Productivity and the Investment Climate: What Matters Most?

Transcription

1 Publc Dsclosure Authorzed Publc Dsclosure Authorzed Productvty and the Investment Clmate: What Matters Most? Fabano Bastos and John Nasr Abstract Publc Dsclosure Authorzed Publc Dsclosure Authorzed Ths paper explores the lnks between the nvestment clmate and frm-level productvty and attempts to dentfy whch dmensons of the nvestment clmate matter most for productvty. The analyss s based on data collected n a recent Investment Clmate survey of garment and food processng frms across fve countres n Eastern Europe and Central Asa. The paper uses the frst prncpal components of a seres of ndcators to summarze broad aspects of the nvestment clmate and dentfy those most mportant n determnng productvty. The results ndcate that compettve pressure s the most crtcal factor n the nvestment clmate, accountng for more varaton n frm-level productvty than nfrastructure provson or ssues related to government rent seekng and bureaucratc burden. Ths suggests that to mprove productvty, ncrease output, and reduce poverty, polcymakers should focus reform efforts on removng barrers to entry and creatng open, hghly compettve markets. World Bank Polcy Research Workng Paper 3335, June 2004 The Polcy Research Workng Paper Seres dssemnates the fndngs of work n progress to encourage the exchange of deas about development ssues. An objectve of the seres s to get the fndngs out quckly, even f the presentatons are less than fully polshed. The papers carry the names of the authors and should be cted accordngly. The fndngs, nterpretatons, and conclusons expressed n ths paper are entrely those of the authors. They do not necessarly represent the vew of the World Bank, ts Executve Drectors, or the countres they represent. Polcy Research Workng Papers are avalable onlne at 1

2 Productvty growth s the sne qua non for economc growth and consequently poverty allevaton. Whle factor accumulaton s mportant, ncreases n captal and labor nputs alone do not explan cross-country dfferences n growth. Economc research has shown that mprovements n the effcency wth whch countres use ther nputs ther total factor productvty (TFP), explans most of the ncrease n output for fast-growng economes. No country has experenced sustaned economc growth wthout also achevng rapd gans n productvty. The questons then for polcymakers are these: What determnants of productvty cause producers n one country to be more effcent than those n compettor countres? And where should reform efforts be targeted to have the greatest mpact on productvty and thus poverty reducton? In a global economy where technology dffuses rapdly and captal s moble, the persstence of productvty dfferences among countres can be largely explaned by dfferences n the nvestment clmate the polcy, nsttutonal, and regulatory envronment n whch busnesses must operate. Countres where property rghts are secure, the government provdes effcent servces, and nfrastructure s well developed are consdered to have a good nvestment clmate. And a good nvestment clmate reduces the cost of dong busness and leads to hgher and more certan returns on nvestment. But merely knowng that the nvestment clmate matters s not enough. Polcymakers are constraned by lmted resources, and the path of reform s dffcult and often slow. Whle some polcy reforms can be nsttuted relatvely quckly, many changes such as buldng 2

3 nfrastructure and mprovng the qualty of the workforce take years. To be most effectve, polcymakers must be able to prortze and drect ther efforts to where they wll have the largest and quckest mpact. Ths paper drectly lnks the nvestment clmate to frm performance and dentfes dmensons of the nvestment clmate that account for cross-country dfferences n productvty. And t presents evdence that compettve pressure s the most mportant of these dmensons n determnng productvty and thus the area where polcymakers should frst focus ther reform efforts. In hghly compettve and dynamc economes, neffcent frms are drven out and survvng busnesses are forced to seek contnuous mprovement. Ths effect has been clearly llustrated n such countres as Chna and Inda, whch have made enormous gans n productvty after lberalzng ther markets and openng them to the world economy. Many studes have lnked nsttutons and growth at the macroeconomc level (see Hall and Jones 1999). These studes rely on cross-country samples and use proxes at the country level for such factors as government effcency, polcy uncertanty, and the securty of property rghts. The nvestment clmate approach, a closely related concept, strengthens the nsttutonal lterature by provdng mcroeconomc foundatons. Ths paper follows the lead of Dollar, Hallward-Dremeer, and Mengstae (2003) n usng data from recent World Bank Investment Clmate Surveys (ICS) to drectly lnk ndcators of the nvestment clmate to frm-level productvty. It uses data from a recently completed survey of fve transton economes n Eastern Europe and Central Asa to estmate total 3

