Silviase/jgov_v1.4_full_ann huggingface dataset:
Figure 3: Energy-GNP ratio (benchmark model)

to about 1.4%, i.e., almost half of the 1973 value and since then has shown only an slight upward trend. The simulation result $ ^{11} $ shows that energy price fluctuations influence the energy-GNP ratio, but not substantially. The energy-GNP ratio does decrease after 1973 and 1979, but the extent is very limited. In addition, in the model, the energy-GNP ratio continues to rise in the mid-1980s reflecting the downturn in the relative price of energy, while in the actual data the ratio declines. In sum, it is concluded that the substitution effect alone fails to account for the drop in the energy-GNP ratio.

### 4.2 The role of energy-saving technological progress

The previous subsection suggested that the energy-GNP ratio generated by the model overpredicted the actual data. This suggests the possible presence of energy-saving technological progress.

There are a number of empirical studies that have sought to examine the role of energy-
saving technological change. For instance, Popp (2002), using U.S. patent data from 1970 to 1994, looks at the impact of increases in energy prices on energy-efficiency innovations. He finds that the rise in energy prices has a statistically significant positive impact on energy-efficiency innovations. On the other hand, Newell, Jaffe, and Stavins (1999) investigate whether energy prices affect the energy efficiency of new models of energy-using consumer durables, such as room air conditioners and gas water heaters, and conclude that for some products the direction of innovation is influenced by changes in energy prices. For Japan, Fukunaga and Osada (2009) measure energy-saving technological change by estimating time-varying biases of technical change. They report that the bias of technical change for energy input in the 1980s was energy-saving.

Another strand of studies deals with energy-saving technological change from a theoretical perspective. Alpanda and Peralta-Alva (2010) introduce technology-specific capital and irreversible investment in a two-sector model and succeed in generating the drop in the energy-output ratio observed in the United States after the first oil crisis. Meanwhile, Hassler, Krusell, and Olovsson (2012) developed a neoclassical growth model with non-renewable resources and measured the level of energy-saving technology in the United States, assuming perfect competition in input markets. They find that energy-saving technological progress started in 1973 and, using their model, moreover show that it is essential for generating the actual time path of real energy.

In this paper, the level of energy-saving technology is measured as follows. First, $ z_{t} $ is added into the production function:

$$ Y_{t}=(\Gamma_{t}H_{t})^{1-\theta}\bigg[(1-\mu)K_{t}^{\frac{\varepsilon-1}{\varepsilon}}+\mu(z_{t}E_{t})^{\frac{\varepsilon-1}{\varepsilon}}\bigg]^{\frac{\varepsilon}{\varepsilon-1}\theta}. $$

where $ z_{t} $ is the level of energy-saving technology. In macroeconomic analyses, the level of technology, such as $ z_{t} $ here, is generally estimated as a residual. However, that strategy does not work in this case, since there are two unknowns $ \{\Gamma_{t}, z_{t}\} $ but only one equation (Equation 9), given the parameter values and the actual time series for $ Y_{t}, H_{t}, K_{t} $ and $ E_{t} $ . To estimate $ z_{t} $ , the first-order condition for energy use shown below is used, and Equations (9) and (10) are solved simultaneously for $ \Gamma_{t} $ and $ z_{t} $ :

$$ p_{t}=\theta\mu\left(\frac{Y_{t}}{B_{t}}\right)\left(\frac{B_{t}}{z_{t}E_{t}}\right)^{\frac{1}{\varepsilon}}z_{t}, $$
Variables	1970-79	1980-89	1990-98	1970-1998
TFP	2.46%	3.52%	1.58%	2.58%
Modified TFP	2.19%	1.96%	1.01%	1.77%
Energy-saving technology	5.47%	6.60%	1.65%	4.37%

Table 2: Average annual growth rates of exogenous variables

where $ B_{t} \equiv \left[(1 - \mu)K_{t}^{\frac{\varepsilon-1}{\varepsilon}} + \mu(z_{t}E_{t})^{\frac{\varepsilon-1}{\varepsilon}}\right]^{\frac{\varepsilon}{\varepsilon-1}} $ . Since the effect of energy-saving technological progress is removed from TFP growth, $ \Gamma_{t} $ is now renamed “modified TFP.” The measured level of energy-saving technology, TFP and modified TFP are displayed in Figure 4 and the average annual growth rates of these series in each period are summarized in Table 2.

Figure 4: Measured level of energy-saving technology, TFP and modified TFP. The initial levels are normalized to 100.

