77323 THE -WORLD BANK ECONOMIC REVIEW, VOL. 14, NO. 2: 309-30 Trade Reform Dynamics and Technical Efficiency: The Peruvian Experience Da M. Semenick Alam and Andrew R. Morrison Markets around the world are becoming more competitive because of changing operat- ing and regulatory environments. One such change—the loosening of trade restrictions— is a macroeconomic policy shift that should have a microeconomic impact on industrial efficiency. Specifically, competitive pressure should discipline or eliminate inefficient producers. This article explores whether or not there is such a dynamic link. It uses a previously unexploited data set to gauge the impact of the 1990 Peruvian reform on plant-level technical efficiency. The results support the argument that the degree of pro- tection and the level of efficiency are inversely related. The chill winds of competition should have favorable effects on industrial effi- ciency according to the neoclassical paradigm. Leibenstein (1966) was the first to state explicitly that "proper motivations" should discipline firms, forcing them to become more efficient or perish. Many industries—such as trucking, air travel, and banking—have faced one such motivational factor: reduced regulation. In a study of the U.S. airline industry, for example, Alam and Sickles (2000) find support for the hypothesis that resource utilization in the industry became more efficient as market forces compelled the airlines to economize after the 1978 deregulation. Similarly, Alam (2000) finds that the U.S. banking industry, which underwent substantial deregulation and notable financial innovations during the 1980s, experienced sustained productivity growth during most of that decade. In the international arena one motivational factor is the heightened competi- tion arising because countries have adopted trade liberalization strategies, the most conspicuous examples being the North American Free Trade Agreement (NAFTA) and the European Economic Community (EEC). In the case of developing countries several empirical studies have confirmed a positive link between trade reform and efficiency, yet many researchers continue to have doubts about the Ila M. Semenick Alam is with the Department of Economics at Tulane University, and Andrew R. Morrison is with the Social Development Division at the Inter-American Development Bank. Their e-mail addresses are UaMlamQitulane.edu and andrewm@iadb.org. The authors thank Peru's Ministry of Industry, Tourism, Integration, and International Trade Negotiations, particularly Jaime Garcia and Julia Hernandez, for supplying the data used in this study. They also thank Fatima Ponce, Jaime Saavedra, and Jorge Vega for their invaluable help and Gerald Granderson, Myriam Quiipe-Agnoli, Mark Johnson, and the anonymous referees for useful comments on an earlier version of this article. © 2000 The International Bank for Reconstruction and Development /THE WORLD BANK 309 310 THE WORLD BANK ECONOMIC REVIEW, VOL. 14, NO. 2 impact of trade liberalization on performance.1 Despite the intuitive appeal of the efficiency hypothesis, empirical studies have been Limited in part by the scar- city of plant-level data from developing countries. This article contributes to the ongoing debate by examining the relationship between trade reform and industrial efficiency in Peru, focusing on the impact of the reform and liberalization program initiated in 1990 after many years of im- port substitution industrialization. Our article is unique in that we use a new panel data set and introduce to the literature a nonparametric, mathematical programming methodology that allows us to estimate time-varying, producer- specific efficiency levels. It is important to study efficiency dynamics because our data set spans a period in which Peru's policies toward protectionism changed dramatically. We study Peru because the empirical evidence concerning the rela- tionship between trade reform and industrial efficiency in developing countries is not definitive; most studies confinn a positive link, but some have failed to detect such a connection. Peru should provide a valuable test about the generalizability of the well-studied Chilean case (see, for example, Tybout, de Melo, and Corbo 1991 and Liu 1993). I. THE DISMANTLING OF PROTECTIONISM IN PERU Peru was one of the last Latin American countries to abandon import substitu- tion industrialization as a development strategy. Import substitution was designed to protect domestic "infant" manufacturing industries from imports through the use of protective instruments, such as tariffs, quotas, exchange rate controls, and price and wage controls. The death knell for this policy sounded with the presi- dential election of Alberto Fujimori, who took office in July 1990 and imple- mented a far-reaching neoliberal reform package. Other leaders of Peru had attempted to introduce neoliberal reforms, but never completed those reforms. The Belaunde administration (1980-85) substantially reduced tariffs on imports.2 In January 1981 the maximum tariff fell from 60 to 35 percent and then to 32 percent. By the end of 1981, 98 percent of all registered items could be imported without a duty, up from only 38 percent in 1978. The government relaxed regulation of foreign investment and announced plans to priva- tize state-owned enterprises. The impact on domestic industry was severe: manu- facturing output fell nearly 20 percent between 1980 and 1983, and idle capacity in manufacturing rose to more than 54 percent. Bowing to strong pressure from Peruvian industrialists, the Belaunde administration abandoned the reform pack- age and restored nominal tariff rates to their levels before the reform. The Garcia government (1985-90) was characterized by populist policymaking, heterodox stabilization, and a continuation of import substitution industrializa- 1. Most notable among them are Pack (1988), Krugman (1994), and Rodrik (1995). This issue it rlic-iire-d later in the article. 