WPS3891
Protection, Openness and Factor Adjustment:
Evidence from the manufacturing sector in Uruguay
Carlos Casacuberta (Universidad de la República)
Néstor Gandelman (Universidad ORT)*
Abstract
This paper uses a panel of manufacturing firms to analyze the adjustment process in capital, blue collar
and white collar employment in Uruguay during a period of trade liberalization when average tariff
protection fell from 43 to 14 percent. Desired factor levels arising from a counterfactual profit
maximization in the absence of adjustment costs are calculated, generating a measure of factor shortages
or surpluses. The average estimated output gap for 19821995 is 2%. Our policy analysis shows that
trade openness affected the adjustment functions of all three factors of production. Highly protected
sectors adjust less when creating jobs (reducing labor shortages) than sectors with low protection. This
may be due to fears of policy reversal in highly protected sectors. Also, highly protected sectors adjust
more easily (than low protection sectors) when destroying jobs (reducing labor surpluses), especially in
the case of blue collar labor. This suggests that trade protection may in fact destroy rather than create
jobs within industries, as firms in highly protected sectors are more reluctant to hire and more ready to
fire than firms in sectors with low protection. The results for capital are qualitatively similar but
quantitatively smaller, suggesting that trade protection plays less of a role in explaining adjustment costs
for capital. Interestingly, exportoriented sectors have lower adjustment costs for blue collar labor, but
not for white collar employment or capital, suggesting that exportled growth may be successful in
reducing blue collar unemployment.
World Bank Policy Research Working Paper 3891, April 2006
The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange
of ideas about development issues. An objective 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. Policy Research Working Papers are available online at http://econ.worldbank.org.
* The authors thank Marcelo Olarreaga, Gabriela Fachola and participants at the 2005 Annual Meeting of the BCU
for valuable comments. Fachola also helped with the database and the construction of the capital series. Financial
support from the Word Bank Research Support budget is also gratefully acknowledged.
1
1. Introduction
The traditional microeconomic textbook model assumes that the level of employment and capital
used by firms is optimal at any point in time. But since adjusting employment and capital is
costly to firms, they often deviate from what would be optimal in the absence of frictions.
Understanding the way firms react to employment and capital shortages can shed light on many
issues of microeconomic and macroeconomic nature. In this paper we analyze such adjustment
process, based on a panel of manufacturing establishments in Uruguay.
The Uruguayan economy evolved from inward looking, based on state interventionism and
import substitution protectionist policies, towards an outward looking orientation, with more
reliance on markets as resource allocation mechanisms and exports as the growth engine. A first
phase of trade reform took place in the 1970s, accompanied by a quick financial liberalization
process. Later, trade liberalization was deepened in the 1990s, combining a gradual unilateral
tariff reduction with the creation of Mercosur, an imperfect customs union between Argentina,
Brazil, Paraguay and Uruguay.
A byproduct of the trade liberalization process in Uruguay was that manufacturing firms
switched to more capital intensive technologies. Specifically, in the 1990s, there were high job
destruction rates as reported in Casacuberta, Fachola and Gandelman (2004). We focus on the
adjustment process, and our main concern is with the effect of protection levels and trade
liberalization on the adjustment functions for capital and white and blue collar employment.
The objectives of the paper are: i) to present an estimation of the adjustment functions for labor
(blue and white collar) and capital, ii) to study the differences in the adjustment process for each
factor of production and iii) to analyze the impact on these adjustment functions of tariff barriers,
trade liberalization and international exposure.
Our main results confirm the lumpy and discontinuous nature of firms' adjustment process.
Large shortages of one factor lead to less responsiveness in adjustment in the creation side of
other factors but larger adjustment in the destruction side. Adjustment costs reduce the volatility
of factor usage but on average they implied an annual gap between desired and observed output
of 2%. Less protected sectors adjust a larger fraction of the gap in the creation side but a lower
fraction in the destruction side.
The paper proceeds as follows. Section 2 presents the policy experiments and the basic
definitions of factor growth rates, factor shortages and adjustment functions. Shortages are
defined with respect to some targeted desired levels. Section 3 details the methodology to obtain
these desired levels. Readers not interested in the technicalities of this procedure may skip this
section. Section 4 presents the results and analyzes the effects of policy changes in the
adjustment process of firms and finally section 5 concludes.
2
2. Labor and capital adjustment functions
In the traditional model without adjustment costs, the employment (capital) choice of the firms
depends only on current shocks and future expectations. In the presence of adjustment costs, it
also depends on past employment (capital) decisions and in the gap between the actual level of
employment (capital) and the "desired" level. We will use the notation B*, W* and K* and B, W
and K for the desired and actual levels of blue collar labor, white collar labor and capital
respectively. A key step in the present methodology is the construction of this "desired" level.
The growth rates of labor and capital inputs are defined as the ratio between the input changes
and the averages between its past and present values. These definitions follow Davis and
Haltiwanger (1992), and Davis et al (1996).1 Using the notation for the rates of growth, we
have
Bjt = Bjt  Bjt1
1 (B + Bjt1 )
2 jt
Wjt = Wjt  Wjt1
1 (W + Wjt1 ) (1)
jt
2
Kjt = Kjt  Kjt 1
1 (K + Kjt )
2 jt 1
Before a firm adjusts its factors of production, the employment (capital) shortage at time t can be
defined as the difference between the desired level of employment (capital) at time t and the
actual level at time t1. Paralleling the previously defined growth rates, the shortage rate is
expressed as a fraction of the average between the present desired level and the past observed
level. Therefore, employment (blue and white collar respectively) and capital shortages (ZBjt,
ZWjt, ZKjt) are:
1A feature of these growth rates is that they are bound between 2 and 2. There is a monotonic relation between the
rates of growth so defined and the usual ones.
