the extent that low productivity can act as an early warning signal, policy ... Act to discourage the concentration of economic power was replaced by a new competition law ... growth averaged around 6% during 1991 2004, perhaps reflecting the effect of ... 2 The economic reforms program was initiated following measures ...
EERI Economics and Econometrics Research Institute
Economic reforms and manufacturing productivity: Evidence from India
Saibal Ghosh
EERI Research Paper Series No 32/2010 ISSN: 2031-4892 EERI Economics and Econometrics Research Institute Avenue de Beaulieu 1160 Brussels Belgium Tel: +322 299 3523 Fax: +322 299 3523 www.eeri.eu Copyright © 2010 by Saibal Ghosh
Economicreformsandmanufacturingproductivity: EvidencefromIndia SaibalGhosh 1
1Department of Economic Analysis and Policy, Reserve Bank of India, Central Office, Fort, Mumbai 400 001. Mail: saibalghosh@rbi.org.in.Iwouldliketothank,withoutimplicating,Prof.AmilPetrinfortheclarificationsoftheuseof theproductivitymethodology.Needlesstostate,theviewsexpressedandtheapproachpursuedinthepaperreflectsthe personalopinionoftheauthor.
1
Economicreformsandmanufacturingproductivity: EvidencefromIndia Abstract Usingdataon2digitindustryfor19812004,thestudyexaminestheassociationbetweengrowth intotalfactorproductivityandeconomicreforms.Accordingly,wefirstcomputeindustrylevel productivity growth using advanced econometric techniques and thereafter ascertain the time frame over which economic reforms impact productivity. The evidence suggests that productivity growth is not reliably higher after reforms than prior to reforms. In addition, the findings indicate that it is primarily the interest rate channel that is important in explaining changes in productivity. Among macroeconomic policies, trade reforms and industrial delicensingappeartobeinstrumentalinexplainingproductivitychanges.
JELclassification:D24,L60,O47 Keywords: economic reforms; total factor productivity; Levinsohn Petrin; Indian manufacturing. 1.
Introduction
A major focus of any structural reforms program is to put the country on a
higher growth trajectory on a sustainable basis. A key component of achieving sustainable growth is to register consistent improvements in productivity. In fact, a significant body of literature has confirmed that the gap in income per capita between richandpoorcountriesisassociatedwithlargecrosscountrydifferencesintotalfactor productivity (Klenow and RodriguezClare, 1997; Hall and Jones, 1999; Howitt, 2000; Klenow and RodriguezClare, 2005). Whether and to what extent such productivity differencescanbetracedtodifferencesinmanufacturing productivityremainsanarea ofongoingdebate.Evidenceonthiscountisfarfromunambiguous:somestudiesreport manufacturing productivity as an important factor for differential economic growth acrossnations(VanBart,1993;VanBartandPilat,1993;DollarandWolff,1993),others findittobemuchlessrelevant(Cavesetal.,1982;Harrigan,1999).
2
We employ the natural experiment of the economic reforms to examine the interface between economic reforms and total factor productivity (TFP) growth of industriesatthetwodigitlevel,usingIndiaasacasestudy.Theproductionfunctionis estimatedfollowingthemethodologysuggestedbyLevinsohnandPetrin(2003)soasto controlforendogeneityproblemsthatemanatefromthesimultaneouschoiceofinputs andproductivitybytheindustry. Studies on this aspect for India report changes in productivity (either improvementordeclines)inthepostreformperiod,butdonotquantifytheperiodover whichsuchproductivitygainsoccur.FollowingfromAghionetal.(2005),sincethepre reform productivity (and consequently, the technological capability) of industries is expected to differ significantly, it seems likely that economic reforms would further magnify the productivity differentials. Accordingly, we explore the time period over whichchangesinproductivityaccruetoindustries.Inaddition,wealsointroduceaset of industrylevel variables to ascertain which set of factors play an important role in influencingproductivity. The choice of India as a case study rests on three considerations. First, India is presently one of the most important emerging economies with a rich history of industrialcontrols.Thesecontrolswereintroducedintheaftermathofindependencein order to dovetail investment into desirable areas within a mixed economy framework throughaprocessofindustriallicensing.Second,likemostdevelopedeconomies,India hasalargeanddiversifiedmanufacturingsector.Overtime,industrieshavetendedto developdistinctcharacteristics,drivenbyacombinationofregulatorypoliciesaswellas factors internal to the organization. The question therefore, remains as to what extent productivity varies across industries. Third, India has a rich history of industrylevel database.Thecrosssectionalandtimeseriesvariationinthedatamakesitamenableto rigorous statistical analysis and provides an ideal laboratory to examine the factors affectinggrowthinTFPanditsinteractionwitheconomicreforms. The study contributes to the extant literature in a few important ways. First, it expandstheliteratureonindustrialproductivityinthecontextofanemergingeconomy.
3
The study of productivity is relevant because productivity is a catchall measure of performance.Thus,productivityanalysismaybepertinenttothoseinvolvedinmergers andacquisitionsissues,likeindustrypractitionersandcompetitionauthorities.Also,to theextentthatlowproductivitycanactasanearlywarningsignal,policypractitioners canutilizeproductivitymeasuresasanadditionalmonitoringinstrument. Second, the study is also related to the channels of monetary transmission. Followingfromtheliterature,wedistinguishbetweenthefinancialacceleratorchannel, in addition to the traditional interest rate channel, by constructing proxies that act as determinants of these channels. By regressing the productivity responses on a set of independent variables that acts as proxies for these channels, we are able to discern which sets of variables are influential in explaining the variation in manufacturing productivityresponseintheIndiancase. Third, the paper examines the role of institutions, focusing on labor laws in generalandindustryleveltradeunionism,inparticular.BesleyandBurgess(2004),for instance, document that states with more prolabor regulation had lower levels of manufacturing development. These states also exhibited higher levels of unionization. Sanyal and Menon (2005) also uncover evidence that statelevel labor regulation variablessuchasnumberoflaborcourts,numberofregisteredunionsandnumberof mandays lost owing to labor disputes act as significant disincentives on firm location. Judgedfromthisstandpoint,itcanbearguedthattheregulatoryframeworkgoverning industrialdisputescouldbeanimportantingredientinfluencingindustrialproductivity, anaspectwhichthestudyseekstoexplore. Fourth,thepaperalsoexploresthemicroandmacroeconomicfactorsinfluencing productivitygrowth.Observershavehighlightedtheroleofseveralfactors,bothatthe microeconomic level such as industry size, capital intensity and leverage as well as macroeconomic level including trade, industrial and financial policies in influencing productivitygrowth,althoughnonehaveconsideredthesefactorsinaholisticfashion. By taking on board both the microeconomic (industry characteristics) as well as the
4
macroeconomicfactors,itprovidesafarmorecomprehensivepictureofthereasonsfor productivitychangesacrossindustriesthanthatconsideredbypreviousresearchers. The reminder of the analysis continues as follows. In Section 2, we provide an overview of the Indian industrial experience, as appropriate, and the position of this paperinthatcontext.Section3describesthemethodologytobeappliedintheempirical sections. Section 4 discusses data issues. Section 5 estimates the coefficients of the production function, from which the industry level productivity measures are calculated. Section 6 studies the determinants of manufacturing productivity and explorestheinterfacebetweenreformsandproductivity.Contextually,italsohighlights theroleplayedbyvariousindustrylevelfactors.Section7concludesthepaper. 2.
