Estimating Plant-Level Marginal Costs and Mark-ups in the Turkish
Manufacturing Industry
Erol Taymaz
Kamil Yilmaz
METU
Koç University
April 2015
Abstract
Using data on the value and quantity of inputs and outputs we calculate plant-level
input and output price indices. We then apply Olley=Pakes methodology to
estimate cost functions for 13 three-digit SIC industries over the 1990-2000 period
The resulting plant-level marginal costs and mark-ups are analyzed in a couple of
ways. First, we inspect the evolution of marginal costs and mark-ups over time.
While plant-level mark-ups gradually declined in 1996 and after, we observed no
significant decline in marginal costs over the same period. We then hypothesize
that the main factor behind the decline in mark-ups could be the increased import
competition after the Customs Union (CU) between Turkey and the EU went into
effect in January 1996. Finally, fixed-effect regressions of plant-level mark-ups on
SIC 4-digit sector tariff rates, import-penetration rates, export-output ratios as well
as plant-level characteristics provided statistical support for the possible impact of
the decline in tariff rates and the increase in import-penetration rates on mark-ups.
JEL Codes: D22, D24, F14, L11, L60
Keywords: Cost Function, mark-ups, plant-level data on product and input prices,
trade liberalization, Customs Union, Olley-Pakes Approach
•
This paper is written as part of the TUBITAK project no 113K392. The authors thank TUBITAK
for its generous support. The authors also Unal Tonsur for their abled research assistance.
I. Introduction
In the estimation of firm level production functions the potential correlation between
input-levels and the unobserved firm-specific shocks has been a source of concern for
applied researchers since Marschak and Andrews (1944). The same problem arises in
the dual problem of cost function estimations in the form of correlation between output,
quasi-fixed input levels and the unobserved firm-specific shocks.
The potential simultaneity bias arises from the fact that some shocks are observed by the
managers but not by outsiders. When the manager observes a large non-price shock to
the firm’s costs s/he may respond by changing the firm’s output level and investment.
Once the shock takes place, variable costs are affected, and the manager adjusts the
production level. For an outside observer, both the cost of production and the level of
production are changed simultaneously. In the presence of simultaneity, OLS estimates of
cost functions parameters will be biased, which in turn implies biased estimates of plant
level marginal costs and mark-ups calculated from the cost function parameters.
In an attempt to address this concern in the case of production function estimations a
number of solutions have been used including fixed effects, and instrumental variables
solutions. In the recent literature methods that do not require instruments have been
developed for production function estimations (Olley and Pakes, 1996, Levinsohn and
Petrin, 2000). Olley and Pakes (1996) approach relies on the inclusion of a proxy
variable for the productivity term. The proxy controls for the part of the error correlated
with inputs; thus identification relies on variation in output and inputs unrelated to firm
specific productivity. The motivation for the proxy is derived from a structural model of
an optimizing firm.
Levinsohn and Petrin (2000) differs from Olley and Pakes (1996) mainly because it
proposes to use material inputs, instead of investment, as the proxy for the productivity
shock. In this project, we used only Olley-Pakes methodology because in the cost
function estimations the material input use is also endogenous.
We build upon this literature and extend it to the estimation of the cost function under the
potential presence of simultaneity bias. In order to estimate the cost function one has to
1
assume competitive factor markets, where factor price are determined. Furthermore, the
estimation frameworks for the cost and production functions differ in their assumptions
on the exogeneity of variables. If a production function is estimated, output is
endogenous and while technology and input quantities are exogenous. In the dual cost
function, however, production costs and input quantities are endogenous while input
prices, output as well as the level of technology are exogenous. Thus, whenever it is
reasonable to assume that prices and the output quantity are indeed exogenous, it is
preferable to use a cost rather than a production function in estimations. As Berndt (1991)
argued these assumptions seem more reasonable with disaggregate data. Finally, we
focus on the short-run cost function, taking capital stock as given. As we have well
defined measures of capital stock, and not a good measure of the price of capital, we can
estimate the short-run cost function, rather than the long-run cost function where all
inputs are variable.
We use plant-level panel data series collected by TURKSTAT through annual
manufacturing establishment surveys from 1990 through 2000. From 1981 to 2002
TURKSTAT used to collect a very rich set of information through annual survey of
manufacturing establishments.
Applying Olley-Pakes methodology to TURKSTAT’s plant-level panel data, we
estimated unbiased cost function parameters for 13 3-digit ISIC manufacturing industries.
Using the estimated cost function parameters and the output prices at the firm level we
obtained panel data series of marginal costs and mark-ups.
The second half of the empirical analysis focuses on the analysis of marginal costs and
mark-ups in different industries and over time. In particular we analyze how the
distribution of marginal costs and mark-ups change over time, and especially after the
implementation of the Customs Union between Turkey and the EU in 1996. One would
expect mark-ups to go down following the implementation of the Customs Union. Our
results show that while the distribution of the marginal costs have not changed much
before or after the Customs Union, mark-ups declined significantly once the CU went
into effect in 1996. Finally, we undertake multivariate regression analysis to show that
2
mark-ups vary directly with import tariff rates and inversely with the sector level import
penetrations rates.
II. Estimation Methods
The majority of the empirical studies of firm behavior in the manufacturing industry
focus on the estimation of the firm (enterprise) or plant (establishment) level production
functions. In particular, many researchers used the micro data to analyze changes in the
firm/plant behavior in response to changes in the external environment in which the firm
operates.
