Using new household survey data for 1995 and 2002, we investigate the size of China's urban-rural income gap, the gap's contribution to overall inequality in China, and the factors underlying the gap. Our analysis improves on past estimates by using a fuller measure of income, adjusting for spatial price differences and including migrants. Our methods include inequality decomposition by population subgroup and the Oaxaca-Blinder decomposition. Several key findings emerge. First, the adjustments substantially reduce China's urban-rural income gap and its contribution to inequality. Nevertheless, the gap remains large and has increased somewhat over time. Second, after controlling for household characteristics, location of residence remains the most important factor underlying the urban-rural income gap. The only household characteristic that contributes substantially to the gap is education. Differences in the endowments of, and returns to, other household characteristics such as family size and composition, landholdings, and Communist Party membership are relatively unimportant.

a b s t r a c tDémurger, Sylvie, Gurgand, Marc, Li, Shi, and Yue, Ximing-Migrants as second-class workers in urban China? A decomposition analysis In urban China, urban resident annual earnings are 1.3 times larger than long-term rural migrant earnings as observed in a nationally representative sample in 2002. Using microsimulation, we decompose this difference into four sources, with particular attention to path-dependence and statistical distribution of the estimated effects: (1) different allocation to sectors that pay different wages (sectoral effect); (2) hourly wage disparities across the two populations within sectors (wage effect); (3) different working times within sectors (working time effect); and (4) different population structures (population effect). Although sector allocation is extremely contrasted, with very few migrants in the public sector and very few urban residents working as self-employed, this has no clear impact on earnings differentials, because the sectoral effect is not robust to the path followed for the decomposition. The second main finding is that the population effect is robust and significantly more important than wage or working time effects. This implies that the main source of disparity between the two populations is pre-market (education opportunities) rather than on-market. Journal of Comparative Economics 37 (4) (2009) 610-628.

Using new household survey data for 1995 and 2002, we investigate the size of China's urban-rural income gap, the gap's contribution to overall inequality in China, and the factors underlying the gap. Our analysis improves on past estimates by using a fuller measure of income, adjusting for spatial price differences and including migrants. Our methods include inequality decomposition by population subgroup and the OaxacaBlinder decomposition. Several key findings emerge. First, the adjustments substantially reduce China's urban-rural income gap and its contribution to inequality. Nevertheless, the gap remains large and has increased somewhat over time. Second, after controlling for household characteristics, location of residence remains the most important factor underlying the urban-rural income gap. The only household characteristic that contributes substantially to the gap is education. Differences in the endowments of, and returns to, other household characteristics such as family size and composition, landholdings, and communist party membership are relatively unimportant.

In urban China, urban resident annual earnings are 1.3 times larger than long term rural migrant earnings as observed in a nationally representative sample in 2002. Using microsimulation, we decompose this difference into four sources, with particular attention to path dependence and statistical distribution of the estimated effects: (1) different allocation to sectors that pay different wages (sectoral effect); (2) hourly wage disparities across the two populations within sectors (wage effect); (3) different working times within sectors (hours effect); (4) different population structures (population effect). Although sector allocation is extremely contrasted, with very few migrants in the public sector and very few urban residents working as self-employed, the sectoral effect is not robust to the path followed for the decomposition. We show that the migrant population has a comparative advantage in the private sector: increasing its participation into the public sector does not necessarily improve its average earnings. The opposite holds for the urban residents. The second main finding is that population effect is significantly more important than wage or hours effects. This implies that the main source of disparity is pre-market (education opportunities) rather than on-market.

We estimate productivity growth for 33 industries covering the entire Chinese economy using a time series of input-output tables covering 1982-2000. Capital input is measured using detailed investment data by asset and labor input uses demographic information from household surveys. We find a wide range of productivity performance at the industry level. We then show how these industry growth accounts may be consistently aggregated to deliver a decomposition of aggregate GDP growth. For the 1982-2000 period aggregate TFP growth was 2.5 percent per year; decelerating from a rapid rate in the early 1980s to negative growth during 1994-2000. The main source of growth during the 1982-2000 period was capital accumulation, with a small negative contribution from the reallocation of factors across industries. IntroductionWhile it is widely agreed that the Chinese economy has grown rapidly since the reforms started in 1978, there is disagreement about both the magnitude and sources of that growth. Was the dominant factor the accumulation of capital, total factor productivity growth, or the restructuring of the economy from agriculture to manufacturing and services? A question related to the structural transformation of the economy is how estimates of aggregate GDP growth may be reconciled with the estimates at the industry level. These questions are difficult to answer given the quality and quantity of data available. The answers to them, however, are important in understanding the effects of past economic policies and hence to devise future policies.Note: We thank two anonymous referees for helpful comments. We also thank members of the National Accounts Department, NBS, who helped us with the data: Xu Xianchun, Qi Shuchang, Liu Liping, Dong Lihua and Zhao Tonglu. We are also grateful for the assistance of Li Xiaoqin and Ma Xiangqian from Beihang University. *Correspondence to: Jing Cao, School of Economics and Management, Tsinghua University, 100084, Beijing, P.R. China (caojing@sem.tsinghua.edu.cn). 485This paper estimates the sources of growth of industry output-the growth of capital, labor and intermediate inputs, and total factor productivity (TFP). To do this we introduce newly developed data, including a time series of input-output tables and estimates from a survey of the labor force. Our measures account for the changing composition of the labor force and investment. The second aim of the paper is to discuss how these industry measures may be aggregated to GDP. We describe three aggregation approaches to highlight the methodological issues of separating out the roles of factor accumulation, factor reallocation and sectoral total factor productivity growth: (i) aggregate production function; (ii) aggregate production possibility frontier (PPF); and (iii) direct Domar-weighted aggregation. The first approach may be familiar to many readers; the aggregate PPF method relaxes the strict assumptions of that approach and allows us to identify the effects of reallocating value-added across industries. The third method...