While randomized evaluations are ideal to identify the causal effects of credit constraint relaxations, by design these cater to a relatively small sample of the entire population. Whether a large scale national reform would replicate these findings is important to under- stand. In this paper, I look at a major overhaul in the agricultural credit delivery process in India in 1998, known as the Kisan Credit Card (KCC) program, and evaluate the impacts of this policy. In doing so, I end up with an apparent puzzle based on the empirical findings. The structure of this paper is therefore designed to present the data findings leading up to the puzzle and concluding with discussions on what the puzzle means and what could explain it.
The targeted group for this credit reform was rural agricultural households, generally involved in farming and other related occupations. Ease of delivering agricultural credit, reasonable interest rates and relaxation of monitoring norms were the key features of this program. Reports from the Planning Comission of India (2002) suggest that by 2000-01, KCCs constituted almost 71% of the total production credit disbursement by commercial
banks. It was also the dominant mode of production credit delivery for other banks. The report also suggests that in the first two years, close to 4 million credit cards were issued with a total disbursal of credit lines worth 50 bllion INR (1 billion USD approximately).
Although this was a major policy reform, to date there has been little convincing evidence of the impacts of this program. Chanda (2012) uses post-policy state level data from 2004-
2009 to see if growth in KCC issues lines up with increases in agricultural productivity. There are other government of India commissioned descriptive reports like the Planning Commission report mentioned above and Samantara (2010). In this paper, I use a country wide district panel dataset to evaluate the causal effects of this program on agricultural output and technology adoption. I also use household data to estimate the impacts of this program on a wide range of outcomes including income, consumption and borrowing.
The reach of formal financial institutions is not universal in most developing countries. This is because banks would want to select into richer regions unless they are administratively required to setup branches in unbanked locations. This makes formal credit markets less accessible to the poor in these areas. The KCC reform therefore provides an opportunity to add to this literature of how access to formal credit institutions can help the poor sections of the society in line with Burgess and Pande (2005). The unique feature of the KCC program was that it catered exclusively to the agricultural sector. Although in this paper, I am not able to distinguish whether the effects of KCC operate through channels of new access to credit or expansion of credit to the ones who already had some access. As a result most of my estimations should be viewed as a bundle of reduced form effects.
This paper takes advantage of rules in implementation of the policy to generate plausibly exogenous variation in access to this program to identify causal effects of the reform. The identification strategy relies on variation across three main dimensions. First is the time dimension. The policy was implemented in 1998 and I look at the outcomes in years before and after the policy. Second, is the political alignment dimension, ie, whether the state government is ruled by a party aligned with the central government in the federal structure of India. Political alignment has been widely regarded to be important for policy implemen-
tation and performance (see Chibber et al 2004, Iyer and Mani 2012 and Asher and Novosad
2015). The final source of variation comes from how the rolling out of these credit cards was implemented. The KCCs could only be issued through formal banks and not by any other agency. I use district level variation in the number of bank branches already setup prior to the policy to proxy for access to this program.
I propose to identify the causal effect of the policy by the interaction of these three variables. The effect is identified by looking at the difference in outcomes after and before the policy in districts with more bank branches over districts with fewer bank branches in states that are ruled by political parties aligned with the central government after controlling for these differences in districts in the states not aligned with the center. I use pre-policy data to show that these regions were not already different along the relevant dimensions to provide support to the identifying assumption that any differences post-policy are attributable to the program.
I find that increased access leads to significantly higher production levels. Rice is the major crop of India and I find an aggregate increase in production by 88 thousand tonnes (metric ton) per year on average which is between 1/3 to 1/4 of an increase compared to the mean. The Food and Agricultural Organization’s FAOSTAT indicates that in 2012, the value of rice produced in India is over 40 billion US dollars which makes it the most valuable crop of India. Rice has consistently been the major crop of India in terms of overall value for years (See – faostat.fao.org). Corresponding to this large change, I find that technology adoption has also been significant. Crop production area under high yielding variety (HYV) seeds increased by around 71 thousand hectares at an aggregate level which is just under a 1/3 increase compared to the mean. This suggests that with increased access to credit, districts exposed to the program fared significantly better in terms of porduction and technology adoption. Using household data, I corroborate some of these results. I find suggestive evidence of increases in rice production for farmers even though estimated imprecisely. I am constrained by the fact that the household data comes only from a sample of farmers and not the universe, unlike the district panel data described above which contains all rice production
in the districts. I find that revenue from sales of rice is higher for farmers potentially exposed to KCC.
