Discussion Papers In Economics
And BusinessLong-Run Fiscal Multiplier for Autonomous Prefectures in China

Yingxin SHI and Mototsugu FUKUSHIGE

Discussion Paper 13-10

Graduate School of Economics andOsaka School of International Public Policy (OSIPP)
Osaka University, Toyonaka, Osaka 560-0043, JAPANLong-Run Fiscal Multiplier for Autonomous Prefectures in China

Yingxin SHI and Mototsugu FUKUSHIGE

Discussion Paper 13-10

June 2013

Graduate School of Economics andOsaka School of International Public Policy (OSIPP)
Osaka University, Toyonaka, Osaka 560-0043, JAPANLong-Run Fiscal Multiplier for Autonomous Prefectures in China

Yingxin SHI
Department of Economics & Management, Dalian Nationalities University
18, Liaohe West Road, Jinzhou New Area, Dalian 116600, China
Mototsugu FUKUSHIGE1
Graduate School of Economics, Osaka University
1-7, Machikaneyama-cho, Toyonaka, Osaka 560-0043, Japan

Abstract
We overcome the problems of data availability and investigate the fiscal multipliers in autonomous
prefectures in China. We first estimate the long-run elasticity of gross regional production withrespect to fiscal expenditure in autonomous prefectures, using autoregressive distributed lag models.
The estimated long-run elasticity is much less than unity, however, and the estimated fiscal
multipliers for prefectures are between 0.61 and 4.93, with an average of 1.93. These results
indicate that additional fiscal expenditure is still effective in increasing local income and promoting
economic growth for most of the autonomous prefectures.

Keyword: Fiscal Multiplier; Autonomous Prefecture; China; autoregressive distributed lag model

JEL Classification: O11, E62, H72.

1Corresponding author.

E-mail address: mfuku@econ.osaka-u.ac.jp (M. Fukushige)
Phone & Fax: +81-6-6850-5248 (M. Fukushige).

1. Introduction
There are 56 nationalities in the People’s Republic of China, with the Han ethnicity
accounting for more than 90% of the population; the remaining population constitute ethnic
minorities. A national minority regional autonomy system is practiced in places where ethnic
minorities gather and live.

These autonomous areas are classified into top three levels of thenational administrative divisions: five provinces, 30 prefectures and third level 117 counties and 3
banners. Gustafsson and Shi (2001) and Gustafsson and Sai (2009) estimate the income gaps
between ethnic areas and the areas where the Han ethnicity is in the majority; the former authors
investigate at the provincial level and the latter at the village level.

Kajitani and Hoshino (2009)

also investigate the gaps in per capita gross regional product (GRP) and the disposable income ofethnic regions between provinces and within provinces (prefectural level). They point out that theincome inequality between provinces is smaller than that within provinces (between prefectures).
In this paper, we focus on the autonomous prefectures. These prefectures account for
only 4.2% of the population but 24.8% of the area of the country.

Geographically, there is one suchprefecture in the Eastern Region, two in the Central Region and the other 27 are in the Western
Region. Of course, the Western Region has focused on “Western Development” since 2000 and theLaw of Promoting Western Development was enacted in 2004. Construction of a “Harmonious
Society” and boosting domestic demand, which are recent political issues in China, also focus on theautonomous prefectures not only by its cultural variations but also by their rich natural resources.
However, these areas are still less developed and face several problems: poverty, environmental
problems, and lack of educational infrastructure, health services and so on. Therefore, the central
government and provincial government provide fiscal subsidies to these prefectures to increase theirfiscal expenditures. To evaluate the effectiveness of this fiscal policy, we need to investigate theeffects of fiscal expenditures on local income or production. In other words, we should examine1the magnitude and significance of the fiscal multipliers.
Several studies have examined the relationship between fiscal policy and economic growthin China.

For example, Batisse (2002) and Hsing and Hsien (2005) analyze national level

economic growth and the effects of macroeconomic policies.

He, Zhang and Zhang (2009) and Liu(2009) estimate national-level fiscal multipliers. Huang and Chen (2012) shed light on another sideof fiscal policy. They study intergovernmental transfer equalization effects.
expenditures themselves have also been carried out.

