panel of manufacturing establishments in Uruguay. The Uruguayan economy evolved from inward looking, based on state interventionism and import ...
Openness and Adjustment Functions: Evidence from the manufacturing sector in Uruguay Carlos Casacuberta (Universidad de la República) Néstor Gandelman (Universidad ORT)* First Draft June 2005 Abstract Using a panel of manufacturing firms this paper analyzes the adjustment process in capital, blue collar and white collar employment in Uruguay. Desired factor levels arising from a counterfactual profit maximization in absence of adjustment costs are calculated, and hence factor shortages (surpluses) are obtained. Adjustment functions are estimated as the percentage of the shortage gap closed by the firm, as a function of all factor shortages. Our results confirm the lumpy nature of factor adjustment, the relevance of nonlinearities as a feature of the process and the interdependence between factor shortages. The average estimated output gap for 19821995 is 3%. Our policy analysis shows that the trade openness process affected the adjustment functions of all three factors.
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 seventies, accompanied by a quick financial liberalization process. Later, trade liberalization was deepened in the nineties, combining a gradual unilateral tariff reduction with the creation of Mercosur, an imperfect customs union between Argentina, Brazil, Paraguay and Uruguay. A by-product of the trade liberalization process in Uruguay was that manufacturing firms switched to more capital intensive technologies. Specifically, in the nineties, there *
We gratefully acknowledge Gabriela Fachola for her work with the database and in particular for the construction of the capital series and her valuable comments. All errors remain responsibility of the authors.
were high job destruction rates as reported in Casacuberta, Fachola and Gandelman (2004). Here we focus on the adjustment process, and our main concern is with the effect of 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 trade liberalization and international exposure. 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. 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. In order to make the results comparable to other studies, 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
∆B jt =
B jt − B jt −1
(
)
1 B jt + B jt −1 2 W jt − W jt −1 ∆W jt = 1 W jt + W jt −1 2 K jt − K jt −1 ∆K jt = 1 K jt + K jt −1 2
(
)
(
)
1
A 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.
2
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 t-1. 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:
ZB jt =
B*jt − B jt −1
(
)
1 * B jt + B jt −1 2 W *jt − W jt −1 ZW jt = 1 * W jt + W jt −1 2 K *jt − K jt −1 ZK jt = 1 * K jt + K jt −1 2
(
)
(
)
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:
AB jt = AW jt = AK jt =
∆B jt ZB jt
∆W jt ZW jt
∆K jt ZK jt
The next step is to characterize such adjustment functions in terms of the shortages in all three factors. It is particularly 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 factors. In particular, the adjustment functions are not restricted to be linear and we allow for different intercept and slope for positive and negative shortages. The basic specifications omitting the asymmetric interactions for positive shortages are:
3
ABjt(ZBjt ,ZWjt ,ZKjt ) = λ0 + λ1ZWjt2 + λ2ZWjtZKjt + λ3ZWjtZBjt + λ4ZB2jt + λ5ZBjtZKjt + λ6ZK2jt AWjt(ZBjt ,ZWjt ,ZKjt ) =ν0 +ν1ZWjt2 + v2ZWjtZKjt +ν3ZWjtZBjt +ν4ZB2jt +ν5ZBjtZKjt +ν6ZK2jt AKjt(ZBjt ,ZWjt ,ZKjt ) =κ0 +κ1ZWjt2 +κ2ZWjtZKjt +κ3ZWjtZBjt +κ4ZB2jt +κ5ZBjtZKjt +κ6ZK2jt In practice, the estimated models are the following:
(1)
∆Bjt (ZBjt ,ZWjt ,ZK jt ) = ZBjt ABjt (ZBjt ,ZWjt ,ZK jt ) =
[
ZBjt λ0 + λ1ZWjt2 + λ2 ZWjt ZK jt + λ3 ZWjt ZBjt + λ4 ZB2jt + λ5 ZBjt ZK jt + λ6 ZK 2jt (2)
∆Wjt (ZBjt ,ZWjt ,ZK jt ) = ZWjt AWjt (ZBjt ,ZWjt ,ZK jt ) =
[
ZWjt ν0 + ν1ZWjt2 + v2 ZWjt ZK jt + ν3 ZWjt ZBjt + ν4 ZB2jt + ν5 ZBjt ZK jt + ν6 ZK 2jt (3)
] ]
∆K jt (ZBjt ,ZWjt ,ZK jt ) = ZK jt AK jt (ZBjt ,ZWjt ,ZK jt ) =
[
ZK jt κ0 + κ1ZWjt2 + κ2 ZWjt ZK jt + κ3 ZWjt ZBjt + κ4 ZB2jt + κ5 ZBjt ZK jt + κ6 ZK 2jt
]
If there is a degree of 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 the above mentioned adjustment equations. We focus basically on trade reform, which consisted of the signature of the Mercosur treaty in 1990, and of unilateral tariff reductions along the nineties. The first step is then to estimate a pre and post reform sets of 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.
