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Exempel 10, Sid 5 What factors affect economic growth in China? 1 Background In the late 1970βs, Deng Xiaoping initiated the open door policy, a free-market reform. The reform took two decades to finalize and included opening the country to foreign direct investments, allowing for entrepreneurs to start their own businesses, privatizing state-owned enterprises and removing price controls.1 Before the reform, China had just been through the Cultural Revolution that had killed millions of people and left the country broke. China was really in need of a change. From 1978 to 2010 the economy grew by an average of 9,5% every year and the private sector now accounts for 70% of GDP.2 In 2001 China entered the World Trade Organization, increasing international trade and helping the economy grow further.3 China is still a poor country, despite positive outcomes of the market reform. GDP is increasing every year but the annual per capita income is only $4900.4 The reason for this relatively small figure is Chinas huge population. The majority of the population is still fit for work but since the one-child policy was implemented 30 years ago, the population growth is slowing down while people get older. This means todayβs young people will have to support a huge ageing population. Does the population growth have a positive or negative impact on the economy? What other factors are important for China to consider when analyzing growth? These questions are the starting point of this thesis. Research objective My research question is the following: What factors affect economic growth in China? More specifically, I will research how factors health care, education, foreign direct investment per capita, population growth, household savings and initial GRP per capita affect economic growth measured in GRP per capita in China.5 Theory The theoretical background will be based on the Solow model of exogenous growth. The model explains the long-term growth of an economy and uses a number of factors to determine their impact on growth. The factors are savings, investments, population growth, technological progress and increased human capital.6 The dependent variables in my econometric function are based on these reasons. 1 Gang, 2005. 2 Ibid. 3 Ek, 2007, p. 3. 4 Dauerstädt & Stetten, 2005, p. 227. 5 GRP is short for Gross Regional Income, which is the GDP for each province or region. 6 Burda & Wyplosz, 2001, p. 45. 2 The basic version of the model includes two factors of production, capital (K) and labor (L). But, it can be augmented with other factors, which is explained later on. Capital includes machines, railways and other physical assets. Y equals output. π = πΉ(πΎ, πΏ) Because capital depreciates, savings and investments need to be equal or larger than the depreciation rate.7 When actual investments equal required investment (depreciation rate), output is neither rising nor falling. This is the steady state, K*. If the capital stock is at K0, investments are bigger than the depreciation rate and the capital stock will be higher next year. At K1, investments are not enough to replace the capital that wears out, and the capital stock will be lower next year. This way the capital stock always moves towards K*. The model assumes decreasing marginal productivity of capital and labor.8 This means that if a firm has a number of workers and a number of machines, increasing the machines by one unit will increase the output as well. The ratio Ξπ/ΞπΎ is called the marginal productivity. If we continue to increase the number of machines, holding labor constant, the output will increase, but at a decreasing rate, i.e. decreasing marginal productivity. The model also assumes diminishing returns to scale.9 If both factors double, output will also double, i.e. increase by 100%, if there are constant returns to scale. When there are diminishing returns to scale, output will increase by less than 100% even though both factors double. Because of diminishing returns, you cannot continue to increase labor or capital in order to increase output, as, in the end, there will be no effect on output. We need other factors as well, like technological progress and increased human capital.10 They are also included in my thesis as the variables βhealthβ and βeducationβ. Improved health care can affect GDP/capita in two ways, both as increased human capital, which ought to increase growth, but also as increased population because improved health care causes people to live longer. Population is included as Y/L where output decreases as labor increases. Since economic growth is measured in GDP/capita in my research, I have included βpopulationβ as a variable because income per capita decreases as population increases. 7 Ibid., 50. 8 Ibid., 48. 9 Ibid., 49. 10 Ibid., 58. 3 Literature There have been quite a few studies on economic growth in China, and on economic growth in general. The papers on economic growth in China that I have read so far either include just one or two variables or they are extremely thorough with many more variable than I will ever be able to use. Still, I think they form a good basis for my research. The paper βCan the Augmented Solow Model Explain China's Economic Growth? A CrossCountry Panel Data Analysisβ by Ding and Knight researches Chinaβs economic growth using Solow models and econometrics. Ding and Knight use panel data over the period 1980-2000 and examine the growth difference between China and other countries, and try to find the extent to which the growth difference can be explained by the Solow model.11 They use the three different Solow models, the textbook model, the model augmented by human capital