As can be seen, the growth in the energy-saving technology in the 1970s and 1980s was substantial. Indeed, the average annual growth rates of energy-saving technology were 5.47% and 6.60% in the 1970s and 80s, respectively. These improvements in energy-saving technology partially account for the growth of TFP, resulting in the lower rate of growth in modified TFP. For example, the growth rate of TFP in the 1980s was 3.52% whereas the growth rate of modified TFP was only 1.96%.

As a whole, the average annual growth rate of TFP over 1970-1998 was 2.58%, whereas the
modified TFP growth rate was only 1.77%. This suggests that about $ 31.4\% \simeq 100*(1-(1.77/2.58)) $ of the TFP improvements in this period is attributable to energy-saving technological progress. This number may be too large and there are at least two possible reasons to think why this might be the case. First, the definition of energy here excludes nuclear power due to the lack of price data. This implies that substituting nuclear power for fossil fuels results in the improvements in energy-saving technology. In Appendix 2, it is briefly examined to what extent the growth of energy-saving technology is mitigated by the inclusion of nuclear power. Second, during the period, Japan's industrial structure changed from one dominated by heavy industries (such as steel and shipbuilding) to one dominated by knowledge-intensive industries (such as electronics), which decreased energy use. This change is not directly related to energy-saving technological progress but is included in the measured $ z_{t} $ .

Another problem with the series of $ z_{t} $ is that it suggests that the level of energy-saving technology occasionally declines — something that runs counter to our perceptions of technology. The likely reason is that $ z_{t} $ is estimated as a residual so that it contains other elements which are not related to energy-saving technological progress. Thus, short-term fluctuations in $ z_{t} $ need to be regarded with a degree of caution; however, from a longer-term perspective, Figure 4 is likely to present a relatively accurate picture of improvements in the level of energy-saving technology.

The stop of the improvements in energy-saving technology in the 1990s is also eye-catching and this may be triggered by the following two possible reasons. First, the chronic slump of Japanese economy in the 1990s, well known as the "Lost Decade" may have weakened the firms' incentive to increase the R&D expenditure related to energy-saving technology. According to the "Survey of Research and Development" provided by Ministry of Internal Affairs and Communications, the average annual growth rates of R&D expenditure (natural sciences and engineering only) were 14.6% and 9.9% in the 1970s and 80s whereas only 2.2% in the 1990s. Second, Japan's energy efficiency may have reached some kind of upper-bound in the late 1980s. In fact, Agency for Natural Resources and Energy (2012) shows that the primary energy supply relative to real GDP is 2.6 in the U.S. and 2.3 in the European Union (27 countries) in comparison with 1.0 in Japan in 1990. In sum, while the estimated levels of energy-saving technology probably have some factors not related to the true technological progress in energy efficiency, it captures, at least, the trend of the improvements in energy-saving technology.

Before examining the impact of the energy-saving technology in the energy-GNP ratio, it would
Figure 5: Impulse responses to a 10% energy-saving technology shock

be useful to look at the impulse responses of aggregate variables to a stochastic energy-saving technology shock as in the standard real business cycle models in order to understand the role of energy-saving technology in the model. Thus, it is temporally assumed that the energy-saving technology follows a first-order autoregressive process and that the persistent parameter is 0.9 for simplicity. Figure 5 displays the impulse responses to a 10% positive energy-saving technology shock. The improvements in energy-saving technology require less energy to produce the same amount of output, leading to the decrease in energy use (in this case -9%). This increase in energy-saving technology, however, raises the marginal products of capital and labor, resulting in the rise in investment and hours worked. In total, value added increases by 0.25% due to the 10% positive energy-saving technology shock. Since the improvements in energy-saving technology decrease the energy use and increases the value added, this impulse response analysis implies that the energy-saving technology could be a good candidate for explaining the drop of the energy-GNP ratio.

Finally, the impact of energy-saving technological progress on the dynamics of the energy-GNP ratio is examined. In addition to the relative energy price, the time path of the level of energy-saving technology obtained in Section 4.2 is fed into the model. Figure 9 presents the simulation results generated by the model taking the only substitution effect into account, labeled
Figure 6: Energy-GNP ratio with energy-saving technology

as “Benchmark” and the model taking the substitution and the energy-saving technology effects into account, labeled as ‘Energy-saving technology.’ This time, the model with energy-saving technological progress closely fits the data. The reason is as follows. After 1973, the level of energy-saving technology starts to increase considerably, and by the end of the 1980s, it has more than tripled. Due to energy-saving technological progress, the energy required to produce the same amount of output for a given level of capital stock and hours worked declined. As a result, the energy-GNP ratio decreased over this period.

