metadata: authors: 0: author: Roberts, Mark J.
1: author: Tybout, James R.
count: Chile,Colombia
docna: 0: docna: Size rationalization and trade exposure in
            developing countries
docty: Policy Research Working Paper
subtopic: Construction Industry,Common Carriers Industry,Food & Beverage Industry,General Manufacturing,Pulp & Paper Industry,Plastics & Rubber Industry,Labor Markets,Trade Policy,International Trade and Trade Rules,Rules of Origin,Business Cycles and Stabilization Policies,Trade and Multilateral Issues
teratopic: Education
abstracts: cdata!: Given the lack of direct evidence
            regarding industrial adjustment in response to trade
            liberalization, this paper tackles some very basic
            questions. Specifically, in LDCs, how is trade orientation
            correlated with the size distribution of plants and with
            plant-level labor productivity? It begins with a simple
            model that summarizes some effects of trade exposure on
            producer size and productive efficiency that have been
            stressed in the recent analytical and simulation literature.
            It then examines annual plant-level data from Chile and
            Colombia to determine whether these effects can be
            confirmed. The empirical results indicate that, over the
            long run, higher trade exposure is correlated with smaller
            plant sizes, controlling for industry and country effects.
            However, the mix of high versus low productivity plants is
            not strongly associated with trade exposure. Both of these
            findings cast doubt on the mechanisms linking trade, plant
            size, and productivity in a number of recent analytical and
            simulation studies.
geo_reg_key: 119228,517191
repnme: repnme: Size rationalization and trade exposure in
            developing countries
display_title: Size rationalization and trade
            exposure in developing countries
listing_relative_url: /research/1991/02/700065/size-rationalization-trade-exposure-developing-countries
topicv3: Manufacturing Sectors m1326521 1281,Trade m1326924 698,Trade Policy and Integration m1327407 775,Trade Policy m1327640 776,Labor Productivity m1504060 2756
publishtoextweb_dt: 2010-07-01T00:00:00Z
historic_topic: Education
origu: Policy, Research & Ect Affairs
no_of_pages: 44
model_extractions: **Policy, Re"uch, and External Affairs**

# **Trade Policy**

WPS **594** This paper -- a product of the Trade Policy Division, Country Economics Departmentis part of a larger cffort in PRE to study industrial competition, productive efficiency, and theii relation to trade regimes. Copies are available free from the World Bank, 1818 H Street NW, Washington DC 20433. Please contact Sheila Fallon, room N10-035, extension 37947 (39 Pages).

is part of a larger Common wisdom dictates that increased exposure to global markets increases the elasticity of demand perceived by domestic producers, which in tum shifts production toward larger, more efficient plants. Rationalization of production is more pronounced when there are few barriers to entry and exit of firms, because inefficiently small plants are induced to shut down.

Simulation models support the perceived wisdom that liberalization of imperfectly competitive industries in developing countries results in larger plants and more efficiency. But there is little microeconometric evidence to confirm the adjustment mechaniisms these models assume.

To see if these effects could be confirmed, Roberts and Tybout examined annual plant data from Chile and Colombia, using a simple model that summarizes some effects of trade exposure on producer size and productive efficiency. They found thal: Increased exposure to import competition appears to clearly _reduce_ the size of all plants in both the short run and (especially) the long run. The popular belief that trade liberalization will increase average plant size in import-compeling sectors is not supported by recent Chlilean and Colombian experience. This may mean that liberalization does not necessarily improve productivity, but their flndings are not strong enough to warrant strong conclusions.

* The results depend greatly on whether barriers to finns' entry and exit are high or low. The effects of changing output levels, import and export shares, and effective protection rates are systematically moderated by the possibility of easy entry or exit. It could be that output adjustment by incumbent plants has less of a role when the number of plants adjusts to shifts in demand. Or it could mean that high tumover reflects competitive pressure and reduces the marginal impact of foreign competition on market structure.

* Long-run and short-run correlations of trade regimes and distribution of plant size are quite different. Slhort-run correlations associate exports with relatively large plants; long-run correlations associate exports with relatively small plants. Roberts and Tybout suggest caution in basing policy decisions on either finding.

These findings cast doubt on the mechanisms linking trade, planl size, and productivity in recent analytical and simulation studies.

F **The** PRE Working Paper Series disseminates **Lhe** findings of work under way **in the** Bank's Policy, Research, and **Extemal**<sup>**7**</sup> Affairs Complex. Anobjective of the series is to get these findings out quickly, even ifpresentations are less than fully polished. The findings. interprctations, and conclusions in these papers do not necessarily **reprcSCnt** official Bank policy.

i'rodki,ced by **the** I'RE Disnlillcalik)T! CC!nler
# Size Rationalization and Trade Exposure in Developing Countries

by Mark J, Roberts and James R Tybout

# Table of Contents

|1.|Ove|rview|1|
|---|---|---|---|
|II.|The|ories<br>Linking Trade Regime and Size Rationalization|3|
||A.<br>|The Analytics<br>of Size Rationalization<br>Under Imperfect<br>Competition<br> <br>|3|
||B.<br>C.|Demand<br>Shifts and Rationallzation<br>Robustness|4<br>7|
|Ill.|Em|pirical<br>Methodology|8|
||A.<br>|The Data and Country Backgrounds<br> <br>|8|
||B.|An Empirical<br>Framework<br>for Plant Size and Productivity<br>Analysis|12|
|rv.|Res|ults:<br>Between Country Estimates|16|
||A.<br>|The Employment<br>Size Distribution<br> <br> <br>|16|
||B.|Predicted<br>Employment<br>Size Distribution<br>Under Alternative<br>Trade Regimes|20|
||C.|<br>Distribution<br>of Labor Productivity|22|
|V.|Res|ults:<br>Within Country EsUmates|24|
||A.<br>B.|Employment<br>Size Distribution<br>Labor Productivity<br>Distribution|24<br>25|
|VI.|Sum|mary|26|
|Tables|||29|
|Biblio|graphy||34|
|Footno|tes||37|

0 Funding was provided by the NBER and the World Bank research project, "Industrial Cornpetition. Productive Efficiency, and Their Relation to Trade Regimes,' RPO 674-46. We have benefitted from the commnents of Robert Baldwin. Ann Harrison, Anne Krueger, Robert Lipsey. Ramon Lopez, Peter Petri. Larry Samuelson, and Marius Schwartz. We also wish to thank Constantina Backinezos, Jin-Sung Park. and Lili Liu for research assistance.
SIZE RATIONALIZ ION AND TRADE EXPOSURE IN DEVELOPING COUNTRIES Mark J. Roberts James R. Tybout

# I. OVERVIEW

Several reasons are often cited why exposure to foreign competition should increase plant size and productivity in less developed countries (LDCs). First, ioreign competition reduces the market power that domestic producers may derive from scale economies, rationed credit markets, or institutional constraints. Consequently, reductions in protection should expand output among these prccucers and allow better exploitation of scale economies. Similarly, when competitive discipline is absent, the resultant cushion of monopoly profits may allow inefficiently small, wasteful domestic firms to survive. Finally, even if profits are competed away through entry or the threat of entry, limited domestic demand can lead to inefficiently small-scale production in markets for differentiated products, where Chamberlinian competition prevails.

