Grain Promotion and Food Consumption: Analysis of Chinese Provincial and Household Data

Transcription

1 Grain Promotion and Food Consumption: Analysis of Chinese Provincial and Household Data Zhu Jing (Nanjing Agricultural University), Denise Hare (Reed College), Zhong Funing (Nanjing Agricultural University), and Zhou Zhangyue (James Cook University) September 3, 2012 Abstract China has long struggled to achieve food security. In the era of a planned economy, local self-sufficiency was dictated by policy. With China s transition to a market economy, different policy schemes have been utilized, with greater emphasis on the role of market forces, especially in conjunction with China s accession to WTO in However, self-sufficiency in grain production remains a deeply rooted goal, and interventionist measures towards its achievement are still viewed as the most direct and effective means of food security. This paper examines how the well being of China s rural population, proxied by measures of food consumption, is affected by the promotion of grain production. JEL Codes: O12, Q12, Q18 1

2 Introduction China has long struggled to achieve food security. In the era of a planned economy, local self-sufficiency was dictated by policy. The result was two-fold: grain production was encouraged, but it came at the cost of either substitution away from other higher-value crops or exploitation of marginal land. As a result of these inefficiencies, farmers income, purchasing power, access to food, and potential benefits from international trade were reduced, even as local grain production may have increased (Zhong, 2001). With China s transition to a market economy, different policy schemes have been utilized, with greater emphasis on the role of market forces, especially in conjunction with China s accession to WTO in However, self-sufficiency in grain production remains a deeply rooted goal, and interventionist measures towards its achievement are still viewed as the most direct and effective means of food security. A recent example is China s first Outline of Mid- and Long-term Plan for National Food Security, issued in November of 2008, in which it is stipulated that the country will seek to stabilize grain sown area and achieve greater than 95 percent grain self-sufficiency (National Development and Reform Commission, 2008). Continuing in this vein, the current five-year plan, released in March 2011, reiterates the goal of stabilizing grain sown area while at the same time stimulating production (through targeted investment in grain growing regions and compensation to growers) in order to boost production capacity (USDA Foreign Agricultural Service, 2011). Policy reform coupled with market penetration over the last thirty years has radically changed the rural economic environment. Households have more access to 2

3 national and global markets in both their production and consumption spheres; and while government policy serves to influence choices, it no longer dictates behavior as in earlier eras. Given the wider scope of production and consumption options, government efforts to ensure self-sufficiency in grain may be more costly, in terms of rural household well being, than was previously the case. Notably, a more dynamic and diversified rural economy may have increased the opportunity cost of resources devoted to grain production. Additionally, market integration facilitates greater household specialization in production, reducing if not eliminating the need for subsistence-oriented production. This paper examines how the well being of China s rural population, proxied by measures of food consumption, is affected by the promotion of grain production. We perform parallel analyses of data at the provincial and household levels, taking advantage of the time-series dimensions of both data sets, to examine the effects of grain promotion, and how it may have changed subsequent to WTO accession. Our results suggest that, indeed, the production of grain imposes some burden on those who grow it, and its negative impact may be especially strong for those living in regions still targeted for grain promotion in the post-wto era. The paper is organized as follows. The following section provides a brief review of the literature describing China s recent policy environment surrounding grain production. Subsequently the data and regression methods we use to examine the impact of grain promotion are introduced. We then describe the results of our analysis, and conclude. 3

4 Literature review With a population-land ratio of nearly six rural residents per hectare of cultivated land (National Statistical Bureau, 2010), China s comparative advantage is in laborintensive production. Within the agricultural sector, this means it is more efficient in production of vegetables, fruits, and livestock than in cereals, oil seeds, and cotton. On the eve of WTO accession, many academic and policy researchers forecast that China s grain farmers were among those who would suffer the most when subjected to international price competition. Since WTO accession, both the export of labor-intensive products and the import of land-intensive products have increased (Chen, 2008). However, despite the fact that China s agricultural imports and exports moved in the direction of its comparative advantage, with the exception of soybeans, the predicted large volume of grain imports did not materialize. China continued to export corn and rice for several years after WTO accession (Lohmar, et al., 2009). To explain this departure from expectations, Lohmar and his coauthors conjecture that one contributing factor is slower growth in the demand for grains resulting from the changing diets that accompany China s rising incomes and urbanization. In the post WTO years, China completed liberalization of most agricultural product markets. Policies were increasingly directed towards facilitating more efficient markets through investments in infrastructure and development of other supporting institutions, rather than direct intervention (Huang and Rozelle, 2008). In contrast to earlier eras, grain prices are now mostly market driven, and marketing of grain is largely conducted by private traders (Gale, et al., 2005). Regional patterns of comparative advantage and differences in local conditions are increasingly factored into policy 4

