Quality change and productivity improvement in the Japanese economy

Japan and the World Economy 17 (2005) 1–23

Quality change and productivity improvement in the Japanese economy Derek Boswortha,*, Silvia Massinib, Masako Nakayamab a

Intellectual Property Research Institute of Australia, University of Melbourne, Victoria 3010, Australia b Manchester School of Management, UMIST, P.O. Box 88, Manchester M60 1QD, UK Received 18 June 2002; received in revised form 6 June 2003; accepted 11 July 2003

Abstract This paper provides a new view of the rate of productivity growth in Japan, and the contribution of productivity improvements to the growth of the Japanese economy. Many studies point to the high rates of labour and total factor productivity growth in Japan during the early post-War period as the principle cause of the rise of the Japanese economy. The present study, however, emphasises the role of quality change in Japan’s development. In order to do this, quality-constant measures of both inputs and outputs are derived using time series hedonic regression techniques. These qualityconstant series are then used to revise the traditional partial and total factor productivity measures. We demonstrate that all of the traditional measures of productivity are biased insofar as their construction does not fully account for quality change. The essential message is that, in Japan, quality change accounts for significant parts of the true growth in both inputs and outputs, leading to underestimation of their growth rates in the official statistics. However, given the nature of the ratios employed in measuring productivity, the implications for factor productivity growth are more complex. # 2003 Published by Elsevier B.V.

1. Introduction1 Many authors explicitly or implicitly point to the role of productivity change in explaining the Japanese economic miracle, as well as, in more recent years, pointing * Corresponding author. Tel.: þ44-161-2003438; fax: þ44-161-2003505. E-mail address: [email protected] (D. Bosworth). 1 The authors would like to thank participants at the Scottish Economics Conference, Edinburgh, 2001, and the Schumpeter Conference, Manchester, 2000 for their helpful comments and encouragement.

0922-1425/$ – see front matter # 2003 Published by Elsevier B.V. doi:10.1016/j.japwor.2003.07.001

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to the slowdown in productivity improvements. The more general productivity literature, not explicitly focusing on Japan, has shown considerable interest in the implications of measurement issues, in particular, the potential bias arising from quality change. To date, this literature appears to have focused on the implications for labour productivity growth, arguing that the miss-measurement of output growth, by failing to recognise the improvements in quality, leads to an under-estimate of the rate of labour productivity growth (Lichtenberg and Griliches, 1989). Gordon (1992) argues, for example, ‘‘. . . it is likely that measurement issues bias downwards the growth rate of manufacturing output in every country, due to inadequate adjustments of price indexes for quality change, but more outside the US due to the absence of a computer price deflator.’’ In a recent paper, we have demonstrated that Japanese growth is significantly underestimated by the failure of the official deflators to hold quality-constant (Bosworth et al., 2002). The rationale for the present paper, however, concerns the need for consistency in the treatment of quality not only on the output side, but also on the input side, when measuring productivity growth. There is an unacceptable asymmetry if, for example, output growth is adjusted upwards to reflect quality change in the basket of goods produced, if a similar adjustment is not made to the labour input used in the production of that output. This does not mean that output per person or per person hour are not of interest from an income or welfare perspective, however, from a productivity perspective it seems important to be consistent in the treatment of quality. If quality is forced into the output measure by the use of quality-constant deflators, then it should also be present in the labour input measure. This becomes clear when we turn to capital productivity or total factor productivity (TFP) (i.e. where the welfare interpretation of output per person or per person hour is not relevant). We argue below that it is not only important that productivity measures should encompass both quality and volume changes, but that it should be possible to decompose productivity growth into these two components, in order to obtain a clearer picture of the sources of economic growth and welfare improvement. It might be argued that what we propose in this paper is not new, as adjustments for ‘‘quality’’ have been an important theme of the growth accounting literature as it has ‘‘chipped-away’’ at the Solow residual factor (for example, Solow, 1957; Jorgenson and Griliches, 1967 and Jorgenson and Fraumeni, 1992). In practice, however, we demonstrate below that there are also related problems in the treatment of quality in the growth accounting literature. First, the weights applied in the aggregation process are crucial, and may be biased if they are not ‘‘pure-quality’’ weights and include an inflationary component. In particular, we demonstrate that this problem does not disappear through the use of divisia indices. In much the same way as for the measurement of productivity, the miss-measurement of the various weights can lead to biases in the measurement of both inputs and outputs. The traditional growth accounting approach is also immensely resource intensive. The methodology proposed in the present paper, however, can be applied at almost any level of aggregation, and it uses stochastic techniques that provide statistical tests of the relative importance of the quality versus inflationary components. Our basic approach is a hedonic one, but, in place of attributes and technical characteristics, the present paper uses intellectual property data, such as patents, which have been widely adopted as indicators of technological change at both micro and macro levels (Griliches, 1991; Patel and Pavitt,

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1995). However, given the level of aggregation we are working at, we not only use patents, but also trade marks, designs and utility models. We argue that all of these forms of intellectual property are linked to product innovation. In the case of labour, we follow the growth accounting literature in using occupational mix and educational measures, but also a variety of other indicators drawn from other streams of the literature. Section 2 continues with a brief review of the empirical estimates of productivity growth in Japan, before discussing the conceptual issues that are fundamental to the present paper. In particular, we deal with the separation of the inflationary and quality components of the inputs and outputs, as well as the weights to be used in aggregation. Section 3 discusses the data used in the present study, the time series hedonic methodology and the empirical results obtained from the regressions for the Japanese economy. Section 4 then constructs comparative measures of productivity change based upon the original, official deflators and our revised quality-constant deflators. We also demonstrate the method of decomposition of quality change in both inputs and outputs, as well as in the weights used in aggregation. Finally, Section 5 draws the main conclusions of this exercise.