4
Finally, we find it useful to calculate what would be the output gap attributable to the adjustment costs. This 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 exercise may skip this section and proceed directly to section 4. 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:
(
)
Y jt = K jtα B jt H jt β W jt µ E jt γ M jtφV jt 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. There is also an inverse demand function for the firm, given by:
Pjt = Y jt
−
1 η
D jt
where D is a demand shock and η is the elasticity of demand. Firms face competitive factor markets with the following total costs for blue collar labor, white collar labor, capital, energy and material:
5
(
ωB(L jt ,H jt ) = w0t B jt 1 + w1t H jt
δ
)
ωW (Wjt ) = PWtW jt ωK (K jt ) = PKt K jt ωE(E jt ) = PEt E jt ωM (M jt ) = PMt M jt 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. This function is indexed by parameters w0 (straight-time blue collar wage), w1 (overtime premium) and δ (marginal wage elasticity). 2 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 = ln P for input prices, and subscripts are omitted to simplify notation):
(
)
~ ~ ~ ~ ~ ~ η η − 1 ~ η ~ −ln PK −lnα − β B + H − µW − γE − φM −V − D + ~ η −1 η η −1 K= η α− η −1
(
)
(
)
~ ~ ~ ~ ~ ~ η η − 1 ~ η ~ η ~ − ln − D + PB − lnβ + 1 + PB1 H δ − αK − βH − µW − γE − φM − V 0 ~ η −1 η η −1 η −1 B= η β− η −1
(
)
~ ~ ~ ~ ~ ~ η η − 1 ~ η ~ η ~ ~ η − ln − D + PB − lnβ + PB1 + B + lnδ − αK − βL − µW − γE − φM −V 0 η −1 ~ η −1 η η −1 η −1 H= η β −δ η −1
2
As 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.
6
(
)
~ ~ ~ ~ ~ ~ η ~ η η −1 ~ − ln PW − lnµ − αK − β B + H − γE − φM − V − D + ~ η −1 η η −1 W= η µ− η −1
(
)
~ ~ ~ ~ ~ ~ η η −1 ~ η ~ − ln PE − lnγ − αK − β B + H − µW − φM −V − D + ~ η −1 η η −1 E= η γ− η −1
(
)
~ ~ ~ ~ ~ ~ η η −1 ~ η ~ − ln PM − lnφ − αK − β B + H − µW − γE −V − D + ~ η −1 η η −1 M= η φ− η −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, blue-collar employment and white collar employment as functions of the parameters of the model to be estimated and observed variables as follows:
(
) [
(
)] (
)
[
(
)] (
) (
)
~ η η−1 ~ ~ ~ ~ ~ η ~ ~ −ln − D − βH −γE −φM −V + − β − µ PK −lnα + β PB0 −lnβ + 1+ PB1 Hδ + µ PW −lnµ ~ η−1 η η−1 K= η α+ β + µ− η−1
~ η η−1 ~ ~ ~ ~ ~ η ~ ~ −ln −D − βH −γE −φM −V + −α− µ PB0 −lnβ + 1+ PB1 Hδ +α PK −lnα + µ Pw −lnµ ~ η−1 η η−1 L= η α+ β + µ− η−1
7
(
) [
(
)] (
)
~ η η −1 ~ ~ ~ ~ ~ η ~ ~ −ln − D − βH − γE −φM −V + −α − β PW −lnµ + β PB0 −lnβ + 1+ PB1 Hδ + α PK −lnα ~ η −1 η η −1 W= η α+ β + µ− η −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 frictionless levels would be the ones observed in absence of adjustment costs just for one period, while the desired levels would be the ones observed in absence of adjustment costs in all periods, as noted for instance by Cooper and Willis (2004). Following Eslava et al (2005), the desired levels of capital and employment are approximated, up to a constant, by the frictionless levels of capital, white-collar and blue-collar employment according to
K *jt = K jtθ Kj B *jt = B jtθ Bj W jt* = W jtθ Wj
(
)(
)
(
where K *jt , K jt , B *jt , B jt and W jt* , W
jt
) are respectively the desired and frictionless
levels of capital, blue-collar and white collar employment. The firm specific constants to be estimated are θ Kj , θ Bj andθWj . Following Caballero, Engel and Haltiwanger (1995, 1997) θ Kj , θ Bj and θ Wj 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 percentage gap in output (Y*/Y-1) is:
K *jt −1 = K Y jt jt Y jt*
α
8
B *jt B jt
β
W jt* W jt
µ
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 that we use,
(
)
Y jt = K jtα B jt H jt β W jt µ E jt γ M jtφV jt we compute our measure of total factor productivity as:
(
)
~ ~ ~ ~ ~ V jt = Y jt − αˆ K jt − βˆ B jt + H jt − µˆ W jt − γˆE jt − φˆM jt where αˆ , βˆ , µˆ ,γˆ and φˆ 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. These are estimated across 250 bootstrapped samples. 3.3.2. Demand shock estimation We also estimate establishment level demand shocks based on the inverse demand equation given by:
Pjt = Y jt
−
1 η
D jt
The inverse demand function is estimated in logs, and the demand shock recovered as the residual from the regression of the log of Pjt in Yjt:
ˆ = ln P + εˆ ln Y d jt = ln D jt jt jt where ε = -1/η. In order to identify the elasticity of the demand equation we estimate a
9
two equation system of demand and supply, using three stage least squares. Supply shifters include our total factor productivity estimate and input prices, 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 15%. 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:
(
ωB (L jt , H jt ) = w0t B jt 1 + w1t H jt δ
)
.3
Bils (1987) and Cooper and Willis (2004) estimate for the U.S. the wage marginal elasticity δ to be 2. Eslava et al (2005) working with Colombian firms calibrate δ to 2 and w1 to the legally overtime premium and estimate from their data the straight-time wage w0. Instead, we use a non linear least squares procedure to estimate the parameters w0, w1 and δ. Due to difficulties in attaining convergence in the iterative process, the estimation was carried out in two steps. First taking as given δ = 2 we estimated w0 and w1, and then taking our estimate of w1 as given we optimized with respect to w0 and δ in a second stage. Table 3 shows the results of these estimations. 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. Figures 4 to 6 plot the median actual and desired factor levels and Figure 7 shows the median shortages for the three production factors. Until 1985 we observe surpluses (i.e. negative shortages) in all three factors of production and since then the desired levels are slightly above the observed ones. Over time there seems to be a trend in for smaller factor gaps. Consistently the output gap presented in Figure 8 is negative in the first half of the eighties and positive afterwards. Although the yearly gaps are as large as 10% of actual output, over the fourteen years covered by this study the average output gap is 3%. Table 4 displays the pairwise correlations between the observed, the frictionless and desired input levels. All correlations are high suggesting the models predicts reasonably 3
It 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
well. Turning now to the adjustment functions themselves, we estimate the parameters in the equations (1), (2) and (3) 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 positive and negative shortages, i.e. we allow for different levels as well as slopes of the adjustment for positive and negative shortages. 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 positive and negative shortages, with the exception of capital, where there is a difference in the slope only. 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 adjustment functions for white and blue collar employment and capital are displayed in figure 9. 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. To observe the effect of the rest of the factors in each adjustment function, figures 10 to 12 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 percentage of the adjustment is plotted as a function of the shortage. Negative shortages 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. The estimation shows there is an asymmetric behavior in the adjustment process. First, for small values of the observed shortage, white and blue collar employment adjustment functions 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 exceed the desired ones, i.e. the firm finds it easier to create labor than to destroy it. 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 our results statistically significant.
11
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 negative shortages 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 three 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 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 tan 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 in 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 10 to 12). 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).