5 formulation. As such, policies forcing local self-sufficiency in production of grain have been relaxed, although farmers in major grain producing areas are still encouraged, in various ways, to grow grain (Jiang, 2008). Various policy measures, ranging from price supports and direct subsidies, to training and infrastructure investment, and even financial awards and public recognition, demonstrate the central government s favoritism for grain production over other agricultural pursuits. Carter, Zhong, and Zhu (2012) report that, in 2009, billion yuan was spent by the central government in support of the grain subsector, representing more than one-third of a total agricultural support budget, and in stark contrast to less than 1 billion yuan spent in support of the livestock subsector. Support is provided for improved grain quality, greater mechanization, and larger production scale (China Agricultural Yearbook Editing Committee, 2010). At the national level, numerous policy documents 1 uniformly proclaim the 95 percent self-sufficiency target in grain consumption along with the usage of grain sown area as an indication of policy compliance, the allocation of oversight responsibility to provincial governors, and the implementation of various rewards to local governments in the form of centrally-funded financial injections into local budgets as well as promotions and other prizes for local officials to be tied directly to the region s grain output levels. Heilongjiang, Liaoning, Jilin, Inner Mongolia, Hebei, Jiangsu, Anhui, 1 Among those referenced here are Outline of Mid and long-term plan for national security ( ) stipulated by the State Council in July 2008; Plan for improving grain production capability by 50 million tons nationally stipulated by the State Council in November 2009; A few opinions on promoting agricultural science and technology to enhance continuously the supply capability of agricultural products (No. 1 document issued by the central government in 2012); and The Grain Law (draft) issued February 22,

6 Jiangxi, Shandong, Henan, Hubei, Hunan, and Sichuan are identified as key producers of surplus grain in excess of provincial consumption needs. As an illustration of regional targeting, nearly 93 percent of the 6.5 billion yuan budgeted to support increased grain production in 2010 was to directed to these 13 key grain producing provinces (National Development and Reform Commission, 2009). Local governments in major grain producing regions also have gone on record to show their support for meeting national targets. For instance, one county in the southern part of Hunan province, at the beginning of 2012, stated its intention to make grain promotion a top priority for the year, calling on government employees and party members to become model grain producers and to encourage friends and relatives to follow their examples, all in an effort to achieve national recognition (Dang, 2012). Also mentioned is the role that grain production will play in cadre evaluation unsatisfactory performance in this single area can be very unfavorable to promotion, even if all other targets have been met. Finally, area sown to grain is noted as an indicator of compliance, with 1.58 million mu noted as the overall county goal. Pronouncements similar to these can be found for nearly every province in China. As our data will show in the next section, area sown to grain in China actually began to decline well before (and perhaps somewhat in anticipation of) China s WTO accession. Explaining why the decline predates the structural adjustment that would be expected to accompany China s move towards the market and greater integration into the world economy, Han (2005) cites the low domestic grain prices of the pre-wto years. In fact Han (2005) goes on to suggest that at the time of WTO entry, huge accumulations of grain stocks may have had the unintended effect of depressing prices and serving to 6

7 disincentivize grain production, even during years when other policies aimed to encourage it. The year 2004 was a landmark in several respects. For one, the downward trend in area planted to grain was reversed, with both planted area and grain output posting increases in subsequent years. This change took place as China phased out its longstanding practice of extracting agricultural surplus through various taxes and fees, and replaced it with the introduction of agricultural subsidies. With respect to grain production, these subsidies initially paid farmers 10 yuan per mu for area planted to grain (Gale, et al., 2005). In spite of the new subsidy, Gale, et al. (2005) argue that a steep increase in grain prices is the primary motivation for the increases in grain acreage, as world price movements have increased the profitability of grain growing. Heerink, et al. (2006) concur that subsidies for grain growing have contributed little toward increased levels of grain output, arguing instead that these income supports release households from liquidity constraints and facilitate transitions into more lucrative activities such as livestock production. Funds allocated towards support of grain production have increased in recent years, with more resources available to subsidize seeds, machinery, and other agricultural inputs. Direct subsidies for growing grain range from 10 to 15 yuan per mu (People s Daily Online, 2011). Given a policy environment that is more mindful of market forces, coupled with the switch from sticks (quotas) to carrots (subsidies and price supports) in communicating policy objectives, one might expect that promotion of grain production could be less damaging than was experienced in the past. In the following sections, we will show 7

