Storage infrastructure and agricultural yield: Evidence from a capital investment subsidy scheme

Chatterjee, Somdeep Article Storage infrastructure and agricultural yield: Evidence from a capital investment subsidy scheme Economics: The Open-Access, Open-Assessment E-Journal Provided in Cooperation with: Kiel Institute for the World Economy – Leibniz Center for Research on Global Economic Challenges Suggested Citation: Chatterjee, Somdeep (2018) : Storage infrastructure and agricultural yield: Evidence from a capital investment subsidy scheme, Economics: The Open-Access, Open- Assessment E-Journal, ISSN 1864-6042, Kiel Institute for the World Economy (IfW), Kiel, Vol. 12, Iss. 2018-65, pp. 1-19, https://doi.org/10.5018/economics-ejournal.ja.2018-65 This Version is available at: https://hdl.handle.net/10419/183502 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Vol. 12, 2018-65 | October 25, 2018 | http://dx.doi.org/10.5018/economics-ejournal.ja.2018-65 Storage infrastructure and agricultural yield: evidence from a capital investment subsidy scheme Somdeep Chatterjee Abstract In a developing economy, the availability of storage infrastructure is considered essential for two purposes; the reduction of post-harvest losses resulting in food shortage, and allowing for gains from inter-temporal trade due to potential arbitrage opportunities arising out of volatility in food grain prices. This paper provides empirical evidence on a lesser studied impact of storage infrastructure, viz, agricultural yield. The author exploits potentially exogenous variation generated by the intensity of access to a capital investment subsidy program for construction and renovation of rural godowns in India to identify causal effects of better storage on yield. He finds that the program led to an increase in rice yield by 0.3 tons per hectare, approximately a 20% increase compared to the baseline. A potential mediating channel for such an effect would be reduced storage costs facilitating better investments in productive inputs. As supportive evidence, the author finds that fertilizer consumption increased by 21% in response to the intervention. JEL Q12 Q18 O12 O13 Keywords Storage; yield; fertilizer consumption; Grameen Bhandaran Yojana Authors Somdeep Chatterjee, Business Environment Group, Indian Institute of Management Lucknow, Lucknow, India, [email protected] Citation Somdeep Chatterjee (2018). Storage infrastructure and agricultural yield: evidence from a capital investment subsidy scheme. Economics: The Open-Access, Open-Assessment E-Journal, 12 (2018-65): 1–19. http://dx.doi.org/10.5018/economicsejournal.ja.2018-65 Received July 3, 2018 Published as Economics Discussion Paper July 17, 2018 Revised September 28, 2018 Accepted October 18, 2018 Published October 25, 2018 © Author(s) 2018. Licensed under the Creative Commons License - Attribution 4.0 International (CC BY 4.0) Economics: The Open-Access, Open-Assessment E-Journal 12 (2018–65) 1 Introduction The United Nations Development Programme’s (UNDP) ‘2030 agenda’ includes 17 new sustainable development goals, one of which is responsible consumption and production (Goal 12).1Among the stated targets of Goal 12 is the objective to ...reduce food losses along production and supply chains, including post-harvest losses by 50%. Gustavsson et al. (2011) estimate that about 1.3 billion tonnes of food produce are wasted or lost each year globally. Much of this is due to lack of adequate storage facilities and other associated logistical concerns. The availability of storage infrastructure in terms of adequate warehouses or godowns to preserve crop produce is therefore of immense global importance, especially in light of the UNDP agenda. Alternately, from the point of view of farmers, especially in developing countries, a major motivation for acquiring storage services would be to cope with price volatility and seasonal fluctuations. Many farmers in developing countries rely on credit and successful repayment of their debts are likely to be contingent upon the possibility of selling their produce at favorable prices. However, in the absence of storage facilities, lean season and peak season variation coupled with market price dynamics may compel farmers to sell a lot of produce at low prices as determined by market conditions and conversely, not being able to sell enough when the prices are higher. Therefore, from a pure welfare perspective of the farmers, access to storage infrastructure seems important as well. While the above motivations should be sufficient to encourage the development of storage infrastructure worldwide in general, and developing countries in particular, it may still be interesting to explore if access to better storage has other potential benefits. For instance, does improved storage infrastructure increase or decrease the motivation of farmers to produce? We know that better storage can help reduce loss of food grains but if farmers already accounted for the potential of losses due to lack of proper storage facilities, will they increase or decrease their production once they have access to storage? In this paper, I study a capital investment subsidy scheme for construction