Risk-Coping in Low-Income Populationscru2/econ731_files/Mark/LectureNotes3.pdfRisk-Coping in...
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Risk-Coping in Low-Income Populations
Coping with income fluctuations important problem when incomes low
Agriculture has high-frequency fluctuations: intra and inter-annual variability
How to maintain consumption in face of fluctuating incomes?
1. Reduce income fluctuations: mitigate effects of production shocks on income
2. For given income realization, take action to maintain consumption
Sector/timing income/production consumption
ex ante: prior to realization of
shock
Contracts: share tenancy
Asset allocation:
crop diversification
irrigation investment
plot diversification
Occupational diversification
Save
Insurance contract
Social insurance arrangements:
marriage
migration
ex post: after realization
? Borrow
Sell assets (dissave)
Transfers
Labor supply
Production and risk
Does 1) absence of insurance, 2) dislike of risk affect productivity?
Structure
1. Utility of farmers:
c c 1 2U = V(: , F ) V >0, V <0 (dislike variability)
c cwhere : = average consumption, F = consumption sd
2. Technology (CRS):
Farm assets differ in two dimensions: profitability and
contribution to risk (e.g., irrigation pump versus plow)
B T: = Wf("): av. profits and average weather(T)
B T variability in profits and weatherF = W'(")F
where W = total productive wealth of the farmer
1... n" = {" " }, the farmer’s asset portfolio,
i =with " share of asset i in total productive wealth of farmer
f() reflects the productivity of the farmer’s portfolio
'() reflects the riskiness of the farmer’s portfolio
3. Constraints:
c B: = :
c BF = k(W)F k’<0, 0 # k # 1
Perfect insurance: k = 0 No insurance: k = 1
4. Behavior: maximize utility subject to technology and
constraints
Results (FONC):
i nPerfect insurance: f - f = 0 (production efficiency)
1 i n 2 i n TImperfect insurance: V (f - f ) = - V (' - ' ): k
implies:
2If farmers dislike risk (V <0) and are uninsured (k�0),
A. Inefficient production
B. Positive relationship between marginal
contribution to profit levels and to profit
variability across assets and farmers:
1 n 1 nf /f = ' /' overinvest in risk-reducing
assets
Absent insurance market means farmers must
trade-off profitability and risk reduction
C. If wealthy farmers are less risk-averse or better
able to insure, then wealthy farmers will have
more profits per unit of wealth and more variable
incomes per unit wealth
Important issue: Absence of insurance market implies that poorest
farmers are most inefficient because they are poor:
they choose to be inefficient!
But policy-makers, observing inefficient poor farmers but not knowing
the root cause, could conclude:
a. Provide technical assistance, training for poor
b. Not engage in equalizing land reform (why give land to the
inefficient?)
Thus, need to know whether its missing insurance, or incompetence:
Empirical questions: Do we find A, B, C?
Two challenges: characterize technology and measure weather
1. Specify a profit function:
We want a flexible function (not Cobb-Douglas!) to characterize
technology: normalized (by total wealth portfolio) quadaratic
kt i i ikt i j ij ikt jkt i i ikt t T t ktProfits/W: B = G$" + 1/2EE * " " + G(" T + ( T + ,
(Specification also includes year effects (prices) and a farmer effect)
kt i i ikPortfolio riskiness: ' = sqrt((G(" ) )2
2. Measure weather: what aspects matter?
What if principal asset, store of value is used for consumption-smoothing?
“Stocking out” then means out of business
Example: use of bullocks in Indian ICRISAT villages
What’s a bullock?
1. Bovine male used for traction - animal power
2. Use of bullocks (pair) necessary for agricultural production in monsoon
agriculture (bovine economy)
3. Ownership of bullocks also necessary: problem of rental (moral hazard again)
4. Good also as buffer stock: transportable
ICRISAT Facts: 1. Important part of portfolio
2. High turnover - sales and purchases (9.5% in national survey)
3. 60% of bullock sales to buyers outside the village
4. Turnover seems related to consumption-smoothing
5. Farmers own too few
Puzzle: If bullock ownership is necessary and bullocks are useful also as
store of value, why do farmers hold so few?