These positive effects of trade exposure are widely held to apply, both in developing and in industrialized economies. Nonetheless, analytical models show that they need not obtain. Whether trade liberalization improves efficiency depends criticallv on the distribution of output adjustments across plants with diff?ring unit costs (Rodrik, 1988a). This depends, in turn, on factor intensities, the pattern of demand shifts, the nature of competition, and the extent to which entry and exit are possible (e.g., Buffie and Spiller, 1986; Brown, 1989). When technology and innovation are endogenous, further ambiguities result (Rodrik, 1988b).
2 Simulation models support the received wisdom that, in LDCs, liberalization of imperfectly competitive industries results in larger plants and higher efficiency (Condon and de Melo, 1986; Devarajan and Rodrik, 1988. 1989; de Melo and Roland-Holst, forthcoming). Disturbingly, however, there is very littie micro-econometric evidence confirming the adjustment mechanisms that these models assume. For example, Bhagwati (1988) concludes: "Although the arguments for the success of the [outward-oriente **d** evelopment scrategies] based on economies of scale and X-efficiency are plausible, empirical support for them is not available." Pack (1989) goes furthe., climing that the link between trade liberalization and productivity growth has not been established at all.' Given the lack of direct evidence regarding industrial adjustment in response to trade liberalization, this paper tackles some very basic questions. Specifically, in LDCs, how is trade orientation correlated with the size distribution of plants and with plant-level labor productivity? We begin with a simple model that summarizes some effects of trade exposure on producer size and productive efficiency that have been stressed in the recent analytical and simulation literature. We then examine annual plant-level data from Chile and Colombia to determine whether these effects can be confirmed.

The empirical results indicate that, over the long run, higher trade exposure is corre'.ated with smaller plant sizes, controlling for industry and country effects. However, the mix of high versus low productivity plants is not strongly associated with trade exposure. Both of these findings cast doubt on the mec'anisms linking trade, plant size, and productivity in a number of recent analytical and simulation studies.
3

# II. THEORIES LINKING TRADE REGIME AND SIZE RATIONALIZATION

# A. The **Analytics** of Site Rationalization Under Imperfect Competition

To motivate our empirical work, we begin with an expository model that generates several predictions familiar from the trade and development literature.2 First, assume that within each industry, domestically produced goods are perfect substitutes, and domestic firms are Cournot quantity competitors vis a vis one another. Also, let the domestic product be an imperfect substitute for imports, so that the demand curve faced by domesi'.c producers may be written as P - P(Q,0), where Q **-** Eqi, qi is the output of the ith producer, and 0 is the set of factors that determine exposure to world markets.<sup>3</sup> This set includes quantitative restraints (QRs), tariffs, and the - real exchange rate. Finally, define Ci F + qic1 to be the total costs of producing qi borne by the ith plant (i-l,n), where F and ci are constants. The presence of marginal cost heterogeneity is meant to reflect differences in managerial abilities, credit market access, and capital stocks.4 As is well known, the first-order condition for profit maximization under Cournot competition is: Accordingly, summing equation (1) over all plants, equilibrium output and price in this market depend only on the sum of marginal costs and not on the distribution of marginal costs across plants (e.g., Bergstrom and Varian, 1985): (2) nP(Q,Q) + QPQ(Q,O) Ci
4 Given n, and assuming PQ < 0, theae is thus a negative monotonic relationship between Nc1 and the equilibrium industry outp' t, Q. In turn, given Q, each plant's output q1 is determined recursively by equation (1).

If market entry and exit are free, the number of firms is endogenous. To characterize equilibrium in this case, we require that the last and least efficient plant (plant n) covers costs, and that all potential firms not in the market to anticipate losses upon entry. Sorting plants in order of increasing average cost, this condition amounts to: (3) Cn+,/qu+l > P(Q,il) > Cn/qn where q3,, is the output level the the n+lth (potential) plant would cAooze if it were to enter the market.

# B. Demand Shifts **and** Rationalixation

We can now review predictions atout the link between demand shifts and the size distribution of plants. Hereafter, any shift that induces average cost reductions through plant size adjustments will be said to have "tationalized" industry.5 In our framework this can occur two ways -- either by increasing output levels overall and reducing average fixed costs, or by shifting market shares toward large, low marginal cost plants and reducing average variable costs,<sup>6</sup> To exposit the conditions under which trade liberalization induces such shifts, it is convenient to assume a linear demand schedule with both the intercept and the slope dependent upon trade regime:
5 (4) P-aQ, Then, if entry is not possible, equilibrium is described by the following n+2 conditions: From these equations, the effect of demand shifts induced by trade reforms follow easily. Suppose that, beginning from autarky, trade is liberalized. This type of reform has the effect of placing domestic producers in large world markets, where there are many other oroducers and substitute products. Regaraless of whether the domestic product is exportable or importcompeting, one would expect its demand elasticity to rise. We . ;vte the consequences of such an elasticity increase by pivoting the demand curve through the pre-reform equilibrium point, reducing both a and 0. By equation (5.2) P must fall, so Q must rise, and industry-wide average fixed costs must fall. The ratio qj/Q does not depend on 0; thus if a were not changing, all plants would expand proportionately, and average variable costs would be unaffected. But reductions in a dampen the expansion of each plant by the same absolute amount (equation 5.3), allowing large plants to expand at Faster rate.<sup>5</sup> So trade reforms that increase the elasticity of demand without
6 shifting it inward reduce average costs, both by shifting production toward low cost producers and by increasing industry-wide output.

Of course elasticity effects are not the only possible effect of increased foreign competition. Trade reforms that amount to tariff reductions or real currency appreciation may act mainly to reduce domestic demand for import-competing products. If this causes a contraction in total output, average fixed costs will rise for the affected industries, at least partly offsetting any fall in average variable costs. Although many simulation models allow for this contractionary effect of liberalization, it has not usually proved to be dominant.6 Now consider the adjustments that occur when entry and exit are possible. Suppose trade liberalization shifts demand inwatd (reduces a), with or without an increase in elasticity. By equation 5.2, P must fall, so the smallest, least efficient firms will begin to take losses and exit, reducing both n and Zci.<sup>7</sup> In the initial equilibrium c, < P (equation 3), so before price adjusts, this exit will have reduced nP more than it reduced Zci. Accordingly, to restore equilibrium Q must contract more and P must fall less than they would have if exit were not possible (equation 2). In sum, compared to the case of no exit, efficiency effects are stronger for two reasons: The least efficient plants leave the market entirely, and remaining plants face less contractionary pressure. By analogous logic, free entry and exit exacerbate the reduction in productive efficiency associated with outward shifts of the demand curve, as might accompany quotas or increases ir the tariff rate: Small, inefficient firms are induced to enter and take market shares from incumbents. This consequence of market expansion through protection is another familiar story in the literature.8
7

# C. **Robustness**

Though far from comprehensive, the expositLon above gives an idea of the size rationalization effects that have recently been stressed in the literature. In particular, exposure to foreign competition<sup>can increase</sup> plants' size by increasing the el.asticity of demand. Even if exposure to competition reduces plant size by contracting demand, it is<sup>likely to hit the</sup> most inefficient plants hardest. Hence, unless returns to scale<sup>are</sup> important, efficiency gains are still likely. Finally, the<sup>positive effects</sup> of liberalization are larger when entry and exit are possible because inefficient plants will be forced out of the market, allowing<sup>those producers</sup> who remain behind to.operate on a larger scale.

