Grain into gold? The impact of agricultural income shocks on rural Chinese households∗ Jessica Leight April 9, 2014 Abstract This paper seeks to estimate the effect of positive lump-sum income shocks experienced by Chinese households beginning in 1993, analyzing the evolution of an unusual grain quota system. This system mandated that households sell part of their grain output to the state at a below-market price that increased over time, generating positive income shocks that varied cross-sectionally in accordance with crop composition. The identification strategy seeks to exploit climatic variation in the propensity to cultivate different crops in conjunction with variation in the quota price over time to identify quasi-random variation in the size of the income shocks. The results suggest that investment in agriculture falls as households gain increased income, while there are sharp increases in non-staple consumption; there is also evidence of increased employment in state-owned enterprises and in household industrial enterprises. This is consistent with the hypothesis that increased growth in agriculture could lead to increased investment in non-agricultural activities. 1 Introduction The question of the degree of complementarity between agricultural and industrial development has long been of primary interest to economists. A substantial literature written in the early postwar years argued that dramatic growth in agriculture was the necessary prerequisite for any similar industrial “take-off” in the developing world (Lewis 1954, Nurkse 1953, Rostow 1960). At the same time, an alternate view prominently espoused by Hirschman (1958) held that agriculture had few linkages to stimulate growth in other sectors of the economy and thus the emphasis should be on “unbalanced growth” that favored industry. More recently, policy analysts have cited the East Asian miracle economies as well as a range of other case studies as evidence that substantial agricultural growth has to precede industrial expansion in a developing economy (Birdsall, Ross & Sabot 1995, Mellor 1995). Clearly, an increase in the return on investments in agriculture will generate both a substitution effect, unambiguously positive for investments in agriculture, and a positive income shock for rural households. Theorists of positive linkages between agricultural and industrial ∗ Preliminary and not for citation. Email: jessica.leight@williams.edu 1 growth have variously argued that this increase in agricultural income leads to an increase in demand for agricultural inputs or consumption goods that engenders industrial growth; generates outmigration of labor into other sectors; or stimulates new investments by rural households in non-agricultural activities. Identifying the effect of a quasi-exogenous increase in agricultural income on rural households’ economic decisions can serve to clarify whether any of these hypotheses hold, and potentially contribute to the ongoing debate about the relationship between agricultural and industrial growth. This paper estimates the impact of a large positive income shock to agricultural households on investment in agriculture and non-agricultural activities as well as consumption in rural China. In the post-1983 period, all rural households in China were required to sell a fixed quota of grain to the state at a below-market price as part of the so-called Household Responsibility System implemented following decollectivization and described in more detail below. However, the central government began to raise this price rapidly in the mid-1990s (Huang & Rozelle 2002), a shift that was equivalent to a large reduction in the size of the lump-sum tax imposed on rural households (Huang 1998), or conversely, a sizeable positive income shock. Crucially, however, the size of this shock varied systematically as a function of the composition of crops cultivated, given the stark difference in the treatment of different crops under the quota system (Huang, Rozelle, Ha & Li 2002). The identification strategy in this paper exploits this cross-sectional variation in a differencein-difference framework that compares the patterns of investment in agricultural and nonagricultural activities over time in households that receive income shocks of varying sizes as a result of the increase in quota prices. Predominantly rice-growing areas systematically experience larger income shocks given that they are subject to larger mandatory quota quantities than predominantly wheat-growing areas. While cultivation of rice is itself an endogenous household decision, the specification of interest will analyze the impact of increasing quota income for areas that have a greater propensity to cultivate rice based on their climatic conditions, conditional on province-year fixed effects. Moreover, the rural Chinese context is a particularly fruitful one in which to examine the impact of rural income shocks given that widespread rural industrialization allows for many households to diversify over a range of economic activities, and allows the identification of changes in this pattern of diversification over time. The results show that an increase in income leads to a decrease in agricultural investment and an increase in non-agricultural investment as well as labor outside the household. There is also a substantial increase in consumption, particularly of non-staple goods, and a large increase in borrowing. This suggests that the income effect on agricultural investment is negative, while it is positive for a range of measures of investment in non-agricultural activities. Moreover, the implied magnitudes are substantial: the local average treatment effect suggests that each 100 yuan (approximately $15) of quota income earned by households induced to cultivate rice by their climatic conditions increases their probability of starting an industrial household business or obtaining external employment in a state-owned enterprise by 50%. The results are robust to a series of specification checks. There is no evidence that these 2 patterns reflect differential shocks in policy or other output prices that are correlated with the quota price, or differential trends in areas with different climates. There is also no evidence that the results reflect strategic behavior by households designed to manipulate the assigned quota quantity, or differential quota enforcement. This paper contributes to the extant literature on the relationship between economic and industrial development at the micro level. In the theoretical literature, Matsiyama (1992) argues that the relationship between industrial and agricultural growth should depend on the relative openness of the local economy, and Kogel & Prskawetz (2001) construct a two-sector model in which an increase in agricultural productivity matches the stylized facts of the British Industrial Revolution. Empirically, research has been hampered by the absence of many plausibly exogenous shocks to local economic development. Reardon, Delgado & Matlon (1992) analyze the relationship between rural income and income diversification in Burkina Faso and also provide a summary of an older literature analyzing correlations between income and entry into new rural sectors in both South Asia and Africa. More recently, Foster & Rosenzweig (2004) estimate the impact of shocks to the returns to agriculture in India induced by the adoption of Green Revolution technology and find that industrial growth is fastest in areas where agricultural growth is lagging. Jedwab (2011) analyzes cocoa booms in Ghana and Ivory Coast and finds that positive price shocks in cocoa lead to an increase in urbanization. Kaboski & Townsend (2011) evaluate the impact of a governmentsponsored microcredit program as a major income shock in rural Thailand on consumption and investment, but do not consider effects across different sectors. A related literature has examined the impact of government cash transfer programs on household investment and entry into new sectors. Gertler, Martinez & Rubio-Codina (2012) and Sadoulet, de Janvry & Davis (2001) find that households who benefit from cash transfer programs in Mexico invest cash transfers in productive assets, resulting in a long-term increase in consumption; Gilligan, Hoddinott & Taffesse (2008) find similar results examining a government assistance program in Ethiopia. Other literature found that old-age pensions in South Africa lead to increased migration (Ardington, Case & Hosegood 2009), in contrast to earlier work that found that the pension lead to a decrease in labor supply by prime-age adults (Bertrand, Mullainathan & Miller 2003). The paper proceeds as follows. Section 2 outlines the institutional structure facing rural Chinese households in this period, and Section 3 describes the data. Section 4 presents the identification strategy and the first stage. Sections 5 and 6 presents the two-stage least squares results and a set of robustness checks, and Section 7 concludes. 