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Farmers’ Income, Indebtedness and Agrarian Distress in India

  • ICAR-National Institute of Biotic Stress Management

Abstract and Figures

The paper examines farmers’ income, indebtedness and suicides. It concludes that income of farmer is low mainly due to low harvest prices, high cost of inputs and small operational holding size. Low incomes coupled with higher consumption needs force small farmers into high-interest debt trap. There is a need to increase public investment in farm infrastructure, strengthen direct benefit transfer schemes for purchase of inputs, improve institutional credit delivery mechanisms and widen safety nets in rural areas. The recent farm policy related to encouraging Farmer Producer Organizations and contract farming could potentially increase small farmers bargaining power and scale economies to utilise market opportunities.
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* Principal Scientist
(Agricultural Economics)
ICAR-Central Research
Institute for Dryland
Agriculture, Hyderabad.
** Principal Scientist
(Agricultural Economics)
ICAR-Central Marine
Fisheries Research Institute,
*** Assistant Professor
O P Jindal Global University
Sonipat, Haryana.
Email: arnab@jg
Long neglect of public
investment in the farm
sector, especially in
irrigation and market
infrastructure, has
forced small and
marginal farmers to
invest in infrastructure
with borrowed money
from the private money
lenders at exorbitant
interest rates. This has
eventually increased
their indebtedness,
thus, partly contributed
to the agrarian distress.
Key Words: Farmers’ Income, Indebtedness, Farmer’s Suicides, Sources of Credit,
Agrarian Change
Farmers’ Income, Indebtedness
and Agrarian Distress in India
- A Amarender Reddy*, S S Raju** and Arnab Bose***
The paper examines farmers’ income, indebtedness and
suicides. It concludes that income of farmer is low mainly due
to low harvest prices, high cost of inputs and small operational
holding size. Low incomes coupled with higher consumption
needs force small farmers into high-interest debt trap. There
is a need to increase public investment in farm infrastructure,
strengthen direct benefit transfer schemes for purchase of
inputs, improve institutional credit delivery mechanisms and
widen safety nets in rural areas. The recent farm policy related
to encouraging Farmer Producer Organizations and contract
farming could potentially increase small farmers bargaining
power and scale economies to utilise market opportunities.
1. Introduction
Expanding access to formal credit continues to remain a
key strategy for promotion of agricultural development and
livelihood diversification (Ramprasad, 2019; Chichaibelu and
Waibel, 2017 and2018; Alpanda and Zubairy, 2017; Misra,
2019; Reddy and Kumar, 2006). As per the Reserve Bank of
India (RBI) guidelines, banks have to allocate 40% of the
total net bank credit to the priority sector and within it 18%
to agriculture. Despite the expansion of institutional credit,
most of the rural households in general and small and tenant
farmers in particular still depend on informal sources of credit.
The debt-asset ratio of the rural households had risen over the
years from 1.6% in 1992 to 2.5% in 2013, indicating that farmers
liabilities are increasing faster than their assets (Datta et al.,
2018; NSSO, 2016; Rajakumar et al., 2019). Many of them are
unable to secure adequate financial assistance. Farmers suicide
in the recent past is also on the higher side - 48,104 individual
Farmers’ Income, Indebtedness and Agrarian Distress in India
dependents on agriculture committed suicide between 2013 and 2016 (Desmond, 2016;
Merriott, 2016; Mohanty and Lenka, 2019; Agarwal and Agrawal, 2017). Of the total
reported suicide cases, 55% were farmers and the remaining 45% were agricultural
labourers (Lok Sabha, 2018). According to the All-India Survey of Rural Debt and Invest-
ment (NSSO, 2014), the number of indebted farmers had risen from 25% of the total
rural households in 1992 to about 46% in 2013. Tenant farmers are more vulnerable
to income shocks and farm distress related suicides – they account for 80% of farmers’
suicides in the country, although they constitute only 10.4% of the total farmers in India.
The stagnant output prices, increasing cost of cultivation especially labour, declining
average size of operational holdings and increasing share of tenant farmers who depend
mostly on informal sources for credit are some of the reasons for the increased farm
distress (Chand et al., 2015).
The number of agricultural loan accounts increased from 11.08 crore in 2015 to 12.09
crore in 2017, and outstanding credit from Rs. 11.85 lakh crore to Rs. 14.36 lakh crore.
The outstanding loans per account increased from Rs. 1.06 lakh to Rs. 1.18 lakh during
the same period. Some studies point out that most of these loans originate in urban
areas like Delhi and Mumbai, that too in the month of March, which is a lean season for
agricultural operations. This has, indeed, reflected in the rising share of urban areas in
the total agricultural credit increased from 14.9% in 1991 to 33.1% in 2011 (RBI, 2018).
It hints at the phenomenon of absentee landlords who live in urban areas having more
access to credit from formal sources rather than the actual cultivators who live in rural
areas. The actual cultivators and tenant farmers with small operational holdings are not
able to access formal credit channel, and hence, are forced to depend on informal credit
at exorbitant interest rates. Given this background, this paper tries to examine farmers
income and consumption gap, availability of agricultural credit, rural indebtedness,
distress and farmers suicides. Further, the paper explores various policy options in order
to reduce rural distress.
2. Data and Methodology
This paper primarily relied on the two sets of data, namely, National Sample S urvey
Office (NSSO) All India Debt and Investment Survey, 2012-13 (hence forth NSSO,
2014) and National Bank for Agriculture and Rural Development (NABARD) All India
Rural Financial Inclusion Survey 2016-17 (henceforth NABARD, 2018). They publish
data on the sources of farmer’s income among different land classes and monthly per
capita consumption expenditure (MPCE) deciles in India. The unit-level data from the
Comprehensive Cost of Cultivation Scheme (CCS) of Government of India was also
used to understand the farm size and profitability relationship in the case of Telangana.
Literature survey of micro-case studies was also done to understand micro dynamics of
indebtedness, farm suicides and their interaction.
THE MICROFINANCE R EVIEW Volume XII(1) January-June 2020
The paper uses international poverty line to measure poverty in terms of purchasing
power parity (PPP, current international $) exchange rate rather than nominal exchange
rate in dollars. In October 2015, the World Bank updated the International Poverty Line
(IPL), a global absolute minimum, to PPP $1.9 per day (Adams and Page, 2005; Churchill
and Smyth, 2017). When measuring international poverty of a country, the international
poverty line at PPP is converted to local currencies at 2011 price and then to the prices
prevailing at the time of the relevant household survey using the Consumer Price
Index (CPI). In the year 2012-13, PPP exchange rate (PPP$= Rs. 17) was used as against
nominal exchange rate of Rs. 45 per dollar. Accordingly, PPP $ 1.9 was equivalent to Rs.