and renovation of rural godowns in India to answer this question. The answer to this question is important from two perspectives. First, from the perspective of a policy maker in a developing country, if better storage can lead to better productivity, then policies and incentives to promote storage facilities should be worth taking up as it would potentially increase the welfare of agents in the economy. Second, from the perspective of entrepreneurs in the warehousing business, farmers maybe encouraged to acquire their services if they can be convinced that access to storage leads to better yield. At the end of the day all the farmer cares about is his own productivity and if if his output per unit of land cultivated can go up due to some intervention or innovation, it is likely that the farmer would adopt that technology or make use of that service. The Grameen Bhandaran Yojana (GBY) was such a program introduced in India in 2001-02 to provide the farming community with scientific storage facilities and enable them to avoid deterioration and wastage of the food grain production. 2 I exploit the intensity of penetration of this scheme along with potentially exogenous variation provided by the access to this program 1 More about the 2030 agenda can be found here: http://www.in.undp.org/content/india/en/home/post-2015/ sdg-overview/ 2 The National Bank for Agricultural and Rural Development, India has laid out the details of this scheme on their website: https://www.nabard.org/content.aspx?id=593 www.economics-ejournal.org 2 Economics: The Open-Access, Open-Assessment E-Journal 12 (2018–65) in terms of time and institutional features of implementation to find the causal effects of storage facilities on agricultural yield for rice, which is a major food crop of the country. The empirical framework relies on the natural experiment of introduction of the GBY. This facilitates the inference about the counterfactual scenario, ie, what would have happened in the absence of GBY. I find that enhanced access to GBY leads to an increase in rice yield. This helps answer the above research question because this finding implies that in the absence of GBY, rice yield would have been lower. Therefore, with better storage facilities the farmers appear to be motivated to improve their production and in the counterfactual scenario with no (or less) access to storage, rice yields would have been lower. This suggests that farmers do account for post-harvest losses when making their production decisions and input choices. I use several years of pre-GBY data to perform falsification exercises in support of the counterfactual assumptions and identification strategy. I also present a simple theoretical framework with concave production functions and storage costs to motivate and support the empirical analysis. The rest of the paper is organized as follows. In Section 2, I present some background of this research by relating the study to existing literature and providing information about the policy implementation. I also discuss the theoretical premise of this study by presenting a very simple conceptual model. In Section 3, I discuss the estimation of the causal effects of the program. I first lay down the identification strategy and then discuss data sources and present the regression specification. Section 4 presents the main results and falsification tests. Section 5 describes a robustness check and Section 6 concludes. 2 Background In this section, I present some details about the GBY program and discuss the literature to which this paper adds. Broadly speaking, the GBY might facilitate gains from inter-temporal trade by allowing for storage which has been considered a problem in the literature in this field. Much of the lack of such arbitrage gains are attributed to price volatility, excess consumption in current periods, giving away of food grains to friends and relatives, many of these being related to the idea of not having adequate access to storage infrastructure. I conclude this section by describing a very simple conceptual model which can help understand the theoretical channel through which better storage may affect production and yield. 2.1 The Grameen Bhandaran Yojana - A Capital Investment Subsidy Program The GBY was launched as a capital investment subsidy program by the government of India in 2001-02. The idea was to subsidize the setup of new godowns and renovate existing ones which were becoming dysfunctional. The government realized that a major problem faced by farmers in the country was lack of retention infrastructure for their food grain produce and this may lead to huge post-harvest losses and increased consumption by the producers themselves to prevent spoilage. Among the stated objectives of the program, capacity enhancement for storage with associated facilities in farming areas was prioritized to enable quality control of the output and better marketability. From this it is fairly obvious that the government was aware of the issues www.economics-ejournal.org 3 Economics: The Open-Access, Open-Assessment E-Journal 12 (2018–65) associated with price volatility and strived to protect the farmers’ interests in this