12
It should be kept in mind that our results consider the case of changes in the adjustment function of one factor when shortages of the other two factors move in the same direction, i.e. they both increase or decrease at the same time. We do not find that the blue collar adjustment functions when the other shortages increase or decrease together is the smalles of the three. This is because, although the positive coefficiente on the interaction between white collar and blue collar shortages is not significant, their interaction effects are of the opposite signs This may reflect that blue and white-collar labor may be complements while capital and blue collar labor are rather substitutes. 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 anti-export bias that characterized previous import substitution policies. With the recovery of democratic institutions in 1984, 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 non-tariff barriers. In 1990, a program of scheduled tariffs reductions began, and the signature of the Mercosur treaty led to the establishment of an imperfect trade union with the 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 a sectoral shock (trade liberalization) affects the way firms respond to idiosyncratic shortages. Industries that were more open from the beginning should experience lower adjustments due to the general higher international exposure. Descriptive statistics of our policy and firm variables are presented in Table 6. We find that the average tariff were reduced significantly from an average of 43% to 14% between 1985 and 1995. Tariff changes accelerated from a mean -2,55 annual percentage points before 1990 to -3,01 after 1990. The average ratio of exports over sales remained basically unchanged over 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 anti-inflation 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, 1982-1989 and 19901995, and estimate the adjustment functions in each one of them. In the second period was when most of the import tariffs reductions took place and the Mercosur was
13
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 one for observations in 1990–1995 and zero elsewhere. Table 7 displays the estimated pre and post 1990 adjustment functions. In turn, figures 8, 9 and 10 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. However this shifts affects differently each of the factors of production. A clear pattern emerges in the creation side for both types of labor: the level changes with the slope remaining roughly constant. In the case of white collar labor, there is basically no effect on the destruction side. The blue collar adjustment pattern differs after and before 1990. While for the whole period functions the intercept is higher in the creation side, the pre 1990 function shows the opposite: a larger fraction of the gap is closed in the destruction side. The post 1990 function shows a much higher fraction of the gap closed when shortages are positive. Though there is a very small fraction of adjustment when negative shortages are small in absolute value, the negative side of the adjustment function becomes steeper after 1990. In the case of capital there is a slight increase in the slope in the creation side and the adjustment function becomes much flatter in the destruction side pointing towards lower adjustment costs and 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, while figures 16, 17 and 18 show the estimated functions for tariff reductions of 0, 2 and 4 percentage points. A pattern emerges in which the fraction of the gap actually adjusted decreases for all factors in the creation side, while increases in the destruction side. Firms in sectors that experience higher tariff reductions and had to destroy employment and capital 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. The change in white collar affects only the intercept term of the adjustment function. In blue collar both slopes are affected and in capital the impact of tariff reductions in on the slope of the destruction side.
14
4.5. Tariff trade barriers 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. Figures 19, 20 and 21 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 employment. The effect for capital is only on the slope and of a much smaller magnitude. 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. 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 it is 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 22, 23 and 24 show the shifts in the estimated functions for export shares of 0, 25 and 50 percentage points. 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. It is interesting to note that the effect on the destruction side is of a different size for white and blue collars. Sectors more export oriented, when their desired blue collar employment level is below the actual one, they adjust a higher share of the gap than sectors oriented to the domestic market. On the other hand, for higher shortages, more export oriented sectors when they would decide to destroy white collar employment, they adjust a smaller share than more domestic market oriented firms. There does not seem to be significant differences between export oriented and domestic market oriented firms with respect to capital 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, he paper confirms a number of regularities that the previous literature on adjustment functions has highlighted.
15
Our investigation confirmed 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, market-induced, increasing returns in the adjustment technology). The evidence provided seems to confirm a pattern that has important nonlinear features, hence consistent with such constraints. Thus, it often happens that after several periods of relatively passive investment response to shocks, it is possible to observe feverish reactions. Naturally, 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. This 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 3%. On the other hand, the paper intended to assess the effects of trade liberalization on firm’s adjustment process. The constraints arising from the adjustment cost functions may in turn become an important part of the policy analysis. Our results point to a significant shift in the adjustment functions for all the production functions 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. Sectors less protected 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 in the context of Mercosur, those sectors more highly protected were probably those that faced the largest 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) : 359-383. Caballero, R., E. Engel and J. Haltiwanger (1995) Plant-Level Adjustment and Aggregate Investment Dynamics, Brookings Papers on Economic Activity 2: 1-54. 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 1982-1995. The survey-sampling frame encompasses all Uruguayan manufacturing establishments with five or more employees. A more up to date database has not been released yet, but analyzing the available dataset is useful, since most of the trade liberalization was undertaken between 1986 and 1996, when the average tariff went from 36 percent to 11 percent. In this sense, the goals of the paper and the expected conclusions, especially on the effects of trade liberalization on the adjustment functions, are not expected to significantly change with the inclusion of more years. 4 The INE divided each four digits 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). This selection criterion makes our database biased towards large firms. 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. In total, we have 810 different establishments present in at least one period. There are 392 starting in 1982, of which just 185 make it to 1995. The 1988 sample, is composed of 366 establishments included for the first time in that year, and 398 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 avoid overestimation of the amount of depreciation by calculating an average depreciation rate by type of asset – building, machinery and others – by industrial sector and by 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 ijtx = K ijtx −1 + I ijtx − δ jtx K ijtx −1 with 4
Some micro data after 1995 may later become available. This data may probably be matched with our database in further projects.
18
δ jtx
∑D = ∑K
x ijt
i
x ijt −1
i
where i indexes firms; j the industrial sector, t the year and x stands for: machinery, buildings or other capital assets. K is the capital stock; I is amount invested; δ 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. Due to lack of information, the depreciation rate remains fixed at its 1988 rate.
