1. Ka-fu Wong © 2007 ECON1003: Analysis of Economic Data Lesson0-1 Lesson 0: Understanding China

2. Ka-fu Wong © 2007 ECON1003: Analysis of Economic Data Lesson0-2 Gross Regional Product of China (100 million yuan) (from http://www.stats.gov.cn/tjsj/ndsj/2005/indexee.htm) Region Gross Regional Product (100 million yuan) 20002001200220032004 Beijing2478.762845.653212.713663.104283.31 Tianjin1639.361840.102051.162447.662931.88 Hebei5088.965577.786122.537098.568768.79 Shanxi1643.811779.972017.542456.593042.41 Inner Mongolia1401.011545.791756.292150.412712.08 Liaoning4669.065033.085265.666002.546872.65 Jilin1821.192032.482246.122522.622958.21 Heilongjiang3253.003561.003882.164430.005303.00 Shanghai4551.154950.845408.766250.817450.27 Jiangsu8582.739511.9110631.7512460.8315403.16 Zhejiang6036.346748.157796.009395.0011243.00 Anhui3038.243290.133553.563972.384812.68 Fujian3920.074253.684682.015232.176053.14 Jiangxi2003.072175.682450.482830.463495.94 Shandong8542.449438.3110552.0612435.9315490.73 Henan5137.665640.116168.737048.598815.09 Hubei4276.324662.284830.985401.716309.92 Hunan3691.883983.004140.944638.735612.26 Guangdong9662.2310647.7111735.6413625.8716039.46 Guangxi2050.142231.192455.362735.133320.10 Hainan518.48545.96597.50670.93769.36 Chongqing1589.341749.771971.302250.562665.39 Sichuan4010.254421.764875.125456.326556.01 Guizhou993.531084.901185.041356.111591.90 Yunnan1955.092074.712232.322465.292959.48 Tibet117.46138.73161.42184.50211.54 Shaanxi1660.921844.272101.602398.582883.51 Gansu983.361072.511161.431304.601558.93 Qinghai263.59300.95341.11390.21465.73 Ningxia265.57298.38329.28385.34460.35 Xinjiang1364.361485.481598.281877.612200.15

3. Ka-fu Wong © 2007 ECON1003: Analysis of Economic Data Lesson0-3 Cross-sectional data Cross-sectional data in statistics and econometrics is a type of one-dimensional data set. Cross-sectional data refers to data collected by observing many subjects (such as individuals, firms or countries/regions) at the same point of time, or without regard to differences in time. Analysis of cross-sectional data usually consists of comparing the differences among the subjects.

4. Ka-fu Wong © 2007 ECON1003: Analysis of Economic Data Lesson0-4 Cross-sectional data Region Gross Regional Product (100 million yuan) 20002001200220032004 Beijing2478.762845.653212.713663.104283.31 Tianjin1639.361840.102051.162447.662931.88 Hebei5088.965577.786122.537098.568768.79 Shanxi1643.811779.972017.542456.593042.41 Inner Mongolia1401.011545.791756.292150.412712.08 Liaoning4669.065033.085265.666002.546872.65 Jilin1821.192032.482246.122522.622958.21 Heilongjiang3253.003561.003882.164430.005303.00 Shanghai4551.154950.845408.766250.817450.27 Jiangsu8582.739511.9110631.7512460.8315403.16 Zhejiang6036.346748.157796.009395.0011243.00 Anhui3038.243290.133553.563972.384812.68 Fujian3920.074253.684682.015232.176053.14 Jiangxi2003.072175.682450.482830.463495.94 Shandong8542.449438.3110552.0612435.9315490.73 Henan5137.665640.116168.737048.598815.09 Hubei4276.324662.284830.985401.716309.92 Hunan3691.883983.004140.944638.735612.26 Guangdong9662.2310647.7111735.6413625.8716039.46 Guangxi2050.142231.192455.362735.133320.10 Hainan518.48545.96597.50670.93769.36 Chongqing1589.341749.771971.302250.562665.39 Sichuan4010.254421.764875.125456.326556.01 Guizhou993.531084.901185.041356.111591.90 Yunnan1955.092074.712232.322465.292959.48 Tibet117.46138.73161.42184.50211.54 Shaanxi1660.921844.272101.602398.582883.51 Gansu983.361072.511161.431304.601558.93 Qinghai263.59300.95341.11390.21465.73 Ningxia265.57298.38329.28385.34460.35 Xinjiang1364.361485.481598.281877.612200.15