2 2.1 Institutional background Household Responsibility System in China Between 1962 and 1978, agricultural production in China was highly collectivized. The primary unit of production was the so-called production team, a unit consisting of 20 to 30 households 3 that jointly farmed agricultural land and sold the resulting output, distributing the associated income to participating laborers according to a system of workpoints intended to reward labor, skill and political commitment. The team farming system followed an even more extreme form of collectivization implemented during the Great Leap Forward between 1958 and 1962, in which land and labor were collectivized in communes of 6,000-8,000 households (Chinn 1979). Throughout this period of institutional change, the overarching imperative of agricultural policy under Mao was to maximize grain production in order to subsidize the politically important urban population and support a major drive to industrialization, a goal enforced using substantial mandatory production quotas and low procurement prices. By 1978, the cumulative impact of these policies in the agricultural sector was disastrous, leading to low rural income, land degradation and a severe undersupply of non-grain crops (Walker 1984). As a result, the new government led by Deng Xiaoping, acceding to power shortly after Mao Zedong’s death, introduced major changes in agricultural policy. First, the household was reinstated as the primary unit of agricultural production under a system variously known as household contracting or the household responsibility system. Each household was provided with an allocation of “contract” land for its own use, while land title continued to be held by the village. Land was also subject to periodic mandatory reallocations by village leaders (Keliang, Prosterman, Jianping, Ping, Reidinger & Yiwen 2007). The household also committed to the delivery of a fixed amount of quota grain sold to the state at a preset price, in addition to taxes paid. Excess production could then either be sold to the state at a higher, above-quota price, or at rural markets (Lin 1992), with the household having full rights over residual, post-quota income. At the same time, the state’s system of agricultural targets and agricultural procurement was overhauled. Prices for government procurement of most agricultural goods, previously so low that they often did not cover costs, were raised substantially. In addition, the previous complex system of targets for sown area, inputs, production and yield for a variety of agricultural productions was simplified to government procurement targets for key agricultural goods only (Lin 1992). These changes were implemented in a piecemeal fashion between 1979 and 1983, beginning with a few isolated provincial or local experiments, and subsequently spreading widely to a point of almost total decollectivization by the end of 1983 (Unger 1985). The establishment of the household responsibility system led to a substantial increase in the growth rate of agricultural output, which had been only 2% over the previous 25 years. Between 1978 and 1984, agricultural output increased nearly 8% annually. One analysis estimated that roughly half of this growth was due to increased use of inputs, particularly fertilizer, and half to the assignment of land use rights to households via the household responsibility system (Lin 1992). 2.2 Grain policy A key dimension of agricultural policy in the post-Mao period has been mandatory grain procurement by the state, widely known as the grain quota system. The grain procurement system has also been the subject of repeated reforms since 1983, usually occurring in a cycle in which 4 an attempt at liberalization of sales is followed by inflation or declining supplies and then a rapid retrenchment. In 1985, it was announced for the first time that procurement contracts were to become voluntary, a change retracted the following year. In 1993, there was another abortive attempt at liberalization in which the central government announced that the grain bureaus responsible for procurement in rural areas would be privatized and thus converted into trading companies. This initiative was held in check and then reversed following a drop in production and a rise in prices (Sicular 1995). These periodic reversals reflect a consistent stance of governmental unease toward grain markets and their inclination to regularly impose new controls on those markets to ensure procurement targets are met and the goal of self-sufficiency in grain production is not imperiled (Oi 1999).1 Following the abandonment of these attempts at reform, the quota system has remained in place with no major structural changes. The evidence suggests that grain quotas are mandatory and enforced. The volume of grain quotas did decline after 1995 relative to earlier in the decade; subsequent surveys have found that state procurement accounts for 25-30% of rural households’ grain output (Dewen Wang & Rozelle 2003, Sicular 1995). Dewen Wang & Rozelle (2003) provide a detailed analysis of the mechanics of the operation of the quota system during the period examined here. Quota prices were set by the central government, while quota quantities in form of grain deliverable per household were generally set by county leaders for all villages within their jurisdiction, and varied with village characteristics. The authors also confirm that the quota obligation uniformly comprises “well less than total production.” In the primary sample of interest in this analysis, quota sales account on average for 10% of rural households’ grain output between 1993 and 2002. Figure 1 shows a histogram of quota quantity as a proportion of total grain quantity produced at the household level (averaged over all years in which the household is observed). It is evident that the vast majority of households sell less than 20% of their total output as quota sales. While there is no direct evidence about quota enforcement (i.e., quota sales as a proportion of the quota quantity fixed) in the primary years of analysis, evidence from earlier surveys in the same sample suggests that 95% of quota quantity contracted is delivered.2 Households who fail to produce the quota quantity may face the obligation either to purchase grain at the market price and resell it to the government, or pay the equivalent in cash. Empirically, evidence from this dataset suggests both cases are rare.3 More importantly, there was a substantial increase in procurement prices beginning in 1995 1 Commercial production of grain in China has remained minimal. Literature about the evolution of agriculture has noted that production of pork, poultry and eggs has shifted to commercial farms, but there is no evidence of important commercial production in other crops (Fuller, Beghin, Cara, Fabiosa, Fang & Matthey 2003). 2 Village leaders report the quota quantity contracted in surveys in 1990, 1991 and 1993; households report this data in surveys between 1986 and 1991. 3 In this data, village leaders report in surveys in 1990, 1991 and 1993 what fraction of overall quota sales in their village were re-sales of purchase grain or cash payments by households who did not produce the grain themselves; these sales accounted for only 1.5% and 6% of total quota sales, respectively. Households also report in surveys between 1986 and 1991 what fraction of their quota sales corresponded to re-sales and cash payments, and report these unusual sales were equal to 1.4% and 1.7% of the total, respectively. 5 in response to declining production and an increased emphasis on grain self-sufficiency at the provincial level (Huang & Rozelle 2002). This increase has been identified as one of the key factors in the dramatic decline in rural poverty observed in China in the 1990s (Ravallion & Chen 2007), and is equivalent to a reduction in the magnitude of the lump-sum tax imposed on farmers via the mandatory quota system (Huang 1998). It is also useful to note that quota policy was far from monolithic, and in particular, the treatment of different grains was very different. Rice consistently was the crop most heavily penalized by the quota policy. Procurement prices for rice were lower, and rose more slowly. In addition, the quota quantity consistently constituted a higher proportion of total production for rice producers than for wheat producers (Huang et al. 2002). Quota policy penalized rice more heavily because first, the government particularly valued the establishment of large stocks of government-owned rice in order to meet urban demand for its consumption, and second, wheat and other grain crops were subject to greater competition from imports. The identification strategy in this paper will exploit this systematic variation in quota policy by crop in order to generate cross-sectional variation in the magnitude of the income shock induced by shifts in the quota price over time. 2.3 Conceptual framework This section will outline a very simple conceptual framework that will be useful in describing the quota policy’s effects. Assume households produce grain output G, and quantity Q must be sold at the quota price Pq . Remaining output is sold at the market price Pm . The quota price is fixed prior to the harvest; the market price is determined following the harvest and can take one of two values. A higher market price is denoted P̄m and a low market price is denoted P m . Households’ income I under the quota system can thus be written as follows. I = QPq + (G − Q)Pm (1) The implicit lump-sum tax imposed by the quota system is Q(Pm −Pq ). The size of this implicit tax varies with the state of the world in a given year: it will be larger when the market price is higher, and smaller when the market price is lower. Accordingly, the quota serves to dampen the variability of income in a given year. An increase in the quota price Pq will increase mean income, while the variance of income across states in a given year will be unaffected (i.e., the risk-dampening function of the quota system will be maintained). 