Firm productivity in Israeli industry 1979–1988

JOURNALOF

Econometrics

Journal

of Econometrics

65 (1995) 175-203

Firm productivity in Israeli industry 1979-1988 Zvi Griliches*-a,b, “Department

of Economics, bNBER, ‘Central

Harvard

Haim Regevc

University,

Cambridge,

MA

Bureau of Statistics,

Cambridge, 02138,

MA

02138.

USA

USA

Jerusalem,

Israel

Abstract An analysis of a large panel data set on Israeli industrial firms finds that most of the growth in aggregate productivity comes from productivity changes within firms rather than from entry, exit, or differential growth; that firms which will exit in the future have lower productivity performance several years earlier (the ‘shadow of death’ effect); and that, overall, there was little total factor productivity growth in Israeli industry during 1979-1988 (another ‘lost decade’). Key words: Production function; Productivity; c/assiJcation: C81; D24; L60; 047

Israeli industry; Panel &ta

JEL

1. Introduction

This paper is part of a larger study of firms in Israeli industry (mining and manufacturing). It uses almost all of the data assembled by the Central Bureau of Statistics (CBS) on individual industrial firms to create a consistent panel data

* Corresponding

author.

We are indebted to the US-Israel Binational Foundation, the Bradley Foundation, the Guggenheim Foundation, and the National Science Foundation for financial support of this work and to the Central Bureau of Statistics, the Maurice Falk Institute for Economic Research in Israel, and the NBER for help with data, computing, and facilities. We have benefited from the reseach assistance of Eli Berman, Judy Hellerstein, and Yuval Nachtom, and the advice of Arye Bregman, Melvyn Fuss, Reuben Gronau, and Moshe Sicron.

0304-4076/95/$07.00 0 SSDI 030440769401601

1995 Elsevier Science S.A. All rights reserved U

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set, allowing one to observe the growth in the output and productivity of these firms over time and their turnover, and to investigate their correlates. The present paper describes the evolution of this population of firms, seeks factors affecting their productivity growth, and calculates the impact of exit, entry, and differential growth on the aggregate productivity of Israel’s industrial sector. Additional analyses of these data, primarily for the earlier part of our time period, appear in Bregman, Fuss, and Regev (1991, 1992). The paper starts with a brief description of the overall trends during our period of analysis (1979-88). It continues with a description of our data set and definitions of the major variables. The substantive analysis follows, focusing first on the contribution of firm mobility to changes in aggregate industrial productivity, and then on an analysis of productivity differences across firms and time, using the production function framework. A computation of total factor productivity growth rates and some concluding comments close the paper.

2. The period of analysis During the period surveyed, 1979-88, the Israeli economy underwent a number of changes. The period began with a variety of ‘stop-go’ policies in repeated attempts to arrest inflation. It was characterized by rather slow growth (by Israeli standards) in both output and productivity. Inflation accelerated in 1982-85 until the stabilization program of 1985 brought it down dramatically and led to a strong post-stabilization recovery in some industrial sectors. The economy entered another slow growth phase in 1988, due largely to the world-wide slowdown in the growth of electronics and the decline in defense expenditures; it appears to have recovered somewhat in 1990-91. The overall picture of these years appears in Fig. 1, which shows total production, capital, labor, and an index of total factor productivity (TFP) for Israeli industry, drawn from aggregate data sources. The figure reveals that the slowdown in productivity growth rates that began in the mid-1970s (see BenPorath, 1986) continued throughout much of our period, with a short-lived revival in the mid-1980s and resurgence towards the end of the period. All told, both the 1970s and the 1980s were periods of slower growth in productivity, at least relative to the levels achieved by Israeli industry earlier. The 1990s appear to have started with a significant increase in industrial production (from 6.3 percent in 1990 and 6.7 percent in 1991, to 8.8 percent in 1992; figures are seasonally adjusted), partly due to the increased immigration of Russian Jews. As the detailed data available to us only go as far as 1988, we cannot bring our analysis entirely up to date at this moment, but we hope to analyze the more recent period in the near future.

qf Econometrics 65 (199.5) I75- 203

Z. Griliches, H. Regev / Journal

r

, ,8

, ,6

. .

....

.. .

.

.

.

.

. ..

177

.. . . .. ..

.

Labor

till 1979 ‘00

‘81

1

I

‘82

‘83

II

‘84

II

‘85

‘86

I

‘87

‘88

I

‘89

I

‘90

I4

‘91

‘92

Source: CBS “IndustrialIndexes’ and Bank of Israel

Fig. 1. Labor,

capital

stock, production,

and TFP,

1979-1992

(index: 1979 = 100).

3. The data base

The data used in this paper are based on the Industrial Surveys carried out regularly by the CBS since 1955. We organized these data into a time series panel, thus making it possible not only to look at the structure of firms in a given year, but also to observe their development over time. We augmented these data by merging them with information from other surveys and sources of variables needed for our production function and related analyses: fixed capital, R&D expenditures, quality of labor, detailed output and input price indices, and firm age and mortality. The Industrial Surveys provide information on the usual variables at the firm level: sales, expenditures, labor, inventory change, and investment. Additional information came from the monthly reports made by the same firms to the CBS and from occasional surveys on capital, labor skill, and R&D. The basic data from the Industrial Surveys were used to construct measures of gross output, intermediate inputs, and labor inputs. The monetary variables were converted to constant prices-i.e., to measures of output and intermediate input quantities -in two stages: First, all nominal monetary magnitudes were deflated by the CPI (CPI 1979 = 1.0) and converted to constant 1990 dollars using the appropriate exchange rate. In the second stage the data were deflated again, using detailed (three-digit) local sales and export and import price indices (relative to the CPI). The share of exports in a firm’s sales was used as its export weight in calculating the overall production price index, and the relevant output

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coefficients from the input-output tables were used as weights in the calculation of the intermediate inputs price index. Labor input is measured in person-year equivalents; the ratio of gross output in constant prices to person-years worked is our central measure of productivity. These data are analyzed at the firm level and summarized separately for each of the 22 distinct two-digit level industrial groupings. The basic data for the other variables used (capital, R&D, and labor quality) are much more sparse. Two Capital Surveys were conducted in Israel, one in 1968 and the other in 1982, covering only a subsample of our firms. Where available, these data were used as a benchmark to construct capital stock and capital services measures, based on the additional investment data (deflated by appropriate price indices) and the perpetual inventory method. For most smaller firms we did not have an appropriate benchmark, and capital services levels were imputed statistically, using information on investment in the sample and its relation to the estimated capital services and other variables in the subsamples with available benchmark data. The resulting estimates seem reasonable in the various cross-sections but may be too imprecise to be used in the analysis of first differences and longer-term growth rates. Capital services were defined as the sum of estimated depreciation and interest on the net stock of capital (at 5 percent), plus the cost of equipment and building rentals (see Regev, 1993, for more details). The CBS conducts regular surveys on R&D expenditures. In some years it covers all the firms in our sample and in some years -only a subsample. These data were used to calculate R&D capital stock and services, using the same procedure as for fixed capital services, assuming a depreciation rate of l/7. Data on the occupational mix of the labor force in 1988 appear in a special Survey of the Structure of Labor Force in Industry (1989). This survey distinguishes between engineers, other academics, technicians, and other workers. Similar data were available for part of the sample for 1978 and 1982, based on information from the Ministry of Industry and Trade (Shaliv, 1989). In the rest of the sample, missing values were imputed using the 1982 and 1988 data and tabulations by size and subindustry. Since the data on other academics were not comparable between the surveys, we lumped them with ‘other’ workers and created an index of technical-scientific labor ‘quality’ (per worker) with different groups weighted by approximate relative wage weights (engineers = 2, technicians = 1.75, other workers = 1). The 1978 values of the index were used for the first year, 1979, and the average of 1982 and 1988 values for 1985. To avoid double counting, we subtracted the number of R&D engineers and technicians before computing this measure (and also from our total labor input measure). Their contribution is already represented in the R&D variable. For most of the large firms in the sample all the variables are available and the data are ‘cleaner’. To check on the influence of our imputations we constructed a subsample of larger firms, defined as firms that had 50 + person-years in at

