July 2008 Agriculture and Foods Economics Prayoga Wiradisuria Government Spending and Rural Development in China: 1993-2004 period analysis GRADUATE SCHOOL OF ASIA PACIFIC STUDIES

Content Background: China’s rural development China rural welfare development China’s agriculture value added China’s public spending Data calculation (Price adjustment, stock value, ln transformation) Rural development equation system Conclusion Appendix 1,2,3,4

Population living under $1 a day (% pop) Background: China’s rural development* China is interesting country for this particular class study because of the following reasons: China has been the fastest growing major nation for the last quarter century with more than 10% GDP growth rate which is accompanied by more than 8% per-capita income growth rate Such economic growth has been successfully reduced poverty rate. Percentage of people living under $1 a day has been reduced greatly from 64% in 1981 to 9.9% in 2001 However, due to large poverty base, China is now still sitting as the second largest poverty contributor to the world’s poor after India In 2004, 60% of China’s population is in rural which are engaged in agriculture activities While known for its industrial growth, China also experiencing significant agriculture growth. In 2001 China is positioned in number 9 for agricultural growth. For major country category it is positioned in number 1, or the fastest growth. 45% of China population (which means almost 600 million people) are in agriculture sector. This magnitude should attract government’s attention through public spending prioritization. Rest of world 37% China 22% India 41% Contribution to world’s poor China agricultural regions 87 96 90 84 01 1981 99 93 9.9 *Sources include: FAO, WDI

China is interesting country for this particular class study because of the following reasons:

China has been the fastest growing major nation for the last quarter century with more than 10% GDP growth rate which is accompanied by more than 8% per-capita income growth rate

Such economic growth has been successfully reduced poverty rate. Percentage of people living under $1 a day has been reduced greatly from 64% in 1981 to 9.9% in 2001

However, due to large poverty base, China is now still sitting as the second largest poverty contributor to the world’s poor after India

In 2004, 60% of China’s population is in rural which are engaged in agriculture activities

While known for its industrial growth, China also experiencing significant agriculture growth. In 2001 China is positioned in number 9 for agricultural growth. For major country category it is positioned in number 1, or the fastest growth.

45% of China population (which means almost 600 million people) are in agriculture sector. This magnitude should attract government’s attention through public spending prioritization.

To measure rural life or welfare development, author took Percentage of people living under international poverty line of $1 a day as a proxy. Assuming that share of poverty in urban and rural remains the same throughout the years, this data can then be used to measure rural poverty reduction progress. Due to some missing years, interpolation approach was undertaken. China Rural welfare development *UN MDG Database **Author calculation 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2002 2003 2004 1993 1994 1995 1996 1997 1998 1999 2000 2001 -15.1% 0.8% -11.9% -10.5% CAGR After Interpolation** Original data* Data from survey Interpolation data 11.1

China has the largest number of agriculture value added in 2004 in the world, and second after Indonesia in terms of its annual growth rate. Author therefore chose China for this study. Agriculture includes forestry, hunting, and fishing, as well as cultivation of crops and livestock production. Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. It is calculated without making deductions for depreciation of fabricated assets or depletion and degradation of natural resources. The origin of value added is determined by the International Standard Industrial Classification (ISIC). Data are in constant 2000 U.S. dollars and obtained from WordBank’s World Development Indicator database China’s agriculture value added *Compounded Annual Growth Rate 1999 1993 2004 2002 2000 1998 1996 1994 2003 1995 1997 2001 CAGR*: +3.7% AGVA (in million) 107 Brazil Indonesia France India Japan USA China 205 109 7 largest in 2004 (in billion) CAGR* (1993-2004) 3.7% 2.3% 2.5% 1.8% 3.8% -0.7% 4.6%

China has the largest number of agriculture value added in 2004 in the world, and second after Indonesia in terms of its annual growth rate. Author therefore chose China for this study.

Agriculture includes forestry, hunting, and fishing, as well as cultivation of crops and livestock production.

Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. It is calculated without making deductions for depreciation of fabricated assets or depletion and degradation of natural resources. The origin of value added is determined by the International Standard Industrial Classification (ISIC).

Data are in constant 2000 U.S. dollars and obtained from WordBank’s World Development Indicator database

Agriculture, Infrastructure and Education spends are those spending categories assumed to bring impact to rural development and therefore is chosen to be further analyzed whether they have given significant correlation to poverty reduction as the proxy for rural development in China. The nature of the data available gives Education spends which includes health. While Infrastructure spends includes industry and utility. Data is obtained from ADB Key Indicator 2007 statistics. China’s public spending Agriculture (in billion Yuan) Infrastructure (in billion Yuan) Education (in billion Yuan) 514.4 2004 2001 2000 1999 1998 1995 1994 1997 1993 1996 2003 2002 For computational purpose, this data will then be: Adjusted for price changes Calculated for its stock values Transformed into Logarithmic values Interpolated data

Agriculture, Infrastructure and Education spends are those spending categories assumed to bring impact to rural development and therefore is chosen to be further analyzed whether they have given significant correlation to poverty reduction as the proxy for rural development in China.

The nature of the data available gives Education spends which includes health. While Infrastructure spends includes industry and utility.

Data is obtained from ADB Key Indicator 2007 statistics.

For computational purpose, this data will then be:

Adjusted for price changes

Calculated for its stock values

Transformed into Logarithmic values

Public spending data calculation Prices changes adjustment For price changes adjustment, author uses China GDP deflator (1993-2004)* as follows: 113 *Source: World Bank’s WDI Database **Using formula and assumptions given for class project Stock values calculation** Logarithmic transformation The purpose is to make the regression result easier for analysis To incorporate the accumulated impacts of the spending Agriculture (AG) Infrastructure (INFRA) Education (EDU) For year 0 For year t AG INF EDU Ln(AG) Ln(INF) Ln(EDU) Also: POV AGVA Ln(POV) Ln(AGVA)

Regression analysis Data 26.2 25.8 26.0 25.6 Ln(POV) Ln(AGVA) Ln(AG) Ln(INF) Ln(EDU) 10 6 8 , Correlation 1 0.996989 0.990884 0.96326 -0.96522 ln(INF) 1 0.997748 0.969471 -0.95917 ln(AG) 1 0.973358 -0.95694 ln(EDU) 1 -0.97067 ln(AGVA) 1 ln(POV) ln(INF) ln(AG) ln(EDU) ln(AGVA) ln(POV)

Equation system POV = + 20.837 - 2.427 AGVA R 2 = 0.94 (1.413) (.190) AGVA = + 4.879 + .397 AG R 2 = 0.94 (.204) (.032) AGVA = + 5.263 + .438 INF R 2 = 0.92 (.191) (.039) AGVA = + 5.355 + .279 EDU R 2 = 0.95 (.155) (.021) 113 From the correlation shown earlier, regression analysis where made for the following equations: All variables are logarithmic transformed Number in parentheses are standard errors All statistically significant at 1 percent level (1) (2) (3) (4)

Conclusion 113 Rural development in China which is represented by percentage of poverty (POV) is reduced by agriculture value added (AGVA) in high correlation. This proves the greater agriculture value added, the less poverty. Agriculture value added (AGVA)is also in high correlation with agriculture spending (AG), infrastructure spending (INF), and education spending (EDU). The correlations are positive which means increase in AG, INF, or EDU, will result in increase in AGVA. Agriculture value added (AGVA) seems to be influence the most by infrastructure spending (INF) as shown in equation 3. The second most impact to AGVA is given by agriculture spending (AG) and followed by education spending (EDU) This analysis suggests that infrastructure development, which believed to provide rural with access to market, capital, resources, et cetera which will expose rural people to better prosperity should then be prioritized in the case of limited budget situation. Further study is required to understand more the connection between public spending and rural development in China using local/provincial level data and better representing rural development proxy

Rural development in China which is represented by percentage of poverty (POV) is reduced by agriculture value added (AGVA) in high correlation. This proves the greater agriculture value added, the less poverty.