Figure 7 displays the comparison of the simulation results for other aggregate variables. As can be seen, the model with energy-saving technology also does a better job in accounting for the dynamics of other aggregate variables. This is due to the fact that the better model prediction of the energy use driven by the improvements in energy-saving technology also improves the model explanation power for other aggregate variables.

## 5 Conclusion

In this paper, a simple neoclassical growth model with energy as an input is constructed, and it is examined to what extent the model can account for the observed decline in the energy-GNP ratio. The findings can be summarized as follows. First, the substitution effect resulting from a rise in
Figure 7: Comparison of the simulation results

the relative price of energy alone cannot explain the movement in the energy-GNP ratio. Second, the estimated level of energy-saving technology improved substantially during the 20 year period from 1970 to the late 1980s. And third, when energy-saving progress is incorporated, the model can account for the decrease in the energy-GNP ratio.

There are some possible extensions to improve the analysis presented here. First, as discussed in Section 4.2, because the energy-saving technology level is estimated as a residual, it probably contains other factors which are not related to energy-saving technological change. This means that it is necessary to estimate “pure” energy-saving technological change. Second, since energy-saving technological change is treated as exogenous, endogenizing technological progress represents another potentially interesting extension (See some discussions in Popp et al., 2010). These issues are left for future research.
## References

[1] Agency for Natural Resources and Energy., 2012. Annual Report on Energy. Tokyo: EnergyForum.

[2] Alpanda, S., Peralta-Alva, A., 2010. Oil Crisis, Energy-Saving Technological Change and the Stock Market Crash of 1973-74. Review of Economic Dynamics 13, 824-842.

[3] Atkeson, A., Kehoe, P. J., 1999. Models of Energy Use: Putty-Putty versus Putty-Clay. American Economic Review 89, 1028-1043.

[4] Backus, D. K., Crucini, M. J., 2000. Oil Prices and the Terms of Trade. Journal of International Economics 50, 185-213.

[5] Chen, K., Imrohoroğlu, A., Imrohoroğlu, S., 2006. The Japanese Saving Rate. American Economic Review 96, 1850-1858.

[6] Conesa, J. C., Kehoe, T. J., Ruhl, K. J., 2007. Modeling Great Depressions: The Depression in Finland in the 1990s. Federal Reserve Bank of Minneapolis Quarterly Review 31, pp. 16-44.

[7] Cooley, T. F., (Eds.). 1995. Frontiers of Business Cycle Research. Princeton: Princeton University Press.

[8] Fukunaga, I., Osada, M., 2009. Measuring Energy-Saving Technical Change in Japan. Bank of Japan Working Paper Series No. 09-E-5.

[9] Hassler, J., Krusell, P., Olovsson, C., 2012. Energy-Saving Technical Change. NBER Working Paper No. 18456.

[10] Hayashi, F., Prescott, E. C., 2002. Japan in the 1990s: A Lost Decade. Review of Economic Dynamics 5, 206-235.

[11] Hayashi, F., Prescott, E. C., 2008. The Depressing Effect of Agricultural Institutions on the Prewar Japanese Economy. Journal of Political Economy 116, 573-632.

[12] Kim, I., Loungani, P., 1992. The Role of Energy in Real Business Cycle Models. Journal of Monetary Economics 29, 173-189.
[13] Miyazawa, K., 2009. Estimation of the Elasticity of Substitution Between Oil and Capital. Economics Bulletin 29, 655-660.

[14] Newell, R. G., Jaffe, A. B., Stavins, R. N., 1999. The Induced Innovation Hypothesis and Energy-Saving Technological Change. Quarterly Journal of Economics 114, 941-975.

[15] Otsu, K., 2009. A Neoclassical Analysis of the Postwar Japanese Economy. The B.E. Journal of Macroeconomics 9 (1).

[16] Popp, D., 2002. Induced Innovation and Energy Prices. American Economics Review. 92, 160-180.

[17] Popp, D., Newell, G. R., Jaffe, B. A., 2010. Energy, the Environment, and Technological Change. Handbook of the Economics of Innovation 2, 873-938.
## Appendix 1

This Appendix describes the construction of the data series for the current study.

## Energy

In this paper, the energy-related variables are real energy use, $ E_{t} $ , and the relative price of energy, $ p_{t} $ . The data source for the energy-related variables is the “Trade Statistics of Japan” published by the Ministry of Finance. As briefly explained in Section 1, although the “General energy supply and demand balance” published by the Agency for Natural Resources and Energy has greater detailed information such as what percentage of the total domestic supply of primary energy comes from the nuclear power generation, the “Trade Statistics of Japan” is used in this paper since the latter contains the energy price data which is essential to examine the impact of the substitution effect and to estimate the series of energy-saving technology.

