The cost of living in China: Implications for inequality and poverty

Transcription

1 The cost of living in China: Implications for inequality and poverty Ingvild Almås and Åshild Auglænd Johnsen Norwegian School of Economics and ESOP, University of Oslo, and University of Stavanger SSDEV, Alto di Canazei July 2013

2 This paper Proposes a complementary approach to measuring real incomes for in China: Calculates regional prices in China using the Engel-curve approach. Makes incomes comparable across both time and space. Provides new estimates of inequality and poverty.

3 China since reforms started in 1978 From centrally planned, low-productive, low inequality and widespread poverty to high growth, more market orientation and poverty reduction. Forces contributing to differences in the cost of living (COL) within China: Provinces differ in terms of geographical size, population, resources and development. Different reforms and reform outcomes urban/ rural regions. = The cost of living matters to comparisons of real incomes and poverty measurement.

4 Measuring cost of living (COL) 1. Consumer Price Index (CPI): inter-temporal, set in a base-year, not cross-sectional. 2. Spatial Price Index (SPI): inter-temporal, cross-sectional, but highly data-intensive. = Aggregation techniques for CPI and SPI are not welfare consistent. Literature: Brandt and Holz (2006), Gong and Meng (2008), Chen and Ravallion (1996, 2007). Nakamura, Steinsson and Liu (mimeo), Feenstra et al (2013).

5 Official statistics and the CPI The reliability of official statistics: Chinese prime minister Li Keqiang (2007): China s GDP is man-made and therefore unreliable. All other figures, especially GDP statistics, are for reference only. Evaluating Liaoning s economic activity by focusing on electricity consumption, the volume of rail cargo and the amount of bank loans disbursed. The CPI: Not very transparent. Little info on weighting and construction.

6 This paper Reasonal to expect regional prices to differ within China: Matters to comparisons of real incomes and poverty measurement. This paper estimates welfare consistent spatial price indices and subsequently revises inequality and poverty measures for 1995 and 2002: Across provinces (rural and urban). 1. Size of Chinese GDP reported by the World Bank. 2. Poverty reduction in China and the Millennium Development Goals.

7 World Bank studies on poverty in China % of population Poverty Headcount Ratio Year C&R (2013): $1.25 at 2005 PPP C&R (2013): $2 at 2005 PPP R&C (2007): Chinese rural/ urban poverty lines C&R (2008): $1.08 at 1993 PPP, no COL adjustment C&R (2008): $1.25 at 2005 PPP, no COL adjustment C&R (2008): $2 at 2005 PPP, no COL adjustment C&R (2008): $1.25 at 2005 PPP, COL adjustment C&R (2008): $2 at 2005 PPP, COL adjustment Poverty reduction : somewhere between 23% and 48%. Poor in China 2005: somewhere between 10% and 52%.

8 Main findings From 1995 to 2002: The urban-rural price gap is reduced. Increased inequality. Poverty reduction has been more moderate than suggested by official measures and the World Bank.

9 Identifying spatial price indices - the Engel Curve Approach Engel s law: A household s budget share for food is inversely related to household real income. Estimating Engel curves for food and identify price levels through this (Hamilton 2001; Costa 2001, Almås 2012): Identical households with the same budget share for food also have same real income. Systematic regional differences in measured real incomes reveal price level differences. = Estimate welfare consistent price indices.

10 Model Hamilton, AER (2001) The almost ideal demand system (AIDS) specification for household h: m h,p,u,t = a+b 1(ln y h,p,u,t ln P p,u,t)+b 2(ln p f c,u,t ln p n c,u,t)+θx h,p,u,t +ɛ h,p,u,t, (1) N m h,p,u,t = a + b 1(ln y h,p,u,t ) + d p,u,td p,u,t + θx h,p,u,t p=1 + b 2(ln pc,u,t f ln pc,u,t) n + ɛ h,p,u,t (2) The price level of urban (u)/rural (r) area of province p relative to urban Beijing in year t can be expressed as follows: d p,u,t = b ln d p,u,t P p,u,t = e ( dp,u,t /b) (3)

11 Data - Chinese Household Income Project Series (CHIP) Independent household income survey: Years 1988 (not utilized), 1995 and Collected by an independent international group of economists in collaboration with the Economics Institute of the Chinese Academy of Social Sciences (CASS). Access: DSDR (Datasharing for Demographic Research)/ ICPSR (Inter-University Consortium for Political and Social Research) Households Mean HHsize Households Mean HHsize Rural Urban Total

