Use of plant level micro-data for SME innovation policy evaluation in Japan

Transcription

1 RIETI Dscusson Paper Seres 01-E-006 Use of plant level mcro-data for SME nnovaton polcy evaluaton n Japan MOTOHASHI Kazuyuk RIETI The Research Insttute of Economy, Trade and Industry

2 RIETI Dscusson Paper Seres 01-E-006 Use of plant level mcro-data for SME nnovaton polcy evaluaton n Japan 1 Kazuyuk Motohash 2 Mnstry of Economy, Trade and Industry (METI), Japan And Research Insttute of Economy Trade and Industry (RIETI), Japan November 2001 [Abstract] Japan s SME polcy has reached a major turnng pont. That s, tradtonal lftng up SMEs as a whole type polcy has been revsed toward pro-competton polcy to nduce entrepreneurshp and nnovaton n SMEs. In ths paper, ths polcy drecton and new nnovaton promoton schemes ntated by METI are evaluated by usng plant-level mcro data. Census of Manufacturng base longtudnal mcro-data are lnked wth the lst of frms partcpatng n SME nnovaton polcy scheme under the Law on Creatve Actvtes n SMEs and the Law on Supportng Busness Innovaton n SMEs. Plant level pattern on ndustral dynamcs suggests both polces for new busness start-up and nnovaton creaton n exstng frm are mportant. In addton, postve effects on sale growth by partcpatng n a program of the Creatve Actvty Laws are observed. JEL classfcaton: C35, L10, L50 Keywords: frm s growth, SME nnovaton polcy, program evaluaton, sample selecton model 1 Orgnal verson of ths paper s ttled Use of Frm Demographc Data for SME Polcy Evaluaton, whch was presented at the CAED01 conference n Aarhus, Denmark, October Executve Deputy Drector at Research and Statstcs Dept of METI and Vstng Fellow of RIETI. (motohash-kazuyuk@met.go.jp). Vews expressed n ths paper are the author s and do not necessarly reflect those of hs organzatons. 1

3 1. Introducton Japan s SME polces have reached a major turnng pont. They used to be amng at lftng up SMEs as a whole, focusng on the gap n productvty and wages between SMEs and large corporatons, durng the 30 years snce the end of the Second World War. Now they are n the process of transformaton to a polcy that sees SMEs as the source of entrepreneurshp and nnovaton whch leads to economc dynamsm and supports ndvdual hgh growth SMEs. Based on ths fundamental dea, METI s undertakng drastc reform of SME polcy by varous means, ncludng the amendment of the SME Basc Law that took place n As background to ths knd of polcy drecton, analyss at the establshment or frm level of offcal statstcs such as the Census of Establshments and Enterprses and the Census of Manufacturng, has provded mportant mplcatons on dynamc frm demography and heterogenety of SMEs. The dualstc structure theory, whch concentrates on the gap between large corporatons and SMEs, s based on a comparson of an average large corporaton and an average SME. However, we have come to realze that, n the areas of R&D, nnovaton ndces, and busness performance factors such as employment and productvty, compared to large corporatons, SMEs have an amazngly large varaton (METI, 1999). Also, as seen n the fact that almost all large corporatons began as SMEs, t s mportant not only to conduct analyses based on the statc structure of frm demography, but also to consder dynamc frm-level trends reflectng ts expanson, contracton, entry and ext. The heterogenety of SMEs mples that there s no average SME, whch used to be the target of tradtonal SME polces n the era of the dualstc structure theory. Instead, we need to create specfc polces for a varety of purposes, keepng n mnd the SMEs to be targeted, for example, the buldng up of front-lne SMEs such as hgh-tech ventures, busness nnovatons of exstng SMEs, safety net polces to protect SMEs from negatve macro-economc shock, etc. To that end, t s necessary to carry out accurate analyss of the actual condtons of economc actvty usng establshment- or frm-level mcro-data rather than aggregate data by ndustry or frm sze. Snce the md-1990s, METI has been actvely complng establshment and frm-level panel data. 3 3 Motohash (2001) provdes an overvew of METI s actvtes n the development of longtudnal mcro datasets. 2

4 In developng a mcro-data base, t s common for OECD countres to use admnstratve data, such as tax records, busness regsters and admnstratve records as a bass for lnkng statstcal survey data, but ths does not happen n Japan. On the other hand, large-scale census surveys are conducted by means of vsts to each establshment by enumerators, so that t s possble to grasp the entry and ext of the establshment, and to construct clean data for dynamc establshment demography, through statstcal surveys. Currently, METI s nvolved n a project to develop a comprehensve establshment and frm data base, by lnkng frm-level data from the Basc Survey of Busness Structure and Actvty, and establshment data from varous knds of census statstcs such as the Census of Manufacturng, Census of Commerce, and Census of Selected Servce Sectors. In ths paper, establshment level-dynamcs of the Japanese manufacturng sector are analyzed by usng longtudnal data sets from the Manufacturng Census. In addton, I wll present analytcal works on the evaluaton of METI s polcy measures to promote SME nnovaton. Frst, I wll provde a general descrpton of METI s SME nnovaton promotonal measures,.e. the Law for the Promoton of Creatve Busness Actvtes of Small and Medum Enterprses and the Law on Supportng Busness Innovaton of Small and Medum Enterprses. Ths s followed by a secton concernng establshment-level dynamcs and ther mplcatons for SME nnovaton polcy. Then I wll provde models and results of analytcal works on SME nnovaton polcy evaluaton wth mplcatons for consderng future SME nnovaton polces. 2. METI s SME Innovaton Polcy (1) Reform of METI s SME Innovaton Polcy In response to the change n the basc SME polcy drecton mentoned above, n 1999 METI totally revewed ts SME support polces. Several laws to promote SMEs exst to acheve varous specfc polcy purposes, such as to vtalze regonal clusters and to streamlne dstrbuton system for SMEs. The most fundamental of these systems s the SME Modernzaton Promoton Law, a law passed n 1963 whch encourages the promoton of nvestment n SME facltes, n order to abolsh the dualstc structure between large corporatons and SMEs n terms of productvty and wages. The SME Modernzaton Promoton Law contrbuted to the mprovement of productvty, through 3