Industrialpolicyandgrowth The introduction of the concept of a socialist economy in the 1960s with its
concomitant focus on poverty reduction, egalitarianism and social equality meant that the Federal government pursued highly restrictive policies with respect to trade, industry and finance. The process of transition towards selfreliance, driven to an overarching extent by concerns of ‘export pessimism’ amongst developing nations nestedonthelogicofheavyindustryorientedindustrializationwithinaclosedeconomy framework. Such a policy engendered the need for industrial licensing whereby firms hadtoapplyforalicenseforsettingupnewunitsorforcapacityexpansion.Ineffect, thepolicyexertedmultiplecontrolsoverprivateinvestmentthatlimitedareasinwhich privateinvestorswereallowedtooperateandalsodeterminedthescaleofoperations, thelocationofnewinvestmentsandeventhetechnologyemployed.Thiswasbuttressed byahighlyprotectivetradepolicy,oftenprovidingtailormadeprotectiontoeachsector of industry. The costs imposed by these policies have been extensively studied (Bhagwati and Desai, 1965; Bhagwati and Srinivasan, 1971; Mookherjee, 1995), and by 1991,aconsensusemergedontheneedforgreaterliberalizationandopenness. The structural break engendered by economic reforms laid strong emphasis on enabling markets and globalization coupled with lower degree of direct government
5
involvement in economic activities. The list ofindustries reserved solely for the public sectorwasgraduallyscaleddownandreducedtothree:defenseaircraftsandwarships, atomicenergygenerationandrailwaytransport.Theprocessofindustriallicensingby the Federal government was abolished, except for a few hazardous and environmentallysensitive industries. The requirement that investment by large houses need a separate clearance under the Monopolies and Restrictive Trade Practices Act to discouragetheconcentrationofeconomicpowerwasreplacedbyanewcompetitionlaw thatfocusedonregulatinganticompetitivebehavior. The net effect of these measures was a modest improvement in industrial growth. From an average of 4% in the 1970s and around 6.5% in the 1980s, industrial growth averaged around 6% during 19912004, perhaps reflecting the effect of liberalization of various controls. Over the entire period beginning 1980 through 2004, industrialgrowthhasbeenroughlyoftheorderof6.1%(Kohli,2006). Concomitantwiththeprocessofderegulation,therehavealsobeenattemptsto ascertainwheneconomicreformshaveledtoanyperceptiblechangesinmanufacturing productivity.Studiesonthisaspectareinconclusive,atbest.Earlystudiesdocumenteda decline in growth in TFP during the 1970s and a turnaround (driven primarily by an increase in labor productivity) in the first half of the 1980s (Ahluwalia, 1991). These findings were echoed in several other studies (Ray, 2002; Krishna and Mitra, 2003; Pattanayak et al., 2003; Unel, 2003) which also reported improvements in TFP, post reforms. Others (Goldar and Kumari, 2003) have, however, uncovered evidence that economic reforms adversely impacted productivity. By way of example, Goldar and Kumari (2003) indicate a fall in the growth rate of TFP in Indian manufacturing from 1.9%perannumduring19811991to0.7%during199198.Balakrishnanetal.(2000)and Srivastava (2001) also identify a slowdown in TFP growth in Indian manufacturing in the postreform period. Contextually, using data on a sample of over 3500 firms covering both the pre and postliberalization period, Balakrishnan et al. (2006) find limitedevidenceinsupportofamovetoamorecompetitivemarketstructure.
6
The methodology of TFP computation in several of these studies however, exhibits certain shortcomings. To address this deficiency, we employ advanced econometrictechniquestocomputeproductivityandsubsequentlyrelateittothesetof factors,bothattheindustryandeconomywidelevel,toascertainthefactorsinfluencing them. TheanalysiswhichcomesclosesttothespiritofthepresentpaperisAghionetal. (2005). Using data on 3digit manufacturing industries for 16 major Indian states covering198097,theyaddresstheissueastohowtechnologicalcapabilityofindustries affectstheirresponsetoa‘shock’,definedasthetradeliberalizationin1991.Although this shock was common across firms in the same industry; however, firms in different states in the same 3digit industry varied in terms of their level of prereform productivity,whichweretakenasaproxyoftheirtechnologicalcapability.Theresults demonstrated that stateindustries with higher prereform technological capability exhibited greater increases in total factor productivity (TFP), following reform. The presentanalysisalsoseekstodeciphertheresponseofindustriestoashock.Incontrast toAghionetal.(2005)however,the‘shock’inthepresentcaseistheeconomicreforms program initiated in 1992.2 Unlike their analysis however, our focus is on the productivitychangeacrossindustries. Toanticipatetheresults,theevidencesuggeststhatproductivityimprovements arereliablylowerafterreformsthanpriortoreforms.Inaddition,thefindingsindicate that both interest rate channel as well as financial accelerator channel is important in explainingproductivity,althoughtherelativeimportanceofthevariouschannelsvaries markedly. Besides, several macroeconomic factors such as trade policy and credit availabilityarefoundtobeimportantinexplainingproductivitychanges. 3.
Methodologyforproductivitycomputation
2 The economic reforms program was initiated following measures announced in the Union Budget on July 24, 1991. GiventhattheIndianfinancialyearspansfromfirstdayofAprilofagivenyeartothelastdayofMarchofthefollowing year,theyear199192hasbeentakentorepresents1992,andlikewiseforotheryears.
7
The traditional approach for productivity measurement estimates a production function and subsequently utilizes the residuals not explained by the factor inputs (capital, labor) as a proxy for total factor productivity. However, when estimating the productionfunction,itisimportanttoaccountforthecorrelationbetweeninputlevels and productivity, as profit maximizing firms respond to increase in productivity by increasinguseoffactorinputs.Therefore,methodsthatignorethisendogeneitysuchas OLSorthefixedeffectsestimatorcouldprovideinconsistentparameterestimates. To address this deficiency, we employ a modification of the semiparametric approach. This framework was originally suggested by Olley and Pakes (1996) and subsequentlymodifiedbyLevinsohnandPetrin(2003).ThemethodologybyOlleyand Pakes enables consistent estimation of the coefficients of a production function, taking onboardthetwopossiblesourcesofbias,sampleselectionbiasandasimultaneitybias.3 Theformerproblemishandledbymodelingtheexitdecision.4Thelatter,ontheother hand, is solved by inverting an investment function, which is affected by unobserved productivity(See,forinstance,PetrinandLevinsohn,2008). Levinsohn and Petrin (2003) contend that the monotonicity condition required fortheinversionoftheinvestmentfunctionmaynotbevalidduetocapitaladjustment costs. The monotonicity condition for investment is replaced by an equivalent requirementforanintermediateinputfunction. 4.
Databaseandsample The basic source of the data is theAnnual Survey of Industries (hereafter, ASI)
database. The Economic and Political Weekly (EPW) has created a systematic, electronic database using ASI results for the period 197374 to 200304. Concordance has been worked out between the national industrial classifications (NIC) used till 198889 and that used thereafter (NIC1970, NIC1987 and NIC1998) and comparable series for 3
Theselectionbiasreferstothefactthatmanyfirmswouldhaveleftthemarketduringthesampleperiod.Itis,therefore, reasonable to imagine that the unobservable productivity variable and the decision to leave the market are correlated, causing a potential bias. The simultaneity problem is related to the correlation between the unobservable productivity variableandtheamountofinputschosenbythefirm. 4Giventhedataisatthe2digitindustrylevel,theissueofsampleselectionbiascannotbedirectlyfactoredintoaccount, sincetheexitoffirmsisunobservable.
