The Impact of Technology Shocks on the Japanese Business Cycle -An empirical analysis based on Japanese industry data-
November 2005 February 2006 (revised)
Tsutomu Miyagawa (Gakushuin University) Yukie Sakuragawa (Atomi University) Miho Takizawa (Hitotsubashi University)
* This paper was presented at the workshop in Gakushuin University, Social Economic Research Institute in Osaka University, Institute of Statistical Research, and The 2nd book conference for the research project ‘Empirical Analyses of Economic Institutions’ organized by Professors Fumio Hayashi, Charles Horioka, and Ken Ariga in Tokyo . An anonymous referee, Professors Ken Ariga, Fumio Hayashi, Charles Horioka, Kazuo Ogawa, Fumio Ohtake, Kaoru Hosono, Hidehiko Ishihara, Makoto Saito, Masaya Sakuragawa, and participants of the workshops helped us to improve our paper. Our research is supported by Grants-in-aid No. 12124202, No. 14203001, and No. 15330043, and by the Hi-Stat project of Hitotsubashi University (COE program) financed by the Japanese Ministry of Education, Culture, Sports, and Science and Technology. All remaining errors are the authors’ responsibility.
Abstract In order to investigate the impacts of technology shocks on the recent Japanese business cycles, we construct the aggregate technological measure from industry-based data. Our approach is to estimate production function by industry, by controlling for returns to scale factor and unobserved factor utilization. We find that positive technology shocks result in a contraction of labor input on impact. This result implies that the standard RBC model is not supported and the new Keynesian model or the labor reallocation model is a candidate to explain the Japanese business cycles. From further empirical studies, we find that the labor reallocation model is plausible for explaining the Japanese business cycles. Keywords: Real business cycle model; New Keynesian sticky price model; Labor reallocation model; Solow residual; The BFK measure of technology index; Impulse response. JEL Classification code: E32, E47 Tsutomu Miyagawa Professor of Economics, Department of Economics, Gakushuin University 1-5-1, Mejiro, Toshima-ku, Tokyo, 171-8588, Japan E-mail:
[email protected] Yukie Sakuragawa Faculty of Management, Atomi University 1-9-6 Nakano Niiza-shi Saitama, 352-8501, Japan E-mail:
[email protected] Miho Takizawa Graduate School of Economics, Hitotsubashi University 2-1 Naka, Kunitachi City, Tokyo 186-8603, Japan E-mail:
[email protected]
1
1. Introduction After more than a decade of stagnation, the Japanese economy finally appears to be regaining momentum. Yet, although a number of explanations have been advanced, the reasons for that stagnation are still not well-understood One of the most influential explanation proposed is that put forward by Hayashi and Prescott (2002). Employing a standard neo-classical growth model, they argued that the stagnation of the Japanese economy during the 1990s was primarily the result of a decline in total factor productivity (TFP). According to the standard RBC model, movements in TFP which is calculated as the Solow residual represent technological progress in Hayashi and Prescott(2002) However, Hall (1988, 1990) has already pointed out that not only technological progress but also demand factors such as aggregate demand, the utilization rate of capital and labor, and labor hoarding behavior affect fluctuations in TFP. Then, a number of Japanese economists have focused on causes of fluctuations in TFP in the Japanese economy in order to confirm the argument of Hayashi and Prescott(2002) Following Hall (1988, 1990) and Basu and Fernald (1995), (1997), Kawamoto (2004) as well as Miyagawa, Sakuragawa, and Takizawa (2005) (hereafter MST) examined what factors explained fluctuations in TFP in Japan by estimating production functions by industry. Kawamoto (2004) came to the conclusion that the utilization rates of labor and capital played a crucial role in the fluctuations in TFP. In contrast, MST (2005) concluded that a technological factor which they calculated by subtracting the effect of increasing return to scale, utilization rate, and demand externalities from the traditional Solow residual was a key factor of the fluctuations in TFP and supported RBC (real business cycle) model. Though the above results examined causes of fluctuations in TFP, they did not analyze how technology shocks affect macroeconomic variables. In the standard RBC model, a positive technology shock leads to an increase in labor input and other inputs immediately because firms are not constrained by the demand which firms face. However, in a new Keynesian dynamic general equilibrium model, the demand which firms face constrains their production and labor hours reduces in the short run, even when technology shocks occur. Based on the above theoretical features of the two models, Galí (1999)
showed
that in most advanced countries technology shocks reduced labor hours in the short run. This finding has inspired a number of studies examining the response of macroeconomic variables, such as labor hours, output and investment, to technology shocks in order to
2
determine which of the two models, the RBC or the new Keynesian dynamic model, is supported by the evidence. One such study is that by Basu, Fernald and Kimball (2004; hereafter: BFK). In contrast with Galí
who estimated a structural VAR model to examine the effects of
technology shocks, BFK constructed a technology measure by assuming that firms optimized cost function under imperfect competition and subtracting effects of utilization rate in labor input and capital input and scale factors from the conventional Solow residual. They estimated production function parameters considering the utilization rates of labor and capital by industry and aggregated a technology measure in each industry. They recognized this aggregate technology measure as an aggregate technology shocks and examined the response of labor hours to the technology shocks. As a result, they showed that the aggregate technology shocks reduced labor hours like Galí (1999). Using the technology measure developed by BFK (2004), Malley, Muscatteli, and Woitek (2005) and Marchetti and Nucci (2005) tackled this issue by using industry-level or firm-level data. The former paper proposed a sticky wage model in addition to the standard RBC model and new Keynesian sticky price model and paid attention to the response of real wage to technology shocks as well as the response of labor hours. Their results implied that the standard RBC model or the sticky wage model is more supportive than the new Keynesian model. The latter paper divided their dataset into firms that tended to stick their prices and those with flexible prices and showed that technology shocks in firms with stickier price had contractionary effect on labor inputs. The purpose of our paper is to investigate responses of aggregate variables to a positive technology shocks by using industry-based data and measuring technology shocks following the methodology of BFK (2004). There have been a couple of researches in this field. Braun and Shioji (2004) examined the responses of work hours to technological shocks by using the VAR method considering the criticism of Galí’s method from Christiano, Eichenbaum and Vigfusson (2003). Their estimation results supported a positive response of work hours to a positive technology shocks. In contrast with the results by Braun and Shioji (2004), Kawamoto and Nakakuki (2005) showed the contractionary effects of a positive technology shocks. Following BFK (2004), they used industry-based data (KEO database constructed Kuroda et. al (1996), and Nomura (2005)) and measured the index of technology shocks by estimating production functions by industry. Aggregating technology shocks in each industry and estimating bivariate VAR model with aggregate technology shocks and labor hours, they found the negative response of labor hours to technology shocks.