2. Measuring productivity growth In this section, we report on Japan’s productivity performance, based on standard partial and total factor productivity ratios. The discussion then turns to a number of key conceptual issues surrounding the measurement of real inputs and outputs, before considering their implications for partial and total factor productivity ratios, and the associated growth accounting measures. Finally, this section considers the issue of the measurement of the weights used in TPF ratios and in growth accounting approaches. 2.1. Productivity growth in Japan There is little need to provide a comprehensive review of Japanese productivity growth—a number of examples will suffice to show that the bulk of the literature suggests, at least until recent years, rates of growth of labour and total factor productivity in excess of most of Japan’s industrial competitors. Based upon OECD data, for example, Bosworth et al. (1996, p. 404) demonstrate that, over the period 1960–1968, Japan shows an average rate of growth of labour productivity of over 11 per cent per annum, which is in excess of all the other OECD countries. The performance of Japan is also better than the other countries reported in each of the periods 1968–1973 and 1973–1979, as well as for the 1963–1989 period as a whole. The second feature of the results, however, is the slow-down in productivity growth, with the period up to 1973 exhibiting significantly higher rates of change than after 1973. Other authors have pointed to the need to control for hours of work and the greater capital intensity of the Japanese economy (Gordon, 1995), but Japan still has a higher growth in output per person hour than any of the other reported countries, averaging over one percentage point per annum more than the USA over the period 1979–1992. The results are similar when we move to the total factor productivity measure—again Japan performs better than all of the other countries (with the apparent exception of Italy). In this instance,

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Japan’s performance is around one and a quarter percentage points per annum higher than that of the USA. Based on data for the period 1973–1980, Griliches and Mairesse (1990) confirm that the ‘‘productivity of labour in manufacturing continued to increase much faster in Japan than in the United States.’’ (op cit. p. 317). However, their main aim was to investigate the source of this differential performance. In doing so they took individual firm data, which has some similarities with our choice of data for the incorporated sector (though not at the individual firm level). Their examination of the role of R&D suggested that its contribution to the explanation of productivity was small (although significant) and that it also did not account for difference in growth rates between the two countries (op cit. p. 330). What they did find, however, was a striking difference in the contribution of physical capital in the two countries in two respects: first, double the growth rate of capital per employee in Japan; second, the two-fold impact on productivity for the same addition to capital in Japan (op cit. p. 332). The latter is of some interest to the present paper, because it might relate to the under-estimation of the true capital growth in Japan, because of the failure of official statistics to account for improvements the quality of capital. We return to this below. The more rapid productivity growth of Japan has produced a differential between the level of Japanese productivity and that of many other countries, such as the UK. According to Kagomiya (1993), for example, by the end of the 1980’s labour productivity in Japan was 19 per cent higher than the UK in manufacturing and TFP was 12 per cent higher. As a consequence, assuming that factor prices do not take up the whole of this differential, this gives Japan a significant competitive advantage and appears to explain her rapid economic development and growth. A more recent study by Chang and Luh (2000) reports on the wealth of evidence about productivity growth amongst the Asia-Pacific countries, before proceeding to construct new estimates based on 19 APEC member economies. The estimates are derived using linear programming methods, using a Malmquist distance function index of productivity. The data are divided into two separate decades (i.e. 1970–1980 and 1980–1990). The authors are interested in somewhat different aspects to that of the present study (see Bosworth et al., 2001), and we adopt a much more Japanese-oriented view of their findings. Continuing our comparison with the USA – Chang and Luh (2000, p. 559) report that the capital-labour ratio grew by over 135 per cent in Japan between 1970 and 1980, compared with 18.5 per cent in the USA. The corresponding growth from 1980 to 1990 was 65.5 per cent for Japan and 25.7 per cent for the USA. The Malmquist index of TFP performance suggested that Japan’s relative position compared to the other 18 APEC countries deteriorated during the decade 1970–1980 (so did the US, but by slightly less), and improved marginally during the period 1980–1990 (as did the US). The authors go on to disaggregate these results in more detail, but we do not report on that discussion. 2.2. Role of product quality in real output measurement We begin by discussing the existing concerns over the way in which real output, RY, is extracted from a nominal output series (i.e. measured in current prices), NY, by deflating the

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data using a price index, p. The central issue arises because prices can change for two reasons, one reflecting the movement in the quality of the product and the other a ‘‘pure price’’ change. This issue occurs because the real output measure should not only reflect changes in the volume of production, but also changes in the quality of the output produced. Thus, the ideal output measure is the volume of output in some ‘‘physical’’ sense, multiplied by an appropriate quality index. To obtain this from nominal output data, we require a quality-constant deflator, which ensures that, when the nominal output measure is divided by the deflator, quality is forced into the measure of real output. As output prices have two components,  the first of which reflects quality and the second is the quality-constant p ¼ pðQÞpðQÞ, price index (i.e. the ‘‘pure price’’ effect), real output, RY, is obtained in the following way, RY ¼ pðQÞY ¼

NY  pðQÞ

(1)

and it can be seen that the quality component, pðQÞ is forced into the real output measure, which comprises quality and volume, Y, components. The decomposition of p into its two parts is precisely what the ‘‘matched products’’ and ‘‘hedonic price’’ techniques are designed to do (Berndt, 1991). However, while many statistical offices apply matched product techniques, only the US applies hedonic measures and only in the case of computer prices. It has been demonstrated that the ‘‘matched products’’ approach generally fails to control for quality and, indeed, will perform worse in situations where there is rapid quality change—which is precisely the time when controlling for quality is most important (Stoneman et al., 1992). It is for this very reason that the US introduced hedonic procedures for computers. It is easy to demonstrate the effect of the failure to account for all of the quality change. This occurs if the price index used as the deflator contains both the quality  and ‘‘pure price’’ components, Y ¼ NY=pðQÞpðQÞ, where Y is the volume of output. The extent to which the two estimates differ depends upon the quality component of price, p(Q). Insofar as the growth accounting literature uses official measures of output, they are also likely to use underestimates of the rate of growth of real output in sectors or economies with faster rates of quality growth. 2.3. Implications of quality for productivity measurement The immediate implication appears to be that labour productivity growth, calculated as the rate of change in output per person or per person hour is underestimated to the extent that quality change is excluded from the output measure. Further consideration of this, however, is that employment (or person hours) is no longer a valid measure of labour input, at least in a productivity context (although it may still have some income or welfare interpretation). For example, if real output (appropriately measured) and person hours are constant in a given area of production, but the output is being produced by a lower-quality (lower-paid) workforce, labour productivity in the sense we mean in the present paper is increasing. This is precisely the pattern observed across the product life cycle, as a standard product is produced by lower quality (and less well paid) labour. In the same way, labour productivity will be falling if a given real output, with constant person hours is produced by

