metadata: authors: 0: author: Tybout, James
1: author: de Melo, Jaime
2: author: Corbo, Vittorio
count: Chile
docna: 0: docna: The effects of trade reforms on scale and
            technical efficiency : new evidence from Chile
docty: Policy Research Working Paper
subtopic: Construction Industry,Common Carriers Industry,Food & Beverage Industry,Technology Industry,General Manufacturing,Pulp & Paper Industry,Plastics & Rubber Industry,Technology Innovation,International Trade and Trade Rules,Inflation,Business Cycles and Stabilization Policies,Trade and Multilateral Issues
teratopic: Water Resources
abstracts: cdata!: This paper shows how the industrial
            structure and performance changed after Chile's
            dramatic trade liberalization. A comparison of the 1967 and
            1979 censuses shows little improvement in productivity
            overall, but these figures don't separate the effects
            of trade liberalization from the effects of recession, high
            interest rates, and real appreciation. To isolate the
            effects of trade liberalization, the authors compared
            industries in which protection was significantly reduced
            with industries in which it was not. It was found that of
            the industries for which protection was lifted, the smallest
            plants tended to expand output more. Cross-plant estimates
            of returns to scale dropped significantly. These findings
            are consistent with the view that exposure to foreign
            competition forces suboptimally small producers toward
            minimally efficient scale. Also, production levels became
            higher and more uniform across plants in those industries
            undergoing dramatic reductions in protection. Taken
            together, these results support the received wisdom that
            increased exposure to trade improves competition within an industry.
geo_reg_key: 119228,517191
repnme: repnme: The effects of trade reforms on scale and
            technical efficiency : new evidence from Chile
display_title: The effects of trade reforms on
            scale and technical efficiency : new evidence from Chile
listing_relative_url: /research/1990/08/700196/effects-trade-reforms-scale-technical-efficiency-new-evidence-chile
topicv3: Machinery and Equipment m1326517 1916,Manufacturing Sectors m1326521 1281,Inflation m1326709 0870F4727ACED1CF8525815C00471347,Trade m1326924 698,Trade Policy and Integration m1327407 775
publishtoextweb_dt: 2010-07-01T00:00:00Z
historic_topic: Water Resources
origu: Policy, Research & Ect Affairs
no_of_pages: 49
model_extractions: <!-- Start of picture text --> Plc,Research, and External Affk're<br>Trade Policy<br>WPS 481<br><!-- End of picture text -->

- This paper- a product of the Trade Policy Division, Country Economics Department was prepared for the World Bank research project, "The Effects ofTrade Regimes on Industrial Competition and Efficiency" (RPO 674-46). Copies are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Rebecca Sugui, room NIO-031, extension 37951 (44 pages, including tables).

How did industrial structure and performance change after Chile's dramatic trade liberalization?

A comparison of the 1967 and 1979 censuses shows little improvement in productivity overall - but these figures don't separate the effects of trade liberalization from the effects of recession, high interest rates, and real appreciation.

To isolate the effects of trade liberalization, Tybout, de Melo, and Corbo compared industries in which protection was significantly reduced with industries in which it was not. Several findings emerged.

First, in industries for which protection was lifted, the smallest plants tended to expand output more. Cross-plant estimates of retlims to scale dropped significantly. These findings are consistent with the view that exposure to foreign competition forces suboptimally small producers toward minimally efficient scale.

Second, production levels became higher and more uniform across plants in those industries undergoing dramatic reductions in protection.

Taken together, these results support the received wisdom that increased exposure to trade improves competition within an industry.

The PRE Working Paper Series disseminates the findings of work undcr way in the Bank's Policy, Research, and Extemal AffairsComplex. An objective oftheseries is to get these findings outquickly. even if presentations are less than fullypolished. The findings, interpretations, and conch:-ions in thcse papers do not necessarily represent official Bank policy.

Produced bv the PRE Dissemination Center
||Table of Contents||
|---|---|---|
|I.<br>Ove|rview|1|
|II.<br>A G|lance at the Data|2|
|A.|Changes in the Chilean Economy Between<br>the Census Years|2|
|B.|Industry-wide<br>Changes in Structure|3|
|C.|Sector-Specific<br>Structural Change|6|
|III.<br>Bet|ter Measures of Efficiency Changes|11|
|A.|Indices of Scale and Technical Efficiency|11|
|B.|Dealing with Imperfect Data|15|
|C.|The Estimated Effect of Trade Reform on<br>Productive Efficiency|17|
|IV.<br>Con|clusion Remarks|23|
|Appendix 1:|Production Technolgoy Estimates|24|
|Bibliograph|y|30|
|Appendix 2:|Data Preparation Procedures|32|
|Bibliograph|y|44|

# List of Tables

|Table|1 Aggregate Industrial Structure. 1967 and 1979|4|
|---|---|---|
|Table|2 Cross-Industry<br>Rank Correlations--Changes in<br>Protection with Changes in Industrial Characteristics|<br>9|
|Table|3 Values and Rank Correlations of Protec, Scale,<br>Effic and Disper|19|
||Figure||
|Figur|e 1 Efficiency Inferences from Technology Estimates|13|

This paper was prepared for the World Bank research project "The Effects of Trade Regimes on Industrial Competition and Efficiency" (RPO 764-46). We thank Raul Galleguillos and Jose Miguel Sanchez for their painstaking work in preparing the data used in this study, and Julie Stanton for very able research assistance. Helpful comments from Bela Balassa, Zvi Griliches, Gene Grossman, Ramon Lopez, Cristian Moran, Dani Rodrik and three anonymous referees were received on an earlier draft.
1

# I. OVERVIEW

For several reasons, development economists routinely argue that trade protection reduces industrial sector efficiency. First, in markets characterized by entry barriers, the absence of foreign competition allows domestic producers to enjoy monopoly power and excess profits. Consequently, these firms may fail to produce at minimum efficient scale (achieve "scale efficiency") and/or to get the maximum possible output from their input bundles (achieve "technical efficiency' or 'X-efficiency"). Second, in markets characterized by Chamberlinean competition, trade protection may attract inefficiently small producers, causing similar increases in average production costs.<sup>1</sup> It is sometimes claimed that these two intra-industry effects of protectionism are more important sources of welfare loss than the traditional comparative advantage effects.

