Capital Formation and Development in

Jikun Huang Center for Chinese Agricultural Policy Chinese Academy of Sciences

And

Hengyun Ma College of Economics and Management Agricultural University

A Report Submitted to FAO, Rome

August 2010 Capital Formation and Agriculture Development in China

1. Introduction In the post World War II era, modern development economists mostly agree that the role of agriculture and rural development is absolutely an integral part to process of nation building and healthy development (Johnston and Mellor, 1961; Johnston, 1970). Agriculture plays five important roles in the development of an economy: i) supplying high quality labor to factories, constructions sites and the service sector; ii) producing low cost food which will keep wages down for workers in the industrial sector; iii) producing fiber and other crops that can be inputs to production in other parts of the economy; iv) supplying commodities that can be exported and earn foreign exchange which can help finance imports of key technology packages and capital equipment; v) raising rural incomes. The view toward agricultural and rural development in the modern world has changed dramatically in the past several decades. Traditionally, agriculture was thought of an inferior partner in development. Since the size of the sector falls during development, it was logically considered that it could be ignored. Why is that leaders would ever want to invest in a sector that is shrinking? Some academics urged policy makers to treat agriculture like a black box from which resources could be costlessly extracted (Lewis, 1954). All investment was supposed to be targeted at the industry and the cities. As a low productivity sector, it did not deserve investment. Unfortunately, countries that took this path seriously soon found out that while such a strategy may work in the initial years of development, in the longer run it slowed development and often ended up in failure (Timmer, 1998). Neglect of agriculture meant that a large part of the population was left out of the development process. If those in the low productive part of the economy were not invested in, they found it difficult to shift to the developing parts of the economy. Dual economies grew apart. It was found that in many cases, production in agriculture fell and food prices rose. Many households fell into isolated subsistence. When this happened, of course, the stability that is required for growth disappears and development stagnates and can even go into reverse. There are many examples of countries that encountered these difficulties, such as, Argentina, Mexico, Nigeria and even to some extent the Former Soviet Union. In contrast, during the last century nations that grew fast and entered the ranks of developed nations — for example, Japan and Korea—frequently found that heavy investment in agriculture was an integral part of their development strategy. China is actually one of such typical examples. Observers reported widely about the discontent of China’s rural populations, not the least due to the heavy burden of fees and taxes before early 2000s (Esarey et al., 2000). Village leaders were required to finance most local public infrastructure as well as their village’s operating budgets with the fees assessed on villagers (Liu et al., 2009). Local governments in many areas imposed heavy tax burdens on farmers (Tao et al., 2004). In some villages poor households paid more than 30% of their annual earnings in fees and taxes. During this time the government transferred little in the way of fiscal support to the rural economy. Indeed, as late as 2002

2 the total amount of subsidies targeted to the agricultural sector by the Ministry of Finance (MOF) was only 100 million yuan (MOF, 2008). This amount is extremely small given the size of China’s rural population. Subsidies to agriculture from the central government amounted to less than 0.007% of the value of agricultural output, only around 0.1 yuan per capita. Most of the subsidies went to enterprises and local government; even it is unclear if farmers really benefited at all. However, in the nowadays, China’s leaders has dramatically changed their policies towards agriculture since the mid-2000s, moving from taxing agriculture to financing agriculture. After 2003, things appear to have suddenly changed in the direction of agricultural policy. In 2003-2005 the leaders abolished taxes and fees (Luo et al., 2007). In 2004, subsidies to farmers rose to 14.5 billion yuan (MOF, 2005). By 2005 instead of the net flow being from rural households to the government’s fiscal coffers, the flow reversed. Between 2004 and 2008 subsidies from the MOF to the agricultural sector rose by more than 2.5 times and reached 95 billion yuan in 2008.1 In 2009, subsidy further raised by nearly 30% (from an already high base) and reached 123 billion Yuan. The total local tax and fee bill of farmers was zero. Moreover, according to the MOF, most of the subsidy payments went directly to farmers, instead of as before, to agricultural enterprises and government agencies. Beside emerging trends in subsidizing agriculture, public investments in agricultural and rural development have also been growing substantially. What triggered this turnaround in the beginning of this century? Policy documents suggest that leaders in China consider the growth of agriculture, particular grain production, is necessary condition for social stability and ensuring national food security and therefore a foundation for its successful economic development. At the same time, policy makers stated explicitly that they wanted investment in agriculture and rural sector and subsidies to increase farmers’ incomes for reason of rising rural and urban income gap. The primary reason investment contributes to growth and development is that it contributes to domestic capital formation. The nexus between capital formation in and for agriculture and agricultural growth, and agricultural growth and food security and poverty alleviation are very well articulated in the literature. Further, domestic capital formation is fundamental for increasing production and Total Factor Productivity (TFP) and consequently for sustainable development and reduction in poverty. The overall goals of the paper are to investigate the structure, magnitude and trends of capital formation in , to analyze of the determinants of agricultural investment and capital formation, and to provide policy options for promoting appropriate agricultural investment and capital formation for stimulating sustainable food production in China. To achieve these goals, this study will have the following five specific objectives: 1) To provide overview of China’s agricultural development and food security in the past three decades; 2) To examine trends, stocks, and shifting in composition of agricultural

1 According to government sources, there are four types of subsidy payments, including ‘grain subsidy’ (in Chinese—liangshi butie), ‘input subsidy’ (nongzi zonghe butie), ‘quality seed subsidy’ (liangzhong butie), and ‘agricultural machinery subsidy’ (nongjiju butie). The first two subsidy payments accounted for 82% of total subsidies in 2008.

3 investments by both public and private sectors; 3) To examine capital formation and its determinants of major public investments and private investment at aggregate level; 4) To examine capital formation and its determinants at farm level and by major commodity; and 5) To assess the impacts of investment/capital formation and sources of investment on agricultural production and total factor productivity (TFP) for major agricultural commodities. The rest of the paper is organized as follows. The next two sections present economic and agricultural growth, structural changes, and key development strategies and policies that led to the changes in China agricultural development. Then the following two sections provide details on capital investment and capital stock formation in agricultural sector by both public and private sectors, determinants of agricultural capital stock formation, the impacts of agricultural capital stock on agricultural production. In section 6, stochastic production frontier function is constructed and then total factor productivity (TFP) is estimated and decomposed into technical efficiency (TE) and technological change (TC) for each of major agricultural commodities and overall agricultural sector. The last section summarizes the findings with a few major conclusions.

2. Overall Economic Performance and Agricultural Growth in China 2.1 Economic Growth and Structure Change China’s economy has experienced remarkable growth since the reforms were initiated in 1978 and pushed forward by a number of subsequent policy initiatives. Since the mid- 1980s, rural township and village-owned enterprises development, measures to provide a better market environment through domestic market reform, fiscal and financial initiatives, the devaluation of the exchange rate, trade liberalization, the expansion of special economic zones to attract foreign direct investment (FDI), state-owned enterprise (SOE) reform, agricultural trade liberalization and many other policy efforts have contributed to China’s economic growth. In response, the annual growth rate of gross domestic product (GDP) was nearly 10 percent between 1979 and 2008 (NSBC, 2009) Rapid economic growth has been accompanied by significant structural changes in the economy. While the average annual growth of agriculture averaged about 5 percent throughout the entire reform period, the growth rate of the economy as a whole and of the industrial and service sectors were faster (Table 1). In fact, since 1985, the growth of industry and service sector has been two to three times faster than agriculture. Because of the differences in the sectoral growth rates, share of GDP of agriculture sector (primary industry) has fallen from 40.0 percent in 1970 to 11.3 percent in 2008, while correspondingly, share of GDP of service sector has increased from only 13.0 percent in 1970 to 40.1 percent in 2008 (Table 2). These figures highlight the ironic feature of agricultural development; the more transformative role that agriculture plays means that the pace of development will rise and the share of agriculture in the economy will fall. The shifts in the economy can also be seen in employment. Agriculture employed 81 percent of labor in 1970. By 2008, however, as the industrial and service sectors grew in important, the share of employment in agriculture fell to 39.6 percent, while the shares of

4 employment other industries increased from 10% and 9% in 1970 to 27.2% and 33.2% in 2008, respectively (Table 2). Actually, by 1995, there had been more than 150 million farmers working off the farm (Rozelle et al., 1999), and by 2000, the number had risen to more than 200 million (Rozelle and Swinnen, 2004). Clearly from the figures on the economic structure of the economy—both from an output and employment perspective, agriculture is performing in a way that is consistent with the beginning of the transformation of China’s overall economy—from agriculture to industry and from rural to urban (Rozelle et al., 1999; Nyberg and Rozelle, 1999). 2.2 Agricultural Growth and Production Structural Changes The ups and downs that characterized the performance of agriculture in the pre-reform period disappeared after 1978. Whatever metric of success that there was in agricultural production in China during the 1950s, 1960s and 1970s was surpassed during the reform era and agriculture finally began to carry out its various roles in the development process. Compared to the early and mid-1970s when the value of gross domestic product of agriculture rose by 2.7 percent annually, the annual growth rate more than tripled to 7.1 percent during the initial Reform period, 1978 to 1984 (Table 3). Although during the later reform periods (1985-1995; and 1996-2000) the annual growth rates have slowed (around 4 percent or so in real terms), these are still extraordinarily high rates of agricultural growth over such a sustained time period. At least in the early reform period, output growth—driven by increases in yields— occurred in all subsectors of agriculture. Between 1978 and 1984, grain production, in general, increased by 4.7 percent per year. Production rose for each of the major grains— , wheat and maize. While sown area did not change during this time, annual growth rate of yields for grains in general more doubled between the late part of the pre-reform era and the early reform period. During the early reform period (1978-1984), the growth of yields of all major grain exceeded the growth of yields during the early and mid-1970s. Far more fundamental than rises in output and yields of the grain sector, China’s agricultural economy has steadily been remaking itself from a grain first sector to one that is producing higher valued cash crops, horticultural goods and livestock/aquaculture products. Like the grain sector, cash crops, in general, and specific crops, such as cotton, edible oils and vegetables and fruit, also grew rapidly in the early reform period when compared to the 1970s. Unlike grain (with the exception of land-intensive staples, such as cotton), the growth of non-grain sector continued throughout the reform era. Hence, in the case of many commodity groups the high growth rates, which have exceeded those of grains during almost the entire Reform era, are continuing to accelerate or at least maintain the high rate of growth. Clearly, the agricultural sector is playing a major role in providing more than subsistence (e.g., grain); it is supplying oilseeds for the edible oil sector; horticultural products for retail food sector and cotton for the textile sector.2 China also is moving rapidly away from a crop-first agriculture. The rise of livestock and fishery sectors outpaces the cropping sector, in general, and most of the subcategories of cropping. Livestock production rose 9.1 percent per year in the early reform period and has continued to grow at between 6.5 to 8.8 percent since 1985. The fisheries subsector is

2 The fall in cotton production during the later reform period has more to do with pest infestations than lack of incentives. Since the late 1990s, there has a been a revival of the cotton sector, production wise as the advent of insect resistant, genetically modified cotton has overcome this problem (Huang et al., 2002).

5 the fastest growing component of agriculture, rising more than 10 percent per year during most years of the Reform era. The rapid and continuous rise in livestock and fisheries has steadily eroded the predominance of cropping. Meanwhile, as can been seen from Appendix Table 1, crop production distribution also apparently vary across region over time due to the economic regionalization. For example, used to account for 7.15% of total wheat production in 1985, while it only accounts for 0.80% in 2008. Similarly, used to account for 9.43% of total wheat production in 1985, while it accounts for 3.79% in 2008. However, Henan used to account for 16.13% of total wheat production in 1985, while it approximates 30% (it is 27.13%) in 2008. The same remains for , and , etc. The same spacious production change also can be observed for rice, maize and canola production in China. 2.3 Productivity Trends and Rural Incomes While it is possible that agricultural productivity trends tell a somewhat different story of how transition affects agricultural performance that for the case of output (as was the case in the pre-reform period), this is not the case in reform China. First, as is known from the literature (Lardy, 1983), output per unit of land (or yields) all rose sharply. In addition, for the entire reform period, trends in agricultural labor productivity, measured as output per farm worker, parallel those of yield. Moreover, it is also possible that partial and more complete measures of productivity move in opposite directions (as it did in the pre-reform period), most of the evidence from the literature shows that, in fact, total factor productivity (TFP) trends move largely in the same direction as the partial measures. Several series of TFP estimates have been produced for China’s agriculture (McMillan et al., 1989; Fan, 1991; Lin, 1992; Wen, 1993; Huang and Rozelle, 1996; Fan, 1997; Jin et al., 2002). The studies uniformly demonstrate that in the first years after reform (1978 to 1984), comprehensive measures of productivity (either constructed TFP indices or their regression-based equivalents) rose by 5 to 10 percent per year. Although Wen (1993) worries that TFP quit growing in the post- reform period (1985 to 1989), Fan (1997) and Jin et al. (2002) demonstrate that during the 1990s, TFP continues to rise at a rate of around 2 percent per year. In other words, the estimates of TFP changes in China show that measures of TFP generally move in a manner consistent with the partial ones. Moreover, rates of TFP rise of 2 percent annually are certain not low in international context (e.g., according to Alston and Pardey, 1998). The US, many Western European countries and Australia have grown by around 2 percent per year in the post-World War II era). In part due to rising productivity, and perhaps also due to the increasing (allocative) efficiency associated with specialization, shifting to the production of more higher value crops and livestock commodities and the expansion of off farm work, rural incomes during the reforms have steadily increased (Table 4). Between 1980 and 2000, average rural per capita incomes have risen (in real terms) from 667.9 to 2253.4 yuan. This annual rise (6%) is remarkable and is as high as the growth rates experienced in Japan and Korea during their take-off years. During the New Millennium, rural income experienced another high annual growth rate (7.73%). As can be seen, the structure of rural per capita income has evidently changed. For example, the income share from crop farming has sharply declined from nearly 50% in 1985 to only 30% in 2008. Hence, it seems

6 surprising the amount of attention given to the rural income problem by the media; the problem, however, no doubt is rooted in the relative rise between the rural and the urban that both started from a higher base and rose faster than rural incomes. The inequality between rural and urban also has a parallel with the rural economy, between those that began relatively rich and those that began the period relatively poor and is probably the more serious problem. The growth rate rural per capita income of those in the richest docile is higher than average, more than 8 percent annually. In contrast, although incomes are rising (at 2-3% annually) for those in the lowest docile, the rates of increase are far lower than the richest. This implies, of course, that in relative terms the poorest of the rural poor are falling behind. Importantly, although rises in income (and income inequality) are clearly related closely with the ability of rural households to gain access to off farm employment (Riskin and Khan, 2001), agriculture has been shown to play an inequality mitigating role. Two factors are responsible for this (Rozelle, 1996). First, agricultural income is distributed more evenly to begin with. Second, the poor are proportionately more involved in agriculture. Because of these two characteristics, it has been shown that increases in agriculture income lead to a lower Gini coefficient and other measures of inequality.