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an increasingly high-quality (higher-paid) workforce. The appropriate measure should be real output per ‘‘quality adjusted employee hour’’. Thus, in the present paper, we view the price of the ith input, pi, as also comprising an  and quality (pi ðQÞ) component. The weighting of any inflationary or pure price (pi ðQÞ) input, Xi, by a quality component, p(Q)i, can be viewed as just another form of factor augmenting technological change (Kumbhakar, 2002, pp. 245–246). The wage is viewed as a price in a similar way to output or capital, which, for any individual or group of individuals, comprises a quality and a quality-constant, ‘‘pure-inflation’’ component. There is a range of empirical literature that considers either inflationary pressures on wages or the effects of quality change (see Section 3 below). The resulting measure of total factor productivity is best written in the multiplicative (log-linear) form, Y pðQÞY Y pðQÞ  Y  TFP ¼ ¼ (2a) ðpi ðQÞXi Þai pi ðQÞai X ai i i and, in rate of change form, 0

TFP ¼

0 X pðQÞ  ai pi ðQÞ 0

i

! þ

0

Y

X

0

!

a i Xi :

(2b)

i

In understanding these expressions, remember that pi(Q) represents the part of the price that the firm is paying for the quality (increased quality) of the ith input, which should therefore be reflected in either increased output volume or quality if TFP is to rise. Again, we have shown the separation into the quality and volume components. In each case, as well as with the measures of labour and capital productivity, it can be seen that, insofar as the official deflators fail to control for quality in their price deflators, then the resulting official estimates of productivity change will also be miss-measured. The implications of properly accounting for quality in productivity ratios are not clear. In the case of labour productivity, for example, ensuring the deflators are completely quality-constant will tend to raise output per person, but adjusting the labour input for quality will have the opposite effect if labour quality is rising. In addition, given that movements in quality for inputs and outputs may occur at different rates during different periods, the new measures may even behave quite differently over time. 2.4. Implications of quality for the growth accounting approach Growth accounting attempts to take quality change into account through the substitution from lower- to higher-quality inputs. In this way, they are able to measure changes in ‘‘quality’’, as they ‘‘. . . define growth rates of capital and labour productivity as the differences between growth rates of input measures that take substitution into account and measures that ignore substitution.’’ (Jorgenson and Fraumeni, 1992). Thus, after the initial ‘‘macro’’ model of Solow (1957), the growth accountants have worked from the ‘‘bottomup’’, for example, using wage-weighted occupation and education employment measures. It needs to be borne in mind, however, that the weights applied are based upon relative

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 If the actual wages, which have a quality, wðQÞ and an inflationary component, wðQÞ. weighted sum is written, X wj EðQÞ ¼ Ej (3) w j where, j is the jth type of labour, E(Q) the growth accounting measure of the labour input (which reflects quality through substitution effects), and w is the average wage. The use of the divisia index form in Eq. (3) is purported to help reduce problems with measuring year on year changes of the price index, as only relative values are utilised. However, we can show that this measure gives rise to an under-estimate of the effects of quality change. Allowing for the quality and ‘‘pure wage’’ effects, we can write, " #  XwðQÞj  wðQÞ X j  Ej EðQÞ ¼ w0 ðQÞj w0 ðQÞ (4) Ej ¼ j  wðQÞ wð QÞ j j If we translate this into rates of change by totally differentiating Eq. (4) and dividing both  sides by wðQÞwðQÞEðQÞ, we obtain,   0 0 0 X 0  þ Ei EðQÞ ¼ w0 ðQÞi þ w0 ðQÞ (5) i

 0 both appear. The problem with the divisia form of the price where w ðQÞ i and w0 ðQÞ weights can now be clearly seen. While it helps to eliminate up-ward year on year changes in inflation impacting on the measure of the quality adjusted employment, it has the same effect on any unaccounted quality component of the wage. This component arises not from the substitution of one employment group for another, but from the quality improvements within each employment group. Thus, if all groups improve equally in quality, then  0 and quality improvements within each employment group ðwðQÞj =wðQÞÞ ¼ 1 and w0 ðQÞ are ignored in the construction of w0 ðQÞ0 i . There are two implications of this result. First, the use of relative wages fails to capture any of the within-group quality change. Second, the ‘‘pure-inflation’’ component of the wage should not be present in the weights shown in Eq. (5). In current growth accounting studies, it is only ‘‘absent’’ from the weights because growth accountants assume it away (i.e. they assume all markets are highly competitive and labour is paid the value of its marginal product). In practice, this is an empirical question that requires testing. 0

0

3. Data and methodology 3.1. Empirical specifications In this paper, in stark contrast to the micro-growth accounting techniques, we adopt an aggregate time-series approach based on regression techniques in order to separate the inflationary and quality components of price change. We have already reported on this approach with regard to the Japanese producer price index (PPI) in a paper that re-estimates the rate of economic growth in Japan (Bosworth et al., 2002), and hence we simply briefly

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review these results. In this earlier work, we adopted a hedonic regression equation of the form, pt ¼ f ðZt ; Ptn ; Utn ; Dtn ; Ttn Þ

(6)

where pt is the producer price index and Zt denotes the inflationary component of price change, which includes measures of fuel prices and capacity utilization. The variables Pt, Ut, Dt and Tt represent the product quality or attributes, proxied by patents, utility models, designs and trademarks respectively. The list of IP variables has been extended from just patents (Griliches, 1991) to include other measures of product innovation. These enter the equation appropriately lagged by n periods (n can vary from variable to variable), to show the effect of technical change on the price deflator. Bosworth et al. (2001) provide a more complete justification for all of the variables. In the present study, however, estimation of Japanese productivity also requires the separation of quality and ‘‘pure price’’ effects in the wage and capital price indexes. The specification for the capital price index is a simple replication of Eq. (6), except the dependent variable is now capital prices, pK, and the patents, utility models, design and trademark data also relate specifically to capital goods, pKt ¼ gðZt ; PKtn ; UKtn ; DKtn ; TKtn Þ

(7)

