Trade costs and household specialization: evidence from Indian farmers

Transcription

1 Trade costs and household specialization: evidence from Indian farmers Nicholas Li University of Toronto May 20, 2015 Version 0.3 PRELIMINARY DO NOT CITE Abstract Farmers in developing country produce a large fraction of the goods they consume. I use Indian household survey data from to look at how prices and trade costs affect the decisions of Indian households about whether to farm and what to farm. I document substantial heterogeneity across Indian districts in the production, buying and selling behaviors of farmers and a large decline in the importance of home-produced consumption between 1987 and 2010 driven by exit from farming and increased specialization of farmers. I formalize these empirical patterns by combining a Roy model of occupational choice with a Ricardian trade model for agricultural varieties. Comparative advantage between farming/non-farming and comparative advantage across locations for crop varieties interact with trade and marketing costs to shape farm activity. I use the expansion of the Public Distribution system in the 1990s and 2000s to examine how marketing frictions affect farm production decisions and find evidence consistent with the model. The findings highlight the impact of a farmer consumption advantage on agricultural productivity in the presence of high marketing and trade costs and suggest large welfare gains for individual farmers from market integration compared to autarky. Thanks to Mu-Jeung Yang, Dan Trefler, Loren Brandt, Xiaodong Zhu, Diego Restuccia, Gustavo Bobonis, David Atkin, Andres Rodriguez-Clare, Kala Krishna, Lorenzo Caliendo, Trevor Tombe, Palermo Penano, and seminar participants at the University of Toronto, University of Calgary, and Stanford CGID 2014 conferences. All errors are my own. nick.li@utoronto.ca

2 2 NICHOLAS LI 1. Introduction In 2014 there were over 570 million farms worldwide, 475 million of which were under 2 hectares in area (Lowder et al. (2014)). In India alone there were over 50 million farm households defined as main source of income being self-employment in agriculture of which over 35 million were under 2 hectares. 1 The majority of these small farmers (often described as peasant or subsistence farmers) consume a large share of their own output. In this paper I examine the link between selection into farming and home consumption and their relation to trade and marketing costs. As noted in a recent paper by Gollin and Rogerson (2014) agricultural production has certain technological features production dispersed across locations with high trade costs and preference features food as a necessity that suggest an important role for trade costs in the decision of whether to farm and what to farm. While recent work in trade has highlighted the importance of comparative advantage across crops for agricultural productivity within countries (Donaldson (2012), Costinot and Donaldson (2014), Sotelo (2014)) and across countries (Tombe (2014), Swiecki (2014)), marketing costs broadly interpreted as the the relative cost of acquiring a good through own-production versus through market exchange may also play an important role in determining how individual households specialize (Fafchamps (2012)). The combination of food as necessity with high trade and marketing costs generates a consumption advantage for farmers that may drive selection of less productive farmers into farming and allocation of land to crops with lower market value, lowering agricultural productivity. The food problem identified by Schultz (1953) may thus operate at a household scale as well as a country scale. In this paper I use data from India s National Sample Survey over the period and the Institute for the Semi-Arid Tropics (ICRISAT) Village Dynamics in South Asia (VDSA) over the period to examine patterns of consumption out of home production for farmers. This represents a novel application of household consumption surveys that are available for many countries and takes advantage of the fact that farm households are perhaps the only type of household with substantial home production of goods and an active trade margin with the rest of the world. I show that the aggregate home share of consumption has fallen from 14% to 1 These figures come from the Indian National Sample Survey. Figures cited in Lowder et al. (2014) find as many as 137 million holdings, which also includes land holdings by households who derive their main income from agricultural wage labor or non-agricultural activities.

3 TRADE COSTS AND HOUSEHOLD SPECIALIZATION 3 7% in India between 1987 and This is due to exit from farming and increased integration into local markets by farmers. There is wide dispersion in home shares across farmers related to a number of factors. Conditional on farm size, farmers in districts with more roads, distribution-sector workers, and consumer food subsidies have lower home shares, and these districts also have a lower fraction of farmers. I show that this decline in home shares for farmers is related to crop specialization. I also show how within-district wedges between retail and farm-gate prices for food products can be quite large, providing a consumption advantage to farmers in terms of the labor for goods exchange rate, and that the magnitude of these wedges can be as large as retail price differences across districts. While across-district price gaps do not appear to change much over this period, I show that there has been a substantial decline in market/home price wedges that is largely driven by reforms to the Public Distribution System, which expanded over the 1990s and 2000s to provide large consumer subsidies for rice and wheat, costing over 1% of India s GDP. Motivated by these patterns in the data I develop a simple model combining elements from the trade and structural change literature capturing the two margins driving home-produced consumption in the data, selection into farming and specialization conditional on farming. The model combines a Roy model of selection into farming based on comparative advantage (similar to Lagakos and Waugh (2013)) with a Ricardian model of comparative advantage across locations (as in Eaton and Kortum (2002) and subsequent work on agricultural trade). I show that the combination of marketing costs, affecting farmer vs. non-farmer prices, and external trade costs, affecting retail price gaps across locations, determines the consumption advantage of farmers. The farmer consumption advantage increases the size of the agricultural sector and the ratio of non-farmer to farmer income. In the absence of marketing costs, external trade liberalization has no effect on selection into farming, while in their presence it can stimulate movement out of farming. Holding external trade costs constant, a reduction in marketing cost can also sharpen the effects of comparative advantage across districts, leading to a substantial increase in trade, greater specialization by farmers and movement out of farming. This is consistent with the observation that farmer home shares have fallen despite limited evidence of retail price convergence or lower segmentation across districts 2 2 Despite some reforms aimed at liberalizing internal agricultural markets, work by Atkin (2013) and Mallory and Baylis (2012) suggests that markets remained segmented into the 2000s.

4 4 NICHOLAS LI Returning to the data, I exploit variation across districts in the implementation of major Public Distribution System reforms in the 1990s and 2000s and road construction to examine the reduced-form impact of trade and marketing costs on the dimensions of specialization mentioned above. The effects of the PDS on production that I identify are novel in the context of national consumer food subsidy programs, which are usually only analyzed from the perspective of nutrition and poverty effects (Tarozzi (2005) and Dreze and Khera (2013)). However, they are consistent with a large body of work analyzing non-separability of consumption and production decisions for farm households (de Janvry and Sadoulet (2006)) and highlight the unique status of food for production decisions by poor households. I also use changes in PDS and roads as instruments for the district-level market/home wedge in order to estimate the elasticity of household market consumption with respect to the relative price of market/home consumption, finding elasticities around 2.8. This is on the lower end of estimates for agricultural trade elasticities in the trade literature based on Eaton and Kortum (2002)-style Ricardian frameworks, but is the only such national-level estimate at the household level to the best of my knowledge. This relatively low elasticity, combined with a very high share of food consumption from the market even for farmers suggests very large welfare gains from allowing farmers to trade with the market relative to household food autarky. This paper is related to several strands of literature in trade and development. Recent work in trade highlights how transport costs affect agricultural production patterns across regions of India (Donaldson (2012)), the U.S. (Costinot and Donaldson (2014)) and Peru (Sotelo (2014)). Tombe (2014) and Swiecki (2014) look at international agricultural trade and trade costs in two-sector models, where trade may potentially interact with distortions across sectors. As these papers look at representative agents for each location and trade costs across retail markets, the role of individual farmers and marketing costs is unclear. In macro development there is a long tradition of examining non-homotheticity, cross-sector productivity differences and agricultural employment (Schultz (1953), Restuccia et al. (2008), Adamopolous (2011), Lagakos and Waugh (2013), Gollin and Rogerson (2014), Gollin et al. (2014)) and more recently a renewed attention to misallocation of inputs across individual, heterogeneous production units (Restuccia and Adamopolous (2014)). My paper focuses on how consumption and production decisions are jointly determined by productivity, trade and marketing costs for individual households. I show that a con-

5 TRADE COSTS AND HOUSEHOLD SPECIALIZATION 5 sumption advantage of farming may be one factor affecting sorting and (measured) productivity differences across sectors and individual farms, beyond the usual factors (e.g. distortions in input markets, especially for land and labor) that are used to explain the existence of a large, low productivity agricultural sector with many small, low productivity farms. The important role for marketization costs I identify is consistent with misallocation and distortions arising from poor infrastructure, low productivity in distribution/retail (Lagakos (2014)), and high markups by traders and middle-men (Atkin and Donaldson (2013)) but relates these to production decisions. Finally, my paper also relates to a classic development literature focused on market failures in agriculture and non-separability of consumption and production (see de Janvry and Sadoulet (2006) for a review). My contribution here is to look at the choice of whether to farm and what to farm with a nationally representative Indian survey that measures home consumption shares and home/market wedges. In addition to examining the factors that contribute to the farmer choices in the crosssection and over time, I provide direct evidence of how a large reform to consumer food subsidy policy in India affected production decisions. The rest of the paper is organized as follows. Section 2 presents some basic facts about home consumption and retail/farm-gate price wedges in India. Section 3 develops a model motivated by these facts that combines selection into farming and crop specialization and provides an illustrative numerical simulation. Section 4 provides reduced form estimates of how two major policies road construction and reforms to India s Public Distribution System affected farm production decisions, and structural estimates of a household-level trade elasticity I use to quantify welfare gains and gauge the potential magnitude of differences in marketing costs across districts. Section 5 offers some concluding remarks. 2. Data and descriptive evidence While virtually all households engage in some non-market production of (marketable) services, many do not produce any of the goods that they consume. Farmers, particularly in the developing world where food makes up a large share of the budget, are an important exception. Household consumption surveys collected routinely by national statistical agencies to measure nutrition and poverty or construct expenditure shares for consumer price indexes typically collect data on home produced

6 6 NICHOLAS LI goods in addition to market expenditures on goods. While differing survey methods, reporting periods, disaggregation of goods, and methods of imputing the value of home produced goods make international comparisons difficult, a cursory international comparison presents some clear patterns. Table 1 presents data from the World Bank LSMS for Uganda, Timor, Peru, Guatemala and India for various years, ordered from lowest to highest GDP per capita. As countries get richer, the fraction of food consumed out of home production falls for the average household, value added per worker falls and the fraction of households that own or lease in agricultural land declines. Looking within the subset of households that consume at least one percent of food out of home production, the home share tends to decline as countries get richer which is related to increased specialization in production relative to consumption. While the number of food varieties surveyed varies a lot across surveys, a general pattern is that the home share of food expenditures is higher than the home share of food varieties, a pattern that is consistent with specialization in staple production (as opposed to cash crops/commodities) and/or a lower relative price for the home produced varieties (leading to higher expenditure shares provided the food varieties are substitutes and not complements). 3 Table 1: Subsistence farmers across countries Uganda 2009 Timor 2000 India 2009 Peru 1994 Guatemala 2000 GDP per capita USD Home share food Agric. VA/worker Households with land Conditional on home food share 1% Home share food exp Home share food varieties Survey food varieties All data from World Bank LSMS and World Development Indicators except for India. To allow for accurate comparisons over time and across locations and households, I focus on patterns in India s National Sample Survey. The survey is based on a 30 day 3 Note that this pattern is also consistent with home bias as described in Atkin (2013) and it is not implausible that for some households selection into farming is driven by food preferences.