3. Major Drivers of Agricultural Growth 3.1 Sources of Growth and Agricultural Investments Past studies have already demonstrated that there are a number of factors that have simultaneously contributed to agricultural production growth during the reform period. The earliest empirical efforts focused on measuring the contribution of the implementation of the household responsibility system (HRS), a policy that gave individual farmers control and income rights in agriculture. These studies concluded that most of the rise in productivity in the early reform years was a result of institutional innovations, particularly the HRS (Fan 1991; Lin 1992). More recent studies show that since the HRS was completed in 1984, technological change has been the primary engine of the agricultural growth (Huang and Rozelle 1996; Fan 1997; Fan and Pardey 1997; Huang et. al. 1999 and Jin et al. 2002). Improvements in technology have by far contributed the largest share of crop production growth even during the early reform period. When examining the sources of the technology shifts, Jin et al. (2002) empirically demonstrate the cross province differences in investment into R&D by the government have had the largest effects on technological improvements. Between 1990 and 2005, investment in R&D nearly tripled. China is one of the only countries in the world in which agricultural R&D expenditures as a share of AgGDP is rising. Moreover, China is investing in far more than conventional agricultural technology. Since the late 1990s, China has greatly expanded its investment into plant biotechnology. By the mid-2000s China’s public investment into plant biotechnology was the largest in the world. China’s agricultural leaders believe that past gains in no small part are from government-supported technologies; they believe their current investments in agricultural R&D will play an important role in driving agricultural output in the future. Transportation and market infrastructure have also improved remarkable since the early 1990s—which serve to raise the return to farmers at the farmgate. Huang and Rozelle (2006) show that China’s food markets have become highly integrated since the late 1990s. Not only do integration measures show that prices in one region are highly linked

7 to prices in other regions, our work also suggests that the efficiency of moving commodities across the nation is improving. In fact, when measuring efficiency in terms of the percentage change in price for every 1000 kilometers of distance from port (between 4-7%), in efficiency terms China’s agricultural marketing is comparable with that in the US. Irrigation has played a critical role in establishing the highly productive agronomic systems in China (Wang, 2000). The proportion of cultivated area under irrigation increased from 18 percent in 1952 to a level at which about half of all cultivated land had been irrigated after the early 1990s (NSBC, 2001). However, rising demand for domestic and industrial water uses poses a serious constraint to irrigated agriculture and increasing water scarcity has come to be seen as a major challenge to the future food security and well-being of people especially in the northern region. Wang et al. (2005a,b) shows that the water management reform has been helping increase the efficiency of water use in north China, although the scope for such reform in the long run is somewhat limited. 3.2 Agricultural Subsidies In 2004 China launched its new path of development. Instead of taxing farmers and charging them fees to provide basic services in their rural homes, the government has taken decisive action to eliminate almost all taxes and fees. In addition, since 2004, the government has begun to subsidize farmers. Since 2004, subsidies have grown fast. In 2004, the government gave out 14.52 billion yuan. The amount had climbed to 95 billion yuan in 2008 and 123 billion yuan in 2009. While the government initially launched the subsidy program (at least nominally) to increase grain production and enhance food security, as it turns out the implementation has made the program non-distortive. According to a recent study by Huang et al. (2009) demonstrates that changes over time in a farmer’s grain subsidy have no impact on grain production. Likewise, so-called input subsidies have no effect on the level of input investment by farmers. As a result, in recent years the government really has begun to rely on the program for an alternative policy target: increasing the welfare of rural households. 4. Agricultural Investment and Fixed Capital Formation Agricultural investment is often divided by public investment and private investment. Public agricultural investment covers a range of productivity enhanced and environmental improved investments, particular in agricultural research and development (R&D), agricultural extension, agricultural infrastructure (e.g., irrigation and drainage system), and soil and land improvement. Technology and knowledge related investments (e.g., agricultural R&D investment, education and training) are embodied in the factors (e.g., labor; land; seeds, chemicals and other capital inputs) used in agricultural production. While most of agricultural infrastructure investment form fixed capital in the production. Private investment can also be divided by individual farmers (or farm household) and enterprises. Because agricultural investment by enterprises is very minimal, this paper examine only those investments by individual households. In this section, we only look at agricultural capital construction investment and its fixed capital formation in agriculture by public sector and farm households. 4.1 Public Investment and Capital Stock Table 5 presents the public capital construction investment as well as final agricultural fixed capital stock formation during 1985-2008. As can be seen from Table 5, industrial fixed capital construction investment increased rapidly in the first period (1986-1995), rising 22.2% annually (from a low base). However, it almost stagnated during the second period (1996-2000), with only 3.3% annual growth rate. But during the New Millennium,

8 industrial fixed capital construction investment roared sharply, with a 50.3% of annual growth rate in 2001-2008. This made fixed capital construction investment in industry rise from 434.9 billion yuan in 2001 to 7540.5 billion yuan in 2008. Capital construction investment in agriculture also increased rapidly, but its growth pattern differed from that in industry. During the first period (1986-1995), the growth of capital construction investment in agriculture was very slow. Annual growth rate was only 2.0% in 1986-1995. Concerns on stagnation of grain production and slowed down of overall growth of agriculture in the 1995-2000 (Table 1), China has invested substantially in agriculture since the late 1990s (Table 5). Annual capital investment in agriculture grew rapidly with annual growth rate of 30.5% in 1995-2000, investment increased from 14.3 billion in 1996 to 41.5 billion in 2000. Moreover, this growth rate continued, even higher in 2001-2009, making the annual capital investment in agriculture rise to 420.9 billion by 2008. However, it should be noted that the annual capital construction investment in agriculture was only small part of that in industry, most of them being about only 5%. Actually, vast capital construction investment in agriculture did not start until 2004 in China (Figure 1). Similarly, capital stock formation in agriculture by public sector followed the same pattern as capital investment in agriculture during 1985-2008 (Figure 1). Clearly due to slow capital investment during 1985-1995, capital stock formation in agriculture was slow by public sector in China in this period. Actually, it declined 1.5% annually in 1985-1995. As addressed by Huang and Ma (1998), capital actually was outflow from agricultural/rural sector to industry/urban sector during this period. However, the capital stock began to grow moderately in 1996-2000 (4.5% annually) and extremely fast (20.8 annually) in 2001-2008 (Table 5). As reviewed previously, this is most likely because agricultural tax was abolished after 2003 and a large volume of budget was transferred to subsidize agriculture after 2004 (refer to footnote 1). While provincial capital construction investment and capital stock in general have grown overtime, the rates of growth differ largely among provinces (Appendix Table 2). Capital stock data in agriculture for each province are unavailable, and only a few studies did estimate China’s capital stock but all of them are already dated. For example, Chow (1993) constructed a series of sectoral capital stock for China for the period of 1952- 19853, while Li (1993) also estimated a series of aggregate capital stock for the period of 1985-1998. Therefore, to carry out this study, we have to construct a new series for it if we model the behavior of capital stock formation in agriculture by public sector using a panel dataset. To construct this series, we start to set the national capital stock (408.4 billion yuan) in agriculture estimated in 1985 (Table 5) by Chow (1993) as a base. Then we estimate provincial agricultural capital stock by dividing this national agricultural capital stock number using the shares of provincial aggregate capital stock in 1985 estimated by Li (1993). And then we use the following equation to construct agricultural capital stock series for each province: = −δ + (1) Kit Kit −1(1 ) Iit

3 Chow (1993) estimated agricultural fixed capital stock in 1985 to be 129.16 billion yuan at 1985 price. In this study this number was deflated to become 408.4 billion yuan at 2000 price.

9 Where Kt is current agricultural capital stock, Kt −1 is previous year capital stock, δ is

capital depreciation rate (5%), It is current year capital investment, and i indicates individual province. Both capital construction investment and stock formation in agriculture for each province are presented in Appendix Table 2 for two years in 1986 and 2008. It is clear that both capital construction investment and capital stock formation in agriculture vary largely across province. The sizes of investment and capital stocks depend on the size of provinces and importance of agriculture in the local economy, while the growth rates reflect the efforts and commitments made by the local government to invest in agriculture. However, this study has found that major agricultural production provinces not only have a larger volume of capital construction investment and therefore capital formation, but also have a fairly quick growth rate of agricultural capital stock formation. For example, the largest investment was found in Henan and Shandong (53.8 and 50.6 billion yuan, respectively) in 2008, followed by Hebei (38.7 billion), (32.5 billion), Heilongjiang (30.9 billion), (28.8 billion) and Sichuan (26.8 billion). Their growth rates of capital investment in agriculture were over 25% annually in 1986- 2008, especially these growth rates reached 30% for the two largest agricultural provinces, Henan and Shandong. As a result, this makes the fixed capital stock formation in agriculture grow fairly fast in most provinces. The fixed capital stock formation reached 118.3, 147.5, 119.5, 95.0 and 81.0 billion yuan in 2008 for Henan, Shandong, Hebei, Heilongjiang and Inner Mongolia, respectively. The capital stock formation over these five provinces summed to 561.3 billion yuan, accounting for 36.1% of national aggregate capital stock formation in agriculture in 2008. In addition, it is interesting to find that rapid increase in capital formation in agriculture took place in some geographical remote provinces. For example, the annual growth rates were 24% and 23% in Inner Mongolia and , respectively, and 16-19% in , , and in 1986-2008. Though the reasons are unclear, this may have something to do with their relative low level of capital investment in the early period, the national supporting policy towards the western China, and regional characteristics or advantages in agricultural production. For example, Xinjiang has become major cotton production base, accounting for nearly 40% of national total cotton production in recent years. Central government has also invested substantially in Xinjiang’s agricultural since late 1980s. Similarly, Heilongjiang is major soybean and production in China. Dairy and sheep wool in Inner Mongolia account for one quarter of national production. Rapid development of regional specific commodities in these regions can promote local and central government to increase investment in agriculture because they may play a more important role in increasing rural income. These changes in both capital construction investment and fixed capital stock formation are expected to have a significant effect on agricultural productivity growth over region and vice versa. 4.2 Farm Household Agricultural Capital Investment and Stock In this study, we examine fixed capital construction investment in agriculture by rural households for the reason mentioned early. Due to data availability, we further limit our

10 analysis on fixed capital stock formation in agriculture and the quantity of major agricultural machinery possessed by rural households. The results are presented in Table 6 for average rural household. Overall, the agricultural capital investment by farmers and the growth rate of fixed asset have been rising overtime. The agricultural fixed asset was under 2000 yuan per rural household measured at 2000 price in the mid 1980s (Table 6), while this number rose to about 2400 yuan by the mid 1990s with an annual average growth rate of 2.29% in 1985-1995. The growth rate accelerated in 1996-2000 (4.36%), making the agricultural fixed assets per household rise to more than 3300 yuan by 2000. The fastest growth of agricultural fixed assets per household was found in 2001-2008 (6.4% annually). By 2008, agricultural fixed assets per household reached 5400 yuan. Apparently, the rural household agricultural fixed assets grew slightly from public capital construction stocks. The former shows an accelerated growth trend, while the later was stagnated in 1985- 1995 and then followed a much higher growth rate than the former in recent years (Figure 3). Rising investment in agriculture by households is particular evidenced in agricultural machinery (Table 6). Agricultural machinery was only 1.1 KW per rural household in China in 1985, while it rose to 1.55 KW by 1995, with average annual growth rate of 3.49%. In the following period (1996-2000), its annual growth rate raised to 7.37%. The investment continued to grow after 2000 with annual growth rate of more than 5% in 2001-2008. By 2008, agricultural machinery power reached 3.20 KW per rural household. Large-medium tractors per rural household display almost the same growth pattern as agricultural machinery. The growths of machinery and tractors per rural household are also consistent with that of agricultural productive fixed assets per rural household (Figure 2). Rising wage in recent years may partially explain these rising capital investments by farm households in China. Appendix Table 3 shows regional agricultural fixed assets per rural household and Appendix Table 4 displays regional agricultural machinery per rural household The variations for them across region are also obvious. Similarly, regional household capital investment in agriculture increased faster in 1996-2008 than in 1985-1995. Many agricultural provinces show a more apparent and rapid growth in capital investment in agriculture. For example, the annual growth rate for Hebei province increased from 2.65% in the first sub-period to 8.36% in the second sub-period (last column, Appendix Table 3). The same can be found for Shandong province. It is surprising, however, that some rich and developed regions did not apparently increase their capital investment in agriculture more than others. This can be observed for , Tinjian, , and by comparing the two growth rates of last columns in Appendixes Table 3 and 4. This may be explained by the fact that the off-farm employment rate is much higher in rich provinces than in less developed provinces. Overall, these observations are also expected to have a significant effect on regional agricultural capital stock formation and agricultural production performance, which are analyzed in the following sections.

5. Modeling Capital Formation and Its Impacts on Agriculture

11 In the previous sections, we have presented the capital investment and capital stock formation in agriculture by both public and farm households in China over time and across regions. In this section, we will empirically model the behavior of capital stock formation and its impacts on agriculture. 5.1 Empirical Model and Variable Definitions Following Chand and Kumar (2004), we analyze determinants of public and farm household capital stock formation and their impacts on agriculture growth by defining the following three Cobb-Douglas production equations system: (2)  AgFCS = f[AgFCS(1), AgGDP(1), AgGDPR(1),Z]   HAgAS = f[HAgAS(1), AgFCS(1), AgCredit, Income(1), RoRPI(1), Z]   AgGDP = f[AgFCS(1), HAgAS(1),AgLabor, CropSown, Rainfall, AgGDPR(1), Z]

Where, AgFCS is log of aggregate fixed capital stock in agriculture by public sector measured in 10,000 yuan; AgGDP is log of gross domestic product from agriculture measured in 10,000 yuan; AgGDPR is terms of trade for agricultural relative to whole economy measure by taking the ratio of agricultural GDP to GDP; HAgAS is log of agricultural fixed assets invested by farm households measured in 10,000 yuan; AgCredit is log of institutional credit supplied by all kinds of formal banks to agricultural sector during a year measured in 10,000 yuan; Income is aggregate rural net income calculated by per capita net income times by total rural population and measured in 10,000 yuan; RoRPI is rate of return to private investment, measured by taking the ratio of agricultural GDP to aggregate fixed assets in agriculture by rural household sector; AgLabor is log of aggregate agricultural employment measured in 10,000 persons; CropSown is log of aggregate crop sown areas measured in 1000 hectares; Rainfall is ratio of rainfall during Jun-Sep (major crop growing season) to whole year aggregate; Z is a vector of provincial dummy variables to capture the variations in GDP growth, public and farm household capital stock formation in agriculture. Data included are 31 provinces over 1985-2008. The numbers in the parentheses are time lag (in 1 year). All values are deflated by 2000 real price. Private capital formation (HAgAS) is affected by several factors. Most of the studies in the literature have considered public capital formation (AgFCS), amount of institutional credit supplied to agriculture (Credit), rural household income (Income) and the rate of return to private investment (RoRPI). It may be difficult to capture the impact of public capital formation on that in private sector because the effect of former lag is realized on the latter. Institutional credit is directly related to private investment as medium- and long- term loads are given for the purchase of agricultural fixed assets. As RORPI improves and income increases, RORPI and Income are also expected to have a positive influence on private investment. Slow public capital investment in agriculture is often said because the high share of agricultural GDP (AgGDPR) and the increase in agricultural tax/fee. This impact may likely vary over time and over policy settings. For example, during the initial era of

12 economic development, agricultural growth may be unable to increases public agricultural capital investment because large part of funds need to be investment in industry and other sectors. In other words, large share of agricultural GDP may lead to decrease of public agricultural capital investment. Both public and private capital investment in agriculture are expected to have positive impact on the growth of agricultural GDP. Labor and land are also expected to have a positive impact on agricultural GDP. Natural environment (rainfall) is also important to agricultural production. 5.2 Estimated Results and Analyses The data used to run above equations of system are obtained from China Statistical Yearbook, China Agricultural Statistical Yearbook, and China Rural Statistical Yearbook, various issues. Most of them are explained in the previous sections and also can be referred to the footnotes in relevant Tables. Next, we empirically estimate this three equation model simultaneously to investigate determinants of public and farm household capital stock formation and their impacts on agricultural GDP. 5.2.1 During the Whole Period The estimated results are shown in Table 7 for the whole study period (1985-2008). The regression results are fairly good and R-squares range from 0.961 to 0.999. The lag dependent variables for all three equations have statistically significant and positive parameters, which are what we expect. Besides these, Table 7 also shows several interesting results. Public Capital Stock Formation:  Public capital stock formation is closely correlated with agricultural GDP. The estimated results show that the parameter of AgGDP(1) in AgFCS equation is positive and statistically significant (column 1) though the magnitude (0.0935) is very small (column 1, Table 7). One percent increase in agricultural GDP can result in about 0.09% rise in public capital stock formation in agriculture.  There is a strong evidence of political economy of public investment in agriculture. While agricultural growth has contributed to public capital stock formation, its contribution is less than the growth from the rest of economy. The estimated parameter for AgGDPR(1) is negative (-0.133) and statistically significant (column 1), which implies that, after controlled for the size of agriculture (AgGDP), as the share of agriculture in the economy falls (or as the shares of industry and service sectors in the economy rise), public investment in agriculture rises. The results are consistent with the political economy that has been found in the literature (Andersen and Hayami, 1986; Lindert, 1991). As agricultural share in the economy falls, it gains political power and public investment rises. In other words, in the beginning stage of economic development, during which agriculture or primary industry dominates the whole economy, public capital investment would be very limited in agriculture. Private (Farm Household) Capital Stock Formation:  While there is a week complementary of agricultural investment between public and private sectors, the relationship is not statistically significant. The estimated

13 parameter of AgFCS(1) in farm household capital stock equation is positive (0.0426) but statistically insignificant (column 2, Table 7).  There is strong correlation between farmers’ capital investment in agriculture and their ability to invest in the economy. This result is evidenced from two parameters estimated for agricultural credit (AgCredit) and farmer’ [Income(1)] (column 2). These findings imply that more access to agricultural credits for farmers and increases in their income could result in more private (rural household) capital investment in agriculture.  Finally, household capital investment in agriculture is positively correlated with rate of return to private investment since RoRPI(1) is positive and significantly different from zero. The rate of return to private investment actually indirectly impact household capital investment in agriculture (Chand and Kumar. 2004). As RoRPI improves and farmer’s income also increases, which has a significant impact on household capital investment in agriculture. This finding is also expected since rural households are also reasonable in investing their money. GDP Agriculture Growth:  AgGDP is closely correlated with public capital stock formation [AgFCS(1)]. The estimated parameter or elasticity (0.1692) for AgFCS(1) is positive and significantly different from zero (column 3). This implies that 1% increase in public capital stock can generate about 0.17% rise in agricultural GDP. Therefore, public capital investment played an important role in agricultural production in China and will be more important in the future. But, private capital investment played an even more important role in agricultural production in China. The estimated parameter shows that 1% increase in private capital stock can generate about 0.38% rise in agricultural GDP, which is twice of elasticity of public capital stock. However, this situation has changed overtime (see discussion below).  Crop sown area has a significant impact on agricultural production growth (column 3). Arable land is still one of key factors that affect agricultural production in China. Therefore, arable land protection law is critical important in China given its large population base and limit in agricultural land.  The share of agriculture in GDP is negatively associated with agriculture growth (see parameter for AgGDPR(1) in column 3, Table 7). That is, the smaller the agriculture in the economy, the faster its growth. Actually, this may imply that agricultural production growth finally needs technical and financial supports from industry.  Agricultural labor input is found to have no statistically significant impact on agricultural GDP. It may be explained by the large rural labor surplus in most years of studies period. This finding is consistent with the agricultural economic literature in China, which show wage had not been increased until recent years. . 5.2.2 In Sub-Periods: 1985-1995 vs 1996-2008 We also run the model separately for two sub-periods (1985-1995 and 1996-2008) in order to examine whether there are any significant behavior changes in determinants of private and public capital stock and the impact of capital stock on agricultural economy. These results are presented in Table 8.