Is it the problem of stocking out because of the inability to insure/borrow?
What would happen to bullock ownership and the efficiency of production if
insurance and/or an assured source of income were provided farmers?
Bullock model:
max E0GT$t(1/1-()(Ct - Cmin)
1-(
Bt = Gj"jDtj + Gj"jDtjTt Dtj = 1 if the # of bullocks (Bt) = j
Ct = Bt - pbbt+1 if Ct > Cmin
Ct = Cmin if Bt + pbBt < Cmin (stock out and bakruptcy)
Bt = Bt-1 + bt
Estimates and simulations
Risk-Coping and Marital Arrangements: India
The efficient risk-sharing model eliminates idiosyncratic risk -
independent shocks to households - but not aggregate community-level
risk
Given spatial covariance of risk, especially in agriculture, want partners
in risk pooling arrangement who are spatially separated
1. Household migrants: evidence from Africa (Lucas and Stark,
JPE 1986) that remittances compensatory
2. Patrilocal exogamy: marry outside the village
households export daughters, import daughters-in-law
risk-sharing is centrifugal l force for family: incentive to
spread the family spatially
(returns to specific experience induce a centripetal force:
incentive to stay together)
marriage creates a risk-sharing partner:
1. Creates inventive to risk share: member of household
A in household B, so if household A still cares about
the former member of A, A will help B in bad times
(altruism)
2. Creates a monitoring capacity: if the former A-
member still cares about origin household A, will act as
resident agent providing state verification
Implication of marriage as a risk-coping institution:
1. Marital partners will come from spatially-separated families: marital migration
2. Set of destination households for daughters will be spatially diverse: diversified
portfolio of marital partners
a. two daughters will not marry partners from either their own village or the same
village elsewhere
3. Households linked by marriage will be similar with respect to permanent
characteristics: assortative mating on capacity to provide help (recall borrower-group
self-selection)
4. If family groups can transfer information at less cost, given that marital search over
long distances is costly, marriages will be between family members
5. If the cost of marriage is positively related to its remoteness (search costs), then
households better able to self-insure will have shorter-distance marriages (wealth)
Empirical questions:
1. Does distance really matter for reducing risk covariance? How much separation is
required to obtain covariance reduction?
2. How important are inter-village transfers, as opposed to intra-village transfers?
3. What proportion of marriages are spatially exogamous, occur among partners from
different villages?
4. Are the marital portfolios spatially diverse?
5. Is consumption-smoothing really augmented by marriage? Is the consumption of
households with more marital partners more insured against income fluctuations?
6. Are households that face more income risk and are less protected have marital partners
that are more spatially separated?
Evidence from a survey of 115 marriages in 10 ICRISAT villages in 1984: locations of
husbands of all daughters of head and locations of origin of all daughters-in-law
Findings: 92.2% of married women came from or went to another village (exogamy)
average distance = 33 km (sd=60), maximum distance = 750 km
in households with two or more married women (daughters or daughters-in-
law) 94% not from or located in same village
only 14 of 115 marriages involved partners who were not blood relatives
in value terms 59% of transfers came from outside the village (26.6% of
loans)
Findings on spatial covariance:
use times-series information on daily rainfall, profits, wages in 6
ICRISAT villages
measure: distance between each of the 6 villages = 15 independent pairs
regress: correlation coefficient rijk for each variable k between village i and
village j on the distance between i and j for the 15 village-pairs:
rijk = $kdistanceij, where k = rainfall, profits, wages
Findings on consumption-smoothing and marriage:
compute variance in consumption Fc2 and farm profits FB
2 for each ICRISAT
farm household over 10 years
want to know the relationship between a household’s consumption variability
and its profit variability, and how it is mitigated by household characteristics
regression across households:
Fc2 = *1FB
2 + *2FB(wealth) + *3FB2(# of marriages) + *4FB
2(marital distance) + *5FB2(# of migrants)
dFc2/dFB
2 = *1 + *2(wealth) + *3(# of marriages) + *4(marital distance) + *5(# of migrants)
expect: *1 > 0; *2,*3,*4,*5 < 0
I. Micro Credit Institutions
Problems of the credit market in a setting with imperfect information:
1. Adverse selection (hidden characteristics). Banks cannot tell poor risks from good ones
among borrowers. Terms of loan affect who borrows and ability to repay. What is an effective
method of screening?
