Revisiting the relationship between fiscal health expenditure and

economic growth: a bootstrap panel Granger causality analysisHighlights:

This study revisits the causal relationship between fiscal health expenditure and

economic growth in China with the bootstrap panel Granger causality analysis.

According to economic and policy differences, China is divided into the eastern

region and the Midwest in this paper.

There is one-way causality running from fiscal health expenditure to economic

growth for Jiangsu and the other eight provinces.

Apart from Shaanxi, economic growth has little effect on fiscal health

expenditure in the 29 provinces.

Abstract: With economic growth and health care reform, development of the health care system has made great progress in China. This paper applies the bootstrap panel Granger causality test, which is suitable for small sample sizes, to analyze the relationship between fiscal health expenditure and economic growth in China from 1990–2015. The results indicate geographic heterogeneity in their relationship. There is one-way causality running from fiscal health expenditure to economic growth for Jiangsu and the other eight provinces. Conversely, economic development has a unidirectional impact on fiscal health expenditure in Shaanxi. In addition, for the remaining provinces, the causality between economic growth and fiscal health expenditure is not significant.Key word: Fiscal Health Expenditure; Economic Growth; Proportion of Elderly Population; Bootstrap Panel Granger Causality Test

Introduction

In 2015, the national fiscal health expenditure was 1,185.5 billion RMB, which represents an increase of 16.45% compared to that in 2014 and is 3.46% higher than the financial expenditure in the same period. The State Council issued a relevant document to stress the urgency of health care reform and will increase financial expenditure in health care. It is well known that health care expenditure, the most important health investment, is the main source of healthy human capital. Barro (1996) found that healthy human capital could increase economic growth by increasing labor productivity. In addition, with the development of economic and living standards, the government attaches great importance to health care and thereby increases the total amount of fiscal health. A large number of researchers have studied the impact of economic development on health spending. Kleiman (1974) found that economic growth is a vital factor affecting health care expenditure. Baltagi (2010) reported that health expenditure is a necessity in OECD countries. Few studies have involved the interaction of health spending and economic growth. Therefore, this paper studies the two-way Granger causality between fiscal health expenditure and economic growth and provides policy suggestions for health care reform in China. This paper will enrich the existing research in the following aspects. First, the research method, a bootstrap panel Granger causality test, is suitable for small sample sizes and fully takes into account the heterogeneity and relevance of the subjects. Additionally, this method applies to our research objectives. Second, this study investigated the bilateral causality between economic growth and fiscal health expenditure. Lastly, the study imposes that the relationship between economic growth and public health expenditure varies widely from province to province. The remainder of this paper is organized as follows: Section 1 introduces the relationship between health expenditure and economic growth in four aspects, section 2 describes the data and descriptive statistics, section 3 introduces the theory and application of the bootstrap panel Granger causality test and relevant tests, section 4 explains the empirical analysis of economic growth with fiscal health expenditure and puts forward policy suggestions, and section 5 concludes the paper.

1 Literature review

There are four study results that have been found between health expenditure and economic growth. First, health expenditure has a one-way impact on economic growth, which is mainly related to the indirect human capital effect of health expenditure. Second, economic growth affects health expenditure, mainly regarding whether health expenditure is a necessity or a luxury. Third, the interaction between economic growth and health expenditure has been discussed as to whether there is a two-way causality between economic growth and health expenditure. Finally, a number of scholars have found that there is no significant correlation between health expenditure and economic growth.