5. Ka-fu Wong © 2007 ECON1003: Analysis of Economic Data Lesson0-5 Regional Nominal GDP in Year 2004 RegionGDP (100 million yuan) GDP per capita (yuan) Beijing4283.3137058 Tianjin2931.8831550 Hebei8768.7912918 Shanxi3042.419150 Inner Mongolia2712.0811305 Liaoning6872.6516297 Jilin2958.2110932 Heilongjiang5303.0013897 Shanghai7450.2755307 Jiangsu15403.1620705 Zhejiang11243.0023942 Anhui4812.687768 Fujian6053.1417218 Jiangxi3495.948189 Shandong15490.7316925 Henan8815.099470 Hubei6309.9210500 Hunan5612.269117 Guangdong16039.4619707 Guangxi3320.107196 Hainan769.369450 Chongqing2665.399608 Sichuan6556.018113 Guizhou1591.904215 Yunnan2959.486733 Tibet211.547779 Shaanxi2883.517757 Gansu1558.935970 Qinghai465.738606 Ningxia460.357880 Xinjiang2200.1511199

6. Ka-fu Wong © 2007 ECON1003: Analysis of Economic Data Lesson0-6 2004 Regional Nominal GDP Summary Statistics (100 million yuan) Mean5265.82 Standard Error783.88 Median3495.94 Mode#N/A Standard Deviation4364.43 Sample Variance19048264.72 Kurtosis1.10 Skewness1.29 Range15827.92 Minimum211.54 Maximum16039.46 Sum163240.43 Count31 Tibet Guangdong

7. Ka-fu Wong © 2007 ECON1003: Analysis of Economic Data Lesson0-7 Regional Nominal GDP in Year 2004 (sorted by GDP) RegionGDP (million yuan)GDP per capita (yuan) Tibet211.547779 Ningxia460.357880 Qinghai465.738606 Hainan769.369450 Gansu1558.935970 Guizhou1591.904215 Xinjiang2200.1511199 Chongqing2665.399608 Inner Mongolia2712.0811305 Shaanxi2883.517757 Tianjin2931.8831550 Jilin2958.2110932 Yunnan2959.486733 Shanxi3042.419150 Guangxi3320.107196 Jiangxi3495.948189 Beijing4283.3137058 Anhui4812.687768 Heilongjiang5303.0013897 Hunan5612.269117 Fujian6053.1417218 Hubei6309.9210500 Sichuan6556.018113 Liaoning6872.6516297 Shanghai7450.2755307 Hebei8768.7912918 Henan8815.099470 Zhejiang11243.0023942 Jiangsu15403.1620705 Shandong15490.7316925 Guangdong16039.4619707

8. Ka-fu Wong © 2007 ECON1003: Analysis of Economic Data Lesson0-8 2004 Regional Nominal GDP per Capita Summary Statistics (yuan per person) Mean14079.39 Standard Error1912.88 Median9608.00 Mode#N/A Standard Deviation10650.44 Sample Variance113431828.11 Kurtosis7.14 Skewness2.50 Range51092.00 Minimum4215.00 Maximum55307.00 Sum436461.00 Count31 Guizhou Shanghai