7 TRADE COSTS AND HOUSEHOLD SPECIALIZATION 7 recall period 4 and collects basic household characteristics and detailed consumption data for about 100,000 households in the years , , , , and Sampling is based on two-stage stratification with first-stage units (villages and city blocks) randomly sampled within a state, and households sampled within each first-stage unit. 5. The most disaggregated geographic unit that can be tracked over time, geocoded and matched to other data sets is the district. District boundaries change over time as districts are subdivided, but using 1961 boundaries there are about 300 districts in India with the median district area being 7500 sq.km (which would make it the 116th ranked Metropolitan Statistical Area in the United States). The consumption survey consists of a list of individual items such as rice, wheat, flour, milk, chicken, chick peas, spinach, etc. Households are asked to report the quantity and value of goods purchased from the market, received as gifts or in exchange, or home produced. While goods purchased through the market are valued at the actual transaction price, the value of goods received as gifts or in exchange are valued at average local retail prices and home produced goods are valued at ex farm or ex factory rate not including any element of distributive service charges. 6 In addition to market purchases, beginning in the survey records quantities and values of rice and wheat purchased through the public distribution system, a period that coincides with the beginning of a transition of the public distribution system from a universal entitlement scheme to a more targeted one offering fixed quantities of grains at prices well below market rate to households with ration cards. The list of items in the survey is fairly stable over time but there has been a decrease in food categories (some categories were combined) and addition of new types of consumer goods like home electronics. To be consistent I aggregate up to a common set of 134 food categories as this is my 4 In the survey used both a 7 and 30 day recall period for the same households, while in the survey used the 7 day period for some food items and households and 30 day period for others. Consequently measured consumption and expenditures for may not be exactly comparable to other survey rounds. 5 Richer households were oversampled and receive a lower sampling weight 6 In earlier years of the survey cash, home-produced, and total were recorded separately but in more recent years home production and total are the two categories recorded. In the survey round home and market consumption are not recorded separately instead households were asked whether consumption was out of cash, home, or both. We treat both as home production for reasons discussed below but this, along with the recall period issue mentioned earlier, suggests caution when comparing the data with other years.

8 8 NICHOLAS LI main focus. Out of the 134 food categories, 102 are potentially home-producible and account for a steady 78% of all food expenditures for the average household between 1987 and Goods like flour and bread are not considered home-producible as any processing of foods by the household subsequent to purchase is not recorded; these goods are only recorded if they are purchased from the market but not if they are consumed at home. By restricting analysis of home shares of food to a consistent set of 102 home-producible foods I aim to capture the decision of whether to grow rice or purchase it in the market, abstracting from the decision of whether to make ricecakes at home or purchase them in the market which is closer to a service margin of marketization than the goods one I examine here. This also lowers the importance of quality and product heterogeneity when analyzing prices although there is still lots of scope for quality variation across unprocessed agricultural goods. Non home producible food goods are thus treated as equivalent to non-food goods for the rest of the analysis. 7 Between and the aggregate share of consumption out of home production declined from 14% to 7% in India (22% to 14% when restricting to food consumption). Table 2 presents a breakdown of these aggregate figures across three types of rural households farmers (self-employed in agriculture), agricultural laborers, other rural residents and urban households along with average real expenditures and land holdings. 8 Farmers make up about a quarter of all households but own the vast majority of the land and have substantially higher shares of home consumption (overall and out of food). Agricultural laborers are poorer than other rural households but otherwise are very similar in terms of land holdings and consumption out of home production, while urban households are the richest but have the least land and trivial levels of consumption out of home production. The table makes it clear that the overall decrease in consumption out of home production is coming 7 Note that there are some home-producible goods outside of food recorded in the survey particularly fuel in the form of dung cakes and firewood, but also clothing but their expenditure shares are very small and they are less related to occupational choice. 8 There is some issue about how to classify farmers. I use the NSS classification of household type self-employed in agriculture which is based on the source of the majority of household income in the preceding year, but farmers could also be defined based on household industry (which would include agricultural laborers), land ownership or share of food out of home production. The aggregate patterns are similar qualitatively but different definitions of farmer will obviously lead to different quantitative conclusions. There is also an issue with urban classification, as urban areas are classified partly on the basis of a decennial population census and areas that were previously classified as rural sometimes become urban. See appendix figures 11 and 12 for the entire distribution of food home shares by household type or using a 0.1 hectare land threshold.

9 TRADE COSTS AND HOUSEHOLD SPECIALIZATION 9 from two sources: a decrease in the share of households that are classified as farmers, and a decrease in the home share conditional on being a farmer. Part of the decline in overall home share is also driven by the decline in food share over time since most non-food goods are not home-producible, but even within food there is a substantial decrease in the home produced share of food for farm households. Table 2: Farmers and home production Farmer Ag.laborer Rural other Urban Farmer Ag.laborer Rural other Urban Share of households Real PCE Land (ha) Home share Food share Home share of food All data from the Indian NSS. Focusing on farmers only, Figure 1 shows that there is considerable heterogeneity in the distribution of household-level and district-level mean home shares of food, with a spike at zero capturing farmers entirely engaged in non-food (particularly cotton) and cash crop production (e.g. oil seeds). The entire distribution has shifted to the left between 1987 and While there are many geographic features that influence the home share of food for farmers, a map displaying district-level means of home share (figure 2) shows that overall home shares are lowest in Southern India and coastal areas and highest in more mountainous, remote and landlocked regions in the North, North-East and North-West. 9 As the map suggests, there is also a positive correlation (0.37, significant at the 1% level) between the fraction of households that are farmers and the mean district farmer home share. Given that the number of food varieties consumed by Indian households varies with income and location characteristics (Li (2013)), which may affect home shares with no change in production patterns, I also look at direct evidence of specialization. Table 3 provides some evidence of increasing specialization over time from multi- 9 I hope to explore some of these geographic factors in more detail in future drafts.

10 10 NICHOLAS LI Figure 1: Dispersion and decline in food home shares. Left panel is individuals, right panel is district-level means. Blue line represents 1987, red line represents ple sources. Panel A uses International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) micro-data for the periods and , which collects detailed crop output and value data at the farmer-plot level for a few villages. In the early period farmers are fairly diversified, producing on average 6.4 crops, but this declines to 3.4 by the later period. This specialization is also reflected in a rise of the Herfindahl index, constructed using value shares for each crop in total output (with value equal to one for households producing a single crop). The ICRISAT microdata also provides further corroboration of the decline in the home share of food for farmers from an alternative source. Panel B returns to the NSS household consumption data, which does not measure production explicitly but allows a proxy for food production under the assumption that any food products produced by the farmer are also consumed in the same month if this assumption is violated then this is an underestimate of the number of crops produced. The number of distinct crops produced can be constructed in this way for a district, village or household. In all three

11 TRADE COSTS AND HOUSEHOLD SPECIALIZATION 11 Figure 2: District mean farmer home shares Size of dot corresponds to fraction of households who are farmers, color legend maps on to home shares. cases there is a clear downward trend in the number of varieties produced, and the number for farmers in the 2009 NSS round is quite similar to the one in the ICRISAT micro data. Looking beyond farmers to include all rural households, there is a drop in the number of crops produced but the median rural household is still producing a food crop in the later period, consistent with earlier evidence that many households that derive most of their income from agricultural labor and/or non-agricultural activities still engage in production of food crops. Finally, in panel C I use the ICRISAT Village Dynamics in South Asia (VDSA) district-level data which contains crop areas, yields and prices for 16 major crops 10. For these 16 crops I construct district-level Herfindahl indexes for land shares and value shares, both of which indicate growing district-level specialization over time. I also calculate a rough estimate of the districtaggregate share of consumption produced within-the district for the first 10 crops. This requires combining the ICRISAT VDSA output estimates with aggregate con- 10 These are rice, wheat, sorghum (jowar), pearl millet, maize, finger millet, barley, chick pea, pigeon pea, ground nut, sugar, cotton, sesamum, rapeseed/mustard seed, castor seed and linseed