regard. Whether the policy eventually worked or not remains an empirical question examined in this paper, but the advent of the program seems to be grounded by some theoretical premise. An associated objective of the government was to encourage private sector entities and cooperative societies to invest in agriculture by allowing them to setup warehouses for storing food grains via this program. The government invited participation in the program to construct rural godowns from self-help groups, non-government organizations, farmers or a group of farmers, any private proprietary or partnership firm and so on. 3 Indian districts are sub-divided into rural and urban areas based on the local governments of those areas. Municipalities and municipal corporations essentially look after issues in urban regions whereas rural areas have local self governments known as panchayats in a three-tier federal structure. The only geographic restriction in setting up these godowns was that it had to be outside of the municipal corporation areas, ie, must be restricted to rural regions. The individual private entrepreneur who would construct a rural godown would decide on the storage capacity of the unit independently with the condition of having a minimum capacity of 100 tonnes and a maximum capacity of 10,000 tonnes of grain, to be eligible for subsidy under the scheme. 4 There were some specific engineering requirements which were laid down as well to be eligible for subsidies, such as adequate protection from rodents, birds etc, sufficient ventilation, robust structure conducive to weather conditions in the area and so on. The major highlight of this program was the assistance from the government in terms of providing subsidies for setting up such godowns. The subsidy was linked to institutional credit and would only be made available to projects funded by banks and other recognized formal financial institutions. Loans would be given out to the entrepreneurs and the banks would be responsible for collecting the repayments. The subsidy would be provided by the government through the banks in terms of favorable loan terms. The subsidy rates were fixed as described in Table 1. From the initial disbursement of the first instalment of the loan, a term limit of roughly 15 months was considered for the completion of the project. 2.2 Related Literature The traditional literature on the effects of storage mainly focusses on the risk-response of households in terms of storage as a savings or investment device (Saha and Stroud 1994). More recently, researchers have started taking into account the impact of programs and policies that promote storage on various outcomes of interest. For example, Femenia (2015), in a general equilibrium setup, shows that providing subsidies for setting up private storage facilities (much like the GBY model) would eventually destabilize the market and Gouel (2013) presents a framework to compare various storage policies in a standard setting with well defined risk preferences of consumers. There is a large body of literature, particularly in the domain of agricultural economics, acknowledging the problem of post-harvest losses, summarized in a meta-analysis by Affognon et al. (2015). However, convincing empirical estimates of the magnitude of these losses are 3Details found here: http://agricoop.nic.in/sites/default/files/1_0.pdf 4 based on geographic and topographic conditions, special discretionary relaxations were sometimes considered under the consideration of NABARD for setting up smaller godowns, particularly in hilly areas. www.economics-ejournal.org 4 Economics: The Open-Access, Open-Assessment E-Journal 12 (2018–65) Table 1: Credit-Linked Subsidy Assistance under GBY Category Subsidy Rate Max Ceiling (as percentage of capital cost) (in 100,000 INRs) Women Farmers 33.33% 62.50 Self help groups and co-operatives (women run) 33.33% 62.50 Scheduled Castes and Scheduled Tribes (SC/ST) Entrepreneurs 33.33% 62.50 Co-operatives run by SCs/STs 33.33% 62.50 All Farmers (other than women) 25% 46.87 Agriculture graduates, cooperatives and State/ Central Warehousing Corporations 25% 46.87 All other individuals, firms etc 15% 28.12 Renovation of godowns of cooperatives 25% N.A. Notes: The category column indicates the category of entrepreneurs undertaking the godown construction project. There were special considerations for historically marginalized sections of the society, ie, SCs and STs and also for women. largely unavailable and one has to rely on reports coming out of the World Bank and other similar orgranizations for descriptive data on these numbers. 