TÜSİAD-KOÇ UNIVERSITY ECONOMIC RESEARCH FORUM
WORKING PAPER SERIES
PRODUCTIVITY RESPONSE TO REDUCTION IN TRADE BARRIERS:
EVIDENCE FROM TURKISH MANUFACTURING INDUSTRY
Şule Özler
Kamil Yılmaz
Working Paper 0704
January 2007
________________________________________________________________
TÜSİAD-KOÇ UNIVERSITY ECONOMIC RESEARCH FORUM
Rumeli Feneri Yolu 34450 Sarıyer/Istanbul
Productivity response to reduction in trade barriers:
Evidence from Turkish manufacturing plants*
Sule Ozler
Kamil Yılmaz
UCLA
Koç University
January 2007
Abstract
We examine the effects of trade policy changes on the evolution of plant productivity. Plant level
productivities are estimated for the 1983-96 period following the procedure of Olley and Pakes
(1996). Industry averages indicate that productivity gains are largest in import competing
industries with highest gains reaching to 8% per year during periods of rapid decline in protection
rates. A decomposition of industry level productivity gains also suggests important differences
across sectors by trade orientation. Though reallocation of market shares to more productive
plants are important in both export oriented, and import competing sectors, within plant
productivity improvements are significant only in export-oriented sectors. We also investigate the
effects of changes in protection rates on plant level productivities using regressions that control
for endogeneity of protection rates (tariff and non-tariff). We find that productivity improvements
resulting from declining protection levels are statistically significant and economically important,
especially in import competing sectors. This analysis also suggests that there is a huge degree of
heterogeneity, measured by plant size, in response to changes in protection rates.
JEL Classification: F13, D24, C14.
Keywords: Production function; semi-parametric estimation; protection; import penetration;
domestic competition.
*
The database used in this study is the TurkStat’s (SIS) Industrial Analysis Data Base. We thank many at the SIS for their
efforts in establishing the procedures that has allowed us to use the data set at the SIS premises, and for providing me with
the data set. Among those are former Presidents Sefik Yildizeli and Omer Gebizlioglu, Nurgul Ogut, Emine Kocberber,
Selmin Altin, Ilhami Mintemur, Ali Gunes, and Akin Bodur. We also thank seminar participants at Bilkent University,
Middle East Technical University, Pennsylvania State University, Sabancı University, University of Maryland, College
Park, University of Pennsylvania and participants at GDN and ERF Annual Conferences for helpful comments. This work
has benefited from a financial grant from the Economic Research Forum for the Arab Countries, Iran and Turkey. The
contents and recommendations do not necessarily reflect the views of the Economic Research Forum.
Corresponding author: Kamil Yılmaz, Department of Economics, Koc University, Rumelifeneri Yolu, Sariyer, Istanbul
34450, Turkey. [email protected]
I. Introduction
There is a large body of literature, theoretical as well as empirical, analyzing the impact of trade
reforms on productivity and economic growth. In both sets of literature there is plenty of controversy
(see Tybout 2000, 2003, Epifani, 2003, and Erdem and Tybout, 2004 for recent reviews). The
theoretical trade literature, for example, provides conflicting predictions on the impact of trade reform
on productivity. On the one hand, trade openness might increase productivity through its disciplining
affect of imports or by increasing variety of available inputs, or by serving as a channel for technology
diffusion.1 On the other hand, trade openness might reduce productivity by diminishing incentives of
domestic producers to invest in superior technologies when faced with reduced market shares (Rodrik
1988, 1991).
Evidence on impact of trade policy reforms on productivity growth is also mixed. Crosscountry studies of Edwards (1993) and Ben-David (1993), which report beneficial impact of trade
openness, have been criticized on methodological grounds by Rodriguez and Rodrik (2000) and
Rodrik (1999). In general, cross country studies are criticized because of the difficulty of controlling
institutional and country specific factors across countries, as well as on the grounds that the important
question is what would have happened had the country not adopted the policy changes (Srinivasan and
Bhagwati 2001). Applied general equilibrium models, as stated by Kehoe and Kehoe (1994) yield a
negligible impact of Nafta on the U.S. and Canada, while a one-two percent per year growth in
productivity for Mexico. Results from studies that use industry level data are also mixed. Tybout, de
Melo and Corbo (1991) find no evidence of overall productivity improvement in Chile after its trade
liberalization, and Tybout and Westbrook (1995) report weak evidence linking trade liberalization to
productivity in Mexico. On the other hand Harrison (1994) find positive impact of trade reform in
Ivory Cost, and Ferreira and Rossi (2003) report a large positive impact in Brazil.
While the importance of working with plant level data sets due to high degree of plant
heterogeneity in industries have been pointed out in several studies (see Olley and Pakes, 1996,
Roberts and Tybout, 1996, Aw, Chen and Roberts, 1997), recent increased access to plant level data
sets (spanning a sufficiently long period of time in countries that have dramatically liberalized their
trade regimes) have made burgeoning of more studies possible (see Tybout, 2003, for a review).
1
Melitz (2003) develops a dynamic industry model with heterogeneous firms to analyze the role of international trade in
productivity gains.
1
Evidence in this literature mostly point to increased productivity gains during increased trade
openness.2 There is less agreement on the specific causes of productivity gains partly due to
difficulties in delineating several mechanisms that may be operating simultaneously. There is
evidence suggesting that these gains result from disciplining affect of imports, especially in import
competing sectors, and intra plant improvements (for recent examples see Pavcnik (2002), Muendler
(2004), and Hay (2001), as well as evidence suggesting that participation in international activities is
behind intra plant productivity improvements (see Clerides, Lach and Tybout 1998, Aw, Chen and
Roberts 1997 and Kraay, Soloaga, and Tybout 2006).
We contribute to this literature by providing evidence on the productivity response to trade
barrier reductions for the Turkish manufacturing plants. We analyze plant level data for 23 three-digit
SIC industries spanning the period of 1983-96, which is a period of significant changes in trade policy.
We calculate plant level total factor productivity implementing an estimation method developed by
Olley and Pakes (1996), which uses investment as the proxy to address the potential simultaneity bias
in production function estimations. We then analyze total factor productivities obtained from
production function estimations. An inspection of productivity evolution over time suggests that
during periods of most rapid decline in protection rates productivity gains are largest. We also find
that productivity gains in import competing sectors during these periods are higher than other sectors.
A decomposition of industry level productivity gains also suggests important differences across sectors
by trade orientation. Though reallocation of market shares to more productive plants are important in
both export oriented, and import competing sectors, within plant productivity improvements are
significant only in export-oriented sectors.
Finally, we investigate effects of changes in protection rates (tariff and non-tariff) on plant
level productivities using regressions that control for endogeneity of protection rates (tariff and nontariff). In doing so an important maintained hypothesis of our paper is heterogeneity of plant level
responses to trade barrier reductions. Specifically, in addition to our measure of protection rate we use
its interaction with beginning of the period plant size, and the plant size measure on its own. We find
the following results. The decline in nominal (and effective) protection rates is associated with
productivity improvement, especially in import competing sectors; larger plants have higher
productivity in comparison to smaller plants; the increase in productivity of large plants in response to
2
There is some evidence that the outcome may not be universal.. In India, for example, studies with slightly different
methodological results find opposing results (see Epifani 2003).
2
a decrease in protection rates is higher than smaller plants. This may be interpreted as suggesting that
as import regime openness leads to increased competitive pressure from imports, large plants, with an
already existing productivity advantage over smaller plants mobilize their resources to increase
productivity further. In several other recent studies reductions in tariff rates are found to increase
plant level productivity (Fernandes (2007) for Colombia, Schor (2004) and Muendler (2004) for
Brazil). The marginal effect of tariffs for large and small firms does not differ in Schor (2004), while
it does in Fernandes (2007) with higher marginal effects for larger firms.
The paper is organized as follows. In the next section, we provide a brief history of the Turkish
trade regime since 1980 and describe the plant-level data set we use in our analysis. In Section III a
brief presentation of Olley-Pakes method is presented. In section IV patterns in sector level total
factor productivity estimates are described. Section V contains the results of plant level total factor
productivity analysis. Section VI is the conclusion section.