In the estimation of plant-level production functions the potential correlation between
input-levels and the unobserved firm-specific shocks has been a source of concern for
applied researchers since Marschak and Andrews (1944). The cost function can be
obtained as the dual of the production function. A similar simultaneity problem arises in
the estimation of plant-level cost functions, in the form of correlation between output and
the unobserved firm-specific cost shocks.
The underlying intuition for this concern is that a firm that is subject to a large adverse
(or favorable, for that matter) shock to its production costs may respond by changing its
production level along with the input levels. If this is true, due to the simultaneity of
production costs and the output the ordinary least squares (OLS) estimates of the cost
functions would yield biased parameter estimates, thus biased estimates of firm level
marginal costs as well as the mark-ups. In addition to the simultaneity bias, OLS
estimation suffers from selection bias, which is a result of the fact that the firms that are
subject to significant increases in production costs may end up exiting the market.
Similar concerns are also valid in production function estimations using plant-level data.
In an attempt to address this concern in the framework of production function analyses a
number of solutions have been used including fixed effects, and instrumental variables
solutions. Fixed effect estimations do not solve the problem of simultaneity because it
assumes that the simultaneity bias is constant for each firm over time, an assumption not
likely to hold in real life. The use of instrumental variables for the output (such as
demand shifters) is recommended but most of the time it is difficult to find relevant
demand shifters that varies at least at the sector level if not at a firm level.
3
In recent literature, methods that do not require instruments have been developed for
production function estimations (Olley and Pakes, 1996, Levinsohn and Petrin, 2000). In
this section we build upon this literature and extend it to the estimation of cost functions
under the potential presence of the simultaneity bias.
Let us describe a Cobb-Douglas production function for firm i at time t (suppressing the
firm index i):
qt = β 0 + β p ltp + β a lta + β e et + β m mt + β k kt
(1)
where q denotes output, l p and l a denote blue-collar (production) and white-collar
(administrative) employees, e denotes energy, m denotes material inputs and k denotes
capital stock. All variables are in logarithms.
The dual of the profit maximization is the cost minimization which results in the
following short-run variable cost function for firm i at time t (suppressing the firm index
i):
ct = β!0 + β! p wtp + β!a wta + β!e pte + β!m ptm + β!k kt + β!q qt
(2)
where ct denotes the total short-run production costs, qt is the output, wtp and wta are the
wage rates for blue-collar (production) and white-collar (administrative) employees, p te is
the price of energy, p tm is the price of material inputs, and k t is the capital stock which
is fixed in the short-run. All variables are represented in log-levels.
Cost minimization implies that elasticity of the cost function with respect to variable
input prices adds up to 1: β! p + β!a + β!e + β!m = 1 . As a result, we impose this restriction
directly in the cost function estimation and normalize the variable input prices and the
short-run variable costs with the wage rate for white-collar (administrative) employees.
With this transformation, we can rewrite the cost function to be estimated, with new
variables and the estimation error term, qt :
c!t = α 0 + α p w! tp + α e p! te + α m p! tm + α k kt + α q qt + εt
4
(3)
~p, ~
To be more specific, c~t , w
p te and ~
p tm are total short-run costs, the wage rate of the
t
blue-collar employees, the price of energy and the price of material inputs, respectively,
divided by the wage rate of white-collar (administrative) employees. As a result, the
elasticity of short-run costs to administrative wages is equal to 1− α p − α e − α m .
In addition, we can test for the presence of increasing or decreasing returns to scale in the
production process, by testing whether or not the output elasticity of the short-run costs
( α q ) is less than or greater than one. When there are increasing returns to scale, an X
percent increase in output leads to less than an X percent increase in total costs, so that
the average cost of production should decrease as a result.
Another important parameter is the elasticity of the short-run total costs with respect to
the capital stock. It has to be negative because an increase in the capital stock, while
holding everything else constant, will lead to an increase in the productivity of the
variable inputs and hence lower costs of production. As a result, we will also check
whether or not α k ≤ 0 .
Finally, we included the plant-specific cost shock term in equation (3) which has two
components: a plant-specific cost component, ω t , and an idiosyncratic component η t .
The latter term affects the short-run variable costs without any change in output. The
first term, ω t , is not observed by the econometrician, but observed by the plant
management. As a result it affects the plant manager’s output decision along with
variable input decisions. A simultaneity problem arises when there is contemporaneous
correlation both within firm i and across time t between ε t and the firm’s output, q.
One solution to the potential simultaneity bias problem is to assume that ω t is plantspecific and time-invariant, and estimate the cost function in equation (3) using a fixed
effects model. If the assumption holds, the fixed effects estimation is expected to remove
the effects of time-invariant component of plant-level cost shock. The assumption of
unchanging plant specific cost shocks, however, may be a source of concern during times
of large adjustments. Furthermore, assuming constant plant-level cost shocks prevents
us from addressing how plant-level costs change over time.
5
An alternative approach that can be adopted is the instrumental variables estimator. The
instrumental variables approach relies on finding variables that are correlated with the
output, but uncorrelated with the error term, ω t . However, almost always, instrumental
variables approach suffers from the drawback of finding instruments that satisfy the
above properties.
As we highlighted above, the problem of simultaneity was first raised in the case of
production function estimations with micro data. Olley and Pakes (1996) proposed a new
approach to remedy the simultaneity bias problem production function estimation by
including a proxy for the productivity term. The proxy controls for the part of the error
correlated with inputs; thus identification relies on variation in output and inputs
unrelated to the firm specific productivity term. The motivation for the proxy is derived
from a structural model of an optimizing firm.