3
Though our method in the paper is similar to Kawamoto and Nakakuki (2005), our data is different from theirs. We use the Financial Statements Statistics of Corporations (hereafter FSSC) published the Ministry of Finance covers not only the manufacturing sector but also non-manufacturing sector, while Kawamoto and Nakakuki (2005)’s paper focused on the manufacturing sector. In addition, our dataset is quarterly, while the dataset in Kawamoto and Nakakuki (2005) is annual. The merit using the quarterly data is that we can capture the features of the business cycles well. Our study examined more models than Kawamoto and Nakakuki (2005). They studied the plausibility of the two representative models; the standard RBC model and the new Keynesian sticky price model. However, we examined an alternative model as well as the standard RBC model and the new Keynesian model. In the case that negative response of labor hours is observed, we study the plausibility of the labor reallocation model as an alternative model of the new Keynesian model as BFK (2004) and Marchetti and Nucci (2005) showed. The remainder of this paper is organized as follows. In the next section, we explain the cost minimizing firm behavior developed by BFK (2004) and estimate production function for each industry. Like MST (2005), we use FSSC quarterly data. Using the estimation results for each industry, we construct the measure of technology shocks and study its features. In the third section, we examine the effect of aggregate technology shocks on macroeconomic variables. Especially, we focus on the impact of technology shocks on labor variables to examine which model provides a more plausible explanation of the recent Japanese business cycles. In this analysis, we find the negative response of labor hours in all estimations of the whole period. Then, the new Keynesian sticky price model or labor reallocation model is a candidate to explain the business cycle in this period. In the fourth section, we examine which model is more plausible to explain the Japanese business cycle. 2. Measuring BFK index of technology improvements In this section, following BFK(2004), we describe the behavior of a cost minimizing firm. According to this behavior, we estimate production function for each industry by using industry-level data and measure the index of technology improvements. Then, we aggregate technology measure and examine its features. Gross output in industry i is given by
Yit = F ( Lit , Z it , M it , Θit ) ,
(1)
4
where Yit denotes gross output, Lit stands for effective labor input, Z it for effective capital services, M it for intermediate inputs of energy and materials, and Θit is
(= N it H it E it ) is decomposed into three factors: the number of employees N it , labor hours per worker H it , and labor effort Eit . Similarly, Z it comprises the capital stock K it and the capital utilization rate U it , such that Z it = K itU it . The function F (⋅) is assumed to be homogenous of degree γ in total inputs. We observe only the number of employees N it , labor hours per worker H it , capital stock K it , and intermediate inputs M it . Labor effort Eit and the capital utilization rate U it are not observed.
defined as the technology index. Here, Lit
Taking the log of all variables in equation (1), differentiating them with respect to time, and separating the observed and unobserved inputs, we obtain
Δy it = γ i (Δxit + Δa it ) + Δθ it ,
(2)
where
Δxit ≡ cLi (Δnit + Δhit ) + cZi Δkit + cMi Δmit Δait ≡ cLi Δeit + cZi Δuit . For any variable Qit , Δqit represents the growth rate of Qit and c ji denotes the cost-based share of factor j (=L, Z, M). Following BFK (2004), we assume that firm i faces adjustment costs to investment and hiring. Moreover, we assume that higher utilization or higher effort raises the firm’s costs. Similarly, longer labor hours also raise the firm’s costs because of the need to pay a shift premium (e.g. for overtime, night-time and week-end work). The firm minimizes the present value of expected costs subject to equation (1), capital accumulation and labor hiring: ∞
s
Et ∑ [∏ (1 + rτ ) −1 ][WNG ( H , E ) X (U ) + PM M + WNΨ (O ) + PI KΛ ( J )] , (3) N K H , E ,U , M , J , O s −t τ =t Min
(we omit the subscript i) subject to K i t +1 = J it + (1 − δ ) K it , and
N i t +1 = Oit + N it ,
where rt is the discount rate of the firm, W is a base wage which does not include the costs of overtime work.. PM is the price of intermediate inputs, PI is the price of investment goods, O is the hiring net of separations and J is the gross investment. The functions Ψ (⋅) and Λ (⋅) are adjustment cost functions of labor hiring and
5
investment, respectively. The function G ( H , E ) specifies how the hourly wage depends on the length of the workday and workers’ effort and X (U ) is the shift premium. Thus,
WNG( H , E ) X (U ) represents the total compensation associated with changes in labor hours, the labor effort, and capital utilization, WNΨ (O changing the number of employees, and PI KΛ ( J
K
N
) represents the total cost of
) represents the total cost of
investment. δ is the depreciation rate. After obtaining the first order conditions of Equation (3), the following equation implicitly relating E and H is lead.