5 It is nonetheless well established that the reduction in post-harvest losses is a very important objective particularly for developing countries. This paper contributes to this strand of literature by providing empirical estimates of such an intervention targeting reducing post-harvest losses on actual yield of food grains. The other strand of literature that this paper relates to is in the field of access to new agricultural technology. Specifically, papers like Conley and Udry (2010) focus on adopting a new technology by many farmers in a community and study the role of learning, Duflo, Kremer and Robinson (2011) study investment decisions of farmers in Kenya with respect to fertilizer purchases concluding that subsidies may help procrastinating present-biased farmers to make a welfare improving investment decision, Emerick (2018) in the context of India finds that there exist trading frictions among farmers in terms of adoption of new technology and interventions like door-to-door sales may alleviate some of these concerns. This paper is most closely related to Basu and Wong (2015) and Aggarwal, Francis and Robinson (2018) which study the effects of experimentally providing farmers with some storage facilities. Aggarwal, Francis and Robinson (2018), in the context of interventions in Kenya, conclude that access to storage may act as an investment instrument rather than a savings instrument and based on their estimations suggest that interventions that help farmers store grain could have 5 For instance, the World Bank published a “Missing Food" report on post-harvest losses in the context of sub-Saharan Africa to be found here: http://siteresources.worldbank.org/INTARD/Resources/MissingFoods10_web_final1.pdf www.economics-ejournal.org 5 Economics: The Open-Access, Open-Assessment E-Journal 12 (2018–65) better welfare implications compared to encouraging savings in bank. A potential channel through which these effects operate is embedded in an idea from behavioral economics, known as, mental accounting (Thaler 1999). Mental accounting, in this context, involves earmarking the saved grains for future use rather than falling prey to temptations of consumption at current periods. Basu and Wong (2015) find that their storage intervention (providing free weather-sealed drums and sacks for food grain storage in West-Timor) had no overall effect on consumption smoothing or health but find that it led to increases in reported income and non-food consumption. This is indicative of the fact that if a large-scale well intentioned storage intervention is directed towards farmers, at the least one would expect the budget set to expand opening up many more consumption-possibilities. 2.3 Conceptual Framework While some of the results above look at the effects of storage from the perspective of better consumption, in this paper, I approach the issue a little differently. The basic premise of this paper is to estimate if better storage can lead to better production. Going by the findings of Basu and Wong (2015), it is understandable that the mediating channel would be a favorable budget constraint. If this is so, it is natural to also expect that farmers may be encouraged to invest more in inputs (as suggested by Aggarwal, Francis and Robinson 2018). If the investment is made in productive inputs, one would expect that output and hence yield per unit of land, would rise. In this section, I present a simple model of a profit maximizing producer (who is also the seller) choosing inputs in the presence of storage costs, to describe this potential channel. Consider a standard concave production function y=f(X) where y denotes the output and x denotes any representative input (or vector of inputs) in the production process. Concavity of production function implies f0(x)>0 and f00(x)<0 . Assume that the market price is denoted p and the producer sells a fraction α of the produce in the market and stores (1−α) times the produce. Without loss of generality, assume that input x has a per-unit cost of c . Additionally, the producer incurs a per-unit cost ψ towards storage of the product. The producer chooses input x to maximize profit π. We can write out the objective function of the producer as follows: maximize xπ=p·α·f(x)−c·x−ψ·(1−α)·f(x)(1) The optimal choice of x must satisfy the first order condition of profit maximization given by: ∂π ∂x=p·α·f0(x)−c−ψ·(1−α)·f0(x) = 0 (2) Solving for xwe can find the following condition: f0(x) = c p·α−ψ·(1−α)(3) www.economics-ejournal.org 6 Economics: The Open-Access, Open-Assessment E-Journal 12 (2018–65) Clearly, there is a positive relationship between the marginal productivity term f0(x) and the storage cost ψ . If storage costs decline, ψ falls and as a result f0(x) falls. Given the concavity of the production function, a lower f0(x) implies a higher value of x . So with decrease in storage costs, optimal input choice increases and given that the input has positive productivity, the output, and hence yield, should be higher. The idea to be explored here is that an intervention like GBY leads to better storage infrastructure and hence may play an important role in reducing the ψ , which eventually implies, following this model, that output should go up through the mediating channel of productive investments in input. Such investments are now facilitated by lower storage costs leading to favorable budget constraints. 3 Estimation In this section, I first discuss the identification strategy of this paper to estimate causal effects of the storage intervention on agricultural yield and other outcomes of interest. Then I go on to discuss the data sources and present the regression specifications in the empirical framework section. 