Slightly simplifying, we can adopt the Olley and Pakes model to the case of cost function
estimation with micro data. We know the cost minimization given the production
technology is the dual of the profit maximization given the prices of output and inputs.
Short-run variable costs are assumed to be a function of the firm’s state variables (capital,
output and plant specific cost shock), factor prices, and a vector of state variables of other
firms. Factor prices are assumed to be common across firms and evolve exogenously.
However, firms are subject to uncertainty about future market structure (which consists
of firm specific state variables for all active firms). Each period, the firm chooses its
variable factors (labor, material inputs and energy), output and a level of investment,
which together with the current capital stock determines the stock of the next period.
Investment demand function is then written as follows.
it = it (ω t , k t ) .
For positive values of investment Pakes (1994) shows that investment is strictly
increasing in the unobserved productivity shock. Hence, it (ω t , k t ) can be inverted to
yield:
ω t = (it , k t ) .
As such the unobservable firm specific cost shocks can be expressed in terms of
6
observable variables, and hence can be controlled in the cost function estimates.
Using the above expression for the unobservable cost function, equation (3) can be
rewritten as follows:
e
~ p +α ~
~m
c~t = α 0 + α p w
t
e pt + α m pt + α q qt + φ (it , k t ) + η t
where
(4)
φt (it , k t ) = α 0 + α k ⋅ k t + ωt (it , k t ) .
Consistent parameter estimates of the coefficients on output and input prices 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 Olley and Pakes, 1996).
To obtain the separate effect of capital on costs from its effect on a plant’s
investment, a second stage is required. The identification of the effect of capital on
production costs is obtained from the assumption that capital slowly adjusts to the shock.
Towards this end, the observed cost shock is decomposed into its expected and
unanticipated components:
ω t = E[ω t | ω t −1 ] + ξ t ,
In the second stage of the estimation, in period t, capital is assumed only to respond to
E[ω t | ω t −1 ]. (In the first stage of the estimation the variable inputs respond to both ω t
and η t .) Using the decomposition of production costs and the estimated coefficients of
the first stage, the cost function can be then rewritten as follows:
e
*
~ p −α ~
~m
.
c~t* = c~t − α p w
t
e pt − α m pt − α q qt = α k k t + g (ωt −1 ) + η t
(5)
where g (ω t−1 ) = α 0 + E [ω t | ω t−1 ] and η t* = ξ t + η t . Since a by-product of the first stage is
an estimator for ω t −1 , estimation of equation (5) is possible and yields consistent
estimates of α k .1
Levinsohn and Petrin (2003) introduce a new method by building on ideas developed in
Olley and Pakes (1996). The authors prove that like investment, intermediate inputs can
1
Olley and Pakes (1996) use a series expansion as well as a kernel estimator for this stage.
7
also solve the simultaneity problem. Levinsohn and Petrin (2003) point out three
arguments emphasizing the potential advantages of their method. The first is that
intermediate inputs generally respond to the entire productivity term, while investment
may only to the “news” in the unobserved term. A second advantage is that intermediate
inputs provide a simpler link between theory and estimation since they are not typical
state variables. Finally, and perhaps most importantly, investment proxy is only valid
for firms reporting non-zero investment. Large adjustment costs, often lead to a very
high level of zero investment reporting (about 41% of our sample), whereas firms always
report positive intermediate input use, such as energy. Concern with potential truncation
bias that may arise from excluding zero investment observations makes the choice of
intermediate input proxy an attractive alternative.
In presenting the results from the cost function estimations we will only report the OP
estimates. We do, however, estimate OP also by incorporating plant exit in the
estimation procedure (as in Pavcnik, 2002) so as to control for the selection bias.
III. Data
In this study we use a data set, collected by the Turkish State Institute of Statistics
(TURKSTAT) for the Turkish manufacturing industry. From 1983 to 2001,
TURKSTAT periodically conducted Census of Industry and Business Establishments
(CIBE).2 In addition, TURKSTAT used to conduct Annual Surveys of Manufacturing
Industries (ASMI) at establishments with 10 or more employees.3 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.4 For this study we use a sample that matches
2
Since the formation of the Turkish Republic CIBE has been conducted 7 times ( in 1927, 1950, 1963,
1970, 1980, 1985, and 1992).
3
TURKSTAT also collected data on establishments with less than 10 employees. However, up to 1992
data on these establishments were collected only during CIBE years. From 1992 to 2001 TURKSTAT
collected annual data for establishments with less than 10 employees but, using a sampling method.
4
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
8
plants from CIBE and ASMI for the 1990-2000 period. Unfortunately, not all key
variables needed for this study have been collected for establishments in the 10-24 size
group. Thus our sample consists of plants with 25 or more employees. Finally, we limit
the sample only to private establishments.5 In the resulting sample we have 49,915
plant-years for 11,733 plants in 23 three-digit SIC industries.
Real value of annual output is obtained by deflating the plant’s total annual sales
revenues by its output price index constructed as the weighted average of the plant’s
product prices.
Material inputs include all purchases of intermediate inputs. The nominal value of total
material input use by each plant is deflated by the material input price index for the
corresponding plant, constructed as the value-weighted average of the prices of all
material inputs used by the plant.
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 TURKSTAT.
Labor is the number of paid employees in a given year. Wage rates for the production
(blue-collar) and administrative (white-collar) employees are the average for the whole
year and obtained directly from the survey results.
Finally, capital stock series is constructed using the 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,
observations reflect the concerted effort by TURKSTAT to include all establishments in the CIBE years
(Ozler (2001)).