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179

least one of the relevant four years. This subsample comprises about 40 percent of the firms in the overall sample and accounts for approximately 70 percent of the total industrial labor input.’ Averages of the main variables from the production account (and of some of the augmented variables) are presented in Table 1. These averages show that the performance of Israeli industry was relatively poor at the beginning of our period, but improved considerably towards the end of the period: the period began with a wide gap between the gross margin (value added minus labor costs) and the estimated cost of capital services (fixed capital and R&D); this gap was Table

I

Main variables,

A. Labor

Person-years (thousands)

B. Production

C. Capital 7. 8. D. Human 9.

1979-1990

(1990 dollars)

1979/80

1982183

1985186

1988

1990

288.3

217.8

299.4

286.7

277.5

input

1.

2. 3. 4. = 2-3 5. 6.=45

full sample,

account

(thousands

Production Intermediate Value added Labor cost Gross margin services (thousands

of 1990 dollars 61.19 43.71 17.48 15.61 1.81

of 1990 dollars

Fixed capital R&D

per person-year) 69.30 46.39 22.9 I 19.54 3.37

73.74 49.55 24.19 18.25 5.94

77.82 49.41 28.41 21.21 7.20

86.16 57.62 28.54 21.41 7.13

_

per person-year)

4.20 0.43

5.58 0.51

6.15 0.71

1.64 0.71

1.07

I .09

1.11

1.1 I

capital Labor

quality

index

Figures for 197991988 are based on the full panel; figures for 1990 are based on a new sample. Person-years are for the beginning of each period. Labor increase in 1985 is due in part to the adjustment of the sample; Capital services = Depreciation + 5% of net capital; R&D depreciation rate = l/7. Labor quality index = 1 + (engineers + 0.75 technicians)/total employees.

‘Several changes and recalculations have been made since the first draft of this paper (NBER working paper no. 4059) appeared. The original draft mentioned ‘materials’ inputs, referring to input materials and energy. This designation has been changed to ‘intermediate’ inputs, and expanded to include other input expenses, such as administrative expenses (legal and accounting), local taxes, etc. (see definitions), but does not include financing and rental expenses. These changes necessitated reestimation of the various coefficients, producing somewhat different results, largely improvements, which have been incorporated into the present paper.

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Z. Griliches, H. Regec 1Journal qf Econometrics 65 (1995) 17% 203

almost closed by 1988 despite an increase of nearly 80 percent in capital services per person-year.

4. Resource mobility and productivity growth The number of firms in our panel changes from year to year because of the exit and dissolution of firms, the entry of new firms (sampled from additions to the National Insurance register), and because of occasional sample adjustments due to undercoverage or large changes in the number of employees per firm. To analyze these data efficiently we focus on three subperiods: 1979-82, 1982285, and 1985-88, and construct for each subperiod a consistent data set divided into continuing firms (those present at both the beginning and the end of the period) and those that ‘closed’ (exited) and ‘opened’ (entered) by the end of the period (the latter grouping appears in the tables on the ‘replaced’ line under the beginning and end of the period, respectively). This definition causes a slight underestimate of total turnover, ignoring firms that entered or exited in midperiod. Except for the elimination of a small number of obvious outliers, we tried to retain all the firms for which the CBS collected economic data during this period. Table 2 lists the number of firms in our sample for each of the subperiods analyzed: 1979-82, 1982-85, and 1985-88, and an estimate of the total number of industrial firms in Israel. These numbers enable us to compute turnover rates per year, defined as the sum of absolute value of changes (entry pIus exit) over a period, divided by the number of firms or workers at the beginning of the period; they thus measure the amount of gross ‘churning’ in the number of firms and workplaces. On average, about l/8 (per year) of the firms in our panel are turnovers. Fig. 2 shows the firm turnover rates for each of our 22 two-digit level industrial groupings. There is quite a bit of variability in these numbers across industries, with higher firm turnover occurring in the textiles, wood, electronics, clothing, and precision and optical industries, and smallest in mining and quarrying. Fig. 3 presents similar numbers for job turnover rates, which are about half as large as the firm turnover rates. There is a strong positive correlation between firm and employment turnover rates. The major exception is the electronics industry, with above-average firm turnover but below-average employment turnover rates. The components of the employment turnover measure are shown in Appendix Table A.1.’ We begin our substantive analysis by looking at various aspects of labor productivity and its change over time. Table 2 groups firms by their ‘mobility’

* Similar analyses

can be found in Baldwin

(I 990), Dunne (1989). and Regev (1989).

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Table 2 Firms, person-years,

production

and value added,

Total Beginning

by period

and turnover Replaced

Continuing

Beginning

End

1,646 1,616 1,759

305 265 227

247 238 244

4,619 4,719 5,284

4,619 4,779 5,284

1,587 1,404 1,189

1,620 1,612 1,771

257,387 256,792 283,043

256,970 273,395 269,285

29,308 20,967 16,338

22,980 11,132 18,994

End

Beginning

1,951 1,881 1,986

1,893 1,854 2,003

1,646 1,616 1,759

6,206 6,183 6,473

6,239 6,391 7,055

279,950 291,127 288,279

End

A. Firms in panel 1979-1982 1982-1985 1985-1988 B. Firms

in population

1979-1982 1982-1985 1985-1988 C. Person-years 1979- 1982 1982-1985 198551988

286,695 271,759 299.38 1

D. Production 1979-1982 1982-1985 1985-1988

61.19 69.30 73.14

E. Value added 1979-1982 1982-1985 1985-1988 Beginning

per person-year

per person-year 17.48 22.91 24.19

(thousands

of 1990 dollars)

69.15 73.42 77.82

63.07 71.61 75.34

(thousands 22.83 24.34 28.41

71.24 74.88 79.05

44.61 41.53 46.62

46.03 50.83 60.35

23.59 25.12 29.06

11.50 11.79 12.33

14.32 12.53 19.18

of 1990 dollars) 18.16 23.84 24.89

= exited by the end of the period; end = entered

during

the period.

status: ‘continuing’ and ‘replaced’ (opened and closed). Panels D and E of the table give two measures of average productivity for each group: gross output and value added (both in constant prices) per year of labor input. These are aggregate estimates inflated by appropriate sampling weights. Table 3 shows the associated growth rates for the total industry aggregate and separately for the subset of ‘continuing’ and ‘replaced’ firms. (Note that these aggregate numbers do not yet control for within-group shifts.) Table 4 shows the same data for each of the 22 two-digit industrial sectors, while Fig. 4 compares the average productivity of exiting and of entering firms, relative to stayers. Having seen in Table 2 that the productivity of entering and exiting firms is quite different from the overall average, we ask how much of the growth in

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Mining and quarrying Machinery Basic chemic. and pharm. Transport equipment Basic metal Other chemicals Dairy, meat, fish products Average Rubber Textiles Metal products Plastic Printing and publishing Paper Electrical equipment Beverages, tobacco 8 0th. Mineral products Miscellaneous Leather Wood Electronic equipment Clothing Precision and optical equip.

