Productivity, Training, and Human ResourceManagement Practices - Disentangling Complex

Interactions using a Survey of JapaneseManufacturing Firms1

Masako KurosawaNational Graduate Institute for Policy Studies

Fumio OhtakeInstitute of Social and Economic Research, Osaka University

Kenn Ariga2

Institute of Economic Research, Kyoto University

Abstract

This paper analyzes the determinants of firm level training and its im-pact on productivity using a survey of manufacturing factories in Japan.The analysis shown below generates mixed blessing. We find importantbut varied interactions among various aspects of human resource man-agement practices (HRMPs) and trainings. Although most of the find-ings on the determinants of Off-the-Job Training (OffJT) are in line withthe preceding empirical studies, but, those determinants explain little ofthe variations of the On-the-Job Training (OJT). We also find statisti-cally significant impacts of OffJT on productivity, but we fail to detectsignificant impact of OJT on productivity. We also fail to detect signifi-cant complementarity or substitutability in production function betweentraining and various HRMPs. These findings indicate that the determi-nants and productivity impacts of training and other HRMP are far morecomplicated and heterogenous than what the conventional wisdom sug-gests.

JEL Classification numbers J24,J31, M53, and M54

1 This research is partially funded by JSPS Grant-in Aid Sceintific Research. We thank Sinpei Sanoat Osaka University for his extremely able research assistance. We wish also to thank Naoki Mitani, TaejonKim, Giorgio Brunello and participants in Coneference on Education and Training (January 2005, KyotoJapan) for their comments. Naturally, none of them are responsible for any remaining errors.

2 Corresponding author: Institute of Economic Research, Kyoto University, Yoshida Honmachi, SakyokuKyoto 606-8501 Japan E-mail: ariga@kier.kyoto-u.ac.jp. Phone:+81-75-753-7123,-7198(fax)

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1 Introduction

Various subfields of economics try to identify and unearth the underlying factors re-sponsible for the large disparities in economic performances across countries, sectors,firms, and various units within a firm. We know that such a disparity commonly existseven among apparently homogeneous set of economic units. Moreover, the magnitudeis often large enough to dwarf those explicable in terms of conventional analysis ofproduction function. An important candidate accounting for the productivity differ-ence is the quality of labor inputs, i.e., human capital which cannot be accounted forby conventional measures of human capital, such as education attainment3. We alsolearned from the small but growing literature investigating the impact of innovativehuman resource management practices (HRMPs) that they do also account for a signif-icant portion of the unexplained productivity residual. For example, Ichiniowski andShaw (2003) find ’6.7% productivity difference between a line with the most innovativehuman resource practices and a line with the most traditional human resource manage-ment system (p.164)’ (among comparable steel finishing lines, even after controllingfor environments, technology and human capital elements of the lines in question).We contribute to this literature by analyzing the data from a survey that we conductedamong establishments in manufacturing industry in Japan. In particular, we estimatethe impact of workplace training and HRMPs on establishment level productivity.

Workplace training can be an important source of human capital accumulation. Incomparison with the vast literature on the impact of formal education on productivity,empirical studies on the impact of training on productivity are scarce and often limitedin its scope. Reliable data on workplace training is often difficult to find. The problemis even more serious in the case of informal on the job training. By its very nature,such training is ad hoc and it takes place spontaneously, often without well definedtimetable. Hence they are difficult to trace and document, even inside the firms. An-other reason for the difficulty is the heterogeneity and diversity of trainings. Settingaside the heterogeneity of the types of training, the training intensity differs greatlyacross various characteristics of individual employees, industry sectors, establishmentssize, and types of job. The diversity often remains large even after we control forthese measurable characteristics. A standard view on the observed diversity is that theystem from the large disparities in costs and returns from training. In principle, a fullfledged empirical research on the impact of training should in corporate these factors,including the heterogeneity in production technology, work organizations, and aspectsof HRMPs.

As is the case in many similar studies done in the past, our sample factories are di-verese in the amount and distribution of training investments. Since the survey coversthe entire spectrum of manufacturing sectors, they also differ greatly in size, tehcnol-ogy, location, and their human resource management policy. Although the diversity isa potential source of variations in training investments and their impacts, they may alsocreate a host of problems in our econometric analysis. For one thing, our identification

3 An important strand of the research in this measurement issue concerns the diversity among firms incompositions of employees, which is likely to be correlated in important way with difference in organizationcapital and workplace practices. See Abowd et al (2004).

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of the determinants of trainings is made more difficult if different types of trainings areinduced by different factors and their impact on worker productivity differ consider-ably. The fact that each training is designed to meet needs and objectives specific toindividual firms or factories can also hinder our analysis unless we have the type ofdata which help us identifying such causality.

By the same token, a particular choice of HRMP by a firm should also reflect man-agement decisions based on diverse set of information, most of which we simply donot observe. The upshot of these rudimentary observations is that both training andHRMPs should be treated as endogenous variables when we investigate their impactson productivity. In principle, our ultimate research goal is to conduct structural esti-mations of all these determinants in a unified framework. Our immediate objective inthis paper is far more modest and exploratory in nature. Specifically, this paper in-vestigates in an exploratory manner various aspects of human resource managementpolicies, with particular focus on firm level training so that we have a broad picture onthe determinants of HRMPs and their correlations with productivity. Fortunately, oursurvey data includes detailed information on various human resource practices and at-titudes towards work place training. We investigate how the training investment and itsimpact on productivity are influenced by these variables. This paper estimates the de-terminants and the impact on productivity using the survey of manufacturing factoriesin Japan. As far as we know, our is the first econometric study applied to Japan4.

Exploiting the rich set of such informations not only on the variety of HRM prac-tices and training but also highly disaggregated information on industry, technology,and other attributes of sample factory, we investigate to what extent these elements canexplain productivity differentials which remain inexplicable in conventional estimatesof production functions.

The results of the analysis shown below are mixed. We find important but varied in-teractions among various aspects of human resource management practices (HRMPs)and trainings. Although most of the findings on the determinants of Off-the-Job Train-ing (OffJT) are in line with the preceding empirical studies, factors responsible forOffJT variations explain little of the variations of the On-the-Job Training (OJT). Inparticular, incidences of OffJT are positively correlated with: firm size, labor union,hi-tech firms, innovative HRMPs, and high quality workers. None of these factors havesignificant positive impact on OJT. We also find statistically significant impacts of Of-fJT on productivity, whereas we fail to detect significant impact of OJT on productiv-ity. We also fail to detect significant complementarity or substitutability in productionfunction between training and various HRMPs.

Another notable finding is the robust and statistically significant impact on produc-tivity by Kaizen meetings (small group meetings to discuss daily problems in operation,and to propose methods for improvements). Comparison of wage with productivity re-gressions also reveal that these measured impacts on productivity do not necessarilytranslate into comparable wage increase. Whereas the impact on wage by OffJT issignificant, it is typically smaller than the comparable impact on productivity, while

4 The only possible precedent to our study is Kurosawa (2001) which exploits a survey conducted inKitakyusyu area of Japan. Compared to this survey, our survey is much larger in size and more representativeof the underlying population of manufacturing firms.

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the impact of Kaizen meeting on wage is essentially nil. These findings indicate thatthe determinants and productivity impacts of training and other HRMP are far morecomplicated and heterogeneous than what the conventional wisdom suggests.

The sequel of the paper is organized as follows. In the next section, we report thesurvey we conducted. Section 3 is the main body of the paper in which we report oureconometric analysis. A brief conclusion is in Section 4.

2 The Survey on Training and HRM among Manufac-turing Firms in Japan

2.1 A Brief Review of the Survey

In February and July 2003, we conducted a survey of manufacturing factories (es-tablishments) in Japan. This survey was mailed to 12,100 sample factories, drawnrandomly from the population of manufacturing factories. We obtained 530 valid re-sponses from the first round (February 2003), and 300 more from the second round(July 2003).

Although the response rate was low, it is surprisingly even across establishmentssizes: except for the largest category with more than 10,000 employees, the responserate does not vary much across size [Table A1 in Appendix]. Since our mailing listcovers roughly 20% of all the manufacturing establishments in Japan and also that theresponse rate is fairly flat across sizes, we believe that our sample represents fairly wellthe population of manufacturing factories in Japan.

Our survey consists of 40 major questions mailed to the head of each establish-ments. The questions fall into one of the following four major categories: (1) Employ-ment size and composition, (2) Work organizations and human resource managementpolicies, (3) Training and self learning, and (4) Production data and attributes of thefactory.

In Appendix, we provide English translation of the questionnaire as they are rele-vant to the variables used in the survey. Appendix also includes additional tables forconditional means of training variables according to answers for questions in (2).

2.2 Training data

2.2.1 Measurement Issues

Our sample from the survey consists of 830 manufacturing establishments (factories).We constructed a short panel for year 2001 and 2002 and the estimation results reportedbelow exploit the panel structure of the data.

Although most of our variables used in the regressions are self explanatory, wechose somewhat unconventional methods to measure OJT intensity. In order to mea-sure the intensity of OJT, we employed two different methods and variables.

OJT is difficult to measure, especially for outside researchers. By far the mostimportant reason for this difficulty is ad hoc and spontaneous nature of this type of

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training. OJT is often interspersed with self learning. For these reasons, it seemsquite likely that those receiving OJT may not recall with accuracy the frequency andthe length of these sporadic episodes. It is also likely that inter-personal differencesin the ability to recall those incidences affect the survey outcome. Our strategy toavoid this problem is to focus instead on the time spent by those who provide OJT. InQ24 of the survey [see Appendix], we asked the time allocation of supervisor/foremanamong major daily tasks:(1) Meeting, (2) Monitoring and supervision, (3) training andinstruction (of subordinates), (4) paper work, data collection, (5) other activities. Weuse (3) training/instruction as our measure of the time spent on OJT by those trainers.We use the decomposition of employees at sample factories to obtain the aggregatehours spent on OJT by trainers. We then divide this aggregate hours (per day) by thenumber of regular production workers. The variable thus computed, OJT_prod, is theOJT training given to a production worker per day, and it is our first measure of OJTintensity.

The second measure focuses on the training needs for the novice5. We ask threequestions to measure OJT for the new employees. First, we ask the share of employ-ees who are not fully capable of performing tasks due to lack of enough experiences.Second, we ask for how many months for these workers need to be trained to be fullycapable of performing required tasks. We finally ask the average hours per month spentby these workers on OJT. These three variables are multiplied to obtain aggregate hoursspent on OJT on these types of workers. OJTnew_prod, OJTnew_eng, OJTnew_manare, respectively, for production, engineer and administrative workers.

Neither of the two types of the measures can cover all the OJTs taking place insidesample factories. For example, the first measure may not capture OJTs conducted bysenior fellow workers, rather than by supervisor/foreman. It is also possible that pro-fessional trainers provide OJTs to newly hired workers, rather than by regular staffs atproduction lines. The second measure obviously does not cover OJTs given to work-ers other than those considered ill-trained6. We decided to use the first variable as ourpreferred measure of OJT. The main reason is the coverage: although the first mea-sure may not capture trainings conducted by those other than supervisors or foreman, itseems reasonable to assume that these are the people who are primarily responsible fortraining workers at workplace, especially in our sample of manufacturing factories, andit is difficult to imagine that others such as team leaders or senior production workersconduct OJT without provision, guidance or supervision by these groups of workers.