II. Changes to the Turkish Trade Regime and Data
II.1. Changes to the Trade Regime
The onset of structural reforms in Turkey dates back to January 1980. An important component of the
reform package consisted of policy changes to achieve greater trade openness. In the first couple of
years of the program export increase was targeted through measures such as export tax rebates,
preferential export credits, foreign exchange allocations and the duty-free access to imports. During
this period (1980-1983) the total subsidy rate received by manufactured goods exporters was around
20-23% (Milanovic, 1986).
Though some steps were undertaken towards elimination of import barriers during 1980-83
period it was not until 1984 that major changes to the import regime were announced. These changes
entailed elimination of both tariffs and quantitative restrictions. The import regime was based on
classification of commodities into three groups: ‘prohibited’ list, ‘imports subject to permission’ list,
and ‘liberalized’ list. With the changes announced in 1984 around 60% of 1983 imports were no
longer subject to restrictions or approvals by authorities. The number of commodities in the
‘prohibited’ list, which was around 500 in 1984, was reduced to almost zero by 1985. The
3
commodities in the ‘subject to permission’ list, which accounted for 46% of manufactured imports in
1984, was reduced to 22% in 1986 and 6% in 1988. Quantitative restrictions were completely phased
out by 1990 (Togan 1994). Changes in quantitative restrictions are argued to have resulted in
considerable elimination of trade barriers. It is suggested that the wedge between the domestic and
international price of imports imposed by quantitative restrictions was 50% in 1980, and that it
declined by 10% every year, falling down to 20% in 1984, and 10% in 1985, and finally to zero by
1986 (Krueger and Aktan 1992).
In addition to the relaxation of quantitative restrictions, the 1984 import program entailed
significant changes in the tariff structure. Tariffs on imports of intermediate and capital goods were
reduced. Though tariffs on imports of consumer goods and on imports of goods that would compete
with domestically produced goods were increased this did not lead to an increase in overall nominal
protection rates, because imports of the goods in these categories were severely restricted before 1984.
The output-weighted average nominal tariff rate for the manufacturing industry stood almost
unchanged from 75.8% in 1983 to 76.9% in 1984, however, declined to 40% in 1990 and to 20.7% in
1994.
II.2. Data
The Turkish State Institute of Statistics (SIS) for the Turkish manufacturing industry collects the plant
level data set used in this study. SIS periodically conducts Census of Industry and Business
Establishments (CIBE).3 In addition, the SIS conducts Annual Surveys of Manufacturing Industries
(ASMI) at establishments with 10 or more employees.4 The set of addresses used during ASMI are
those obtained during CIBE years. In addition, every non-census year, addresses of newly opened
private establishments with 10 or more employees are obtained from the chamber of industry.5 For
3
Since the formation of the Turkish Republic CIBE has been conducted 7 times (in 1927, 1950, 1963, 1970, 1980, 1985,
and 1992).
4
SIS also collects data on establishments with less than 10 employees. However, up to 1992 data on these establishments
were collected only during CIBE years. Since then SIS collects annual data for establishments with less than 10 employees
but, using a sampling method.
5
Thus plant entry can be observed in every year of the sample. Though not reported here, in the CIBE years we observe a
larger number of new plants, and a higher fraction of smaller plants. Both of these observations reflect the concerted effort
by the SIS to include all establishments in the CIBE years.
4
this study we use a sample that matches plants from CIBE and ASMI for the 1983-96 period.6
The data is well suited for our purposes because it contains information on variables that are
commonly used in estimation of firm level production functions. Specifically, the data includes value
of sales, number of employees, values of material inputs, electricity, fuels and investment (details of
variable construction are relegated to the Appendix). Unfortunately, not all the key variables needed
for this study have been collected for establishments in the 10-24-size group.7 Thus our sample
consists of plants with 25 or more employees.
We limit the sample to only on private establishments.8 In the resulting sample we have
61,054 plant years for 10,350 plants in 23 three-digit SIC industries. We do not select the plants that
were in the sample period through the entire period and hence use an unbalanced data set. However,
entry or exit each constitutes a small percentage of total number of plants within each year as can be
seen in Table A1 of the Appendix. In 1993, following the CIBE year 1992, the number of entering
plants shows a dramatic increase indicating the concerted effort by SIS to identify new plants. Even in
that year continuing plants constitute about 75% of the total number of plants.
When we turn to explaining plant level total factor productivity (in Section V below) in
addition to the plant level data set we use some sector level and economy wide variables. First,
sectors are classified into three groups as import competing, export oriented and non-tradable sectors
based on sector level import, export and sales values (see Appendix for details). Second, trade
protection rates are calculated by incorporating information on tariff and non-tariff barriers (see
Appendix for details). As can be seen in Table A2 of the Appendix output weighted nominal
protection rates show a declining trend.9 Despite the general declining pattern of nominal protection
rates, however, there are significantly different sub periods. Specifically, the decline in manufacturing
wide protection rate is about 50% during 1984-85, 6% during 86-88, 64% during 1989-93, and 38%
during 1994-96 (by 1989 the level of the protection rate had already declined to about 50% from initial
6
The ASMI and CIBE data are available in a machine-readable form starting from 1980. For this study we limited the
sample for the post 82 period primarily because in the years prior to 1983 the quality of data is less reliable and much work
is needed for its improvement.
7
During the 1983-92 period 10-24 size group, and 24+ group were administered different survey forms.
8
The unit observed in the data is a plant, not a firm. However, in Turkish manufacturing sector almost entirety of the
plants is single plant establishments.
9
Though the series reported is yearly, as described in the Appendix some of these years contain imputed values. We use
such values in our discussion of descriptive statistic but do not in our regression analysis.
5
value of about 148%).
Real wages is another sector level variable included in this study. It is important to note here
that during the sample period real wages were primarily policy driven rather than market determined.
After the coup d’etat in September 1980 real wages in Turkey were suppressed until the last two years
of the decade. In the late 1980s and early 1990s, on the other hand, real wages increased reflecting
government’s shift towards populist policies.10 As can be seen in the Appendix (Table A3) there is a
significant increase in real wages during the sample period.
The final sector level variable is 4-firm concentration ratios (CR4). Average concentration
ratios for the whole manufacturing sector as well as the trade-orientation groups are presented in
Appendix Table A4 (though they are used at 3-digit level in our analysis). An inspection of the table
indicates that as would be expected non-tradables has the highest concentration ratio, followed by
import competing and exports oriented sectors respectively. In both the non-tradable and export
oriented sectors there is a significant decline by 1990. The import competing sectors on the other hand
experienced an increase in the concentration ratios in the late 1980s followed by a decline in the 1990s.
Lastly we use real exchange rate (RER) as a macroeconomic variable that indicates the
competitiveness of Turkish economy with respect to the “rest of the world”.
In Figure A1 of the
Appendix RER is presented for the period. The figure indicates that the Turkish Lira’s had a
considerable real appreciation during the late 1980s is followed with significant depreciation in the
early 1990s.
III. Plant Level Productivity Estimation
Estimates of plant level productivity in this study are obtained by an implementation of Olley and
Pakes (1996) (OP from here on). As is well known the method is developed to address potential
simultaneity biases that arise in production function estimations. This is illustrated by considering a
Cobb-Douglas production function in log-levels as described below (at time t for firm i (suppressing
the firm index i):
10
Following the opening of the political competition in 1987 the Prime Minister Turgut Ozal and his government
switched to economic policies that were expected to increase their chances of winning the general election. This was meant
to give in the demands of various segments of the society, including the labor. Real wages in the public sector companies
increased by more than the double between 1988 and 1991, and continued with steep hikes into the mid-nineties. The
public sector wage hikes were followed by similar increases in the private sector.
6
yt = β 0 + β l ⋅ lt + β k ⋅ k t + β ι ⋅ ι t + ω t + η t
(1)
where y t is output, l t is the variable input and k t is the capital stock and, ιt is intermediate inputs.
