Estimating Plant-Level Marginal Costs and Mark-ups in the...

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.

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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

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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

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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.

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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):

tktmteata

ptpt kmellq ββββββ +++++= 0 (1)

where q denotes output, pl and al 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 =!β0 + !βpwt

p + !βawta + !βe pt

e + !βmptm + !βkkt + !βqqt (2)

where tc denotes the total short-run production costs, tq is the output, wtp and wt

a are the

wage rates for blue-collar (production) and white-collar (administrative) employees, etp is

the price of energy, mtp is the price of material inputs, and tk 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: !βp + !βa + !βe + !βm =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, tq :

!ct =α0 +α p !wtp +αe !pt

e +αm !ptm +αkkt +αqqt +εt (3)

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To be more specific, tc~ , ptw~ , e

tp~ and mtp~ are total short-run costs, the wage rate of the

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 −αe −αm .

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

( qα ) 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 αk ≤ 0 .

Finally, we included the plant-specific cost shock term in equation (3) which has two

components: a plant-specific cost component, tω , and an idiosyncratic component tη .

The latter term affects the short-run variable costs without any change in output. The

first term, tω , 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 tε and the firm’s output, q.

One solution to the potential simultaneity bias problem is to assume that tω is plant-

specific 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.

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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, tω . 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.

),( tttt kii ω= .

For positive values of investment Pakes (1994) shows that investment is strictly

increasing in the unobserved productivity shock. Hence, ),( ttt ki ω can be inverted to

yield:

),( ttt ki=ω .

As such the unobservable firm specific cost shocks can be expressed in terms of

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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:

ttttqmtm

ete

ptpt kiqppwc ηφααααα ++++++= ),(~~~~

0 (4)

where ),(),( 0 ttttkttt kikki ωααφ +⋅+= .

Consistent parameter estimates of the coefficients on output and input prices can then be

obtained using a semi-parametric estimator (for example by modeling tφ 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:

[ ] tttt E ξωωω += −1| ,

In the second stage of the estimation, in period t, capital is assumed only to respond to

[ ]1| −ttE ωω . (In the first stage of the estimation the variable inputs respond to both tω

and tη .) Using the decomposition of production costs and the estimated coefficients of

the first stage, the cost function can be then rewritten as follows:

*1

* )(~~~~~tttktq

mtm

ete

ptptt gkqppwcc ηωααααα ++=−−−−= − . (5)

where g ωt−1( ) =α0 +E ωt |ωt−1[ ] and ttt ηξη +=* . Since a by-product of the first stage is

an estimator for 1−tω , estimation of equation (5) is possible and yields consistent

estimates of kα .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.

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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 c onducted 7 times ( in 1927, 1950, 1963, 1970, 1980, 1985, and 1992). 3TURKSTAT 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

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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.

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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 blue-

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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

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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 (α̂q ) 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

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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

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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 mark-

ups 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. Import-

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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 mark-

up.

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, mark-

ups 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.

16

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

17

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 export-

output 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 mark-

ups, 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.

18

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20

Table 1. Cost Function Estimates a la Olley and Pakes (1996)

Sector Blue-Collar

Wage Energy Price

Material Input Prices Output

Capital Stock (t-1)

311 0.069 0.493 0.415 0.958 -0.020

(5.8)** (26.0)** (23.6)** (138.4)** (-3.20)**

312 0.031 0.607 0.333 0.991 -0.070

(1.27) (15.7)** (9.4)** (76.0)** (-4.38)**

321 0.145 0.554 0.292 0.951 -0.013

(15.3)** (38.7)** (22.2)** (178.5)** (-4.44)**

322 0.206 0.442 0.347 0.945 -0.027

(18.4)** (26.4)** (22.6)** (138.5)** (-3.02)**

331 0.147 0.629 0.175 0.934 -0.018

(4.23)** (15.2)** (6.3)** (47.2)** (-1.62)

352 0.187 0.713 0.122 0.877 -0.098

(6.82)** (22.3)** (5.4)** (50.4)** (-2.11)*

356 0.047 0.682 0.240 0.959 -0.071

(2.49)* (25.4)** (11.1)** (78.1)** (-3.08)**

369 0.164 0.738 0.072 0.844 -0.007

(8.06)** (31.5)** (4.9)** (67.8)** (-0.25)

371 0.014 0.621 0.320 1.058 -0.076

(0.62) (18.4)** (11.2)** (101.3)** (-15.7)**

381 0.107 0.685 0.212 0.933 -0.015

(7.53)** (34.9)** (12.7)** (103.7)** (-2.02)*

382 0.141 0.685 0.146 0.892 -0.012

(7.38)** (30.8)** (8.7)** (70.5)** (-1.57)

383 0.111 0.807 0.084 0.940 -0.012

(6.03)** (40.6)** (6.9)** (78.4)** (-1.52)

384 0.089 0.762 0.121 0.965 -0.033

(4.54)** (29.5)** (6.2)** (75.6)** (-4.43)**

+ p<0.10; * p<0.05; ** p<0.01

21

Table 2. White-Collar Wages Coefficient and Constant Returns to Scale Hypothesis

Sector White-

Collar Wage

Constant Returns to Scale

Hypothesis 311 0.023** 0.958** 312 0.029+ 0.991 321 0.009 0.951** 322 0.005 0.945** 331 0.049* 0.934** 352 -0.022 0.877** 356 0.031** 0.959** 369 0.026* 0.844** 371 0.045** 1.058** 381 -0.004 0.933** 382 0.028* 0.892** 383 -0.002 0.940** 384 0.028* 0.965**