5
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
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 4-digit ISIC 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.
IV. Estimation Results
A. Cost Function Estimates
In this section we present our cost function coefficient estimates using Olley and Pakes
(1996) method, discussing them in detail. In Table 1 coefficient and standard errors of
OP estimates for 13 three-digit ISIC industries are presented.
We estimated the cost
function for 13 sub-sectors because many of the 3-digit sub-sectors do not have sufficient
number of observations. While the number of observations far exceeded 1000 in 9 out of
13 sub-sectors, it was below the 1000 mark in four of them. As we estimate the cost
function for a subset of three-digit ISIC industries, and for each plant the capital stock is
measured as of the beginning of the year, the size of the data set used in the cost function
estimations shrinks to 33,813 plant-year observations.
All parameter estimates have expected signs and an overwhelming majority of these
estimates are statistically significantly different from zero. The fact that the number of
observations for each industry is high increases the reliability of the estimation results. In
all sectors, capital stock has the expected negative sign, which shows that the lagged
capital stock reduced variable costs, holding other factors unchanged. The coefficient
estimate of the capital stock is not statistically significant in the manufacture of other
non-metallic mineral products (369) industry only. In three other sectors, namely, the
manufacturing of wood and cork products, except furniture (331), the manufacture of
machinery except electrical (382) and the manufacture of electrical machinery (383), the
elasticity of the variable costs with respect to capital stock is statistically significant at the
10-15 percent level.
The results reported in Table 1 show that variable costs of production responds to
changes in the energy prices the most, followed by the material input prices and blue10
collar wage rates, respectively. While the elasticity of variable costs with respect to
energy prices fluctuates between 0.5 and 0.8, that of the material input prices fluctuate
between 0.07 and 0.42, and the wages of the blue-collar production workers fluctuate
between 0 and 0.18. When we consider them together the sum of the three coefficients is
close to one. Actually, we do not expect it to be equal to one, because price of one of the
inputs (namely the administrative (white-collar) wages) is implicitly estimated, as all
other input prices and variable costs are normalized with the administrative wages.
Therefore, subtracting the estimated input price coefficients reported in Table 1 from one,
we obtain the estimated elasticity of variable costs with respect to the administrative
wage rate. The resulting estimates are presented in Table 2. In addition to the value of the
estimated coefficient, we highlight the statistical significance of the estimated parameters
with ** and * at the one and five percentage levels.
Based on the regression results, we can now discuss the elasticity estimates. The
elasticity of the variable costs with respect to administrative wages is positive and
statistically significantly different from zero in the manufacturing of food and beverages
(311), the manufacturing of wood and cork products, except furniture (331), manufacture
of plastic products not elsewhere classified (356), the manufacture of other non-metallic
mineral products (369), iron and steel basic industries (371), manufacture of machinery
except electrical (382) and manufacture of transport vehicles (384). Even though the
coefficient estimates of the administrative wages in these industries are as expected, they
are, nevertheless not as sizeable. The estimated elasticity of variable costs with respect to
administrative wages is around 0.05, if not lower. This is not an unexpected result. Even
though, administrative personnel, engineers and technicians play important roles in the
manufacturing industry, their salaries form only a small fraction of total variable costs.
Hence an increase in the wage rate paid to administrative employees will not increase the
variable costs by a significant margin.
The last issue we would like to discuss about the cost function estimates of Table 1 and
Table 2 is about the economies of scale. The estimated output elasticity of the variable
costs is lower than one in all sectors, except for the, iron and steel basic industries (371).
In the manufacturing of iron and steel basic industries, the elasticity is 1.058 and
statistically significantly different from 1, indicating that there are diseconomies of scale
11
in this sector. This implies that as the output increases the average variable cost increases
in the iron and steel sector. In all other sectors the data support the presence of economies
of scale and average variable cost declining with the level of production. This result is
consistent with the findings of other papers (see de Loecker et. al. (2011)).
B. Customs Union and Increased Import Competition
The results we obtained so far showed that the direct estimation of the cost function
produces quite reliable estimates. However, this is just the beginning of the empirical
analysis: Multiplying the sector-level output elasticity estimate ( α̂ q ) with plant-level
variable costs and dividing by plant-level output we obtain an estimate of the marginal
cost of production at the plant level. Finally, the logarithm of the ratio of the plant-level
price and the marginal cost gives us the plant-level mark-ups. As we emphasized at the
beginning, we obtain the estimates of marginal costs and mark-ups by directly using the
cost function estimates rather than deriving them under stringent assumptions from the
production function estimates (see de Loecker 2011).
In Figure 1, we first plot the average marginal costs and mark-ups for the whole
manufacturing industry over the 1990-2000 period. Before we plot the marginal costs,
however, we make sure to divide the marginal costs with the plant-level input price index
in order to remove the inflation effect. Once normalized with input prices, we can
analyze how the distribution of marginal costs across plants varies over time along with
the distribution of mark-ups.
While the average marginal costs declined slowly (by 15 percent) from 1991 to 1995, the
average mark-ups increased by 22 percent over the same period. The first half of 1990s
was a period of frequent elections. After the general election of 1987, the country had
local elections in 1989, followed by early general elections in 1991, and local elections in
1994. Frequent elections combined with fierce competition among center right parties led
to a heavy reliance on populist economic policies.
