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.
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.
2.1 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 causal 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.
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