12 12 NICHOLAS LI sumption estimates of rural and urban households in the district from the NSS, and assuming that if consumption is below production then all of the consumption is out of local production. 11 This measure suggests that districts have become more open with respect to these 10 goods and the import share of consumption from other districts has risen. Appendix figure 13 presents the trend towards specialization over the entire period. As with home shares, there is substantial dispersion in the degree of specialization across districts. Panel A of figure 3 shows the dispersion in specialization (Herfindahl index in area) across districts for various years, while Panel B shows the fraction of land used to grow rice and wheat, which shows a clear pattern of polarization over time with some areas becoming more specialized in those crops. Figure 4 presents the district-level mean of crops per farmer or total district crops estimated from the NSS data under the assumption outlined above, and shows a similar wide dispersion in the extent to which the typical farmer is specialized or grows many crops. I next turn to prices, which may help explain the declines in farming and home production between and Table 4 presents the average price paid by farmers, agricultural laborers, other rural residents, and urban residents relative for selected food products relative to the average farm-gate price for 1987 and 2009, where prices here are derived by dividing the total value (actual or imputed) by total quantity. The data reveal a very important aspect of Indian agriculture the purchasing and selling price of rice and wheat look very different than other crops. India s food security policy relies on two policies. On the purchasing side, the Food Corporation of India sets national minimum support prices for farmers achieved through procurement, which sometimes requires growing stockpiles when the open market price would have fallen below this (especially true for wheat in recent years). This policy applies to 25 crops but principally to rice and wheat, which are then allocated to states to distribute through the Public Distribution System (PDS). Until all Indians were theoretically entitled to a fixed amount of rice and wheat through this system at a price close to the farm gate price, with the central government effectively subsidizing the entire cost of distribution; in practice, the reach of fair price or ration shops, the quality, availability, and actual prices charged for rice and wheat varied tremendously across states and locations. Beginning in 1992 the 11 I only use the first 10 crops as the other 6 are mostly consumed in processed form making consumption difficult to estimate. Consumption for the other 10 goods is less affected by this although for rice and wheat in particular there is non-trivial consumption in processed form.

13 TRADE COSTS AND HOUSEHOLD SPECIALIZATION 13 Table 3: Agricultural specialization Mean Median Mean Median A. ICRISAT micro (farmer-level) data Farmer count of crops Crop value Herfindahl index Home share of food B. NSS data District count of crops Village count of crops Farmer count of crops Rural household count of crops C. ICRISAT VDSA (district-level) data 16 crop area Herfindahl index crop value Herfindahl index District home share See text for description. Revamped PDS program specifically targeted tribal/remote areas and offered prices at 50% of the cost of procurement to below poverty line (BPL) households in these areas. In 1997 the Targeted PDS made this theoretically universal, with all BPL households entitled to a fixed amount or rice and/or wheat at 50% of economic cost, and in 2002 this was expanded again to offer additional grains at even more subsidized prices to the poorest households (the AAY). The actual generosity and accessibility of the PDS system varies substantially across states as the program was under their administrative control. 12 The data in table 4 indicates that the combination of support prices for farmers and subsidies for consumers leads to smaller differences between the prices paid by different households and the farm-gate price, with the the ratio of consumer prices to farm-gate prices falling below one in some cases by 2009 (particularly for agricultural laborers, typically the poorest households). Other goods exhibit much higher ratios of consumer prices to farm-gate prices, ranging from 10%-20% for 12 See appendix figure 10 for a map of PDS shares of rice and wheat in

14 14 NICHOLAS LI Figure 3: ICRISAT VDSA: A. Herfindahl index for land allocation and B. specialization in rice/wheat pulses to 25% to 40% for more perishable products like fruits and vegetables. Farmers typically pay lower prices than other household types because at least some of their consumption comes from goods they produce themselves if every farmer produced and consumed every agricultural product themselves the ratio would be one. The magnitude and evolution of selling versus purchasing prices for Indian foods displayed in table 4 highlights the potential consumption advantage of being a farmer the ability to secure food at prices considerably below the prevailing retail prices in the district and how this advantage could be affected by government policy. The farm gate prices derived from NSS consumption data vary modestly within district and even within villages, where some crops exhibit slightly higher prices for larger and richer farmers (e.g. the the within-village elasticity of farm-gate rice prices to land holdings and household size are 1.2% and -0.6% respectively). In the rest of the paper I choose to focus on district-level mean farm-gate and retail prices (in rural areas) for two reasons. First, there is no way to link village-level data

15 TRADE COSTS AND HOUSEHOLD SPECIALIZATION 15 Figure 4: NSS 2009: District-level mean number of crops per farmer and total number of crops over time or to any village characteristics or other data sets. Second, it is frequently the case that retail and farm-gate prices are not observed for the same district making it difficult to calculate retail/farm-gate wedges at the village-level these wedges are only observed in about 1/4 to 1/3 of villages for the most commonly produced and consumed goods, rice and wheat, and even less frequently (in the 2% to 6% range) for others. At the district-level coverage is closer to 87% for rice, 68% for wheat, and in the 20-50% range for other goods. While some geographic variation within villages is lost this also reduces sampling error and lowers the impact of outliers. The reason retail and farm-gate prices are often not observed in the same village is related to the fact that it is very rare to observe a household that consumes out of home production and market production about 2% or less of farmer households consume both home and market versions of a food product during the survey month and this share is fairly stable over time, with the exception of rice and wheat where simultaneous home consumption and non-home consumption rises to about 12% and

16 16 NICHOLAS LI Table 4: District average price paid vs. farm-gate price Farmer Ag.laborer Rural other Urban Farmer Ag.laborer Rural other Urban Rice Wheat Chickpea Pigeon Pea Potato Onions All data from the Indian NSS. 6% in recent years when PDS purchases are included in non-home consumption. This pattern is consistent with the observation that farm-gate prices are typically below retail prices, so it would never make sense for a farm household to buy and sell the same good unless its production was constrained to below desired consumption (this is more likely for the rural households with the smallest land holdings who derive most of their income from agricultural wage labor or other sources and are therefore not classified as self-employed in agriculture by the NSS). 13 The International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) Village Dynamics in South Asia (VDSA) compiles average farm harvest prices from wholesale markets ( mandis ) collected by state governments, offering an alternative source of data. These prices do not exactly correspond to farm-gate prices as they may include some distribution cost component, and the coverage across goods is relatively low. These prices are highly correlated with the mean district farm-gate prices in the NSS data but tend to be lower, which may be due to timing and aggregation issues. Using either NSS mean district farm-gate prices or VDSA district harvest prices I construct a wedge index as the aggregate expenditure share weighted average ratio of market to farm-gate or harvest prices, which captures the average price of the average consumption bundle in the district at retail prices versus farm-gate prices. 13 One issue with aggregation by district is that this includes observations collected at different points of the year, whereas villages are sampled with a single 30-day period. While I do not focus on seasonality in this paper there is some modest seasonal variation in home shares but it does not appear to vary widely across years. The main results of the paper are robust to using district-quarter level aggregation although this leads to somewhat lower coverage of goods for reasons discussed above.

17 TRADE COSTS AND HOUSEHOLD SPECIALIZATION 17 Figure 5 presents a kernel density plot of these district-level average wedges for 1987 and The upper-left panel shows that in 1987 these ratios were typically above one (around 1.1) but with considerable dispersion, and by 2009 the average wedge had fallen and a number of districts had wedges below one due to the wide availability of subsidized rice and wheat. The upper-right panel shows that excluding rice and wheat there was minimal change in these wedges. Restricting to just 11 major goods with price data from the VDSA (rice, wheat, sorghum, pearl millet, maize, finger millet, barley, chick pea, pigeon pea, minor pulses and ground nuts) the bottom two panels present the distribution of district wedges using the NSS and the VDSA farmer prices while the VDSA wedges are somewhat higher on average (but also more likely to be below one in 1987), both distributions show a clear leftward shift by 2009 due to the PDS rice and wheat subsidies. In interpreting these marketing wedges as trade costs, it is worth keeping two things in mind. First, the retail goods may embody different quality than the home produced goods both positively (farmers selling their best quality crops, use of packaging) and negatively (loss of freshness). Second, purchasing retail goods involves additional time costs for households beyond the financial costs, and these costs may be non-trivial for rural households (see Li (2013) for evidence from time-use data). This is beyond any risk and insurance based advantages to home production such as insuring consumption against price movements. Given the numerous restrictions on agricultural trade across districts and states (see the appendix in Atkin (2013) for a detailed discussion) and high transport costs, there is also considerable variation in retail prices across Indian districts. Figure 6 shows the substantial dispersion across districts relative to the median price. Comparing the across-district retail price gaps in figure 6 to the within-district retail/farmgate wedges in figure 5, it becomes clear that the within-district wedges are relatively large, which indicates that marketing costs are potentially as important as trade costs for farm decisions. There also appears to be little change in across-district retail price dispersion between with rice and wheat again being the notable exceptions due to different implementation of the PDS across states and districts. Altogether, the evidence on prices indicates a quantitatively important retail/home wedge within districts and a shift in these wedges driven by variation in the PDS subsidy policy over time and across districts. Within a village and during the same 30 day periods households face the same

18 18 NICHOLAS LI Figure 5: Within-district market/home wedge dispersion/decline menu of retail and farm-gate prices so comparing farmers within a village provides the best evidence on how farm production and consumption decisions vary with farmer characteristics. Figure 7 documents how the home share out of goods that are home-producible (essentially non-processed foods) or out of total expenditure vary with land or income for four-person farmer households in Farmer households with more land typically have a higher home share but this flattens out quite rapidly for the home share of total expenditure. As farmers get richer the home share initially rises but it then declines, particularly for the home share of total expenditure. These patterns are consistent with the fact that the home-producible goods have a lower income elasticity than the goods that cannot be produced at home. While it may seem surprising that richer/larger farmers have a higher share of home consumption for home-producible items, this is consistent with the trade perspective offered in the next section larger farms operate more like large open economies facing 14 The patterns shown here hold in regressions with village fixed-effects but I leave this out to give a sense of the scale on the Y-axis

