TFP Growth in Turkey ( Tham Khao Layout )

8/12/2019 TFP Growth in Turkey ( Tham Khao Layout )

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Total Factor Productivity Growth inTurkish Manufacturing Industries: AMalmquist Productivity Index

Approach

SERPIL SEVILAY SENTURK

KTH Economics ofInnovation and

Growth

Master of Science ThesisStockholm, Sweden 2010


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Total Factor Productivity Growth inTurkish Manufacturing Industries: AMalmquist Productivity Index

ApproachSERPIL SEVILAY SENTURK

Master`s Thesis in Economics of Innovation andGrowth (30 ECTS credits) at the School of ArchitectureRoyal I and the Built EnvironmentnstituteSupervisof Technology year 2010or at ABE was Hans LfExaminer was Hans Lf

RoyalInstituteofTechnologySchoolofArchitectureandtheBuiltEnvironment

KTHABESE100 44 Stockholm, Sweden


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Total Factor Productivity Growth in TurkishManufacturing Industries: A Malmquistroductivity Index ApproachPAbstract

This thesis aims to estimate and analyze the total factor productivity (TFP) growth ratesof public and private manufacturing industries in Turkey over the period 1985 to 2001using DEA linear programming technique. The empirical results indicate that TFPincreased by 0.56% for the entire manufacturing industry, by 0.51% for public sectorsand by 0.60% for private sectors. The impact of the currency crisis in Turkey in 1994 onthe manufacturing industry has also been discussed. I find that the crisis affected theTurkish manufacturing industry as a whole along with its public and privatecomponents negatively in terms of TFP growth.


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Contents

CHAPTER1- INT RODUCTION.....................................................................................................................................1

1.1BACKGROUND..................................................................................................................................................................................... 11.2LITERATUREREVIEW........................................................................................................................................................................ 2

1.3REPORTOUTLINE.............................................................................................................................................................................. 5 CHAPTER2THEORYANDHYPOTHESES..............................................................................................................6

CHAPTER3 METHODOLOGY... .................................................................................................................................9

3.1DATA ENVELOPMENTANALYSIS (DEA) ............ ............. ............. ............. ............ ............. ............. .............. ............. ............ ....... 9

3.2MALMQUISTTFPINDEX............................................................................................................................................................... 13

CHAPTER4- DATASOURCES...................................................................................................................................16

CHAPTER5- EMPIRICALRESULTS.........................................................................................................................18

CHAPTER5CONCLUSION.......................................................................................................................................24

R

EFERENCES..................................................................................................................................................................26


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Chapter1- Introduction

1.1Background

Productivity occupies a critical role in accelerating the speed of economic growth. It hasbeen well defined in neoclassical growth model that the growth of output is the sum ofgrowth of labor, capital accumulation growth and the growth of productivity (Ramsey,1928; Solow, 1956). Hence, the shifts in the production frontier are partly due to the

productivity or efficiency, given fixed number of factor inputs. Moreover, real businesscycle models (RBC) extended the Ramsey (1928) model by including economicfluctuations and give emphasis to the role of shocks of technology in the economy(Kydland and Prescott, 1982). However, neoclassical growth models take productivityor technological progress as exogenous. On contrary, endogenous growth modelsconsider technological process as endogenous, challenging the neoclassical models withexpectations of finding another driving force.

Since Solow (1957), researchers attempt to explain output growth by the accumulationof factor inputs and total factor productivity (TFP) growth. TFP is defined as the share ofoutput that cannot be explained by the amount of inputs used in production so that theefficiency and intensity of the utilization of inputs determines the level of TFP. In thestandard business cycle literature, TFP is highly correlated with output and hoursworked. Shocks to TPF are propagated by procyclical investment and labor supply,creating fluctuations in output and labor productivity. Solow (1956) stresses that TFPgrowth must be included in the aggregate neoclassical production function in order tosustain the longrun growth in per capita income. Yet, how to make the TFP growthendogenous remained as the main problem for decades. The problem was how to coverthe fixed costs of innovation in a perfectly competitive market with constant returns toscale in capital and labor. Romer (1990) and Aghion and Howitt (1992) came up with asolution to this problem by awarding the innovator monopolistic rights over theinnovation which are protected by the patenting system.


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There are also some studies which associated the greater part of the gap in income percapita between developed and poor countries to the differences in TFP. As the effects ofglobalization spread to the world economies, countries rely ever more on to advance

their efficiency of production in order to remain competitive in the global world.Following this trend, Turkey has launched a pervasive exportoriented policy regime,changing the earlier importsubstitution policies.

