POLICY RESEARCH WORKING PAPER   2131
Efficiency Wage and Union                                              In this study of Mexican ir-l
unions appear to bargain
Effects in Labor Demand                                                principally about employmen:
and Wage Structure in                                                  rather than wages and firrs
appear to pay efficiency
M exico                                                                wages above market clearing
to reduce turnover. Since
minimum wages are not
An Application of Quantile Analysis                                    binding, whatever labor
market segmentation is
William F. Maloney                                                     observed arises
Eduardo Pontual Ribeiro                                                endogenously, and is not due
to union- or government-
induced distortions.
The World Bank
Latin America and the Caribbean Region
Poverty Reduction and Economic Management Sector Unit
May 1999



Poicy RESLARCF1 WORKINC PAPER 21 3 1
Summary findings
Applying quantile analysis to detailed firm-level data      workers, suggesting that unions prevent workers from
from Mexico, Maloney and Ribeiro study determinants         being paid too far below the median for tlheir skill level.
of demand and wages for two classes of labor.                 Maloney and Ribeiro identify significant efficiency
Unions appear to have a strong impact on how much        wage effects where firms pay above market clearing to
unskilled labor is employed but not on wages. This         prevent labor turnover both in labor demand and in the
suggests an extreme example of "efficient bargaining"      wage equations. Since minimum wages are not binding
rather than the nmore common "monopoly union"              and the union impact on wages is small, this suggests that
behavior. The impact on productivity is, by definition,     whatever segmentation exists emerges endogenously and
negative, but unions could also be said to be forcing      is not due to union- or government-induced distortions.
firms to use "appropriate technology" (less capital and       Maloney and Ribeiro offer the first use of quantile
more workers), increasing the total amount of labor         analysis to analyze labor demand at the firm level, and
employed in the economy. The only impact on wages           one of the first uses of correct standard errors in two-
appears for the tenth (lowest) quantile of unskilled        stage least-squares quantile regression.
This paper-a product of the Poverty Reduction and EconomicManagement Sector Unit, LatinAmerica and the Caribbean
Region - is part of a larger effort in the region to understand the functioning of developing country labor markets. Copies
of the paper are available free from the World Bank, 1818 H Street NW, Washington DC 20433. Please contact Tania
Gomez, room 18-102, telephone 202-473-2127, fax 202-522-2119, Internet address tgomez@aiworldbank.org. Policy
Research Working Papers are also posted on the Web at http://www.worldbank.org/html/dec/Publications/Workpapers/
home.html. The authors rnaybe contacted atwmaloney@worldbank.orgorribeiro@vortex.ufrgs.br. May 1999. (43 pages)
The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about
development issues. An obhective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The
papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this
paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the
countries they represent.
Produced by the Policy Research Dissemination Center



Efficiency Wage and Union Effects in
Labor Demand and Wage Structure in Mexico:
An Application of Quantile Analysis
William F. Maloney
The World Bank
Eduardo Pontual Ribeiro'
Universidade Federal do Rio Grande do Sul
We thank Carlos Arango, Omar Arias, Gustavo Gonzaga, Daniel Hamermesh and Lyn Squire for helpful
comments and to Tania Gomez for vital assistance. We are also grateful to the Mexican National Institute of
Statistics, Geography and Information (INEGI) for the use of the data. INEGI is in no way responsible of any
incorrect manipulation of the data or erroneous conclusions drawn from it. The views expressed here are those
of the authors only, and should not be associated with the World Bank and its member countries. Contacts:
wmaloney@worldbank.org ribeiroPvortex.ufrgs.br



I. Introduction.
The evidence to date suggests that both union power and efficiency wage behavior
may have large effects on the structure and dynamics of labor markets. The literature
documenting their effect on wages, in particular, is vast. However, as Blanchflower et. al.
(1991) note, the impact on employment of unions has received relatively little study, and that
of efficiency wages has attracted even less. Further, as Nickell and Wadhwani (1991) argue,
the existence of both phenomena simultaneously complicates efforts to distinguish between
competing models of union behavior.' Their work and that of Hendricks and Kahn (1991)
are among the very few that analyze employment determination in the presence of efficiency
wages and two types of union bargaining: the "right to manage" (RTM) or "monopoly union"
type where unions attempt to set the wage but let firms choose the level of employment, and
the "efficient bargaining" (EB) type where unions bargain over both (Oswald 1991 and
Layard and Nickell 1990). Using a large panel from the UK manufacturing sector, Nickell and
Wadhwani find no evidence of union influence on employment and mixed evidence of
efficiency wage effects. Hendricks and Kahn find EB effects in their study of the demand for
police in the US.
This paper builds on this work in two ways. First, it approaches these issues using
quantile analysis that more completely characterize the distributions of wages and labor
demanded than can be done using the conditional mean based linear regression approaches
(OLS, 2SLS) that are standard. Despite assumptions of identical agents, in fact the samples
may show substantial heterogeneity and large differences in the impacts of regressors across
lFor discussions of the theory of efficiency wages see Stiglitz (1974), Krueger and Summers (1988), Phelps
(1994) and Weiss (1990). For a review of the literature on union impacts, see Lewis (1986).
1



quantiles. For example, union power might be expected to impinge more strongly among
those who receive a relatively high wage given their human capital. But, alternatively, unions
may put a floor under the wage such that workers whose productivity is very low given their
nominal human capital and leave the rest of the wage distribution market determined.
Efficiency wage effects would probably also be expected to be most prevalent among those
paid "above the market" given their human capital. The results show the power of this
technique to uncover differences that would ordinarily go undetected. The paper offers the
first use of quantile analysis in firm level analysis of labor demand and the first use of correct
standard errors in two stage least square quantiles.
Second, it analyses Mexico, a country with unique institutional, economic and
political characteristics that make it an important case study to add to the literature for two
reasons. First, we may observe union behavior that, although theoretically plausible is
contrary to that commonly found. The manufacturing sector studied here is heavily
unionized: 18% of firms have no union representation, and the rest have a median
unionization rate of 70%. However, the massive Labor Congress (CT) which embraces the
Confederation of Mexican Workers (CTM), the Revolutionary Federation of Workers and
Peasants (CROC), the Federation of Government Workers (FSTSE) and roughly 38 other
labor organizations has had a longstanding and close relationship with the governing
Revolutionary Institutional Party (PRI). Particularly since 1987 with the inception of the
Pacto Social- a joint agreement of labor, business and the government to promote price
stability- unions have closely coordinated wage demands with national stabilization
objectives. This has made possible extreme downward flexibility of wages during recent
2



crises and may have curtailed union power along this dimension.2 In addition, it can be
argued that in the absence of unemployment insurance, employment enters more heavily in
union objective functions than in the industrialized countries. The particular constraints and
elements of union utility may give rise to EB outcomes with implications for the structure of
wages, and the overall level of employment distinct from those generally anticipated. It also
may permit a test of the assertion (see Nickell and Wadhwani, Layard and Nickell among
others) that the wage-employment relation may slope upward.
Second, the Mexican labor market is well suited to testing for efficiency wage effects.
Though a longstanding literature explains dualistic LDC labor markets by government or
union interference in the wage setting process,3 Mexico's minimum wage was not binding in
the period we study (Bell 1997), and if unions focus primarily on employment, then the wage
structure and whatever dualism is observed may be emerging endogenously through
efficiency wage effects. The existence of a large non-unionized sector permits isolating such
effects whose manifestations can sometimes be indistinguishable from the outcomes of union
bargaining. Further, since the Mexican Constitution prohibits firing of workers except in
extreme circumstances we are arguably testing for one particular variety of efficiency effect
arising from the prevention of turnover (Stiglitz 1974).
The data set we work with is exceptionally rich. It permits conditioning on numerous
dimensions of firm heterogeneity as well as offering some that may conceivably be associated
with efficiency wage effects. It provides evidence on the dynamics of unionized firms and
2 Though in November of 1997, the New Union of Workers (UNT, .7-1.5 million workers) split from the CTM
largely over what was perceived to be excessive responsiveness to government initiatives, across the period
analyzed here some analysts have seen a decline in union influence both within the PRI and overall. This
discussion partly based on Collier and Collier (1991) and Brooks and Cason (1998).
3



their behavior in the use of three inputs: unskilled and skilled labor and, to a lesser degree,
capital. Disaggregating labor promises an improvement over the vast majority of efficiency
bargain papers as we may avoid a composition bias in the demand for labor due, for example,
to the substitutability of types of workers. Further, we can go beyond the standard (static)
labor demand literature with worker types (e.g. Hamermesh, 1993, and references therein)4
that has not allowed for the possibility of employment decisions occurring along a contract
curve as in the EB solutions instead of the standard labor demand curve.
The paper is organized as follows. The following section presents the theoretical
background and the testable implications. The third section discusses quantile analysis. The
fourth presents the data set used in the paper and the fifth the empirical results. The last
section concludes with a summary of results.
Ila. Analytics: (Overview)
Efficiency Wages:
The extensive literature on efficiency wages provides a rationale for firms to
voluntarily pay wages above the market clearing level. One common variant of these models
arises from the difficulty of monitoring individual workers and the lack of any penalty from
being caught "shirking" - any activity, or lack thereof, that might be detrimental to the firm. If
wages are market clearing, a worker fired for shirking can simply get another job at the same
wage. However, if all firms pay higher than market clearing wages, unemployment will be
3 See, for example, Harris and Todaro(1970) See Esfahani and Salehi Isfahani (1989) as an example of
modeling LDC dualism in an efficiency wage context.
4Hamermesh also points out the clear advantages of using microdata and the dearth of such studies.
4



created in the economy that creates a disincentive to being laid off and hence to shirking.
Since, in many Latin American countries, workers can be fired only with difficulty,
the "turnover" variant of efficiency wage models is probably more appropriate: firms must
invest resources in workers when they are hired, perhaps through training or through the
process of recruitment, that will be lost if the worker leaves. Hence, it is worthwhile for firms
to pay higher wages and raise the opportunity cost of leaving.
Interviews with Mexican entrepreneurs in the survey used here support this view.
Roughly 30% stated that the resignation of recently trained workers was a problem. This is
almost certainly an understatement for two reasons. First, "recently" may not capture the
relevant period of return on the investment in the worker. Second, if the firm is already
paying the optimal efficiency wage to prevent workers from leaving, it will not report
excessive turnover as a problem. Of those reporting frequent resignations after training, 58%
do something to raise the total well-being of the worker after training, 28% raise remuneration
without promoting the worker, and 40% take measures that increases the wage of the worker,
including promotions (see Appendix I).
The efficiency wage argument is particularly compelling in LDCs where firms may
absorb a larger share of education costs due to poorly functioning education systems. Thus,
firms will be very concerned about preventing workers they train from moving to another
firm. In addition, in countries where self-employment (formal or informal) are considered
desirable destinations, it is possible that workers enter formal salaried work to accumulate
' As Marquez and Ros (1990) noted, and has been confirmed by later studies, wages of similar workers rise with
firm size, much as they do in industrialized countries. Further, Marquez (1990), Abuhadba and Romaguera
(1993) and Schaffner (1998) find efficiency wage effects in the patterns of wage differentials that are strong
and highly correlated among Chile, Venezuela, and Brazil and the U.S.. This suggests that the conditional wage
dispersion (wages adjusted for human capital) and rigidities may be emerging endogenously and are not due to
5