Note that the lag n may not only differ between variables but also with Eq. (6) above. The case of labour quality is somewhat different. Here the wage, measured as monthly contractual earnings, is the focus of attention. As we noted in Section 2 there is a range of literature relevant to explaining the wage or changes in wages. The literature broadly divides between explaining the wage level by personal characteristics and attributes, and explaining the rate of change of wages by the level of demand, trade unionism, etc. The former stems mainly from work on the earning’s function (see, for example, Mincer, 1974), with years of schooling, educational attainment, training, occupation, age, etc. as explanatory variables. Thus, the focus of this work is almost exclusively on the ‘‘quality’’ of the individual as measured by their personal attributes (see Berndt, 1991, pp. 150–223). If estimated as a hedonic equation using a panel data set, it could be used to derive the ‘‘pureinflation’’ wage changes, but this has been far from the main focus (although some work has been carried out on unionisation and wage differentials). The second approach, which focuses on the rate of change in wages, has concentrated on one of two aspects, both of which are closely linked with ‘‘pure-inflation’’ wage changes, rather than any aspect of labour quality. The first has been the market equilibrating models that use ECM techniques to test the rate of adjustment of wages to their equilibrium value. The second have been concerned with the sources of inflationary pressure, such as excess demand in the product and labour markets and union pushfulness (see, for example, Layard and Nickell, 1985). However, the inclusion of labour quality variables in these specifications has been shown to reduce the apparent magnitude of the union mark-up (for reviews of the relevant literatures see Bosworth et al., 1996). In the present study, we continue with a regression-based approach, drawing on both of these literatures. After some experimentation, we adopted a two-stage procedure to save on degrees of freedom. Other more direct approaches, which we do not report on here, however, yield similar results in the decomposition of quality and ‘‘pure price’’ effects. The

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estimates reported below were obtained from a regression equation with a range of possible ‘‘labour quality’’ variables, wt ¼ hðZt ; OSt ; At ; TNt ; EDt ; TUt Þ

(8) 2

where, OS is a measure of occupational structure, A denotes the average age of employees, TN a measure of the average tenure of employees, ED the average educational level, and TU the proportion of the workforce in unions. Note that, in this aggregate time series data, unlike the individual-level, micro data, A and TN are likely to be closely correlated and play very similar roles in the explanation of the wage. 3.2. Key variables and descriptive statistics All of the statistics reported in this paper are taken from the Japanese Yearbook of Statistics, and detailed discussion of each series can be found in Bosworth et al. (2001). Output prices are represented by the producer price index, while the fuel price index (FPI) and the capacity utilisation index (KU) have been used to capture the pure-inflationary price changes. The profiles of PPI and FPI are quite similar, although FPI shows the wellknown peaks due to the two oil shocks in the early and late 1970s (these are present to a smaller extent in the PPI profile). Related to the oil shocks, we note that the capacity utilisation index moves in an opposite direction to the PPI and FPI series, at least until the late 1980s, when the Japanese economy was booming. In the 1990s, the three series show a similar pattern, this time because of the recession that hit the East Asian region. After a short recovery, the financial crisis in the late 1990s shows its effects on the three series. The IP variables, Pt, Ut, Dt and Tt are taken from the Science and Technology section of the same source. There are two choices to be made here, between: (i) applications and grants/registrations; (ii) domestic, foreign and total IP. A priori, we preferred grants and registrations to applications, as those that do not enter into force can be argued not to represent an improvement on the existing state of the art. However, applications appear to work much better in a number of our regressions, for reasons that we outline below. In some of our specifications, it appears a priori more sensible to have a measure of the rate of change of the IP stock. This is calculated using, for example, patents in force, as a measure of the patent stock, PS. The rate of change of the stock, PS0 , is approximated by PRt/PSt, which represents patent grants divided by the stock. The patterns of IP applications appear to be slightly smoother than the equivalent registrations, particularly in the case of patents. Among the IP variables, utility models and designs follow a pattern more similar to the price index series, showing an upward trend until the early 1980s, and then declining. An exception is the pattern of patents applica-

2 We undertook a prior regression to construct OS, in order to save on degrees of freedom. The first stage followed the growth accounting literature, in investigating the changing occupational breakdown of the Japanese economy, with a view to obtaining weights to aggregate the occupations into a single measure, OS, OCCt ¼ hðZt ; Oj ; . . . ; Ont Þ, where Oj denotes the proportion of individuals in the jth occupation and OS is the weighted sum of the occupations, where the weights are the parameter estimates. The estimates were made omitting a base occupation and, in subsequent regressions, any occupations that were insignificantly different from the base group that did not adversely affect the diagnostics.

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tions, which shows an upward trend over the whole period, including the 1980s and 1990s. A decrease in the number of patent applications in the early-mid 1990s reflects the economic recession mentioned above. While registrations or grants are generally preferred to applications in technological change studies (i.e. because they have been screened by the IP examiners, and only those with sufficient novelty are eligible for IP protection), the ‘‘jumps’’ in registration behaviour induced by the new laws and administrative procedures3 suggest that the reverse might be true in this instance. In practice, we are able to show that both applications and registrations give very similar results if the sample period is restricted to end in 1991. However, if more recent years are included, applications continue to work in the regressions but registrations or grants do not. 3.3. Empirical results 3.3.1. Order of integration The Dickey–Fuller unit root tests show all our variables in the data-set are integrated of order 1, I(1), except utility models for capital goods. From a visual analysis of our series we expected them to be I(1), hence we undertook the test on the series differenced twice as suggested by Dickey and Pantula (1987). In general, when using non-stationary series, it is possible to falsely conclude that a causal long-run relationship exists between the two series. However in our study, we are assuming there is a clear effect of technical change on price series. In fact, we expect to find a significant relationship between the lagged technological change variables and the current price index. Moreover we expect to find long run relationships, i.e. in levels, between the price deflator and the technical change variables, rather than the short run relationships, as analysed by differentiating the series, given the different nature of the processes measured by these variables. Technical change occurs over a longer time span compared to price movements. This is also reflected in the kind of time series normally available, monthly, quarterly and yearly series for prices, whereas IP data are normally provided as yearly series, with sometimes highly erratic changes between years. Consequently, we started our estimations from the most simple and general model, which regress the price deflator on lagged IP variables. This section now continues by presenting the main empirical results. A large number of equations with various combinations of variables and different time lags, as well as different functional forms have been estimated. The present study only reports those with good properties and significance levels.4 The empirical results first report on the output price, then, results for the capital goods price index regressions are provided and, finally, the wage regression results are presented. 3

With regard to patent registrations we can identify two main "jumps": the first between 1991 and 1992 and the second between 1994 and 1996. The first break in the trend is due to the introduction of electronic applications by the Japanese Patent Office. The second break is likely to be due to changes in the patents and utility models law in 1993 (Granstrand, 1999). In addition, the procedure for utility models changed in 1994 to become faster in response to a general shortening of the product life cycle. See Bosworth et al. (2002) for further discussion. 4 More detail about the results, particularly regarding the producer price index, can be found in Bosworth et al. (2002).