Leaving 1994 aside, as the year of economic crisis, the economy was mostly in an
expansionary mode over the period. This was also the period during which export
oriented growth strategy lost its glamour and the central bank showed its desire to keep
12
the inflation under control despite the ever increasing fiscal budget deficits. In 1989, the
government decided to liberalize the capital flows, which led to a jump in private capital
inflows. As an outcome, Turkish Lira appreciated in real terms. The outcome of the
existing policy environment was the ever-growing domestic demand, whose benefits
could be reaped by domestic producers in the form of higher sales and mark-ups.
The upward trend in the average mark-ups was reversed as the Customs Union between
the EU and Turkey went into effect in 1996. The average mark-up declined by 15 percent
in 1996 and after a year of hiatus it declined by 18.5 percent in 1998. Despite a short
reversal 1999, the average mark-up declined again in 2000 and ended up 36 percent
lower than its level in 1995. From 1995 to 2000, the average marginal costs increased
by 8 percent only.
Figure 1 shows the impact of increased import competition on mark-ups in the
manufacturing industry. As import competition increased in and after 1996, the
manufacturing prices declined substantially, while marginal costs of production did not
record a commensurate decline. As a result, the ability of plants/firms to generate higher
profit margins was constrained.
While the behavior of average mark-ups and marginal costs reveal substantial
information about the effect of the CU on the manufacturing industry, we think that it is
not sufficient to look at the behavior of the average rates. Instead, it will be more
conclusive to analyze how the behavior of the distribution of mark-ups and marginal
costs changed over time. For that reason, we plot the behavior of the distribution of
marginal costs in every even year from 1990 to 1996 and in Figure 2, and in every even
year from 1996 to 2000 in Figure 3. The two figures provide information consistent with
the one obtained from Figure 1: holding everything else constant there was little
discernable decline in the distribution of marginal costs after the implementation of the
CU.
The plots of the distribution of mark-ups over time in Figure 4 (1990, 1992, 1994 and
1996) and Figure 6 (1996, 1998 and 2000) reveal information that supports the results we
obtained from the analysis of average mark-ups over time. While there was little leftward
move in the distributions of marginal costs over time, the distributions of mark-ups have
13
moved to the left significantly with the implantation of the CU in 1996 and the
subsequent years.
When we put the mark-ups for even years in one graph (see Figure 6) the leftward
movement of the distribution of mark-ups becomes even more discernable. While there
was little decline in mark-ups from 1990 to 1994, starting in 1996 the distribution of
mark-ups moved gradually to the left. Consequently, we can conclude that the results we
obtained from the behavior of full distribution of mark-ups and marginal costs over time
are consistent with the results we obtained from the average marginal costs and mark-ups.
We used the OECD industry classifications that divide manufacturing sub-sectors based
on the characteristics of the production process. We consider four groups of industries:
Resource intensive, labor intensive, scale intensive and specialized suppliers. We then
checked the mark-ups for each of the sub-industry groups. Our results are plotted in
Figures 7-10. The leftward move of the mark-ups was most discernable in the case of
labor-intensive sectors, followed by scale intensive and specialized supplier industries.
Mark-ups in the resource intensive industries were affected the least from the increased
import competition from EU imports.
Once we completed the graphical analysis of the behavior of marginal costs and markups over time, we can now move to the regression analysis. It was shown that following
the CU average import tariffs in the manufacturing industry declined. Furthermore,
import penetration rates increased especially in 1996. These together allow us to study
the impact of these measures on the short-run variable costs of the manufacturing plants.
For that reason, we undertake separate fixed-effect regressions of marginal costs and
mark-ups on import tariffs, 4-digit ISIC sector level import penetration rates as well as
the sector level export output ratio and plant level characteristics.
The results are presented in Tables 3 through 6. Irrespective of using the current or
lagged import tariff rates, and irrespective of using other sector-level variables, plant
characteristics and lagged dependent variables in the regressions, plant-level marginal
costs and mark-ups tend to increase with sector-level import tariff rates.
Furthermore, when we include the import-penetration rates, both import tariffs and
import-penetration rates have statistically significant impact on mark-ups. Import14
penetration rates are defined as the ratio of imports to total sales in the domestic market,
which is equal to the imports plus output of the domestic industry minus its exports. We
incorporate both the import tariffs and import-penetration rates in the regressions because
imports can be affected through other trade measures such as quantitative restrictions,
anti-dumping duties, technical specifications and standards.
As the data on these
measures are not readily available, it would make more sense to use import-penetration
rates at the sector level as a direct measure of import competition faced by domestic
producers. As can be seen in Figure 12, average import tariffs dropped by 2 points from
10 percent to 8 percent in 1996 and 1997. This is still a significant decline. Yet, the
import competition increased even further as measured by the import-penetration rates.
The average import-penetration rate increased from 15.8% in 1995 to 20% in 1996.
Furthermore, when import tariffs decline significantly it does not necessarily impy that
import competition would increase at the same rate. As can be seen, in Figure 12, while
import tariff declined from 22% in 1990 to 7% in 1993, import penetration did not
change effectively. Therefore, along with import tariff rates it make a lot of sense to
include the import penetration rate in the plant-level analysis of marginal cost and markup.
Along with the import penetration rates we also include export-output ratios in the plant
level marginal and mark-up regressions. Irrespective of using the contemporaneous or
lagged import penetration rates in the marginal cost and mark-up regressions, the
coefficient estimates for import penetration rates are statistically significant. According to
our estimates, a 10 percent increase in import penetration rates leads to a 3 percent
increase in marginal costs next period, it lowers the plant level mark-ups by 4 percent
next period. Increased competition leads to a decline in the sales of domestic firms. As
their production scale decreases marginal costs tend to increase. In the meantime, markups decline more than the increase in marginal costs, reflecting the decline in domestic
firms’ prices as a result of increased competition. When we consider the
contemporaneous effect, it is statistically and economically more significant than the
lagged effect. Therefore, we can conclude that the increased competition by imports
improved welfare by lowering domestic prices and making goods cheaper for domestic
consumers.