4 factor productvty and econometrcally demonstrate the mpact of the nvestment clmate on productvty. But ths paper also goes a step further than prevous earler papers, and ranks the broad measures of the nvestment clmate by ther relatve mportance n explanng the varaton n productvty across countres. Ths rankng technque can help polcymakers use the results of the productvty analyss to desgn and mplement polces for creatng a pro-growth, and thus pro-poor, nvestment clmate. The Data The data used for ths study were drawn from an nvestment clmate survey conducted by the World Bank n 2003 across fve transton countres: the Kyrgyz Republc, Moldova, Poland, Tajkstan, and Uzbekstan. The survey used an dentcal nstrument n each country to conduct face-to-face ntervews wth frms managers and bookkeepers or accountants. The questonnare was smlar to that used n the Busness Envronment and Enterprse Productvty Survey conducted by the European Bank for Reconstructon and Development and World Bank n But t was amended to collect accountng data needed to estmate TFP and to mprove comparablty wth the World Bank s core productvty and nvestment clmate survey nstrument. The survey, known as the Busness Envronment and Enterprse Productvty Extenson Survey, was wde rangng: n addton to the accountng data, t sought both quanttatve and qualtatve nformaton on nvestment clmate ndcators, ncludng managers perceptons of the busness envronment. 4

5 The survey was conducted smultaneously across all fve countres, makng a useful pont-n-tme comparson. 1 In each country a sample of 100 enterprses was drawn randomly from all sze categores, wth the sample equally splt between enterprses whose man operatons were n the garment sector and those whose man operatons were n food processng. As far as was possble, the food processng sample was drawn from frms classfed under NACE code 15.1 (producton and preservng of meat and meat products). Restrctng the sample to two sectors helped ensure the accuracy of the productvty estmaton. Both sectors produce tradable goods n reasonably compettve markets. Frms n the sample were therefore lkely to face common prces, reducng the chance that the nomnal value of output (and thus productvty) was nflated because of market power. Snce the sectors are narrowly focused, t s also lkely that the selected frms were usng smlar technologes. Ths leads to a more homogeneous producton functon and makes t easer to attrbute the resdual to dfferences n effcency rather than dfferences n technology or market structure. Although over 500 frms were surveyed, many dd not report the full range of nvestment clmate measures needed for the study. Others were engaged only n part n garment producton or food processng, dervng the rest of ther earnngs from servces, tradng, 1 The survey was admnstered and supervsed n all fve countres by a sngle frm, and the enumerators underwent tranng together to reduce varaton among countres due to dfferences n mplementaton methodologes. Because of worres about survey fatgue followng the large Busness Envronment and Enterprse Productvty Survey less than a year earler, the sample was lmted to 100 frms n each country. 5

6 or other types of actvtes. Stll others reported numbers that were not credble, partcularly n response to questons on frm performance and labor nputs. Once outlers, ncomplete observatons and observatons on frms that were not solely manufacturers were removed, 362 observatons remaned 192 n food processng and 170 n garments (see appendx tables A1.1 and A1.2 for descrptve statstcs). Transton and the Investment Clmate The fve transton countres n the sample provde a unque opportunty to explore the mpact of the nvestment clmate and lberalzaton efforts. At ndependence all fve countres nherted centrally planned economes wth heavly dstorted markets. But ther response to ths stuaton vared. Poland moved aggressvely to lberalze and nsttute market reforms. It rapdly transferred productve assets to prvate hands, welcomed foregn nvestment, allowed easy entry of new startups, reduced government regulatons, and concentrated on mprovng the regulatory envronment for prvate busness. At the other extreme, Uzbekstan took a much more conservatve approach. The government contnues to drect nvestment and allocates a substantal share of the country s economc resources. Foregn nvestment and foregn trade (beyond commodtes) have grown slowly. Uzbek offcals clam that the slow pace of reform softened the shock of transton and reduced the fall n output suffered by all transton 6

7 economes when they lost guaranteed markets and subsdes. 2 However, the falure to adopt market reforms and encourage competton has choked off growth durng the recovery and led to lower productvty levels. 3 The other Central Asan countres, the Kyrgyz Republc and Tajkstan, appear to have an nvestment clmate more lke Uzbekstan s than Poland s, whle Moldova s nvestment clmate appears to be somewhere n the mddle. Managers n the Moldovan sample report hgher levels of competton and better nfrastructure than those n the Central Asan countres, but worse performance on these measures than n Poland. The transton economes nherted nfrastructure of poorer qualty than that n OECD countres, though of much better qualty than that n most developng countres. All the transton economes had a well-educated workforce, wth very hgh lteracy. In the 2002 Busness Envronment and Enterprse Productvty Survey managers n most of the 27 transton economes ncluded reported that the bggest constrants to prvate sector operatons centered on governance ssues, the regulatory regme and the uncertan polcy envronment. For managers n the smaller sample of transton economes used n ths paper, macroeconomc uncertanty was the bggest concern, reflectng the transton experence 2 Ths nformaton comes from ntervews wth Uzbek offcals conducted as part of the World Bank s recent nvestment clmate assessment n Uzbekstan. 3 Uzbekstan suffered a smaller declne n output than any other transton economy. Uzbek output at ts lowest pont, n 1995, was stll 85 percent of the level n Several factors may have mtgated the declne n output. The country had a smaller ndustral base to begn wth, ts man exports were commodtes (such as gold and cotton) that were easy to reorent for export to the world market, and ol and energy producton ncreased enough durng ths perod to make Uzbekstan self-suffcent n these resources. 7