Based on human capital theory, scholars examined the impact of health expenditure on economic growth from different perspectives. González-Páramo (1994) notes that public services, such as education investment and health expenditure, will enhance the overall quality of the labor force, increase labor productivity, and indirectly promote economic growth, that is, the human capital effect of health expenditure. McDonald and Roberts (2002) and Mankiw et al. (1992) have shown that health human capital is more effective for economic growth than education human capital. Rivera and Currais (1999a, 1999b, 2003) and Heshmati (2001), based on OECD data for different time intervals, found that health care spending promotes economic growth. Rivera and Currais (2004) also affirm the positive effects of health expenditure on economic growth in a study of Spanish data from 1973–1993, while the article emphasizes that this promotion comes from current health spending rather than health investment. Bishai and Simon (1987) used computer simulation technology to study the relationship between economic growth and health expenditure and found that the experimental results depended on technological development. On the one hand, if technological progress is slow, health expenditure will stimulate economic growth in the short term and hinder growth in the long term. On the other hand, if technological advances occur quickly, health expenditure will promote per capita income growth. Hartwig (2010) constructed the Granger causality model using data of per capita health expenditure and per capita GDP of 21 OECD countries from1970–2005 to test the long-term economic effects of health expenditure in developed countries. The results showed that for countries with higher levels of economic development, health expenditure will curb long-term economic growth. Gong et al(2012)show that given the crowding-out effect of health investments on material capital , excessive health investment will hamper economic growth.

Research on the impact of economic growth has focused on the income elasticity of health expenditure. Kleiman (1974) suggested that income level is an important factor influencing health expenditur. Correa and Namkoong (1992) also noted that economic conditions have a significant impact on health policies. Subsequently, scholars have devoted extensive research on the income elasticity of health expenditure. Blomqvist and Carter (1997) noted that health care is a labor-intensive commodity, whose price increases with the level of income; thus, the health expenditure increases with the level of economic development. At present, there is controversy in academia regarding whether health expenditure is a necessity or a luxury. Baltagi and Moscone (2010), based on data of 20 OECD countries from 1971–2004, found that the income elasticity of health care expenditure was less than 1; thus, health expenditure was a necessity. Similarly, Lv and Zhu (2014) presented that the income elasticity of health expenditure is between 0.71 and 0.78 on the basis of data from 42 African countries from 1995–2009. Meanwhile, this study also found that compared to lower-income countries, the income elasticity of health expenditure is higher in upper-middle-income countries. On the other hand, Hitris (1997) and Mehrara et al. (2010) both insist that health expenditure is a luxury when studying the

income elasticity of health expenditure based on some OECD countries. Clemente et al. (2004) conducted empirical studies on three aspects of total health expenditure, public health expenditure and private health expenditure. In addition, the result suggests health expenditure is a luxury. In view of the effects of structural changes, Esteve and Martinez-Zahonero (2007) tested the long-term economic effects of health expenditure with data of the Spanish region from 1960–2001. The results show that there were two structural changes in 1971–1972 and 1991–1992. Although health expenditure is a luxury, the income elasticity of health spending decreases. Santiago et al. (2013) applied data on 31 OECD countries from 1970–2009 and found that health expenditure is a necessity in the short term and a luxury in the long term. In addition, in countries with higher private spending, economic growth has a more pronounced effect on health expenditure.

Additionally, a bilateral causality between health expenditure and economic growth has been found in some studies (Amiri and Ventelou (2012), Chaabouni,and Abednnadher (2014)). Wang (2011), using the data of 31 OECD countries from 1986–2007 as samples, found that health expenditure contributes to economic growth while economic growth inhibits health expenditure. At the same time, the article used the quantile model to examine the impact of health expenditure on economic growth under different economic development levels and the impact of economic growth on health expenditure under different health expenditure levels. Although the research does not reach a definitive conclusion, it provides a new direction for follow-up study. Sami et al. (2016) considered the impact of economic development in different counties. The sample countries were divided into low-income, middle-income, and high-income countries. The study indicated economic growth and health care expenditure are mutually reinforcing with different levels of development.

In addition, some researchers have noted that there is no obvious correlation between health expenditure and economic growth (Hansen and King (1996), Devlin and Hansen (2001)).