Fig. 2. Firm turnover

rate, by industry,

1979-1988

(annual

rates),

Transport equipment Basic chemic. and pharm. Mining and quarrying Electronic equipment Other chemicals Dairy. meat, fish products Machinery Rubber Paper Textiles Metal products Beverages, tobacco, 0th. Basic metal Electrical equipment Printing and publishing Plastic Average I===== k :< Mineral prod&is Precision and optical equip. Leather Wood Clothing Miscellaneous

Fig. 3. Labor

1

I

I

I

,

I

0

2

4

6

6

10

turnover

rate, by industry,

1979-1988

(annual

rates)

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183

average productivity occurs within firms and how much is the result of the mobility of resources between them, both as the result of exit and entry and also as the consequence of differential growth of surviving firms. Such a decomposition of changes in aggregate productivity can be derived using a very simple accounting framework. At any point in time, the contribution of a particular firm to aggregate productivity (at any level of aggregation) is w,q,, where 4, is its own level of productivity (say, gross output per labor-year) and w, is its relative weight in the aggregate. For the measurement of labor productivity the relevant weight is the firm’s total labor input. In our case, because we are dealing with a sample of firms, the weight is further inflated using the appropriate sampling ratio. Three findings from Tables 2-4 and Fig. 4 are worth emphasizing: (a) The average productivity of exiting firms is significantly lower than that of the continuing ones. (b) Entering firms are somewhat more productive than exiting firms, but the difference is large only in the last period. (c) The aggregate growth rates of labor productivity are very similar for the total sample and for the continuing subset of firms, indicating that most of the changes in aggregate productivity come primarily from changes within firms rather than from different exit and entry rates and various weight shifts. Finding (a) is true for most of our industrial groupings separately; finding(b) is true for most (but not all) of the industries examined in Fig. 4.

Basic metal Electrical equipment Leather Mineral products Beverages, tobacco, 0th. Miscellanaous Machinery Wood Metal products Clothing Textiles Electronic equipment Printing and publishing Average Plastic Paper Other chamlcals Rubber Basic chemic. and pharm. Transport equipment Dairy, meat, fish products Precision and optical equip. Mining and quarrying 0 Fig. 4. Production

per person-year,

0.5

1 .o

1.5

constant prices (entrants/stayers

2.0

2.5

relative to exiters/stayers).

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184

175- 203

Table 3 Percent

growth

in labor productivity,

by period and turnover

rates, constant

Continuing

Total A. Gross

(annual

prices) Replaced

production

197991982 1982-1985 1985-1988

2.66 2.25 6.36

2.65 1.I1 6.15

- 0.01 8.63 15.28

5.30 1.75 12.91

5.21 1.34 12.60

1.74 6.10 28.78

B. Value added 197991982 1982-1985 1985- 1988

Table 4 Growth in labor productivity, per person-year)

by industry

197991988

annual

rates (percent

Full sample Total Mining and quarrying Dairy, meat, fish products Beverages, tobacco, others Textiles Clothing Leather Wood Paper Printing and publishing Rubber Plastic Basic chemicals and pharmaceuticals Other chemicals Mineral products Basic metal Metal products Machinery Electrical equipment Electronic equipment Transport equipment Precision and optical equipment Miscellaneous

3.76 8.21 - 0.20 0.15 5.41 2.02 3.74 3.27 1.84 - 3.40 1.51 0.04 5.30 6.84 1.97 3.73 2.99 7.96 3.17 IO.55 6.6 1 6.37 4.37

growth

in production

Large firms 1.13 9.58 - 0.37 0.98 6.05 3.12 3.10 3.41 1.55 - 3.37 1.89 - 0.23 5.27 7.3 1 2.36 5.47 3.66 9.9 1 3.87 10.86 6.66 6.5 1 5.05

Z. Griliches.

H. Regeu / Journal

The change in a firm’s contribution wrq, - wt- rqt- I = wWq

of Econometrics

65 (1995)

I75-

185

203

to the total can be decomposed

as follows:

+ @wM4

where x(a) = (x, + xt_ ,)/2 designates the period average for a variable and dx = x, - x,_ 1 its change. This decomposition is meaningful for continuing firms, allowing us to separate the contribution of within-firm productivity growth from the between-firm shifts in the relative weight of high- versus low-productivity firms. We cannot do the same, however, for firms that exit and enter the sample during the period. Instead, we treat them as one firm and make a direct comparison of the change in the average productivity between all entering and exiting firms, and also compute the change of their weight in the total. Fig. 5 shows the results of this decomposition, by industry, for the average growth rate in gross production per person-year for the whole 1979-88 period (it is constructed by averaging the separate calculated values for each of the three subperiods). The results using gross value added instead of production

Printing and publishing Dairy, meat, fish products Plastic Beverages, tobacco and 0th. Rubber Paper Clothing Mineral products

I I

I a

Mobility Within

! :

I

Electrical equipment

Miscellaneous Basic them. and pharm. Precision and optical equip. Other chemicals Mining and quarrying Electronic equipment

Fig. 5. Production

per person-year,

‘within’ and ‘mobility’

effect (percent

change

per year)

3 The numbers in Table 4 and Fig. 5 are not exactly comparable because the former uses value-added in overall (CPI) constant prices while the calculations summarized in Fig. 5 are based on individual firm-weighted three-digit level industrial price deflators.

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were similar, and are shown in Appendix Table A.2. As implied by the results shown in Table 2, most of the growth in labor productivity occurs within firms, with mobility, i.e., the sum of the replacement effect (differences in the productivity of entering versus exiting firms) and the weight shift (the movement of employment from low-productivity to higher-productivity firms) accounting for only a small fraction of the overall growth. Again, there are some significant deviations from the average results to be noted in this figure. For some industries, such as clothing, paper, beverages and tobacco, plastic, food, and printing and publishing, the within-effects on production are actually negative while mobility is positive. For food and printing and publishing, the within effect is sufficiently negative so as to generate a negative total. Whereas the mobility effect is positive on average, exceptions do occur (e.g., in food and basic metals). A more detailed look at these differences between industries is warranted and is part of our agenda for future research. So, too, is a study of the subsequent performance of the new entrants, to assess their long-run contribution to the growth of industrial productivity.

5. Productivity

differences across firms

In this section we use a production function framework to look at the dispersion of labor productivity across firms and over time and to search for some of the factors that may account for it. This framework is summarized by the following equation, which is assumed to hold for each of our firms: y = Xb + Zc + lm + u,

(1)

where y is the vector of T logarithms (in each year) of production per person-year, X is a matrix of the logarithms of the conventional input variables (intermediate inputs and capital services per person-year), and Z is a matrix of control variables (e.g., dummy variables for size, age, and location) and other variables of interest such as R&D and labor quality. b and c are the parameter vectors that we wish to estimate, while lm + u represents the unmeasured determinants of y - errors and ‘disturbances’. The equation is written in this form to emphasize the m component, which is assumed to be specific to a firm and unchanging over time. It varies across firms and can be thought of either as a random firm component of the overall disturbance, or as a set of individual firm intercepts. (I is just a T x 1 vector of ones, where Tis the number of years or observations available for each firm.) Major estimation issues arise in such models because of the possibility that m might be correlated with the values of X (and Z) and because the b’s and c’s _ the parameters of the production function - may differ across time, industries, and firms. We deal with these issues in several ways. First, by ignoring the