Disturbingly, the body of empirical evidence supporting these claims is ambiguous. For example, Rodrik (1988a) notes that "there is practically no direct evidence on the importance of scale economies in specific industrial sectors of the developing countries.' Similarly, Pack (1988) observes that "to date there is no clear confirmation of the hypothesis that countries with an external orientation benefit from greater growth in technical efficiency in the component sectors of manufacturing." Finally, Bhagwati (1988) concludes "although the arguments for the success of the export promotion strategy based on economies of scale and X-

> 1/ For more detail on arguments involving scale effects, entry and exit, see Roberts and Tybout (1990). For a compact review of the literature linking trade and technical efficiency in LDCs see Rodrik (1988b).
2 efficiency are plausible, empirical support for them is not available.' One is left wondering whether the received wisdom is more a matter of faith than fact.

The lack of clear evidence is not accidental. To examine the intra-industry effects of trade reforms directly, one must make "before and after' comparisons of a large sample of plants. Sufficient data are rarely available for the appropriate countries at the appropriate times. Fortuitously, however, the Chilean trade liberalization of 1974-79 was preceded and followed by industrial censuses, providing a natural experiment. This paper is an effort to document the evidence that emerged.

The paper is divided into three remaining sections. After some background on Chilean reforms, section II presents descriptive measures of the size distribution and productivity of each industry in each census year. Changes in these measures across census years are then correlated with industry-specifi **c** hanges in effective protection rates. The results are suggestive, but suffer from conceptual problems, measurement error and missing data problems. Hence section III corrects for these shortcomings and re-examines patterns of association. The results reinforce and extend the findings in section II: Although overall industrial efficiency did not improve between the two census years, the 3-digit industries that underwent relatively large reductions in protection improved relative to others.

# II. A GLANCE AT THE DATA

# A. Changes in the Chilean Economy Between The Census Years

In this section we take a first look at the data to familiarize the reader with general magnitudes and sources of variation. But before
3 discussing descriptive statistics, it is worth reviewing the major changes that took place in Chile between the two census years.<sup>2</sup> Like most developing countries, Chile pursued an import-substituting industrialization strategy in the 1950s and 1960s. By 1967 the first year for which we have plant-level data -- quantitative restrictions (QRs) were widespread. The cross-sectoral dispersion in tariff rates was extremely high, and the average effective protection rate for manufacturing was over 100 percent (Behrman, 1976). In addition, market entry and exit were limited by extensive controlR on the domestic credit market and by labor laws that made worker dismissal difficult. Ainally, price controls on domestic commodity markets were pervasive.

By 1979 -- the other year for which we observe plants -- the situation had changed dramatically. All QRs hibd been removed in 1974, and between 1975 and 1979, the average effective protection rate rate had been brought dowc below 15 percent, while cross-sectoral dis ion in tariff rates had been virtually eliminated. Thus by 1979, Chile had achieved one of the lowest and most uniform protection structures in the world. The domestic labor and financial markets had also been deregulated and price controls on sales in the domestic market had been removed.

# **B.** Industry-wide Changes in Structure

Table 1 summarizes the ways in which industrial structure, productivity and exposure to foreigr. competition changed between the two census years. Referring to the top panel, notice that the dramatic drop in protection was accompanied by clear increases in import shares, export

# 2/ See Corbo (1985) for further details.
Table 1: AGGREGATE INDUSTRIAL STRUCTLFRE, 1967 AND 1979

||1967<br>(1)|1979<br>(2)|Ratio<br>(2)1(1)|
|---|---|---|---|
|Market Conditionsa||||
|Effective Protection|120Z|15%|.13|
|Export Share|42|132|3.25|
|Import Penetration Rate|20Z|29T|1.45|
|Intra-Industry<br>Trade Index|.14|.28|2.00|
|Manufacturing Unemp. Rate|5Z|132|2.60|
|Plant Size Distributionb||||
|Average Gross Output per Plant|37,241|<br>37,425|1.01|
|Number of Plants|7,060|<br>6,771|<br>.96|
|10th output percentile|1,430|<br>1,051|<br>.74|
|25th output percentile|3,006|<br>2,358|<br>.78|
|median output|.,203|<br>5,723|<br>.79|
|75th output percentile|20,972|<br>16,126|<br>.77|
|9Gth output percentile|75,536|<br>60,442|<br>.80|
|99th output percentile|507,189|595,724|1.17|
|Factor Use and Productivityb||||
|Value Added per Plant|21,403|<br>15,978|<br>.75|
|Workers per Plant|39|34|<br>.88|
|Efficiency Labor per Plantc|188|185|<br>.98|
|Capital per Plantd|30,760|<br>59,853|1.95|

- a All figures refer to manufacturing. Trade flow data are taken from de Melo and Urata (1986). Let X, M and Q represent exports, imports and domestic production, respectively. Then the export share is X/Q; the import penetration rate is M/(Q+M-X), and the index of intra-industry trade is 1.0 - IX-MI/(X+M). Effective protection figures are taken from Behrman (1976) and Aedo and Lagos (1979). We omit the glass industry in calculating mean effective protection rates because its 1967 value of 3500X was an extreme outlier. Unemployment figures come from Banco Central de Chile (1986).

- b These figures refer to the census data after cleaning. Variables valued in 1967 prices were converted to 1979 prices.

- c wage bill divided by the minimum wage, which is held constant in real terms across census years.

- d figures are based on plants reporting complete capital stock data. Such plants constituted 38 percent of the sample in 1967 and 35 percent in 1979.
5 shares, and intra-industry trade. So trade liberalization apparently did place domestic manufacturers in more direct competition with their foreign rivals. Also, however, it combined with macro stabilization policies to create unemployment problems that still lingered in 1979.

The middle panel of table 1 describes the plant size distribution. Here notice that output per plant remained surprisingly stable between 1967 and 1979, while the number of plants fell slightly. This lack of expansion presumably reflected the same forces that created recession and unemployment during the second half of the 1970s. Notice alsc that, while the largest plants expanded, most others contracted. So if small plants were below minimum efficient scale in 1967, they were probably more so after the liberalization.

Measures of value-added per unit input ive a crude sense for productivity levels; these can be inferred from the bottom panel of table 1. Referring to th, last column, note that value-added per plant fell more rapidly than workers per plant (however measured) between 1967 and 1979, so labor productivity dropped. Similarly, for those plants reporting complete capital stock data, value-added per unit capital fell.<sup>3</sup> Taken together, these results suggest an unequivocal fall in total factor productivity for the manufacturing sector as a whole between 1967 and 1979. Like the low output growth, this productivity dr3p probably reflected the relatively slack demand for manufactured products in 1979, coupled with a gradual

> 3/ Bear in mind, however, that measurement problems are particularly acute for capital stocks, which are reported by only a fraction of the plants and are expressed in historic cost deflated by a general capital stock price index.
6 change in the mix of products and the nature of technology.4 Measurement 5 error is also likely, especially in capital stocks and x1& price deflators.