8 evidence to the contrary. We argue that China s grain self-sufficiency policy continues to impose a negative impact on those charged with responsibility for growing grain. Data and methods Our data include two different levels of aggregation. The household-level data are from annual surveys conducted by the Research Center for Rural Economy of the Ministry of Agriculture, covering more than four thousand farm households in five provinces: Jilin and Heilongjiang in the northeast; Anhui and Zhejiang in the southeast; and Sichuan in southwestern China. We have a total of 57,913 observations over the years Unfortunately, the identification codes do not always represent unique households across all years of the sample. Nonetheless, it is clear that many households remain in the dataset for multiple years, as evidenced by consistent population and land area measures. However, we cannot be completely certain of discerning when a given identification code changes to a different household. For this reason, we have been very cautious about exploiting the panel nature of these data, and will offer several versions of fixed effect results from the household data. Careful scrutiny of the dataset was carried out and observations of suspicious quality were deleted. Altogether a total of 88 observations were deleted, and we are left with a final household dataset of 57,825 observations. At the provincial level, we have data for a longer time period ( ), for twenty-five provinces, yielding 600 observations. Data for Chongqing and Hainan have been merged into Sichuan and Guangdong, respectively. Tibet is dropped for lack of data on some variables. We also exclude Beijing, Shanghai, and Tianjin as most residents of 8

9 these administrative regions are urban. These data were collected and prepared by the National Statistical Bureau and published in annual editions of the Chinese Statistical Yearbook. Unlike in the household data, observations are clearly associated with a given province, allowing us to easily exploit the panel nature of the data. To examine the effect of grain cultivation on individual consumption outcomes, we estimated the following model in both datasets: where the dependent variable is either per capita grain consumption or per capita food expenditure. Grain is defined to include cereals, beans, and tubers, which are aggregated by weight 2 and recorded in kilogram consumed, per person. Food expenditure includes both cash expenditure and consumption in kind (valued at market prices), and is recorded in real yuan per capita. We would prefer to use total food consumption as our dependent variable, but due to difficulty both in collecting the data, and in aggregating across different types of food, expenditure is as close as we can get. Following a demand function approach, we include prices and income. For each of the two dependent variables, a price variable is constructed by taking the ratio of the relevant price index (grain or food), and dividing by the general rural CPI. The base year for all of these series is 1984, and they record changes in prices across both time and 2 Beans are converted to grain equivalent in the ratio of 1:1 while tubers are converted at the rate of 5:1. 9

10 province 3. Due to concerns about simultaneity between consumption and income, we use a lagged value of income in the provincial regressions. Unfortunately, the absence of a completely unique household identifier prohibits us from using lagged income in the household regressions. The variable that measures the percent of total sown area dedicated to grain is the main object of inquiry. In the household regressions, this is the ratio of area put under production of wheat, rice, or corn, to the total sown area. In the provincial case, this is the ratio of land sown to any grain crop (cereal, bean, or tuber) to total sown area. In addition to the variables noted above, the household level regressions also include a control for the amount of land (measured in mu per capita) under cultivation. Our provincial and household regressions all contain year fixed effects. In the provincial analysis, the first set of results we present is generated from pooled cross section data, while subsequent models control for province fixed effects. In the household analysis, we also first provide results of regression on pooled cross section data. But in the spirit of controlling for fixed effects, and absent any absolutely unique household identifier, we experiment with different variables to try to identify clusters of observations arising from a single household. For each of the two dependent variables we offer four alternative household fixed effects models. The first uses the household identification number coupled with the village identification number to uniquely define a household effect. The next uses the household-village identification number along with a constant household population size. The third uses the household-village identification number along with a constant household land amount. The fourth uses all of these the 3 For years prior to 1993, the price indices record only the time series (and not the provincial) dimension of price changes. 10