3 Data The data employed here is the China Research Center for the Rural Economy (RCRE) panel, collected in a sample of 299 villages in 13 provinces in China every year between 1986 and 2001, excluding 1992 and 1994. A randomly selected sample of households in each surveyed village forms the panel; the mean number of households in a village-year cell is 69. The surveys prior to 6 1993 were considerably briefer and did not collect data on a number of the outcomes of interest in this paper; accordingly, the primary analysis is restricted to the post-1993 panel. In addition, the analysis employs climatic data from a network of stations in China collected by the Carbon Dioxide Information Analysis Center (CDIAC); both temperature and precipitation are recorded on a monthly basis. Using data on the latitude and longitude of the county centroid, measures of temperature for each county and year are constructed from the station data by interpolating using the inverse distance weighting method. Each interpolation employs only data from stations within 150 kilometers of the county centroid. This data set also provides data on the locally measured price for both mandatory grain quota sales and market sales, which is useful in light of the widespread heterogeneity in grain policy already discussed.4 Both the quota and the market price are observed at the household level, and calculated by dividing the reported household value of sales at the market (quota) price by the reported household quantity of sales at that price. Crucially, grain quota sales are not reported separately by type of grain. Accordingly, it is only possible to estimate a mean quota price in each village-year cell; the price cannot be accurately measured for various types of grain. Figure 2a shows the market and quota price of grain by year between 1993 and 2002. A large gap between the quota price and market price is evident early in the period, despite the fact that both market and quota prices are increasing; in 1993, the quota price is 30% lower than the market price. By the end of the period, the quota price is nearly equal to the market price (around 3% lower on average). Figure 2b shows the quota and market price in the full sample of surveys beginning in 1986. The data between 1986 and 1991 will not be employed in the primary analysis due to the absence of a number of primary outcomes of interest as discussed above, but will be used in robustness checks. While the price for grain quota sales varies over time, some of this variation is driven by underlying variation in the market price. Accordingly, the mean quota price in each village v in province p in year t is regressed on the corresponding market price in the following equation; λvp denotes a village fixed effect for village v in province p that absorbs any systematic crosssectional variation in price. Observations corresponding to the top and bottom 1% of observed market and quota prices are trimmed to avoid undue influence of outliers.5 q m Pvpt = βPvpt + λvp + vpt (2) The residual from this regression is denoted P̃vpt . 4 After they have fulfilled their grain quota, rural households also have the option to sell their excess production to the government at a higher, negotiated procurement price. Huang et al. (2002) provide data that these negotiated procurement prices are generally intermediate between the quota price and the wholesale market price. In this data, it is impossible to distinguish between genuine market sales and sales at the higher price paid by government buyers. However, no distinction is made between these two types of sales, as both represent the price that the rural producer would face for the marginal unit of production, which is generally not the quota price. The market price is thus used to denote the price for marginal grain sales. 5 This regression is run at the village-year level, rather than at the household level, due to the large number of missing variables at the household level corresponding to households that report no market sales in a given year. At the village-year level, every village-year cell has sufficient data to estimate a market price and a quota price. 7 It is important to note that this measure of the price wedge between the quota and market price is by construction uncorrelated with the year-on-year variation in the market price. P̃vpt captures only the component of the quota price that does not reflect underlying variation in the market price. In the primary analysis, it will be used as an instrument for the observed quota price to capture variation in the magnitude of the income shock to the household represented by shifts in the quota price over time. Table 1 provides some summary statistics about the primary sample. The average household consists of four members, cultivating an area of around 1.4 hectares primarily in grain. 90% of households own at least one productive asset (e.g., animals, tools or machinery); around a third of them report a non-agricultural business, and more than half report that at least one household member is engaged in labor outside the household. Net income is around $800 annually, or around $200 per capita. 4 Identification strategy 4.1 Variation in crops and quota quantity The primary independent variable of interest in this analysis is quota income, defined as the income received by households from their grain quota sales. Quota income shifts over time and space as a function of changes in both the quota quantity and the quota price. Analysis of government quota policy in China typically indicates that quota quantities, once set by county leaders, change only incrementally, while the central government has discretion over the grain quota price and will change it annually in accordance with shifting policy goals (Rozelle, Park, Huang & Jin 2000, Lin 1991). Accordingly, a plausible null hypothesis is that the primary source of variation in quota income stems from cross-sectional variation in quota quantity interacted with a time-varying price: ∆R = Q ∗ ∆P̃ . This hypothesis can be confirmed in the data. Table 2 shows the results from a series of regressions that decompose the variation in quota quantity and price across space and time. The first four columns show regressions of the quota quantity and then the price on village fixed effects and year fixed effects; note the measure employed here is again the quota price residual, P̃ . As predicted, village fixed effects account for around 30% of the variation in quota quantity, while year fixed effects account for only 1%. For the quota price, however, the results are exactly inverted: village fixed effects account for less than 1% of the variation in the quota price, and year fixed effects account for nearly 50%. Accordingly, it seems plausible to employ a time-invariant, cross-sectional variable correlated with quota quantity and interact it with price P̃ in order to generate an instrument for quota income. Given the widespread discussion in the literature of the systematic cross-sectional differences in quota policy implementation that corresponds to variation in crop composition, variation in the propensity to cultivate rice — heavily penalized by the quota system — will be the key source of variation this analysis will exploit. In China, as in other countries, the suitability of a particular area for rice cultivation is 8 partially determined by temperature: more specifically, the total accumulated temperature over a year for days with temperature greater than 10 degrees Celsius. Analysis of the ecology of rice cultivation in China has argued that the cumulative temperature must exceed 2000 degrees to cultivate one rice crop and 4500 degrees to cultivate two crops (Shao, Fan, Liu, Xiao, Ross, Brisco, Brown & Staples 2001). This induces an (unsurprising) correlation between the average temperature and area of land cultivated in rice, as well as — in this context — the quota quantity. The key temperature variable employed in this analysis will be the mean cumulative temperature of days with temperature greater than 10 degrees Celsius over the years recorded from 1986 to 1994, the period corresponding to the panel years of the RCRE survey in which weather station data has been published.6 This variable, denoted total temperature, is then normalized to have mean zero and standard deviation one. Correlations between the temperature variable as constructed and area cultivated in rice can then be estimated, conditional on province-year fixed effects; standard errors are clustered at the level of the county, the level at which temperature is measured. Note that for the vast majority of the sample, only one village is sampled in each county, and thus cross-village heterogeneity is roughly equivalent to cross-county heterogeneity.7 Province-year fixed effects are denoted νpt , area cultivated in rice by household i in village v, province p and year t is denoted Aivpt and quota quantity is denoted Qivpt . The equations of interest are thus written as follows: Aivpt = βT empvp + νpt + ivpt (3) Qivpt = βT empvp + νpt + ivpt (4) Columns 5 and 6 of Table 2 show the results, suggesting that a one standard deviation increase in the total temperature increases the area cultivated in rice by about 31% and the quota quantity by around 45% for localities within the same province and year. This is an effect of substantial magnitude, and it does not rely on the cross-provincial heterogeneity evident in China between southern provinces that primarily cultivate rice and northern provinces that primarily cultivate wheat. In this data set, about 50% of the variation in the rice area cultivated is accounted for by province-year fixed effects. Accordingly, the identification strategy exploits about half of the variation in crop area. 4.2 First stage This analysis suggests that temperature interacted with a time-varying measure of quota price may be an appropriate instrument for quota income. Column 1 of Table 3 shows the first stage of interest, regressing quota income on the interaction of temperature and price conditional on village and province-year fixed effects; standard errors are clustered at the village-year level.8 6 Because the data only reports monthly mean temperature, not daily mean, the mean monthly temperature is imputed to every day within that month. 