8
various two and threedigit industries have been prepared. From the database, the series on output and input (undeflated) has been extracted for various twodigit industries, aggregating a total of 23 industries.5 Data have been culled out on several variables: gross value added, total persons engaged in industrial units (number of workers), number of factories, net income, gross fixed capital formation, loan outstanding, working capital, interest payments and value of intermediate inputs (separateseriesconstructedformaterialsasalsopowerandfuel). The empirical section estimates a CobbDouglas production function having a measureofoutputasdependentvariableandtheeinputsasexplanatoryvariables.The inputs are labor, capital and intermediate inputs. Following Levinsohn and Petrin (2003), two intermediate inputs are used as proxy variables for productivity. These include expenses on fuels and raw material expenditures. Labor is taken as the total numberofworkersinthefirm. Following Levinsohn and Petrin (2003), a real capital stock series K was constructedusingtheperpetualinventoryequation,asgivenbytheexpression:
Kt
(1 G ) K t 1 I t (1)
whereIt isinvestmentandisthedepreciationrate.FollowingCaselli(2005),theinitial capitalstockK0iscomputedasK0=I0/(+),whereI0isthevalueofinvestmentseriesin the first year of its availability and is the average geometric growth rate for the investment series between the first year with available data (i.e., 1974) and 1980. We assumethevalueforthedepreciationrate,of5%,followingearlierevidenceforIndia (Unel,2003). Output is measured as gross value added, consistent with the literature (Ahluwalia,1991;Goldar,1986;BalakrishnanandPushpangadan,1994;Unel,2003).One advantage of using gross value added rather than gross output is that it allows
5 The list of industries (along with industry code) include: Food and beverage (15), Tobacco (16), Textile (17), Wearing apparel (18), Leather and footwear (19), Wood and straw (20), Paper (21), Publishing and recording media (22), Coke, petrol and fuel (23), Chemicals (24), Rubber and plastic (25), Other nonmetallic minerals (26), Basic metal (27), Metal products(28),Machineryandequipment(29),Office,accountingandcomputermachinery(30),Electricalmachinery(31), Radio,TV,etc(32),Medicalequipmentetc.(33),Motorvehiclesetc.(34),Othertransportequipment(35),Furniture(36) andElectricity,gasandwatersupply(40),
9
comparison between industries that are employing heterogeneous raw materials (Grilliches and Ringstad, 1971; Verner, 1998). Another advantage is that gross value addedaccountsfordifferencesandchangesinthequalityofinputs(SalimandKalirajan, 1999).ConstantvalueswereobtainedbydeflatingallnominalvariablesbytheWPIfor separateindustrygroups,obtainedfromtheOfficeoftheEconomicAdvisor,Ministryof CommerceandIndustry,GovernmentofIndia. 5.
Estimationofmanufacturingproductivity Thefirststepofthealgorithminvolvestheestimationofthefollowingpartially
linearequation:
y it
J o J l l it J k k it J c cit J e eit ht (cit , k it ) H it (2) wherelowercaselettersrepresentnaturallogarithms;yisoutput,lislabor,cis
communications, e is intermediate inputs, k is capital and is the random error term. The function h(.) is estimated by a polynomial series expansion where terms up to the fourthdegreeofcitandkitareutilized. The first step in the estimation allows one to obtain consistent estimates of the variablefactorcoefficients,iande.Oncetheseparametersareobtained,wecompute theterm:
y itP
^
^
y it J l l it J e eit (3) This term is then regressed on a polynomial series in {cit, kit}. The fitted value ^
fromtheregressionisdenotedas\ t (c it , k it ) . Inthesecondstep,consistentestimatesforkande areobtainedthroughnon linearleastsquaresappliedtoexpression(4)asunder:
y itP
^
J o J e eit J k k it g [\ t 1 (cit 1 , k it 1 )] J o J c cit J k k it 1 ] [ it K it (4) whereitistheinnovationterminproductivity. Table 1 presents the production function coefficients estimated through the
LevinsohnPetrin (LP) algorithm, alongside the coefficients obtained through OLS methodology.
10
[Table1.Estimationofproductionfunctionparameters] The estimates reveal that the coefficient on the freely variable input, labor, is higherunderOLSmethodascomparedtoLPprocedure.Illustratively,underOLS,the coefficientonworkerequals0.241;thesameunderLPmethodis0.228,confirmingthe theoretical and empirical observations of Levinsohn and Petrin (2003). The bias in the coefficient on capital depends on the extent of correlation among the inputs and the productivityshocks.Inthiscase,theestimatesunderOLSestimatearehigherthanthat underLPmethodology. Industrylevel (natural log of) total factor productivity is computed as the differencebetweenactualandfittedoutput,accordingas:
Z it
^
^
^
^
y it J l l it J k k it J c c it J e eit (5) AkintoTopalova(2007),weconstructanindexofproductivityasthelogarithmic
deviation of an industry from the reference industry’s productivity in the base year. More specifically, we subtract the productivity of the industry with the median log outputinthebaseyearfromtheestimatedfirmlevelTFP. Table2reportsthesummarystatisticsforthemeasureofTFPgrowthaccording totheirstatuswithregardtoindustry.Averageproductivitygrowthappearstobethe highest in coke and the lowest for textiles; as many as 11 industries have average productivitygrowthinexcessoftheoverall industrywideproductivitygrowthfigure. Without loss of generality, the productivity growth numbers appear to be consistent withpreviousstudiesforIndia. [Table2.Descriptivestatisticsforproductivityscores] 6.
Determinantsofproductivity
6.1
Whatfactorsdriveindustryproductivity?