Although these effects are often stressed, they '.re not gauranteed. There is no reason why liberalizations might not contract demand for domestic products so severely as to increase average costs -- particularly when fixed costs and entry barriers are significant. Moreover, as various<sup>authors have</sup> shown, alternative analytical frameworks expand the range<sup>of possible</sup> outcomes. For example, if static Cournot quantity competition<sup>is replaced</sup> with another equilibrium concept, firms adjust their output levels differently in response to demand shifts. The monotonic ne.itive relationship between plant size and average variable costs might tt,en be broken, and it would no longer necessarily hold that shifting producticn toward<sup>large plants improves</sup> efficiency. Still more outcommes are possible if one endogenizes<sup>marginal</sup> costs, allowing for changes in factor prices, X-efficiency, and learning-bydoing. Finally, domestic product differentiation can be introduced. This not only opens the possibility of cross-plant variation in the degree<sup>of</sup>
# a

competition from foreign substitutes, it also allow endogenous adjustments in the length of production runs.

Given these qualifications, it is clearly an empirical question whether trade liberalization will: (1) increase the average scale of production; (2) shift market shares toward large producers; and (3) bring with it productivity improvement. The remainder of this paper is devoted to ar econometric examinination of these issues.

## III. EMPIRICAL METHODOLOGY

## A. **The Data and** Country Backgrounds

In this section we examine cross-country and inter-temporal contrasts in trade exposure, plant size distributions, and labor productivity distributions for evidence on the empirical relevance of the theoretical effects reviewed in Section II. To do this we utilize annual census data covaring all manufacturing plants with at least 10 workers in Colombia and Chile.<sup>9</sup> But before turning to the empirical models these data support, it is useful to review the cross-country differences and within-country time series fluctuations in trade policies and industrial performance that allow us to identify parameters.

### Chilei°

The Chilean data used in this paper cover the period 1979-35; we begin our overview with the years immediately preceding. like much of Latin America, Chile pursued an inward-oriented development strategy in the 1960s. The system of incentives -- including tariffs, quotas, e:cchange rate policy, and domestic market regulations -- favored manufacturing at the expense of
9 agriculture and import-competinig producers over exporters (Corbo, 1985). This bias int3nsified in the early 1970s. By 1973 average cariff rates exceeded 100%, prior deposit requirements for importers created heavy additional surcharges, and a complex system of multiple exchange rates prevailed.

In 1973, the military seized power and began implementing radical policy changes. In addition to fiscal austerity and price stabilization programs, the new government rapidly implemented lajssez faire micro reforms. The new administration sold public enterprises, decontrolled prices and interest rates, and dismantled trade barriers. The average nominal tariff rate fell from 105 percent in 1974 to 12 percent in 1979.

Although the industrial sector initially suffered from recessionary macro conditins, recovery began in 1976 and continued into 1981. Several features of this recovery were noteworthy. First, the reductions in industrial employment that accompanied the 1974-75 recession continued during the 1976-81. ra_over,, so that labor productivity increased dramatically. Second, the balance of trade in industrial products worsened considerably during che latter part of the recovery period. The trade liberalization was partly responsible. but there was also considerable exchange rate appreciation beginning ill 1979. Third, during 19'6-1981 a handful of powerful conglomeratps ("grupos") emerged and consolidated control over both financial and industrial enterprises.

By the end of 1982, the Chilean economy was again in serious trouble. The exchange rate had been overvalued for some time, and tradeable sector producers had undergone a protracted profit squeeze. Large capital inflows were necessary tu finance the current account deficit, yet international credit was evaporating, exacerbating firms' financial stress with very high
10 interest rates. The government finally devalued, but the financial soundness of the economy had already been undermined, and a major recession followed. Unemployment reached roughly -O% in 1983.

To help the economy recover, the government took various steps to ease firms' financial problems. This relief, in addition to devaluation, a mild increase in tariff protection, and a reduction in the corporate income tax from 38% to 10%, facilitated a quick industrial sector recovery. As the recovery continued, average tariff levels were gradually dropped, falling from a peak of 36% in September 1984 to 15% in 1988.

To summarize, our sample period includes the end of a major trade liberalization and economic recovery (1979-81), a severe recession that was accompanied by devaluation and mild increases in protection (1982-83), and a sustained recovery with a return to very low levels of protection. Table 1 presents time series on trade exposure and average workers per plant (an index of average plant size). Note that the ratio of imports to output grew substantially over the period 1979-82, then fell (with devaluation and increased protection) after 1982. Both total manufacturing employment and average plant size declined continuously after 1979 until the recovery began **iii** 1984.

Popular sentiment has it that the Chilean industrial sector is now one of the most efficient in Latin America. Although the government's approach to anti-trust policy is essentially laissez faire, it is commonly held that the discipline of foreign competition prevents firms from exercising much market power and forces inefficient firms to reform or shut down. The "grupos" are still in evidence, but they too are considered efficient competitors by most observers.
# Colombia

The Colombian data base spans 1977-1987<sup>but, as with Chile, it is</sup> instructive to begin with a review of years<sup>preceditng. In 1967, the Colombian</sup> government began to abandon its<sup>traditional inward-looking</sup> development strategy in favor of export promotion<sup>policies, a modest degree of trade</sup> liberalization, and greater exchange rate flexibility.<sup>Exports were</sup> encouraged with duty drawback schemes,<sup>tax incentives,</sup> and special credit facilities. Imports were liberalized by scaling back prior licensing requirements, eliminating prohibited lists, anu reducing<sup>average nominal</sup> tariff rates.<sup>11</sup> During this period of export promotion<sup>and trade liberalization</sup> there was growth in the aggregate economy as<sup>well as in the volume of imports and</sup> exports. Real GDP grew at an annual<sup>average rate of 6.3 percent over the</sup> 1967-75 period, and the manufacturing<sup>sector grew at an annual rate of 8.8</sup> percent. But beginning in late 1975,<sup>significant</sup> changes in Colombia's macroeconomic environmenc began to influence<sup>trade policy and the real</sup> exchange rate. Specifically, substantial increases in world coffee<sup>prices and</sup> increased foreign borrowing contributed<sup>to large foreign exchange inflows</sup> which resulted in increased inflation.<sup>Substantial real appreciation</sup> resulted, which tended to hurt tradeable<sup>goods producers in the industrial</sup> sector. Accordingly, between 1976 and the early 1980s,<sup>efforts to liberalize</sup> the trade regime proceeded at a<sup>slower pace.</sup> The trend toward liberalization stalled completely in the early 1980s. In 1980, approximately 69 percent<sup>of all commodities did not require import</sup> licenses. But in 1981 only 36 percent<sup>of all commodities</sup> were classified in the free import category, and this percentage<sup>fell continuously through 1984.</sup>
12 By that time only .5 percent of all commodities could be freely imported, 83 percent required licenses, and 16.5 percent were prohibited. Liberalization resumed in 1985 and 1986 but not enough to return to 1980 levels.

The time series patterns in Colombian trade exposure are reported in Table 1. There is a marked increase in import penetration and a marked decline in export shares over the period of currency appreciation, 1977-82. Over the same period, total manufacturing employment and average plant size declined. Finally, note the contrasts between Chile and Colombia in terms of trade exposure, total industrial employment, and average plant size. Both the total manufacturing sector and the average plant size are larger in Colombia. Moreover, imports, and to a lesser degree exports, are smaller in Colombia as a share of domestic production. This partly reflects differences in the size of the two countries but probably also reflects Colombian trade policy, which never came close to the degree of openness found in Chile.<sup>12</sup> For example, while Chile essentially eliminated QRs, they remained a prominent feature of Colombian trade policy throughout the sample period. Similarly, while Chile had achieved uniform 10 percent tariffs by 1979, Colombian tariffs remained around 30 percent after substantial cuts in 1974.