Plant specific error term, ε t is composed of a plant-specific productivity component, ω t , and an i.i.d.
component, η t . The latter term has no impact on the firm’s decisions. The productivity term, ω t ,
which is not observed by the econometrician, is known by the firm, and it impacts the firm’s decision
rules. A simultaneity problem arises when there is contemporaneous correlation both within firm i and
across time t between ε t and the firm’s inputs in the firm specific sequences.11
To address the simultaneity problem OP use investment to proxy for the part of the error
correlated with inputs where investment demand function is then written as follows:
it = it (ω t , k t ) .
For positive values of investment it (ω t , k t ) is inverted to yield ω t as a function of capital and
investment ω t = (it , k t ) . Even though we leave the firm’s exit decision in this exposition, OP account
for exit; we present results that estimate OP with and without exit for comparisons with other
methods.) Substituting this expression into equation (1) yields output in terms of observable variables:
y t = lt ⋅ β l + β ι ⋅ ι t + φ t (it , k t ) + η t ,
(2)
where φ t (it , k t ) = β 0 + β k ⋅ k t + ω t (it , k t ) . Consistent parameter estimates of the coefficients on the
variable inputs can then be obtained using a semi-parametric estimator (for example by modeling φ t as
a polynomial series expansion in capital and investment as in OP)12.
A separate effect of capital on output from its effect on a plant’s investment is obtained in a
second stage by assuming that ω t follows a first order Markov process and capital does not
11
In the case of a two input production function, when both capital and labor are correlated with the productivity shock, but
labor’s correlation is significantly higher, and that labor and capital are correlated with each other, the parameter estimate
of the labor coefficient will tend to be overestimated and the parameter estimate of capital will be underestimated. It is
generally not possible to sign the biases of the coefficients when there are many inputs all of which potentially have
varying degrees of correlation with the error term ε t .
12
An important feature of the Turkish economy relevant to our undertaking is presence of macroeconomic cycles during
the period under consideration as reflected in the cyclical pattern of real GNP growth rate. In our estimations we take
these cycles into account by using dummies for the periods of expansion and contraction. More specifically we distinguish
between for time periods: 1984-1987 (expansion); 1988, 1989 and 1991 (contraction); 1990, 1992, 1993, 1995, 1996
(expansion); and 1994 economic crisis.
7
immediately respond to the innovations in productivity, where the innovation in productivity is defined
as:
ξ t = ω t − E [ω t | ω t −1 ] .
Under these assumptions consistent estimates of β k is obtained from the estimation of the following
equation:
y t* = y t − lt ⋅ β l − β ι ⋅ ι t = β 0 + β k ⋅ k t + E [ω t | ω t −1 ] + η t*
(3)
where, yt* is output net of labor’s contribution and η t* = ξ t + η t . Since a by-product of the first stage
is an estimate of ω t a consistent estimate of E [ω t | ω t −1 ] can be obtained and estimation of equation (3)
yields consistent estimates of β k .13
The production function estimates are presented in Table 1. As can be seen in Table 1 the
coefficient estimates of material inputs are the largest in all industries (averaging about 0.70 across
industries). The next largest is the labor coefficient, followed by the elasticity of energy. Note also
that with the exception of capital stock elasticity almost all of the coefficients are estimated
statistically significantly at standard levels of confidence and are of expected sign. Scale elasticity
estimates exceed one in several industries (food miscellaneous, footwear, furniture, other chemicals,
glass, nonferrous metals, fabricated metals, non-electrical machinery, and transport equipment).
In the next section we present comparisons of total factor productivity estimates obtained from
OP with other estimators that have been commonly used in the literature such as OLS, fixed effects
and instrumental variables. As an alternative to OP estimates we have also conducted estimations
based on the method developed by Levinsohn and Petrin (2003). The method, which is built upon OP,
relies on employing intermediate inputs, instead of investment as the proxy variable to solve the
simultaneity problem. Since in many data sets, including ours, the investment variable is zero for a
large fraction of the observations (presumably due to adjustment costs) LP has a practical advantage of
not truncating the observations with zero investment values. However, the LP method also requires
that productivity shock is monotonically increasing in capital stock and the proxy input. In our data
this condition does not hold for many industries, independent of whether we use intermediate inputs,
13
Olley and Pakes (1996) use a series expansion as well as kernel estimator for this stage. Also note that a constant can not
be identified separately from the polynomial expansion in investment and capital.
8
or energy as a proxy input. Furthermore, when the monotonicity condition holds for more than one
variable input LP method has an inherent inconsistency. In several industries we observe that
monotonicity condition holds for more than one variable input. Thus comparisons based on LP
estimates are not presented below.
IV. Total Factor Productivity
Before turning to plant level estimations of the next section we first compute and analyze
industry level total factor productivity (calculated as output share weighted plant level productivities)
to gain some insights into stylized facts of the period. Total factor productivity for plant i, in year t is
TFPit = y it − βˆl lit − βˆιι it − βˆ k k it
(4)
where TFPit is the logarithm of the total factor productivity, y it is the log level of real output for plant
i at time t. Omitting the subscripts, l , ι and k represent log levels of labor, intermediate inputs and
capital for plant i at time t. βˆ s with the appropriate subscripts are parameter estimates obtained from
production function estimates.
Sector level productivity in year t is defined as output share weighted average of plant level
productivities:
TFPt = ∑θ it ⋅ TFPit
i
where, θ it is output share of plant i in total industry output in year t.
TFP level and growth rates for the manufacturing sector are presented in Figures 1a and 1b. In
both figures we present TFP level and growth rates based on parameter estimates obtained from OP
(which are presented in Table 1). We also present TFP level and growth rates based on OLS, fixed
effects (FE) and instrument variables (IV). These figures suggest that in our data set TFP level and
growth rates based on OP do not deviate substantially from the others when plant level productivities
are aggregated. The largest gap is in 1996 when TFP level based on OP estimates is about 3% lower
than OLS and IV estimates and only 0.8% higher than the FE estimate. An inspection of growth rates
indicates that FE estimates fluctuate more than others. Though there does not appear to be a large
difference at this level of aggregation simultaneity bias at plant level analysis is still an important
9
source of concern.
We next describe comparisons of TFP growth rates in periods of high protection rates to those
in periods of low protection rates. The sub-periods compared are those that are described in the data
section (50% during 1984-85, 6% during 86-88, 64% during 1989-93, and 38% during 1994-96.)
In
Table 2 we present the comparisons for the manufacturing sector as a whole and by trade orientation of
manufacturing industries. In calculation of subsector TFP growth rates we use either unweighted
annual averages, or annual averages weighted by the group’s output share in manufacturing, and
present them in sections A and B of the table. The results for the manufacturing sector as a whole
indicates that on average the Turkish manufacturing industry attained 1.94 % TFP growth per annum
between 1983 and 1996. There is, however, a substantial difference across sub-periods. The
comparisons presented in columns (4)-(7) indicates that large productivity gains take place during
periods of large decreases in protection rates (during 1984-85, and 1989-93 productivity gains are
7.4% and 5.6% per annum, respectively).
Sub period comparisons by industry trade orientations yields the largest productivity gains in
import competing sectors irrespective of the method of averaging. As can be seen in Table 2- Part A column (1) the productivity growth in import-competing sectors (3.0%) is more than three times that of
productivity growth in the export-oriented sectors (0.9%). In Part B the conclusion is similar in that
the contribution of import-competing sectors to the productivity growth in the manufacturing industry
as a whole is almost three times the contribution of the export-oriented sectors. Also note that even
though the average productivity growth in non-traded sectors is 1.7% throughout 1983-96, its
contribution to the manufacturing industry productivity growth is less than 0.1%.
To gain insights into whether changes in industry level TFP growth result from within plant
changes of productivity growth or between plant shifts we decompose changes in log productivity
levels as in Haltiwanger (1997)14:
∆TFPt = TFPt − TFPt −1
∆TFPt = ∑ θ i ,t −1 ∆TFPit + ∑ ∆θ it (TFPi ,t −1 − TFPt −1 ) + ∑ ∆θ it ∆TFPit +
i∈C
∑θ (TFP
i∈Ent
it
it
i∈C
i∈C
− TFPt −1 ) − ∑ θ i ,t −1 (TFPi ,t −1 − TFPt −1 )
(5)
i∈Ext
14
Pavcnik (2002) decomposes the deviation of plant productivity from a reference plant in the base year as in Olley and
Pakes (1996). Since we decompose the changes in productivity levels the reference plant drops out.