9. Ka-fu Wong © 2007 ECON1003: Analysis of Economic Data Lesson0-9 Regional Nominal GDP in Year 2004 (sorted by GDP per capita) RegionGDP (100 million yuan)GDP per capita (yuan) Guizhou1591.904215 Gansu1558.935970 Yunnan2959.486733 Guangxi3320.107196 Shaanxi2883.517757 Anhui4812.687768 Tibet211.547779 Ningxia460.357880 Sichuan6556.018113 Jiangxi3495.948189 Qinghai465.738606 Hunan5612.269117 Shanxi3042.419150 Hainan769.369450 Henan8815.099470 Chongqing2665.399608 Hubei6309.9210500 Jilin2958.2110932 Xinjiang2200.1511199 Inner Mongolia2712.0811305 Hebei8768.7912918 Heilongjiang5303.0013897 Liaoning6872.6516297 Shandong15490.7316925 Fujian6053.1417218 Guangdong16039.4619707 Jiangsu15403.1620705 Zhejiang11243.0023942 Tianjin2931.8831550 Beijing4283.3137058 Shanghai7450.2755307

10. Ka-fu Wong © 2007 ECON1003: Analysis of Economic Data Lesson0-10 Time Series Region Gross Regional Product (100 million yuan) 20002001200220032004 Beijing2478.762845.653212.713663.104283.31 Tianjin1639.361840.102051.162447.662931.88 Hebei5088.965577.786122.537098.568768.79 Shanxi1643.811779.972017.542456.593042.41 Inner Mongolia1401.011545.791756.292150.412712.08 A time series is a sequence of data points, measured typically at successive times, spaced at (often uniform) time intervals.

11. Ka-fu Wong © 2007 ECON1003: Analysis of Economic Data Lesson0-11 Nominal Gross Domestic Product (100 million yuan)

12. Ka-fu Wong © 2007 ECON1003: Analysis of Economic Data Lesson0-12 Per Capita Nominal GDP (yuan per person)

13. Ka-fu Wong © 2007 ECON1003: Analysis of Economic Data Lesson0-13 Nominal GDP and Per Capita nominal GDP

14. Ka-fu Wong © 2007 ECON1003: Analysis of Economic Data Lesson0-14 Nominal GDP and Per Capita nominal GDP

15. Ka-fu Wong © 2007 ECON1003: Analysis of Economic Data Lesson0-15 Real GDP and Per Capita Real GDP

16. Ka-fu Wong © 2007 ECON1003: Analysis of Economic Data Lesson0-16 Panel data Region Gross Regional Product (100 million yuan) 20002001200220032004 Beijing2478.762845.653212.713663.104283.31 Tianjin1639.361840.102051.162447.662931.88 Hebei5088.965577.786122.537098.568768.79 Shanxi1643.811779.972017.542456.593042.41 Inner Mongolia1401.011545.791756.292150.412712.08 A data set containing observations on multiple phenomena observed over multiple time periods is called panel data.

17. Ka-fu Wong © 2007 ECON1003: Analysis of Economic Data Lesson0-17 Example Financial Intermediation in China All bank financial intermediation rate and GDP per capita in Chinese provinces 1997. Note: Excludes Beijing, Tianjin, Shanghai, and Tibet. Source: Park, Albert, and Kaja Sehrt (2001): “Tests of Financial Intermediation and Banking Reform in China,” Journal of Comparative Economics 29: 608–644. Provincial data of 1997 reveals a striking inverse relationship between financial intermediation and GDP per capita. This pattern suggests that the allocation of financial resources across provinces may be highly inefficient, with richer provinces being taxed relative to poorer provinces.

18. Ka-fu Wong © 2007 ECON1003: Analysis of Economic Data Lesson0-18 Example Fiscal development in China The negative relationship reveals that the revenue capacity to support the public payroll in poor counties is much weaker than in more developed ones. Economic development level and rent from land development. Source: Zhang, Xiaobo (2006): “Fiscal decentralization and political centralization in China: Implications for growth and inequality,” Journal of Comparative Economics 34: 713–726.