# B An Empirical Framework for Plant Size and Productivity Analysis

We wish to develop regressions that use trade exposure proxies to explain variations in the size and productivity of plants across 3-digit industries, countries, and time. First, to summarize plant sizes for industry i, country J, year t, we rank plants by ascending employment level and find the employment cut-offs for the 10<sup>h,</sup> 25<sup>th,</sup> 5 0<sup>th,</sup> 7 5<sup>th,and</sup> 9 0<sup>th percentiles.13</sup> Similarly, to summarize productivity distributions for each observation, we
13 rank plants by output per worker and find cut-offs for the same percentiles. Hence we generate five size measures and five productivity measures, each of which can serve as a dependent variable:

- ln(EMPkijt) logarithm of che kth percentile of the employment size - distribution (k 10, 25, 50, 75, 90) - ln(PRDkijt) logarithm of the the kth percentile of the - productivity (output per man) distribution (k 10, 25, 50, 75, 90)

This approach not only allows us to study variation in median plant sizes and median labor productivity (i.e., changes in the 50 th percentiles), it permits us to analyse changes in the shape of the size and productivity distributions, picking up such things as the growth or disappearance of very small plants.

We express all percentiles in logarithms to facilitate analysis of their rates of change and the associated shifts in output shares.

To explain distributional shifts, we regress each of the ten variables above on proxies for various types of demand shifts. For industry i, country j, year t, the explanatory variables we work with are:

-

- lnQijt log of real industry output

ln(M/Q)ijt- log of the ratio of imports to output ln(X/Q) ijtlog of the ratio of exports to output

- TURj - mean turnover rate. The turnover rate is the sum of the industry's entry and exit rates. These rates are averaged across all years for each industry in each country to get a "long run" value that is specific to each industry in each country.
14

-

- ERPij log of the mean effective rate of protection.<sup>Given that</sup> Chilean protection was essentially uniform during<sup>the sample</sup> period, variation in this protection measure is<sup>due only to</sup> Colombia. For Chile, we set this variable<sup>to 0. Colombian</sup> figures are averages of effective protection measures<sup>for</sup> 1979, 1984 and 1985 reported in Cubillos and Torres<sup>(1987).</sup>

Hence, for example, one possible regression explaining<sup>the kth employment</sup> percentile is:

- (7) EMPkijt - OllnQt + 621n(M/Q)ijt + 031n(X/Q)±jt + 64TUR1<sup>i+p</sup> 5<sup>TURijlnQijt</sup> + 06 TURijln(M/Q)ijt + 0 7TURijln(X/Q)ijt + **A.** j + pjt + jt

Here- lnQ proxies total market size, while<sup>ln(X/Q) and ln(M/Q) proxy exposure</sup> to international markets. (When interpreting coefficients on these latter variables, it must be kept in mind that<sup>the regression has already controlled</sup> for total output.) The average turnover<sup>rate, TUR, is used as a measure of</sup> the extent, and thus the ease, of entry and<sup>exit into an industry over time.</sup> High turnover rates are consistent with<sup>low sunk costs of entry, and hence</sup> should reflect the potential for competitive pressures f;om domestic rivals. Also, as discussed in section II, the sensitlvity of size distributions to demand shift should depend upon the ease<sup>of entry and exi.. We therefore</sup> interact our turnover variable with the trade<sup>variables in the regression</sup> equations. Finally, to control for<sup>the industry-specific</sup> technology effects and country-specific macro conditions, represented by A and **_j._** respectively, industry and time dummies are included. 14Equation 7 can, of course, also be estimated using productivity percentiles, PRDk, as dependent variables.

As seen in Table 1, there are fairly<sup>significant and persistent cross-</sup> country differences in trade exposure<sup>and average plant size, but plant size</sup>
l5 fluctuations within each country over time are smaller. This<sup>suggests that</sup> the patterns of correlation will depend upon<sup>the type of estimator applied to</sup> the panel data. For example if we use a<sup>"between" estimator, parameters are</sup> identified with cross-country differences in the (temporal) mean values of the variables. Averaging Equation 7 across<sup>time gives:</sup> 15 Parameter estimates of uj in this model will reflect country-wide contrasts between the Chilean and Colombian<sup>size distributions,</sup> while Ai estimates will reflect technological and other industry-specific factors common to both countries that determine the size distribution<sup>for industry</sup> "i". The remaining parameters reflect correlations once these factors are controlled for. Because variables are averaged over time,<sup>the estimates might</sup> be viewed as reflecting long-run correlations. 16 To examine the robustness of our findings to alternative measures<sup>of trade exposure we will also</sup> estimate the model using ERP rather than ln(X/Q)<sup>and ln(M/Q).</sup> An alternative estimator of Equation 7 does not involve<sup>averaging over</sup> time. Rather, it identifies parameters by treating<sup>a single industry, country</sup> and year as the unit of observation. If we control<sup>for technology differences</sup> with country-specific industry dummies, and<sup>we control for macro effects with</sup> country-specific time dummies, the resultant<sup>"within" estimates should reflect</sup> the time series correlations of size or productivity<sup>distributions with</sup>
16 industry-specific trade policy. These estimates address the question of how much rationalization occurs within a country in the short run as trade exposure changes. They will be more sensitive to hysteresis effects than the "between" estimates, so entry and exit are likely to play a smaller role in the short run. Bear in mind also that this estimator will not pick up the dynamics of adjustment processes -- all correlations are contemporaneous. Finally, given that the variable ERP does not vary through time, we are unable to check the robustness of our "within" regression by replacing ln(X/Q) and ln(M/Q) with the effective rate of protection.

# IV. RESULTS: BETWEEN COUNTRY ESTIMATES

# A. The Employment Size Distribution

Table 2 presents regression coefficients for the employment size distribution using the "between" estimator. Explanatory variables are listed on the left-hand side of the table and percentiles across the top. Each column in each panel summarizes a separate regression. The top panel was estimated using import and export shares as the measure of trade exposure and the bottom panel was estimated using effective rates of protection. Note that overall, the fit is very tight, and both trade patterns and turnover appear to matter a great deal. 17 Looking across columns in the top half of Table 2, one sees that an increase in import share is associated with a reduction in all size percentiles, controlling for the level of industry output. These results suggest that, contrary to the findings of many simulation models, the elasticity effects of import competition on plant size are not dominant.
17 Rather, demand contraction, factor market effects, and other forces associated with increased import competition apparently lad to smaller plants.18 We defer the issue of whether this means efficiency losses accompany liberalization to section IVC below.

Notice next that large plants appear to contract relatively more in the face of import competition, so even the market share effects of trade liberalization appear to be absent. This result is not as robust as the negative correlation between trade exposure and size, as will be seen presently. Nonetheless, possible explanations are worth listing. First, drawing on the simple analytics of section III, it is possible that trade exposure actually reduces demand elasticities. Second, and more plausibly, it may be that imported goods do not compete with the kinds of goods small plants produce, so large plants bear most of the adjustment burden. Third, industries with large plants may be more effective at lobbying for import protection.