skills and financial capital, and then quit to open their own business.
Both theories imply that the offers workers can get outside the firm (the outside wage),
as well as the probability of being able to get ajob at that wage (the hiring rate) should be
important to determining the wage that is set in the firm, as well as to the quantity of labor
hired.
Union Bargaining.6
The most common view postulates that unions maximize utility, which may be a
function of both the wage received by union members and the level of employment, subject to
a constraint representing combinations of the two that firms are willing to pay, the labor
demand curve. In the "Right to Manage" view unions would identify the level of the wage
that maximizes their utility, and firms simply set the level of employment.
However, if the firm is a monopolist or oligopolist and earns excess profits, then both
unions and firms may be better off by coming to a bargain that pushes them off the labor
demand curve. Figure 1 traces out a series of iso-profit curves- combinations of the wage and
level of employment such that the firm earns the same level of profits. A lower curve implies
a higher rate of profits. The apex of each curve is necessarily cut by the labor demand curve:
the firm maximizes profits subject to any given wage, that is, it chooses the level of
employment that puts it on the highest iso-profit curve possible. Point P represents one such
point. As the wage rises or falls, the firm's optimal level of hiring traces out the labor demand
curve, the locus of all apexes of iso-profit curves. Employment either below or above the
profit maximizing level (100 at WO) necessarily implies that the firm earns fewer profits and is
either government or union intervention.
6 Graphs taken from and discussion based on Borjas (1996).
6



thus on a higher iso-profit curve. Therefore the iso-profit curves must slope downward on
either side of the intersection with the labor demand curve.
As point M in figure 2 shows, a better deal for both workers and firms can be
negotiated than that at "Right to Manage" equilibrium at point M. Here, the union's utility
curve is tangent to the demand curve, but not to the iso-profit curve of the firms. Thus, the
willingness of workers and firms to trade off employment for wages is not equal, and the
equilibrium is not efficient. Two alternate and more efficient bargains where the two curves
are tangent can easily be seen, both of them off the labor demand curve. First, at point R,
Unions reach a higher level of utility, UR compared to UM while firms are earning the same
level of profits. Alternately, at Q, unions are no worse off while firm profits are higher.
Which bargain, R, Q or perhaps Q', where both are better off, are "efficient bargains" and lie
on the contract curve. The contract curve is the set of efficient bargains ranging along the
line PZ from P, where workers have no bargaining power and take the market wage W* and
the firm takes all profits, 7c*, to Z where 7Tz represents the level of profits below which the
firm would go out of business, and the union captures all of the monopoly rents. This iso-
profit curve also suggests that the maximum wage workers could ever gain would be W. and
then only if it cares very little about employment.
These bargains along PZ, however, are clearly not efficient from a production point of
view: at any bargain except P, more workers are being hired than the firm would hire in the
absence of a union, E*. This "featherbedding" is a way of transferring firm profits to workers
through the creation of unnecessary positions, rather than wages. The final equilibrium
clearly depends then on the goals of the union as captured in the shape of its utility function,
that jointly with the firm's iso-profit functions determines the contract curve, and the union's
7



relative bargaining strength, which determines the position of the final bargain along the
contract curve.
Both union objectives and bargaining power in Mexico may be different from those in
industrialized countries for a variety of reasons. First, like much of Latin America during the
1980's and early 90's, job growth has been slow relative to population growth. Second, as is
the case with most of its neighbors, Mexico has no system of unemployment insurance and
employment stability may be more highly valued than wages. Third, since the post-revolution
inception of the Institutionalized Revolutionary Party (PRI) in 1929, the major unions have
had a longstanding and close relationship with the government. Particularly since 1987 with
the inception of the Pacto- a joint agreement of labor, business and the government to
promote price stability- unions have closely coordinated wage demands with pacto guidelines.
These factors taken together may lead to an emphasis on employment creation, relative to
pushing up wages in the union utility function.
The next section details how empirically it is possible to detennine whether the type of
bargaining occurring as well as if efficiency wage effects are important.
Ilb. Analytics (detail):
Broadly following Nickell and Wadhwani and Layard and Nickell we postulate a firm
facing a downward sloping product inverse demand curve do with shift term, a. Its real
revenue function
R(N,Q2,e,) = F(N,Q,e)d(F(.),ar) Rt> O,R2 >0,R3 > 0
8



is a function of the labor it hires, N, the stock of other factors including capital, management
ability, technology, Q, and also efficiency wage effects on the productivity of labor, e. Among
these is the ratio of the inside wage, W, to the expected alternative outside wage, E(Wa).
Firms bargain with a union whose utility
u=U(W,E(Wa),N) UI>O,U2<O,U3>O
depends on the wage, the expected outside wage, and employment. In the "right to manage"
model the union bargains for a level of W, and lets the firm choose the level of employment.
However, if the union cares about employment as well, then its utility is maximized over both
N and Wand the outcome is determined jointly in an "efficient bargain" with the firm. In this
case, the firm moves off the demand curve it would face in the RTM scenario and onto the
contract curve.
The result of a standard Nash bargaining model yields a system of equations, both for
employment and the wage. The firm solution is a system of equations for labor demand and
wages of an (implicit) form such as
N = N(W, E(Wa), Z2,e, Ov)
W= W(E(Wa),Z2,e, Ow)
that reflect the compound effects of the two utility functions, as well as union bargaining
power over employment, EN and the wage, EO,. Z2 contains variables that determine the
position of the labor demand relation, such as Q,and o. The expected outside wage enters both
through the union utility function, and efficiency wage effects.
Several empirically testable predictions derive from the Nickell and Wadhwani and
Layard and Nickell framework which are testable with the Mexican data:
9



a. If unions bargain solely over the wage, then union power will be captured entirely in
the wages paid by the firm and free-standing proxies for union power should have no effect in
labor demand functions. Alternatively, if unions also bargain over the level of employment,
the union proxy should enter positively in the demand equation. In the extreme case that
unions do not bargain over the wage, but only employment, the union terms should be
insignificant in the wage equation.
b. Since the workers' alternative, the expected outside real wage adjusted for the
probability of getting a job, enters both in the firm's calculation of the optimal efficiency
wage as well as the union utility function, its predicted sign and magnitude are ambiguous in
cases where union power is present.7 To avoid this problem, we will work with both
unionized and non-unionized sectors to search for efficiency wage effects.
c. The sign of the employment/wage elasticity depends on whether unions have more
power bargaining over employment or over wages.
d. As union power over employment determination e rises, the elements of Z2 (Q, and
c) should lose influence in the labor demand equations.
We are particularly concerned with how these effects vary across a heterogeneous
sample. Most obviously, if unskilled workers are represented by unions more than the skilled,
we may observe different union and efficiency wage effects for each group. However the
quantile analysis detailed below also allows us to investigate whether these factors impinge
differently across the conditional distribution within these two samples.
7Nickell and Wadhwani argue that the appearance in the demand function of outside wages indicates the
presence of efficiency wages unless unions both bargain over employment and more importantly, have a non-
standard objective function, with the sign depending on the size of the standard employment-wage elasticity.
10



III. Empirical Methodology
Conditional mean regression estimators, such as Ordinary Least Squares, are
traditionally used to estimate the relations such as those posited above. Minimizing the
squared sum of errors allows estimating the values of the parameters that predict the mean of
the dependent variable, conditional on a set of explanatory variables chosen. However, if the
sample is not completely homogeneous, such techniques may hide differential effects of the
regressors across the distribution that may be a critical part of the story being told. Further, if
there are large outliers, or the distribution of the disturbances is non-normal, conditional mean
estimators may be inefficient and often biased.
These concerns can be reduced somewhat by estimating the conditional median
regression where half the errors lie above, and half below the fitted curve. Quantile analysis,
introduced in Koenker and Bassett (1978), extends this analysis to estimating curves where
approximately T% of the errors will be negative and (I 00-c)% of the errors will be positive.' If
the errors are i.i.d., slicing the distribution at different quantile levels has little effect on
parameter estimates and little information is lost in a single measure of the conditional central
tendency, such as the parameters generated by OLS or median regression. However, figure 3
shows that asymmetries or heteroskedasticity in the distribution of errors may lead to
substantially different estimates of the impact of the variables under study.
The problem of estimating an equation with endogenous explanatory variables under
quantile analysis was addressed successfully by Powell (1983). A two stage method, where a
least square regression is run on the first stage and median regression on the second as in
2SLS, was shown to generate consistent estimates with asymptotically normal distributions
11



under weaker assumptions than least squares. This special case of a two-stage quantile
regression (2SRQ) was generalized for any quantile by Chen and Portnoy (1996).
In all the empirical work below, we present results of the quantile analysis at c=
50 (the conditional median regression) completely, -r= 10 where 10% of the deviations lie
below the estimated regression, and t= 90 where 90% lie below. Appendix II presents the
standard conditional mean regressions, whether OLS or 2SLS for reference. In all cases they
are very close the median regression.
Correct Standard Errors for Two Stage Regression Quantiles
Standard errors estimates for regression quantiles have been studied in Buchinsky
(1995) for models with exogenous regressors. Based on a Monte Carlo study, the author
recommends the use of the design matrix bootstrap, as this method is valid under many forms
of heterogeneity (heteroscedasticity), with a small reduction in efficiency in iid samples,
compared to other methods. As in our model we cannot reject apriori heterogeneity
(confirmed by LS based heteroscedasticity tests), so we choose this method to estimate the
covariance matrix of the regression parameter vector.
The method amounts to sampling pairs (Yi *,x,*) in a regression model Yi = xi',' + u
to generate a pseudo samnple of the data and obtaining an estimate b* of ,. The process is
repeated B times and the B estimates of ,B are used to construct the covariance matrix. The
pseudo sample can be of size n, the original sample size, as in this paper, and B should be
large enough to guarantee a small sample variability of the covariance matrix. We chose
s The technique has generally been applied to estimating returns to education, (Buchinsky 1994).
12



B=200, based on the literature. The use of the design matrix bootstrap method for models with
endogenous regressors can be argued for using the results of Freeman and Peters (1994) on
bootstrapping 2SLS models and the analogy principle of estimation in Mansky (1988). In the
present case, the covariance matrix for the labor demand equations were obtained sampling
the triplets (yi *,xi*, zi*), where z, is the vector of instruments, or exogenous variables in the
system and xi includes the endogenous explanatory variables. Both first and second stage
regressions are then run to obtain the estimates of the parameter vector ,B for each of the B
samples.
IV. Data:
We employ the Encuesta Nacional de Empleo, Salarios, Tecnologia y Capacitacion
(ENESTYC), the National Survey of Training, carried out by the Mexican Official Statistics
Institute (Instituto Nacional de Estadistica, Geografia e Informatica, INEGI) for the year
1992 which contains detailed information on firms specific variables relating to employment,
technology, capital stock, etc. A 1995 Survey was also available that had the advantage of
collecting data on share of the work force unionized at the firm level. However, it lacked
information on the human capital of the work force and because the period it spanned
contained the Tequila crisis in December 1994 and the beginning of the ensuing recession, we
work primarily with what may be considered a more "normal" period of relative prosperity.
13