14 Although most results for these two sub-periods are similar with those found for the whole period, there are also several interesting findings that reveal the changes in China’s agricultural capital stock formation.  The most interesting finding is the relationship between agricultural GDP and public investment in agriculture. In the early period (1985-1995), when the share of agriculture in GDP was about 20% to 30%, the growth of agriculture indeed had resulted in decline in public investment in agriculture. The estimated parameter for AgGDP was -0.0195 and statistically significant (column 1, Table 8) in 1985-1995. However, the estimated parameters for agricultural GDP share was positive (0.0489) and statistically significant, which implies that as the share of agriculture in the economy fell overtime, investment in agriculture from public sector also fell in early period of development. This was a period China’s recorded a negative growth of agricultural capital stock (column 3, Table 5) and a period the farmer was taxed heavily (Huang et al., 2009). However, as agricultural GDP shares continued to fall from 19.7% in 1996 to 11.% in 2008 (NSBC, 2009), the political ’s agricultural policy is presented. The growth of agriculture had positive impacts on public investment (see the significant parameter of AgGDP, column 4) in 1996-2008. Moreover, the fall in share of agriculture in the economy has started to affect agricultural investment. The estimated parameter (-0.2455, column 4, Table 8) for agricultural GDP share shows that agriculture has gained much attention in public investment as its share in the economy fell or as the nation has been developed largely based on industry and service sectors’ expansion. By this late development period, China already had health fiscal and financial system. The policy has been moved from taxing agriculture to investing and subsidying agricultural and rural economy.  The roles of credit and farmer’s income on agricultural capital formation have also changed significantly overtime. For example, in the first period (1985-1995), farmers’ agricultural investment was mainly driven by their own cash or income. The estimated parameter for farmer’s income (0.4213) shows that doubled farmer’ income could lead to 42.13% increase in his/her agricultural capital stock (column 2, Table 8). But the analysis does not find any significant effect of agricultural credit on farmer’s investment in agriculture in 1985-1006 (the parameter for AgCredit, column 2). However, agricultural credit has becoming one of important determinants in farmer’s capital investment in agriculture in the last period of this study, 1996-2008 (column 5). Meantime, while farmer’s income is still important in determine agricultural investment, the magnitudes of its impacts have fallen overtime (comparing the parameters of income variable in columns 2 and 5). These may be explained by the fact that farmers in China have been moving from more subsistence agriculture to more commercialized and diversified agriculture.  As China’s market continuing liberalized and land tenure becoming more secure, the rate of return to private investment (RoRPI) was found to have significantly positive impact on private capital investment in agriculture in 1996-2008, while it was not the case in early period (1985-1995). The estimated parameter RoRPI was

15 0.0863 and statistically significant in 1996-2008 (column 5), while it was not the case in 1985-1995 (column 3).  The impacts of public capital stock (AgFCS) in agriculture were found to have significant and more important role in agriculture in the late sub-periods studied. Indeed, we do not find any significant impact of agricultural capital formation on agricultural growth in 1985-1995, the estimated parameter was insignificant (column 2, Table 8). However, the impacts have been substantial since the late 1990s. The elasticity of agricultural GDP with respect with agricultural capital stock from public sector rose to 0.2680 (column 6). While there could be several reasons for insignificant impacts of public capital stock on agricultural growth in the early stage of development (e.g., insignificant investment and large rural surplus which substituted for capital input), this finding may indicate that there is sequence of agricultural investment. In the later stage of development, when rural wage started to rise in the past 10 years, which also evidenced from shifting the parameter for agricultural labor input in agricultural GDP from significant in 1985-1995 to insignificant and negative (-0.0152) in 1996-2008, capital formation in agriculture has become much more important. This suggests that in order to enhance agricultural production productivity China must increase public capital investment in agriculture.  Both public and private capital investment in agriculture have been found to have a positive correlationship in the late sub-period studied. The relationship between public and private capital investment behaviors are mixed, dependent upon many factors and the stage of development. First, it may be expected that public capital investment can drive private capital investment in agriculture, but it cannot be expected that private capital investment can drive public capital investment in agriculture. Second, it may be not expected that public capital investment can drive private capital investment in agriculture in the early stage of development when land tenure was not secure and farmer’s income was low. These economic assumptions are evidenced from the estimated parameters. Comparing the estimated coefficients of AgFCS (columns 2 and 5, Table 8), it is found that AgFCS had a significant impact on HAgAS in the late sub-period rather than in the early sub-period. The estimated elasticity shows that a 10% increase in public capital investment in agriculture could lead to 1.133% increase in private capital stock in agricultural (column 5).

6. Agricultural TFP and Impact of Capital Formation In previous section, we empirically estimated capital formation in agriculture by public and private sectors and investigated the behaviors of capital formation and their impacts on agricultural GDP growth for China. It may be easy to understand that increasing capital investment in agricultural can almost certainly increase agricultural output and therefore agricultural GDP growth. Many factors can affect agricultural total factor productivity (TFP) performance. One of the most important factors is technological change (TC). TFP change can be decomposed into TC and technical efficiency (TE) and even other components (e.g., allocative efficiency, scale efficiency). For most of the cases various technological changes are generally embodied in capital stock formation and physical material inputs. Therefore, it is more interesting to investigate whether agricultural capital

16 investment has any significant influence on agricultural TFP performance. It is also interesting to find whether there are any differences in such impacts of capital stock formation between public and private sectors. Therefore, this section first estimates agricultural TFP growth for both aggregate agricultural and major individual crop commodity. Then we investigate the influence of capital formation (and other factors) on TFP growth for both aggregate agriculture and major individual crop commodity for China. 6.1 Agricultural TFP Estimates China’s agricultural total factor productivity and its decomposition into technical efficiency and technological change have been well estimated and documented (Wen, 1993; Fan, 1997; Tian and Wan, 2000; Allan Rae et al., 2006; Hu and McAleer, 2008; Jin et al., 2002, 2008; Chen et al., 2008). In this study, we follow traditional approach as used in the literature and use the household agricultural production cost and revenue data to estimate agricultural TFP and its growth pattern and try to correlate possible findings to capital formation in agriculture in China. This sub-section includes two parts: i) model and variables, and ii) estimated results and analyses. 6.1.1 Model and Variables The stochastic frontier production function (Aigner et al., 1977; Meeusen and van den Broeck 1977) has been the subject of considerable recent production efficiency research with regard to both extensions and applications (Battese and Coelli 1995). Stochastic production function analysis postulates the existence of technical inefficiency of production of firms involved in producing a particular output, which reflects the fact that many firms do not operate on their frontiers but somewhere below them. Many theoretical and empirical studies on production efficiency have used stochastic frontier production analysis (e.g., Coelli et al., 1998; Kumbhakar and Lovell 2000). As panel data permit a richer specification of technical change and obviously contain more information about a particular firm than does a cross-section of the data, recent development of techniques for measuring productive efficiency over time has focused on the use of panel data (Kumbhakar et al., 1999; Henderson 2003). Panel data also allow the relaxation of some of the strong assumptions that are related to efficiency measurement in the cross-sectional framework (Schmidt and Sickles 1984). In the rest of this paper, we adopt a panel data approach to measure and decompose TFP for several key sub-sectors of China’s livestock economy. Allan Rae et al. (2006) estimate and decompose TFP of China’s livestock commodities, and Jin et al. (2009) and Tian and Wan (2000) estimate and decompose TFP of China’s crops by employing a stochastic frontier production function and the same data source. Therefore, for detailed description of data source and stochastic production frontier function specification can also refer to the above three literatures. In this particular study, we define the stochastic frontier production function in translog form: 1 (3) lnY =α + ∑ β ln X + β t + ∑ ∑ β ln X ln X it 0 j j jit t 2 j k jk jit kit 1 + β t 2 + ∑ β ln X t −u + v 2 tt j jt jit it it where ln denotes the natural logarithm, i indexes the provinces, t indexes the annual

17 observations over time; Yit is total provincial output; the Xjs are the input variables, t is a

σ 2 time trend to capture trends in productivity change, vit is assumed to be an iid N(0, v )

+ 2 random variable, independently distributed of the uit ; and uit is iid N (mit, σ u), mit = zitδ where zit is a vector of explanatory variables. Note that the non-negative inefficiency term δ uit is obtained by truncation at zero of the normal distribution with mean zit and

2 variance σ u (Battese and Coelli 1995) .

There are several specifications that make the technical inefficiency term uit time- varying, but most of them have not explicitly formulated a model for these technical inefficiency effects in terms of appropriate explanatory variables. We define the technical inefficiency function uit as follows:

=δ +δ + δ (4) uit 0 1t ∑ 2i Di where D are provincial dummies. Since there are serious econometric problems with two-stage formulation estimation (Kumbhakar and Lovell 2000), our study simultaneously estimates the parameters of the stochastic frontier function (2) and the model for the technical inefficiency effects (3). The likelihood function of the model is presented in the appendix of Battese and Coelli (1993). σ 2 = σ 2 + σ 2 The likelihood function is expressed in terms of the variance parameters u v and

γ ≡ σ 2 σ 2 γ u / , and is an unknown parameter to be estimated. The stochastic frontier function may not be significantly different from the deterministic model if γ is close to 1 (Coelli, Rao and Battese 1998, p.215). On the other hand, if the null hypothesis γ = 0 is σ2 accepted, this would indicate that u is zero and thus the term uit should be removed from the model, leaving a specification with parameters that can be consistently estimated by ordinary least squares. We use the FRONTIER 4.1 computer program developed by Coelli (1996) to estimate the stochastic frontier function and technical inefficiency models simultaneously and this program also permits the use of our unbalanced panel data. Empirically, crop yield is chosen as independent variables (Y). Capital, labor, and irrigation are chosen as independent variables (X). Noted all of variables are expressed on a per mu basis. 6.1.2 Results and Analyses σ2 The estimated results for equation 4 are fairly robust. All estimates of parameters, u and γ , are statistically significant, indicating that inefficiency exists and frontier specification is correct. Over 50% of parameters estimated are statistically significant for aggregate agricultural model and for individual crop models, and also all χ2 tests demonstrate that

18 translog model specification is significant from Cobb-Douglas production function specification for aggregate agriculture and all crops. The detail estimated coefficients and standard errors are not reported in the text because the aim of our analysis here is to focus on TFP estimate for agriculture and major crops, but they are available upon request. The empirical estimated results of TFP and its decomposed two components, technical efficiency (TE) and technological change (TC), are presented in Table 9 for national aggregate agriculture and crops and Table 10 for regional aggregate agriculture and crops. The commodities include agriculture as a whole, six grain crops (wheat, corn, early indica, middle indica, late indica and japonica rice), one cash crop (cotton) and one oil crop (soybean) at the national aggregate levels. For National Agriculture and Crops: National agricultural TFP in China increased rapidly in the past two decades, almost exclusively due to technological change (Table 9). The estimated results show that agricultural TFP growth increased faster and faster over three periods. Annual TFP growth rate was only 0.62% in 1985-1995 and 1.88% in 1996-2000, but it grew 5.34% each year in 2001-2008 (row 1). As the analysis shown, this is mainly due to technological change (TC). The TC, the embodied knowledge and capital formation, was faster compared with the earlier periods. For example, the TC was only 0.61% annually over 1985-1995, while it rose 3.09% each year over 1996-2000. Finally, TC grew at over 5.0% per year in the New Millennium (row 3). However, technical efficiency (TE) was almost stagnate over 1985-1995 and over 2001-2008 and even negative growth over 1996-2000 (row 2). For crops, they almost followed the same patterns of TFP change, but their TFP growth rates were slower as aggregate agriculture may include faster TFP growth commodities. For example, mean TFP growth rates were faster over the 2001-2008 than over the 1996- 2000. This scenario was actually shaped by the pattern of TC growth rates across the last two periods. In other words, TFP growth was primarily determined by TC. However, variations in TFP growth pattern are still obvious. For example, TFP growth for soybean was 4.7% per year during the last period of 2001-2008, which was mainly resulted from TC growth (3.8% annually). However, TFP growth rates for wheat and corn were about 2.3% and 3.4% annually, which was almost determined by both technical efficiency (TE) improvement and technological change (TC). For middle indica rice, TFP growth was nearly zero because TE and TC were almost offset each other. For Regional Agriculture and Crops: To observe the TFP growth patterns across region for aggregate agriculture, we present the results in Table 10. As can be seen from the table, the agricultural TFP growth rates have become faster and faster over three periods nearly for all provinces. Many had a negative TFP growth rates in 1985-1995, but most move to positive TFP growth rates in 1996-2000 and become large positive number in the recent period. In 2001-2008, TFP annual growth rate ranged from about 2% to 8%, most of them concentrate approximately on 5% annually. And it is also clear that the TFP growths are almost completely driven by technological change (TC). Though all regions nearly demonstrate a similar TFP growth pattern, some variations can still be observed. To see the TFP growth and decomposition patterns across region for individual crop, one can refer to the results in Appendixes Table 5-12. with the following major findings. First,

19 the TFP growth patterns vary obviously across crops. For example, both TE and TC change extremely across regions for wheat during the last period (columns 8-9, Appendix Table 5), while they are fairly consistent across region for corm during the last period (columns 8-9, Appendix Table 6). For indica rice, the TFP growth was mainly driven by technological change for early indica rice (columns 7-9, Appendix Table 7), while the TFP growth was mainly driven by technical efficiency improvement for early indica rice for middle indica rice (columns 7-9, Appendix Table 8). Second, the TFP growth patterns also vary largely across regions. For example, the TFP growth rates are over 3.0% in some provinces (e.g., Guangxi and Hainan), while the TFP growth rates are even -1.5% in other provinces (e.g., Zhejiang and Anhui) (column 7, Appendix Table 9). The same can be found for japonica rice (column 7, Appendix Table 10), for cotton (column 7, Appendix Table 11), and for soybean (column 7, Appendix Table 12). Third, the TFP growth patterns change over time. For example, almost all of the TFP growth rates are larger over 2001-2008 than over 1996-2000 for wheat (column 7, Appendix Table 5). All of the TFP growth rates are positive over 2001-2008, but most of them are negative over 1996-2000 for corn (columns 7 and 4, Appendix Table 6). The same can found for early indica rice (columns 7 and 4, Appendix Table 7). In contrast, the TFP grew faster over 1996-2000 than over 2001-2008 for cotton (columns 4 and 7, Appendix Table 11). After estimated TFP and its decomposition for agricultural and individual crop, some reconciliation and implications that are related to capital investment and formation in agriculture in China may be drawn based on our observations and analyses.  Agricultural TFP growth rates were actually closely correlated to capital investment and formation in agriculture in China. As is found previously, public capital formation in agriculture was actually negative (-1.5% annually) over 1986- 1995 (last column, Table 5), correspondingly, agricultural TFP growth was very slow (0.62%) over the same period (column 3, Table 9). However, as capital stock in agriculture increased (annual 4.5% growth rate) over 1996-2000, as a result, agricultural TFP growth rate rose to 1.88% annually over the same period. The fastest growth (20.8%) of public agricultural capital stock occurred in the New Millennium (2001-2008), agricultural TFP also reached its highest growth (5.34%). This observation may most likely imply that public agricultural capital formation is a forever determinant of agricultural TFP growth in the future.  Although there is no single relationship between private agricultural capital formation and individual crop TFP growth, overall, the positive relation between them are also evidenced in overall agricultural TFP and many agricultural commodities (comparing growth rates in Tables 6 and 9)..  Agricultural capital formation, especially embodied technology, may not only be correlated with technological change, but it also significantly improves technical efficiency. Technical efficiency normally means training and extension, etc. However, ‘training and extension’ cannot significantly improve technical efficiency without arming labor by advanced tools. For example, to save quality seed cost needs refined sower; to save irrigation cost needs refined sprinkler, and to harvest crops needs combine on time, etc. Clearly, these situations are not easily solved even by the most complicated training programs. Therefore, agricultural capital formation has also

20 something to do with the improvement of technical efficiency. The questions left only are what kinds of capital investment are needed. 6.2 The Role of Capital Formation in TFP growth After decomposition of TFP growth for agriculture and individual crop, this sub-section will investigate the determinants of TFP growth for agriculture and individual crop. We will use our modeling to try to find some robust evidence for our findings and implications in the previous section. As previously first present our model and variables, and next empirically estimate the models, and then analyze the results and observe the role of capital stock formation in agriculture on TFP growth for both agriculture and individual crop. Our empirical goals are: i) to model econometrically whether the capital stock formation in agriculture has any significant impact on both aggregate agriculture and individual commodity production; ii) to investigate whether the role of capital stock formation in agriculture changed over time, over crop, and over region; iii) meanwhile, to observe how other production factors affect agricultural TFP growth during the different reform periods in agriculture over both crops and regions. 6.2.1 Model and Variables As modeling capital stock formation in agriculture, this section employs a simple Cobb- Douglas production function to model the determinants of TFP and the impacts of capital stock and other factors on TFP for agriculture. The empirical production function is defined as follows: = β + β + β + β (5) lnTFPit 0 1 ln AgFCSit 2 ln HAgASit 3 ln AgCreditit + β + β + 4 ln AgLaborit 5 ln Fertilizerit vit Where, independent variables were defined as previously. To run about regression, we need to transform the TFP growth series year by year to a normalized index series based × on 1985 by exp(TFPt ) exp(TFPt-1) . Since this sub-section is just to investigate the impact of major production factors on TFP growth and pattern of determinants of TFP growth, therefore, we did not have to incorporate all production factors into our model. Similarly, we also use a panel data framework to regress above Cobb-Douglas production function for both agriculture and individual crop. 6.2.2 Results and Analyses The estimated parameters and their t-statistics are presented in Table 11 for aggregate agriculture, Table 12 for regional agriculture, and Table 13 for national individual crop (wheat, corn, early indica, middle indica, late indica, cotton and soybean, respectively) at regional level. As can be seen, all R2 s are fairly high, meaning goodness of fit is high for all regression results. Many coefficients are significant and their signs are also reasonable. For aggregate agriculture, we choose both public capital stock formation (AgFCS) and private capital stock formation (HAgAS) in agriculture as capital inputs, because they have different sources of agricultural investment and different context of investment (e.g., the former mainly for agricultural irrigation system and the later mainly for agricultural machinery) and therefore represent different investment behavior. Therefore, they are assumed to have different impact on agriculture TFP growth. However, for individual crop