The advantage of using household data is being able to observe borrowing patterns. Using a cross-section of households, I find that households are more likely to have fewer but larger loans with exposure to KCC. I also find that they are more likely to have larger bank loan sizes if exposed to KCC. These effects seem to be larger for those households which report cultivation as their main source of income and for rice farmers. This is reassuring because most of the production effects observed using the district data seem to suggest that rice farmers would be most affected by this policy.
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p> The results from the househ
old data analysis suggest that KCCs did not lead to new borrowing. An obvious conclusion that maybe drawn from this is that KCCs did not provide new access to credit but even then large increases in production are documented. What explains this? Two things maybe at work here. First, KCCs expanded credit options for the already unconstrained. I find some supportive evidence for this. Using a selected sub-sample of borrowers already borrowing from banks, I find that such households are borrowing more in response to the KCC reform. However, this is likely to be a very small fraction of the overall cohort of borrowers. The second possibility is that KCCs increased the risk tolerance of farmers. Farmers may view KCCs as an insurance against possible future setbacks. Before KCCs, they were probably saving up for such contingencies and hence investing less in technology. Now, with the advent of KCCs, they are aware that in the event of a mishap, they can fall back on KCCs and therefore invest more in the present period and therefore record higher production with no higher borrowing.
The rest of the paper is organized as follows. Section 2 provides background information. Section 3 describes the empirical strategy. Section 4 explains the Data. Section 5 presents results and Section 6 concludes.
2 Background
2.1 The Kisan Credit Card Program
Agriculture constitues roughly a fifth of the total GDP of India and employs two out of three Indian workers. In the late nineties, agriculture started opening up to the market rather than being limited to subsistence farming. Agricultural credit has played an important role in developing the market for such produce and help improve the condition of farmers in the country. However, the finance and credit institutions present in the country prior to 1998 were deemed inefficient by several reports and experts and as a result the Kisan Credit Card program was envisaged. This scheme was launched in 1998 and was introduced for the first time in the budget speech of the Finance Minister of India in the parliament. Within a year after its inception around 5 million cards were issued to farmers. Prior to 1998, the system of agricultural credit delivery was complicated. A multi agency approach was used where borrowers had to go through several layers of bureaucracies depending on the purpose of their loans (Samantara 2010). KCC also brought about a revolving credit regime as opposed to the existing demand loan system (Chanda 2012).
At its inception, the KCC was not a traditional credit card that is commonly used. The card was a mere documentation for identifying the individual and his credit line with a given bank. It did not have features that allowed payments at merchant outlets. This also makes the presence of banks an important dimension for identifying the intensity of reach of the program. The way to use a KCC was to visit the bank branch in person and withdraw a certain amount of money which could then be used for purchases. This also ruled out the possibility of banks monitoring the usage of the loans.
The most important feature of this credit product was the ease of availability of loan. Some banks laid down rules for eligibility like having title to an acre of irrigated land. On fulfiling this criterion, the farmer would be eligible for a loan with a bank without any collateral requirement for an amount upto 50,000 INR (around 1000 USD back then). The KCC accounts were largely valid for 3 years and repayment time frames spanned upto a year.
On successful replayments and responsible credit use, these accounts were renewable but the initial approval was given largely without any bacground checks. As pointed out above, a big difference from existing crop loans was that the usage of the KCC loans were not monitored whereas most agro-credit was tied to agricultural use or purchase of inputs, fertilizers etc. So, a farmer could get a KCC account and use the amount for personal consumption.
In a way KCC provided the best available source of personal credit to poor farmers. The biggest advantage over microfinance institutions were that KCC was operational through formal banks and charged a very reasonable interest rate of around 7% per annum as opposed to as large as 36-40% rates charged by self help group microcredit institutions. The approval process was also very simple and was a single window exercise as the only criterion was ownership of an acre of irrigated land. Many banks have recorded allowance of credit limits in excess of 50,000 INR but in such cases they often asked for collaterals. Therefore larger scale farmers who are financially in a better off situation were only likely to go for these loans. There was no clause to my knowledge which restricted large farmers from opening a KCC account.