5 the modernzaton of SME equpment, n the era when for ndustral productvty the scale of profts was mportant, and SMEs was handcapped due to the low level of captal ntensty. However, as ntangble nvestments such as nvestments n human resources and R&D, rather than tangble equpment nvestments, become ncreasngly mportant, assstance schemes based on the SME Modernzaton Promoton Law have become obsolete and less able to meet the needs of SMEs. Also, the SME Modernzaton Promoton Law s desgned so that the sectors whch should be modernzed are specfed, and all companes belongng to those sectors receve fnancal assstance. However, as the dversty of SMEs expands, the usefulness of supportng schemes by ndustry has decreased. METI decded to abolsh the SME Modernzaton Promoton Law n 1999, and worked to prepare a new scheme to support SME nnovaton. The Law on Supportng Busness Innovaton of Small and Medum Enterprses was establshed, to support a wde range of nnovaton for SMEs, such as the development of new products and new producton methods, rather than nvestment n equpment. At that tme, the Law Concernng Promoton of the Advancement of SMEs nto New Felds, whch supports the transfer of type of busness of SMEs nvolved n busness felds that are not dong well, was combned wth new nnovaton support schemes. Also, the streamlnng and ntegraton of the Law for the Promoton of Creatve Busness Actvtes of SMEs, whch supports the openng of companes and technologcal development, was consdered. However, snce ths law s effectve untl 2003 and many SMEs are stll makng use of the law s assstance polcy schemes, t was decded to leave that partcular law n place as s. Accordngly, the current METI schemes to promote SME nnovaton consst of the two pllars of the Law for the Promoton of Creatve Busness Actvtes of SMEs establshed n 1995 and the Law on Supportng Busness Innovaton of SMEs passed n (2) Law for Promoton of Creatve Busness Actvtes of SMEs The schemes based on ths law are fundamentally composed of one part to assst new busness openngs and another part to assst technologcal development. The former ncludes systems that recognze measures for taxaton-related postponement of nvestment by ndvdual nvestors n venture busnesses, and drect fnancal support for venture busnesses through local venture organzatons. For the latter, frst t s necessary for SMEs to submt proposals related to technologcal 4

6 development projects. If t s judged that the contents of the project n queston feature newness, and moreover f the project seems to have a hgh probablty of successful completon n terms of captal and human resource allowances, subsdares, low-nterest fnancng and taxaton-related support polces can be receved. Although t s also possble for proposals to be put forward by groups of several SMEs, n almost all cases applcatons are from ndvdual SMEs. As of June, 2001, about 7,400 proposals had been approved. (3) Law on Supportng Busness Innovaton of SMEs The schemes based on ths law 4 begn wth the submsson of proposals related to broadly determned nnovaton, ncludng both product and process nnovaton, as s the case for the Law for Promoton of Creatve Busness Actvtes of SMEs. However, for the Law on Supportng Busness Innovaton of SMEs, t s also necessary to ndcate the performance target n the proposal by nnovaton actvtes supported by ths scheme. 5 In addton, for each approved project, t s necessary to report to the government the state of progress of the project, and the government wll conduct follow-ups, ncludng examnaton of the progress made towards the performance target. Therefore, whle the Law for Promoton of Creatve Busness Actvtes of SMEs requres strct ex-ante examnaton process for project approval, the Law on Supportng Busness Innovaton of SMEs dffers n that ts focus s more on follow-ups afterwards than on examnaton at the applcaton stage. For ths latter law also, a group of SMEs can submt a proposal, but n almost all cases the applcatons are made by ndvdual SMEs. As of June, 2001, about 1,400 proposals had been approved. (4) Objectves of SME nnovaton polces In order to conduct evaluatve analyses of the programs of each of these two laws, t s mportant to clarfy objectves and frms targeted by these polces. Both laws are schemes to promote SME nnovaton, whch s the top prorty of METI s SME polces, snce new busness openngs and 4 The Law for Promoton of Creatve Busness Actvtes of SMEs s composed of the nnovaton support part and the emergency avodance safety net part, for busness felds n whch there has been an extreme worsenng of condtons as a result of external shocks, such as dramatc changes n compettve condtons. Here I descrbe the nnovaton support part schemes. 5 The performance target can be made ether by value-added growth rate or by labor productvty growth rate. Targeted growth rate should be no less than 9% for three-year plans, 12% for four-year plans, and 5