Off-the-Job Trainings (OffJT) are also measured in two alternative manners. First,we asked monetary opportunity costs of OffJT, excluding external expenses, such astuitions to attend vocational college courses, fees for external instructors, etc. Weasked sample firms to compute such costs using total man hours spent by trainees and

5 This is the approach employed in Barron, Berger and Black (1997).6 We could of course have asked directly on the intensity of OJT. We decided not to take this strategy. First

of all, we doubt it very much that OJT incidences are accurately recorded and documented to allow thefactory representative to answer this question with any accuracy. We also suspect that the degree to whichsuch question can be answered with reasonable precision may systematically depends on organizationalcharacteristics of the firm and thus the statistic is not only noisy but possibly biased. For example, somefirms have centralized training center. At those firms, information collection and storage on training isalso centralized and individual factories do not have any available data on training.

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trainers and their respective average hourly wages.The second measure of OffJT is the total man hours spent on OffJT. We asked

first how many employees received OffJT (#of incidences times # of employees perincidence) , and then we asked average hours spent on each session of OffJT. Againafter many trial and errors, we decided to use the first variable as our choice of OffJTmeasure. The primary reason for choosing the reported internal cost is the sample size.The internal cost measure of the training has the highest response rate compared toman hours estimate or the total monetary cost including the external payments.

A third measure that we focus in the subsequent analysis is not a part of trainingbut it is closely related. The variable qchrt measures the average hours spent (percapita, per year) on meetings in which production workers discuss various measures toimprove productivity, improve product quality, reduction of defects, etc. At the cost ofover-simplifications, we called it kaizen meetings.

2.2.2 Workplace Training in Japan

Table 1 offers summary statistics of these measures of training, qchrt, and other majorvariables.

The mean of OffJT hours per production worker per year is 11.65, and that of OJTin our preferred measure is .35. The latter measure is the number of hours of trainingper working day. Using 237 as the annual average work days reported in Monthly LaborSurvey, we get 82.95 hours per year per production worker. The total hours of trainingin our survey is 94.6 hours per production worker per year. The mean internal cost ofOffJT per employee is 11.7 thousands yen, which translates into 5.9 hours equivalentof the average hourly wage rate of the production workers. The corresponding totalOffJT per employee is 19.5 thousand yen, which is comparable to 21.4 thousand, ac-cording to Basic Survey on Human Resource Development [hereafter BSHR](NoryokuKaihatsu Kihon Chosa) by Ministry of Health, Labor and Welfare. This is a nation-ally representative survey of companies with more than 30 full time employees and thefigure given above are for the fiscal year 2002, excluding employee opportunity costof training. The data on incidence of OffJT indicates that the mean ratio of the totalnumber of production workers trained to the total number of production workers is .44.

How do they compare against the available data on workplace training? Accordingto BSHR, workplace training in Japan has been declining for some time. For example,in 1986, roughly 80% of the surveyed firms conducted some form of OffjT, whereasless than 50% (48.7%) of the firm answered yes to the same question in 2002. As wesaw above, the mean incidence rate in our survey is roughly the same as this figurein BSHR,2002. Although the most recent survey suggests some recovery in trainingincidence, it seems clear that Japanese firms invest significantly less on training now,compared to 10 or 20 years before. Data on training expenditures paint a similar pic-ture: the share of (out of pocket) training cost in the total labor cost declined from.3-.4% during the 1980’s, to below .3% during the 1990s and early 2000s. Anyway,the data on incidence of OffjT in our survey seems comparable to other types of datain Japan. The data also suggests that incidence of training at these sample firms arelarger than the average of the major OECD countries. The data in OECD EmploymentOutlook 2004, shows that the average participation rates in employer provided training

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ranges between 10 to 60%. Also, the OECD data indicates that 83 hours of the totaltraining hours per production worker in our data is almost the double of the highest na-tional average among European countries (annual hours spent on training per employeeranges between 5 to 40 hours)7.

The data on OJT one of us collected for manufacturing firms8 in Thailand showsroughly 32 hours of OJT per year per production workers, whereas, in the same survey,the average for those working at Japanese subsidiary firms is close to 45 hours. Thus83 OJT hours per year in our data is comparable with the Thai data. On the other hand,Barron et al (1997) reports the figures taken from the two employer surveys in the USfor the average total hours of OJT during the first 3 months of employment. They rangefrom 144 to 178 hours, or, 48 or 59 hours per month. Our own data also asks similarquestions [Q23] and our sample mean is 402 cumulative hours of OJT needed to traina newly recruited worker. The corresponding average monthly OJT for the novice is53 hours. Thus the data on OJT for novice production staff suggests comparable levelof OJT investment to the similar US survey data.

All in all, although scarcity of comparable data makes it difficult to place our ownmeasure of OJT in proper context, we believe that our data is consistent at least witha popular notion that Japanese firms, especially manufacturing firms, invest heavily intraining, especially in OJT.

Turning back to our own survey data, in the lower panel of Table 1, we supply cor-relation matrix among the training measures. One thing that we notice immediatelyis the lack of significant correlations of OJT_prod with other training variables. Inparticular, the covariance with the OffJT variables are all insignificant (at 1% signifi-cance level) and many of them are negative. There are many other covariances amongtraining variables which are also not significant.

Although we lack any strong empirical regularity or theoretical prediction to evalu-ate these findings, the weak correlations among training variables are at least suggestiveof the possibility that the determinants of cost and benefit from training can differ sub-stantially across types of training even within the same establishment or firm. As wewill show later on, the lack of strong correlations among training variables is due atleast partially to the underlying heterogeneity among different types of trainings, aswell as their determinants. When we convert OffJT measures into per capita, they donot exhibit any significant correlation with the employment size, which also applies toOJT measures as well as to qchrt.

2.3 Human Resource Management Policy and Training

In order to characterize the sample establishments in a manner consistent with ourresearch objective, we choose two sets of variables related to human resource manage-ment practices and training.

Our first choice of an indicator variable, hrmp, represents the degree to which eachsample factory adopts various human resource management practices. The survey asks

7 Notice, however that our data covers informal OJT which OECD data does not. See Bassanini, Booth,Brunello, De Paola, and Leuven (2005) for in-depth internatioal comparisons of firm level training.

8 See Ariga and Brunello (forthcoming).

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if the sample factory adopts various measures on: work hours (e.g., flextime, longvacation), personnel management (job rotation, career advancement assistance), com-pensation (ESOP, stock-option, gain sharing), and work organization (QC circle, jobmeeting). The full list of practices covered in the questionnaire is shown in Q16 in Ap-pendix. We have listed 22 such practices and asked if the sample factory employ thosepolicies. After experimenting with a variety of techniques to build a proxy variable tosummarize these measures, we decided to use cluster analysis to divide the sample into6 broad groups so that hrmp=1,2,..6 represents the grouping of sample establishmentsinto 6 distinct types with respect to employment of HRMPs9. Roughly speaking, hrmpproxies in ascending order the degree to which sample factories adopt HRMPs. Hencethe samples with hrmp=1 are those with minimum HRMP adoption rate and hrmp=6being the highest (See Table A3 in Appendix for the details).

We next present a proxy variable representing the training policy, summarizingthe outcome of 4 questions we asked [Q27S1-Q27S4 in Appendix]. We used clusteranalysis to categorize the responses into 5 groups [trview](We denote this grouping byTG1,TG2,..TG5). 5 groups thus constructed have the following characteristics. SeeAppendix and Table A2 for the details.

Very briefly, we characterize each group. TG1 samples have the most standardcharacteristics associated with a large manufacturing firms in Japan. Multi skillingas the primary objective of training; more than 90% of the sample use supervisor’sevaluation to measure the impact of training. TG 4 is distinguished from the others forits emphasis on training as the means to help new workers to learn the necessary skillsand samples in this group can be characterized by intensive investment in newer andinnovative HRM policies [see Q6,Q7, Q22 in Table A2]. Although TG 1 and 4 differin their emphasis, both groups employ intensively large varieties of HRM practices andthey pay close attention to trainings.

On the other extreme is TG 5 who pay the least attention to the training. TG 5 hasthe highest share of samples who are unsure of any tangible effects of training. TG3 is similar overall to TG 5, but the average scores in HRM variables are somewhathigher than TG 5. TG 2 is somewhere in between these two extremes. Another notablecharacteristics of TG 2 is that they tend to emphasize career development and selflearning. In other words, TG 2 places more emphasis on voluntary nature in improvingwork efficiency and pay respect to employees’ choice.

Among the survey questions regarding training policy, Q27S1 [Traineffect] de-serves special attention. We ask sample establishments to choose one among the fouralternatives for the most important effect of training:

1. To help and speed up the learning by the novice of skills necessary for jobs2. To facilitate multi-skilling3. To smoothen and expedite the start-up process of new product line/ newly in-

stalled equipment9 Since cluster analysis is sensitive to the choice of the initial ’seeds’ from which the grouping selection

is made, we used 6 polar samples as the initial set to form these groups. For this purpose, we grouped these 22questions into two. First group corresponds to various means and policies aimed to enhance individualemployee welfare and productivity, whereas the second group includes those for group or firm based meansand incentives. Then we chose 6 polar cases using group wise adoption shares as the benchmark .

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4. Not sure if trainings are effective in delivering any tangible outcomes.Answer to this question also suggests potentially diverse routes and patterns through

which the training can influence the productivity of workers. By far the most straightforward answer is (1). If this is indeed how training influence the productivity, the im-pact should be concentrated in the novice, and the net impact on productivity will beconcentrated in a relatively short span of time. On the other hand, the second answer,to facilitate multi-skilling, has a different implication. The impact of such training willbe more widespread, and possibly longer lasting. Moreover the impact of multiskillingon actual productivity can be difficult to detect unless ’multi-skilled’ workers are actu-ally engaged in works requiring multiple skills, the observation of which can be verydifficult for outsider observers. In fact, if we take an extreme view that multi-skillingacts more like an inventory against unexpected swings in labor demand, the impact ofmulti-skilling can be seen only in cases of significant reshuffling of employees acrossdifferent units or tasks within units. Finally, in the case of training at the start-up of newproduct line or equipment, the impact may be even more difficult to detect primarilybecause such a training cannot be observed independently from investment activity.

The triple, hrmp, trview, and traineffect jointly represent the spectrum of samplefirms in terms of their overall emphasis on HRM, and in particular, their training poli-cies. We use these three measures and compute conditional means of the training vari-ables. The results are shown in Table 2. Two points are immediate. First of all, HRM(hrmp) characteristics grouping does predict significantly the intensities of OffJT. Byand large, the correlation is stronger between hrmp and OffJT. The contrast amongsamples across hrmp is strongest in qchrt. Compared to samples with hrmp=1, thosewith the highest hrmp spend five times more (in hours per capita) on qchrt. Comparedto these two measures, OJT does not exhibit any consistent variations across HRMPgroups.

As a matter of fact, Table 2 suggests that the adoption of HRMP and the intensity ofOJT are largely independent, and if any, the intensity seems lower at firms with higherscores of HRMPs.

On the other hand, somewhat different pictures emerge from conditional means oftraining variables according to training policy grouping [trview].First of all, we findsignificant differences between the two top groups, groups 1,2 and 4. We find thatGroup 2 has highest average OJT intensities, whereas Group 4 has the highest averageintensity in OffJT and also in qchrt. These findings conform to our characterizationabove in that Group 4 on average employs most of the newer HRM practices, whereasGroup1 and 2 employ more traditional HRM practices among Japanese firms.