32.3. Hence a family of five members needed to earn a minimum of Rs. 4,845 per month
in 2012-13 to remain above the poverty threshold income (Table 1).
We had calculated average poverty gap to meet the international poverty line (Rs.
4,845/per month/household) by deducting mean income from the poverty line. The
poverty gap index is a measure of the intensity of poverty and it estimates the depth
of poverty by considering how far, on the average, the poor are from that poverty line
(Imai et al., 2012), that is,
Poverty gap = (poverty threshold income-actual income)*100/poverty threshold income.
3. Income and Consumption Gap
In 2012-13, the average income of the farmer was just Rs. 6,427 per month or Rs.
77,124 per annum, as per the data collected from NSSO (2014). The average household
income was 32.7% above poverty line. However, the poverty gap was observed to be
5.9% among farmers possessing less than 0.01 hectares of land and 14.3% for those with
0.01 to 0.4 of hectares of land. Income was found to be higher only for households whose
landholding size was above 2 hectares. The income and consumption gap also indicate
that small farmers had to incur debt as their monthly consumption levels was higher
than their income. Arguably, these small farmers had to depend on informal money
Table 1: Monthly Income-Consumption Gap of Farmers (2012-13) (Amount in Rs)
Size class Income Culti- Animal Non-farm Total Consumption Income gap Poverty
of land from vation rearing business income expenditure to meet current gap
possessed wages consumption (%)
(ha) (Income-consumption
<.01 2902 30 1181 447 4560 5108 -548 5.9
.01-.4 2386 687 621 459 4153 5401 -1248 14.3
.41-1 2011 2145 629 462 5247 6020 -773 -8.3
1.01-2 1728 4209 818 593 7348 6457 891 -51.7
2.01-4.0 1657 7359 1161 554 10731 7786 2945 -121.5
4.01-10.00 2031 15243 1501 861 19636 10104 9532 -305.3
>10 1311 35685 2622 1770 41388 14447 26941 -754.2
All sizes 2071 3081 763 512 6427 6223 204 -32.7
Source: Autho rs’ calculatio n based on data colle cted from NSSO (2014)
Farmers’ Income, Indebtedness and Agrarian Distress in India
lenders to meet their consumption needs as they lacked collateral, land or other assets.
This pushes them into a vicious cycle of indebtedness.
The monthly income and expenditure of agricultural households are available
across ten deciles of monthly percapita consumption expenditure (MPCE). They can
be considered as income surrogates, thus, the 1st decile would reflect the lowest income
group and 10th decile the highest income group (Figure 1). Average incomes remained
too high in the case of 10th decile group. In all the other deciles, there was hardly any
surplus of income after meeting consumption needs.
Poverty gap to meet the international poverty line was 20.1% among 1st decile, 12% in
2nd decile, 3.1% 3rd decile and 1.2% among 4th decile class of MPCE (Table 2). It indicates
Table 2: Decile-wise and Source-wise Monthly Income of Farmers and Poverty Gap in 2012-13
MPCE Source-wise share of monthly income (in %) Average Poverty
decile Wages Cultivation Animal Non-farm All monthly gap
class rearing business income(Rs.) (%)
1 44.7 39.6 12.4 3.3 100 3870 20.1
2 38.1 43.6 15.1 3.2 100 4263 12.0
3 36.5 43.6 12.3 7.6 100 4697 3.1
4 35.6 43.4 15.4 5.6 100 4789 1.2
5 37.2 44.7 11.9 6.2 100 5471 -12.9
6 35.1 45.5 14.1 5.3 100 5830 -20.3
7 29.4 51.6 10.5 8.5 100 5703 -17.7
8 29.8 50.7 11.0 8.5 100 6122 -26.4
9 32.6 50.3 9.7 7.4 100 7430 -53.4
10 26.2 50.6 11.4 11.8 100 12458 -157.1
All 32.2 47.9 11.9 8.0 100 6426 -32.6
Source: Autho rs’ calculatio n based on data colle cted from NSSO (2014)
Income gap(Rs.)
Income and consumption (Rs.)
Decile group
Figure 1: Decile Wise Monthly Income and Consumption Gap (In Rs.)
Income(Rs.) Consumption(Rs.) Income gap(Rs.)
Source: Authors’ calculation based on data collected from NSSO (2014)
THE MICROFINANCE R EVIEW Volume XII(1) January-June 2020
that this bottom 40% of the farm households were even below the poverty line and,
thus, they would have hardly had any surplus to invest in the farm sector.
Rural Distress is Widespread
The prevalence of farm distress is mirrored by the number of suicides of farmers
(Table 3). The suicides of those engaged in the agricultural sector, including farmers and
agricultural labourers, were high in drought-prone states like Maharashtra, Telangana
and Karnataka and also in agriculturally rich states like Punjab and Andhra Pradesh.
Suicides were low in Bihar, West Bengal, and Uttar Pradesh which were less developed
states. Higher number of suicides in the agriculturally rich states is probably because
farmers in those states were more exposed to neoliberal policies of privatisation,
diversified faster to cash crops and faced input and output market fluctuations and
uncertainties, whereas low suicides in less developed states was because agriculture in
those states was dominated by food crops with less market-orientation (Patnaik, 2007).
However, farm distress was widespread even in high-productivity states.