3
ZBjt = B*jt  Bjt 1
1(B * + Bjt )
2 jt 1
*
ZWjt = Wjt  Wjt 1
1 (W * + Wjt ) (2)
2 jt 1
ZKjt = K*jt  K jt 1
1 (K * + Kjt )
2 jt 1
Following Eslava et al (2005) adjustment functions (ABjt, AWjt, AKjt, for blue and white collar
employment and capital, respectively) are defined as the fraction of each shortage that is actually
closed. Hence adjustment functions are defined as follows:
ABjt = Bjt
ZBjt
AWjt = Wjt
ZWjt (3)
K
AK = jt
jt ZK jt
The next step is to characterize such adjustment functions in terms of the shortages in all three
factors. It is relevant to consider the case in which the adjustment function in each of them is not
independent of the shortages observed in the other two. We follow a parametric strategy in which
we allow capital and labor shortages to depend on their own shortage, on the other factors
shortages and on interactive terms. In particular, the adjustment functions are not restricted to be
linear and we allow for different intercept and slope for shortages and surpluses (or negative
shortages). We do so because the causes of adjustment costs are different in the creation and
destruction side. For instance, hiring new employees entails search, recruiting and training costs
while firing current employees is associated with severance payments and eventual effects on the
moral of the remaining employees. The basic specifications omitting the asymmetric interactions
for positive shortages are:
4
ABjt(ZBjt,ZWjt,ZKjt )=0 +1ZWjt +2ZWjtZKjt +3ZWjtZBjt +4ZB2jt +5ZBjtZKjt +6ZK2jt
2
AWjt(ZBjt,ZWjt,ZKjt )=0 +1ZWjt +v2ZWjtZKjt +3ZWjtZBjt +4ZB2jt +5ZBjtZKjt +6ZK2jt
2
(4)
AKjt(ZBjt,ZWjt,ZKjt )=0 +1ZWjt +2ZWjtZKjt +3ZWjtZBjt +4ZB2jt +5ZBjtZKjt +6ZK2jt
2
In practice, the estimated models are the following:
Bjt =ZBjt 0 +1ZWjt +2ZWjtZKjt +3ZWjtZBjt +4ZB2jt +5ZBjtZKjt +6ZK2jt
[ 2 ]
Wjt =ZWjt 0 +1ZWjt +v2ZWjtZKjt +3ZWjtZBjt +4ZB2jt +5ZBjtZKjt +6ZK2jt
[ 2 ]
(5)
Kjt =ZKjt 0 +1ZWjt +2ZWjtZKjt +3ZWjtZBjt +4ZB2jt +5ZBjtZKjt +6ZK2jt
[ 2 ]
The significance of the non linear terms would indicate that a firm with a larger gap between
desired and actual factor levels adjusts more, hence this would be evidence of the presence of
fixed costs associated with adjustment. These fixed costs cause the adjustment decisions to be
lumpy. In other words if there is lumpiness in the adjustment process, then the percentage of
adjustment towards the desired levels for each factor is expected to be increasing in the absolute
value of the shortage of that factor.
Our policy exercises will be framed in terms of an extended version of equation (5). We focus
basically on tariff protection and trade reform. The first step is then to estimate pre and post
Mercosur adjustment functions to detect shifts in the response of firms arising from changes in
the environment. However, since this does not allow isolating the reform effect from other
factors also present in the period, we also study the interactions of a set of policy variables with
the adjustment functions. In our policy experiments we use tariffs, changes in tariffs, and a
measure of export orientation of firms.
Finally, we find it useful to calculate the output gap attributable to adjustment costs. The output
gap follows straightforward from the production function and the actual and desired input levels.
3. Estimation of desired input levels
A technical description of the methodology for the estimation of the desired input levels follows.
The reader interested mostly in the results of our exercises may skip this section and proceed
directly to section 4.
5
3.1. Frictionless factor levels
To obtain the firm's desired factor input levels, our procedure starts by estimating the firm's
frictionless factor demands, based on prior estimates of plant level productivity and demand
shocks. Estimating the frictionless input levels allows obtaining in turn the desired input levels.
Frictionless levels correspond to those levels of inputs that the firm would choose in absence of
adjustment costs, and are derived from the firm's optimization problem.
The firm's production function is assumed to be:
Yjt = K jt BjtH jt Wjt Ejt M Vjt
( )
jt (6)
where K is capital, B is blue collar employment, H are blue collar hours, W is white collar
employment, E is energy, M are materials and V is total factor productivity shock.2
There is also an inverse demand function for the firm, given by:
1
Pjt =Yjt 
Djt (7)
where is the elasticity of demand and D is a time and firm specific demand shock capturing all
factors other than firms' own price affecting demand.
Firms face competitive factor markets with the following total costs for blue collar labor, white
collar labor, capital, energy and material:
B(Ljt,Hjt )= w0tBjt 1+w1tHjt
( )
W(Wjt )= PWtWjt
K(Kjt )= PKtKjt (8)
E(Ejt )= PEtEjt
M(Mjt )= PMtMjt
where PW is the white collar wage, PK is the user cost of capital, PE is the per unit cost of energy
and PM is the per unit cost of materials. In the case of blue collar employees, total compensation
is the product of employment Bjt times a wage function that depends on total hours Hjt. This tries
to capture the fact that the marginal wage is not constant. As the firm tries to increase hours per
worker, it must resort to overtime hours and a premium must be paid at least for some workers.
2For simplicity all firms are assumed to have the same production function.
6
This function is indexed by parameters w0 (straighttime blue collar wage), w1 (overtime
premium) and (marginal wage elasticity). 3
After taking logs, the first order conditions for both types of employment, hours, capital, energy
and materials yield the following system of equations (where X denotes the frictionless levels,
~
X = ln X for variables and P = lnP for input prices, and subscripts are omitted to simplify
~
notation):
(P ) ~ ~ ~ ~ ~ ~
~ ln1D+1 ~K ln B+HWEMV
K =1 ~
 (9)
1
(P ln + ) (1+P )
~ ~ ~ ~ ~ ~ ~
H K  H  W E M V
~ ln1D+ ~B0 1 1 B1
B =1 ~
(10)

1
~ ~ ~ ~ ~ ~ ~ ~
H =
~ 1 ln1D+1 ~B0 ~ (P ln +1PB1 +B+1lnKLW EMV
)
 (11)
1
(P ) ~ ~ ~ ~ ~ ~
~ ln K  B +HE M V
W =1 ln1D+ ~W ~ 1
 (12)
1
(P ) ~ ~ ~ ~ ~ ~
~ ln K B+HW M V
E =1 ln1D+ ~E ~ 1
 (13)
1
3As in Caballero and Engel (1993), our functional form for the blue collar compensation implies that in the absence
of employment adjustment costs, the firm would always choose the same number of hours per worker, and adjust to
productivity and demand shocks only varying employment.
7
(P ) ~ ~ ~ ~ ~ ~
~ ln1D+ ~M 1 ln K B+HW E V
M =1 ~
 (14)
1
In absence of adjustment costs for hours, energy and materials, the frictionless levels of those
inputs coincide with the observed levels. Therefore, the first order conditions can be reduced to a
system of three equations and three unknowns. After solving it, we can write the log of the
frictionless levels of capital, bluecollar employment and white collar employment as functions
of the parameters of the model to be estimated and observed variables as follows:
~ ~ ~ ~
K=
~ 1
ln1D ~ HEMV +1PK ln +PB0 ln+ 1+PB1H +PW ln
(~ ) [~ ( )] (~ )
++ (15)
1
~ ~ ~ ~
L=
~ 1
ln1D ~ HEMV +1PB0 ln+ 1+PB1H +PK ln +Pwln
[~ ( )] (~ ) (~ )
++ (16)
1
(~ ) [~ ( )] (~ )
~
W =1
ln1D ~ HEMV +1 PW ln +PB0 ln+ 1+PB1H + PK ln
~ ~ ~ ~
++ (17)
1
3.2. Desired factor levels and output gap
Frictionless levels are not the same as the desired ones. Both concepts differ in that the desired
levels are the ones observed if adjustment costs are momentarily removed, while frictionless
levels are the ones observed in absence of adjustment costs in all periods. Bertola and Caballero
(1994) state reasonable conditions under which the desired levels can be approximated, up to a
constant, by frictionless levels.
8
K*jt = K Kj jt
B*jt = B Bjjt (18)
Wjt = W Wj
*
jt
where K *jt , K
( ),(B* ) and (W* )
jt , B ,W are respectively the desired and frictionless levels of
jt jt jt jt
capital, bluecollar and white collar employment. The firm specific constants to be estimated are
Kj,Bj andWj.
