firms tend to increase male rather than female production workers.

It is interesting to note that the unemployment control is positive for the semi-skilled group and negative or neutral for the highly skilled and low-skilled groups. This suggests that employers increase the relative share of production workers as overall unemployment rises.

We find that capital and highly skilled workers are complements while capital and semiskilled workers are substitutes. This result is consistent with Tan (2000). Also, capital and semiskilled male production workers are substitutes while capital and semi-skilled female production workers are complements.

Finally, technology is biased toward the use of highly skilled workers. This implies that technology is skill-using for highly skilled workers but skill-replacing for semi-skilled and unskilled workers. Furthermore, technical change is skill-using for male and female highly skilled workers and skill-replacing for the semi-skilled male production workers. Tan (2000) finds that, in Malaysia, technical change is skill-using only for male highly skilled workers.

# 4. Time **of benefit from technology**

Probit models of technology adoption are informative but ill-suited to dynamic processes (L6pez-Acevedo 2001). To analyze the temporal relationship of labor demand and TA, we use event analysis. As Tan (2000) suggests, comparing the period of TA to skill-mix changes in previous and consecutive time periods facilitates effective event analysis. The variable r represents the time period relative to TA; the period of adoption is _r_ = 0 , the period preceding adoption is r= -1, and the period following adoption is r= 1. Since companies adopt technology at different times, the period in which r = 0 differs between firms, Also, we define that T = 0 in all periods for a firm that never adopts technology. Since we only have point data for 1992, 1995, and 1999, rranges from -2 to 2. Using information on rfrom all firms that adopt technology, we 10
estimate the _v-profile_ of skill shares, or the relationship of when a firm hires skilled labor to when it adopts technology. We measure the v-profile relative to _r_ = 0 to allow comparison with firms that adopted technology in the same period, and with firms that never adopted technology.

We want to know how employers vary skill-mix in the years preceding and following TA. A regression model relating skill shares to rmay be written as follows:

**_SiJt_ =l+** **_,loAXt+_** _+ fl2iZy 1 +_ /337' _T +,6 4lnUR + sit_ (3)

where: _Sjjt_ = the share of workers of skill groupj in firm i during period t; X = a vector of firm and industry characteristics; Z = a vector of dummy variables for each rbetween -2 and +2; _T,_ = a time trend term, and _UR_ = the unemployment rate, included as a control for macroeconomic changes.

Here, the A2 coefficients trace the v-profile of skill share j relative to r = 0. We use models of similar specification to examine how productivity and wages vary with r.

Table 4 reports the estimated coefficients of _r,_ in relation to _r_ = 0. These v-profiles appear graphically in Figures 2 through 8. We fit these graphs with cubic spline to show the underlying trends in these variables over -r more effectively. In essence, they relate firm hiring patterns to TA timing.

Table **4.** Estimated **Coefficients of rfor Skill Shares, Productivity and Wages** of **TA Firms**

|_r_<br>**Highlyskilled**|**SkillShares**<br>**semiskilledd**|**Unskilled**|**Productivity**|**Wag**<br>**Highlyskilled**|**esbySkillGrou**<br>**Skilledand**|**n**<br>**Unskilled**|
|---|---|---|---|---|---|---|
|-2<br>-0.7193*|-2.8499|3.8857|**-1.0721**<br>**|-1.3990**|-1.1136**|-0.9067**|
|**-1**<br>-0.5144*|-7.6369**|9.0491**|-0.5273**|-0.7277**|-0.7224**|-0.3480**|
|0|||||||
|1<br>0.2992*|3.2059**|-3.0640**|0.5745**|0.7215**|0.6064**|0.5846**|
|2<br>0.5262**|3.0331**|-4.5258**|1.0297**|1.4301**|1.1697**|1.1401**|

***** Significant at **10%** level; ** Significant at 5% **level.** _Notes:_ Regressions included dummy **variables** for 9 **manufacturing divisions, indicators for small,** medium **and large** firms, dummy variables for foreign firms and joint-ventures, and **a** control **for macroeconomic changes.**

11
Figure 24. Skill Shares Pre- and Post-TA Relative to Non-TA Firms

|15.9388S-<br>I<br>-2<br>SHAREO|F HIGHLY SKILLED WORKERS|I<br>2<br>AND ADOPTION|
|---|---|---|
|71.0987<br>22.4253<br>-2<br>SHARE OFSKI|time<br>LLEDAND SEMI-SKILLEDWOR|2<br>KERS ANDADOPTION|
|859857<br>o<br>20.3028-<br>-2<br>SHARE|~~~~~~<br>0<br>S<br>time<br> OFUNSKILLEDWORKERSAN|<br>2<br>DADOPTION|
Figure 2 and Table 4 show that prior to TA, the share of highly skilled workers changes little relative to the time of TA. The share of highly skilled workers among total firm employment is fairly small. Although we observe an increase in the number of highly skilled workers hired after TA, the increase is also small relative to the total number of workers in the firm. Figure 3 shows that the share of skilled and semi-skilled production workers increases before and after TA. By the second period after TA, the share of skilled and semi-skilled workers has risen considerably. Figure 4 shows that the share of less-skilled workers is actually larger before TA, but falls postadoption.