Samantara (2010) points out that a major reason why KCC was launched was to inte- grate the various credit needs of farmers, from personal consumption to festival expenditure, education, health and agricultural needs, into one comprehensive product. Earlier a farmer had to weigh multiple options based on the purpose of his loan. KCC made it a one stop procedure wherein he could withdraw the requisite amount and use it for any purpose what- soever. All the bank cared about was the timely repayment and not the usage. This was a major shift from the pre-existing agro-credit policy in India which was called the Agricul- tural Credit Delivery System. Under that system, a multi-product multi-agency approach was adopted. Policy makers in the country had planned this in a way such that specific needs of farmers could be addressed by specific credit products. A farmer could go to a bank for purchase of a particular input and get a loan against that purchase. The idea was more like financing purchases rather than giving out cash loans. From such a scheme KCC came as a welcome change which sought to replace the multi-product approach in favor of a cash
credit approach in a single comprehensive product. As might be already evident from this discussion, KCC was intended to address the short term credit needs of farmers and not the longer term needs. Since there was no monitoring, one could not rule out the possibility of withdrawing cash from these accounts and using them for consumption purposes. At present, Kisan Credit Cards are available as differentiated products with various banks coming out with various varieties and features.
Overall the Kisan Credit Card program should be viewed as a bundle of reforms in one. It not only aimed to relax credit constraints by making loans available to the ones constrained prior to 1998 but also provided a source of flexible credit. KCCs could potentially finance a lot of purchases, not just agricultural inputs and therefore have wider social consequences. Since KCCs were a source of cheaper credit, one might also view it as expansion of credit options for the ones already having access to other forms of credit. Unconstrained farmers may now be attracted to borrow at cheaper rates and finance their short term credit needs.
2.2 Conceptual Framework and Related Literature
To estimate the true causal effects of access to credit one would ideally want to generate random variation in access to financial institutions. There is a rich literature comprising of experimental studies along these lines (Angelucci, Karlan and Zinman 2015, Attanasio et al
2015, de Mel, Mckenzie and Woodruff 2008, Augsburg et al 2015, Banerjee et al 2015, Crepon et al 2015, Tarozzi, Desai and Johnson 2015). Apart from this there is a quasi-experimental literature which looks at policy reforms in the formal financial sector to answer a similar question (Burgess and Pande 2005, Banerjee and Duflo 2014). Government policy reforms are usually not randomly assigned, therefore identifying the c
ausal effects of such programs is challengin
g even though it is important to understand the mechanisms behind such policies aimed at removal of borrowing constraints.
Most recent studies on the role of credit access focus largely on this aspect of mechanisms of credit delivery (Karlan and Morduch 2009). This paper is the first to objectively evaluate the Kisan Credit Card scheme using a district panel dataset and extends this literature
by looking at this large scale national reform in credit delivery mechanism. In the Indian context, Banerjee and Munshi (2004) and Banerjee and Duflo (2014) study the role of credit constraints on firms and businesses. However the role of credit constraints in agricultural occupations has been little studied till date. This paper also contributes to the literature by attemtping to fill this void.
An important question that arises here is whether this program should be viewed as enhanced ‘access’ to agricultural credit or ‘expansion’ of credit to the ones who already had access to credit? The existence of credit constraints and impediments to borrowing are major roadblocks in developing economies which is why governments may want to innovate by reforming the system of credit delivery. If the main objective is to improve the condition of the poor, one would imagine that removing the borrowing constraints would be important, or in other words a program like KCC should have given ‘access’ to credit to the ones who never had the chance to borrow before. The starting point of the analysis is to understand how we expect credit access to affect the credit constrained? If KCC relaxed credit constraints and people unable to borrow elsewhere could now borrow under this program, economic theory and existing empirical evidence would lead us to expect multiple effects.
First, if households invest in productive assets or the borrowed funds are used to finance improvements in technology of agricultural production, we expect their agricultural income to be higher. Second, if we aggregate these effects, overall production of crops should be higher and overall adoption of new technology should also be higher. Third, composition of consumption may change. Banerjee et al (2015) find such evidence in a microfinance experiment but the idea is applicable to a broader country wide setting as well because in essence we are thinking of the impact of relaxation of credit constraints per se. Finally, since this was a national level formal lending program, one would expect that with enhanced access informal lending would go down and be substituted by more formal sector loans.
The flip side however, is that from a lender’s perspective, such a policy may attract poor quality borrowers. This leads to issues of adverse selection. Asubel (1991) discusses credit card markets in the US and how lowering interest rates are far from ideal from a bank’s
perspective as bad borrowers may select into borrowing at lower rates. KCC lending was usually at a much lower rate of interest than market rates or informal lending rates prevalent among microfinance institutions. This would have meant that the adverse selection issue was likely to be severe under this program. Also since new borrowers are unlikely to have ever engaged in credit dealings, their perception about their own future stream of income determining their repaying ability is likely to be myopic. Melzer (2011) and Bond, Musto and Yilmaz (2009) point out these problems about ‘misinformed’ borrowers underestimating their future repayment commitments.