In so far as sources of income was concerned, among agricultural households
at all India level, income from cultivation was 48% of their monthly income, wages
contributed 32%, animal rearing contributed 12% and non-agricultural business another
8%. The income from cultivation as percentage of the total monthly income was higher
in Telangana, Madhya Pradesh, Punjab and Uttar Pradesh. The share of wage income
was higher in West Bengal, Kerala, Andhra Pradesh and Bihar. The share of income
from animal rearing was higher in Gujarat and Andhra Pradesh. The share of income
from non-farm business was higher in Kerala and West Bengal. Literature suggests
Table 3: State-wise Monthly Income and Consumption Gap and Poverty Gap
Suicides in agricultural Source-wise share of Income and consumption
sector* (2014-16) monthly income (in %) gap (Rs)
State Total Share of Wages Culti- Animal Non-farm Income Consu- Income gap Poverty
suicides farmer (%) vation rearing business mption income- gap
(%) (%) (%) (%) consumption) (%)
Bihar 17 0 37 48 8 7 3557 5485 -1928 26.6
West Bengal 230 0 53 25 6 16 3980 5888 -1908 17.9
Uttar Pradesh 700 40 23 58 11 8 4924 6230 -1306 -1.6
Andhra Pradesh 2352 39 42 34 18 7 5979 5927 52 -23.4
Madhya Pradesh 3809 53 21 65 12 2 6209 5019 1190 -28.2
Telangana 3392 85 23 67 6 4 6311 5061 1250 -30.3
Rajasthan 492 1 34 43 13 10 7349 7521 -172 -51.7
Maharashtra 11956 68 29 52 7 11 7385 5762 1623 -52.4
Gujarat 1309 10 34 37 24 5 7926 7672 254 -63.6
Karnataka 4416 62 30 56 7 7 8832 5889 2943 -82.3
Kerala 1338 10 44 30 5 21 11889 11008 881 -145.4
Punjab 459 75 26 60 9 4 18059 13311 4748 -272.7
India 36332 55 32 48 12 8 6427 6223 204 -32.7
Note: * Suicide s in agricultural s ector refers t o suicide by farmer s and agricultural l abourers.
Source: Autho rs’ calculatio n based on data colle cted from NSSO (2014) and Nation al Crime Records Bur eau (2016)
Farmers’ Income, Indebtedness and Agrarian Distress in India
that the increased share of income from wages, animal rearing and non-farm business
contributed to a reduction in the suicide rates among farmers, even though their average
income levels remained low (Ravallion and Chaudhuri, 1997; Joshi et al., 2004).
Rainfed Areas and Farm Distress
Farmer suicides are correlated with the share of the rainfed area at the state level.
Suicide rates are higher in the states with the higher rainfed area (Figure 2). The R2 value
of the regression is 0.29, which is considered significant as the data is cross sectional
(Maddala and Kajal, 1992). There are many studies which confirms to this hypothesis
especially in Telangana and Vidarbha areas of Maharashtra (Behere and Behere, 2008).
In rainfed areas, the farm yields are low and fluctuate based on the monsoons. Farmers
also incur huge costs for digging private bore wells, as the public canal and tank irrigation
are not available. In rain fed areas, it is very difficult to accumulate surplus and invest
in technologies that increases yield and reduces risk. They incur huge crop losses almost
once in three years.
Distress is More Among Small and Tenant Farmers
With little surplus generated over the years, small and tenant farmers are unable
to invest in productivity-enhancing technologies. The problem of low private invest-
ment gets compounded by the reduction in public investments since the early 1990s,
which eventually increased cost of cultivation of marginal and small farmers (Fan, et al.,
Punjab UP
Telang ana
y = 0.02x + 0.55
R² = 0.29
0 10 20 30 40 50 60 70 80 90 100
Suicide rate
(number per 100000 farmers)
Rainfed area(%)
Figure 2: Relationship Between Percentage of Rainfed Area
and Suicide Rates of Farmers, 2014-16
Source: Author’s calculation based on data collected from National Crime Records Bureau (2016)
THE MICROFINANCE R EVIEW Volume XII(1) January-June 2020
2008). In contrast, large farmers could invest their surplus income on farm technologies,
resulting in an increase in yield and scale economies.
In the last decade, there are perceptible changes in the farming sector that favoured
large farms. The rapid and widespread farm mechanisation, development of new plant
varieties, rising wage rates in rural areas, opportunities for higher education for both
men and women, and outmigration of male workers contributed to the increased scale
economies and higher returns on large farms compared to small farms. The scale
economies mainly emerged from the cost reduction through expanding mechanisation
to all operations. The experience in Telangana shows that in maize crop, small farms’
profits were only Rs. 20,100 per hectare, while large farms gained Rs. 35,000 per hec-
tare (Table 4). Similarly, in the case of paddy, profits were Rs. 25,300 and Rs. 31,800 per
hectare respectively, for small and large farms. The net returns of tenant farmers were
much below that of the small farms. Faced with the situation of rising cost and declining
returns, the small and tenant farmers are caught in debt trap.
Extent of Indebtedness
Recent data shows that total institutional agriculture credit disbursement had crossed
Rs. 11 lakh crore to meet the credit needs of 12 crore farmers, but this had favored large
farmers. Many small and tenant farmers continue to depend on non-intuitional credit
Table 4: Profitability of Small and Large Farms in Telangana, Triennium Ending 2010 (Amount in Rs)
Crop Farm size Gross Cost Cost Net returns Net return
returns/ha A2/ha C2/ha over cost A2/ha over cost C2/ha
Maize Small(own) 48.3 28.2 48.9 20.1 -0.6
Large(own) 57.7 22.7 41.3 35.0 16.4
Tenant 47.8 31.0 51.7 16.8 -3.9
Paddy Small(own) 56.6 31.3 60.1 25.3 -3.5
Large(own) 59.7 27.9 48.7 31.8 11.0
Tenant 54.5 34.4 63.2 20.1 -8.7
Arhar Small(own) 24.5 21.5 45.6 3.0 -21.1
Large (own) 27.2 15.4 25.3 11.8 1.9
Tenant 25.5 23.7 47.8 1.9 -22.3
Source: Dire ctorate of Economi cs and Statistic s (2019).
Table 5: Average Amount of Outstanding Loan per Farmer in 2012-13 and 2015-16
Size class of Outstanding loan, 2012-13 Loans taken, 2015-16
land Average Indebted Institutional Non- All Indebted Institutional Non-
possessed amount farmers (%) institutional (Rs. 1000) farmers (%) institutional
(ha) (Rs. 1000) (%) (%) (%) (%)
<.01 31.1 42 15 85 78.0 46 71 29
.01-.4 23.9 47 47 53 76.5 39 59 41
.41-1 35.4 48 53 47 82.7 43 69 31
1.01-2 54.8 56 65 35 120.0 46 80 20
2.01-4.0 94.9 67 68 33 203.8 50 78 22
4.01-10.00 182.7 76 72 29
>10 290.3 79 79 21
All sizes 47.0 52 60 40 107.1 44 72 28
Source: NSSO (2014) and NABARD (2018): NAFIS 2016-17.
Farmers’ Income, Indebtedness and Agrarian Distress in India
agencies (Table 5). According to NSSO (2014), 52% of the farmers were indebted in
2012-13. As per NABARD (2018)1 data, 44% of the farmers had taken loans in 2015-16.