Agriculture value added (AGVA)is also in high correlation with agriculture spending (AG), infrastructure spending (INF), and education spending (EDU). The correlations are positive which means increase in AG, INF, or EDU, will result in increase in AGVA.

Agriculture value added (AGVA) seems to be influence the most by infrastructure spending (INF) as shown in equation 3. The second most impact to AGVA is given by agriculture spending (AG) and followed by education spending (EDU)

This analysis suggests that infrastructure development, which believed to provide rural with access to market, capital, resources, et cetera which will expose rural people to better prosperity should then be prioritized in the case of limited budget situation.

Further study is required to understand more the connection between public spending and rural development in China using local/provincial level data and better representing rural development proxy

Appendix-1: Regression analysis of equation (1) 113 -2.003433191 -2.85060305 -2.0034332 -2.850603054 1.63E-07 -12.7666 0.190107063 -2.427018123 ln(AGVA) 23.98471811 17.68833558 23.984718 17.68833558 4.12E-08 14.74709 1.412924187 20.83652685 Intercept Upper 95.0% Lower 95.0% Upper 95% Lower 95% P-value t Stat Standard Error Coefficients 1.042090835 11 Total 0.006024 0.06024144 10 Residual 1.62848E-07 162.9857 0.981849 0.981849395 1 Regression Significance F F MS SS df ANOVA 12 Observations 0.077615359 Standard Error 0.936410933 Adjusted R Square 0.942191757 R Square 0.970665626 Multiple R Regression Statistics

Appendix-2: Regression analysis of equation (2) 113 0.468299163 0.326631595 0.46829916 0.326631595 1.98E-07 12.50262 0.031790561 0.39746538 ln(AG) 5.334508527 4.423964549 5.33450853 4.423964549 3.77E-10 23.87939 0.204328375 4.87923654 Intercept Upper 95.0% Lower 95.0% Upper 95% Lower 95% P-value t Stat Standard Error Coefficients 0.16668589 11 Total 0.001002 0.010022266 10 Residual 1.98432E-07 156.3156 0.156664 0.156663624 1 Regression Significance F F MS SS df ANOVA 12 Observations 0.03165796 Standard Error 0.93386067 Adjusted R Square 0.93987334 R Square 0.96947065 Multiple R Regression Statistics

113 Appendix-3: Regression analysis of equation (3) 0.52461697 0.3523365 0.52461697 0.3523365 4.95663E-07 11.34182009 0.038660174 0.438476735 ln(INF) 5.689502604 4.836361166 5.689502604 4.836361166 9.40031E-11 27.49026699 0.1914471 5.262931885 Intercept Upper 95.0% Lower 95.0% Upper 95% Lower 95% P-value t Stat Standard Error Coefficients 0.16668589 11 Total 0.00120232 0.012023199 10 Residual 4.95663E-07 128.636883 0.154662691 0.154662691 1 Regression Significance F F MS SS df ANOVA 12 Observations 0.034674486 Standard Error 0.920656035 Adjusted R Square 0.927869123 R Square 0.963259634 Multiple R Regression Statistics

113 Appendix-4: Regression analysis of equation (4) 0.325451163 0.232794143 0.325451163 0.232794143 1.01083E-07 13.42421804 0.02079247 0.279122653 ln(EDU) 5.700389718 5.010181833 5.700389718 5.010181833 9.69994E-12 34.57601835 0.154884398 5.355285776 Intercept Upper 95.0% Lower 95.0% Upper 95% Lower 95% P-value t Stat Standard Error Coefficients 0.16668589 11 Total 0.000876327 0.008763273 10 Residual 1.01083E-07 180.2096299 0.157922617 0.157922617 1 Regression Significance F F MS SS df ANOVA 12 Observations 0.029602826 Standard Error 0.942169069 Adjusted R Square 0.947426426 R Square 0.973358324 Multiple R Regression Statistics