11
This section examines the determinants of productivity. We classify the set of possible factors under three heads: the interest rate channel (IRC), the financial acceleratorchannel(FAC)andlabormarketcharacteristics(LMC).Table3providesthe empiricaldefinitionsofthevariablesandthedatasource. [Table3.Variabledescription] Underthefirstchannel,weconsiderthreevariables:interestcost,investmentand thefactastowhetheranindustryisinthetradedsector.High(interest)costindustries are likely to be more sensitive to interest rate changes. If such costs act as a brake on investment, this is likely to manifest itself in lower productivity, indicating a negative coefficient on this variable. Second, investmentintensive industries will have higher capital stock in relation to output and the more sensitive will be the industry with respecttoanincreaseinthecostofcapital,whichcoulddampenproductivity.Finally, industries in the tradable goods sector might have better placed to access to foreign currency earnings. If diversification of sources of revenue impels them to raise productivity,thiswouldsuggestapositivecoefficientonthisvariable. The second channel is based on the financial accelerator theory. The first indicator is leverage. Levered industries are likely to encounter greater difficulties in obtaining new funds from the market. Based on this conjecture, we expect a negative influenceofleverageonproductivity.Ontheotherhand,totheextentthatleverageratio is an indicator of borrowing capacity, more levered industries might be able to obtain loans at better terms, suggesting a positive coefficient on this variable. Second, industrieswithhighcoverageareexpectedtobemorecreditworthyandtherefore,likely toexhibithigherproductivity.Industrieswithhigherworkingcapitalrequirementshave higher shortterm financing requirements. If higher shortterm financing requirements translate into higher (resp., lower) productivity, the coefficient on this variable is expectedtobepositive(resp.,negative). Finally,thesizeofanindustry(size)isoftenusedasanindicatorforthedegree ofasymmetricinformationproblemsinlendingrelationships.Agencycostsareusually
12
assumedtobesmallerforlargeindustriesbecauseoftheeconomiesofscaleincollecting andprocessinginformation.Asaresult,suchindustriesareabletofinancethemselves directlythroughfinancialmarketsandarelessdependentonbanks.Tocontrolforthe fact that size varies significantly across industries, we normalize this variable by definingitastheratiooftotalnumberofworkersinanindustrydividedbythenumber offactories. The final variable is related to labor market features. If an industry is highly unionized, retrenchment could prove difficult. Trade union membership represents an important constituent of bargaining power of workers and it seems likely that highly unionized industries could exhibit lower productivity. To address this aspect, we includeavariableunion.Amajorlimitationofthisvariableisthatthenumberoftrade unionmembersisreportedonlyfromunionssubmittingreturns.Moreover,submission of these returns is purely voluntary. As a result, these numbers could be under estimates, since it does not take cognizance of the members of nonreporting unions. Notwithstanding these deficiencies, trade unionism is an important component of bargainingpowerofworkersthathelpssteerthecourseofnegotiationsalongadefined path. Accordingly,weestimatethefollowingequationforindustrysattimetasgiven by(6):
TFPGs ,t
D 1 [ IRC ] s ,t D 2 [ FAC ] s ,t D 3 [ LMC ] s ,t D 4 [YD]t D 5 [ ID] s Q s ,t (6)
where TFPG is the industryspecific, timevarying measure of total factor productivitygrowth,obtainedusingtheLPalgorithm;IRC,FACandLMCarethesetof variables under these three channels, as elucidated earlier. The inclusion of industry fixedeffects(ID)absorbsunobservedheterogeneityinthedeterminantsofproductivity that are industryspecific, while the year dummies (YD) control for macroeconomic shocks common to all industries, although neither YD nor ID are reported in the regressions.Finally,istheerrorterm.
13
[Table4.Effectofindustrycharacteristicsonchangeinproductivity] Table 4 reports the results. We have, on average, data for 27.7 years for each industry,hencethemaximumnumberofindustryyearsis523.Toascertaintherelative importanceofeachofthesechannels,models(1)through(3)sequentiallyincorporatethe three channels before combining them together in Model 4. Throughout, the reported standard errors take on board the serial correlation in the data by keeping all observationsthatbelongtothesameindustrytogether(i.e.,clusteredstandarderrors). Inthefirstsetofregression,investmentbearsanegativesign,supportiveofthe factthattheinterestratechannelplaysanimportantroleinaffectingproductivity.The coefficientoninvestmentindicatesthatariseininvestmentby10%lowersproductivity by around 1.5 percentage points. The magnitude is statistically significant at the 0.01 level.Thecoefficientoninterest,conformingtoaprioriexpectations. On the other hand, the financial variable that seems to work more consistently with the financial accelerator hypothesis in explaining productivity growth is coverage and size. Illustratively, industries exhibiting higher coverage ratios are better able to containthecostsofcapital,whichismanifestinhigherproductivity.Thecoefficienton size is positive. The result is consistent with previous findings which suggests that productivity is higher in large firms (Snodgrass and Biggs, 1995; Lee and Tang, 2001; Nuccietal.,2006). Thethirdmodelexaminestheroleofinstitutions,inparticular,theroleoftrade unionism. The coefficient on the variable union is negative and statistically significant. ThefindingisincontrastwithpreviousresultsforIndia,whichreportlimitedimpactof institutional factors on productivity (Topalova, 2007), but is consistent with the contention that more unionized firms are less likely to exhibit productivity improvementsinthelongrun(Hirsch,1997), The final model (4) combines all the explanatory variables together in a single specification. Most of the earlier results carry over in this case as well. As well, in the fully augmented model, leverage bears a negative and significant sign, implying that
14
levered industries could be constrained in accessing funds, which could adversely impacttheirproductivity.Thefitofthemodelishighestinthiscase:model(4)explains roughly17%ofthevariationinthedependentvariable. 6.2
Doeconomicreformsmatterforproductivity? A significant body of literature in India notes that Indian manufacturing
witnessedsignificantproductivitygains,postreforms,althoughseveralothersreportto the contrary. It does not, however, explore the time frame over which such changes occur at these enterprises. Illustratively, studies which report improvements in productivity after reforms simply note that productivity tended to be substantially higher after reforms than in the period prior to reforms. Others ((Srivastava 2001; Das 2003a;KaurandKiran,2008)uncoverevidenceofadeclineinTFP.Srivastava(2001)for instancereportdeclineinTFPduringtheperiod199091to199798to2%perannumas comparedto3.6%during198081to199091.TheestimatesbyDas(2003a)alsosuggest thatTFPgrowthinpost1991reformperiodtobeeithernegativeorintherangeof02% formostindustries. Toinvestigatethisfurther,table5presentstheresultsofformaltestsforchanges in TFPG by industry around the time when economic reforms occurred. Two sets of resultsarereported.Inone,theaveragelevelsofTFPGoveronetofiveyearsfollowing reforms[POST(5,1)]arecomparedwiththeaveragelevelsduringtheperiodofoneto five years before reforms [PRE(1,5)]. In another, the average levels of TFPG over the period of one to ten years following reforms [POST(1, 10)] are compared with the averagelevelsovertheperiodonetotenyearspriorto reforms[PRE(10,1)].Sincethe prereformtechnologicalcapabilityofdifferentindustriesisexpectedtobesignificantly different,thiswouldsuggestthatthetimeframeoverwhichproductivitygainsaccrueto industriescoulddifferaswell. [Table5.Univariatetestsofproductivitychange:Prevs.postreforms]
15
Theevidencesuggeststhatintheshortrun,growthinproductivity,onaverage, is higher after reforms as compared to that obtaining prior to reforms. In the small window case, across most of the industries, the TFPG in the onetofive years after reformsexceedsthatintheonetofiveyearspriortoreforms.Theresultsarereversedin thelongwindowcase,whereinitisobservedthatTPFGafterreformsistypicallylower ascomparedtothatobtainingintheprereformperiod.Inneithertheshortnorthelong window case are these differences statistically significant. Consider, for instance, basic metalsindustry.TheTFPGintheonetofiveyearspriortoreformsis0.082.Thisrisesto 0.103, post reforms. In case of the long period, these corresponding numbers are 0.061 and 0.038, respectively. None of these differences are however, significant at conventionallevels.