1.2LiteratureReview

Early studies on productivity are based on factor productivities such as laborproductivity or capital productivity. Over the time, studies started to focus on TFPbecause it reflects the effectiveness of whole inputs as well as the technological change.The importance of the frontier approach arises from its availability to express TFP bythe combination of several different components. Nishimizu and Page (1982)decompose TFP growth into two components, namely technical efficiency change andtechnological progress. As a result, the differences in TFP growth rates of countries have

been used to explain the determinants of economical advancement. Since the developingcountries look for ways to get to the level of economic prosperity as high as thedeveloped countries, many studies use TFP as the main tool to determine the sources ofgrowth.TFP growth has been studied extensively by many researchers as it is considered to be one of

the most important factors of rapid growth in Asia. Young (1992) studies the TFP growth in

Hong Kong economy for the time period 1961 to 1986 and estimates an average TFP growth

rate of 0.34.Mahadevan (2002a) measures TFP growth in food, chemical, textile andfabricated metal industries over the period 1980 to 1994 in South Korea. She uses thestochastic frontier approach and finds that productivity is the main driver of outputgrowth. She also concludes that the increase in export affects TFP growth positively andthe technical efficiency change is positive except for the food and textile industries. Inanother study, Mahdevan (2002b) applies DEA technique using the dataset that covers28 subsectors of Malaysian manufacturing industry between 1981 and 1996. She reportsthat the source of TFP growth is technical efficiency change rather than technological

progress.


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the other two industries.Zaim and Takn (2001) use nonparametric production frontier approach in order tocompare the performances of public and private manufacturing sectors in Turkey. Theyuse a panel data on 28 subsectors defined at threedigit level for the years between 1974and 1991. They deduct that the private sector is more efficient than the public sector

according to the overall productivity growth and the technical efficiency ofmanufacturing sector in Turkey is following a declining trend for the whole period.

Hsieh and Klenow (2009) examine the effects of resource misallocation on TFP and compares

India and China with the US. Their findings suggest that if India and China reached the US

efficiency level through reallocation of capital and labor to equalize marginal products, China

would enjoy TFP gains of 30% to 50% and India would experience a greater gain from TFP,

i.e. 40% to 60%.

Edwards (1998) extends the previous studies by using a panel of 93 developing and developed

countries during 1980-90. He shows evidence of an average growth rate of 0.3%. He also

constructs a new openness index in order to understand the relationship between TFP and

trade orientation. Furthermore, Miller and Upadhyays (2000) study also focuses on 83

developing and developed countries from 1960 to 1989 with a fixed effects approach. They

rank the countries according to their TFP growth performances and Turkey is in the 50thplace.

Krueger and Tuncer (1982) and Nishimuzu and Robinson (1984) studied the total factorproductivity growth in manufacturing industries in Turkey for 1960s and 1970s.Krueger and Tuncer report higher total factor productivity growth estimates for thepublic sector which is in line with Yildirim (1989) and Uygur (1990)`s studies for almost

the same time interval. Nishimuzu and Robinson calculate the total factor productivitygrowth rates for Japan, Korea, Turkey and Yugoslavia. Their findings support that thetotal factor productivity change in Turkey is higher than Yugoslavia but lower thanrea. They nJapan and Ko also present industry ra kings within countries.

Taymaz and Saatci (1997) analyze technical efficiency in textile, cement, and motorvehicles industries using a plant based panel data for 1987 1992 period. They estimatestochastic production frontiers using the model developed by Battese and Coelli (1995).They conclude that average rate of technical efficiency in cement industry is higher than


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Author(s) Years Data TFP estimator

Krueger and Tuncer(1982) 1963-1976

2-digit subsectors of MI

Public/private

Cob-douglasproduction function

Nishimizu andRobinson (1984)

1953-1973

2-digit subsectors of MI

Turkey, Yugoslavia, Japan,Korea

Translog productionfunction

Zaim and Taskin(1997)

1974-19913-digit subsectors of MI

Public/private

DEA MalmquistTFP Index

Karadag, nder andDeliktas (2002)

1990-1998Total MI - Regional

Public /PrivateDEA Malmquist

TFP Index

nder, Deliktas andLenger (2003)

1990-1998Total MI - Regional

Public /PrivateSFA

Karadag(2004)1980-2000 Total MI - Regional

Private

DEA MalmquistTFP Index

Alvan (2006) 1990-2000Total MI

Public/Private

2-Deflator GrowthAccountingApproach

Alvan and Gosch(2010)

1980-2001 Total MI

Public/private

TDA

* Thetabledoesnotcoverthestudiesbasedonafirmlevelorcountryleveldata.Table1:A brief summary of related studies on TFP growth in manufacturing industry in

T kur eyThe regional performance differential in Turkish manufacturing industry receivedattention of a number of studies in the literature. nder et. al. (2003) estimated totalfactor productivity changes for manufacturing industry in eighteen provinces in Turkey.They use a panel data for the years 19901998 in order to construct a translog stochasticproduction function in their analysis. Karada (2004) measured total factor productivitygrowth of private sector in Turkish manufacturing industry using a panel data coveringthe period between 1980 and 2000.