12 Data - Chinese Household Income Project Series (CHIP) Heilongjiang Inner Mongol Jilin(R) Xinjiang Tibet Qinghai Gansu Ningxia Shanxi Beijing Tianjin Hebei(R) Shandong(R) Liaoning Shaanxi(R) Henan Jiangsu Anhui Shanghai Sichuan Hubei Chongqing Zhejiang (R) Hunan(R) Jiangxi(R) Guizhou(R) Fujian Yunnan Fujian, ROC Guangxi Guangdong Taiwan Hong Kong Macau Hainan Red: Data Coverage for both 1995 and (R) means data only on rural households: Guizhou, Hebei, Hunan, Jilin, Jiangxi, Shaanxi, Shandong, Zhejiang.

13 How to deal with consumption out of self-production? Important for poor (rural) households (60 percent in 1995 and 40 percent in 2002). But: Have only self-production for rural areas. Evaluation not obvious. Main estimation: Include self-production for rural households. Evaluate it at market value. Run separate estimations for urban and rural households. Find urban-rural price gap from a pooled regression based on market purchases normalizing so that the 2002 gap is equal to the identified gap.

14 Robustness analysis 1. Main estimation: Rural/ urban separately. 2. Robustness check: Pooled regression, market purchases (not self-production). 3. Robustness check: Including housing: Rural/ urban separately. Pooled regression, market purchases (not self-production).

15 Price levels (SPI) and real incomes Rural 1995 Urban mean_c Fitted values SPI mean_c Fitted values SPI Rural 2002 Urban mean_c Fitted values SPI mean_c Fitted values SPI

16 Main findings: Prices Table: Price change from 1995 to 2002 COL CPI All Rural Urban

17 Main findings: Prices Table: Price change from 1995 to 2002 COL CPI All Rural Urban Urban areas: supply-related factors and administrative price could explain low prices in the short run, productivity gains, tariff reductions, reforms of SOEs, technology adoption and a large rural labor supply could explain low prices in the long run. Rural areas: global integration and convergence with international prices in traded goods prices less likely to affect more remote rural areas. Urban-rural price gap: Substantially reduced. This price convergence may be a result of liberalization or decreasing transport costs within China.

18 Main Findings: Inequality Table: Change from 1995 to 2002 A. Inequality: Gini Index COL CPI All Rural Urban

19 Main Findings: Poverty Head Count Index Table: Change from 1995 to 2002 B. Poverty Head Count: $1 a day COL CPI World Bank All Rural Urban C. Poverty Head Count: $2 a day COL CPI World Bank All Rural Urban

20 Main Findings: Poverty Gap Index Table: Change from 1995 to 2002 D. Poverty Gap: $1 a day COL CPI World Bank All Rural Urban E. Poverty Gap: $2 a day COL CPI World Bank All Rural Urban

21 Summary of inequality and poverty results, all China Gini $1 HC $2 HC $1 PG $2 PG Engel-based World Bank CPI-based Figure: Changes in Inequality and Poverty.

22 Evaluating our results We consider an alternative poverty measure to evaluate our results: Night-time light: Satellite images from the United States Air Force Defense Meteorological Satellite Program (DMSP). Poverty line: not having access to any night-time light ( Poverty in China as seen from outer Space, Almås, Johnsen and Kotsadam (mimeo)).

23 Evaluating our results Estimating poverty in China using an alternative poverty line defined by zero nighttime light prevalence. We provide two measures: 1. Spatial measure: Areas without any night-time light are defined as poor areas. 2. Head-count ratio: People living in areas with no night-time light prevalence are defined as poor. Provides a constant real poverty threshold over time and across all of China. Anchors the poverty line to a transparent and clear definition related to basic needs and well-being. Escapes the main issues related to the quality of income data and comparability across time and space.

24 Night-time light in China in 1992 and 2010

25 The Night-time Light Approach: other studies Night-time light corresponds well to economic activity: Alesina et al. (2012): Inequality. Henderson et al. (2012): Growth. Chen and Nordhaus (2011): Growth. Others: Doll et al. (2006), Ghosh et al. (2010), Michalopoulos and Papaioannou (2013), and Sutton et al. (2007).

26 Snapshot: data Population data for 1990 and 2010: Gridded Population of the World (CIESIN, 2005). Uses the best possible census and administrative data and data for several periods. 5 km 2.: 1990, 1995, 2000, 2005, 2010, and Satellite lights/ night-time light: Data: US Air Force Defense Meteorological Satellite Program. The satellites circle the earth 14 times per day. 1 km yearly observations on nighttime light for the period 1992 to 2010.