7 actve new product development and busness feld transfer actvtes n SMEs become a source of strength for the dynamsm of the economy as a whole. In fact, a large proporton of the creaton of new employment n the past was brought about by hgh-growth SMEs, whch can be observed n many OECD countres (OECD (1996)). At the same tme, t s also true that SMEs are handcapped n the aspects of acquston of captal and human resources. When SMEs start new operatons, there are many managers who pont out obstacles n the acquston of captal and personnel (MITI (2000)). Also, we can suppose that the detrmental effect of market falure that occurs n the fnancal and labor markets as a result of lack of symmetry of nformaton, etc. s more severe for SMEs. Polcy for promotng SME nnovaton can be justfed though ths way. Both SME laws have the objectve of supportng SME nnovaton, but the SMEs targeted by each are slghtly dfferent. As I mentoned at the begnnng of ths paper, a frm whose scale s below a certan level s known as an SME, but n realty SMEs show extreme dversty, and we also have to consder the dynamc changes nvolved, such as expanson and contracton of frm sze. For the Law for Promoton of Creatve Busness Actvtes of SMEs, snce t s a scheme to bascally support hgh-tech ventures, technologcal development projects are expected to have a hgh degree of newness, and the purpose s to manly support hgh-rsk, hgh-return projects. For the Law on Supportng Busness Innovaton of SMEs, n contrast, the chef am s to promote the revtalzaton of exstng companes through a broad range of nnovatons. When we consder the promoton of SME nnovaton, we tend to thnk of companes that have advanced technology and grow dramatcally. However, those knds of corporatons comprse an extremely small percentage of all SMEs, so t s an mportant polcy ssue to carry out revtalzaton of the other knds of companes that comprse the vast majorty of SMEs. Based on ths way of thnkng, the Law on Supportng Busness Innovaton of SMEs has as ts man goal the promoton of the challenge for as many SMEs as possble to undertake nnovatons n products and processes. 3. Data (1) Manufacturng Census Panel Data The longtudnal dataset used for polcy evaluaton n ths paper s based on plant-level survey data 15% for fve-year plans. 6

8 compled by the Manufacturng Census. The Manufacturng Census used to be an annual survey of all establshments, but recently the complete census survey has been undertaken only n years that end n 0, 3, 5 or 8, whle n other years there s a supplementary survey of only establshments wth four or more employees. The survey conssts of Survey A for establshments wth 30 or more employees, and the smpler Survey B, amed at establshments wth 29 or fewer employees. The total number of establshments covered s about 650,000, of whch about 60,000 fall nto the Survey A category. The Manufacturng Census survey s conducted by survey staff who have been apponted n each geographcal dstrct for on-ste surveyng, so that the openng of new establshments and the closng of exstng ones are accurately reflected n the lst of establshments n the survey. Every year, the survey s conducted by usng the dentfcaton number for each establshment, whch s called the establshment code, so that the longtudnal data can be compled based on ths code. The data used n ths paper are annual panel data from 1986 and 1999, and are unbalanced panel data reflectng a sgnfcant number of entres and exts of establshments. It should be noted that a complete census survey s not conducted every year, but only establshments wth four or more employees are surveyed n some years. Therefore, the dentfcaton code table for all establshments from 1986 to 1999 was compled frst, and each year s data for establshments wth four or more employees were lnked to ths ID code table 6. The total number of establshments appearng n the table s 1,234,828. The number of establshments wth four or more employees was 437,574 n 1988 and 373,713 n 1998, and 236,565 of them appear n datasets throughout the perod from 1988 to It should be noted that the establshment-level turnover of these datasets not only reflects entres and exts of establshments, but also ncludes changes n the number of establshments across the survey threshold. The share of 1-3 employee establshments n the total number s 41.9%, but ths accounts for 5.4% n employment and 1.8% n value added n Therefore, t s assumed that bases assocated wth mssng these establshments are small for the employment and productvty analyss provded later n ths paper. In addton, t s observed that the attrton rate of very small 6 Establshments wth 1-3 employees are surveyed n complete census years, but ndvdual plant data are not avalable n a computer-readable format. Therefore, data for 4+ establshments are used for all years n ths paper. 7 Ths number of 4+ establshments s measured n census years, such as 1988 and 1998, nstead of

9 frms s very hgh, and such fragle SMEs are not good control samples for analyzng the mpact of SME nnovaton promoton polcy. The varables covered n ths dataset are shpments, materal nput, number of employees, wages, the four-dgt Japanese Industral Classfcaton (JSIC) code, etc. Informaton on captal nputs such as nvestments and the book-value captal stock amount s avalable only for establshments wth 10 or more employees. Real value added for each establshment n each year s calculated by usng nput and output deflators at the three-dgt JISC level. 8 (2) Lnkage wth Lst of Frms Subject to SME Polcy Analyss on the effectveness of nnovaton polcy was carred out by lnkng a lst of the frms that are recevng polcy assstance, such as subsdares or low-nterest fnancng systems, wth the manufacturng census longtudnal data just mentoned above. Lnkage has been conducted by aggregatng establshment census data to the frm level by usng frm dentfers frst, and matchng them by the frm s name and address, wth the lst of frms supported by the two laws on SME nnovaton promoton. As of June, 2001, there are about 7,400 companes that are subjects of the Law for Promoton of Creatve Busness Actvtes of SMEs that began n 1995 (of these, about 2,800 are those whose man busness are manufacturng). And the Law on Supportng Busness Innovaton of SMEs that began n 1999 has about 1,400 companes as subjects (of these, about 800 are those whose man busness are manufacturers). We were able to make a lnkage for 1,360 companes (3,123 establshments) for the former law and 392 companes (1,004 establshments) for the latter law. Moreover, when a frm s the subject of SME polces, nnovaton benefts such as new technology and new product development tend to appear n all the establshments owned by the frm n queston. Therefore, the analyss n the followng sectons s conducted at the establshment level. 4. SMEs n Compettve Envronment and Implcatons for Innovaton Polcy METI s preparng varous knds of support schemes for the purpose of promotng nnovaton, takng the vew that SMEs are a source for the creaton of employment and economc dynamsm. In and 1999, snce the census year survey s more relable than that n other years. 8 The deflator at the three-dgt JSIC level, wth 176 sectors for manufacturng, s compled by usng nformaton from nput-output tables n Japan. 8