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1.3ReportOutline

Chapter 2 provides theoretical framework on the factors influencing TFP growth andexpected results together with the hypotheses. Chapter 3 gives an extensive descriptionof the constructed model used to calculate the TFP growth of the subsectors of themanufacturing industry. Chapter 4 represents the empirical results and thenterpretations on the results. Chapter 5 concludes.i


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Chapter2TheoryandHypotheses

The motivation of this paper is to estimate TFP using DEA Malmquist index approachand analyze the results both in aggregate and subsectoral levels as well as to provide acomparison of public and private sectors.As can be seen from Table 1, none of the previous studies covers all this specificationstogether. Only three of the related studies use DEA Malmquist TFP Index to measure TFPgrowth in manufacturing industry in Turkey. Zaim and Taskin (1997), investigate theTFP growth at subsectoral level but their dataset covers an earlier time period. Karadag,nder and Deliktas (2002) study the TFP growth in manufacturing industry only inaggregate level. The data Karadag (2004) uses cover nearly the same period but he isalso examining only the aggregate level manufacturing industry. In this respect, thisstudy can be regarded as a recent application of Zaim and Taskins study.In the aggregate level manufacturing, there are a lot of determinants affecting overallTFP growth. As Akinlo (2005) reports that some of the most important macroeconomic

factors that affect TFP growth in manufacturing industry are openness to the worldeconomy, economic stability, infrastructure investment and knowledge accumulation.Edwards (1998) and Tinakorn (2001), among others, report that the more the economyis open the more rapidly it grows. The main reason that the openness of trade affects thegrowth of the economy favorably is that it contributes productivity by increasing theavailability of cheaper intermediate goods thanks to import and access to larger markets

and superior technology. In the beginning of 1980s, trade orientation in Turkey changed.By the export promotion and the import liberalization activities Turkey became moreopen to the world economy. Thus, I expect this transformation to affect the TFP growthpositively during the period I investigate.Inflation is the most commonly used indicator of macroeconomic stability in empiricalstudies. Many authors point out that inflation affects economic efficiency negatively(Renelt, 1992; Fischer, 1993; Andres and Hernando, 1997). Between 1985 and 2001

Turkish economy was very instable because the economy experienced very high


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operates at higher factor productivity than the private industry.

Celasun (1998) highlights the outcomes of the currency crisis in 1994 and claims thatthe Turkish economy shrank by 6%, which is the highest annual output contraction in

the history of Turkish Republic. Likewise, Turkish Lira was devalued approximately50% against the US$, the reserves of the Central Bank dropped to its half, the interest

inflation rates during the period. So, I expect this determinant to affect TFP growthnegatively.

Technological progress is the result of R&D activities generating knowledge. Abdih andJoutz (2006) question the Romer (1990) endogenous model studying the cointegrationproperties of data on TFP and knowledge stock as measured by the patent applicationsand find a strong longrun cointegrating relationship between them. The number of totalpatent applications in Turkey rose from 132 in 1985 to 3051 in 2001, suggesting asimilar pattern that allows me to expect the existence growth in TFP during the sameperiod.Taking all these effect into consideration, three of the four are expected to have positiveimpact on TFP growth in Turkish manufacturing industries between 1985 and 2001. Inflation

which is an indicator for economic stability is the only one that is expected to have negative

effect. Therefore, for the aggregated result, I expect an increase in TFP.

This thesis explores the differences in TFP growth between the public and private

manufacturing sectors, as well. The general implication about the issue is to assume that

private sector is more efficient that the public sector. However, the empirical evidence cannot

suggest a clear conclusion about superiority of private sector on public counterparts. Bishop et

al. (1994) argues that privatization may have achieved restructuring the organizational

framework in favor of productivity in developed nations but it usually has not occurred in

developing countries. Besides, Florio (2004) concludes that some other variables such as

market structure explain the performance of public enterprises more relevantly than the

private ownership. Krueger and Tuncer (1982) mention some of the advantages of public

sectors in Turkey during 1980s. Public sector enterprises have easier access to import licenses

and enjoy tax reductions in imported goods as well as domestically-produced goods. They payless interest over their borrowings. There have happened outright subsidies to production in

public firms. Keeping all of these in mind, I expect that public manufacturing industry


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rates reached incredibly to the value of 400%. In order to stabilize the economy, aStandBy agreement with International Monetary Fund (IMF) was signed on April 5th,1994 but it did not achieve to adjust the structural measures so that the crisis deeply

depressed the economy. Because of the fact that the macroeconomic environment wasvery unstable in 1994 and afterwards, I expect to find greater TFP growth values in themanufacturing industry during 19851994 than that of the period 1995 to 2001.In a nutshell, the hypotheses to be tested in this study can be listed as follows:

Hypothesis 1: The overall TFP of manufacturing industries at an aggregate level for the period

1985-2001 increases.

Hypothesis 2: The public manufacturing industry shows a better performance in terms of TFPgrowth than its private counterpart.

H

ypothesis 3: The currency crisis occurred in 1994 affected the TFP growth negatively.


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productivity index, among others.