2. The discussion of trade reform under the Belaunde administration is drawn from Conaghan, Malloy, and Abugattas (1990) and Pastor and Wue (1992). Alam and Morrison 311 tion. Saavedra Chanduvi (1996:2) provides a concise and accurate description of the Peruvian industrial sector at the end of the Garcia government: The Peruvian manufacturing sector developed for three decades [1960-90] sheltered by a set of tariff and nontariff barriers that permitted it to enjoy very high—and in some cases infinite—levels of protection . . . The pattern of trade and industrial production were so distant from that dictated by comparative advantage that Peru came to produce automobiles and com- puters for the internal market. The Fujimori government took the first step toward dismantling import sub- stitution industrialization in October 1990 by consolidating tariff categories. The number of tariff categories dropped from 56 to 3, with rates of 15, 25, and 50 percent. In March 1991 these rates were reduced further to 5, 15, and 25 per- cent, with 82 percent of all goods subject to the 15 percent rate. The magnitude of the reform is revealed by the decline in average tariff rates, which fell from 66 percent in July 1990 to 17 percent in March 1991 (Escobal 1992 and Saavedra Chanduvi 1996). The mean effective rate of protection fell from more than 90 percent in July 1990 to 36 percent in December 1990 and then to less than 30 percent in March 1991—the month that additional structural reforms were added to the reform package (table 1). Disaggregated by three-digit Standard Industrial Classification (Sic) codes, the percentage declines in effective rates of protection between July 1990 and March 1991 are impressive, falling almost 98 percent for nonferrous metals, 81 percent for clothing, 79 percent for other chemical products, and 76 percent for furniture. The smallest decline was in iron and steel production, al- though the drop was still 22 percent. The mean decline was 63 percent. Also notable, the standard deviation of the effective rates of protection fell from more than 70 percent in July 1990 to less than 20 percent in March 1991. Although tariff reform was the major departure from import substitution, other key elements of the 1990 reform package included removal of wage and price controls, increases in the prices of and elimination of subsidies to public services, reduction in public sector employment, unification of a multiple exchange rate system, efforts to increase tax collection, elimination of restrictions on capital flows, and liberalization of interest rates (Quijandria 1995). In 1991 and 1992 many of these reforms were deepened: the government removed interest rate ceil- ings on dollar-denominated deposits and loans, instituted a private pension sys- tem, and created an agency to regulate the behavior of private firms and protect consumers' rights. LL THEORETICAL LINKS BETWEEN TRADE REGIME AND INDUSTRIAL EFFICIENCY There are many arguments explaining why more open trade regimes lead to more efficient industrial production. Perhaps the most basic is that returns to 312 THE WORLD BANK ECONOMIC REVIEW, VOL. 14, NO. 2 Table 1. Effective Rates of Protection, by Three-Digit Standard Industrial Classification, Peru, 1990-91 (percent) SIC code and name July 1990 December1990 March 1991 311-12 Food products' 132.7 42.8 34.5 313 Beverages and tobacco 197.2 62.6 49.5 321 Textiles'1 -All -26.1 -24.0 322 Clothing 261.6 87.6 48.5 323 Leather products 151.2 45.1 53.8 331 Furniture 132.0 46.0 31.9 341 Paper products 63.9 40.4 35.7 342 Publishing 85.1 33.1 21.8 351 Basic chemicals 66.5 20.8 30.1 352 Other chemical products 131.0 34.4 27.3 355 Rubber products 88.7 35.9 30.3 356 Plastic products 88.7 35.9 30.3 369 Nonmetallic mineral products 83.1 34.0 27.2 371 Iron and steel production 42.0 21.2 32.8 372 Nonferrous metalsb -45.1 -1.5 -1.1 381 Miscellaneous metal products 89.3 50.2 35.3 382 Nonelectric machinery 30.2 21.0 20.8 383 Electric machinery1 91.8 40.8 34.6 390 Other manufactured products 73.1 50.9 36.9 Mean 90.6 35.5 29.3 Standard deviation 71.9 23.6 17.5 Note: The effective rate of protection measure* the percentage by which value added can increase over the free-trade level as a consequence of a tariff structure. This rate for a sector is ( V T - V-w)rVvp where V T is value added under trade policies and V v it value added at world prices (Krugman and Obstfeld 1994). The effective rate of protection captures protection of intermediate and final goods. A negative rate implies that input industries are particularly favored. This table includes only those sector* for which price deflators are available. Thus sic 324 (footwear) and SIC 384 (transport equipment) are not included. a. The average of milk products, flour and bakery products, and odier food products'. b. The negative rates indicate higher tariffs on input imports than on final goods. The increases in mese rates between July 1990 and March 1991 are the result of tariff reductions on inputs due to trade liberalization. c The average of machinery and equipment for industrial and professional use and home appliances and devices. Source: Banco Central de Rcierva del Peru. entrepreneurial effort increase as exposure to foreign competition rises (Corden 1974, Martin and Page 1983, and Tybout 1992a). A second argument is that increasing returns to scale imply lower costs per unit as output increases (Pack 1988 and Tybout 1992a). For this argument to be complete, however, a reduc- tion in protectionism must be accompanied by an increase in domestic output— a conclusion that is far from certain, since increased competition may force pro- ducers to exit instead of expand (Tybout 1992a). Several authors (Pack 1988, Grossman and Helpman 1991, and Edwards 1992) argue that greater openness may accelerate developing countries' adoption of technological innovations originating in industrial countries. From the viewpoint of the new growth theory the creation of larger markets through trade liberaliza- Alam and Morrison 313 tion (and market-based exchange rates) will raise demand for products, leading to more investment in product development and innovation (Tybout 1992a).3 Two other effects may be important: share effects and residual effects. If more efficient plants gain market share as a result of exposure to foreign competition, industrywide efficiency should rise, even if no scale economies are present (Bond 1986, Roberts and Tybout 1991, and Tybout and Westbrook 1995). Tybout and Westbrook (1995) coin the term "residual" effects, noting that this "catchall category" includes capacity utilization, externalities, learning-by-doing, and mana- gerial effort.4 HI. EMPIRICAL LINKS BETWEEN TRADE REGIME AND EFFICIENCY Two approaches, mirroring two different techniques for measuring productiv- ity, have been used to test empirically for a relationship between the type of trade regime and industrial productivity: studies of total factor productivity (TFP), usu- ally based on secondary data sources available at the one- or two-digit SIC level, and estimations of production functions using plant-level data. Total Factor Productivity Studies Several studies have estimated TFP and linked its evolution to changes in trade