8 (fgure 1). (Whle there were some dfferences among countres, the rank orderng was smlar across countres, so only the sample averages are reported here.) As n almost every country where nvestment clmate surveys have been conducted, managers also perceved both the cost and accessblty of fnance as a problem. And whle they perceved nfrastructure, access to resources, and technology as mportant constrants, they consdered them less so than the polcy envronment. Fgure 1 Share of Frms Identfyng Each Investment Clmate Factor as a Major Constrant n Fve Transton Economes Macroeconomc nstablty (nflaton, exchange rate) Regulatory polcy uncertanty Tax rates Cost of fnancng (such as nterest rates) Tax admnstraton Access to fnancng (such as collateral) Corrupton Customs and trade regulatons Antcompettve or nformal practces Busness lcensng and operatng permts Crme, theft, and dsorder Legal system and conflct resoluton Labor regulatons Sklls and educaton of avalable workers Electrcty Access to land Transport Telecommuncatons Percent Note: Fgure shows sample averages across the Kyrgyz Republc, Moldova, Poland, Tajkstan, and Uzbekstan. Source: World Bank, Busness Envronment and Enterprse Productvty Extenson Survey, [ Measurng the Investment Clmate Managers broad perceptons of the nvestment clmate gve some ndcaton of the most constranng busness problems. But to understand the effect of the nvestment clmate on 8

9 frm performance requres specfc measures of ndcators. The nvestment clmate survey collected data on a wde varety of quanttatve as well as qualtatve, or perceptonbased, ndcators of the nvestment clmate. Whle percepton based ndcators are useful, they must be augmented by quanttatve ndcators to gve an accurate pcture of the nvestment clmate. The reason s that managers have dfferent frames of reference. An ssue perceved as a major problem n one country may actually mpose a smaller cost on busnesses n that country than t does n a country where the problem s rated as merely moderate. So whenever possble t s mportant to gather quanttatve measures of a problem (for example, how much companes pay n brbes on average) rather than just perceptons (how bg a problem corrupton s). There are a vast number of possble ndcators and the challenge s to narrow ths large selecton down to a manageable number that accurately descrbe the nvestment clmate. In dong so, researchers face two major problems: endogenety and multcollnearty. Ths analyss treats the nvestment clmate ndcators as exogenous determnants of frms performance. However, hgh-performng frms may be proactve n reducng ther own nvestment clmate constrants, producng a smultanety bas n the estmaton exercse. For example, a well-managed, productve frm may be able to work wth authortes to lmt nspectons or secure a more relable power supply. To address ths endogenety ssue, the emprcal analyss uses average nvestment clmate ndcators taken across 9

10 frms n the same country, regon, and sector. To the extent that ndvdual frms are less lkely to nfluence averages and provded that the sample cuts are sensbly chosen to reflect pockets of busness envronments, usng averages s a vald way to nstrument out the smultanety problem. 4 Among the possble nvestment clmate ndcators are multple ndcators that address a smlar major ssue, gvng rse to multcollnearty. For example, many ndcators relate to the qualty of nfrastructure such as the prevalence of e-mal, the number of frms wth ther own power generators and the number of power outages n a gven perod. It s not clear how mportant any partcular ndcator s. The qualty of nfrastructure affects all frms, but whch ndcator matters most to a partcular frm depends on ts crcumstances and ts producton technque. Typcally, all of the ndcators that address a broad dmenson of the nvestment clmate are hghly correlated and move together. Consequently, t s not possble to know the mportance of any one partcular ndcator snce t may be servng as a proxy for other, more relevant varables. Investment clmate s a vast concept, and a tenson naturally arses between ts breadth and a parsmonous quanttatve approach. From a practcal perspectve ths study s prevented from workng wth as many nvestment clmate ndcators as would be preferred, snce correlaton among them wll eventually dsrupt the econometrc exercse. One soluton used n past studes s to restrct the analyss to a subset of nvestment clmate ndcators and accept the omtted varable bas. Although ths approach can yeld 4 Another tradeoff that researchers face when decdng how many crtera on whch to base the sample cut s that a thck cut wll mtgate the endogenety problem but generate lttle varaton n the regressor, whle a thn cut wll have the opposte effect. 10