Other commonly used efficiency estimation methodologies include parametrictechniques like stochastic frontier approach and regression approachs such as translog

Chapter3Methodology

Koopmans (1951) was the first to provide a definition for technical efficiency in terms ofinputs and outputs. Debreu (1951) introduced a technical efficiency measure byresource utilization in order to produce the given output with less input. His study wasdeveloped by Farrell`s (1957) work which suggested calculating productive efficiency byconstructing a production frontier using multiple inputs and single output. Charnes et al(1978) developed a linear programming technique, Data Envelopment Analysis (DEA),to estimate a production frontier and measure relative efficiency of decisionmaking

o accunits (DMUs) by taking multiple inputs and multiple outputs int ount.Farrell (1957)`s study on efficiency measurement and Caves et al. (1982)`s study onproductivity measurement inspired Fre et al. (1992) to improve Malmquist indexintroduced by Malmquist (1953) as a quantity index of input utilization. They developMalmquist productivity index based on productivity change calculations using DEA. Inthis study, I use their Malmquist index methods to calculate total factor productivity(TFP) of the subsectors of manufacturing industry in Turkey.

3.1DataEnvelopmentAnalysis(DEA)

DEAbased Malmquist productivity index approach has been used extensively tomeasure performance in various sectors as banking, education, medical services,tourism, manufacturing, agricultural activities and telecommunication. Berg et al. (1992)employ it to understand how deregulation affected the performance of Norwegian banksbetween 1990 and 1992, Fre et al. (1994) use it to investigate the productivity progressof hospitals in Sweden, Coelli et al. (2005) apply it to calculate agricultural productivitydifferences between 93 countries, Madden and Savage (1999) measure catchup,innovation and efficiency in telecommunications in 74 countries using Malmquist


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stochastic production function estimation. DEA has some advantages over both of thesemethodologies. Fre et al. (1989) and Chavas and Cox (1990) mention thatnonparametric techniques perform better than parametric techniques in some

situations. The translog index approach is referred as an inconsistent method because itconsiders TFP growth erroneously by measuring technical change and disregardingtechnical inefficiency (Fried et al., 1993). The advantages that DEA provides in empiricalresearch are briefly summarized by Majumdar (1997) as follows:

DEA is a multivariate technique that can take multiple inputs and outputs intoaccount. It can estimate technical efficiency in case of joint input usage and joint

output production. DEA is a nonparametric approach that does not require an assumption about themathematical form of the production function. This feature makes it verypractical because it is usually hard to determine the functional relationshipbetween the productive factors and the product.

It is not necessary to make assumptions about the technology employed by firms.

In order to understand the effects of technological change on technical efficiencysome other analysis should be conducted. DEA carries out individual observation optimization because it is frontier

oriented and does not estimate central tendencies. However, regressionapproaches use averaging technique and estimate a single parameter for allobservations.

DEA allows data set to be static or dynamic. When panel data is used, it estimatesthe optimum frontier for a firm with respect to efficiency characteristics of allent years.DMUs for that year and all the observations for that firm for differ

DEA can estimate scale efficiency without using the input prices.However, DEA has some drawbacks. First, it is highly sensitive to the data used. So, theinput and output data should be chosen cautiously considering theoretical and practicalissues and the measurement errors in the data should be minimized. Secondly, DEA is anonstatistical technique implying that the estimates are not dependent upon any


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statistical distribution. Therefore, it does not allow for statistical tests and ignores thestatistical noise. These problems are considered carefully and reduced to a minimum inthis study. In order to minimize the first problem, the input and output data is selected

as suggested by Karada et al. (2002) and Zaim and Taskin (2001). I limit the number ofinputs by using only three inputs and choose total output instead of value added asoutput in order to reduce the potential measurement errors.DEA is a linearprogramming methodology. It uses input and output data regarding anumber of decision making units (DMUs) in order to create a linear frontier surface withrespect to a number of discrete intervals over the data points. The distance between the

observed data point and the frontier gives the technical efficiency of each DMU.DEA can be applied inputoriented or outputoriented. When the inputoriented optionis chosen, DEA constructs the frontier for each DMU by searching the possible highestproportional decrease in input utilization by holding the output level constant. For theoutputoriented option, DEA constructs the frontier for each DMU by searching thepossible highest proportional increase in output production by holding the input levelconstant. Moreover, DEA allows the user to choose between two options for the returnsto scale characteristic of the technology applied: Constant returns to scale (CRS) andvariable returns to scale (VRS). When CRS is assumed for the technology, both the inputoriented and the outputoriented options give the same results for technical efficiencyscores. In case of VRS technology applies DEA gives different results in inputorientationand outputorientation.In this study, I assume that the technology is CRS because I deal with sector based

aggregate data which makes it insensible to use VRS technology. GrifellTatje and Lovell(1995) shows that Malmquist TFP changes cannot be calculated accurately if VRS isassumed for the technology. Since I assume CRS for the technology, the decisionbetween the orientation models does not affect the results. However, I use outputoriented model because I believe that the profit maximizing behavior is more commonlyadopted in the manufacturing sector rather than the costminimizing behavior.According to neoclassical model the main objective of a DMU is to maximize the profits

(Hunt and Morgan, 1995 and Hanedan et al., 2006 ). The remainder of this section


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provides an informative synopsis on outputoriented DEA model and Malmquist TFPIndex under CRS.