Previous studies have enriched our understanding of the relationship between fiscal health expenditure and economic growth from both the theoretical and empirical perspectives, but most have focused on developed countries. In contrast to domestic and foreign research, studies of China have the following deficiencies. First, when using national and regional data, the differences between provinces are ignored. Applying inappropriate homogeneity assumptions to parameters will seriously affect the robustness of the empirical results. Second, research of China is mainly focused on the relationship between total health expenditure and economic growth, and there is a lack of discussion of fiscal health expenditure. Third, studies of provincial data ignore the interaction between provinces. Economic growth and health expenditure both have a strong spatial spillover effect, and this spillover effect will be reinforced with political, economic and cultural exchanges among provinces. Finally, combined with foreign studies, we can see that the proportion of the elderly population has a significant impact on health expenditure. With continued population aging, its inhibitory effect on economic growth is more and more significant. However, current

studies of the China have rarely included the proportion of the elderly population in the analysis system.

2 DataAccording to economic development and geographical location, the Chinese

Mainland is divided into eastern, central and western economic zones. The eastern region includes Beijing, Tianjin, Hebei, Liaoning, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, Hainan; the central region includes Shanxi, Jilin, Heilongjiang, Anhui, Jiangxi, Henan, Hubei, Hunan; and remaining provinces are in the western region. There are great differences in developmental policies between the eastern region and the Midwest (the Midwest refers to the central and western regions) because of economic development. The strategy for the middle-region development and western region development is implemented in the western and central regions. Therefore, the subject of this dissertation is divided into the eastern region and the Midwest.

Because of missing data from Chongqing, Sichuan, Hong Kong, Macao and Taiwan, this study chooses panel data of 29 provinces from 1990–2015 to study the relationship between economic growth and fiscal health expenditure. The variables in this article include the real gross domestic product per capita (PRGDP), the real fiscal health expenditure per capita (PRFHE) and the proportion of elderly people over 65 (P65). The data are from the China Statistical Yearbook 1990–2015. As there is no separate fiscal health expenditure in China Statistical Yearbook 1990–2007, we take one-quarter of the science-education-culture-health expenditure as the fiscal health expenditure, as from previous study. Table 1 Summary statistics of PRGDP in 29 provinces Region Province Mean Max Min Std.dev

The eastern region

Beijing 15779.73 31345.44 4611.602 8880.874

Tianjin 14245.48 31610.39 3517.534 9430.428Hebei 5802.791 11869.7 1357.591 3575.446Liaoning 8659.885 19345.12 2434.384 5555.409Shanghai 17582.07 30760.48 5569.708 7655.209Jiangsu 10364.58 25993.53 1944.466 7431.18Zhejiang 10452.74 22893.91 2007.893 6575.704Fujian 8304.373 20010.09 1512.94 5451.614Shandong 8094.165 18918.36 1569.963 5539.324Guangdong 9416.482 19844.33 2319.319 5456.495Hainan 5156.188 12018.16 1433.032 3130.977

The Midwest Shanxi 5025.411 10356.85 1347.961 3318.635

Jilin 6254.008 15104.51 1584.044 4491.24Heilongjiang 5910.567 11699.98 1859.893 3230.054Anhui 4189.437 10590.43 976.4489 2994.992Jiangxi 4283.84 10830.03 1101.155 3063.596Henan 4752.274 11541.17 1035.657 3384.159

Hubei 5681.438 14930.93 1454.477 4099.007Hunan 4862.691 12599.12 1146.606 3586.576Inner Mongolia 8033.787 20997.73 1325.659 7140.066Guangxi 4056.944 10359.57 921.9197 2833.855Guizhou 2791.695 8797.34 779.896 2345.381Yunnan 3560.44 8492.196 1061.351 2216.58Tibet 3718.724 9366.953 1101.351 2478.945Shaanxi 5151.311 14049.07 1128.016 4295.391Gansu 3352.428 7768.274 1037.871 2240.657Qinghai 4813.295 12154.56 1480.804 3485.091Ningxia 5043.268 12888.77 1300.000 3864.656Xinjiang 5484.395 11880.36 1647.351 3203.656

Table 1 and 2 show the summary statistics of the per capita real GDP and per capita real fiscal health expenditure in 29 provinces. The provinces with the highest and lowest mean per capita real GDP are Shanghai and Guizhou, respectively. However, the provinces with highest and lowest mean per capita real fiscal health expenditure are Beijing and Hunan, respectively. The per capita real GDP of the eastern provinces is generally higher than that in the Midwest provinces. However, there is no significant gap between the eastern provinces and the Midwest provinces for the per capita real fiscal health expenditure.Table 2 Summary statistics of PRFHE in 29 provincesRegion Province Mean Max Min Std.dev