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Table 1 Descriptive statistics for variables used in estimation Variables

Definition

Means

Standard deviations

Price equations, 1960–1998 PI Price index for all commodities FPI Fuel price index PAa Patent applications UMA Utility model applications DA Design applications TMA Trademark applications PR Patent grants UMR Utility model registrations DR Design registrations TMR Trademark registrations PFb Patents in force UMF Utility models in force DF Designs in force TMF Trademarks in force

80.93 108.41 216.60 129.46 45.38 136.58 55.68 43.24 28.12 90.75 382.71 290.42 169.11 813.46

24.15 52.57 120.60 61.52 9.40 53.84 40.21 14.58 8.88 51.16 214.80 106.73 85.85 355.15

Capital goods variables, 1967–1995 KPI Price index for capital goods KP Patent applications for capital goods KUM Utility model applications for capital goods KD Design applications for capital goods KTM Trademark applications for capital goods

87.93 124.89 69.07 7.49 23.27

15.48 62.28 28.40 1.69 5.55

Wage equation variables, 1968–1997 CE Monthly contractual earnings (index form) AGE Average age of workers DUR Average years of service OR Ratio of union member workers to total OCC Occupational variable (constructed) GF Ratio of new graduates to total employees

71.52 36.67 9.68 29.56 85.99 1.67

32.22 2.01 1.39 4.24 29.47 0.25

Note: the means and standard deviations vary with the sample period and with the number of lags used in the regression. Source: Japanese Yearbook of Statistics. a All IPR variables are in thousands. b IPRs in force are for 1960–1997.

3.3.2. Producer price index regressions The first model to be estimated is the price index for all commodities. Table 1 sets out the definition and descriptive statistics for the variables. To mitigate the difference of measurement scales between price indices and IPRs, all the IPRs are presented in thousands. The variable pt is the producer price index (PPI), while the attempt to capture purely inflationary pressures, Zt, was represented by the fuel price index (FPI) and capacity utilisation (KU), although we did investigate other proxies for this influence. Of these two measures, the FPI was always preferred, consistent with the fact that Japan is an economy that highly dependent on foreign sources of fuel, the price of which is largely exogenously determined. Some of the partial correlations between explanatory variables are quite high, especially between trademarks and patents, although there are other examples. In principle, these

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could introduce multicollinearity problems in the estimation of our models and, on occasion, there may be problems in obtaining robust coefficient estimates. In practice, this does not appear a major problem. In addition, in terms of the separation of price and quality effects, the key correlation in our specifications concerns the fuel price and the IP variables. Here we generally see much lower correlations. These correlations are further weakened when we move to the current FPI and lagged IP, as reported in the results below. Some care needs to be taken, however, in the case of specifications that use both FPI and utility model or design applications. A key test lies in the robustness of the coefficient estimates. The first stage is to attempt to separate the effects of pure price and quality change from the producer price index. Based upon a number of exploratory estimations we found that PPI and FPI cointegrate, that is their movements over time are well represented as short term changes around a long term relationship, with the inclusion of some dummies corresponding to the oil shocks. Given this finding, therefore, we started from a very simple model exploring the effects on PPI of lagged patent grants and the current FPI. As we noted above, our a priori expectation was that patent grants ought to provide a better measure of technical change than applications. Hence, we began with the grant and registration data. Although our feasible data-set for this model is 1961–1998, we only obtained a good model for the shorter period of time 1961–1991, after which the patent registration series shows a diverging pattern compared to the PPI.5 As we have already noted, the significance of the patent grant variable falls away if the data period is extended to 1998, for the reasons outlined above. Hence, we also explored the role of patent applications. The results suggest that patent applications not only work just as well as patent grants over the period 1961–1991, but also applications continue to work well when the sample period is extended to 1998. The results now suggest a somewhat longer lag on the patent variable, as might be expected given the delay between application and grant. In addition, the model includes a dummy variable for 1980, which follows the oil crisis of 1979. Some slight residual autocorrelation emerges but not at a worrying extent (both using DW, which lies in the inconclusive interval, and the AR F-test). Correcting for residual autocorrelation using autoregressive least squares, however, provides similar coefficients. The lagged residual is significant at around the 10 per cent level, but the similarity of the coefficients suggests that autocorrelation is not a major problem in the previous regression. There are some issues to take into account in models that introduce additional IP variables. First, the short sample is not long enough to build models with a high number of explanatory variables. Second, the correlation between our IP variables may cause some problems in terms of obtaining robust coefficient estimates. In practice, patents and trademarks appear to do broadly the same job in these regressions, and we found it difficult to obtain meaningful coefficient estimates on these variables when they were both included in the model. Further evidence of this problem can be found in the fact that the best two 5 The results in levels for the period 1961–1991 show that, in addition to FPI, the patent registration variable lagged one period is also highly significant. The R2 is high, as expected in a ‘‘levels’’ regression, and the Fstatistic is significant at the 1 per cent level. The diagnostics suggest that there are no problems of heteroskedasticity or normality.

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Table 2 Price index hedonic regression results Sample

Constant FPI

PA (2)

UMA (1) DA (3)

ID80

Ad. R2

DW

1

1962–1998

–

1.29a

–

0.9820

1.47

3

1963–1998

0.0376 (0.0126) –

12.859 (3.5568) 13.239 (3.2266) 13.027 (3.3213)

0.9789

1963–1998

0.1493 (0.0050) 0.1532 (0.0047) 0.1402 (0.0060)

–

2

19.1530 (1.6831) 16.2100 (1.8378) 13.0190 (2.8205)

0.9809

1.42

0.2924 (0.0110) 0.2650 (0.0138) 0.2596 (0.0166)

0.2547 (0.0990)

Figures in parentheses are standard errors. a Lies in inconclusive area at 1 per cent level but suggestive of positive first-order correlation at 5 per cent level.

models include either patents applications lagged two periods and designs applications lagged three periods, or patents applications lagged two periods and utility model applications lagged one period. These results are shown in Table 2. The coefficients on FPI (at around 0.26–0.29) and lagged patent applications (at around 0.14–0.15) are largely unchanged between the alternative models. The first model, which includes just the patent IP measure, shows evidence of slight autocorrelation, which can be corrected by using autoregressive least squares. While the lagged residuals in the ALS version stand to be significant at the 1 per cent level, there is little change in the parameter estimates, although, where included, the coefficient on lagged designs rises slightly. The dummy variable relating to the pre- and post-1980 period continues to be significant throughout. We continued to check whether patent registrations out-performed applications in any of these models. However, the registration version of the model only had good diagnostics over the shorter period, 1961–1991, and consistently poor diagnostics when extended to the full data period. Neither the introduction of a dummy variable or a trend variable for the post-1991 period corrected the problem with the registration model, giving unsatisfactory diagnostics and adversely affecting the coefficient of the patent registration variable. In general we can conclude from these models that applications tend to better represent the impact of technological change on price indexes over the longer period. Finally, we discuss the introduction of trademarks into the model. As noted above, trademarks tend to do broadly the same job as patents, and it is not possible to obtain a satisfactory specification when both variables are included together. Interestingly, trademarks were not subject to legal and administrative changes in the early 1990s, and it is possible to find a specification for the entire sample period through to 1998, in which trademark and design registrations (appropriately lagged) are both significant and the diagnostics are acceptable. This suggests that, if this exercise were undertaken at a sectoral level, trademarks might well play a useful role for those areas of activity where there is little or no patenting activity.6 6 Indeed, when using either Japanese or foreign IP series, rather than the total of both, we only found meaningful results when using trademarks and designs.

