Silviase/jgov_v1.4_full_ann huggingface dataset:
relationship between pollution and income is an important first step. $ ^{2} $

We then specify the reduced-form equation in accordance with our analytical interests. Our specific concern regarding the EK curves for the sample provinces in China is whether the provincial EK-curve trajectories have shifted downward or upward, depending on the dominance of either the latecomer’s advantage or its disadvantage $ ^{3} $ ; in other words, whether the levels of environmental pollution have been affected not only by the level of per capita income following the EK curve, but also by the speed of development among the provinces. If a sample province reaches a certain level of development later among the provinces and then lowers its environmental pollutants, we speculate that the province does not repeat the EK-curve trajectories already experienced by the developed provinces; it should enjoy the latecomer’s advantage by absorbing the progress in environmental know-how, skills, and technology. On the contrary, if the later development in sample provinces is linked with higher pollution, the provinces may suffer from the disadvantage caused by the “pollution heaven” scenario. Therefore, we will include a term representing the speed of development among the provinces into the ordinary regression model of the EK curve, to identify the regional latecomer’s effects. The speed of development of a sample province in a certain year is specified as the ratio of the GDP level of that province relative to the maximum GDP level among provinces (equivalent to the GDP of Shanghai province) in that year.

Another variable that seems to be important in the time-series EK curve analysis is concerned with a nation-wide time effect in environmental management and technology. For example, the more recent, the more technological transfer China may have received as a developing country, or the more pollution China may have suffered from through the “pollution heaven” scenario in the world. The variable on a nation-wide time effect can be specified as a time-trend, which is simply represented by the number of year.

In a sense, the latecomer’s effects in China’s environmental management and technology appear to be divided into two effects: a regional latecomer’s effect, depending on the speed of provincial development, and a national latecomer’s effect, shown as a nation-wide time effect — because a nation-wide time effect is caused by the fact that China is a developing country.

Based on analytical interests mentioned above, we specify the modified EK curve
model as follows.

$$ \mathbf{E M S_{i t}}=\boldsymbol{\alpha}_{0}+\boldsymbol{\alpha}_{1}\mathbf{G D P_{i t}}+\boldsymbol{\alpha}_{2}\mathbf{G D P^{2}_{i t}}+\boldsymbol{\alpha}_{3}\mathbf{S P E E D_{i t}}+\boldsymbol{\alpha}_{4}\mathbf{Y E A R_{i t}}+e_{i t} $$

where i is the province index, t is the time index, and e is the error term. The dependent variables EMS is measure of the per capita industrial emissions: waste water (WWP), waste gas (WGP), and solid waste generated (SWP). The per capita waste gas emissions has three components: sulfur dioxide discharged (SUP), soot discharged (SOP), and dust discharged (DUP). As for the independent variables, GDP is the real provincial GDP per capita. SPEED represents the speed of provincial development, specifically the ratio of the GDP level of a certain province relative to the maximum GDP level among provinces in a certain year. YEAR, a nation-wide time effect in environmental management and technology, is indicated by number of years as a time trend. $ ^{4} $

To verify the inverted-U shapes of the EK curves, the signs and magnitudes of $ \alpha_{1} $ and $ \alpha_{2} $ , the coefficient of GDP should be examined. Environmental emissions can be said to exhibit a meaningful EK curve with the real GDP per capita, if $ \alpha_{1}>0 $ and $ \alpha_{2}<0 $ , and if the turning point, $ -\alpha_{1}/2\alpha_{2} $ is a reasonably low number. Of particular importance is the coefficient of SPEED, $ \alpha_{3} $ , which is useful for identifying the dominance of the latecomer's advantage or its disadvantage at the provincial level. The positive sign of $ \alpha_{3} $ , the downward shift of the trajectories, indicates that the latecomer's advantage surpasses its disadvantage, while the negative sign of $ \alpha_{3} $ , the upward shift of the curve, reveals the dominance of the latecomer's disadvantage. The coefficient of YEAR, $ \alpha_{4} $ would have negative sign in case that China, as a nation, enjoys the nation-wide technological progress and positive sign when she suffers from the “pollution heaven” effect in the world.