The eastern region

Beijing 188.635 504.641 37.657 144.047

Tianjin 106.787 372.752 27.208 97.274Hebei 53.334 213.089 10.104 60.907Liaoning 60.100 190.259 17.078 58.519Shanghai 140.266 371.548 34.499 94.252Jiangsu 63.430 240.712 10.954 66.911Zhejiang 75.283 259.172 13.208 72.023Fujian 64.277 270.492 14.208 71.034Shandong 50.991 210.626 10.438 58.523Guangdong 66.560 250.296 17.164 62.757Hainan 76.970 326.326 16.818 92.704

The Midwest Shanxi 60.827 234.604 13.453 65.499

Jilin 69.385 264.012 16.237 76.463Heilongjiang 58.858 212.503 13.595 61.144Anhui 54.394 233.700 7.352 71.207Jiangxi 57.278 258.249 8.998 72.624Henan 49.681 223.867 7.093 64.011Hubei 56.487 260.342 9.133 69.300Hunan 48.663 215.232 8.508 62.351Inner Mongolia 80.393 302.810 14.602 92.031Guangxi 59.086 255.162 10.066 74.201

Guizhou 65.019 302.220 8.850 84.117Yunnan 71.558 263.549 14.286 73.138Tibet 163.065 573.120 31.632 157.534Shaanxi 66.796 287.953 10.927 83.640Gansu 67.480 284.427 11.896 80.701Qinghai 121.862 500.001 20.410 139.968Ningxia 84.351 328.043 16.352 96.054Xinjiang 82.599 305.722 13.015 85.474

3 Methodology

3.1 The cross-sectional dependence test and slope homogeneity test

An important assumption in the bootstrap panel causality test is that there is a cross-sectional dependence among subjects. As economic growth and fiscal health expenditure have strong spatial spillover influence, a province's economic growth or

fiscal health spending will have an impact on neighboring provinces. In the case of

cross-sectional dependency, the SUR analysis model is more effective than the pooled OLS model, because the SUR model takes into full account the cross-sectional dependence among the subjects. Pesaran (2006) suggests ignoring the cross-sectional dependence among different subjects will lead to substantial bias and size distortion. Therefore, the cross-sectional dependence test is the key to selecting an effective estimation method and ensures the robustness of the experimental results. At present, widely used cross-sectional dependence tests mainly include CDBP, CDlm, CD and LMadj.

At the same time, the other important condition of the bootstrap panel causality analysis is that the slope coefficient is heterogeneous among the subjects. Because of differences in policy, economic development level and factor endowment among the provinces, its response to the same shock vary. Ignoring this difference and applying a joint hypothesis to the slope coefficient will affect the robustness of the empirical analysis results. Meanwhile, Breitung (2005) proposed that the homogeneity assumption of the parameters is unable to reflect the heterogeneity arising from country-specific characteristics. Currently, frequently used slope homogeneity tests

include Swany,~Δ

and

~Δadj.

3.2 Bootstrap panel Granger causality test

Because of the existence of cross-section dependence and slope homogeneity, this study examines the relationship between economic growth and fiscal health expenditure using the bootstrap panel Granger causality test proposed by Kónya (2006). Bootstrap panel causality test is based on the principle of repeated sampling,

making full use of the informational characteristics of data. It overcomes the shortcomings of small sample sizes in causality analysis, making the results of empirical research more robust. Kónya (2006) proposed that because of the generation of country-specific critical values, the method is both robust to the unit root test and the co-integration test, and the model also applies to the causality test of non-stationary sequences. It is worth noting that the choice of variable level plays a crucial role in the research results in causality analysis because different forms of variables may lead to a loss of dynamic trends in the series. The model estimates the described system using SUR to impose a zero limit on the causal relationship using Wald's principle, and finally generates a bootstrap critical value. Because of the province-specific Wald test and province-specific bootstrap critical values, it is not necessary for the Wald test to require a joint hypothesis for all provinces in the panel. The causality analysis includes two sets of equations that can be written as follows.