HGH ( H , E ) EGE ( H , E ) = , G( H , E ) G( H , E ) where Gs ( s = H , E ) denotes the derivative of the labor cost function with respect to
s . The elasticity of labor cost with respect to E and H must be equal because the elasticity of effective labor input with respect E and H is equal in terms of disutility of labor. From this relation and the assumption of convexity and normality in G , we can express the unobserved intensity of labor utilization E as a function of observed hours per worker H , i.e. E = E ( H ) , E ' ( H ) > 0 . Definig
ξ as the elasticity of effort
with respect to hours and log-linearlizing E = E ( H ) , we find the relation that
de = ξ dh . From the first order conditions of Equation (3), we also obtain an equation implicitly relating U and H
FZ Z / F HGH ( H , E ) −1 UX ' (U ) =[ ] [ ] FL L / F G( H , E ) X (U ) where Fs ( s = L, Z , M ) denotes the derivative of the production function with respect to s . We rewrite the above equations as follows. First, as in Hall(1990), cost minimization implies that the ratios of output elasticities are proportional to factor cost shares, i.e.
FZ Z / F α K = . Second, define g ( H ) as the elasticity of labor cost with FL L / F α L
respect to hours: g ( H ) =
HGH ( H , E ( H )) . Third, define x(U ) as the elasticity of G ( H , E ( H ))
6
labor cost with respect to the workweek of capital: x(U ) =
AX ' (U ) . Then we can X (U )
express the following equation,
x(U ) = (α K / α L ) g ( H ) . Denoting the elasticity of labor cost with respect to hours as η and the elasticity of labor cost with respect to the capital utilization rate as
ω and assuming that
(α K / α L ) is constant, we obtain the relation da = ⎛⎜η ⎞⎟ dh . Thus, we find that the ⎝ ω⎠ rate of change of effort and the utilization rate are proportional to the growth rate of hours. Arranging the above relations, we rewrite Equation (2), using only observable variables, as
Δy it = γ i Δxit + γ i {ξc zi + ⎛⎜η ⎞⎟c Li }Δhit + Δθ it ⎝ ω⎠
(4)
= γ i Δxit + φi Δhit + Δθit , where
φi = γ i {ξc Zi + (η / ω )c Li } .
This is the equation we estimate in order to measure the index of technology improvements for 33 industries. The main data set is constructed from FSSC data. Real gross output is real sales after inventory adjustment. Production factors are constructed as follows. Capital stock is calculated by perpetual inventory method. For our nominal investment data, we use the increase in tangible fixed assets excluding land and construction in progress from the detailed descriptions of the transactions in tangible fixed assets in the FSSC. Deflator in investment goods and average depreciation rate are taken from JIP database (Fukao et, al,(2003)). For the labor force series, we use the number of employees provided in the FSSC. To construct the labor input series on a man-hour basis we adjust the number of employees by hours worked provided in the
Report on the Monthly Labor Survey. Real intermediate input series are calculated by subtracting real value-added from real output. Real value-added is defined as a sum of real labor cost, real depreciation value and real operating income. We adjust the discontinuity of FSSC data every April by using the method in Ogawa and Kitasaka (1998). 1 The classification of industry is described in Table A-1. The descriptive statistics are shown in Table A-2. We use the 3SLS method to overcome the problem of 1
More detailed description of the data construction was written in MST (2005). 7
simultaneity between exogenous technology shocks and the inputs used in production. We use the following variables as instruments: the diffusion index of financial institutions’ lending attitude published by Bank of Japan, the rate of change in the price of intermediate inputs, the diffusion between the current temperature and the average one, and the nominal exchange rate.
2
Conserving the number of coefficients, we aggregate the 33 industries into three groups (durable manufacturing industry (industry nos. 5, 10-19), non-durable manufacturing industry (industry nos. 2-4, 6-9), and non-manufacturing industry (industry nos. 1, 20-33) and impose the restriction that the coefficient on the utilization rate ( φ ) is the same within the three industry groups. We restrict our estimation period to the time from the first quarter of 1983 to avoid the inclusion of distortions brought about by the two oil shocks. Our estimation results are shown in Table 1. They suggest that the majority of the 33 industries display constant returns to scale, confirming the results of MST (2005) who carried out the estimation of Equation (4) without restriction with respect to utilization rate. The estimated returns to scale parameters ( γ ) are 1.14 for the durable manufacturing sector, 1.18 for the non-durable manufacturing sector, and 1.10 for the non-manufacturing sector. The coefficient on the utilization rate in each sector is negative and insignificant. Our results imply that the conventional Solow residual is affected by increasing returns to scale in some industries. However, we do not observe any effect of the utilization rate on the fluctuations of the conventional Solow residual. These results are conflict with those of BFK (2004) and Kawamoto and Nakakuki (2005). 3 (Insert Table 1) We now turn to the calculation of the technology measure at the aggregate level by using the above estimation results. Weighing industries using the Domar weight is calculated we aggregate the technology measure for each industry.