Let the number of the DMUs in a data set be n and DMUi

is the ith

DMU where i=1,2,,n.Let the number of outputs and inputs of the ithDMU be m and k respectively. DEA storesthe output quantities in output vector, yi,and the input quantities in the input vector, x i.tes he following th N DMUs: detailDEA crea t vectors for the i DMU and matrices for all

Vectors: i i1 i imy = [m x 1] = [y , y 2,.., y ].., xim]xi= [k x 1] = [xi1, xi2,

i= [n x 1], weightsMatrices: Y= [n x M] = y

X= [n x K] = x

Then, in outputorientation, DEA aims to solve the linear programming problem; LPP(1), for the ithDMU under the assumption of is a scalar value.

12

LPP (1)

For the problem LPP (1), cannot be less than 1. 1 defines the proportional increasein output production and 1/ gives the technical efficiency score for the i thDMU. ivector contains information about the peers of the ithDMU if it is inefficient. The efficient

DMUs that define the efficiency surface for the ithinefficient DMU are the peers for thatDMU. DEA solves an LPP and produces a and a vector for each DMU.

,max ,st 0iiy Y +

ix X 0

,

i ,

i 0 ,


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3.2MalmquistTFPIndex

Fre et al. (1982) define Malmquist production index by distance functions that arecalculated using DEA. They compose their Malmquist production index using twocomponents: Change in the efficiency and change in the efficiency frontier. Distancefunctions make it redundant to identify the intention such as cost minimization orrevenue maximization in order to describe a multivariate production technology. Inputdistance functions define the production technology by considering minimumproportional reduction of input vectors for a given output vector. Output distancefunctions define the production technology by considering maximum proportionalincrease in output vectors for a given input vector. I consider output distance functionssince I use output oriented DEA in this study.Let P(x) be the feasible production function that represents the set of all output vectors;y, that can be produced using a given input vector; x. Under the assumption oftechnology satisfies the axioms listed by Coelli et al. (1998, Chapter 3), the outputdistance function is defined as:

do(x,y) = min{: (y/)P(x)}do(x,y) cannot take a value greater than one if it is an element of feasible productionfunction. If the output vector, y, is placed on the outer boundary of P(x) the distancefunction will be equal to 1. If the output vector, y, is placed outside the feasible P(x) the

bdistance function will e greater than 1.DEA Malmquist TFP index is used to calculate the ratio of the distances of eachobservation relative to the technology frontier and measure the TFP difference betweentwo observations. The TFP change index for the ithDMU between the period t andperiod t+1 is defined by Fre et al. (1994) as follows:

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miys,xs,yt,xt= dityit1, xit1dtyt, xti i i x

dit1yit1, xit1dt1y t, xti i i 12 (1)

yit, xit notions in the equation (1) represents the output produced by i thDMU during theperiod t and the input used by ithDMU during the period t+1, respectively. The


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dityit1, xit1 term in equation (1) refers to the distance between the observation at theperiod t+1 to the technology at the period t and dit1yit, xit term represents the distancebetween the observation at the period t to the technology at the period t+1. If miisgreater than one it indicates an increase in the TFP change from period t to period t+1. Avalue of miless than one points to a decrease in the TFP change from period t to periodt+1. Equation (1) can be expressed as equation (2).

miys,xs,yt,xt= d it1yit1,xit1dityit,xit TECHNICAL EFFICIENCY CHANGE CHATCHING UP EFFECT

x d ityit1,xit1dit1yit1,xit1 xd ityit,xitdit1yit,xit TECHNICAL CHANGE

INNOVATION EFFECT

12 (2)

In equation (2), the first multiplier is technical efficiency change between periods t andt+1 in the outputoriented model described by Farrell (1957). It represents the ratio ofthe technical efficiency in period t to the technical efficiency in period t+1. The secondmultiplier in equation (2) corresponds to the technical change which is a geometricmean of the shift in the technology frontier between the periods t and t+1.

In this study, required distance functions for the Malmquist TFP index are calculatedusing DEA. For each DMU in the panel data there are four distance functions to becomputed in order to measure the TFP change between the periods t and t+1. Therefore,he following four LPPs must be solved:t

( )1

1 1 1,, max

+ + +

=

t t ti i id y x ,

st 0t 1 t 1i iy Y+ + + t 1 t 1i ix X 0+ +

, ,

i 0 , LPP 2

14


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LPP 3

( )1

,,d y max

=

t t ti i ix ,

st 0t ti iy Y + t ti ix X 0

, ,

i 0 ,

( ) 1 ,d y , max = t+1 t t i i ix ,

st 0t t 1i iy Y + + t t 1i ix X 0

+

, ,

i 0 , LPP4

LPP5

Different from the standard DEA LPPs, the parameter obtained from the solutions toLPP 4 and LPP 5 can be greater than 1 because the production points are measured upto technologies from different periods. Especially in LPP 5, it is likely to be greater than 1since the observation is compared to the technology from a former period.