Following Caballero, Engel and Haltiwanger (1995, 1997) Kj ,Bj andWj can be determined as
the ratio between the actual and frictionless capital, blue collar and white collar employment, for
the year where investment and employment growth for each type take their median values
respectively. It is then implicitly assumed that, in the year of the median employment growth and
median investment, the desired and the actual adjustment of employment and capital respectively
coincide.
To define the output gap we make the extra assumption that total factor productivity is an
exogenous shock not dependent on the levels of the inputs. Given the production function and
the previous assumption that the desired and actual hours, materials and energy consumption
coincide, the desired output is:
Yjt = K*jt B*jtH jt Wjt Ejt M Vjt
* ( ) *
jt (19)
Adding firms' output and desired output it is straightforward to estimate the output gap.
3.3. Estimation of various variables and parameters
3.3.1. Productivity shock estimation
We use Levinsohn and Petrin's (2003) methodology to obtain a measure of total factor
productivity by estimating a production function where an electricity consumption variable is
used to control for unobservables. Such method specifically controls for two problems in this
type of estimations: the selection problem (i.e. in a panel a researcher would only observe the
surviving firms, hence those likely to be the most productive), and the simultaneity problem (the
input choices of firms conditional on the fact that they continue to be in activity depend on their
productivity).
Given the production function specification in equation (6) we compute total factor productivity
as:
9
V~jt = Y~jt ^K jt  ^ Bjt + H
~ (~ ~ ) ^
jt Wjt ^Ejt ^M jt (20)
where ^ ,^ ,^ ,^ and ^ are the estimated factor elasticities for capital, blue collar employment
hours, white collar employment, electricity and materials respectively, and all variables are
expressed in logs. The estimated coefficients of the production function are shown in Table 1.
The null hypothesis of constant returns of scales is not rejected, though is not imposed. The
standard errors are estimated across 100 bootstrapped samples.
3.3.2. Demand shock estimation
We also estimate establishment level demand shocks based on the inverse demand equation (7).
The inverse demand function is estimated in logs, and the demand shock recovered as the
residual.
d jt = lnDjt = lnPjt + ^ lnYjt
^ (21)
where =  . In order to identify the elasticity of the demand equation we estimate a two
1
equation system of demand and supply, using three stage least squares. Supply shifters include
total factor productivity and a sector wage index, while time and industry effects are also
included. Results are presented in Table 2.
3.3.3. Input prices and compensation function estimation
Our database has input prices for goods, white collars, materials and energy. They all vary across
years and four digit sectors. For the user cost of capital we use a constant value of 10%. The only
parameters remaining to be estimated are those of the compensation function for blue collar
workers.
The postulated compensation function for blue collars is stated in (8).4 Bils (1987) and Cooper
and Willis (2004) estimate for the U.S. the wage marginal elasticity to be 2. Eslava et al (2005)
working with Colombian firms calibrate to 2 and w1 to the legally overtime premium and
estimate from their data the straighttime wage w0. We also calibrate to 2 and perform a non
linear least squares procedure to estimate the parameters w0, and w1. Table 3 shows the results of
this estimation.
4It would be desirable to postulate a similar function for white collar workers too, but our database does not include
information on hours worked by white collars.
10
4. Adjustment functions and the effects of policy changes in the adjustment process
4.1. Estimated adjustment functions
In this section we present our baseline adjustment function estimations. Figures 1 to 3 display the
histograms of the estimated shortages. Their distributions are roughly symmetric. Table 4
presents summary statistics on the desired, frictionless and actual input levels. All correlations
are high suggesting the models predicts reasonably well. For the whole manufacturing sector the
level of desired white collar jobs is 15% above the actual one and the desired blue collar jobs and
capital are 10% above the actual ones.
Figure 4 shows the mean and median output gap defined as the ratio between firm's desired
output and actual output. The mean output gap for the whole period is 2%. The gap follows the
Uruguayan business cycle. In 1982, Uruguay suffered a deep exchange rate and financial crisis
that led to three years of recession. In such years the desired output was below the observed one.
In 1985, the economy started to recover but the desired output was still lower than the actual one.
The next five years are expansion years and firms tend to desire more employment and capital
than what they actually had implying positive output gaps. Due to inflationary problems the
government in 1990 undertook contractionary fiscal policies that led to a halt in GDP growth that
was resumed two years later. This implied the negative gaps in the early nineties and the positive
ones of the last years of our sample.
Turning now to the adjustment functions themselves, we estimate the parameters in equations (5)
by panel fixed effects regressions. For each factor separately we generate a dummy variable that
takes the value of 1 when the shortage is positive and 0 otherwise. Interacting this dummy with
the factor shortage and with the cube of the shortage we allow for asymmetric effects of
shortages and surpluses, i.e. we allow for different levels as well as slopes of the adjustment for
shortages and surpluses.
The adjustment functions for white and blue collar employment and capital are displayed in
figure 5. Since our specification implies that every shortage in every factor and the interactions
between them can potentially have an effect on the adjustment of each factor, we present our
baseline estimation setting the shortages of other factors at zero. The percentage of the
adjustment is plotted as a function of the shortage. Negative shortages would indicate that the
past level of the input is above the desired one (factor surplus), hence to close this gap the firm
needs to decrease this factor, and it finds itself in the job or capital destruction side. Conversely,
positive shortages show a past level of the input below the desired one, hence if the firm wants to
close the gap, it will be in the factor creation side, i.e. it will invest or hire.
Table 5 reports the estimated coefficients of the baseline adjustment functions. The significance
of the Pos interactions variable shows that the adjustment function in all cases is asymmetric
with respect to shortages and surpluses, with the exception of capital, where there is a difference
in the slope only.
Figure 5 also shows that there is an asymmetric behavior in the adjustment process. First, for
small values of the observed shortage, white and blue collar employment adjustment functions
11
show an upward shift in the positive side. This means that firms tend to adjust a larger fraction of
the gap between the desired and the actual employment when the observed levels are below the
desired ones, i.e. firm finds it easier to create labor than to destroy it except when the destructive
adjustment is large.
Since in most cases the cross product terms that include the adjusting factor are significant, we
can infer that, as conjectured, shortages of other factors are relevant to understand the adjustment
process. The negative sign of this cross shortages terms imply that large shortages of one factor
lead to less responsiveness in adjustment in the creation side of other factors but larger
adjustment in the destruction side. To observe the effect of the rest of the factors in each
adjustment function, figures 6 to 8 show separately the adjustment function of each factor, where
the shortages in the other factors are set at their mean values, and their mean values plus and
minus one standard deviation respectively.
The lumpiness of the adjustment process is shown by the fact that the size of the adjustment is
increasing in the absolute value of the shortage observed in almost all cases, both in the creation
and the destruction side. Our results also confirm nonlinearity of the adjustment process, since
nonlinear terms are in all tables statistically significant.