We investigate weak instrument problem in our estimation. F-value in the first stage of 3SLS estimation is 3.54. This value is not high compared to the value Staiger and Stock(1997). However, F-values of the estimations without one instrument ( nominal exchange rate, diffusion index, or temperature) are similar to the basic estimation. In addition, the results in these estimation are also similar to the basic estimation. 3 Using detrended labor hour data constructed by Hodrick=Prescott Filter, we tried to estimate Equation (4). However, we could not find the significant effect of utilization rate on the Solow residual. 2
8
33
Δθ t = ∑ ( i =1
Svit )Δθ it 1 − cMt
(5)
where S vi is the share of the value-added in industry i in the total of the 33 industries. We call Δθ t ‘the BFK measure of technology index’. 4 The statistical features of the BFK measure are summarized in Table 2. The table shows that the mean of the BFK measure in all industries does not decrease from the 80s to 90s, although it is not depart from the mean of the traditional Solow residual. This result is consistent with that in Kawamoto (2005). However, the variances of the Solow residual are covered by those of the BFK measure. These results and Figure 1 imply that the fluctuations of the conventional Solow residual is explained by the fluctuations of the BFK measure of technology index and fluctuations in utilization rates and labor hoarding behavior does not explain the movement in the traditional Solow residual. This implication is contrast to the result in Kawamoto (2005). (Insert Table 2 and Figure 1) 3. The response of labor indices to technology shocks BFK (2004) recognized that their measure of technology index fluctuated only by technology shocks and examined the response of output and factor inputs to technology shocks. In this section, we examined the same study as BFK by using our BFK measure. In the standard RBC model, positive technology shocks induces an increase in output and factor inputs. First, we examine simple correlations between the BFK measure of technology index and output or factor inputs. Previous works chose several types of labor inputs such as labor hours per worker, total man-hour and number of employees. In our study, we also provide three types of labor input: labor hours per worker (h), total man-hours (nh), and number of employment (n). Table 3 provides the coefficients of correlation between the purified technology
When the production function does not have a constant return to scale, Domar weight is expressed as follows,
4
33 S Δθ t = ∑ ( vit )Δθ it . i =1 1 − γcMt
However, as we find constant return to scale in many industries in Table 1, we construct aggregate technology index by using Equation (5). 9
residual and various macroeconomic indicators. The results indicate that the contemporaneous correlation between the BFK measure and output is positive and significant. On the other hand, the contemporaneous correlation with investment is insignificant. As for the labor indices, the correlations are negative and significant for all three indicators except the correlation between employment and the BFK measure. Finally, the correlations between the BFK measure and the conventional Solow residual are significant and positive,. Thus, based on the results presented in Table 3, we can say that the simple RBC model is not supported (Insert Table 3) Next, we regress output and factor inputs on current and lagged BFK measure of technology index. The estimating equation is as follows:
Δqt = const. + ∑ α 1i Δθ t −i + ∑ α 2 j Δqt − j i =0
(6)
j =1
q = v, j, h, nh, n where v is aggregate output (=value added). We estimate Equation (6) for the two different periods: the entire observation period (1983:1-2002:4), and the period of stagnation (1991:2-2002:4). 5 Based on the Akaike and Schwartz information criteria, we choose two lags of Δθ and Δq . Table 4 presents the results of the estimations. The estimation of output yields significant positive coefficients on the contemporaneous technology shocks in all estimation periods. Then, positive responses of output to technology shocks appear as Table 3 indicates. In contrast, in the estimation of investment, the coefficients on contemporaneous purified technology shocks are insignificant in all estimations. However , the coefficients on the lagged technology shocks are positive and significant, suggesting that technology shocks stimulate investment with a lag due to the adjustment cost of investment. (Insert Table 4) When the labor indices are dependent variables, all coefficients on the
We omit the results of the estimation for the period before the stagnation (1983:1-1991:1), because the signs of coefficients are same as the estimation results of the two periods.
5
10
contemporaneous technology shocks ( Δθ ) are negative in all estimations. Especially, in the estimations where labor hour and man-hour are dependent variables, the coefficients of Δθ are significant in all estimations. The above results suggest that the standard RBC model is not supported in the Japanese business cycle. To confirm the results of Table 4, we have to investigate the dynamic responses of macro variables to technology shocks. Using the results of Equation (6), we examine the impulse responses of aggregate variables to technology shocks. Figures 2 shows the accumulated dynamic impulse responses to a one–standard-deviation shock in the technology shocks based on the estimation results in the whole period.
6
(Insert Figure 2) Figure 2 shows the output response to a positive technology shock. The response is positive and consistent with the results obtained by BFK (2004) and Kawamoto and Nakakuki (2005). However, the labor input response differs from the results of BFK (2004) and Kawamoto and Nakakuki (2005). In the short-run, a negative labor input response is found, except with respect to employment. This result is in line with BFK (2004) and Kawamoto and Nakakuki (2005). However, in the long-run, the response of labor hours per worker remains negative in our estimation, while the other studies found that the response became positive. Finally, the response of investment to a positive technology shock increases gradually and keeps positive in the long run. This result which is similar to BFK (2004) but in conflict with Kawamoto and Nakakuki (2005) indicates that there exists adjustment cost of investment. As a result of Figure 2, we confirm that the standard RBC model is not supported in the whole period. 4. Can alternative models explain the Japanese business cycles? – the sticky price model vs. labor reallocation model In the last section, we find that the standard RBC model cannot explain the Japanese business cycles. In this section, we would like to specify a model which can explain the negative response of labor inputs to technology. We will focus on new Keynesian sticky price model and labor reallocation model as BFK (2004) suggested. 7 We thank Dr. Kawamoto of Bank of Japan for teaching the program of bootstrap method calculating confidence intervals of impulse responses. 7 BFK (2004) considered two more alternative models: one was a time-to-learn model and the other a ‘cleansing effect of recessions’ model. 6
11
In the sticky price model, a monopolistically competitive firm faces a downward demand curve. In the money-in-utility model, aggregate demand depends on the real money balance. Under the sticky price condition, aggregate demand does not increase without monetary accommodation. In this environment, a positive technology shock decreases labor input under constant demand. The alternative model we consider is the labor reallocation model. Lilien (1982) showed that assuming an uneven response among industries to positive technology shocks, reallocation costs prevent labor from moving to industries that respond positively and therefore reduce aggregate labor inputs in the short run. If the first model can explain the Japanese economy, price level does not respond to technological shocks. To examine the response of the general price level, we estimate the following equation.
Δpt = const. + ∑ β1i Δθ t −i + ∑ β 2 j Δmst −i (or ∑ it −i ) i =0
i =0
(7)
i =0
In Equation (7), we will examine whether
β10 =0 or not. If β10 ≠ 0 , the sticky price
model is not supported.
Δp is change in general price level. We use consumer price index or GDP deflator as an indicator of general price level. In this estimation, we control the effect of monetary policy on the general price level. We choose two types of instruments of monetary policy; money supply and short-term nominal interest rate. We take M1 ( Δms )as an indicator of money supply and overnight call rate ( Δi ) as an indicator of nominal interest rates. The estimation period is from 1983:1 to 1998:4, because zero interest policy was taken and the definition of money supply was changed after 1999. Table 5 shows the estimation results of the Equation (7). After controlling monetary policy, we find the negative and significant response of price change to a positive technological shock. This result implies that the sticky price model is not supported.