Variables:
Core Variables:
Wages and Employment: Following Roberts and Skoufias (1997) and others the wage and
labor stock of skilled (Wsand Ns, respectively) and unskilled labor (Wuand Nu, respectively)
are derived as weighted averages of subcategories within each. The weights for constructing
the labor variables are the full wage (wage, social security and other non-wage benefits) per
worker that capture the relative "marginal product" of each subclass. This generates a
compound measure of "efficient units" of skilled or unskilled labor with the least productive
subclass of labor as the numeraire in each.9 The wage is then the total payments to the
subclasses of labor divided by the labor measure, which, in practice is simply the wage of the
numeraire subclass. The average schooling of the unskilled is about half of the skilled
workers.
Value Added (Value Add.): the value of total 1991 output minus the expenses in materials and
energy in million Pesos.
Human Capital Variables:
Schooling (School and School2): Average years of schooling of the employed workers in
each skill level in the firm, where the years of schooling were obtained from 7 levels.
Experience(Experience and Experience2): Average tenure in the firm of workers within each
sub-class of labor.
9 This approach is arguably preferable to simply assuming that each subclass of workers has identical
productivity in the aggregation. In the skilled category are found directors (directivos), Professionals
(profesionista), Technical workers (tecnicos), Administrative Employees (empleados administrativos) and
Supervisors (supervisores). Among the Unskilled are professional workers (obreros profesionales), specialists
(especializados) and general workers (general).
14



As Dickens and Katz note, the most thorough test for efficiency wages are those that
are able to cross individual level human capital variables with plant level characteristics and
control for both. We are able only to control for the mean level of schooling in the plant and
the mean tenure of each category of workers within the plant. Though not a good measure of
individual experience, the latter is a good proxy for the accumulation of firm specific human
capital and arguably better than the potential experience variable (age-education) found in
many articles (see, for example, Lam, and Schoeni 1991).
Union and Efficiency Wage Variables:
Union Density (Union): The 1995 ENESTYC tabulates union density (ratio of firm employees
affiliated to a union) by individual firm while the 1992 only tabulates a dummy for the
presence of unionization in the firm. Under the assumption that union structure changes little
over two years, we assign a value of zero to the union density variable if the 1992 dummy is
zero and the median sectoral value from the 1995 survey if the 1992 dummy is unity.
Outside(alternative) wage (Wa): Log of the median sectoral wage, at the 4-digit industry
level.
Hiring rate(Hiring): In the cross sectional context, the aggregate unemployment rate
employed by Nickell and Wadhwani is not useful'°. We instead use the sectoral hiring rate
(number of hires over level of employment in the sector), as a measure of the probability of
finding a job if you leave (with your skills). This is more consistent with a labor turnover view
of efficiency wages.
'0 The cross-section nature of the data and the impossibility of identification of the firm's regional location
precludes the use of an regional average wage, or typical informal sector earnings.
15



Quality Control (Qual. Con.): dummy for firms that have quality control of output.
Productivity after Training (Training): dummy for firms that indicated increases in
productivity after implementing training programs.
Two size and eight sectoral dummies.
Shift parameters in Z2
Capacity Utilization (Cap. U): average capacity utilization as reported by the firm in 1991.
Productivity: labor productivity measured as output per unit of labor. 11
Capital Labor Ratio (KIL): Log of the ratio of the reported value of capital stock over labor
force.
Corporate: dummy for firms that belong to a corporation with multiple branches.
Foreign: dummy for firms with more than 50% foreign ownership.
Age (Age and Age2): age of the plant in years.
Export: dummy for firms with 10% or more of sales to other countries.
Automated: percentage of capital stock value of automated machinery.
Competiveness: dummy for firms that identify their product as "competitive" against imports.
Research and Development (R & D): dummy for firms with positive R&D expenses in 1991.
Technology Acquisition (Tech.): dummy for firms with positive expenses in technology
acquisition in 1991.
Observations with missing, incomplete, or zero entries for employment, output or
capital stock were dropped. We also only include privately owned firms and those with over
16 employees."2
" To avoid a division bias in the productivity coefficient we use previous year productivity, as in Borjas
(1980). Dropping this variable from the regressions does not change the results noticeably in general.
16



Table 1 presents the summary statistics of the variables employed. A few general
observations seem important. First, mean schooling of "unskilled" workers is half of those
identified as "skilled," and the mean unskilled experience is slightly smaller than that of
skilled workers, possibly due to higher turnover. Second, only 17,8% of the firms do not
have their workers associated with a central sindical (union) so there are roughly five times
the number of observations in the union sample than the non-union sample. This can make
comparisons of the significance of effects occasionally ambiguous. Within the union sample,
the mean unionization rate is 55%. Third, Mann-Whitney tests show that the unconditional
distributions of most variables differ between the unionized and unionized samples."3 This
suggests that the two samples differ in fundamental ways and perhaps should not be combined
during the analysis.
Va. Empirical Results: Wage Equations
By contrast to the standard competitive model where firms take wages as given, the
framework above makes it clear that wages and employment are determined jointly and hence
constitute a system of equations to be estimated. However, standard wage equations with
employment omitted can be thought of as a reduced form and can be estimated using one step
estimators such as least squares or median regression. Broadly following Dickens and
Katz(l 987) and Nickell and Wadhwani we estimate the log linear approximation
Ws,,u = Warwa + ere + yuU + hyh + XyX + 8w
'2Micro fimns (up to 15 employees), are extremely underrepresented in the sample and their heterogeneity cannot
be captured with the sample weights provided. INEGI has a specific survey of micro firms that is probably better
suited to their analysis.
17



where wa is a vector of proxies for the expected alternative wage, e a vector of other possible
efficiency wage related variables, u, the union power measure, h a vector of human capital
variables, x the vector of firm related characteristics, including those in Z2.
1. General Results
Tables 2.1-2.2 present the quantile results for the non-union and union groups
respectively. In all cases, the regressions are significant at the 0 % level. The pseudo-R2 of
the median regression specifications explains 29% of the variance for skilled unionized,
1 1.5% for those unskilled, and for the non-unionized sample 3 7.8% and 15.6% respectively.'4
Several of the shift variables enter with expected signs and magnitudes although others
are more ambiguous. The impact of productivity on wages in both samples is of comparable
size as in the literature (Wadhwani and Wall 1991) although it enters insignificantly for
skilled non-union workers and of significantly larger coefficient for non-union unskilled.
Among union firms, productivity is insignificant at the 1 O' quantile suggesting that for this
part of the distribution, union power may possibly delink productivity from wages. Exporters
pay significantly more to skilled union workers in the 1O' quantile and unskilled union in the
50t" and 90" quantile, although the indicator of product competitiveness is never significant in
any regression suggesting that greater openness does not obviously depress wages. Foreign
firms pay skilled workers less across all quantiles and unskilled workers significantly more in
the 90th quantile. R& D enters positively at the median only for non-unionized skilled
workers, technology purchases enter strongly and of the predicted sign only in the unionized
]3 Standard equality of mean and variances tests were not employed as the unconditional distributions are clearly
non-normal.
1 Pseudo R2 defined as squared correlation between original and fitted observations. The standard version
based on the decomposition of total variation between fitted and residual values is not correct for quantile
18



sector (with the exception of the 90 quantile), indicating that union firms use more
productive technology. K/L enters positively for non-union unskilled workers at the 90th
quantile. Automated never enters significantly.
These results are similar to those from the OLS regressions presented in Appendix 2.
Appendix figures Al. 1 -Al.4 show that the predicted relation between wages and employment
is very similar in both cases.
2. Union Effects
The union and non-union samples were run separately for two reasons. First, we are
interested in isolating efficiency wage effects that, as in the case of the outside wage, are
sometimes hard to disentangle from union effects. Second, there may be serious problems of
selection bias in measuring union premia. In preliminary regressions, we find that a union
dummy in the combined sample suggests that firms with unions pay 15.2% more to skilled
workers and 9.25% more than non-union firms and the continuous union density variable
enters positively and significantly as well. However, it is very difficult to know whether
unions cause wage differentials, or whether unions are more likely to be found in certain types
of firms who also pay higher wages. As an example, schooling enters significantly across all
sub-samples, but experience enters only in the union sample. The average union worker with
five years of experience would make roughly 12% more than his non-union counterpart and
the unskilled perhaps 8%. Constraining the experience coefficient to be equal across sub-
samples could give rise to differentials of the magnitudes of the union dummies. If the
differing coefficients represent rigidly enforced seniority base promotions or wage hikes, then
analysis.
19



the differential might legitimately capture union power (see Borjas 1996). However, if firms
use production techniques that require more on-the-job training and also make them more
prone to unionization, then the differentials capture legitimate differences in human capital
rather than union power.
As mentioned earlier, Mann-Whitney tests show that the unconditional distributions of
most variables differ between the unionized and non-unionized sample, and Chow tests for the
equality of the coefficients between union and non-union firms strongly reject at the 1% level,
consistent with the view that union and non-union firms may be fundamentally different. As a
strategy that partially alleviates the selection bias problem, we test for the impact of union
power within the sample of firms with unions.
In a very surprising result, the free-standing union term virtually never enters
significantly as would be predicted by traditional right to manage models.'5 The labor demand
regressions in the next section will cast doubt on the obvious interpretation that unions have
no power. But the quantile regressions also reveal a story hidden to standard techniques. For
the upper quantiles, there is no impact on unskilled wages. However, for the 1 O quantile a
strong and positive coefficient emerges on the union density term while the human capital
variables that are important for the other quantiles largely disappear. An interpretation is that
workers who earn little given measured human capital, are helped by unions. If for example,
a worker's unobserved characteristics, such as reliability and diligence, dictate a low wage
relative to those who, on paper, appear similar, unions will push them toward the average for
their class. To the degree that this measures distortion in the wage distribution, it appears to
'5 This contrasts with the findings of Panagides, A. and H.A. Patrinos (1994). However, their finding of union
impact on wages probably arises from the fact that they could not control for firm size or other charateristics.
20



be confined to the I0th quantile. Overall, union density does not appear to have a major
impact on unskilled wages.
A striking result is the strong negative impact of union density on skilled wages,
precisely the opposite effect found in the combined regression. This may be due to more
successful unskilled worker bargaining over firm rents (distributed in forms other than
wages), or it may be related to a desire to reduce the wedge between skilled and unskilled
remuneration for equity reasons.
3. Efficiency Wage Effects
Outside wages enter for all union worker types and most quantiles, skilled non-
unionized workers at the median and the 10th quantile for unskilled non-union. The
significance of the outside wage in both the union and non-union samples, although less
strikingly, suggests that its influence is more than simply a reference for union bargaining.
Further, the magnitude of the impact for unskilled workers at the 10th quantile is double that
for the union sector and almost double for skilled workers at the 50t. For both samples, at the
90th quantile, outside wage effects drop dramatically in significance and magnitude for skilled
workers. A possible interpretation is that workers paid well given their human capital are
likely to receive a larger share of total remuneration and the efficiency wage premium in
unobservable non-wage benefits.
Somewhat counterintuitively, the hiring rate enters with unexpected sign although
insignificantly in all but the unionized skilled worker sample at the 50th and 90' quantiles.
Whether training had been undertaken that was perceived as productivity enhancing has the
predicted sign for non-union skilled workers and high wage unskilled non-union workers.
21