3.1 Identification Strategy Empirically identifying the effects of improved storage infrastructure on agricultural production and yield is difficult. Firstly, there maybe several factors correlated with better storage infrastructure which may also affect output and hence linear regressions using a simple OLS framework is likely to suffer from omitted variables bias. One such omitted variable could be the presence of better nonstorage infrastructure in general which may lead to overall impacts on the agricultural production in the region. The other major concern is that better storage infrastructure maybe available in those areas which already have better yield. In other words, entrepreneurs may selectively choose to locate in areas that are flourishing and hence a simple OLS model will suffer from selection bias and reverse causality issues. The ideal way to estimate such an effect would be to randomly assign better storage facilities to some regions. Then a means comparison of the average output in these regions could provide reliable causal estimates of storage on productivity. Even though the GBY was a targeted program towards improving and upgrading storage facilities in rural India, the policy was not randomly allocated to regions, as is usual with a national level program. So essentially the program in principle affected all regions of the country, ruling out the possibility of cross-sectional variation in terms of rolling out of the policy. However, the institutional features of implementation of this program provides an interesting quasi-experimental setup that can allow us to identify the causal effects of GBY on outcomes of interest using the intensity of the program reach. Since assistance was linked to institutional sources of credit, the availability of banking infrastructure in a given region is likely to be heavily correlated with the intensity of the program. To avoid issues arising out of the unlikely scenario of banking infrastructure improving in a region in response to this policy, I restrict attention to the cumulative infrastructure available at the start of the program. So I use the number of existing bank branches at the district levels as of 2001-02 as a potential source of exogenous variation in access to GBY. However, just comparing regions with www.economics-ejournal.org 7 Economics: The Open-Access, Open-Assessment E-Journal 12 (2018–65) more banks to those with less banks would not give true causal estimates of GBY on outcomes like agricultural yield because bank infrastructure potentially leads to several other financial and economic benefits that may spur on agricultural growth. Doing a difference-in-difference estimation seems attractive exploiting the time dimension of the policy introduction and comparing more and less banked regions before and after 2001-02. However, even such a setup is not sufficient to make a causal claim of GBY because it is quite possible that with other competing programs like the Kisan Credit Card scheme, for instance, yield would naturally be higher in more banked regions post-2002 and this may not entirely be attributable to the GBY (see Chatterjee 2018). To address these issues, while continuing with the cross-sectional bank infrastructure variation and inter-temporal variation, I incorporate a third dimension to the identification strategy, ie, an intensity measure of the GBY program using actual reach of the program. This idea is similar to a lot of applied econometrics studies done in the field of development economics (like Duflo 2001). I look at the intensity of the program in the first 3 years, ie, 2002-2005 at the state levels to get an idea as to which states were ahead in terms of GBY implementation. State level variation can occur because of several reasons ranging from the alignment of the state government with the central government, available state level infrastructure, market conditions in the state, taxation policies etc. I use a fuzzy triple difference estimation by comparing districts with more banks as of 2001-02 to others in states with more GBY access as of 2005 to others, before and after the introduction of the program. I define GBY access in terms of total number of new godowns constructed in the first 3 years of the program at state levels. Therefore, based on the intensity measure of program reach, define GBY districts as the ones with above-median number of rural bank branches in states with above-median number of new godowns constructed as of 2005. All other districts would be like the control group of this quasiexperiment. The identifying assumption of this fuzzy triple difference estimation paradigm is that the difference in average yield in GBY districts before and after 2001-02 would be no different from the difference in average yields for the non-GBY districts between the same time periods, in the absence of the program, after controlling for other differences between the cross-sections contained within the definition of GBY and non-GBY districts. Since this is an assumption about the counterfactual, there is no clear way to test for the validity of this. However, since I have multiple years of data, I perform a falsification exercise and provide support for this claim at a later section. So, any changes in average yield over time between GBY and non-GBY districts would be the causal effect of GBY on yield, based on this assumption and empirical framework.6 3.2 Data Data used in this paper comes from three sources. First, the Village Dynamics of South Asia (VDSA) database by ICRISAT which provides a district level panel dataset for various agriculture related variables. 