8
(Insert Table 5) Next, we turn to examine the plausibility of a labor reallocation model. To investigate whether the labor reallocation model explain the negative response of labor We tried to estimate Equation (7) for the two sub-periods: the 80s and the 90s. The results of the estimations are same as those for the whole period.
8
12
inputs, BFK (2004) introduced an additional variable representing the dispersion of technology shocks into their estimation. 9 Following Lilien (1982) and Mills, Peroni, and Zervoyani(2005), the dispersion measure of technology shocks can be expressed as follows:
Dispt = [∑ vi (ψ it ) 2 ]
where
1
(8)
2
ν i is the share of value-added in industry i andψ it is the residual of the
regression of technology index in industry i on the BFK measure of technology index Although the usual Lilien Measure uses Δθ i instead of
ψ it in (8), we use ψ it in order
to omit the effect of the aggregate technological shock. Using (8), we estimate the following equation:
Δqt = const. + ∑ α 1i Δθ t −i + ∑ α 2 j Δqt − j + ∑ α 3k Dispt −k i =0
j =1
(9)
k =0
q = h, nh, n Table 6 presents the estimation result of Equation (9). In the estimation for the whole period, our results are contrast to those of BFK (2004). 10 As in the regression of labor inputs on the technology shocks (Table 4), the coefficients on technology shocks are negative and significant. However coefficients of the contemporaneous dispersion measures are negative and significant. The result indicates that real inflexibility in the labor market contributes to the negative response of the labor input to the technological shock in the short run. (Insert Table 6)
As a result of Tables 5and 6, the labor reallcation model can trace the Japanese economy more than the new Keynesian sticky price model.
In contrast with BFK (2004), Marchetti and Nucci (2005) tested which model of the two models trace the real economy by separating out in their firm-level dataset firms with stickier prices and then examined the response of labor input to a technology shock. 10 The estimation results for the 80s and for the 90s are similar to the results for the entire period. 9
13
5. Conclusion We have examined three types of business cycle models to explain the Japanese business cycles: the standard RBC model, the new Keynesian sticky price model, and labor reallocation model. A number of studies in the US and Europe have focused on the responses of labor inputs to technology shocks to examine which model has more explanatory power to the real economy. If we observe the negative responses of labor inputs to technology shocks, the new Keynesian sticky price model or the labor reallocation model tends to explain the business cycle, In contrast, the positive responses of labor inputs support to the standard RBC model These models have different policy implications in the short run. The standard RBC model implies that policy interventions to the real economy are ineffective. The new Keynesian sticky price model suggests that the monetary policy can affect the real economy in the short run. The labor reallocation model requires the structural reform removing the real inflexibility in the labor market. To understand which model can explain the features of the recent Japanese business cycles, we examined the impact of technology shocks to labor inputs by using the methodology in BFK (2004). First, we estimate production functions by industry and measure technology index by industry which is not affected by increasing return to scale, utilization rates, and labor hoarding behavior. Next, aggregating the industry-level technology indices, we then examined the response of various macroeconomic variables to technology shocks and investigated which model explains the recent Japanese business cycles. The findings of our study can be summarized as follows. (1)
The aggregate technology measure does not change from the 80s to the 90s. This result is consistent with that in Kawamoto (2005), However, in contrast to his result, the fluctuations in the technology measure cover the movement of the traditional Solow residual and the utilization rate and labor hoarding behavior do not affect the fluctuations in the traditional Solow residual, This implication is consistent with MST (2005).
(2)
In the regression of labor input variables on the aggregate technology measure, the responses of labor inputs to technology shocks are negative in all estimations. In addition, this negative response is persistent. This implies that the standard
14
RBC model does not capture the features of the Japanese business cycles. (3)
To specify a business cycle model which can explain the negative response of the labor inputs, we examine the features of the new Keynesian sticky price model and labor reallocation model. We cannot find price stickiness from the negative and significant response of price change to technological shocks. In contrast, the dispersion measure of technology shocks can explain the negative response of labor inputs. As a result, the labor reallocation model is more plausible to explain the Japanese business cycles than the new Keynesian sticky price model. Our finding that the labor reallocation model is plausible for the explanation of the Japanese economy is consistent with that in Miyagawa, Ito, and Harada (2004) which emphasized that the misallocation in the labor market was a main cause of the productivity slowdown in Japan. Our results does not support that monetary policy is effective in the short run
because the sticky price model does not explain the fluctuations in the Japanese economy. Rather, we recommend structural reform policies to restore the Japanese economy as the negative responses of labor inputs to the technological shock persists in the long run.
15
References Basu, S. and J. G. Fernald (1995) “Are Apparent Productive Spillovers a Figment of Specification Error? ” Journal of Monetary Economics, vol.36, pp.165–188. Basu, S. and J. G. Fernald (1997) “Returns to Scale in U.S. Production: Estimates and Implications,” Journal of Political Economy, vol.105, pp.249–283. Basu, S., J. G. Fernald, and M. Kimball (2004) “Are Technology Improvements Contractionary?” NBER Working Paper Series No. 10592, forthcoming in
American Economic Review. Braun, T. and E. Shioji, (2004), “Nihon ni okeru gijutsuteki shock to sorodojikan [Technology Shocks and Work Hours: Evidence from Japan]”
Keizai Kenkyu
vol. 55, no.4, pp. 289–298 (in Japanese). Caballero R. J., T. Hoshi and A. K. Kashyap (2004) “Zombie Lending and Depressed Restructuring in Japan,”
Online:
. Christiano, L., M. Eichenbaum, and R. Vigfusson (2003), “What Happens After a Technology Shock?” NBER Working Paper No. 10254. Fukao, K., T. Inui, H. Kawai, and T. Miyagawa, (2003)“Sectoral Productivity and Economic Growth in Japan, 1970-98,” T. Ito and A. K. Rose eds., Growth +
Productivity in East Asia, University of Chicago Press, 2004, pp.177-227. Fukao, K. and H. Kwon (2005) “Why Did Japan’s TFP Growth Slow Down in the Lost Decade? An Empirical Analysis Based on Firm-Level Data of Manufacturing Firms,” forthcoming in The Japanese Economic Review. Galí, J. (1999) “Technology, Employment and the Business Cycle: Do Technology Shocks Explain
Aggregate
Fluctuations?”