Studies on fiscal

Chen (2013) and Yu and Tsui (2005)

investigate the determinants of provincial-level local government size and prefectural- andcounty-level government expenditures, respectively. Additionally, Zheng et al. (2013) point out
that special and political factors are important in determining investment in regional infrastructure.
However, there are no studies on the effects of subsidies from the central or provincial government
to prefectural- or subprefectural-level governments and economies.
We obtained our data from each autonomous prefecture’s Statistical Office.
investigate the fiscal multipliers in each autonomous prefecture.

We then

We first estimate the long-run

elasticity of fiscal expenditures with respect to GRP in autonomous prefectures, using an
autoregressive distributed lag (ARDL) model.

The estimated long-run elasticity is considerably

less than unity, however, and the prefecture-level multipliers are between 0.61 and 4.93, with anaverage of 1.93. This result indicates that additional fiscal expenditure is effective in increasing
local income and promoting economic growth for most of the autonomous prefectures.
The outline of the paper is as follows. In Section 2, we discuss recently used estimation
methods for fiscal multipliers.

We then consider the limitations of the data for the autonomous

prefectures and develop the ARDL model. In Section 3, we present the estimation results andexamine the fiscal multiplier for individual autonomous prefectures.
conclude the paper and discuss the remaining research issues.

2

Lastly, in Section 4, we2. Model for Estimating Fiscal Multipliers
Recently, most of the studies on fiscal multipliers estimate time series models such as
vector autoregressive (VAR) models or structural vector autoregressive (structural VAR) models andtheir impulse response functions.

For example, Ilzetzki, Mendoza and Végh (2013) estimate

structural VAR models for 44 countries and estimated the impact multipliers and cumulative
multipliers. Their results show that the fiscal multipliers in industrial countries are larger thanthose in developing countries.

Tang, Liu and Cheung (2013) estimate time-varying VAR models

for the ASEAN countries and find causal relationships from tax collections to GDP, whereas there isno significant causality from government expenditure to GDP. Nijkamp and Poot (2004) conductmeta-analysis and conclude that government consumption multipliers are not significant, whereas
those from government infrastructure spending are significant.
For China, neither the simple VAR approach nor structural VAR approach has been used to
examine fiscal multipliers.

Other approaches, however, have been use to estimate fiscal multipliers.

For example, He, Zhang and Zhang (2005) estimate national-level short-run and medium-term fiscal
multipliers using an input–output table and a DSGE model.

Their estimates of the short-run

multipliers are between 0.80 and 0.84, but that of the medium-term multiplier is about 1.10. Liu(2009) uses a Keynesian model and estimates fiscal multipliers at the national and provincial levels;
those estimates are between 1.53 and 5.15.
In this paper, we focus on the fiscal multipliers in autonomous prefectures in China.

Weuse data for 30 prefectures between 2005 and 2010 with some missing observations. To estimate
the effects of fiscal expenditures on gross regional products (GRP), we assume that fiscal
expenditure is an exogenous variable in determining GRP. Therefore, we pool the 30 prefecturesand estimate an ARDL model with a dummy variable for each prefecture to control for the3

idiosyncratic differences among prefectures.
First, we set up the following ARDL model (note that the subscript i denotes individualautonomous prefectures and the subscript t denotes time),

𝐾

𝐾

𝐾

𝑙𝑛𝑌𝑖𝑡 = 𝛼 + ∑ 𝛽𝑠 𝑙𝑛𝑌𝑖,𝑡−𝑠 + ∑ 𝛾𝑠 𝑙𝑛𝐺𝑖,𝑡−𝑠 + ∑ 𝛿𝑠 𝐼𝑛𝑓𝑙𝑡−𝑠 + 𝜖𝑖𝑡
𝑠=1

𝑠=0

𝑠=0

where Y and G are GRP and fiscal expenditure in each autonomous prefecture, respectively.
Infl is the national-level inflation rate, which is calculated using the consumer price index for rural
households from the 2012 Statistical Yearbook of China.