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D. Bosworth et al. / Japan and the World Economy 17 (2005) 1–23

It was decided to use the second equation of the two specifications discussed for three reasons. First, the adjusted R2 gives the highest value. Second, each of the diagnostic statistics shows satisfactory properties. More importantly, the two time lags for patents and utility models are within expected lengths whereas that of design in the third equation is 3 years, which a priori seems too long. The resultant hedonic price index regression model used to a construct quality-adjusted price index is then written as: PPIt ¼ 16:210 þ 0:2650FPIt þ 0:1532PAt2 þ 0:0376UMAt1 þ 13:239ID80t (9) The coefficients of the IPR variables can be interpreted as a change in the price index caused by a unit change (thousands) of IPRs. Therefore, an increase of 1000 in patent applications results in an increase of 0.153 percentage points in the price index after 2 years and an increase of 1000 in utility model applications causes an increase of 0.038 percentage points in the price index after one year. Overall, the coefficients of FPI, the inflationary variable are from 0.25 to 0.29 in these equations, which implies that a one point increase in FPI generates an increase of 0.25–0.29 percentage points in the price index. 3.3.3. Capital goods price index regression The next model to be estimated is a hedonic price index for capital goods. Definitions and descriptive statistics are again presented in Table 1. As mentioned earlier, due to a shorter period over which the data are available, the sample size here is more limited. However, it is expected that nearly 30 observations can serve as a reasonable sample to estimate the relationship between capital goods prices and the IP measures. As in the case of aggregate IPRs, patents and utility models exhibit a negative partial correlation, but the value is close to zero. However, patents remain quite strongly correlated with both designs and trademarks, but much less so with the measures of Z (i.e. the FPI). The resulting regression results are presented in Table 3 below. Again, we have chosen these specifications from a wider range of results with various measures of the IP variables and lagged effects. The first equation is linear in form, corresponding to the results in Table 2 above. They suggest that a unit increase in the fuel price index increases capital goods prices by about 0.16 percentage points (the ‘‘pure-inflation effect’’), while a 1000 increase in the number of capital goods patents (lagged one period) increases the capital goods price index by just over 0.18 percentage points. In this equation, the trademark variable (also lagged one period) is also close to being significant at the 5 per cent level, showing a larger coefficient than in the case of patents (i.e. a 1000 increase in capital goods trademarks leads to just over a 0.28 percentage point increase in the capital goods price index). Thus, the patent and trademark coefficients comprise the ‘‘quality effect’’. For the capital goods price index, the log–log form equations appear, on purely statistical grounds, to show potentially better results (see the second and third rows of the table). In a log–log form regression model, coefficients of independent variables can be interpreted as the elasticity of a dependent variable with respect to each independent variable. From the two double-log equations in the table, the elasticity of the capital goods price index with respect to FPI is about 20 per cent, while the elasticity with respect to patents is about 34 per cent. However, in those two equations, the coefficients on the design variable have negative signs, suggesting that the capital price elasticity with respect to

D. Bosworth et al. / Japan and the World Economy 17 (2005) 1–23

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Table 3 Capital goods price index hedonic regression results Dependent Sample variable

Constant

FPI

KP (1)

KPI

1968–1996 1968–1995

0.1610 (0.0117) –

0.1829 0.2847 (0.0144) (0.1627)

ln KPI ln KPI

1968–1995

40.001 (3.4437) 2.3242 (0.0706) 2.3826 (0.0731)

–

KTM (1)

–

ln FPI

Ad. R2

DW

0.9471

1.03a

0.2278 0.9767 (0.0404) 0.3097 0.1824 0.9745 (0.0140) (0.0408)

1.02a

ln KP

ln KP (1)

–

–

0.2093 0.3426 (0.0136) (0.0148) 0.2145 – (0.0144)

ln KD

–

1.13

Figures in parentheses are standard errors. a

Lies in inconclusive area at 1 per cent level but suggestive of positive first-order sereal correlation at 5 per cent level.

design is around minus 20 per cent. Given that the IP variables serve as an indicator of quality change, these equations are rejected on a priori grounds. Therefore, the linear form equation, presented in the first row of the table, is chosen as the best model to construct the index: KPIt ¼ 40:001 þ 0:1610FPIt þ 0:1829KPt1 þ 0:2847KTMt1

(10)

3.3.4. Wage index regression The last model to be estimated is that of wages. For the wage equation, various types of labour quality variables and a labour union variable have been introduced. As stated in the beginning, a variable was constructed for occupational structure. The construction of this variable was entirely based on statistical properties. Thus, the wage was first regressed on the occupational breakdown of the workforce, along with FPI.7 Then, only those variables with significant coefficients were taken to construct the occupational structural variable with the intercept term. The occupation variable, OCCt is calculated as: OCCt ¼ 132:00 þ 10:323PTt  4:0400CLt  1:9707AFFt  29:862MINt  8:0888TCt  1:3952CPPt

(11)

where PT denotes professional and technician, CL clerical and related, AFF workers in agriculture, forestry and fishing, MIN miners, CPP craftsmen and production process workers (all the variables are measured as a percentage of the total workforce). Having obtained this variable as a proxy for occupational structure, Table 1 shows the descriptive statistics used in the model. It should be borne in mind that some variables used here are measured in ratios, so they may yield a large coefficient. In particular, GF (the ratio of new graduates to total employees) takes very small values throughout the period, since it is the ratio of new graduates, not cumulative graduate employees, to the total number of employees. Examination of the partial correlations reveals that, as expected, age and tenure are strongly correlated. Age is also highly correlated with the wage variable. The trade union membership ratio is negatively correlated with the other variables. 7

One occupation was dropped as the base group.