11 sgnfcant results, there are always questons of whether the IC varables used provde a representatve descrpton of the nvestment clmate and whether the strength of the results s merely due to the partcular selecton of ndcators. In an attempt to overcome both problems, ths study reduces the dmensonalty of the data by usng prncpal component analyss. 5 The approach frst dentfes broad aspects of the nvestment clmate measured by the survey, collects a few seres that map nto each of them, and then extracts the man varaton commanded by each aspect through the use of ther respectve frst prncpal component. It essentally treats all of the underlyng nvestment clmate ndcators as proxes for three broad measures of the nvestment clmate descrbed below: rent predaton, nfrastructure, and competton. Rent predaton The rent predaton measure s constructed from survey responses on the costs mposed by brbery, bureaucracy, and nspectons. Hgher levels of predaton typcally have an adverse effect on performance. The cost of brbery or the brbe tax s the estmated amount of unoffcal payments frms typcally made to publc offcals n the prevous year as a percentage of sales. The cost of bureaucracy or the tme tax s the percentage of senor management tme spent n dealng wth publc offcals on the applcaton and nterpretaton of laws and regulatons and on gettng or mantanng access to 5 See Manly (1994) for an ntutve ntroducton to prncpal component analyss and Marda, Kent, and Bbby (1997) for a more techncal exposton. 11

12 publc servces. The tme tax s a proxy for how burdensome government red tape s. The cost of nspectons s measured as the number of days n the prevous year spent on nspectons and requred meetngs wth government offcals. Hgher numbers of nspectons lower performance because more nspectons provde more opportuntes for rent seekng; t can generally be assumed that the more nspectons there are, the more lkely they are to be rent seekng actvtes. Table 1 Mean Indcators of Rent Predaton by Country Indcator Kyrgyz Republc Moldova Poland Tajkstan Uzbekstan Inspectons (days) Brbe tax (percent) Tme tax (percent) Note: See text for defntons of ndcators. Source: World Bank, Busness Envronment and Enterprse Productvty Extenson Survey, The Kyrgyz Republc appears to perform the worst on the rent predaton measure, wth the hghest values for nspectons and the brbe tax (table 1). Poland has the lowest brbe tax but the hghest tme tax. Stll, t s dffcult to say that any one country suffers from the hghest level of rent predaton. It has been suggested that there may be large measurement error because n some countres managers are unwllng to talk about government rent seekng. However, the ntervew teams provded no evdence of systematc measurement error. Infrastructure The nfrastructure measure ndcates the effect of nadequate nfrastructure on frm performance. A composte measure of the average number of days frms experenced 12

13 nterruptons n power, water, and telephone servce n the last year, t serves as a proxy for the overall state of nfrastructure. The European countres clearly have a superor nfrastructure, wth Poland s stronger than Moldova s (table 2). Frms n Poland, on average, experenced only 1.5 power outages durng the prevous year and those n Moldova 2.3 compared wth 37.3 n Tajkstan and 15.8 n the Kyrgyz Republc. Water and telephone outages tell a smlar story. Frms n Uzbekstan reported fewer servce outages on average than those n the other Central Asan countres, but stll substantally more than those n the European countres. These mcroeconomc results mrror country-level data. For example, the European countres had the most telephone manlnes per 1,000 people and the most Internet hosts per 10,000 people n 2000, wth Poland havng the most developed telecommuncatons sector. Table 2 Mean Indcators of Infrastructure by Country Indcator Kyrgyz Republc Moldova Poland Tajkstan Uzbekstan Servce nterruptons n prevous year Power Water Telephone Telephone manlnes per 1000 people, Internet hosts per 10,000 people, Source: For servce nterruptons, World Bank, Busness Envronment and Enterprse Productvty Extenson Survey, 2003; for other data, World Bank, World Development Indcators database. Competton 13

14 Compettve pressure s the drvng force behnd productvty mprovements. In closed economes wth sheltered markets enterprses have lttle ncentve to nvest n new products or cost savng measures. But n open, hghly compettve economes enterprses must constantly strve to mprove ther compettve poston to survve. The competton measure uses a seres of percepton questons regardng the mportance of domestc and foregn competton for decsons to ntroduce new products and to reduce the costs of exstng products. It also ncludes a queston on the number of compettors n frms man product lne as a gauge of market power. Table 3 Mean Indcators of Competton by Country Indcator Kyrgyz Republc Moldova Poland Tajkstan Uzbekstan Importance of domestc competton for decsons a To ntroduce new products To reduce costs Importance of foregn competton for decsons a To ntroduce new products To reduce costs Compettors n man product lne Average net nflows of foregn drect nvestment (percentage of GDP), a. Average rankng by frms on a scale of 1 (not at all mportant) to 4 (very mportant). Source: For foregn drect nvestment, World Bank, World Development Indcators database; for all other data, World Bank, Busness Envronment and Enterprse Productvty Extenson Survey, By all measures based on managers perceptons, Poland has a much more compettve economy than the Central Asan countres or even Moldova (table the 3). On average Polsh managers ranked pressure from domestc compettors as 3.3 n mportance (wth 3 meanng farly mportant and 4 very mportant), whle n other countres average rankngs ranged between 2.14 and 3.0. As expected, the lowest scores appeared n Uzbekstan, 14