regime. One of the earliest is by Michaely (1975), who finds a correlation be- tween the reduction in quantitative restrictions and increased TFP in Israel in the mid-1950s. Nishimizu and Robinson (1986) decompose TFP growth into the shares accounted for by the expansion of domestic demand, the expansion of exports, and import substitution. They use data from Japan, the Republic of Korea, Tur- key, and Yugoslavia for subperiods between 1955 and 1973. Their results are intriguing: although domestic demand accounts for the largest share of TFP growth for all countries in all subperiods, export expansion frequently plays an impor- tant role. Import substitution, in contrast, contributes negatively to growth in most cases. More recently, Edwards (1994) calculates differences in TFP growth between 1987-91 and 1978-82 for manufacturing sectors in six Latin American coun- tries. Although he warns against inferring causal relationships, he does remark that Chile and Costa Rica, the two countries that began trade reform earliest among the six, had the largest increases in the rate of TFP growth.J Haddad, de Melo, and Horton (1996) study the productivity of Moroccan manufacturing sectors following a modest and gradual reform process aimed at heightening com- petition and improving technical efficiency. Despite data limitations, the authors find evidence suggesting that TFP grew as a result of the reform program. In an 3. However, entrepreneurs may be less likely to develop new products because trade liberalization makes available a wider variety of imported substitutes (Tybout 1992a). 4. They also include technological innovation in this class, but we follow Edwards (1992) and Grossman and Helpman (1991), who include it as a separate class. 5. The other four countries in the study are Argentina, Bolivia, Mexico, and Uruguay. 314 THE 'WORLD BANK ECONOMIC REVIEW, VOL. 14, NO. 2 effort to determine the robustness of the positive link between trade liberaliza- tion and .economic performance, Edwards (1997) uses several estimation tech- niques, time periods, functional forms, and measures of openness. He extends his analysis to include 93 countries and finds that countries with greater trade barri- ers experienced slower productivity growth. As Edwards (1994) points out, one drawback of TFP studies is their aggregate nature, which may obscure diverse sectoral responses to trade policy.6 Further- more, as Pack (1988) and Havrylyshyn (1990) observe, TFP studies have pre- sented mixed results. Havrylyshyn (1990:10) sums up this problem: The evidence [on the relationship between trade reform and efficiency] from studies of TFP is weak and ambiguous. Some evidence of positive links between trade policy and productivity growth certainly exists... But many c a s e s . . . are ambiguous, and some suggest a negative relation. Production Function Studies The second major approach to investigating the relationship between trade policy and industrial efficiency calls for estimating plant-level production func- tions, deriving estimates of efficiency from those functions, and examining the Links between the efficiency estimates and trade policy. Tybout, de Melo, and Corbo (1991) estimate deterministic production functions at the three-digit SIC level for Chile for the years 1967 and 1979. From the production function pa- rameters, they create Sic-level indexes of efficiency levels and dispersion of effi- ciency levels with 1967 as the base year. They find that reductions in tariff pro- tection are correlated with increases in efficiency and decreases in the variance of efficiency scores. The authors conclude that additional plant-level studies—using panel data and other trade liberalization events—must be performed to help verify their findings. Tybout and Westbrook (1995) use a similar technique to analyze reform in Mexico. They estimate production and cost functions for 1984 (before trade reforms were implemented) and for 1990 (after reforms were implemented) and then use correlation analysis to determine whether changes in efficiency at the three-digit SIC level are correlated with changes in various measures of trade policy. In addition to deterministic measures of efficiency, stochastic frontier produc- tion functions have been a popular technique for estimating production tech- nologies. Early efforts use cross-sectional data collected before and after liberal- ization to examine changes in efficiency levels. Handoussa, Nishimizu, and Page. (1986), for example, find significant increases in technical efficiency among Egyp- tian public sector firms during a period of trade liberalization beginning in 1973. More recent work has taken advantage of the availability of firm-level panel data. Iiu (1993), for example, assumes a Cobb-Douglas production function and, using 6. This u not an insurmountable problem. Nishimizu and Robinson (1986), for example, examine determinants of TFP growth by country at the sectoral level. Although they do not include explicit trade policies in their list of determinants, ntch an analysis is possible. Alam and Morrison 31S panel data for 1979-86 from Chile, finds that the mean efficiency levels of sur- viving plants tend to be higher than those of exiting plants.7 By and large, pro- duction function approaches have produced empirical results confirming that trade liberalization improves efficiency (Havrylyshyn 1990).8 IV. DATA The data we use to estimate plant-level efficiency were provided by the Peru- vian Ministry of Industry, Tourism, Integration, and International Trade Nego- tiations (MTTlNa), which conducts an annual survey of manufacturing plants with more than 20 employees.9 Our data cover 1988-92—two years before and two years after the implementation of economic reform in July 1990. The.coverage of the survey is reasonably good since completion was obligatory. Our output variable is the value of total production. We use six inputs as explanatory variables: capital stock, raw materials, electricity, payments for in- dustrial services, and blue- and white-collar workers.10 All of the data are re- corded in value terms with the exception of the labor variables. Capital stock is the value reported for the end of the previous calendar year, and workers are measured in physical (not efficiency) units. Tybout (1992b) points out that firm- level data on capital stocks of developing countries may be subject to measure- ment error. To check the consistency of the capital measure in our data set, we calculate capital-to-output ratios (K/Q) for all establishments. There were some unrealistic ratios, so we dropped the observations corresponding to the largest and smallest 10 percent of all K/Q values from the sample.11 7. Liu's classification of exiting, entering, and surviving firms is based on "intertemporal patterns of missing values for each plant" (Liu 1993:220). Thus if a plant exits the sample and does not reappear, it is classified as an exiting plant. However, this plant just may have stopped filing information while continuing to produce. Similarly, entering plants may be survivors that did not file in preceding years. Still, there is no superior panel data set for Latin American industries. 