PRGDP1, t=α1,1+∑i=1

l y1

β1,1 , iPRGDP1 ,t−i+∑i=1

l x1

γ1,1 , iPRFHE1 ,t−i+∑i=1

lz 1

δ 1,1 ,iP651 , t−i+ε1,1 , t

PRGDP2, t=α1,2+∑i=1

l y1

β1,2 , iPRGDP2 ,t−i+∑i=1

l x1

γ1,2 , iPRFHE2 ,t−i+∑i=1

lz 1

δ 1,2 ,iP652 , t−i+ε1,2 ,t

…

PRGDPN ,t=α1 , N+∑i=1

l y1

β1 , N ,iPRGDPN ,t−i+∑i=1

lx1

γ 1 ,N ,iPRFHEN , t−i+∑i=1

l z1

δ1 , N , iP65N ,t−i+ε1 , N , t

And

PRFHE1 ,t=α 2,1+∑i=1

ly2

β2,1 ,iPRGDP1 ,t−i+∑i=1

lx2

γ 2,1 ,i PRFHE1, t−i+∑i=1

l z2

δ2,1 ,i P651 ,t−i+ε2,1 ,t

PRFHE2 ,t=α 2,2+∑i=1

ly2

β2,2 ,i PRGDP2 ,t−i+∑i=1

lx2

γ 2,2 ,i PRFHE2 ,t−i+∑i=1

l z2

δ2,2 , iP652 ,t−i+ε2,2 ,t

…

PRFHEN ,t=α 2 , N+∑i=1

ly2

β2 , N ,i PRGDPN , t−i+∑i=1

lx2

γ2 , N , iPRFHEN ,t−i+∑i=1

lz 2

δ 2, N ,iP65N , t−i+ε 2, N ,t

In these equations, PRGDP is the real gross domestic product per capita, on behalf of the level of economic development; PRHEC is the real fiscal health expenditure per capita to measure the health investment of government; P65 refers to

the proportion of elderly people over 65 to indicate the population aging level; t（=

1，...，T） is the time period; N（= 29） is the number of sample provinces, and l is

the lag length. In the regression analysis, each equation has a different preset variable, and the error term can be correlated by cross-section, thus we can treat these equations as an SUR system. For each province, the possible Granger causality has

the following four conditions: (i) if not all γ1 ,iare zero but allβ2 ,i are zero, then there is a one-way Granger causality from PRFCE to PRGDP; (ii) if all γ1 ,iare zero but not allβ2 ,i are zero, then there is a one-way Granger causality from PRGDP to PRFCE; (iii) if neitherγ1 ,i norβ2 ,i is zero, then there is a two-way Granger causality between PRGDP and PRFCE; and (iv) if bothγ1 ,i andβ2 ,ii are zero, then there is no Granger causality between PRGDP and PRFCE.

Before proceeding with the SUR system, we need to determine the optimal lag length. The results of the causality test may vary with different lag structures, thus determining the optimal lag length is critical to the robustness of the empirical result. According to Kónya (2006), the maximum lag length can be different among variables but must be consistent in the same equation. In large panel data systems, it is assumed that there is a lag length of 1–4, and we selected the optimal lag length by minimizing the Schwarz Bayesian Criterion in this study.