10
The first term, the within-plant component of productivity growth is based on plant level
changes, weighed by preceding year’s output shares in the industry. The second term is the betweenplant component. It reflects changing output shares of firms, weighted by the difference between each
plant’s own productivity level and the average productivity in the sector in the previous year. As such,
an increase in a plant’s output share contributes positively to the between-plant component only if the
plant has higher than average industry productivity in the previous year. The third term is the
covariance term, which allows the plant’s output share to change along with its productivity from one
period to another. The last two terms in equation (5) capture the effect of net entry by subtracting the
productivity of exiting plants from the productivity of entering plants.
The results of this decomposition are presented in Table 3. Several interesting patterns emerge.
First, total factor productivity growth is negative in the years leading to the stagnation of 1988-89 and
in the crisis year of 1994. Second, the within component is quite important both in positive
productivity growth years as well as years with productivity decline. Third, the between component of
productivity growth is negative throughout the sample period, but it is relatively small, while the
covariance term is uniformly positive and large. Clearly, shift in output towards plants that experience
an increase in productivity accounts for an important component of the average industry productivity
growth. Finally, contribution of net entry is in general negligible in comparison to the contribution of
continuing plants.
The message of these results is that though the relative contribution of
productivity improvements resulting from within plant improvements and those resulting from
reallocation of output across plants vary over the years, overall they both appear equally important.
As can be seen from Table 3, the results for the whole manufacturing sector largely hold for
different sub-periods or different industry groups by trade orientation. Only during the 1989-96
period significant within productivity improvements are observed for import competing, and nontraded industries. The contribution of within-plant component of productivity growth is also positive
for the whole sample in both industry groups, but the impact is much smaller compared to the 1989-96
period. Presence of positive within plant productivity gains may indicate importance of plant level
changes in technologies, or organization of production.
V. Explaining Productivity at the Plant-Level
The results in the previous section suggest that high productivity growth in the manufacturing sector
took place during periods of a decline in protection rates. To explore linkages between protection rates
11
and plant level productivity we regress our total factor productivity measure on nominal protection
rates. In this investigation we also control for macroeconomic, sector level and plant level
characteristics that are expected to condition plant productivity. Specifically, macro and sector level
variables we control for are the real exchange rate (as an indicator of demand shifts between tradable
and non-tradable sectors), lagged values of sector level real wages (changes of which were primarily
policy induced), four firm concentration ratios (as an indicator of domestic competition) and lagged
values of nominal protection rates. Size indicators (measured by number of employees), entry and exit
indicators are the plant level variables included in our estimations. Next we turn to a more detailed
description of our specification.
Real exchange rate (RER) is included because real exchange rate (RER) appreciation might
increase demand for non-tradables and decrease demand for domestically produced traded goods. If
plants do not adjust their inputs instantaneously and have some spare capacity, the demand fluctuations
induced by RER appreciation (depreciation) could lead to an increase (decrease) in measured
productivity for plants in the non-tradable goods sector and a decrease (increase) in measured
productivity for plants in the export oriented and import competing sectors.
Our sample period, 1983-96, also includes a period of rapid increase in both public and private
sector real wages (see Table A8 for the average real wage rates in the manufacturing industry and its
subcategories). Evidence suggests that these increases were not a consequence of productivity
increases of the previous years as one might expect15. On the contrary, productivity increases appear
to have followed policy induced increases in wages. After the coup d’etat in September 1980 real
wages in Turkey were suppressed until the last two years of the decade. In the late 1980s and early
1990s, on the other hand, real wages increased reflecting government’s shift towards populist
policies.16
It is likely that faced with the rapid increase in wages, many firms were forced to
undertake replacement investment in order to keep the unit labor costs under control. In addition,
managerial and organizational changes that took place might have reduced X-inefficiencies. Thus, we
include lagged sector average real wage rate (obtained at the four-digit SIC level).
15
The results of one of our ongoing projects show that in the Turkish manufacturing industry wages Granger-cause
productivity during the 1983-1996.
16
Following the opening of the political competition in 1987 the Prime Minister Turgut Ozal and his government
switched to economic policies that were expected to increase their chances of winning the general election. This was meant
to give in the demands of various segments of the society, including the labor. Real wages in the public sector companies
increased by more than the double between 1988 and 1991, and continued with steep hikes into the mid-nineties. The
public sector wage hikes were followed by similar increases in the private sector. (see Appendix Table A8)
12
We also include 4-firm concentration ratios (CR4) in our productivity equation. This variable
is introduced so as to capture the possible impact of domestic competition separately from impetus of
imports as a source of competitive pressure on domestic plant productivity. In an imperfectly
competitive market, where a small number of firms can effectively grab rather large shares of domestic
sales of domestically produced output the smaller firms’ ability to attain higher levels of productivity
will be curtailed. Even if smaller plants can increase productivity, the market power of large plants
will limit their ability to expand their market shares and hence returns to increased productivity will
not be realized fully. In addition, as shown by Hopenhayn (1992) an increase in the market power of
the largest plants can be a result of increased entry costs which is also expected to have an adverse
impact on productivity of incumbent plants. As a result, one would expect the plant level productivity
to be inversely related to the intensity of domestic market competition.
To capture the impact of trade policy changes we use lagged values of nominal protection rates
(NPR). A concern with estimations of productivity on measures of trade policy is the endogeneity of
trade policy. In other words, government authorities may increase trade protection in response to
pressures from industries with productivity disadvantage. The choice of which industry should be
more protected is far from random. However, the political economy of trade protection appears to
have changed very little during the period under consideration. The year by year Spearman rank
correlation of nominal protection rates among the 23 industries during the period is above 80%. These
numbers suggest that the structure of protection did not change much during the period. Thus, using
industry dummy variables that control for these time-invariant characteristics of political economy of
trade reforms can significantly reduce any potential biases.
Turning to plant level variables that are expected to have an impact on plant level productivity,
plant size (measured by number of employees) is found as an important variable in the existing
literature. (see Tybout 2000)
Several studies suggest that larger plants are more productive than the
smaller ones, thanks to the presence of scale economies. Since plant size may be influenced by
increased exposure to trade (see Roberts and Tybout, 1991), and thus correlated with nominal
protection rates, we use the plant size measured at the beginning of the sample observations for each
plant (i.e. measured in the entry year of the plant).17 Using these measures we create four size groups:
17
The use of initial size may not solve the potential collinearity problem entirely as it is possible that in a market that is
subject to trade regime openness the prospect of increasing import competition may result in increasing the size of entering
13
size group 1 (25-49 employees) size group 2 (50-99 employees), size group 3 (100-249 employees)
and size group 4 (250+ employees). In our estimations, in addition to using the size indicators on
their own, we also use their interactions with the NPR variable as an explanatory variable. Other plant
level variables are entry and exit indicators. Findings in the literature largely suggest that plants that
cannot attain a minimum level of productivity tend to exit. On the other hand, there is no definitive
reason for entering plants to have higher productivity in their first year in the industry compared to the
continuing plants.
With the above considerations the productivity equation we estimate is as follows 18:
TFPi ,jt = λ 0 + λ1 NPR j ,t −1 + λ 2 S i ,t + λ 3 S i ,t NPR j ,t −1 + λ 4 ⋅ W j ,t −1 + λ 5 CR 4 t + λ 6 RER t + λ 7 Ent i + λ8 Ext i + µ i ,t
(6)
where
TFPitj = log of plant i’s (which is in sector j) total factor productivity in year t,
NPR j ,t −1 = log of nominal protection rate for industry j in year t-1,
Si = indictor of plant size group
W j ,t −1 = log of sector j’s (4-digit) annual average real wage rate in year t-1,
CR4j = log of 4-firm concentration ratio at the 3-digit sectoral level.