The coefficients on the interaction between TUR and ln(M/Q) are significantly positive, which implies that the size effect of trade exposure is more substantial in low turnover industries. Given that import expansion is associated with output contraction, this is consistent with the theory reviewed earlier: more size adjustment occurs when exit is not easy. Alternatively, the results might be interpreted to mean simply that the discipline of foreign competition matters more in industries where the discipline of potential entry is less important. Here again, the larger effect for the higher percentiles is supportive of the hypothesis that imports compete more directly with big plants. In either case, the data confirm the arguments of Buffie and Spiller (1986), Rodrik (1988a), and others that it is
18 critical to take ease of entry into consideration when predicting the effect of regime changes on size distributions.

Turni..g next to export shares, one finds the direction of the r.ects is similar: high trade exposure is associated with smaller plant sizes, and the effect is strongest in industries with low turnover. This pattern is generally supportive of the premise that both ln(X/Q) and ln(M/Q) measure exposure to foreign markets. However, the effect of ln(X/Q) now weakens as we move to higher percentiles, so most of the contrast between "open" and "closed" markets appears to be showing up among small plants. This same pattern holds for the interaction between ln(X/Q) and TUR. We have no ready explanation for this finding, but it may indicate that small plants are relatively more important export suppliers.

Given import and export shares, larger industry-wide output levels have an effect on the size distribution that is qualitatively identical to that of trade exposure. Larger domestic production is associated with relatively more small producers, especially in low turnover industries. We can offer several. observations on this somewhat surprising result. First, if larger markets are more competitive, one would expect to see this correspondence between plant size and trade exposure. Second, it must be remembered that ln(Q) enters the variables ln(X/Q) and ln(M/Q) negatively. Hence, the total effect of an increase an output holding M and X fixed is given by the sum of the output coefficient and the negative of the import and export coefficients. For example, a unit increase in ln(Q) holding X and M fixed shifts the loth percentile rightward by .184 + .204 - .268 > 0. The negative coefficient on output in the regression equations implies that a proportionate increase in Q, X, and M is associated with a smaller size distribution of plants.
19 Since industry duimies are already included, the level of turnover only controls for country-specific differences in turnover rates. These can be due to cross-country differences in product mixes within given industries, or to differences in credit markets and other determinants of sunk costs.<sup>19</sup> The pattern that emerges is expected: high turnover is associated with a relatively large number of small plants.

To check the robustness of the findings concerning trade exposure and plant size, we next replace the trade exposure measures ln(X/Q) and ln(M/Q) with the effective protection measure ERP.<sup>20</sup> The coefficient on ERP in the regressions can be interpreted as the difference in size distributions that is correlated with differences in effective protection rates, controlling for country-wide plant-size differences, and for indus try specific effects.

Results are reported in the bottom half of Table 2. Note first that there is a positive correlation of the employment size distributions with effective protection. Just as with the X/Q and M/Q measures of trade exposure, higher rates of effective protection are associated with larger plant sizes. Moreover, the ize effect is less extreme in high turnover industries. In both these senses the results conform to the findings in the top half of Table 2: demand contraction and other effects associated with high trade exposure appear to dominate elasticity effects.

However, comparing the different size percentiles, one finds that the statistically significant effects of increased protection appear in the lower percentiles, which suggests that small plants expand at a relatively rapid rate when protection is increased. Contrary to our earlier findings, these results are consistent with the hypothesis that trade exposure increases
20 demand elasticities, thereby inducing rationalization by forcing small plants to contract relatively more.

Finally, in the ERP regressions we see that larger domestic production and higher turnover are both associated with rightward shifts in the size distribution. Both of these patterns are present across all the percentiles. This same pattern was reported in the top half of Table 2 for the 75th and goth percentiles. However, the l0th through 50th percentiles tended to decline with increased output or turnover in the regressions based on ln(X/Q) and ln(M/Q). These do not strike us as important anomalies because, as discussed above, the size shift associated with output increas6s is positive for all table 2 percentiles when X and H are held fixed. Also, our turnover variable is mainly useful in interaction terms; the level effects of entry barriers are essentially controlled for with industry duzmies.

To summarize the robustness of the 'between' estimates, we conclude that the correlation between trade exposure and the employment size distribution is clearly negative in the long run, and the magnitude of the effect is clearly moderated by ease of entry or exit.<sup>21</sup> However, whether small or large plants adjust more in percentage terms to increases in exposure depends upon the measure of exposure that is used. Perhaps effective protection measures are most relevant for policy analysis since these are most directly controlled by the government.

B. Predicted Employment Size Distribution under Alternative Trade Regimes Given that the regression models use interaction terms between turnover and trade exposure, it is difficult to infer the magnitudes of predicted differences in the employment size distribution under alternative trade
21 regimes. Accordingly, Table 3 presents predicted values of the employment size distributions based on regression results from Table 2.

The top panel illustrates how the employment size distribution shifts as the import shars rises, the middle panel illustrates how it shifts as the export share rises, and the bottom panel illustrates .hifts with changes in effective protection. The left size vf the table describes a low turnover industry while the right side corresponds to a high turnover industry. Within each panel, columns present "low", "medium' and "high" export or import shares.<sup>22</sup> Finally, rows of the table give predicted employment levels for the 5th through 95th percentiles, as well as the mean and standard deviation of the employment distribution.

First, focussing on the size distribution for low turnover industries, the leftward shift in the size distribution as import shares increase is marked. For example, the mean plant size falls from 73.4 to 31.1 employees as the import share rises. This leftward shift is particularly large for the 7 5<sup>th,</sup> 90<sup>th, and9</sup> 5<sup>th</sup> percentiles. Similarly, both the mean and the standard deviation drop substantially with increases in import share. Recall, however, that high turnover moderates the extent to which import shares reduce plant size. This appears in Table 3 when one moves from the low turnovter to the high turnover figures, especially among large plants.

Relative to import shares, export shares appear to covary less with the employment size distribution. For example, among low turnover industries, the mean plant size declines only from 54.2 to 51.1 employees as the export share increases. Also, although plants in high turnover industries are generally more concentrated in the lower employment ranges, changes in export shares appear to have little effect on location or shape of the distribution.
22

> The bottom panel of Table 3 reports predicted percentiles of the size distribution when the effective rate of protection is varied. The most substantial change occurs in the upper percentiles of the size distribution for low turnover industries. Increases in the effective rate of protection are correlated with an increase in the size of the larger plants, but the increase **is** not **as** large as that associated with changes in import penetration.

# C. Distribution of Labor Productivity

The empirical results thus far have shown that high trade exposure is associated with relatively small-scale production, controlling for other factors. Does this **mean** that trade exposure worsens productivity? To examine this issue more directly, we next apply our empirical model to the distribution of labor productivity across plants. This not only allows us to determine the overall direction of productivity shifts with trade exposure, it also speaks to such questions as whether shifts are concentrated among the least productive plants.

Table 4 reports "between-country" regression results fo- the percent.les of the labor productivity distribution. The top half of the table measures trade exposure with import and export shares while the bottom half uses effective rates of protection. The first result to notice is that significance levels are much lower than those associated with size distributions. Hence reductions in labor productivity do not obviously accompany reductions in scale. Notice next that differences in the import share between countries are positively correlated with differences in the percentiles of the productivity distribution, while the export share is
23 negatively correlated. This negative correlation of exports ard productivity could reelect the limitations of single-factor productivity measures: low labor productivity may be due to high labor intensity without implying low total factor productivity, since capital is not controlled for. Moreover, the Hechscher-Ohlin models suggests that trade liberalization should stimulate exports of labor-intensive products, so this omitted variable bias in our productivity measure will be correlated with trade patterns.