On balance, the evidence in table 5 appears to indicate that, over a long time
span, productivity growth for key industries declines after economic reforms. The evidencehowever,doesnotcontrolforthegenerallevelofeconomicactivitybeforeand after the economic reforms. They are therefore, not capable of distinguishing between changes in industry attributes arising from ordinary fluctuations in economic activity andthoseduetochangesinattributesintrinsictotheindustry. To investigate this further, we perform a series of multiple regressions that enable us to detect changes in industry attributes occurring during economic reforms, whilecontrollingfortheeconomicenvironment.Accordingly,weincludethreeindicator variables.ThevariablePREequalsoneiftheobservationisoneorfiveyearspriortothe year of reforms, else zero. The Year0 variable equals one in 1992 (year of reforms) and zero, otherwise.6 Finally, the variable POST equals one if the observation is for one to five years after the year of reforms, zero otherwise. Finally, we include the set of observable industrylevel controls, as also industry dummies. We repeat the analysis
6ThestatementonindustrialpolicytabledintheIndianParliamentinJuly1991laidtheframeworkforderegulationof theindustrialsector.Thecomprehensivesetofreformswereinitiatedoverthenextoneandahalfyears.Followingfrom thislogic,weconsidertheyear1992(correspondingtotheperiod1991Aprilto1992March)astheyearofreforms.The Year0dummyvariableisbasedonthisconsideration.
16
using both the short and long window to ascertain the time span over which productivitygainsaccruetoindustries. [Table6.Regressionresults:Productivitychangeandeconomicreforms] AsTable6shows,productivitygrowthdoesnotexhibitanyperceptibleriseprior to economic reforms. The coefficient on PRE is not significant in Models (1) (4). The evidence to suggest that productivity growth improves after reforms, at least in the shortrun,ismuchmorecompelling,sincethecoefficientonPOSTisstronglysignificant intwoofthefourcases.ThedifferencesbetweenthecoefficientsofPREandPOSTare however,notstatisticallysignificant.Sinceallregressionscontrolforindustrydummies, this indicates that productivity changes are not reliably higher post economic reforms thanduringtheperiodpriortoreforms. In the longer run, the evidence does not provide any conclusive evidence. Consider for instance Model (8). Although the coefficients on both PRE and POST are positive and statistically significant, the differences between these coefficients are not statistically significant, indicating that productivity improvements in the postreform periodarenotstatisticallyhigherascomparedtotheprereformregime. 6.3
Reformsandproductivity:Adisaggregatedlook
AmajorfocusofthereformsprocessinIndiahasbeentheissueastowhichset
of reforms exerted a perceptible impact on productivity growth. A significant body of literatureattributesthesametotradereforms(KrishnaandMitra,1998;ChandandSen, 2002;Das,2003b;Topalova,2007),whereasothersfocusontheimportanceofreoriented industrial policies (Kalirajan and Bhide, 2004); yet others point to the ease of credit availability as a crucial ingredient for improved productivity performance (Reddy, 2005). To explore this further, we examine the impact of each of these policies on productivitygrowth. We include three sets of measures to capture the impact of the respective policies. Under trade policy, following Pursell (2007), we employ the nominal rate of
17
protection (NRP) which captures the effect of lowering tariffs on output and intermediateinputs.IfloweringNRPraisesproductivitygrowth,thecoefficientonthis variablewouldbenegative.7Underindustrialpolicy,followingAghionetal.(2008),we employ a dummy variable which equals one beginning from the year in which an industry was delicensed (delicensed), else zero. To the extent that delicensing of an industryisassociatedwithimprovedproductivity,apositivecoefficientonthisvariable isexpected.Finally,underfinancialpolicies,weemploytheratioofgrossbankcreditas aratiotoGDP(credit),withanexpectedaprioripositivesign.8Dataongrossbankcredit areobtainedfromtheReserveBankofIndiadatabase(RBI,2007).Allregressionscontrol forindustryspecificfeaturesasalsodummiestoaccountforindustrylevelandbusiness cycleconsiderationsthatareotherwisenotaccountedforintheanalysis.
[Table7.Regressionresults:Productivitychangeandmacroeconomicpolicies]
The results are set out in Table 7. The coefficient on NRP is negative and
significant at the 0.10 level with a point estimate of 0.005. In other words, higher protection rate lower productivity growth: a 10 percent rise in NRP lowers TFPG by roughly0.05.TheevidenceconcurswithstudiesonIndianmanufacturingbySivadasan (2006)andTopalova(2007),whichindicatesabeneficialimpactoftradeliberalizationon productivity. Next, we examine whether industrial policies have had a salutary impact on TFPG.Inthesecondspecification,thecoefficientondelicensingispositiveandstrongly significant. This is in conformity with recent research which shows that delicensing played a major role in increasing productivity in India’s formal manufacturing sector, resulting in significant productivity gains (Chari, 2009). The credit variable displays an observedpositivesign,butisnotstatisticallysignificant.
7
Estimatesofnominalratesofprotection(NRP)for1993,1995,2001and2004havebeenextractedfromPursell(2007).It isassumedthattheNRPremainunchangedduringinterveningyears.SeealsoNouroz(2001) 8The data on deployment on gross bank credit (GBC) across industries does not exactly correspond with the industry names, since the data sources are different. As a result, it is not possible to obtain the GBC/GDP ratio for certain industries;hencethenumberofindustries/observationsarelowerwhencreditisemployedasanindependentvariable.
18
Summing up, the analysis testifies that economic reforms have not had any perceptible influence on the growth in manufacturing productivity; the conventional interest rate variables besides the financial accelerator variables are relevant in explaining productivity differentials across industries. Importantly, among major economicpolicies,tradeliberalizationanddelicensingseemstohavebeeninstrumental inproductivitygrowth. 7.
Summaryandconclusions
The reforms exercise in India, undertaken as part of the overall restructuring
since the early 1990s, was aimed at improving the growth prospects of the economy. Centraltotheprocesswasimprovingtheproductivityinthemanufacturingsector.Most studies on industrial productivity fail to account for the sample selection and simultaneity bias and therefore, arrive at misleading conclusions of productivity changes.
In this context, the present study employs advanced econometric techniques to
compute productivity of Indian manufacturing sector since 1980 that encompasses the economicreformsprogram.Industrylevelproductivitymeasureswereobtainedasthe differencebetweenactualandexpectedoutput,wherethelatteristhefittedvaluefrom theestimationofaproductionfunction.Theestimatedproductionfunctionfollowsfrom the strategy suggested by Levinsohn and Petrin (2003) to account for endogeneity problems. In the second stage of the investigation, we evaluate the factors affecting manufacturing productivity. The evidence indicates that both interest rate channel as well as financial accelerator channel is important in explaining productivity change, althoughtherelativeimportanceofthevariousfactorsundereachofthechannelsvaries markedly. In particular, across these two channels, the investment and interest cost variables seem to exert a notable impact on productivity growth. The results indicate thatamongmajoreconomicpolicies,tradeliberalizationanddelicensingseemstohave playedthemostsignificantroleinexplainingproductivitychange.