15 where government polces have restrcted competton. By contrast, Poland has taken a number of steps to lberalze and open ts economy to competton, many n preparaton for jonng the European Unon. Poland has prvatzed all but some of ts older, heavy ndustres (such as mnng and shpbuldng), and many new busnesses have entered the market snce transton began. The rate of foregn drect nvestment s one way to llustrate the relatve openness of the European economes n the sample. In net nflows of foregn drect nvestment averaged 5.1 percent of GDP n Poland and 4.0 percent n Moldova, but only 1.9 percent n Tajkstan and 0.8 percent n Uzbekstan. Prncpal Components and the Underlyng Investment Clmate Varables Prncpal component analyss s used to pn down weghts for a lnear combnaton of the underlyng nvestment clmate varables such that the resultng seres (the frst prncpal component) has maxmum varance. Intutvely, the goal s to construct one artfcal seres that can summarze the behavor of a group of underlyng varables that descrbe smlar aspects of the nvestment clmate. Only the frst prncpal components are used because havng a sngle seres for each dmenson of the nvestment clmate wll be crucal when the analyss attempts to rank the dmensons by ther relatve ablty to explan the varaton n frm-level productvty. 6 6 The analyss s restrcted to the frst prncpal components because they adequately characterze the broad dmensons of nvestment clmate yet yeld a parsmonous lst of varables that explan the effect of the nvestment clmate on frm performance. The egenvalues assocated wth each of the frst prncpal components are greater than one, whle those assocated wth each of the second prncpal components are 15

16 Snce the frst prncpal components are used to summarze dfferent dmensons of the busness envronment, they can be expected to be correlated wth the underlyng (ndvdual) nvestment clmate varables. Indeed, the frst prncpal components are strongly assocated wth ther correspondng nvestment clmate varables (averaged for each country-regon-sector slce; table 4). Table 4 Correlaton between Investment Clmate Varables and Frst Prncpal Components Rent predaton Infrastructure Competton Underlyng varables Correlaton Underlyng varables Correlaton Underlyng varables Correlaton Inspectons Power Domestc, new products Brbe tax Water Foregn, new products Tme tax Telephone Domestc, reduce costs Foregn, reduce costs Num. of Compettors Source: Authors calculatons. Estmatng Productvty Frm-level productvty estmates are generated for the sample by fttng a Cobb-Douglas producton functon after poolng across sectors and countres. Equaton 1 gves a general form of the producton functon: (1) Q = AK α L β M δ Here A s a term that captures the nfluence of the nvestment clmate on the producton functon. It s n effect the resdual porton of output not accounted for by nputs of ether only slghtly greater or less than one. Ths fact gves some assurance that relyng only on the frst prncpal component does not nvolve throwng away too much nformaton. 16

17 captal (K), labor (L), and raw materals and fuel (M) and can be ascrbed to the busness clmate. It s modeled here as a functon of observable nvestment clmate ndcators, x. Takng logs, the equaton for ths general form can be wrtten as: (2) q α k βl δm = φ( x ) The left sde of ths equaton s conventonally known as total factor productvty (TFP), and n the model used here t s a functon of the nvestment clmate. The concept of nvestment clmate has an mportant country-specfc component, and as a result, ncludng country dummy varables n a regresson may wash away much of the effects of the nvestment clmate ndcators. But t s mportant to take nto account country-specfc effects when estmatng coeffcents on captal and labor. The analyss thus proceeds n two steps. Frst the basc producton functon s estmated wth country dummes but wthout nvestment clmate measures. Then, usng the coeffcents from ths regresson, TFP s calculated and regressed on broad nvestment clmate measures. The producton functon s estmated n logs. In addton, t s assumed that frms n the sample are prce takers and that they purchase captal and raw materals at world prces. Thus, the dollar value of captal and materals and the dollar value of output can be compared across countres. These are strong assumptons, but gven the hghly compettve nature of the selected sectors they are not unreasonable. Wage rates dverge across countres so that labor nput s measured by the number of workers, not the value of labor used. The regressors nclude nputs (materal, labor, and captal), capacty utlzaton, schoolng, sectoral and country dummy varables. The basc model s 17

18 augmented wth capacty utlzaton to account for effcency loss due to unused capacty and low market demand. 7 The schoolng varable s a weghted average of educaton years of the workforce and captures elements of labor qualty. 8 Nomnal values are converted to U.S. dollars usng nomnal exchange rates. 9 The regresson s estmated usng ordnary least squares wth Whte correcton for heteroskedastcty and allows for clusterng wthn countres. The specfcaton s as follows: (3) ln ( Output ) = cons + β + β u ln ( CapUtl ) + β s m ln ( Materal ) + β ln ( Schoolng ) + β SectoralDu mmy+ t l ln ( Labor ) + β c ln ( Captal ) 4 n= 1 β n D n + ε The dfferent producton technologes used by the two sectors could mean a need to estmate separate equatons for each sector. In an unreported exercse the equaton s therefore estmated wth sectoral dummy varables nteracted wth the nput coeffcents. The parameter estmates are not jontly sgnfcant, however. Consequently, the data are pooled across sectors to take advantage of a large sample sze and the results of the producton functon estmaton are reported n Table 5. Moldova s chosen as the base country, and country-specfc productvty gaps expressed n relatve percentage terms are retreved from the estmated coeffcents on the country 7 Capacty utlzaton was estmated by smply askng managers what they consdered ther capacty utlzaton to be. No effort was made to defne a standard number of shfts or correct for dfferences n the length of the workweek. 8 There was no relable nformaton on worker tranng or experence n the Busness Envronment and Enterprse Productvty Extenson Survey. 9 Usng nomnal rather than real exchange rates ntroduces bas because many countres ntervene to support the value of ther currency. Uzbekstan n partcular has an overvalued exchange rate. But n comparsons of nternatonal compettveness nomnal exchange rates make more sense because these are what exporters face. 18