It is also important to think about potential general equilibrium effects of this program. Are there any spillovers? For example, if some farmers get credit cards whereas others do not, maybe they have a competitive advantage over the ones who did not get this card and this might lead to perverse welfare implications. Similarly, if KCCs are very attractive and result in high profits for farmers, this maybe an incentive for non-farmers to take up agricultural occupations which in turn may affect non-agricultural sectors in the rural areas.
3 Empirical Strategy
There are two parts in my empirical strategy. I have the twin objective of evaluating the overall effects of access to credit on production outcomes on average and also whether access to credit through such a reform is useful for intended beneficiaries. To this end, I use two different datasets. The first is a district panel dataset and the second is a cross-sectional household dataset.
Identifying the causal effects of having a KCC on agricultural outcomes using survey data is difficult because KCCs were not randomly assigned to households. Also, using a cross sectional dataset, it is not possible to use time varying access to the scheme either. It turns out that there is no clear idea even in government documents in terms of how these cards were rolled out. To overcome these issues, I propose an identification strategy that relies on plausible exogenous variation in the reach of this program to find causal effects of
the program. Apart from the time dimension (program introduced in 1998) which provides variation in the access to the program over the span of the data, there are two different cross sectional dimensions that give us a sense of which regions might have had more access to these cards after the policy. I use an interaction of these dimesions to identify effect of the policy.
The KCC program was announced by the Finance Minister of India in his budget speech in 1998 and the implementation began soon after. The government at the center was ruled by the Bharatiya Janata Party (BJP) led National Democratic Alliance (NDA) coalition. However, not all state governments were run by the NDA coalition. Since the implementation of this policy required a lot of work at the grass roots in terms of setting up infrastructure, spreading awareness, nudging banks to implement this policy and the like, one can under- stand that the role that state governments and officials at the village and block levels who are employed by the state governments would have had an important role to play in the penetration of this policy in those states. This gives one potential source of variation in the policy. I use an indicator variable aligned which takes the value 1 if the state in question was ruled by the BJP or one of its NDA allies in 1998 and 0 otherwise. The idea is that aligned states would probably have earlier or quicker access to this policy whereas the opposition parties may choose to be slack in the policy implementation in the states where they are in power, out of several motives including the fact that they would want the scheme to be projected as a failure for the ruling coalition and take advantage of this in future elections themselves.
The first real governmental study on the program outreach was done in 2002 by the Planning Commission of India. They published a report with tables on the state wise coverage of Kisan Credit Cards as of March 2000, which is 2 years into the program. The coverage rates were basically the number of KCCs issued by various banks as a percentage of total operational land holdings in the concerned state. So this gave an idea as to how many farmers were potentially reached or covered under the policy within the first two years of the policy at a state level. If we observe that aligned states actually were implementing
the policy faster than the other states, we might be more confident about the use of this dimension to identify the effects of the program. Table 1 provides supportive evidence. I find that coverage in aligned states is almost 2.5 times the coverage in rest of the states and the difference is statistically significant at the 99% level of confidence as indicated by the p-value.The second dimension that I bring to this analysis of variation in access is a technicality that the policy had. These credit cards
could only be given out through ban
ks. So it is understandable that areas with more banks are likely to be able to roll out these cards faster than the ones which are unbanked or have fewer banks. However, there may be concerns that banks opened up or positioned or repositioned themselves based on the policy announcement in markets where KCC lending would flourish more. To account for this issue I use bank data at the baseline year, ie, 1998 and not after the policy. I use district level existing bank branches data from 1998 to enumerate the number of branch offices of banks at the time of announcement of the policy. This gives us another potential exogenous source of variation in the intensity of coverage of the program. I create the variable bank98 to denote the number of bank branches in a given district in 1998 and use the indicator variable morebanks which takes the value 1 for districts with number of banks above the mean of bank98.
Finally, I use a variable to capture the time of exposure to the policy and controlling for pre-existing differences along the above cross sectional dimensions over time. This variable takes the value 1 if the observation is post-1998, ie, after the KCC reform and zero otherwise.