7 There are only seven pairs of villages located in the same county out of 149 villages in the core sample. 8 The top 5% of observations of quota income are trimmed to remove the influence of outliers. 9 Again, the price variable employed is uncorrelated with the market price. Rivpt = βT empvp × P̃vpt + λvp + νpt + ivpt (5) A one-standard deviation increase in the interacted instrument leads to an increase in quota income of around 15%, and the relationship has substantial predictive power with a F-statistic around 22. Column (2) shows the first stage with the sample restricted to villages that are homogeneous in rice or wheat cultivation. This will be the primary first stage of interest, and the sample restriction and its justification will be outlined further in the next section. The exclusion restriction for this specification requires that an increase in the quota price has no differential impact across areas with different climatic conditions, as measured by the total temperature, other than a varying lump-sum income shock. 4.3 Specification checks There are a number of potential challenges to this specification. First, the quota price could in fact be a price shock, rather than a pure income shock. Second, the implicit decomposition of quota income postulated is Q × P̃ , where Q is treated as time-fixed and endogenous and P̃ as time-varying and exogenous. However, there could be changes in quota quantity over time that are also correlated with the instrument (a first stage in ∆Q), or systematic cross-sectional variation in price (a first stage in P̃ ) that would render this decomposition invalid. Third, shifts in temperature could induce systematic changes in quota income other than those mediated by changes in rice area cultivated. Each of these potential challenges will be addressed in turn. Quota price as an income shock The key assumption throughout this analysis is that the quota price is not the price of the marginal unit of grain sold and thus does not affect a household’s decision about the optimal level of production; i.e., a shift in the quota price is an income effect rather than a price effect. However, there are several ways in which this assumption could be violated: substitution in and out of agriculture, substitution between crops, and the presence of households for which quota production is equal to total production. The first potential channel for a price effect of the quota price is relevant if shifts in the quota price induce households to substitute in and out of agriculture entirely. The status of rural households who are not cultivating grain in the quota system is unclear; some surveys have found that these households are still required to provide grain they have purchased or an equivalent cash payment, even if they do not cultivate grain themselves (Brandt, Rozelle & Turner 2004). Regardless of whether these households are completely exempt or are subject to an equivalent tax, however, when they optimize their production decisions and decide whether to cultivate grain again in the next year, the quota price represents the price of the marginal unit of grain production. Unsurprisingly, however, complete exit from grain cultivation is rare among rural households in China; only 10% of households in the sample report even one year in which they do not cultivate grain, and on average these households still report cultivation in about half the years 10 surveyed. However, for analytical clarity, all households that do not report grain cultivation in every year have been dropped from the analysis to focus purely on households for whom a shift in quota price can be viewed unambiguously as an income shock. Importantly, this population of households that are not purely agricultural is balanced across areas of different temperature, and thus dropping these households does not create differential patterns of selection into the sample in treatment and control areas. This strategy may pose a challenge for external validity, as the resulting estimates cannot be extrapolated to households for whom exiting agriculture entirely is a meaningful counterfactual. However, given the extremely small number of households that show this pattern and the fact that a much more common empirical regularity — as will be elaborated further below — is households that simultaneously pursue agricultural production, non-agricultural household production and/or employment outside the household — this does not seem to be a major concern. A second channel through which an income effect could be a price effect is if households switch crops in response to changes in the quota price and begin selling a larger quantity of rice as their mandated quota (rather than the smaller mandated quantity of wheat) when the price increases. While this may not systematically be a logical decision if the quota price remains below the market price, as it does on average, it could be locally optimal if the quota price is close to the market price in some village-years. In this case, if households have some discretion over the quantity they sell, then a change in the quota price can no longer be plausibly interpreted as a change in a lump-sum tax. Alternatively, if non-compliant or partially compliant households choose to comply with the quota system when the price increases, this could also be problematic for the identification strategy. For these households, the marginal price remains the market price throughout, but the characteristics of the households earning quota income may then be systematically different when the price is high compared to when the price is low. In order to test this hypothesis, villages are classified as heterogeneous or homogeneous in the primary grain crops of interest (rice or wheat) using a simple rule: any village in which the total amount of both rice and wheat cultivated over all households and years reported exceeds zero are denoted as heterogeneous cultivators.9 The remaining villages (constituting approximately 57% of all observations) are classified as homogeneous cultivators. The test used to designate a village as homogeneous is quite stringent in order to ensure that the resulting classification truly reflects climatic suitability for rice and wheat and not individual selection into different crops. Columns 3 and 4 of Table 3 show the results of the following regression, estimated for both homogeneous and heterogeneous villages, denoted “Hom” and “Het” respectively. Standard errors are clustered at the village-year level. Qivpt = β P̃vpt + λvp + ivpt (6) The objective is to test whether quota quantity varies year-on-year with changes in quota price, 9 Observations reporting cultivation of less than .01 hectare rounded down to zero. 11 conditional on cross-sectional fixed effects, in homogeneous and heterogeneous villages. The results show that the relationship in homogeneous villages is close to zero and insignificant, while the coefficient in heterogeneous villages is large and significant. This suggests that either households are crop-switching or there are other inconsistencies in quota implementation in heterogeneous areas that allow for differential compliance, a reasonable assumption given that enforcement of grain policies is likely to be more challenging in these areas. Accordingly, the primary sample will also be restricted to villages that are homogeneous in grain production of rice or wheat. This also applies to subsequent specification checks reported in Table 3. A third channel through which the income effect of quota production could become a price effect is if households are constrained in quota production: i.e., they are selling all their grain production as quota production. Only around 1% of household-years in the relevant sample report grain production equal to quota sales; accordingly, this does not seem to be a large firstorder effect. Columns 5 and 6 of the same table show the results of the following regression, where Civpt is a dummy for households that are constrained in quota production. The objective is to test whether there are more constrained households (in levels) in areas with different total temperature, or a significant shift in the number of constrained households in areas with different temperature when the price begins to increase. Both coefficients are highly insignificant, suggesting that households on the margin of quota production are not a significant source of bias in these results. Civpt = βT empvp + νpt + ivpt (7) Civpt = βT empvp × P̃vpt + λvp + νpt + ivpt (8) Decomposition of quota income Columns 7 and 8 of Table 3 show the results of two regressions that test the decomposition of quota income. The objective of these regressions is to check whether residual variation in quantity over time and variation in quota price can be treated as exogenous conditional on province-year fixed effects: i.e., to test whether there is a first stage in ∆Q or P̃ . To do so, equation (3) is re-estimated with new dependent variables Qres,ivt , defined below, and P̃vt , the quota price. Qres,ivt = βT empvp + νpt + ivpt (9) P̃vt = βT empvp + νpt + ivpt (10) Qres,ivt is defined as the residual from regressing the quota quantity Qivpt on village fixed effects, corresponding to the residual variation in quota quantity after cross-sectional variation by village is partialled out: Qivpt = λvp + ivpt (11) The results show coefficients that are small in magnitude relative to the standard deviation of the dependent variable (both are around .05 of a standard deviation) and insignificant, 12 confirming the hypothesis that the only robust correlation is between mean quota quantity by village and temperature.10 Relationship between climatic conditions and quota quantity The proposed specification also assumes