19
We evaluate the period over which economic reforms are manifest in productivity changes. Here again, the evidence suggests that improvements in productivityarenotreliablyhigherafterreformsthanpriortoreforms.Theresultsare remarkably robust. It is apparent in simple univariate comparisons as well as in multivariate regressions that control for industry and year effects. Judged thus, there seems to be a role for macroeconomic policies in impacting productivity in the post reformsperiod.TheIndiancaseisatestimonytothisfact. References Aghion, P., R.Burgess., S.Redding and F.Zilibotti (2005). Entry liberalization and inequalityinindustrialperformance.JournaloftheEuropeanEconomicAssociation 2/3,291302. Aghion, P., R.Burgess., S.Redding and F.Zilibotti (2008). The unequal effects of liberalization: Evidence from dismantling the license raj in India. American EconomicReview98,13971412. Ahluwalia,I.J.(1991).ProductivityandGrowthinIndianManufacturing.OxfordUniversity Press:NewDelhi. Balakrishnan, P., and K.Pushpangadan (1994). Manufacturing industry: A fresh look. EconomicandPoliticalWeekly39(October),202835. Balakrishnan, P., and K.Pushpangadan (2002). TFPG in manufacturing: The 80s revisited.EconomicandPoliticalWeekly47(January),32325. Balakrishnan,P.,andM.SureshBabu(2003).GrowthanddistributioninIndianindustry inthenineties.EconomicandPoliticalWeekly48(September),39974005. Balakrishnan,P.,K.PushpagandhanandM.SureshBabu(2000).Tradeliberalizationand productivity growth in manufacturing: Evidence from firmlevel panel data. EconomicandPoliticalWeekly(October),367982. Balakrishnan, P., M. Parameswaran, K.Pushpagandhan and M.Suresh Babu (2006). Liberalization,marketpowerandproductivitygrowthinIndianindustry.Journal ofEconomicPolicyReform9,5573. Balakrishnan,P.,K.PushpagandhanandM.SureshBabu(2000).Tradeliberalizationand productivity growth in manufacturing: Evidence from firmlevel panel data. EconomicandPoliticalWeekly(October),367982. Besley, T., and R.Burgess (2004). Can labor regulation hinder economic performance? EvidencefromIndia.QuarterlyJournalofEconomics119,91134. Bhagwati,J.andT.N.Srinivasan(1975).ForeignTradeRegimesandEconomicDevelopment. NewYork:ColumbiaUniversityPress. Bhagwati, J., and P.Desai (1970). India: Planning for Industrialization. London: Oxford UniversityPress.
20
Caselli, F. (2005). Accounting for cross country income differences. In P. Aghion and P.Durlauf(Eds.)HandbookofEconomicGrowth.ElsevierScience,679741. Caves, D., L.Christensen and W.E.Diewert (1982). Multilateral comparisons of output, inputandproductivityusingsuperlativeindexnumbers.EconomicJournal92,73 86. Chand, S., and K.Sen (2002). Trade liberalization and productivity growth: Evidence fromIndianmanufacturing.ReviewofDevelopmentEconomics6,12032. Chari, A.V. (2009). The aggregate productivity effects of entry and output restrictions: AnanalysisoflicensereforminIndia.WorkingPaper.CornellUniversity. Conway, P., R. herd and T. Chalaux (2008). Product market regulation and economic performanceacrossIndianstates.OECDWorkingPaperNo.600.OECD:Paris. Das, D.K. (2003a). Manufacturing productivity under varying trade regimes: Indian in the 1980s and 1990s. Working Paper No. 107. Indian Council for Research on InternationalEconomicRelations:NewDelhi. Das, D.K. (2003b). Quantifying trade barriers: has protection declined substantially in Indianmanufacturing?WorkingPaperNo.105.IndianCouncilforResearchon InternationalEconomicRelations:NewDelhi. Dollar, D., and E.N.Wolff (1993). Competitiveness, Convergence and International Specialization.Cambridge,MA:MITPress. EconomicandPoliticalWeeklyResearchFoundation(2007).AnnualSurveyofIndustries: 197374to200304.Mumbai. Fajnzylber,P.,andD.Lederman(1999).Economicreformsandtotalfactorproductivity growthinLatinAmericaandtheCaribbean,195095:Anempiricalnote.World BankPolicyResearchWorkingPaperNo.2114.WorldBank:WashingtonDC. Ghosh, S. (2009). Industry effects of monetary policy: The Indian case. Indian Economic Review44,89105. Goldar,B.N.(1986).ProductivitygrowthinIndianindustry.NewDelhi:AlliedPublishers. Goldar, B.N. (2002). Trade liberalization and manufacturing employment: The case of India.InternationalLaborOrganizationEmploymentPaperNo.34.ILO:Geneva. Goldar, B.N. (2004). Productivity trends in Indian manufacturing in the pre and post reforms periods. Working Paper No. 137. Indian Council for Research on InternationalEconomicRelations:NewDelhi. Goldar, B.N., and A. Kumari (2003). Import liberalization and productivity growth in Indian manufacturing industries in the 1990s. The Developing Economies 41, 436 60. Goldar,B.N.andV.S.Renganathan(2008).Importpenetrationandcapacityutilizationin Indian industries. Institute of Economic Growth Working Paper No. 293. IEG: NewDelhi. GovernmentofIndia.IndianLaborYearbook.LaborBureau:Shimla(variousyears). Grilliches, Z., and V.Ringstad (1971). Economies of Scale and Form of the Production Function.Amsterdam:NorthHollandPublishingCompany. Hall,R.,andC.Jones(1999).Whydosomecountriesproducesomuchmoreoutputper workerthanothers?QuarterlyJournalofEconomics114,83116.
21