19 TRADE COSTS AND HOUSEHOLD SPECIALIZATION 19 Figure 6: Across-district rural market prices trade costs, which end up with smaller import shares than small open economies and with other explanations like a minimum efficient scale of production for each crop and with the fact that many smaller farmers get a substantial proportion (up to 50%) of their income from wage labor. The ICRISAT farmer-level data offers some additional insight into how farm production and consumption patterns vary with size. The first panel of figure 8 uses the 2009 ICRISAT data to measure the fraction of each crop-farmer combination that is sold (as opposed to consumed/stored) during the year. I plot the kernel density across all farmers, which reveals a distinct bimodal pattern with a large number of crop-farmer cells that are almost entirely home consumed (sale fraction below 20%) and a decent number that are almost entirely sold (sale fraction above 20%), with a smaller number of crop-farmer cells that are evenly split between home consumption and sale to the market. The selling crops are often, but not always, non-food cash crops like cotton and oil seeds. Comparing large farmers (the top 10%, with over hectares of land) to small farmers (the bottom 10%, with under hectare)

20 20 NICHOLAS LI Figure 7: NSS: Farmer characteristics and home shares. Sample is all four-person farmer households in in the ICRISAT villages, we see that the small farmers have a higher density of goods meant for home production relative to for market sale and the large farmers have the opposite. The second panel of figure 8 provides further evidence that the subsistence motivation of smaller farmers results in different crop choices. Not only is the fraction of output sold increasing in total hectares under cultivation, but the share of land devoted to goods with a low market yield is also declining. For each ICRISAT village I measure the average market value of output per hectare by multiplying yield by the harvest price and dividing by area under cultivation. I then measure the fraction of land for each farmer that is devoted to crops that are in the bottom 50% and bottom 25% of crops in terms of revenue per hectare. Using either measure, there is large, sharp decline in the fraction of land devoted to these marginal crops going from low to modest size farms. This shows that richer farmers differ not only in how much they sell to the market but also in which crops they produce.

21 TRADE COSTS AND HOUSEHOLD SPECIALIZATION 21 In addition to farmer characteristics like farm size, there are many location characteristics that potentially drive the across-district differences in home shares and specialization observed in figures 3 and 4. Some of these are likely related to timeinvariant geographic features like distance from the coast, climate, soil and terrain ruggedness, and proximity to major cities. Some of these geographic features may affect trade costs directly, but they are also likely to affect the physical productivity of different crops, including those that are more (cereals) and less (sugar cane, oil seeds, cotton) likely to be important from a consumption point of view, which makes it difficult to interpret their effects without a crop productivity model 15 The district-level variables that seem most likely to affect trade costs across districts and retail/farmer price wedges within districts include local all-weather road density (from the ICRISAT VDSA data set), the importance of the public distribution system (measured as the fraction of all rice and wheat sold through the system), and the size of the distribution sector (measured as the proportion of workers whose principal industry is wholesale and retail trade or transport, storage and communication services, drawn from schedule 10 of the NSS). The top row of figure 9 presents a scatter plot and linear regression fit of the relationship between these three characteristics and the district mean home share of farmers in All three variables display a negative and statistically significant correlation with the district mean farmer home share, suggesting that trade and marketing costs are strongly related to farmer production decisions. The second row presents a similar scatter plot where the Y axis variable is the fraction of households who are farmers. For road density and the size of the distribution sector, there is a significant negative correlation, although for the distribution sector this is partly by construction. For the PDS the relationship is slightly positive, but it is worth bearing in mind that the size of the PDS in is not random and if farmers tend to be poorer than the average household (and are concentrated in areas with even poorer agricultural laborer households) accurate targeting by the PDS would be expected to produce this pattern. The third row of figure 9 shows kernel density plots for these district-level variables in and (I use for the PDS plot as this variable is not available for ). In all three cases there is a substantial rightward shift as road density, the size of the distribution sector and the generosity of the PDS have 15 I plan to collect district-level data on these characteristics to get a better sense of the factors driving cross-sectional dispersion and use the FAO GAEZ models to predict productivity.

22 22 NICHOLAS LI increased. Summarizing the facts in this section, there are large differences in the extent of food home-production in India across locations, with a large decline over time driven by a combination of lower home-consumption shares for farmers and a decrease in the fraction of households engaged in self-employed agriculture. These differences appear to be driven by specialization in production and also appear to be related to observable trade and marketing costs (retail/farmer price wedges) as well as variables related to trade costs like roads, the size of the distribution sector and government intervention in food markets. Between 1987 and 2010 there were major reforms to India s Public Distribution System which resulted in much lower retail prices for rice and wheat relative to farm-gate prices, but the effect was quite heterogeneous across districts.

23 TRADE COSTS AND HOUSEHOLD SPECIALIZATION 23 Figure 8: ICRISAT: Farmer characteristics and production

24 24 NICHOLAS LI Figure 9: District trade costs and home share

25 TRADE COSTS AND HOUSEHOLD SPECIALIZATION Model As the change in consumption of home-produced food in India is driven by changes within farmers and the choice to farm, I model both these decisions by combining a Roy model of occupational choice from Lagakos and Waugh (2013) with a standard Ricardian trade model from Eaton and Kortum (2002). Within a location (district) households have similar productivity in agricultural crops crop comparative advantage exists only across districts but households differ in their productivity in occupations outside of farming (which may include agricultural labor). Households face trade costs which drive a wedge between retail prices in different districts and also marketing costs which drive a wedge between the farm-gate price for farmers and the retail price that non-farmers have to pay. The model highlights the role of trade and marketing costs for generating a consumption advantage for farmers that affects farm production decisions, selection into farming, and farmer/non-farmer income gaps Preferences Households have standard Stone-Geary preferences over agricultural goods (the necessity), denoted by a, and other goods, denoted by m: U = (q a ) α (q m + m) 1 α (1) While other goods are a homogeneous variety, the agricultural good is a composite of varieties. There is a continuum of agricultural varieties of measure one, and constant elasticity of substitution (CES) across varieties: ( 1 q a 0 ) σ q σ 1 σ 1 σ ai di (2) The household budget constraint is given by 1 0 q aip ai di + p m q m I. Given additive separability between agricultural and other goods, the agriculture variety problem can be solved separately given some spending level X a. The demand functions for agricultural varieties and for the agricultural composite and other good are standard.

26 26 NICHOLAS LI 3.2. Production Each household in a location receives two random productivity draws an independent draw x from some distribution of productivity for the other good m (with CDF M(x)), and a series of productivity draws for each agricultural variety that are perfectly correlated within a location but vary across locations. The idea is that some households may be better or worse at non-farming relative to farming in a location, but comparative advantage for agricultural varieties is tied to climate, soil and other geographic characteristics common to households in a location. The agricultural productivity draws A i are from a Frechet distribution with scale parameter T and shape parameter θ (P r(a i < z) = e T z θ ). Farmers thus face a production constraint given by 1 0 y i/a i di 1. All producers are small and there is perfect competition. In the absence of any frictions or other locations, farmer households would produce all of the agricultural varieties and trade them with producers of the other good. ) 1 1 σ The quantity of a farmer s output is given by T 1/θ (where γ Γ ( θ+1 σ with Γ the γ θ gamma function) while the quantity of a non-farmer s output is their x draw. Denoting the fraction of farmers as π a, equilibrium in the labor market satisfies: π a = P rob(x P a P m T 1/θ γ ) (3) where we are simply comparing the income of the marginal non-farmer to their income in agriculture. Higher agricultural productivity (T ) would increase the farmers holding P a /P m constant, as would higher relative prices for agricultural goods P a /P m. The productivity of the marginal non-farmer is denoted x and under standard distributional assumptions x (π a ) is a monotonically increasing function of π a such that the larger the number of farmers, the more selected (hence more relatively productive) are the non-farmers. Closing the model requires equilibrium in one of the goods markets, e.g. setting demand equal to supply for the good m market: (1 α) ( π a P a T 1/θ /γ + (1 π a )P m x(π a ) ) mαp m = (1 π a )P m x(π a ) (4) where x(π a ) x xdm(x), the average productivity of non-farmers given the cut-off 0 productivity x defined by π a. I assume throughout that productivity is high enough that households consume both types of goods. These two conditions pin down the

27 TRADE COSTS AND HOUSEHOLD SPECIALIZATION 27 two unknowns π a and P a /P m. At this point, the model is virtually identical to the Lagakos and Waugh (2013) except they model both agricultural and non-agricultural productivity as independent (or possibly correlated) draws from a Frechet distribution. The main insight from this model is that relative (average) productivity in a sector will be falling in the employment share of that sector, and the combination of non-homotheticity and low aggregate productivity will tend to lead to a large and relatively low productivity agricultural sector Frictions and trade With trade and marketing costs and multiple locations, farmers no longer produce a homogeneous agricultural good q a but may instead produce a subset of potential agricultural varieties, importing some from other locations. For simplicity assume there are N+1 symmetric locations, where each location has different agricultural variety productivity draws from the Frechet distribution. Trade across locations incurs an iceberg trade cost denoted by d. In addition to the trade cost, farmers and non-farmers may no longer face the same prices. A farmer sourcing from their own production pays a cost p ai (the farm-gate price) while a non-farmer must pay price τp ai with τ an iceberg marketing cost. In this setup, given the Frechet distribution, farmer productivity in units of the agricultural composite is now given by ( T + NT (dτ) θ ) 1/θ γ (5) which is increasing in the number of other locations and decreasing in the trade and marketing costs incurred to import varieties from farmers in those locations (to an extent which depends on the strength of comparative advantage given by 1/θ). This farmer productivity also captures their effective price of consumption for the agricultural composite good. Non-farmers face a different price for agricultural goods as they must incur a marketing cost τ even for the varieties purchased from their location. The wedge between the agricultural composite price paid by non-farmers and farmers is given by wedge W W ( T + NT (dτ) θ ) 1/θ > 1 (6) (τ θ T + N(dτ) θ 1/θ )