that the positive relationship between a climatic measure of the propensity to cultivate rice and quota quantity is mediated entirely by change in the area of rice cultivated. If this were not the case, then it could be that areas with different climatic conditions are endogenously selecting into higher quota quantities due to unobserved political or economic characteristics, and this correlation could be problematic if these unobserved characteristics are also time-varying in conjunction with the quota price. In order to test this hypothesis, a second residual is estimated from the regression of quota quantity on the area cultivated in rice: Qivpt = βAivpt + ivpt (12) This residual is denoted Qares,ivt and is regressed on the temperature measure in the following specification. Qares,ivt = βT empvp + λvp + ivpt (13) The results are shown in Column 7 of Table 3. The relationship between the quota quantity residual and the temperature measure is small in magnitude (around .17 of a standard deviation) and statistically insignificant. Thus again, there is no strong evidence that a shift in climate is correlated with changes in other unobservable characteristics beyond the relationship with rice area. To sum up, the specification checks are consistent with the shocks to quota income representing income shocks that vary cross-sectionally in accordance with temperature and propensity to cultivate rice, and over time as the quota price varies. Again the first stage of interest, restricting the sample to homogeneous villages only, is shown in Column (2) of Table 3. The F statistic is 13, indicating that weak instrument bias is not a major challenge for this analysis. Alternate climatic measures This analysis may raise the question about why temperature is used as a primary measure of propensity to cultivate rice, rather than rainfall, given that rainfall is also widely identified as a key determinant of the suitability of a region for ricegrowing. Unsurprisingly, there is also a correlation between mean reported rainfall and the area cultivated in rice and in this sample. However, the correlation between rainfall and quota quantity is weak and inconsistent, primarily because (unlike temperature) there also seems to be a direct relationship between rainfall and quota quantity that is not mediated through rice area: in other words, the test implemented by estimating equation (13) fails. 10 This result may initially seem counterintuitive given the evidence in the literature cited above that the mean quota price is also generally lower for rice, which would be expected to induce a similar correlation between quota price and a propensity to cultivate rice. In fact, this correlation is evident in a cross-section across provinces, but not within provinces. Residual price variation within a province-year is idiosyncratic. 13 In fact, there is a relatively strong independent relationship between rainfall and industrial employment (conditional on province-year fixed effects), and the literature has highlighted that areas with greater relative industrialization generally have lower grain quotas (Dewen Wang & Rozelle 2003). This independent correlation renders the first stage between rainfall and quota quantity weak. For temperature, the comparable correlation between total temperature and industrial employment is weak and statistically insignificant (T-statistic of 1.2). The results will be tested for robustness to potential bias induced by varying industrialization correlated with climatic conditions in Section 6. 5 Results 5.1 Ordinary least squares The ordinary least squares specification of interest can be written as follows, where Xivpt denotes economic outcomes of interest and the primary independent variable is Iivp,t−1 , lagged income from grain quota payments. Village and province-year fixed effects are included, and standard errors are clustered at the village-year level. Xivpt = βIivp,t−1 + λvp + νpt + ivpt (14) Eight summary measures will be employed as dependent variables: agricultural sown area and production, agricultural inputs, non-agricultural labor and investment, labor outside the household, migration, grain consumption, non-grain consumption, and borrowing. For each class of outcome, summary variables are generated by standardizing each variable to have mean zero and standard deviation one and calculating the mean. For the ordinary least square specification, only the results for the summary outcomes are reported, with the full set of results provided in the appendix.11 This specification has a clear source of bias: namely, the endogenous determination of quota quantity by county leaders. Dewen Wang & Rozelle (2003) find in regressing quota quantity on a range of explanatory variables at the village level in a multivariate framework that quota quantity is generally positively correlated with both income and the relative salience of agriculture. Evidence on this point can also be drawn from this dataset by regressing the same economic outcomes of interest Xivpt on quota quantity in 1993, Q1993 ivp , again with village 11 The summary measure for agricultural production is the mean of total area sown, area sown in grain, wheat, rice and cash crops, and grain and cotton produced. The summary measure for agricultural inputs is the mean of quantity of grain seeds, cash crop seeds and fertilizer employed as well as dummy variables for owning any agricultural assets, tools, animals or machinery. Non-agricultural investment is the mean of dummy variables for investing labor or capital in an industrial, construction, transportation or retail household business. Outside labor is the mean of a dummy variable for working in an outside enterprise and working in a collective, SOE or private enterprise. Migration is the mean of dummy variables for working outside the township or outside the county, a dummy equal to one if there is a reported change in household size, and a dummy for receiving remittances. Grain consumption is quantity grain reported consumed; non-grain consumption is the mean of vegetables, oil, meat, milk, eggs and sugar consumed. The summary measure for borrowing is simply the variable for whether a household has obtained a loan. 14 and province-year fixed effects. Xivpt = βQ1993 ivp + λvp + νpt + ivpt (15) This regression captures whether households with greater quota quantities at the start of the period show differential patterns in primary economic outcomes in subsequent years, conditional on village and province-year fixed effects. The results shown in Panel A of Table 4 show coefficients that are generally strongly positive: households with higher quota quantities at the start of the period show more rapid growth in agricultural inputs and outputs, are more likely to report non-agricultural household businesses and to migrate, and consume more grain and non-grain items. Accordingly, the OLS estimates are expected to show strong upward bias on measures of investment in agriculture and non-agricultural businesses, as well as an upward bias on measures of consumption. Panel B of Table 4 reports the OLS estimates, where quota income is measured in hundreds of yuan. All the coefficients are positive, though the coefficients on outside labor and borrowing are insignificant. The magnitudes imply an increase in quota income of 100 yuan (around $16 dollars) leads to an increase in agricultural investment of between .4 and .6 standard deviations, and an increase in consumption of between .2 and .6 standard deviations. 5.2 Two-stage least squares Panel C of Table 4 shows the results from re-estimating (14) in a two-stage least squares framework, employing T empvp × Pvp,t−1 as an instrument for Iivp,t−1 . This table reports only the summary measures; the full set of regression results is reported in Table 5. The first notable result in Columns 1 and 2 in Panel C is that the sign of the estimated coefficients for agricultural production and agricultural inputs is reversed, and those coefficients are now negative and significant: in other words, the marginal complier household, induced to experience a larger increase in quota income because of the climatic suitability of its land for rice, actually invests less in agriculture. A hundred-yuan increase in quota income in the prior year leads to a decline of around .7 standard deviations in a mean variable of agricultural sown area and production, and the disaggregated results in Panel A of Table 5 show a decline in every measure of agricultural area sown and production (sown area in grain, wheat, rice and cash crops, and production of grain and cotton, the primary cash crop). There is also a decrease of around .4 standard deviations in agricultural inputs. Disaggregated results show a significant negative impact on the value of grain seeds purchased and the probability of owning an agricultural asset. There is no significant impact on cash seeds or the value of fertilizer employed, while the probabilities of owning an animal or tools and machinery as agricultural assets exhibit imprecisely estimated declines. The coefficient in Panel C Column (3) of Table 4 suggests there is no significant average effect on the probability of investing in a non-agricultural household enterprise, while the disaggregated results in Panel C of Table 5 show results for dummy variables for investing labor or capital in enterprises of each enumerated type. There is evidence of an increase in the probabil15 ity of supplying labor to a household industrial enterprise and a corresponding decline in retail that roughly counterbalance each other, suggesting households may be creating new types of businesses. There is no effect on the probability of non-labor investment in a non-agricultural activity. For outside employment, by contrast, the