21 TFP growth model, we empirically incorporate only private capital stock formation (HAgAS) into equation (5) to represent capital factor as we do not have data on public capital stock formation for each crop. In fact, we found that some of regression results are not expected if we use aggregate public capital stock formation in individual crop models. For Agriculture National First, we investigate the impacts of capital stock formation in agriculture and other agricultural production factors on aggregate agricultural TFP growth. From Table 11, the following interesting observations can be drawn:  Public capital stock formation (AgFCS) in agriculture has played significant role in agricultural TFP growth. Moreover, it is also observed that public capital formation in agriculture played even more important role in agricultural TFP growth over the late sub-period. For example, the elasticity of AgFCS was 0.1373 over 1985-1995, while it rose to 0.1487 over 1996-2008. This means that a 10% increase in AgFCS could lead a 1.487% increase in agricultural TFP growth over 1996-2008. Namely, the contribution of public agricultural capital formation to agricultural TFP growth has increased by 8.3%.  Private capital formation in agriculture has positive and statistically significant impact on agricultural TFP, particular in the late period (1996-2008). Observing the coefficient of HAgAS found that HAgAS was significant in whole period (1985- 2008) and the later period (1996-2008). This is most likely because private sector is price taker and therefore private capital investment in agriculture is closely correlated with the rate of return to private capital investment (refer to column 5, Table 8). The positive but insignificant impact of private capital formation on agricultural TFP in the first period (1985-1995) may be explained by the following factors. In the early stage, as presented early (Table 8), RoRPI was not significantly correlated with agricultural TFP growth in 1985-1995. As a result, there might not have much incentive for private sector to invest in agricultural capital in this period. However, as market continued to be liberalized and land tenure became more secure over the late sub-period, RoRPI was found to have significant impact on private capital formation in agriculture, which therefore significantly improved agricultural TFP performance recently. In fact, this finding is consistent with our findings presented in Section 5.  The impact of fertilizer input on agricultural TFP growth was positive and statistically significant for whole study period and the late period. This may reflect the quality improvement of chemical and efficiency of fertilizer use have been improved over time despite there was overuse of chemical fertilizer in China (Huang et al., 2008).  The impact of labor on agricultural TFP was surprising negative and statistically significant in the second period (1996-2008). If this is true, it implies that the quality of labor employed in agriculture had fell in the recent years. As TFP has accounted the quantity of inputs (e.g., labor) from its calculation, the remaining part is the quality of labor. So our results may be consistent with recent reports in media and elsewhere that China’s agriculture has be coming “99-38-61” – people working in agriculture are old (99 for old age), women (8th March, Women Day) and kid (1st June, Children Day).

22 For Agriculture Regional In regional analysis, we focus on the impacts of capital formation on agricultural TFP performance for some selected major agricultural production regions or provinces. These include Shandong, Henan, , , Hebei, Heilongjiang and Guangdong. The estimated parameters and t-statistics of Agricultural TFP model are presented in Table 12 for the above seven selected provinces. While the results are generally support the findings from the national study, there are also some new evidences from the regional analyses. It is observed that there are variations in the impact of public capital formation on agricultural TFP over time. This may be caused by variations in public and private investment behaviors, which can be due to the disparity of regional economy development and rural income level. However, except for Jiangsu and Heilongjiang, all show that public capital formation in agriculture has always been having significant impact on the agricutural TFP growth over the whole study period. This finding is basically consistent with that as found for national agriculture. Not all provinces display private capital formation has a significant influence on agricultural TFP performance over the late sub-period. However, they either changed their sign (from negative to positive) of coefficients (e.g., Shandong, Hubei, etc) or increased their elasticities (e.g., Henan). This probably indicates that provincial private capital formation has gradually begun to have a role in agricultural TFP improvement. For Crops National The analysis by crop for China as a whole presented in Table 13 has shown similar results as those for aggregate agriculture. For example, almost for all crops, private capital formation (HAgAS) in agriculture has significant impact on crop TFP growth over both whole period and the late sub-period. Negative impacts of labor input on crop TFP are also evidenced. Comparing the estimated coefficients, we found that almost all of marginal output effects for private capital formation are much larger than those of labor and fertilizer. This most likely suggests that increasing private capital investment in agriculture by rural household sector is more important than labor and fertilizer in this stage of development. For Crops Regional Appendixes Table 13 to 19 present the regression results of individual crop TFP and three major production factor inputs for the selected provinces. It is noticed that the numbers of observations (at most 24) are much smaller at the provincial level than at national level (at least several hundred). This means that the results may not be very stable for the individual provincial analysis. However, this section is only to check whether the evidence found in the national analyses is also confirmed by the analyses at individual provincial level. The important is to compare the possible impact between three major agricultural production factors. Based on Appendixes Tables 13-19, we have the following observations:  More cases where farmer’s capital stock (HAgAS) has significant positive impact on crop TFP growth can be found over the latest sub-period than over the earlier sub- period. As counted, for example, there are four significantly positive cases for wheat

23 over 1996-2008 but only two case over 1985-1996 (Appendix Table 13), three cases for corn over 1996-2008 but only one case over 1985-1996 (Appendix Table 14), three cases for cotton over 1996-2008 but none over 1985-1996 (Appendix Table 15). The same can be said for early indica rice (Appendix Table 16), middle indica (Appendix Table 17), late indica (Appendix Table 18) and soybean (Appendix Table 19).  More cases of significant positive impact on crop TFP growth at provincial level can be found for HAgAS than for labor and fertilizer in the latest sub-period than in the earlier sub-period. As counted, for example, there are four cases for HAgAS but only one and none for labor and fertilizer for wheat TFP growth. The more obvious examples can be found for cotton and early indica, where there are three and six cases for HAgAS respectively but none for labor and fertilizer. The same can be said for other three crops.  However, there are more negative impact cases found over 1996-2008 than over 1985-1995 for labor and fertilizer. As counted, for example, there are three and five negative impact cases over 1996-2008 but none over 1985-1995 for labour for cotton and early indica TFP growth (Appendixes Table 15 and 16). Almost the same scenarios can be found for fertilizer over crops. Overall, combining all above observations, we can conclude that: i) private capital formation in agriculture has more significant positive impact on crop TFP growth in 1996- 2008 than in 1985-1995; ii) private capital formation also has more positive impact on crop TFP growth than labor and fertilizer; and iii) increasing private capital formation in agriculture by rural household sector is likely more important than increasing labor and fertilizer input at present development stage in China.

7. Conclusions and Policy Implications (TBC later). In this study, we first introduce the agricultural role in whole national economy, and then we review the view and strategies toward agricultural and rural development in the modern world. We especially review the policy changes for agricultural and rural development in China. Then we systematically show the economic growth and structure changes for China’s national and agricultural economies. Then,we try to identify the drivers of agricultural productivity growth. In the next three core sections, we first describe and estimate agricultural investment growth and capital stock formation in agriculture by both ‘public’ and ‘private’ sectors in China. We statistically display that both ‘public’ and ‘private’ investment in agriculture have obviously been increasing over time, and compare the development trends between public and private investment. Then we econometrically model the behaviors of public and private capital formation and GDP growth over two separate reform periods. Finally, we empirically decompose the TFP growth for both aggregate agriculture and individual crop and then investigate the impact of capital formation on the TFP growth over time and across province. The major conclusions and policy implications are followed: Firstly, capital investment in agriculture has rising significantly by both government and farmers in the past 2-3 decades, particular in recent years. Capital stock formation in agriculture by public sector increased sharply due to the rising of capital investment since

24 the middle 1990s. In the recent years, China has been in transition from taxing agriculture to invest and subsidize it agriculture. Secondly, as China now is in early stage of agricultural policy transition, political economy would suggest that there will be more public investment in and more subsidies for agriculture in the coming years. It should be noted that while government has significantly increased its capital investment in agriculture, capital investment in agriculture still accounts for very small share in total public capital investment in China. However, given rapid growth of China’s economy, particular in industry and service sectors, agriculture has been gaining increasing attention by policy makers. China now is in the stage that the nation can offer to support its agricultural development with rising fiscal income from non-agricultural sectors. Thirdly, increased public investment in agriculture has also induced farmer’s capital formation in agriculture. This is a win-win scenario on agricultural investment. To raise farmers’ investment in agriculture, public investment is one of necessary conditions (though not sufficient conditions). Fourthly, credit policy, overall growth of farmer’s income, rural wage, and comparative agriculture are important factors that facilitate farmers’ investment in agriculture. Indeed, these are essential conditions for farmers to investment and increase their agricultural capital. Recent rising rural labor and migrant wage suggests that farmers will substitute more labor with capital inputs. Demand for credit will rise. Although we show that agricultural credit has significant and positive effects on farmers’ agricultural capital investment, how to finance millions of small farmers through formal credit/banking system is a challenging task that China should pay much more attention to in the coming years. Keeping agriculture more comparative (or higher return) will lead to more investment by farmers, but more comparative agriculture also needs investment. Fourthly, the accumulated agricultural capital stock has played critical important in agricultural GDP growth in China, particular in recent period. The return to capital investment was much higher and significant in later development periods than that in early stages. There is likely a sequence and logic of large capital investment in China. China seems in right time to transfer is agricultural policy by enhancing its investment. Fifthly, our analyses find that China has a very health TFP growth. Indeed, the growth rate of agricultural TFP has been accelerated in the recent decade. This is evidenced from the analyses at both national and regional levels. Rising TFP growth are also observed in nearly all crops. It is worth to note that, to keep comparative agricultural sector in the economy and in the international market, agricultural TFP has to increase. Sixthly, public agricultural capital formation has played important role in rising China’s agricultural productivity or TFP. Among all factors examined, public investment has showed the most consistent and positive impacts on agricultural TFP in all analyses conducted in this study. Moreover, its marginal impact or the return to public investment has been rising overtime. Seventhly, private (or farmers) capital formation in agriculture has also played important role in rising agricultural TFP. The impacts have increased significantly in recent period (compared with insignificant impacts in early period). Private capital investment also has more positive impact on crop TFP growth than labor and fertilizer.

25 Eighthly, the TFP decomposition analyses also show that technological change is a primary driver of the TFP growth in China’s agriculture. This is a remarkable result as the rest of world has been concerning falling in agricultural R&D and stagnate of agricultural technology changes. Technology change is the other important form of capital formation in agriculture – embodied capital formation through agricultural R&D investment (e.g., modern inputs such modern varieties, improved agricultural chemicals, and etc) and human capital investment (e.g., education and training). On the other hand, this study also finds that there is still some room for China’s agriculture to improve its technical efficiency. Last but not least, labor (rather than human capital) and fertilizers have become less important for agricultural TFP growth in recent years. This provides further evidence of sequence of agricultural policy and capital investment in agriculture. There is also evidence of falling labor “quality” in agricultural sector.

26 References Anderson, K. and Y. Hayami. 1986. The Political Economy of Agricultural Protection: East Asia in International Perspectives. Sydney, London, and Boston, Allen & Unwin. Aigner, D. J., C. A. K. Lovell and P. Schmidt. 1977. Formulation and Estimation of Stochastic Frontier Production Function Models. Journal of Econometrics 6:21-37. Allan Rae, Hengyun Ma, Jikun Huang and Scott Rozelle. 2006. Livestock in China: commodity-specific total factor productivity decomposition using new panel data. American Journal of Agricultural Economics 88(August):680-695. Alston, J., G. Norton and P. Pardey. 1998. Science Under Scarcity. Oxford University Press, Oxford, England. Battese, G. E., and T. J. Coelli. 1993. A Stochastic Frontier Production Function Incorporating a Model for Technical Inefficiency Effects. Working Papers in Econometric and Applied Statistics No 69, Department of Econometrics. University of New England. Armidale. Battese, G. E., and T. J. Coelli.1995. A Model for Technical Inefficiency Effects in a Stochastic Frontier Production Function for Panel Data. Empirical Economics 20:325-332. Battese, G.E., and T.J. Coelli.1995. A Model for Technical Inefficiency Effects in a Stochastic Frontier Production Function for Panel Data. Empirical Economics 20:325-332. Chand, Ramesh and Parmod Kumar. 2004. Determinants of Capital Formation and Agriculture Growth Some New Explorations. Economic and Political Weekly 25(December):5611-.5616. CHEN, Po-Chi, Ming-Miin YU, Ching-Cheng CHANG, Shih-Hsun HSU, 2008. Total factor productivity growth in China's agricultural sector. China Economic Review 19(December): 580-593. Chow, Gregory C. 1993. Capital formation and economic growth in China. The Quarterly Journal of Economics 108(August):809-842. Coelli, T. 1996. A Guide to Frontier Version 4.1: A Computer Program for Stochastic Frontier Production and Cost Function Estimation. CEPA Working Paper 96/07, Department of Econometrics, University of New England, Armidale. Coelli, T., Rao, D., and Battese, E. 1998. An Introduction to Efficiency and Productivity Analysis. Massachusetts: Kluwer Academic. Esarey, A., T.P. Bernstein and Lu, L. 2000. Taxation Without Representation in Contemporary Rural China, Cambridge: Harvard University Press.

27 Fan S. 1991. Effects of Technological Change and Institutional Reform on Production Growth in Chinese Agriculture. American Journal of Agricultural Economics 73, 266-275. Fan S. 1997. Production and Productivity Growth in Chinese Agriculture: New Measurement and Evidence. Food Policy 22 (June): 213-228. Fan, S. and P. Pardey. 1997. Research Productivity and Output Growth in Chinese Agriculture. Journal of Development Economics 53 (June):115-137. Henderson, Daniel J. 2003. The Measurement of Technical Efficiency Using Panel Data. Working paper, Department of Economics, State University of New York at Binghamton. Hu, Baiding and Michael McAleer, 2008. Estimation of Chinese agricultural production efficiencies with panel data. Mathematics and Computers in Simulation 68: 475-484. Huang, J and S. Rozelle. 1996. Technological Change: Rediscovering the Engine of Productivity Growth in China's Agricultural Economy. Journal of Development Economic 49, 337-369. Huang, J. and C. Chen. 1999. Effects of Trade Liberalization on Agriculture in China: Commodity and Local Agricultural Studies. United Nations, ESCAP CGPRT Centre, Bogor, Indonesia. Huang, J., R. Hu, J. Cao, and S. Rozelle, 2008. Training Programs and in –the –field Guidance to Reduce China’s Overuse of Fertilizer Without Hurting Profitability, Journal of Soil and Water Conservation, Vol. 63, No. 5 (2008), pp: 165-167. Huang, J. and S. Rozelle and H. Wang. 2006. Fostering or Stripping Rural China: Modernizing Agriculture and Rural to Urban Capital Flows. Developing Economies 44(1): 1-26. Huang, J. C. Pray, S. Rozelle and Q. Wang. 2002. Plant Biotechnology in China. Science 295(January 25):674-677. Huang, J., H. Ma. 1998. Agricultural fundamental role over two decades of agricultural reform observed from funds flow. Reform 5:56-63 (in Chinese). Huang, J.. Y. Liu, W. Martin, and S. Rozelle. 2009. Changes in Trade and Domestic Distortions Affecting China’s Agriculture. Food Policy 34 (2009): 407-416. Jin, S., J. Huang, R. Hu, and S. Rozelle. 2002. The Creation and Spread of Technology and Total Factor Productivity in China’s Agriculture. American Journal of Agricultural Economics 84(November): 916-939. Jin, S., H. Ma, J. Huang, R. Hu and S. Rozelle. 2009. Productivity, efficiency and technical change: measuring the performance of China’s transforming agriculture, Journal of Productivity Analysis, No. 8 (2009), pp: 1-17.