Larger levels of industry output, holding import and export shares fixed, are correlated with a rightward shift in the labor productivity distribution. This could reflect increased capacity utilization or exploitation of scale economies in the larger country. High turnover industries also have higher productivity levels. As was seen in the employment distributions, high turnover tends to reduce the magnitude of the import, export and output correlations. Finally, the country dummy variable is positive and significant. This can simply reflect differences in the units of measurement. However, with the exception of the country dummy and output level among higher productivity plants, virtually none of the remaining coefficients are statistically significant. Unlike the employment size distribution, there is little evidence here that productivity differences across the two countries are related to trade exposure.

The bottom half of Table 4 reports regression results using the effective rate of protection as the measure of trade exposure. Again, output and turnover are correlated with a rightward shift in the productivity distribution. Increased trade protection is correlated with higher productivity, especially for the least productive plants, but once again, none of these coefficients are statistically significant. In short, based on our
24 (admittedly crude) measure of productivity, there is no clear evidence that differences in trade expo;ure between sectors in Cclombia and Chile are correlatea with differences in the distribution of plant-level labor productivity.

# V. RESULTS: WITHIN COUNTRY ESTIMATES

As reviewed in section III, an alternative way to identify our model is to use the within-country temporal variation in the data. This approach picks up the short-run associations between trade exposure, output levels, and the size and productivity distributions. The top panel of Table 5 presents results for the employment size distribution and the lower panel presents results for the productivity distribution.

# A. Employment Size Distribution

Fluctuations in import shares show ,. negative association with plant sizes, just as in all the "between" country regressions. Now, however, this association is so weak statistically that it makes li:tle sense to talk of short-run rationalization effects. Because we are limitinY the "within" model to contemporaneous effects, we find this low significance unsurprising.

More surprisingly, time series fluctuations in export shares correlate positively with the percentiles of the size distribution, although they are negatively correlated with percentiles in the "between" regressions. Though weaker than in Table 2, these correlations are still statistically significant. So in the short run, output growth due to export share expansion is associated with relatively rapid employment growth. In terms of rationalization, the growth in employment is concentrated among large plants.
25 We see no obvious explanation for this contrast<sup>between the "within' and the</sup> "between" results.

The coefficients on the output variable indicates that the correlation of industrial output with plant sizes is generally positive.<sup>This reflects a</sup> combination of output adjustments by incumbencs and entry or exit. However, given that most turnover takes place among small plants, shifts in<sup>the higher</sup> percentiles reflect mainly the expansion and contraction of incumbents (Roberts, 1989; Tybout, 1989). Finally, in industries where turnover<sup>is high,</sup> the positive correlation between output and size is relatively<sup>muted.</sup> Overall, the patterns of contemporaneous correlation between<sup>the</sup> percentiles of the employment size distribution are much less systematic<sup>than</sup> the between country estimates. Systematic rightward<sup>or leftward movements of</sup> the size distribution are not obvious in the regression results. This suggests that while the across-country differences in trade exposure<sup>are</sup> correlated with differences in t.he entire size distribution of<sup>plancs, the</sup> time-series differences in trade appear to have a more random effect<sup>on plants</sup> within the size distribution. This may mean that differences in<sup>the plant</sup> size distribution between the countries reflect underlying structural differences in the size of markets, openness to trade and other factors.<sup>In</sup> contrast time series fluctuations in the size distribution within<sup>each</sup> industry and country reflect idiosyncratic aspects<sup>of the market and time</sup> period.

# B. Labor Productivity Distribution

The bottom half of Table 5 reports results for the labor productivity distribution using the within-country variation.<sup>Import share has no</sup>
26 significant effect on the shape of the distribution. In contrast, an increase in the export share is positively and significantly correlated with the 10Th, 25<sup>th, and</sup> 50<sup>th percentiles of the productivity</sup> distribution but negatively correlated with the 7 5 th and **90 th** percentiles. That is, higher export shares are correlated with higher productivity for the less productive plants but lower productivity for high productivity plants.

Expansion in output over time leads to productivity improvements. This can result from either increased use of capital or scale economies in high output periods. Finally, as we have seen throughout this paper, the import, export, and output correlations are lower in magnitude in high turnover industries. In particular, the turnover results could arise if high turnover industries are less capital intensive or have technologies with less scale economies. Demand fluctuations in these industries then have less effect on an individual plant's labor productivity and thus less effect on the distribution across plants.

Overall, the within estimator indicates little evidence of rationalization with variation in trade exposure over time. The productivity changes over time are largely explainable with variation in capital utilization.

# VI. SUHKARY

It is often argued that when domestic markets are imperfectly competitive, increased exposure to global markets should rationalize production. Such exposure is believed to increase the elasticity of demand perceived by domestic producers, which in turn should shift production toward the large, efficient plants. The rationalization effects should be especially
27 marked when there are low barriers to entry and exit because inefficiently small plants will be induced to shut dowr.. This paper is the first attempt we know of to confront these :heories of rationalization with actual data on the size distribution of plants from developing countries.

Several striking results emerge. First, increased exposure to import competition appears to clearly reduce the size of all plants in both the short run and the long run, but especially in the latter. Whether large plants shrink relatively less depends upon the way in which we measure exposure to world markets: Increases in import shares are associated with relatively rapid shrinkage by large plants, but reductions in effective protection correlate with relatively little shrinkage by large plants. Either way, it appears that models that predict trade liberalization will increase average plant size in import-competing sectors do not describe recent Chilean and Colombian experiences. This may mean that productivity improvements have not accompanied liberalization, but our findings on this issue are not strong enough to warrant strong conclusions.

Second, as theory suggests, it makes a great deal of difference whether one is analyzing industries with high or low entry barriers. The effects of changing output levels, import shares, export shares, and effective protection rates are systematically moderated by the possibility of easy entry or exit. One interpretation is that there is less role for output adjustment by incumbent plants when the number of plants adjusts to demand shifts. Alternatively our results could simply mean that high turnover reflects coraipetitive pressure, and reduces the marginal impact of foreign competition on market structure.
28 Third, the "long-run" correlations of trade regimes and size distributions are quite different from the short-run year-to-year correlations. Not only are the effects of trade exposure stronger in the long run, but the correlations of export shares change sign. The short run correlations show exports associated with relatively large plants while the long run correlations show exports associated with relatively small plants. We trust the long run figures more, because we limited our short-run analysis to simultaneous correlations, and have not attempted to model the dynamics of adjustment. Nonetheless, the short run findings suggest caution in extracting policy recommendations from our figures.