Overall, institutional sources contributed to 60% of outstanding loans in 2012-13 and 72%
in 2015-16. Both surveys revealed that the amount of outstanding loan and percentage of
indebted farmers increased with farm size owing to better access to institutional credit.
Indebtedness to institutional sources has increased steeply as farm size increased, and
thereby, suggesting how important is the quantum of land possessed for getting loans
from institutional sources.
What is more, the outstanding loans from institutional sources like banks carried low
interest rates (mostly below 12% per annum), compared to non-institutional sources like
moneylenders or input dealers that were available for over 20% interest rate per annum
(Figure 3). Though majority of institutional loans were taken by farmers with below
15% interest per annum, majority of small and tenant farmers were not able to get loans
from institutional sources under unavoidable circumstances. Because farm returns often
turned negative, taking recourse to loans at exorbitant interest rates continued to push
small and tenant farmers into a debt trap.
Some of the southern states like Andhra Pradesh, Telangana, Kerala and Karnataka
had a large percentage of indebted farmers (Table 6). The amount of debt per agricultural
household was also higher in these states. States with higher percapita incomes like
Kerala and Punjab had higher average loan outstanding per household. The marginal
and small farmers of Andhra Pradesh, Telangana, Kerala and Karnataka were excessively
Amount outstanding (%)
Interest rate(%)
Figure 3: Distribution of Amount of Outstanding Debt by Rate of Interest
institutional non-institutional
Source: NSSO (2014)
1 NABARD All India Rural Financial Inclusion Survey 2016-17
THE MICROFINANCE R EVIEW Volume XII(1) January-June 2020
burdened with a large amount of debt compared to their counterparts in Bihar, Gujarat,
Uttar Pradesh, Madhya Pradesh and West Bengal.
4. Low Capital Formation among Small Farms
Due to higher interest rates and also non-profitable agricultural activities, small and
marginal farmers in the bottom deciles (of household asset holding) were not able to
spend equivalent to top 10% of the households in fixed capital investments (Figure 4).
Inadequate farm investment would seriously impede achieving higher farm productivity.
1643 1957
4568 3950 4950
9108 7984
18283 17688
31 31
41 47
# of households reporting
Average amount (Rs.)
Decile class of hh asset holding
Figure 4: Rural Households Reporting Fixed Capital in 2012-13)
Average amount(Rs.) % of hhs reporting
Source: NSSO (2014)
Table 6: State-wise and Landholding Size-wise Average Amount of Outstanding Loan per Agricultural Household in 2012-13
(Amount in Rs. ’000)
State Landholding size % of indebted
<.01 .01-.4 .41-1 1.01-2 2.01-4.0 4.01-10 >10 All sizes farmers
Andhra Pradesh 241 74 89 105 162 350 249 123 92.9
Telangana 56 58 79 103 110 137 269 94 89.1
Kerala 169 159 194 347 607 751 1573 214 77.7
Karnataka 36 78 63 99 125 232 367 97 77.3
Rajasthan 169 33 43 68 103 155 153 71 61.8
Maharashtra 10 45 23 46 58 207 387 55 57.3
Punjab 13 25 52 164 229 327 927 120 53.2
West Bengal 6 15 20 33 33 44 276 18 51.5
Madhya Pradesh 9 12 15 27 63 117 195 32 45.7
Uttar Pradesh 22 16 22 46 108 125 218 27 43.8
Gujarat 7 12 25 31 83 162 115 38 42.6
Bihar 7 14 13 34 28 42 149 16 42.5
India 31 24 35 55 95 183 290 47 51.9
Source: NSSO (2014)
Farmers’ Income, Indebtedness and Agrarian Distress in India
5. Indebtedness and Farmer Suicides
Figure 5 shows the relationship between farmers’ indebtedness and suicide rates.
The analysis reveals that indebtedness has a positive but insignificant influence on the
farmer suicide rates. Farmers in Bihar, Uttar Pradesh, Madhya Pradesh and Chhattisgarh
had indebtedness between 40% and 50%, but suicide rates were higher in Chhattisgarh
and Madhya Pradesh than in Uttar Pradesh and Bihar. Haryana, Gujarat, Tamil Nadu,
Kerala, Karnataka and Telangana were the other states with high levels of farmers
indebtedness, but suicides rates were higher only in Kerala, Karnataka and Telangana.
Hence, there may be many factors other than their level of indebtedness influencing
farmers’ suicide rates.
Agricultural Households are More Indebted
According to NABARD (2018), the average debt of agricultural households was Rs.
1.07 lakh of which 72% was contributed by institutional sources, while among non-
agricultural households it was Rs. 75,688 of which 65% was by institutional sources.
Overall, in rural areas, the average loan per household was Rs. 91,852, of which 69%
was accounted by institutional sources (Table 7).
Across all decile class of MPCE, the incidence of indebtedness among agricultural
households was higher than non-agricultural households (Figure 6). Level of indebted-
ness increased as income increased, that is, households in the upper deciles of MPCE
y = 0.02x + 0.3
R² = 0.14
0 10 20 30 40 50 60 70 80 90 100
Farmer suicide rate
(number per 100000 population)
Indebtedness (% of farmers)
Figure 5: Farmers' Indebtedness and Farmer Suicide Rate
Source: Authors’ calculation based on data extract ed from NSSO (2014), NABARD (2018): NAFIS 2016-17
and National Crime
Records Bureau (2016)
THE MICROFINANCE R EVIEW Volume XII(1) January-June 2020
Table 7: Average Loan Taken by Borrowing Households in 2015-16
Source of loan Agricultural households (Rs.) Non-agricultural households (Rs.) All households (Rs.)
Institutional 77473(72%) 48970(65%) 63645(69%)
Non-institutional 29610(28%) 26718(35%) 28207(31%)
All 107083(100%) 75688(100%) 91852(100%)
Note: Figure s in brackets indic ate percent to total
Source: NABARD (2018): NAFIS 2016-17.
agricultural households non-agricultural housholds All households
% indebtedness
Figure 6: Incidence of Indebtedness by Decile Class of MPCE
in 2015-16
12910 All households
Source: NABARD (2018): NAFIS 2016-17
agricultural households non-agricultural housholds All households
Amount in Rs.1000)
Figure 7: Average Outstanding Debt by Decile Class of MPCE
in 2015-16
12910 All households
Source: NABARD (2018)
NAFIS 2016
Farmers’ Income, Indebtedness and Agrarian Distress in India
were relatively more indebted than those in the bottom decile classes. A similar trend
was also observed in the case of outstanding debt (Figure 7). A typical agricultural
household in the 10th decile would have an average outstanding debt amounting to Rs.