15
In our plant-level marginal cost and mark-up regression we also included sector level
export-output ratios as explanatory variables. Export output ratios tend to lower the
marginal costs and mark-ups, when we consider them contemporaneously. However,
when we include the lagged rather than the contemporaneous export-output ratios in our
regression, the coefficient estimates are no longer statistically significant. We conclude
that the negative coefficient estimates for the export-output ratios can be due to sector
level variation.
Finally, the plant-level characteristics that we included in the marginal cost and mark-up
regressions are in general statistically insignificant, especially when they are included as
lagged variables in the corresponding equation. We think plant level must have already
captured by the plant-fixed effects. That is why they are not statistically significantly
from zero.
IV. Conclusions
This paper is a contribution to the literature on plant level empirical analysis of the
impact of trade liberalization on domestic competition, productivity and cost structure.
Indeed it is the first paper in the literature that calculates the plant level input and output
price series from detailed plant-level data on input and output product prices.
Once plant level input and output price indices are calculated, applying the methodology
introduced by Olley and Pakes (1996), we estimated cost functions for 13 three-digit ISIC
industries over the 1990-2000 period. We showed that all but one of the 13
manufacturing sub-sectors analyzed have displayed increasing returns to scale.
Furthermore, we showed that variable short-run costs are much more elastic with respect
to energy prices than the blue-collar wages and the material input prices.
Based on the cost function parameter estimates, we estimated plant level marginal costs
and mark-ups and analyzed these estimates in several different ways. First, we inspected
their evolution over time and across industries. We show that while plant level marginal
cost distributions did not change much over time, plant level mark-ups increased in the
first half of 1990s. After this increase, mark-ups started declining in 1996, which happens
to be the year when the CU agreement went into effect and continued to do so in
16
subsequent years. These results as a whole are the direct evidence of the increased
import competition following the implementation of the CU in 1996.
Once we estimated plant level marginal costs and mark-ups and study their behavior over
time, we analyzed whether the marginal costs and mark-ups are responsive to changes in
nominal protection rates as measured by import tariffs as well as other measures of
import competition. Towards that end, we estimated fixed-effect regressions of marginal
costs and mark-ups on import tariffs, 4-digit sector import penetration rates and exportoutput ratios as well as several plant characteristics. The results showed that even after
taking into account the possible impact of other factors, marginal costs and mark-ups
respond statistically significantly to changes in nominal protection rates as well as to
import penetration rates. Decreases in protection rates reduce marginal costs and markups, whereas an increase in import penetration rates does reduce mark-ups only.
Based on bivariate and multivariate fixed effect regression analyses we, therefore,
conclude that increased import competition has significant impact on marginal costs and
mark-ups.
17
Bibliography
Baily, M. N., C. Hulten, D. Campbell, 1992, “Productivity Dynamics in Manufacturing
Plants”, Brooking Papers: Microeconomics 1992, 187–225
Berndt, E., 1991, The Practice of Econometrics: Classic and Contemporary, AddisonWesley.
Blundell, R. and S. Bond, 1999, “GMM Estimation with Persistent Panel Data: An
Application to Production Function,” Working paper W99/4, The Institute for
Fiscal Studies.
De Loecker, J. 2011. “Product Differentiation, Multiproduct Firms, and Estimating the
Impact of Trade Liberalization on Productivity,” Econometrica 79(5), 1407–1451.
De Loecker, J., P. K. Goldberg, A. K. Khandelwal and N. Pavcnik, 2012, “Prices,
Markups and Trade Reform,” mimeo.
Ericson, R. and A. Pakes, 1995, “Markov-Perfect Industry Dynamics: A Framework for
Empirical Work,” Review of Economic Studies, 62, pp. 53-82.
Foster, L., J. Haltiwanger and C. J. Krizan, 2001, “Aggregate Productivity Growth:
Lessons from Microeconomic Evidence”, in E. Dean, M. Harper and Ch. Hulten
(eds.), New Developments in Productivity Analysis, Chicago University Press.
Gökçekuş, Ö., 1997, “Trade Liberalization and Productivity Growth: New Evidence from
the Turkish Rubber Industry”, Applied Economics, 29 (5), 639-645.
Griliches, Z. and H. Regev, 1995, “Firm Productivity in Israeli Industry 1979–1988,”
Journal of Econometrics 65, 175–203.
Klette, T. J., and Z. Griliches, 1996. “The Inconsistency of Common Scale Estimators
When Output Prices are Unobserved and Endogenous”, Journal of Applied
Econometrics 11 (4), 343–61.
Levinsohn, J. ve A. Petrin, 2003, “Estimating Production Functions Using Inputs to
Control for Unobservables,” Review of Economic Studies, 70, 317-342.
Marschak, J. and W. Andrews, 1944, “Random Simultaneous Equations and the Theory
of Production,” Econometrica, 12(3-4), 143-205.
18
Melitz, M. J., 2003, “The Impact of Trade on Intra-industry Reallocations and Aggregate
Industry Productivity,” Econometrica 71(6), 1695–1725.
Olley, S. ve A. Pakes, 1996, “The Dynamics of Productivity in the Telecommunications
Equipment Industry,” Econometrica, 64, 1263-1297.