This paper is a first step in the direction of micro-based examinations of the rationalization hypothesis. Though suggestive, much remains to be done. Aside from modeling the dynamics of adjustment, we hope to study the relationship between average costs and size, and the degree to which plants adjust costs endogenously with changes in the trade regime.
29

**TABLE 1**

# **TRADE EXPOSURE AND MARKET SIZE IN COLOMBIA AND CHILE**

|**XYar**|<br>**ImRort S**<br>|**harea **<br>|**Export S**<br>|**hare**b<br>|Total Em<br>|olov.c<br>|<br>Plant S|ize**d**|
|---|---|---|---|---|---|---|---|---|
||Colomki|Chile|Colombia|Chile|Colombia|Chile|Colombi|a Chile|
|1977<br>|<br>.246<br> <br>||.100<br>||402.7||77.0||
|1978<br>|<br>.262<br> <br>||.088||410.7||79.0||
|1979<br>|<br>.250<br> <br>|.528<br>|.092<br>|.086<br>|<br>420.8<br>|229.0|78.7|55.6|
|1980<br>|<br>.328<br> <br>|.600<br>|.108<br>|.105|<br>419.8|209.8|77.2|56.4|
|1981<br>|<br>.363<br> <br>|.762<br>|.055|.060|<br>404.3|194.2|75.0|57.5|
|1982<br>|<br>.375<br> <br>|.758<br>|.053<br>|.088|<br>394.2|155.2|69.9|51.3|
|1983<br>|<br>.329<br> <br>|.637<br>|.047<br>|.088|<br>374.5|147.3|74.6|52.9|
|1984<br>|<br>.297<br> <br>|.762<br>|.047<br>|.081|<br>372.6|164.2|74.4|56.6|
|1985<br>|<br>.264<br> <br>|.701|.051<br>|.072|<br>360.0<br>|174.2|69.7|60.9|
|1986<br>|<br>.289<br> <br>||.061<br>||368.6||68.5||
|1987|<br>.287||.065||397.5||71.0||
|**Ave.**|.299|.678|.070|.083|<br>393.2|182.0|74.1|55.9|

- a Manufactured imports as a share of domestic manufactured output b Marnfactured exports as a share of domestic manufactured output i'otal manufacturing employment, in thousands Average number of workers per plant in the manufacturing sector
**30**

**TABLE 2**

BETWEEN ESTIMATES OF EMPLOYMENT SIZE DISTRIBUTION £ (Absolute values of t-statistics in parenthesis)

||**Trade ErDo**|**sure Measu**|**red withIm**|**gort andE **|**xport Shares**|
|---|---|---|---|---|---|
||||**Percent**|**ile**||
||10th|25th|50th|75th|90th|
|ln(M/Q)|-.184*|<br>-.317*|<br>-.432|-.5/3*|<br>-1.10*|
||(2.59)|<br>(2.78)|<br>(2.03)|<br>(2.24)|<br>(2.98)|
|ln(X/Q)|-.204*|<br>-.333*|<br>-.367*|<br>-.168|.004|
||(6.24)|<br>(6.36)|<br>(3.76)|<br>(1.43)|<br>(.022)|
|ln(Q)|-.268*|<br>-.496*|<br>-.414*|.251|.129|
||(3.81)|<br>(4.40)|<br>(1.97)|<br>(1.00)|<br>(.353)|
|TUR|-7.60*|-14.17*|-10.02|13.43|14.72|
||(3.31)|<br>(3.86)|<br>(1.46)|<br>(1.63)|<br>(1.23)|
|TR*ln(M/Q)|<br>.446*|<br>.663*|<br>1.11*|1.39*|2.72*|
||(3.04)|<br>(2.82)|<br>(2.31)|<br>(2.64)|<br>(3.56)|
|TUR*ln(X/Q)|<br>.772*|<br>1.29*|<br>1.51*|.695|.188|
|-|(5.43)|<br>(5.67)|<br>(3.54)|<br>(1.36)|<br>(.254)|
|TUR*ln(Q)|.691*|<br>1.21*|<br>.974*|<br>-.564|-.722|
||(4.65)|<br>(5.09)|<br>(2.19)|<br>(1.06)|<br>(.935)|
|ChileDummy|<br>-.019|.055|-.039|-.291|-.037|
||(.260)|<br>(.473)|<br>(.179)|<br>(1.12)|<br>(.097)|
|R<sup>2</sup>|.887|.903|.842|.866|.765|
||**Trade Expo**|**sure Measu**|**red with Ef**<br>|**fective Pr**<br>|**otection Rates**|
||||**Percenti**|**le**||
||10th|25th|50th|75th|90th|
|ERP|.244*|<br>.352*|<br>.361|.332|.368|
||(3.41)|<br>(2.52)|<br>(1.78)|<br>(1.j7)|<br>(1.36)|
|ln(Q)|.296*|<br>.422|.545|1.15*|1.24*|
||(2.39)|<br>(1.78)|<br>(1.58)|<br>(4.03)|<br>(2.70)|
|TUR|14.05*|<br>19.08|21.96|43.73*|45.28|
||(2.33)|<br>(1.64)|<br>(1.31)|<br>(3.13)|<br>(2.03)|
|TUR*ERP|-.707*|<br>-1.05*|<br>-1.01*|<br>-1.04*|<br>-1.11|
||(4.45)|<br>(3.43)|<br>(2.29)|<br>(2.84)|<br>(1.89)|
|TUR*ln(Q)|-.876*|.1.18|-1.41|-2.67*|<br>-2.73|
||(2.29)|<br>(1.62)|<br>(1.32)|<br>(3.03)|<br>(1.93)|
|Chile Dummy|<br>.003|-.038|-.113|-.517|-.474|
||(.014)|<br>(.097)|<br>(.198)|<br>(1.09)|<br>(.623)|
|R<sup>2</sup>|.664|.587|.596|.836|.647|

aIndustry dummies were included in the regressions but are not reported. *Significantly different from zero at the .05 level using a two-tail test.
**31**

# **TABLE 3**

# **PREDICTED EMPLOYMENT SIZE DISTRIBUTION UNDER ALTERNATIVE LEVELS** OF **TRADE EXPOSURE**

(Table entries are number of employees in kth percentile plant)

# **Low** Turnover Industries High Turnover Industries

|||Import Sha|re|I|mport S|hare|
|---|---|---|---|---|---|---|
|Percentile|<br>low|<br>moderate|<br>high|<br>**low**|<br>modera|<br>te high|
|5th|10.6|10.1|9.4|9.6|9.6|9.5|
|10th|11.7|10.9|9.8|10.8|10.5|10.1|
|25th|16.7|14.5|11.6|13.9|12.9|11.5|
|50th|31.9|26.8|20.5|23.0|21.5|19.4|
|75th|73.6|59.0|42.1|61.4|57.0|50.8|
|90th|199.2|131.0|69.2|110.8|97.0|79.3|
|95th|276.8|188.2|104.6|222.4|108.3|146.1|
|Mean|73.4|52.1|31.1|56.7|48.0|37.3|
|Std. Dev.|93.0|61.1|32.2|87.1|65.7|42.7|
||**Lov Tur**<br>E|**nover Ind**<br>x2ort Sha|**ustries**<br>re|**Hish**Tur<br>E|nover I<br>xRort S|ndustrie<br>hare|
|**Percentile.**|**low**|moderate|high|low|modera|te**high**|
|5th|10.2|10.1|10.1|9.2|9.6|9.7|
|10th|11.2|10.9|10.8|9.8|10.5|10.9|
|25th|15.0|14.5|14.2|11.5|12.9|13.7|
|50th|27.3|26.8|26.5|18.3|21.5|23.3|
|75th|59.4|59.0|58.7|52.8|57.0|59.2|
|90th|124.9|131.0|134.1|90.5|97.0|100.6|
|95th|200.2|188.2|182.4|188.2|188.3|188.3|
|Mean|54.2|52.1|51.1|47.3|48.0|48.4|
|Std. Dev.|66.7|61.1|58.4|70.9|65.7|63.2|

