رشد بهره وری کشاورزی در چین: اندازه گیری سطح مزرعه در مقابل اندازه گیری کل

We measure agricultural productivity growth in China using alternative data sets: farm level data for Jiangsu province, national data, and provincial aggregate data for Jiangsu. For all three data sets, productivity growth was estimated to be strong during the immediate post-reform 1978–1987 period. According to the farm level data, productivity growth then slowed from 1988 to 1996. Alternatively, the national and provincial aggregate figures showed continued high productivity growth in the 1990s. These findings suggest that aggregate data may blur the true picture with regard to agricultural productivity growth in China.

It is generally accepted that the rapid output growth in China's agriculture from 1979 to 1984 was due to significant productivity gain Lin, 1992 and Wen, 1993. In most developing countries such as China, agricultural productivity gain is central to the growth of national wealth (Johnson, 1997). The continuation of agricultural productivity growth in China is particularly important, as more than 300 million workers remain in agriculture (nearly 50% of the country's total labor force).1 An increase in rural incomes, through further agricultural productivity gain, would help close the relatively wide urban–rural income gap.2 The coastal-centered economic boom slowed down in the late 1990s and the reform of China's state owned enterprises resulted in high urban unemployment (Saywell, 1998). This suggests that China's overall development policy will continue to discourage labor movement from the countryside to the cities and therefore the rural economy itself will be viewed as a key to future national economic growth. Labor is an abundant resource in rural China and a large percentage of the labor force is used in grain production. However, grain cultivation is a relatively low-return activity, and the marginal labor productivity in grain is small. Further economic reform in the countryside would encourage farmers to withdraw from grain in favor of other crops or activities. However, agricultural reform is slow moving in China. From 1998, the central government reasserted its emphasis on “grain self-sufficiency” and introduced renewed government control over grain prices, by prohibiting private agents in the grain market (Crook, 1998). According to national aggregate data, total factor productivity (TFP) in China's agriculture increased by 55% from 1979 to 1984 (Wen, 1993). This jump in productivity was unprecedented in the developing world, and most of the rapid change was attributed to the Household Responsibility System (HRS), which was a one-off institutional change.3 After the effects of the HRS petered out, a policy issue that surfaced in the late 1980s and early 1990s was a slowdown in the growth of investment in agriculture.4 Despite this apparent investment slowdown, we report a surprising result in this paper. Namely, the tremendous agricultural productivity gains enjoyed by China in the 1980s continued well into the 1990s. The total factor productivity index (TFPI) increased by almost 50% from 1988 to 1996, according to national aggregate statistics, and using Wen's (1993) methodology. Are these strong agricultural productivity gains in the 1990s plausible? This question is the motivation for our paper. We hypothesize that the continued high productivity gains in the 1990s seems unrealistic due to potential data aggregation biases5 and because of recent concerns over the reliability of China's national agricultural production statistics.6 The purpose of this paper is to measure post-reform agricultural productivity growth in China using farm level (i.e., household) data and to compare the results to both national and provincial aggregate data, for the 1978–1996 time period. Measuring productivity growth is a complicated task, even in western economies where data are more reliable than is the case for China.7 Previous studies of China's agricultural growth have all used very aggregate national or provincial data, even though the theory is based on microeconomic decision-making relationships at the individual firm level. Our disaggregate household level data are unique and they were obtained from farm cost surveys in Jiangsu Province, one of the most progressive agricultural provinces in China. Comparing the household results with those from aggregate data, we find that the productivity estimates tell a similar broad story (of high productivity growth) from 1978 to 1987, and then the estimates diverge from 1988 to 1996.8 The productivity growth rates estimated with national and provincial aggregate data remain quite high in the second time period but those obtained with household data show a significant slowing of the productivity growth rate (see Fig. 1). We suggest that the 1988–1996 productivity results from the household data are more convincing and more consistent with expectations, given the slowdown in investment in China's agriculture in the 1980s and the financial problems experienced by China's farmers in the 1990s (Statistical Yearbook of China, 2000). According to the Jiangsu household data, the TFPI increased by 19% from 1990 to 1996, much less than the 44% growth implied by the national aggregate figures and the 61% implied by the Jiangsu provincial figures.The rest of this article is organized as follows. In Section 2, we describe the data and method used in this paper. In Section 3, we report results for national productivity indices. In Section 4, productivity results are reported for Jiangsu province, using both farm level household data and provincial aggregate data. Section 5 provides concluding comments.

Most previous studies of efficiency gains in China's agriculture have used aggregate provincial or national data. The results of these past studies have not been called into question because they examined the immediate postreform period and it is generally accepted that this was a period of significant productivity gain that lasted almost a decade. The largest sources of postreform efficiency gains were institutional change, and increases in chemical fertilizer application, increased mechanization (including electricity for pumping water and mechanized plowing), and technical change. In the subsequent decade, investment in China's agriculture slowed considerably and actually fell in real terms over a large part of the 1985–1995 period. When we take an aggregate approach to measuring productivity gain in this second decade (after reform) the results are striking. We find that estimates of annual efficiency gains continue to be very high, a somewhat implausible outcome. This puzzle provided motivation for us to measure China's productivity growth using a disaggregate approach. By using national, provincial, and household data for China, we find that the three data sets result in agricultural productivity growth estimates that are relatively high for the 1978–1987 time period, immediately following economic reform. The national data suggests productivity growth of 8.1% per year during this period compared to 7.3% for the Jiangsu provincial aggregate data and 3.8% for the Jiangsu household data. However, both aggregate data sets show much higher productivity growth from 1988 to 1996, compared to the results from the Jiangsu household level data. The estimated annual productivity growth rate for this period was 5.6% using national data and 6.7% using provincial data, versus 1.9% using the household data. The estimated TFP growth rate for the cropping sector was 2.9% per year from the 1988 to 1996 time period using Jiangsu's household data. We believe the household results are more plausible because of reduced investment at the time and the fact that increases in farm product prices from the mid-1980s were accompanied by rising farm input prices in China. In comparing the input, output, and TFP indexes, we find that the aggregate data likely overestimate labor usage, but at the same time, underestimate the large cost increases associated with nonlabor inputs. The relatively high growth rates of the aggregate national and provincial output indexes could be due to many factors. The exaggeration of livestock figures is clearly a problem with the aggregate data. A second major problem might relate to the inclusion of intermediate inputs in the aggregate GVAO. A third explanation is that the aggregate data overstate the total value of agricultural output, from the bottom up, due to political incentives to overestimate production. In addition, some important cost items might be left out of the aggregate data. These include indirect costs such as depreciation of fixed assets. Certainly, there are other reasons that might explain our findings and we cannot even rule out the possibility that the aggregate results are more accurate than the farm level results. Nevertheless, our results cast doubt on the efficacy of using an aggregate approach when studying agricultural productivity gains in China.