1.86 lakh, whereas a non-agricultural household would carry an average debt of Rs. 1.34
lakh in 2015-16.
6. Source of Credit
The role of institutional sources in providing credit to farm households showed an
appreciable rise between 2012-13 and 2016-17, from 60% of their total debt to 72.3%
(Table 8). In both periods, the relative share of commerical banks remained very high;
and their share increased to 54% in 2016-17 from 42.9% in 2012-13, thus, accounting
for bulk of the rise in the relative share of institutional sources. There is a perceptible
fall in the share of the money lenders from 25.8% in 2012-13 to 9.4% in 2016-17. Both
NSSO (2014) and NABARD (2018) had indicated an increasingly larger role of banks and
cooperatives and dwindling share of moneylenders, and friends and relatives in meeting
credit needs of farm households.
More Loans are Used for Domestic Uses
Majority of the loans were used for sundry domestic needs (32%), housing purpose
(21%) and medical expenses (17%), which are not immediately productive (Table 9). But
they are urgent in nature. Most of
the small and marginal farmers are
forced to take loans from informal
sources, as several domestic needs
do not qualify for credit from institu-
tional sources like banks. Ultimately
these loans would be taken from
informal sources.
Table 8: Average Loan Taken from Various Sources by Farm Households in 2012-13 and 2016-17
Source of loan 2012-13 2016-17
Amount(Rs) Share (%) Amount(Rs) Share (%)
Commercial bank 20163 42.9 57825 54
Cooperatives 6956 14.8 6425 6
SHG/MFIs 5247 4.9
SHG-Bank linked 4390 4.1
Other institutions 987 2.1 3534 3.3
Total Institutional Sources 28106 60 77421 72.3
Relatives& friends 5029 10.7 15313 14.3
Money lenders 12126 25.8 10066 9.4
Landlord 376 0.8 4069 3.8
Input supplier 1363 2.9 127 0.1
Total non-institutional sources 18894 40 29575 27.6
Total 47000 100 107083 100
Source: NSSO (2014) and NABARD (2018): NAFIS 2016-17
Table 9: Purpose-wise Loans taken by Borrowing Households (2015-16)
Purpose % of Household
Sundry domestic needs 32
Capital expenditure for agricultural purposes 25
Housing purpose 21
Running expenses for agricultural purposes 19
Medical expenses 17
Running/capital expenses for non-agricultural enterprises 13
Educational purpose 6
Source: NABARD (2018): NAFIS 2016-17
THE MICROFINANCE R EVIEW Volume XII(1) January-June 2020
7. Indebtedness and Farmers’ Suicides: Evidences from Case
Numerous studies have pointed out that farmers suicidal death rates exceeded those
of the general population (Mishra, 2006). Breaking down the National Crime Records
Bureau (NCRB) data for 2014, it is observed that the overall suicide rate of farmers
in the country was 15.8 per 1,00,000 people, which was 50% higher than suicide rate
of the general population. State-wise analysis indicate disparities across states with
suicide rate of farmers remaining higher in geographically contiguous states including
Maharashtra (2568), Telangana (898), Madhya Pradesh (826), Chhattisgarh (443)
and Karnataka (321). These states recorded suicide rate of 28.7 per 1,00,000 farmers.
Together these states accounted for 30% of farming population but over 60% of farmer
suicides in the country. In 2014, suicides of small farmers constituted 44.5% of the total
suicides, marginal farmers accounted for 27.9%, medium farmers were 25.2%, and large
farmers accounted for 2.3%. Amongst the major causes of farmers’ suicides, reason of
Table 10: Summary of Micro Studies of Farmers Distress, Suicides and Debt
Author/year Area/sample Results
Behere and Behere Report on farmers Money lenders were the predominant source of credit (28.4%)
(2008) suicides (Vidarbha) Only 4% access to land development banks
Chhikara and Kodan Haryana, secondary Positive relation between farm size and percentage of credit
(2013) data from formal sources.
47% of credit to marginal farmers; 62% of small and 75% of large
farmers got from formal sources
Gedela (2008) Telangana; 37 suicide Suicide victim families obtained 70% of credit from informal
victim families; sources; it was only 53% for non-suicide families; victim families also
37 control had higher debt from informal sources.
Value of livestock in victim families was Rs. 20,000; in control Rs. 27,000
Communicate less regularly with relatives
Kale (2011) Vidarbha; 40 suicide Formal sources contributed to 76% of the total credit of victim families;
victim families; 40 control whereas it was 96% for non-suicidal families
Mishra(2006) Maharashtra(111 72% of the suicide reported families (treatment) indebted to informal
treatment; 106 control) sources, while it was 38% for non-suicide controls
Suicides also had higher amount of debt
Kale et al., (2014) Interview with 200 All families were indebted
suicide victim families 51.5% indebted from both formal and informal sources
in Vidarbha 47% indebted from formal sources
99% families did not have subsidiary income/ employment
Heads of the families were more prone to suicides
Nagthan, et al A small case-control Marriages of sisters/daughters increased the debt burden
(2011) study in Karnataka Addiction to alcohol
(30 cases, 30 controls) Financial illiteracy
Reddy, 2012 18 villages in Semi-arid Interest on institutional credit was below 7% due to interest subvention
scheme, interest from micro-finance institutions and friends and relatives
range from 12% to 24%, while from input dealers, landlords and traders
ranged was above 20% and reaches beyond 36% in some cases.
Source: Authors’ compilation.
Farmers’ Income, Indebtedness and Agrarian Distress in India
indebtedness topped (20.6%), followed by farming related issues (17.2%), family problems
(20.1%), illness (13.2%), alcohol (4.4%) and others (24.5%). A review of most of the micro
level studies, presented in Table 10, identified indebtedness as the predominant single
factor associated with farmer suicides (Gruere and Sengupta, 2011; Kale,2011; Nagthan
et al., 2011; Kennedy and King, 2014; Gedela, 2008).
Dongre and Deshmukh (2012) found that farmers in the Vidarbha region of
Maharashtra ranked debt as the most important reason for farmer suicides, followed
by addictions, environmental problems, and price issues, amongst others. Two other
studies concluded that unpaid loans were a correlate of those who committed suicide
(Gruere and Sengupta, 2011; Mishra, 2006). Kale (2011) found that in a small sample
from Vidarbha, 95% of farmer suicide victims were indebted, while of control house-
holds, this was only 25%. Another study in the same region found that 197 of 200 victims
(98.5%) were indebted (Kale, 2014). Mishra (2006) also found that debt was the most
common factor in Maharashtra at 86.5%, followed by deterioration in the farmers
economic status (73.9%).