Apart from real GDP per capita, speed of development, and nation-wide technological progress, there are also likely to be exogenous factors that affect emissions. For instance, climate and geography vary widely among provinces and may well be correlated with emissions. Insofar as these factors cause the error term e to be correlated among provinces for a given period, pooled cross-section estimates that ignore this correlation will be inefficient. To address this issue, we must specify an error-components model, in which:
$$ e_{\mathrm{it}}=r_{\mathrm{i}}+u_{\mathrm{it}} $$

where $ r_{i} $ is the province effect, and $ u_{it} $ is the remaining error term. In choosing between fixed-effects and random-effects estimation, an important issue is whether the province effect is correlated with the explanatory variables. In the absence of such a correlation, random-effects estimation is consistent and efficient. In contrast, if such a correlation exists, there may be omitted-variable bias, necessitating fixed-effects estimation. According to the statistics of the Wu-Hausman test (Hausman 1978), which is used to help choose between these two approaches, we use a random-effects estimation on all emissions (see Table 1-1).

#### 3.3.2 Estimation Results and Interpretations

Table 1 lists the results of the estimation on the per capita industrial emissions of waste water, waste gas, and solid waste generated. First, we must verify the shape of the EK curve of each emission index. In all the cases, the estimates for the coefficients $ \alpha_{1} $ and $ \alpha_{2} $ have the signs of the inverted-U shapes, which are different from zero as high levels of significance, and the turning point indicates a feasible number around 30,000 yuan per capita. All the indices for the industrial emissions can, therefore, be said to reflect a meaningful, inverted-U shaped EK curve with the real GDP per capita in sample provinces. Second, we must see if the trajectories show a downward shift or an upward shift, namely whether the regional latecomer's advantage or its disadvantage dominates in the environmental management of latecomer's provinces. In the case of waste water, the estimate for the coefficient of SPEED, $ \alpha_{3} $ , is positive and discernible, thereby representing the downward shift of the trajectories and the dominance of the regional latecomer's advantage. On the other hand, the case of total waste gas and solid waste generated has the negative and discernible coefficient of $ \alpha_{3} $ , showing the upward shift of the trajectories, the dominance of the regional latecomer's disadvantage. Third, we must identify a nation-wide time effect in environmental management and technology. In the case of waste water, sulfur dioxide discharged and soot discharged, the coefficient of YEAR, $ \alpha_{4} $ , is negative and discernible, thereby representing the nation-wide technological progress – national latecomer's advantage.

The estimation results above can be interpreted as follows. First, the provincial panel data proved the validity of the EK curve hypothesis in all the industrial emissions in China.

Second, the regional latecomer’s advantage was verified in the case of waste water discharged, while its disadvantage was identified in waste gas emissions and solid waste generated. The contrasting result appears to come from two backgrounds concerning
environmental policy performances against each emission. One is about the difference in the timing of when the regulatory legal framework was formulated. The water pollution prevention and control law was promulgated in 1984 at the earliest stage in the series of environmental policy actions, while the air pollution prevention and control law and the solid waste pollution prevention and control law were promulgated later in 1987 and 1995 respectively (World Bank, 2001). The other is concerned with the difference in the effectiveness of policy actions. The pollution levy system, covering the pollution charges for water, air and solid waste, is one of the most representative pollution control policies. However, the system is often criticized for having design deficiencies: pollution fees are often lower than the marginal cost of abatement required to meet emission standards, and pollution levies are assessed only on above-standard emissions. Industrial enterprises choose to remain noncompliant and pay the levy only on their excess pollution (World Bank, 2001). As far as the levy on water pollution is concerned, however, its level is reported to be higher than the marginal abatement costs in many cases (Wang and Wheeler, 1996), and also to have been assessed not on above-standard emission but on total volume of emissions since 1993 (Matsuoka, 2000). In these senses, water pollution control may have been mature and well-designed compared with the air and solid waste pollution control. This difference in policy performances may have created the contrasting result on regional latecomer's effects between the case of waste water and that of waste gas and solid waste. Regional latecomers may have enjoyed the dissemination effects of nation-wide progress in water pollution control, while the latecomers may have stayed behind from the progress or even suffered from the "pollution heaven" effect in air and solid waste pollution. In cases where pollution leakage among provinces occurs in the area of waste gas emission and solid waste, the estimated EK curve with inverted-U shapes may be unsustainable, namely a "historical artifact."