7 I use data from 15 major states (comprising above 80% of the population of the country) for the years 1990 to 2010 to account for a period of roughly 20 years, such that we have 6 It is worth noting that non-GBY districts do not mean that these districts have zero access to GBY, it is just because of the way we have defined a GBY district for expositional simplicity. 7 The appendix presents some of the summary statistics of variables used as outcomes or controls in the regression that follow, from the VDSA dataset. www.economics-ejournal.org 8 Economics: The Open-Access, Open-Assessment E-Journal 12 (2018–65) Table 4: Robustness Checks Barren Land Non Agricultural Land (1) (2) (3) (4) GBY -1.043 7.875 5.567 2.591 (7.733) (4.913) (8.904) (5.364) District FE Yes Yes Year FE Yes Yes Yes Yes Controls Yes Yes Yes Yes R20.04 0.95 0.25 0.95 Observations 4028 4028 4028 4028 Mean of Dep Var 47.88 thousand hectares 62.95 thousand hectares Notes: The sample contains the population of districts for 15 major states of India for a period between 1990–2010 coming from the VDSA database. All columns report results from different regressions. The coefficient GBY is the causal effect of the GBY program on outcomes, as described in the estimation strategy section. All regressions control for relevant baseline dummy variables and double interactions. Controls include gross cropped area, gross irrigated area, total number of markets, annual actual rainfall. Robust standard errors clustered at the district level are reported in parentheses. *** p<0.01 **p<0.05 *p<0.1 harvest losses coming down essentially makes the budget constraint favorable for the farmer and hence he can now invest in fertilizers etc, leading to an increase in yield. A contemporaneous policy was the System of Rice Intensification (SRI) that was being adopted by Indian farmers since 2001(Sharma 2014). However, there is no documented evidence that the SRI program affected banked districts in more godown states differentially, even though the timing was the same as GBY. This is why the triple difference estimation design is very robust because it generates identification variation across two cross-sections and one time dimension and unless some other program affects exactly these three dimensions together, the estimated effects above must be that of GBY and not anything else that is being ignored empirically. 6 Conclusions In this paper, I study the Grameen Bhandaran Yojana which was a capital investment subsidy scheme introduced by the Indian government in 2002 for construction and renovation of rural godowns. I find that the program led to increase in rice yield and fertlizer consumption. I do not find any changes in land use patterns suggesting that the mediating channel for increased yield could be reduced costs of storage facilitating investment in productive inputs. While previous studies in the field have recognized the importance of reducing post harvest losses from the point of view of reducing food wastage and improving storage infrastructure to allow inter-temporal www.economics-ejournal.org 15 Economics: The Open-Access, Open-Assessment E-Journal 12 (2018–65) arbitrage benefits due to price volatility of food grains, the idea of better storage infrastructure increasing productivity remains little explored. This paper fills that void by presenting, to the best of my knowledge, the first empirical estimates of the impact of the GBY intervention on agricultural yield. To identify the causal effects of the program, I used a fuzzy triple difference estimation design exploiting the inter-temporal variation in the reach of the program coupled with institutional features of implementation and actual program intensity. Future research may be directed towards estimating the impact of GBY on production costs which may enable calculating the elasticities of the GBY effect on yield through the cost reduction channel. This paper does not make any specific claim about the channels through which storage infrastructure affects yield and/or fertilizer use but just presents basic reduced form results in what is known as a “Intent-to-treat" (ITT) approach in the quasi-experimental econometric literature. For instance there maybe concerns that farmer?s willingness and returns from recommended doses of fertilizer play bigger roles in fertilizer consumption than access to GBY and so on, which is precisely why a two-stage least squares (2sls) estimation paradigm has been avoided. 9 . A 2sls would require making strong assumptions about the exclusion restriction which might lead to aspersions caste on the estimates reported. Acknowledgements I thank Chon-Kit Ao and Indranil Biswas for useful discussion in conceptualizing the framework underlying this paper and the co-editors of Economics for handling this manuscript and providing regular feedback. Additionally, I thank two anonymous referees and an anonymous invited reader for insightful comments and suggestions which have enriched this paper further. Research assistance from Shreya Kapoor is duly acknowledged. 