# C. Sector-Specific Structural Change

**For** several reasons, we caution against viewing th.e patterns of adjustment in table 1 as driven exclusively, or even mainly, by trade policy. First, as already noted, the avail4tle price indices may exhibit general biases over time, and some of the variables are measured with error. Second, and more importantly, Chile 4;;derwent many major policy changes between 1967 and 1979, including hy>,. inflation during 1973, a major recession during 1974-76, exchange rate appreciation thereafter, and large increases in ti,e real interest rate. In terms of influence on manufacturing sector aggregates, these forces could easily have masked the effects of commercial policy.

Comparisons of adjustment patterns among the different three-digit industries are more likely to reveal something about the effects of trade liberalization. This is because all industries were subject to roughly the same measurement errors and changes in macro conditions, but different

- 4/ Reductions in the ratio of industrial value added to industrial output are typically observed as the process of economic development unfolds. See, for example, Chenery, Syrquin, and Robinson (1986).

- 5/ We used official price series at the three-digit indust-ial clasificati n level from Chile's Instituto Nacional de Estadastica to deflate outputs, and combined these prices with the 1977 Chilean inputoutput table to impute sector-specific intermediate input price deflators. Experimentation with other price indices did not signficantly change our findings. Further details of our data preparation are available upon request.
7 industries underwent very different amounts of change in protection. Therefore, deviations of particular industries from the typical pattern of adjustment probably partly reflected the amount of new foreign competition that these irM'stries faced.

To see whether changes in trade exposure correlate with changes in structure and performance, we construct a set of industry-specific variables. First, we measure the change in protection between 1967 and 1979 with: where Rjt as the effective protection rate for the jth .ndustry in year t.6 Simildrly, we construct a set of industry-specific measures of change in size, factor use, and productivity. But for these variables, we provide extra detail by looking at the shape of the entire cross-plant distribution in each year -- just as in the middle panel of table 1. For example, we measure changes in the size distribution of plants wtih the following six variables: Here Qn. represents the output of the plant at the nth percentile during year t, given that plants in this industry and year have been sorted in order of increasing output. So if median plant size falls between the census years for industry j, RQ50j will be less than one. Also, if the shape of the size distribution changes, this will be reflected by different values for the different percentiles. We constuct analogous variables to 6/ Estimates of the effective rates of protection by industry and year are taken from Behrman (1977) and Aedo and Lagos (1986).
8 measure changes in value-added (RVn), employment (RLn), value-added per efficiency unit (RVEn), value-added per unit capital (RVKn), gross output per efficiency unit (RQEn), and gross output per unit capital (RQKn).<sup>7</sup> We choose these variables for several reasons. First, use of the ratio form makes it possible to ignore any macro shock or data bias that affects all industries equally.<sup>8</sup> It also allows us to ignore industryspecific effects that do not vary over time. Se-!ond, by including both gross output and value-added in our set of variables, we can see whether the findings hinge critically on measurement problems with the latter.

Different three-digit industries presumably react differently to changes in protection. So, to avoid forcing all industries into the same mold, we use Spearman rank correlations to look for dominant patterns of association between PROTEC and the various measures of structure and performance. These correlations are reported in Table 2 for each variable and percentile, along with associated t ratios.

Several conclusions can be drawn. First, sectors with relatively large declines in protection have shown a somewhat greater tendency toward emplcyment reductions. For example, the correlation between changes in protection and changes in the 50<sup>th employment percentile is .260,</sup> indicating that reductions in protection are associated with reductions in employment among plants with no more than the median number of workers.

- 7/ Ratios involving capital are constructed using only the subset of plants reporting complete capital stock data.

- 8I By 'equally,f we mean that the shock or bias scales the ratio of interest by an amount that is independent of j.
9 Table 2: CROSS-INDUSTRY RANK CORRELATIONS -- CHANGES IN PROTECTION WITH CHANGES IN INDUSTRIAL CHARACTERISTICS

|||Pe|rcenti|les (n)|||
|---|---|---|---|---|---|---|
|PROTEC correlated with:|<br>10th|25th|50th|75th|90th|99th|
|Gross Output<br>(RQn)|-.327<br>(1.50)|-.193<br>(.86)|-.062<br>(.26)|.157<br>(.69)|.133<br>(.59)|<br>.108<br>(.47)|
|Value Added<br>(RVAn)|-.481<br>(2.39)|-.338<br>(1.56)|-.251<br>(1.13)|-.168<br>(0.74)|.003<br>(0.01)|<br>.054<br>(.23)|
|Employment<br>(RLn)|.251<br>(1.13)|-.055<br>(.25)|.260<br>(1.17)|.520<br>(2.67)|.397<br>(1.89)|<br>.251<br>(1.13)|
|Efficiency ..abor<br>(REn)|.075<br>(.33)|-.018<br>(.08)|.348<br>(1.61)|.395<br>(1.87)|.222<br>(.99)|<br>.181<br>(.80)|
|Value Added<br>per Efficiency<br>LaboL (RVAEn)|-.577<br>(3.08)|-.434<br>(2.10)|-.410<br>(1.96)|-.304<br>(1.39)|-.414<br>(1.98)|-.071<br>(.31)|
|Value Added<br>per unit<br>Capital (RVAKn)|-.392<br>(1.85)|-.246<br>(1.11)|-.199<br>(.88)|-.323<br>(1.48)|-.236<br>(1.06)|-.221<br>(.98)|
|Gross Output per<br>unit Efficiency<br>Labor (RQEn)|-.451<br>(2.20)|-.418<br>(2.01)|-.462<br>(2.27)|-.319<br>(1.47)|-.516<br>(2.63)|-.022<br>(.09)|
|Gross Output per<br>unit Capital (RQKn)|-.218<br>(.96)|-.143<br>(.62)|-.158<br>(.70)|-.414<br>(1.98)|-.419<br>(2.01)|-.358<br>(1.67)|

Absolute values of t ratios are in parentheses This reduction is most striking among plants in the third largest quartile, and is stongest when employment is measured in terms of number of workers. Second, however, reductions in protection are associated with higher value added and output among the smallest plants. This effect is stongest in the lowest decile, and is nonexistent among plants above the median. So there
10 is some evidence that, as protection falls, small plants increase production levels toward minimum efficient scale, shedding labor at the same time.<sup>9</sup> Given that labor use falls and output rises as protection is reduced, it is unsurprising that value-added per efficiency worker and output per efficiency worker both rise. As with the output effects, these responses are concentrated among the smaller plants.<sup>10</sup> This finding supports X-efficiency arguments linking trade exposure and productivity.