( )1

,,d y max

=

t t+1 t+1i i ix ,

st 0t 1 ti iy Y+ + t 1 ti ix X 0+

, ,

i 0 ,


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deflated by GDP deflator and expressed in 1987 prices.Capital(K), total capacity of power equipments installed at the end of the year in termsof horse power is used as one of the inputs. The reason I use this proxy is that sufficientdata on net capital stock could not be obtained and it is a strong indicator for capital asSaracoglu and Suicmez (2006) implies.

Chapter4- DataSources

The methodology explained in the previous section is applied to estimate sector basedTFP growth rates for manufacturing industries in Turkey for each year in the period19852001. The data used in this study is obtained from the TurkStat (Turkish StatisticalInstitute). The 8 subsectors2in the data are defined at 2digit level of the InternationalStandard Industrial Classification Revision 2. I used Revision 2 instead of Revision 3 inorder to avoid inaccurate comparison between years due to the content change inRevision 3 in 1992. The data contains information collected from all public firms and the

private establishments with 10 or more employees. Although, most of the manufacturingfirms in Turkey employ less than 10 people their contribution to value added is only 6%(Saracoglu and Suicmez, 2006). So that, holding this group out of estimations isconsidered to be not problematic. Information in the data is reported separately forpublic and private sectors for all industries.Three inputs and one output are used to calculate Malmquist TFP Index using DEA. I uselabor (L), capital (K) and raw materials (R) as inputs as suggested by Zaim and Taskin(1997), Karadag et. al (2002) and Saracoglu and Suicmez (2006), among others. For theoutput variable, there are two common measures used in the literature: Total outputand value added. I prefer to use total output because the DEA procedure is highlysensitive to the variables used. As measuring value added requires more calculation it ismore likely to increase the measurement errors. Summary statistics of the data is presentedin Table 2.Output(Q)

, can be used in physical or value terms. As, I compare different manufacturingindustries producing different products taking the physical quantities into accountwould give inconsistent results. In order to use an aggregate measure, real value ofoutput in terms of million Turkish Liras is used as output in the calculations. All nominalvalues are


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Labour (L), the total number of hours worked in production calculated by multiplyingthe number of employees and the number of hours worked.RawMaterials(R),

is the real value of input in terms of million Turkish Liras. The valuesare deflated using GDP deflator in order to express them in 1987 prices.ID Sector

Output*

(TL,1987prices)

RawMaterials*

(TL,in1987prices)Capital*(HP/year) Labor*(hour)

31 private 64566857728 44394004480 998235 193889313

public 25937954334 14555405673 510590 110303966

total 90504812424 58949409852 1508826 304193279

32 private 85632016866 55925081510 1523877 599216411

public 3289264915 1967855686 130100 48265895

total 88921282921 57892938089 1653977 647482306

33 private 5430729577 3512240241 203783 37707534

public 749471976 469970317 37201 6320390

total 6180201577 3982210620 240984 44027924

34 private 13135821553 8046409758 256597 39986429

public 2790973380 1774039567 300310 19099505

total

15

926

794

903

9820

449

160 556

907

59

085

935

35 private 66125932183 40600086648 1011303 127661885

public 59713452152 30825787753 788617 34220392

total 125839385540 71425875245 1799920 161882278

36 private 23855964582 11000611418 1266360 128653427

public 1938637897 1016509240 155818 13580019

total 25794602345 12017120798 1422177 142233445

37

private

33959

116

198

25

216

191

789 814

252

69

138

778

public 13694312508 8462219836 1209785 69159272

total 47653429007 33678411656 2024037 138298050

38 private 96041883768 59185131881 1389281 362584333

public 3936264282 1876602022 195051 41714019

total 99978148924 61061734400 1584333 404298351

*Geometricaverage

Table2: Summary statistics of variables for manufacturing subsectors, 1985 2001


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Chapter5- EmpiricalResults

The Malmquist TFP change indexes are calculated using DEAP 2.1 linear programdeveloped by Coelli et. al (1998). The results are interpreted from three different pointsof views in this section. First, the annual TFP changes of total manufacturing industryare estimated and compared to the other studies that investigate the TFP changes in thesame period using different methods. Secondly, the differences between the TFP growthrates of public and private manufacturing sectors are evaluated. Lastly, the estimatedTFP growth rates of eight subsectors of the manufacturing industry are given.

A summary of the annual performances of the manufacturing industry in Turkey as awhole is presented in Table 3. The table contains the annual average TFP changes and itscomponents for the manufacturing industry in Turkey for the entire time period. Asmentioned before, if the reported index is less than 1, it indicates a worsening in theperformance in the subsequent years. On the other hand, if the reported index is greaterthan 1, it means that the productivity of the manufacturing industry is improved.