2. Griliches. H. Regev 1 Journal of Econometrics 65 (1995)

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187

possibility of correlated firm effects and simply fitting an overall pooled version of Eq. (1). Second, by looking at individual years or subperiods for changes in the major coefficients of interest. Third, by including sector and industry dummy variables to control for some of the underlying heterogeneity. Fourth, by estimating the equation in difference (growth rates) form: (Yt -

Y,-1)

=(X,

-

x,-db

+ (Z, -

zt-Ik

+ u’,

(2)

which eliminates the lm term and the associated biases from the estimating equation (a’ = u, - ut_ 1). Because of the unbalanced nature of our data, this fourth form results in a loss of observations; to minimize this loss, we compute such differences separately for each of the subperiods and pool the results. The final approach is to assume that the observed average factor shares, s, are good estimates of the individual level b’s, and use them in a TFP growth calculation: TFPG = dy - (dX)s = (dZ)c + e,

(3)

where dy and dX is shorthand for the respective differences defined in (2) and the focus here is both on computing TFPG itself and on estimating c, controlling now both for the biases due to the possible presence of the individual firm effects (m) and the potential simultaneity of X (its possible correlation with both m and u). Such ‘control’ does not come without cost, however. By using growth rates we ignore the information available in the cross-sectional dimension and may magnify the role of measurement errors in what remains. Also, in assuming that b = s we use the competitive assumption that the relevant prices are equal to their respective marginal products, which could be the very same hypothesis which one wishes to test (see Griliches, 1986, for a more detailed discussion of some of these issues). With this background in mind we look first at Table 5, which presents the results of estimating a pooled (over time and firms) production function, focusing primarily on some of the additional variables (besides the conventional measures of capital, labor, and intermediate inputs) which might help to explain the differences in observed productivity. We also present results for the subsample of the large firms, mainly because much of the capital data for small firms are imputed and it is not entirely clear how much one can really gain from ‘fabricating’ so much data (especially given the relatively low fits of the estimating equations: R2 of about 0.45 or less) and because there is also a question whether these data are really missing at random - a prerequisite for consistency of such imputation methods (see Griliches, 1986, for additional discussion of this range of issues). The first column of Table 5 lists our most inclusive cross-sectional estimates, based on pooling all periods (1979 to 1988) and approximately 7,700 observations in the full sample and 3,800 in the larger-firms subsample. Table 6 gives similar results for each of the four cross-sections separately. To check the

188

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65 (1995)

175- 203

Table 5 Pooled

regressions

- dependent

variable:

production

per person-year

Unweighted

Observations R2

Root MSE

Weighted

Full sample

Larger

7,142 0.863 0.300

3,828 0.865 0.279

firms

Full sample 1,742 0.869 0.266

(2) t.

(3) Coef.

1.445 0.688 0.058

62.4 156.5 10.9

I.341 0.728 0.049

45.0 III.9 7.0

1.393 0.68 1 0.062

66.2 147.7 12.0

0.026

- 0.019

4.7 - 1.4

0.03 1 0.010

4.9 - 0.7

0.040 0.013

10.0 I.4

0.406

6.7

0.497

6.3

0.740

14.0

Scale (ref. = 50-99 worker-years) 5-49 employees 0.003 100-299 employees - 0.014 300+ employees - 0.012

0.4 - 1.2 - 0.7

- 0.021 - 0.007 - 0.005

- 1.6 - 0.7 - 0.3

0.010 0.02 I 0.033

0.9 I.8 2.7

Sector (ref. = private) Reg. stock market Histadrut Kibbutz Public sector

- 0.035 0.029 0.042 0.061

- 2.0 2.1 3.2 2.1

- 0.035 0.013 0.073 0.036

- 2.1 1.9 1.0 1.3

- 0.068 0.005 0.047 0.018

- 5.9 0.5 3.1 1.4

-

~ -

2.3 2.7 3.4 1.7 0.9 2.7

-0.111 - 0.066 - 0.073 - 0.050 - 0.006 - 0.000

-

-

-

0.3 - 3.2 - 0.4

0.068 - 0.030 0.000

3.3 - 2.6 0.1

- o.ooo

- 0.0

- 0.025 - 0.043

- 2.9 - 4.6

-

~ -

-

-

0.076 0.034 0.074 0.073 0.036 0.043

~ 3.6 _ I.9 - 4.3 _ 4.3 1.6 1.6

0.01 I - 0.019 0.058

1.2 - 2.2 5.8

(1) Coef. Intercept Intermediate inputs Capital services R&D variables R&D capital No R&D Quality

services

of labor

Branch (ref. = electronics) Food Textiles Printing, paper Wood, minerals Chemicals, plastic Metal, machinery Life cycle Estab. Estab. Estab.

(ref. = established 1963-76 1950-62 before 1950

Mobility Closed Closed Closed Opened Opened Opened

(ref. = stayers) 1979982 1983385 1986688 1979982 1983-85 1986 +

Year dummies Year 1982 Year 1985 Year 1988

0.035 0.037 0.05 I 0.026 0.012 0.035

after 1976) 0.004 - 0.03 I - 0.003 -

0.100 0.058 0.092 0.063 0.028 0.098

4.9 3.7 6.4 3.8 1.4 4.0

-

0.112 0.104 0.097 0.125 0.150 0.237

(4) r

5.8 3.6 3.5 2.3 0.4 0.1

3.1 3.7 4.2 4.9 1.2 3.4

(5) Coef.

0.083 0.053 0.064 0.017 0.012 0.027

(6) 1

6.1 4.1 4.3 1.1 I.0 2.4

(ref. = 1979) - 0.017 - 0.070 - 0.05 1

- 1.7 - 6.6 - 4.5

- 0.009 - 0.06 1 - 0.026

- 0.7 - 4.5 - 1.8

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robustness of the results, several alternative estimates are presented based on the cleaner subsample of larger firms and also using the sampling weight and size measure to weight each of the observations in the regression. The latter procedure is intended to approximate more closely what one might get for weighted ‘average’ coefficients at the aggregate level if individual firm coefficients do differ across firms and firms are weighted by their contribution to total production. One can group 1. 2. 3. 4. 5. 6. 7. 8.

the estimated

coefficients

into eight groups:*

Intermediate inputs and fixed capital services R&D capital and labor quality (as a proxy for human Size (scale) Sector and type of ownership Industry groupings (branch) Establishment year (life cycle) Mobility status Year dummies.

capital)

Looking first at intermediate inputs- this is the most important variable in accounting for differences in gross labor productivity, both in terms of the size of its coefficient and its statistical significance. Focusing primarily on the estimates from the weighted regression in Table 5 we find that the coefficient of about 0.68 is higher than the share of intermediate inputs in industry as a whole, which had drifted down from 71 to about 63 percent over the period surveyed (calculated from Table 1). This discrepancy could be due to the simultaneity bias in the estimation of this coefficient, a topic to which we return below. Perhaps not coincidentally, the estimated coefficient of capital services, 0.062 (for the weighted full sample; Table 5) is smaller than the implied share of capital in Israeli industry, which rose from about 3 to 9 percent of gross output during the period surveyed (computed from Table 1 by dividing the gross margin by the respective production figures) and close to the 0.055 estimated by Bregman et al. (1991, Table 1, column 2) for their much smaller and ‘cleaner’ 1982 cross-section. Both estimates might be subject to serious downward bias due to unavoidable errors in the construction of the capital variable. On the other hand, the residually estimated capital share probably overestimates the true output elasticity of physical capital. It also contains some returns to R&D and to inventory and working capital, neither of which are included in our definition of capital services. In fact, the sum of the two estimated coefficients, capital services and R&D, comes close to the residual capital share in the latter half of our period.