Another asymmetry is given by the fact that estimated adjustment functions display a smaller
slope in the creation side than in the destruction side. The differences in the slopes can be
understood together with the differences in the intercepts. The higher intercept in the creation
side indicates higher adjustment for firms with small factor shortages, while a relatively flat
slope of the adjustment schedule shows that they are able only to undertake smaller adjustments
when there is a high positive shortage. On the contrary, the lower adjustments for small surpluses
are associated with firms closing higher percentages of the gaps when surpluses become large
enough in absolute values. A natural interpretation of this result is that there are larger
adjustment costs associated to factor destruction (severance payments, loss of specific human
capital, etc.) than to factor creation (search, training, etc.).
Comparing the different factor adjustment functions, both in the creation and the destruction
side, the slopes for white collar are larger than for blue collar labor. Such features can be seen as
related to the differences in adjustment costs for each factor. Labor unions tend to be stronger in
industries more intensive in blue collar labor; hence this can be related to lower adjustment
levels when shortages are large in the destruction side. The white collar adjustment function has
higher slope than blue collar adjustment on both sides. When the shortage is small in absolute
value, adjustment is lower in white collar than in blue collar. Conversely, if the shortage is large
in absolute value, a larger proportion of the gap is closed for white collars than for blue collars.
Probably this relates to the fact that white collar labor includes workers with specific human
capital, which is difficult to create. Therefore firms probably may be willing to accept small
shortages without adjusting, but the adjustment will be fuller when the shortage becomes large in
absolute value. For instance, consider a firm that has more clerks that needed, but these clerks
are familiar with the workings of the firm: if this shortage is not too large, the firm may prefer to
keep these extra workers. On the other hand, if blue collars have less specific training, they may
12
be more easily disposed. On the creation side, hiring an extra clerk implies higher training costs;
hence the firm may prefer to use the existent workers more intensively if the shortage is small. If
the shortage becomes large enough, the cost of the extra hours will be higher than the training
cost of the newly hired white collar workers.
Finally, the effects of the shortage of the other factors in the adjustment function are captured
both by the direct effect and the cross product effects terms, but their impact comes mostly from
the latter. A negative sign of the coefficients of the cross product of the shortages (see Table 5)
causes that the higher the shortages of the other factors, the higher the adjustment in the
destruction side, and the lower the adjustment in the creation side (see figures 6 to 8). Many
firms downsized and even exited over the period of trade liberalization. This implied the
simultaneous destruction of labor (both white and blue collar) and capital. This explains why
higher shortages in absolute value for capital (white collar employment) provoke higher
adjustment on white collar (capital). According to the evidence presented in Casacuberta,
Fachola and Gandelman (2004), firms in order to remain competitive switched towards more
capital intensive production methods. Therefore, the lower the shortage in capital (employment),
the higher the adjustment in employment (capital).
4.2. Trade reform effects in the adjustment process of firms
Vaillant (2000) describes the trade liberalization process in Uruguay, finding that trade policy in
the nineties sought to continue and deepen the openness process started in the seventies, intended
to end the antiexport bias that characterized previous import substitution policies. With the
recovery of democratic institutions in 1985, political pressure for the modification of trade policy
grew, but the government did not modify the main policies, and there was only a slightly higher
protection as a result of the use of nontariff barriers. In 1990, a program of scheduled tariff
reductions began, and the signature of the Mercosur treaty led to the establishment of an
imperfect customs union with its neighboring countries.
To study the impact of trade liberalization and international exposure on the adjustment process
we estimate several adjustment functions. We are looking at the way sectoral shocks (protection
level, trade liberalization) affect the way firms respond to idiosyncratic shortages. Industries that
were more open from the beginning should experience lower shifts in their adjustment functions
due to the generalized higher international exposure.
A problem with using tariffs or change in tariffs in the right hand side of equations is that they
may possibly be endogenous. In our case this problem is less severe due to the fact that Uruguay
is a relatively minor player integrated with its larger neighbor economies in Mercosur. Hence the
common external tariff and the changes in Uruguayan tariffs to converge to the trade block
protection level are basically affected by Argentinean and Brazilian political players and beyond
control for local firms.5
Descriptive statistics of our policy and firm variables are presented in Table 6. We find that the
average tariff was reduced significantly from an average of 43% to 14% between 1985 and 1995.
On average, annual tariff changes accelerated from 2.1% before 1990 to 3.0% after 1990.
5This is discussed in more detail in Casacuberta, Fachola and Gandelman (2004)
13
The average ratio of exports over sales did not change drastically over the years. The start of the
1990 decade is associated not only with trade openness, but also with a significant appreciation
of the peso, linked to an exchange rate based antiinflation policy, which caused a deterioration
of relative prices that affected exporting firms. Towards the end of our sample period (1995)
only a slight recovery of the ratio of exports to total sales is observed.
4.3. Before and after 1990
The simplest experiment is to break the panel in two periods, 19821989 and 19901995, and
estimate the adjustment functions in each one of them. In the second period was when most of
the import tariff reductions took place and the Mercosur was established.
To analyze the pre and post Mercosur shifts in the adjustment functions, we interact the intercept
and each factor's own shortage terms (allowing for asymmetric effects in the creation and
destruction sides) in the adjustment equations with a time dummy that takes the value 1 for
observations in 19901995 and 0 elsewhere. Table 7 displays the estimated pre and post 1990
adjustment functions. In turn, figures 9, 10 and 11 show respectively the pre and post adjustment
function estimations for each of the three factors we are analyzing.
The results lead to conclude that there was effectively a shift in the adjustment functions between
both periods. A clear pattern emerges in the creation side for the three factors of production: the
level increases while the slope remains roughly constant. This implies that firms adjust a higher
fraction of their shortages suggesting lower adjustment costs at least for the creation side.
The blue collar adjustment pattern differs before and after 1990. While for the whole period
adjustment functions (figure 5) the intercept is higher in the creation side, the pre Mercosur
function (figure 10) shows the opposite: a larger fraction of the gap is closed in the destruction
side than in the creation side. The post Mercosur function shows a much higher fraction of the
gap closed when shortages are positive. Though there is a very small fraction of adjustment when
surpluses are small in absolute value, the negative side of the adjustment function becomes
steeper after Mercosur.
For white collar labor, there is basically no effect on the destruction side. In the case of capital
the adjustment function becomes flatter in the destruction side, pointing towards lower
lumpiness.
4.4. Changes in import taxes
We also estimate adjustment functions to study the effect of changes in tariffs with our shortage
terms. Again a policy variable is interacted with each factor's own shortage effect on the
adjustment functions. Table 8 displays the estimated coefficients. In all regressions at least one
policy interaction is significant. Figures 12, 13 and 14 show the estimated functions for tariff
reductions of 0, 2 and 4 percentage points.
14
While for capital the impact of tariff reductions is really minor, a pattern emerges for both types
of labor, in which the fraction of the gap actually adjusted decreases in the creation side, while
increases in the destruction side. Firms in sectors that experienced higher tariff reductions and
had to destroy employment adjusted a larger proportion than those not so exposed. On the
creation side it was the opposite: firms with lower tariff reductions adjusted a larger proportion
of their shortages.