28 Johnston, B. 1970. Agriculture and Structural Transformation in Developing Countries: A Survey of Research. Journal of Economic Literature 8: 101-145. Johnston, B. F. and Mellor, J. W. 1961. The Role of Agriculture in Economic Development. American Economic Review 51(September):566-593. Kumbhakar, S. C., and C. A. K. Lovell. 2000. Stochastic Frontier Analysis. Cambridge: Cambridge University Press. Kumbhakar, S. C., Heshmati, A., and Hjamarsson, L. 1999. Parametric Approaches to Productivity Measurement: A Comparison Among Alternative Models. Scandinavian Journal of Econometrics 101:404-424. Lardy, N. R. 1983. Agriculture in China's Modern Economic Development. Cambridge: Cambridge University Press. Lewis, W. Arthur. 1954. Economic Development with Unlimited Supplies of Labor. The Manchester School 22 (May):139-91. Li, K. W. 2003. China’s capital and productivity measurement using financial resources. Economic Growth Center, Yale University. Lin, J. 1991. Prohibitions of Factor Market Exchanges and Technological Choice in Chinese Agriculture. Journal of Development Studies 27(4):1-15. Lin, J. 1992. Rural Reforms and Agricultural Growth in China. American Economic Review 82, 34-51. Lindert, P. H. 1991. Economic Perspectives on the Historical Evolution of Agricultural Policies, in Agriculture and the State: Employment and Poverty in Developing Countries, ed. C Peter Timmer, Ithaca, N. Y., and London: Cornell University Press. Liu, C., Zhang, L., Luo, R. and Rozelle, S. 2009. Infrastructure Investment in Rural China: Is Quality Being Compromised during Quantity Expansion? The China Journal 61(January):105-130. Luo, R., Zhang, L., Huang, J., Rozelle, S. 2007. Election, Fiscal Reform and Public Goods Provision in Rural China. Journal of Comparative Economics 35(3):583-611. McMillan J, Walley J, Zhu L. 1989. The Impact of China’s Economic Reforms on Agricultural Productivity Growth. Journal of Political Economy 97, 781-807. Meeusen, W., and J. van den Broeck. 1977. Efficiency Estimation from Cobb- Douglas Production Functions with Composed Error. International Economic Review 18: 435-44. MOF [Ministry of Finance]: 2005-2008, various reports, The website of Ministry of Finance, China. http://www.gov.cn/jrzg/2008-11/13/content_1148414.htm

29 NBSC (National Statistical Bureau of China). China Rural Economy Statistical Yearbook, various issues from 1982 to 2007. Beijing (China): State Statistical Press. NBSC (National Statistical Bureau of China). Statistical Yearbook of China, various issues from 1981 to 2007. Beijing (China): China Statistical Press. Nyberg, A. and S. Rozelle. 1999. Accelerating China's Rural Transformation. The World Bank, Washington DC. Riskin, C. and A. Khan. 2001. Inequality and Poverty in China in the Age of Globalization New York, NY: Oxford University Press. Rozelle S. and J. Swinnen. 2004. Success and Failure of Reform: Insights from the Transition of Agriculture. Journal of Economic Literature XLII (June): 404-456. Rozelle, S. 1996. Stagnation Without Equity: Changing Patterns of Income and Inequality in China’s Post-Reform Rural Economy. The China Journal 35 (January): 63-96. Rozelle, S. G. Li, M. Shen, A. Hughart and J. Giles. 1999. Leaving China’s Farms: Survey Results of new Paths and Remaining Hurdles to Rural Migration. China Quarterly 158 (June):367-393. Schmidt, P., and R.C. Sickles. 1984. Production Frontiers and Panel Data. Journal of Business and Economic Statistics 2:367-74. Tao, R., Lin, Y., Liu, M. and Zhang, Q. 2004. Rural Taxation and Government Regulation in Chin. Agricultural Economics 31(2-3):161-168. Tian, W., and G. Wan. 2000. Technical Efficiency and its Determinants in China’s Grain Production. Journal of Productivity Analysis 13:159-74. Timmer, P. 1998. The Agricultural Transformation, Chapter 7 (pp. 113-135) in Carl Eicher and John Staatz (eds.) International Agricultural Development, Third Edition The John Hopkins University Press: Baltimore, MD. Wang, J. 2000. Property Right Innovation, Technical Efficiency and Groundwater Management: Case Study of Groundwater Irrigation System in Hebei, China, 2000, Ph.D. Thesis, Chinese Academy of Agricultural Sciences Wang, J. Z. Xu, J. Huang and S. Rozelle. 2005b. Incentives to Water Management Reform: Assessing the Effect on Water Use, Productivity and Poverty in the Basin. Environment and Development Economics. 10, 769-799. Wang, J., J. Huang and S. Rozelle. 2005a. Evolution of Tubewell Ownership and Production in the North China Plain. Australian Journal of Agricultural and Resource Economics 49 (June):177-195. Wen, G. 1993. Total Factor Productivity Change in China’s Farming Sector: 1952- 1989. Economic Development and Cultural Change 42, 1-41.

30 Table 1. The annual growth rates (%) of China’s economy, 1979-2005.

Reform periods 1979-84 1985-95 1996-2000 2001-2005

Gross domestic products 8.8 9.7 8.2 9.6

Agriculture 7.1 4.0 3.4 3.9 Industry 8.2 12.8 9.6 10.7 Service 11.6 9.7 8.2 10.2

Foreign Trade 14.3 15.2 9.8 25.0

Import 12.7 13.4 9.5 25.5 Export 15.9 17.2 10.0 24.6

Rural enterprises output 12.3 24.1 14.0 NA

Population 1.40 1.37 0.91 0.63

Per capita GDP 7.1 8.3 7.2 9.0

Note: Figure for GDP in 1970-78 is the growth rate of national income in real term. Growth rates are computed using regression method.

Source: NSBC, Statistical Yearbook of China, various issues.

31 Table 2. Changing structure of China’s national economy, 1980-2008

Year Industrial shares in GDP Industrial shares in employment

Primary Secondary Tertiary Primary Secondary Tertiary industry industry industry industry industry industry

1970 40.0 46.0 13.0 81.0 10.0 9.0 1980 30.2 48.2 21.6 69.0 18.0 13.0 1985 28.4 42.9 28.7 62.0 21.0 17.0 1990 27.1 41.3 31.5 60.2 21.4 18.3 1991 24.5 41.8 33.7 60.0 21.4 18.6 1992 21.8 43.5 34.8 58.7 21.7 19.6 1993 19.7 46.6 33.7 56.4 20.9 22.7 1994 19.9 46.6 33.6 54.3 22.7 23.0 1995 20.0 47.2 32.9 52.9 22.9 24.1 1996 19.7 47.5 32.8 50.5 23.5 26.0 1997 18.3 47.5 34.2 49.9 23.7 26.4 1998 17.6 46.2 36.2 49.8 23.5 26.7 1999 16.5 45.8 37.8 50.1 23.0 26.9 2000 15.1 45.9 39.0 50.0 22.5 27.5 2001 14.4 45.2 40.5 50.0 22.3 27.7 2002 13.7 44.8 41.5 50.0 21.4 28.6 2003 12.8 46.0 41.2 49.1 21.6 29.3 2004 13.4 46.2 40.4 46.9 22.5 30.6 2005 12.2 47.7 40.1 44.8 23.8 31.3 2006 11.3 48.7 40.0 42.8 25.3 31.9 2007 11.1 48.5 40.4 40.8 26.8 32.4 2008 11.3 48.6 40.1 39.6 27.2 33.2

Source: China Statistical Yearbook, 1981-2009.

32 Table 3. The rapid development of China’s agricultural economy by commodity, 1970-2005. Commodity Pre-reform Reform periods

1970-78 1978-84 1985-95 1996-2000 2001-2005

Agricultural Gross 2.7 7.1 4.0 3.4 3.9 Domestic Product

Grain total Production 2.8 4.7 1.7 0.03 1.1 Sown area 0.0 -1.1 -0.1 -0.14 -0.7 Yield 2.8 5.8 1.8 0.17 1.8 Rice Production 2.5 4.5 0.6 0.4 -0.8 Sown area 0.7 -0.6 -0.6 -0.5 -0.8 Yield 1.8 5.1 1.2 0.8 0.0 Wheat Production 7.0 8.3 1.9 -0.6 -0.4 Sown area 1.7 -0.0 0.1 -1.6 -3.1 Yield 5.2 8.3 1.8 1.0 2.7 Maize Production 7.4 3.7 4.7 -1.3 5.6 Sown area 3.1 -1.6 1.7 0.8 2.7 Yield 4.2 5.4 2.9 -0.9 2.9

Total cash crop area 2.4 5.1 2.1 3.5 1.5

Cotton Production -0.4 19.3 -0.3 -1.9 6.5 Sown area -0.2 6.7 -0.3 -6.1 5.3 Yield -0.2 11.6 -0.0 4.3 1.2

Edible oil crops 2.1 14.9 4.4 5.6 0.8

Vegetable area 2.4 5.4 6.8 9.5 3.1 Fruit Orchards area 8.1 4.5 10.4 2.0 2.4 Outputs 6.6 7.2 12.7 10.2 21.0

Meat 4.4 9.1 8.8 6.5 4.9 (pork/beef/poultry) Fishery 5.0 7.9 13.7 10.2 3.6

Note: The numbers in the table are annual growth rates computed using regression method. Growth rates of individual and groups of commodities are calculated based on production data, while sectoral growth rates are calculated based value added in real terms. Sources: NSBC, 1980-2007 and MAO, 1980-2007.

33 Table 4. Changing structure of rural per capta net income in China’s agricultural economy over time

Year Total income Of which (%) per capita Farming Animal Forestry Fishery Husbandry

1980 667.9 - - - - 1985 1257.4 48.15 1.55 11.16 0.90 1990 1338.6 48.10 1.10 12.54 1.04 1993 1464.3 47.58 1.37 9.16 0.99 1994 1563.2 48.36 1.06 8.00 0.90 1995 1724.9 49.13 0.86 7.08 0.99 1996 1944.3 47.99 0.84 7.32 0.91 1997 1549.4 59.77 1.23 11.71 1.33 1998 2140.2 42.89 0.86 7.76 1.04 1999 2219.2 39.91 0.98 7.14 1.12 2000 2253.4 37.01 1.00 9.20 1.20 2001 2350.0 36.50 0.93 8.96 1.22 2002 2478.4 35.01 1.03 8.51 1.30 2003 2594.0 33.78 1.12 9.37 1.33 2004 2796.3 35.98 1.16 9.23 1.24 2005 3044.6 33.72 1.41 8.71 1.31 2006 3306.6 32.33 1.53 7.40 1.15 2007 3642.8 31.49 1.43 8.09 1.14 2008 3956.3 29.97 1.39 8.35 1.16

Annual growth rates (%) 1980-1985 13.49 54.24 - 11.53 - 1986-1995 3.21 15.01 8.18 9.68 15.89 1996-2000 3.76 -2.54 8.60 10.12 11.40 2001-2008 7.73 7.44 16.96 9.40 9.70

Source: China Statistical Yearbook, 1981-2009. Income per capita is measured at 2000 price.

34 Table 5. Capital construction investment and fixed capital formation (billion yuan), 1985-2008

Year Industrial capital Agricultural capital Agricultural fixed construction investment construction investment capital formation

1985 - - 408.4 1986 53.2 10.1 398.1 1987 68.3 11.4 389.6 1988 81.3 10.9 380.9 1989 82.3 9.9 371.8 1990 95.3 13.0 366.2 1991 114.7 14.3 362.2 1992 145.8 15.3 359.4 1993 200.5 15.1 356.5 1994 276.2 13.7 352.4 1995 323.6 12.0 346.8 1996 372.7 14.3 343.7 1997 412.0 15.7 342.2 1998 416.9 45.6 370.7 1999 385.0 35.8 388.0 2000 424.4 41.5 410.1 2001 435.0 47.8 437.3 2002 523.6 42.4 457.9 2003 749.8 52.2 487.1 2004 2777.7 180.1 642.8 2005 3771.8 217.4 828.0 2006 4735.4 253.5 1040.1 2007 5985.2 299.5 1287.6 2008 7540.5 420.9 1644.1 Annual growth rate (%) 1986-1995 22.2 2.0 -1.5 1996-2000 3.3 30.5 4.5 2001-2008 50.3 36.5 20.8

Note: All values ware measured at 2000 price. Chow (1993) estimated agricultural fixed capital stock in 1985 to be 129.16 at 1985 price, which was deflated to become 408.4 at 2000 price, and then this number was distributed across provinces according to the shares of provincial total capital stock in 1985 estimated by Li (2003). After 1985, provincial agricultural capital stock series are constructed by previous year stock depreciated at the 5% annually plus current year agricultural capital construction investment deflated by 2000 price. Exchange rate in 2008 was: 1 US$ = 6.95 yuan (RMB) Source: China Statistical Yearbook, various issues.

35 Table 6. Average rural household’s agricultural productive fixed assets and machinery and annual growth rate, 1985-2008.

Year Agri productive fixed Agricultural Machinery Large-medium tractor assets (yuan/household) (kw/household) (per 100 household)

1985 1908.1 1.10 0.45 1986 1900.6 1.17 0.44 1987 1897.5 1.23 0.44 1988 1785.7 1.27 0.42 1989 1594.3 1.31 0.39 1990 1753.3 1.29 0.37 1991 2160.9 1.30 0.35 1992 2207.7 1.33 0.33 1993 2396.7 1.38 0.31 1994 2340.6 1.46 0.30 1995 2393.2 1.55 0.29 1996 2799.8 1.64 0.29 1997 2865.2 1.80 0.29 1998 2988.4 1.91 0.31 1999 3083.3 2.06 0.33 2000 3321.7 2.18 0.34 2001 3519.5 2.26 0.45 2002 3745.1 2.36 0.37 2003 4108.0 2.42 0.39 2004 4244.5 2.56 0.45 2005 4844.7 2.71 0.55 2006 5026.0 2.87 0.68 2007 5284.4 3.01 0.81 2008 5433.2 3.20 1.17

Annual growth rate (%) 1985-1995 2.29 3.49 -4.30 1996-2000 4.36 7.37 4.06 2001-2008 6.40 5.09 14.63

Note: Agricultural fixed assets was measured at 2000 price. Source: China Statistical Yearbook, 1986-2009.

36 Table 7. Modeling the behavior of public and private capital formation in agriculture and GDP agriculture over whole study period (1985-2008)

Variables AgFCS HAgAS AgGDP

(1) (2) (3) AgFCS(1) 1.0734*** 0.0426 0.1692*** (116.21) (1.55) (7.99) AgLabor - - 0.1473 - - (1.68) CropSown - - 0.4142*** - - (4.09) Rainfall - - -0.0055 - - (-0.13) AgGDPR(1) -0.1329*** - -0.3115*** (-11.61) - (-9.47) AgGDP(1) 0.0935*** - - (7.34) - - HAgAS(1) - 0.6475*** 0.3799*** - (11.38) (14.54) AgCredit - 0.0505*** - - (2.66) - Income(1) - 0.2897*** - - (5.13) - RoRPI(1) - 0.2634*** - - (5.48) -

Obs 678 678 678 R2 0.9934 0.9604 0.9745 Chi2 101286 16610 25992 # of parameters 34 36 37

Note: Figures in parentheses are calculated t-values. Provincial dummy variables are included but not reported. ***, ** and * stand for 1%, 5% and 10% significant level, respectively.

37 Table 8. Modeling the behavior of public and private capital formation in agriculture and GDP agriculture over two reform periods 1985-1995 1996-2008 AgFCS HAgAS AgGDP AgFCS HAgAS AgGDP (1) (2) (3) (4) (5) (6) AgFCS(1) 0.9358*** 0.1074 -0.0120 0.9976*** 0.1133*** 0.2680*** (75.56) (1.17) (-0.07) (49.82) (3.13) (17.85) AgLabor - 0.4596* - - -0.0152 - (1.66) - - (-0.22) CropSown - 0.8863** - - -0.0708 - (2.07) - - (-1.00) Rainfall - -0.0133 - - -0.0064 - (-0.18) - - (-0.26) AgGDPR(1) 0.0489*** - -0.1295* -0.2455*** - -0.1910*** (8.37) - (-1.68) (-8.97) - (-6.11) AgGDP(1) -0.0195*** - - 0.2166*** - - (-5.03) - - (4.37) - - HAgAS(1) - -0.4844** 0.4731*** - 0.5107*** 0.1659*** - (-2.16) (10.24) - (4.66) (7.24) AgCredit - 0.0488 - - 0.0507*** - - (0.91) - - (2.75) - Income(1) - 0.4213*** - - 0.1574 - - (3.71) - - (2.24)** - RoRPI(1) - 0.0678 - - 0.0863 - - (1.02) - - (1.78)* -

Obs 280 280 280 398 398 398 R2 0.9998 0.9464 0.9685 0.9917 0.9799 0.9946 Chi2 1140000 4944 8910 48196 19482 73169 # of para. 32 34 35 34 36 37

Note: Figures in parenthese are calculated t-values. Provincial dummy variables are included but not reported n the table. ***, ** and * stand for 1%, 5% and 10% significant level, respectively.

38 Table 9. Decomposition of annual growth of total factor productivity for China’s agriculture and major crops over three periods (%)

Crop Decomposition 1985-1995 1996-2000 2001-2008

AgGDP TFP 0.616 1.883 5.339 TE 0.011 -1.170 0.315 TC 0.605 3.089 5.008

Wheat TFP 1.037 -0.481 2.316 TE 0.663 -0.372 1.215 TC 0.372 -0.109 1.087 Corn TFP 1.400 -1.620 3.353 TE 2.948 -1.499 1.959 TC -1.503 -0.122 1.367 Early indica TFP -0.168 1.320 3.012 TE 0.000 0.002 0.002 TC -0.168 1.318 3.010 Middle indica TFP 1.011 -0.228 -0.069 TE 0.867 0.875 0.939 TC 0.143 -1.094 -0.998 Late indica TFP -0.877 -0.433 1.354 TE 0.397 -0.072 0.158 TC -1.269 -0.361 1.194 Japonica TFP 2.700 1.403 1.603 TE 0.050 -0.334 0.513 TC 2.649 1.742 1.084 Cotton TFP 0.228 3.443 1.491 TE -1.166 2.114 0.358 TC 1.411 1.301 1.129 Soybean TFP 1.036 4.661 4.703 TE -0.669 0.807 0.854 TC 1.717 3.823 3.817

Note: AgGDP is measured at 2000 price. Total factor productivity (TFP) is simply decomposed into technical efficiency (TE) and technological change (TC), namely, TFP=TE+TC, which are estimated using a stochastic production frontier function.