1 Kijtx -1 = Kijtx − Iijtx ⋅ 1 − δx j88
(
)
7.2. Tariffs We use data on import tariffs for the period 1985-1995 from Vaillant (2000) and Casacuberta, Fachola and Gandelman (2004).
19
8. Tables Table 1 Levinsohn-Petrin productivity estimator Dependent variable represents revenue Group variable (i): id Time variable (t): anio
y
Coef,
Std. Err.
z
w bh m k elec
0,152 0,230 0,321 0,216 0,216
0,024 0,038 0,052 0,073 0,066
6,210 6,000 6,210 2,960 3,250
Wald test of constant returns to scale:
Number of obs = Number of groups =
6635 807
Obs per group: min = avg = max =
1 8,2 14
P>z [95% Conf. Interval] 0,000 0,000 0,000 0,003 0,001
Chi2 =1,51 (p = 0,2197)
20
0,104 0,155 0,220 0,073 0,086
0,200 0,305 0,422 0,358 0,346
Table 2 Demand shock estimation Three-stage least squares regression Equation ydemand yoferta
Obs 6635 6635
Parms 28 16
RMSE 1,584 1,647
R-sq 0,077 0,002
chi2 299,589 18,341
P 0,000 0,304
Coef,
Std, Err,
z
P>z
y demand Price
[95% Conf, Interval]
-1,358
0,423
-3,210
0,001
-2,187
-0,530
Y supply Price V lpmat
0,182 0,071 -0,831
0,257 0,023 0,220
0,710 3,050 -3,770
0,478 0,002 0,000
-0,322 0,025 -1,262
0,687 0,117 -0,399
Endogenous variables: y pr Exogenous variables: estimated productivity shock from production function, log of price on raw materials year dummies (supply) _3 digit ISIC industry dummies (demand)
21
Table 3 Compensation function estimation Non linear least squares First stage (delta taken as given and equal 2) Source
SS
Df
MS
266446377,0 53866500,3
1 7050
266446377 7640,63834
Total
320312878
7051
45428,007
comp
Coef.
Std. Err.
t
P>t
[95% Conf.Interval]
0,809 1,17E-07
0,014 5,73E-09
56,390 20,460
0,000 0,000
0,780 0,837 1,06E-07 1,29E-07
Model Residual
w0 w1
Number of obs = F( 1, 7050) = Prob > F = R-squared = Adj R-squared = Root MSE = Res, dev, =
7048 34848,7 0,000 0,832 0,832 87,4 83021,0
* Parameter delta taken as constant term in model & ANOVA table (SE's, P values, CI's, and correlations are asymptotic approximations)
Second stage (w1 taken as estimation from first stage) Source
SS
Df
MS
Model Residual
355629920 53855587,2
2 7050
177814960 7639,09039
Total
409485507
7052
58066,5779
Number of obs = F( 2, 7050) = Prob > F = R-squared = Adj R-squared = Root MSE = Res, dev, =
Coef, Std, Err, t P>|t| [95% Conf.Interval] w0 0,823 0,013 63,840 0,000 0,798 0,848 delta 1,993 0,006 347,780 0,000 1,982 2,005 (SE's, P values, CI's, and correlations are asymptotic approximations)
22
7048 23264,19 0,000 0,869 0,868 87,4 83019,6
Table 4 Pariwise Correlations: Frictionless, Desired and Actual Factor Levels
FW DW W
FW 1,00 0,53 0,70
FB DB B
FB 1,00 0,53 0,62
FK DK K
FK 1,00 0,29 0,55
23
DW
W
1,00 0,67
1,00
DB
B
1,00 0,63
1,00
DK
K
1,00 0,59
1,00
Table 5 Estimated parametric adjustment functions Baseline specification White collar adjustment Constant 0,05462 [0,02072]*** Pos Shortage 0,10974 [0,03616]*** (ShortageW)^2 0,16161 [0,00915]*** (ShortageW)^2*Pos -0,04402 [0,01467]*** (ShortageB)^2 -0,00472 [0,01082] (ShortageB)^2*Pos (ShortageK)^2
Blue collar adjustment 0,13856 [0,02096]*** 0,09533 [0,03332]*** 0,02742 [0,00989]***
Capital adjustment 0,00864 [0,02107] 0,04907 [0,03275] 0,00322 [0,00891]
-0,0124 [0,00888]
0,00239 [0,00745]
0,05982 [0,01025]*** -0,02838 [0,01435]** 0,00281 [0,00715]
-0,05343 [0,01098]*** -0,05944 [0,00884]*** 0,0233 [0,01120]** 5317 700 0,27
0,00866 [0,01006] -0,00036 [0,01106] -0,02871 [0,00821]*** 5317 700 0,26