8. Pack (1988:372) dissents from this view. 9. MTTiNa, Estadtstica manufacture™, datos a ravel de establedmiento (various years). To our knowledge, we are the first researchers to be given access to these data. 10. We do not use other input categories reported in the data set, such as fuel, replacement parts and accessories, and containers, because there are many missing values. We deflate all of the variables, except for labor, using data obtained from MmNCi, Estadbtica industrial mensual (various years). The MmNa deflators were available for 1988,1989,1990, and 1993; deflators for 1991 and 1992 had not been processed so we have interpolated to obtain diem. Since price deflators are not available at thefirmlevel, some of the measured cross-firm variation in productivity is capturing firm-specific variation in prices; without firm-level price indexes, we cannot address this problem. (This observation was contributed by an anonymous referee.) 11. Tybout (1992b) provides a more ftfgant, econometric solution to this measurement error problem. He uses indirect least squares to instrument for capitaL Applying this method tofiveChilffln mft^T^pctiiring industries, he finds that, although die relative sizes of the input coefficients rhangr, returns to scale are not affected. Unfortunately, Tybout's solution is not feasible in the nonparametric, mattwrnariral programming framework we use here. However, by rwninmg the middle 80 percent of observations, we attempt to reduce die jnflnrnrr of outliers without introducing bias. This approach may affect parameter estimates and die level of efficiency but should not influence temporal patterns, die main focus of our investigation. To test diis observation, we also perform our analysis using all observations and find that each dependent variable maintained die same (direct or inverse) relationship with efficiency as is observed with die filtered data (see section VI). 316 THE WORLD BANX ECONOMIC REVIEW, VOL. 14, NO. 2 V. METHODOLOGY To measure the level of technical efficiency in Peruvian industries, we use the linear programming method of data envelopment analysis. This method com- pares an entity's observed level of performance with its theoretically possible level of performance. This best-practice level of performance is determined by creating a production frontier based on the firms that produce the largest amount of output(s) for a given level of input(s) or, conversely, those that minimize the amount of input(s) needed to produce given levels of output(s). Efficiency Measurement The production technology, S, is defined as all feasible combinations of inputs and outputs, feasible meaning that the combination of inputs is able to produce the levels of outputs. For each point in time t=l,...,T, there are n = 1 , . . ., N firms, each consuming / = 1 , . . . , / inputs to produce k = 1 , . . . , K outputs. Thus Xfa is the amount of input / used by firm n in period t, and y^ is the level of output k produced by firm n in period t. All input and output observations are positive. Assuming a contemporaneous production set, for each time period t, input and output observations from only that time period are used; separate production frontiers are calculated for each industry. As an example, assume that there is only one time period. In a one-input, one- output activity, S may be illustrated as in figure 1. Efficiency measures are calcu- lated as the distance, A, from each point to the efficiency frontier. An output- based distance function, OD, is defined as:12 Figure 1. Production Technology in Input-Output Space Production s Output frontier / #, B c / A a / O Input Note: Letters in input-output space represent firms with different input-output combinations. = max{X I (i/X, y) e S). Note 12. Similarly, an input-based distance function, ID, is written as: JD(z, y) • that under constant returns to scale the values obtained from the output-oriented and input-oriented approaches are simply reciprocals. Alam and Morrison 317 (1) OD(x,y) = min{X I (x, ylX) e S}. Holding the input vector x constant, this expression expands the output vector y as much as possible without exceeding the boundaries of S. If a firm is output- efficient, it has a value of 1 for this expression, whereas if it is output-inefficient, the value is less than 1. Data Envelopment Analysis Charnes, Cooper, and Rhodes (1978) first introduced data envelopment analy- sis to the economics literature; it has since found multiple applications. One reason that these studies have proliferated is that linear programming methods, in general, do not require price information. This is an empirical advantage since often the only data available are physical units of inputs and outputs. It also has widespread appeal because it requires neither the assumption of cost minimization or profit maximization nor the specification of a production func- tion. Since data envelopment analysis is nonparametric, it does not confound the effects of inefficiency with misspecification of the functional form, a signifi- cant problem of parametric production function approaches.13 Furthermore, it is able to compute the relative efficiency of each firm under study, which may have multiple inputs and outputs, with any software program that has linear programming capabilities. Data envelopment analysis, as its name suggests, envelopes observed pro- duction points. It creates a flexible piecewise linear approximation to model the best-practice reference technology. It is flexible in that constraints can be placed on the linear program to account for constant, decreasing, increasing, or variable returns to scale. Radial measures of levels of technical efficiency can then be developed for firms that operate inside the convex hull of the data. We obtain the efficiency score in outputs or, equivalent^ the value of the output distance function for an observation of input(s) and output(s) for firm m at time t, (xmt, ym), from the following linear programming model: (2) [OD(xmm £ 1), or variable (Ljun = 1) returns to scale. For further details see Seiford and Thrall (1990) and Fire, Grosskopf, and Lovell (1985). We assume constant returns to scale in this study, since this restriction was tested on this data set and not rejected. Table 2. Unweighted and Output-Weighted Mean Efficiency Scores by Standard Industrial Classification, Peru, 1988-92 1988 1989 1990 1991 1992 Number Un- Output- Number Un- Output- Number Un- Output- Number Un- Output- Number Un- Output- SIC of weighted weighted of weighted weighted of weighted weighted of weighted weighted of weighted weighted code plants mean mean plants mean mean plants mean mean plants mean mean plants mean mean 311 235 0.59 0.72 175 0.75 0.86 171 0.57 0.75 174 0.70 0.78 158 0.70 0.81 312 52 0.84 0.92 34 0.82 0.87 31 0.84 0.94 34 0.83 0.94 32 0.78 0.84 313 80 0.72 0.80 66 0.72 0.90 64 0.58 0.84 63 0.71 0.89 65 0.68 0.89 321 214 0.72 0.78 166 0.75 0.80 144 0.71 0.79 141 0.80 0.84 131 0.78 0.83 322 135 0.74 0.89 79 0.84 0.92 68 0.80 0.91 65 0.83 0.92 57 0.80 0.91 323 53 0.89 0.92 39 0.87 0.94 35 0.92 0.92 32 0.90 0.93 31 0.87 0.88 331 41 0.86 0.96 .27 0.86 0.95 14 0.87 0.95 30 0.81 0.87 30 0.88 0.90 341 46 . 0.81 0.85 34 0.89 0.98 33 0.89 0.96 30 0.91 0.90 24 0.97 0.97 342 94 0.79 0.83 49 0.86 0.88 50 0.83 0.92 49 0.86 0.93 41 0.83 0.89 351 81 0.67 0.70 62 0.79 0.81 63 0.78 0.89 57 0.82 0.85 54 0.85 0.95 352 158 0.69 0.78 114 0.69 0.76 114 0.75 0.78 106 0.71 0.79 88 0.79 0.86 355 26 0.80 0.98 16 0.94 0.99 17 0.95 0.98 18 0.92 0.99 16 0.89 0.99 356 129 0.69 0.82 87 0.82 0.88 81 0.69 0.76 83 0.82 0.77 73 0.89 0.92 369 58 0.78 0.83 36 0.71 0.87 36 0.77 0.93 37 0.78 0.89 32 0.78 0.85 371 22 0.96 0.99 17 0.90 0.99 15 0.95 0.97 14 0.96 0.97 15 0.89 0.95 372 16 0.94 0.91 17 0.80 0.84 15 0.95 0.99 15 0.86 0.99 16 0.95 0.99 381 157 0.66 0.79 100 0.74 0.89 90 0.67 0.80 85 0.74 0.82 80 0.76 0.86 382 80 0.64 0.76 45 0.80 0.91 44 0.81 0.93 46 0.76 0.92 34 0.89 0.97 383 88 0.39 0.43 69 0.54 0.63 55 0.72 0.85 56 0.79 0.90 S5 0.72 0.87 390 46 0.84 0.94 33 0.87 0.94 34 0.87 0.96 29 0.87 0.96 27 0.86 0.94 Source: Authors' calculations based on data from Peru's Ministry of Industry, Tourism, Integration, and International Trade Negotiations. 320 THE WORLD BANK ECONOMIC REVIEW, VOL. 14, NO. 2 1992. As with the unweighted mean efficiency scores, there was a marked trend of increasing efficiency over time. Out of the 20 industries, 16 showed an improve- ment in output-weighted mean efficiency between 1988 and 1992. In almost all cases—the sole exception being nonferrous metals (SIC 372) in 1988—the output-weighted means were higher than the unweighted means for 1988 and 1992. In general, the more efficient firms were producing more of the output and, hence, the output-weighted means were higher than simple averages. Trade liberalization should reduce the dispersion of efficiency scores within a given sector and—to the extent that tariff rates in different sectors converge— among sectors. First, consider the possibility of efficiency scores converging among sectors. The difference between the highest and lowest unweighted mean scores declined substantially between 1988 and 1992. This was also the case for weighted mean scores. In fact, the decline in the spread between highest and lowest scores was identical for weighted and unweighted means: from 57 to 29 percent be- tween 1988 and 1992.15 Second, consider the issue of convergence within sec- tors. The standard deviation of the unweighted efficiency scores declined in 14 of 20 industries (table 3).16 Determinants of Mean Efficiency Scores In addition to comparing efficiency levels among industries and over time, we are also interested in the determinants of these efficiency scores. For our analysis we use mean efficiency scores, as opposed to individual-plant efficiency scores, because the data set does not contain plant-level data other than inputs and out- puts. 17 Thus we are Limited to an aggregate analysis of the determinants of sectoral efficiency. We explore two possible determinants of efficiency at the three-digit sic level: commercial policy and industrial structure. Commercial policy is mea- sured by rates of effective protection; these data are produced by the Peruvian Central Bank. Industrial structure is measured by the Herfindahl index of indus- trial concentration, calculated from the same data set used to produce the data envelopment analysis efficiency scores. Before presenting our econometric results, a word of caution is in order. Other important macroeconomic events in Peru between 1988 and 1992—exchange rate overvaluation, hyperinflation, changes in the real interest rate—could over- whelm the effects of changes in trade policy and market structure. In addition, our data have potential econometric problems: price indexes may be biased over time because of hyperinflation, and some variables, especially capital stock, may be measured with error. These econometric problems further complicate the at- 15. Page (1980), for example, has a more detailed data source and is able to use experience of entrepreneurs, age of the plant, and education level of the plant's labor force as regressors. 16. This decline was due almost entirely to tbc rise in the mean of the least-efficient industry, since the highest mean score was already close to 100 percent. 17. \Pe do not report standard deviations calculated on the basis of output-weighted efficiency scores in table 3, because shifts in output to more efficient firms may increase the standard deviation rather than lower it. In other words, because of the weighting procedure, the standard deviation now reflects two factors: the standard deviation per se and the distribution of output. Only the former is of interest as a tea of convergence. Table 3. Standard Deviations of Unweighted Efficiency Scores by Standard Industrial Classification, Peru, 1988-92 1988 1989 1990 1991 1992 ' Number Number Number Number Number SIC of Standard of Standard of Standard of Standard of Standard code plants deviation plants deviation plants deviation plants deviation plants deviation 311 235 0.23 175 0.20 171 027 174 0.22 158 021 312 52 0.18 34 0.20 31 0.17 34 020 32 0.21 313 80 0.27 66 0.25 64 029 63 024 65 0.25 321 214 0.21 166 0.20 144 0.22 141 0.17 131 0.19 322 135 0.22 79 0.18 68 0.23 65 0.19 57 0.21 323 53 0.14 39 0.18 35 0.14 32 0.15 31 0.14 331 41 0.17 27 0.17 14 0.15 30 0.21 30 0.18 341 46 0.19 34 0.16 33 0.17 30 0.12 24 0.05 342 94 0.22 49 0.18 50 0.19 49 0.16 41 0.17 351 81 0.28 62 023 63 0.22 57 020 54 021 352 158 0.23 114 0.23 114 022 106 0.22 88 0.21 355 26 0.22 16 0.15 17 0.11 18 0.14 16 0.18 356 129 0.23 87 0.18 81 021 83 0.18 73 0.14 369 58 0.22 36 026 36 024 37 0.20 32 023 371 22 0.07 17 0.16 15 0.11 14 0.07 15 0.15 372 16 0.10 17 0.23 15 0.08 15 0.18 16 0.09 381 157 0.26 100 022 90 023 85 0.20 80 0.20 382 80 0.25 45 021 44 022 46 024 34 0.19 383 88 0.25 69 028 55 0.27 56 0.19 55 0.24 390 46 0.19 33 020 34 0.17 29 0.18 27 0.18 Source: Author*' calculations based on data from Peru's Ministry of Industry, Tourism, Integration, and International Trade Negotiations. 