4A ninth set of location

dummy variables, for Jerusalem and two development areas, was also included. Because they were never very significant, either statistically or economically, they are not shown separately in the tables.

regressions,

- 0.018 0.03 1 0.052 0.063

Sector (ref. = private) Reg. stock market Histadrut Kibbutz Public sector

0.028

0.002 - 0.027

0.019 - 0.042 - 0.064

of labor

services

1.403 0.679 0.051

- 0.5 1.3 2.1 1.3

1.1 - 2.0 - 2.3

0.2

0.2 - 1.2

34.9 89.9 5.1

- 0.03 1 0.047 0.05 1 0.025

0.049 0.022 0.000

0.059

0.031 0.000

1.415 0.65 1 0.082

- 0.9 1.7 1.8 0.4

2.4 0.9 0.0

0.4

2.6 0.0

32.3 74.0 7.6

- 0.055 - 0.003 0.078 - 0.024

0.003 - 0.036 - 0.000

0.292

0.035 - 0.021

1.349 0.716 0.065

- 1.4 - 0.1 2.6 - 0.4

0.2 - 1.4 - 0.0

2.3

2.8 - 0.7

28.3 73.1 5.7

- 0.063 0.034 - 0.008 0.228

- 0.044 - 0.006 0.028

0.659

0.024 - 0.045

1.488 0.703 0.042

(7) Coef.

(6) r.

(5) Coef.

(3) Coef.

(1) Coef. (4) r.

1,993 0.867 0.287

1,937 0.844 0.366

1,872 0.871 0.295

1,940 0.894 0.256 (2) r.

1988

per person-year 1985

production 1982

variable:

1979

full sample - dependent

Scale (ref. = 50-99 worker-years) 5-49 employees 100-299 employees 300+ employees

Quality

R&D variables R&D capital No R&D

Intercept Intermediate inputs Capital services

Observations R2 Root MSE

Cross-section

Table 6

- 2.0 1.3 - 0.3 3.9

- 2.3 - 0.3 0.9

6.8

2.5 - 1.8

35.8 81.5 4.1

(8) r.

(ref. = electronics)

Closed 1986-88 Opened 1979-82 Opened 1983-85

Mobility (ref. = stayers) Closed 1979-82 Closed 1983-85

Life cycle (ref. = established Estab. 1963-76 Estab. 1950-62 Estab. before 1950

Food Textiles Printing, paper Wood, minerals Chemicals, plastic Metals, machinery

Branch

- 0.062

- 0.092 - 0.042

after 1976) - 0.076 - 0.011 0.016

0.023 - 0.060 0.042 - 0.009 0.054 0.02 1

- 2.7

- 4.6 - 2.0

- 2.3 - 0.7 0.9

0.9 - 2.5 1.6 - 0.3 2.3 0.9

- 0.068 - 0.020

- 0.078

- 0.038 - 0.007 0.023

0.079 0.043 0.025 0.035 0.060 0.090

- 2.8 - 0.6

- 3.5

- 1.2 - 0.4 1.1

2.6 1.5 0.8 1.2 2.1 3.4

-

- 0.145 - 0.065 - 0.007

0.003 - 0.050 - 0.024

0.108 0.009 0.101 0.034 0.012 0.050

3.1 0.3 3.0 1.0 0.4 1.7

- 5.2 - 2.0 - 0.2

0.1 - 2.2 - 1.0

-

- 0.077 0.014 0.063

0.036 - 0.068 - 0.038

- 0.141 - 0.121 - 0.176 - 0.100 - 0.148 -0.011

5.0 4.6 6.3 3.3 5.6 0.5

- 2.6 0.5 2.0

1.5 - 3.5 - 1.8

-

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The possibility remains, however, that the smaller estimated elasticity of capital represents truly lower returns to fixed capital. The R&D variable used is the logarithm of deflated R&D capital services per unit of labor, which was calculated in a similar way to fixed capital services. Its coefficient (0.04) is both statistically and economically significant. It is about half the size of the physical capital elasticity and implies that the rate of return to R&D, as of 1988, was more than five times higher than the rate of return to investment in physical capital! A dummy variable is added for firms reporting no formal R&D at all. We expected it to be positive, picking up some of the equivalent informal activity in smaller firms, but it comes out negative, often significantly so, underscoring the importance of the R&D variable. We intend to pursue a more detailed analysis of the role of R&D in Israeli industry in a later paper. Among the other variables, the role of quality of labor is most important, both substantively and statistically. The estimated coefficient of 0.40 in the unweighted version and 0.74 in the weighted one is higher than one might expect for a pure ‘quality’-of-labor index, which should have a coefficient on the order of the coefficient of labor quantity (the latter is estimated implicitly at 0.30). The higher estimate for the labor quality index may reflect the omission of other important variables, such as quality-of-capital measures and the fact that the labor quality index is not fully inclusive. The actual estimates are somewhat unstable over time (see the separate annual estimates in Table 6) becoming larger and more sigificant towards the end of the period. This may reflect both the better quality of our estimates of this variable in 1988, and possibly the rising importance of skills and education in the economy over time, a trend that became apparent in the United States and other countries in the 1980s. What is interesting about this finding is that it uses productivity data directly to validate the evidence on returns to education, rather than deriving it, as is usually done, from income data and earnings function estimation. The findings discussed above are rather insensitive to sample composition or its weighting, though some of the coefficients, especially for R&D and quality-of-labor variables (and also some of the time dummies) rise and become more significant when the observations are weighted. This happens because in the larger firms these variables are more important and get more weight in the total. Direct measures of firm size turn out not to be particularly important. We imposed a constant returns to scale formulation (partly in order to preserve the confidentiality of the data) after learning from experimentation that it held approximately in our data (Bregman, Fuss, and Regev, 1992, estimate the elasticity of scale in their data at 0.96). We include among the variables four (employment) size-class dummy variables whose coefficients are neither very stable nor economically significant, though they do indicate some increasing returns to scale when the regressions are weighted to give larger firms a larger weight in the final results.

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The next finding of interest is what one might call the ‘shadow of death’ effect. ‘Doomed’ firms (those that will exit in the future) are significantly less productive in the present. Perhaps not surprisingly, the dummy variables for ‘closed’ are significantly negative, implying lower productivity on the order of 6 to 13 percent. This is consistent with the Olley and Pakes (1992) model, and implies that low productivity will be a major cause of exit when we turn to an explicit analysis of its determinants. Firms that entered (‘opened’) early in the period of analysis are also somewhat less productive, though that may be the result of our overestimating their beginning capital stock (for lack of good benchmarks). But it is also consistent with the higher mortality rate observed for these firms in the first years of their existence. The age effect is less clear and is confounded with both vintage and selection effects. Over time, the productivity of older surviving firms (those established before 1962) falls relative to the levels of firms established later (see Table 6). In subsequent work we plan to extend our panel backwards in time, through the 1970s and 1960s and investigate the life cycle of these firms in greater detail. Among the other variables, the notable findings are: higher-than-average productivity in the electronics, chemicals, minerals, and machinery industrial groupings, and lower-than-average productivity in the textile and apparel, and food groupings. Among the organizational classifications, kibbutz enterprises show a modest but consistent productivity advantage-possibly due to undercounting of the total resources devoted to production in these enterpriseswhile firms registered on the stock market show a disadvantage (perhaps because of some principal-agent conflict and the resulting over-investment). In Table 7 we show the results of using firm differences over time. These are based on the ‘continuing’ firms in each of the three subperiods. That is, we have three sets of three-year growth rates, each for a slightly different population of firms, which we pool together in estimating one overall growth rate regression (adding period intercepts). Looking at such differences we can control for various unmeasured or badly measured permanent aspects of these firms. We also reduce, to some extent, the simultaneity problem by removing a large component from the error term. But we do increase the possible influence of measurement errors, since they may now represent a larger fraction of the remaining variance in our variables. Also, by looking at differences across time, we eliminate the direct influence of all the unchanging firm-descriptive variables. Now, when such variables are included, it is equivalent to adding a time interaction, asking, for example, if the residual rate of growth (the time coefficient in the production function) was different for kibbutz firms. Perhaps surprisingly, the growth regressions tell a rather similar story with a slightly lower capital services coefficient and a similar R&D capital services coefficient. There is also some indication that large firms experience somewhat higher productivity growth rates, that the kibbutzim fell behind in this period,