Finally, and less strikingly, output per unit capital and valueadded per unit capital tend to rise most in those industries where the reductions in protection were most dramatic. Hence, given that reductions in protection are associated with increases in both output per employee and output per unit capital, any standard index of total factor productivity growth constructed from these data would probably correlate negatively across industries with changes in protection.

- 9/ Comparing plants' employment levels in Chile (1979-1986) and Colombia (1977-1987), Roberts and Tybout (forthcoming) found that high rates of protection were associated with relatively large plants -- both in terms of output and in terms of employment -- once industry and country effects were controlled for. This finding is consistent with the present paper's findings regarding correlations of protection with workers per plant, but not with its findings regarding output per plant.

- 10/ Efficiency effects aside, a reduction in effective protection can cause an increase in value-added to the extent that technology allows plants to substitute away from intermediate goods usage as their relative price rises. We are grateful to Ramon Lopez for this point.
11

## **III. BETTER MEASURES OF EFFICIENCY CHANGES**

**All** of the above is very suggestive, but it glosses over two types of measurement problems. The first is that table 2 is limited to single factor productivities. Accordingly, it does not allow us to measure **overall productivity** of the input bundle, or to distinguish between scale effects and X-efficiency effects. The second problem concerns data quality. Value-added and capital stock figu -es are likely to be biased because of inflation-distorted bookkeeping. Moreover, even without inflation, book values of capital stocks reflect historic cost rather than economic worth, and the discrepancy in values should increase as the capital ages. Finally, for many enterprises capital stock figures are **missing entirely.** This section presents an attempt to come to grips with all of these shortcomings.

# **A. Indices Of Scale and Technical Efficiency**

First, by going to a parametric representation of production technologies, we can easily construct better measures of efficiency. Assume that intermediate inputs are used in fixed proportion to gross output, and let the maximum value-added attainable with given amounts of capital (K*) and labor (E) be represented by the production function f(K* ,E). Then returns to scale are simply the sum of the elasticities of f( ) with respect to its arguments. If this sum exceeds one for a given vector of inputs, the associated plant is said suffer from scale inefficiency. Also, if the ith plant is using inputs (K*i,Ei) and producing value added Vi, then ei = f(K*i,Ei) - Vi is an index of the
12 plant's level of technical inefficiency.11 Our objective is infer something about the changes in these efficiency measures that accompany trade liberalization. Ignoring econometric and data problems for the moment, imagine we are able to obtain consistent estimates of the following Cobb-Douglas production function for each industry and year: Here D1 through D3 are dummies that control for things like type of firm ownership, ul is a disturbance term, and all other variables are assumed to be measured in logarithms. (More detailed variable definitions are provided in the appendix.) Then inferences regarding scale and technical efficiency can be based on estimates of a, Po. P1. and var(ul).

To see how, refer to figure 1. Here we represent f(K*,E) indirectly with its dual, the technically efficient cost function g(V). This allows us to work in two dimensions. All variables are in logarithms, so l/g'(V) reflects returns to scale, and gt ) follows the usual pattern of increasing returns in low output ranges, then constant or diminishing returns. Dots and asterisks are hypothetical data points; they represent cross-sectional observations on plants in a givenindustry. All data points lie above g(V) to indicate that the

> 11/ This definition, which began with the work of Farrell (1957), is fairly standard for empirical work. For literature surveys, see Forsund, Lovell and Schmidt (1980) and Schmidt (1985).
FIGURE 1 W <!-- Start of picture text --> / g(v)<br>AFTER - /<br>BF ORE<br>. * B FO<br>*<br>/ / / 10<br>VALUE ADDED (v)<br>I415>/<br>,/'<br><!-- End of picture text -->
14 associated plants are not operating at maximum technical efficiency. 12 Assuming that deviations from the efficient technology are Hicksneutral, the horizontal distance from the cost function for the ith plant is a monotonic positive function of its technical inefficiency level, ei.

Suppose that trade liberalization forces some plants in the increasing returns region to expand- or exit the market, eliminating the data points represented by asterisks in figure 1. Then Cobb-Douglas estimates of the returns to scale (a+P1) are likely to fall, as the "before" and "after" regression lines make clear.<sup>13</sup> The cost function intercept is also likely to fall, or equivalently, the production function intercept (Po) is likely to rise. Finally, given that the Cobb-Douglas technology implies a log-linear relationship between costs and output, it should fit the data better after plants in the increasing returns range have been eliminated. So the variance in ul (C1<sup>2</sup> ) is likely to drop with trade liberalization unless offseting changes in measurement error occur.

> 12/ With measurement error in value added, some observed data points might fall below the cost function. Measurement error does not alter the analysis unless its variance changes across census years in a way that is correlated with changes in protection rates. We assume this does not occur.

- 13/ Returns to scale estimates should fall for the estimated production function as well as the estimated cost function, since cost is simply an index of factor usage. Interpreted this way, figure 1 may be viewed as representing the inverse production function.
15 There is another reason that 012 may fall and _(Po)_ may rise as efficiency improves. Imagine that heightened exposure to foreign competition forces all plants to move toward the efficient technology, so that the data points in figure 1 drift to the right, and cluster more closely above the efficient cost function. Then the residual variation and the intercept of the estimated cost function would fall.<sup>14</sup> These effects can occur without any change in the estimated returns to scale, so by examining the changes in _Po,_ 012, and a+f2 together, we may be able to learn something about the relative importance of scale effects versus technical efficiency effects.

# B. Dealing with Imperfect Data

Problems of missing data and measurement error complicate the analysis. As mentioned above, capital stock figures do not accurately reflect true stocks, and for many plants they are missing entirely. We deal with these problems by employing an estimator that combines errorsin-variables techniques developed by Goldberger (1972) with missing data treatments exposited in Gourieroux and Monfort (1981). This estimator provides maximum likelihood estimates for equation (1), given the following relationships (Tybout, 1990):

> 14/ Much of the micro-econometric literature on efficiency measurement is devoted to capturing these two effects (e.g., Schmidt, 1985). We depart from this literature somewhat because of the data problems discussed in the following subsection.
16 Here K is book value of capital stock, which differs from the true stock by the measurement error u3, and is missing entirely for a fraction of the sample. Also Z is an instrument for the true capital stock (see the appendix for details). When estimating the system (1) through (3), we assume that all disturbances are independent of one another, and labor and capital are exogenous.15 Also, we assume that the missing data pattern is "ignorable" (e.g., Little, 1982), and hence does not create selectivity bias.<sup>16</sup>

- 15/ This exogeneity assumption is justifiable if firms choose input levels before observing current period shocks to technology and to real input prices (e.g., Zellner, Kmenta, and Dreze, 1966). This condition seems to suit 1967 particularly well, given that labor laws were very strict, and labor dismissal was difficult. Nonetheless, we experimented with an alternative version of the model in which it was assumed that firms observe current period wages without error and freely adjust employment levels to maximize short-run profits. This extension yielded implausible results, suggesting the approach taken here is a closer approximation to behavior.

