Swapnil
Swapnil Landge
In this paper, I want to study the impact of the farm loan waiver program on agricultural productivity and output growth. Farm loan waiver, waiving of the loan taken by the farmer against the farmland as collateral from the private or public bank to do farming or nonfarming activities. In recent history, farm loan waiver has become a very popular program amongst politicians to win the elections. but the real question is how this waiver program overall impacts the structural change in the developing countries and is thus benefitting all the farmers equally or only large farmers. In the last decade alone, Various Indian states (Provinces) have cumulatively written off a whopping Rs 4.7 lakh crore ( $ 0.6 trillion) from farmer loans.The farm loan waiver program has been unique to India but a similar kind of program has also been observed in other developing countries. In this paper, I will try to analyze data and model for India but as further research, this model can be taken up for other countries. In this paper, I will analyze the followings
- Impact of farm loan waiver program on the agricultural productivity and on output growth after the first loan waiver program was implemented with the help of Econometric Methods such as Difference-in-Differences (DID) or Regression Discontinuity Designs (RDD) or Synthetic Control Method (SCM) available for Program Evaluation.
- If i decide the to use Synthetic Control Method (SCM) then i will estimate the agriculture productivity and output growth in absence of a farm loan waiver program.
A brief history of the farm loan waiver program in India
Loan waivers are a fairly recent phenomenon when viewed in the context of India’s seventy-year democratic history. The first loan waiver was announced about three decades ago in 1987 by the then Chief Minister of Haryana, Chaudhary Devi Lal due to the pressure from the newly emerged strong political organization of the farmer community on the national political stage ( as a result of the green revolution, Mandal commission). Alongside the strengthening of the political organization of the farmer community came a number of demands ranging from subsidies for inputs such as fertilizers, farm equipment, and power to minimum support price (MSP) for farm produce and more recently the calls for loan waiver. It is instructive to see that shortly after the loan waiver announcement in his state, Devi Lal went on to be appointed the Deputy Prime Minister under the V.P. Singh government in 1989. Here as well, the central government of the day declared the first agricultural loan waiver at the national level. Post these early announcements there have been nineteen waivers as listed in the table below
Table 1: Farm loan waivers in India
source:-https://pib.gov.in/newsite/printrelease.aspx?relid=180924
There was a big gap between the first wave of waivers (1987,90) until next waiver program (2006). Since 2006, farm Loan waiver programs have been a very populist program amongst state governments(politicians). They think that the promise of this program will help them to win elections and bring them to power again. Nineteen of the eighteen waivers listed above came from state governments while only two came from the central government. This suggests that waivers are largely a matter of state policy. I will focus on this state-centric feature of loan waivers to reveal the difference in GDP and productivity growth of the states which received farm loans vs the state that didn’t receive it to consider the weights for Synthetic control methods.
There has been a vast variety of literature available on farm loans specific to India. Papers like Sainath 2008, Patel 2017 supportive of the farm waiver argue that waiver helps to relieve the persistent debt stress debt overhang faced by farmers following years of rural stagnancy. Patel (2017) hypothesizes that loan waivers would help distressed farmers overcome the debt baggage and enable them to make productive investments. Another set of papers criticizes the farm loan waiver program like Kanz (2008) empirically showed that the beneficiaries of the 2008 loan waiver granted by the central government of India tend to make lower investments and have less productive farms than similar non-beneficiaries. A negative view is also reflected in Shylendra’s (1995) study of the national loan waiver of 1990. His empirical evidence demonstrates that loan waivers primarily benefit the better-off households and waivers adversely impact the repayment behavior of borrowers.
The third category of work presents a more nuanced view. Mukherjee, Subramanian, and Tantri (2014) differentiate the impact of loan waivers on distressed and non-distressed borrowers. Their research shows that waivers have had a positive effect on the loan performance of distressed beneficiaries but have had no effect on non-distressed beneficiaries. Further, they find that loan waivers also lead to rationing of future credit by banks to the non-distressed borrowers.
Many papers have looked into this issue from rural banking or credit facility perspective. Government interventions by way of loan waivers lead to a moral hazard encouraging farmer households to borrow for consumption purposes (as against income generation) and making them less cautious because the punishment for such things is low (Tanika & Aarti, 2017a). A paper-like Rao 2008 mentioned that blanket waiver of all loans can cause serious distortions and encourage indiscipline among farmers who can afford to pay, and thus severely affect the future flow of credit to the sector. Even (Mahajan, 2008) also states that this scheme will spread the tendency to default on such loans, a practice that will work against the goal of financial inclusion. Once a loan waiver is announced, banks usually stop giving loans to farmers qualifying for loan waivers during the next loan cycles (Kanz 2016; Giné and Kanz 2018). Rath (2008) points out that those farmers, who had already repaid their loans before the announcement of loan waivers, feel cheated and therefore are reluctant to pay fresh loans.
From an empirical point of view, Kanz, M. (2016) used Regression Discontinuity Analysis (RDD) to study the impact of debt relief on the economic decisions of recipient households. Giné, X. and Kanz, M. (2017) used Difference in difference (DID) estimation to estimate the impact of debt waiver on the credit market and the real economy. Mukherjee et al., 2017) used RDD and DID Data to study the causal effect of debt relief on the loan performance of distressed and non-distressed farmers.De and Tantri (2016) used RDD and DID Data To estimate the effects of ex-post loan repayment behavior of the debt relief recipients and their access to new credit. Mishra et al. (2017) used RDD. Data To estimate the impact of debt waiver on beneficiaries’ savings and consumption. Christopher, R. (2012) used RDD To estimate the impact of debt waiver on beneficiaries’ wealth and well-being.
Ample research is done on Indian farm loans and farm loan waivers but i didn’t find much research which is studying the impact of the farm loan waiver on agricultural productivity and output growth except the paper by Giné, X. and Kanz, M. (2017) which study the credit market impact and real effects of “Agricultural Debt Waiver and Debt Relief Scheme” (adwdrs), enacted by the government of India in 2007-08
Difference-in-difference Model
(Productivity)it=(alpha)_i+(Beta)_t+(gamma) L_it +(Yeta) X_it + (Epsilon)_it
Alpha=State fixed effect
Beta =Fixed effect
Gamma=measures the elasticity of real outcomes with respect to program exposure.
X=vector of time-varying controls
L=state exposure to the loan waiver