4.5. Tariff trade barrier levels
This exercise is similar to the previous experiment, but instead of using the import tax change in
the firm's sector as a shifter of the adjustment functions, we use the import tax level. Table 9
shows the regression coefficients. Most policy interaction terms are statistically significant for
blue collar and capital. For white collar only the constant shifter for the creation side changes
significantly. Figures 15, 16 and 17 display the estimated functions for tariff levels of 10, 20 and
30 percent points.
Lower tariff levels are associated to higher adjustment on the creation side, especially for blue
collar jobs but also for white collar jobs and capital. The destruction side seems not to change
with tariff levels in the case of white collar adjustment functions. For capital and blue collar
labor, higher tariff levels are associated with lower adjustments in the destruction side, the
opposite than the creation side.
This is an indirect way of showing that protection may in fact destroy jobs, rather than create. If
shocks to firm are iid, our result implies that protection will lead to lower levels of employment.
The reason may have to do with firms' expectations. For instance suppose there is a generalized
positive demand shock. A firm in a highly protected sector will not adjust completely in the
presence of adjustment costs (e.g., firing workers) unless the government has credibly committed
to maintain protection. If there is any risk that the tariff will go down, then the firm may be more
reluctant to hire many workers than a similar firm in other sector that is not exposed to the risk of
the government reducing tariffs. The same applies on the job destruction side. A highly protected
firm that suffers a negative shock will be more likely to fire workers if the government's tariff is
not a credible permanent policy.
4.6. Export market orientation
Uruguay's industrial structure was in the mid eighties basically composed by a reduced number
of traditional products exporting firms and by sectors developed under the imports substitution
process. We estimate how the adjustment functions of the firm are affected if they are more
export or domestic market oriented, as measured by the percentage share of export sales in the
firm's total sales.
To do so we interact the percentage of exports over sales variable with each factor's own
shortage on the adjustment functions. Table 10 shows the estimated coefficients, while figures
18, 19 and 20 show the shifts in the estimated functions for export shares of 0%, 25% and 50%.
15
In those more export oriented firms the percentage of adjustment is larger both in the creation
and destruction sides for blue collar labor, implying lower adjustment costs. Firms more export
oriented, when their desired blue collar employment level is below the actual one, they adjust a
higher share of the gap than firms oriented to the domestic market. The causality may go in the
opposite direction. In order to be able to export, firms need to be more efficient, and therefore
need to have more ability to adjust factor inputs.
There does not seem to be a clear significant pattern between export oriented and domestic
market oriented firms with respect to capital and white collar adjustment functions.
5. Conclusions
This paper intends to use micro data to improve our understanding of the effects of policy
measures on the adjustment of factors of production. On the one hand, the paper finds evidence
supporting a number of regularities that the previous literature on adjustment functions has
highlighted.
Our investigation confirms that aggregate investment and job creation might be seen as the result
of lumpy and discontinuous microeconomic decisions. Individual adjustment constraints depart
significantly from the constraints implicit in the quadratic adjustment cost model. There are
several sources of irreversibilities (technological, marketinduced, increasing returns in the
adjustment technology). The evidence provided seems to confirm a pattern that has important
nonlinear features, hence consistent with such constraints. This impacts the use of all factors of
production, particularly employment.
The existence of adjustment costs implies that the desired levels of white and blue collar
employment and capital often deviate from the observed ones. In our data these deviations imply
that the yearly gaps might be above 10%. To have an idea of the importance of the adjustment
costs, it is useful to consider that for the whole period the average output gap is of 2%.
On the other hand, the paper intended to assess the effects of protection and trade liberalization
on firms' adjustment process. The constraints arising from the adjustment cost functions may
become an important part of the policy analysis. Our results point to a significant shift in the
adjustment functions for all the production factors before and after 1990, corresponding with
significant changes in the trade openness process. The shifts point towards larger fractions of the
gaps closed in the creation side, and lower in the destruction side.
Trade policy as measured by tariffs levels and their reductions also proved to significantly shift
the adjustment functions. Firms in less protected sectors have shown higher adjustment fractions
in the creation side and lower in the destruction side, particularly for blue collar labor. Sectors
facing larger tariff changes, adjust less in the creation side, particularly for blue collars, and more
on the destruction side. In the context of tariff reductions of Mercosur, those sectors more highly
protected were probably those that faced the largest tariff reductions.
16
6. References
Black S. and L. Lynch (1997) How to compete: the impact of workplace practices and
information technology on productivity, NBER WP 6120.
Bils, M., (1987) The cyclical behavior of marginal cost and price, The American Economic
Review, Vol 77 No. 5.
Caballero, R., and E. Engel (1993) Microeconomic adjustment hazard and aggregate dynamics,
Quarterly Journal of Economics 108 (2) : 359383.
Caballero, R., E. Engel and J. Haltiwanger (1995) PlantLevel Adjustment and Aggregate
Investment Dynamics, Brookings Papers on Economic Activity 2: 154.
Caballero, R., E. Engel and J. Haltiwanger (1997) Aggregate employment dynamics: building
from microeconomic evidence, American Economic Review 87 (1): 115137.
Cassoni, A, G. Fachola and G. Labadie (2001) The Economic Effects of Unions in Latin
America: Their Impact on Wages and the Economic Performance of Firms in Uruguay,
Research Department WP R466, Inter American Development Bank.
Casacuberta, C., G. Fachola, and N. Gandelman, (2004) The Impact of Trade Liberalization on
Employment, Capital and Productivity Dynamics: Evidence from the Uruguayan
Manufacturing Sector, Journal of Policy Reform, Vol 7 (4)
Cooper, R., and J. Willis, (2004) The economics of Labor adjustment: mind the gap, The
American Economic Review, Vol 94 No. 4
Eslava, M., J. Haltiwanger, and M. Kugler, (2005) Heterogeneous adjustments of employment
and capital after factor market deregulation. Mimeo.
Davis, S., and J. Haltiwanger, (1992) Gross job creation gross job destruction and employment
reallocation, Quarterly Journal of Economics, 107(3)
Davis, S., J. Haltiwanger, and S. Schuh, (1996) Job creation and destruction, MA: MIT Press
Levinsohn, J., and A. Petrin, (2003) Estimating production functions using inputs to control for
unobservables, Review of economic studies, Vol 70 (2).
Levinsohn, J., A. Petrin, and B. Poi (2003) Production function estimation in Stata using inputs
to control for unobservables, Stata Journal, 4 (2).
Vaillant, M., (2000) Limits to trade liberalization: a political economy approach, Ph.D.
dissertation, Universiteit Antwerpen UFSIA.
17
7. Appendix: Data
In this paper we exploit Uruguayan establishment level data covering a considerably long period
of time. We use annual establishment level observations from the Manufacturing Survey
conducted by the Instituto Nacional de Estadística (INE) for the period 19821995. The survey
sampling frame encompasses all Uruguayan manufacturing establishments with five or more
employees.
The INE divided each four digit International Standard Industrial Classification (ISIC) sector in
two groups. All establishments with more than 100 employees were included in the survey; the
random sampling process of firms with less than 100 employees satisfies the criterion that the
total employment of all the selected establishments must account at least for 60% of the total
employment of the sector according to the economic Census (1978 or 1988).