4 Empirical results and policy recommendations

4.1 The result of the cross-sectional dependence test and the slope

homogeneity testTo ensure the robustness of the empirical results, we crossed the cross-sectional

dependence test and the slope homogeneity test first, and the results are shown in table 3. The results show that there is a significant cross-sectional dependence among the provinces at traditional significance levels. This means that a province's fiscal health expenditure or economic growth will affect other areas, thus it is necessary to take into account the dependence in causality analysis. The slope homogeneity test results show that, except for the ~∆adj test, the tests reject the original hypothesis of slope homogeneity at the conventional significance level. As a result, the causality between economic growth and public health expenditure may differ from one area to another. Based on the results, the presence of cross-sectional correlation and slope heterogeneity make the bootstrap panel Granger causality test more effective than the pooled OLS model.Table 3 Cross-sectional dependence and slope homogeneity testMethod The eastern region The Midwest

Test Stat P-value Test Stat P-value

Cross-sectional dependence testCDBP 327.391 0.000 945.072 0.000CDLM 25.971 0.000 45.280 0.000CD 15.420 0.000 23.262 0.000LMadj 113.538 0.000 208.167 0.000Slope homogeneity test~Δ 15.462 0.000 15.256 0.000

~Δadj0.650 0．258 0.640 0.261

Swamy83.354 0.000 109.533 0.000

4.2The Granger causality test of fiscal health expenditure on economic

growth

In table 4, from a nationwide perspective, only Jiangsu, Zhejiang, Fujian, Hainan, Anhui, Guangxi, Yunnan, Qinghai, and Xinjiang pass the significance test, indicating there is a causality running from fiscal health expenditure to economic growth. These provinces all show a positive effect of fiscal health expenditure on economic growth. As is known, fiscal health expenditure is an important source of healthy investment and has a strong effect on human capital. It can promote economic growth by improving the health of workers, enhancing the labor market participation rate and working efficiency and prolonging working hours of workers. However, there are 20 provinces that did not pass the test from the results. This shows that the effect of fiscal health expenditure on economic growth is not significant in most provinces. Therefore, the Chinese government needs to further promote health care reform, enhance health care investment, optimize the structure of health care expenditure, and widen the fields and channels of health care infrastructure. At the same time, the government should pay more attention to the efficiency of health inputs, avoiding duplication and waste of health resources, and improve the human capital effect of health expenditure finally. Yip and Hsiao (2009) indicated that the Chinese government can improve the operation efficiency of the health care system by changing the payment method of suppliers and developing third-party purchasing institutions that represent the interests of the people.Table 4 Public health expenditure does not cause Granger economic growth

Area province Wald statistics

Bootstrap critical value1% 5% 10%

The eastern region

Beijing 12.382 76.328 28.616 20.191Tianjin 1.437 33.305 18.915 13.833Hebei 0.673 29.638 17.055 12.187Liaoning 11.328 31.369 17.699 12.968Shanghai 16.076 44.055 24.883 18.103

Jiangsu26.330**（+）

35.104 20.722 14.976

Zhejiang31.280**（+）

39.216 22.304 16.150

Fujian40.639***（+）

31.594 18.426 13.718

Shandong 0.0349 29.979 17.172 12.604Guangdong 0.278 33.228 18.433 13.409

Hainan26.009**（+）

31.460 18.975 14.042

The Midwest

Shanxi 2.175 57.894 35.164 25.457Jilin 0.795 50.089 29.551 21.641Heilongjiang 7.108 49.574 27.590 20.110

Anhui73.987**（+）

77.247 42.197 30.626

Jiangxi 1.491 53.850 30.712 22.086Henan 12.991 54.922 32.833 23.875Hubei 10.896 67.321 38.158 26.952Hunan 17.468 60.818 35.950 27.156Inner Mongolia 7.365 72.985 40.583 29.256

Guangxi69.883**（+）

80.778 47.668 35.268

Guizhou 3.132 54.145 30.023 21.683

Yunnan27.688*（+）

59.791 33.100 24.420

Tibet 0.836 82.704 46.733 32.798Shaanxi 0.768 77.478 44.851 32.921Gansu 4.291 51.284 29.854 21.636

Qinghai70.715***（+）

68.080 36.845 27.024

Ningxia 23.863 72.447 40.277 29.362

Xinjiang35.825**（+）

60.764 34.468 25.028

Notes: 1. *, ** and ***Indicate significance at the 0.1, 0.05 and 0.01 levels, respectively