RERt = log real exchange rate measured annually,
Ent i = entering plant indicator,
Ext i = exiting plant indicator,
µ i,t = error term.
We estimate equation (6) for the manufacturing industry as a whole as well as separately for
industry groups by their trade orientations. As can be observed in Figure 2 productivity levels differ
among industry group with import competing industry (IC) showing the highest productivity
improvement through the period.
plants. A cursory inspection of the data, however, does not yield support for this concern. In particular, a regression of log
initial employment level on lagged log nominal protection rate does not yield statistically significant parameter estimates.
18
We should also remind here that as was discussed in the data section we run these regressions only for those years for
which we have data available on lagged protection rates (1984, 1985, 1989-1992 and 1995).
14
Our first set of estimates is presented in Table 4. In the specification for this table we use three
digit sector indicators, but leave out entry and exit plant indicators, as well as plant specific fixed
effects. Let us start our discussion by focusing the results on the nominal tariff (NPR) variable. An
inspection of NPR coefficients tells an interesting story about interaction between plant size and
nominal protection rates. First, note that for the manufacturing sector as a whole we don’t find any
impact of nominal protection rate on productivity for the smallest plant size group (left out group with
25-49 employees). However, the coefficient estimates for the interaction terms are negative and
statistically different from zero for each of the remaining size groups. In fact, as the size group
increases the magnitude of the estimated coefficient increases.
An inspection of the size indicators used on their own yields that larger plants have a higher
productivity in comparison to smaller plants. The results pertaining size indicators then indicate that
not only do larger plants have higher productivity relative to smaller plants but also their productivity
in response to a decrease in protection rates is higher. This may be interpreted as suggesting that as
import regime openness leads to increased competitive pressure from imports, large plants, with an
already existing productivity advantage over smaller plants mobilize their resources to increase
productivity further.
A comparison of the results pertaining NPR and S across industries by trade orientation
indicates that the IC sector (import competing) results mimic the entire manufacturing sector results.
The EO (export oriented) sector results are quite interesting in that the smallest group (the left out
dummy variable) appear to benefit from higher protection rates as indicated with a positive and
statistically significant parameter estimate. As size increases, however, the impact becomes negative
as in the import competing sectors. We don’t find any significant affect on the NT (non-traded) sector.
The parameter estimates for NPR indicate that a decrease of nominal protection rates has an
economically important affect on productivity. A coefficient estimate of -0.184 (-0.078-0.106) for the
largest size group in the IC sector, for example, imply that a 15% (say from 80% to 68% or from 40%
to 34%) decline in protection rates distributed uniformly across time would lead to a 2.76% increase in
the average productivity.
Other variables used in our specification have the anticipated signs. Lagged sector-average
wage rate has positive and statistically significant impact on plant level productivity in the
manufacturing industry as a whole and in each of the sub-sectors. The real exchange rate coefficient
15
estimate is positive for the tradable sectors and it is negative for the non-traded sectors. The 4-firm
concentration ratio (CR4), has a statistically significant affect at reasonable levels of confidence only
in import competing sectors and has no affect in others. 19
Panel B of Table 4 differs from panel A by inclusion of exit and entry indicators. Since the
results pertaining to the variables we have discussed so far stay qualitatively similar in Panel B we
focus our discussion only on the entry and exit indicators here. The coefficient estimates for the exiting
plants are negative and significantly different from zero for the manufacturing sector as well the sub
sectors. The coefficient of the entry variable is estimated with a statistically significant coefficient
only at low levels of confidence for the whole manufacturing sector and it is not found to be
statistically significant in subgroups. Overall, the results yield that while entering plants do not have
any productivity superiority over the continuing plants those plants that do exit tend to have lower
productivity compared to continuing plants. The estimates indicate that in their final year in the
industry exiting plants tend to be approximately 4% less efficient than continuing plants.
In the next set of specifications, presented in Table 5, we incorporate plant fixed effects in
addition to the already existing industry indicators. With this change we drop size indicators, though
the interaction of size with nominal protection rate remains. Qualitatively all the results presented in
Table 5 are as the same as to those already discussed except for entry and exit indicators. Plant entry
and plant exit indicators both are not statistically significant at reasonable levels of confidence, in
contrast to the findings we report earlier.
Sensitivity of the Results
In assessing the results presented so far there are a couple of issues of import. First, one may argue that
real exchange rate cannot capture all the variation in the macroeconomic environment.
Second, it
may be argued that effective protection rates, instead of the nominal protection rates, ought to be used.
We next turn to a discussion of estimations that address these potential sources of concern.
In panel A of Table 6 we present results from a specification that includes year indicators (year
indicators themselves are not reported in the table). This specification of course necessitates that the
19
We also used the 8-firm concentration ratio and the Herfindahl index as measures of domestic competition. The results
are very similar to the ones with four-firm concentration ratio.
16
real exchange rate measure is not longer included. The results in Table 6 indicate that the qualitative
results of Table 5 continue to hold for the manufacturing sector as a whole and with respect to each of
the sectoral divisions. The only difference with the inclusion of the year indicators instead of the real
exchange rate is the decline in the absolute values of the coefficient estimates for the nominal
protection rate for plants with less than 50 employees. .
Throughout our analysis we used nominal protection rates as a measure of trade policy.
However, to the extent that products of a sector and the inputs used in that sector have different
nominal protection rates, effective protection rates (EPR) will deviate from nominal protection rates
As is well known measurement of EPRs is not an easy task and EPRs are inevitably subject to
substantial measurement error. Nevertheless, in Panel B of Table 6 we present a set of results based on
EPR. Though the coefficient estimates of EPR and its interaction with size indicators are smaller than
the comparable NPR estimates (Table Panel B) they continue being statistically significant.
V. Conclusions
In this paper we examine the response of plant productivity to changes trade policy. Using an
unbalanced panel of 26,839 plant year observations for the 1983-96 period we estimate plant
productivities. The estimation is undertaken following the procedure of Olley and Pakes (1996) so as
to eliminate potential simultaneity biases that are present in OLS estimations.
We analyse plant productivities in several different ways. First we create industry averages
and inspect their evolution over time, and across sectors by trade orientation. We observe that
productivity gains are largest in import competing industries with highest gains reaching to 8% per
year during periods of rapid decline in protection rates. Next we decompose the productivity gains to
those resulting from reshuffling among plants and those resulting from within plant productivity
improvements. We find again find that there are important differences across sectors by trade
orientation. Though reallocation of market shares to more productive plants are important in both
export oriented, and import competing sectors, within plant productivity improvements are significant
only in export oriented sectors.
Finally we investigate the effects of changes in protection rates on plant level productivities
using regressions. In these regressions we control for endogeneity of protection rates (tariff and nontariff) as well as other potential important determinants of productivity improvements during the
17
period under consideration, such as intensity of domestic competition, government induces wage
changes and real exchange rate changes. We find that productivity improvements resulting from
declining protection levels are statistically significant and economically important, especially in import
competing sectors. Our analysis also suggests that there is a huge degree of heterogeneity, measured
by plant size, in response to changes in protection rates.
18
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21
Table 1. Olley-Pakes Production Function Estimates (1983-96)
Material
S.E. Energy** S.E.
Inputs**
Capital
S.E.
Scale
Elasticity
0.004
0.007
0.021
0.053**
0.041*
0.031
0.005
0.017
0.035
1.041
1.064*
0.968
2508
588
311
0.091
0.053
0.098
0.016
0.004
0.008
0.016
0.020
0.052**
0.041**
0.071**
0.093**
0.006
0.009
0.013
0.028
1.023
1.011
1.076
1.134**
3911
2374
371
193
0.014
0.020
0.098
0.068
0.013
0.019
0.038
0.047*
0.038
0.018
0.935
1.142**
399
236
0.017
0.083
0.011
0.112**
0.011
1.084
546
*
SIC
Sector
Labor**
S.E.