28 28 NICHOLAS LI The Frechet distribution provides simple expressions (in descending order) for the 1 Farmer s home share in agriculture λ =, the share of agricultural consumption of non-farmers sourced from local farmers, θ, the share of agricultural 1+N(dτ) θ τ τ θ +N(dτ) θ N(dτ) consumption of non-farmers sourced from farmers in other locations θ, and τ θ +N(dτ) θ the share of agricultural consumption of farmers sourced from farmers in other locations θ. I suppressed agricultural productivity T here for simplicity (they N(dτ) 1+N(dτ) θ cancel out if identical), but note that an implication of this model is that more productive farmers with large T will typically have higher home shares in agriculture (but possibly lower home shares overall when including the other good), consistent with the data. Equilibrium in this model involves a similar equilibrium condition in the m good market given by ( ( (1 α) π a P ) ) a T + NT (dτ) θ 1/θ /γ + (1 πa )P m x(π a ) mαp m = (1 π a )P m x(π a ) but now the labor-market clearing/occupational selection condition involves the wedge W π a = P rob(xp m + m (P a ( T + NT (dτ) θ ) 1/θ γ + m ) (7) W α ) (8) With W > 1 the effective consumption of agricultural goods for farmers is higher than their market income to an extent that depends on the local marketing costs τ (but also depends on productivity and trade costs with other locations) and the importance of the agricultural good in utility, which is related to both α which is the expenditure share in the limit, and the size of γ which captures the fact that the agricultural good is a necessity. In rich, highly productive locations the consumption advantage conferred on farmers W may be of limited importance for utility and occupational sorting even in the presence of substantial marketing costs, because the share of these agricultural goods in consumption is small. Together, these two equations again pin down the equilibrium relative price of the a and m goods P a /P m and the fraction of households that are farmers π a. Unlike the frictionless equilibrium, prices matter not just for occupational incomes but also for consumption prices, with both trade and marketing costs playing a potentially important role. In this model both the marketing cost τ and the across-location trade cost d increase W, the consumption advantage of being farmer, leading to a

29 TRADE COSTS AND HOUSEHOLD SPECIALIZATION 29 larger share of households in farming and lower relative productivity in agricultural (typically measured in units of the other good m this is the implicit assumption in the valuation of farmer total expenditures using farm-gate prices for home-produced consumption). A decline in W driven by a decline in either τ or d moves households into the m sector and out of farming, lowering π a and the average income/total expenditure gap between non-farmers and farmers in both nominal and real terms. The non-homotheticity increases the importance of this consumption advantage channel at lower levels of aggregate productivity. Consider a reduction in external trade costs d. In an environment with no marketing costs, this external trade liberalization effectively boosts farm productivity and welfare. However, it would have no effect on π a, as a compensating decline in the agricultural terms of trade P a /P m would exactly offset the change in farmer productivity. The farmer consumption advantage does not exist and is unaffected (W = 1 pre and post liberalization). 16 There is also no change in relative nominal on real consumption between farmers and non-farmers. In an environment with marketing costs, this external trade liberalization lowers the consumption advantage of farmers because many of the goods that non-farmers previously would have bought from local farmers become cheaper (W decreases). The set of goods that farmers can produce more cheaply for themselves than they can exchange with farmers in other locations also falls. Consequently, farmers with relatively high productivity in non-farming will exit farming and both nominal and real income for non-farmers falls relative to farmers (real income falls by less as the prices for non-farmers relative to farmers rises). The agricultural terms of trade (P a /P m ) fall as before, and farmers also become less autarkic. Consider now a reduction in the marketing cost τ. This results in a decline in W and a decrease in the fraction of households engaged in farming. Farmers become less autarkic, the agricultural terms of trade fall, and both nominal and real income for non-farmers relative to farmers falls. Although there is no change in the measured retail price gaps and trade costs between locations, agricultural trade increases because the gap between home productivity and market prices shrinks. Households and entire locations can become less autarkic in terms of agricultural goods through this mechanism despite constant geographic price dispersion through the decline in 16 Note that this would not be the case with preferences that featured non-unitary elasticity of substitution.

30 30 NICHOLAS LI farming and the decline in production for home consumption by farmers. Indeed since farmers consume less imported varieties than their non-farming peers, exit from farming will tend to increase the volume of agricultural trade by more than a model where the volume of trade is proportional to the home share of a representative farming household. Note that in this economy, when income is low and all households are farmers the gains from trade (relative to autarky, or due to changes in trade costs) are identical to the standard Eaton and Kortum (2002) Ricardian model and can thus be expressed using the formula from Arkolakis et al. (2012) given by Û = (λ /λ) 1/ɛ where λ is the initial home share of agriculture and λ is the new one (gains relative to autarky can be assessed by setting λ = 1) and ɛ is the trade elasticity (which in the Eaton and Kortum (2002) Ricardian model is just the Frechet shape parameter θ). 17 Table 5 presents results from a simple simulation of the model using a uniform distribution of non-farm productivities, using NSS data for for the calibration. I compare a baseline scenario roughly calibrated to the data scenarios where I reduce the marketing cost to zero and hold trade costs constant, reduce the trade cost to zero and hold marketing costs constant, and reduce both. Under my calibration reduction of either cost lowers the share of farmers only modestly but can generate larger drops in the non-farmer/farmer income (total expenditure) ratio and very large drops in the home food share of farmers and aggregate population. Compared to a large reduction in trade costs (from 1.5 to 1), a modest reduction in marketing costs (from 1.2 to 1) generates larger declines in the share of farmers and non-farmer/farmer income ratios but a more modest decline in the home share of farmers. Beyond a more realistic calibration, there are several basic extensions of the model that might yield additional insights. First, allowing for an elasticity of substitution less than one between the agricultural good and the m good, which is common in the structural change literature, would amplify the effects of marketing and trade costs in terms of increasing the size of the agricultural sector. This would also allow productivity gains from trade integration in an environment with no marketing costs to affect the decision to farm or not. 18 Second, adding farmer heterogeneity would not 17 With positive consumption of the m good, the welfare expression will be more complicated and in practice would depend on how productivity in the m good sector responds to trade and the substitutability of agricultural goods for the m good. 18 Note however that the potential for trade to affect the decision to farm or not despite no terms of

31 TRADE COSTS AND HOUSEHOLD SPECIALIZATION 31 Table 5: Model calibration and simulation Calibration Data Baseline No mkt cost No trade cost No frictions Farmer share Non-farmer/farmer income Food share farmer Food share non-farmer Price index for A Non-farmer/farmer price Home share farmers Home food share farmers Whole pop. Home share Aggregate local food share Baseline calibration uses θ = 2.5, N = 9, d = 1.5, τ = 1.2, m = 45, α = 0.15, non-farm productivity x distributed U[5, 25] overturn the basic results but would allow for a more realistic calibration that also took into account differences in home shares within locations. Allowing for households below the income threshold required to start purchasing the m good would allow the income distribution to play some role as well, as the Stone-Geary preferences only allow average incomes to matter when this never occurs. Third, adding a perfectly competitive land rental sector would allow for larger land holdings when more households exit agriculture but the overall effect would be similar. Allowing for misallocation of land is a more interesting but substantial extension. 19 Finally, a more realistic model would need to go beyond the simple two sector framework common to the macro growth literature and allow for three or possibly four sectors farmers have a modest consumption price advantage over agricultural laborers (poorer on average) and other rural residents (richer on average), and a substantial consumption price advantage over urban residents (much richer on average). Given that many trade effects for agriculture/non-agriculture prices or elasticity of substitution greater than one might be viewed as an advantage of my approach. 19 Note that in this model, with a perfectly competitive frictionless land market, land is allocated proportional to productivity in market production, but since smaller farmers are more home-oriented in their production pattern, they will appear to have even lower output per hectare when crop outputs are evaluated at market prices. In this sense the model implies endogenous misallocation due to output frictions, rather than the usual misallocation due to exogenous land and other input frictions.

32 32 NICHOLAS LI non-farmer households in rural areas engage in some home production, and that farming is determined by a majority of income criterion, it might also be advantageous to model farming as a continuum rather than a discrete occupational choice as in Lagakos and Waugh (2013). 4. Estimation While the model suggests a role for trade and marketing costs in selection into farming and farmer production decisions and there are correlations in the cross-section and time-series, but the more formal analysis in this section leverages the panel structure of the data to examine changes in specialization across occupations and crops in response to changes in district-level trade and marketing costs. I also provide some preliminary evidence on the elasticity of market consumption with respect to its price using the market/home wedges described earlier. I use this parameter to derive some rough estimates of welfare gains and the magnitude of differences in unobserved market/home frictions over time and across districts Reduced form Table 6 presents regressions using district-level outcomes using the ICRISAT VDSA data, which covers 16 major states between 1966 and The main outcome variables of interest are district crop specialization measured using the 16 major crop Herfindal index in crop area discussed earlier and occupational specialization measured as the fraction of the population engaged in farming. Also of interest is the value of agricultural output for the 16 major crops, constructed by multiplying quantities and wholesale market prices in the VDSA data. The independent variables capturing trade and market access are log district population, log district all-weather road length, and a national market access measure derived from India highway maps collected by Allen and Atkin (2015). 20 All regressions include district and year fixed effects. Standard errors are clustered at the district level. 20 I follow Allen and Atkin (2015) and construct a population weighted market access measure for each district j given by Mktaccess jt = pop it i neqj. Allen and Atkin (2015) calculate a highway hours 1.5 ijt travel time in hours that varies as the Indian highway system expands during the sample period. All measures are linearly interpolated and extrapolated given that data are missing for many years but I also report resorts for select years where the market access variable can be measured directly (except for population which must still be interpolated from the decennial census).