American
Economic
Review,
vol.89,
pp.249–271. Hall, R. E. (1988).”The Relation between Price and Marginal Cost in U.S. Industry,”
Journal of Political Economy, vol.96, pp.921–947. Hall, R. E. (1990) “Invariance Properties of Solow’s Productivity Residual,” in P. Diamond (ed.) Growth, Productivity, Unemployment, Cambridge, MA: MIT Press. Hayashi, F. and E. C. Prescott (2002) “The 1990s in Japan: A Lost Decade,” Review of
Economic Dynamics, vol.5, pp.206–235. Kawamoto, T. (2005), “What Do the Purified Solow Residuals Tell Us about Japan’s Lost Decade?” Monetary and Economic Studies 23, Institute for Monetary and Economic Studies, The Bank of Japan, pp. 113-148.
16
Kawamoto, T. and M. Nakakuki (2005), “Purified Solow Residual in Japan’s Manufacturing: Do Technology Improvements Reduce Factor Inputs?” mimeo. Kuroda, M., K. Shinpo, K. Nomura, and K. Kobayashi (1996), KEO Database, Tokyo: Keio University Press, (in Japanese). Lilien, D. (1982), “Sectoral Shifts and Structural Change in the Japanese Economy,”
Journal of Political Economy vol. 90, pp. 777–793. Malley, J. R., V. A. Muscattli, and U. Woitek (2005) “Real Business Cycles, Sticky Wages or Sticky Prices? The Impact of Technology Shocks on US Manufacturing,”
European Economic Review, vol.49, pp.745–760. Marchetti, D. J. and F. Nucci (2005) “Price Stickiness and the Contractionary Effect of Technology Shocks,” European Economic Review, vol.49, pp.1137–1163. Mills, T.C., G. Pelloni, A. Zervoyianni (1995), “Unemployment Fluctuations in the United States: Further Tests of the Sectoral-Shifts Hypothesis,” The Review of
Economics and Statistics, Vol.77, No.2, pp.294–304. Miyagawa, T., Y. Ito, and N. Harada (2004) “The IT Revolution and Productivity Growth in Japan,” Journal of the Japanese and International Economies, vol.18, pp.362-389. Miyagawa, T., Y. Sakuragawa and M. Takizawa (2005), “Productivity and the Business Cycle in Japan: Evidence from Japanese Industry Data,” forthcoming in The
Japanese Economic Review. Nishimura, K., T. Nakajima and K. Kiyota (2005), “Does the Natural Selection Mechanism Still Work in Severe Recessions? Examination of the Japanese Economy in the 1990s,” Journal of Economic Behavior and Organization, vol.58, pp.53-78. Nomura, K. (2005), Shihon no Sokutei [Measurement of Capital Stock], Tokyo: Keio University Press, (in Japanese). Ogawa, K. and S. Kitasaka (1998) Shisan shijo to keiki hendo: gendai nihon keizai no
jissho bunseki [The Asset Market and Business Fluctuations in Japan], Tokyo: Nihon Keizai Shimbun, Inc (in Japanese). Staiger, D. and J. Stock (1997) “Instrumental Variables Regression with Weak Insturments," Econometrica, vol.65, pp.557-586.
17
Table1: Parameter estimates 1983:1-2002:4
A. Returns-to-Scale (γi) Estimates Non-Durable Manufacturing Non-Manufacturing 2 1.192 1 1.157 (0.106 ) (0.164 ) 3 1.228 20 1.016 (0.134 ) (0.019 ) 4 1.377 21 1.133 (0.209 ) (0.054 ) 6 1.063 22 1.106 (0.194 ) (0.253 ) 7 1.132 23 1.144 (0.097 ) (0.291 ) 8 1.126 24 1.281 (0.099 ) (0.260 ) 9 1.138 25 1.282 (0.060 ) (0.142 ) 26 0.666 (0.217 ) 27 0.927 (0.143 ) 28 1.054 (0.150 ) 29 1.455 (0.264 ) 30 1.146 (0.334 ) 31 1.216 (0.077 ) 32 0.837 (0.312 ) 33 1.124 (0.134 ) B. Coefficient on Hours Per Worker Durables Manufacturing Non-Durable Manufacturing Non-Manufacturing 0.155 -0.070 0.041 (0.145 ) (0.196 ) (0.140 )
Durable Manufacturing 5 1.098 (0.099 ) 10 0.892 (0.232 ) 11 1.845 (0.247 ) 12 1.137 (0.101 ) 13 1.307 (0.098 ) 14 1.019 (0.101 ) 15 1.105 (0.042 ) 16 1.100 (0.069 ) 17 1.026 (0.089 ) 18 0.944 (0.124 ) 19 1.016 (0.067 )
1) Standard errors in parentheses.