Inter-industry wage differentials (Krueger and Summers 1988) as captured by sectoral
dummies are not very strong in most sectors after controlling for human capital variables in
firms, contrary to much of the literature. On the other hand, firm size effects, are strongly
present as in Schaffner (1998). For both union and non-union firms, apparently similar skilled
workers in small firms of between 16 and 100 workers make roughly 50% less in wages and
benefits than they would in a firm of over 250. In the event that this is in fact, due to
efficiency wage considerations arising, perhaps from difficulties of monitoring, the implicit
segmentation emerging endogenously among formal enterprises is very large.
Vb. Empirical Results: Labor Demand
The data allow the estimation of system of skilled and unskilled labor demand
finctions and hence the examination of unions' impact on the substitutability of factors and
the allocative efficiency of firms. We estimate log-linear approximations to both equations.
fls, u = W,& + /Jqq + Wa/h + /iuu + x,& + a
n,.,s is the log of labor demand for skilled (NS) or unskilled labor (NU), w=log(WS, WU) the
vector of log own and cross wages16 q, log firm output, w, the vector of proxies for own and
cross-expected alternative outside wages (log outside wages and the sectoral hiring rates), u
the measure of union bargaining power, x the vector of other firm characteristics.
Ideally, the labor demand equation would be estimated using instruments for wages
due to possible measurement error, random productivity shocks, and the fact that unions may
16 As in Roberts and Skoufias we do not have a measure for the capital services price. We use the corporation
dummy as well as the level of automated machinery and firm size dummies to differentiate the firms on their
opportunity costs of capital services.
22



bargain over both wages and employment simultaneously.'7 However, good instruments prove
difficult to find. We are not working with panel data and, as the previous section suggests, the
most complete model for the unskilled wage explains relatively little of the variance. The
results, as Roberts and Skoufias found for Colombia, were counter-intuitive."8 Output should
be considered endogenous also, due to measurement error (current output different from the
output used in the decision making of the firm) and the presence of unobserved firm specific
shocks that affect output and employment. This is confirmed by Durbin-Hausman-Wu
(Davidson and MacKinnon, 1993) tests. With more success we instrument using the capital
stock (and its square), capacity utilization levels and other firm specific technology variables.
Chow tests for the equality of coefficients between union and non-union firms for skilled and
unskilled workers respectively reject at conventional significance levels. Again, it is clearly
not appropriate to combine the samples and we report separate regressions for each group.
1. General Results
Tables 3.1 and 3.2 present the results from a static labor demand equation for the non-
unionized and unionized sectors respectively. The regressions are broadly consistent with
standard factor demand theory (see, for example, Chambers 1988 and Hamermesh) and other
empirical studies. Own-wage elasticities strongly suggest a downward labor demand relation
'7 Hours composition bias (Hamermesh) does not seem to be a problem in our data as the majority of firms did
not change the number of weekly and daily shifts across a six month period in 94-95, during the tequila crisis.
f  In both union and non-union firms using OLS, we have an upward sloping demand curve for skilled labor. In
the former a flat demand curve for unskilled workers appeared with "wrong" signed outside wage effects for
both (yet consistent with the wrong insignificant inside wages coefficients). The large union effect was
maintained for unskilled. Hiring rates never entered significantly. The (naive) R2 for unskilled fell from .68 to
.15. We attribute the results to the unavailability of good instruments (hinted by the non-rejection of Durbin-
Hausman-Wu tests for unskilled workers) and present only the results with non-instrumented wages and lagged
output.
23



in both sectors. Output elasticities are statistically similar in both samples, as expected by
theory under the hypothesis of a homothetic technology. Their small magnitude suggests that
firms are operating in the downward sloping part of the long run average cost curve, as in a
monopolistic competition model. Cross-wage effects are zero for all median regressions."9
Morishima elasticities of substitution between types of labor are in the 60-35% range, and if
we assume that total labor costs are half that of the capital services (10% of the capital stock)
the capital-unskilled elasticity of substitution labor seems to be around 0.5 and nearly twice
that of the capital-skilled labor, as in the references in chapter 3 of Hamermesh.20
Estimating the relation across quantiles adds additional information.   Perhaps
unsurprisingly, firms who have a large workforce after adjusting for the variables in the
regression show higher output elasticities and hence smaller scale economies across all sub-
samples operating closer to the long run minimum of the average cost curve. This is most
striking for the case of non-union skilled workers where the 10' quantile has an output
elasticity of .22 which rises to .58 in the 90' quantile. Figures A2.1-2.4 in the appendix
illustrate what these differences imply for the employment trajectory for each quantile level.
The 9O"h quantile employ vastly more additional workers for an additional unit of output than
the firm at the conditional median and this appears particularly to be the case for unskilled
labor where the distribution appears far less symmetric than is the case for skilled workers.
'9 Both labor types seem to be substitutes for capital, with the cross price unskilled labor-capital elasticity larger
in absolute value than the skilled-capital elasticity. Roberts and Skoufias results suggest also monopolistic
competition and skilled and unskilled workers have negative cross price elasticities, i.e., they are complements.
20 Morishima elasticities of substitution measure how much the ratio of factor used, rather than simply the
amount of one factor, changes with respect to one of the factor prices. In unionized firms, the ratio of labor
types mean wage costs is close to one and about 0.85 for non-union. We use the fact that factor demands are
homogeneous of degree zero in prices to obtain the capital elasticities.
24



Less obviously, these firms with lower economies of scale also show higher
elasticities of substitution. For skilled workers, own elasticities quadruple and double for
non-union and union firms respectively across the quantile range and also substantially
increase for unskilled workers although less dramatically. This, increase across quantiles can
be seen in appendix figures A2.1-2.4. No clear pattern emerges for cross elasticities across
quantiles.
Several of the proposed demand shift variables enter significantly and of expected
sign. Capacity utilization, as a measure of factor usage is positive in all regressions although
clearly significantly so only for unskilled at the 50' quantile for non-unionized and for the
10k in the both the unionized groups and 50 for the unskilled. The virtual absence of any
significant effect among skilled workers with the exception of a counterintuitive negative
effect for the 1 O'hquantile in the union sample suggests skilled labor hoarding. Exporting
firms hire roughly 15-20% more unskilled and perhaps 0- to 4% more skilled workers (never
significant) as would be expected given that 82% of these firms engage in some maquila work
that is particularly labor intensive.
Foreign firms tend to hire more workers, relative to national although only statistically
significantly so across all quantiles of the union skilled sample. Older union firms have more
skilled workers although not significantly more unskilled. Whether the firm is part of a large
corporation enters positively for all groups for at least the lower quantile, and not for the
upper quantile.
25



2. Union Effects
Although the previous section suggested that there were no union effects on wages,
union density has a strong positive effect on the level of unskilled employment across all
quantiles. This suggests a unique example of efficient bargaining where unions accept the
market wage, but cause firms to move off the demand curve to hire more labor. The
regressions suggest that across all quantiles, for each 1% of the work force that is unionized,
unskilled employment appears to increase by roughly 2.5%. Despite this, we find little
reduction in the magnitude of the shift termns, such as capacity utilization, foreign ownership,
and membership in a larger corporation as the analytical overview suggests should happen,
and the clear downward sloping demand relation is preserved. This suggests that the position
on the contract curve may not be "too far" from the demand curve. However, estimates of the
impact of unions on productivity (not shown) confirm the intuition that there is a significant
adverse effect arising from the additional labor hired.
Somewhat counterintutively, the regressions for skilled workers find a negative union
effect, although one that is only remotely significant is the top quantile where a I point rise in
the percentage unionized leads to a roughly equal equivalent fall in the number of skilled
laborers. This might suggest that, among firms with conditionally high levels of employment,
unions represent primarily unskilled workers and "crowd out" those more skilled.
Alternatively, perhaps when unions seek to organize, they pick firms with conditionally large
labor forces, but within this group choose those with relatively more unskilled workers.
As might be expected, union firms have statistically significant, although not
substantially smaller own wage elasticities that might suggest slightly less flexibility in the
allocation of unskilled workers. However, the pattern is reversed for skilled workers and the
26



large employment firms appear to be driving this result. For conditionally small firms, the
union sample for unskilled labor has a higher point estimate than that of the non-union sector
and at the median, there is no noticeable difference.
The presence of unions has little impact on output elasticities. A firm with 10% higher
output is likely to hire roughly 4% more skilled. This is not necessarily surprising since a
greater hiring rate of workers by unionized firms occurs on top of a larger base, leaving the
elasticity virtually unchanged.
In sum, unions appear to concentrate their efforts on featherbedding, but it is not
obvious that they reduce flexibility in allocating factors. It also appears that unions have the
effect of reducing the dispersion of elasticities across quantiles.
3. Efficiency wage effects.
There is evidence of efficiency wage effects in the demand functions as well. As
Nickell and Wadhwani show, the expected outside wages and hiring probabilities can enter in
the demand function in the unionized sector even when there are no efficiency wage effects
because unions may use them for reference. However, the outside wage, and the probability
of hiring enters strongly for unskilled non-union workers. In the union sector, the hiring rates
are significant at the median for the skilled but outside wages are, in general, either counter-
intuitive or insignificant
Focusing on the non-union sector, the quantile regressions suggest that the outside
wage effects are fairly consistent across quantiles-unimportant for skilled workers and of
similar magnitudes and significant for unskilled. No clear story emerges from the coefficients
on hiring rates.
27