Third, nation-wide technological progress—the latecomer’s advantage in environmental management and technology at a national level—was identified in the case of waste water, sulfur dioxide discharged, and soot discharged. The effect seems to reflect the fact that China, as a developing country, is obtaining the technological transfer in water and air pollution control from advanced countries through governmental cooperation and private direct investment. For example, the Japanese government has provided official development aid (ODA) in the area of environmental protection, including air and water pollution control, as a top priority. Especially, the “Environmental Development Model City Project,” which has been promoted since 1997 based on Japan-China governmental agreement, has contributed to the enhancement of acid rain preventive measures and the measures for effective use of
desulfurized by-products (OECC, 2001).

### 4. Concluding Remarks

In this study, using the analytical framework of the EK curve, we set out to examine, with a focus on regional environmental states in China, whether regional latecomers enjoy the spillover effects of nation-wide progress in environmental management and technology, or stay behind from environmental progress, or even suffer the “pollution heaven” effects through the relocation of polluters toward those regions. The analysis focused on such industrial emissions as waste water, waste gas (sulfur dioxide discharged, soot discharged, and dust discharged as its components) and solid waste in Chinese provinces. We carried out a regression analysis using provincial panel data to examine the existence of the EK curve pattern and to see whether the latecomer’s advantage or its disadvantage dominates in the environmental management of undeveloped Chinese provinces.

The study’s main findings are as follows. First, the provincial panel data proved the validity of the EK curve hypothesis in all the industrial emissions in China. Second, the regional latecomer’s advantage was verified in the case of waste water, while its disadvantage was identified in waste gas and solid waste. Third, a nation-wide technological progress—the national latecomer’s advantage—was identified in the case of waste water, sulfur dioxide, and soot. We speculate that the contrasting result on regional latecomer’s effects between waste water, waste gas, and solid waste comes from the difference in policy performances: the mature and well-designed water pollution control may have regional latecomers enjoy the spillover effects of nation-wide progress, while the premature and ineffective control of air and solid waste pollution may cause the pollution leakage on regional latecomers. We also presume that nation-wide technological progress in waste and air pollution control may reflect the fact that China, as a developing country, is obtaining technological transfers from advanced countries. The results imply the urgent necessity to enhance the policy performances on the air and solid waste pollution control to make the EK curve pattern sustainable.

However, this study may only be an initial step in the analysis of the environmental latecomer’s effects in Chinese provinces. Analytical issues still remain that need to be addressed. First, the EK curve estimation model might be more sophisticated in terms of adopting dynamic panel modeling with the semi-parametric generalized additive model framework (see Auffhammer and Carson, 2008). Second, another challenge is to have our analysis focus on environmental policy performances. We can insert policy
variables into the EK curve estimation model and conduct policy simulation in terms of shifting the EK curve (see Panayotou, 1997).
Figure 1. The EK curve on waste water, waste gas, and solid waste in main provinces
Table 1-1. Estimation results using provincial panel data

Variables	Waste Water (ton)	Waste Gas (1000 cubic meter)	Solid Waste (ton)	Among Waste Gas
Sulfur (ton)	Soot (ton)	Dust (ton)
Const.	1.96*10-3***	1.25*10-2	-2.41	0.29	0.32**	0.08
(10.12)	(0.80)	(-0.25)	(1.87)	(2.34)	(0.63)
GDP	1.63*10-3***	1.95*10-3***	8.94*10-5***	9.76*10-7***	2.59*10-7*	1.75*10-7
(7.71)	(9.56)	(8.31)	(5.75)	(1.76)	(1.23)
GDP²	-3.66*10-8***	-2.53*10-8***	-1.24*10-9***	-1.81*10-11***	-5.56*10-12***	-4.54*10-12**
(-12.33)	(-7.63)	(-8.23)	(-7.64)	(-2.68)	(-2.26)
SPEED	31.5***	-1.28*10***	-1.12***	2.17*10-3	-4.85*10-3	-5.67*10-3
(5.02)	(-2.63)	(-3.21)	(0.39)	(-1.13)	(-1.58)
YEAR	-0.98***	-0.06	1.45*10-3	-1.41*10-4*	-1.55*10-4**	-3.81*10-5
(-10.10)	(-0.77)	(0.30)	(-1.82)	(-2.28)	(-0.57)
Adj R**2	0.46	0.57	0.44	0.18	0.04	0.02
Turning Point	2.23*10-4	3.85*10-4	3.6*10-4	2.7*10-4	2.33*10-4	1.93*10-4
<the>	<the>	<the>	<the>	<the>	<the>	<the>
Chi-Sq. Statistic	7.65	0.00	1.01	4.12	0.1	0.12
Chi-Sq. d.f.	4	4	4	4	4	4
Prob.	0.11	1.00	0.90	0.39	1.00	1.00
Estimation Type	Random	Random	Random	Random	Random	Random

Table 1-2. Summary of estimation results

Variables	Waste Water	Waste Gas	Solid Waste	Sulfur	Soot	Dust
EK curve	Inverted-U	Inverted-U	Inverted-U	Inverted-U	Inverted-U	Inverted-U
Latecomer's Effect
Regional	Advantage	Disadvantage	Disadvantage	-	-	-
National	Advantage	-	-	Advantage	Advantage	-

Note:

The T-value is shown in parentheses.