coefficient on the summary variable for outside employment is relatively large, around .24 standard deviations, albeit imprecisely estimated. This increase is clearly driven by a large increase in the probability of employment in a stateowned enterprise, evident in Panel D of Table 5. No effect is detected on migration, which may be surprising given that it could be hypothesized that households would use an income windfall to fund the transactional costs of labor migration. However, these results should be considered with the caveat households may have motives to misreport migration, either because it is not officially sanctioned or to maximize the size of the village-allocated land plot to which they are entitled. Finally, Columns (5) through (8) in Panel C of the summary measures suggest large positive effects on the consumption of non-grain (i.e., non-staple foods) and on borrowing, with no effect on consumption of staple grains.12 The aggregate consumption variable increases by a full standard deviation, primarily driven by large increases in consumption of meat, sugar and eggs shown in the disaggregated results in Panel E of Table 5. The probability of accessing credit increases 8 points on a base probability of 18 percent, a relative increase of nearly 50%. Taken together, these results suggest that rural households in China that experience income shocks show a clear pattern of behavior. They disinvest in agriculture — i.e., the income effect for agriculture is negative — and seek to establish industrial household businesses or obtain wage employment in state enterprises. They also consume more luxury goods, and access substantial new sources of personal loans. Comparing the OLS and 2SLS results, the differences between the two specifications seem consistent with the evidence of differential trends for high quota quantity villages presented in Panel A of Table 4. Households with greater quota quantities early in the period show more rapid increases in agricultural area and production, agricultural inputs, migration and grain consumption; the OLS estimates show a strong positive bias relative to the 2SLS estimates in each case. There was no evidence of differential trends for outside labor or borrowing, where the OLS estimates are not significantly different from the 2SLS estimates. The only two cases in which the differential trend is not consistent with the bias are non-agricultural labor (where the OLS and 2SLS estimates are not significantly different), and non-grain consumption, where the OLS estimate is in fact biased downwards. To assess the magnitude of the estimated effects in the two-stage least squares specification, in this sample the mean quota quantity is 200 kilograms and the increase in the quota price from trough to peak is around .4 yuan per kilogram (from 1 yuan/kilogram to 1.4 yuan/kilogram), 12 Previous research in Jensen (2008) presented experimental evidence that rice and wheat are Giffen goods for households in poverty in urban areas in China, suggesting a negative income effect. There is no robust evidence of a negative income effect in this data, though it cannot be ruled out; it is also plausible that the demand curves of grain for rural and urban households may be very different. 16 implying an increase in quota income of around 80 yuan, or a proportionate increase of 40%. Given that the coefficients are per 100 yuan of quota income, the effective magnitudes of the effect observed over this period are around 20% lower than the magnitudes indicated by the coefficients reported here. Relative to household net income, the average level of quota income is only around 3% for households at the median, with the caveat that net income may be measured very imprecisely. However, for households at or below the 25% percentile of income, the average level of quota income jumps to around 20% of net income. For these households, the change in quota policy thus generates a permanent increase in annual income of almost 5% - a shift that seems to have large implications for their economic decisions. 6 Robustness checks This section presents a number of robustness checks on the primary results. First, I run placebo tests in a pre-period where the quota price exhibited limited variation. Second, I show that the quota price is not correlated with any systematic cross-sectional shocks in either policy or prices that could be a source of bias. Third, I re-estimate the primary specification with controls for the lagged market price, as well as separate time trends in areas with differing temperatures and differing levels of industrial employment. Fourth, I examine evidence of strategic household behavior in the determination of the quota quantity, and strategic quota enforcement. 6.1 Placebo tests The fundamental identifying assumption of the main analysis requires that there is no unobserved variable correlated with fluctuations in the quota price that also has a disparate impact across areas with different climatic conditions. A useful test of this assumption is to evaluate whether trends in major economic outcomes are parallel between those areas in a period without major changes in the quota price. Between 1986 and 1991, the quota price P̃ , again defined as the unexplained residual in a regression of market on quota price, showed no major fluctuations; this is evident in Figure 2b. Accordingly, I can evaluate whether parallel trends are observed across areas with different total temperature in this period, using a more limited set of outcomes that are reported in these earlier surveys.13 In order to implement this test, I regress the economic outcomes of interest on village fixed effects and an interaction between province fixed effects and a linear time trend. Xivpt = λvp + νp × t (16) 13 Agricultural area is the mean of total area sown, grain area sown, cash area sown, and grain production; agricultural inputs is the mean of dummy variables for owning any productive asset and dummy variables for owning animals, tools or machinery; non-agricultural business in the mean of dummy variables for investing any labor or capital into the specified types of non-agricultural businesses; migration is the mean of dummy variables for working outside the township or outside the county, a dummy equal to one if there is a reported change in household size, and a dummy for receiving remittances; and grain consumption is quantity grain reported consumed. 17 The residuals from this regression are then plotted by year, dividing the sample of villages into high-temperature and low-temperature villages based on whether the primary measure of total temperature is above or below the median. The graphs are shown in Figure 3. Trends over this period seem generally parallel for agricultural area sown, migration and grain consumption. For agricultural inputs, there is some divergence early in the period but the trends align after 1988, and there is some evidence that non-agricultural labor is increasing more rapidly in low-temperature areas after 1990. However, there is no evidence of systematically diverging trends that would be a major source of bias. 6.2 Shocks correlated with quota price The existence of other shocks correlated with changes in the quota price that also exhibit systematic cross-sectional variation would also be problematic for the postulated identification strategy. In order to test whether there is evidence of this type of shock, I estimate the primary reduced form specification employing two sets of dependent variables. The first includes measures of the price of agricultural output sold for the most commonly produced crops; the second includes a number of local policy variables, including the number of local officials recorded in the sample, taxes and collective fees.14 The dependent variables are normalized to have mean zero and standard deviation one, and the equation estimated for each outcome Yivpt is the following: Yivpt = βT empvp × P̃vpt + λvp + νpt + ivpt (17) The results are shown in Table 6. The first regression in each panel shows the result of a regression employing a summary variable that is the mean of the other variables estimated, and the coefficients are insignificant for both summary measures. In Panel A, the price of three crops is significantly correlated with the instrument, though the effects are small in magnitude, less than .1 of a standard deviation. In Panel B, significant effects are evident only for collective levies. Thus it seems clear that correlated policy or price shocks are not a significant source of bias. 6.3 Alternate specifications The next specification check re-estimates the primary equation allowing for a number of additional controls. First, I re-estimate the primary specification controlling for lags and leads of the market price and allowing the effect of the market price to vary across different quantiles of total temperature. While the quota price measure P̃ is uncorrelated with the market price by construction, it may be correlated with lags or leads of the market price.15 Denoting 14 For agricultural prices, it is only a subset of the total sample that reports market sales for a given crop, accounting for a lower (and fluctuating) number of observations. There are also some missing observations for policy outcomes, accounting for the lower number of observations. 15 Note the lags and leads of the market price refer to the year relative to the year in which quota income is measured. In the main specification, economic outcomes are regressed on quota income from the previous year. The lagged market price is thus the market price two years prior to the measurement of economic outcomes; the leading market price is the market price in the same year that economic outcomes are measured. 