The data for the whole period are actually obtained from two sub sample sets: from 1982 until
1988 and from 1988 until 1995. In 1988 the Second National Economic Census was conducted.
After that, the INE made a major methodological revision to the manufacturing survey and
changed the sample of establishments. The statistical analysis was also performed controlling for
the sample of origin. Firms entering the sample in 1988 behave similarly than firms from the old
sample. Differences in behavior in white collar and capital adjustment are small. For the firms in
the new sample the slope of the blue collar adjustment function is higher in the creation side
while lower in the destruction side. It is hard to evaluate differences by sample in the sign or size
of the policy impacts.
In total, we have 627 different establishments present in at least one period. There are 208
starting in 1982, of which just 185 make it to 1995. The 1988 sample, is composed of 304
establishments included for the first time in that year, and 254 from the old sample not all of
which are to be followed in subsequent years.
7.1. Capital
To construct the establishment capital stock series, we follow a methodology close to Black and
Lynch (1997). The 1988 Census reports information on the capital stock. We use machinery
capital. We avoid overestimation of the amount of depreciation by calculating an average
depreciation rate by industrial sector and year. The resulting depreciation rate is then used for all
firms within each sector yearly. We further exclude the value of assets sold in our measure of
capital, assuming assets have been totally depreciated at that point. Thus, the equation for
estimating the capital stock for years later than 1988 is:
K jit = K jit + I jit itK jit
1 1 (22)
with
18
D jit
it =
x j
K (23)
jit
j
where j indexes firms; i the industrial sector, t the year. K is the capital stock; I is amount
invested; is the depreciation rate; and D is depreciation in pesos.
For years before 1988, the equation is reversed and each year's capital is obtained by subtracting
each year's investment and applying a depreciation factor. The depreciation rate before 1988 was
not available and was estimated using 1988 data. We ran a simple OLS model for the log of total
depreciation conditional on the log of gross output, capital stock, total hours and electricity
usage. Using this model we predicted the before 1988 depreciation levels.
Kjit = Kjit Ijit 1^jit
( ) 1
1 (24)
7.2. Tariffs
We use data on import tariffs for the period 19851995 from Vaillant (2000) and Casacuberta,
Fachola and Gandelman (2004).
19
8. Tables
Table 1
LevinsohnPetrin productivity estimation
Coefficients Std. Err.
White collar 0,148*** 0,026
Blue collar hours 0,234*** 0,036
Materials 0,314*** 0,053
Machinery capital 0,120* 0,063
Electricity 0,200*** 0,088
Number of observations 5903
Number of establishments 685
Wald test of constant returns to scale: Chi2 =1,51 (p = 0,22)
Note: Dependent variable is gross output. All variables are in logs
* significant at 10%; ** significant at 5%; *** significant at 1%
Table 2
Demand shock estimation
Threestage least squares regression
Obs Parameters
Demand equation 5903 9
Supply equation 5903 16
Coef, Std. Err.
Demand equation
Price 1,156* 0,619
Supply equation
Price 0,863* 0,520
Total factor productivity 0,006* 0,003
Wage Index 0,376** 0,166
Note: Dependent variable is gross output.
Endogenous variables: gross output and price.
Exogenous variables not reported: year dummies (supply) and 3
digit ISIC industry dummies (demand)
* significant at 10%; ** significant at 5%; *** significant at 1%
20
Table 3
Compensation function estimation
Non linear least squares
Coefficient Std. Err.
w0 0,816*** 0,016
w1 1,24E07*** 6,36E09
Delta 2
Number of obs 6198
Rsquared 0.832
Adj Rsquared 0.844
Note: * Parameter delta taken calibrated to 2.
significant at 10%; ** significant at 5%; *** significant at 1%
Table 4
Summary statistics: actual, desired and frictionless factor levels
Mean values
Variable Observations Mean Std. Dev.
DW 5512 37 75
W 5512 32 63
DB 5512 115 208
B 5512 105 168
DK 5512 275333 788947
K 5512 249379 657317
Pariwise Correlations:
FW DW W
FW 1.00
DW 0.75 1.00
W 0.72 0.85 1.00
FB DB B
FB 1.00
DB 0.66 1.00
B 0.65 0.88 1.00
FK DK K
FK 1.00
DK 0.66 1.00
K 0.63 0.82 1.00
Note: K = actual capital, FK = frictionless capital, DK= desired capital
Idem with B, FB, DB and W,FW,DK for blue and white collar
21
Table 5
Estimated parametric adjustment functions
Baseline specification
White collar Blue collar Capital
adjustment adjustment adjustment
Constant 0.074 0.166 0.060
[0.022]*** [0.023]*** [0.021]***
Pos Shortage 0.137 0.1406 0.026
[0.039]*** [0.037]*** [0.036]
(ShortageW)^2 0.189 0.029 0.006
[0.011]*** [0.012]** [0.013]
(ShortageW)^2*Pos 0.103
[0.017]***
(ShortageB)^2 0.009 0.099 0.025
[0.015] [0.012]*** [0.013]*
(ShortageB)^2*Pos 0.071
[0.019]***
(ShortageK)^2 0.012 0.021 0.179
[0.011] [0.010]** [0.007]***
(ShortageK)^2*Pos 0.137
[0.013]***
(ShortageW)*(ShortageB) 0.042 0.0011 0.011
[0.014]*** [0.013] [0.016]
(ShortageW)*(ShortageK) 0.025 0.001 0.001
[0.012]** [0.014] [0.012]
(ShortageB)*(ShortageK) 0.026 0.058 0.059
[0.016] [0.0121]*** [0.011]***
Observations 4945 4945 4945
Number of id 627 627 627
Rsquared 0.3 0.29 0.37
Note: Pos Shortage: Dummy=1 if Shortage is positive
ShortageW. ShortageB. ShortageK are the shortages for white collar. blue
collar and capital. Standard errors in brackets
* significant at 10%; ** significant at 5%; *** significant at 1%
22
Table 6
Policy and firm variables
Descriptive statistics
tariff (%) Tariff
change Export/sales
1982 . . 0.17
1983 . . 0.20
1984 . . 0.20
1985 42.53 . 0.19
1986 38.96 3.47 0.19
1987 35.49 3.46 0.19
1988 32.64 3.46 0.14
1989 32.07 0.57 0.16
1990 31.50 0.57 0.16
1991 24.59 6.97 0.15
1992 20.68 3.91 0.15
1993 17.09 3.54 0.15
1994 17.11 0.02 0.15
1995 14.01 3.14 0.16
All period
Mean 26.3 2.8 0.2
standard dev 9.9 2.7 0.3
Percentile 50 24.9 2.9 0.0
Percentile 90 40.0 0.1 0.8
Before 1990
Mean 34.7 2.1 0.2
standard dev 8.2 1.8 0.3
Percentile 50 34.6 2.2 0.0
Percentile 90 44.5 0.3 0.8
1990 and after
Mean 21.3 3.0 0.2
standard dev 6.9 2.9 0.3
Percentile 50 19.8 3.1 0.0
Percentile 90 31.4 0.0 0.7
23
Table 7
Estimated parametric adjustment functions
Pre and post Mercosur estimation
White collar Blue collar Capital
adjustment adjustment adjustment
Constant 0.089 0.367 0.071
[0.027]*** [0.029]*** [0.026]***
Pos Shortage 0.083 0.180 0.055
[0.048]* [0.044]*** [0.042]
(ShortageW)^2 0.174 0.022 0.008
[0.014]*** [0.012]* [0.012]
(ShortageW)^2*Pos 0.107
[0.024]***
(ShortageB)^2 0.005 0.029 0.020
[0.014] [0.017]* [0.013]
(ShortageB)^2*Pos 0.012
[0.023]
(ShortageK)^2 0.010 0.021 0.191
[0.011] [0.010]** [0.009]***
(ShortageK)^2*Pos 0.148
[0.017]***
(ShortageW)*(ShortageB) 0.038 0.001 0.003
[0.014]*** [0.013] [0.016]
(ShortageW)*(ShortageK) 0.027 0.008 0.009
[0.012]** [0.014] [0.012]
(ShortageB)*(ShortageK) 0.025 0.058 0.048
[0.016] [0.012]*** [0.011]***
Constant*Mercosur 0.035 0.307 0.022
[0.032] [0.031]*** [0.03]
Pos Shortage*Mercosur 0.099 0.487 0.081
[0.048]** [0.043]*** [0.042]*
(ShortageW)^2*Mercosur 0.025
[0.018]
(ShortageW)^2*Pos*Mercosur 0.002
[0.028]
(ShortageB)^2*Mercosur 0.107
[0.020]***
(ShortageB)^2*Pos*Mercosur 0.090
[0.029]***
(ShortageK)^2*Mercosur 0.062
[0.014]***
(ShortageK)^2*Pos*Mercosur 0.060
[0.021]***
Observations 4945 4945 4945
Number of id 627 627 627
Rsquared 0.31 0.33 0.38
Note: Pos Shortage: Dummy=1 if Shortage is positive. ShortageW. ShortageB.