311
312
313
321
322
323
324
331
332
341
351
352
355
356
361
362
369
371
372
381
382
383
384
Food
Food Miscellaneous
Beverages
Textiles
Wearing Apparel
Leather Products
Footwear
Wood Products
Furniture
Paper
Industrial Chemicals
Other Chemicals
Rubber Products
Plastics
Ceramics
Glass
Nonmetal Minerals
Iron and Steel
Nonferrous Metals
Fabricated Metals
Non-electrical Machinery
Electrical Machinery
Transport Equipment
0.176
0.136
0.148
0.007
0.018
0.037
0.774
0.825
0.681
0.005
0.008
0.024
0.038
0.062
0.108
0.151
0.231
0.266
0.251
0.008
0.013
0.034
0.040
0.728
0.686
0.641
0.774
0.004
0.006
0.020
0.025
0.068*
0.209
0.032
0.035
0.730
0.819
0.228
0.033
0.661
No of
Obs.
0.210
0.256
0.147
0.208
0.033
0.016
0.025
0.016
0.563
0.661
0.700
0.748
0.016
0.010
0.013
0.010
0.110
0.049
0.074
0.068
0.015
0.008
0.014
0.008
0.081
0.117**
0.064*
0.051*
0.033
0.009
0.023
0.023
0.964
1.083**
0.985
1.075
446
1123
528
1036
0.370
0.257
0.265
0.060
0.031
0.018
0.478
0.558
0.533
0.034
0.024
0.007
0.198
0.143
0.204
0.042
0.017
0.009
0.002
0.137**
-0.071
0.030
0.018
0.041
1.049
1.094*
0.931
176
280
1794
0.167
0.210
0.015
0.024
0.741
0.735
0.006
0.013
0.071
0.062
0.007
0.014
0.068**
0.070**
0.011
0.013
1.048
1.078*
1120
421
0.210
0.253
0.160
0.270
0.013
0.018
0.015
0.016
0.723
0.683
0.758
0.635
0.007
0.010
0.008
0.009
0.065
0.023
0.052
0.084
0.007
0.010
0.007
0.008
0.077**
0.111**
0.061**
0.110**
0.011
0.010
0.010
0.014
1.075**
1.070*
1.031
1.099**
2018
1786
1518
1423
Notes: Standard errors are based on bootstraps with 1000 replications. **, * and + indicates statistical significance at the 1, 5, and 10% levels. Labor,
energy and material input elasticities are significantly different from zero at the 1% significance level.
22
Figure 1a. TFP Level Estimates for Manufacturing Industry –
Based on Alternative Estimation Methods
1.30
1.25
1.20
1.15
1.10
1.05
1.00
0.95
83
84
85
86
OP
87
88
89
OLS
90
91
92
93
Fixed Ef f ects
94
95
96
Inst. Var.
Figure 1b. TFP Growth Rate Estimates for Manufacturing Industry
Based on Alternative Estimation Methods
0.15
0.10
0.05
0.00
83
84
85
86
87
88
89
90
91
92
93
94
95
-0.05
-0.10
OP
OLS
Fixed Ef f ects
23
Inst. Var.
96
Table 2. TFP Growth Rates (%)
A. Annual averages
Sector
Manufacturing
Import Competing
Export Oriented
Non-traded
84-96
84-88
89-96
84-85
1.9
3.0
0.9
1.7
-0.3
0.2
-0.7
-0.8
3.4
4.8
2.0
3.2
7.4
8.5
6.8
2.7
86-88 89-93 94-96
-5.4
-5.4
-5.7
-3.2
5.6
7.4
3.9
3.9
-0.3
0.4
-1.2
2.1
B. Annual averages weighted by the group’s output share in manufacturing
Manufacturing
Import Competing
Export Oriented
Non-traded
84-96
84-88
89-96
84-85
1.94
1.41
0.46
0.07
-0.3
0.1
-0.4
0.0
3.3
2.2
1.0
0.2
7.4
3.8
3.5
0.1
86-88 89-93 94-96
-5.4
-2.3
-3.0
-0.1
5.6
3.4
1.9
0.2
-0.3
0.2
-0.6
0.1
Manufacturing
Industry
1984
4.6 1.8 -1.8 4.4
4.4 0.2
0.16
1985
10.2 6.5 -1.9 5.7
10.2 0.0
0.32
1986
-7.4 -7.7 -2.5 4.3
-6.0 -1.5 -0.21
1987
-5.7 -6.8 -2.2 4.0
-5.0 -0.7 -0.54
1988
-3.2 -5.3 -2.2 4.6
-3.0 -0.2 -0.46
1989
4.2 1.8 -1.6 3.8
4.0 0.2
0.40
1990
6.0 4.2 -1.0 2.7
5.9 0.1
0.17
1991
3.9 0.6 -0.3 3.4
3.7 0.2
0.16
1992
6.7 4.8 -1.9 3.5
6.3 0.4
0.39
1993
7.1 5.1 -1.2 3.1
7.0 0.0
0.17
1994
-4.9 -8.1 -1.6 5.4
-4.3 -0.6
0.02
1995
3.6 1.8 -3.5 5.5
3.7 -0.1 -0.02
1996
0.2 -2.3 -1.6 4.3
0.3 -0.1 -0.08
Period Average for Trade-Orientation Based Industry Groups
Import
84-96
3.0 0.7 -1.6 3.8
3.0 0.1
0.2
Competing 84-88
0.2 -1.4 -1.8 3.6
0.4 -0.2
-0.1
89-96
4.8 2.1 -1.5 4.0
4.5 0.2
0.3
Export
84-96
0.9 -1.3 -2.1 4.7
1.3 -0.3
-0.1
Oriented
84-88
-0.7 -3.2 -2.5 5.7
0.0 -0.7
-0.2
89-96
2.0 -0.2 -1.8 4.1
2.1 -0.1
0.0
Non-Traded
84-96
1.6 0.2 -1.2 2.5
1.6 0.1
0.1
84-88
-0.8 -1.8 -1.5 2.0
-1.3 0.5
0.3
89-96
3.2 1.5 -1.0 2.9
3.4 -0.2
0.0
24
Exit
Entry
Net Entry
Continuing
Total
Covariance
Between
Within
Total
Table 3. TFP Growth Decompositions (%)
-0.01
0.29
1.26
0.15
-0.25
0.24
0.06
0.00
0.01
0.13
0.57
0.08
-0.01
0.1
0.1
0.1
0.3
0.5
0.1
0.1
-0.2
0.2
Figure 2. OP estimates of TFP
Manufacturing Industry and Trade-based Industry Groups
1.40
1.30
1.20
1.10
1.00
0.90
83
84
85
86
87
88
89
Manuf acturing
90
IC
25
91
92
EO
93
94
95
NT
96
Table 4. Plant-level Productivity, Protection and Size - OLS Estimates
ALL
Panel A
IC
EO
NT
ALL
Panel B
IC
EO
NT
Lagged NPR
-0.006
[0.006]
0.051**
[0.008]
-0.007
[0.032]
-0.007
[0.006]
0.050**
[0.008]
-0.013
[0.032]
Lagged NPR *
Size 2 dummy)
Lagged NPR *
size 3 dummy)
Lagged NPR *
size 4 dummy)
Size 2 dummy
-0.028** -0.037** -0.035**
[0.008] [0.012] [0.011]
0.0002
[0.048]
-0.028** -0.037** -0.034**
[0.008] [0.012] [0.011]
0.004
[0.047]
-0.048** -0.050** -0.075**
[0.009] [0.016] [0.012]
0.015
[0.032]
-0.048** -0.050** -0.074**
[0.009] [0.016] [0.012]
0.018
[0.032]
-0.161** -0.106** -0.246**
[0.013] [0.018] [0.018]
-0.07
[0.043]
-0.160** -0.106** -0.245**
[0.013] [0.018] [0.018]
-0.066
[0.043]
0.096**
[0.031]
0.132**
[0.047]
0.117**
[0.041]
0.087
[0.186]
0.093**
[0.031]
0.130**
[0.047]
0.114**
[0.041]
0.064
[0.186]
Size 3 dummy
0.205**
[0.034]
0.597**
[0.050]
0.223**
[0.062]
0.367**
[0.067]
0.283**
[0.044]
0.899**
[0.069]
0.147
[0.136]
0.742**
[0.191]
0.202**
[0.034]
0.592**
[0.050]
0.222**
[0.062]
0.363**
[0.067]
0.279**
[0.044]
0.892**
[0.069]
0.123
[0.137]
0.705**
[0.191]
0.135**
[0.007]
0.083**
[0.008]
0.162**
[0.009]
0.095**
[0.029]
0.135**
[0.007]
0.083**
[0.008]
0.162**
[0.009]
0.099**
[0.029]
0.019
[0.012]
-0.182**
[0.032]
0.029+
[0.015]
0.146
[0.116]
0.018
[0.012]
-0.181**
[0.032]
0.028+
[0.015]
0.14
[0.116]
0.115**
[0.018]
0.098**
[0.029]
0.073**
[0.026]
-0.412**
[0.120]
0.114**
[0.018]
0.097**
[0.029]
0.072**
[0.026]
-0.409**
[0.120]
Exit dummy
---
---
---
---
-0.041**
[0.009]
-0.030*
[0.015]
-0.040**
[0.012]
-0.120*
[0.049]
Entry dummy
---
---
---
---
-0.018*
[0.008]
-0.025+
[0.014]
-0.014
[0.010]
-0.053
[0.052]
26839
0.640
10229
0.327
15656
0.711
954
0.286
26839
0.640
10229
0.327
15656
0.711
954
0.292
Size 4 dummy
Lagged Average
Sector Wage
Concentration ratio
(4-firm)
Real Exchange Rate
Number of Obs.