18 temperature quantile dummies as νq , I estimate the following equations: m Xivpt = βIivp,t−1 + λvp + νpt + νq × Pvp,t−2 + ivpt Xivpt = βIivp,t−1 + λvp + νpt + νq × m Pvp,t + ivpt (18) (19) The results are shown in Panels A and B of Table 7, and are consistent with the primary results; again, there is a decline in agricultural production and investment and an increase in consumption and borrowing. Next, I add distinct time trends in each of the four quantiles of total temperature, denoted νq × ηt . If the results in fact reflect differing trends over time in areas with different climatic conditions, then adding these trends should render the relationship between quota income and economic outcomes insignificant. Xivpt = βIivp,t−1 + λvp + νpt + νq t + ivpt (20) The results are shown in Panel C of Table 7 and show coefficients that are consistent in both magnitude and pattern of significance with the primary results. As previously outlined, another potential challenge is that the climatic measure of interest might be correlated with relative industrialization. Though insignificant, there is a positive correlation between temperature and industrial employment that could be a source of bias. In order to test this hypothesis, the equation is re-estimated with a different set of time trends for each quartile of a measure of industrial employment Ivp , mean days worked in non-household enterprises in the village. νind denotes dummy variables for quartiles of industrial employment, yielding the following specification. Xivpt = βIivp,t−1 + λvp + νpt + νind t + ivpt (21) The results in Panel D of Table 7 are again consistent with the previous conclusions and show no evidence that differential trends among villages with different levels of industrialization are driving the results. 6.4 Determination of quota quantity and quota enforcement Another potential source of bias in these results could be strategic behavior by households around the quota quantity. If the determination of the quota quantity is responsive to household behavior, then households facing variation in the quota price may manipulate their consumption or investment decisions in an attempt to lower or increase their quota. If their incentives to do so vary across areas with different climatic conditions, then the observed patterns could simply reflect households’ efforts to manipulate the quota target. In order to test this hypothesis, I regress lagged economic outcomes, employing the same summary measures previously employed, on quota quantity and the interaction of quota quantity 19 with temperature, as well as household fixed effects φivp and province-year fixed effects. Xivp,t−1 = β1 Qivpt + β2 Qivpt × T empvp + φivp + νpt + ivpt (22) The objective is to test whether households’ economic decisions in a year seem to determine the quota quantity they face in a subsequent year, and if this relationship varies systematically across areas with varying temperature. The results are shown in Table 8. The estimated coefficients are generally insignificant and β2 is uniformly so, with the exception of a positive coefficient when non-grain consumption is the dependent variable. The results from this regression suggest, perhaps surprisingly, that households with higher non-staple consumption may actually face lower quota quantities. However, this effect is not evident in high-temperature areas. Given that on average the quota price remains below the market price in all years, households in low-temperature areas may have an incentive to raise their consumption to lower their quota quantity, but this would generate bias in the opposite direction of the observed effect: households in lower temperature areas, generally characterized by lower quota income, would show higher consumption. An additional source of bias could arise if quota enforcement differs systematically across areas with different climatic conditions. In that case, quota income could itself endogenously reflect other political conditions, and this could be a source of bias if trends in political conditions are correlated with quota prices. Unfortunately, enforcement cannot be measured contemporaneously in the primary dataset, as households do not separately report the quota quantity assigned and the quota quantity sold. However, some data is available that can shed some light on variable quota enforcement and resources for quota enforcement. First, the household surveys report whether households include a member who is a government employee, a village cadre, or a party member. I can test whether quota quantity is significantly correlated with these measures of political influence, and if the correlation varies across villages with different climatic conditions by estimating the following equation, where Xivpt denotes characteristics of the household. Qivpt = β1 Xivpt + β2 Xivpt × T empvp + λvp + νpt + φivp + ivpt (23) In addition, I employ data from a separate survey of village leaders that reports the quantity of quota grain delivered relative to the amount contracted in the years 1990 and 1991, as well as the number of public officials in the village (a rough measure of enforcement capacity) reported in six different years. I regress these measures on temperature, conditional on year fixed effects. Table 9 reports the coefficients of interest. While there is some evidence of favoritism in the quota quantity system for government employees (though interestingly, not for village cadres or party members), the degree of favoritism does not vary with climatic conditions. There is no evidence in variation in village personnel who might participate in quota enforcement, or in the actual proportion of contracted quantity delivered early in the 1990s. As previously mentioned, the quota enforcement system seems robust enough to ensure that more than 95% of quota 20 grain contracted is in fact delivered. 7 Conclusion The vast majority of the population of most developing countries in the world continues to live in rural areas. Accordingly, understanding the implications of increases in income for the economic activities of rural households is crucial in understanding how rural growth will affect overall growth in developing countries, and particularly the shifts in investment between agricultural and non-agricultural activities. This paper analyzes the evolution of an unusual institution in rural China, the grain quota system, to estimate the impact of a large income shock on household economic behavior. This system effectively imposed a lump-sum tax on rural households that declined in magnitude over time as the quota price increased, and also varied systematically cross-sectionally in correlation with crop composition. The identification strategy exploits cross-sectional variation in the climatic suitability of different areas for different crops in conjunction with time variation in the quota price to generate a source of quasi-exogenous variation in quota income. The results indicate that the effect of a positive income shock on investment in agriculture is strongly negative, while there are positive effects on industrial investment, employment in state-owned enterprises, consumption of non-staple goods, and borrowing. Richer households therefore seek to reduce their investment in agriculture and diversify into other sectors, particularly industrial sectors. This is a necessary condition, though not sufficient, for an increase in income for primarily rural and agricultural households to increased investment in non-agricultural sectors. . 21 8 Figures and Tables Figure 1: Quota production and total production Figure 2: Quota and market prices over time (a) (b) 22 Figure 3: Pre-trends prior to quota price increase (a) Agri. area sown (b) Agricultural inputs (c) Non-agricultural labor (d) Migration (e) Grain consumption 23 Table 1: Summary statistics Variable Mean Standard deviation Obs. Household size Sown area Grain sown area Productive assets Non agri. business Outside labor Net income 4.086 1.418 1.253 .905 .328 .532 6669.1 1.441 .927 .817 .294 .529 .499 88.115 16553 16435 16482 16570 16570 16570 16332 Table 2: Variation in quota quantity and price Quota quan. (1) Quota price (2) (3) (4) Temperature Mean Obs. R2 Fixed effects Clustering 52153 .313 Village None 52153 .017 Year None 40557 .002 Village None 40557 .491 Year None Rice area (5) Quota quan. (6) .111 (.054)∗∗ 150.176 (71.338)∗∗ .351 35577 .019 Prov.-year County 321.825 35986 .008 Prov.-year County Notes: The regressions in columns (1) through (4) include only the specified set of fixed effects. The independent variable in columns (5) and (6) is the mean total summed temperature for all days above 10 degrees Centigrade, normalized to have mean zero and standard deviation one. The fixed effects employed and the clustering of standard errors are as specified in the table. Asterisks indicate significance at the ten, five and one percent level respectively. 24 Table 3: First stage and specification checks First stage Quota income (1) (2) Price x Temp. Quota quan. (3) (4) 29.347 31.069 (6.106)∗∗∗ (8.304)∗∗∗ .001 (.001) Quota Price Clustering Sample selected Price (8) Quan. rice res. (9) .006 (.004) 93.043 (91.169) .002 (.001) Temperature Mean St. dev. Obs. F Fixed effects Specification checks Infra. dummy Quan. res. (5) (6) (7) -13.291 (18.870) 2.464 30.855 (7.562) (9.668)∗∗∗ 193.819 184.376 339.469 298.706 234.157 226.837 725.109 827.111 32167 19118 23004 17553 22.915 13.856 .106 10.114 Village + Prov.-year Village Vill.-year Full Hom. Vill.-year Hom. Het. .016 .126 21621 .86 Prov.-year County Hom. .016 0 -.001 .126 639.055 .229 21621 21621 21621 1.57 .488 1.708 Vill. Prov.-year Prov.-year + Prov.-year Vill.-year County County Hom. Hom. Hom. 3.698 497.183 21465 1.024 Prov.