19 dummy varables. 10 The results show large productvty gaps between countres (fgure 2). Although the sample s drawn from enterprses n the same ndustres and usng smlar technologes, productvty s hgher for enterprses n countres wth a more compettve and less costly nvestment clmate. Frms n Poland are 28 percent more effcent than those n Moldova, whle frms n Uzbekstan, the least effcent, are 26 percent less effcent than those n Moldova. Fgure 2 Productvty Gaps between Countres wth Moldova as the Base Poland Tajkstan Uzbekstan Kyrgyz Republc Source: Authors calculatons. In the second step the coeffcents estmated n equaton 3 are used to derve the measure of frm-level productvty: (4) ln TFP ˆ βˆ u ln = ln ( Output ) βˆ ( CapUtl ) βˆ s m ln ( Materal ) βˆ ln ( Schoolng ) l ln ( Labor ) βˆ c ln ( Captal ) Table 5 Producton Functon Results Ln (Output) Coeffcent Standard error 10 Productvty gap for country n = [ exp ( β n ) 1 ] * 100, for any country n other than Moldova. 19

20 Ln (Materal) 0.72* Ln (Labor) 0.22* Ln (Captal) 0.07* Ln (CapUtl) 0.08** Ln (Schoolng) 0.40** Dummy Food Poland 0.25* Tajkstan 0.15* Uzbekstan 0.30* Kyrgyz Republc 0.29* * Sgnfcant at the 1 percent level. ** Sgnfcant at the 10 percent level. Source: Authors calculatons. Country dummes are ntentonally not ncluded n the second equaton (snce, as noted, ncludng them washes away much of the effect of the nvestment clmate varables). Ther role n the analyss s smply to allow for cleaner estmates of nput elastctes and coeffcents on capacty utlzaton and schoolng n the frst regresson. The measure of performance (log-tfp) s regressed aganst control varables ncludng age, percentage of sales exported, percentage of foregn ownershp, and the frst prncpal components of each of the three broad dmensons of nvestment clmate rent predaton, nfrastructure, and competton. The level of exports s ncluded because t s often argued that exportng s a learnng mechansm, whch enables companes to mprove productvty by learnng from customers and facng nternatonal competton. Lkewse, foregn ownershp may ncrease productvty f foregn nvestors brng new technologes and management technques. The analyss uses ordnary least squares wth Whte correcton for heteroskedastcty and allows for clusterng wthn countres. The specfcaton s as follows: 20

21 (5) ln TFP = Cons + β β 5 Infrastructure 1 Age + β2exports + β3foregn Ownershp + β4 Re nt _ b + β 6 Competton b + ε predaton + The rent predaton and nfrastructure varables are constructed n a such way that hgher values denote worse condtons, whle the opposte s true for the competton varable. The results confrm the expected sgns of the nvestment clmate varables and clearly ndcate that nvestment clmate (understood as the jont sgnfcance of the nvestment clmate regressors) matters for frm performance (table 6). 11 Table 6 Total Factor Productvty Regresson Results Coeffcent Standard Regressor error Age Foregn ownershp Exports (percentage of sales) Rent Predaton * Infrastructure * Competton * Constant * * Sgnfcant at the 1 percent level. Note: R2 s Usng the Wald test, the null hypothess that coeffcents on nvestment clmate regressors are jontly equal to zero s rejected at the 1 percent level. Source: Authors calculatons. What Matters Most? Competton The precedng analyss clearly demonstrates the mportance of competton, rent predaton, and nfrastructure n determnng frm-level productvty. However, for the analyss to be useful requres another step dentfyng the relatve mportance of these dmensons of the nvestment clmate and thus the areas of reform that wll generate the largest payoff. Because the nvestment clmate dmensons are prncpal components, 11 Unreported results from a specfcaton that drectly ncludes a number of nvestment clmate ndcators ndcate that foregn ownershp and exportng behavor are sgnfcant. But when the prncpal component approach, whch better captures the overall nvestment clmate, s used, these two varables no longer appear to be sgnfcant. 21