2. Enforcement. How do banks ensure that clients comply with terms of loan? Terms of loans
affect choice to repay. What is an effective mechanism to enforce repayment?
3. Moral hazard (hidden action). Loan terms create incentives to increase risk, and thus reduce
ability to repay. How can clients be efficiently monitored so they utilize the loan appropriately?
4. Verification costs. Costly to distinguish ability to repay from choice to default. How can
these auditing costs be minimized?
These problems acute among poor:
1. If a project fails, poor more unlikely to be able to repay loan.
2. How do you punish poor people? little to expropriate (“grim strategies”)
3. Ratio of transaction costs (verification) to loan size high (why are loans small?).
But, also:
1. Rural villages are typically small and the poor are immobile: communities have a great deal
of information about members.
2. Community may be able to impose social sanctions.
Credit sources: commercial banks, government banks, village moneylenders, other institutions
- micro credit
Micro-credit institutions exploit advantages of communities:
1. Require borrowers to form small groups in which all borrowers are jointly liable for each
other’s loans: joint liability lending
2. Intensive monitoring of clients.
3. Promise repeat loans for responsible borrowers as a group.
Grameen Bank
1. Lends to 2 million people in 36,000 villages (1999)
2. Lending funds obtained mainly from external institutions (subsidy?)
3. Loans made to self-selected groups of 5 people in the same village (optimal?)
4. Joint liability, with threat of no more loans in event of default
5. Compulsory weekly savings.
6. Surcharges on loans
7. 4 and 5 used to form emergency consumption insurance fund
8. Groups participate in non-financial activities (training)
Micro-Credit Issues
1. Why should we expect these institutions to solve fundamental credit problems? Theory
2. Are they necessary, compared to what? Contrast with collateral requirements. Theory
3. What is the evidence on:
A. Improvement in repayment rates.
B. Alleviating poverty
C. Solvency and sustainability: are these institutions profitable?
Framework
B = loan amount i = interest rate charged by institution
D = return on money institution can get if does not make loan
p = probability of project success (loan can be repaid)
u* = value of alternative use of borrower’s time
C = collateral (object of value that is forfeited if loan cannot be
repaid)
What makes for good collateral?
1. Appropriability 2. Absence of risk 3. Absence of
moral hazard
Examples: financial assets, animals, jewelry, land
Which is best?
Expected value of loan B made by institution (no-profit constraint):
E(B) = p(B(1+i) - B(1+D)) + (1- p)(C - B(1+D)) = 0
Participation constraint of borrower:
p(Y - B(1+i)) + (1- p)(-C) > u* (u* may vary across
borrowers)
Note: limited liability: cannot expropriate any monies if loan not
repaid, beyond C (may be due to poverty)
Roles of collateral and joint liability
Case 1: No asymmetric information or strategic behavior
C and i are perfect substitutes (both lender and borrower are
indifferent):
dE(B)/dC = (1- p) > 0 dE(B)/di = pB > 0
dv/dC = -(1- p) < 0 dv/di = -pB < 0
(but collateral requirement can be used to circumvent interest
ceiling)
Case 1: Adverse selection
Assume that there are two observationally-identical borrower types
a and b with different p’s: pa > pb
Problem: bank cannot distinguish, so bases loan cost on average of
p’s: low-risk borrowers subsidize high-risk; some low-risk won’t
want loans - repayment rates low as high-risk borrowers are
advantaged
A. Screening using collateral
a prefers loans with higher C and lower i compared to b
Bank can offer two different contracts: low C-high i and high C-
low i, lowering average risk of borrowers and increasing
repayment rates overall: collateral is a screening mechanism
But very poor have no collateral
B. Joint liability lending
joint liability lending substitutes for absent individual collateral.