-year County Hom. Notes: The independent variables are temperature, quota price and the interaction between the two, normalized to have mean zero and standard deviation one. The dependent variable is quota quantity in Columns (1) and (2); a dummy for whether household quota sales is equal to total production in Columns (3) and (4); the residual of quota quantity regressed on village fixed effects in Column (5); quota price in Column (6); the residual of quota quantity regressed on rice area cultivated in Column (7); and quota income in Column (8). “Hom.” denotes villages that are homogeneous in rice and wheat production, while “Het.” denotes villages that are heterogeneous. The fixed effects employed and the clustering of standard errors are as specified in the table. Asterisks indicate significance at the ten, five and one percent level respectively. 25 Table 4: Primary results Agri.prod. Agri. input Non agri. Outside labor Migration Grain cons. Other cons. Borrowing (1) (2) (3) (4) (5) (6) (7) (8) Panel A: Quota quantity and differential trends Quota quantity 1993 Obs. .747 (.159)∗∗∗ .580 (.100)∗∗∗ .095 (.045)∗∗ .017 (.033) .254 (.056)∗∗∗ .309 (.072)∗∗∗ .060 (.016)∗∗∗ .015 (.053) 15556 15556 15556 15556 15556 15556 15556 15556 Panel B: Ordinary least squares Quota income Obs. .470 (.029)∗∗∗ .306 (.022)∗∗∗ .0006 (.022) .050 (.013)∗∗∗ .160 (.016)∗∗∗ .177 (.014)∗∗∗ .050 (.005)∗∗∗ .006 (.006) 16570 16570 16570 16570 16570 16570 16570 16570 Panel C: Two-stage least squares Quota income Obs. -.459 (.265)∗ -.402 (.213)∗ .052 (.243) .190 (.227) .073 (.184) .073 (.133) .197 (.068)∗∗∗ .167 (.089)∗ 16570 16570 16570 16570 16570 16570 16570 16570 Notes: All specifications include village and province-year fixed effects, and standard errors clustered at the village-year level. The independent variable in Panel A is quota quantity in 1993; the independent variable in Panels B and C is quota income, lagged, in hundreds of yuan. Summary measures employed as dependent variables are calculated as follows and then standardized to have mean zero and standard deviation one. The summary measures for agricultural production, agricultural inputs and non-agricultural labor represent the mean of all variables reported in Panels A, B and C respectively of Table 5. Outside labor is the mean of Panel D, Columns (1) through (4); migration is the mean of Panel D, Columns (5) through (8). Grain consumption is simply grain consumption as reported in Table 5, Panel E, Column (1); non-grain consumption is the mean of the other variables in Table 5, Panel E. The summary measure for borrowing is simply the variable for whether a household has obtained a loan. Asterisks indicate significance at the ten, five and one percent level respectively. 26 Table 5: Two-stage least squares Panel A: Agriculture Quota income Mean Obs. Sown area (1) Grain (2) Wheat (3) Rice (4) Cash (5) Grain prod. (6) Cotton prod. (7) -.763 (.439)∗ -.364 (.353) -.019 (.036) -.010 (.013) -.365 (.153)∗∗ -90.293 (142.872) -9.989 (5.551)∗ 1.418 16435 1.253 16482 .416 16432 .204 16538 .977 16427 2255.035 16503 18.244 16569 Panel B: Agricultural inputs Grain seeds Cash seeds Fertilizer (1) (2) (3) Quota income 17.470 Obs. Assets (4) Animals (5) Tools (6) Machinery (7) -5.092 (2.445)∗∗ .363 (.249) -20.175 (34.056) -.046 (.021)∗∗ -.071 (.048) -.044 (.029) -.036 (.027) .768 16325 573.532 16459 816.419 16428 .905 16570 .328 16570 .683 16570 16570 Panel C: Non-agricultural household activities Quota income Mean Obs. Labor Indus. (1) Cons. (2) Trans. (3) Retail (4) Indus. (5) Investment Cons. (6) Trans. (7) Retail (8) .039 (.020)∗ .005 (.014) .011 (.016) -.051 (.019)∗∗∗ .008 (.015) .004 (.009) .006 (.014) -.029 (.017)∗ .080 16570 .050 16570 .075 16570 .123 16570 .069 16570 .027 16570 .047 16570 .095 16570 Panel D: Outside labor Outside (1) Collective (2) SOE (3) Private (4) Outside (5) Outside (6) Household size (7) Remittances (8) Quota income .053 (.048) .016 (.037) .041 (.018)∗∗ -.045 (.038) .035 (.047) .029 (.039) -.075 (.063) -7.063 (31.787) Mean Obs. .532 16570 .184 16570 .062 16570 .177 16570 .333 16570 .193 16570 4.086 16553 95.555 16568 Panel E: Consumption Grain (1) Veg. (2) Oil (3) Meat (4) Milk (5) Eggs (6) Sugar (7) Total non grain (8) Quota income 29.863 (38.281) 1.126 (1.132) 2.097 (1.286) 9.480 (3.405)∗∗∗ .088 (.073) 2.830 (1.004)∗∗∗ 1.302 (.679)∗ 16.585 (5.733)∗∗∗ Mean Obs. 986.662 16511 24.376 16406 33.904 16570 57.660 16570 .179 16427 15.209 16345 8.036 16541 138.901 16570 Notes: All specifications include village and province-year fixed effects, and standard errors clustered at the village-year level. The independent variable is quota income, lagged, in hundreds of yuan, instrumented by the interaction of the total temperature index and the quota residual. Asterisks indicate significance at the ten, five and one percent level respectively. 27 Table 6: Robustness checks: correlated shocks Panel A: Agricultural prices Sum. 1 (1) Sum. 2 (2) Rice (3) Wheat (4) Corn (5) Pork (6) Cotton (7) Soy (8) Veg. (9) Price temp int. -.0004 (.022) -.039 (.033) -.012 (.021) .023 (.029) -.038 (.039) -.059 (.034)∗ .102 (.053)∗∗ -.130 (.031)∗∗∗ -.002 (.007) Obs. 11697 11586 1028 2527 5737 5240 3577 2554 2507 Panel B: Policy measures Summary (1) Cadre (2) Party (3) State inputs (4) Taxes (5) Coll. levies (6) Fees (7) Fines (8) Price temp int. -.004 (.012) .003 (.011) -.004 (.006) .009 (.006) -.017 (.039) .055 (.015)∗∗∗ -.023 (.034) -.068 (.065) Obs. 13153 13130 13149 13153 12943 13067 12984 13010 Notes: All specifications include village and province-year fixed effects, and standard errors clustered at the village-year level. The dependent variables (prices and policy measures) are normalized to have mean zero and standard deviation one. The independent variable is the primary instrument of interest, the interaction of the total temperature index and the quota residual. Asterisks indicate significance at the ten, five and one percent level respectively. 28 Table 7: Robustness checks: alternate specifications Agri. prod. (1) Agri. input (2) Non agri. (3) Outside labor (4) Migration (5) Grain cons. (6) Other cons. (7) Borrowing (8) Panel A: Summary measures with controls for lagged market price Quota income Obs. -.507 (.285)∗ -.440 (.231)∗ .045 (.273) .190 (.243) .139 (.208) .130 (.154) .218 (.074)∗∗∗ .189 (.100)∗ 16125 16125 16125 16125 16125 16125 16125 16125 Panel B: Summary measures with controls for leading market price Quota income Obs. -.465 (.274)∗ -.371 (.210)∗ .039 (.257) .155 (.239) .070 (.194) .064 (.142) .205 (.071)∗∗∗ .197 (.101)∗ 16158 16158 16158 16158 16158 16158 16158 16158 Panel C: Trends by quartile of temperature Quota income Obs. -.417 (.185)∗∗ -.255 (.152)∗ .248 (.209) .065 (.164) -.090 (.142) -.115 (.099) .151 (.047)∗∗∗ .161 (.067)∗∗ 16570 16570 16570 16570 16570 16570 16570 16570 Panel D: Trends by quartile of industrial employment Quota income -.432 (.265) -.441 (.257)∗ .023 (.275) .409 (.275) .176 (.220) .106 (.152) .236 (.092)∗∗ .177 (.098)∗ Obs. 16570 16570 16570 16570 16570 16570 16570 16570 Notes: All specifications include village and province-year fixed effects, and standard errors clustered at the village-year level. The independent variable is quota income, lagged, in hundreds of yuan, instrumented by the interaction of the total temperature index and the quota residual. Results in Panels A and B include a control for the lagged or leading market price, respectively, interacted with quantiles of total temperature; Panels C and D include separate time trends for each quartile of total temperature and separate time trends for each quartile of industrial employment, respectively. The dependent variables are summary indices with mean zero and standard deviation one; definitions of the indices are provided in the notes to Table 4. Asterisks indicate significance at the ten, five and one percent level respectively. Table 8: Endogenous determination of quota quantity Quota quantity Quota quantity int. Obs. Lagged agri. prod. (1) Lagged agri. input (2) Lagged non agri. (3) Lagged outside labor (4) Lagged migration (5) Lagged grain cons. (6) Lagged other cons. (7) Lagged borrowing (8) .117 (.185) .071 (.202) -.092 (.169) .108 (.159) .065 (.132) -.137 (.131) -.224 (.120)∗ .082 (.113) -.238 (.121)∗ .157 (.117) .010 (.153) .056 (.134) -.086 (.041)∗∗ .124 (.044)∗∗∗ -.274 (.263) .163 (.244) 16306 16306 16306 16306 16306 16306 16306 16306 Notes: All specifications include village and province-year fixed effects, and standard errors clustered at the village-year level. The dependent variables are lagged summary indices with mean zero and standard deviation one; definitions of the indices are provided in the notes to Table 4. The independent variables are quota quantity and the interaction of quota quantity with the total temperature index, both standardized to have mean zero and standard deviation one. Asterisks indicate significance at the ten, five and one percent level respectively. 29 Table 9: Variation in quota enforcement Quota quantity (1) -46.726 (5.969)∗∗∗ Government employee int. 1.615 (5.506) Cadre (2) (3) Party Village Economic Cadres Quota fulfillment committee committee committee (4) (5) (6) (7) (8) Government employee Cadre int. Party member Party member int. 5.207 (7.566) -9.501 (8.377) .455 (5.716) -4.833 (7.816) Temperature Mean Obs. Fixed effects Clustering 226.357 226.357 226.357 14314 14334 14338 Village + prov.-year Village-year .107 (.156) .144 (.140) .572 (1.634) -.166 (.312) -.006 (.013) 3.922 185 Year Village 4.897 185 Year Village 4.034 185 Year Village 6.869 319 Year Village .955 126 Year Village Notes: The dependent variable in Columns (1) through (3) is quota quantity; the independent variable is the specified household characteristic, and the same characteristic interacted with temperature. 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