16

D. Bosworth et al. / Japan and the World Economy 17 (2005) 1–23

Table 4 Hedonic wage regression results Sample 1 2

Constant

1968–1998 41.930 (2.3070) 1968–1998 80.059 (32.665)

FPI

AGE

OCC

GF

ID72

ID86

Ad. R2

DW

0.0820 (0.0060) 0.0697 (0.0121)

–

1.0297 (0.0167) 0.9430 (0.0760)

8.8485 (1.8324) 8.4726 (1.8471)

6.6290 (1.7870) 6.4785 (1.7786)

10.310 0.9974 (1.7251) 9.4944 0.9964 (1.8488)

1.16

1.2991a (1.1102)

1.10

Figures in parentheses are standard errors. a Insignificant at 5 per cent level.

The regression results for the wage equation are set out in Table 4. The two presented here have acceptable properties. To retain normality, dummy variables for 1972 and 1986 are included in the model. In both equations, OCC and GF have significant coefficients with expected signs. When AGE and DUR are introduced together, DUR always shows a negative sign, due to the high correlation between them. As shown in the model (1), even when not included together with DUR, the coefficient of AGE is insignificant. Overall, OCC and GF always show significant coefficients. Based upon the marginally better adjusted R2 and DW statistic, it was decided to construct a quality-adjusted wage index using model (1). The wage equation can be written as: CEt ¼  41:930 þ 0:0820FPIt þ 1:0297OCCt þ 8:8485GFt  6:6290ID72t þ 10:310ID86t

(12)

In Table 4, the coefficients on FPI are even smaller than in the earlier equations, although they both remain significant at the 1 per cent level. In addition, both OCC and GF exhibit highly significant coefficients with expected signs.

4. Implications of the results for growth and productivity improvement 4.1. Growth in output and inputs We now use the above results to show what difference controlling for quality makes to the official measures of inputs and outputs. Figs. 1 and 2 provide information about the pattern of quality change in the Japanese economy, formed from the weighted sums of the various variables other than those which represent pure-inflationary forces (e.g. the weighted sum of the IP variables in the output price equation). In particular, Fig. 1 demonstrates that the quality of both output and the two inputs increased significantly over the period. While it appears from Fig. 1 that the three variables move together in the early and late parts of the sample period, with labour quality growing significantly more strongly than the other two in the 1970s, Fig. 2 shows that this is an illusion. Fig. 2 suggests that the input qualities relative to output can be divided into two halves, one pre- and one post1980. The pre-1980 period was one in which input quality rose more quickly than output quality, a situation which was reversed in the post-1980 period. It can also be seen that the growth in labour quality outstrips that of capital in the first half of the period, only to fall

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140

Index (1990=100)

120 100 80 60 40 20 0 1962

1967

1972

1977

1982

1987

Output

Capital

Labour

1992

1997

Fig. 1. Quality improvements in inputs and outputs.

more rapidly vis a vis output until the late 1980s, after which they show much the same pattern. These changes in quality drive a large wedge between the official measures of outputs and inputs and those that incorporate quality changes (see Bosworth et al., 2001, 2002 for a detailed discussion). In particular, the differences between the official and revised, qualityinclusive measures appear to be larger for output and the labour input than for the capital input. Again, it can be deduced that these changes in quality relative to output are likely to have important implications for the various measures of productivity. 160

Index (1990=100)

140

120

100

80

60

40 1962

1967

1972

1977 Capital

1982

1987

1992

Labour

Fig. 2. Changes in the relative quality of capital and labour (relative to product quality).

1997

18

D. Bosworth et al. / Japan and the World Economy 17 (2005) 1–23

4.2. Implications of quality for factor productivity measures In this section, we compare the measures of productivity based on the ‘‘official’’ deflators, which do not allow for quality change, and the corresponding series that incorporate quality, constructed using the quality-constant deflators outlined above. Here we report labour productivity, capital productivity and total factor productivity. In each case the labour and capital inputs are adjusted for utilization rates (i.e. person hours and capital utilization). The total factor productivity measures reported in this section are constructed using traditional weights that reflect factor shares, we will return to the implications of adopting more consistent weights later in the paper. Fig. 3 shows three alternative measures of labour productivity, all three measures are indices with a value of 100 in 1970. The measure based upon the official deflator for output lies approximately in the middle of the other two. It rises by a factor of just over 4.5 over the period 1967–1997. The highest line at the end of the period shows the quality adjusted output per person hour. This line has a far steeper slope than the other two because output quality has grown significantly over the period, and this measure has no commensurate adjustment to the quality of the labour input. Thus, output per worker hour, by this measure, grows by a factor of about 7.5 over the period 1967 to 1997. The final line in Fig. 3 uses the same quality adjusted measure of output, in this instance divided by the corresponding quality adjusted measure of the labour input. It can be seen that this reduces the apparent rate of labour productivity improvement (and, indeed, it shows a smaller increase over the period than the official series), but the series still rises by a factor of 3.5 over the period from 1967.

600

Index (1970=100)

500

400

300

200

100

0 1960

1965

1970

1975

1980

1985

1990

Real value added per person hour Quality adjusted real value added per person hour Quality adjusted real value added per quality adjusted person hour

Fig. 3. Labour productivity.

1995

D. Bosworth et al. / Japan and the World Economy 17 (2005) 1–23

19

130 120 110

Index (1970=100)

100 90 80 70 60 50 40 1961

1966

1971

1976

1981

1986

1991

1996

QARCI Real value added per unit capital utilised QARCI Quality adjusted value added per unit capital utilised QARCI Quality adjusted real value added per unit quality adjusted capital utilised

Fig. 4. Capital productivity.