19. Ka-fu Wong © 2007 ECON1003: Analysis of Economic Data Lesson0-19 Example Development of Motorcycle industry in China Production grew rapidly in the early 1990s and, by 1995, it exceeded the production peak in 1981 in Japan, which had been the largest producer in the world. The export of motorcycles, mainly to Asia and Africa, began when the growth of domestic consumption stagnated in the late 1990s. Exports have increased rapidly since 2000 reaching three million in 2003, which accounts for 20.6% of the total number of motorcycles produced. (However, these data do not cover small producers operating without government permission.) Source: Sonobe, Tetsushi, Dinghuan Hu, and Keijiro Otsuka (2006): “Industrial development in the inland region of China: A case study of the motorcycle industry,” Journal of Comparative Economics 34: 818–838.

20. Ka-fu Wong © 2007 ECON1003: Analysis of Economic Data Lesson0-20 Example Township and village enterprises in China The figure illustrates changes in employment and output value of TVEs in addition to their importance in the national economy over a twenty-year period. The share of TVE output in GDP is measured by the ratio of TVE value- added to national GDP of secondary industry. Initially, COEs were more important than PEs in terms of number of employees and output value but, over time, their positions reversed. Employment in COEs declined to less than 40 million following the record high level reached in 1995 of more than 60 million. By contrast, the employment of PEs shows an upward trend during the period and reaches nearly 100 million by 2002, which accounts for around 20 percent of the rural labor force. Likewise, aggregate output of COEs stagnated at around four trillion yuan since the mid-1990s, whereas the output of PEs grows rapidly from 1992 and reaches nine trillion yuan in 2001. This growth of TVEs is attributable to both enterprise privatization and the establishment of private enterprises. Source: Ito, Junichi (2006): “Economic and institutional reform packages and their impact on productivity: A case study of Chinese township and village enterprises,” Journal of Comparative Economics 34: 167–190

21. Ka-fu Wong © 2007 ECON1003: Analysis of Economic Data Lesson0-21 Example Return to Schooling in China The plotted log-income differences may be interpreted crudely as returns to schooling. Assuming a schooling gap between elementary-school and college graduates of 10 to 12 years, the implied marginal rate of return per year of schooling for college graduates was approximately 5.5% shortly after the beginning of the Communist era and it decreased to about half that in the very early years of reform. After reform, the earnings premium for college education accelerated sharply beginning around 1990. Source: Fleisher, Belton M., and Xiaojun Wang (2005): “Returns to schooling in China under planning and reform,” Journal of Comparative Economics 33: 265–277.

22. Ka-fu Wong © 2007 ECON1003: Analysis of Economic Data Lesson0-22 Example Regional disparities in China On average, the coastal regions registered relatively higher growth than the inland provinces following the reforms and into the 1990s. Although the inland regions experienced growth similar to that of the coastal regions during the 1980s, these regions fell considerably behind their coastal counterparts during the 1990s when real GDP per capita increased by only 95 percent in the inland regions but by 144 percent in the coastal regions. In fact, GDP per capita in purchasing power terms in the coastal regions began to catch up with that of the Southeast Asian emerging economies, e.g., Malaysia, Philippines, Indonesia and Thailand, in the early 1990s and even surpassed that of these countries in 1996 Fu, Xiaolan (2004): “Limited linkages from growth engines and regional disparities in China,” Journal of Comparative Economics 32: 148–164.

23. Ka-fu Wong © 2007 ECON1003: Analysis of Economic Data Lesson0-23 Part 1 of Problem set #1 Download xmarriage.xls from our class website and perform the following tasks: Compute the ratio, the registered cross-border marriages per marriage registrations approved in each region, for the year 2000. Summarized and briefly describe this ratio of year 2000 across regions. For example, which province has the highest cross- border marriages? Why? Plot the ratio against GDP per capita of year 2000. Do you see any relationship between the two variables?

24. Ka-fu Wong © 2007 ECON1003: Analysis of Economic Data Lesson0-24 - END - Lesson 0: Understanding China