The average TFP increase for entire time period is 0.6% so that Hypothesis 1 cannot berejected. This result indicates that factors in favor of TFP growth are relatively dominanton the factors against it. The manufacturing industry experienced two sharp decreasesin productivity between 19931995 and 19982000. The most significant improvementin TFP is between 1995 and 1997. The results are in line with the findings of Saracogluand Suicmez (2006) who measure the TFP growth in Turkish manufacturing industry ataggregate level for the same period. Among the two components of TFP growth,

technical change is found to have a greater impact. When the efficiency change index issmaller than 1 at a specific point in time, the technical change index can eliminate thenegative effects that are induced by contracted efficiency change. Hence, technicalchange helps generate improved TFP growth rates but efficiency change usually fails too so.d


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Years EfficiencyChange TechnicalChange TFPChange

1985/86 0,931 1,100 1,025

1986/87 1,125 0,916 1,030

1987/88 0,973 1,053 1,025

1988/89 1,026 0,964 0,989

1989/90 0,977 1,030 1,006

1990/91 1,006 1,021 1,026

1991/92 0,973 1,059 1,031

1992/93

1,031

1,004

1,036

1993/94 1,001 0,979 0,980

1994/95 0,934 1,016 0,949

1995/96 1,097 0,916 1,005

1996/97 0,878 1,183 1,039

1997/98 0,899 1,126 1,013

1998/99 1,093 0,869 0,951

1999/2000 1,164 0,841 0,979

2000/01 0,940 1,075 1,011

Average 1,003 1,010 1,006

Table3:Annual average changes in TFP and its components in manufacturing industry

Table 4 presents a subsectoral analysis and reveals that the average TFP growth ofprivate manufacturing sector is slightly higher than their public counterparts. The TFPgrowth of private sector increased by 0.60% in average, yet public sectors TFP grewonly 0.51% in average during the whole time period. This result confirms that theHypothesis 2 is rejected. As mentioned earlier, this result is somewhat unexpectedbecause public sector enterprises in Turkey have some advantages over the private

sector such as tax reductions and easy access to import licenses. Indeed, the privatesectors higher TFP growth is due to the technological progress. Even though the


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technical efficiency rate of private sector experienced a slowdown, its ability to adapt tothe technological progress made it perform better than the public sector.

ISIC Definition Sector EfficiencyChange

TechnicalChange

TFPChange

31

Food, Beverages and Tobacco

public 1,000 0,996 0,996

private 1,002 1,027 1,029

total 1,001 1,011 1,012

32Textile, Wearing Apparel and

Leather Industries

public 0,991 1,002 0,994

private 1,007 0,996 1,003

total 0,999 0,999 0,998

33Wood and Wood Products, Including

Furniture

public 1,004 1,004 1,008

private 1,003 1,001 1,003total 1,003 1,002 1,005

34 Paper and Paper Products, Printing

and Publishing

public 0,994 1,006 1,000

private 0,993 1,004 0,997

total 0,993 1,005 0,998

35Chemicals and Chemical, Petroleum,

Coal, Rubber and Plastic Products

public 1,000 1,003 1,003

private 0,994 0,993 0,988

total 0,997 0,998 0,995

36 Non-Metallic Mineral Products,

except Products of Petroleum andCoal

public 1,003 1,008 1,012

private 1,005 1,001 1,006total 1,004 1,004 1,009

37

Basic Metal Industries

public 1,002 1,006 1,008

private 0,988 1,012 1,000

total 0,995 1,009 1,004

38Fabricated Metal Products,

Machinery and Equipment

public 1,015 1,005 1,020

private 1,001 1,022 1,023

total 1,008 1,013 1,021

Average public 1,0011 1,0037 1,0051private 0,99911,0001 1,00691,0053 1,00601,0056total

Table4:The TFP change of Turkish manufacturing subsectorsThe diversity of TFP growth across subsectors of manufacturing industry is one of themain interests of this paper. The most efficient manufacturing subsector over theentire period is Manufacture of Fabricated Metal and Products, Machinery andEquipment (ISIC38). The TFP of this sector is increased by 2.1%. The technical efficiency


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of four of the subsectors, namely BasicMetalIndustries(ISIC37), Manufacturing of Textile,Wearing Apparel and Leather Industries (ISIC32), Manufacturing of Wood and Wood

Products Including Furniture (ISIC33) and Manufacturing of Paper and Paper Products,

Printing

and

Publishing

(ISIC34)

decreased.