ShortageK are the shortages for white collar. blue collar and capital. Mercosur is a
dummy that takes the value 1 for all observations after 1989. Standard errors in brackets
* significant at 10%; ** significant at 5%; *** significant at 1%
24
Table 8
Estimated parametric adjustment functions
Tariff changes effect estimation
White collar Blue collar Capital
adjustment adjustment adjustment
Constant 0.026 0.167 0.062
[0.030] [0.030]*** [0.031]**
Pos Shortage 0.196 0.116 0.038
[0.050]*** [0.047]** [0.048]
(ShortageW)^2 0.201 0.032 0.006
[0.015]*** [0.013]** [0.014]
(ShortageW)^2*Pos 0.076
[0.023]***
(ShortageB)^2 0.006 0.044 0.022
[0.015] [0.018]** [0.016]
(ShortageB)^2*Pos 0.026
[0.026]
(ShortageK)^2 0.009 0.033 0.179
[0.013] [0.012]*** [0.013]***
(ShortageK)^2*Pos 0.119
[0.020]***
(ShortageW)*(ShortageB) 0.047 0.002 0.013
[0.015]*** [0.013] [0.018]
(ShortageW)*(ShortageK) 0.025 0.013 0.016
[0.014]* [0.015] [0.014]
(ShortageB)*(ShortageK) 0.036 0.040 0.063
[0.018]** [0.014]*** [0.013]***
Constant*Open 0.019 0.002 0.017
[0.007]*** [0.006] [0.007]**
Pos Shortage*Open 0.012 0.007 0.020
[0.009] [0.009] [0.009]**
(ShortageW)^2*Open 0.005
[0.004]
(ShortageW)^2*Pos*Open 0.006
[0.005]
(ShortageB)^2*Open 0.015
[0.003]***
(ShortageB)^2*Pos*Open 0.027
[0.006]***
(ShortageK)^2*Open 0.008
[0.004]**
(ShortageK)^2*Pos*Open 0.008
[0.005]
Observations 4278 4278 4278
Number of id 627 627 627
Rsquared 0.33 0.31 0.31
Note: Pos Shortage: Dummy=1 if Shortage is positive. ShortageW. ShortageB.
ShortageK are the shortages for white collar. blue collar and capital. Open is the
annual change in tariff levels. Standard errors in brackets
* significant at 10%; ** significant at 5%; *** significant at 1%
25
Table 9
Estimated parametric adjustment functions
Tariff level effect estimation
White collar Blue collar Capital
adjustment adjustment adjustment
Constant 0.052 0.181 0.057
[0.051] [0.047]*** [0.051]
Pos Shortage 0.289 0.778 0.192
[0.080]*** [0.071]*** [0.074]***
(ShortageW)^2 0.207 0.022 0.009
[0.028]*** [0.012]* [0.013]
(ShortageW)^2*Pos 0.136
[0.044]***
(ShortageB)^2 0.002 0.177 0.022
[0.015] [0.031]*** [0.015]
(ShortageB)^2*Pos 0.083
[0.047]*
(ShortageK)^2 0.008 0.026 0.119
[0.013] [0.011]** [0.024]***
(ShortageK)^2*Pos 0.102
[0.035]***
(ShortageW)*(ShortageB) 0.047 0.012 0.007
[0.015]*** [0.013] [0.017]
(ShortageW)*(ShortageK) 0.022 0.010 0.001
[0.013] [0.015] [0.013]
(ShortageB)*(ShortageK) 0.030 0.056 0.052
[0.017]* [0.013]*** [0.01193]***
Constant*Tariff 0.00074 0.01419 0.002
[0.002] [0.002]*** [0.002]
Pos Shortage*Tariff 0.006 0.026 0.008
[0.003]** [0.002]*** [0.002]***
(ShortageW)^2*Tariff 0.001
[0.001]
(ShortageW)^2*Pos*Tariff 0.002
[0.002]
(ShortageB)^2*Tariff 0.003
[0.001]***
(ShortageB)^2*Pos*Tariff 0.002
[0.002]
(ShortageK)^2*Tariff 0.001
[0.001]*
(ShortageK)^2*Pos*Tariff 0.000
[0.001]
Observations 4507 4507 4507
Number of id 627 627 627
Rsquared 0.32 0.34 0.32
Note: Pos Shortage: Dummy=1 if Shortage is positive. ShortageW. ShortageB.