- 16/ This assumption can be relaxed (Tybout, 1990). However, experiments in which we did so suggested that selectivity bias was minimal, so we opted for the simpler estimator.
17 C. The Estimated Effect of Trade Reforms on Productive Efficiency Estimates of the system (1) through (3) for each industry and year are presented and discussed in appendix 1. We use the results to construct industry-specific indices of the changes in returns to scale, average efficiency level (i.e., production function intercept), and dispersion in efficiency levels between 1967 and 1979. For the jth industry (j=l, 21), these measures are: Here hats indicate estimated values, and subscripts outside brackets refer to the year of the census from which the estimates were obtained. As with our descriptive statistics of section II.C, these indices are relatively robust with respect to industry-specific distortions in estimators. Such distortions will not matter if they bias estimators by the same factor in each year, or (when comparing across industries) if the factor of bias is common to all industries.

The top panel of Table 3 presents estimated values for each parameter ratio of interest, industry by industry, along with associated PROTEC values. As in table 1, there is still no evidence of overall improvements in productive efficiency for the manufacturing sector. Of the 21 manufacturing industries analyzed, only 10 showed reductions in estimated returns to scale, only 9 showed evidence of Hicks neutral
18 productivity improvements, and only 5 registered reductions in our index of efficiency dispersion.

Recall, however, that many macro shocks hit the Chilean manufacturing sector between the census years, masking the effects of commercial policy in sector-wide analyses. Therefore, to isolate the influence of trade reforms from shocks that are common to all industries, we once again turn to cross-industry comparisons. Spearman rank correlations and t ratios for PROTEC, SCALE, EFFIC, and DISPER are reported in the bottom panel of table 3. Here notice that relatively large reductions in protection are associated with relatively marked declines in the estimated returns to scale. Although this association is statistically weak, it builds on our table 2 findings. There we saw that small plants tend to drop out or grow as protection is removed; here we find evidence that this process generates some efficiency gains.

More strikingly, the intercept of the production function appears strongly negatively associated with the level of protection, as does the dispersion in output levels conditioned on inputs. These correlations are partly due to a flattening of the regression line, as the correlation between SCALE and EFFIC makes clear.<sup>17</sup> However, SCALE does not correlate as strongly with reductions in protection as does EFFIC, so we are tempted to conclude that such reductions are associated with movements toward the efficient technology across a wide range of

> 17/ Even when the underlying population remains unchanged, sampling error is likely to induce a negative correlation between the intercept and the sum of the slope coefficients, so we caution against reading too much into the high t ratio for the rank correlation of SCALE and EFFIC.
19 Table 3: VALUES AND RANK CORRELATIONS OF PROTEC, SCALE, EFFIC and DISPER

|Est|imated Pa|rameter|Ratios||
|---|---|---|---|---|
|Industry|PROTEC|SCALE|EFFIC|DISPER|
|food|.24||.97|1.89|
|beverages|1.47|1.02|1.06|1.46|
|textiles|.19|1.01|1.14|2.44|
|apparel|.98|.95|1.02|2.21|
|leather products|.96|.99|.54|3.10|
|footwear|1.01|.99|.83|4.10|
|wood and cork|1.13|1.26|.53|2.86|
|wood furniture|1.11|1.18|.76|2.10|
|pulp and paper|.60|1.18|.24|.90|
|printing|1.31|1.00|.91|3.13|
|indus. chemicals|.69|.98|.94|1.61|
|other chemicals|.95|1.17|.81|2.33|
|rubber products|.28|1.04|3.66|.98|
|plastic products|.50|.88|1.41|2.06|
|glass*|.03|.72|1.49|1.19|
|non-metal minerals|1.13|1.13|.65|3.41|
|iron and steel|.87|.79|1.14|2.04|
|metal products|.60|1.08|.85|2.72|
|non-elec. mach.|.64|.92|1.42|.94|
|elec. mach.|.20|.90|1.72|.94|
|transport equip.|.39|1.06|.77|.99|

||Spearman|Rank Correlationsi|*|
|---|---|---|---|
||SCALE|<br>EFFIC|DISPER|
|PROTEC|.387|<br>-.523|.546|
||(1.83|)<br>(-2.68)|(2.85)|
|SCALE||-.731|.239|
|||(-4.67)|(1.07)|
|EFFIC|||-.461|
||||(-2.26)|

* This industry exhibited such ar. extreme reduction in measured protection that we redid the analysis without it. None of the results (available on request) were significantly affected.

** t ratios are in parentheses
20 plant sizes. That is, the apparent changes in overall eificiency were probably not exclusively attributable to small inefficient plants expanding or dropping out.

Taken together, the rank corelations in table 3 are wholly consistent with the table 2 finding that industries undergoing the most reduction in protection showed the most productivity improvement. They also appear to confirm the adjustment processes discussed in connection with figure 1. Nonetheless, there are alternative plausible explanations for our findings. Consider the scale result first. Changes in returns to scale are negatively associated with changes in average plant size across the two censuses, as one would expect.<sup>18</sup> So it appears that expansions in plant size do partly account for reductions in estimated returns to scale. But other factors may also be at work. Specifically, our data deflation is based on the presumption that products are homogeneous at the 3-digit level. This is obviously unlikely, and could lead to misinterpretations. That is, when value added is deflated by an industry price deflator, we are not really isolating a physical quantity, even if intermediate inputs are always used in fixed proportion to output. This means that if big firms achieve larger mark-ups because of market power, they will appear to have more output per unit input -- that is, they will appear more scale efficient. Moreover, if reductions in protection force these firms to price competitively, it will appear that returns to scale in the

> 18/ The rank correlation between average plant size changes and scale changes is about -.30.
21 industry have decreased.19 This is surely a result in favor of trade liberalization, but not the one we have stressed.

Several alternative explanations also<sup>ccme to mind for the</sup> technical efficiency findings. For example, the positive correlation between PROTEC and DISPER might have been<sup>generated by a reduction in</sup> product diversity within certain 3-digit<sup>industries, making intra-</sup> industry technology more uniform across plants.<sup>This, too, would reduce</sup> observed deviations from the estimated<sup>technology. As for the</sup> association between PROTEC and EFFIC,<sup>it might reflect a downward shift</sup> of the efficient cost functiz-n for<sup>industries exposed to world</sup> competition. If this explains table<sup>3, dispersion about the average</sup> isoquant need not have fallen most in<sup>the industries</sup> with the greatest average efficiency improvement. Given that the rank correlation between DISPER and EFFIC is only -.13 (t=-.55),<sup>this interpretation</sup> of the results appears to have some merit.