Harrigan, J. (1999). Estimation of crosscountry differences in industry production functions.JournalofInternationalEconomics47,26794. Hirsch,B.T.(1997).Unionizationandeconomicperformance:Evidenceonproductivity, profits,investment,andgrowth.InF.Mihlar(ed.)Unionsandrighttoworklaws. TheFraserInstitute:VancouverBC,pp.3570. Howitt, P. (2000). Endogenous growth and crosscountry income differences. American EconomicReview90,82946. Kalirajan, K., and S.Bhide (2004). The postreform performance of the manufacturing sectorinIndia.AsianEconomicPapers3,12657. Kaur, M., and R. Kiran (2008). Indian manufacturing sector: Growth and productivity under the new policy regime. International Review of Business Research Papers 4, 13650. Klenow, P., and A.RodriguezClare (1997). The neoclassical revival in growth economics: Has it gone too far? In B.Bernanke and J.Rotemberg (Eds.) NBER MacroeconomicsAnnual.Cambridge,MA:MITPress,73102. Klenow, P., and A.RodriguezClare (2005). Externalities and economic growth. In P. AghionandP.Durlauf(Eds.)HandbookofEconomicGrowth.ElsevierScience,817 61. Kohli, A. (2006). Politics of economic growth in India (Part I). Economic and Political Weekly(April),125159. Krishna,P.,andD.Mitra(1998).Tradeliberalization,marketdisciplineandproductivity: NewevidencefromIndia.JournalofDevelopmentEconomics56,44762. Lee,F.,andJ.Tang(2001).MultifactorproductivitydisparitybetweenCanadianandUS manufacturingfirms.JournalofProductivityAnalysis15,11528. Levinsohn, J., and A.Petrin (2003). Estimating production functions using inputs to controlforunobservables.ReviewofEconomicStudies70,317342. Mookherjee, D. (1995). Indian Industry: Policies and Performance (Editor). Oxford UniversityPress:NewDelhi. Nouroz, H. (2001). Protection in Indian manufacturing: An empirical study. Delhi: MacMillanIndia.Ltd. Nucci,F.,A.F.PozzoloandF.Schivardi(2006).Isfirmproductivityrelatedtoitsfinancial structure:Evidencefrommicroeconomicdata.InM.MalgariniandG.Piga(Eds.) Capital accumulation, productivity and growth: Monitoring Italy 2005. Palgrave MacMillan.P.177198. Olley,G.,andA.Pakes(1996).Thedynamicsofproductivityinthetelecommunications equipmentindustry.Econometrica64,12631297. Pattnayak,S.S.,andS.M.Thangavelu(2005).Economicreformandproductivitygrowth inIndianmanufacturingindustries:Aninteractionoftechnicalchangeandscale economies.EconomicModeling22,60115. Petrin, A., and J. Levinsohn (2008). Measuring aggregate productivity growth using plantleveldata.Availableat
22
Pursell, G., N. Kishor and K. Gupta (2007). Manufacturing protection in India since independence. Australia South Asia Research Center Working Paper No. 7, AustraliaNationalUniversity,ASARC:Canberra. Ray,S.C.(2002).DidIndia’seconomicreformsimproveefficiencyandproductivity?A nonparametric analysis of the initial evidence from manufacturing. Indian EconomicReview37,2357. Reddy, Y.V. (2005). Importance of Productivity in India. Address delivered at the Annual Conference of Indian Economic Association at Andhra University, Vishakapatnam,India.Availableat ReserveBankofIndia(2008).HandbookofStatisticsontheIndianEconomy.RBI:Mumbai. Rodrik,D.(2005).Whywelearnnothingfromregressingeconomicgrowthonpolicies. Availableat Salim, R.A., and K.P. Kalirajan (1999). Sources of output growth in Bangladesh food processing industries: A decomposition analysis. The Developing Economies 37, 35574. Sanyal,PandN.Menon(2005).Labordisputesandtheeconomicsoffirmgeography:A studyofdomesticinvestmentinIndia.EconomicDevelopmentandCulturalChange 53,82554. Sivadasan, J. (2006).Productivity consequencesof product market liberalization: Micro evidence from Indian manufacturing sector reforms. Ross School of Business WorkingPaperNo.1062.AnnArbor,MI:UniversityofMichigan. Snodgrass,D.R.andT.Biggs(1995).Industrializationandsmallfirmspatternsandpolicies. SanFrancisco:InternationalCentreforEconomicGrowth. Srivastava,V.,(1996).Liberalization,ProductivityandCompetition:APanelStudyofIndian Manufacturing.OxfordUniversityPress:NewDelhi. Srivastava,V.,(2001).TheimpactofIndia’seconomicreformsonindustrialproductivity, efficiency and competitiveness: A panel study of Indian companies 198097. DiscussionPaper.NationalCouncilofAppliedEconomicResearch:NewDelhi. Topalova,P.(2007).Tradeliberalizationandfirmproductivity:ThecaseofIndia.Paper presented at the Ninth European Trade Study Group Conference, Athens University,September.Availableat(Accessed10.02.2008) Trivedi, P., A.Prakash and D.Sinate (2000). Productivity in major manufacturing industriesinIndia,197374to199798.DevelopmentResearchGroupStudyNo. 20.ReserveBankofIndia:Mumbai. Unel, B. (2003). Productivity trends in Indian manufacturing during the last two decades.IMFWorkingPaperNo.02.IMF:WashingtonDC. Van Bart, A. (1993). Comparative levels of manufacturing productivity in postwar Europe:Measurementandcomparisons.OxfordBulletinofEconomicsandStatistics 52,34374. VanBart,A.,andD.Pilat(1993).ProductivitylevelsinGermany,JapanandtheUnited States: Differences and causes. Brookings Paper on Economic Activity (Microeconomics)2,169.
23
Verner, D. (1998). Are wages and productivity affected by human capital investment andinternationaltradeinZimbabwe?PolicyResearchWorkingPaper2101.The WorldBank:WashingtonDC.
24
Table1.Estimationofproductionfunctionparameters OLS Constant 1.570(0.147)*** Ln(workers) 0.241(0.027)*** Ln(fuel) 0.100(0.043)** Ln(rawmaterials) 0.358(0.026)*** Ln(capital) 0.443(0.051)*** Timeperiod 19812004 Industry,N.Obs 23;547 Waldtest: 21.86 ConstantReturnstoScale (0.00)*** Standarderrorsinparentheses ***,**and*indicatesignificanceatthe1,5and10%level,respectively
LP 0.228(0.077)*** 0.190(0.049)*** 19812004 23;547 14.66 (0.00)***
Table2.Descriptivestatisticsforproductivityscores
N.Obs
Mean
Median
SD
Minimum
Maximum 0.917
25 percentile 0.056
75 percentile 0.151
Allindustries TFPG TFPGbyindustry
523
0.046
0.041
0.202
0.839
BasicMetals Chemicals Coke Electricity ElectricalMachinery Food Furniture Leather Machinery&eqpt. Medicaleqpt. Metalproducts Motorvehicles Office,accountingetc. Othernonmetallicmin.(NMM) Othertransporteqpt. Paper Publishingetc. Radio,TV,etc Rubber Textile Tobacco Wearingapparel Wood
23 23 23 17 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23
0.052 0.057 0.099 0.079 0.023 0.045 0.043 0.032 0.028 0.038 0.033 0.052 0.054 0.042 0.045 0.024 0.057 0.059 0.071 0.006 0.057 0.064 0.012
0.060 0.051 0.097 0.064 0.020 0.019 0.070 0.043 0.020 0.034 0.038 0.053 0.091 0.041 0.049 0.010 0.030 0.032 0.051 0.020 0.042 0.061 0.002
0.197 0.119 0.413 0.176 0.157 0.105 0.326 0.298 0.083 0.189 0.085 0.161 0.310 0.148 0.178 0.204 0.152 0.200 0.142 0.108 0.234 0.175 0.197
0.391 0.176 0.839 0.043 0.065 0.100 0.461 0.548 0.158 0.507 0.140 0.230 0.664 0.267 0.341 0.371 0.156 0.363 0.175 0.182 0.446 0.140 0.605