Consistent with characterization both in terms of training policy and HRM prac-tices, samples in Group 3 overall have the lowest averages in all of the three measuresand the Group 5 is the second lowest, except for OJT.

Finally, we also tabulate conditional means of training intensities across traineffect.For two training measures, the characterizations using traineffect do not conform wellwith the conditional means shown in the table. In particular, for OJT, samples whoanswered ’Not sure if trainings are effective in delivering any tangible outcomes’ hasthe highest average. For OffJT, those who emphasize multi-skilling as the objective oftraining has the lowest average. In the case of qchrt, those who answered ’to smoothen

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and expedite the start-up process of new product line/ newly installed equipment’ as theobjective of training has the highest average. Even though these findings on trainef-fect may be eventually reconciled after some implicit theorizing10, the results certainlydo not suggest that the perceived objectives in training can be usefully exploited todetermine the training intensities.

2.4 SummaryAll in all, the preceding analysis of the survey results do not lend strong support tothe view that training intensities, choice of training policies, and other aspects of HRMpractices are all strongly inter-linked to form a coherent whole of a long run corporatestrategy for human resource management. At best, qualitative information on trainingpolicy and overall HRM practices can be useful in interpreting the differential emphasison various types of training. The only exception seems to be the stronger correlationswith qchrt, amount of time spent on various kaizen meetings. One way to interpretthis observation could be that the training intensities depend primarily on productiontechnologies and skill requirements, which in turn can be regarded as largely exogenousfrom the viewpoint of the design of HRM policies, whereas other related measures toimprove production efficiency, such as the one represented by qchrt, may depend moreon overall HRM policies, and less on the production technology.

3 Econometric Analysis

3.1 IntroductionThis section reports the regression analysis of training intensity and its impact on firmlevel productivity. In the analysis that follow, we also incorporate information on HRMpolicies and other qualitative information on various aspects of employment relationand trainings. Our estimation strategy is simple and we summarize it below.

Consider a following set of equations for trainings, HRMPs and productivity:

Qjt = Q(Kjt , L

jt , NT

jt , FT

jt ,KT

jt ,H

jt , ZQ

jt , ) + u

jQ + εjQt (PF)

NT jt = N(ZIjt ) + ujN + εjNt (OJT)

FT jt = F (ZIjt ) + ujF + εjFt (OFFJT)

KT jt = K(ZIjt ) + ujK + εjKt (QCHRT)

Hjt = H(ZIjt ) + u

jH + εjHt (HRMP)

wherein Qjt , NTjt , FT

jt , KT

jt , and Hj

t are log of the real value added, OJT training,OffJT training, Kaizen meeting, and HRMP policy variables, respectively, at samplej factory in year t. The uj ≡ {ujQ, u

jN , u

jF } are establsihement specific time invari-

ant noises, and εjt ≡ {ujQt , u

jNt , ujFt , u

jKt , ujHt } are serially independent noises. The

10 One somewhat cynical interpretation regarding the higher intensities of training among those who an-swered ’not sure if there exits any tangible effect of training’, would be: these are the sample facto-ries who overinvest in training to the extent that the marginal effect is not significantly different from zero.

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log real value added is regressed over conventional factor inputs, Kjt,,capital (as mea-

sured in terms of the book value of structure and equipment), and Ljt ,labor (in totalman hours), as well as two training intensities and kaizen meeting, HRM and trainingpolicies, and a set of exogenous variables, ZQjt .

We consider two alternative specifications. In the first specification, we treat KT jtand Hj

t as econometrically exogenous in (PF) equation. Hence we instrument twotraining intensities. In the second specification, we treat both KT jt and Hj

t as en-dogenous so that all these four variables will be instrumented using ZIjt which jointlysatisfy exclusion restrictions for IV/GMM estimations.

We allow each equation to have two additive noises, one specific to each samplefactory, and the other time varying noises, εjt . Since we have strong reasons to suspectthat firm specific noises (uj ≡ {ujQ, u

jN , u

jF }) are not mutually independent so that

E(ujQ|FTjt ) 6= 0 (NonOrth)

E(ujQ|NTjt ) 6= 0

Hence the OLS estimates of (PF) can be biased. Similar observations can be made onKT jt andHj

t as well. Given the possible correlation among εjMt ,

E(εjMt |εjNt ) 6= 0 (NonOrth2)M 6= N,M,N = Q,N,F,K,H

in view of the well known tendency that trainings tend to be counter-cyclical, we alsosuspect that the fixed effect regressions may not suffice to remove the correlationsamong the residuals.

Our strategy is therefore to use a proper instrument variable among ZIjt to purgethe effect of the correlations among error terms. One way to interpret training inten-sity equations (OJT), (OffJT) is to consider them as reduced forms for the first orderconditions on the optimal level of training intensity. By construction, such a first ordercondition (Euler equation) equates the marginal benefits from incremental change intraining intensity to the marginal cost of training.

B(T jt , ZBjt ) = C(T jt , ZC

jt )

∴ T jt = T (ZBjt , ZC

jt )

wherein ZBjt , ZCjt are vectors of exogenous variables which shift marginal benefit

and cost. One stark way to demonstrate why and how these equations arise is simply toidentify the marginal benefit of training as the marginal impact of training on currentand future productivity.

B(T jt , ZBjt ) =

∂Qjt

∂T jt

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Then as far as the firm specific productivity noise appear also in ∂Qjt

∂T jt, ujQ and/or εjMt appear

necessarily in (OJT) and (OffJT) equations, which is tantamount to say that uj and εjtare mutually correlated11.The equation above also serves as a guideline in determiningappropriate instruments for the training variables: such variables should affect trainingcost to the firms but do not directly interact with productivity.

3.2 Intensity of Training

We start with the determinants of the intensity of trainings. Given the discussions in thelast section, it is prudent to investigate the endogeneity of qualitative variables on HRMand training policies. For this purpose, we offer three sets of regression results in thissubsection. First, we offer OLS estimates of three training variables using HRM andtraining policy variables as exogenous explanatory variables. Next, we offer regressionresults without these qualitative variables. At the end of the subsection, we reportregression results for HRMP cluster dummy variables.

3.2.1 OLS Estimates

The results are shown in Table 3. We start with the impacts on OffJT by variables influ-encing the quality of employees. Most of the coefficients for this category of variablesindicate strong positive impact of quality of workers on OffJT. For example, HSqual-ity, respondent’s evaluation of the quality of newly recruited high school graduates (indescending order, 1 the highest and 5 the lowest), shows the large impact on OffJT ofthe variable. The negative and significant impact of the vacancy applicant ratio on OJTmay suggest that this type of training is counter-cyclical. It is also possible that thetighter local labor market condition is likely to lower the quality of the new recruit.The share of engineers and professional employees (Eng.prof.Share) is also significanton OffJT.

Turning now to the employer characteristics, we find the contrasting impacts of totalman hours and the average work-hour per capita, both of which exert positive impactson OffJT, whereas the impact on OJT are negative (stable across different specifica-tions and estimation methods). The immediate implications of these results are notclear. For one thing, to the extent that the variable represents the scale of the sampleestablishment, our reading of the impacts are that OffJT is larger at larger establish-ments, and the other way around for OJT. On the other hand, another variable, averagework hours per regular employee, carries the same signs, positive for the OffJT and thenegative for OJT. A possible interpretation of this result is the pro- (counter-) cyclical-ity of training incidence. Available evidence strongly points out that the training areconcentrated during the time with slow business, which is consistent with the negativeimpact on OJT, but not with the result on OffJT12.

We also find that the recent installment of new capital equipment (Newcap) tendsto have positive impact on OffJT, but the impacts are insignificant. We also find no

11 One alternative which is not employed in this paper is to estimate 3SLS. See Ariga and Brunello (2004).12 This result on OffJT still holds when we replace the training cost by those inclusive of external cost.Bassanini et al (2005) find that the participation rate for the firm sponsered training in EU member countriesis strongly counter-cyclical.

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significant impact by the recent adoption of new production method/work organization(Newline). The impact on OffJT by Union (dummy variable if the sample establishmenthas a labor union) is also highly significant and positive, but it is negative on OJT.Whether or not the sample factory has R&D facility has positive impact on OffJT, butnot significant. The same is true for Expo (=1 if the factory sells its output overseas)and multi-plant (=1 if the firm has more than one production facilities).

Turning now to training policy variables, we have a few rather unexpected results.To begin with, we find strong correlation of the training with the respondent’s policiesand perception on the major impact of training. First of all, as we expected, Trview vari-able exerts some impacts on OffJT[TOLS-1 and 3 in Table 3]. Compared to the firmsbelonging to cluster 1 (default), those in clusters 2 and 3 tend to have substantiallylower training level (by the order of .4 to .7). This is consistent with the character-ization of these cluster groups in the section 2. In particular, firms in Group 1 paymore attention to the training policy, use various means to measure the effectiveness oftraining, and make full use of the assessment of training in promotion decisions. Notsurprisingly, therefore, these firms tend to invest more on training. The results on OJTis weaker, but the point estimates on Trview are all negative.

The impact of HRMP cluster variable on OffJT is also consistent with the character-izations in the last section. Sample factories practicing more innovative HRM policiesdo tend to invest more on OffJT1314. Somewhat surprisingly, the impact of qchrt ontraining intensities are also not significant at all, and often negative. Neither HRMP orqchrt have significant impact on OJT.

Finally, lag_subsidy is the dummy variable indicating if the sample factory waseligible for special subsidy for training expenses for the year 2002 (for 2003) or 2001(for 2002). The regression results show consistently correct (positive) signs but theyare not statistically significant (more on this variable below).

In sum, the results shown in Table 3 supports the popular hypotheses regarding thedeterminants of training for OffJT. The level of training is increasing in quality of em-ployees, technology level of firms, and scale of operation. Firms re-train workers whenthey introduce new machinery. Firms with innovative HRM practices tend also to investmore on training. The regression results on OJT supports none of these conventionalwisdom. As a matter of fact, our regressions leave most of its variations unaccountedfor as indicated by low R2. To put it bluntly, the measured variations in OJT remainmysteriously orthogonal to most of the variables we collected in the survey. We comeback to this issue later on at the end of this section.

3.2.2 Training regressions without HRMPs

Given the positive associations among training intensities, training policy and HRMPvariables, it is not entirely clear if the latter two set of variables can be treated asexogenous variables influencing the training intensity. We incorporate this concern in

13 The positive association between HRMPs and formal training is also found in Kuorosawa(2001). UnlikeOffJT, OJT seems more common among smaller, low tech firms, another finding in common with this paper.14 The result shown in Table 3 treats cluster variables as a set of dummies. Other specifications usingHRMP as continuous variables also yield similar results.

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two ways. TOLS5-TOLS8 in Table 4 show the training regressions without HRMP ortraining policy variables. The last 4 columns of Table 4 report seemingly unrelatedregression for OffJT,OJT,qchrt,and HRMP. Finally, Table 5 reports multinomial logitand ordered probit regressions for HRMP.