322 THE WORLD BANK ECONOMIC REVIEW, VOL. 14, NO. 2 tempt to uncover the links among trade policy, market structure, and industrial efficiency^. Despite these observations, however, it should be noted that, although all industrial sectors faced the same macroeconomic events, they were subject to different changes in trade policy and market structure. Thus although it is diffi- cult to identify macroeconomic determinants of productivity change, since trade policy and industrial concentration are more sector-specific, their influence on productivity is easier to capture.18 Industries with high effective rates of protection before stabilization still had high rates after stabilization (p = 0.82; table 4). This pattern also holds for industrial concentration (p = 0.83) and mean efficiency levels (p = 0.80). Both contemporane- ous and lagged Herfindahl indexes are significantly positively correlated with mean efficiency levels (0.50 < p < 0.66). Correlations between Herfindahl scores and effec- tive rates of protection are negative but generally insignificant; the same is true for correlations between effective rates of protection and mean efficiency levels. Correla- tions between effective rates of protection and standard deviations are positive (as expected) for the unweighted case and negative for the standard deviations calcu- lated on the basis of output-weighted means (see note 16). To test whether there is a relationship among industrial concentration, effec- tive rates of protection, and efficiency at the industry level, we perform several regressions. We hypothesize that higher levels of industrial concentration and effective rates of protection are associated with lower efficiency scores. The logic behind the first inverse relationship is that the smaller is the Herfindahl index, the less concentrated and more competitive is the industry, which should lead to greater efficiency. The reasoning behind the second inverse relationship is that, as effective rates of protection decline with trade liberalization, increased compe- tition compels firms to become more efficient. We also include a third covariate: the square root of the number of plants in each sector, JFT. Caves and Barton (1990) show a link between the number of firms used to estimate technical efficiency and the resulting efficiency score. The expected relationship is an inverse one, which at first appears counterintuitive, since one might expect that, as the number of firms increases, competition in- creases, driving average efficiency upward. What is actually occurring, however, is a purely statistical phenomenon: as the number of observations drawn from a distribution increases, so do the number of extreme values (both high and low). Caves and Barton (1990:60) argue that the relationship between estimated tech- nical efficiency and the number of observations may be similarly linked. In other words, the more draws taken from a distribution, the more likely the researcher is to encounter a highly efficient plant, which makes all other plants in the sample less efficient in comparison. Caves and Barton observe that the range of effi- ciency values increases at a rate close to JR. Thus we estimate our equations with and without this regressor. 18. Tybout, de Melo, and Corbo (1991) offer this justification for nrnmining the effect of trade policy on industrial efficiency in Chile between 1967 and 1979. Table 4. Pearson Correlations between Mean Efficiency Levels, Effective Kates of Protection, and Herfindahl Indexes of Industrial Concentration, Peru, 1990-92 OUTPUT OUTPUT OUTPUT OUTPUT MEAN MEAN STD STD WGTD WGTD WGTD WGTD STD ERP90 ERP92 HERF90 HERF92 EFF 90 EFF 92 DEV 90 DEV 92 EFF 90 EFF 92 STD DEV 90 DEV 92 ERP90 1 ERP91 .0.82*" 1 HERF90 -029 -0.18 1 (021) (0.45) HERF92 -0.42* -025 0.83*" 1 (0.06) (028) MEAN -028 -0.05 0.66*" 0.63"* 1 EFF 90 (024) (0.84) MEAN -0.43* -0.16 0.51** 0.50** 0.80"* 1 EFF 92 (0.06) (0.50) (0.02) (0.03) (<0.01) STD 0.38* 0.15 -0.73"* -0.70"* - 0 . 9 1 " * -0.80*** 1 DEV 90 (0.10) (0.54) STD 0.45" 020 -0.34 -0.39* -0.58"* -0.85*" 0.69*" 1 DEV 92 (0.05) (0.40) (0.14) (0.09) (<0.01) (<0.01) (<0.01) OUTPUT -0.16 0.07 0 . 7 2 " ' 0.67*** 0.87*" 0.63*** - 0 . 7 1 * " -0.39* WGTD EFF 90 (0.49) (0.78) (<0.01) (<0.01) (<0.01) (<0.01) (<0.01) (0.09) OUTPUT -0.30 -0.01 0.61*" 0.64*" 0.65*** 0.80*** -0.60*" -0.56*" 0.64"* 1 WGTD EFF 92 (020) (0.96) (<0.01) (<0.01) (<0.01) <0.01) (0.01) (0.01) 0 01 \ OUTPUT -0.30 -0.16 OSS*** 0.83*" 0.71"* 0.56* -0.77*" -0J8* 0.76*" 0.61"* 1 WGTD STD DEV 90 (020) (0.50) (<0.01) (<0.01) (<0.01) (0.01) (<0.01) (0.10) OUTPUT -0.41* -020 0.85*** 0.98*" 0.71"* 0.56*" -0.76*" -0.42* 0.73* *» 0.69*** 0.86*** WGTD STD DEV 92 (0.07) (0.41) (<0.01) (<0.01) (<0.01) (0.01) (<0.01) (0.06) 'Significant at the 10 percent level. * *Significant at the 5 percent level. * * * Significant at the 1 percent level. Note: p-values are in parentheses. ERP90 is the effective rate of protection in Jury 1990; ERP91 is the effective rate of protection in March 1991. HERF90 is the Herfindahl index in 1990; HERF92 is the Herfindahl index in 1992. MEAN EFF 90 is the mean efficiency level in 1990; MEAN EFF 92 is the mean efficiency level in 1992. STD DEV 90 is the standard deviation of efficiency in 1990; STD DEV 92 is the standard deviation of efficiency in 1992. OUTPUT WGTD EFF 90 is the output- weighted mean efficiency in 1990; OUTPUT WGTD EFF 92 is the output-weighted mean efficiency in 1992. OUTPUT WGTD STD DEV 90 is the standard deviation of output-weighted efficiency in 1990; OUTPUT WGTD STD DEV 92 is the standard deviation of output-weighted efficiency in 1992. Source: Effective rates of protection come from unpublished data from the Peruvian Central Bank. Herfindahl indexes and mean efficiency levels are computed by the authors using data from Peru's Ministry of Industry, Tourism, Integration, and International Trade Negotiations. 