11 household-village identification number along with constant household population and land area (in this last case, the household per capita land area variable becomes redundant and so is dropped from the estimation). Both the dependent variables are logged, as is income, to facilitate interpretation of results as elasticities (prices and percent area sown to grain are not logged because they already are specified as percentages, in addition percent area sown to grain sometimes takes zero values). Using Breusch-Pagan tests, we fail to reject the null hypothesis of homoskedasticity in the provincial-level food expenditure regression, but reject in all other cases, at the 95 percent confidence level, or better. We are unable to test for autocorrelation in the household data (due to lack of a completely accurate household identifier), but find evidence of first-degree autocorrelation in the provincial grain consumption model. As a result of the diagnostic tests, we report heteroskedasticity-consistent standard errors for both the household food expenditure and grain consumption model results, while we report Newey-West standard errors (which correct for both heteroskedasticity and autocorrelation) for the provincial grain consumption model results. We worry about whether the direction of causality implied by our model is appropriate. Ideally we could use instrumental variables to test whether the area of land planted to grain is endogenous to our measures of food consumption. This might be the case, for instance, if preferences for grain consumption over other types of food led to increased grain acreage. However, lacking any suitable instruments, we are unable to test for or correct endogeneity. Instead, we utilize the time series dimension of the provincial panel data to perform Granger causality tests, using 3 lags of the variable measuring area 11

12 planted to grain and three lags of the referenced consumption variable, along with price and lagged income. For food expenditure, we find that percent area planted to grain Granger causes food expenditure, while food expenditure does not Granger cause area planted to grain. Adding year and province dummies to the regressions, there is no evidence of Granger causality in either direction. For grain consumption, we find no support for Granger causality in either of the basic models, but adding year and province dummies to the regressions yields a near finding of Granger causality from grain sown area to grain consumption (the p-value is 0.11). Once again there is no evidence of Granger causality in the other direction (from grain consumption to grain sown area). Therefore, the evidence we have points uniformly to causality running in the prescribed direction, from grain sown area to food consumption, and not the reverse. Figures 1 to 4 illustrate the time trends observed in our data. We observe our dependent variables (Figure 1) to exhibit similar trends across both datasets, with food expenditure rising over the sample period while grain consumption falls. The relative prices of both grain and food (Figure 2) experienced two upward surges, the first being in the early to mid 1990s and again starting in around In both cases, the relative price of grain experienced a more pronounced increase than did food. Data on the percentage of cultivated land sown to grain is illustrated in Figure 3. In the first panel, averaging across all provinces, we observe grain sown area to decline in most years until 2003, when the trend turned upward. In the second panel we observe averages constructed across seven provinces groups, constructed to resemble, as closely as possibly, China s seven zones defined in pursuit of agricultural structural strategic adjustment as laid out in the 12 th Five Year Plan (USDA Foreign Agricultural Service, 12

13 2011). 4 Here we see the evidence of growing regional specialization over time. Whereas nearly all provinces allocated between seventy and eighty percent of their cultivated land area to grain production in 1985, by 2008, we see percentages ranging from almost ninety percent in the Northeast Plain to a little more than fifty percent in South China. This finding is consistent with Tan (2008) as well as Yin, et al. (2006) who report that subsequent to the 1980s, grain production has become more concentrated in China s northern region relative to the south. In Figure 4, we compare the percent grain sown area variable across the two data sets, by province. Within each province, the trends in area sown to grain out of total area sown are roughly similar between the household and provincial datasets. Typically, the provincial percentages exceed those reported at the household level. No doubt this is the result of the broader definition of grain (including tubers and beans) that is used by the National Statistical Bureau in constructing the provincial level statistics. The difference is particularly striking for Heilongjiang province, where soybeans are an important crop. So far our data have shown evidence that despite fluctuation in grain and food prices, food expenditures in rural China have consistently trended upwards while grain consumption has trended down. Meanwhile differences in regional patterns of grain cultivation have become more pronounced, suggesting more specialization over time. If cultivation of grain has become more concentrated in regions with a comparative advantage in grain, then it is possible that the opportunity costs of the land and other 4 Group 1 (Northeast Plain) includes Heilongjiang, Jilin, and Liaoning; Group 2 (Huanghuaihai Plain) includes Hebei, Henan, and Shandong; Group 3 (Yangtze River Basin) includes Qinghai, Sichuan, Hubei, Jiangsu, and Anhui; Group 4 (Fenwei) includes Shaanxi and Shanxi; Group 5 (Hetao) includes Inner Mongolia and Ningxia; Group 6 (South China) includes Yunnan, Guizhou, Guangxi, Hunan, Guangdong, Jiangxi, Fujian, and Zhejiang; Group 7 includes Gansu and Xinjiang. 13