VI. Conclusion
Two provocative findings emerge from this paper. First, Mexican unions do not seem
to show "right to manage" behavior and their impact on wages appears relegated to putting a
floor under the conditionally least well paid workers (10Oh quantile). Nonetheless, they do
greatly increase the quantity of unskilled workers hired and appear to "crowd out" skilled
workers in what appears to be an extreme form of Efficient Bargaining. The impact on
productivity is, by definition negative, but the flip side may be that unions are forcing firms to
use "appropriate" technology that uses relatively less capital and more unskilled workers. As
Layard and Nickell have noted, the general equilibrium effects on the overall level of labor
employed in the economy are clear: if unions bargain over employment as well as wages, as
seems to be the case here, employment in the union sector should be higher, under a smaller
than unity elasticity of substitution, as also seems to be the case here. It is therefore possible
that far from reducing the level of employment by rationing workers into the informal sector,
Mexican unions may be preserving low skilled jobs, at the cost of smaller wages.2'
The second important finding is that there appears to be strong evidence of efficiency
wage effects. A tentative conclusion would be therefore, that whatever segmentation is
observed, defined as equivalent workers earning different wages, is more likely to be
emerging from the efficiency wage effects than from union effects. This would suggest that,
even in the absence of minimum wages or union power, substantial segmentation will remain
in Mexico as well as other LDCs.
21 For an analysis of the differential impact on labor markets of economic reform under different types of union
behavior, including the "featherbedding" found here, see Devarajan, Ghanem and Thierfelder (1997).
28



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Table 1 - SUMMARY STATISTICS
Non-Union (n=73 1)                             Union (n=3,421)
Variable     Mean Std.Dev.    Min Median    Max  Mean  Std.Dev.  Min.  Median   Max
Foreign        0.331   0.471   0.000   0.000   1.000   0.198   0.398   0.000   0.000   1.000
Age            17.036  13.414   1.000  12.000  99.000  25.188  16.123   1.000  23.000  99.000
Cap. U.*       75.104  19.339   5.000  80.000 100.000  74.479  18.102   1.000  80.000 100.000
Exporter        0.438   0.496   0.000   0.000   1.000   0.241   0.427   0.000   0.000   1.000
R & D           0.334   0.472   0.000   0.000   1.000   0.383   0.486   0.000   0.000   1.000
Tech.           0.454   0.498   0.000   0.000   1.000   0.508   0.500   0.000   1.000   1.000
Qual. Cont.     0.988   0.110   0.000   1.000   1.000   0.997   0.057   0.000   1.000   1.000
Corporate*      0.252   0.434   0.000   0.000   1.000   0.257   0.437   0.000   0.000   1.000
Union                                                  0.680   0.074   0.477   0.703   0.854
Ws              9.422   0.907   5.991   9.573  11.352   9.827   0.726   5.849   9.909  12.110
Ns              1.587   1.011   0.000   1.444   6.685   1.973   0.956   0.000   1.890   6.472
Wu              7.765   0.754   5.758   7.744  10.134   7.983   0.688   5.659   8.032  11.160
Nu              3.449   1.342   0.000   3.466   8.964   3.842   1.135   0.000   3.809   8.606
School- S*     11.962   2.120   3.000  12.150  18.500  12.150   1.708   4.909  12.233  17.843
School-U       6.603   1.497   3.000   6.450  12.000   6.873   1.321   3.000   6.783  12.000
Experience-S    5.495   3.385   0.294   4.483  25.640   6.631   4.000   0.065   5.663  32.927
Experience-U   4.124   3.787   0.000   3.000  38.000   5.817   4.864   0.000   4.118  40.000
Value Add.*     8.274   1.717   1.792   8.343  14.878   9.250   1.597   1.386   9.263  15.072
Productivity- S    6.686   1.204   1.068   6.750  12.472   7.277   1.116   0.372   7.328  13.008
Productivity-U    4.824   1.441  -0.508   4.797  10.811   5.408   1.241  -2.635   5.435  10.659
Wa-U           7.837   0.210   6.742   7.899   8.189   7.847   0.193   6.742   7.825   8.189
Wa-U           9.563   0.325   7.784   9.578  10.182   9.641   0.293   7.784   9.708  10.182
Automated*     12.726  23.325   0.000   0.000 100.000  11.837  22.601   0.000   0.000 100.000
Log(K)          7.716   1.980   2.303   7.690  14.526   9.021   2.000   1.609   9.131  15.735
Log K/Nu        3.186   1.838  -2.896   3.232   9.573   4.013   1.666  -3.626   4.191  10.125
Log K/Ns        4.342   1.635  -0.984   4.397  10.978   5.040   1.514  -1.686   5.194  10.877
Hireing-U       7.512   3.972   0.322   6.628  15.240   6.857   3.468   0.322   6.052  15.240
Hireing-S.      1.079   0.512   0.000   1.043   2.641   1.199   0.602   0.000   1.091   2.641
Training        0.211   0.408   0.000   0.000   1.000   0.236   0.425   0.000   0.000   1.000
Competitive     0.512   0.500   0.000   1.000   1.000   0.631   0.483   0.000   1.000   1.000
Source: author's calculation from ENESTYC '92. Large, medium and small firms only (from 16 employees on).
* - Mann-Whitney test does not reject equality of distributions between union and non-union samples. See
variable definitions in text.
32



Table 2.1: Quantile Wage Regressions-Non-Union
Skilled                                                                Unskilled
10%                     50%                     90%                     10%                     50%                     90%
coef           s.e      coef            s.e     coef            s.e     coef            s.e     coef            s.e     coef             s.e
Constant            4.00         (3.13)      1.49         (2.07)    7.37 b        (3.76)    -0.22         (3.72)    4.45 b        (2.20)      4.24 a      (2.30)
Productivity         0.02        (0.06)    0.04           (0.04)    0.04          (0.05)    0.09 b        (0.05)    0.12 b        (0.04)      0.09 b       (0.04)
Wa                   0.22        (0.34)    0.57 c         (0.20)    -0.02         (0.36)    0.80 a        (0.43)    0.30          (0.28)      0.31         (0.29)
Hireing             -0.14        (0.25)   -0.09           (0.12)    0.18          (0.15)   -0.02          (0.02)   -0.01          (0.02)      0.01         (0.01)
School               0.46        (0.30)    0.41 c         (0.14)    0.32 b        (0.14)    0.25          (0.18)    0.29 b        (0.13)      0.44 c       (0.17)
School2             -0.02        (0.01)    -0.01 b        (0.01)   -0.01 a        (0.01)   -0.02          (0.01)   -0.02 b        (0.01)    -0.03 c    .(0.01)
Experience          -0.04        (0.06)   -0.03           (0.05)    0.01          (0.04)   -0.02          (0.03)   -0.01          (0.02)      0.01         (0.02)
Experience2          0.00        (0.00)      0.00         (0.00)    0.00          (0.00)    0.00          (0.00)    0.00          (0.00)      0.00         (0.00)
R&D                  0.03        (0.17)    0.17 b         (0.08)    0.07          (0.10)   -0.03          (0.15)    0.13          (0.12)      0.11         (0.08)
Tech.                0.05        (0.14)    0.07           (0.08)    0.03          (0.10)    -0.05         (0.17)    0.03          (0.11)    -0.08          (0.08)
Foreign              0.19        (0.13)    0.15 b         (0.07)    0.11          (0.12)    0.00          (0.12)    0.04          (0.08)      0.40         (0.13)
Training            -0.06        (0.15)    -0.04          (0.08)    -0.13         (0.10)    0.06          (0.12)    0.00          (0.13)      0.13         (0.12)
Corporation          0.61 c      (0.24)    0.30 b         (0.13)    0.21          (0.15)    0.01          (0.17)    0.08          (0.15)      0.26         (0.15)
Foreign             -0.19        (0.18)    -0.19          (0.11)    -0.07         (0.12)    0.10          (0.13)    0.03          (0.10)    -0.03          (0.12)
Exporter             0.00        (0.00)    0.00           (0.00)    0.00          (0.00)    0.00          (0.00)    0.00          (0.00)      0.00         (0.00)
Automated            0.13        (0.13)    -0.10          (0.07)   -0.10          (0.10)    0.06          (0.11)   -0.04          (0.08)      0.03         (0.08)
Competitive         -0.17        (0.31)      0.00         (0.34)    0.31          (0.27)    0.06          (0.70)   -0.27          (0.23)      0.07         (0.20)
Qual.Cont.           0.01        (0.04)    0.00           (0.02)    0.01          (0.03)   -0.03          (0.03)   -0.03          (0.03)      0.00         (0.03)
K/L                 -0.29         (0.34)    0.08          (0.16)    0.22          (0.15)    -0.09         (0.23)    0.12          (0.14)      0.34 b       (0.16)
Sector 32           -0.65        (0.41)    0.16           (0.22)    0.16          (0.31)    0.21          (0.26)    0.15          (0.15)      0.11         (0.20)
Sector 33            0.20        (0.31)    0.05           (0.19)    0.18          (0.26)   -0.09          (0.36)    0.23          (0.21)      0.61 b       (0.29)
Sector 34            0.09        (0.37)    0.29 a         (0.18)    0.32          (0.20)    -0.34         (0.27)    0.11          (0.19)      0.44 b       (0.21)
Sector 35            0.06        (0.44)   -0.14           (0.29)    0.41          (0.29)   -0.07          (0.44)   -0.14          (0.19)      0.05         (0.22)
Sector 36            0.26        (0.48)    0.15           (0.23)    0.46          (0.34)    -0.15         (0.31)   -0.34          (0.29)    -0.33          (0.43)
Sector37            -0.15        (0.32)   -0.01           (0.15)    0.31          (0.19)   -0.25          (0.36)    0.11          (0.15)      0.11         (0.11)
Sector38            -1.17 a      (0.68)    -0.81          (0.72)    0.21          (0.46)   -0.66          (0.72)   -0.12          (0.36)      0.32         (0.45)
Sector39            -0.88 c      (0.22)    -0.65 c        (0.13)    -0.56 c       (0.14)   -0.40 b        (0.17)   -0.34 c        (0.11)    -0.41 c        (0.11)
Medium              -0.13        (0.16)    -0.13          (0.09)    -0.07         (0.11)   -0.10          (0.14)   -0.12          (0.12)    -0.17          (0.12)
Small               -0.26 b      (0.12)    -0.48 c        (0.10)   -0.29          (0.18)   -0.50 b        (0.21)   -0.46 c        (0.10)    -0.70 c        (0.20)
R2                   0.37                    0.38                    0.36                    0.12                    0.16                     0.13
Wald (27df)    231.84 c                   346.45 c                 133.70 c                 54.20 c                139.70 c                 151.88 c
Note: Sample Sizes:Non-Union n=731, Union n--3422. Median Chow tests for equality of union and non-union coeff. (27d.f) 80.57 (c) skilled,
75.11 (cXunskilled). Wald is aChi-Squaretest of regression significance. a Sig. at 10%, b 5%, c 1%. R2=squaredcorrelation between actual and predicted.
33