33 TRADE COSTS AND HOUSEHOLD SPECIALIZATION 33 I report results using the whole sample and the select years where market access is better measured. The first two columns show that district crop specialization, measured by the area Herfindahl, is increasing in both national market access and local roads. To interpret the magnitude of these effects, the average change in local roads and national market access over the period were similar at about 1.43 log points, leading to a 1.1 and 3.6 percentage point increase relative to an absolute increase in the district mean Herfindahl index of 3.5 percentage points over this period. In the 2009 cross-section the 90th/10th percentile district difference is 2.06 for local road density and 0.88 for national market access, predicting a difference of 1.6 and 2.2 percentage points, relative to 56 percentage points in the actual data. This suggests a lot of the cross-sectional heterogeneity in crop specialization is unrelated to trade costs. Columns 3 and 4 capture the fraction of the population that are farmers. As I include log population and log (farmers + agricultural laborers) as controls this can also be interpreted as the ratio of households engaged in agriculture that are primarily self-employed rather than wage laborers. The results indicate that both local market access (population density and roads) and national market access lead to exit from farming. The fraction of farmers fell by about 8 percentage points over the entire period, with the observed increase in market access accounting for in the range of 2-3 percentage points. Columns 5 through 7 look at the log value of agricultural output. Not surprisingly this is increasing in the population engaged in agriculture, but the data seem to suggest that increasing the ratio of farmers to agricultural laborers holding the sum constant lowers the value of output. Local market access (roads, population) and national market access both increase the value of crop output. Column 7 repeats column 5 but adds time-varying controls for the three district-level characteristics that are most likely to affect the value of agricultural output the fraction of gross cultivated area that is irrigated, the fraction planted with high-yielding varieties (HYV), and total chemical fertilizer use. The effects of market access seem to be fairly stable adding these controls, consistent with the idea that market access is not simply working through modern technology and input adoption but also through crop choice. Quantitatively, market access seems to account for about 1/4 of the increase in crop revenue over this period. Table 7 looks at household-level outcomes using the NSS data over the

34 34 NICHOLAS LI Table 6: ICRISAT VDSA: trade costs and district-level outcomes (1) (2) (3) (4) (5) (6) (7) Outcome Area Herfindahl Share of farmers Log value of output Sample Years All Select All Select All Select All Population *** *** 2.200*** 2.470*** 1.808*** (0.0262) (0.0299) ( ) ( ) (0.205) (0.222) (0.210) Agric. pop *** *** 0.969*** 0.759*** 1.033*** (0.0303) (0.0434) (0.0102) ( ) (0.160) (0.226) (0.149) Farmers *** *** (0.0276) (0.0370) (0.109) (0.184) (0.117) Local roads * ** *** ** 0.266*** 0.343*** 0.247*** ( ) ( ) ( ) ( ) (0.0207) (0.0313) (0.0226) Market access ** ** *** *** 0.671*** 0.417*** 0.761*** (0.0106) (0.0102) ( ) ( ) (0.0851) (0.0864) (0.0777) Frac. irrigated 1.183*** (0.165) Frac. HYV 0.258** (0.0999) Fertilizer *** (0.0196) Constant 0.303** 0.331*** 0.533*** 0.514*** *** *** *** (0.118) (0.126) (0.0206) (0.0194) (0.565) (0.639) (0.777) Observations 12,793 1,453 12,793 1,453 12,791 1,452 9,998 R-squared Number of districts Robust standard errors clustered by district in parentheses *** p<0.01, ** p<0.05, * p<0.1 All regressions include district and year fixed effects. All independent variables in logs except irrigation and High Yielding Varieties (HYV). All year sample includes , select sample includes years of non-interpolated market access variable 1969, 1977, 1988, 1996, and See text for full description of other variables. period. I use the , , , and survey rounds. 21 The market/trade cost variables I consider are the local roads, local population, and national market access mentioned earlier but also the value of the PDS subsidy per person. This measure is constructed at the district-level for rural areas, as the average market price over average PDS price times the quantity purchased through the PDS, divided by the rural population. I sum this value for PDS rice and PDS wheat and convert to 1966 rupees for reference, this measure rises from 0 in 1987 to 0.17 in 1993 and 1.1 by 2009 for the average rural household. Because the PDS subsidy may affect farming decisions through its effects on harvest prices beyond its effect on the market/home 21 I omit the round because of differences in the recording period (30 and 7 days, instead of just 30 days) and the measurement of home consumption (the survey asks for total value and total quantity with a flag for cash, home, or both rather than separate values and quantities for home and market consumption). Both of these make comparisons across years more difficult with this survey round.

35 TRADE COSTS AND HOUSEHOLD SPECIALIZATION 35 relative price, I control for district rice and wheat market prices as well. In addition to district and year fixed effects I include household controls for a quadratic in log real per capita monthly expenditure and a quadratic in household size. The first four columns present results using all rural households, where outcomes of interest are the number of home produced crops, the home consumed share of home-producible food, the home consumed share of all consumption, and a dummy variable for production of rice/wheat since this may be most affected by expansion of the PDS subsidy for these crops during the sample period. The results indicate an important role for local roads and the PDS subsidy for household specialization, as increases in either variable lead to less crops (including the main staples rice and wheat) and lower home consumption shares. Surprisingly national market access has essentially zero effect. Column 5 is a linear probability model where the outcome is being a farmer ( self-employed in agriculture ). Both PDS and local roads reduce the probability of being a farmer in a rural area. Columns 6 through 9 repeat columns 1 through 4 but condition on being a farmer and include a quadratic in log land. The results are qualitatively and quantitatively similar to the earlier columns, with one exception national market access seems to have a positive effect on the number of crops produced and the home produced share of food. Both log roads and the PDS variable increase by about one over the period, and the cross-district variation in 2009 is also similar in magnitude (2.4 between the 90th and 10th percentile districts, compared to 2.06 for log road length in km per square kilometer). The coefficients thus imply that local road construction has a larger effect on the number of crops than PDS, but a smaller effect on the home shares and the choice to be a farmer. Note that while district and time fixed effects address the numerous time-invariant differences across district and national-level policies (including national-level agricultural internal and external trade liberalization, national minimum support prices for farmers, etc.), the results are still just conditional correlations. While the changes within districts over time in PDS are plausibly exogenous reflecting different implementation of national-level policy reform across states the growth of roads and market access may be correlated with expected economic growth and agricultural development. While some researchers have argued that some road projects are exogenous with respect to otherwise similar districts e.g. districts that lie along the golden quadrilateral constructed between 1999 and 2009 linking up India s four

36 36 NICHOLAS LI Table 7: NSS data: trade costs and farmer-level outcomes All rural households Rural farmers only (1) (2) (3) (4) (5) (6) (7) (8) (9) Outcome Num.crops Home share food Home share all Prod.rice/wheat Farmer=1 Num.crops Home share food Home share all Prod.rice/wheat Log local roads *** *** *** *** *** *** *** *** *** (0.0856) ( ) ( ) ( ) (0.0106) (0.138) ( ) ( ) (0.0117) PDS value/person *** *** ** *** *** *** *** ** ** (0.0381) ( ) ( ) ( ) ( ) (0.0834) ( ) ( ) (0.0141) Log market access 0.459* *** ** (0.273) (0.0205) (0.0118) (0.0315) (0.0240) (0.417) (0.0259) (0.0154) (0.0437) Log population * (0.462) (0.0472) (0.0287) (0.0658) (0.0721) (0.803) (0.0515) (0.0304) (0.0732) Constant *** *** *** *** *** *** *** * *** (4.281) (0.440) (0.251) (0.561) (0.602) (7.168) (0.487) (0.282) (0.669) Observations 200, , , , ,553 75,212 75,174 75,212 75,212 R-squared Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 All regressions include district and year fixed effects. Other controls include quadratic in real per capita expenditures and household size, market prices of rice and wheat and quadratic in log land for farmer households. Sample includes rural areas of , , , and NSS rounds. largest cities (Datta (2012), Ghani et al. (2013) and Asturias and Garcia-Santana (2014)) this type of variation appears to have poor predictive power with my data. Given the panel nature of the data, it is possible to assess the plausibility of the exogeneity assumption by examining variables forwarded one period. This is similar to testing for pre-trends in a difference-in-difference setting only applied to a fixed effects regression with continuous variation over time in the main variables of interest. The first four columns of table 8 provide at least some evidence for exogeneity by looking at the effects of one-period forwarded PDS and local road variables on the probability of being a farmer, home food share and crop choice. Future roads do appear to have some significant negative effect on the fraction of farmers and home share of food, but the magnitudes are about halved and the significance is marginal (10% level). There is no similar effect for number of crops or consumption of home rice/wheat. For the PDS value per person variable there is no statistical significance for the forwarded variable and the coefficients are all an order of magnitude smaller. Another potential source of variation is from the cross-section. While less plausibly exogenous, I can use district by round dummies to flexibly control for numerous policy factors and construct village-level variables that capture infrastructure and market access and the PDS. I proxy for infrastructure and market access by construct-