Finally, despite our very encouraging<sup>findings, it should be</sup> stressed that they reflect a number<sup>of somewhat arbitrary decisions</sup> regarding data preparation and model<sup>specification. First, there was</sup> some specification search involved<sup>in choosing the capital stock</sup> instrument. Initially electricity consumed<sup>was used, and when it became</sup> clear that this led to very inaccurate<sup>estimates, we switched to</sup>

> 19/ Using the same data, de Melo and Urata<sup>(1986) found that price-cost</sup> margins decreased slightly between<sup>the two census years. They</sup> attribute this to increased competitive pressures from liberalization.
22 machinery and equipment.20 Second, our protection estimates for 1967 are crude. Third, with respect to specification, it must also be recognized that our crude measure of labor input is probably not strictly exogenous. So if labor usage depends on output demand and some omitted variables -- e.g. the wage-price ratio -- changes in the omitted variable can change the estimated relationship between labor and output. (Changes in the structure of protection alter the cross-sectoral pattern of wage-price ratios, and hence in principle could change the apparent pattern of scale economies.) About these shortcomings little could be done. However, we did test for robustness by trying alternative variable definitions and model specifications. With respect to data, we experimented with two alternative industry-specific deflators for gross output and found that our results were robust to the selection of industry-specific gross output deflator (results available upon request). We also developed an extension of the model treating labor as endogenous (see footnote 15). Unfortunately, this yielded implausibly low estimates for returns to scale, probably reflecting problems with our wage variable and the imposition of too much structure on the data.2<sup>1</sup>

> 20/ However, tests for the association between protection and efficiency were not performed until we had settled on our instrument, nor did we even compare results from the two census years industry-byindustry. Hence our search in itself did not bias the results in favor of our priors.

- 21/ Os'r data do not specify location of establishments, so we are unable to control for the possibility that firms in different localities may face different wage-price ratios.
23

# IV. CONCLUDING REHMRXS

This paper provides new micro evidence on the relationship between choice of development strategy and industrial sector performance. Our contribution departs from the existing literature in several respects. First, unlike virtually all other evidence, ours is at the establishment level. Thus we are able to address theories of intra-industry adjustment much more directly than has been possible in the past. Second, there is dramatic variation in the trade regime between our sample years. This gives our analysis a laboratory-like flavor, and creates sufficiently large changes in industrial performance to be detectible despite measurement problems. Finally, unlike much of the literature, our study tracks a single country through time, eliminating the complicating influence of country-specific effects.

Although the findings are suggestive, much more plant-level work must be done before we can be confident of the "stylized facts" regarding intra-industry adjustments to trade reforms. We are currently attempting to generate further evidence by analyzing additional liberalization episodes and by exploiting panel data that follow individual plants through time.
24

# **Appendix** 1: Production TechnoloRy Estimates

This appendix presents more detail on our estimates of equations (1) through (3) of the text.

# A. Variable Definitions

The variables we used to estimate these equations are presented in table Al. We call the reader's attention to several details. First, following Maddala (1977) we use a size dummy (D3) as an instrument for the capital stock. This dummy also appears in the production function (1) because imposing an exclusion restriction would have vastly complicated estimation (Tybout, 1990). Empirically, coefficients on D3 in equation (1) are all very close to zero, so the only cost of proceeding this way is apparently a small loss of power.

Second, notice that Z, which serves as another instrument for the true capital stock, is defined to be machinery and equipment. This reflects several considerations. One is that machinery and equipment are almost always reported.22 (When total capital is missing, it is usually because firms have rented their buildings, land, or vehicles.)

> 22/ This is less true in 1979 than in 1967. Obviously, observations that do not include machinery and equipment must be excluded from the estimation altogether. Details of our data preparation are available upon request.
25

# Table A!: VARIABLE DEFINITIONS FOR SECTION III ESTIMATES

- V = logarithm of value added corrected for inflation distortions and expressed in 1979 prices

- E = logarithm of labor, expressed in efficiency units<sup>23</sup>

- Dl = 1 if the firm is a proprietorship, 0 otherwise

- 24

- D2 = 1 if the firm is a partnership, 0 otherwise

- D3 = -1 if **t1.1.** firm is among the smallest third of the industry<sup>sample;</sup> 0 if the firm is among the middle third of the industry sample; and 1 if the firm is among the largest third of the industry sample

- K = logarithm of the reported value of the capital stock, i.e., machinery and equipment plus vehicles, plus land and buildings. (This variable is considered "missing" if at least one component takes a zero or missing value.)

- Z = logarithm of machinery and equipment

The other consideration is that the instrument Z should be correlated with true capital, but not with the measurement error u2. If measurement error

- 23/ This rariable is constructed as the wage bill divided by the minimum wage. The wage bill includes an imputation for owners and family help. See Griliches and Ringstad for a similar definition. In earlier work (Corbo and deMelo, 1985), we tested several specifications of the labor variable to deal with heterogeneity in the labor force and found that labor input measured by the total number of unskilled equivalent workers was the best.

- 24/ Partnerships could not be distinguished in the 1967 data, so all firms with between 1 and 10 owners were considered partnerships in that year.
26 in capital is essentially due to the longer term items -- i.e., buildings and land -- machinery and equipment satisfy this condition.25

# B. Parameter Estimates

Table A2 reports estimated output elasticities with respect to capital(a) and labor (P1), their sum (a+pl), the intercept (Po), and 2 2 2 estimates for aOl, a2 and a3 To give some sense for the overall fit, we 2 2 include total variation in V (a2) for comparison with al. Numbers in parentheses are asymtotic "z' ratios for the null hypothesis that the coefficient estimated immediately above is zero except in the case of returns to scale, where the null is that returns to scale are unity. Finally, the number of observations used for each industry (n) and the number for which capital stocks are observable (nc) are also reported.

Observe first that the scale estimates are generally very plausible, although the labor coefficient is sometimes larger than one would expect. We found that although the scale estimate a+pl was insensitive to the instrument (Z) used, choice of instrument did affect the breakdown of returns to scale between a and _Pl._ So, although the scale estimates seem robust, these individual magnitudes should be viewed with some caution.

Note next that the estimated variances of ul are always significantly positive, while those of u2 and u3 sometimes are not.