14 resources devoted to grain have declined. In the next section, we will explore this question in greater detail using multivariate regression analysis. Results Our first table of statistics (Table 1) offers a glimpse into differences in returns across various types of crops typically grown in rural China. Data for constructing this table are taken from the Agricultural Commodity Cost and Return Survey, compiled by the National Development and Reform Commission. The figures here represent real net profits earned (in 1985 yuan), per mu of cultivated land. Average returns to cotton, apples, and vegetables exceed returns to all four grains in every five-year interval examined, often by a considerable margin. Among the remaining two, tobacco and sugar cane, returns to the cash crop drop below returns to rice in three instances for tobacco and one instance for sugar cane, but always exceed returns on the other three grain crops. Subsidies applied to purchase of inputs, such as seeds or machinery, are already included in the net profit calculations presented here. Direct subsidies for grain growing are not included, but on the order of yuan per mu, their influence on crop choice will be small. Regression results from the household level data are presented in Table 2, while Table 3 contains the provincial level counterparts. (Summary statistics of all regression model variables are included as an appendix.) The price and income variables work best in the provincial food expenditure regressions, where we observe a consistent pattern of the negative price and positive income coefficients. We might expect a better fit of prices and income variables in the provincial regressions as they measure averages rather than 14

15 individual household outcomes, for which the idiosyncratic component may be larger. The food expenditure income elasticities, as measured in the provincial data, range from 0.36 to Though positive, their magnitudes are much smaller in the household regressions. That the elasticities are all less than one is consistent with work by Gale and Huang (2007) showing that income growth rates exceed food consumption growth rates in recent years, especially for rural households. With respect to grain consumption, the signs of the coefficients on prices and income are mixed in both the household and provincial regression results. From a theoretical perspective it is unclear what sign these coefficients should take. Given their dual role as both producers and consumers, rural households may experience a positive income effect when grain prices rise, enabling them to increase rather than decrease their consumption. At the same time, grain may be an inferior good, so that as income increases, grain consumption may fall rather than rise. Therefore, the lack of any consistent pattern in signs does not warrant serious concern. Finally, it also has been noted that over the period in question, structural change (in the form of market development, increased access to refrigeration, etc.) may better explain changes in food demand than prices and income, though the former are largely omitted from our regressions (Gale, et al., 2005). The main variable of interest is the ratio of grain sown area to total sown area. Measuring a uniform effect across all provinces our results are overwhelmingly consistent with the hypothesis that increased grain acreage leads to lower levels of both food expenditure and grain consumption. At the provincial level, a one percentage point increase in grain sown area percent leads to an approximate percent reduction in 15

16 food expenditure (columns 1 and 2, Table 3), and anywhere from to percent reduction in grain consumption (columns 4 and 5, Table 3). Among households, increases in grain sown area have a consistently negative effect that is about 10-fold smaller than that measured at the province level, a one percentage point increase in household land area devoted to grain reduces food expenditure by about percent (Table 2, columns 1-5). The household level grain consumption story is a little more complicated. In the pooled cross section, we find that a one percent increase in grain acreage yields a percent increase in grain consumption (Table 1, column 6). However, including controls for household fixed effects, the coefficient turns negative, and is significantly different from zero in two out of four cases (Table 1, columns 7-10). Inconsistency between the pooled cross section and fixed effect household results may be reconciled by noting that heterogeneity across households in dietary preference may lead some households to both produce and consume more grain. However, controlling for this preference through the household fixed effect, we once again arrive at the conclusion that more area sown to grain leads to less consumption of grain, as well as lower food expenditure. It is notable that we find a substantially larger magnitude of the effect of grain acreage at the provincial level than we do at the household level. One plausible explanation is that growing grain imposes an externality on other households in the community by depressing local grain prices. As a result, the effect of high acreage devoted to grain is much more damaging at the larger observational unit of the province than it is at the smaller observational unit of the household. 16