22 ther coeffcents do not have a scale that can be easly nterpreted (Greene 2000, p. 258). Moreover, because many of the ndcators that comprse the nvestment clmate dmensons are qualtatve, t s unclear how to construe changes n them. To gude polcy, we need to be able to rank the relatve mportance of each of the nvestment clmate dmensons n explanng the varaton n productvty across frms. However, there s no easy way of assessng relatve performance of regressors n a multple regresson context when, as s certanly the case here, they are not completely ndependent. One way s to take a fracton-of-varance approach, but ths requres a natural orderng of the regressors, somethng not present n ths case. Another possble soluton, and the one chosen here, s to apply a methodology proposed by Kruskal (1987). 12 Kruskal s approach bulds on the smple concept of partal correlaton coeffcents, whch can be used whenever t s necessary to measure the assocaton between two varables after accountng for the common nfluence of other factors. A one-dmensonal ndex capturng the relatve explanatory power of each nvestment clmate regressor s calculated by averagng over a sequence of squared partal correlaton coeffcents generated from all possble orderngs n whch such regressors can be consdered (see appendx 2 for detals on the procedure used). The normalzed ndex scores ndcate that competton accounts for far more varaton n frm-level productvty than nfrastructure and rent predaton (fgure 3). The competton 12 See also Johnston and DNardo (1997) for an exp oston of Kruskal s methodology. 22

23 measure explans more than 3.7 tmes as much varaton as the rent predaton measure. Next n mportance s nfrastructure, whch explans 1.73 tmes as much varaton as rent predaton, though less than half as much as competton. Fgure 3 Normalzed Scores wth Rent Predaton as the Base Rent predaton Infrastructure Competton Source: Authors calculatons. Concluson Ths paper not only provdes strong evdence of the lnk between the nvestment clmate and frm-level productvty but also demonstrates an approach for rankng dfferent dmensons of the nvestment clmate. The results ndcate that compettve pressure s the most mportant factor drvng productvty levels. A good supportng nfrastructure and a nonpredatory regulatory envronment are nvaluable, but they are not suffcent. It s pressure from market competton that drves frms to mprove effcency. Open economes that foster such pressure wll see faster growth and greater poverty allevaton. 23

24 Ths fndng suggests that the relatvely quck steps governments can take to ncrease competton wll have a bg payoff n frm performance even as the slow, expensve process of upgradng nfrastructure takes place. It also ndcates that hgh levels of fxed nvestment, especally n nfrastructure, wll not be enough to spur growth. Uzbekstan, for example, has had relatvely hgh levels of fxed nvestment but low productvty growth, suggestng that the nvestment expendtures have not been partcularly effcent. Investment must be accompaned by lberalzaton, whch allows market competton to ensure that resources are effcently allocated. 24

25 Appendx 1 Summary Statstcs Table A1.1 Frm Characterstcs, Full Sample Characterstc Observatons Frm sze (employees) Ownershp Domestc 334 Some foregn 28 Exports (percentage of sales) None Table A1.2 Country Descrptve Statstcs Country Characterstc Observatons Mean Medan Kyrgyz Republc Frm sze (employees) Foregn ownershp (percent) Exports (percentage of sales) Sales (thousands of U.S. dollars) Moldova Frm sze (employees) Foregn ownershp (percent) Exports (percentage of sales) Sales (thousands of U.S. dollars) Poland Frm sze (employees) Foregn ownershp (percent) Exports (percentage of sales) Sales (thousands of U.S. dollars) 67 2, Tajkstan Frm sze (employees) Foregn ownershp (percent) Exports (percentage of sales) Sales (thousands of U.S. dollars) Uzbekstan Frm sze (employees) Foregn ownershp (percent) Exports (percentage of sales) Sales (thousands of U.S. dollars) Total Frm sze (employees) Foregn ownershp (percent) Exports (percentage of sales) Sales (thousands of U.S. dollars)

26 Appendx 2 Kruskal Methodology There s no clear way to determne the relatve mportance of ndvdual regressors n a multple regresson when there s no natural orderng to the regressors. However, Kruskal (1987) proposes an ntutve and practcal soluton, derved from basc statstcal concepts, that attempts to deal wth the ambguty produced by the correlaton structure of the explanatory varables. The procedure as mplemented n ths paper s as follows. For each one of the three broad nvestment clmate dmensons rent predaton, nfrastructure, and competton the analyss calculates the four dfferent partal correlaton coeffcents arsng from all permutatons n whch a partcular dmenson can be consdered after holdng all controls (age, exports, and foregn ownershp) fxed. To further llustrate the procedure, let s consder rent predaton. The goal s to study how much varaton n frm-level productvty ths dmenson can account for after the effect of some frm characterstcs s removed. Because rent predaton s lkely to be correlated wth the other two dmensons (nfrastructure and competton), ambguty arses when tryng to determne how much varaton each dmenson commands. It s mpossble to know whether the correlaton between log-tfp and rent predaton should be measured after or before removng the common nfluence of nfrastructure, competton, or even both. That leaves four possble measures of correlaton. Orderng 1: Partal correlaton coeffcent between log-tfp and Rent Predaton holdng frm controls fxed. 26