39 Table 10 (to be continued). Decomposition of annual growth of total factor productivity (TFP) for agriculture across province over three reform periods (%)

Province 1985-1995 1996-2000 2001-2008 TFP TE TC TFP TE TC TFP TE TC (1) (2) (3) (4) (5) (6) (7) (8) (9) Beijing -1.499 0.078 -1.575 1.576 -0.120 1.697 4.705 0.025 4.679 -2.830 -0.622 -2.222 -0.455 -1.364 0.921 3.161 -0.626 3.810 Hebei -0.403 0.815 -1.208 -2.353 -2.839 0.499 4.971 2.342 2.568 -1.458 -0.904 -0.559 -2.071 -4.094 2.109 1.855 -2.194 4.139 Mongolia 0.229 0.363 -0.134 2.115 -0.631 2.763 4.546 -0.162 4.715 Liaoning 1.062 1.169 -0.105 2.680 -0.356 3.047 5.373 0.380 4.974 Jilin 2.457 1.250 1.192 3.658 -0.157 3.821 5.678 0.091 5.582 Heilongjian -0.073 0.301 -0.373 0.524 -2.445 3.043 6.547 1.445 5.029 g Shanghai 0.432 0.212 0.219 3.650 -0.785 4.471 8.223 0.208 7.999 Jiangsu -0.063 -0.014 -0.049 2.670 -0.143 2.817 5.079 0.123 4.949 Zhejiang 0.069 0.017 0.051 2.106 -0.035 2.142 4.173 -0.024 4.197 Anhui -0.313 -1.139 0.836 0.257 -2.609 2.943 4.930 0.260 4.658 2.799 1.651 1.129 3.951 -0.128 4.084 6.245 -0.103 6.354 1.450 -0.257 1.711 5.019 0.326 4.677 5.729 0.191 5.528 Shandong -0.956 -0.279 -0.679 -1.907 -3.427 1.573 4.780 1.654 3.075 Henan -0.334 -0.483 0.150 0.196 -1.875 2.111 3.876 -0.101 3.982 Table 10 (continued). Decomposition of annual growth of total factor productivity (TFP) for Agriculture across province over three reform periods (%)

Province 1985-1995 1996-2000 2001-2008 TFP TE TC TFP TE TC TFP TE TC (1) (2) (3) (4) (5) (6) (7) (8) (9) Hubei 0.860 -0.189 1.051 3.222 -0.986 4.250 6.605 0.522 6.052 0.930 -0.033 0.963 3.225 -0.185 3.416 5.149 0.104 5.040 Guangdong 0.586 0.035 0.551 3.251 -0.073 3.326 5.926 -0.093 6.025 Guangxi 2.638 1.255 1.366 2.240 -1.650 3.955 6.618 0.538 6.047 Hainan 2.459 -0.012 2.472 4.862 -0.003 4.865 7.351 -0.061 7.417 - - - 3.263 -2.034 5.407 8.041 1.035 6.935 Sichuan 1.806 -0.067 1.875 4.572 0.112 4.456 6.486 0.147 6.330 2.469 -0.640 3.129 0.696 -4.561 5.508 8.336 1.462 6.775 Yunnan 0.660 -0.768 1.440 3.962 0.110 3.848 5.980 -0.156 6.145 -0.115 -1.731 1.644 5.966 1.721 4.173 2.449 -2.290 4.850 -1.247 -2.400 1.181 2.160 -1.955 4.197 8.506 2.241 6.128 -1.910 -2.498 0.602 5.995 2.613 3.296 6.180 0.876 5.257 1.058 0.223 0.834 -2.589 -5.517 3.099 8.866 3.705 4.977 Ningxia 0.546 -0.262 0.810 0.475 -2.899 3.474 6.740 1.447 5.218 Xinjiang 0.566 0.305 0.260 2.002 -1.223 3.264 5.052 -0.539 5.621

41 Table 11. Determinants of Agriculture TFP and the impact of capital formation on TFP.

Variables Coefficients t-statistic

1985-2008: AgFCS 0.1962*** 17.06 HAgAS 0.0564*** 3.38 AgLabor -0.4567*** -10.56 Fertilizer 0.1450*** 7.48 R2 0.7488 - Obs 753 -

1985-1995 AgFCS 0.1373** 2.49 HAgAS 0.0154 0.99 AgLabor -0.0437 -0.62 Fertilizer 0.0005 0.03 R2 0.5139 - Obs 383 -

1996-2008 AgFCS 0.1487*** 9.96 HAgAS 0.1103*** 5.02 AgLabor -0.5850*** -9.28 Fertilizer 0.1512** 3.03 R2 0.8982 - Obs 415 -

Note: AgGDP and AgFCS were measured (100 million yuan) at 2000 price. HAgAS was aggregate (10000 yuan) at 2000 price. AgLabor was aggregate manday (10000). Fertilizer was aggregate (10000 ton). Provincial dummy variables were included bu not reported in the table. ***, ** and * stand for 1%, 5% and 10% significant level, respectively. Table 12. Determinants of agriculture TFP and the impact of capital formation on TFP in the selected major agricultural provinces Variables Shandong Henan Hubei Jiangsu Hebei Heilongjiang Guangdong Coef. t Coef. t Coef. t Coef. t Coef. t Coef. t Coef. t 1985- 2008: AgFCS 0.139 5.06 0.113 3.52 0.217 7.28 -0.046 -0.69 0.155 7.54 0.238 4.35 0.280 3.22 HAgAS -0.119 -2.05 0.363 4.62 0.106 1.74 0.002 0.05 -0.056 -0.82 0.099 1.04 0.338 2.42 AgLabor -0.257 -1.00 -0.407 -1.74 -0.943 -11.79 -0.884 -10.17 -0.316 -1.43 0.177 1.88 1.144 2.49 Fertilizer 0.079 0.90 -0.241 -3.06 0.110 3.17 0.001 0.01 0.025 0.26 -0.065 -0.86 0.717 3.60 R2 0.868 - 0.886 - 0.993 - 0.971 - 0.913 - 0.925 - 0.866 - Obs 24 - 24 - 24 - 24 - 24 - 24 - 24 - 1985-1995 AgFCS 0.170 0.33 -0.567 -1.18 -0.585 -1.28 -0.974 -1.25 1.703 2.15 3.264 2.39 -0.341 -1.12 HAgAS -0.160 -1.75 0.168 1.22 -0.006 -0.13 -0.035 -1.39 -0.142 -0.70 -0.198 -1.15 0.009 0.29 AgLabor -0.340 -0.81 -0.856 -1.27 -0.289 -1.24 -0.155 -0.52 -0.428 -0.90 1.181 1.33 -0.179 -1.18 Fertilizer 0.101 0.48 -0.172 -0.89 0.067 1.58 -0.621 -1.32 0.572 1.79 -0.105 -0.79 -0.032 -0.64 R2 0.712 - 0.845 - 0.915 - 0.622 - 0.665 - 0.564 - 0.775 - Obs 11 - 11 - 11 - 11 - 11 - 11 - 11 - 1996-2008 AgFCS 0.208 11.71 0.251 4.12 0.157 2.25 0.117 3.12 0.240 3.51 -0.015 -0.14 0.297 4.56 HAgAS 0.075 1.60 0.250 1.21 0.192 1.34 0.189 3.55 -0.245 -1.12 0.309 5.02 0.313 2.44 AgLabor 0.031 0.15 0.159 0.58 -1.048 -4.44 -0.459 -6.15 1.177 1.36 -0.056 -0.70 2.288 3.95 Fertilizer -1.054 -5.71 -0.668 -1.61 0.136 0.56 0.748 4.12 0.428 0.41 0.467 1.17 0.522 2.08 R2 0.989 - 0.950 - 0.988 - 0.996 - 0.980 - 0.988 - 0.969 - Obs 13 - 13 - 13 - 13 - 13 - 13 - 13 -

43 Note: see Table 10. The provinces selected in this table are either they have a large agricultural GDP share or are major crop producing area.

44 Table 13. Determinants of Crop TFP and the impact of capital formation on TFP.

Variables Wheat Corn Early Indica Middle Indica Late Indica Japonica Rice Cotton Soybean Coef. t Coef. t Coef. t Coef. t Coef. t Coef. t Coef. t Coef. t 1985- 2008: HAgAS 0.093 7.86 0.060 4.71 0.031 1.51 0.069 4.74 0.136 3.85 -0.104 -3.69 0.269 10.63 -0.104 -3.71 AgLabor - -4.01 - -4.16 - -2.01 0.030 1.75 0.117 3.53 -0.158 -4.20 -0.222 -5.68 -0.160 -4.27 0.060 0.084 0.043 Fertilizer 0.006 0.69 0.003 0.28 0.053 4.71 - -5.86 0.094 4.98 0.055 3.10 0.005 0.26 0.053 3.02 0.045 R2 0.710 - 0.620 - 0.590 - 0.683 - 0.543 - 0.328 - 0.692 - 0.334 - Obs 566 - 530 - 242 - 242 - 216 - 390 - 287 - 390 0.00 1985-1995 HAgAS 0.025 1.54 0.011 0.41 - -0.67 0.020 0.94 0.045 0.75 0.048 1.19 0.094 1.62 0.047 1.18 0.008 AgLabor - -2.12 - -0.67 0.119 4.04 - -1.55 0.232 2.30 -0.310 -5.24 0.158 1.76 -0.327 -5.69 0.062 0.032 0.075 Fertilizer 0.004 0.46 - -2.23 0.030 4.83 - -4.72 0.061 2.83 0.078 2.62 0.028 1.24 0.066 2.29 0.026 0.041 R2 0.620 - 0.460 - 0.600 - 0.464 - 0.521 - 0.655 - 0.443 - 0.672 - Obs 272 - 232 - 113 - 102 - 87 - 184 - 181 - 184 - 1996-2008 HAgAS 0.078 3.47 0.175 8.65 0.064 2.43 0.064 4.40 0.204 4.14 -0.267 -4.50 0.153 3.92 -0.267 -4.50 AgLabor - -3.59 - -1.20 - -5.39 0.044 3.15 0.079 1.69 -0.093 -1.82 -0.181 -4.03 -0.093 -1.82

45 0.089 0.034 0.139 Fertilizer - -1.09 0.050 1.79 - -1.63 - -2.03 -0.045 -0.53 0.060 2.89 0.196 4.03 0.060 2.89 0.016 0.073 0.027 R2 0.860 - 0.820 - 0.880 - 0.932 - 0.671 - 0.490 - 0.798 - 0.490 - Obs 294 - 298 - 129 - 140 - 129 - 206 - 206 - 206 - Note: see Table 10.

46 2000

1800 405 Agri capital formation Agri capital investment 1600 355

1400 305

1200 255

1000 205

800 155

600 105

400 55

200 5 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008

Figure 1. Aggregate capital construction investment and capital stock formation in agriculture by public sector (billion yuan), 1985-2008

47 6000 3.5

5500 3.0 5000

4500 2.5

4000 2.0 Household Ag Machinery 3500 1.5 3000

2500 Household Ag Fixed 1.0 Assets 2000 0.5 1500

1000 0.0 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007

Figure 2. Per rural household aggregate agricultural fixed assets (yuan) and agricultural machinery (KW), 1985-2008

48 2250

2100

1950

1800

1650

1500 Total Private Public 1350

1200

1050

900

750

600

450

300

150 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007

Figure 3. Aggregate capital stock, and private and public capital stock in agriculture (billion yuan), 1985-2008

49 Appendix Table 1. Share (%) of provincial production in China, 1980-2008

Province Wheat production Rice production Maize production Canola production

1980 2008 1980 2008 1980 2008 1980 2008

Beijing 0.73 0.29 0.21 0.00 1.42 0.53 1.14 0.48 Tianjin 0.50 0.47 0.22 0.05 0.92 0.51 0.53 0.51 Hebei 6.96 10.87 0.59 0.29 10.59 8.69 3.56 5.32 Shanxi 2.15 2.25 0.05 0.00 4.20 4.12 1.43 0.98 Inner Mongolia 1.49 1.37 0.03 0.37 2.22 8.50 1.02 1.40 Liaoning 0.10 0.04 1.68 2.63 10.44 7.17 3.73 4.54 Jilin 0.30 0.02 0.77 3.02 8.10 12.55 2.08 2.26 Heilongjiang 7.15 0.80 0.57 7.91 8.31 10.98 3.07 2.09 Shanghai 0.37 0.16 0.83 0.47 0.06 0.01 1.45 0.38 Jiangsu 10.22 8.88 8.78 9.23 2.22 1.22 9.15 4.22 Zhejiang 1.44 0.19 8.41 3.44 0.25 0.07 6.07 2.75 Anhui 6.17 10.38 5.53 7.21 0.62 1.73 4.36 4.70 Fujian 0.41 0.01 4.80 2.65 0.00 0.08 2.09 2.96 Jiangxi 0.16 0.02 8.49 9.70 0.02 0.04 3.19 4.29 Shandong 13.88 18.09 0.53 0.58 13.19 11.38 7.60 6.95 Henan 16.13 27.13 1.27 2.31 8.51 9.73 4.36 7.95 Hubei 4.83 2.93 7.42 7.99 1.37 1.36 4.77 5.64 Hunan 0.44 0.03 13.88 13.17 0.34 0.77 8.06 8.01 Guangdong 0.43 0.00 11.60 5.23 0.10 0.38 3.50 5.50 Guangxi 0.05 0.00 7.20 5.77 1.77 1.25 5.89 4.73 Hainan 0.00 0.00 0.00 0.75 0.00 0.04 0.61 0.80 Chongqing 0.00 0.52 0.00 2.76 0.00 1.48 2.13 3.04 Sichuan 9.43 3.79 11.07 7.80 9.78 3.84 14.49 9.44 Guizhou 0.63 0.38 2.32 2.40 3.38 2.36 2.25 2.91 Yunnan 1.42 0.74 2.77 3.24 4.20 3.19 2.57 4.75 Tibet 0.33 0.23 0.00 0.00 0.01 0.01 0.02 0.03 Shaanxi 4.17 3.48 0.54 0.43 4.39 2.91 1.97 1.59 Gansu 4.35 2.38 0.01 0.02 1.42 1.60 1.08 0.93 Qinghai 1.02 0.37 0.00 0.00 0.00 0.01 0.18 0.19 Ningxia 0.90 0.57 0.24 0.35 0.14 0.90 0.13 0.18 Xinjiang 3.86 3.61 0.18 0.21 2.02 2.56 0.29 0.48 Source: China Statistical Yearbook, 1979-2009.

50 Appendix Table 2. Fixed capital construction investment and fixed capital stock formation in agriculture by public sector cross provinces.

Province Agri fixed capital construction Agri fixed capital stock formation investment (billion yuan) (billion yuan) 1986 2008 Annual 1986 2008 Annual growth % growth % Beijing 0.08 2.81 17.9 16.52 14.54 -1.1 Tianjin 0.04 3.73 23.2 10.80 12.05 0.9 Hebei 0.05 38.67 34.8 11.91 119.47 21.2 Shanxi 0.08 9.54 24.1 10.85 29.77 8.8 Inner Mongolia 0.06 28.82 32.2 6.14 80.97 24.0 Liaoning 0.12 32.51 28.8 56.65 98.67 4.7 Jilin 0.07 20.37 29.1 8.28 56.16 17.3 Heilongjiang 0.19 30.89 25.9 14.49 95.04 17.0 Shanghai 0.07 0.84 12.1 28.74 15.47 -5.0 Jiangsu 0.08 11.18 25.5 33.05 43.72 2.4 Zhejiang 0.10 7.32 21.7 18.42 38.63 6.4 Anhui 0.18 18.26 23.4 11.48 49.30 12.9 Fujian 0.09 10.26 24.3 7.12 34.34 14.0 Jiangxi 0.07 13.48 26.9 8.19 40.19 14.2 Shandong 0.16 50.60 30.1 24.83 147.49 16.0 Henan 0.19 53.76 29.2 18.32 118.34 16.8 Hubei 0.25 20.65 22.3 15.16 57.84 11.8 Hunan 0.09 15.28 26.2 10.08 47.50 13.8 Guangdong 0.49 13.19 16.2 22.29 48.94 6.8 Guangxi 0.12 16.38 25.3 5.97 44.07 18.1 Hainan 0.00 2.17 - 2.19 18.55 19.5 Chongqing 0.01 11.35 38.0 0.03 29.86 78.6 Sichuan 0.15 26.84 26.7 16.17 71.60 13.2 Guizhou 0.04 5.09 25.2 4.54 15.39 10.7 Yunnan 0.10 16.62 26.0 6.12 45.10 18.1 Tibet 0.01 1.60 28.9 1.55 7.52 14.1 Shaanxi 0.06 13.18 27.6 11.29 41.93 11.6 Gansu 0.19 8.42 18.9 6.25 30.31 14.1 Qinghai 0.04 3.15 22.5 2.71 12.40 13.5 Ningxia 0.05 4.38 22.1 2.44 14.45 16.0 Xinjiang 0.18 15.11 22.2 6.26 75.02 23.0

Note: Agricultural capital construction investment (called agricultural fixed assets investment after 2003) and stock formation are measured in billion RMB and deflated by 2000 price.