17 Utilizing the provincial data to measure separate effects for each of the policy regions (province groups) illustrated in the lower panel of Figure 3, we note that the impact of additional acreage sown to grain varies considerably by region (Table 3, columns 3 and 6). Increasing area sown to grain by one percentage point in Province Group 2 (which includes Henan, Hebei, and Shandong) reduces food expenditures by percent. For provinces in the South China region (Province Group 6, including Yunnan, Guizhou, Guangxi, Hunan, Guangdong, Jiangxi, Fujian, and Zhejiang), the corresponding figure is Examining the impact of growing grain on grain consumption, the magnitude of the effect rises to a decline of percent, in response to a one percent increase in area sown to grain, for Henan, Hebei, and Shandong. This effect is the largest in magnitude, though Province Groups 3 (Qinghai, Sichuan, Hubei, Jiangsu, and Anhui), 4 (Shaanxi and Shanxi), and 7 (Gansu and Xinjiang) also experience negative effects that are statistically significant. Further illustrating the dietary impact of grain production, Figure 5 plots the grain sown area coefficients from Table 3, columns 3 and 6, against actual grain sown area at the end of our sample interval, It is notable that the scatter plot does not reveal a pattern consistent with specialization according to comparative advantage. In contrast, the strongest negative impacts of grain production are experienced in regions that tend to fall in the middle of the grain sown area percent spectrum. It is notable that Province Group 2 experiences a relatively large negative impact from grain growing, both with respect to grain consumption and food expenditure, even though it continues to plant nearly 70 percent of its sown area to grain. All three provinces in this group (Henan, 17

18 Hebei, and Shandong) are among those that are most heavily targeted in campaigns to encourage grain production. Conclusions and Implications Promotion of self-sufficiency in grain over other forms of agricultural production led to tragic consequences in China s not-so-distant past. Rural economic reforms and the subsequent introduction of market mechanisms significantly lessened the problems caused by over-reliance on grain production. In the post-wto era, further market reforms have led to even greater regional specialization, partially attenuated to domestic comparative advantage. As a result, grain growers in rural China may be less disadvantaged than in the past. Many farmers today face far fewer constraints in deciding what to produce and what to eat, due to improved market functioning. However, it must be noted that there is still a high opportunity cost associated with putting land into grain cultivation, and our data show that regional specialization according to comparative advantage cannot fully explain recent patterns of grain production, there must be other forces at work. The production of grain still imposes a burden on those who grow it, though in the recent years the government subsidy may have reduced the burden to some extent. This means that farmers in regions that have been targeted to grow grain bear an opportunity cost for producing the surplus grains for supply to other regions. Local officials, mindful of national grain self-sufficiency goals, may feel pressure to encourage farmers to devote more cropland to grain in return for budget relief and career advancement. These households who allocate a higher share of 18

19 their scarce land resources to grain crops than the market would dictate consume less grain and less other food because of lower earnings. Our findings have important implications for China s future grain production policies. If the government wishes to continue to boost China s grain capacity beyond the market equilibrium, then policy initiatives must be developed to ensure that grain growers get adequately compensated for their efforts in producing surplus grains. If the country s food security benefits are sufficient to justify the costs incurred by producing excess grain, then returns to growers must be high enough to sustain desired production levels. Alternatively, policy makers in China may wish to consider whether they are better served by allowing market signals to drive production decisions entirely, with the goal of better diets to be achieved through the rising incomes that would result from a more efficient allocation of resources. 19