Adjusted R2
-0.078**
[0.008]
-0.078**
[0.008]
Notes: Industry indicators are included. All variables are in logarithms. Sample includes 1984, 1985, 1989,-1992, 1995. Standard errors are in square brackets.
Plant’s employment in the first year in the sample is used to determine plant size (25-49; 50-99; 100-249; 250+). Average wage rate for the 4-digit SIC sectors
are used as the wage variable. **, * and + indicate statistically significant coefficient estimates at the 1%, 5% and 10% level, respectively.
26
Table 5. Plant-level Productivity, Protection and Size – Plant Fixed Effects Estimates
Panel A
ALL
Lagged NPR
Lagged NPR *
size 2 dummy)
Lagged NPR *
size 3 dummy)
Lagged NPR *
size 4 dummy)
Lagged Average
Sector Wage
Concentration ratio
(4-firm)
Real Exchange Rate
Exit dummy
Entry dummy
Number of Obs.
Adjusted R2
IC
Panel B
EO
NT
-0.026** -0.076**
0.008
0.072
[0.007]
EO
NT
-0.027** -0.077**
0.007
0.066
[0.009]
[0.044]
[0.007]
[0.009]
[0.044]
-0.02
-0.159*
-0.033** -0.053**
-0.02
-0.154*
[0.014]
[0.071]
[0.010]
[0.014]
[0.071]
-0.039** -0.058** -0.049**
0.007
-0.038** -0.057** -0.048**
0.012
[0.012]
[0.015]
[0.061]
[0.012]
[0.015]
[0.061]
-0.105** -0.099** -0.108**
-0.224*
-0.104** -0.099** -0.107**
-0.219*
[0.013]
[0.020]
[0.017]
[0.087]
[0.013]
[0.020]
[0.017]
[0.087]
0.103**
0.093**
0.078**
0.056*
0.103**
0.093**
0.078**
0.061*
[0.007]
[0.010]
[0.009]
[0.027]
[0.007]
[0.010]
[0.009]
[0.027]
-0.013
-0.210**
0.02
-0.027
-0.014
-0.211**
0.019
-0.029
[0.014]
[0.031]
[0.016]
[0.118]
[0.014]
[0.031]
[0.016]
[0.119]
0.113**
0.097**
0.058*
-0.222+
0.113**
0.101**
0.056*
-0.231+
[0.016]
[0.027]
[0.024]
[0.125]
[0.017]
[0.027]
[0.024]
[0.126]
---
---
---
---
-0.001
0.016
-0.007
-0.058
[0.010]
[0.016]
[0.013]
[0.053]
---
0.017*
0.017
0.019+
0.022
[0.015]
10229
[0.010]
15656
[0.041]
954
0.591
0.841
0.548
[0.010]
-0.034** -0.054**
[0.010]
---
[0.015]
[0.020]
---
---
ALL
26839
10229
15656
954
[0.008]
26839
0.795
0.591
0.841
0.549
0.795
IC
[0.010]
[0.015]
[0.020]
Notes: Industry indicators are included. All variables are in logarithms. Sample includes 1984, 1985, 1989,-1992, 1995. Standard errors are in square brackets.
Plant’s employment in the first year in the sample is used to determine plant size (25-49; 50-99; 100-249; 250+).Average wage rate for the 4-digit SIC sectors are
used as the wage variable. **, * and + indicate statistically significant coefficient estimates at the 1%, 5% and 10% level, respectively.
27
Table 6. Plant-level Productivity, Protection and Size – Plant Fixed Effects Estimates: Sensitivity
Panel A
Nominal Protection Rate
Year indicators
Dependent variable
Indicators
Lagged Protection
Lagged Protection*
size 2 dummy)
Lagged Protection *
size 3 dummy)
Lagged Protection *
size 4 dummy)
Lagged Average
Sector Wage
Concentration ratio
(4-firm)
Real Exchange Rate
Exit dummy
Entry dummy
Number of Obs.
2
Adjusted R
Panel B
Effective Protection Rate
ALL
IC
EO
NT
-0.006
-0.019
0.017+
0.122**
-0.014** -0.027** -0.007**
0.008
[0.008]
[0.014]
[0.010]
[0.046]
[0.002]
[0.003]
[0.003]
[0.051]
-0.02
-0.159*
-0.002
-0.017**
0.003
-0.043
[0.014]
[0.072]
[0.003]
[0.006]
[0.004]
[0.068]
-0.037** -0.055** -0.047**
0.019
-0.009*
-0.016*
-0.008+
-0.004
[0.012]
[0.015]
[0.061]
[0.004]
[0.007]
[0.005]
[0.072]
-0.099** -0.092** -0.107**
-0.175*
-0.029** -0.020** -0.032**
-0.117*
[0.013]
[0.020]
[0.016]
[0.084]
[0.004]
[0.007]
[0.006]
[0.051]
0.093**
0.073**
0.082**
0.029
0.102**
0.103**
0.076**
0.02
[0.008]
[0.011]
[0.011]
[0.040]
[0.007]
[0.010]
[0.010]
[0.036]
0.012
-0.186**
0.043*
0.135
-0.024+ -0.164**
0.015
0.004
[0.014]
[0.033]
[0.017]
[0.168]
[0.014]
[0.031]
[0.016]
[0.166]
---
---
---
---
0.065**
0.044
0.048*
-0.297+
[0.016]
[0.028]
[0.023]
[0.159]
-0.032** -0.052**
[0.010]
[0.015]
[0.020]
ALL
IC
EO
NT
-0.016+
0
-0.018
-0.107+
-0.009
0.01
-0.019
-0.064
[0.010]
[0.016]
[0.013]
[0.055]
[0.011]
[0.016]
[0.014]
[0.074]
0.021*
0.016
0.017
0.036
0.032**
0.021
0.032**
0.015
[0.009]
[0.015]
[0.011]
[0.044]
[0.009]
[0.016]
[0.011]
[0.044]
26839
10229
15656
954
25098
9918
14403
777
0.800
0.605
0.844
0.565
0.795
0.585
0.842
0.557
Notes: Industry indicators are included. All variables are in logarithms. Sample includes 1984, 1985, 1989,-1992, 1995. Standard errors are in square brackets.
Plant’s employment in the first year in the sample is used to determine plant size (25-49; 50-99; 100-249; 250+).Average wage rate for the 4-digit SIC sectors are
used as the wage variable. **, * and + indicate statistically significant coefficient estimates at the 1%, 5% and 10% level, respectively.