Results of training regressions in Table 4 indicate some loss in explanatory powerfor OffJT when we remove these qualitative variables (R2 drops from .5 to .45 ). Acomparison of TOLS-1 with TOLS-5 reveals some important changes in the estimatedcoefficients. Log(man-hour) variable becomes highly significant. On the other hand,self_learning become insignificant in TOLS5- TOLS-8 for HRMP and logit and probitmodels in Table 5 show how we can interpret the difference. Both HRMP and OffJTare highly correlated with Log(man-hour) whereas the average man-hour per capita isnot significant in HRMP regressions. This indicates that HRMP is positively correlatedwith factory size. It is also strongly negatively influenced by self_learning variable.This dummy variable is unity for sample factories which consider self learning helpindividual employees to seek alternative jobs elsewhere. Not surprisingly, such a firmtends not to invest on HRM policies designed to enhance individuals skills and thosedesigned to help individual employee career development.

By and large, TOLS-5 and 8 exhibit similar patterns on the determinants of OffJTand HRMP. However, there also exists important differences The tendency for firms toadopt innovative HRMP is significantly influenced by the stability and the quality ofemployees, as can be seen from the strong positive impact of union and R&D (dummyfor having R&D facility) and negative impact of separation rate. This point is alsoconfirmed in multinomial logit model shown in Table 5. Firms invest more in HRMPif they are: larger in size, with R&D facilities, with new capital investment. Also, theresults suggest that firms invest more in HRMP if employees are younger, not novice(with tenure more than 1 years at the firm), unionized, and less likely to quit. In short, itappears that the adoption of HRMPs are more sensitive to the quality and characteristicsof employees than OffJT.

Table 4 shows the results of seemingly unrelated regressions in which we incorpo-rate possible correlations in the error terms in OffJT,OJT,qchrt,and HRMP regressions.Results do not differ markedly from OLS. Given that the sample sizes differ signifi-cantly between TOLS5-TOLS8 and SUR, it is safe to say that the results do not findsignificant correlations among residuals of these regressions. Estimated error covari-ance matrix is close to diagonal. Breusch-Pagan test indicates that we cannot reject thenull that the error terms are uncorrelated at 5% confidence level.

All in all, although we do confirm some similarity between HRMP adoption andOffJT, we find no strong evidence that the endogeneity of HRMP and other qualitativevariables contaminate in important manners the OLS regressions on training variables.

3.3 Production Function

We report two sets of regression estimates for the production functions. The dependentvariable is log of real value added. Table 6 shows the OLS for the base case, and Table7 shows the estimates of base cases with instrumental variables

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3.3.1 OLS Estimates

Let us start with OLS in Table 6. It is easy to see that coefficients on capital and labor(measured in total man hours of regular employees15) are both highly significant andthe estimates conform to the standard estimates of this type of production functions.The sum of the two coefficients are fairly close to unity. Most of regression estimateseither fails to reject, or marginally reject at 5% level, the null (F-test) that the sum ofthe coefficients is unity. The coefficient on labor is larger than that on capital, oftenclose to 2 to 1 in the magnitudes, as conventional wisdom suggests.

Variables representing the quality of labor are in expected signs, including the shareof professional and engineers in the regular employee, and the share of female employ-ees, although only the last variable is statistically significant. The impact of havingan R&D facility (R&D) and the (labor) Union variables are both positive and signifi-cant. The dummy variable representing recent installment or replacement of new capi-tal equipment is also positive and significant.

Somewhat surprisingly, almost none of HRMP or training policy variables are sta-tistically significant in explaining productivity. POLS-2 and 6 shows the impact onHRMP dummies. Recall that this set of dummy variables represents in increasing or-der the degree of adoption of HRMPs, with HRMP=1, the lowest as the benchmark.Although point estimates are all positive except for one, none of them are significanteven at 10% level. The estimate for HRMP=6 is even negative. Recall that this dummyhad by far the strongest positive impact on OffJT. Both m-logit and ordered probit re-gressions consistently show that this group of firms are large and capital intensive, withstable and highly educated labor force, and R&D intensive.

Training policy cluster variables (Trview, POLS-3 and 7) does somewhat better.Compared to the cluster group 1, the highest ranking group in their intensive and com-prehensive training policies, all other group dummies carry negative signs and one ofthem is significant, which is also consistent with the training regressions results inTable 3. Finally, Traineffect [POLS-4 and 8]variables are totally insignificant.

With all these control variables incorporated in regressions, the impact of OffJTis found to be positive and all significant at least at 10% level. An increase in onestandard deviation of the variable, 1.69, should increase productivity by 8.8-10.6%.The variable is the log of the internal cost (including the opportunity costs) of OffJTtrainings so that the estimated coefficients corresponds to the returns from training.The coefficients ranges from .052-.063, which is smaller than the typical estimates ofthe return from training, but still comparable to the typical estimates on returns fromeducation.

The impact of qchrt, hours per production staffs spent on kaizen meetings, is alsopositive and often highly significant. An increase in this variable by one standarddeviation, 25, is predicted to increase productivity by as much as 12% (.005*25=.125).

On the other hand, the estimated impact of OJT on productivity is negative andnever statistically significant [POLS5-9]. Given the limitation of our proxy for repre-

15 Strictly speaking, in order to avoid double counting, hours spent on training should be subtracted fromthe total man hours. This modification does not change in any important manner the estimated impactsof trainings and other variables.

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senting unknown true intensity of OJT, we also tried alternative measures but none ofthese alternative measures bring about significantly different results16.

3.3.2 IV Estimates

When we use instrumental variables for the training and HRMPs, results shown in Ta-ble 7 are similar but differ in several important ways from OLS estimates. Our instru-ments are the following four variables. First two are answers to two questions regard-ing the quality of recently recruited high school graduates, HSquality and HSQchange[Q14 and Q15]. The lag_subsidy is a dummy variable representing whether or not thesample establishment is eligible for a subsidy program which was instituted during theprevious year to smoothen the employment adjustments. Specifically the program ap-plies to firms in specified sectors of industry and provide subsidy to training programsconducted by employers. Eligible sectors for this subsidy program are chosen by thegovernment on the basis of whether the sector as a whole has experienced contractionin business activities. The last one is the number of years since the establishment ofthe sample factory. We also experimented by adding two other instruments; one isself_learning, the variable explained in the training regressions above, and the other isthe average annual work hours per capita.

Many variables become insignificant without comparable declines in the estimatedcoefficients. Among those variables is OffJT. The impact remains positive but no longersignificant (except for one case). The estimated coefficients are typically larger than theOLS estimates but respective standard errors even larger, indicating the instrumentalvariable methods adds significant additional noise to the variable.

3.3.3 Diagnostic Checks

We suspect that training and institutional variables may have lost significance in the2SLS estimation probably because of the weak instruments as well as of smaller samplesizes. The bottom row of Table 7 shows the partial F-statistics (explanatory power ofexcluded instruments) for each endogenous variable. Low (some even less than 2)value of F statistics for qchrt indicates indeed that regressions do suffer from weakinstrument for this variable, whereas the first stage regressions seems satisfactory forOffJT17.

Nevertheless, variables we have used as instruments did satisfy the rank conditionin that they were each statistically significant (except for lag_subsidy) in the respectivetraining/institution incidence function (or they were each partially correlated with therespective training/institutional incidence), and they also satisfied over-identifying re-strictions. This can be seen from Hansen’s J-statistic which indicates that we cannotreject the null of exogeneity of the excluded instruments at 5% confidence level for thebase set of instruments, as well as self_learning and wkhr, the average work-hour. On

16 We also ran IV regressions includingOJT using the same set of instruments used for other endogenousvariables. None of these regressions (not shown) generated significant estimates for the impact ofOJT.Wealso found partial F for the excluded instruments is extremely low (less than 1) for the first stage regressionsfor OJT.17 See Stock, Wright and Yogo (2002) for the diagnostic checks useful for identifying the weak instrumentproblem.

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the other hand, C statistic for the additional instruments suggests that the wrkhr, the av-erage work hour, may not be appropriate for the instrument (p value is a marginal .12).Finally, conditional on those instruments being valid, none of the training/institutionalvariables we utilized could reject the null hypothesis that they are exogenous at con-ventional significance level [Hausman’s F statistic].

Overall, our IV regressions do satisfy the exogeneity conditions but the instrumentsare perhaps weak and we conclude that the loss of the significance in OffJT and qchrtis a reflection of this problem.

3.3.4 Other Specifications

We also experimented alternative specifications including the products of training andHRM variables. The idea is to see if significant complementarity exists between HRMand training in the production. Results (not shown) do not lend strong support forcomplementarity in HRM and training18.

3.4 Discussions on OJT

One of the more surprising results in this paper is about OJT. As we documentedabove, our measure of OJT not only fails to explain the productivity of sample factories,but our econometric studies leave the bulk of the observed variations of the variableunexplained. These results are surprising at least for several reasons. First of all, thereexists plenty of previous (case) studies in Japan which focus on the crucial role of OJTin skill formation of production workers19. Koike, among others, repeatedly argued thecrucial role of OJT in skill formation, at least for production workers in manufacturingindustries, and downplayed the effects of OffJT and team based practices, such as theone represented by qchrt20. Our results indicate almost totally opposite results not onlyon their impacts on productivity but also in wage regressions shown below.

Our results on OJT are surprising also because they are at least suggestive of theimportant differences between the determinants of OJT as opposed to OffJT. Roughlyspeaking, OJT training level is orthogonal to factors which do explain with reasonableaccuracy the level of OffJT,qchrt, or adoptions of various HRMPs. This runs counter tothe generally accepted perception that they are complementary than being substitutes21.

Needless to say, results based upon a single survey must be interpreted with duecaution and we fully acknowledge the danger of over generalization. It is perhaps notwithout merits, however, to suggest a few possible lines of interpretations, which canbe addressed in more satisfactory manner in the future researches. The first and ob-vious interpretation of our results is that our measure of OJT is simply so noisy as tobe meaningless. Given the unconventional method we used in the survey, we take it

18 In some specifications, we did find significant positive impact of the products of OffJT and qchrt,OffJTand HRMP. Given the lack of significant explanatory power of HRMP, we consider these results at bestsuggestive.

19 Koike, Chuma, and Ohta (2001) is the latest example of series of case studies done on skill formation andtraining at Japanese manufacturing industries.20 See Koike (1999), especially pp14-15, 25-29.21 The difference between OJT and OffJT determinants are also emphasized in Kurosawa (2001)

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seriously that we made a poor choice in the measurement. The major problem withthis interpretation, however, is that other alternative measures of OJT as we explainedin section 2.2, generate qualitatively the same results. Our preferred alternative inter-pretation is the one based upon selection bias due to unobservable labor quality. Byconstruction, OJT is concerned with the skill and knowledge needed to perform thecurrent tasks. It is almost surely the case that such OJT training level is negativelyrelated to the skill and current productivity of employees at individual level22. To put itsimply, OJT is likely to be the indispensable device to train workers in order for themto meet a certain set of minimum standards required for a job. Slower the progress oflearning, we should observe more investment in OJT. Aggregated at the factory level,it is even possible that the apparent impact of OJT is negative if factories with largershare of inexperienced workers invest heavily on OJT. This problem is likely to befurther confounded by the fact that OJT is conducted more heavily at the early stageof skill formation, when information on learning capacity and aptitudes of newly hiredworkers are seriously lacking.

To be fair, we have the same problem in the measurement of the impact of OffJT(but likely in the opposite direction). Our consolation is that as far as OffJT at fac-tory level is concerned, such selectivity bias seems to be smaller, given the relativelygood performance of instruments used in IV regressions for OffJT. Although we donot dismiss the possibility that the remaining hidden labor quality generates bias to theestimate, the strong impact of HighSchool_quality variable is at least encouraging signthat our estimates on OffJT can be trusted with greater confidence.23

3.5 Average wages and training

Although somewhat limited in scope, we can use the reported average wage to see if wecan identify the significant impact of training on wages. We use the log of the averageannual wage for production workers.