10 ths sense, t s mportant to evaluate ths hypothess, servng as a lynchpn of METI s SME nnovaton promoton polcy, before program evaluaton on specfc polcy scheme s conducted. In ths secton, usng the manufacturng census longtudnal data, I wll descrbe mcro-level performance of SMEs and ther role n shapng ndustral dynamsm n compettve envronment. There are some stylzed facts on mcro-level dynamcs are commonly found n many countres. Frst, concernng the relatonshp between frm sze and speed of growth, n many countres t can be observed that the smaller the sze of the frm, the greater the speed of expanson of the frm scale, n terms of number of employees (Caves (1998)). Even n countres such as the U.S. and France, where recently there s a growng trend for large corporatons to downsze, a move to expand employment n SMEs can be seen (Motohash (1998)). At the same tme, n contrast to the employment level n large corporatons, where changes occur comparatvely steadly, n smaller companes changes n employment tend to be dramatc, and frequently lead to busness closngs. Therefore, we need to be aware of the possblty that f we look only at the trends of survvng companes, there may be a upward samplng bas on the growth rate. That s, the second stylzed fact s that the smaller the scale of the frm, the greater the varance n the speed of ts growth. The Passve Learnng Model (Javanovc (1982)) can be used as a model to explan such stylzed facts related to frm growth. Ths model s constructed under the assumpton that each frm (owner) has ts own level of ablty, but at the tme of establshng the frm, the frm (owner) does not have such nformaton. In ths sense, nformaton on manageral ablty s passvely learned n the course of conductng operatons, and decson on the expanson or contracton of the frm s made, based on the posteror of manageral ablty, nferred by past nformaton of frm s performance. Accordng to ths model, the smaller the scale of the frm, the greater the varance n growth, and moreover, the greater the speed of growth of survvng companes. Dunne, Roberts and Samuelson (1988). In contrast, there s another model n whch the owner s aware of hs/her own level of ablty and the relevant market condtons, and there s actve explotaton by the owner whereby management decsons take place as proftablty parameters change stochastcally over tme (Ercson and Pakes (1995)). A major dfference between ths model and the passve learnng model les n whether or not the ablty parameter changes over tme,.e. tme nvarant for passve learnng model, vs tme varant for actve explotaton model. An emprcal test to dstngush between two models s 9

11 provded n Pakes and Ercson (1998). It has been shown that n the U.S., companes nvolved n the dstrbuton busness follow the style of the passve learnng model, whle those n manufacturng tend to follow a pattern close to the actve explotaton model. Dependng on whether the economy actually follows a passve learnng model or an actve explotaton model, polcy mplcatons change dramatcally. That s, n the passve learnng model, as a frm grows and gets older, the speed of sze growth decreases and becomes stable due to more precson on nferrng ts manageral ablty. Therefore, dynamc economc changes occur only as a result of ext of low-productvty frms out of the market and frm and productvty growth n newly establshed frms. In ths world, t s mportant to promote new frm openngs and to remove obstacles to frms exts. In contrast, the actve explotaton model assumes that managers are facng compettve pressure from other frms, and actvely explotng any possblty to mprove ts performance. In ths world, polcy for nursng compettve envronment for nnovaton creaton s mportant, and revtalzaton of exstng SMEs can be also a polcy target. By means of the ndustral statstcs panel data, we can see the relatonshp between the entry and ext of establshments and frm scale and productvty. In Table 1, all establshments are classfed nto groups by lookng at and whether or not the establshments n queston survved n each year of the complete census,.e., 1988 (T=1), 1990 (T=2), 1993 (T=3), 1995 (T=4) and 1998 (T=5). For example, Group 123 denotes establshments that exsted n 1988, 1990 and 1993, but not n 1995 and Hence, these establshments must have exted out of the market between 1993 and In another example, the establshments whch survved through all the years are depcted n the dagram as Frstly, t s clear that those establshments that survved over all years are comparatvely large and have a hgh level of labor productvty. Further, establshments whch opened between 1989 and 1990 and whch survved up tll 1998 (Group 2345), and establshments whch opened between 1996 and 1998 (Group 5) also have comparatvely hgh productvty. On the other hand, establshments whch also opened between 1989 and 1990 but closed by 1997 (Groups 2, 23, and 234), were small n sze and ther productvty level was relatvely low from the begnnng. If we look at employment and productvty growth, compared to establshments whch contnue to exst over tme, establshments whch opened n 1989 or later have a relatvely hgh growth rate. In partcular, durng the perod, when there was a dramatc decrease n the number of 10