In this paper, because we have only six

years of data, we set a maximum lag length K of two and select the model that minimizes Schwarz’s
Bayesian information criterion (SBIC). In the estimation, we transform the model to estimate thelong-run effect of the autoregressive parameters and distributed lags as follows,

𝐾−1

𝐾−1

𝐾

∗

𝑙𝑛𝑌𝑖𝑡 = 𝛼 + 𝛽 𝑙𝑛𝑌𝑖,𝑡−𝑠 + ∑ 𝛽𝑠+ Δln𝑌𝑖,𝑡−𝑠 + 𝛾 ∗ 𝑙𝑛𝐺𝑖,𝑡−𝑠 + ∑ 𝛾𝑠+ Δln𝐺𝑖,𝑡−𝑠 + ∑ 𝛿𝑠 𝐼𝑛𝑓𝑙𝑖,𝑡−𝑠 + 𝜖𝑖𝑡
𝑠=1
𝑠=1
𝑠=0

where Δln𝑌𝑖,𝑡−𝑠 = 𝑙𝑛𝑌𝑖,𝑡−𝑠 − 𝑙𝑛𝑌𝑖,𝑡−𝑠−1 and Δln𝐺𝑖,𝑡−𝑠 = 𝑙𝑛𝐺𝑖,𝑡−𝑠 − 𝑙𝑛𝐺𝑖,𝑡−𝑠−1.

In this model, Infl

was not transformed because we do not need to estimate it long-run effect.

The relationships

between the coefficients of the original model and transformed model are as follows,𝛽 ∗ = ∑𝐾
𝑠=1 𝛽𝑠and𝛽𝑠+ = − ∑𝐾
𝑘=𝑠 𝛽𝑘 .

This transformation is also useful when we estimate the long-run elasticity of fiscal expenditure (ε)
with respect to GRP; we can estimate it as follows,

4

ε=

𝛾∗
⁄(1 − 𝛽 ∗ ).

Of course, other economic variables may be candidates for explanatory variables.

For example, aninternational trade flow variable may be a candidate. Poncet (2003) investigates the trade flows ofChinese provinces and Jiang (2011) points out the importance of openness for TFP growth at theprovincial level.

However, we cannot access these data and autonomous prefectures are lessconnected to international trade systems, so we do not include these variables.

3. Empirical Results
Most of the data are provided by each autonomous prefecture’s Statistical Office:government expenditures and nominal GRP between 2005 and 2010 in each autonomous prefecture.
Missing observations are fiscal expenditures for Ganzi Autonomous Prefecture in 2005 and those for
Huangnan Autonomous Prefecture between 2005 and 2008. The total number of observations is
173 and we use 115 for estimation because we set the maximum lag length equal to two in the model
selection process. All the variables except the inflation rate are deflated by the national-level
consumer price index for rural households, which is obtained from the 2012 Statistical Yearbook of
China and the base year is 2005. If local price indexes were available, we could pay attention to
local price differentials and deflate the nominal data by these indexes. For example, Brandt andHolts (2006) constructed local price index by the provincial level prices and consumption baskets in
China. However, as we cannot access the data to construct a price index for each prefecture, we
adopt the national-level consumer price index for rural households as a proxy for prefectural prices.
We use the index for rural households instead of that for urban areas because most autonomous
prefectures are in rural areas.

As for the estimation method, we pool all the observations and apply
5the least squares with dummy variables (LSDV) method. This is a estimation method for panel
data and its estimator is consistent estimator even when the assumptions that the estimator of therandom effect model is efficient and consistent does not hold.
We select the explanatory variables, apart from the dummy variables, for the LSDV
method to minimize SBIC. The estimated results are shown in Table 1. We call the model before
model selection the “Full model” and that after selection by SBIC the “Selected model by SBIC.”
In the selected model, the variable Infl and its lagged values are omitted, and only lagged GRP and
instantaneous fiscal expenditures are included as explanatory variables in addition to the prefecture
dummy variables. The estimated R-squared for the selected model is high at 0.9958 and diagnostic
tests for missspecification (RESET), using squared and cubic fitted values as explanatory variables,
are not significant. Next, using this selected model, we investigate the effects of fiscal policy.
The calculated elasticity of GRP with respect to fiscal expenditure is 0.497, which meansthat a 1% increase in fiscal expenditure increases GRP by about 0.5%. To evaluate the effects offiscal expenditures on GRP we use this elasticity to calculate the fiscal multiplier.

We canapproximate the fiscal multiplier for each prefecture as,Ym = ε∙ .
GThis approximation is also used in Liu (2009). Using averages of the ratios of GRP to fiscal
expenditure for each prefecture during the sample period, we estimate the average fiscal multiplierfor each prefecture in Table 2.