and augmented by structural change.12 The variables used are similar to mine: the growth in GDP per capita, the level of GDP per capita, investment-output ratio, the share of agriculture in GDP, and growth rate of population.13 The result is that the model augmented with both human capital and structural change best explains the economic growth in China. They find that capital investment is the most important component of growth, accounting for 54% of total growth.14 Other important factors are improvements in factor productivity through structural change and slower population growth rate. They also find that the level of education in China is crucial to the growth difference between China and other developing countries.15 Rosenqvist and Lundblad have carried out a similar research to mine in the paper βUtvecklingsländers ekonomiska tillväxtβ, except their focus is developing countries in all parts of the world. The theoretical background for this research is once again the Solow model of growth. The dependent variable is growth of GDP per capita and the independent variables are health, education, GDP per capita, level of corruption, FDI per GDP, economic freedom, change in population growth and system of government.16 Their data only includes the year 2003, and using econometrics, they have created a linear regression model. Their result shows that the variables that have the greatest impact on growth are system of government, FDI per GDP and decreasing population growth.17 11 Ding & Knight, 2008, p. 1. 12 Ibid., 6. 13 Ibid., 17. 14 Ibid., 30. 15 Ibid., 32. 16 Rosenqvist, 2012, p. 1. 17 Ibid., p. 22. 4 Methodology The method used will be econometrics, a way to measure economic data. Since my only source of data is statistics, this is an entirely quantitative approach. I will form a function consisting of the factors I aim to research. The dependent variable, to the left of the equal sign, is growth in GRP per capita for each province. On the other side are all the independent factors, i.e. the factors being researched. When using econometrics, I will first find data for all variables. The program used, R Commander, will give me coefficients, which will tell me the level of impact that each independent variable has on the dependent variable. This allows me to perform an analysis of the result (see section Analysis of findings). This is the function that I have formulated: Ξ πΊπ π πΉπ·πΌ πΊπ π = π½! + π½! π»ππππ‘β! + π½! πΈππ’! + π½! + π½! Ξπππ! + π½! πππ£πππ! + π½! +π πππππ‘π ! πππππ‘π ! πππππ‘π ! GRPi denotes Gross Regional Product (provincial GDP) in province i. Healthi is measured by life expectancy in province i. Educationi is measured by literacy rate in % in province i. FDI/capitai is short for Foreign Direct Investment per capita in province i. ΞPopi is population growth in province i. Savingi is annual household savings in province i. GRP/capitai is the initial level of GRP in in province i. π½! is the intercept and π is the error term. Data I will include data for all 22 provinces plus five autonomous regions and four municipalities, a total of 31 observations. I plan to use data for the years 2000-2010 in my research but I have yet to decide in what way. The best way would be to use panel data as that allows me to include all the variables for all the years chosen. This method is quite difficult to perform and is something that I have never done before so it might be overambitious. One option is to choose only two years, for example 2000 and 2009, perform a linear regression for each year and make a make a comparison. The results should be interesting as the world economy was very different in 2009 as compared to the year 2000. Another option is to perform linear regressions on year averages, for example 2000-2007 and 2008-2010, in order to include as many years as possible but still be able to make a comparison between the two periods. All the data used is from the National Bureau of Statistics of China.18 18 National Bureau of Statistics of China, 2012. 5 Analysis of findings When running a regression (i.e. compiling data for all variables and running it through the program R Commander), I will get one coefficient for each variable instead of the π½:s in the function above. For example, π½! indicating education might come out as 0,2 which means that if the literacy rate increases by 1%, GRP per capita increases by 0,2%. If π½! for population growth comes out as -0,7 it means that if the population in one province increases by 1% then GRP per capita will decrease by 0,7%. Of course, the result might not be as I expect it to be, for example population growth might come out as a positive coefficient. In my analysis, I will have to find a reason for the given coefficients. Timeframe Week 1-3: Collect data and run regressions. Week 4-6: Write theory, method and background. Week 7-8: Finish analysis. 6 Bibliography Dauderstädt, Michael, och Jürgen Stetten. China and globalisation. Leibniz: Leibniz Information Centre for Economics, 2005, 226-β234. Ding, Sai, och John Knight. Can the Augmented Solow Model Explain China's Economic Growth? A Cross-βCountry Panel Data Analysis. Oxford: University of Oxford, 2008. Ek, Anna. The Impact of FDI on Economic Growth. Bachelor Thesis, Jönköping: Högskolan i Jönköping, 2007. Gang, Fan. China Is a Private-βSector Economy. den 22 August 2005. http://www.businessweek.com/magazine/content/05_34/b3948478.htm (accessed 25 03 2012). National Bureau of Statistics of China. Annual Data. http://www.stats.gov.cn/english/statisticaldata/yearlydata/ (accessed 25 03 2012). Rosenqvist, Johan, och Therese Lundblad. Utvecklingsländers ekonomiska tillväxt. Bachelor Thesis, Huddinge: Södertörns Högskola, 2012. 7