12 employees n the manufacturng ndustry n general, those establshments whch opened n 1996 or later showed a growth trend. Snce there s also a growth trend n labor productvty for these establshments, rapd expanson n establshment scale s ndcated. The foregong observatons suggest that Japan s manufacturng ndustry follows a pattern that can be explaned by the Passve Learnng Model, n whch the frm scale s small for comparatvely young establshments, whle employment and labor productvty has large varance. However, there are some evdences that there are dynamc trends n SMEs wth a steady, relatvely large scale, as well. For establshments that survved over tme, I nvestgated employment and labor productvty growth by groupng based on whether or not changes n the JSIC three-level ndustral classfcatons between 1988 and 1993 or between 1993 and 1998 occurred. As you can see from Table 2, establshments wthout a JSIC change tend to have a slghtly large scale and hgher productvty, but the dfference s not all that large. On the other hand, lookng at growth n labor productvty, those establshments wth a JSIC change show a hgher growth rate. Snce the labor growth rate s at almost the same level as for those establshments wthout a JSIC change, ths mples that ths growth n productvty was brought about by an expanson n added value. Thus, t s true that economc dynamsm s brought about not only by establshment entry and ext, but also by busness nnovaton for creatng new markets on the part of exstng establshments. As shown n the above observatons, SMEs can be a potental sprng of economc dynamsm leadng to expanson of employment and growth n added value durng sluggsh overall economc condtons n Japan. At the same tme, t s also true that SMEs are always n an unstable poston n changng economc and manageral condtons. Tradtonal SME polces are focused on reducng the handcaps of SMEs compared to large corporatons n areas such as the lack of economc resources and dffcultes n obtanng captal and personnel. However, t has become more mportant to formulate polces to develop SMEs as sources of growth. From ths vewpont, the concern s how to support SMEs wth growth potental, and how to nduce nnovatve actvtes for transformng potentalty nto actual growth. In ths case, the promoton of hgh-growth venture busnesses s of course mportant, but t s also no less mportant to encourage busness nnovaton n exstng corporatons, n order to revtalze the whole economy. 11

13 5. Evaluaton of SME Innovaton Polcy Evaluaton of METI s SME nnovaton polcy n ths secton addresses the followng two questons. The frst s whether or not polcy support offered under the two nnovaton promoton schemes has reached SMEs wth the targeted characterstcs,.e., relatvely new and hgh-tech frms for the Law for Promoton of Creatve Busness Actvtes of SMEs (Creatve Actvty Law: CAL) and relatvely stable exstng frms for the Law on Supportng Busness Innovaton of SMEs (Busness Innovaton Law: BIL). Snce the selecton process of both schemes s ntated by partcpants, t s mportant to check whether each frm has appled to the rght scheme n lne wth the polcy-makers ntentons. 9 The second queston s whether or not polcy programs have an mpact on a frm s busness performance, such as growth n sales, employment and productvty. In ths program evaluaton, t s mportant to check the margnal effect of program partcpaton, because partcpatng frms mght have performed well even wthout program partcpaton. It s mpossble to conduct an expermental study n the socal scence feld, but ths knd of queston can be addressed by usng datasets wth good control samples (Jarmn and Jensen (1997)), as s the case for ths study. Ths performance evaluaton study s conducted only for the Creatve Actvty Law, because the Busness Innovaton Law just started n 1999, and no data on after-program performance are avalable. Summarzed statstcs based on manufacturng census longtudnal data wth SME nnovaton polcy partcpant dentfers are provded n Table 3. It was found that for both schemes, partcpant frms are relatvely large n employment sze, and hgh n labor productvty, average wage and captal/employment rato. In addton, partcpatng frms show better busness performance n the growth rates of sales, employment and labor productvty. It s mportant to keep n mnd the tmng of polcy support to see whether ths comes from orgnal manageral ablty or from program partcpaton. The scheme under the Creatve Actvty Law started n 1995, and the startng year of the project for each partcpant s dstrbuted from 1995 to 2001, whle the Busness Innovaton Law has just started n It s observed that partcpants showed better performance well before program partcpaton for both laws. Table 3 also shows nformaton on the establshment age and changes n ndustral classfcaton at the three-dgt JSIC level from 1988 to In ths respect, CAL partcpants and BIL partcpants show dfferent patterns, that s, the share of younger 9 It should be noted that ths selecton process by SMEs s not completely endogenous, because an applcant for some supportng scheme often asks program offcers for advce on the scheme best fttng ther needs. 12

14 establshments and JSIC change are greater than average for CAL partcpants, but smaller than average for BIL partcpants. To control for ndustry and sze effect on the dstrbuton of busness performance, I conducted probt analyss for both CAL establshments and BIL establshments. As can be seen n Table 4, labor productvty growth premums before program partcpaton dsappear for both laws after controllng for ndustry, sze and other effects. In contrast, ex-ante premums on sales growth rate do not dsappear for ether law, and ex-ante employment growth rates have a postve correlaton wth BIL partcpaton. Accordngly, a frm wth growth ntenson, nstead of mprovng effcency, s lkely to apply for the nnovaton promoton schemes. In addton, ths frm s presumed to have the manageral ablty to expand ts busness, and to gan a sutable market share. It should be noted that postve and statstcally sgnfcant coeffcents to dummy varables for frms born after 1994 can be found not only for CAL partcpants, but also for BIL partcpants. Therefore, the smaller share of these frms of the BIL partcpants n Table 3 s based, presumably due to the sze characterstcs of BIL large establshments wth a larger share. The Busness Innovaton Law s desgned to stmulate product or process nnovaton for relatvely stable exstng SMEs, n contrast to the Creatve Actvty Law, whch promotes hgh-tech ventures. However, t was found that BIL partcpants and CAL partcpants have smlar characterstcs,.e., they are both relatvely new, large n sze and growng faster than average. The frst look at the manufacturng census longtudnal data suggests an overlappng of polcy targets by the two laws. The performance evaluaton study was conducted for the Creatve Actvty Law. The scheme under the Creatve Actvty Law started n 1995, and each partcpatng frm s conductng projects on R&D, product development, etc., accordng to the approved plan lastng from one to fve years. The dstrbuton of the tmng of the project s presented n Table 5. Out of the 3,123 establshments n ths dataset, about 500 establshments start the project each year, and 855 establshments are stll actve n Snce the project covers the whole process from product development to the marketng of new products, most partcpants fnshed the development stage n the frst few years. Therefore, n the followng analyss, post-project performance evaluaton s provded for CAL partcpants, whch started the project before OLS estmates of the effect of CAL partcpaton on the sales growth rate from 1996 to 1999 are 13