In order to facilitate the comparison with the regression on log of value added, Ta-ble 8 shows the regression results using exactly the same specifications as we used forthe production function regressions, except for one variable: we used average tenureof production workers in wage regressions, instead of average tenure for all the em-ployees. One thing we notice immediately is that the wage regressions are qualitativelyquite similar to the production function regressions. In particular, we confirm the gen-

22 See Ariga and Brunello (2003) in which we find the positive impact of worker quality on OffJTand the negative impact onOJT in the case of manufacturing firms surveyed in Thailand.23 There are other equally plausible explanations for our findings on OJT. In principle, training mea-sures in terms of hours spent on training may run the risk of double counting in our production function es-timations because the total work hour is included already as an explanatory variable. Using the time spent bysupervisors for the measure of OJT can aggravate the problem because not only the time spent by train-ers but also those spent by trainees are part of the total man-hours. To the extent most of OffJT in-structors are from outside of the sample factory, this problem may be more serious in OJT. [We did try to rec-tify the problem by estimating total man hours net of training, but none of these regressions made sig-nificant change.]

There may also exist trade-off between long-term productivity increase and short term efficiency in thetime allocation of supervisors activity. More intense monitoring, and tighter control of production process,for example, may increase short run efficiency (which is likely to appear in our measured value added)but the long term gain from OJT may be sacrificed.

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eral tendency that the impact of OffJT is positive and highly significant. Impacts ofOJT and qchrt are never significant and often in the wrong sign. Recall, however, thatthe latter variable had consistently positive impact on value added.

Another significant difference between wage and value added regressions is theimpact of HRMP and training policies. Wage regressions show clear and consistentpattern that employees at firms with highly innovative HRMPs are paid better. In factthe point estimate suggests that those at highest HRMP scores earns roughly 20% morecompared to those at the lowest HRMP score group, whereas the impact of HRMPvariables are almost never statistically significant in value added regressions.

When we compare the estimated coefficients in wage and value added regressions,we also find that the OffJT coefficients on log wage is significantly smaller than thecoefficient on log of value added24. This confirms our premise that the worker and thefirm share the benefit from training. On the other hand, time spent on Kaizen Meetings,qchrt, generates significant impact on value added but the employees do not seem toreceive any wage increase. In the case of Kaizen meeting, the gain in productivity isspecific to the group of workers in a small unit and whatever the enhancement in theirskills is highly specific to the technology and work organization they belong to. Henceemployees do not pay for the cost or directly benefit from such meetings. Moreover,even if the skills acquired through such meetings are general, factors such as labormarket imperfection could make it seem as if they are specific. Finally, Kaizens areoften designed to improve working conditions as well as productivity. Wage may wellbe reduced if the amenity of shop floor is improved by Kaizen meetings.

Before closing, we note that OJT training is never significant in wage regressions[not shown], which is in line with the previous findings in production function esti-mates.

Highly stable and robust estimates of wage regressions lend some support to ourassessment that the OLS estimates of value added regressions do not suffer in importantway from the endogeneity bias.

4 Conclusion

In this paper, we used a unique survey of manufacturing factories in Japan to estimatethe impact of firm level training and innovative HRM policies on the productivity ofsample factories. We find modest but statistically significant impact of off the jobtrainings on productivity but fails to obtain reliable estimate on the impact of on-the-job training. The regression show mostly similar results when we replace valued addedby wages. The impact of OffJT on wage is significant but not OJT. Furthermore, theimpact of OffJT on productivity is larger than the impact on wages, indicating theexistence of the benefits firms capture through training their workers.

We also find some impact of the HRM practices on productivity and training. Onthe other hand, our study fails to identify strong complementarity among various HRMpractices and trainings. Although our results are far from conclusive on this important

24 Dearden, Reed and Reenen (2000), using a large panel data on British manufacturing industries, also findthat the impact of training on wage is consistently smaller than the one on value added.

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issue, it seems possible that the failure to identify the complementarity in productionfunction or the consistent impact of HRMP on productivity may be attributable to thelimited span of time covered in the data. Indeed, if we consider the result by Jones andKato (2003) as a representative one, it takes a long time for these practices to result inproductivity improvement. Our results in which we find no strong impact of HRMPson productivity may simply reflect the long gestation period needed for the full impacton the productivity to be realized. Given the stronger results for the impact of HRMs ontraining intensity, it is conceivable, however, that HRMPs may reduce the (marginal)cost of trainings, but their benefits are independent from HRMPs.

Given the severe limitations imposed on our estimations, due primarily to thoseon data availability, our results should be interpreted with due cautionary notes. Inparticular it should be noted that the short span of the time covered in the data may beresponsible for some of the week estimates, especially on the impact on productivity.

5 References

[1] Abowd, J.M., J. Haltiwanger, R. Jarmin, J. Lane, P. Lengermann, K.M. McCue,K. McKinney, and K. Sandusky (2004), ’The Relation among Human Capital,Productivity and Market Value,’ mimeo.,

[2] Ariga,K. and G. Brunello (2004), ’Are Education and Training always Comple-ments? Evidence from Thailand,’ forthcoming in this journal

[3] Barron, J.M., M. Berger, and D.A. Black (1997), On the Job Training, UpjohnInstitute

[4] Bassanini, A., A. Booth, G. Brunello, M. De Paola, and E. Leuven (2005), ’Work-place Training in Europe,’ IZA Discussion Paper 1640,

[5] Black, S.E., and L.M. Lynch (2004), ’Measuring Organization Capital in the NewEconomy,’ mimeo.,

[6] Black, S.E., and L.M. Lynch (2001),’How to Compete: The Impact of WorkplacePractices and Information Technology on Productivity,’ Review of Economics andStatistics 83, no. 3 (2001): 434-445

[7] Dearden, L., H. Reed, and J.Van Reenen (2000), ’Who Gains When WorkersTrain? Training and Corportate Productivity in a Panel of British Industries,’ IFSWorking Paper 00/04

[8] Hashimoto, M. (1994), ’Employment-Based Training in Japanese Firms in Japanand the U.S.,’ in L.M. Lynch (ed.) Training and the Private Sector, University ofChicago Press for NBER

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[9] Ichiniowski, C., and K. Shaw (2003), ’ Beyond Incentive Pay: Insiders’ Estimatesof the Value of Complementary Human Resource Management Practices,’ Journalof Economic Perspectives 17(1): 155-180

[10] Kato, T. and M. Morishima (2002), ’The Productivity Effects of ParticipatoryEmployment Practices: Evidence from New Japanese Panel Data,’ Industrial Re-lations 41(4): 487-520

[11] Koike, K. (1999), Shigoto no Keizaigaku (Economic Analysis of Work) [in Japanese],Tokyo: Toyo-keizai

[12] Kurosawa, Masako (2001), ’The Extent and Impact of Enterprise Training: TheCase of Kitakyushu City,’ The Japanese Economic Review, 52(2): 224-242

[13] Stock, J.H., J.H. Wright, and M. Yogo (2002), ’A Survey of Weak Instruments andWeak Identification in Generalized Methods of Moments,’ Journal of Businessand Economic Statistics, 20(4): 518-529

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Appendix: Survey Questionnaire

In this appendix, we provide English Translation of the questionnaire sent out to the sample establishments. Unless otherwise specified, all questions below are concerned with regular full time employees. Except for Q14 and Q15, answers for all the questions below should be supplied separately for year 2001 and 2002. 1. Composition of employees Q1. (1) Number of regular employees, (2) Part time and other types of workers Q2. Share of female employees Q3. Share of employees age 50 or over Q4. Share of 4 year college graduates Q5. Share of employees with less than 1 year of tenure, (1) college graduates, (2)Others Q6. Average years of tenure, for (1) college graduates, (2) Others Q7. Average separation rates per year. Check one: less than 1%, 1-3%, 3-5%, 5-7%,

7-10%, more than 10% Q8. Breakdown (in percentage) of regular employees into the following job categories.

(1) Managers, (2) Supervisors, (3) Professional and engineers, (4) Clerical, and (5) Production and technical workers.

Q9. Percentages of employees in the following categories who are not fully proficient in their current jobs. (1) Professional and engineers, (2) Clerical, (3) Production and technical (for this category of workers, answer separately for regular and non-regular workers)

Q10. Annual average work hours for the following types of employees: (1) production and technical, regular employees , (2) production and technical, non-regular employees, (3) regular employees other than production and technical

Q11. Average annual gross earnings for the following job cateogories. (1) Professional and engineers, (2) Clerical, (3) Production and technical workers

Q12. Average age of employees : (1) Professional and engineer, (2) Clerical, (3) Production and technical workers

Q13. Average number of years at the firm (tenure), (1) Professional and engineer,

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(2)Clerical, (3) Production and technical workers Q14. Recent changes in the average quality of high school graduates hired at your

factory. Check one: (1) substantially lower, (2) somewhat lower, (3) No major change, (4) somewhat higher, (5) substantially higher.

Q15. Average quality of high school graduates hired at your factory in recent years: In comparison with the quality of high school graduates in the region seeking employments. Check one: (1) excellent, (2) above average, (3) around average, (4) below average

2. Human Resource Management Practices Q16. Adoption of the following practices. Check all that apply.

(1) Flex-time* (2) Flexible work schedule*, (3) Long term vacation, (4) MBO (Management by objective) (5) Self evaluation, (6) Job posting within the firm, (7) Career development planning, (8) 360 degree appraisals, or multi rater assessments/feedback, (10) Mentors, (11) Annual salary assessment, (12) ESOP, (13) Stock option, (14) Pay for (group, line) performance, (15) Best employee awards (for the year, quarter, month, etc.), (16) Employee management committee, (17) QC Circle (18) Morning briefing, (19) Regular meeting for Kaizen, (20) Company paid/hosted employee recreation trips, (21) Other recreation activities, (22) Supports for self-learning

*Flex-time: employees are allowed to choose the starting and the ending time of daily work schedule,

provided they meet certain conditions (e.g., work day should start no later than 10am). Flexible work

schedule: within certain limit, employees have discretion on allocation of daily work hours (such as 8

hours in Mondays and Thursdays, 4 hours in Wednesday and Fridays).

Q17 Practices in Q16 which were adopted for the first time in year 2002, or 2001. Q18. If you checked ‘yes’ to (19) in Q16, answer the following. Q18S1 Percentages of employees who participate in Kaizen meeting for: (1) regular

production/technical workers (2) non-regular production/technical workers Q18S2 Average frequencies (per year) and the average length (in hours) of each Kaizen

meeting for regular production/technical workers 3. OffJT Q19. Purpose/contents of OffJT conducted. Check all that apply. (1) New products or services

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(2) Operation of new capital equipments and tools (3) Repair and maintenance of new and old equipments. (4) Computer usage (5) Quality Control (6) Team agenda setting and problem solving (7) Company’s long term strategy

(8) Information System (9) Sales and customer services (10) Presentation skills (11) Communication skills (12) Personell evaluation, leadership (13) Safety (14) Remedial skills in basic arithmetics (15) Others (language, health, etc.) Q20. Total man-hours of OffJT trainings for employees in each type of job category. (1)

Manager, (2) Supervisor, (3) Professional and engineer, (4) of which for the newly hired, (5) Clerical, (6) of which for the newly hired, (7) production and technical, (8) of which for the newly hired

Q21. Average hours per training for (1)-(8) above. Q22. Total annual cost of OffJT: (1) internal (including opportunity) costs of the rents

and instructors of training, (2) external (out of pocket) costs for outsider instructors fee, costs for trainings outside the firm, etc.