Table 2.2: Quantile Wage Regressions-Union
Skilled                                                            Unskilled
10%                   50%                    90%                    10%                    50%                    90%
coef           s.e    coef           s.e    coef            s.e    coef            s.e     coef           s.e     coef           s.e
Constant           -0.40       (1.85)    3.25 b       (0.87)    7.67 c       (1.07)    2.32         (1.53)    2.28         (1.20)     2.83 b      (1.48)
Productivity       -0.01       (0.03)    0.03 b       (0.01)    0.08 c       (0.02)    0.03         (0.03)    0.03 c      (0.01)      0.07 c      (0.02)
Wa                 0.45 c      (0.14)    0.32 c       (0.07)    0.19 b       (0.09)    0.45 b       (0.18)    0.48 c       (0.14)    0.53         (0.15)
Union              -0.88 b     (0.39)   -0.90 c       (0.25)   -0.99 c       (0.32)    0.83 b       (0.41)    0.25        (0.25)    0.31          (0.30)
Hiring             -0.09       (0.06)   -0.08 b       (0.03)   -0.08 a       (0.05)    0.00         (0.01)    0.00 c       (0.01)     0.00 b      (0.01)
School             0.90 c      (0.18)    0.59 c       (0.07)    0.17 a       (0.09)    0.21 a       (0.11)    0.30 c      (0.06)      0.24 a      (0.10)
School2           -0.03 c      (0.01)   -0.02 c       (0.01)   -0.01 b       (0.00)   -0.01         (0.01)   -0.02         (0.01)    -0.01 c      (0.01)
Experience          0.06 c     (0.02)    0.02 c       (0.01)    0.01         (0.01)    0.01         (0.01)    0.02         (0.01)     0.02        (0.01)
Experience2        0.00 b       (0.00)    0.00 b      (0.00)    0.00 a       (0.00)    0.00         (0.00)    0.00 a       (0.00)    0.00 b       (0.00)
R&D                 0.00        (0.05)   -0.02        (0.03)    0.03         (0.04)   -0.04         (0.06)   -0.02 c       (0.03)    -0.01        (0.04)
Tech.               0.14 b      (0.06)    0.12 c      (0.02)    0.03         (0.04)    0.13 c       (0.05)    0.12 a       (0.03)     0.13        (0.04)
Foreign             0.00       (0.05)    0.02         (0.03)    0.05         (0.05)    0.05         (0.06)    0.01         (0.03)    -0.07        (0.04)
Training            0.05        (0.06)    0.02        (0.02)    0.00         (0.04)    0.06 a       (0.06)    0.06         (0.03)    0.04         (0.05)
Corporation         0.26 c      (0.06)    0.15 c      (0.03)    0.14 c       (0.04)    0.02         (0.06)    0.03         (0.04)    0.07         (0.05)
Foreign            -0.21 c      (0.06)   -0.05 a      (0.03)   -0.10 c       (0.03)   -0.05         (0.06)    0.02         (0.03)    0.04 b       (0.05)
Exporter           0.00 b      (0.00)    0.00         (0.00)    0.00         (0.00)    0.00         (0.00)    0.00 b      (0.00)    0.00 c        (0.00)
Automated           0.01       (0.05)   -0.03         (0.02)    0.00         (0.03)   -0.03         (0.05)    0.01         (0.03)    -0.02        (0.04)
Competitive        -0.07        (0.15)    0.18        (0.21)    0.21         (0.15)   -0.07         (0.44)    0.41        (0.27)      0.36        (0.51)
Qual.Cont.         0.02        (0.02)    0.02 a       (0.01)    0.02         (0.01)   -0.03         (0.02)    0.00         (0.01)    -0.03        (0.02)
K/L                -0.15       (0.10)   -0.03         (0.06)    0.05         (0.07)   -0.07         (0.09)   -0.02         (0.05)    -0.07        (0.07)
Sector 32          0.12        (0.21)    0.07         (0.07)    0.09         (0.08)    0.12         (0.15)    0.11        (0.08)    0.05 b        (0.10)
Sector33           -0.02       (0.09)    0.00         (0.06)    0.13         (0.08)    0.11         (0.14)   -0.02        (0.09)    0.05          (0.09)
Sector 34          0.10        (0.08)    0.08         (0.05)    0.09         (0.06)    0.19 b       (0.09)    0.01 c      (0.06)      0.04        (0.08)
Sector 35          0.08        (0.12)    0.11 b       (0.06)    0.02         (0.09)   -0.10         (0.17)    0.01 c      (0.07)    0.10          (0.17)
Sector36           0.10        (0.18)   -0.07         (0.06)   -0.01         (0.12)    0.03         (0.13)   -0.12        (0.08)    -0.15         (0.12)
Sector 37          0.05        (0.08)    0.06         (0.04)    0.09         (0.05)    0.07         (0.08)    0.02        (0.06)      0.01        (0.08)
Sectar38          -0.25        (0.26)    0.04         (0.20)    0.11         (0.08)    0.07         (0.14)    0.07 b      (0.12)    0.03 c        (0.16)
Sector 39          -0.94 c     (0.07)   -0.62 c       (0.04)   -0.58 c       (0.05)   -0.39 c       (0.06)   -0.28         (0.05)    -0.34        (0.05)
Medium            -0.30 c      (0.06)   -0.22 c       (0.03)   -0.22 c       (0.04)   -0.13 b       (0.06)   -0.04 c      (0.03)    -0.13 c       (0.04)
Small             -0.25 c      (0.05)   -0.33 c       (0.04)   -0.52 c       (0.07)   -0.56 c       (0.06)   -0.56 c      (0.04)    -0.76 c       (0.06)
R2                 0.28                   0.29                   0.26                   0.11                  0.11                    0.11
Wald (28 df)    597.40 c                1372.5 c               468.78 c              145.78 c               413.71 c               280.59 c
Note: Sample Sizes:Non-Union n=731, Union n=3422. Median Chow tests for equality of union and non-union coeff. (27d.f) 80.57 (c) skilled,
75.11 (c)(unskilled). Wald is aChi-Squaretestofregressionsignificance. aSig. at 10%., b 5%,c 1%. R2=squared correlation betweenactual andpredicted.
34



Table 3.1: 2SRQ Demand Regressions- Non-Union
Skilled                                                                    Unskilled
10%                      50%                      90%                      10%                       50%                        90%
coef           s.e      coef            s.e      coef             s.e      coef             s.e      coef             s.e      coef             s.e
Constant             0.14        (1.81)      0.05         (1.86)    -0.58          (2.68)      -2.08         (5.46)       2.39         (2.27)       3.80         (3.66)
Value Add.           0.22 b    (0.09)        0.39 c       (0.07)      0.58 c       (0.11 )      0.38 c       (0.1 0)       0.40 c      (0.07)        0.54 c      (0.12)
Ws                  -0.08       (0.07)      -0.22 c       (0.06)    -0.36 c        (0.10 )     -0.14         (0.11 )      -0.01        (0.06)       -0.11        (0.10)
Wu                   0.04       (0.04)      -0.03         (0.04)    -0.10          (0.09)      -0.46 c       (0.12)       -0.63 c      (0.05)       -0.74 c      (0.09)
Wa-s                -0.18       (0.24)      -0.05         (0.19)      0.13         (0.35)      -0.98 b       (0.44)       -1.07 c       (0.26)      -0.93 c      (0.40)
Wa-u                 0.06       (0.34)       0.14         (0.31)      0.21         (0.49)       1.95 b       (0.71)        1.64 c       (0.43)       1.52.c      (0.52)
Hiring-s             0.21 a    (0.13)        0.24 c       (0.09)      0.19         (0.16)      -0.41         (0.27)       -0.11         (0.12)      -0.01        (0.19)
Hiring-u             0.00       (0.01)      -0.02 b       (0.01)    -0.01          (0.02)       0.04         (0.02)        0.02 a       (0.01)       0.04 b      (0-02)
Cap.U.               0.00       (0.00)       0.00         (0.00)      0.00         (0.00)       0.00         (0.00)        0.01 c      (0.00)        0.00        (0.00)
Corporate            0.24 b    (0.11)        0.06         (0.10)      0.18         (0.13)       0.41 a       (0.23)        0.20 a       (0.12)       0.10        (0.13)
Foreign              0.04       (0.12)       0.05         (0.11)      0.13         (0.15)       0.19         (0.22)        0.17         (0.14)      -0.07         (0.18)
Training             0.08       (0.08)       0.05         (0.07)      0.06         (0.11)       0.10         (0.15)        0.09         (0.07)       0.20        (0.16)
Age                  0.01        (0.01)      0.00         (0.01)    -0.02 a        (0.01)       0.02         (0.02)        0.00         (0.01)      -0.02         (0.01)
Age2                 0.00       (0.01)       0.00         (0.01)      0.04 a       (0.02)      -0.04         (0.04)        0.00         (0.01)       0.03         (0.02)
Exporter             0.02       (0.08)       0.07         (0.08)      0.06         (0.11)       0.10         (0.17)        0.26 b       (0.11)       0.24         (0.13)
Automated            0.00        (0.00)      0.00         (0.00)      0.00         (0.00)       0.00         (0.00)        0.00         (0.00)       0.00         (0.00)
Qual.Cont.           0.05        (0.48)     -0.21         (0.29)    -0.19          (0.43)       1.04         (0.91)        0.29         (0.41)       0.45         (0.42)
R&D                  0.07        (0.07)      0.08         (0.06)      0.09         (0. 10)     -0.03         (0.17)       -0.03         (0.08)       0.09         (0.14)
Tech.                0.04        (0.07)     -0.06         (0.06)    -0.18          (0.13)       0.06         (0.16)       -0.02         (0.08)      -0.14         (0.11)
Sector32             0.24       (0.17)       0.20         (0.12)    -0.01          (0.20)      -0.38         (0.45)       -0.11        (0.16)       -0.17        (0.27)
Sector 33            0.25       (0.16)       0.16         (0.13)      0.76 b       (0.36)       0.11         (0.34)        0.03        (0.19)        0.52         (0.43)
Sector 34            0.40 b    (0.20)        0.31 a       (0.18)      0.36         (0.35)      -0.77 a       (0.43)       -0.83 c      (0.25)       -0.63        (0.37)
Sector 35            0.20       (0.19)       0.08         (0.14)    -0.15          (0.22)      -0.67         (0.40)       -0.51 c       (0.21)      -0.49        (0.30)
Sector 36            0.37        (0.44)      0.22         (0.25)      0.33         (0.38)       0.05         (0.51)        0.35         (0.25)       0.09         (0.34)
Sector 37            0.44       (0.31)       0.50 a       (0.29)      0.14         (0.55)       0.20         (0.44)        0.00         (0.25)       0.22        (0.54)
Sector 38            0.42 b    (0.17)        0.33 c       (0.12)      0.26         (0.18)      -0.58         (0.41)       -0.33 b       (0.17)      -0.34        (0.27)
Sector 39           -0.13       (0.31)       0.17         (0.26)      0.20         (0.45)      -0.55         (0.46)       -0.59         (0.38)      -0.21        (0.48)
Small               -0.56 c    (0.18)       -0.70 c       (0.16)    -0.52 a        (0.27)      -1.44 b       (0.29)       -1.25 c      (0.16)       -1.08 c      (0.30)
Medium              -0.26 b    (0.12)       -0.48 c       (0.10)    -0.29          (0.18)      -0.50 b       (0.21)       -0.46 c      (0.10)       -0.70 c       (0.20)
R2                  0.59                     0.62                     0.59                      0.68                       0.71                      0.68
Wald(28 df)       534.04 c               1135.34 c                 436.21 c                  631.38 c                  1365.22 c                  593.50 c
Note: Sample Sizes:Non-Unionn=731, Union n=3422. Median Chowtestsfor equality of union and non-union coeff. (28d.f) 36.23 (b), 130.89(c) (unskilled).
R2 is the squared correlation between actual and second stage fitted. Wald is achi-square test ofregression significance. a sig at 10%, b 5%/6, c 1%.
Instruments for Output:capital stock, its square, sector dummies and interactions and technology variables.
35