I use this framework to run a regression analysis to capture the the causal effect of the policy on outcomes of interest. The interpretation of the estimated regression coefficient in this framework is that it gives us the difference over time (post- and pre-policy) in the given outcome (say, rice production) for households in districts with more banks compared to households in districts with lesser banks in aligned states after controlling for these same differences in non-aligned states. The critical identifying assumption is that this outcome would not have been different for these groups of households had there been no KCC policy. There is no standard way to validate this assumption and identification always assumes this, but the panel structure of the data provides an opportunity to check whether these districts were historically different and already had differential trends even before the policy. If we find that before the policy, differences in outcomes along the above dimensions were not different, we gain confidence that the identifying assumption is plausible. I describe a check for this at a later section and find that before 1998 there were indeed no differences in outcomes in these areas. I exercise adequate caution in interpreting the above described triple-difference as the causal effect of KCC on any outcome of interest by netting out other possible differences coming from demographics like the number of persons in the family, number of children, number of married men and women and also the age and education levels of men and women and other agriculture specific variables like differences in irrigation, rainfall etc. I also net out area specific and time specific constant differences between the households by using variables in the regression known as fixed effects. So the estimated triple difference described above will not be confounded by differences that are largely area specific and/or time specific and therefore makes it more convenient to establish a causal claim.
The fact that prior to the policy, the cross sectional dimensions seem to be similar, leads us into the cross sectional analysis. The dataset that I use is from 2005 which is a post- policy year. I still use the above cross sectional dimensions to generate exogenous variation in access to the policy but do not have the time dimensions anymore. Since there were no differences in these regions prior to the policy, any difference that I find for 2005 can be attributed as a causal effect of the program.
The main outcome that I look at is crop production. As mentioned earlier, rice is the major crop of the country in terms of value. I focus primarily on rice production but also look at the other important crops like wheat and maize. The idea is that with access to credit, farmers may be able to invest more and increase output. Since there is an element of investment behavior attached to credit access, I look at the use of high-yielding variety (HYV) seeds. If farmers would adopt more HYV seeds to increase their production, this would be evidence of technology adoption. I observe all of these outcomes at the district level and use the panel dataset to find effects on these. The cross sectional dataset however has a wide range of other outcomes that are of interest. I briefly describe some of those below.
4 Data
District Production Data
The data for this study mainly comes from 2 sources. First, ICRISAT-VDSA database provides a district panel data set for agricultural outcomes.The ICRISAT has a rich database known as the Village Dynamics of South Asia (VDSA) and makes this available for 19 major states of India. For this analysis I am only focussing on production of rice, wheat, maize and use of HYV seeds. The data contains information on total production, total area under production, gross and net cropped and irrigated areas,, number of markets in district, rainfall etc. Although the dataset provides data from 1966-2011, I focus on the post-1985 period. This is because of two reasons. Firstly, the empirical strategy would require that pre-trends are accounted for among the geographic classifications used to identify the causal effect of the program. One would be worried that in years long before the policy, potential treatment and control groups would have had very different trends in outcomes which would invalidate the analysis. Also, the period before 1986 marks a long history of political turmoil including the emergency days and war with neighboring countries. 1986 gives us a reasonable starting point for the analysis and it is at least 12 years before the KCC program began. Secondly,
the dataset for the early 60s and 70s has lots of missing information, so analysis using those years would in any way lead to lesser power.
Household Survey Data
The second dataset is the Indian Human Development Survey (IHDS)-2005. The first official release of the survey was in 2008 for a survey they conducted in 1503 Indian villages and 971 urban neighborhoods in the year 2005. So, the data in this edition of the survey is based on respondents interviewed in 2005. It was jointly conducted by a team from the University of Maryland, USA and the National Council of Applied Economic Research (NCAER), India. The 2005 survey covered 41,554 households and compiled responses from two interviews each of which lasted for an approximate duration of one hour. I have a wide range of outcome variables to look at including income, consumption per capita, asset ownership, loan and debt details etc. I focus only on the rural sample and exclude the urban households which yields a sample of 26734 households.
Household Crop Data
The IHDS-2005 also surveyed households to collect data at the crop level. There are multiple households producing multiple crops. As will become clear later, most of my main results appear to be driven by rice producers. So I merge the household survey data with the crop files using only those farmers who produce any rice. For my regressions using this dataset, I focus on the households below the 99th percentile to exclude some large outliers. In the sample the mean of rice production for a household is around 25 units measured in tenths of a quintal, the maximum is 2600 which is unusually high. Therefore, I exclude the large outliers who produce above the 99th percentile, which is 200 units in tenths of a quintal.