(ShortageK)^2*Pos (ShortageW)*(ShortageB) (ShortageW)*(ShortageK) (ShortageB)*(ShortageK)
Observations Number of id R-squared Standard errors in brackets * significant at 10%; ** significant at 5%; *** significant at 1%
24
0,20207 [0,00677]*** -0,14962 [0,01145]*** 0,01245 [0,01218] -0,01997 [0,00891] -0,06448 [0,00749]*** 5317 700 0,43
Table 6 Policy and firm variables Descriptive statistics 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 All period mean standard dev Percentile 50 Percentile 75 Percentile 90 Before 1990 mean standard dev Percentile 50 Percentile 75 Percentile 90 1990 and after mean standard dev Percentile 50 Percentile 75 Percentile 90
tariff (%) . . . 43,30 39,80 36,26 32,88 32,04 31,48 24,55 20,65 17,07 17,08 13,97
Tariff change . . . . -3,5 -3,5 -3,5 -0,6 -0,6 -7,0 -3,9 -3,5 0,0 -3,1
Export/sales 16,9% 18,0% 17,9% 16,8% 16,9% 17,6% 14,6% 16,0% 16,2% 14,6% 14,5% 14,6% 15,2% 15,6%
27,54 11 26 35 42
-2,85 3 -3 -1 0,04
0,15 0,3 0 0,12 0,75
36,02 8,59 35,63 40,91 46,8
-2,55 1,72 -3,08 -1,62 0,08
0,16 0,3 0 0,12 0,78
21,04 6,85 19,8 24,96 31,09
-3,01 2,87 -3,14 -0,04 0,03
0,15 0,29 0 0,11 0,69
25
Table 7 Estimated parametric adjustment functions Pre and post Mercosur estimation White collar adjustment Constant 0,06629 [0,02426]*** Pos Shortage 0,04656 [0,04219] (ShortageW)^2 0,14806 [0,01175]*** (ShortageW)^2*Pos -0,03137 [0,01871]* (ShortageB)^2 -0,0029 [0,01083] (ShortageB)^2*Pos
Blue collar Capital adjustment adjustment 0,2681 -0,04152 [0,02602]*** [0,02704] -0,12374 0,11465 [0,03892]*** [0,03854]*** 0,0225 -0,00084 [0,00975]** [0,00880]
0,03729 -0,01307 [0,01421]*** [0,00873] 0,01192 [0,01784] (ShortageK)^2 0,00711 0,00715 0,23914 [0,00754] [0,00705] [0,00847]*** (ShortageK)^2*Pos -0,20544 [0,01376]*** (ShortageW)*(ShortageB) -0,05146 0,00145 0,00836 [0,01098]*** [0,01009] [0,01198] (ShortageW)*(ShortageK) -0,06203 -0,00029 -0,01704 [0,00885]*** [0,01095] [0,00880] (ShortageB)*(ShortageK) 0,02512 -0,02684 -0,05238 [0,01121]** [0,00808]*** [0,00744]*** Constant*Mercosur -0,04027 -0,23563 0,16699 [0,02922] [0,02927]*** [0,03209]*** Pos Shortage*Mercosur 0,13145 0,39067 -0,20846 [0,04490]*** [0,04141]*** [0,04177]*** (ShortageW)^2*Mercosur 0,02586 [0,01517]* (ShortageW)^2*Pos*Mercosur -0,02962 [0,02323] (ShortageB)^2*Mercosur 0,04728 [0,01685]*** (ShortageB)^2*Pos*Mercosur -0,0591 [0,02329]** (ShortageK)^2*Mercosur -0,13502 [0,01309]*** (ShortageK)^2*Pos*Mercosur 0,16935 [0,01869]*** Observations 5317 5317 5317 Number of id 700 700 700 R-squared 0,28 0,29 0,45 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%
26
Table 8 Estimated parametric adjustment functions Tariff changes effect estimation White collar adjustment Constant 0,00238 [0,02912] Pos Shortage 0,15645 [0,04963]*** (ShortageW)^2 0,18388 [0,01343]*** (ShortageW)^2*Pos -0,0353 [0,02103]* (ShortageB)^2 0,00039 [0,01209] (ShortageB)^2*Pos (ShortageK)^2
0,00589 [0,00883]
Blue collar adjustment 0,14005 [0,02887]*** 0,06895 [0,04529] 0,02883 [0,01068]***
Capital adjustment -0,02937 [0,02958] 0,11437 [0,04602]** 0,00375 [0,01059]
0,01753 [0,01566] 0,04385 [0,02204]** 0,02074 [0,00870]**
-0,00698 [0,01164]
(ShortageK)^2*Pos (ShortageW)*(ShortageB) (ShortageW)*(ShortageK) (ShortageB)*(ShortageK) Constant*Open Pos Shortage*Open (ShortageW)^2*Open (ShortageW)^2*Pos*Open
-0,0666 [0,01206]*** -0,05143 [0,01015]*** 0,03689 [0,01302]*** -0,01777 [0,00628]*** 0,00754 [0,00905] 0,00454 [0,00314] 0,00653 [0,00474]
(ShortageB)^2*Open