A comparison of these farmers with those who did not die by suicide showed that the
latter had three times as much debt, and the difference was significant at 95% confidence
level (Mishra, 2006). An investigation of the socio-economic causes of farmers’ suicide
in Karnataka also found agricultural debt as the primary factor leading to 29 out of 30
suicide cases (Nagthan, et al., 2011). Gedela (2008) also found that indebtedness was
one of the statistically significant factors underlying farmers suicide in Andhra Pradesh.
Cash Crops and Farmers Suicide
Kennedy and King (2014) found that ‘‘cash crop cultivators, with marginal landholdings
and debts” were most at risk, and that these three characteristics accounted for 75% of
the variation in the overall male suicide rates seen across the country. There was also
evidence of a positive effect on profit for farmers growing Bacillus thuringiensis (Bt)
cotton arising from higher yields and reduced pesticide costs. Since growing genetically
modified (GM) cotton, farmer suicides had increased only in Punjab. In other states,
farmers suicide rate had gone down since the introduction of the GM crops (Ian, 2014).
Because of the introduction of Bt cotton, there was a significant increase in yield (32%),
gross income (35%) and net income (106%), reduction in cost (17%) and pesticide cost
(18%). However, seed cost also increased by 134% (Herring and Rao, 2012).
Institutional Credit Syphoned off by Urban Absentee Landlords
Sadanandan (2014) shows that after 1989, the percentage of total bank loans going
to agriculture began to reduce sharply, from approximately 20% to 12% in 1994. By the
2000s it had halved, with even less (8%) being lent directly to farmers. This drop in
formal credit going to agriculture is alarming. Further, most of the agricultural credit
originated from the urban centres like Delhi and Mumbai that too not during the sowing
THE MICROFINANCE R EVIEW Volume XII(1) January-June 2020
period. It is likely that agricultural credit is siphoned off by the absentee landlords, and
thereby, reducing the availability of institutional low-interest rate credit to small and
marginal farmers who were actually cultivating the land (Dongre and Deshmukh, 2012).
8. Policy Options
Expansion of Institutional Credit
Although all banks are implementing three-year financial inclusion plans since 2010,
credit reach through formal sources is still limited. The RBI Working Group reported
that while there were over 12.56 crore small and marginal farmers, only around 5.14
crore had accounts as per the priority sector lending returns of scheduled commercial
banks for 2015-16 (RBI, 2019). This translated to only 41% of small and marginal farmers
being covered by the formal credit system. Keeping the low penetration of cheap
and formal sources of credit, Government of India laid a firm road map for opening
brick-and-mortar branches and promoting alternative modes of banking (Reddy, 2006;
Reddy and Malik, 2011). However, the newly opened bank branches declined from 8,749
in 2014-15 to 3,948 in 2017-18. The fall is more perceptible in rural centres with less than
10,000 population. The number of new branches opened in such areas dropped from
3,274 to 1,067 during the three-year period. The number of automated teller machines
(ATMs) also dropped from 2.08 lakh in March 2017 to 2.07 lakh in March 2018. Only
44% of ATMs were located in rural areas, although about 60% population lives there
(RBI, 2018).
Pradhan Mantri Dhan Jan Yojana (PMJDY) added 33.6 crore new basic savings bank
deposit accounts, expanding the base of such accounts to 53.6 crores by March 2018.
Of the 6,60,000 villages, formal sector covered 5,69,547 villages. But most of them
(5,15,317 villages) were covered by business correspondents (BCs) offering limited
services. As much as 80% of adult members had a bank account but half of them rarely
used their accounts. According to a World Bank report, 48% of the bank accounts had
no transactions during the last year against the global average of inoperative accounts
of 25%. Only 13% of Indian adults borrow through formal channels (Demirgüç-Kuntet
al., 2018). To improve the utilisation of bank accounts in various ways, citizens should
have better financial literacy.
Step up public investment in irrigation in dry lands
Kale et al., (2014) found that 69% of suicide victims in Vidarbha had no water source
and relied entirely on monsoon rains for cultivation of crops. Gedela (2008) found that
non-suicide farmers had a higher proportion of their land area irrigated than suicide
victims in Telangana. Poor irrigation may not only be a direct cause of increased debt by
lowering returns and potentially causing crop failures, but it may be partly responsible
for forcing farmers towards moneylenders, as banks may be reluctant to lend to
Farmers’ Income, Indebtedness and Agrarian Distress in India
farmers who lack irrigation facilities because their returns is less assured. Telangana
government aims to bring an additional one crore acre of land under irrigation through
public investment and this may relieve dryland farmers. Stepping up public investment
in irrigation would be one of the solutions.
Subsidiary Occupations
Many micro studies indicated that in addition to cultivation of crops, animal
rearing, dairy, poultry farming, various caste occupations, working as semi-skilled
or skilled workers in construction have increased creditworthiness of farmers and
reduced dependence on exploitative money lenders. Telangana government schemes
like the promotion of food processing industries through crop colonies, sheep rearing,
handlooms and various other rural industries may help in providing subsidiary income
opportunities in rural areas. This idea may be replicated in other parts of the country.
Assistance to Weaker Sections
Reddy (2012) noted that only 60% of outstanding loans were being used for productive
purposes. Chhikara and Kodan (2013) estimated that marginal and small farmers in
Haryana borrowed 23.7% and 20.7% of loans, respectively, to fulfil social obligations
such as ceremonies and marriages. Villagers are spending huge money beyond their
capacity for daughter or sister marriages and health-related expenses (Reddy, 2012).
Under the Kalyana Laxmi/ Shaadi Mubarak scheme, government is giving Rs. 1,00,116 to
a girls family towards meeting marriage expenses, which is a relief to farming families.
Similar schemes have to be implemented in other states to meet unforeseen expenses
or social obligations, so that farmers use the credit for intended purpose, which would
enhance their incomes and livelihoods.
Targeted Income Support
Under the PM-KISAN (Prime Minister-Kisan Samman Nidhi) scheme, the Government
of India provides an income support of Rs. 6,000 per year in three equal installments
to small and marginal farmer families. Some state governments are also implementing
similar schemes with some modifications based on local political and economic situations.