Table 3.2: 2SRQ Demand Regressions-Union
Skilled                                                                    Unskilled
10%                      50%                      90%                       10%                       50%                        90%
coef           s.e      coef             s.e      coef            s.e      coef             s.e      coef             s.e      coef             s.e
Constant             0.56       (0.93)       1.75 b       (0.85)      4.11 c       (1.46)       1.28         (1.58)        3.73        (0.90)        5.82 b      (1.54)
Value Add.           0.42 c    (0.03)        0.46 c       (0.03)      0.53 c       (0.04)       0.35 c       (0.04)        0.40 c       (0.03)       0.43 c       (0.04)
Ws                  -0.23 c    (0.03)       -0.34 c       (0.03)    -0.51 c        (0.04)      -0.04         (0.04)       -0.02         (0.03)      -0.03         (0.03)
Wu                   0.08 c    (0.02)        0.02         (0.02)    -0.04          (0.03)      -0.58 c       (0.03)       -0.62 c       (0.02)      -0.64 c       (0.03)
Wa-s                -0.19 a    (0.11)       -0.04         (0.10)    -0.18          (0.17)       0.31 b       (0.15)        0.08 c      (0.10)        0.25.b      (0.12)
Wa-u                 0.05       (0.16)      -0.07         (0.15)      0.17         (0.27)      -0.17         (0.26)       -0.15 c      (0.17)       -0.49 a      (0.27)
Union               -0.19 a    (0.11)       -0.27         (0.33)    -0.95          (0.48)       2.45 c      (0.53)        2.59 c       (0.38)       2.98 c       (0-53)
Hiring-s             0.11 a    (0.06)        0.10 b       (0.05)      0.06         (0.06)      -0.07         (0.07)        0.08         (0.04)       0.12        (0.08)
Hiring-u             0.00       (0.01)       0.01 a       (0.01)      0.01         (0.01)       0.01         (0.01)        0.00 a      (0.01)        0.00 b       (0.01)
Cap.U.               0.00 b    (0.00)        0.00         (0.00)      0.00         (0.00)       0.01 c       (0.00)        0.00 c      (0.00)        0.00         (0.00)
Corporate            0.04       (0.04)       0.07 b       (0.03)      0.08         (0.06)       0.10 b       (0.04)        0.06 a      (0.03)        0.01        (0.06)
Foreign              0.12 b    (0.05)        0.17 c       (0.04)      0.20 c       (0.06)      -0.04         (0.06)       -0.01        (0.05)        0.09        (0.06)
Training             0.01       (0.04)      -0.02         (0.03)    -0.04          (0.05)      -0.04         (0.04)       -0.03        (0.03)       -0.01        (0.04)
Age                  0.00       (0.00)       0.01 b       (0.00)      0.00         (0.00)       0.00         (0.00)        0.00        (0.00)        0.00        (0.00)
Age2                 0.00       (0.00)      -0.01 a       (0.00)      0.00         (0.01)       0.00         (0.01)        0.01        (0.00)        0.01 b      (0.01)
Exporter             0.03       (0.05)       0.03         (0.03)      0.06         (0.05)       0.17 c       (0.05)        0.23 b      (0.04)        0.26 c      (0.06)
Automated            0.00       (0.00)       0.00         (0.00)      0.00         (0.00)       0.00 a       (0.00)        0.00        (0.00)        0.00         (0.00)
Qual.Cont.          -0.11       (0.26)      -0.08         (0.23)      0.10         (0.35)       0.12         (0.22)        0.15        (0.33)       -0.26        (0.53)
R&D                  0.09 b    (0.04)        0.03         (0.03)      0.03         (0.05)       0.00         (0.05)       -0.03         (0.03)      -0.05        (0.05)
Tech.               -0.02       (0.04)      -0.01         (0.03)    -0.05          (0.04)      -0.02         (0.05)       -0.05         (0.03)      -0.02         (0.05)
Sectr 32             0.06       (0.08)       0.10         (0.06)      0.04         (0.11)       0.06         (0.09)        0.23        (0.07)        0.21 b      (0.09)
Sector 33            0.33 c    (0.09)        0.26 c       (0.08)      0.03         (0.13)       0.19         (0.11)        0.13         (0.08)       0.06        (0.12)
Sector 34            0.17 a    (0.10)        0.36 c       (0.09)      0.19         (0.14)      -0.12 a       (0.13)        0.04 c       (0.09)       0.12         (0.13)
Sector 35            0.30 c    (0.08)        0.33 c       (0.06)      0.11         (0.11)      -0.04         (0.11)        0.00 c       (0.06)       0.04         (0.11)
Sector 36            0.46 c    (0.10)        0.44 c       (0.07)      0.23 b       (0.11)       0.23 b       (0.09)        0.11        (0.08)        0.06         (0.09)
Sector 37            0.35 c    (0.11)        0.28 c       (0.08)      0.10         (0.14)      -0.04         (0.10)       -0.02        (0.11)        0.03        (0.14)
Sector 38            0.35 c    (0.07)        0.39 c       (0.06)      0.25 b       (0.12)       0.06         (0.11)        0.16 b      (0.06)        0.34 c      (0.11)
Sector39             0.03       (0.14)       0.09         (0.12)    -0.12          (0.28)      -0.03         (0.14)        0.01        (0.13)       -0.01        (0.12)
Small               -0.44 c    (0.08)       -0.53 c       (0.08)    -0.65 c        (0.12)      -1.30 c       (0.10)       -1.09 c      (0.07)       -1.08 c      (0.10)
Medium              -0.25 c    (0.05)       -0.33 c       (0.04)    -0.52 c        (0.07)      -0.56 c       (0.06)       -0.56 c      (0.04)       -0.76 c       (0.06)
R2                   0.62                    0.63                     0.62                      0.67                       0.68                      0.67
Wald (29df)    1967.53 c                 3803.92 c                1951.86 c                 3204.40 c                 4210.45 c                  3651.45 c
Note: Sample Sizes-Non-Union n=73 1. Union n=3422. Median Chow tests for equality of union and non-union coeff. (28d.f) 36.23 (b), 130.89 (c) (unskilled).
R2 is the squared correlation between actual and second stage fitted. Wald is a chi-square test of regression significance. a sig at 10%, b 5%, c 1%.
Instruments for Output:capital stock, its square, sector dummies and interactions and technology variables.
36



Appendix 1: Measures to Confront the Problem of Recently
Trained Workers Who Resign, %
Total   Large Medium  Small  Micro
Increase wages               23.1    7.1      7.6     9.5    29.2
Increase other remunerations  4.6    17.5    17        14      0.1
Promote those trained        12.7    39.6    32.8    24.3    6.1
Reduce the # of those trained  8.4    0.2     1.3     1.3    11.5
Reduce the training offered   0.2     0.5      0       0       0.3
Give non-monetary recognition   8.5    12.8    9.8    7.3      8.4
None                         33.5    15.5    26.1      35     34.7
Don't know                    1.2     2.3     0.7     5.2      0
Others                        7.8     3.9     4.1     3.2     9.7
Source: 1992 ENESTYC
37



Appendix 2: OLS Estimates
Table A2.1 - WAGE EQUATIONS
Non-union                                        Union
Skilled            Unskilled             Skilled              Unskilled
Prd        0.036    (0.028)     0.097  (0.025) a    0.036   (0.012) a     0.039    (0.012) a
Lwa        0.384    (0.156) a    0.388  (0.204) b    0.300   (0.057) a    0.433    (0.086) a
Union           ---              ---      ---       -0.865   (0.210) a    0.236    (0.197)
Hirr      -0.002    (0.090)     -0.014  (0.010)     -0.065   (0.029) a    -0.002    (0.004)
Sch        0.424    (0.081) a    0.235  (0.103) a    0.571   (0.058) a    0.238    (0.055) a
Sch2      -0.016    (0.003) a   -0.017  (0.007) a    -0.022   (0.002) a   -0.014    (0.004) a
Exp       -0.021    (0.023)     0.000  (0.015)      0.027   (0.007) a     0.018    (0.006) a
Exp2       0.001    (0.001)     0.000  (0.001)      -0.001   (0.000) a    -0.001    (0.000) a
R_d        0.121    (0.065) b    0.004  (0.064)     0.010   (0.025)       -0.035    (0.026)
Tech.      0.030    (0.063)      0.078  (0.061)     0.097   (0.024) a     0.135    (0.026) a
P.train.    0.197    (0.067) a    0.110  (0.065) b    0.024   (0.025)     0.001    (0.026)
Corp      -0.038    (0.081)     -0.008  (0.079)     0.028   (0.026)       0.067    (0.028) a
Forg.      0.327    (0.096) a   0.127  (0.093)      0.179   (0.030) a     0.025    (0.032)
Export.   -0.123    (0.074) b   0.012  (0.073)      -0.108   (0.026) a    0.024    (0.028)
Auto       0.001    (0.001)     0.001  (0.001)      0.001   (0.000) b     0.000    (0.001)
Cmptl   -0.013    (0.055)       0.026  (0.054)      -0.008   (0.022)      -0.003    (0.023)
Qual.ctr.   0.083    (0.248)    -0.193  (0.242)     0.094   (0.186)       0.238    (0.197)
LogK/L    0.001    (0.019)      -0.026  (0.018)     0.016   (0.008) b    -0.014    (0.008) b
Sect 32    0.046    (0.110)     0.072  (0.101)      -0.008   (0.043)     -0.028    (0.042)
Sect 33   -0.018    (0.143)     0.094  (0.126)      0.063   (0.068)       0.075    (0.069)
Sect_34    0.129    (0.134)     0.200  (0.156)      0.072   (0.056)       0.103    (0.063)
Sect 35    0.162    (0.124)     0.059  (0.132)      0.116   (0.044)A      0.061    (0.047)
Sect 36    0.142    (0.194)     -0.006  (0.183)     0.079   (0.060)       0.078    (0.061)
Sect_37    0.226    (0.233)     -0.192  (0.227)     -0.011   (0.065)      -0.068    (0.064)
Sect 38    0.035    (0.102)     0.107  (0.110)      0.085   (0.038) A     0.065    (0.042)
Sect 38   -0.503    (0.256) b    -0.045  (0.250)    0.031   (0.090)       0.117    (0.094)
Medium   -0.132    (0.077) b    -0.085  (0.075)     -0.229   (0.025) A    -0.091    (0.026) a
Small    -0.702    (0.083) a    -0.349  (0.082) a    -0.676   (0.032) A   -0.335    (0.033) a
Const.    2.912    (1.500) b    3.943  (1.643) a    3.523   (0.680) A     3.082    (0.732) a
R'        0.3987               0.1692              0.2895                0.1191
F - test  17.26 a              5. 30 a             49.43 a               16.39 a
Note: Sample sizes: Non-union n=73 1, Union, n=3,422. Chow tests for equality of union and non-union coeff.(27d.f ) 86.66
a (skilled), 42.37 a (unskilled). a-significant at the 5% level, b-significant at the 10% level.
38