0,0109 [0,01062] -0,00978 [0,01234] -0,03321 [0,01014]*** -0,00006 [0,00595] -0,00342 [0,00842]
0,23226 [0,00961]*** -0,16467 [0,01771]*** -0,00466 [0,01428] -0,01699 [0,01059] -0,07896 [0,00966]*** -0,03269 [0,00665]*** 0,04329 [0,00896]***
-0,01267 [0,00301]*** 0,01742 [0,00477]***
(ShortageB)^2*Pos*Open (ShortageK)^2*Open (ShortageK)^2*Pos*Open
Observations 4318 4318 Number of id 699 699 R-squared 0,3 0,29 Open is the annual change in tariff levels. Standard errors in brackets * significant at 10%; ** significant at 5%; *** significant at 1%
27
0,01704 [0,00284]*** -0,01954 [0,00427]*** 4318 699 0,42
Table 9 Estimated parametric adjustment functions Tariff level effect estimation White collar adjustment Constant -0,01073 [0,04832] Pos Shortage 0,30167 [0,07658]*** (ShortageW)^2 0,20796 [0,02444]*** (ShortageW)^2*Pos -0,10216 [0,03835]*** (ShortageB)^2 0,00284 [0,01163] (ShortageB)^2*Pos (ShortageK)^2
0,00825 [0,00836]
Blue collar adjustment -0,21172 [0,04424]*** 0,70407 [0,06793]*** 0,0226 [0,01013]**
Capital adjustment 0,08294 [0,05354] -0,04425 [0,07320] 0,00333 [0,00995]
0,15358 [0,02446]*** -0,09269 [0,03551]*** 0,00789 [0,00760]
-0,01955 [0,01031]*
(ShortageK)^2*Pos (ShortageW)*(ShortageB) (ShortageW)*(ShortageK) (ShortageB)*(ShortageK) Constant*Tariff Pos Shortage*Tariff (ShortageW)^2*Tariff (ShortageW)^2*Pos*Tariff
-0,06286 [0,01155]*** -0,05506 [0,00961]*** 0,03245 [0,01235]*** 0,00209 [0,00152] -0,00687 [0,00245]*** -0,00157 [0,00083]* 0,00217 [0,00133]
(ShortageB)^2*Tariff
-0,00539 [0,01019] 0,0044 [0,01166] -0,03688 [0,00894]*** 0,01339 [0,00145]*** -0,02402 [0,00214]***
0,10242 [0,02279]*** -0,00595 [0,03191] 0,00463 [0,01352] -0,02879 [0,00995] -0,05966 [0,00869]*** -0,00136 [0,00184] 0,002 [0,00234]
-0,00309 [0,00081]*** 0,00332 [0,00114]***
(ShortageB)^2*Pos*Tariff (ShortageK)^2*Tariff (ShortageK)^2*Pos*Tariff
Observations 4655 4655 Number of id 700 700 R-squared 0,29 0,3 Tariff is the sector average import tariff Standard errors in brackets * significant at 10%; ** significant at 5%; *** significant at 1%
28
0,00314 [0,00075]*** -0,00427 [0,00104]*** 4655 700 0,41
Table 10 Estimated parametric adjustment functions Export share in sales effect estimation White Constant 0,07223 [0,02364]*** Pos Shortage 0,06261 [0,04062] (ShortageW)^2 0,14983 [0,01058]*** (ShortageW)^2*Pos -0,00801 [0,01675] (ShortageB)^2 -0,01648 [0,01109] (ShortageB)^2*Pos (ShortageK)^2
0,00223 [0,00739]
Blue 0,13677 [0,02366]*** 0,07225 [0,03746]* 0,0399 [0,01110]***
Capital -0,0241 [0,02352] 0,10012 [0,03650]*** 0,00151 [0,00960]
0,04845 [0,01168]*** -0,00702 [0,01674] 0,00255 [0,00728]
-0,01348 [0,00926]
(ShortageK)^2*Pos (ShortageW)*(ShortageB) (ShortageW)*(ShortageK) (ShortageB)*(ShortageK) Constant*ExportS Pos Shortage*ExportS (ShortageW)^2*ExportS (ShortageW)^2*Pos*ExportS
-0,0425 [0,01176]*** -0,05578 [0,00912]*** 0,02396 [0,01167]** -0,09988 [0,05650]* 0,29668 [0,09978]*** 0,03501 [0,02447] -0,17782 [0,04108]***
(ShortageB)^2*ExportS
-0,00687 [0,01101] -0,00553 [0,01210] -0,02159 [0,00881]** 0,00187 [0,06489] 0,12109 [0,10141]
0,21637 [0,00762]*** -0,17515 [0,01290]*** 0,01628 [0,01333] -0,01194 [0,00960] -0,06958 [0,00782]*** 0,18301 [0,06916]*** -0,2584 [0,09869]***
0,07506 [0,03335]** -0,09499 [0,04537]**
(ShortageB)^2*Pos*ExportS (ShortageK)^2*ExportS (ShortageK)^2*Pos*ExportS