51 Appendix Table 3. Agricultural productive fixed assets per rural household (yuan at 1985 real price) at yearend and annual growth rates (%) cross province and period

Province 1985 1995 2008 Annual growth rate (%)

1985-1995 1996-2008

Beijing 847.9 1581.0 3112.3 6.43 5.35 Tianjin 1215.6 2293.3 5081.8 6.55 6.31 Hebei 1539.4 1999.8 5682.0 2.65 8.36 Shanxi 1813.5 1163.4 2583.7 -4.34 6.33 Inner Mongolia 3344.0 4379.8 12642.6 2.74 8.50 Liaoning 1967.5 2244.9 6830.4 1.33 8.94 Jilin 6517.7 4272.7 11173.3 -4.13 7.67 Heilongjiang 3035.9 5375.7 12848.6 5.88 6.93 Shanghai 1584.1 1585.6 795.8 0.01 -5.16 Jiangsu 1067.5 1548.9 3687.8 3.79 6.90 Zhejiang 1509.7 4194.0 4811.3 10.76 1.06 Anhui 2136.4 2621.5 5590.9 2.07 6.00 Fujian 1441.4 1985.5 4071.4 3.25 5.68 Jiangxi 1772.5 1785.7 3348.3 0.07 4.95 Shandong 1250.0 1963.9 6184.1 4.62 9.22 Henan 1747.6 2423.1 5055.1 3.32 5.82 Hubei 1457.1 1646.5 3351.9 1.23 5.62 Hunan 1799.2 1193.1 2633.3 -4.02 6.28 Guangdong 2008.8 1857.1 2259.4 -0.78 1.52 Guangxi 2190.8 1595.1 3420.9 -3.12 6.04 Hainan - 2691.8 5392.5 - 5.49 Chongqing - - 2976.9 - - Sichuan 1597.6 1791.3 4078.8 1.15 6.53 Guizhou 2778.3 2909.7 3516.2 0.46 1.47 Yunnan 3034.7 3301.6 5976.6 0.85 4.67 Tibet 10052.0 9462.2 23858.4 -0.60 7.37 Shaanxi 1594.2 1377.5 3204.1 -1.45 6.71 Gansu 2543.7 1960.2 5601.2 -2.57 8.41 Qinghai 6840.5 3609.4 7349.3 -6.19 5.62 Ningxia 2654.5 3831.8 9458.4 3.74 7.20 Xinjiang 3012.3 4748.3 12309.7 4.66 7.60

Data source: China Statistical Yearbook, 1986, 1996 and 2009.

52 Appendix Table 4. Agricultural machinery per rural household at yearend (KW) and annual growth rates (%) cross province and period

Province 1985 1995 2008 Growth rate Growth rate (1985-1995) (1995-2008)

Beijing 2.86 3.75 1.41 2.75 -7.25 Tianjin 3.51 4.73 4.69 3.03 -0.07 Hebei 1.72 3.13 6.45 6.17 5.72 Shanxi 1.59 2.25 3.82 3.53 4.16 Inner Mongolia 1.57 2.62 7.87 5.25 8.83 Liaoning 1.53 1.61 2.90 0.51 4.63 Jilin 1.50 1.90 4.51 2.39 6.88 Heilongjiang 2.48 2.90 5.98 1.58 5.72 Shanghai 1.74 1.29 0.99 -2.95 -2.02 Jiangsu 1.28 1.47 2.44 1.39 3.97 Zhejiang 0.91 1.54 1.91 5.40 1.67 Anhui 0.84 1.48 3.51 5.83 6.87 Fujian 0.79 1.26 1.60 4.78 1.85 Jiangxi 0.91 0.94 3.54 0.32 10.74 Shandong 1.39 2.03 4.95 3.86 7.10 Henan 1.06 1.66 4.63 4.59 8.21 Hubei 1.06 1.17 2.69 0.99 6.61 Hunan 0.78 1.07 2.63 3.21 7.16 Guangdong 0.89 1.30 1.29 3.86 -0.06 Guangxi 0.73 1.30 2.37 5.94 4.73 Hainan - 1.76 3.13 - 4.53 Chongqing - - 1.25 - - Sichuan 0.43 0.61 1.34 3.56 6.24 Guizhou 0.35 0.56 1.86 4.81 9.67 Yunnan 0.77 1.18 2.20 4.36 4.91 Tibet 1.03 1.63 7.96 4.70 12.97 Shaanxi 1.00 1.16 2.42 1.50 5.82 Gansu 1.31 1.78 3.59 3.11 5.54 Qinghai 1.74 2.98 4.29 5.53 2.84 Ningxia 2.17 3.17 6.75 3.86 5.99 Xinjiang 2.36 3.61 5.82 4.34 3.74

Data source: China Statistical Yearbook, 1986, 1996 and 2009.

53 Appendix Table 5. Decomposition of total factor productivity (TFP) for wheat across province over three reform periods (%) Province 1985-1995 1996-2000 2001-2008 TFP TE TC TFP TE TC TFP TE TC (1) (2) (3) (4) (5) (6) (7) (8) (9) Tianjin 2.239 0.051 2.186 1.885 -0.012 1.897 2.882 -0.028 2.910 Hebei 0.951 0.031 0.920 0.729 -0.033 0.762 1.977 0.014 1.962 Shanxi 0.587 -0.917 1.518 0.012 -1.269 1.298 4.093 1.311 2.746 Inner 3.363 1.610 1.725 1.095 0.171 0.922 2.027 -0.036 2.064 Mongolia Jilin 4.770 2.197 2.518 0.461 -0.058 0.519 - - - Heilongjiang 3.672 2.052 1.587 -1.864 -3.783 1.995 5.059 1.815 3.185 Shanghai 0.879 0.002 0.877 1.905 0.013 1.892 2.977 -0.013 2.991 Jiangsu -0.143 0.008 -0.151 -0.119 -0.018 -0.101 1.820 0.036 1.783 Zhejiang -2.315 -1.459 -0.869 4.250 3.066 1.149 0.201 -1.090 1.305 Anhui 0.264 0.052 0.213 -0.685 -0.948 0.266 2.479 1.280 1.183 Shandong 0.660 0.018 0.641 0.438 -0.044 0.482 1.685 0.034 1.650 Henan 0.683 -0.007 0.689 0.445 0.003 0.442 2.520 0.029 2.489 Hubei -0.187 -0.064 -0.123 -1.150 -0.884 -0.269 3.675 2.501 1.146 Sichuan -0.841 0.008 -0.849 -0.795 0.486 -1.275 -0.333 -0.552 0.220 Guizhou 0.547 1.056 -0.504 0.296 2.337 -1.995 -3.063 -2.220 -0.862 Yunnan 1.358 1.450 -0.091 -2.287 -1.485 -0.814 -0.211 -0.670 0.462 Shaanxi -0.176 -0.796 0.625 0.623 0.218 0.404 3.692 1.868 1.791 Gansu -0.698 -0.975 0.279 2.298 2.625 -0.318 1.235 0.641 0.590 Qinghai 0.002 0.047 -0.044 -0.974 -0.385 -0.592 0.433 0.387 0.046 Ningxia -0.926 -1.662 0.748 -0.572 -0.601 0.030 1.173 -0.024 1.197 Xinjiang 2.509 0.025 2.483 1.010 0.007 1.002 2.448 -0.024 2.472

55 Appendix Table 6. Decomposition of total factor productivity (TFP) for corn across province over three reform periods (%) Province 1985-1995 1996-2000 2001-2008 TFP TE TC TFP TE TC TFP TE TC (1) (2) (3) (4) (5) (6) (7) (8) (9) Tianjin 3.129 4.526 -1.337 -3.072 -3.276 0.211 4.162 2.614 1.509 Hebei 2.137 3.672 -1.481 -2.449 -2.459 0.010 3.677 2.194 1.451 Shanxi 1.905 3.538 -1.577 -0.919 -0.773 -0.148 2.613 1.246 1.350 Inner 2.893 4.459 -1.499 -2.289 -2.182 -0.110 3.266 1.788 1.451 Mongolia Liaoning 1.227 2.812 -1.541 -2.718 -2.594 -0.127 3.543 2.108 1.405 Jilin 0.908 2.438 -1.494 -1.751 -1.684 -0.068 2.798 1.391 1.387 Heilongjiang 2.828 4.500 -1.600 -1.426 -1.387 -0.040 2.965 1.466 1.477 Jiangsu 0.453 2.267 -1.774 -1.211 -0.896 -0.318 2.428 1.059 1.355 Anhui 2.338 3.980 -1.579 -1.731 -1.629 -0.104 2.980 1.712 1.246 Shandong 1.919 3.517 -1.544 -0.469 -0.429 -0.039 2.715 1.318 1.380 Henan 2.013 3.596 -1.528 -1.282 -1.181 -0.102 3.154 1.665 1.465 Hubei 1.166 3.060 -1.837 -1.441 -1.081 -0.364 2.626 1.538 1.071 Guangxi 2.222 4.426 -2.111 -1.306 -0.916 -0.393 2.540 1.553 0.972 Chongqing - - - 0.406 0.582 -0.175 1.849 0.909 0.932 Sichuan 0.179 2.091 -1.872 -0.842 -0.384 -0.460 2.170 1.027 1.132 Guizhou 2.075 4.029 -1.878 -0.496 0.037 -0.533 1.507 0.571 0.931 Yunnan 1.516 3.385 -1.807 0.027 0.383 -0.355 1.630 0.565 1.058 Shaanxi 0.604 2.213 -1.574 1.593 1.781 -0.185 1.914 0.576 1.330 Gansu 0.665 2.481 -1.772 0.255 0.539 -0.282 1.656 0.529 1.121 Ningxia -0.496 0.384 -0.877 0.408 0.464 -0.055 1.671 0.317 1.350

56 Xinjiang 3.451 4.958 -1.436 0.704 0.684 0.020 1.796 0.263 1.529

57 Appendix Table 7. Decomposition of total factor productivity (TFP) for early indica across province over three reform periods (%)

1985-1995 1996-2000 2001-2008 Province

TFP TE TC TFP TE TC TFP TE TC

(1) (2) (3) (4) (5) (6) (7) (8) (9)

Shanghai ------1.500 -0.031 -1.470 Jiangsu -0.089 0.368 -0.455 ------Zhejiang -1.272 -0.002 -1.270 -0.301 0.003 -0.304 1.420 -0.001 1.421 Anhui -1.177 0.258 -1.431 -1.288 -0.555 -0.737 1.131 -0.006 1.137 Fujian -1.241 0.001 -1.242 -0.055 0.002 -0.057 1.528 -0.001 1.529 Jiangxi -1.105 -0.049 -1.056 0.542 0.143 0.399 2.331 0.122 2.207 Hubei -0.865 0.001 -0.867 0.207 -0.032 0.239 2.061 0.008 2.053 Hunan -1.230 -0.001 -1.230 -0.169 0.008 -0.177 1.515 0.000 1.515 Guangdong -0.956 -0.020 -0.937 0.373 0.070 0.303 2.495 -0.046 2.541 Guangxi -0.942 0.000 -0.942 0.323 0.002 0.321 1.973 -0.003 1.976 Hainan -3.925 0.139 -4.058 -3.187 0.035 -3.221 -1.950 -0.080 -1.872 Yunnan 1.253 0.107 1.146 ------

58 Appendix Table 8. Decomposition of total factor productivity (TFP) for middle indica across province over three reform periods (%)

1985-1995 1996-2000 2001-2008 Province

TFP TE TC TFP TE TC TFP TE TC

(1) (2) (3) (4) (5) (6) (7) (8) (9)

Jiangsu 0.981 0.889 0.091 -0.534 0.631 -1.158 -0.536 0.094 -0.629 Anhui 1.709 0.861 0.840 0.242 0.792 -0.545 0.496 0.745 -0.247 Fujian 2.030 0.852 1.168 1.154 0.932 0.221 1.475 0.876 0.593 Henan 2.271 0.848 1.410 1.043 0.950 0.092 1.243 0.939 0.300 Hubei 0.728 0.711 0.017 -0.773 0.046 -0.819 -0.243 0.039 -0.282 Hunan ------0.704 0.850 -0.144 Chongqing - - - 0.569 0.719 -0.150 0.391 0.850 -0.455 Sichuan 1.435 0.800 0.630 -0.739 0.219 -0.957 -0.653 0.034 -0.686 Guizhou 1.517 1.025 0.487 0.624 0.978 -0.351 -0.682 0.746 -1.418 Yunnan 1.487 1.180 0.303 0.670 0.902 -0.230 0.537 0.762 -0.223 Shaanxi 1.536 0.881 0.650 0.001 0.889 -0.879 -0.606 0.078 -0.683

59 Appendix Table 9. Decomposition of total factor productivity (TFP) for late indica across province over three reform periods (%)

1985-1995 1996-2000 2001-2008 Province

TFP TE TC TFP TE TC TFP TE TC

(1) (2) (3) (4) (5) (6) (7) (8) (9)

Zhejiang -2.527 0.071 -2.596 -2.637 -0.086 -2.554 -1.502 0.060 -1.561 Anhui -1.130 0.094 -1.223 1.484 2.927 -1.402 -1.431 -0.700 -0.736 Fujian -2.896 -0.501 -2.407 -0.909 0.657 -1.556 2.106 0.154 1.949 Jiangxi 0.642 1.404 -0.752 -0.999 -1.350 0.356 2.118 0.863 1.244 Hubei -2.673 -1.460 -1.231 1.666 1.620 0.045 1.389 -0.030 1.420 Hunan -2.181 -0.087 -2.096 -0.161 0.756 -0.910 0.107 0.067 0.040 Guangdong -2.098 -0.336 -1.768 0.019 1.139 -1.107 1.988 0.835 1.143 Guangxi -2.166 -0.988 -1.190 0.840 1.308 -0.462 3.384 2.223 1.136 Hainan -7.390 -3.619 -3.912 -6.383 -3.533 -2.954 6.360 8.260 -1.755

60 Appendix Table 10. Decomposition of total factor productivity (TFP) for japonic across province over three reform periods (%)

Province 1985-1995 1996-2000 2001-2008 TFP TE TC TFP TE TC TFP TE TC (1) (2) (3) (4) (5) (6) (7) (8) (9) Beijing 0.571 -1.699 2.309 6.464 4.756 1.631 - - - Tianjin 2.623 0.041 2.582 1.162 -0.324 1.491 1.153 0.234 0.917 Hebei 2.614 0.573 2.029 -1.194 -1.804 0.622 1.198 1.181 0.017 Shanxi 0.750 -0.076 0.826 0.347 0.010 0.338 -0.102 0.094 -0.196 Inner - - - -8.747 -9.053 0.336 1.374 1.402 -0.027 Mongolia Liaoning 1.355 -0.524 1.889 0.594 -0.059 0.654 0.672 0.533 0.139 Jilin 1.735 0.015 1.721 0.326 -0.083 0.409 -0.362 0.043 -0.404 Heilongjiang 1.082 -0.238 1.323 1.525 1.311 0.211 -0.550 -0.045 -0.505 Shanghai 1.676 0.081 1.593 0.316 -0.011 0.327 -0.318 0.012 -0.330 Jiangsu 2.210 0.129 2.078 0.893 -0.025 0.918 0.398 -0.022 0.421 Zhejiang 2.500 0.217 2.278 0.972 -0.214 1.188 0.757 0.330 0.426 Anhui 2.785 0.772 1.998 1.764 1.165 0.592 1.180 0.904 0.274 Shandong 2.541 0.196 2.341 1.752 0.862 0.883 0.588 0.324 0.263 Henan 1.824 -0.258 2.087 -2.996 -3.920 0.962 2.421 2.089 0.325 Hubei 1.271 -0.884 2.174 -0.188 -0.934 0.754 0.848 0.656 0.191 Yunnan 2.696 0.106 2.588 1.040 -0.540 1.589 1.521 0.414 1.103 Ningxia 2.160 -0.067 2.228 0.842 -0.023 0.865 0.142 0.001 0.142 Xinjiang - - - 1.619 0.082 1.535 - - -

61 Appendix Table 11. Decomposition of total factor productivity (TFP) for cotton across province over three reform periods (%)

Province 1985-1995 1996-2000 2001-2008 TFP TE TC TFP TE TC TFP TE TC (1) (2) (3) (4) (5) (6) (7) (8) (9) Tianjin -0.667 -2.808 2.203 0.838 -1.164 2.025 Hebei -0.671 -2.785 2.175 11.393 9.320 1.896 1.024 -0.631 1.665 Shanxi 1.295 -0.337 1.638 9.115 7.179 1.806 0.505 -0.853 1.369 Liaoning 1.545 -0.620 2.178 7.162 4.781 2.273 3.309 1.247 2.037 Shanghai 3.735 0.615 3.101 3.334 -0.644 4.004 - - - Jiangsu 3.627 1.098 2.501 4.056 1.374 2.645 0.999 -0.861 1.876 Zhejiang 2.162 -0.435 2.608 4.560 0.676 3.858 3.603 0.300 3.293 Anhui 2.344 0.103 2.239 2.131 -0.181 2.316 1.267 -0.709 1.989 Jiangxi 1.114 0.197 0.915 2.357 1.424 0.920 2.257 1.174 1.071 Shandong 0.319 -1.275 1.614 8.369 6.193 2.049 1.639 -0.157 1.799 Henan 1.056 -0.998 2.075 3.690 1.591 2.066 1.126 -0.863 2.005 Hubei 1.859 0.040 1.818 -0.048 -2.115 2.112 2.685 0.698 1.973 Hunan 2.044 0.556 1.480 -2.933 -4.436 1.572 4.102 2.168 1.893 Sichuan 0.401 -1.365 1.791 4.900 3.173 1.674 -3.188 -5.070 1.982 Shaanxi 3.669 2.120 1.517 9.530 7.778 1.626 -0.280 -1.105 0.834 Gansu 7.081 5.161 1.826 0.694 -0.322 1.020 0.341 -0.579 0.925 Xinjiang 2.028 0.260 1.764 1.462 -0.058 1.521 1.407 -0.081 1.489

62 Appendix Table 12. Decomposition of total factor productivity (TFP) for soybean across province over three reform periods (%)