Assumes that community members know who is a and who is b
Joint liability credit contract: if a borrower’s project succeeds, she
pays loan back but if her partner’s project fails, she also pays an
amount c to the bank - c is then collateral, pledged by partner
Expected value of i’s joint-liability loan with partner j:
E(v)ij = pipj(Y - r) + pi(1- pj)(Y - r - c), where r = Bi
Implication: positive assortative matching by riskiness
Expected gain from b partnering with an a rather than another b:
E(v)ba - E(v)bb = pb(pa - pb)c
Expected loss from a partnering with a b rather than another a:
E(v)aa - E(v)ab = pa(pa - pb)c
So a will never partner with b as long as c>0.
Bank can now screen by offering joint-liability contracts with
different i, c combinations. High-c offer leads to group forming of
low-risk borrowers, with therefore high repayment rates
Case 2: Strategic default and enforcement
Sometimes it is in the interest of borrowers not to repay even when
successful.
Decision rule:
Repay if Y - Bi > Y - C - S, where S=any sanctions
A. Enforcement using collateral
Borrowers now not indifferent between C and i:
Higher C lowers return to default; higher i increases default
gain
In high-risk (low p) settings with no collateral, banks cannot just
raise i in order to break even - that increases default risk, lowers
repayments; high i and low repayment rates go together
If no collateral, only recourse: threat of cut-off from future loans
B. Enforcement using joint-liability contracts
Joint liability credit contract: All in default if any member does not
repay, and no one in group ever receives a future loan
Decision rule now:
Repay if Y - 2Bi > Y - S
Does joint liability lead to higher repayment?
Consider two cases:
1. One group member defaults and other repays both: better
2. One group member defaults, the other is willing to pay
own debt but not partners: worse than individual liability
So benefit in this case not obvious, depends on distribution of Y’s
What are problems with threat of loan cut-off?
Requires credit monopoly - now Grameen bank’s monopoly
being eroded
Case 3: Moral hazard (p is now a choice)
A. Choosing risk with collateral requirements
Max {p(Y - r) + (1 - p)(-C) - 1/2kp2}
p* = (Y - r + C)/k
dp*/dr = -1/k < 0, dp*/dC =1/k >0
interest rate is like a tax on effort; increasing C induces more effort
But very poor have no collateral: lower effort and lower repayment
What is equilibrium level of p in the “collateraless” economy?
Take into account zero-profit constraint: p*r = D, then
sp*2 - Yp* + D = 0, so p* = (Y + sqrt(Y2 - 4Dk))/2k
B. Joint liability lending and risk choice
joint liability lending substitutes for absent individual collateral.
Assumes that community members can monitor group members’
effort
Again, joint liability credit contract: if a borrower’s project
succeeds, she pays loan back but if her partner’s project fails, she
also pays an amount c to the bank - c is then collateral, pledged by
partner
Assume that all choose the same p cooperatively, then the problem
solved by each partner is:
Max {p(Y - r) + p(1 - p)(-c) - 1/2kp2}
p* = (Y - r - c)/(k - 2c), with
dp*/dr = -1/(k - 2c) < 0, dp*/dc = (2(Y - r) - k)/(k - 2c)2 >0
What is level of p in the “joint-liability” economy?