324 THE WORLD BANK ECONOMIC REVIEW, VOL. 14, NO. 1 We are able to calculate efficiency scores for plants in 25 sectors, but three—wood and cork products (Sic 332), glass and glass products (sc 362), and the manufacture of professional, scientific, and measurement and control equipment including photo- graphic equipment and optics (SIC 385)—do not have data on effective rates of pro- tection. We dropped two other industries—footwear (sc 324) and transport equip- ment (SIC 384)—because we were unable to obtain deflators for these classifications. Thus there are 20 observations for each year. Also die earliest year for which we could obtain effective rates of protection is 1990; therefore our regressions cannot go back to 1988 or 1989. We also do not have effective rates of protection for 1992, so we must use 1991 as the postrefonn year. Clearly, this will tend to understate the efficiency gains~resulting from liberalization, since firms will not have adjusted com- pletely to the change in regime by the end of 1991. 1 ' REGRESSION RESULTS. In the absence of the.^N" regressor, the results of the regression using 1990 data (table 5, column 1) provide no support for our hypothesis. Although the coefficient on tradeprotection is negative (indicating that industries with higher levels of protection have lower mean efficiency scores), it is not statistically significant The coefficient on industrial concentration, as measured by the Herfindahl index, is statistically significant, but its sign is the opposite of what we would expect more concentrated industries have higher efficiency scores. However, once we control for the statistical factor identified by Caves and Barton, the results do support our hypothesis (table 5, column 2). All three coef- ficients have the expected negative sign, the effective rate of protection is signifi- cant at the 10 percent level, and JN~ is significant at less than 1 percent. The coefficient on the Herfindahl index, although not significant at standard levels, is suggestive of an inverse relationship. Note that JK~ and the Herfindahl index are significantly, inversely related (p = -0.61 and -0.66 for 1990 and 1991, respec- tively). This high degree of collinearity makes it difficult to isolate the link be- tween market concentration and efficiency and, hence, may explain why the Herfindahl.coefficient has a higher p-value. The results for 1991 tell a similar, although less striking, story. The signs on the regressors are negative, but only ,/N" is statistically significant (table 5, col- umn 4). Again, if JFT is not included (table 5, column 3), the effective rate of protection is insignificant, and the Herfindahl index, although significant, has the wrong sign.20 19. In terms of the tign of each parameter estimate, the result! are the same whether we use the complete data set or the 80 percent subset However, with the complete data set the parameter estimates, although similar in magnitude, are typically lower, and most are not significant. By using the 80 percent subset, we thus are able to measure more precisely the relationship between trade reform and efficiency, which is obscured when outliers are present. 20. An alternative interpretation of the Herfindahl index is as a measure of the underlying differences in efficiency within the industry: efficient plants get larger, resulting in higher concentration. This is a possible explanation for its positive sign. Note also that the Herfindahl index can be written as the sum of a "variance equivalent" (or dispersion component that measures the within-industry variance in size) and the inverse of a "numbers equivalent" (or numbers component that equals 1 over the number of firms). Alam and Morrison 325 Table 5. Determinants of Mean Efficiency at the Three-Digit Level Independent . | 1990 data 1991 data Pooled data Disequilibrium" variable (V (2) (3) (4) (S) (6) (7) (8) Constant 0.022 0.128** -0.062 0.018 -0.021 0.070 -0.020 0.047 (0.77) (0.04) (035) (0.74) (0.70) (0.11) (0.77) (0.48) Effective rate of protection11 -0.030 -0.051* -0.003 -0.107 -0.034 - 0 . 0 5 6 " -O.101 -0.189* (0.46) (0.09) (0.98) (0.24) (0.29) (0.03) (0.40) (0.10) lnHerfindahl index 0.104"" -0.077 0.060**'• -0.043 0.083*** -0.054* 0.061*1' -0.027 (< 0.01) (0.12) (0.01) (0.21) (<0.01) (0.07) (0.02) (0.52) VN -0.068*** -0.040* ** -0.052* ** -0.034* * (< 0.01) (<0.01) (0.01) (0.02) Year dummy*' 0.023* -0.008* (0.04) (0.03) R1 0.45 0.74 031 0.62 0.39 0.67 0.30 0J0 F 6.97*** 1 5 . 5 " * 3.74* * 8.60*** 7.97* ** 17.99*** 3.70** 5.31* (< 0.01) (< 0.01) (0.05) . (<0.01) (< 0.01) (<0.01) (0.05) (0.01) Number of observations 20 20 20 20 40 40 20 20 •Significant at the 10 percent level. "Significant at the 5 percent level. "•Significant at the 1 percent level. Note: p-valucs are in parentheses. Dependent variable is the natural log of the mean efficiency (core at the three-digit SIC level. a. Data from 1992 a n regressed on data from 1991. b. Estimated coefficient times 100. c N is the number of plants in each a c (see tables 2 and 3). d. Year dummy = 0 for 1990; 1 for 1991. Source: Authors' calculations. In order to determine if the parameter estimates are insignificant because of the high degree of collinearity, we run a regression pooling the data for both 1990 and 1991 (table 5, columns 5 and 6). As expected, with twice as many observations, all of the coefficients in the pooled regression can be measured more precisely and become significant. They also have the expected sign (when VN"is omitted, the point estimates are significant, but, again, Herfindahl has the incorrect sign). These results suggest that the expected relationship among mar- ket structure, trade reform, and efficiency does exist, but it is hard to detect unless there is a sufficient number of observations so that ordinary least squares can separate out the effects of individual regressors on efficiency. The fact that the econometric results are stronger for 1990 than for 1991 should not be surprising. Almost 30 years of import substitution industrializa- tion produced a strong negative relationship between rates of effective protection and efficiency levels. In the year spanning 1990 and 1991, firms may not have adjusted completely to the new tariff structure. There are several reasons for such a delayed response. The first reason is the credibility of the reform—past govern- This would explain why, when we include both JfJ and the Herfindahl index, the coefficient on JR is negative. We are grateful to an anonymous referee for these observations. 