The energy-related variables are constructed based on the methodology developed by Atkeson and Kehoe (1999). real energy use $ E_{t} $ at time t is calculated as follows:

$$ E_{t}\equiv\sum_{i}P_{i,0}Q_{i,t}, $$

where i denotes the type of energy. In the analysis here, there are three types of energy: petroleum, coal, and liquid natural gas. $ P_{i,0} $ is the price of type i energy in the base year, which is 1990. Note that $ P_{i,0} $ is the CIF (cost, insurance and freight) price converted into Japanese yen, so that exchange rate changes are already taken into account. $ Q_{i,t} $ is the quantity of imported type i energy in year t.

To construct the relative price of energy, the energy price deflator at time t, denoted as $ DEF_{t}^{P} $ , is derived as follows:

$$ D E F_{t}^{P}\equiv\frac{\sum_{i}P_{i,t}Q_{i,t}}{\sum_{i}P_{i,0}Q_{i,t}} $$

Then the relative price of energy, $ p_{t} $ , is constructed by dividing the energy price deflator by the GNP deflator, whose base year is also 1990:
$$ p_{t}\equiv\frac{D E F_{t}^{P}}{D E F_{t}^{V}}, $$

where $ DEF_{t}^{V} $ is the GNP deflator at time t.

## Other variables

The working age population is defined as the number of people between ages 15-64 and is obtained from the “Census population” published by the Ministry of Internal Affairs and Communications. The average weekly hours worked per employed person $ (\ell_{t}) $ is from the “Monthly Labor Survey” published from the Ministry of Health, Labor and Welfare and the number of employed persons $ (M_{t}) $ is available from the 1968 SNA (System of National Account) produced by the Economic and Social Research Institute, Cabinet Office. Then, the empirical counterpart of $ h_{t} $ is calculated as follows,

$$ h_{t}=\frac{\ell_{t}*M_{t}}{N_{t}*16*7}. $$

Following Otsu(2009), it is assumed that the maximum number of hours worked per day is 16.

The rest of the variables are obtained from the 1968 SNA. The 1968 SNA is used for analysis since 1993 SNA is not available before 1980. As the models described in this paper contain no government sector, it is required to adjust the data from 1968 SNA for matching up series in the models. $ ^{12} $ In the 1968 SNA, real value added (real GNP in this paper) is decomposed into the following parts:

$$ V_{t}=C_{t}+X_{t}+G_{t}+NX_{t}+NFP_{t} $$

where $ V_{t} $ is real value added, $ C_{t} $ is real “Private final consumption expenditure”, $ X_{t} $ is the sum of real “Gross fixed capital formation” and real “Change in inventories”, $ G_{t} $ is real “Final consumption expenditure of government”, $ NX_{t} $ is real “Net exports (excluding real energy imports)”, and $ NFP_{t} $ is real “Net factor payment.” The nominal variables are converted into the real ones by dividing them by a constant 1990 yen deflator.
Following Hayashi and Prescott (2002), the real “Final consumption expenditure of government” is included in $ C_{t} $ and both the real “Net exports (excluding real energy imports)” and the real “Net factor payment” are incorporated in $ X_{t} $ . That is,

$$ V_{t}=C_{t}^{^{\prime}}+X_{t}^{^{\prime}} $$

where $ C_{t}^{'} $ is the sum of $ C_{t} $ and $ G_{t} $ , $ X_{t}^{'} $ is the sum of $ X_{t} $ , $ NX_{t} $ , and $ NFP_{t} $ . $ V_{t} $ , $ C_{t}^{'} $ , and $ X_{t}^{'} $ are the empirical counterparts of valu added, aggregate consumption and aggregate investment in this paper, respectively.

Finally, the capital stock series are constructed using a perpetual inventory method. The initial capital stock, $ K_{1970} $ is set so that $ K_{1970}/V_{1970} = 1.29,^{13} $ which is the value taken from the dataset constructed by Hayashi and Prescott (2002). The subsequent series of capital stock are obtained by the law of motion for capital stock, Equation (3).

## Appendix 2

As discussed in Section 4.2, one of the shortcomings that the estimated level of energy-saving technology has is that substituting nuclear power generation for fossil fuel energy is interpreted as improvements in energy-saving technology. In this Appendix, the crude estimation of the energy supply by nuclear power generation is attempted and it is examined to what extent the inclusion of nuclear power generation influences the level of energy-saving technology and other simulation results.

Since the imports data are used to construct the real energy use, one way to include the nuclear power generation into the definition of energy use is to use the imported quantity of uranium ore, a raw material of nuclear power generation. The drawback of this approach, however, is that the spent nuclear fuel is reused repeatedly so that it is difficult to obtain the price information.