9I thank an anonymous referee for raising this issue www.economics-ejournal.org 16 Economics: The Open-Access, Open-Assessment E-Journal 12 (2018–65) References Affognon, Hippolyte, Christopher Mutungi, Pascal Sanginga and Christian Borgemeister (2015) “Unpacking Postharvest Losses in Sub-Saharan Africa: A Meta-Analysis", World Development, 66: 49–68. https://www.sciencedirect.com/science/article/pii/S0305750X14002307 Aggarwal, Shilpa, Eilin Francis and Jonathan Robinson (2018) “Grain Today, gain Tomorrow: Evidence from a Storage Experiment with Savings Clubs in Kenya", Journal of Development Economics, 134:1–15. https://www.sciencedirect.com/science/article/pii/S0304387818303365 Basu, Karna and Maisy Wong (2015) “Evaluating Seasonal Food Storage and Credit Programs in East Indonesia", Journal of Development Economics, 115: 200–216. https://www.sciencedirect. com/science/article/pii/S0304387815000310 Chatterjee, Somdeep (2018) “The Curious Case of Farmer Credit Cards: Higher Output with No Increase in Borrowing", mimeo Conley, Timothy and Christopher Udry (2010) “Learning about a New Technology: Pineapple in Ghana", American Economic Review, 100(1): 35–69. https://www.jstor.org/stable/27804921 Duflo, Esther (2001) “Schooling and Labor Market Consequences of School Construction in Indonesia: Evidence from an Unusual Policy Experiment", American Economic Review, 91(4): 795–813. https://www.jstor.org/stable/2677813 Duflo, Esther, Michael Kremer and Jonathan Robinson (2011) “Nudging Farmers to Use Fertilizer: Theory and Experimental Evidence from Kenya", American Economic Review, 101(6): 2350– 2390. https://scholar.harvard.edu/files/kremer/files/nudging_farmers_aer.101.6.2350.pdf Emerick, Kyle (2018) “Trading Frictions in Indian Village Economies", Journal of Development Economics, 132: 32–56. https://www.sciencedirect.com/science/article/pii/S0304387817301293 Femenia, Fabienne (2015) “The Effects of Direct Storage Subsidies under Limited Rationality: A General Equilibrium Analysis ", Agricultural Economics, 46: 715–728. https://doi.org/10.1111/ agec.12187 Gouel, Christophe (2013) “Rules Versus Discretion in Food Storage Policies ", American Journal of Agricultural Economics, 95(4): 1029–1044. https://academic.oup.com/ajae/article/95/4/1029/ 93356 Gustavvson, Jenny, Christel Cederberg, Ulf Sonnesson, Robert van Otterdijk and Alexandre Meybeck (2011) “Global Food Losses and Food Waste", FAO (UN) Study Report.http://www.fao. org/docrep/014/mb060e/mb060e00.pdf MOA (2010) “Guidelines for Seed Production of Hybrid Rice", Department of Agriculture and Cooperation, Ministry of Agriculture, Government of India. Accessed on 26-08-2018 from http://vikaspedia.in/agriculture/crop-production/package-of-practices/ hybrid-rice-seed-production/view www.economics-ejournal.org 17 Economics: The Open-Access, Open-Assessment E-Journal 12 (2018–65) Thaler, Richard (1999) “ Mental Accounting Matters", Journal of Behavioral Decision Making, 12: 183–206. https://faculty.chicagobooth.edu/richard.thaler/research/pdf/mentalaccounting.pdf Saha, Atanu and Janice Stroud (1994) “A Household Model of On-Farm Storage under Price Risk", American Journal of Agricultural Economics, 76(3): 522–534. https://www.jstor.org/ stable/1243663 Sharma, Rita (2014) “More Rice from Less Water", The Hindu (July 7, 2014) https://www. thehindu.com/opinion/op-ed/more-rice-from-less-water/article6183223.ece www.economics-ejournal.org 18 Economics: The Open-Access, Open-Assessment E-Journal 12 (2018–65) A Appendix: Summary Statistics Table A.1: Summary Statistics Observations Mean Standard Deviation Rice Production (in ’000 tonnes) 11708 175.646 284.257 Wheat Production (in ’000 tonnes) 11708 170.956 320.411 Gross Cropped Area (in ’000 hectares) 9295 578.746 360.023 Rice Cropped Area (in ’000 hectares) 11708 106.762 149.949 Wheat Cropped Area (in ’000 hectares) 11708 78.213 97.219 Gross Irrigated Area (in ’000 hectares) 9295 155.784 215.979 Irrigated Area for Rice (in ’000 hectares) 10787 43.954 78.221 Irrigated Area for Wheat (in ’000 hectares) 10787 65.814 96.140 Total Number of Markets Available 10878 13.625 16.387 Annual Rainfall (in millimeters) 9344 1125.234 749.792 Consumption of Fertilizers (in ’000 tonnes) (Nitrogen, Potash and Phosphate) 11610 32.232 40.910 Notes: The results are from the VDSA database using the full available span of the data for all states. I use only a subsample of the data from here restricting the analysis to 15 major states (comprising above 80% of the population of the country) for the years 1990 to 2010. www.economics-ejournal.org 19 Please note: You are most sincerely encouraged to participate in the open assessment of this article. You can do so by either recommending the article or by posting your comments. Please go to: http://dx.doi.org/10.5018/economics-ejournal.ja.2018-65 The Editor © Author(s) 2018. Licensed under the Creative Commons License - Attribution 4.0 International (CC BY 4.0)