The estimated long-run elasticity of GRP with respect to fiscal

expenditure is considerably less than unity, however, and the estimated fiscal multipliers forprefectures are between 0.61 and 4.93, with an average of 1.93.

Apart from six prefectures(Gannan, Linxia, Golog, Qizilsu, Ganzi and Aba) the other 24 prefectures have fiscal multipliers that6are larger than unity. This result means that GRP increases by more than the associated increase in
fiscal expenditure in most prefectures.

Compared with previous studies, the estimated fiscal

multipliers are larger than those of He, Zhang and Zhang (2009) and marginally smaller than thoseof Liu (2009).

4. Conclusion
We estimated long-run fiscal multipliers for the autonomous prefectures in China, using an
ARDL model with panel data. The estimated long-run elasticity of GRP with respect to fiscal
expenditure is less than unity, however, and the estimated fiscal multipliers for prefectures are
between 0.61 and 4.93 Additionally, there are six prefectures whose fiscal multipliers are less thanunity.

This result indicates that additional fiscal expenditure is effective in increasing GRP andpromoting economic growth for most autonomous prefectures. This result is also informative forreconsidering fiscal subsidies from central and provincial governments for autonomous prefecturesin China because those subsidies increase the fiscal expenditures in autonomous prefectures. In
other words, the results shed new light on the effects of fiscal policy that have not been investigatedpreviously because of data limitations.
Finally, there are some remaining problems.

First, the coefficients of the model areconstant across the 30 autonomous prefectures. This assumption is somewhat restrictive because
these 30 prefectures might face different natural and economic environments and their elasticity
GRP with respect to fiscal expenditure might vary.

In such situations, we should estimate aseparate model for each autonomous prefecture. However, in this paper, we only have six years ofdata, so we had no choice but to apply the LSDV method to obtain stable estimates. In the future,
if suitable data are available, we will estimate separate prefectural models to obtain fiscal multipliers.
Furthermore, to evaluate the size of the fiscal multipliers for the autonomous prefectures, they7

should be compared across prefectures. Furthermore, in considering subsidies for ethnic regions
generally, we should focus not only on economic development but also on the conformability or
happiness of residents, e.g., as investigated by Knight et al. (2009). However, such a topic isbeyond the scope of this paper.

Acknowledgments
We are very grateful to the Statistical Office in each autonomous prefecture for providing data on
fiscal expenditure, tax collections and GRP. Shi is also grateful to SEAC (2013-GM-022) and
DLNU (ZJ13RWYB002) for their financial support of the joint research on this topic with
Fukushige.

8References
Batisse, C. (2002) Dynamic externalities and local growth: A panel data analysis applied to Chinese
provinces. China Economic Review 13, 231-251.

Brandt, L. & Holz, C.A. (2006) Spatial price differences in China: Estimation and implications.
Economic Development and Cultural Change 55, 43-86.
Chen, C.-H. (2013) Fiscal decentralization, collusion and government size in China’s transitional
economy. Applied Economic Letters 11, 699-705.
Gustafsson, B. & Sai, D. (2009) Villages where China’s ethnic minorities live. China Economic
Review 20, 193-207.

Gustafsson, B. & Shi, L. (2003) The ethnic minority-majority income gap in rural China duringtransition. Economic Development and Cultural Change 51, 13-86.
He, D., Zhang, Z. & Zhang, W. (2009) How large will be the effect of China’s fiscal stimulus
package on output and employment? Pacific Economic Review 14, 730-744.

Hsing, Y. & Hsien, W.-J. (2005) Impact of monetary, fiscal and exchange rate policies on output in
China: A VAR approach. Economics of Planning 37, 125-139.

Huang, B. & Chen, K. (2012) Are intergovernmental transfers in China equalizing? China Economic
Review 23, 534-551.

Ilzetzki, E., Mendoza, E.G. & Végh, C.A. (2013) How big (small) are fiscal multipliers? Journal of
Monetary Economics 60, 239-254.

Jiang, Y. (2011) Understanding openness and productivity growth in China: An empirical study of
the Chinese provinces. China Economic Review 22, 290-298.

Kajitani, K. & Hoshino, M. (2009) Determinants of intergovernmental fiscal transfers in internal
China and redistribution effect. Asian Studies 55(1), 19-39.