27 Orderng 2: Partal correlaton coeffcent between log-tfp and Rent Predaton holdng frm controls and Infrastructure fxed. Orderng 3: Partal correlaton coeffcent between log-tfp and Rent Predaton holdng frm controls and Competton fxed. Orderng 4: Partal correlaton coeffcent between log-tfp and IC Rent Predaton holdng frm controls, IC Infrastructure, and IC Competton fxed. There s no reason to beleve that one orderng s better than any other, snce there s no partcular order n whch the dfferent nvestment clmate dmensons mpact frm performance. So a strategy s needed to deal wth the nformaton contaned n such a sequence of partal correlaton coeffcents. A natural and ntutve approach would be to plot the four partal correlaton coeffcents assocated wth each one of the three dmensons studed. Ths would provde a vsual representaton of how log-tfp and dfferent nvestment clmate dmensons are correlated when dfferent varables are held fxed. To do so, the followng notaton s used (fgure A2.1): Let P1 denote the partal correlaton coeffcent between frm performance (P) and nvestment clmate dmenson 1, holdng frm controls fxed. Let P1.2 denote the partal correlaton coeffcent between frm performance (P) and nvestment clmate dmenson 1, holdng frm controls and nvestment clmate dmenson 2 fxed. 27

28 Let P1.23 denote the partal correlaton coeffcent between frm performance (P) and nvestment clmate dmenson 1, holdng frm controls, nvestment clmate dmenson 2, and nvestment clmate dmenson 3 fxed. Fgure A2.1 Absolute Value of Partal Correlaton Coeffcents Permutaton of orderngs 0.7 Absolute value of partal correlaton coeffcent P1 P2 P3 P1.2 P2.1 P3.1 P1.3 P2.3 P3.2 P1.23 P2.13 P3.12 Partal Correlaton Coeffcents 1 - Rent Predaton 2 -Infrastrucutre 3 - Competton Source: Authors calculatons. The fgure llustrates the perls of lookng at partal correlaton coeffcents wthout consderng alternatve orderngs n whch left-out varables are held fxed. The fgures for each nvestment clmate dmenson fluctuate as the nfluence of the other two dmensons s added or removed. More mportant, the vsual representaton stll seems convoluted and unable to delver a clear message about whch dmenson matters the most. If the explanatory power of competton s measured by P3 and the explanatory 28

29 power of nfrastructure by P2.13, nfrastructure would appear to be more powerful but f P2 s used rather than P2.13, the opposte would be the case. To address ths ssue, Kruskal proposes a way of summarzng the nformaton n the fgure. The method nvolves transformng each set of four partal correlaton coeffcents nto a sngle number by squarng and averagng the coeffcents, formng the followng ndexes: Equaton A Kruskal ndex for Rent predaton = 0.25[( P1) + ( P1.2) + ( P1.3) + ( P1.23) 2 ] Equaton A Kruskal ndex for Infrastructure = 0.25[( P2) + ( P2.1) + ( P2.3) + ( P2.13) 2 ] Equaton A Kruskal ndex for Competton = 0.25[( P3) + ( P3.1) + ( P3.2) + ( P3.12) 2 ] It s now possble to provde a rankng of nvestment clmate dmensons by ther ablty to account for the observed varaton n frm-level productvty. Moreover, after normalzng the Kruskal scores, t s also possble to provde a quanttatve estmate of how much more varaton the leadng dmenson can account for compared wth the others (table A2.1). Table A2.1 Kruskal Scores of Explaned Varaton Investment clmate dmenson Average of squared partal correlaton coeffcents Normalzed score (wth rent predaton as the base) Rent predaton Infrastructure Competton Source: Authors calculatons. 29

30 References Dollar, Davd, Mary Hallward-Dremeer, and Taye Mengstae Investment Clmate and Frm Performance n Developng Economes. World Bank, Development Research Group, Washngton, D.C. Greene, Wllam H Econometrc Analyss. 4th ed. Upper Saddle Rver, N.J.: Prentce Hall. Hall, Robert E., and Charles Jones Why Do Some Countres Produce So Much More Output per Worker Than Others? Quarterly Journal of Economcs 114 (1): Johnston, Jack, and John DNardo Econometrc Methods. 4th ed. New York: McGraw-Hll. Kruskal, Wllam Relatve Importance by Averagng over Orderngs. Amercan Statstcan 41 (1): Manly, Bryan F. J Multvarate Statstcal Methods: A Prmer. 2nd ed. New York: Chapman & Hall. Marda, K. V., J. T. Kent, and J. M. Bbby Multvarate Analyss. 6th ed. New York: Academc Press. 30