Other Data
My identification strategy also relies on variations across three dimensions, coverage of
KCCs, number of bank branches in 1998
and political alignment of state governments with the cen
ter as of 1998. I look up media reports and open source information available online to match whether the political party ruling a state was part of the ruling coalition at the center. In particular I look up the name of the Chief Minister of the states in 1998 and note down his political party. Then I check if that political party was part of the ruling coalition at the center, ie, National Democratic Alliance or NDA. I use data from the Reserve Bank of India website to list the number of bank branches and branch offices in each district. I also use data from the Planning Commission of India publication of 2002 for state level access to KCCs by number of land holding covered under the scheme in 2000 to support the idea that political alignment was important in terms of the reach of the program.
Do households own a Kisan Credit Card?
The IHDS-2005 includes a question for households on whether anybody in the family owns a KCC or not. This is only a 0-1 binary variable. The ideal scenario to describe the true causal effect of access to credit on outcomes would be to do what is known as a 2-stage least squared regression (2sls) by using an instrumental variable for access to credit. So if the KCC program was an instrument for access to credit, then ideally we would want to run a regression of access to credit on the identifying variables and divide the coefficient estimates above by this coefficient. However, there remain some concerns regarding the interpretation of the instrumental variable.
First, the ideal channel we have in mind would be actual borrowings and usage of the credit card and not the mere possession of this card. The only way that enhanced access to credit through possession of this card would lead to increases in income is if people actually borrowed using this card. Second, since we have just a single time point, the year 2005, which is seven years after the policy was implemented, all the coefficients reported using this binary variable would be under-estimates. For example, if a household had the KCC for 7 years, and we believe it was constrained prior to that, then the coefficients from the triple difference estimates above are relevant over a period of time while the household has
benefitted from access to credit. So if for this household we consider a change in some outcome, it is not just an instantaneous rise but an overall change. If we divide this by the estimates using the binary variable of KCC which just takes into account 1 period of time, the potential 2-stage least squares coefficient (which is the quotient of these two) would be hugely overestimated. So we would either need to multiply the binary varialbe regression coefficient by the number of years the household had the card for (the information for which is unavailable) or deflate the triple difference estimates by some factor.
Second, the binary variable for having a KCC is not the perfect proxy for ‘access to credit’ which would be the main dependent variable in our regression model to do the 2SLS regressions. It is also quite possible that a single household had multiple KCCs but this would show up as a 1 on the binary variable, the same as a household with just 1 KCC. To avoid these problems, I do not use this as an outcome variable in my regressions. However, roughly comparing the means of this variable in areas potentially exposed more to the program to the areas exposed less, I seem to find a positive difference, but this is merely suggestive and therefore I do not interpret this as causal. The mean of this variable for the entire sample is around 4% which makes any estimation using this as a dependent variable less convincing.
5 Results
5.1 Results using District-Panel Dataset
5.1.1 Effects on Crop Production and Technology Adoption
Table 2 reports results on effects of credit access through more exposure to KCC program on crop production outcomes. Rice is by far the major crop of India in terms of value of output. I find from column 1 that annual district production of rice increases by about 88 thousand tonnes with more exposure to KCC. This is quite a big effect compared to the mean of 285 thousand tonnes which suggests that impediments to borrowing severely constrain the scale of production. One possible interpretation of this is while farmers are credit constrained, they can put a smaller area under crop production, use lesser inputs and have little or no
access to advanced production technology. With access to credit, these are less of problems and as a result we expect to see a surge in production, to the extent that is found in Table 2. India had a major drought in 2002 which affected several rice farmers. Rainfall was about
56% below normal in July and almost 22% less rain was recorded overall (see Bhat 2006). In general this should not impact my analysis. However, there maybe concerns that banked districts in aligned states might have responded differently in terms of providing support to the agricultural system and therefore it confounds the estimate somewhat. I find that the point estimates are not very different if we exclude 2002 which alleviates these concerns. These results are not reported but are available upon request.
A possible mediating channel for an increase in rice production could be adoption of technology. Existing studies have shown that credit constraints are important hindrances in adoption of technology (Croppenstedt, Demeke and Meschi 2003). Mukherjee (2012) uses Indian household data to show that access to banks leads to better adoption of High Yielding Variety (HYV) seeds in production. Since the KCC program intended to provide more credit access, it is interesting to examine whether the relaxing of credit constraints has a similar effect as Mukherjee (2012) on aggregate. Column 4 in Table 2 suggests that overall crop area put under HYV seeds usage is higher by 71 thousand hectares with exposure to KCC. This is suggestive evidence that access to credit leads to some technology adoption.