One, two, or three asterisks indicate that a coefficient estimate is significantly different from zero at 10, 5, or 1% percent level, respectively.

Sources: China Statiscal Yearbook(1889-2008)
## References

Auffhammer, Maximilian, and Richard T. Carsonb. 2008. “Forecasting the Path of China’s CO $ _{2} $ Emissions Using Province-level Information.” Journal of Environmental Economics and Management 55 229–247.

Borghesi, Simone. 1999. “The Environmental Kuznets Curve: A Survey of the Literature.” European University Institute, Florence. Available online at http://www.Feem.It/web/activ/wp/abs99/85-99.pdf. Processed.

Dasgupta, Susmita, Benoit Laplante, Hua Wang, and David Wheeler. 2002. “Confronting the Environmental Kuznets Curve.” Journal of Economic Perspectives 16(1):147-68.

De Bruyn, S. M., J. C. J. M. van den Bergh and J. B. Opschoor. 1998. “Economic Growth and Emissions: Reconsidering the Empirical Basis of 166 Journal of Economic Perspectives Environmental Kuznets Curves.” Ecological Economics 25:2, pp. 161-75.

Grossman, G., and A. Krueger. 1991. "Environmental Impacts of the North American Free Trade Agreement," NBER Working Paper 3914.

___. 1993. "Environmental Impacts of the North American Free Trade Agreement," in The U.S.-Mexico Free Trade Agreement. P. Garber, ed. Cambridge: MIT Press, pp. 13-56.

___. 1995. “Economic Growth and the Environment.” Quarterly Journal of Economics 112:353–378.

Hausman, J. A. 1978. “Specification Test in Econometrics.” Econometrica 46(6).

Irie, Y., Y. Kobayashi, and T. Morita. 2000. Policy Analysis on Alternative Development Paths Based on the Environmental Kuznets Curve—Empirical Proof Using Time Serial Data on $ SO_{2} $ and Simulation Analysis. Breakaway session at the 2000 meeting of Society for Environmental Economics and Policy Studies, 30 September, at Tsukuba International Conference Room, Japan.

Liu, X. Z., Heilig, G. K., Chen, J. M., & Heino, M. 2007. “Interactions Between Economic Growth and Environmental Quality in Shenzhen, China's First Special Economic Zone”. Ecological Economics, 62, 559–570.

Lopez, R. 1994. “The Environment as a Factor of Production: The Effects of Economic Growth and Trade Liberalization.” Journal of Environmental Economics and Management 27:2, pp. 163-84.

MacBean, Alasdair. 2007. “China’s Environment: Problems and Policies.” The World Economy, Volume 30 Issue 2: 195 – 362.

Martin, P. and D. Wheeler. 1992. “Prices, Policies, and the International Diffusion of Clean Technology: The Case of Wood Pulp Production,” in International Trade and the Environment. Patrick Low, ed. Washington, D.C.: World Bank, pp.197-224.

Matsuoka, S., R. Matsumoto, I. Kawauchi, and M. Iwase. 2000. “The Capacity Building of Environmental Management in East Asia.” In Viewpoint to East Asia 11–4: 92–105.

Matsuoka, S., et al. 2000. “Country Report.” In Viewpoint to East Asia 11–4: 106–155.
Nahman, A. and G. Antrobus. 2005. “The Environmental Kuznets Curve: A Literature Survey.” South African Journal of Economics Vol. 73:1.

Overseas Environmental Cooperation Center. 2001. Cases of Japan – China Environmental Cooperation.

Panayotou, T. 1997. “Demystifying the Environmental Kuznets Curve: Turning a Black Box into a Policy Tool.” Environment and Development Economics 2:4, pp.465-84.

Rothman, D.S. 1998. “Environmental Kuznets Curves – Real Progress or Passing the Buck? A Case for Consumption-based Approaches.” Ecological Economics 25:177-194.