ShortageK are the shortages for white collar. blue collar and capital. Tariff is
the sector average import tariff. Standard errors in brackets. * significant at
10%; ** significant at 5%; *** significant at 1%
26
Table 10
Estimated parametric adjustment functions
Export share in sales effect estimation
White Blue Capital
Constant 0.089 0.169 0.042
[0.026]*** [0.026]*** [0.024]*
Pos Shortage 0.095 0.105 0.073
[0.043]** [0.041]** [0.040]*
(ShortageW)^2 0.179 0.040 0.003
[0.012]*** [0.013]*** [0.013]
(ShortageW)^2*Pos 0.071
[0.019]***
(ShortageB)^2 0.031 0.096 0.028
[0.014]** [0.014]*** [0.014]**
(ShortageB)^2*Pos 0.047
[0.021]**
(ShortageK)^2 0.013 0.014 0.187
[0.011] [0.010] [0.008]***
(ShortageK)^2*Pos 0.160761
[0.015]***
(ShortageW)*(ShortageB) 0.024 0.023 0.018
[0.015] [0.013]* [0.017]
(ShortageW)*(ShortageK) 0.016 0.004 0.004
[0.013] [0.015] [0.013]
(ShortageB)*(ShortageK) 0.023 0.048 0.063
[0.017] [0.013]*** [0.011]***
Constant*ExportS 0.090 0.005 0.073
[0.060] [0.068] [0.063]
Pos Shortage*ExportS 0.330 0.187 0.210
[0.107]*** [0.111]* [0.100]**
(ShortageW)^2*ExportS 0.018
[0.028]
(ShortageW)^2*Pos*ExportS 0.190
[0.052]***
(ShortageB)^2*ExportS 0.072
[0.044]*
(ShortageB)^2*Pos*ExportS 0.127
[0.065]*
(ShortageK)^2*ExportS 0.033
[0.022]
(ShortageK)^2*Pos*ExportS 0.121
[0.039]***
Observations 4862 4862 4862
Number of id 618 618 618
Rsquared 0.3 0.3 0.38
Note: Pos Shortage: Dummy=1 if Shortage is positive. ShortageW.
ShortageB. ShortageK are the shortages for white collar. blue collar and
capital. Exports is the share of firms sales that is exported. Standard errors
in brackets * significant at 10%; ** significant at 5%; *** significant at 1%
27
9. Figures
Figure 1. White Collar shortages  Histogram
.184854
noitca
Fr
0
1.95676 1.99851
ZW
Figure 2. Blue Collar shortages  Histogram
.226592
noitca
Fr
0
1.95011 2
ZB
Figure 3. Capital shortages  Histogram
.197787
noitca
Fr
0
1.97092 2
ZK
28
Figure 4. Firms' Outuput Gap
10%
8%
6%
4%
2%
0%
2%
4%
6%
82 83 84 85 86 87 88 89 90 91 92 93 94 95
19 19 19 19 19 19 19 19 19 19 19 19 19 19
mean median
Figure 5  Adjustment functions
Baseline estimation (all other shortages=0)
1,0
0,9
0,8
0,7
0,6
0,5
0,4
0,3
0,2
0,1
0,0
2 1,6 1,2 0,8 0,4 0 0,4 0,8 1,2 1,6 2
White Collar Blue Collar Capital
29
Figure 6  Adjustment functions  White collar
1,0
0,9
0,8
0,7
0,6
0,5
0,4
0,3
0,2
0,1
0,0
2 1,6 1,2 0,8 0,4 0 0,4 0,8 1,2 1,6 2
All other shortages = mean Other shortages = mean+sd Othre shortages= mean sd
Figure 7  Adjustment functions  Blue collar
1,0
0,9
0,8
0,7
0,6
0,5
0,4
0,3
0,2
0,1
0,0
2 1,6 1,2 0,8 0,4 0 0,4 0,8 1,2 1,6 2
Other shortages= mean Other shortages = mean+sd Othre shortages= mean sd
30
Figure 8  Adjustment functions  Capital
1,0
0,9
0,8
0,7
0,6
0,5
0,4
0,3
0,2
0,1
0,0
2 1,6 1,2 0,8 0,4 0 0,4 0,8 1,2 1,6 2
All other shortages = mean Other shortages = mean+sd Othre shortages= mean sd
Figure 9  Adjustment functions  White collar
1,0
0,9
0,8
0,7
0,6
0,5
0,4
0,3
0,2
0,1
0,0
2 1,6 1,2 0,8 0,4 0 0,4 0,8 1,2 1,6 2
Post Mercosur Pre Mercosur
31
Figure 10 Adjustment functions  Blue collar
1,0
0,9
0,8
0,7
0,6
0,5
0,4
0,3
0,2
0,1
0,0
2 1,6 1,2 0,8 0,4 0 0,4 0,8 1,2 1,6 2
Post Mercosur Pre Mercosur
Figure 11  Adjustment functions  Capital
1,0
0,9
0,8
0,7
0,6
0,5
0,4
0,3
0,2
0,1
0,0
2 1,6 1,2 0,8 0,4 0 0,4 0,8 1,2 1,6 2
Post Mercosur Pre Mercosur
32
Figure 12  Adjustment functions  White collar
1,0
0,9
0,8
0,7
0,6
0,5
0,4
0,3
0,2
0,1
0,0
2 1,6 1,2 0,8 0,4 0 0,4 0,8 1,2 1,6 2
No change in tariffs 2 percent tariff reduction
4 percent tariff reduction
Figure 13  Adjustment functions  Blue collar
1,0
0,9
0,8
0,7
0,6
0,5
0,4
0,3
0,2
0,1
0,0
2 1,6 1,2 0,8 0,4 0 0,4 0,8 1,2 1,6 2
No change in tariff 2 percent tariff reduction
4 percent tariff reduction
33
Figure 14  Adjustment functions  Capital
1,0
0,9
0,8
0,7
0,6
0,5
0,4
0,3
0,2
0,1
0,0
2 1,6 1,2 0,8 0,4 0 0,4 0,8 1,2 1,6 2
No change in tariffs 2 percent tariff reduction
4 percent tariff reduction
Figure 15  Adjustment functions  White collar
1,0
0,9
0,8
0,7
0,6
0,5
0,4
0,3
0,2
0,1
0,0
2 1,6 1,2 0,8 0,4 0 0,4 0,8 1,2 1,6 2
Tariff=10% Tariff= 20% Tariff =30%
34
Figure 16  Adjustment functions  Blue collar
1,0
0,8
0,6
0,4
0,2
0,0
2 1,6 1,2 0,8 0,4 0 0,4 0,8 1,2 1,6 2
0,2
Tariff=10% tariff= 20% Tariff=30%
Figure 17  Adjustment functions  Capital
1,0
0,9
0,8
0,7
0,6
0,5
0,4
0,3
0,2
0,1
0,0
2 1,6 1,2 0,8 0,4 0 0,4 0,8 1,2 1,6 2
Tariff = 10% Tariff = 20% Tariff = 30%
35
Figure 18  Adjustment functions  White collar
1,0
0,9
0,8
0,7
0,6
0,5
0,4
0,3
0,2
0,1
0,0
2 1,6 1,2 0,8 0,4 0 0,4 0,8 1,2 1,6 2
No exports Exports = 25% of sales Exports = 50% of sales
Figure 19  Adjustment functions  Blue collar
1,0
0,9
0,8
0,7
0,6
0,5
0,4
0,3
0,2
0,1
0,0
2 1,6 1,2 0,8 0,4 0 0,4 0,8 1,2 1,6 2
No exports Exports = 25% of sales Exports = 50% of sales
36
Figure 20  Adjustment functions  Capital
1,0
0,9
0,8
0,7
0,6
0,5
0,4
0,3
0,2
0,1
0,0
2 1,6 1,2 0,8 0,4 0 0,4 0,8 1,2 1,6 2
No exports Exports = 25% of sales Exports = 50% of sales
37