Özler, Ş. and K. Yilmaz, 2009, “Productivity Response to Reduction in Trade Barriers:
Evidence from Turkish Manufacturing Plants,” Review of World Economics, Vol.
145(2), 339-360.
Pavcnik, N., 2002, “Trade Liberalization, Exit, and Productivity Improvements: Evidence
from Chilean Plants,” Review of Economic Studies, 69 (1): 245-276
Taymaz, E. and G. Saatçi, 1997, “Technical Change and Efficiency in Turkish
Manufacturing Industries,” Journal of Productivity Analysis, 8, 461-475.
Taymaz, E., E. Voyvoda and K. Yılmaz, 2008, Türkiye İmalat Sanayiinde Yapısal
Dönüşüm ve Teknolojik Değişme Dinamikleri, ERC Çalışma Metni 0804, Orta
Doğu Teknik Üniversitesi.
Taymaz, E. and K. Yılmaz (2007), “Productivity and Trade Orientation: Turkish
Manufacturing Industry Before and After the Customs Union,” Journal of
International Trade and Diplomacy, 1(1), 127-154.
Tybout, J. (2000), “Manufacturing Firms in Developing Countries: How Well Do They
Do, and Why?” Journal of Economic Literature, 38, 11-44.
Yaşar, M., R. M. Rejesus ve İ. Mintemur (2004), “Is there Evidence of Creative
Destruction in the Turkish Manufacturing Sector? Lessons from a Cross-industry
Analysis of Aggregate Productivity Growth”, Applied Economics, 36 (17), 19371945.
19
Table 1. Cost Function Estimates a la Olley and Pakes (1996)
Sector
311
312
321
322
331
352
356
369
371
381
382
383
384
Blue-Collar
Wage
0.069
(5.8)**
0.031
(1.27)
0.145
(15.3)**
0.206
(18.4)**
0.147
(4.23)**
0.187
(6.82)**
0.047
(2.49)*
0.164
(8.06)**
0.014
(0.62)
0.107
(7.53)**
0.141
(7.38)**
0.111
(6.03)**
0.089
(4.54)**
Energy
Price
0.493
(26.0)**
0.607
(15.7)**
0.554
(38.7)**
0.442
(26.4)**
0.629
(15.2)**
0.713
(22.3)**
0.682
(25.4)**
0.738
(31.5)**
0.621
(18.4)**
0.685
(34.9)**
0.685
(30.8)**
0.807
(40.6)**
0.762
(29.5)**
Material
Input
Prices
0.415
(23.6)**
0.333
(9.4)**
0.292
(22.2)**
0.347
(22.6)**
0.175
(6.3)**
0.122
(5.4)**
0.240
(11.1)**
0.072
(4.9)**
0.320
(11.2)**
0.212
(12.7)**
0.146
(8.7)**
0.084
(6.9)**
0.121
(6.2)**
+ p<0.10; * p<0.05; ** p<0.01
20
Output
0.958
(138.4)**
0.991
(76.0)**
0.951
(178.5)**
0.945
(138.5)**
0.934
(47.2)**
0.877
(50.4)**
0.959
(78.1)**
0.844
(67.8)**
1.058
(101.3)**
0.933
(103.7)**
0.892
(70.5)**
0.940
(78.4)**
0.965
(75.6)**
Capital Stock
(t-1)
-0.020
(-3.20)**
-0.070
(-4.38)**
-0.013
(-4.44)**
-0.027
(-3.02)**
-0.018
(-1.62)
-0.098
(-2.11)*
-0.071
(-3.08)**
-0.007
(-0.25)
-0.076
(-15.7)**
-0.015
(-2.02)*
-0.012
(-1.57)
-0.012
(-1.52)
-0.033
(-4.43)**
Table 2. White-Collar Wages Coefficient and
Constant Returns to Scale Hypothesis
Sector
311
312
321
322
331
352
356
369
371
381
382
383
384
WhiteCollar Wage
0.023**
0.029+
0.009
0.005
0.049*
-0.022
0.031**
0.026*
0.045**
-0.004
0.028*
-0.002
0.028*
Constant
Returns to Scale
Hypothesis
0.958**
0.991
0.951**
0.945**
0.934**
0.877**
0.959**
0.844**
1.058**
0.933**
0.892**
0.940**
0.965**
+ p<0.10;* p<0.05; ** p<0.01
21
Table 3. Marginal Costs and Import Tariffs
Current Import Tariffs
(1)
(2)
0.066
--
(0.004)**
Lagged Import Tariffs
(3)
(4)
0.059
--
(0.006)**
--
0.063
--
0.056
(0.005)**
Lagged Marginal Cost
Adjusted R2
N
--
--
0.97
33,802
0.97
24,452
(0.005)**
0.105
0.104
(0.014)**
(0.014)**
0.97
24,452
0.97
24,452
Controlled for year and 4-digit sector fixed effects; * p<0.05; ** p<0.01
Table 4. Marginal Costs and Trade Liberalization
Current Explanatory
Variables
Import Tariffs
Import Penetration Rate
Export-Output Ratio
Capital/Labor Ratio
Lagged Explanatory
Variables
0.042
0.042
0.050
0.049
(0.006)**
(0.006)**
(0.006)**
(0.006)**
0.043
0.043
0.028
0.029
(0.007)**
(0.007)**
(0.006)**
(0.006)**
-0.027
-0.027
0.003
0.003
(0.006)**
(0.006)**
(0.005)
(0.005)
--
-0.016
--
(0.005)**
Foreign Share
--
0.001
(0.005)
--