Selden, T.M. and D. Song. 1994. “Environmental Quality and Development: Is There a Kuznets Curve for Air Pollution Emissions?” Journal of Environmental Economics and Management, 27: 147-162.

Shafik, N. 1994. “Economic Development and Environmental Quality: An Econometric Analysis.” Oxford Economic Papers 46, Supplement: 757-773.

Shen, J. Y. 2006. “A Simultaneous Estimation of Environmental Kuznets Curve: Evidence from China”. China Economic Review, 17(4): 383–394.

Shen, J. Y., & Hashimoto, Y. 2004. “Environmental Kuznets Curve on Country Level: Evidence from China. Discussion Papers in Economics and Business 04–09,” Osaka University, Graduate School of Economics and Osaka School of International Public Policy (OSIPP).

Song, T., T. Zheng and L. 2008. Tong. “An Empirical Test of the Environmental Kuznets Curve in China: A Panel Cointegration Approach.” China Economic Review, 19: 381-392.

Stern, D.I. 1998. “Progress on the environmental Kuznets Curve?” Environment and Development Economics 3: 173-196.

Stokey, N. L. 1998. “Are There Limits to Growth?” International Economic Review 39: 1-31.

Suri, V. and d. Chapman. 1998. “Economic Growth, Trade and Energy: Implications for the Environmental Kuznets Curve.” Ecological Economics 25:195-205.

Wang, Hua and David Wheeler. 1996. “Pricing Industrial Pollution in China: An Econometric Analysis of the Levy System.” Policy Research Working Paper No. 1644. World Bank, Policy Research Department, Washington, D.C.

World Bank. 1992. “Development and the Environment.” World Development Report. Washington, D.C.: Oxford University Press.

___. 2001. CHINA - Air, Land, and Water: environmental priorities for a new millennium. Washington, D.C.
ESRI Discussion Paper Series No.253

Japan’s Public Pension: The Great Vulnerability to Deflation

by

Mitsuo Hosen

November 2010

Economic and Social Research Institute

Cabinet Office

Tokyo, Japan
Japan’s Public Pension:

The Great Vulnerability to Deflation $ ^{*} $

Mitsuo Hosen

Executive Research Fellow, ESRI

## Abstract

Deflation since the mid-1990s has had a serious negative impact on the Japanese economy in many respects. This paper focuses on the financial vulnerability of Japan's public pension schemes to deflation. It is demonstrated in the paper that deflation made real pension benefits substantially higher than originally planned, and thus considerably worsened the financial conditions of the country's public pension schemes. This development also has serious implications for intergenerational distribution, since the younger and future generations need to bear the costs of these higher pension benefits at a later stage. Needless to say, ending deflation is a top priority, but at the same time it is imperative to improve Japan's public pension schemes to avoid being financially impaired by deflation.
### 1. Introduction

## (1) Objectives of the paper

In the current institutional setting in Japan, deflation increases the level of benefits of real pensions, while keeping the real pension contribution schedule constant; it thus has a negative impact on Japan's public pension finance. In this sense, Japan's public pension schemes are not neutral to deflation. This paper intends to explain this non-neutrality of public pension schemes to deflation and to quantify its impact on public pension finance.

I have argued in several previous papers that Japan's public finance has been greatly aggravated by deflation since the mid-1990s. Under deflationary circumstances, tax receipts tend to decrease more than proportionately due to progressive nature of personal income and other taxes, while public expenditures tend to decrease less than proportionately because of downward rigidity of some expenditure items. Public pension benefits are a typical and the most important example of such items.

## (2) Scope of the paper

Japan has implemented a two-tier pension system since 1986. All residents aged 20-59 are compulsorily covered by the first-tier National Pension. In the second-tier, employees in the private sector are compulsorily covered by the Employees' Pension, and civil servants in central and local governments as well as private school personnel are compulsorily covered by Mutual Aid Associations. In addition to these public pension schemes, there are corporate pension funds and private savings plans on a voluntary basis. These third-tier supplementary schemes, however, fall outside the scope of this paper.

## (3) Motivation behind the paper

An important factor behind my writing this paper is the much-worse-than-expected financial conditions of public pension schemes, which were revealed recently. In 2004 the Japanese government carried out a drastic pension reform to restore both the financial sustainability of public pension schemes and the public trust in them. It is stipulated in this 2004 Public Pension Reform Law (hereafter referred to as “the 2004