Sample and Data Sources of Quantitative Analysis of Institutional Framework of China and how it Impacts the Development

        The research employed the secondary annual time series data for the year 1996-2017. The reason of this sample size is the availability of data. The data of institutional variable is collected from World Governance Indicators that is available from 1996 to onwards. This study used Cobb Douglas Production Function to derive the model

Variable Operationalization of  Quantitative Analysis of Institutional Framework of China and how it Impacts the Development

As the institutional quality is the major consideration of the study so there is need to add the variables of institutional quality.

For the estimation the six governance indicators are used the explanatory variables whereas the GDP as the dependent variable. Like the other previous studies, this model also includes the other control variables.

Gross fixed capital formation is usually used for the proxy of K and Y represents the output that is known as Gross Domestic Product (GDP) so we can replace the K by GFCF, L by LF (Labor Force), and Y by GDP in the equation (1.1). GDP is used as a proxy of development in China.

Empirical Model of  Quantitative Analysis of Institutional Framework of China and how it Impacts the Development

Hence, the equation (1.1) can be rewritten as following in order to estimate the concerned variables of the study.

All the variables are collected in form of Local Chinese Currency Unit in Million in the constant term from 1996-2017.

The models can be estimated empirically to find out the role of institutional quality and in the development of China. The model is estimated on Stata software and results are presented in the next section.

Descriptive Statistics of  Quantitative Analysis of Institutional Framework of China and how it Impacts the Development

The summary of descriptive statistics is presented in the table 1 whereas; the table 2 shows the pair wise correlations. As shown in the tables different variables have been used for the institutional quality and for the regression, the other control variables are used.

        Above table represents the summary statistics of variables i.e. mean, standard deviation, minimum, and maximum value. It can be observed from the table that government effectiveness has highest mean value i.e. 57.80526 while voice and accountability has lowest mean value i.e. 6.717368. Following the mean values government effectiveness and voice and accountability has highest and lowest standard deviation respectively. Minimum and maximum value of each variable can also be observed in the table.

    The above figure shows the plot of each variable from 1996 to 2017 that is the sample size of this research. Longed and NFL represents log of gross domestic product and log of labor force respectively.

        The above shows the correlation between the variables. The negative correlation can be observed between voice and accountability and GDP, political stability and GDP, government effectiveness and voice and accountability, regulatory quality and voice and accountability, rule of law and voice and accountability, gross fixed capital formation and voice and accountability, labor force and voice and accountability, government effectiveness and political stability, regulatory quality and political stability, rule of law and political stability, gross fixed capital formation and political stability, labor force and political stability, control of corruption and regulatory quality, gross fixed capital formation and control of corruption, and labor force and control of corruption. All other variables are positively correlated to each other.

ADF (Augmented Dicky Fuller) test is applied on each variable of equation (1) to check the stationarity of variables.

Results from ADF test shows that all the variables are stationary at first difference. It was the necessary condition for co-integration relationship among variables, which is fulfilled. Now we can say that variables are integrated of order one. Now the confirmation of cointegration among variables is needed. Johansson cointegration test can be applied for this purpose. But we 

    Above table contains the coefficients of estimated parameters of long run, along with their standard errors, p-values and significance status. It can be observed from the results that voice and accountability, political stability, government effectiveness, rule of law, and gross fixed capital formation are significant at 1% which means that these variables significantly affect the development of China in long run. On the other hand, regulatory quality, control of corruption, and labor force do not affect the development of China in long run. China is highly capital intensive country; this might be the reason that the impact of labor force is not significant in long run. Regulatory quality and corruption, since 1996 (the sample size of this research), are under control in China; this might be the reason that regulatory quality and control of corruption are insignificant in the long run in development of China.

        The results in above table show that the effect of voice and accountability and rule of law is negative in the long run on the development of China. On the other hand, political stability, government effectiveness, and gross fixed capital formation positively and significantly impacts the development of China in the long run. The coefficient value of political stability is very high i.e. 124.2891 which shows that the contribution of political stability to development of China is highest among other variables.

        Breusch–Godfrey test is utilized to check the serial correlation among the variables at optimal lags. H₀ is no serial correlation at lag order, as p value is greater than 0.05 at lag 1 so H₀ is accepted. And it is concluded that there is no sign of serial correlation among variables at lag 

        The figure represents the forecasting of each variable for the period from 2018 to 2028 i.e. 10 years. The graphical representation of forecasting shows the possible trends of each variable from 2018 to 2028. The forecasting shows that GDP of China will be increasing till 2019 then it will be constant till 2028. On the other hand, the voice and accountability, political stability, government effectiveness, regulatory quality, and rule of law will be decreasing in China till 2019 and then after some time, these variables seemed to be constant till 2028. Furthermore, forecasting indicates that control of corruption have been increasing in 2018, then it seemed to be constant till 2028. Moreover, the forecasting trends of gross fixed capital formation and labor force are more or less same i.e. decreasing in 2018, increasing till 2019, and constant till 2028.

Policy Implication of  Quantitative Analysis of Institutional Framework of China and how it Impacts the Development 

Policy implication is totally based on the empirical results. It is recommended to Government of China that it should increase the gross fixed capital formation to enhance the development of country in the long run.

Better political stability and government effectiveness bring significantly positive change in the development of China. So Chinese government should ensure the stability of these institutional indicators and must take these institutional indicators into high consideration.

Optimal level of voice and accountability and rule of law should be maintained in China to make its impact positive on the development of China.

References of Quantitative Analaysis of Institutional Framework of China and how it Impacts the Development

Acemoglu, D., & Robinson, J.A. (2012) Why Nations Fail: The Origins of Power, Prosperity and Poverty, London: Profile Books.

Barro R.J. (1997). Determinants of Economic Growth: A Cross-Country Empirical Study. MIT Press

Qian, Yingyi (1999). The institutional foundations of China’s market transition, Paper Prepared for World Bank’s Annual Conference on Development Economics, Washington, D.C.

Saima N., Nasir I. and Mohammed A.K. (2014). The impact of Institutional Quality on Economic Growth: Panel Evidence. The Pakistan Development Review, 53(1), 15-31

Shanker, D. (2003). Developing countries, China and economic institutions, Social Science Research Network, from http://papers.ssrn.com/so13/papers.cfm/abstract_id=277928

World Bank Report (2008). GDP and economic indicators of China, from http://www.worldbank.org