(0.000)
Skilled Labor Share
--
0.013
--
-0.012
--
Adjusted R2
N
-0.017
(0.024)
--
(0.009)
Lagged MC
0.000
(0.001)
(0.023)
Imported M&E Share
-0.009
-0.023
(0.009)**
0.101
0.101
0.103
0.103
(0.014)**
(0.014)**
(0.014)**
(0.014)**
0.98
24,445
0.98
24,391
0.98
24,444
Controlled for year and 4-­‐digit sector fixed effects; * p<0.05; ** p<0.01
22
0.98
24,386
Table 5. Dependent Variable – Mark-up
(1)
Current Import Tariffs
(2)
(4)
0.042
0.028
(0.014)**
(0.019)
Lagged Import Tariffs
0.053
0.048
(0.017)**
(0.016)**
Lagged Mark-up
Adjusted R2
N
(3)
0.35
33,804
0.34
24,452
0.135
0.135
(0.020)**
(0.020)**
0.35
24,452
0.35
24,452
Controlled for year and 4-­‐digit sector fixed-­‐effects; * p<0.05; ** p<0.01
Table 6. Dependent Variable – Mark-up
Current Explanatory
Variables
Import Tariffs
Import Penetration Rate
Export-Output Ratio
Capital/Labor Ratio
Lagged Explanatory
Variables
0.047
0.044
0.065
0.066
(0.022)*
(0.022)*
(0.018)**
(0.018)**
-0.096
-0.096
-0.047
-0.049
(0.022)**
(0.022)**
(0.016)**
(0.016)**
-0.042
-0.043
0.007
0.006
(0.017)*
(0.017)*
(0.015)
(0.015)
--
-0.011
--
(0.028)
Foreign Share
--
0.039
--
(0.015)*
Skilled Labor Share
--
-0.005
--
-0.063
--
Adjusted R2
N
0.001
(0.002)
--
(0.072)
Lagged Mark-up
0.028
(0.016)
(0.002)*
Imported M&E Share
0.003
(0.028)
0.014
(0.075)
0.134
0.133
0.134
0.134
(0.020)**
(0.020)**
(0.020)**
(0.021)**
0.35
24,445
0.35
24,391
0.35
24,444
Controlled for year and 4-digit sector fixed effects; * p<0.05; ** p<0.01
23
0.35
24,386
2.6 -­‐2.1 2.5 -­‐2.2 Mark-­‐ups 2.4 -­‐2.3 2.3 -­‐2.4 2.2 -­‐2.5 2.1 -­‐2.6 Marginal Costs 2 -­‐2.7 1.9 -­‐2.8 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 1.5
Figure 1. Average Marginal Cost and Mark-up Over Time (1990-2000)
0
.5
Density
1
1990
1992
1994
1996
-4
-3
-2
marginal costs normalized with input prices
-1
kernel = epanechnikov, bandwidth = 0.0708
Figure 2. Distribution of Marginal Costs Over Time (1990-1996)
24
1.5
0
.5
Density
1
1996
1998
2000
-4
-3
-2
marginal costs normalized with input prices
-1
kernel = epanechnikov, bandwidth = 0.0589
1.5
Figure 3. Distribution of Marginal Costs Over Time (1996-2000)
0
.5
Density
1
1990
1992
1994
1996
1
2
mark-ups
3
4
kernel = epanechnikov, bandwidth = 0.0946
Figure 4. Distribution of Mark-ups Over Time (1990-1996)
25
.5
0
Density
1
1996
1998
2000
1
2
mark-up
3
4
kernel = epanechnikov, bandwidth = 0.0677
1.5
Figure 5. Distribution of Mark-ups Over Time (1996-2000)
0
.5
Density
1
1990
1992
1994
1996
1998
2000
1
2
mark-ups
3
4
kernel = epanechnikov, bandwidth = 0.0946
Figure 6. Distribution of Mark-ups Over Time (1990-2000)
26
1.5
0
.5
Density
1
1996
1998
2000
1
2
mark-up
3
4
kernel = epanechnikov, bandwidth = 0.0834
1.5
Figure 7. Resource Intensive Sector - Distribution of Mark-ups (1996-2000)
0
.5
Density
1
1996
1998
2000
1
2
mark-up
3
4
kernel = epanechnikov, bandwidth = 0.0607
Figure 8. Labor Intensive Sectors - Distribution of Mark-ups (1996-2000)
27
.6
0
.2
Density
.4
1996
1998
2000
1
2
mark-up
3
4
kernel = epanechnikov, bandwidth = 0.1818
.6
Figure 9. Specialized Supplier Sectors - Distribution of Mark-ups (1996-2000)
0
.2
Density
.4
1996
1998
2000
1
2
mark-up
3
4
kernel = epanechnikov, bandwidth = 0.1818
Figure 10. Scale Intensive Sectors - Distribution of Mark-ups (1996-2000)
28
2.5 -­‐2 Mark-­‐ups 2.4 -­‐2.1 2.3 -­‐2.2 2.2 -­‐2.3 Marginal Costs 2.1 -­‐2.4 2 -­‐2.5 1.9 -­‐2.6 1.8 -­‐2.7 1.7 -­‐2.8 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 Figure 11. Automotive Industry – Average MC and Mark-ups (1990-2000)
0.25 0.2 0.15 0.1 0.05 0 1990 1991 1992 1993 1994 1995 Import Penetra8on Rate 1996 1997 1998 1999 2000 Import Tariffs Figure 12. Average Import Tariff Rate and Import-Penetration Rate
29