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Table I Pooled

growth

rate regressions

~ dependent

variable:

growth

in production

Unweighted

Observations R2 Root MSE

Weighted

Full sample

Larger

5,018 0.652 0.095

2.68 1 0.657 0.089

(1) Coef. Intercept Intermediate inputs Capital services

per person-year

(2) t.

firms

(3) Coef.

Full sample 5,018 0.703 0.080 (4) 1.

(5) Coef.

(6) r.

0.015 0.660 0.048

2.3 82.8 4.2

0.017 0.670 0.052

2.0 60.9 3.5

0.035 - 0.010

2.4 ~ 2.4

0.029 - 0.009

2.0 - 2.0

0.028 - 0.016

3.3 - 5.6

0.138

0.9

- 0.001

- 0.0

0.658

6.5

0.005 0.010 0.016

1.3 2.5 2.7

0.008 0.013 0.020

1.6 2.4 2.9

0.003 0.001 0.009

0.8 0.4 2.3

Sector (ref. = private) Reg. stock market Histadrut Kibbutz Public sector

- 0.010 - 0.001 - 0.005 0.017

- 1.4 - 0.2 - 0.0 1.6

0.010 - 0.002 - 0.013 0.015

- 1.4 - 0.4 - 2.0 1.5

0.003 0.004 - 0.012 0.017

0.8 1.1 - 2.5 4.3

Branch (ref. = electronics) Food Textiles Printing, paper Wood, minerals Chemicals, plastic Metal, machinery

-

-

-

-

-

-

R&D variables R&D capital No R&D Quality

services

of labor

Scale (ref. = 50-99 worker-years) 5-49 employees 100-299 employees 300 + employees

Life cycle Estab. Estab. Estab.

(ref. = established 1963376 1950-62 before 1950

0.019 0.004 0.026 0.018 0.016 0.001

3.4 0.8 4.6 2.9 3.0 0.1

0.254 0.012 0.032 0.024 0.019 0.000

3.8 1.8 4.2 3.0 2.9 0.0

0.03 1 0.736 0.015

0.019 0.014 0.031 0.013 0.018 0.005

5.9 95.2 1.4

4.9 2.9 6.1 2.4 4.7 1.3

after 1976) 0.02 1 - 0.005 - 0.003

4.1 - 1.4 - 0.9

0.028 - 0.007 - 0.005

3.3 - 1.8 - 1.2

0.028 - 0.005 ~ 0.014

5.1 - 1.9 ~ 4.8

- 0.001 - 0.016

~ 0.1 - 3.5

0.010 - 0.028

1.2 - 3.5

- 0.011 - 0.009

- 1.8 - 1.5

Period dummies (ref. = 1979-82) Period 1982- 1985 - 0.013 Period 198551988 0.008

- 3.7 2.3

- 0.015 0.01 I

- 3.6 2.4

- 0.020 0.018

- 7.1 6.6

Mobility (ref. = stayers) Opened Closed

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Table 8 Growth rate production person-year

function

regression

1979-88

- dependent

variable:

Unweighted

Observations R2

Root MSE

in production

per

Weighted

Full sample

Larger

1,304 0.678 0.036

779 0.722 0.033

(1) Coef.

growth

firms

(3) Coef.

(2) r.

Full sample 1,304 0.791 0.027 (4) r.

(5) Coef.

(6) t.

3.8 38.7 0.1

0.034 0.749 0.007

8.8 53.4 0.4

1.9 - 2.4

0.026 - 0.018

2.7 - 9.4

0.127

0.5

0.286

2.3

- 0.4 - 1.3 1.3

0.003 0.002 0.013

0.8 0.7 2.7

- 0.003 0.003

-

- 0.003 0.004 - 0.005 0.015

- 0.8 1.1 - 1.3 2.0

- 0.004

- 1.0

0.008 0.008 - 0.006 0.02 1

3.4 3.3 - 1.9 7.6

-

-

Intercept Intermediate inputs Capital services

0.016 0.672 0.033

3.3 45.6 1.9

0.030 - 0.010

1.9 - 3.4

0.323

1.8

0.001 0.003 0.014

Sector (ref. = private) Reg. stock market Histadrut Kibbutz Public sector Branch (ref. = electronics) Food Textiles Printing, paper Wood, minerals Chemicals, plastic Metal. machinery

R&D variables R&D capital No R&D Quality

services

of labor

Scale (ref. = 50-99 worker-years) 5549 employees 100-299 employees 300+ employees

Life cycle (ref. = established Estab. 1963-76 Estab. 1950-62 Estab. before 1950

0.018 0.003 0.026 0.014 0.014 0.001

from 1963-76) 0.004 - 0.006 - 0.006

4.5 0.9 6.0 3.1 3.6 0.2

0.7 - 2.3 - 2.7

0.023 0.715 0.002

0.03 1 - 0.007

0.004 - 0.007 0.013

- 1.7 2.0

-

-

0.024 0.009 0.030 0.017 0.014 0.000

- 0.012 - 0.009 - 0.009

0.001

1.0

5.2 1.9 5.7 3.2 3.2 0.1

- 1.1 - 3.3 - 2.9

-

0.017 0.011 0.028 0.009 0.013 0.003

0.002 - 0.008 - 0.017

- 0.4 1.3 1.3

~ -

6.2 3.2 8.1 2.4 5.0 1.1

0.2 - 4.0 - 8.5

and that the ‘shadow of death’ is reflected not only in levels but also in the growth rates of productivity. When estimating separately for each period, the results are very similar when we limit ourselves to the subset of ‘continuing’ firms that existed throughout the 1979-88 period (see Table 8).