4. OJT Q23. The average length of time ( in months) necessary for the newly hired employees to

be fully proficient in their current jobs for: (1) professional and engineer, (2) clerical, (3) production and technical

Q23S1. The average hours of OJT per month during the training period (the answer given above) for: (1) professional and engineers, (2) clerical, (3) production and technical

Q23S2. Trends in the duration of such OJT compared to 3 years ago. Check one: (1) longer (by more than 10%), (2) somewhat longer, (3) no change, (4) somewhat shorter, (5) shorter (by more than 10%)

Q24. Hours spent on average by supervisors (foreman) on the following activities per working day. (1) Meeting, (2) Monitoring and supervision, (3) training and instruction (of subordinates), (4) paper work, data collection, (5) other activities

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5. Self learning Q25. Support for self learning. Check all that apply. (1) supplements for tuitions, and

other expenses, (2) reduced work hours to attend classes, Q26. Firm’s view on self learning by the employees. Check all that apply. [self

learning] (1) help broaden skills and knowledge, (2) valuable if they look for different jobs, (3) help identify those who are highly motivated, (4) irrelevant for work and there is no need to help them

6. Evaluation and measurement of training Q27S1. Views regarding the effectiveness of training. Check one. [trainings] (1) help learning and gettting skills necessary to perform required tasks. (2) enhance capacity to learn and facilitate multi-skilling (3) speed up and smoothen the start-up of newly installed equipments or

production lines (4) its effectiveness not very clear Q27S2 Methods employed at your factory to measure the effectiveness of training.

Check all that apply. (1) Self evaluation of trainings received, (2) evaluation by supervisors, (3) evaluations based on variety of measurements, such as defect rate, productivity, accident rate, etc. (4) certification of specific skills via exam, (5) other methods

Q27S3 Usage of the measured effect of training. Check all that apply. (1) Long-run evaluation of employees, (2) transfers, (3) evaluation of the training program, (4) not utilized

Q27S4 Views on training effects and/or measurement. Check all that apply.(1) We evaluate employee performance and potential as a whole so that we do not need to evaluate the impact of any particular training they receive. (2) Trainings are limited mainly to the novice and we are already aware of the effectiveness of training programs. Hence it is not necessary to measure the effectiveness of training each time. (3) We lack appropriate data and methods to measure the effect of training. (4) We have a plan to develop a program to measure the effectiveness of trainings (but we have not implemented it yet). (5) We have a plan to hire outside consultant to draft such a plan.

Q28. Have you recently installed (replaced) new large scale capital equipments? Q28S1 If the answer is ‘yes’ to the above, when was it?

25

Q28S2 Did you conduct trainings needed for the installation? If yes, days of training conducted for: (1) professionals and engineers, (2) production and technical.

Q29. Have you recently made a major change in production process? Q29S1. If ‘yes’, indicate the year of the change. Q29S2. Did you conduct trainings needed for the change? If yes, days of trainings

conducted for: (1) professionals and engineers, (2) production and technical Q30. Forecast for next 3 years on the sales volume of your factory. Check one.

(1) Increase, (2) No major change, (3) Decrease 7. Factory data Q31 Is your factory also the headquarter of the firm? Q32 The year the factory was established Q33 Total number of factories in your firm (including the sampled) Q34 The major outlet of your product is (1) local market, (2) nation wide, (3)

international market Q35 Labor union at your factory? Q36 R&D facility in your factory? Q37 Cost and profit statements . Fill in the columns below (unit million yen) 2002 2001 2000 Total sales Material and labor cost Book value of fixture Capacity utilization rate Q38 Choose the industry classification code (in 4 digit) Q39 Address and the contact details

26

Table A1 Survey population and response rate

Factory size Sent-out Received Response rate 20-49 employees 4986 279 7.5% 50-99 employees 4030 249 4.8% 100-199 employees 2680 179 6.6% 200-499 employees 1517 67 6.1% 500-999 employees 357 21 5.9% 1000 or more 238 35 13.5%

27

Table A2 HRM Policies across Grouping according to Training Policies Trainiview grouping TG1 TG2 TG3 TG4 TG5

# of samples 206 642 282 176 354

1 Flex-time 0.228 0.232 0.18 0.306 0.115

2 Self scheduling 0.087 0.088 0.078 0.13 0.033

3 Long term vacation 0.145 0.169 0.127 0.204 0.135

4 Objective management, 0.475 0.506 0.333 0.517 0.336

5 Self evaluation, 0.359 0.367 0.333 0.335 0.231

6 Job posting within the firm 0.092 0.077 0.074 0.164 0.056

7 Career development planning 0.111 0.084 0.109 0.238 0.079

8 Multiple evaluations 0.155 0.124 0.085 0.153 0.07

9 Job rotation 0.16 0.158 0.127 0.25 0.09

10 Mentors 0.009 0.01 0 0.022 0.005

11 Annual Salary 0.257 0.158 0.187 0.198 0.121

12 ESOP 0.466 0.392 0.446 0.522 0.35

13 Stock Option 0.033 0.021 0.024 0.045 0.011

14 Pay for Performance 0.165 0.253 0.102 0.159 0.129

15 Award for the best employees 0.611 0.538 0.397 0.505 0.42

16 Worker Management Meeting 0.553 0.531 0.521 0.625 0.451

17 QC 0.655 0.602 0.531 0.687 0.432

18 Morning meeting 0.922 0.89 0.815 0.886 0.858

19 Kaizen meeting 0.538 0.51 0.351 0.431 0.418

20 Company Trips 0.543 0.52 0.478 0.409 0.477

21 Company recreational events 0.398 0.436 0.315 0.522 0.355

22 Self learning 0.49 0.429 0.361 0.562 0.327

HRMP 3.21 3.06 2.66 3.37 2.45

Log(offjt_int) 3.536 3.596 3.131 3.935 3.481

Ojt_prod 0.366 0.371 0.303 0.322 0.367

Qchrt 11.62 11.33 5.627 14 6.599

traineffect=1 0.039 0.529 0.566 0.66 0.351

traineffect=2 0.902 0.104 0.339 0.271 0.206

traineffect=3 0.058 0.33 0.085 0.059 0.135

traineffect=4 0 0.038 0.011 0.012 0.307

Av. # of employees 155 445 463 462 181

28

Table A3 HRMP variable We first divided 21 policies listed in Q16 above into two groups. Means for enhancing individual employee welfare and/or productivity, and those

for improving job allocations [individual]: 1) Flex-time (2) Flexible work scheduling, (3) Long term vacation, (4) Work objective management, (5) Self evaluation, (6) Job posting within the firm, (7)Career development planning, (11) Annual salary，(15) Best employee awards, (22) Subsidies for self-learning

Team, group or firm based means and incentives [team]: (8) Multiple evaluations, (9) Job rotation, (10) Mentors, 12) ESOP, (13) Stock option, (14) Pay for (group, line) performance, (16) Management-employee councils, (17)QC Circle, (18) Morning briefing, (20) Company paid employee recreation trips, (21) Other recreation activities

We computed scores (the number of ‘yes’ to questions in the two groups). The distribution is such that the sample can be divided into 6 distinct groups as shown below.

Individual employee based means Group based means Low Medium High Low Group 1 na na Medium Group 2 Group 3 Group 5 High Na Group 4 Group 6

We picked 6 samples each of which represents well characteristics of one of the 6

groups above and used them as the initial observations to start cluster (means) analysis. The hrmp variable thus constructed conform well to the above characterizations as the statistics below demonstrates.

Individual employee based means Group based means Hrmp sample Mean Min Max mean Max Min 1 373 .76 0 3 1.50 0 3 2 561 1.13 0 2 3.88 3 6 3 357 3.59 3 6 3.65 2 5 4 173 3.61 1 5 6.46 6 9 5 161 6.35 5 9 4.99 2 7 6 35 7.94 6 10 8.03 6 9

29

Table 1 Summary Statistics Variable Obs Mean Std. Dev. Min Max

Log of value added in 2002 million yen Log(vad) 1128 7.07 1.48 0 13.45

Log of Offjt cost in 2002 yen (internal cost only) Log(offjt_int) 853 3.52 1.68 -0.05 10.65

Log of Offjt costs in 2002 yen (internal and out of pocket

expenses)

Log(offjt) 610 4.49 1.50 0.65 10.32

Offjt hours per production staff offjt_prod 864 11.65 183.4 0 4132

Offjt total man hours for administrative staff

(unit: 1000hrs.)

offjt_man 841 4.28 46.65 0 881

Offjt total man hours for engineers (unit: 1000hrs.) offjt_eng 719 7.31 97.41 0 1840

Offjt total man hours for supervisors (unit: 1000hrs.) offjt_sup 758 7.38 110 0 2486

Ojt hours per production staff ojt_prod 1248 0.35 0.56 0 7.97

Ojt cumulative hours for per novice production staff ojtnew_prod 1063 5854 48160 0 .93x10^6

Ojt cumulative hours for per novice engineers ojtnew_eng 874 2326 22471 0 .40x10^6

Ojt cumulative hours for per novice clerical staff ojtnew_adm 1008 490 2667 0 47613

Kaizen meeting (hours per regular production staff) qchrt 1528 9.60 25.62 0 400

HRMP Clusters HRMP 1612 2.58 1.32 1 6

Log of capital stock in 2002 million yen Log(capital) 1029 6.50 1.77 -0.040 14.06

Log of total man-hours (regular employees) Log(man-hr) 1203 12.02 1.10 8.67 18.77

College graduates (%) Univ 1563 14.55 12.74 0 87.3

Age 50 or older (%) Old 1580 25.33 16.51 0 100

Female (%) Female 1596 23.26 20.70 0 98

Professional, engineers (％) Eng.Prof.Share 1488 7.96 9.05 0 63

Average tenure Av.tenure 1329 14.93 6.09 1.67 40.58

Labor Union (dummy) Union 1596 0.50 0.50 0 1

R&D Facility (dummy) R&D 1594 0.37 0.48 0 1

Products exported (dummy) Expo 1586 0.17 0.38 0 1

Multi plants firm (dummy) Multi-plant 1572 0.95 0.22 0 1

30

Table 1 (Continued)

Variable Obs Mean Std. Dev. Min Max

Cluster group for training policies Trview 1612 2.90 1.36 1 5

Principal training effects ( 4 multiple

choices-dummy)

Traineffect 1516 1.92 0.99 1 4

Self learning Helps career advancement (Q26(4)) SelfLearning 1612 0.04 0.20 0 1

Production line changes in the previous year Newline 1512 0.13 0.34 0 1

New capital equipment installed in the previous year Newcap 1540 0.29 0.45 0 1

Separation rate (ranges 1-7 with ascending order) SepRate 1573 0.03 0.03 0.01 0.1

Tenure less than 1 yr. (%) NoviceShare 1461 6.00 16.57 0 100

Subsidy for employment adjustments lag_subsidy 1590 0.55 0.50 0 1

Average tenure of production workers Avten_prod 1433 14.42 6.39 .5 40

Log of average annual earning of production workers Totlogw_prod 1301 5.99 .33 4.87 6.80