37 TRADE COSTS AND HOUSEHOLD SPECIALIZATION 37 ing a village electrification dummy, equal to one if any household in the village consumes electricity. The motivation for this is that all-weather roads and power lines are often closely linked, are both less likely to connect to remote villages, and that electricity directly enables communications technology that may further lower trade costs. I capture PDS access with a village PDS dummy, defined as equal to one if there is a PDS shop in the village (variable only available in rounds 43 and 50) or if any household in the village consumes a positive quantity of PDS rice or wheat. Columns five through eight of Table 8 present these results, which strongly confirm the importance of market access and the PDS for shaping the choice of whether to farm and what farm. Table 8: Robustness: trends and within-district/round variation (1) (2) (3) (4) (5) (6) (7) (8) Forwarded X variables Within district/round variation Outcome Farmer=1 Num.crops Home share food Prod.rice/wheat Farmer=1 Num.crops Home share food Prod.rice/wheat Log local roads (t+1) * * ( ) (0.182) (0.0101) (0.0124) PDS value/person (t+1) ( ) (0.0839) ( ) (0.0115) Village electr *** *** *** ** ( ) (0.0610) ( ) ( ) Village PDS *** *** *** ( ) (0.0740) ( ) ( ) Constant *** ** *** *** *** *** *** *** (0.751) (10.42) (0.566) (0.856) (0.165) (1.303) (0.101) (0.127) Observations 202,832 78,521 78,477 78, ,831 90,511 90,470 90,511 R-squared Robust standard errors in parentheses clustered by district. *** p<0.01, ** p<0.05, * p<0.1 Regressions in columns 1-4 use roads and PDS value/person forwarded by one period (1987,1993,1999,2004,2009) as placebo. Other controls identical to Table 7, see notes. Regressions in columns 5-8 use village electrification and PDS dummies with district/round fixed effects. While the results so far have focused on the overall effects of PDS expansion and PDS access, because the policy specifically targets poor households and specifically subsidizes market purchases of rice and wheat (relative to home production), the effects of the policy should be heterogeneous across farm households. In particular, one would expect to see larger effects of the PDS for Below Poverty Line (BPL) households who are the only ones theoretically entitled to purchase subsidized rice and wheat in most states. 22 One would also expect to see the effect muted in states 22 The one major exception is Tamil Nadu where the PDS expansion was turned into a universal sub-

38 38 NICHOLAS LI where there is a strong comparative advantage in rice or wheat production in these states, rice and wheat will never be the marginal goods which are only home produced because of market/home wedges. Table 9 shows that the effects of districtlevel PDS/person and village-level PDS access are much larger for poor farmers defined as below median per capita expenditure or below 1 hectare of land than for non-poor farmers. The PDS effect for poor farmers is substantially muted in areas with a strong revealed comparative advantage in rice or wheat production, defined as districts with more than 66% of major crop land area devoted to rice and/or wheat in This heterogeneity can be observed for the home share of food and for consumption of home rice/wheat specifically, and provide further evidence in support of the main mechanism in the paper. 23 Table 9: Robustness: sample splits for PDS variables (1) (2) (3) (4) (5) (6) (7) Poor=below median per capita expenditure Poor=below 1ha land Outcome All farmers Non-poor Poor Poor, rice/wheat area Non-poor Poor Poor, rice/wheat area Panel A. Outcome: Home Share of Food PDS/person ** * *** *** ( ) ( ) ( ) (0.0236) ( ) ( ) (0.0241) Village PDS *** *** *** *** *** ( ) ( ) ( ) (0.0107) ( ) ( ) (0.0127) Constant (0.464) (0.415) (0.625) (1.251) (0.562) (0.479) (1.178) Observations 75,174 43,994 31,180 6,768 52,348 22,826 6,815 R-squared Panel B. Outcome: Consume home rice/wheat PDS/person ** *** (0.0140) (0.0147) (0.0177) (0.0394) (0.0172) (0.0143) (0.0388) Village PDS *** *** *** *** *** *** * ( ) ( ) (0.0104) (0.0136) ( ) (0.0118) (0.0166) Constant (0.660) (0.676) (0.856) (2.093) (0.727) (0.843) (1.897) Observations 75,212 44,011 31,201 6,771 52,370 22,842 6,820 R-squared Robust standard errors in parentheses clustered by district *** p<0.01, ** p<0.05, * p<0.1 Other controls identical to Table 7, see notes. Rice/wheat area defined as districts with more than 66% of major crop area sown in rice and wheat in sidy/entitlement. Empirically there is no clear discontinuity/cutoff in per capita expenditure or land when regression a dummy for consumption of PDS rice or wheat although the slope is significantly negative. 23 Although I control directly for local market prices in all PDS regressions, note that this heterogeneity also provides further evidence that the effect of PDS expansion occurs largely through the market/home wedge facing individual households and not through general equilibrium effects on local market and harvest prices for these goods.

39 TRADE COSTS AND HOUSEHOLD SPECIALIZATION Elasticities and welfare One implication of home production for welfare comes directly through prices. Real consumption for farmers is underestimated relative to other rural households since farmer expenditure in the NSS is constructed using lower prices, and for similar reasons real consumption overall, and for farmers specifically, has not risen as much over time as commonly believed because of the decline in home shares (though in India at least, this needs to be balanced against PDS purchases which are valued at the PDS and not market price when constructing household expenditures). Unlike these direct effects, or the real expenditure or crop revenue outcomes in the regressions presented earlier, any structural assessment of trade/marketing costs on farmer welfare requires an estimate of the elasticity of market consumption with respect marketing costs. While there are many costs that potentially affect the decision of whether to produce and consume at home or produce and sell in exchange for market consumption at the margin, the observable cost in my data is the market/home wedge discussed earlier. There are potentially 102 home-producible goods with observable retail and farm gate prices for each district (making up 78% of all food expenditure) but in any given district and period the number actually observed will be lower. By combining these product-level market/home wedges into a single index using Tornqvist weights based on local expenditure shares I construct the weighted cost of the representative consumption bundle at market prices relative to the same bundle at home prices. I then consider estimating a demand curve of the form: ln sharemarket idt = α d + γ t + ɛ ln τ dt + ΩX idt + u idt (9) where τ dt is the district-period market/home wedge index (rural areas only), ln sharemarket idt is the share of all food expenditure on the 102 items purchased from the market (as opposed to out of home production), X idt are other farmer characteristics and I include district and time fixed-effects. The parameter ɛ is the elasticity of market consumption with respect to the price of market goods, which we would expect to be negative. While this equation can be estimated by OLS, it is subject to the usual problems. Measurement error in τ dt is likely severe given the sampling variability inherent in the small number of prices (particularly farm gate prices) observed in a particular district-period in the NSS data. Although fixed effects help deal with time-invariant

40 40 NICHOLAS LI tastes or frictions affecting home shares that could be correlated with equilibrium market/home price wedges, it is plausible that time-varying demand shocks for market consumption within a district may drive up both market consumption shares and market relative to farm gate prices. Finally, it plausible that part of the cost of purchasing from the market vs. home is unobserved e.g.time and convenience, quality differences, factors related to risk and positively correlated with the observable prices. For these reasons, OLS estimation is likely to result in a downward biased estimate of the elasticity ɛ. This suggests an instrumental variables estimation strategy. Given the findings discussed above, two reasonable candidate variables are the PDS subsidy and local roads. Both these variables have a negative first-stage relationship with the market/home wedge for the PDS, or both variables combined, the F-statistic is reasonably high, while for roads alone the first-stage relationship is fairly weak. The PDS variable seems reasonably likely to satisfy the exclusion restriction, as the primary effect is to directly affect the price, with convenience/time differences less relevant. While it is generally believed that there are quality differences with lower quality in the PDS the quality gap is less likely to vary systematically over time within a district in a way that is correlated with the value of the subsidy. The road variable is perhaps less likely to satisfy this restriction, as more local roads may lower the time cost of purchasing from the market relative to at home. Table 10 presents the results of this estimation for the 102 home-producible food categories in the NSS. I restrict the sample to farm households where one might expect to see some reasonable variation in the dependent variable and include controls for land, household size and real expenditure in addition to district and time fixed effects. Column 1 presents the OLS estimate of the elasticity which is about 0. Column 2 uses the PDS instrument only, which lowers the elasticity to about 1.3. Column 3 uses the road variable only which raises the estimate further to 4.2, but the precision falls substantially and the instrument is weak. Column 4 uses both instruments and gives an elasticity of about 1.8. An overidentification test is not rejected at the 5% significance level. These elasticity estimates are lower than are typically found in the trade literature for agricultural goods, with Donaldson (2012) finding an elasticity of 3.8 for colonial Indian districts and Tombe (2014) finding an elasticity of 4.06 for international agricultural trade. However, lower elasticities should be expected if the land owned by individual farmers is more heterogeneous than land at the district or country level or

41 TRADE COSTS AND HOUSEHOLD SPECIALIZATION 41 farmers have less crop substitution possibilities for other reasons, and the standard errors are wide enough to be consistent with higher elasticities. Table 10: Farmer trade elasticity (1) (2) (3) (4) OLS IV PDS IV Roads IV both All home-producible food Elasticity ɛ (0.09) (0.56) (2.33) (0.59) Obs First-stage F-stat OverID test p-value 0.06 Dependent variable is log share of home-producible food purchased from market. Independent variable is log market/home wedge index described in text. Standard errors in parentheses (clustered by district). Regressions include quadratics in land, household size and real expenditure, district and round FE. There are several potential applications of this elasticity estimate beyond calibration of models similar to the one presented earlier. The first is estimation of welfare gains relative to autarky. Arkolakis et al. (2012) show that a class of trade models have welfare gains from trade (vs. autarky) described by the CES compensating variation formula ( λ λ ) 1/ɛ, where λ and λ are the comparison and actual home produced shares of total consumption (and λ = 1 when the comparison state is autarky). This would apply to the model described earlier under the assumption of separability of homeproducible food and other goods, allowing estimation of welfare gains from trade for home-producible food only given the difficulty or even impossibility of observing home prices or consumption for the other goods this is also an assumption that is necessary in practice. Alternatively, without assuming that the market (import) share has a constant elasticity with respect to trade costs, one might use the consumer surplus approximation suggested by Hausman (2003). Under this interpretation, there is