18
Table 2: Solow residual and the BFK measure of Technology index Solow residual 1983:1-2002:4 1983:1-1991:1 1991:2-2002:4 BFK measure 1983:1-2002:4 1983:1-1991:1 1991:2-2002:4
All industries
Durable
Non-Durable
Non-Manufacturing
Mean Variance Mean Variance Mean Variance
-0.33 5.87 -0.25 6.18 -0.38 5.78
0.31 10.21 0.20 2.74 0.39 15.62
-0.25 16.56 -0.33 9.58 -0.19 21.76
0.17 7.53 0.32 10.54 0.07 5.58
Mean Variance Mean Variance Mean Variance
-0.34 5.36 -0.41 4.31 -0.30 6.20
-0.04 12.75 -0.30 4.41 0.14 18.76
-0.48 14.33 -0.86 12.17 -0.22 15.96
-0.17 7.34 -0.16 8.45 -0.17 6.74
19
Table 3: Correlations between the BFK measure and macro variables
1983:1-2002:4 1983:1-1991:1 1991:2-2002:4
Output Investment (value added) *** -0.04 0.57 (6.18) (-0.34) *** 0.05 0.66 (4.91) (0.25) *** -0.06 0.56 (4.50) (-0.44)
Labor hours Man-hours Employment per worker *** *** ** -0.57 -0.52 -0.24 (-6.17) (-5.44) (-2.15) *** ** -0.46 -0.37 -0.07 (-2.85) (-2.23) (-0.37) *** *** ** -0.66 -0.62 -0.34 (-5.83) (-5.31) (-2.46)
1) The figures in parentheses are t-ratios. 2) ***, **, * indicate the estimate is significant at the 1%, 5%, and 10% level, respectively.
20
Solow residual 0.80 (11.88) 0.80 (7.34) 0.82 (9.62)
*** *** ***
Table 4(1): Regression of output and factor inputs on current and lagged technology shocks (1983:2-2002:4) Δv Coefficient C Δθ Δθ(-1) Δθ(-2) Δv(-1) Δv(−2) Δj(-1) Δj(-2) Δh(-1) Δh(-2) Δnh(-1) Δnh(-2) Δn(-1) Δn(-2)
0.64 0.64 0.08 -0.20 -0.23 0.13
adj.R*R D.W. stat. AIC SBIC
0.37 2.02 4.08 4.27
Δj Standard error 0.25 0.10 0.13 0.13 0.12 0.12
Coefficient ** ***
2.44 0.01 1.03 0.29
Δh Standard error 1.07 ** 0.51 0.51 ** 0.51
Δnh
-0.19 -0.32 0.05 0.07
Standard error 0.26 0.13 0.13 0.13
-0.70 -0.03
0.12 0.12
Coefficient
Coefficient **
0.10 -0.52 0.11 0.03
Δn Standard error 0.36 0.17 *** 0.19 0.18
0.03 -0.02 0.01 0.01
Standard error 0.03 0.02 0.02 0.01
0.51 0.32
0.11 0.12
Coefficient
** -0.60 -0.15
0.12 0.11
*** *** -0.67 -0.17
0.28 1.98 7.26 7.44
0.63 2.08 4.45 4.63
0.53 2.32 5.13 5.31
1) ***, **, * indicate the estimate is significant at the 1%, 5%, and 10% level, respectively.
21
0.12 0.11
***
0.57 2.05 0.19 0.37
*** **
Table 4(2): Regression of output and factor inputs on current and lagged technology shocks (1991:1-2002:4) Δv C Δθ Δθ(-1) Δθ(-2) Δv(-1) Δv(−2) Δj(-1) Δj(-2) Δh(-1) Δh(-2) Δnh(-1) Δnh(-2) Δn(-1) Δn(-2) adj.R*R D.W. stat. AIC SBIC
Standard Coefficient error 0.17 0.28 0.61 0.13 *** 0.22 0.16 -0.06 0.16 -0.39 0.17 ** -0.01 0.16
Δj Coefficient 1.22 -0.39 1.20 0.90
-0.69 -0.20
Δh Standard error 1.57 0.77 0.71 * 0.78
0.16 0.16
Coefficient -0.16 -0.40 0.18 0.19
Δnh Standard error 0.32 0.16 ** 0.17 0.17
Coefficient -0.23 -0.65 0.25 0.18
0.17 0.16
0.33 0.28
0.15 0.15
*** -0.63 -0.15
0.31 1.34 7.61 7.85
-0.03 -0.02 0.01 0.00
Standard error 0.04 0.02 0.02 0.02
Coefficient
*** -0.61 0.00
0.35 2.05 4.09 4.33
Δn Standard error 0.44 0.21 *** 0.22 0.23
0.65 2.11 4.41 4.65
0.60 2.37 5.05 5.29
1) Figures in parentheses are standard deviations 2) ***, **, * indicate the estimate is significant at the 1%, 5%, and 10% level, respectively.
22
0.15 0.15
***
0.32 2.10 0.25 0.49
** *
Table 5: The respoonse of price change to the technology shocks (1983:3-1998:4) Δp1 coefficient C Δθ Δθ(-1) Δθ(-2) Δｍ Δｍ(−1) Δｍ(−2)
0.00 -0.13 0.06 0.04 0.02 -0.07 0.02
Δp2 standard error 0.00 0.03 0.04 0.04 0.04 0.04 0.04
coefficient ** ***
*
Δp1 coefficient C Δθ Δθ(-1) Δθ(-2) Δi Δi(-1) Δi(-2)
0.00 -0.14 0.06 0.02 0.00 0.01 0.00
-0.01 -0.43 0.11 -0.27 0.46 -0.67 0.69
standard error 0.00 0.12 0.14 0.13 0.14 0.14 0.13
*** ** *** *** ***
Δp2 standard error 0.00 0.03 0.04 0.03 0.01 0.01 0.01
coefficient *** *** *
**
0.00 -0.52 0.17 -0.43 -0.02 0.08 -0.06
standard error 0.00 0.15 0.17 0.16 0.02 0.03 0.02
*** *** ** **
1) Δp1 represnts change in consumer price index and Δp2 represnts change in GDP deflator, respectively. 2) ***, **, * indicate the estimate is significant at the 1%, 5%, and 10% level, respectively.
