1993-2004 period analysis

1. 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

2. 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

4. 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

7. 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)

8. 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)

9. 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)

11. 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

12. 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

13. 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

14. 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