+ p<0.10;* p<0.05; ** p<0.01

22

Table 3. Marginal Costs and Import Tariffs (1) (2) (3) (4) Current Import Tariffs 0.066 -- 0.059 -- (0.004)** (0.006)** Lagged Import Tariffs -- 0.063 -- 0.056 (0.005)** (0.005)** Lagged Marginal Cost -- -- 0.105 0.104 (0.014)** (0.014)** Adjusted R2 0.97 0.97 0.97 0.97 N 33,802 24,452 24,452 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

Lagged Explanatory Variables

Import Tariffs 0.042 0.042 0.050 0.049 (0.006)** (0.006)** (0.006)** (0.006)** Import Penetration Rate 0.043 0.043 0.028 0.029 (0.007)** (0.007)** (0.006)** (0.006)** Export-Output Ratio -0.027 -0.027 0.003 0.003 (0.006)** (0.006)** (0.005) (0.005) Capital/Labor Ratio -- -0.016 -- -0.009 (0.005)** (0.005) Foreign Share -- 0.001 -- 0.000 (0.000) (0.001) Skilled Labor Share -- 0.013 -- -0.017 (0.023) (0.024) Imported M&E Share -- -0.012 -- -0.023 (0.009) (0.009)** Lagged MC 0.101 0.101 0.103 0.103 (0.014)** (0.014)** (0.014)** (0.014)** Adjusted R2 0.98 0.98 0.98 0.98 N 24,445 24,391 24,444 24,386

Controlled for year and 4-‐digit sector fixed effects; * p<0.05; ** p<0.01

23

Table 5. Dependent Variable – Mark-up

(1) (2) (3) (4) Current Import Tariffs 0.042 0.028 (0.014)** (0.019) Lagged Import Tariffs 0.053 0.048 (0.017)** (0.016)** Lagged Mark-up 0.135 0.135 (0.020)** (0.020)** Adjusted R2 0.35 0.34 0.35 0.35 N 33,804 24,452 24,452 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

Lagged Explanatory Variables

Import Tariffs 0.047 0.044 0.065 0.066 (0.022)* (0.022)* (0.018)** (0.018)** Import Penetration Rate -0.096 -0.096 -0.047 -0.049 (0.022)** (0.022)** (0.016)** (0.016)** Export-Output Ratio -0.042 -0.043 0.007 0.006 (0.017)* (0.017)* (0.015) (0.015) Capital/Labor Ratio -- -0.011 -- 0.003 (0.028) (0.028) Foreign Share -- 0.039 -- 0.028 (0.015)* (0.016) Skilled Labor Share -- -0.005 -- 0.001 (0.002)* (0.002) Imported M&E Share -- -0.063 -- 0.014 (0.072) (0.075) Lagged Mark-up 0.134 0.133 0.134 0.134 (0.020)** (0.020)** (0.020)** (0.021)** Adjusted R2 0.35 0.35 0.35 0.35 N 24,445 24,391 24,444 24,386

Controlled for year and 4-digit sector fixed effects; * p<0.05; ** p<0.01

24

Figure 1. Average Marginal Cost and Mark-up Over Time (1990-2000)

Figure 2. Distribution of Marginal Costs Over Time (1990-1996)

-‐2.8

-‐2.7

-‐2.6

-‐2.5

-‐2.4

-‐2.3

-‐2.2

-‐2.1

1.9

2

2.1

2.2

2.3

2.4

2.5

2.6

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

Mark-‐ups

Marginal Costs

0.5

11.

5D

ensi

ty

-4 -3 -2 -1marginal costs normalized with input prices

1990199219941996

kernel = epanechnikov, bandwidth = 0.0708

25

Figure 3. Distribution of Marginal Costs Over Time (1996-2000)

Figure 4. Distribution of Mark-ups Over Time (1990-1996)

0.5

11.

5D

ensi

ty

-4 -3 -2 -1marginal costs normalized with input prices

199619982000


0.5

11.

5D

ensi

ty

1 2 3 4mark-ups

1990199219941996


26



0.5

1

Den

sity

1 2 3 4mark-up

199619982000


0.5

11.

5D

ensi

ty

1 2 3 4mark-ups

199019921994199619982000


27

Figure 7. Resource Intensive Sector - Distribution of Mark-ups (1996-2000)

Figure 8. Labor Intensive Sectors - Distribution of Mark-ups (1996-2000)

0.5

11.

5D

ensit

y

1 2 3 4mark-up

199619982000


0.5

11.

5D

ensi

ty

1 2 3 4mark-up

199619982000


28

Figure 9. Specialized Supplier Sectors - Distribution of Mark-ups (1996-2000)

Figure 10. Scale Intensive Sectors - Distribution of Mark-ups (1996-2000)

0.2

.4.6

Den

sity

1 2 3 4mark-up

199619982000


0.2

.4.6

Den

sity

1 2 3 4mark-up

199619982000


29

Figure 11. Automotive Industry – Average MC and Mark-ups (1990-2000)

Figure 12. Average Import Tariff Rate and Import-Penetration Rate

-‐2.8

-‐2.7

-‐2.6

-‐2.5

-‐2.4

-‐2.3

-‐2.2

-‐2.1

-‐2

1.7

1.8

1.9

2

2.1

2.2

2.3

2.4

2.5

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

Mark-‐ups

Marginal Costs

0

0.05

0.1

0.15

0.2

0.25

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

Import Penetra8on Rate Import Tariffs

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