326 THE WORLD BANK ECONOMIC REVIEW, VOI. 14, NO. 2 ments had attempted such measures but had not followed through. If firms ques- tioned the Fujimori government's commitment to reform, they may have taken a wait-and-see attitude before attempting to become more efficient. The second reason is vintage effects: even if the reforms were immediately credible, firms needed time to adopt new technologies. The third reason is the strong resistance to change in Peru when the reform package was passed. To test if firms were taking a wait-and-see attitude to the policy change, we run one additional regression (table 5, columns 7 and 8). We regress mean effi- ciency scores from 1992 on the values of the covariates in 1991. All of the coef- ficients have the expected sign; JFT remains significant (at the 2 percent level), and the effective rate of protection becomes significant (at the 10 percent level). Furthermore, the magnitude of the coefficient on the effective rate of protection increases considerably relative to the 1990 regressions: it is -0.051 when we use 1990 covariates and 1990 efficiency measures (table 5, column 2), and it is -0.189 when we use 1991 covariates and 1992 efficiency measures (table 5, col- umn 8). Thus it appears that, before reform, there is a link between tariffs and efficiency, and this effect grows after reform. Furthermore, this relationship ex- hibits a lag; one year after reform, firms have had an opportunity to respond to the drastically different tariff structure. It is certainly possible that the adjustment back to a stable relationship is on- going; it is likely that plants did not respond in earnest until several years after the reform. It would be interesting to extend our analysis to later years to deter- mine if mean efficiencies have continued to change and if the magnitude of the response increased in the long run. But, unfortunately, our data end in 1992, so we cannot analyze this disequilibrium story further. SURVIVING AND EXITING FIRMS. Because we also are interested in how trade liberalization affects efficiency at the plant level, we separate the observations into surviving and exiting plants, following Liu's (1993) approach. Survivors are those plants that reported for all years 1988-92. Exiting plants are those that reported only for 1988, only for 1988 and 1989, only for 1988-90, or only for 1988-91. 21 For 16 of the 20 SIC codes (80 percent) average technical efficiency is higher for survivors than for firms that exited some time during the sample periods (table 6). This pattern substantiates the one observed by Liu, who finds higher technical efficiency among survivors for 17 of the 25 industries (68 percent). Thus this analysis suggests that relatively inefficient plants exit. "We also want to determine if relatively inefficient plants improved their per- formance following trade reform. For such an analysis we need firms that were present both before and after the reform. "We select 1988 as the prereform period (we pick the earliest time period in order to maximize the amount of time that plants had to adjust) and 1991 and 1992 as the postreform period. This limits 21. Plants that did not fall into any of these categories were dropped from the analysis. Unfortunately, there is no other way to identify surviving and exiting firms in our or Liu's data set. See note 7. Atom and Morrison 327 Table 6. Average Technical Efficiency by Plant Cohort, Peru , Exiting plants' Surviving plants* SIC code Mean Number Mean Number 311 0.6339 83 0.6594 83 312 0.8258 24 0.8024 12 313 0.7104 26 0.6928 32 321 0.7194 86 0.7601 70 322 0.7859 73 0.8064 20 323 0.8966 19 0.9070 15 331 0.8200 19 0.8530 7 341 0.8414 23 0.8988 10 342 0.8021 54 0.8610 20 351 0.7290 30 0.7822 . 31 352 0.6888 65 0.7277 45 355 0.8280 9 0.9427 8 356 0.7057 57 0.8085 29 369 0.7316 27 0.7426 17 371 0.9400 10 0.9412 7 372 0.9374 3 0.8925 8 381 0.6855 79 0.7009 37 382 0.7363 45 0.7933 13 383 0.5561 38 0.6016 27 390 0.8450 18 0.8433 12 a. Exiting plants consist of four cohorts (using the same classification system as Liu 1993): those plants that reported only for 1988,1988-89,1988-90, and 1988-91. b. Surviving plants are those that operated over the entire sample period, 1988-92. Source: Authors' calculations. our analysis to surviving plants and plants that did not exit until 1992. On aver- age, 65 percent of plants that exited in 1992 improved or maintained their 1988 efficiency level; for surviving plants, approximately 70 percent on average had an efficiency score after reform that was at least as high as that before reform (69 percent when 1988 is compared to 1991 and 71 percent when 1988 is compared to 1992).n VII. CONCLUSIONS Our goal for this article was to determine if the Peruvian stabilization and reform package of 1990 led to increased technical efficiency in Peru's manufac- turing industries. We found evidence that it did, once we controlled for the num- ber of firms in each sector and distribution of market share. 22. Note that technical efficiency is a relative measure, with the highest level of efficiency equal to 100 percent. We established in the previous paragraph that survivors have higher average technical efficiency than exiting firms. What this means is that firms that are already very efficient (they have high technical efficiency or they define the frontier and have 100 percent efficiency) have less room for improvement, while firms that have low relative efficiency have greater room for improvement. In other words, survivors may not show dramatically greater improvement over time simply because they are already operating at a higher level of efficiency. Thus between 65 and 70 percent of plants in both cohorts (survivors and exiting plants) maintain or improve their efficiency over time. 328 THE WORLD BANK ECONOMIC REVIEW, VOL. 14, NO. 2 This article presented new micro-level evidence regarding the connection be- tween trade policy decisions and industrial sector efficiency. It contributes to the existing literature in several respects. First, it is one of only a few studies that has examined this issue at the plant level. Second, it is the only study to address the effect of trade reform on efficiency in Peru, because we are the first authors granted access to Peruvian industrial sector survey data. Since the panel covers a period during which there was a dramatic shift in policy, we were able to detect a positive relationship between tariff reduction and efficiency gains despite data difficulties. Third, this is one of only a handful of studies that are based on panel data and hence able to follow individual plants through time. However, limited access to quality trade policy measures prevented us from fully exploiting the panel nature of the data. Finally, by using a nonparametric approach to measure efficiency, we mitigated the problems associated with a priori model specification. 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