In this Appendix, it is simply assumed that the inclusion of nuclear power generation into the definition of energy does not affect the relative price of energy. Then, the real energy use $ E_{t} $ is inflated based on the ratio of the energy supply by the nuclear power generation to the energy
Figure 8: Real energy uses and the series of energy-saving technology with and without nuclear power generation

Figure 8 displays the real energy uses and the estimated levels of energy-saving technology with and without nuclear power generation. As can be seen, the discrepancy between the energy uses with and without nuclear power generation expands in the later periods, reflecting the fact that the share of energy supply by nuclear power generation in total supply has had an upward trend since the beginning of the 1970s. The right side of Figure 8 shows that the path of energy-saving technology taking the nuclear power generation into account is located below the original path, indicating that the improvements in energy-saving technology were partially spurious caused by substituting nuclear power for fossil fuel.

Figure 9 shows the effect of the inclusion of nuclear power generation on the energy-GNP ratio. Once the nuclear power generation is considered, the actual energy-GNP ratio in the data shows the higher ratio than in the original data in the 1980s and 1990s due to the prevalence of nuclear power generation. The simulation result, however, also tracks the actual energy-GNP ratio well. This is simply reflecting the fact that the improvements in energy-saving technology dampened in the late 1980s and 1990s, as shown in the right panel of Figure 8. $ ^{15} $ In sum, although the inclusion
Figure 9: Energy-GNP ratio with the inclusion of nuclear power generation into the definition of energy

of nuclear power generation into the definition of energy provides us more purified energy-saving technology, it does not affect the main results in this paper that the drop in energy-GNP ratio was triggered mainly by the improvements in energy-saving technology.
## 第2節 我が国の企業の人材マネジメントの変化と特徴

前節ではグローバル化、技術革新、経営の不確実性等の企業を取り巻く競争環境の変化が企業の人材活用に影響を与えていることをみてきた。

こうした人材活用への影響は、例えば、1985年に16.4%であった非正規雇用労働者比率が、バブル経済の崩壊や90年代末の金融危機以降から始まる厳しい経済状況を経て、2013年には36.7%まで高まっている状況など、様々な形で顕在化している。

本節では、我が国の経済の歴史的な変化と人材マネジメントの変化を振り返るとともに、雇用形態の多様化に応じた近年の人材マネジメントの特徴を分析していく。

## 1 景気後退局面における雇用調整の手法の変化 $ ^{51} $

## ● 石油危機以降から最近までの日本経済と企業の雇用調整の変化

日本経済は、戦後復興、その後の高度経済成長の中で高い経済成長率を実現してきたが、その後、二度にわたる石油危機、バブル経済の崩壊や1990年代末の金融危機、さらに2008年のリーマンショックと多くの景気変動を経験してきた。本節の始めとして、こうした景気の動向が、雇用、労働時間、賃金等にどのように影響を及ぼしてきたのか、とりわけ企業が雇用調整の対応を迫られたと考えられる主な景気後退期に着目してみていこう。

第2−(2)−1図 完全失業率と求人倍率の推移

資料出所 厚生労働省「職業安定業務統計」、総務省統計局「労働力調査」

(注) 1）データは四半期平均値（季節調整値）。また、グラフのシャド一部分は景気後退期。なお、2012年4～6月期から2012年10～12月期については暫定。

2）有効求人倍率及び新規求人倍率については、1973年から沖縄を含む。

3）完全失業率については、1972年7月から沖縄を含む。

4）有効求人倍率及び新規求人倍率については、新規学卒者を除きパートタイムを含む。

5）完全失業率の四半期値は、月次の季節調整値を厚生労働省労働政策担当参事官室にて単純平均したもの。ただし、2011年3月から8月までの数値は総務省統計局により補完推計されている数値を用いた。
第2-(2)-2図 雇用形態別雇用者数の推移

資料出所 2000年までは総務省統計局「労働力調査（特別調査）」（2月調査）、2005年以降は総務省統計局「労働力調査（詳細集計）」（年平均）

(注) 1）2005年から2011年までの数値は、2010年国勢調査の確定人口に基づく推計人口（新基準）に切替え集計した値。

2）2011年の数値は、東日本大震災による補完推計値。

第2-(2)-3図

雇用調整の実施方法別事業所割合の推移

(年・期)

(%)

(年·期)

資料出所 厚生労働省「労働経済動向調査」

（注）シャド一部分は景気後退期。なお、2012年7～9月期から2012年10～12月期については暫定。