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Looking at the period dummies, which implicitly provide an estimate of the growth in average TFP in the industry as a whole, we observe a significant disagreement on what happened, based on alternative views and weightings of the data. In the unweighted full sample the average firm residual productivity declines at around 1.2 percent per year during the first two periods (1979 through 1985) and recovers somewhat from 1985 to 1988, leaving the average firm in 1988 about 5 percent below where it was in 1979 (see Table 5, column 1). In growth rates, however, when the sample remains unchanged, the average productivity growth for ‘continuing’ firms is positive, at about 1.3 percent per year (as can be calculated from Table 7, column 1). Weighting the same firms by their relative importance in Israeli industry does not change the other coefficient estimates much, but substantially changes the estimated period effects, implying a positive average rate of TFP growth: about 1 percent per year in the full sample (Table 5) in levels and about 0.3 percent in growth rates (Table 7). An alternative to the differences regressions is given by the TFP growth calculations reported in Table 8. These calculations do not impose a common coefficient on all firms across all times. Instead, they use individual firm periodaverage factor cost shares as weights. (We did not use the residual capital shares to compute TFP because they were negative, at the individual level, for a large fraction of the firms in the earlier periods.) A closer look at these data indicates that the main conclusion about TFP growth is again sensitive to the weighting of the observations. The average firm had no positive TFP growth in the period as a whole. When weighted appropriately, TFP in the Israeli industry as a whole grew at a rate of 0.9 percent per year during the period 1979-88, although during the last period, which followed the stabilization program, TFP grew at 2.7 percent per year (Table 9). Since our coefficient estimates are close to the cost shares, when we use the estimates of TFP growth as a dependent variable in parallel regressions, they yield similar conclusions: significant #R&D coefficients and low TFP growth for the average (unweighted) firm and higher TFP growth (weighted) for industry as a whole, with much of it coming in the last subperiod (see Table 10). This rather slow growth in TFP is comparable to the 1970s and is significantly below the Israeli experience in the 1950s and 1960s. In this sense, the 1980s may have been another ‘lost decade’ in the development of Israeli industry. Although total output increased significantly during this period, while employment remained essentially unchanged, much of the growth in labor productivity appears to be accounted for by additional investments in capital and material inputs. If there were significant improvements in the technologies used and in the organizational structure of industry, they are not clearly visible in the aggregate data. When TFP growth is computed separately by industry for continuing firms for the period as a whole (Table 1 l), only the chemicals, metals

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Table 9 Average

capita)

share and total factor

productivity 1979-82

A. Average cost Residualb Aggregate B. Growth

(weighted)

capital

0.074 0.155 0.039 per person

0.084 0.112 0.065

Unweighted Weighted

TFP growth

1979988”

0.095 0.105 0.087

0.081 0.124 0.063

year (% per year) 10.62

C. Average

1985588

share

residual in capital

1982-85

(percent

per year) - capital - 0.44 0.60

“Geometric average of the three periods. “Residual share computed by setting individually

5.70

16.37

share calculated

from costs

- 1.61 - 0.42

estimated

0.92 2.66

negative

capital

10.90

- 0.38 0.93

shares to 0

and machinery, and electronics subsectors (over half of the subsectors examined, by their weight in the total) show positive TFP growth. A comparison of the level and differences estimates (Tables 5 and 7) underlines the fact that the bulk of productivity differences across firms is not accounted for by the regression and that such ‘firm’ effects, or unobservable factors, are a relatively permanent aspect of the firm and an important part of the story. For example, the standard error of the (weighted) levels regression in Table 5 is 0.27. That is, the level regression (in spite of its high R*) does not explain productivity differences across firms very well, leaving us with an error whose standard deviation is 27 percent! At the same time, the standard deviation of the error in the differences regressions is less than 10 percent. In terms of variance components, the unobserved firm factors account for over 85 percent of the residual variance. This is the place to note several econometric loose ends. First, the standard errors of the various coefficients reported in Table 5 are probably underestimated and the associated ‘significance’ levels overestimated, because no account is taken of the strong serial correlation in errors for the same firm across time (alluded to in the previous paragraph). This could be adjusted for by implementing an appropriate GLS procedure. That, however, is somewhat complicated for unbalanced panels and is beyond the computer resources of the CBS at this time. Implementing a GLS procedure would also help us to allow for different error variances across time and across those parts of the sample where

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Table 10 Pooled

TFP growth

regressions-dependent

variable:

TFP growth

(cost shares weights)

Unweighted

Observations R2

Root MSE

Weighted

Full sample

Larger

5,017 0.043 0.092

2,68 I 0.069 0.089

(I) Coef.

(2) r.

firms

Full sample 5,017 0.126 0.080

(3) Coef.

(4) r.

(5) Coef.

(6) r.

0.018

2.9

0.020

2.5

0.036

7.3

0.032 - 0.010

2.3 - 2.6

0.025 - 0.010

1.7 - 2.2

0.019 - 0.015

2.2 - 5.5

0.079

0.5

- 0.036

- 0.189

0.496

4.9

0.001

0.007 0.012

0.5 1.9 2.1

0.007 0.013 0.018

I.5 2.5 2.7

- 0.000 - 0.001 0.004

- 0.0 - 0.2 I.2

Sector (ref. = private) Reg. stock market Histadrut Kibbutz Public sector

- 0.014 - 0.004 - 0.009 0.017

- 2.1 - 0.8 - 1.9 I.7

- 0.014 - 0.004 - 0.016 0.014

- 2.0 - 0.8 - 2.5 1.4

0.001 0.001 - 0.015 0.019

0.4 0.3 - 3.3 4.8

Branch (ref. = electronics) Food Textiles Printing, paper Wood, minerals Chemicals, plastic Metal, machinery

-

-

-

-

-

-

Intercept R&D variables R&D capital No R&D Quality

services

of labor

Scale (ref. = SO-99 worker-years) 5549 employees 100-299 employees 300 + employees

Life cycle Estab. Estab. Estab.

(ref. = established 1963376 1950-62 before 1950

0.019 0.002 0.028 0.020 0.019 0.002

3.5 0.5 4.9 3.5 3.7 0.5

0.028 0.013 0.033 0.030 0.023 0.003

4.3 2.0 4.4 3.8 3.5 0.5

0.028 0.020 0.040 0.022 0.026 0.013

7.0 4.3 7.7 4.2 6.9 3.5

from 1976) 0.027 - 0.006 - 0.002

5.5 - I.7 - 0.8

0.043 - 0.004 - 0.005

5.1 - 1.8 - 1.0

0.032 - 0.005 - 0.013

5.7 - 1.7 - 4.3

- 0.003 - 0.015

- 0.7 - 3.4

0.002 - 0.02 I

0.2 - 2.5

- 0.015 - 0.009

- 2.3 - 1.4

Period dummies (ref. = 1979-82) Period 1982- 1985 - 0.014 Period 198551988 0.009

- 4.3 2.7

- 0.017 0.01 I

- 4.1 2.6

- 0.013 0.016

- 5.0 5.9

Mobility (ref. = stayers) Opened 1983-88 Closed 1979-85

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Table 11 Total factor productivity growth, 1979-88, continuing firms by firm characteristics - full sample (percentages, annual rates)

Total

Firms (no. of observations)

Weight (% of total production)

1,305

100

TFP (annual growth rate) 0.92

Branch groups Food Textiles Publ. and paper Wood and minerals Chemicals and plastics Metal, machinery Electronics & elec.

197 203 152 129 200 283 141

22.2 8.0 1.7 6.3 19.6 15.8 20.4

- 0.99 0.01 - 2.00 - 0.71 1.01 1.75 4.26

Scale (worker years) 5-49 50-99 100-299 3Of+

638 262 212 133

15.5 9.5 22.8 52.2

- 0.42 - 0.93 - 0.69 2.36

Sector Private Reg. stock market Histadrut Kibbutz Public sector

846 89 143 197 30

38.9 15.1 16.7 9.1 19.6

- 0.38

- 1.19 3.70

Establishment year 1970-79 1963-76 1950-62 Before 1950

43 417 386 459

0.9 28.0 34.8 36.3

1.50 1.30 1.61 - 0.05

1.62 1.29

we use imputed rather than actual capital estimates. We tried to take care of the sample selectivity problem by using an unbalanced panel and including all of the firms for which data were available. Moreover, we condition our estimates on future selectivity. In addition, the growth rates regressions control for these sources of bias to a large extent, if the serial correlation and simultaneity arise from the presence of unobserved fixed firm components, and if sample selectivity is also largely dependent on these same components. However, we were unable to do much about the remaining sources of simultaneity. We have no reasonable instruments left in our data (we used the investment variable, which gives leverage in the Olley-Pakes work, to impute

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the missing capital data and thus cannot use it again). We evade the simultaneity problem when we use TFP estimates, but they, in turn, do not allow us to ask all the relevant interesting questions. More could be done about the errors in the variables problem, and we intend to address this issue in the future.5 That there may be some payoff from doing so is implied by the fact that when we use two-period averages to estimate the production function, the capital services coefficient (not shown here) rises by more than 30 percent, from 0.051 to 0.067.