The r-profile for productivity appears in Figure 5. Productivity of technology firms is relatively low before TA. But after TA, productivity of technology firms increases. Although the y-axis of the graph represents the log of productivity, the scale is linear and not logarithmic; hence, the productivity of technology firms increases both before and after TA. However, one period after TA, the rate of productivity growth jumps.

**Figure 5. Productivity Pre- and Post-TA**

<!-- Start of picture text --> 6.32951 -<br>2.41272 X _<br>-2 2<br>PRODUCTIVITY AND ADOPTION<br><!-- End of picture text --> Technology increases firm productivity and consequently firm profits. But how do worker wage changes relate to firm profit increases? To address this question, we estimate separate equations for each skill group, with the dependent variable being the logarithm of wages of the skill group. The resulting r-profiles of three skill groups - highly skilled, semi-skilled, and low skilled workers - appear in Figures 6-8.
Figure 6-8. Wage Premiums by Skill Group Pre- and Post-TA <!-- Start of picture text --> 6.90231 -<br>1.18366<br>-2 21<br>HIGHLY SKILLED WORKERS WAGES AND ADOPTION<br>7.66017j<br>3.50962<br>-2 2<br>aim-<br>SKILLED AND SEMI-SKILLED WORKERS WAGES AND ADOPTION<br>8.05372<br>1.93777<br>-2 2<br>Uame<br>UNSKILLED WORKERS WAGES AND ADOPTION<br><!-- End of picture text -->
Wages show similar trends to productivity, as it appears that employers share productivity gains from technology with all skills of employees. However, skilled employees receive significantly more benefit than unskilled employees. Wages of all skills of employees begin rising two periods before TA, and continue rising until two periods after TA. Furthermnore, the growth rates of the wages of all skill groups jump after TA.

# **5. Technology Adoption and Productivity Growth**

As Tan (2000) argues, a production function including variables for technology type and experience with technology is the best approach for investigating formnally the productivity gains from introduction of new technology. The production function also lets us test whether the benefits of experience with technology increase when combined with employee training: where:

- _VA =VA, - VA,,_ or the difference of _VA_ at time t and the average of _VA_ at all times for firm _i._

- _VA_ = value-added (calculated with INEGI's methodology, i.e. the difference between the value of the production of the firm and its expenditure in materials, water, energy and electricity) in real 1997 pesos;

- _K_ = capital assets (not deflated);<sup>4</sup> _L_ = labor input; _Yj TA_ = a vector of interactive indicator variables for thej types of technology adopted;<sup>5</sup> _Yr y_ = a vector of indicator variables for technology experience; Z = a vector of firm and industry characteristics; _i_ = firm under analysis; _t_ = time period; .6 = normal regression error.

To find the value-added of training when new technology is adopted, we include interaction terms for technology type and training. Robust results in Table 5 show that

> 4No deflator is available for fixed assets, due to this we assume that the flow of capital services is adequately reflected in its book value.

> 5 This is a dummy variable that gets assigned a "I" if the firm adopts new technology and provides training, an a "O" otherwise.

15
combining training with TA increases productivity in three of the four categories of technology. On average, when training increases technology's productivity the benefit is more than six percent. These results indicate the importance of complementary investments in worker training to realize the productivity potential of technology. Training has the greatest effect when a firm adopts machinery tools, and the least effect when a firm adopts robots.

**Table** 5. **The value-added of training** with **technology**

|**Dependentvariable:**|**1**|**992-199**|**9**|
|---|---|---|---|
|**Log(value-added)**|**Coeff.**||**t-Stat.**|
|**ProductionFunction**||||
|Constant|9.1594|**|27.833|
|Log(capital)|0.0948|**|3.843|
|Log(total hours worked)|0.1378|**|5.224|
|TypesofTAandTraining||||
|Automatic equipment|0.0635|*|1.742|
|Machinerytools|0.0789|**|2.593|
|Computerized machinery|0.0671|*|1.684|
|Robots|-0.0041||0.976|
|TAExperience||||
|**1**|-0.1029|**|-3.864|
|2<br>___<br>|_<br>-0.0081||0.817|
|Numberofobs.<br>||2,733||
|OverallR<sup>2</sup>||0.4143||

' Significant at 10% level; ** Significant at 5% level. _Note:_ Regressions included dummy variables for 9 manufacturing divisions, foreign firms and joint-ventures.

# 6. **Conclusions**

This paper attempts to understand how TA affects demand for highly skilled, skilled and semi-skilled, and unskilled workers. Both in terms of wages and employment, with TA firms tend to hire more high skilled workers and pay them better. We also analyze the relative times of TA and demand shifts for a given firm. We find that demand for highly skilled workers increases after the adoption of technology, but not before. By the second period after TA, demand for highly skilled, skilled, and semi-skilled workers has risen notably, while the share of employees made up of unskilled workers tends to diminish after the adoption of technology. Additionally, training and increases in human capital magnify technology-driven productivity gains.

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