20 References Carter, Colin A., Funing Zhong, and Jing Zhu, Advances in China s Agricultural and Global Implications,Applied Economic Perspectives and Policy, vol. 34, number 1, pp. 1-36, Chen, Chunlai. China s Agricultural Trade Following its WTO Accession, Chapter 11 in Agriculture and Food Security in China: What Effect WTO Accession and Regional Trade Arrangements? Chen Chunlai and Ron Duncan, eds. 2008: Asia Pacific Press, Canberra. China Agricultural Yearbook Editing Committee, China Agricultural Yearbook 2010, Beijing: China Agricultural Press. Dang, Dan, Hengnan County Mobilizes Party Members and Cadres to Grow Grain, New Year Brings New Grain Measures [Hengnan Xian Fadong Dangyuan Ganbu Lai Zhong Tian, Xinnian Zhua Liang Xin Jucuo] Hengnan County Agricultural Bureau, February 29, 2012, Gale, Fred, Bryan Lohmar, and Francis Tuan. China s New Farm Subsidies, Economic Outlook Report WRS-05-01, February 2005: Economic Research Service, USDA. Gale, Fred, and Kuo Huang. Demand for Food Quantity and Quality in China, Economic Research Report no. 32, January 2007: Economic Research Service, USDA. Gale, Fred, Ping Tang, Xianhong Bai, and Huijun Xu. Commercialization of Food Consumption in Rural China, Economic Research Report no. 8, July 2005: Economic Research Service, USDA. Han, Donglin. Why Has China s Agriculture Survived WTO Accession? Asian Survey vol. 45, no. 6, November/December 2005: University of California Press. Heerink, Nico, Marijke Kuiper, and Xiaoping Shi, China s New Rural Income Support Policy: Impacts on Grain Production and Rural Income Inequality, China and World Economy, vol. 14, no. 6, pp , Huang, Jikun, and Scott Rozelle. Agricultural Development and Policy Before and After China s WTO Accession, Chapter 2 in Agriculture and Food Security in China: What Effect WTO Accession and Regional Trade Arrangements? Chen Chunlai and Ron Duncan, eds. 2008: Asia Pacific Press, Canberra. Jiang, Tingsong. WTO Accession and Food Security in China, Chapter 7 in Agriculture and Food Security in China: What Effect WTO Accession and Regional Trade 20

21 Arrangements? Chen Chunlai and Ron Duncan, eds. 2008: Asia Pacific Press, Canberra. Lohmar, Bryan, Fred Gale, Francis Tuan, and Jim Hansen. China s Ongoing Agricultural Modernization: Challenges Remain After 30 years of Reform, Economic Information Bulletin no. 51, April 2009: Economic Research Service, USDA. National Statistical Bureau, China Statistical Yearbook, Various years: China Statistics Press, Beijing. National Development and Reform Commission, Outline of Mid- and Long-term Plan for National Food Security , Xinhua News Agency, Nov. 13 th, National Development and Reform Commission, Plan to increase grain capacity by 100 billion jin ( ), Xinhua News Agency, April 8 th, People s Daily Online (English), China Allocates More Money for Subsidies to Boost Farm Production: MOF, April 7, 2011, Tan, Shuhao, Impacts of Cultivated Land Conversion on Environmental Sustainability and Grain Self-sufficiency in China, China and World Economy, vol. 16, no. 3, pp , USDA Foreign Agricultural Service, People s Republic of China 12 th Five-Year Plan. Agricultural Section). trans. Joshua Emmanuel Lagos and Zhang Lei. May 2011, Global Agricultural Information Network. Yin, Peihong, Xiuqi Fang, Qing Tian, and Yuling Ma, The Changing Regional Distribution of Grain Production in China in the 21 st Century, Journal of Geographical Sciences, vol. 16, no. 4, pp , Zhong, Funing. China s Accession to WTO: it s Impact on China s Agricultural Sector, 2001: Seminar on Free Trade and Regional Economic Integration, Geneva. Zhu, Jing. Public Investment and China s Long-Term Food Security Under WTO, pp , Food Policy no. 29, 2004: Elsevier. 21

22 Figure 1: Trends in food expenditure and grain consumption over time 22

23 Figure 2: Trends in relative prices of food and grain over time 23

24 Figure 3: National trends in percent of arable land sown to grain over time 24

25 Figure 4: Provincial trends in percent of arable land sown to grain over time 25

26 Figure 4: Provincial trends in percent of arable land sown to grain over time (continued) 26

27 Figure 5: Effects of grain sown area on food expenditures and grain consumption, by province group, plotted against 2008 percent area sown to grain 27

28 Table 1: Reported net profits, per mu, for selected grain and cash crops Grains Paddy rice Wheat Corn Soybeans Cash crops Cotton Tobacco Sugar cane Apples* Vegetables** *Data for apples is from **Data for vegetables is from 1994 and