These schemes need to be scaled up in other states. Some improvement in the scheme is
required such as placing a eligibility limit in terms of maximum land holdings (like 10
acres per farm family) and inclusion of tenant farmers and agricultural labourers into
the scheme with direct transfer of Rs. 5,000 lump sum amounts for each family.
9. Conclusion
The national target of doubling farmers’ income is a step in the right direction.
However, the recent data show that farmers’ income is not increasing mainly due to low
harvest prices, rising cost of inputs, frequent droughts, etc. The structure of the farm
THE MICROFINANCE R EVIEW Volume XII(1) January-June 2020
economy is changing in favour of large farmers who are reaping scale economies through
farm mechanisation. Increasing farmers’ cash needs are not met through institutional
credit sources. Informal credit sources pull farmers into a high interest-bearing debt trap.
Long neglect of public investment in the farm sector, especially in irrigation and market
infrastructure, has forced small and marginal farmers to invest in infrastructure with
borrowed money from the private money lenders at exorbitant interest rates. There is a
need for a policy push (i) to increase public investment in irrigation and infrastructure
especially in drought-prone areas; (ii) to increase the flow of collateral free institution-
al credit at lower interest rates especially to small and marginal farmers, and also to
tenant farmers and agricultural labourers; (iii) to strengthen small farmers institutions
like Farmer Producer Companies (FPOs) to enhance scale economy and also bargaining
power; (v) to channelise more money through farmers welfare or safety net schemes like
PM-KISAN; and, finally, (vi) to encourage private participation and adoption of the latest
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... On the one hand, smallholder farmers in South African are relatively unproductive, producing, at best, just a quarter of commercial farm output (Hendriks, 2014). Similar studies in India revealed that smallholder harmers usually have low incomes mainly due to low harvest prices, high cost of inputs and small operational holding size (Reddy et al., 2019). It is possible that smallholder farmers in the developing world generally face the same challenges as far as productivity is concerned. ...
... For example, the South African government allocated huge financial resources to facilitate the establishment of self-owned or joint ventures businesses to boost entrepreneurial activity, particularly among smallholder farmers (GEM, 2011). Similarly, low incomes, low harvest prices, high cost of inputs and small operational holding size prevent smallholder farmers from breaking the cycle of poverty (Reddy et al., 2019). However, smallholder farmers in some developing countries are provided with small-scale irrigation schemes, farm input subsidies, farm implements, credit facilities and cash grants to even acquire land under land reform programs to encourage and boost their outputs (Ramaila et al., 2011). ...
... Furthermore, public investment in farm infrastructure could be increased, direct benefit transfer schemes for purchase of inputs Frontiers in Sustainable Food Systems 09 strengthened, institutional credit delivery mechanisms improved and safety nets in rural areas widened (Reddy et al., 2019). ...
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... The contemporary agriculture policy supporting Farmer Producer Organizations and contract farming might boost small -scale farmers' negotiating leverage and scaled reductions, allowing them to take advantage of market possibilities. Reddy., et al. (2019). [79] 9. ...
... Reddy., et al. (2019). [79] 9. ...
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... The contemporary agriculture policy supporting Farmer Producer Organizations and contract farming might boost small -scale farmers' negotiating leverage and scaled reductions, allowing them to take advantage of market possibilities. Reddy., et al. (2019). [79] 9. ...
... Reddy., et al. (2019). [79] 9. ...
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... In Fig 1 the monthly income and expenditure of agricultural households are divided among ten deciles (income surrogates) of monthly per capita consumption expenditure (MPCE) considering the 1st decile as the lowest income group and 10th decile as the highest income group. The average income of 10th decile is significantly higher than the other decile groups where there was no scope of income surplus after meeting consumption needs [13]. The income gaps indicate the higher consumption level of the households than their income. ...
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NABARD Funded project
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The Scheduled Castes (SCs) are officially designated groups of people in India. The SCs are sometimes referred to as Dalit. The Scheduled Castes comprise about 16.6 per cent of India’s population (according to the 2011 census). The Constitution (Scheduled Castes) Order, 1950 lists 1,108 castes across 28 states in its First Schedule. For much of the period of British rule in the Indian subcontinent, they were known as the “Depressed Classes”. Since the independence of India, the SCs were given Reservation status, guaranteeing political representation. The Constitution lays down the general principles of positive discrimination for SCs. National Institution for Transforming India (NITI) Aayog guidance, ICAR notified Kotapally mandal of Mancherial erstwhile while Adilabad for overall development of scheduled caste households in a time bound manner with specific budget allocation. As in the Kotapally mandal share of SC population in total population was higher at 25 per cent, while in the district their share is only 15 per cent and only 16.6 per cent in India as per the Census 2011. Upon receiving the approval, the study team of ICARCRIDA visited the mandal and identified three villages for developmental intervention for intensive development of the SC households. The CRIDA team adopted a unique approach called “Problem Driven Iterative Adoption” where in the team has identified the problems faced by the SC households, diagnosed and dissected these problems and evolved solution in partnership with the local stakeholders, mainly farmers. This baseline survey is a part of identifying the specific problems of the farmers and identify solutions in partnership with the farmers.
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Evidence suggests that smallholders are fast becoming one of the largest micro-credit recipient groups in Bangladesh. However, the literature on the effects of micro-credit use among smallholders is surprisingly deficient. This article seeks to rectify this gap by highlighting the ramifications of micro-credit’s foray into the subsistence agriculture sector. It analyses the ostensibly disparate processes of mounting smallholder indebtedness and the phenomenal rise of micro-finance institutions in Bangladesh in light of the country’s broader context of agricultural commoditisation, input subsidy reduction and a systematic lessening of the subsidised agricultural credit system. The article uses the concept of “accumulation by dispossession/encroachment” to argue that persistent borrowing from micro-finance institutions (MFIs) exposes smallholders to the risks and volatilities of the market. Using qualitative insights from a case study of three villages, it demonstrates how the capital accumulation model of Bangladeshi MFIs marginalises smallholders and ensnares them in a perpetual cycle of debts.
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Expanding access to credit remains a key central government strategy for promoting agricultural development and livelihood diversification in south India and more widely. Smallholders borrowing from multiple credit sources are faced with obligations in addition to financial repayment. Available evidence on the consequences of indebtedness extending beyond monetary debt and its influence on vulnerability is incomplete in important ways. This paper presents an integrated vulnerability framework and illustrates the framework through case studies of three pairs of smallholder clients and credit sources. Using process-tracing and progressive contextualization methods, this paper shows the diversity of feedbacks that shape indebtedness and provides examples of social-ecological consequences. Unpacking these consequences in individual cases demonstrates indebtedness as an important root cause of vulnerability, which is in contrast to an examination of proximate causes, such as credit policy or temperature, that is the focus of a large share of scholarship. The paper shows that different credit sources are associated with different sets of obligations, leading to varied livelihood and agricultural consequences. Suggesting ‘credit stacking’ as an important adaptation strategy and research agenda item, the paper makes a plea for careful analysis of the conditions when credit is a factor in adaptive capacity and indebtedness of vulnerability.