28
APPENDIX
DATA CONSTRUCTION
Variables used in production function estimations:
•
•
•
•
•
Real value of output is obtained by deflating the total annual sales revenues of a firm with a three-digit
price deflator constructed by State Institute of Statistics (SIS). This construction has the usual problems
of having “one price” for all firms, and relies on price-taking behavior at the firm level.20 As such the
deflator controls for changes due to industry level demand shocks and changes arising from inflation.
Material inputs include all purchases of intermediate inputs. The nominal value of firm level annual
inputs are deflated using a three-digit material input price deflator constructed by SIS.
Energy series is the sum of electricity usage and fuel consumption. Real value of electricity and fuel
consumed is obtained by deflating the nominal values with the respective price deflators obtained from
the SIS.
Labor is the number of paid employees in a given year.
Capital stock series is constructed by using perpetual inventory method. The database contains only
information on investment. Detailed subcategories of investment are aggregated to buildings and
structure, transportation equipment, and machinery. Since the data does not contain information on
capital stock in any year we construct initial capital stock series for each establishment. Initial capital
stock series (for the year before a plant enters the sample) is computed by assuming that average real
investment undertaken in the first seven years of a plant represent its average investment behavior in the
seven years before the plant is included in the database. Using 5%, 10%, and 20% as the depreciation
rates for buildings, machinery and transportation equipment, respectively, we calculate the initial capital
stock.. For those establishments that are not in the data for seven years we imputed initial capital stock
series. Using initial capital stocks of establishments in the same four-digit SIC activity in that year
generates the imputed values, which have similar attributes (such as similar usage of energy per worker).
We assume that investment occurring in the previous year enters the capital stock this year.
Trade Orientation
The trade orientation of an industry is determined at a three-digit SIC level, on the basis of sector level export,
import and sales values.
•
20
Sectors that export more than 15% of their sales are classified as export oriented, sectors that have import
penetration rate above 15% are classified as import competing, and others are classified as non-traded. If
a sector’s export-output ratio and import penetration rate are above 15%, then the sector is classified as
import competing or export oriented depending on whether import penetration rate is above export-output
ratio or not. Since the definition of trade orientation involves a potential endogeneity we inspected its
stability over time. In other words, we computed the ratios using alternative sub samples. Interestingly,
trade orientation of the three digit industries does not change much over time. In addition, we compared
our classification with that of Erlat (1998) which is based on Krueger et. Al. (1981)’s criterion. 21 The
See Griliches and Mairesse (1995) for a discussion of problems arising from use of one price.
21
The criterion is based on the difference between domestic consumption C, and production Q, per unit of consumption:
T= (C-Q)/C. Using C≡Q-X+M, T is calculated as T= (M-X)/(Q-X+M), where M is imports, X is exports. Obviously, if
a sector is a net exporter, then T is less than 0. The analysis carried in Erlat (1998) leads her to use 0.40 as a cutoff
29
two classification schemes yield remarkably similar results. As in our classification, this alternative is
also stable over time, as reported in Erlat (1988). Classification based on 1983 values is reported in
Table A4 in the Appendix.
Tariff and Non-tariff Trade Barriers
There is not a single series of tariffs and non-tariff barriers at the three-digit SIC level for the entire period.
The data we use are from three different sources Krueger and Aktan (1992), Togan (1994 and 1997). We use
the existing sources in two different ways for two different purposes. Our first purpose is to see the change in
the protection rates over different periods in our sample to identify sub-periods with low or high protection
rates. Towards this end we combine the data from these sources and generate one series.22 Since in
generating this series we extrapolate and impute values the series is subject to measurement error. Thus when
we do a regression analysis of the relation between productivity and protection rates we rely only on Togan
(1994), which provides data for selected years through our entire sample period. We also adjust Togan
(1994) tariff series using information from Krueger and Aktan (1992) on levels of quantitative restriction prior
to 1995.
value to separate non-tradable from import competing sectors. The sectors with T values between 0 and 0.40 are
classified as import competing and those with T values greater than 0.40 as non-tradable.
22
Togan (1994) provides tariff data for 1983, 1984, 1988-1991. Togan (1997) provides tariffs for 1994 and the
projections for 2001 based on the Customs Union agreement with the EU. In both sources data are provided for 50
sectors used in the national input-output tables, most of which correspond to three-digit SIC codes. We convert these
tariff data to three-digit SIC series. Kruger and Aktan (1992) provide data for the period from 1980 to 1989. Their data
combines tariff rates and quantitative restrictions, which were eliminated by 1985. We use Togan’s calculations for 1988
and extend the series to the pervious years using the yearly rate of changes based on Kruger and Aktan. For the years
after 1988 we rely on Togan’s series and impute values for missing years by linearly extrapolating the existing series.
30
Table A1. Panel information – Number of Plants
(1)
(2)
(3)
(4)
Year
Total
Entry
Exit
Continuing
83
84
85
86
87
88
89
90
91
92
93
94
95
96
Total
3,724
3,724
--
--
3,748
442
309
3,019
3,695
336
352
3,133
3,831
408
283
3,223
4,071
460
243
3,347
4,193
433
328
3,488
4,301
450
339
3,525
4,360
443
391
3,563
4,272
416
411
3,526
4,166
333
397
3,562
4,789
935
319
3,549
5,024
707
420
4,045
5,273
556
357
5,607
61,054
707
10350
594
10350
4,240
-42220
Notes:
Entry: plant that was not in the sample in the previous years
Exit: plants that were in the sample in the previous year but are no longer in the sample.
Sum of the columns (2), (3) and (4) is not necessarily equal to column (1) because the plants with one
observation enter and exit in the same year (1,866 plants). The sum of the totals for column 2, 3, and 4 is equal
to 62,920, which is exactly 1,866 more than 61,054.
31
Table A2. Output-weighted average nominal protection rates (percent)
Manuf.
83
84
85
86
87
88
89
90
91
92
93
94
95
96
147.8
116.5
74.5
63.5
69.7
70.4
51.7
41.5
39.6
34.5
25.0
20.7
18.2
15.5
IC
EO
NT
102.3
93.7
74.2
63.1
58.0
60.2
47.1
33.3
30.6
27.3
20.3
16.8
14.2
11.6
190.3
134.4
73.7
63.1
79.4
79.2
53.1
44.3
44.2
39.1
28.2
22.7
20.8
18.9
256.4
203.5
89.6
74.3
96.8
82.8
83.6
93.3
89.7
75.3
50.7
38.2
32.2
26.7
Note: Some years (85-87, 92, 93, 95 and 96) have imputed values. See Appendix I for sources and
computation method. Manuf. indicated manufacturing as a whole. IC, EO and NT indicate import
competing, export oriented and non-tradable sectors, respectively.
32
Table A3. Output-weighted average real wages
(Manufacturing Sector and 1983-based Trade-orientation groups)
Year
83
84
85
86
87
88
89
90
91
92
93
94
95
96
All
IC
EO
NT
1
1
1
1
0.899
0.948
0.865
0.714
0.944
0.993
0.924
0.660
0.901
0.944
0.895
0.606
0.935
0.994
0.907
0.652
0.867
0.948
0.827
0.625
1.055
1.190
0.978
0.866
1.453
1.670
1.261
1.140
1.971
2.259
1.673
1.559
2.296
2.673
1.851
1.659
2.484
2.975
1.836
1.845
1.836
2.385
1.326
1.428
1.983
2.491
1.439
1.671
2.236
2.784
1.593
1.780
Table A4. 4-firm Concentration Ratios
1984
1990
1996
Max
Min
Mean
Variance
Manuf
27.2
27.6
24.1
28.0
24.1
26.6
1.6
IC
32.1
37.9
30.7
38.8
30.7
35.5
7.5
EO
21.4
17.7
16.2
21.4
15.7
17.6
2.3
NT
47.8
39.4
39.6
47.8
34.7
40.2
13.9
33
Figure A1: Real exchange rate
1.6
1.5
1.4
1.3
1.2
1.1
1
0.9
83
84
85
86
87
88
89
90
91
92
93
94
95
96
Note: Real exchange rate is defined as the U.S. consumer price index converted to
Turkish Lira divided by the domestic consumer price index, 1990=1.
34