29 Table 2: Household-level regression results (1) Food Expenditure (2) Food Expenditure (3) Food Expenditure (4) Food Expenditure (5) Food Expenditure Rural Relative Food Price Index * * (0.130) (0.158) (0.175) (0.211) (0.230) Adjusted Per Capita Net Income *** *** 8.09e-05*** 7.92e-05*** 6.79e-05*** (1.07e-05) (8.73e-06) (6.43e-06) (1.03e-05) (8.17e-06) Log of Per Capita Arable Land *** 0.167*** *** 0.566*** ( ) ( ) ( ) (0.0178) Grain Sown Area Percent *** *** * ** ** ( ) (0.0146) (0.0133) (0.0180) (0.0178) Constant 5.291*** 5.219*** 4.975*** 4.863*** 4.955*** (0.139) (0.166) (0.183) (0.220) (0.240) Year Dummies Yes Yes Yes Yes Yes Province Fixed Effects Yes N/A N/A N/A N/A Observations 50,817 50,817 50,817 50,817 50,817 R-squared Number of newid 5,810 Number of idpop 12,805 Number of idarea 19,311 Number of idpoparea 23,780 Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 29

30 Table 2: Household-level regression results, continued (6) Grain Consumption (7) Grain Consumption (8) Grain Consumption (9) Grain Consumption (10) Grain Consumption Rural Relative Grain Price Index * (0.0292) (0.0357) (0.0374) (0.0436) (0.0455) Adjusted Per Capita Net Income 3.61e-05*** 1.94e-05*** -6.44e-06** 5.50e e-06* (3.38e-06) (3.70e-06) (3.15e-06) (3.58e-06) (3.91e-06) Log of Per Capita Arable Land 0.148*** 0.191*** *** 0.610*** ( ) ( ) ( ) (0.0150) Grain Sown Area Percent *** *** ** ( ) (0.0131) (0.0134) (0.0166) (0.0173) Constant 5.409*** 5.414*** 5.450*** 5.302*** 5.605*** (0.0491) (0.0685) (0.0719) (0.0829) (0.0864) Year Dummies Yes Yes Yes Yes Yes Province Fixed Effects Yes N/A N/A N/A N/A Observations 50,841 50,841 50,841 50,841 50,841 R-squared Number of newid 5,810 Number of idpop 12,807 Number of idarea 19,321 Number of idpoparea 23,790 Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 30

31 Table 3: Provincial-level regression results (1) Food Expenditure (2) Food Expenditure (3) Food Expenditure Rural Relative Food Price Index *** *** *** (0.0880) (0.105) (0.1092) Lagged Log of Adjusted Per Capita Income 0.632*** 0.364*** *** (0.0266) (0.0579) (0.0653) Grain Sown Area Percent *** ** (0.0724) (0.152) Grain Sown Area Percent, Province Group (0.6386) Grain Sown Area Percent, Province Group ** (0.3536) Grain Sown Area Percent, province group (0.2230) Grain Sown Area Percent, province group (0.6560) Grain Sown Area Percent, province group (0.4204) Grain Sown Area Percent, province group *** (0.1869) Grain Sown Area Percent, province group (0.2720) Constant 2.771*** 4.414*** *** (0.233) (0.419) Year Dummies Yes Yes Yes Province Fixed Effects No Yes Yes Observations R-squared Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 31

32 Table 3: Provincial-level regression results, continued (4) Grain Consumption (5) Grain Consumption (6) Grain Consumption Rural Relative Grain Price Index * (0.0554) (0.0426) (0.0395) Lagged Log of Adjusted Per Capita Income ** * (0.0236) (0.0421) ( Grain Sown Area Percent ** ** (0.0872) (0.156) Grain Sown Area Percent, Province Group (0.607) Grain Sown Area Percent, Province Group *** (0.226) Grain Sown Area Percent, Province Group * (0.179) Grain Sown Area Percent, Province Group ** (0.443) Grain Sown Area Percent, Province Group (0.347) Grain Sown Area Percent, Province Group (0.145) Grain Sown Area Percent, Province Group *** (0.237) Constant 5.622*** 6.303*** 6.195*** (0.179) (0.273) (0.270) Year Dummies Yes Yes Yes Province Fixed Effects No Yes Yes Observations R-squared Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 32

33 Appendix: Summary statistics Household Data ( ) Mean St Dev Min Max Adjusted per capita food expenditure Per capita grain consumption Rural relative food price index Rural relative grain price index Grain sown area ratio Adjusted per capita income Provincial Data ( ) Mean St Dev Min Max Adjusted per capita food expenditure Per capita grain consumption Rural relative food price index Rural relative grain price index Grain sown area ratio Adjusted per capita income Provincial Data ( ) Mean St Dev Min Max Adjusted per capita food expenditure Per capita grain consumption Rural relative food price index Rural relative grain price index Grain sown area ratio Adjusted per capita income