Knight, J., Song, L. & Gunatilaka, R. (2009) Subjective well-being and its determinants in rural
9

China. China Economic Review 20, 635-649.

Liu, L. (2009) Impact of the global financial crisis on China: Empirical evidence and policy
implications. China and World Economy 17, 1-23.

Nijkamp, P. & Poot, J. (2004) Meta-analysis of the effect of fiscal policies on long-run growth.
European Journal of Political Economy 20, 91-124.

Poncet, S. (2003) Measuring Chinese domestic and international integration. China Economic
Review 14, 1-21.

Tang, H.C., Liu, P. & Cheung, E. (2013) Changing impact of fiscal policy on selected ASEAN
countries. Journal of Asian Economics 24, 103-116.

Yu, Q. & Tsui, K. (2005) Factor decomposition of sub-provincial fiscal disparities in China. China
Economic Review 16, 403-418.
Zheng, X., Li, F., Song, S. & Yu, Y. (2013) Central government’s infrastructure investment acrossChinese regions: A dynamic spatial panel data approach. China Economic Review,
forthcoming.

10

Table 1. Estimation Results
Full model

Selected model by SBIC

Variables

Coefficient/t-value

Coefficient/t-value

𝑌𝑖,𝑡−1.310091*/2.214.481187** /6.334

Δ𝑌𝑖,𝑡−1.076980/.731

𝐺𝑖,𝑡.248764*/2.513

Δ𝐺𝑖,𝑡–.064820/–.677

Δ𝐺𝑖,𝑡−1–.013492/–.434

𝐼𝑛𝑓𝑙𝑡.00955741/.538

𝐼𝑛𝑓𝑙𝑡−1.00226542/.207

𝐼𝑛𝑓𝑙𝑡−2.023547/.661.257662** /7.946

Constant & dummy variables

Yes

Yes

Adjusted R2

.9959

.9958

S.E.

.06546.06645SBIC–84.892–93.145

Log likelihood

172.673

166.692

.901

.179

RESET
Note: ** and * mean statistically significant at 1% and 5%, respectively.
Total number of observations is 115.

Log likelihood is reported from the results of the ML calculation in TSP 5.0.

11

Table 2. Estimated Fiscal Multipliers

Prefecture

Nationality

RegionProvince
Jilin

Estimated fiscal multiplier
1.96

Yanbian

Korean

Northeast

GannanTibetan

Northwest Gansu

0.66

LinxiaHui

Northwest Gansu

0.93

Yushu

Tibetan

Northwest Qinghai

1.29

HainanTibetan

Northwest Qinghai

4.21

Huangnan

Tibetan

Northwest Qinghai

1.39

Haibei

Tibetan

Northwest Qinghai

1.33

Golog

Tibetan

Northwest Qinghai

0.63

Haixi

Mongolian & Tibetan Northwest Qinghai

4.02

Bayingolin

MongolianNorthwest Xinjiang

4.93

Boertala

MongolianNorthwest Xinjiang

2.35

Qizilsu

Qirghiz

Northwest Xinjiang

0.61

Changji

Hui

Northwest Xinjiang

3.67

Ili

Kazak

Northwest Xinjiang

2.05

Xiangxi

Tujia & Miao

Central

Hunan

1.83

Enshi

Tujia & Miao

Central

Hubei

1.96

Qiandongnan

Miao & DongSouthwest Guizhou

1.45

Qianxinan

Buyi & Miao

Southwest Guizhou

1.94

QiannanBuyi & Miao

Southwest Guizhou

1.80

Xishuangbanna Dai

Southwest Yunnan

2.42

DehongDai & Jingpo

Southwest Yunnan

1.43

Nujiang

Lisu

Southwest Yunnan

1.13

Dali

Bai

Southwest Yunnan

2.43

DiqingTibetanSouthwest Yunnan

1.14

HongheHani & Yi

Southwest Yunnan

2.40

WenshanZhuang & Miao

Southwest Yunnan

1.93

Chuxiong

YiSouthwest Yunnan

2.21

Ganzi

TibetanSouthwest Sichuan

0.72

Aba

Tibetan & Qiang

Southwest Sichuan

0.80

Liangshan

Yi

Southwest Sichuan

2.30

12