15 presented n Table 6. The value of sales s used for the busness performance ndcator, snce t s relevant n evaluatng the short-term effects of the program, as compared to the productvty, whch can be acheved over a relatvely long term. In addton, by usng the growth rate, tme-nvarant fxed effects, such as manageral ablty, can be controlled. 10 As compared to non-partcpants, partcpatng establshments acheved about 1.3% more sales, after controllng for employment growth rates and other plant characterstcs such as ndustry and sze class. When all partcpants are splt up by the tmng of the project, startng before 1997 or after 1998, the group before 1997 shows 2.5% more sales growth, whle groups after 1998 show no dfference compared to nonpartcpants. The same regresson was conducted for each sze class n Postve and sgnfcant coeffcents can be found for more than 10 employee establshments, but not for the category of 10 or fewer employees. As s shown n Table 4, CAL partcpants show better performance n sales growth before partcpaton. In addton, there s also an exogenous selecton process by the government for approval of CAL projects. Therefore, n order to derve the pure effect of program partcpaton, we need to control for these selecton bases. In ths paper, Heckman s two-step procedure 11 s used to derve consstent estmates on the effect of CAL partcpaton. The frst model s based on the assumpton that coeffcents for control varables are dentcal for partcpants and non-partcpants. y = β X + γcal + u (1) p * = δ Z + ε, CAL=1 ff * p >0 ; otherwse CAL = 0 (2) The effect of CAL partcpaton s evaluated by the coeffcent after controllng for X n equaton (1). The partcpaton of CAL s determned by equaton (2), but we can observe only CAL (partcpate or not partcpate), and not p*. If the error terms n the two equatons are ndependent of each other, the OLS estmaton of the frst equaton gves us a consstent estmate of but ths may not be the case. By assumng that the error terms of both equatons are jontly normally 10 Usng the growth rate as a dependent varable gves fxed effect estmators n panel data analyss. Takng three-year dfferences gves a better estmate compared to usng shorter year dfferences, such as the frst dfference model, when there are measurement errors n ndependent varables. (Grlches and Hausman (1986)) 11 Heckman s two-step method s descrbed n most standard textbooks n econometrcs. The econometrc technque used n ths paper s derved based on Maddala (1983). 14

16 dstrbuted wth the covarance matrx n (3), the expected values of dependng on CAL=1 or 0. Cov( E( u E( u u u s determned by (4) or (5), σ u σ ue, ε ) = (3) σ ue 1 φ( δ Z ) / CAL = 1) = E( u / ε > δ Z ) = σ ue (4) Φ( δ Z ) φ( δ Z ) / CAL = 0) = E( u / ε < δ Z ) = σ ue (5) 1 Φ( δ Z ) In ths stuaton, Heckman suggested computng δ by probt wth equaton (2) n the frst step, and calculatng (4) or (5) for each observaton, whch s used for the OLS estmate of equaton (1) n the second step. Table 7 provdes the results of ths estmate procedure. 12 Here, I compared CAL partcpants who started the project before 1997 to non-partcpants. The second step regresson s conducted wth or wthout sze dummes. The statstcally sgnfcant coeffcent CAL n the second step s found for the regresson wthout sze dummes, whle t s not found n the regresson wth sze dummes. Ths result mply that CAL effect on sales growth s undertan at ths stage, or the assumpton on the same coeffcents wth control varables for partcpants and non partcpants s too strong. In the second model, the assumpton of the dentcal coeffcents of control varables s relaxed, as follows: y1 1 X + u1 = β (for partcpants; CAL=1) (6) y0 0 X + u0 = β (for non partcpants; CAL=0) (7) Z p * = δ Z + ε, CAL=1 ff * p >0 ; otherwse CAL = 0 (8) and we assume that equaton (9). u 0, u 1 andε are jontly normally dstrbuted wth the covarance matrx n 12 Due to the prohbtvely large sze of the orgnal dataset, the emprcal analyss s conducted based on random samplng data wth 5000 observatons for non partcpatng controllng samples. 15

17 Cov( u 0, u1 σ 0 σ 01 σ 0e, ε ) = σ 01 σ 1 σ 1e (9) σ 1 0e σ 1e The methodology of estmaton s smlar to that of the frst model,.e., estmatng δ wth equaton (8) by probt n the frst step, and calculatng the nverse Mll s rato 13, then conductng OLS estmaton of (6) and (7) separately wth the estmated nverse Mll s rato. In ths model, t s possble to calculate the expected gross beneft for CAL establshments through program partcpaton,.e., to what extent the sales growth rate goes up as compared to the case f the establshment had not partcpated n CAL program, by the followng equaton: E φ( δ Z ) / CAL = 1) E( y0 / CAL = 1) = X ( β1 β 2 ) + ( σ 1e σ e ) (10) Φ( δ Z ) ( y1 2 Z Fg. 1 gves the plant dstrbuton of the value n equaton (10) for partcpants. More than 80% of plants shows postve value and the peaks s n the category from 5% to 10% more growth rate. Therefore, t s possble to say that CAL partcpaton has postve mpact on sales growth, even after controllng for sample selecton. Heckman s two step procedure s a popular approach for program evaluaton analyss by data wth possble selecton bas, but t s based on strong assumpton of the normalty n the dstrbuton of error terms. There are recent studes showng that the parameter estmates are strongly senstve to the dstrbutonal assumptons. In addton, when most of ndependent varables for the frst step and the second step are overlapped as s the case for ths study, regresson results may gve nconsstent estmates due to dentfcaton problem. In ths sense, the results presented here should be read wth great care, and I wll conduct senstvty analyss by usng dfferent knds of model specfcaton, ncludng sem parametrc or non parametrc approach proposed by Heckman (1990), n the future research. 13 φ( δ Z ) for partcpants and Φ( δ Z ) φ( δ Z ) for non partcpants 1 Φ( δ Z ) 16