Fig. 4 shows the results for capital productivity. In the case of the series derived using the official deflators, capital productivity rises in the early part of the period, peaks in 1974 and then falls significantly to a low in 1997. By 1997, capital productivity was 50 per cent down on its 1968 value and 60 per cent down on its peak value. Clearly, adjusting output for quality change and not the capital input, has the effect of raising the capital productivity series towards the end of the period. In this instance, however, the measure has little meaning, and we turn instead to the index where both output and capital are quality adjusted. It can be seen that this series follows the ‘‘official’’ measure quite closely, though there are some minor differences. Thus, it can be said that, broadly speaking, the quality adjustment to capital largely off-sets that for output. The ‘‘double-adjusted’’ series, therefore, also shows a significant fall from its peak in 1972, declining to 60 per cent of its peak value, and to 50 per cent of its starting value. Thus, the capital productivity index shows that gains in labour productivity over the period from the early 1970s were partly the result of the substitution of capital for labour, resulting in a fall in capital productivity. 4.3. Implications of quality for total factor productivity measures In the conceptual discussion surrounding total factor productivity measurement, we noted two major issues. First, that measures based upon official deflators would have errors in both the top (real value added) and bottom (real capital and real labour) of the TFP ratio because of the failure to ‘‘force’’ quality into both the input and output measures. Second,

20

D. Bosworth et al. / Japan and the World Economy 17 (2005) 1–23 0.90 0.80

Factor proportions

0.70 0.60 0.50 0.40 0.30 0.20 0.10

Official labour share

Quality adjusted labour share

Official capital share

Quality adjusted capital share

19 96

19 94

19 92

19 90

19 88

19 86

19 84

19 82

19 80

19 78

19 76

19 74

19 72

19 70

19 68

0.00

Fig. 5. Official and quality adjusted factor shares.

that the weights, as they are traditionally constructed—as factor shares, contain an inflationary component that should not be present. In essence, whenever the rate of inflation differs between inputs and outputs (or between the two inputs), there will be a miss-measurement of TFP. The idea is to use this section to illustrate these two issues using the Japanese data described above. Fig. 5 makes clear just how important the miss-measurement of the weights may be. The factor shares based on the official data are shown in Fig. 5, in the form of 3 year moving averages. As expected, they are mirror images of one another, summing to unity. The pair of lines closest to the centre of the figure are based on the official statistics, and those furthest from the centre are adjusted proportions. The main feature of the weights is that labour’s share exceeds capital share and that although they move apart during the middle of the period, they stay between quite narrow bounds, with little evidence of any major trend. In order to derive the adjusted series, we have multiplied the weights by the ratio of ‘‘pure product inflation’’ to ‘‘pure capital inflation’’ or the corresponding ratio for labour. This has the effect of making the weights reflect just the quality and volume components. The result is shown in the further (outer) two series in Fig. 5. While, as expected, they remain mirror images, they now exhibit a clear long-term trend. Capital share starts from around 22 per cent in 1968 and grows significantly to around 50 per cent by the end of the period. This seems much more consistent with the growing quality and volume of capital as outlined in earlier sections. Such a major change in the weights is clearly going to have important implications if used in the construction of a TFP series. Fig. 6 shows the corresponding results for total factor productivity. The total factor productivity measure based on the official deflators and corresponding unadjusted factor shares reaches a value by the end of the period about 50 per cent higher than its starting

D. Bosworth et al. / Japan and the World Economy 17 (2005) 1–23

21

200 180 160

Index (1970=100)

140 120 100 80 60 40 20

Official TFP - official weights

19 96

19 94

19 92

19 90

19 88

19 86

19 84

19 82

19 80

19 78

19 76

19 74

19 72

19 70

19 68

0

Quality adjusted TFP - official weights

Quality adjusted TFP - quality adjusted weights

Fig. 6. Total factor productivity.

value, although the end of period value is only about 8 or 9 per cent higher than its peak value in 1973. The inclusion of quality in both the input and output measures, without adjusting the weights in a consistent manner, leaves the overall pattern largely unchanged, although the degree of movement is considerably larger from peak to trough in the adjusted series. Changing the weights in the way described above, however, has a more dramatic effect on the TFP measure, which now shows a much more significant rise over the period, whilst maintaining the same general pattern. While the end of period value is 84 per cent higher than the starting value, the peak in 1993 is over 30 per cent higher than the peak that occurred 20 years earlier.

5. Conclusions This paper demonstrates the need to adjust for quality change in order to more fully understand the growth of the Japanese economy and the sources of productivity growth. In particular, we have shown that it is possible to use measures of intellectual property activity, such as patents and designs, to proxy for changes in the quality of products and capital goods at an aggregate level, thus avoiding the need to use disaggregate growth accounting techniques. Similarly, we used measures of the changing characteristics of labour to separate the quality and inflationary components of wage change. The use of time series hedonic methods, therefore, enables separate indices of the trends in quality to be constructed for all products, capital goods and labour. We have shown that the quality of labour rises from an index of about 40 in the mid-1960s to well over 100 by

22

D. Bosworth et al. / Japan and the World Economy 17 (2005) 1–23

the late 1990s. This increase was larger than that of capital and all products, although both of these improved strongly, from an index of about 60 to nearly 120 by the end of the period. However, the pattern of relative quality improvements vary over time, with output quality failing to keep pace with labour quality for the first half of the period, and outpacing labour quality at the end of the period. When these quality components are taken into account, real value added, the real capital input and the real labour input therefore not only show somewhat different year-on-year changes, but, overall, rise much more strongly than the official figures suggest. The effect of adjusting for quality change in output, therefore, has a very important impact in showing much more rapid growth in output per person or, in the present study, output per person hour in the incorporated sector. The impact of adjusting for quality improvements, however, can have a complex impact on a number of the productivity measures as quality change has to be taken into account for both outputs and inputs. Given that labour quality has grown more quickly than output quality over the period as a whole, it is not surprising to find that, overall, adjusting for quality tends to reduce the rate of growth in labour productivity somewhat below the one obtained when using the ‘‘official’’ price deflator. In addition, the pattern is different over time, with the two series (quality adjusted and ‘‘official’’) rising in step for the first third of the period, the quality adjusted series falling below the official figures for the middle third of the period and, finally, the quality adjusted rising more strongly than the official series in the final third. Output per unit of capital rose in the first third of the period, but then fell away significantly as Japan substituted capital for labour. While there are differences between the rates of growth in the quality of output and capital goods, the two series follow each other quite closely over the longer term. Thus, the picture painted by both the quality adjusted output per unit of capital and the corresponding series based on the official deflator are quite similar. The patterns shown by the labour and capital productivity series can then be traced to the measure of total factor productivity, reflecting the three periods described earlier. While adjusting for quality changes in the input and output measures (without adjusting the weights used in aggregation) make relatively little difference to the overall change from the beginning to the end of the period, there is more movement within the period. However, the failure to remove pure price changes from the factor shares that form the weights means that they are miss-measured. This paper shows that such an adjustment has important implications for both the weights themselves and for the resulting TFP series. The weights now show a secular trend towards capital, and the resulting TFP series shows a significantly greater increase over the period as a whole.

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