2. Bootstrap critical values are obtained from 10 000 replications.

4.3 The Granger causality test of economic growth on fiscal health

expenditure

Table 5 shows the results of the Granger causality test between economic growth and fiscal health expenditure. From the empirical result, only Shaanxi passes the significance test, which shows that economic growth has a positive effect on fiscal expenditure. The other 28 provinces did not pass the test. This means that the effect of economic growth on fiscal health expenditure is not significant in the vast majority of provinces in China. Economic growth has less of an effect on public health expenditure in these areas, due to many factors. First, the phenomenon of "focusing on investment and weakening consumption" is common in financial expenditure in China. Local governments need to reasonably control fiscal expenditure to increase capital expenditure in order to achieve rapid economic growth. Taking the west for instance, economic growth is one of the primary objectives of the West Development Strategy. In addition, in the west, economic growth is mainly driven by investment. The government, as the main investor, needs to place more capital into economic

development. Second, with continued health care reform, the tendency of marketization of medical system construction is obvious. Social investment in health care infrastructure is increasing and plays an alternative role to fiscal health expenditure.

Therefore, the government could coordinate the relationship between economic development and health care reform. Local governments should recognize that the ultimate goal of economic development is to improve peoples’ living standards. Health care infrastructure is an important measure to improve peoples’ wellbeing and is of great significance. Next, the government should do a good job of guidance and management to attract more social capital to join health care infrastructure. Due to the finiteness of government resources, it is the right choice to promote the marketization of health care infrastructure to solve the problem of a lack of health care resourcesTable 5: Economic growth does not Granger cause public health expenditure

Area province Wald statistics

Bootstrap critical value1% 5% 10%

The eastern region

Beijing 0.932 46.736 28.185 21.004

Tianjin 4.089 54.950 34.112 26.103

Hebei 0.632 54.391 35.339 27.588

Liaoning 0.029 60.993 36.102 27.729

Shanghai 5.566 62.844 38.298 28.752

Jiangsu 1.671 47.889 30.863 23.801

Zhejiang 3.628 48.717 29.318 21.532

Fujian 2.922 51.521 32.894 24.768

Shandong 10.200 52.391 32.378 24.484

Guangdong 5.991 57.348 34.799 26.935

Hainan 7.819 58.904 36.027 27.979

The Midwest Shanxi 11.374 118.066 71.801 55.059

Jilin 8.698 133.302 77.577 57.827

Heilongjiang 11.504 136.117 78.502 57.974

Anhui 48.686 119.098 69.889 51.824

Jiangxi 6.129 142.588 92.305 69.587

Henan 6.465 127.283 76.709 57.770

Hubei 3.464 125.044 76.148 57.759

Hunan 5.450 129.413 80.865 61.449

Inner Mongolia 15.062 133.606 79.295 60.713

Guangxi 11.463 133.870 80.051 58.805

Guizhou 10.235 129.949 78.804 60.343

Yunnan 10.894 120.266 74.282 54.851

Tibet 28.415 112.411 64.522 46.723

Shaanxi79.722*

*（+）115.351 69.440 51.506

Gansu 7.384 121.410 73.667 55.699

Qinghai 33.470 121.952 73.500 56.752

Ningxia 15.313 127.427 75.278 54.775

Xinjiang 9.463 136.299 79.116 59.294

Notes: 1. *, ** and ***Indicate significance at the 0.1, 0.05 and 0.01 levels, respectively

2. Bootstrap critical values are obtained from 10 000 replications.

5 Conclusion This paper uses a method of bootstrap panel causality analysis to test the

relationship between fiscal health expenditure and economic growth in China. This paper found that there is significant cross-sectional correlation and slope heterogeneity in the relationship between economic growth and fiscal health expenditure. Causality analysis results showed that there is one-way causality running from fiscal health expenditure to economic growth for Jiangsu, Zhejiang, Fujian, Hainan, Anhui, Guangxi, Yunnan, Qinghai, and Xinjiang. Conversely, economic development has a unidirectional impact on fiscal health expenditure in Shaanxi. In addition, for the remaining provinces, the causality between economic growth and fiscal health expenditure is not significant.

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