18 6. Concluson In ths paper, the manufacturng census longtudnal dataset s used for analyss of SME nnovaton polcy. METI s SME polcy s n the process of major transformaton, whch takes nto account the greater and greater growth potental of SMEs and treats them as a source of ndustral dynamsm for the Japanese economy. By establshment-level mcro-data, t s confrmed that small and young plants have greater potentalty for growth, but t s also shown that volatlty of growth rate s hgh for small establshments. In ths sense, the focus of SME polcy should be put on supportng SMEs, so that they do not fall out of the growth path and are able to realze ther growth potental. Analytcal work on evaluaton of specfc polcy schemes, namely, the Law for Promoton of Creatve Busness Actvtes of SMEs and the Law on Supportng Busness Innovaton of SMEs s also provded. Partcpants n these two schemes show hgher sales growth rates before program partcpaton, whch mples that polcy support seems to be provded for SMEs wth greater potentalty for further growth. However, t s also observed that smlar knds of frms are applyng to these two schemes, and that these two supportng schemes could overlap. Although more detaled study on the characterstcs of partcpatng frms s needed, ths nformaton should be taken nto account for the future reform of METI s SME nnovaton polcy. Performance evaluaton of the Creatve Actvty Law was also conducted. In general, the polcy scheme under the Creatve Actvty Law works well. However, further analyss based on more detal data and sem or non parametrc estmaton methodology to control for selectvty bas s needed to show more clear vew on effects of the nnovaton polcy and to provde more specfc recommendatons for mprovement of the exstng polcy scheme. 17

19 Bblography Baly, Campbell and Hulten (1992), The Dstrbuton of Productvty n Manufacturng Plants, Brookngs Papers on Economc Actvtes: Mcroeconomcs, 1992 Caves, R. E. (1998), Industral Organzaton and New Fndngs on the Turnover and Moblty of Frms, Journal of Economc Lterature, vol. XXXI, pp Dunne, T., M. Roberts and L. Samuelson (1988), Patterns of Frm Entry and Ext n US Manufacturng Industry, Rand Journal of Economcs, vol. 19 (4), pp Ercson, R. and A. Pakes (1995), Markov-Perfect Industry Dynamcs: Framework for Emprcal Works, Revew of Economc Studes, 62, pp Grlches, Z, and J. Hausman (1986), Errors n Varables n Panel Data, Journal of Econometrcs, Vol.31, pp Heckman, J. (1990), Varetes n Selecton Bas, Amercan Economc Revew, May 1990, pp Jarmn, R. (1996), Measurng the Impact of Manufacturng Extenson Partnershp, a paper presented for the Internatonal Conference on Comparatve Analyss of Enterprse Data, June 1996, Helsnk Jarmn, R. and B. Jensen (1997), Evaluatng Government Technology Programs: The Case of Manufacturng Extenson, Chapter 14, OECD, Proceedngs: Polcy Evaluaton n Innovaton and Technology: Toward Best Practces, OECD Pars Jovanovc, B. (1982), Selecton and the Evoluton of the Industry, Econometrca, 50, pp Maddala (1983), Lmted Dependent and Qualtatve Varables n Econometrcs, Econometrcs Socety Monographs, Cambrdge Unversty Press MITI (2000), Whte Paper on Small and Medum Enterprses, 2000, Tokyo, Japan MITI (1999), Whte Paper on Small and Medum Enterprses, 1999, Tokyo, Japan Motohash, K. (2001), Development of Longtudnal Mcro-Datasets and Polcy Analyss for Japanese Industral Sectors, REITI, Tokyo, Japan Motohash, K. (1998), Technology, Productvty and Employment: Insghts from Frm Level Mcro-Datasets n France, Japan and the Unted States, mmeo, July 1998 OECD (1996), Technology, Productvty and Job Creaton, vol. 2 : Analytcal Report, OECD, Pars Pakes, A. and R. Ercson (1998), Emprcal Implcatons of Alternatve Models of Frm Dynamcs, Journal of Economc Theory 79, pp

20 Table 1. Productvty and Employment by Establshment Type (entry, stay and ext) Est Type # of obs. VAE63 VAE2 VAE5 VAE7 VAE10 EMP63 EMP2 EMP5 EMP7 EMP EMPG632 EMPG25 EMPG57 EMPG710VAEG632 VAEG25 VAEG57 VAEG % % % -3.54% % -0.41% % -2.17% -2.93%. 9.08% 0.55% 1.69% % -0.68% -0.60% -1.62% 9.62% 1.49% 2.64% 0.46% % % % -1.77% % 2.98% % 0.66% -0.36%. 4.73% 4.53% 0.90% % % % 0.59% % 1.04% % %

21 Table 2. Productvty and Employment by JSIC Change # of obs. Labor Productvty Level Employment Level All JSIC no change JSIC change JSIC change JSIC change # of obs. Labor Productvty Growth Employment Growth All % -0.09% 0.47% 3.16% 1.04% -0.68% -0.60% -1.62% JSIC no change % -0.14% 0.36% 2.75% 1.02% -0.48% -0.61% -1.67% JSIC change % 0.56% 0.75% 4.63% 1.89% -0.99% -0.17% -1.37% JSIC change % -0.68% 0.88% 4.60% 1.20% -1.34% -0.70% -1.47% JSIC change % -0.01% 0.79% 4.04% 0.64% -1.34% -0.79% -1.55% 20