Observations 5213 5213 Number of id 692 692 R-squared 0,27 0,27 Exports is the share of firms sales that is exported. Standard errors in brackets * significant at 10%; ** significant at 5%; *** significant at 1%
29
-0,09828 [0,02327]*** 0,13954 [0,03382]*** 5213 692 0,43
9. Figures Figure 1. White Collar shortages - Histogram .190103
Fr act ion
0 -1.98439
ZW
1.99833
Figure 2. Blue Collar shortages - Histogram .230304
Fr act ion
0 -1.99635
ZB
2
Figure 3. Capital shortages - Histogram .215282
Fr act ion
0 -1.98048
ZK
30
2
Figure 4 -Median capital stock desired and actual 70000 60000 50000 40000 30000 20000 10000 0 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 K
DK
Figure 5 -Median blue collar em ploym ent desired and actual 80,00 70,00 60,00 50,00 40,00 30,00 20,00 10,00 0,00 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 B
DB
Figure 6 -Mean w hite collar em ploym ent desired and actual 30,00
25,00
20,00
15,00
10,00
5,00
0,00 1982
1984
1986
1988 W
31
1990 DW
1992
1994
Figure 7- Blue collar, w hite collar and capital m edian shortages 0,30 0,20 0,10 0,00 -0,10
1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995
-0,20 -0,30 -0,40 -0,50 -0,60 -0,70 -0,80 ZK
ZB
ZW
Figure 8 - Estimated output gap 15% 10% 5% 0% 1982
1984
1986
1988
-5% -10% -15%
32
1990
1992
1994
Figure 9 - 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
White Colllar
0
0,4
Blue Collar
33
0,8
1,2
Capital
1,6
2
Figure10 - 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
All other shortages = mean
0
0,4
0,8
Other shortages = mean+sd
1,2
1,6
2
Othre shortages= mean- sd
Figure 11 - 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
Other shortages= mean
-0,4
0
0,4
0,8
Other shortages = mean+sd
1,2
1,6
2
Othre shortages= mean- sd
Figure 12 - 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
All other shortages = mean
-0,4
0
0,4
Other shortages = mean+sd
34
0,8
1,2
1,6
2
Othre shortages= mean- sd
Figure 13 - 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
Post Mercosur
0,8
1,2
1,6
2
1,6
2
1,6
2
Pre Mercosur
Figure 14- 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
Post Mercosur
0,8
1,2
Pre Mercosur
Figure 15 - Adjustment functions - Capital 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
-0,2 Post Mercosur
Pre Mercosur
35
Figure 16 - 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
No change in tariffs 4 percent tariff reduction
0,8
1,2
1,6
2
2 percent tariff reduction
Figure 17 - 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
No change in tariff 4 percent tariff reduction
0,8
1,2
1,6
2
2 percent tariff reduction
Figure 18 - Adjustment functions - Capital 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
-0,2 No change in tariffs 4 percent tariff reduction
2 percent tariff reduction
36
2
Figure 19 - 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
Tariff=10%
0
0,4
0,8
Tariff= 20%
1,2
1,6
2
Tariff =30%
Figure 20 - 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 21 - 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
Tariff = 10%
-0,4
0
0,4
Tariff = 20%
37
0,8
1,2
1,6
Tariff = 30%
2
Figure 22 - 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
No exports
-0,4
0
0,4
Exports = 25% of sales
0,8
1,2
1,6
2
Exports = 50% of sales
Figure 23 - 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
No exports
-0,4
0
0,4
Exports = 25% of sales
0,8
1,2
1,6
2
Exports = 50% of sales
Figure 24 - Adjustment functions - Capital 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
-0,2 No exports
Exports = 25% of sales
38
Exports = 50% of sales
2