27 Poverty results: The World Bank s and ours Poverty Headcount Ratio % of population Year C&R (2013): $1.25 at 2005 PPP C&R (2013): $2 at 2005 PPP R&C (2007): Chinese rural/ urban poverty lines C&R (2008): $1.08 at 1993 PPP, no COL adjustment C&R (2008): $1.25 at 2005 PPP, no COL adjustment C&R (2008): $2 at 2005 PPP, no COL adjustment C&R (2008): $1.25 at 2005 PPP, COL adjustment C&R (2008): $2 at 2005 PPP, COL adjustment Night time ligth estimate (smooth version)

28 Concluding remarks We estimate welfare consistent price indices for China. Makes it possible to compare price levels across urban Beijing in 1995, rural Beijing 1995, urban Beijing 2002, rural Beijing 2002, urban Yunnan in 1995 and so on. Accepts how people behave secures welfare consistency. We find that the CPIs underestimate the price increase in rural areas and overestimates the price increase in urban areas. Overall we find that the price inflation (all China) is underestimated. We find that inequality has increased whereas official measures find that it has decreased. We find that the poverty reduction is substantially overestimated, both by the World Bank and when using official CPI deflation.

29 Summary of inequality and poverty results, all China Gini HC$1 HC$2 PG$1 PG$2 Other Engel-based CPI-based World Bank Light-based Figure: Changes in Inequality and Poverty

30 How do we control for relative prices? Food prices (cereals, vegetables, meat, and eggs): Rural food prices in the data. Urban food prices in 1995 are collected from the China Price Information Network, which covers 11 food items and 34 cities. The 2002 data on urban food prices are taken from the China Price Statistical Yearbook 2003, which covers 69 food items for 36 cities. In order to to aggregate up to one food price index we use Country-Product-Dummy method (Rao, 1990; 2005) Non-food prices (white fine cloth, soap, matches, bicycle, black and white tv (kerosene, sewing machine, cigarettes, cotton, wrist watches)) Price Statistical Yearbook Series (1992 edition): Non-food prices for 29 cities. These prices are assumed to be representative of the remaining urban part of the province. Conversion table for farm products to industry products (food to non-food ratio for rural areas).

31 Robustness analysis

32 Market based: Prices Table: Price change from 1995 to 2002 COL-M CPI All Rural Urban

33 Market based: Inequality Table: Change from 1995 to 2002 A. Inequality: Gini Index COL-M CPI All Rural Urban

34 Market based: Poverty Head Count Index Table: Change from 1995 to 2002 B. Poverty Head Count: $1 a day COL-M CPI World Bank All Rural Urban C. Poverty Head Count: $2 a day COL-M CPI World Bank All Rural Urban

35 Market based: Poverty Gap Index Table: Change from 1995 to 2002 D. Poverty Gap: $1 a day COL-M CPI World Bank All Rural Urban E. Poverty Gap: $2 a day COL-M CPI World Bank All Rural Urban

36 Summary of inequality and poverty results, all China Gini HC$1 HC$2 PG$1 PG$2 Engel-based World Bank CPI-based

37 Including housing Potentially large measurement problems related to housing consumption. In this robustness check we follow the approach by Khan et al (1993, 1995, 2005) and use rental value of housing. For owner occupied housing: Rental value is calculated by assuming that rental value of housing is 8 percent of the current market value of the house. 8 percent interest on housing debt is subtracted from this. For urban household housing subsidies are added. Some data issues: Rural 1995: the current market value of the house variable is not in the questionnaire. Khan et al provide a variable representing the present value of the house. Urban 2002: The rental value based on an alternative approach - variables such as sanitary facilities and total living area are used to estimate the market value of the present resident house.

38 Including housing: Prices Table: Price change from 1995 to 2002 COL-H CPI All Rural Urban

39 Including housing: Inequality Table: Change from 1995 to 2002 A. Inequality: Gini Index COL-H CPI All Rural Urban

40 With housing: Poverty Head Count Index Table: Change from 1995 to 2002 B. Poverty Head Count: $1 a day COL-H CPI World Bank All Rural Urban C. Poverty Head Count: $2 a day COL-H CPI World Bank All Rural Urban

41 With housing: Poverty Gap Index Table: Change from 1995 to 2002 D. Poverty Gap: $1 a day COL-H CPI World Bank All Rural Urban E. Poverty Gap: $2 a day COL-H CPI World Bank All Rural Urban