Quality of new high school graduates HschoolQuality 1410 2.84 0.60 1 4

Recent changes in the quality of new high school

graduates

HschoolQChange 1430 2.52 0.96 1 5

Year dummy for 2002 Yeardum 1612 0.5 0.50 0 1

Vacancy applicant ratio (prefecture of the factory

location)

Jobapp 1529 0.61 0.15 0.29 0.94

Industry sector dummy industry1 1590 13.15 7.16 1 24

Regional block dummy Block 1529 5.63 2.85 1 12

Annual Work hours (in 1,000 hrs.) AnnualWkhrs 1332 2.09 0.24 0.90 3.2

Years since establishment of factory Years since Est 1576 51.12 29.01 1 341

Has Company trip Company Trip 1612 0.50 0.50 0.00 1.00

Has provisions to help self-learning Help

SelfLearning 1612 0.42 0.49 0.00 1.00

31

Table 1 (Continued) Correlations among Training Variables

ojt_prod (1)

ojtnew _eng (2)

ojtnew _adm

(3)

Ojtnew_prod (4) (4)

Log(offjt)(5)

Log(offjt _int) (6)

offjt_man (7)

offjt_sup (8)

offjt_eng(9)

ojtnew_eng

(2)

0.1635*

ojtnew_adm

(3)

0.1427* 0.8199*

ojtnewprod

(4)

0.1066* 0.7049* 0.7027*

Log(offjt)

(5)

0.004 0.1533* 0.1998* 0.1526*

Log(offjt_int)

(6)

0.0296 0.1542* 0.1853* 0.1460* 0.9426*

offjt_man

(7)

-0.0102 .7309* 0.1095* 0.0555 0.4117* 0.3719*

offjt_sup

(8)

-0.0562 .6746* 0.0279 0.0496 0.1864* 0.2452* 0.9426*

offjt_eng

(9)

0.0139 .7266* 0.1977* 0.0669 0.3800* 0.3471* 0.9858* 0.9740*

offjt_prod

(10)

-0.0297 .6980* 0.0724 0.0561 0.2381* 0.2771* 0.9685* 0.9938* 0.9891*

offjt_adm

(11)

-0.0182 0.6538* 0.0742 0.0533 0.3978* 0.3711* 0.9086* 0.9836* 0.9537*

*indicates that pair wise correlations significant at 1% level

32

Table 2 HRM variables and training Frequency distribution and Conditional Means

Frequency HRM Cluster

1 2 3 4 5 6 Total

1 37 53 60 25 24 5 204

2 113 199 160 78 50 18 618

3 81 92 49 22 23 4 271

4 23 55 25 23 40 6 172

5 111 139 55 20 20 2 347

Trview

Total 365 538 349 168 157 35 1612

Frequency HRM Cluster

1 2 3 4 5 6 Total

1 162 218 145 61 65 18 669

2 85 120 106 49 56 11 427

3 49 102 63 45 23 6 288

4 31 56 29 6 10 0 132

Train- Effect

Total 327 496 343 161 154 35 1516

(2) Log(offjt_int)

means HRM Cluster

1 2 3 4 5 6 Total

1 2.788 3.456 3.295 3.928 4.576 5.285 3.536

2 3.006 3.192 3.514 4.040 4.827 5.088 3.574

3 3.434 3.108 2.953 3.050 2.480 5.851 3.109

4 2.974 2.950 4.590 4.440 4.680 4.605 3.935

5 2.933 3.599 4.152 3.727 3.745 5.451 3.482

Trview

Total 2.998 3.287 3.497 3.945 4.445 5.196 3.520

means HRM Cluster

1 2 3 4 5 6 Total

1 3.083 3.312 3.631 4.373 5.444 6.352 3.694

2 2.601 3.445 3.260 3.744 3.773 5.245 3.366

3 3.577 3.186 3.430 3.795 3.737 3.607 3.461

4 2.397 3.401 4.052 3.101 5.605 - 3.579

Train- Effect

Total 2.998 3.325 3.510 3.983 4.445 5.196 3.540

33

(3)ojt_prod

means HRM Cluster

1 2 3 4 5 6 Total

1 .742 .257 .330 .230 .376 .205 .366

2 .238 .392 .413 .373 .424 .299 .368

3 .206 .174 .566 .366 .512 .066 .307

4 .203 .365 .468 .350 .228 .128 .329

5 .393 .345 .333 .271 .547 1.233 .368

Trview

Total .324 .332 .412 .332 .392 .308 .354

means HRM Cluster

1 2 3 4 5 6 Total

1 .273 .311 .430 .453 .484 .488 .363

2 .457 .309 .502 .300 .285 .165 .379

3 .180 .383 .272 .261 .349 .263 .298

4 .609 .378 .281 .198 .077 - .379

Train- Effect

Total .332 .334 .415 .333 .378 .308 .356

(4) qchrt

means HRM Cluster

1 2 3 4 5 6 Total

1 7.275 12.903 12.745 13.696 10.771 11.664 11.621

2 4.027 9.569 12.559 21.066 8.197 28.530 11.246

3 2.344 4.906 6.003 4.098 11.771 28.416 5.265

4 3.308 7.382 42.874 17.503 8.926 7.572 14.004

5 4.305 8.689 4.768 8.968 7.922 .200 6.599

Trview

Total 4.068 8.608 12.797 15.867 9.305 20.835 9.598

means HRM Cluster

1 2 3 4 5 6 Total

1 3.114 6.510 11.347 6.542 8.125 21.529 7.280

2 5.546 9.238 16.201 15.501 7.156 12.661 10.828

3 7.101 12.672 14.355 31.603 14.790 34.083 15.846

4 4.720 5.493 4.603 1.100 22.000 - 5.761

Train- Effect

Total 4.483 8.350 12.916 16.411 9.311 20.835 9.817

†Samples correspond to those in regression analysis reported in section 3 of the paper.

34

Table 3 Training Incidence (OLS with HRMP as explanatory variables)1

TOLS-1 TOLS-2 TOLS-3 TOLS-4 Dependent Variable log(offjt int) ojt prod log(offjt int) ojt prod HRMP.Train var. Trview HRMP # of Dummies 4 5

Dummy=1 Default Default Default Default Dummy=2 -0.530** -0.185 0.386 -0.148 Dummy=3 -0.598*** -0.225 0.322 -0.094 Dummy=4 -0.280 -0.235* 0.129 -0.226* Dummy=5 -0.139 -0.097 0.350 0.037 Dummy=6 -- -- 1.160*** -0.188

Company Trip 0.114 -0.064 -- -- Help Self Learning 0.423*** 0.089 -- -- Qchrt 0.0002 -0.001 -0.000 -0.000 Log(Capital) 0.0070 0.011 -0.027 0.010 Log(Man-hour) 0.532*** -0.144* 0.611*** -0.129* AnnualWkhrs(1000hrs.) 1.087*** -0.180 1.056*** -0.148 Univ -0.003 0.003 -0.005 0.004** Old -0.002 0.002 -0.005 -0.000 Female -0.001 -0.002 -0.003 -0.003 Eng.Prof.Share 0.020* 0.011* 0.022** 0.011* Avtenure -0.019 0.001 -0.015 0.002 Novice share -0.004 0.001 -0.007 0.000 Union 0.289* -0.198** 0.326* -0.145 R&D 0.121 0.117 0.089 0.130 Expo 0.270 0.057 0.298 0.065 multi-plant 0.249 0.054 0.147 0.042 NewCap. 0.290 0.026 0.251 0.020 NewLine -0.226 -0.101 -0.131 -0.104 Sep.Rate 1.303 0.648 0.316 0.503 HschoolQuality -0.648*** 0.038 -0.518** 0.040 HschoolQChange -0.165** 0.030 -0.133 0.026 Self Learning -0.624* -0.014 -0.434 0.074 Years since Est 0.004* -0.001 0.004 -0.000 Lagged Subsidy 0.181 0.059 0.249 0.046 Yeardum 0.225 .004 0.262 -0.001 Jobapp 0.302 -0.568* 0.510 -0.539 Constant -3.941*** 2.934*** -5.409*** 2.681***

Observations 363 515 363 515 R-squared 0.495 0.206 0.481 0.202 F-Statistic 20.955 2.255 17.889 2.344 P-Value F 0.000*** 0.000*** 0.000*** 0.000***

1 All the regressions in this table include regional and industry dummies. Standard errors are ‘robust’ in the sense that they are computed without stationarity of the residuals. The asterisks, (* ,**,***), indicate that each coefficient is significant at (respectively) 10, 5, and 1% level. See Table 1 and Appendix for the definition of each variable in the table.

35

Table 4 Training Incidence without HRMP/ Training Policy, SUR for Training and HRMP1 TOLS-5 TOLS-6 TOLS-7- TOLS-8 TSUR

Dependent Variable Log(offjt int) Ojt prod qchrt HRMP Log(offjt int) Ojt prod qchrt HRMPLog(Capital) -.028 .000 .057 -.027 -.044 .002 -2.243*** -.017Log(Man-hour) .574*** -.121 2.219** .272*** .633*** -.103** 2.638** .323***Annual Wkhrs(1000hrs.) 1.067*** -.187 -1.051 .023 1.006** -.106 -6.448 .003Univ -.006 .005** -.143* -.006 -.005 .003 -.243*** -.005Old -.005 .002 -.087 -.007* -.006 .003 -.196*** -.008Female -.005 -.002 -.064 -.007** -.004 -.000 -.068 -.009**Eng.Prof.Share .023*** .010* -.002 .007 .020*** .006 .083 -.013Av.tenure -.014 .002 -.173 .000 -.010 -.000 -.040 .011Novice share -.005 -.000 .096** -.003 -.007 .001 .208*** -.004Union .315* -.176** 1.994 .685*** .301* -.021 6.803*** .682***R&D .127 .125* -2.509 .294** .092 .085 -2.895 .280**Expo .269 .041 4.245** -.010 .217 .007 4.790** .011multi-plant .112 .034 1.872 .097 .149 -.082 -1.012 -.280NewCap. .341* .002 -1.306 .257* .319* .054 -.682 .246*NewLine -.168 -.102 2.000 .069 -.168 -.081 3.411 .159Sep.Rate .143 .519 -4.410 -5.944*** -.384 -1.567 28.95 -5.771*HschoolQuality -.516*** .016 -1.953 -.228** -.574*** .007 -1.865 -.414*HschoolQChange -.161** .026 -1.740* -.059 -.172** -.011 -1.439 -.065Self Learning -.298 .052 2.799 .822*** -.338 .181 2.852 .590**Years since Est .004 -.000 0.047* .001 .004* -.000 .064* -.002Lagged Subsidy .252 .063 -0.076 -.001 .251 .046 -.033 -.065Yeardum .241 .014 1.122 .048 .295** .053 1.682 -.026Jobapp .161 -.444 7.502 -.777 .697 -.111 9.037 -1240*Constant -4.597*** 2.491*** -7.134 -.237 -4.886*** 1.449** 11.847 .821

Observations 372 527 546 558 355 355 355 355R-squared .459 .187 .172 .407 .458 .202 .281 .421

1 See footnotes for Table 3

36

Table 4 annex: Correlation matrix of residuals for SUR

Log(offjt_int) OJT_prod qchrt HRMP Log(offjt_int) 1 OJT_prod .054 1 qchrt .068 -.100 1 HRMP .059 -.101 .075 1