Table A2.2 - LABOR DEMAND EQUATIONS - 2SLS
Non-union                                               Union
Skilled                 Unskilled               Skilled                Unskilled
Lq *           0.441    (0.039) a        0.493   (0.049) a      0.480   (0.0 18) a      0.405   (0.019) a
Lws           -0.252    (0.038) a       -0.082   (0.047) b      -0.379   (0.019) a     -0.024   (0.020)
Lwu           -0.057    (0.036)         -0.636   (0.045) a      0.037   (0.016) a      -0.591   (0.017) a
Lwas          -0.044    (0.174)         -1.028   (0.219) a      -0.091   (0.073)        0.195   (0.079) a
Lwau           0.094    (0.255)          1.423   (0.320) a      -0.002   (0.121)       -0.244   (0.132) b
Union           ---       ---            ---       ---          -0.417   (0.243) b      2.689   (0.264) a
Hirrs          0.237    (0.078) a       -0.154   (0.098)        0.081   (0.033) a       0.067   (0.036) b
Hirru         -0.008    (0.009)          0.036   (0.011) a      0.008   (0.004) a       0.001   (0.004)
Capu          -0.001    (0.001)          0.004   (0.002) a      -0.001   (0.001) b      0.003   (0.001) a-
Corp           0.154    (0.068) a        0.190   (0.086) a      0.080   (0.025) a       0.059   (0.027) a
Forg           0.068    (0.082)          0.124   (0.104)        0.155   (0.029) a       0.010   (0.032)
Age            0.002    (0.005)         -0.003   (0.006)        0.004   (0.002) a      -0.003   (0.002)
Age2           0.004    (0.007)          0.003   (0.009)        -0.002   (0.002)        0.008   (0.003) a
Export         0.056    (0.064)          0.233   (0.081) a      0.068   (0.026) a       0.234   (0.028) a
Auto           0.001    (0.001)          0.001   (0.001)        0.000   (0.000)         0.000   (0.000)
Qual.ctr.     -0.215    (0.209)          0.253   (0.263)        -0.060   (0.176)        0.048   (0.192)
R_d            0.073    (0.055)         -0.001   (0.070)        0.042   (0.024) b      -0.039   (0.026)
Tech.         -0.064    (0.054)         -0.020   (0.068)        -0.016   (0.023)       -0.027   (0.025)
Sect 32        0.200    (0.106) b       -0.115   (0.134)        0.075   (0.045) b       0.182   (0.049) a
Sect 33        0.344    (0.120) a        0.161   (0.151)        0.228   (0.065) a       0.163   (0.071) a
Sect 34        0.370    (0.148) a       -0.647   (0.186) a      0.292   (0.065) a       0.010   (0.071)
Sect_35        0.056    (0.114)         -0.456   (0.143) a      0.283   (0.049) a      -0.025   (0.053)
Sect_36        0.268    (0.164)          0.166   (0.207)        0.370   (0.058) a       0.150   (0.063) a
Sect_37        0.368    (0.199) b        0.064   (0.250)        0.253   (0.061) a       0.016   (0.067)
Sect 38        0.361    (0.101) a       -0.331   (0.128) a      0.371   (0.046) a       0.177   (0.050) a
Sect_38        0.057    (0.223)         -0.319   (0.280)        0.100   (0.088)         0.003   (0.096)
Medium        -0.396    (0.076) a       -0.528   (0.095) a      -0.339   (0.030) a     -0.612   (0.033) a
Small         -0.611    (0.109) a       -1.207   (0.136) a     -0.524   (0.050) a      -1.129   (0.054) a
Const.         0.484    (1.538) a        3.789   (1.934) b       1.986   (0.690) a      3.212   (0.752) a
R2            0.6518                    0.6872                 0.6339                  0.6918
F -test      46.77 a                   59.47 a                210.25a                 269.56a
DHW          32.52 a                   19.68 a                 72.44a                 151.53a
Note: Sample sizes: Non-union n=73 1, Union, n=3,422. Chow tests for equality of union and non-union coeff.(26d.f.) 36.23
b (skilled), 130.89 a (unskilled). a-significant at the 5% level, b-significant at the 10% level. * Instruments for output: capital
stock, its square, sector dummies and its interactions and technology variables.
39



Figure 1: Relationship Between Iso-Profit Curves and the Labor Demand Curve
Dollars
Wd
Pu .P
*            . Profits 1100,000
,  ,         ,   D     Profits = $150.000
Employment
0    50           100          150
Figure 2: Bargaining Solutions Between Unions and Firms
l~~~~ l
WU~~~~~~~~~~
Ez                F*               Employment
40



Figure 3: Quantile Regression
goth %
5O" %
1Olh %
41



Figure A1.1 - Labor Demand - Non-union Skilled
25                                                                                                               Figure A1.2 - Labor Demand - Union Skilled
20 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~25
I0                                                                                          20 1
E
10                                                                                       I( 10) 1
2 10~~~~~~~~~~~~~~~~~~~~~~~~~~~~~L77771-1)                                a1
00     1000   2000  3000 4000 oot   6 000                 0000 so0 SO     o
0  8000   9000                  0~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~000                           00
Output
Figure A1.3 - Labor Demand - Non-union Unskilled
Figure A1.4 - Labor Demand - Union Unskilled
144
le 10810B |
CL                                                  8~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~L
a    72 |                              ./. . . . . . . . . . . . . . .0 10)
0     1000   2000   3000   4000   5000   6000   7000   0000   S000
lu t p ute0                                                          1000   2000  3000   4000     0000  6000  i 000  8000   S000
Output
42



Figure A2.1 - Labor Demand - Non-union Skilled                                                      Figure A2.2 - Labor Demand - Union Skilled
25
25
20
20
15                                                                                   SOS
E  0                                                                                              CqS0
1                                                                                               E 1
5
5
0S000fOo00   15000         20000         25000                         0            5000         10000         15000         20000         25000
Wages                                                                                            Wages
Figure A2.3 - Labor Demand - Non-union Unskilled                                                    Figure A2.4 - Labor Demand - Union Unskilled
200                                              .                                                300
250                                                                                               250
200                                                                                               200
300~~~~~~~~~~~~~~~~~~~~~~~
E                                                                                     rq.lo)      S                                                                                      5 0
ES                                                                             -- -   q.(90)     E 150rq(90)
w  ioa                                                                     ~~~~~~~~~~~~~100- -
\~
50
200                    \
0       000     1000     1000    2000    2500    3000      2000     4000                          0       500     1000    1000    2000    2000    3000    2500    4000
Walges                                                                                            Wages
43



Policy Research Working Paper Series
Contact
Title                            Author                   Date              for paper
WPS2110 Life during Growth: International  William Easterly        May 1999           K. Labrie
Evidence on Quality of Life and                                             31001
Per Capita Income
WPS2111 Agricultural Land Reform in Postwar Toshihiko Kawagoe      May 1999           P. Kokila
Japan: Experiences and Issues                                               33716
WPS2112 Industrial Policy afterthe EastAsian  Ashoka Mody          May 1999           S. Kpundeh
Crisis: From 'Outward Orientation'                                          39591
To New Internal Capabilities?
WPS2113 Wage Determination and Gender    Stefano Paternostro       May 1999           N. Nouviale
Discrimination in a Transition   David E. Sahn                              34514
Economy: The Case of Romania
WPS2114 Economic Reforms and Total Factor Pablo Fajnzylber         May 1999           S. Holt
Productivity Growth in Latin America Daniel Lederman                        37859
And the Caribbean (1950-95):
An Empirical Note
WPS2115 Foreign Investment and Productivity  Simeon Djankov        May 1999           R. Vo
Growth in Czech Enterprises      Bernard Hoekman                            33722
WPS2116 Does Child Labor Displace          Martin Ravallion        May 1999           P. Sader
Schooling? Evidence on Behavioral  Quentin Wodon                            33901
Responses to an Enrollment Subsidy
WPS2117 On the Size and Number of Regional Soamiely Andriamananjara May 1999          L. Tabada
Integration Arrangements: A Political                                       36896
Economy Model
WPS2118 Developing Countries and the Next  Anne 0. Krueger         May 1999           L. Tabada
Round of Multilateral Trade                                                 36896
Negotiations
WPS2119 Tax Deductions, Environmental     Ian Parry                May 1999           R. Yazigi
Policy, and the 'Double Dividend'    Antonio Bento                          37176
Hypothesis
WPS2120 Can Africa Export Manufactures?    Ibrahim A. Elbadawi     May 1999           C. Bernardo
The Role of Endowment, Exchange                                             31148
Rates, and Transactions Costs
WPS2121 Fiscal Management in Federal       William Dillinger       May 1999           A. Lara
Democracies: Argentina and Brazil  Steven B. Webb                           88148
WPS2122 Decentralization and Fiscal        William Dillinger       May 1999           S. Webb
Management in Colombia           Steven B. Webb                             38680



Policy Research Working Paper Series
Contact
Title                            Author                   Date              for paper
WPS2123 Access to Land in Rural India      Robin Mearns            May 1999           G. Burnett
82111
WPS2124 Social Exclusion and Land          Robin Mearns            May 1999           G. Burnett
Administration in Orissa, India  Saurabh Sinha                               82111
WPS2125 Developing Country Agriculture and  Bernard Hoekman        May 1999           L. Tabada
The New Trade Agenda             Kym Anderson                                36896
WPS2126 Libert6, Egalit, Fraternit6:       Monica Das Gupta        May 1999           M. Das Gupta
Exploring the Role of Governance                                            31983
In Fertility Decline
WPS2127 Lifeboat Ethic versus Corporate    Monica Das Gupta        May 1999           M. Das Gupta
Ethic: Social and Demographic                                               31983
Implications of Stem and Joint
Families
'WPS2128 Learning Outcomes and School      Gladys Lopez Acevedo    May 1999           M. Geller
Cost-Effectiveness in Mexico:                                               85155
The PARE Program
WPS2129 Agricultural Extension: Generic    Gershon Feder           May 1999           P. Kokila
Challenges and Some Ingredients    Anthony Willett                          33716
for Solutions                    Willem Zijp
WPS2130 Deep Integration, Nondiscrimination, Bernard Hoekman       May 1999           L. Tabada
and Euro-Mediterranean Free Trade  Denise Eby Konan                         36896