Province 1985-1995 1996-2000 2001-2008 TFP TE TC TFP TE TC TFP TE TC (1) (2) (3) (4) (5) (6) (7) (8) (9) Hebei 1.221 -0.039 1.261 1.786 -1.900 3.758 5.040 0.708 4.302 Shanxi -5.674 -5.630 -0.046 9.045 5.761 3.106 6.343 0.762 5.539 Inner -0.742 -1.493 0.763 0.650 -3.272 4.055 4.875 0.664 4.183 Mongolia Liaoning 0.109 -0.843 0.960 4.405 1.671 2.689 3.664 -0.149 3.819 Jilin 0.249 -0.017 0.266 2.882 0.025 2.857 3.882 0.071 3.808 Heilongjiang -1.292 -0.668 -0.628 1.209 0.571 0.635 2.510 0.615 1.884 Jiangsu 3.163 1.191 1.949 -0.828 -4.579 3.931 -2.754 -6.478 3.982 Anhui 1.221 0.783 0.435 0.715 -2.431 3.224 3.118 -1.045 4.208 Fujian -0.409 -2.674 2.327 5.547 0.418 5.107 - - - Shandong 4.344 2.552 1.747 3.551 -0.029 3.581 3.109 -1.106 4.262 Henan 2.466 2.127 0.333 3.243 0.241 2.994 0.783 -2.707 3.587 Hubei 1.061 -0.988 2.070 5.470 0.497 4.948 2.209 -1.542 3.809 Chongqing ------2.134 -3.991 6.379 Yunnan 2.949 -0.795 3.774 10.265 4.341 5.678 -1.804 -7.099 5.699 Shaanxi -1.477 -2.589 1.141 11.946 7.970 3.682 0.946 -2.999 4.067 Ningxia - - - 2.544 2.761 -0.211 - - -

63 Appendix Table 13. Determinants of wheat TFP and the changing impact of capital formation on TFP for major provinces

Variables Henan Shandong Hebei Anhui Jiangsu Sichuan Shaanxi Xinjiang Coef. t Coef. t Coef. t Coef. t Coef. t Coef. t Coef. t Coef. t 1985- 2008: HAgAS - -0.25 0.047 1.96 - -0.15 0.234 2.56 -0.011 -0.33 -0.045 -0.88 0.310 3.01 0.304 15.01 0.007 0.004 AgLabor - -7.17 - -2.07 - -5.63 - -0.38 -0.076 -2.75 0.103 1.62 -0.025 -0.21 0.026 0.91 0.228 0.085 0.283 0.039 Fertilizer - -0.67 - -1.51 0.010 0.96 0.024 0.55 0.011 0.57 0.013 0.61 -0.006 -0.22 0.017 0.75 0.007 0.016 R2 0.931 - 0.899 - 0.953 - 0.593 - 0.459 - 0.615 - 0.704 - 0.934 - Obs 24 - 24 - 24 - 24 - 24 - 24 - 24 - 24 - 1985-1995 HAgAS - -0.67 0.044 1.58 0.117 8.06 0.345 0.77 0.005 0.84 0.015 0.30 -0.006 -0.05 0.300 3.60 0.045 AgLabor - -0.71 0.036 0.45 - - - -0.15 -0.003 -0.13 -0.001 0.00 0.132 0.68 0.048 0.82 0.045 0.276 19.27 0.070 Fertilizer - -1.92 - -1.95 0.045 7.56 0.034 0.37 0.009 1.96 0.028 0.70 -0.003 -0.15 0.024 0.57 0.035 0.017 R2 0.719 - 0.742 - 0.995 - 0.143 - 0.434 - 0.284 - 0.063 - 0.664 - Obs 11 - 11 - 11 - 11 - 11 - 11 - 11 - 11 - 1996-2008 HAgAS - -1.83 0.106 0.75 0.122 0.92 0.380 2.22 0.000 0.00 0.464 3.47 0.568 3.13 0.371 7.98

64 0.125 AgLabor - -6.18 - -0.23 - -1.02 0.033 0.31 0.230 1.47 0.388 4.03 0.151 0.86 0.067 1.09 0.329 0.047 0.165 Fertilizer 0.028 1.15 - -0.62 - -0.19 0.042 0.62 -0.009 -0.09 0.054 1.29 -0.012 -0.18 0.029 1.50 0.018 0.006 R2 0.936 - 0.855 - 0.883 - 0.678 - 0.746 - 0.656 - 0.726 - 0.985 - Obs 13 - 13 - 13 - 13 - 13 - 13 - 13 - 13 -

Note: see Table 10.

Appendix Table 14. Determinants of corn TFP and the changing impact of capital formation on TFP for major provinces

Variables Jilin Shandong Heilongjiang Hebei Inner Mongolia Liaoning Coef. t Coef. t Coef. T Coef. t Coef. t Coef. t 1985- 2008: HAgAS -0.006 -0.12 0.079 0.95 0.206 1.55 -0.010 -0.15 0.125 2.55 0.099 2.02 AgLabor -0.316 -3.01 -0.028 -0.18 0.105 0.48 -0.219 -1.36 0.059 0.65 0.050 0.39 Fertilizer 0.048 1.23 -0.022 -0.42 0.056 0.64 -0.004 -0.12 0.033 0.76 0.024 0.68 R2 0.475 - 0.345 - 0.410 - 0.313 - 0.402 - 0.343 - Obs 24 - 24 - 24 - 24 - 24 - 24 - 1985-1995 HAgAS -0.129 -1.95 -0.050 -0.33 0.354 1.37 0.357 1.97 0.169 1.06 -0.102 -1.30 AgLabor -0.099 -0.70 0.656 2.07 -0.651 -1.33 0.258 1.00 0.413 2.18 -0.197 -1.61 Fertilizer -0.041 -0.85 -0.114 -1.93 -0.022 -0.17 -0.003 -0.09 0.020 0.33 0.022 0.78 R2 0.405 - 0.577 - 0.533 - 0.396 - 0.478 - 0.303 -

65 Obs 11 - 11 - 11 - 11 - 11 - 11 - 1996-2008 HAgAS 0.086 0.84 -0.055 -0.29 0.337 2.64 0.150 0.45 0.120 1.14 0.241 3.64 AgLabor -0.205 -0.96 -0.306 -1.11 0.366 1.85 -0.069 -0.15 -0.027 -0.21 0.668 2.01 Fertilizer 0.108 1.02 0.129 0.89 0.242 1.85 0.212 0.69 0.072 0.74 0.347 1.83 R2 0.680 - 0.712 - 0.623 - 0.626 - 0.639 - 0.749 - Obs 13 - 13 - 13 - 13 - 13 - 13 -

Note: see Table 10.

66 Appendix Table 15. Determinants of cotton TFP and the changing impact of capital formation on TFP for major provinces

Variables Xinjiang Shandong Hebei Henan Hubei Anhui Coef. t Coef. t Coef. T Coef. t Coef. t Coef. t 1985- 2008: HAgAS 0.263 10.85 0.321 2.30 0.258 3.23 0.680 4.19 0.399 2.55 0.300 5.18 AgLabor -0.044 -0.51 -0.078 -0.26 -0.519 -2.39 0.637 1.92 -0.112 -0.67 -0.277 -2.06 Fertilizer 0.008 0.40 0.080 1.24 0.199 3.27 0.341 2.75 0.005 0.10 0.042 1.29 R2 0.875 - 0.694 - 0.822 - 0.569 - 0.728 - 0.870 - Obs 24 - 24 - 24 - 24 - 24 - 23 - 1985-1995 HAgAS 0.149 1.47 -0.086 -0.36 0.690 0.81 0.799 1.45 0.045 0.20 0.365 1.74 AgLabor 0.078 0.39 1.462 1.54 0.010 0.01 0.185 0.28 0.389 1.11 -0.032 -0.10 Fertilizer 0.026 0.68 0.111 1.44 0.315 1.32 0.398 2.49 -0.138 -1.97 0.018 0.36 R2 0.352 - 0.380 - 0.347 - 0.478 - 0.519 - 0.410 - Obs 11 - 11 - 11 - 11 - 11 - 10 - 1996-2008 HAgAS 0.202 5.93 0.087 0.58 0.067 0.36 0.671 1.63 0.505 2.00 0.365 2.05 AgLabor -0.135 -1.84 -0.294 -1.24 -0.793 -3.67 1.054 1.65 0.085 0.31 -0.369 -2.25 Fertilizer -0.006 -0.09 0.026 0.27 0.151 0.66 0.113 0.34 0.380 1.22 0.009 0.05 R2 0.953 - 0.854 - 0.892 - 0.253 - 0.689 - 0.781 - Obs 13 - 13 - 13 - 13 - 13 - 13 -

Note: see Table 10.

67 68 Appendix Table 16. Determinants of early indica TFP and the changing impact of capital formation on TFP for major provinces

Variables Hunan Jiangxi Guangxi Guangdong Hubei Fujian Anhui Coef. t Coef. t Coef. T Coef. t Coef. t Coef. t Coef. t 1985- 2008: HAgAS 0.027 0.65 0.190 3.00 0.147 4.77 0.049 0.56 0.173 3.65 0.085 3.36 0.108 1.87 AgLabor -0.090 -2.40 - -0.94 -0.076 -2.56 -0.197 -4.45 -0.024 -0.62 0.024 0.35 0.173 2.39 0.045 Fertilizer 0.092 6.01 0.074 5.52 0.047 3.28 0.094 3.37 0.078 6.00 0.085 5.89 0.059 3.74 R2 0.667 - 0.793 - 0.816 - 0.540 - 0.764 - 0.661 - 0.640 - Obs 23 - 24 - 21 - 24 - 24 - 24 - 21 - 1985-1995 HAgAS 0.128 4.51 0.026 0.24 0.100 2.02 -0.001 -0.04 -0.033 -0.45 -0.039 -0.88 0.032 0.51 AgLabor 0.081 1.81 0.468 2.93 0.076 0.78 0.253 5.64 0.041 0.85 0.223 1.30 0.301 5.11 Fertilizer 0.033 3.57 0.023 1.23 0.004 0.27 -0.001 -0.05 0.029 1.84 0.035 2.27 0.028 2.33 R2 0.959 - 0.759 - 0.691 - 0.908 - 0.738 - 0.851 - 0.900 - Obs 10 - 11 - 8 - 11 - 11 - 11 - 10 - 1996-2008 HAgAS 0.183 3.11 0.137 1.71 0.149 5.76 0.148 1.16 0.191 3.58 0.089 3.21 0.149 3.07 AgLabor -0.063 -2.04 - -2.22 -0.128 -5.18 -0.335 -6.88 -0.091 -1.79 -0.101 -0.92 0.030 0.49 0.132 Fertilizer -0.041 -1.25 0.026 0.14 -0.093 -1.55 -0.177 -1.65 -0.069 -0.75 -0.011 -0.11 0.041 1.76 R2 0.917 - 0.923 - 0.950 - 0.897 - 0.957 - 0.799 - 0.847 - Obs 13 - 13 - 13 - 13 - 13 - 13 - 11 -

69 Note: see Table 10.

70 Appendix Table 17. Determinants of middle indica TFP and the changing impact of capital formation on TFP for major provinces

Variables Jiangsu Chongqing Hubei Fujian Hunan Anhui Henan Coef. t Coef. t Coef. T Coef. t Coef. t Coef. t Coef. t 1985- 2008: HAgAS 0.052 2.94 0.042 2.96 0.005 0.09 0.123 2.69 0.023 0.93 0.112 2.19 0.075 1.76 AgLabor 0.070 4.25 0.002 0.05 0.051 1.63 -0.266 -2.73 -0.077 -4.1 -0.047 -0.75 -0.133 -2.16 Fertilizer -0.029 -2.42 0.042 2.34 -0.019 -1.84 -0.071 -2.85 -0.014 -0.81 -0.013 -0.63 -0.082 -4.54 R2 0.582 - 0.945 - 0.446 - 0.824 - 0.957 - 0.698 - 0.921 - Obs 22 - 12 - 24 - 24 - 7 - 24 - 23 - 1985-1995 HAgAS 0.045 1.20 - - 0.100 1.09 0.133 1.52 - - 0.136 1.02 -0.278 -1.55 AgLabor -0.006 -0.03 - - -0.101 -1.22 0.152 0.94 - - -0.349 -2.74 -0.173 -0.61 Fertilizer -0.012 -0.39 - - -0.002 -0.12 -0.025 -0.79 - - 0.020 0.65 -0.151 -3.43 R2 0.519 - - - 0.464 - 0.731 - - - 0.629 - 0.781 - Obs 10 - - - 11 - 11 - - - 11 - 10 - 1996-2008 HAgAS -0.039 -1.72 0.042 2.96 0.004 0.15 0.111 4.60 0.023 0.93 0.029 0.96 0.132 2.95 AgLabor 0.030 2.41 0.002 0.05 0.036 2.43 -0.147 -2.27 -0.077 -4.1 -0.042 -1.81 -0.073 -1.93 Fertilizer -0.051 -1.21 0.042 2.34 -0.092 -3.33 0.006 0.13 -0.014 -0.81 -0.016 -2.49 -0.074 -3.25 R2 0.953 - 0.945 - 0.935 - 0.911 - 0.957 - 0.900 - 0.933 - Obs 12 - 12 - 13 - 13 - 7 - 13 - 13 -

Note: see Table 10.

71 72 Appendix Table 18. Determinants of late indica TFP and the changing impact of capital formation on TFP for major provinces

Variables Hunan Jiangxi Guangxi Guangdong Hubei Fujian Anhui Zhejiang Coef. t Coef. t Coef. t Coef. t Coef. t Coef. t Coef. t Coef. t 1985- 2008: HAgAS 0.034 0.59 0.043 0.43 0.361 4.41 0.055 0.43 0.155 1.99 0.089 1.84 -0.114 -0.91 0.010 0.14 AgLabor 0.080 1.47 - -1.67 0.051 0.57 - -1.49 0.018 0.32 0.183 1.34 0.071 0.54 0.241 13.62 0.137 0.101 Fertilizer 0.122 5.82 - -2.05 0.091 2.04 0.239 5.30 0.067 2.25 0.141 4.20 -0.011 -0.22 0.067 0.91 0.048 R2 0.832 - 0.701 - 0.584 - 0.642 - 0.499 - 0.664 - 0.431 - 0.968 - Obs 22 - 24 - 24 - 24 - 19 - 24 - 18 - 18 - 1985-1995 HAgAS 0.281 4.95 - -0.50 0.644 2.65 - -0.14 -0.357 -0.64 -0.112 -0.64 -0.883 -19.92 -0.133 -0.97 0.158 0.022 AgLabor 0.172 1.61 - -0.20 - -0.37 0.423 1.38 0.194 1.04 0.115 0.12 -0.176 -2.44 0.046 0.23 0.086 0.139 Fertilizer 0.072 3.95 - -1.37 0.067 0.86 0.163 2.46 -0.070 -0.55 0.078 0.95 0.020 1.73 0.085 0.50 0.082 R2 0.950 - 0.264 - 0.598 - 0.765 - 0.401 - 0.648 - 0.999 - 0.874 - Obs 10 - 11 - 11 - 11 - 6 - 11 - 5 - 5 - 1996-2008 HAgAS - -0.09 0.009 0.08 0.298 4.13 0.117 0.47 0.158 1.78 0.119 3.30 -0.025 -0.15 0.124 1.48 0.009

73 AgLabor 0.029 0.51 - -1.17 - -2.00 - -2.04 -0.024 -0.29 -0.003 -0.02 0.252 1.83 0.230 12.59 0.112 0.186 0.169 Fertilizer 0.014 0.29 0.140 0.89 - -0.97 - -0.59 0.124 1.04 -0.176 -0.66 0.055 0.54 0.020 0.19 0.182 0.151 R2 0.222 - 0.735 - 0.860 - 0.506 - 0.925 - 0.585 - 0.643 - 0.949 - Obs 12 - 13 - 13 - 13 - 13 - 13 - 13 - 13 -

Note: see Table 10.

74 Appendix Table 19. Determinants of soybean TFP and the changing impact of capital formation on TFP for major provinces

Variables Heilongjiang Jilin Anhui Shandong Henan Coef. t Coef. t Coef. T Coef. t Coef. t 1985- 2008: HAgAS 0.206 5.26 -0.297 -4.73 -0.139 -1.08 0.042 0.48 -0.039 -0.34 AgLabor 0.007 0.22 -0.516 -3.02 -0.374 -3.18 -0.564 -6.24 -0.254 -2.57 Fertilizer -0.015 -0.44 0.081 1.27 0.189 5.07 -0.029 -0.69 0.081 1.57 R2 0.808 - 0.573 - 0.802 - 0.743 - 0.315 - Obs 24 - 24 - 23 - 24 - 23 - 1985-1995 HAgAS 0.048 0.76 0.098 0.74 0.215 1.03 -0.015 -0.07 -0.258 -0.55 AgLabor -0.060 -0.63 0.304 0.82 -0.156 -1.46 0.932 1.47 -0.300 -2.34 Fertilizer -0.006 -0.10 -0.192 -1.32 0.231 1.87 0.026 0.40 0.211 1.85 R2 0.309 - 0.222 - 0.736 - 0.321 - 0.681 - Obs 11 - 11 - 11 - 11 - 11 - 1996-2008 HAgAS 0.197 1.97 -0.475 -6.95 -0.140 -0.32 0.334 2.25 -0.743 -2.19 AgLabor -0.008 -0.17 -0.416 -2.11 -0.662 -1.94 -0.645 -8.71 0.057 0.26 Fertilizer -0.026 -0.55 0.120 2.29 0.186 3.11 -0.133 -3.90 -0.163 -2.15 R2 0.634 - 0.905 - 0.827 - 0.962 - 0.454 - Obs 13 - 13 - 12 - 13 - 12 -

Note: see Table 10.

75 76