> 25/ We also tried electricity consumption as an instrument for capital but found it had low predictive power. (It was also unattractive conceptually because electricity is subject to relatively severe endogeneity problems.) Installed horsepower was an alternative to machinery and equipment, but this variable was only available in the 1967 census.
27 Table A2: INDUSTRY-WIDE PROWUCTION TEfCFLOCIES 1967 VS. 1979

|INDUSTRY/DESCRIPTIO|NYEAR|n/nc|INTERCEPT<br>fGi|CAPITAL<br>1|LABOR|02<br>1|02<br>2|02<br>3|02<br>I|RETURNS<br>TO SCALE<br>r<br>fi|
|---|---|---|---|---|---|---|---|---|---|---|
|312 FOOD PRODUCTS|67|1543/<br>801|2.802<br>(15.48)|0.345<br>(11.89)|0.645<br>(16.36)|0.428<br>(23.36)|0.200<br>(5.09)|0.060<br>(1.62)|2.26|0.990<br>(-0.35)|
||79|1880/<br>696|2.720<br>(15.12)|0.329<br>(11.77)|0.632<br>(15.03)|0.808<br>(28.96)|0.124<br>(2.41)|0.104<br>(2.03)|2.90|0.961<br>(-1.27)|
|313 8EVRAOES|67|429/<br>122|2.383<br>(3.98)|0.389<br>(5.10)|0.682<br>(9.64)|0.806<br>(12.58)|0.248<br>(2.39)|-0.048<br>(-0.49)|2.57|1.071<br>(1.00)|
||79|138/<br>74|2.521<br>(2.66)|0.315<br>(2.66)|0.783<br>(4.37)|1.179<br>(8.17)|0.049<br>(0.26)|0.166<br>(0.87)|4.18|1.098<br>(0.69)|
|321 TEXTILES|67|666/<br>271|3.191<br>(11.40)|0.332<br>(7.75)|0.611<br>(12.64)|0.318<br>(17.90)|-0.025<br>(-0.60)|0.197<br>(4.36)|2.23|0.943<br>(-1.69)|
||79|657/<br>216|3.6a5<br>(12.39)|0.133<br>(3.23)|0.819<br>(12.22)|0.775<br> <br>(17.74)|0.329<br>(2.16)|-0.218<br>(-1.45)|3.17|0.953<br>(-0.91)|
|322 WEARINOAPPAREL|67|582/<br>104|2.569<br>(4.12)|0.504<br>(5.13)|0.613<br>(7.28)|0.413<br>(15.59)|-0.075<br>(-1.49)|0.284<br>(4.55)|1.94|1.118<br>(1.77)|
||79|621/<br>149|2.610<br>(6.36)|0.268<br>(4.04)|0.791<br>(9.30)|0.912<br>(15.47)|0.453<br>(3.16)|-0.243<br>(-1.80)|2.74|1.059<br>(0.93)|
|323 LEATHER PRODUCTS|67|128/<br>56|4.693<br>(6.57)|0.163<br>(1.43)|0.698<br>(6.45)|0.245<br>(8.01)|-0.09<br>(-0.54)|0.316<br>(1.64)|2.06|0.861<br>(-1.71)|
||79|109/<br>49|2.531<br>(3.47)|0.483<br>(4.19)|0.364<br>(1.69)|0.760<br>(7.49)|-0.070<br>(-0.72)|0.200<br>(1.89)|2.86|0.847<br>(-1.08)|
|324FOOTWEAR|67|323/<br>81|3.385<br>(9.22)|0.260<br>(3.72)|0.738<br>(9.57)|0.297<br>(11.02)|0.147<br>(1.16)|0.104<br>(0.84)|2.14|0.998<br>(-0.05)|
||79|2661/<br>67|2.826<br>(4.09)|0.200<br>(1.78)|0.794<br>(5.44)|1.219<br>(11.25)|0.221<br>(0.74)|-0.061<br>(-0.21)|3.34|0.994<br>(-0.06)|
|331 SAWMILLS,WOOD<br>A CORK|67|732/<br>266|5.143<br>(18.46)|0.206<br>(4.62)|0.636<br>(12.20)|0.389<br>(17.88)|0.160<br>(1.50)|0.148<br>(1.38)|1.71|0.842<br>(-4.11)|
||79|502/<br>196|2.737<br>(6.04)|0.210<br>(2.90)|0.847<br>(8.18)|1.116<br>(15.25)|0.278<br>(1.26)|0.083<br>(0.38)|3.34|1.058<br>(0.79)|
|332WOODftRNITLRE|67|416/<br>108|4.041<br>(12.80)|0.194<br>(3.24)|0.714<br>(9.49)|0.375<br>(11.44)|0.539<br>(2.58)|-0.161<br>(-0.83)|1.64|0.908<br>(-1.78)|
||79|368/<br>93|3.063<br>(6.99)|0.162<br>(2.22)|0.908<br>(8.77)|0.789<br>(12.41)|0.651<br>(1.67)|-0.222<br>(-0.59)|2.45|1.070<br>(0.90)|
|341 PULPAND PAPER|67|39/<br>24|3.997<br>(4.46)|0.303<br>(2.36)|0.579<br>(3.38)|0.308<br>(3.88)|0.151<br>(0.77)|0.065<br>(0.35)|3.77|0.882<br>(-1.04)|
||79|68/<br>29|0.978<br>(1.36)|0.623<br>(5.08)|0.417<br>(2.34)|0.277<br>(1.25)|0.372<br>(0.96)|-0.017<br>(-0.14)|4.95|1.040<br>(0.39)|
|342 PRINTING|67|302/<br>76|3.928<br>(11.87)|0.413<br>(7 62)|0.530<br>(7.27)|0.207<br>(8.59)|0.110<br>(1.57)|0.091<br>(1.38)|1.97|0.943<br>(-1.12)|
||79|388/<br>106|3 555<br>(11.85)|0.183<br>(3.94)|0.755<br>(10.34)|0.649<br>(13.47)|0.240<br>(1.65)|-0.129<br>(-0.90)|2.43|0.938<br>(-1.13)|
|351 CHEMICALS<br>(INDUSTRIAL)|67|81/<br>45|4.578<br>(E.47)|0.227<br>(2.21)|0.642<br>(4.44)|0.507<br>(6.65)|-0.348<br>(-2.17)|0.487<br>(2.79)|2.55|0.869<br>(-1.12)|
||79|S1/<br>24|4.303<br>(4.10)|0.309<br>(2.58)|0.542<br>(2.32)|0.817<br>(4.71)|0.247<br>(1.24)|-0.150<br>(-0.79)|Z.00|0.851<br>(-0.81)|
|352 OTHER CHEMICALS|67|206/<br>109|3.841<br>(5.95)|'.478<br>(4.98)|0.335<br>**(3.30)**|0.352<br>(8.44)|0.074<br>(1.10)|0.178<br>(2.55)|2.35|0.813<br>(-2.54)|
||79|188/<br>112|3.120<br>(4.84)|0.343<br>(4.10)|0.606<br>(4.71)|0.820<br>(8.55)|0.321<br>(2.45)|-0.105<br>(-0.86)|4.30|0.948<br>(-0.S1j|
|335RU88ERPRODUCTS|67|61/<br>31|0.925<br>(0.52)|0.784<br>(2.82)|0.176<br>(0.59)|0.564<br>(5.51)|-0.175<br>(-2.48)|0.586<br>(4.22)|3.15|0.960<br>(-0.22)|
||79|83/<br>39|3.128<br>(4.04)|0.240<br>(1.79)|0.761<br>(4.14)|0.552<br>(6.64)|-0.336<br>(-1.82)|0.501<br>(2.49)|3.32|1.001<br>(0.01)|
|356 PLASTIC PRODUCTS|67|100/<br>39|3.122<br>(4.47)|0.257<br>(3 16)|0.817<br>(6.98)|0 291<br>(7.39)|-0.229<br>(-1.54)|0.486<br>(2.79)|2.16|1.074<br>(0.70)|
||79|185/<br>64|4.398<br>(6.38)|0.025<br>(0.24)|0 914<br>(7 06)|0 601<br>(9.59)|-1167<br>(-0.49)|1.537<br>(0.64)|2.34|0.938<br>(-0.69)|
28 Tabi A2: INDTRY-WIDE PROCUCTION TECNOLOXIES 1967 VS. 1979 (continued)