Breusch-Pagan test of independence using Chisq.(6) gives p-value=.076 for the null that errors are independent

37

Table 5 Multinomial Logit and Ordered Probit Models for HRMP1

Multinomial-logit O-probit HRMP(default =1) 2 3 4 5/6* - Log(Capital) .054 .100 -.092 .164 -.017 Log(Man-hour) .235 .420** .662** .918*** .248** Annual Wkhrs(1000hrs.) .717 .525 .942 .116 -.082 Univ -.006 .009 -.064*** -.008 -.005 Old -.013 -.033*** -.037** -.034** -.012*** Female -.004 -.009 -.060*** -.007 -.005** Eng.Prof.Share -.015 -.021 .072*** -.016 .004 Av.tenure -.032 .021 -.065 .017 .005 Novice share -.004 -.003 -.026 -.033** -.006** Union .464 .582* 3.059*** 1.701*** .632*** R&D .138 .085 .592 .755* .273** Expo -.063 .069 .483 -.102 .019 multi-plant -.336 19.53*** 18.95*** -1.053 .213 NewCap. .003 -.278 .147 .557 .166 NewLine 1.118* 1.615*** .973 1.393** .177 Sep.Rate -12.13*** -10.87** -7.418 -36.69*** -6.562*** HschoolQuality .288 .568** -.072 -.390 -.186*** HschoolQChange -.136 .239 -.151 -.464** -.080 Self Learning -.050 1.531** -35.87*** 2.532*** .733*** Years since Est -.000 -.005 .003 -.001 .002 Lagged Subsidy .641** .462 .055 .223 .101 Yeardum .101 .387 .020 .424 .158* Jobapp -1.087 -0.470 -1.337 1.612 .341

Constant -2.766 -27.21 -26.08 -10.26*** -- Observations 558 608 R-squared .22 .119

1 See footnotes in Table 3. O-probit is ordered probit model. * Either HRMP=5, or 6.

38

Table 5 Multinomial Logit and Ordered Probit Models for HRMP1

Multinomial-logit O-probit HRMP(default =1) 2 3 4 5/6* - Log(Capital) .054 .100 -.092 .164 -.017 Log(Man-hour) .235 .420** .662** .918*** .248** Annual Wkhrs(1000hrs.) .717 .525 .942 .116 -.082 Univ -.006 .009 -.064*** -.008 -.005 Old -.013 -.033*** -.037** -.034** -.012*** Female -.004 -.009 -.060*** -.007 -.005** Eng.Prof.Share -.015 -.021 .072*** -.016 .004 Av.tenure -.032 .021 -.065 .017 .005 Novice share -.004 -.003 -.026 -.033** -.006** Union .464 .582* 3.059*** 1.701*** .632*** R&D .138 .085 .592 .755* .273** Expo -.063 .069 .483 -.102 .019 multi-plant -.336 19.53*** 18.95*** -1.053 .213 NewCap. .003 -.278 .147 .557 .166 NewLine 1.118* 1.615*** .973 1.393** .177 Sep.Rate -12.13*** -10.87** -7.418 -36.69*** -6.562*** HschoolQuality .288 .568** -.072 -.390 -.186*** HschoolQChange -.136 .239 -.151 -.464** -.080 Self Learning -.050 1.531** -35.87*** 2.532*** .733*** Years since Est -.000 -.005 .003 -.001 .002 Lagged Subsidy .641** .462 .055 .223 .101 Yeardum .101 .387 .020 .424 .158* Jobapp -1.087 -0.470 -1.337 1.612 .341

Constant -2.766 -27.21 -26.08 -10.26*** -- Observations 558 608 R-squared .22 .119

1 See footnotes in Table 3. O-probit is ordered probit model. * Either HRMP=5, or 6.

39

Table 6 Production Function (OLS)1

POLS-1 POLS-2 POLS-3 POLS-4 POLS-5 POLS-6 POLS-7 POLS-8HRMP.Train var. -- HRMP Trview Traineffect -- HRMP Trview Traineffect# of Dummies -- 5 4 3 -- 5 4 3Log(Offjt int) 0.060* 0.060* 0.063* 0.052 0.064* 0.063* 0.063* 0.053qchrt 0.005* 0.004* 0.005** 0.005* 0.005* 0.004* 0.006** 0.005*ojt prod -- -- -- -- -0.089 -0.090 -0.079 -0.089

HRMP Training Policy DummiesDummy=1 -- Default Default 0.055 -- Default Default 0.027Dummy=2 -- 0.139 -0.090 -0.109 -- 0.144 -0.103 -0.110Dummy=3 -- 0.189 0.072 -0.004 -- 0.180 0.026 -0.075Dummy=4 -- 0.172 -0.496** Default -- 0.168 -0.505** DefaultDummy=5 -- 0.206 -0.200 -- -- 0.179 -0.224 --Dummy=6 -- -.014 --- -- -- 0.004 -- --Log(Capital) 0.305*** 0.301*** 0.299*** 0.298*** 0.309*** 0.306*** 0.306*** 0.303***Log(Man-hour) 0.555*** 0.554*** 0.578*** 0.573*** 0.556*** 0.555*** 0.575*** 0.572***Univ -0.002 -0.003 -0.000 -0.003 -0.002 -0.002 0.000 -0.003Old -0.003 -0.003 -0.003 -0.004 -0.002 -0.000 -0.000 -0.003Female -0.007** -0.007*** -0.008** -0.007*** -0.008*** -0.007** -0.008*** -0.008**Eng.Prof.Share 0.006 0.006 0.006 0.007 0.005 0.006 0.005 0.006Av.tenure 0.006 0.006 0.007 0.006 0.002 0.003 0.002 0.003Union .186 0.154 0.155 0.204* 0.201* 0.173 0.172 0.264**R&D 0.194* 0.198* 0.178 0.236** 0.225** 0.228* 0.211* 0.231**expo -0.155 -.098 -0.220** -0.179* -0.143 -0.140 -0.208* -0.170multi-plant 0.134 0.098 0.107 0.142 0.100 0.068 0.088 0.108NewCap .263*** 0.273*** 0.303*** 0.258** 0.273*** 0.281*** 0.309*** 0.272***yeardum 0.133 -.010 -0.109 -0.092 -0.088 -0.087 -0.100 -0.081jobapp -0.422 -0.385 -0.579 -0.403 -0.530 -0.506 -0.707 -0.506Constant -0.634 -0.635 -0.823 -0.698 -0.51 -0.635 -0.666 -0.560

Observations 408 408 408 406 399 399 399 397R-squared 0.648 0.65 0.657 0.652 0.649 0.66 0.658 0.653test for CRS: 4.480 4.314 3.299 3.347 3.917 3.949 3.093 3.109Prob > F for CRS 0.035** 0.039** 0.07* 0.068* 0.049** 0.048** 0.08* 0.079*

1 See footnote in Table 3.

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Table 7 Production Function IV Estimates1 Equation # PIV-1 PIV-2 PIV-3 PIV-4 PIV-5

Instruments Base Base Base Base+x Base+x+y

Log(offjt_int) .304* -- .349 .175 -.081

Qchrt -- .012 -.008 .018 .048*

Lnkd .277*** .336*** .257*** .311*** .375***

Lnflh .443*** .632*** .435*** .484*** .577***

Univ -.003 .002 -.005 -.002 .002

Old -.004 -.004 -.003 -.001 .002

Female -.003 -.003 -.005 -.005 -.005

Eng.Prof.Share .007 .012** .006 .008 .010

Av.tenure .010 .015 .005 .009 .013

Union .151 .114 .213 .071 -.086

R&D .162 .114 .114 .205 .321*

Expo -.124 .004 -.134 -.228 -.335*

Multi .100 -.037 -.063 .040 .183

yeardum -.134 -.060 -.144 -.135 -.115

Jobapp -.554 -.549 -.682 -.688 -.674

Newcap .227** .331*** .224* .260** .318**

Constant -.146 -1.894*** .650 -.509 -2.157

Observations 380 557 370 370 370

Hansen’s J stat. 2.22 2.093 1.295 1.935 3.675

p-value for J .528 .553 .523 .586 .452

C stat. for x and y -- -- -- .555 2.422

p-value for C stat. -- -- -- .456 .12

Hausman’s F 2.87 0.35 1.62

p-value for F stat. .091* .557 .200 Partial F for 1st stage (p-value in

parenthesis)

4.71 (.000)

2.23(.065) Log(offjt_int): 5.39-6.13 (.000) Qchrt: 1.68-1.94 (.089-.149)

1 Instrumental variables in base case are: lag_subsidy, years_since_established, HighSchoolQuality, and HighSchoolQchange. Base+x adds selfLearning, and base+x+y adds also wrkhr on base case variables.

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Table 8 Wage Function (OLS)1 Dependent Variable: Log of average annual earnings for production workers

WOLS-1 WOLS-2 WOLS-3 WOLS-4 WOLS-5 WOLS-6 WOLS-7 WOLS-8HRMP.Train var. -- HRMP Trview Traineffect -- HRMP Trview Traineffect# of Dummies -- 5 4 3 -- 5 4 3Log(offjt int) .023*** .021*** .021*** .024*** .022*** .021*** .021*** .024***Qchrt -.000 -.000 -.000 .000 -.000 -.000 -.000 .000ojt prod -.003 -.001 -.002 -.004

HRMP Training Policy DummiesDummy=1 -- Default Default 0.037 -- Default Default 0.055Dummy=2 -- 0.022 -0.066** -0.057 -- 0.014 -0.065** 0.065Dummy=3 -- 0.69* -0.149*** 0.001 -- 0.066* -0.137*** 0.007Dummy=4 -- 0.063 -0.033 Default -- 0.069* -0.032 DefaultDummy=5 -- 0.183*** -0.133*** -- -- 0.194*** -0.122** --Dummy=6 -- 0.056 --- -- -- 0.039 -- --Log(Capital) .041*** .040*** .049*** .042*** .044*** .044*** .050*** .045***Log(Man-hour) -.008 -.015 -.015 -.010 -.019 -.028* -.023 -.022Univ .000 -.000 .000 .000 -.000 -.001 -.000 -.000Old -.001 -.001 -.000 -.001 -.001* -.001* -.001 -.001Female -.006*** -.006*** -.006*** -.006*** -.006*** -.006*** -.006*** -.006***Eng.Prof.Share -.000 .001 .000 -.000 .001 .002 .001 .001Avtenure prod .004** .003 .004* .004** .006** .004* .005** .006***Union .114*** .089*** .106*** .115*** .100*** .072** .096*** .096**R&D -.039* -.043** -.035 -.044* -.042** -.046** -.035 -.042*expo .035 .023 .037 .034 .033 .018 .033 .027multi-plant -.046 -.039 -.037 -.053 -.054 -.042 -.043 -.065NewCap .032 .031 .026 .032 .030 .028 .027 .033yeardum -.009 -.011 -.011 -.010 -.007 -.009 -.010 -.008jobapp .139 .170 .107 .126 .166 .203* .131 .155Constant 5.800*** 5.874*** 5.917*** 5.776*** 5.900*** 6.016*** 5.984*** 5.887***

Observations 440 440 440 438 432 432 432 430R-squared 0.632 0.651 0.653 0.637 0.636 0.658 0.653 0.642

1 See footnote in Table 3. †Avten_prod is the average tenure of production workers.

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