22 Table3. Summary Statstcs of Polcy Evaluaton Dataset Creatve Busness All Actvty Innovaton Number of Establshment Employment Labor Producrvty Average Wage Captal/Employment Rato (*) Sales Growth (annaul) Employment Growth (annaul) % 1.79% 1.01% % 0.76% -0.42% % -0.01% -0.49% % -0.69% -1.61% LP Growth (annaul) % 8.57% 9.35% % 1.34% 0.52% % 3.93% 3.34% % -0.04% -0.98% Share of est. open after % 24.3% 24.8% Share of est. open after % 11.7% 12.9% Share of est. open before % 64.0% 62.3% Share of est. JSIC change 32.5% 26.0% 27.9% Share of est. no JSIC change 67.5% 74.0% 72.1% * Captal Stock data s avalable only for establshment wth 10 and more employments 21

23 Table 4a. Probt Analyss (Creatve Actvty Establshment=1) (t-value n parenthses, statstcal sgnfcance at 1% level for *, 5% level for **, 10 Sales Growth, * (7.67) Value Added Growth, (0.89) Employment Growth, (1.00) Labor Prod. Growth, (1.74) Frm Born After 1994(+) 0.15* (72.20) (0.57) (0.72) (0.31) (0.71) Frm Born After 1989(+) (0.89) (1.03) (2.51) (2.42) (2.59) Sngle Plant Frm(++) -0.23** -0.16** -0.16** -0.15** -0.16** (189.75) (58.75) (59.06) (58.26) (58.76) Sngle Plant+Sngle HQ Frm(++) ** ** (20.90) (5.83) (7.13) (6.18) (7.10) Industry Dummy yes yes yes yes yes Sze Dummy yes yes yes yes yes N LogL Note: +: as compared to frm born before : as compare to multple plant frm Table 4b. Probt Analyss (Busness Innovaton Establshment=1) (t-value n parenthses, statstcal sgnfcance at 1% level for *, 5% level for **, 10 Sales Growth, * (17.37) Value Added Growth, (1.90) Employment Growth, * (14.65) Labor Prod. Growth, (0.00) Frm Born After 1994(+) 0.08* (9.04) (0.31) (0.74) (0.77) (0.95) Frm Born After 1989(+) (0.60) (1.18) (1.26) (1.20) (1.20) Sngle Plant Frm(++) -0.31* -0.25* -0.27* -0.27* -0.27* (146.64) (69.63) (87.73) (89.56) (87.94) Sngle Plant+Sngle HQ Frm(++) -0.12* -0.08** -0.11* -0.11* -0.11* (13.52) (4.63) (8.38) (7.69) (8.41) Industry Dummy yes yes yes yes yes Sze Dummy yes yes yes yes yes N LogL Note: +: as compared to frm born before : as compare to multple plant frm 22

24 Table 5. Number of CAL partcpatng establshments # of est. started # of est. actve # of est. fnshed Table 6. OLS Estmate : Impact of Creatve Actvty Law (Dependent Varable= Sales Growth, ) (t-value n parenthses, statstcal sgnfcance at 1% level for *, 5% level for **, 10% 10<EMP All establshments EMP=<10 EMP=<50 50<EMP Creatve Actvty Est * (4.58) Creatve Actvty Est * * 0.029* (started project before 1997) (6.03) (0.79) (5.15) (4.21) Creatve Actvty Est (started project after 1998) (0.55) (0.74) (0.20) (0.04) Employment Growth, * 0.646* 0.608* 0.674* 0.698* (208.60) (208.61) (132.47) (142.13) (79.88) Frm Born After 1994(+) 0.036* 0.036* 0.032* 0.042* 0.042* (46.87) (46.87) (30.35) (32.50) (16.72) Frm Born After 1989(+) 0.011* 0.011* 0.010* 0.011* 0.014* (13.75) (13.76) (9.26) (8.52) (5.95) Sngle Plant Frm(++) * * * * (7.33) (7.32) (6.40) (5.83) (0.43) Sngle Plant+Sngle HQ Frm( * * * * (4.52) (4.52) (4.24) (3.75) (0.18) Industry Dummy yes yes yes yes yes Sze Dummy yes yes N R-square Note: +: as compared to frm born before : as compare to multple plant frm 23

25 Table 7 OLS and 2 Step Estmate : Impact of Creatve Actvty (Dependent Varable= Sales Growth, ) (t-value n parenthses) (statstcal sgnfcance at 1% level for *, 5% level for **, 10% Frst Step (Probt) Second Step(1) Second Step(2) Creatve Actvty Est * (started project before 1997) (0.94) (4.09) Inverse Mlls Rato * (0.61) (4.78) Employment Growth, (0.30) Employment Growth, * 0.600* - (22.80) (22.78) Frm Born After 1994(+) (0.80) (0.69) Frm Born After 1989(+) * 0.020* (0.51) (3.44) (3.44) Sngle Plant Frm(++) * (11.40) (0.58) (0.36) Sngle Plant+Sngle HQ Frm(++) (0.91) (0.18) (0.40) Industry Dummy yes yes yes Sze Dummy yes yes no N LogL R-square Note: +: as compared to frm born before : as compare to multple plant frm 24

26 Fg. 1 :Sales Growth Effect of Creatve Actvty Law (Annaul Sales Growth , N=1519, Mean=+4.12%) 25