However,

the loss incurred from the decreasedtechnical efficiency in these subsectors, except subsector ISIC37, could not becompensated by technological progress and there is a loss in TFP growth.As can be seen in Table 4, three important subsectors for both ownership types, NonMetallic Mineral Products, except Products of Petroleum and Coal (ISIC36), Basic MetalIndustries (ISIC37) and Fabricated Metal Products, Machinery and Equipment (ISIC38)

exhibit relatively high productivity growth mainly sourced by higher technical changerates when compared to other sectors. Sectors coded ISIC36 and ISIC37 are keysuppliers of intermediate inputs (i.e. metal, cement and glass) and ISIC38 is the mainsupplier of the capital inputs for other sectors. These sectors seem to stimulate thetechnological progress of the entire manufacturing industry which is referred to theinkage effect by Zaim and Taskin (1997).l


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1985 994 1 1995 0120 TE TC TFP TE TC TFP31 public 0,967 0,995 0,962 1,044 0,998 1,042

private 1,026 0,998 1,050 0,972 1,030 1,001

total 0,996 0,997 1,005 1,007 1,014 1,02132 public 1,007 1,004 1,010 0,972 1,001 0,973 private 1,027 0,972 1,028 0,973 0,999 0,972 total 1,017 0,988 1,019 0,973 1,000 0,97333 public 0,967 1,011 0,977 1,053 0,996 1,048 private 1,021 1,006 1,009 1,002 0,994 0,996 total 0,994 1,008 0,993 1,027 0,995 1,02234 public 0,982 1,022 1,004 1,010 0,986 0,996 private 1,007 0,987 1,000 0,988 1,005 0,993 total 0,994 1,005 1,002 0,999 0,996 0,99535 public 1,000 1,009 1,009 1,000 0,995 0,995 private 1,011 0,978 1,007 0,975 0,987 0,963 total 1,005 0,993 1,008 0,988 0,991 0,97936 public 1,014 1,021 1,035 0,989 0,993 0,982 private 1,022 1,047 1,033 0,993 0,979 0,973 total 1,018 1,034 1,034 0,991 0,986 0,97737 public 1,007 1,033 1,040 0,995 0,973 0,968 private 1,003 1,019 1,031 0,974 0,987 0,961 total 1,005 1,026 1,036 0,984 0,980 0,96438 public 1,021 1,017 1,038 1,007 0,990 0,996 private 1,015 0,979 1,029 0,985 1,032 1,016

total 1,018 0,998 1,033 0,996 1,011 1,006

Average public 0,995 1,014 1,009 1,008 0,991 1,000 private 1,016 0,998 1,023 0,983 1,002 0,984 total 1,006 1,006 1,016 0,995 0,997 0,992

Table5:Technical Change, Technological Change and TFP Change results for

subperiods


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Table 5 shows that Hypothesis 3 cannot be rejected since the average TFP growthbetween 1985 and 1994 is 1.6%, yet the average growth between 1995 and 2001 is 0.8%. Ismihan and zcan (2005) argue that the main driver of the relatively high TFP

growth rates in 1980s is the change in the trade orientation of the country and thedecline in 1990s are due to the decrease in infrastructure investments and themacroeconomic instability. In 1990s, Turkish economy experienced very high inflationrates with a peak of 106% in 1994.In addition, an initial inspection of the results reveals that both the public and theprivate sectors deteriorated in the second period due to the currency crisis occurred in

Turkey in 1994. Initial inspection of Table 5 also reveals the fact that technical efficiencyand technological change components of TFP decreased together with TFP before the1994 crisis and increased after the crisis. Therefore, I conclude that the crisis had a veryeep impact on the economy as a whole.d


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exposed to.Future research can extend the topic to explore the determinants of TFP and the

magnitudes of their effects using ordinary least squares (OLS) regressions. Furthermore,my dataset did not allow me to study the TFP growth with respect to the intensity of

Chapter5Conclusion

This study investigates the TFP in manufacturing industry in Turkey. DEA basedMalmquist Index approach is employed in order to measure TFP growth of eightsubsectors (2digit manufacturing industries defined by ISIC Rev. 2). A panel dataconsisting of four productive factors, seventeen years, eight subsectors with the publicand private brunches reported separately is used in the empirical study. Theperformances of sixteen decision making units are used to construct the technologyfrontier.

The empirical results indicate that Turkish manufacturing industry achieved TFPgrowth during the entire time period for both public and private sectors as well as intotal. This result supports my expectation defined by Hypothesis 1. Even though therehappened several economic crisis during the whole period, other determinants of TFPgrowth such as investment to knowledge and infrastructure and openness of theeconomy were very helpful to accomplish growth in TFP. Additionally, Hypothesis 2which states that the public sector must have had better productivity measures than itsprivate counterpart is rejected. This result is somewhat unexpected because publicsector in Turkey has a lot of incentives from the government that makes it lesssusceptible to trading problems. The better performance of the private sector may bedue to its ability to quickly alter the allocation of capital and labor used in productionwhen an internal or an external change (i.e. increase in wages, high rate of capitalrenting) happens. Another reason may be that the public sector in Turkey is oftenperceived to be corrupted more bureaucratic and more professional than the privatesector which makes it less efficient. Finally, I find that the Turkish economy sufferedfrom the 1994 currency crisis. Positive TFP growth rates were attained before the crisisbut the growth cannot be sustained during and after the crisis. Neither the public nor theprivate sectors could overcome the instabilities and challenges which they were much


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inputs used in production. Hence, another research could be to investigate the 3digitmanufacturing industries categorizing them as capital intensive, labor intensive andnowledge intensive industries and compare their TFP growth rates.k


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