23
Table 6: Regression of labor inputs on current and lagged technology shocks including the dispersion measure (1983:3-2002:4) Δnh
Δh Coefficient C Δθ Δθ(-1) Δθ(-2) Δh(-1) Δh(-2) Δnh(-1) Δnh(-2) Δn(-1) Δn(-2) disp disp(-1) disp(-2)
-1.79 -0.34 0.00 0.07 -0.86 -0.12
-0.60 0.60 0.70
adj.R*R D.W. stat. AIC SBIC
0.71 2.00 4.23 4.50
Standard error 0.72 0.12 0.12 0.12 0.13 0.12
0.22 0.22 0.23
-0.99 -0.56 0.05 0.03
***
-0.84 -0.24
0.14 0.13
*** *
Coefficient ** ***
Δn Standard error 1.11 0.17 0.18 0.17
0.18 -0.02 0.01 0.01
Standard error 0.11 0.02 0.02 0.01
0.47 0.30 -0.04 -0.03 0.01
0.12 0.12 0.03 0.03 0.03
Coefficient
***
*** *** ***
-0.80 0.73 0.56
0.37 0.34 0.37
0.53 2.33 2.33 2.60
** **
0.58 2.06 0.21 0.48
1) Figures in parentheses are standard deviations 2) ***, **, * indicate the estimate is significant at the 1%, 5%, and 10% level, respectively.
24
*** **
Figure 1: Solow residual and the BFK measure of technology index 0.06 0.04
Solow residual Purified technology residual
0.02 0 1983:1 1984:4 1986:3 1988:2 1990:1 1991:4 1993:3 1995:2 1997:1 1998:4 2000:3 2002:2 -0.02 -0.04 -0.06 -0.08 -0.1
25
Figure 2: Accumulated Impulse Responses to a Positive Technology Shock (1983:3-2002:4)
.8
Labor hours per worker (dh)
Output (dv)
.7
0.9
0.2
.60.8
0.1
0.7
0
.5
0.6
-0.1
.40.5
0.4
-0.2
.30.3
-0.3
0.2
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
-0.4
.2
0.1 2 0
4
6
8
ERROR5V
10
12
14
16
ERROR95V
18
20
-0.5
IMPULSEV
-0.6
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Employment (dn)
Total of man-hour (dnh) 0.4
0.25
0.2
0.2 0.15
0 -0.2
1
3
5
7
9
11
13
15
17
0.1
19
0.05 0
-0.4
-0.05 -0.6
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
-0.1
-0.8
-0.15
-1
-0.2
Investment (dj) 2 1.5 1 0.5 0 1
3
5
7
9
11
13
15
17
19
-0.5 -1
1) Impulse responses to a 1percent improvement in technology, estimated from bivariate VARs where purified tchnology is taken to be exogenous. Dotted lines show 90 percent confidence intervals from 1000 bootstrap simulations.
26
Table A-1: Industry classification table Classification in the Financial Statements Statistics of Corporations 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33
Construction Manufacture of Food Products Manufacture of Textiles Manufacture of Wearing Apparel and Other Textile Products Manufacture of Lumber and Wood Products Manufacture of Pulp, Paper and Paper Products Manufacture of Publishing and Printing Manufacture of Chemicals and Chemical Products Manufacture of Petroleum and Coal Products Manufacture of Stone, Clay and Glass Products Manufacture of Steel Manufacture of Non-Ferrous Metals Manufacture of Metal Products Manufacture of General Machinery Equipment Manufacture of Electrical Machinery Manufacture of Transportation Equipment Manufacture of Precision Machinery and Equipment Manufacture of Ships Other Manufacturing Wholesale Retail Real Estate Land Transportation Water Transportation Other Transportation and Communication Electricity Gas, Waterworks Services for Business Inns, Other Lodging Services for Individuals Movies, Entertainment Broadcasting Other Services
27
Classification in the Japan Standard Industrial Classification (The 10th Revised Edition) 9,10,11 12, 13 14 15 16 18 19 20 21 25 26 27 28 29 30 31 32 31 17,22,23,24,33,34 48,49,50,51,52,53 54,55,56,57,58,59,60,61 70,71 39,40,41 42 43,44,45,46,47 35 36,37,38 79,82,83,86 75 72,73,74 76,80 81 77,78,84,87,88,89,91,92,95
Table A-2: Basic statistics 1983:1-2002:4 ⊿yi Mean 0.0034 Median 0.0055 Maximum 0.7752 Minimum -1.2960 Std. Dev. 0.0942 Skewness -1.1659 Kurtosis 30.4787
⊿xi 0.0038 0.0057 0.6457 -1.0402 0.0782 -0.7185 27.8933
⊿hi -0.0010 0.0023 0.0809 -0.0948 0.0382 -0.1878 2.0213
Jarque-Bera Probability
83657.0 0.0
68391.7 0.0
120.9 0.0
Sum Sum Sq. Dev
9.0481 23.4295
10.0144 16.1462
-2.7283 3.8461
Observations 1983:1-2002:4
2640
2640
2640
⊿θ -0.3436 -0.2943 4.7464 -6.4670 2.3151 -0.2954 2.8275
⊿y 0.7871 0.8530 8.4315 -10.8692 3.2190 -0.6700 4.8721
⊿j 1.0919 0.7228 53.2685 -28.3030 10.3292 1.1981 10.1743
⊿h -0.0860 0.0924 6.0185 -6.1685 3.5877 -0.0490 1.8257
⊿nh 0.0871 -0.1014 8.1591 -6.1157 4.5053 0.3073 1.8847
⊿n 0.1599 0.1152 1.2435 -0.6104 0.3878 0.5177 3.0043
⊿v 0.4280 0.2944 5.6926 -5.8867 2.2339 -0.0463 3.1808
Disp 2.2363 2.0133 6.6750 0.9230 1.0752 1.9056 7.4303
Jarque-Bera Probability
1.26 0.53
17.67 0.00
190.71 0.00
4.63 0.10
5.41 0.07
3.57 0.17
0.14 0.93
113.84 0.00
Sum Sum Sq. Dev
-27.4902 423.4148
62.9676 818.5950
87.3486 8428.6160
-6.8802 1016.8750
6.9642 1603.5210
12.7919 11.8798
34.2433 394.2219
178.9046 91.3283
Mean Median Maximum Minimum Std. Dev. Skewness Kurtosis
Observations
80
80
80
80
28
80
80
80
80