6. Interim conclusions

and future work plans

A major contribution of this work has been the construction of a consistent data set for an inclusive panel of Israeli industrial firms based on the data that the CBS has collected both regularly and occasionally from these firms. Moreover, the methodology developed for this study is being incorporated in the ongoing procedures of the CBS. From 1990 on, the CBS has started a new sample of firms, most of the larger ones being also carry-overs from our current panel. Moreover, it will now collect these surveys annually. The most important obstacle to effective analysis of these data has been the lack of up-to-date information on the stock of the various types of capital for most of our firms. The last capital survey was done in 1982 and covered only a subsample (about a third) of all the firms in the panel. A new and detailed survey of what remains of all the past investments of the last decades is long overdue. As a result of our work, a Fixed Capital Survey (as of the end of 1991) is currently underway, covering also information on capital subsidies. An R&D census survey was carried out for 1990, and from 1991 on this survey will be based on a sample of all ~ not only large ~ R&D firms. An occupational survey is also underway, and other variables such as price indices, turnover data, and firm establishment-year information have become standard components of this data file. We hope that this extensive data construction effort will bear additional fruit in the future, in the form of new studies by ourselves and other researchers. In particular, we shall be focusing more closely on the role that R&D has played in the growth of these firms and also on its more general contribution to the Israeli economy, both in the past (including the data being retrieved for the 1960s and 1970s) and in the present. In pursuing such analyses the reduction in the rate of inflation may help us analyze such data better in the future, since small relative errors in the price deflators can introduce errors which are several times larger than the productivity effects we were looking for. It would also be desirable to have more data on

‘See, for example, Mairesse and Sevestre (1991) for an attempt measurement in capital within the context of similar data.

to do something

about

errors

of

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201

the exogenous variables which determine the input choices and investment decisions of these firms: access to government subsidies, the presence and strength of unions, and movements in relevant export prices. Without such more ‘causal’ variables, it is hard to interpret the production function estimates as reflecting primarily the technological-organizational aspects of production that one would like to uncover. All these reservations notwithstanding, we have learned quite a bit in this work. Productivity growth in Israeli industry was rather slow in the 1980s and only a few industries stood out positively. Among these firms, growth was significantly higher for the R&D and human capital intensive ones. In spite of the large amount of turnover and churning in firms and jobs, most of the productivity growth occurred within firms. Productivity growth in industry as a whole did not come primarily from the exit of failing firms or from the faster growth of more productive firms. What happened within firms was decisive and that is also what needs attention if the productivity performance of Israeli industry is to improve in the future. Appendix Table A.1 Labor mobility by industry, percent change in person-years,

Net change (1) Mining and quarrying Dairy, meat, fish products Beverages, tobacco, others Textiles Clothing Leather Wood Paper Printing and publishing Rubber Plastic Basic chemic. and pharmaceuticals Other chemicals Mineral products Basic metal Metal products Machinery Electrical equipment Electronic equipment Transport equipment Precision and optical equipment Miscellaneous d (3) + (4) + (5) + (6). ’ (3) + (4) - (5) - (6).

- 4.76 2.42 3.45 4.95 1.41 2.80

- 2.22 3.53 3.38 - 5.18 3.97 0.51 - 0.04 - 2.75 - 3.88 - 0.87 - 1.82 ~ 1.96 I.57 - 3.71 5.00 - 1.55

average over the three periods, annual rates Jobs created

Jobs ended

Gross change (2Jb

Cont. (3)

Opened (4)

Cont. (5)

8.40 9.75 14.44 12.23 19.85 18.05 11.25 14.71 13.88 11.23 15.62 7.89 7.45 15.01 13.45 11.76 9.84 13.93 9.13 9.23 16.29 19.46

1.03 4.94 6.05 2.25 5.48 5.71 3.27 6.60 5.04 I .74 5.75 3.34 2.60 3.52 2.61 3.31 2.87 3.71 4.02 2.47 5.66 3.83

0.79 1.15 2.90 1.39 5.15 4.72 4.24 2.52 3.59 1.28 4.05 0.86 I.10 2.61 2.17 2.13 1.13 2.27 1.33 0.30 4.99 5.13

-

5.33 2.54 3.33 5.50 3.80 3.23 3.79 3.70 2.70 6.29 3.28 2.92 2.74 4.24 5.04 3.73 4.20 4.41 3.00 5.66 1.69 4.02

Closed (6) -

1.25 1.12 2.17 3.09 5.43 4.40 5.94 1.89 2.55 1.91 2.54 0.76 1.00 4.65 3.62 2.58 1.63 3.53 0.78 0.81 3.96 6.48

202

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Table A.2 Growth rate production function in production per person-year

Observations R2 Root MSE

regression,

full sample, unweighted

~ dependent

variable:

1979982

1982-U

1985-88

1,648 0.648 0.089

1,613 0.648 0.092

1,754 0.663 0.100

(1) Coef.

(2) f.

(3) Coef.

(4) r.

(5) Coef.

growth

(6) r.

Intercept Intermediate inputs Capital services

- 0.024 0.634 0.08 1

- 2.2 46.5 4.6

0.018 0.701 0.027

1.6 46.7 1.2

0.033 0.665 0.023

2.7 50.7 1.1

R&D variable R&D capital No R&D

0.008 - 0.009

0.4 - 1.2

0.061 - 0.015

2.3 - 2.1

0.032 - 0.004

1.2 - 0.5

0.009

0.2

0.002

0.1

0.081

2.0

Scale (ref. = 50-99 worker-years) - 0.002 5-49 employees 0.021 100~299 employees 300 + employees 0.020

- 0.5 3.2 2.2

- 0.006 0.002

0.1 - 0.9 0.3

0.013 0.011 0.020

2.0 1.6 1.8

Sector (ref. = private) Reg. stock market Histadrut Kibbutz Public sector

-

-

- 0.013 0.005 0.029 - 0.001

- 1.2 0.7 3.3 - 0.1

- 0.002 - 0.001 - 0.028 0.067

- 0.2 - 0.2 - 3.1 3.4

Branch (ref. = electronics) Food Textiles Printing, paper Wood, minerals Chemicals, plastic Metal, machinery

0.012 0.031 - 0.006 0.010 0.012 0.027

-

-

-

-

Quality

services

of labor

Life cycle Estab. Estab. Estab.

(ref. = established 1963376 1950-62 before 1950

Mobility (ref. = stayers) Closed 1983-85 Closed 1986688

0.015 0.005 0.010 0.008

1.3 3.4 - 0.7 1.1 1.3 3.2

after 1976 ) 0.03 1 0.005 0.007

- 0.011 0.005

1.2 0.7 1.2 0.5

0.000

0.045 0.004 0.035 0.016 0.009 0.006

4.5 0.5 3.5 1.6 1.0 0.8

2.6 0.9 1.3

0.007 - 0.012 - 0.013

0.9 - 1.9 - 2.3

- 1.6 0.6

- 0.026

- 3.4

0.014 0.028 0.030 0.039 0.043 0.015

0.020 - 0.008 - 0.003

1.5 2.1 2.9 3.6 4.6 1.7

2.9 - 1.2 - 0.6

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