0.353 0.282 0.917 0.391 0.290 0.288 0.615 0.825 0.254 0.448 0.233 0.412 0.760 0.286 0.426 0.400 0.556 0.477 0.535 0.269 0.829 0.566 0.388
0.100 0.024 0.008 0.301 0.065 0.052 0.302 0.074 0.012 0.047 0.006 0.073 0.158 0.039 0.026 0.154 0.056 0.083 0.004 0.075 0.039 0.109 0.082
0.170 0.118 0.386 0.391 0.290 0.140 0.615 0.134 0.071 0.448 0.063 0.155 0.211 0.149 0.162 0.165 0.556 0.477 0.535 0.080 0.149 0.157 0.081
25
Table3.Variabledescription Variable Interestratechannel(IRC) Capitalintensity Tradable
Notation Investment Traded
Interestcost Financialacceleratorchannel(FAC) Leverageratio Coverageratio Workingcapitalratio Averagefactorysize Labormarketchannel(LMC) Tradeunionism
Interest Leverage Coverage Working Size Union
Empiricaldefinition Grossfixedcapitalformation/grossvalueadded Dummy=1, if an industry is in the traded goods sector,elsezero Interestpayments/grossvalueadded Outstandingloans/capital Netincome/interestpayments Workingcapital/grossvalueadded Ln(Numberofworkers/Numberoffactories) Number of employees listed as trade union members/numberofworkers
Obs. 547 552
Mean 0.361 0.304
Std.Dev. 1.037 0.461
547 547 547 547 547 547
0.195 0.965 4.572 0.793 4.009 0.201
0.085 0.501 4.132 0.529 0.796 0.195
Table4.Effectofindustrycharacteristicsonchangeinproductivity (1) (2) Constant 0.599(0.091)*** 0.173(0.118) Investment 0.159(0.055)*** Interest 1.132(0.180)*** Traded 0.329(0.248) Leverage 0.042(0.047) Coverage 0.014(0.005)*** Working 0.045(0.029) Size 0.053(0.028)* Union Yeardummies Yes Yes Industrydummies Yes Yes Rsquared 0.158 0.111 Period,industry 19812004 19812004 N.Obs;industry 523;23 523;23 Standarderrors(clusteredbyindustry)inparentheses ***,**and*indicatesignificanceatthe1,5and10%level,respectively
(3) 0.041(0.022)* 0.121(0.049)** Yes Yes 0.068 19812004 523;23
(4) 0.687(0.305)** 0.149(0.058)*** 0.916(0.219)*** 0.374(0.291) 0.027(0.014)* 0.004(0.003) 0.027(0.034) 0.020(0.011)* 0.084(0.059) Yes Yes 0.173 19812004 523;,23
26
Table5.Univariatetests:Productivitychangeprevs.postreforms Industrygroup
Summary statistics
Smallwindow
Difference: POST=PRE (ttest) 0.193
PRE(5,1) POST(+1,+5) Basicmetals Mean 0.082 0.103 SD 0.188 0.155 Chemicals Mean 0.053 0.088 0.446 SD 0.059 0.165 Coke Mean 0.013 0.153 1.578 SD 0.088 0.177 Electricity Mean 0.071 0.117 0.438 SD 0.128 0.194 1.039 Electricalmach. Mean 0.115 0.023 SD 0.126 0.154 Food Mean 0.051 0.069 0.284 SD 0.096 0.113 Furniture Mean 0.176 0.096 1.339 SD 0.267 0.367 Leather Mean 0.165 0.038 0.511 SD 0.507 0.228 Machinery&eqpt. Mean 0.024 0.069 0.769 SD 0.072 0.109 Medicaleqpt. Mean 0.033 0.062 0.716 SD 0.275 0.112 Metalproducts Mean 0.053 0.040 0.153 SD 0.133 0.132 Motorvehicles Mean 0.063 0.105 0.407 SD 0.117 0.199 Office,accounting Mean 0.031 0.052 0.309 SD 0.243 0.548 OtherNMM Mean 0.046 0.089 1.816 SD 0.134 0.026 Othertransport Mean 0.112 0.074 0.663 SD 0.100 0.081 Paper Mean 0.153 0.006 1.534 SD 0.171 0.129 Publishingetc. Mean 0.046 0.065 0.249 SD 0.613 0.157 Radio,TV,etc Mean 0.070 0.008 0.531 SD 0.163 0.285 Rubber Mean 0.038 0.041 0.059 SD 0.083 0.108 Textiles Mean 0.067 0.040 0.331 SD 0.125 0.128 Tobacco Mean 0.092 0.055 0.379 SD 0.059 0.206 Wearingapparel Mean 0.179 0.029 1.099 SD 0.044 0.303 Mean 0.049 0.095 3.851*** Wood SD 0.129 0.195 ***,**and*indicatesignificanceatthe1,5and10%level,respectively
Largewindow
PRE(10,1) 0.061 0.149 0.068 0.091 0.114 0.443 0.066 0.185 0.051 0.189 0.077 0.114 0.021 0.326 0.100 0.352 0.035 0.053 0.003 0.207 0.042 0.090 0.057 0.112 0.056 0.190 0.071 0.118 0.049 0.134 0.053 0.239 0.074 0.179 0.091 0.189 0.122 0.177 0.019 0.110 0.068 0.317 0.116 0.123 0.028 0.110
POST(+1,+10) 0.038 0.191 0.050 0.160 0.071 0.426 0.126 0.174 0.005 0.150 0.036 0.103 0.098 0.335 0.022 0.174 0.028 0.105 0.066 0.191 0.017 0.094 0.022 0.193 0.004 0.427 0.006 0.170 0.042 0.239 0.015 0.196 0.028 0.146 0.009 0.223 0.039 0.111 0.003 0.105 0.042 0.176 0.009 0.219 0.004 0.282
Difference: POST=PRE (ttest) 0.297 0.300 0.221 0.653 0.604 0.834 0.807 0.629 0.191 0.701 0.623 0.503 0.403 0.993 0.082 0.392 0.623 0.887 1.257 0.326 0.226 1.339 0.249
27
Table6.Regressionresults:Productivitychangeandeconomicreforms Constant
PRE(10,1)
(1) 0.609 (0.088)*** 0.004 (0.046) 0.027 (0.025) 0.038 (0.016)**
Smallwindow (2) (3) 0.076 0.033 (0.088) (0.012)*** 0.037 0.050 (0.047) (0.044) 0.025 0.01226 (0.022) (0.021) 0.018 0.011 (0.014) (0.012)
(4) 0.582 (0.254)** 0.007 (0.045) 0.028 (0.023) 0.033 (0.016)*
POST(+1,+10)
Year0 PRE(5,1) POST(+1,+5)
IRC Yes No No Yes FAC No Yes No Yes LMC No No Yes Yes Industrydummies Yes Yes Yes Yes POST=PRE 0.15 0.10 0.27 0.03 Ftest(pValue) (0.70) (0.75) (0.61) (0.86) Rsquared 0.125 0.068 0.032 0.136 Period 19812004 19812004 19812004 19812004 N.Obs,industry 523;23 523;23 523;23 523;23 Standarderrors(clusteredbyindustry)inparentheses ***,**and*indicatesignificanceatthe1,5and10%level,respectively
(5) 0.597 (0.094)*** 0.006 (0.056)
Largewindow (6) (7) 0.143 0.040 (0.108) (0.021)* 0.009 0.058 (0.055) (0.053)
(8) 0.655 (0.297)** 0.043 (0.045)
0.028 (0.022) 0.026 (0.021) Yes No No Yes 0.01 (0.94) 0.120 19812004 523;23
0.067 (0.028)** 0.016 (0.026) No Yes No Yes 4.05 (0.05)** 0.076 19812004 523;23
0.014 (0.021) 0.015 (0.022) No No Yes Yes 5.23 (0.00)*** 0.034 19812004 523;23
0.082 (0.031)*** 0.058 (0.021)*** Yes Yes Yes Yes 0.85 (0.36) 0.139 19812004 523,23
Table7.Regressionresults:Productivitychangeandmacroeconomicpolicies Constant
Delicensing
Tradepolicies 0.519 (0.284) 0.005 (0.003)*
Credit
NRP
Industrialpolicies 0.762 (0.293)***
Financialpolicies 0.275 (0.152)*
0.060 (0.029)**
IRC Yes Yes FAC Yes Yes LMC Yes Yes Yeardummies Yes Yes Industrydummies Yes Yes Rsquared 0.173 0.175 Period,industry 19912004 19812004 N.Obs,industry 506;23 523;23 Standarderrors(clusteredbyindustry)inparentheses ***,**and*indicatesignificanceatthe1,5and10%level,respectively
0.029 (0.038) Yes Yes Yes Yes Yes 0.291 19912004 315;22
Combinedpolicies 0.262 (0.383) 0.010 (0.005)* 0.139 (0.050)*** 0.019 (0.041) Yes Yes Yes Yes Yes 0.309 19912004 306;23
28