|I?VJSTRY/DEmsmIPTIm|<br>YEAR|n/nc|NTECEPT<br>A)u|CAPITAL|LABM|_0r2_<br>1|022<br>2|02<br>3a|_02_|RETURNS<br>TO SCALE<br>a|
|---|---|---|---|---|---|---|---|---|---|---|
|362 CLASS|67|S1/<br>27|2.726<br>(2.10)|0.35?<br>(1.84)|0.703<br>(3.35)|0.489<br>(4.69)|0.085<br>(0.48)|0.113<br>_(0.6A)_|3.67|1.060<br>(0.38)|
||79|37/<br>17|4.072<br>(2.80)|0.349<br>(1.66)|0.416<br>(1.62)|O.S85<br>(4. .4)|0.048<br>(0.14)|0.330<br>(0.93)|3.91|0.764<br>(-1.26)|
|369 NON-METALLIC<br>MINERAL PRODUCTS|67|146/<br>67|3.105<br>(7.29)|0.305<br>(4.55)|0.708<br>(6.82)|0.261<br>(8.16)|0.284<br>(1.70)|0.119<br>(0.79)|2.17|1.013<br>(0.18)|
||79|152/<br>43|2.024<br>(3.57)|0.243<br>(2.57)|0.898<br>(5.87)|0.891<br>(8.65)|-0.049<br>(-0.17)|0.294<br>(0.99)|4.41|1.142<br>(1.40)|
|371 IRON AND STEEL|67|52/<br>28|2.624<br>(2.84)|0.449<br>(4.71)|0.619<br>(4.66)|0.301<br>(4.73)|0.030<br>(0.37)|0.081<br>(0.98)|2.82|1.068<br>(0.52)|
||79|41/<br>18|2.997<br>(2.49)|0.463<br>(2.58)|0.387<br>(1.60)|0.618<br>(3.14)|0.363<br>(1.35)|-0.122<br>(-0.57)|4.26|0.850<br>(-0.69)|
|381MErALPRODUCTS|6:|636/<br>238|4.136<br>(1S.98)|0.206<br>(5.12)|0.702<br>(14.85)|0.290<br>(13.93)|0.467<br>(4.59)|-0.207<br>(-2.31)|2.33|0.908<br>(-2.63)|
||79|625/<br>224|3.505<br>(10.40)|0.124<br>(2.49)|0.866<br>(12.76)|0.789<br>(17.40)|0.266<br>(1.27)|-0.077<br>(-0.57)|3.23|0.991<br>(-0.18)|
|382NON-ELECTRICAL<br>MACHINERY|67|289/<br>99|2.705<br>(4.35)|0.063<br>(0.71)|0.905<br>(7.94)|0.903<br>(12.03)|-0.249<br>(-0.34)|0.484<br>(0.66)|3.20|(1.967<br>(-0.36)|
||79|173/<br>71|3.840<br>(5.62)|0.259<br>(3.02)|0.633<br>(4.65)|0.84<br>(9.31)|-0.043<br>(-0.30)|0.156<br>(1.09)|2.89|0.893<br>(-0.90)|
|383 ELECTRICAL<br>MACHINERY|67|g5/<br>45|2.183<br>(2.45)|0.287<br>(2.46)|0.826<br>(S.14)|0.717<br>(6.60)|0.135<br>(0.77)|0.013<br>(0.07)|3.61|1.113<br>(0.81)3|
||79|76/<br>46|3.746<br>(3.79)|0.066<br>(0.45)|0.934<br>(4.57)|0.673<br>(5°83)|1.576<br>(2.19)|-1.429<br>(-2.05)|3.39|1.000<br>(-0.00)|
|38ATRANSPORT<br>EQUIPME?B'T|67|184/<br>76|4.403<br>(7.45)|0.266<br>(3.40)|0.717<br>(6.91)|0.623<br>(9.52)|-0.012<br>(-0.09)|0.159<br>(1.20)|3.00|0.983<br>(-0.20)|
||79|164/<br>65|3.376<br>(6.40)|0.115<br>(1.75)|0.927<br>(8.25)|0.616<br>(8.91)|0.257<br>(0.60)|-0.012<br>(-0.03)|2.75|1.041<br>(0.45)|
29 However, for these latter two disturbances, only 3 out of 84 estimated variances are significantly negative at the 95 percent level.<sup>26</sup> This gives us some degree of confidence that our model is reasonably specified, and we take these variance estimates to shed some light on the importance of capital stock measurement error. In particular, we observe that all but six sectors showed reductions in the amount of measurement error from 1967 to 1979, perhaps reflecting the fact that firms were instructed to adjust their book va.,ues to market worth in the latter year.<sup>27</sup>

- 26/ Negative variance estiamtes could in principle be eliminated by restricting the parameter space when we maximize the likelihood function, bat this would vastly complicate the calculations. (See Tybout (1990) for details.) Given that the variances of u2 and u3 are nusiance parameters with little bearing on our analysis, we do not grapple with this problem.

- 27/ This one-time revaluation was termed the 'Retacacion Tecnica", or .technical adjustment".