42 42 NICHOLAS LI a downward-sloping Marshallian demand curve for market expenditure with respect to the price of the market bundle (holding other prices and income constant) Hausman s formula gives a lower bound on the consumer surplus (area under the demand curve) for any convex demand curve based on the linear approximation mktexp 2(1 ɛ). This can be expressed as a percentage of total expenditure or total home-producible food expenditures as well, e.g. sharemkt 2(1 ɛ). Table 11 provides several estimates of welfare gains based these formulas, using an elasticity estimate of 1.8. Column one uses the Arkolakis et al. (2012) formula while column two uses the Hausman (2003) formula. The first row presents the welfare gain from trade vs. autarky (expressed as a percent of total home-producible food expenditure) for the average farmer and the average rural household in 1987, while the second row presents the same for Autarky here refers only to autarky in production of home-producible foods extrapolating to other goods requires an elasticity estimate for those goods which is difficult given the lack of price or quantity data for home production. The magnitude of welfare gains from trade versus autarky are much larger than typically calculated in the literature, which comes from the fact that import penetration shares are quite high (home shares are quite low), especially compared to large countries, and the trade elasticity is also on the low side of existing estimates. As farmers and rural households become more integrated into the market by 2009, they are increasingly better off relative to autarky. The gains from market exchange vary considerably across districts as well much more than any changes for the average farmer over the period with some districts benefiting immensely from trade and others benefiting considerably less. Gains are substantially lower under the Hausman (2003) linear demand curve approximation, which is always the case but particularly when elasticities are low as they are here. The other potential application of the estimated elasticity is to infer how large the true trade costs facing households accessing local markets must be if observed differences in home consumption shares are only driven by these trade costs. For example, from 1993 (just before the PDS reforms were enacted) to 2009 the average observed market/home wedge decline by 6 percent, of which 4 percent could be explained by the PDS expansion and 1 percent by the increase in local road density. However, the observed decline in market/home wedge was far too small to explain the entire decline in home share over this period for the average farmer the true trade cost would have had to decline by 15 percent to explain a decline of the mag-

43 TRADE COSTS AND HOUSEHOLD SPECIALIZATION 43 Table 11: Welfare gains for farmers CES demand Linear demand Welfare gains vs. autarky (% home-producible food) 1987 avg. farmer 30% 8% 2009 avg. farmer 37% 10% th dist. pctile farmer 23% 7% th dist. pctile farmer 54% 13% 1987 avg. rural hh. 48% 12% 2009 avg. rural hh. 58% 14% Elasticity of mkt.exp. to price = CES welfare based on ACR(2012) formula. Linear demand based on Hausman (2003) formula. nitude observed with an elasticity of Similarly, the 90/10 ratio of mean farmer home shares across districts in is 3.4, which if driven only by differences in marketing wedges implies a 90/10 ratio of market/home wedges of The actual 90/10 ratio of mean market/home wedges is 1.4, which suggests that the observable market/home wedge only captures about two thirds of the difference in the true market/home wedge across districts. This could be due to many factors including some that have nothing to do with trade costs at all but rather technology but it is not surprising that farmers may perceive other advantages to home production besides the averages static price difference, e.g. the time-costs of selling and buying from the market or consumption insurance against price fluctuations due to weather and other factors as documented by Allen and Atkin (2015). 5. Conclusion Schultz (1953) identified the food problem as the combination of low agricultural productivity and high employment in agriculture in poor countries. The presence of high trade costs for agricultural products combined with food being a necessity can

44 44 NICHOLAS LI generate this pattern, and high agricultural trade costs within countries have been shown to further lower productivity through lower comparative-advantage based specialization across crops. This paper argues that the food problem also operates at a smaller scale that of individual households with selection into farming and the choice of cropping patterns by individual farmers responding to market/home price wedges. This additional channel matters because food is important for poor households and farming offers a substantial consumption advantage for food. While misallocation of farm inputs is often attributed to imperfections in land and labor markets or government regulation, in this case it appears to be at least partly related to frictions on the consumption side. The forces I identify imply that in the presence of consumption frictions, farmers will be biased towards subsistence goods with lower market value and many households will choose to be small, relatively unproductive farmers rather than alternatives (even agricultural labor) that cannot achieve a similar exchange rate of food for labor. The effects of these local trade and marketing costs on occupational choice, which allocates land across households and determines farm scale and managerial capability, and crop choice, whereby households allocate land to crops based on relative prices and productivity, lowering productivity. I show that the intervention of the Indian government in agricultural markets through the PDS a policy designed primarily to alleviate poverty and malnutrition has important effects on production, consistent with the non-separability of consumption and production decisions for households. The PDS has led households to exit farming and become more integrated into the market, with expansion during the 1990s and 200s accounting for up to a third of the decline in farmer home food share over this period. Thus farmers are trading more with the market despite limited liberalization of agricultural trade across states/districts and persistently large retail price gaps across locations. While it is beyond the scope of this paper to analyze the net welfare impact of the PDS, it does appear to partly offset the natural distribution cost wedge between market and farmer prices that incentivizes subsistence farming. Thus removal of the PDS would likely entail a rise in subsistence oriented farming at the margin that would lower productivity. Balanced against this is the central government policy in recent years of high Minimum Support Prices (MSPs) for rice and especially wheat that have created windfalls for farmers in politically important states. This policy has incentivized farming. Thus the effects of

45 TRADE COSTS AND HOUSEHOLD SPECIALIZATION 45 reforming the entire Indian food system and shifting towards less regulated domestic agriculture is unclear and merits further study. While India has one of the most interventionist agricultural policies in the world, it is hardly unique among developing and developed countries in distorting food markets. While the role of price support for farmers has been studied extensively, the effect of consumer food subsidies on agricultural production patterns has been less analyzed and deserves more scrutiny in light of the evidence presented here. My findings on the importance of local road density also highlight the importance of considering how trade reaches the interior where many farmers live in addition to the distances and price gaps between major agricultural markets and urban centers.

46 46 NICHOLAS LI References Adamopolous, Tasso, Transportation Costs, Agricultural Productivity, and Crosscountry Income Differences, International Economic Review, Allen, Treb and David Atkin, Volatility, Insurance and Gains from Trade, Working Paper, Arkolakis, Costas, Arnaud Costinot, and Andres Rodriguez-Clare, New Trade Models, Same Old Gains?, American Economic Review, 2012, 102(1), Asturias, Jose and Manuel Garcia-Santana, Misallocation, Internal Trade, and the Role of Transportation Infrastructure, Working Paper, Atkin, David, Trade, Tastes and Nutrition in India, American Economic Review, 2013, 103(5). and Dave Donaldson, Who s Getting Globalized? The Size and Implications of International Trade Costs, Working Paper, Costinot, Arnaud and Dave Donaldson, How Large are the Gains from Economic Integration? Theory and Evidence from U.S. Agriculture, , Working Paper, Datta, Saugato, The impact of improved highways on Indian firms, Working Paper, 2012, 99, de Janvry, Alain and Elisabeth Sadoulet, Progress in the modeling of rural households behavior under market failures, in Essays in Honor of Erik Thorbecke, Kluwer Publishing, Donaldson, Dave, Railroads of the Raj: Estimating the Impact of Transportation Infrastructure, Forthcoming American Economic Review, Dreze, Jean and Reetika Khera, Rural Poverty and the Public Distribution System, CDE Working Paper, Eaton, Jonathan and Samuel Kortum, Technology, Geography and Trade, Econometrica, 2002, 470(5),

47 TRADE COSTS AND HOUSEHOLD SPECIALIZATION 47 Fafchamps, Marcel, Development, agglomeration, and the organization of work, Regional Science and Urban Economics, 2012, 42, Ghani, Ejaz, Arti Grover Goswami, and William R Kerr, Highway to Success: The Impact of the Golden Quadrilateral Project for the Location and Performance of Indian Manufacturing, Working Paper, Gollin, Doug, David Lagakos, and Michael Waugh, The Agricultural Productivity Gap, Quarterly Journal of Economics, 2014, 129(2). Gollin, Douglas and Richard Rogerson, Productivity, transport costs and subsistence agriculture, Journal of Development Economics, Hausman, Jerry, Sources of Bias and Solutions to Bias in the Consumer Price Index, Journal of Economic Perspectives, 2003, 17(1), Lagakos, David, Explaining Cross-Country Productivity Differences in Retail Trade, Journal of Political Economy, and Michael Waugh, Selection, Agriculture, and Cross-Country Productivity Differences, American Economic Review, 2013, 103(2), Li, Nicholas, An Engel Curve for Variety, Working Paper, Lowder, Sarah K., Jakob Skoet, and Saumya Singh, What do we really know about the number and distribution of farms and family farms worldwide?, Background paper for The State of Food and Agriculture 2014, Mallory, Mindy and Kathy Baylis, Food Corporation of India and the Public Distribution System: Impacts of Market Integration in Wheat, Rice, and Pearl Millet, Journal of Agribusiness, 2012, 30(2), Restuccia, Diego and Tasso Adamopolous, The Size Distribution of Farms and International Productivity Differences, American Economic Review, 2014., Dennis Tao Yang, and Xiaodong Zhu, Agriculture and aggregate productivity: A quantitative cross-country analysis, Journal of Monetary Economics, Schultz, T.W., The Economic Organization of Agriculture, New York: McGraw-Hill, 1953.

48 48 NICHOLAS LI Sotelo, Sebastian, Trade Frictions and Agricultural Productivity: Theory and Evidence from Peru, Working Paper, Swiecki, Thomasz, Intersectoral Distortions and the Welfare Gains from Trade, Working Paper, Tarozzi, Alessandro, The Indian Public Distribution System as provide of food security: Evidence from child nutrition in Andhra Pradesh, European Economic Review, 2005, 49, Tombe, Trevor, The Missing Food Problem: Trade, Agriculture, and International Productivity Differences, American Economic Journal: Macroeconomics, 2014.

49 TRADE COSTS AND HOUSEHOLD SPECIALIZATION 49 A Additional Graphs and Figures Figure 10: : Fraction of rice/wheat sold through PDS

50 50 NICHOLAS LI Figure 11: : Distribution of home shares of food by household type

51 TRADE COSTS AND HOUSEHOLD SPECIALIZATION 51 Figure 12: : Distribution of home shares by land holdings

52 52 NICHOLAS LI Figure 13: District-level specialization over time