# **Hish** Turnover Industries

# Low Turnover Industries

# High Turnover Industries

|Percentile|Effect<br>low|ive Rate o<br>moderate|f Protection<br> **high**|Effecti<br>**low**|ve Rate of<br>moderate|Protection<br>high|
|---|---|---|---|---|---|---|
|5th|11.7|11.8|11.9|10.2|10.2|10.2|
|10th|14.2|14.5|14.7|11.3|11.3|11.3|
|25th|21.2|21.7|22.2|15.1|15.1|15.1|
|50th|41.1|42.3|43.4|28.2|28.3|28.3|
|75th|79.7|81.4|82.9|70.1|69.6|69.1|
|90th|173.3|177.7|182.0|159.7|159.0|158.4|
|95th|324.3|339.2|354.1|278.6|281.2|283.7|
|**Mean**|76.3|78.6|**80.9**|67.7|**67.9**|**68.1**|
|**Std.**<br>**Dev.**|86.3|88.9|91.4|95.8|96.8|97.7|
32 TABLE 4 BETWEEN ESTIMATES OF LABOR PRODUCTIVITY DISTRIBUTIONa (Absolute values of t-statistics in parentheses)

||Trade EXDos|ure**Measu**|**red**with I<br>Percent|mport and E<br>ile|xgort Shares|
|---|---|---|---|---|---|
||10th|25th|50th|75th|90th|
|ln(M/Q)|.158|.239|.230|.289|.398|
||(.474)|(.898)|(.890)|(1.08)|(1.58)|
|ln(X/Q)|-.271|-.258*|-.089|- .(45|-.186|
||(1.81)|(2.16)|<br>(.764)|<br>(.i72)|<br>(1.64)|
|ln(Q)|.260|.387|.490|.643*|<br>.619*|
||(.775)|<br>(1.44)|<br>(1.88)|<br>(2.38)|<br>(2.44)|
|TUR|8.26|8.81|7.26|9.27|6.46|
||(.754)|(1.01)|(.854)|(1.05)|(.780)|
|TUR*ln(M/Q)|<br>-.389|-.592|-.644|- 667|-.824|
|--<br>_____|(.550)|(1.05)|(1.18)|(1.17)|(1.54)|
|TUR*ln(X/Q)|<br>.910*|.806|.107|-.064|.513|
||(1.40)|(1.55)|<br>(.213)|<br>(.122)|<br>(1.04)|
|TUR*ln(Q)|-.495|-.516|-.547|-.618|-.312|
||(.708)|(.926)|<br>(1.01)|<br>(1.10)|<br>(.589)|
|Chile Dummy|<br>1.91*|1.88*|<br>1.85*|<br>1.72*|1.55*|
||(5.65)|(7.00)|<br>(7.09)|<br>(6.32)|<br>(6.06)|
|R<sup>2</sup>|.963|.978|.981|.980|.983|
||Trade Expos<br>10th|ure Measu<br>25th|red with Ef<br>Percenti<br>50th|fective Pr<br>le<br>75th|otection Rat<br>90th|
|ERP|.392|.296|.161|.143|.204|
||(2.04)|(1.73)|(1.01)|(.851)|(1.21)|
|ln(Q)|.788*|.715*|.493|.479|.552|
||(2.46)|(2.50)|(1.86)|(1.71)|(1.96)|
|TUR|36.08*|28.30|11.44|8.44|12.05|
||(2.31)|(2.03)|(.884)|(.618)|(.879)|
|TUR*ERP|-.872|-.588|-.031|.104|-.147|
||(2.07)|(1.56)|(.089)|(.281)|(.399)|
|TUR*ln(Q)|-2.49*|-1.97*|-.880|-.628|-.868|
||(2.51)|(2.24)|<br>(1.08)|<br>(.726)|(1.00)|
|Chile Dummy|<br>2.51*|2.48*|<br>2.52*|<br>2.56*|2.45*|
||(4.65)|(5.13)|<br>(5.63)|<br>(5.43)|<br>(5.18)|
|_R2_|.967|.976|.981|.980|.979|

# Trade Exposure Measured with Effective Protection Rates Percentile

**a** Industry dummies were included in the regressions but are not reported. ^Significantly different from zero at the .05 level using a two-tail test.
33 TABLE 5 WITHIN ESTIMATES OF EMPLOYMENT SIZE and PRODUCTIVITY DISTRIBUTIONa (Absolute values of t-statistics in parenthesis)

|||**Emgloymen**<br>|**t Size Dist**<br>**Percentile**|**ribution**||
|---|---|---|---|---|---|
||10th|25th|50th|75th|90th|
|ln(M/Q)|-.048|.010|-.168*|-.115|-.120|
||(.812)|(.144)|<br>(2.06)|(1.03)|(1.09)|
|ln(X/Q)|.035*|.007|-.042*|.071*|.061*|
||(2.35)|(.417)|<br>(2.04)|(2.57)|(2.19)|
|ln(Q)|.163|.270*|<br>.165|.349|.624*|
||(1.58)|<br>(2.27)|<br>(1.17)|<br>(1.82)|<br>(3.26)|
|TUR*ln(M/Q)|.226|-.047|.463|.195|.178|
||(1.19)|<br>(.215)|<br>(1.77)|<br>(.551)|<br>(.502)|
|TUR*ln(X/Q)|-.091|-.026|.146*|-.243*|-.214|
||(1.83)|<br>(.452)|<br>(2.12)|<br>(2.61)|<br>(2.30)|
|TUR*ln(Q)|-.577|-.776*|-.306|-.784|-1.25*|
||(1.76)|<br>(2.05)|<br>(.679)|<br>(1.29)|<br>(2.07)|
|**R2**|.805|.902|.932|.925|.936|
|||**Labor Pr**|**oductivity**<br>Percentil|**Distributi**<br>e|**on**|
||10th|25th|50th|75th|90th|
|ln(M/Q)|.007|-.066|-.011|-.046|.044|
||(.066)|<br>(.712)|<br>(.120)|<br>(.520)|<br>(.432)|
|ln(X/Q)|.053|.076*|<br>.069*|<br>-.056*|.055*|
||(1.89)|<br>(3.25)|<br>(2.95)|<br>(2.55)|<br>(2.12)|
|ln(Q)|.714*|<br>.686*|<br>.982*|.896*|<br>1.29*|
||(3.68)|<br>(4.26)|<br>(6.07)|<br>(5.89)|<br>(7.25)|
|TUR*ln(M/Q)|- .003|.182|.067|.280|-.017|
||(.008)|<br>(.612)|<br>(.225)|(.995)|<br>(.052)|
|TUR*ln(X/Q)|-.101|-.187*|-.187*|.258*|.149|
||(1.07)|<br>(2.39)|<br>(2.38)|(3.50)|(1.73)|
|TUR*lrL(Q)|-.890|-.837|-1.85*|-1.55*|-2.93*|
||(1.44)|<br>(1.63)|<br>(3.59)|(3.21)|(5.18)|
|**R2**|.986|.991|.992|.993|.990|

'Separate industry duzmies **and** time dummies for each country were included in the regressions but are not reported.

*Significantly different from zero and the .05 level using a two-tail test.