Based on the data from the All-India Debt and Investment Surveys, a re-emergence of non-institutional credit agencies in the incidence of household indebtedness is found since the 1990s, especially in the rural areas, reflecting the inadequate social commitments of the institutional agencies due to their contemporary organisational deficiencies. The data, however, do not seem to capture the extent of urban distress in totality. Yet, given the general dearth of evidence on the status of household indebtedness over time, institutions like the Reserve Bank of India and the National Bank for Agriculture and Rural Development should revisit this information to resurrect their roles in strengthening credit delivery to the general population.
This study analyzes the determinants of household over-indebtedness and its persistence for rural household borrowers in Thailand and Vietnam. A household is considered to be over-indebted if it is in default or arrears on a loan or if its ratio of debt service to income exceeds 50 percent. The persistence of over-indebtedness was tested using a Heckman random effects dynamic probit model controlling for the effect of household demographic, socioeconomic, and behavioral characteristics. For Thailand, but not for Vietnam, past experience of over-indebtedness increases the probability of being over-indebted in the present, controlling for other household characteristics. Village support systems in Vietnam may be more effective in delivering households out of over-indebtedness than in Thailand where heavy debt burdens are taken more for granted. Household characteristics that significantly increase the probability of over-indebtedness include poverty, household size, low education, overly optimistic forecasting of income, and a sense of being less well off than other villagers.
Purpose According to the 70th Round of the National Sample Survey published by the Government of India in 2014, the incidence of indebtedness among households in the rural areas of Telangana state, India, is twice that of rural all-India. Around 59% of rural households are indebted in Telangana as against 31% all-India. The objective of this study is to examine the extent and magnitude of indebtedness among rural households in the Medak district of Telangana state. Further, we wanted to identify the sources of credit to these households and for what purpose the loans were utilised. Design/methodology/approach To achieve our objective, we conducted a primary level household survey in one of the distressed districts in newly formed State. We applied Bayesian and LASSO regression method to identify the factors that impact indebtedness of a household. Findings The OLS results based on LASSO regression results show that among all the explanatory variables-principal occupation, use of modern technology, the rate of interest, household medical expenditure, and source of loan are significant, indicating that these variables significantly affect the loan taken by the farmers in the study area. The study shows that alternative sources of non-farm income and promotion of modern technology in agriculture can reduce the incidence of farmers’ indebtedness in India. Originality/value The paper contains significant information with regard to indebtedness. It focuses on the issue troubling the authorities the most. It provides the ground realities of the incidence of indebtedness in Medak, one of the most distressed districts of Telangana, a southern Indian state. There have been very few similar studies done in the newly formed state. The paper has employed an advanced statistical technique, i.e. the Heckman Selection regression technique, to study farmers' indebtedness in India. It provides a means of correcting for non-randomly selected samples, which otherwise can lead to erroneous conclusions and poor policy.
Rural financial markets have grown rapidly in Asian emerging market economies, thus contributing to economic development and the reduction of poverty. At the same time, however, the level of indebtedness of rural households has increased, making households more vulnerable to shocks. One of the reasons for rising household debt is the ease of borrowing simultaneously from a growing number of lending institutions. This paper addresses two major questions. First, does borrowing from multiple sources increase rural households’ risk of over-indebtedness? Second, do over-indebted rural households refinance their outstanding loans through multiple borrowing, running the risk of becoming trapped in a debt cycle? Employing a dynamic random effects bivariate probit model for a unique set of longitudinal household panel data from Northeast Thailand, this study examines the bidirectional relationship between over-indebtedness and multiple borrowing to determine to what extent rural households become trapped in debt cycles. On the one hand, the results indicate that households in Northeast Thailand take on multiple loans, which further increases their risk of becoming over-indebted. On the other hand, our model results do not confirm the widespread notion that over-indebted rural households use multiple loans to refinance unpayable debts.
Few studies of agrarian transition examine what farmers themselves feel about farming. Are they cultivating out of choice or a lack of options? What distinguishes farmers who like farming from those who do not: their personal/household characteristics and endowments? The local ecology and regional economy? Or a mix of these and other factors? Understanding farmer satisfaction is important not only for assessing citizen wellbeing but also for agricultural productivity, since occupational satisfaction can affect a farmer’s incentive to invest and reveal production constraints. Using a unique all-India data-set which asked farmers, ‘Do you like farming?’ this paper provides answers and policy pointers, contributing a little-studied dimension to debates on the smallholder’s future and subjective wellbeing.
In this paper, we build a dynamic stochastic general-equilibrium model with housing and household debt, and compare the effectiveness of monetary policy, housing-related fiscal policy, and macroprudential regulations in reducing household indebtedness. Excessive household debt arises due to exuberance shocks on house price expectations, which drive a wedge between the actual and the underlying fundamental value of houses. The estimated model also features long-term fixed-rate borrowing and lending across two types of households, and differentiates between the flow and the stock of household debt. Our main findings can be summarized as follows: (i) Monetary tightening is able to reduce the stock of real mortgage debt, but leads to an increase in the household debt-to-income ratio. (ii) Among the policy tools we consider, tightening in mortgage interest deduction and regulatory loan-to-value (LTV) are the most effective and least costly in reducing household debt, followed by increasing property taxes and monetary tightening. (iii) Although mortgage interest deduction is a broader tool than regulatory LTV, and therefore potentially more costly in terms of output loss, it is effective in reducing overall mortgage debt, since its direct reach also extends to home equity loans. (iv) Lowering regulatory LTV and mortgage interest deductions from their current levels would be welfare improving, while we find weak support for systematic leaning against household imbalances through monetary policy.
This study explores the relation between Bt cotton adoption and farmer suicides in India. This is undertaken through comparing the debt levels of Bt cotton cultivators with those adopting alternative organic and Non-Pesticide Management (NPM) methods. The study involves a total of 26 participants in three villages in Telangana, India. It argues that measures of indebtedness need to be adopted as part of assessments of both Bt cotton and development policy.