Take into account zero-profit constraint: p*r = D, then
(k - c)p*2 - Yp* + D = 0
so p* = (Y + sqrt(Y2 - 4D(k - c)))/2(k - c)
Evaluating the Performance of Micro-Credit Institutions
Methods
1. Compare repayment rates and well-being of households in
villages with Grameen banks with households in villages
with no bank
A. Where are banks placed? program placement bias
2. Compare repayment rates, well-being of Grameen
borrowers and other loan takers within a village
A. What does theory say about those who form groups?
Theory says loan groups that form contain people
who are better risks - selection bias
3. Compare same Grameen loan-takers before and after the
introduction of the bank with non-loan-takers - no data
Example: Coleman, Journal of Development Economics, 1999)
1. Exploits phasing in of village joint-liability banks in NE
Thailand: sample of bank members and non-members in 14
villages, 8 of which had a bank for a number of years 6 were
assigned a bank but the bank had not set up. However, the
groups had formed in anticipation of the bank coming in
2. Compares members and non-members within villages
without a bank (control villages) to members and non
members in villages with a bank (treatment villages)
Assumes self-selected groups are the same, so only difference
is ability to obtain a loan - the treatment (72 outcomes)
Mobility, Economic Development and Growth
Contrast rural and urban India: both experienced economic growth, but
different patterns of mobility
Institutions play a key role in both contexts
Why Is Mobility in India so Low? Social Insurance, Inequality and
Growth (Munshi and Rosenzweig, 2005)
1, Increased mobility is usually a hallmark of economic development - individuals no longer
tied to land born on or occupations of their families.
2. India appears to be an exception among developing countries:
A. Occupation: For decades, caste-based occupations locked groups of individuals
to narrowly-defined occupations in Bombay
B. Migration:
1. India lags behind other similar countries: UNDP change in percent
urbanized, 1975-2000 by country
2. In rural India, rate of migration by men out of their village fell from 10% to
6% between 1982 and 1999.
Indeed, in most studies of Indian rural economy migration is assumed to be
absent (determinants of local public goods, returns and schooling choice)
0
5
10
15
20
25
30
35
40
45
China Indonesia India Nigeria
1975 2000
Figure 1. Change in Percent Urbanized, by Country, 1975-2000
C. Marriage: tradition to marry within sub-castes (jatis): restrict matches, given age
restrictions, to narrow pool.
1. Bombay: only 7.6% of individuals aged 25-40 in 1991 married outside their
jati in 2001
2. South Indian tea plantations in 2003: 6.2% out-married in same age group;
3. All rural India (except J & K state): 9.1% out-married in same age group.
Explanations for low mobility: ad hoc, incapable of explaining who is mobile
1. Low out-migration: tied to relative high rates of growth in agriculture
(Indian green revolution) - but accelerated growth in past 15 years in urban
industrial sector and a decrease in migration
2. Low out-marriage: tied to need for marriage between similar mates - but
inequality within jatis has risen, and yet no increase in out-marriage
STATE KARNATAKA
CODE CASTE1 ACHARAS2 ACHARS3 ADISAS4 AGASA5 ARAYARU6 BABU7 BEDIGAS8 BILLAVA9 BORI10 BRAHMAN11 CARPENTER12 CHRISTAIN13 DASOBU14 DEBAUGAS15 DEVOUGA16 EDIGAS17 GOLDSMITH (SUNAR)18 GOMOTARY19 GOUA20 GOWDAS21 HADIUA22 HORABALRU23 IJPAR24 JAIN25 JOGI26 KURBA27 KURUBA28 KUUABEE29 LAMBANI30 LARANIO31 LINGAITH32 LUGAYATUS33 LUNUATAT34 MADIDALAS35 MADRASI36 MARATHI37 MEDIGA38 MUSLIM (SAYED)39 MUSWAS40 NAI (BARBER)41 NAIKI42 NAUDABOBI43 NONDAVAS44 OKAURA45 REDY46 SCHEDULE CASTE47 SCHEDULE CASTE/SCHEDULE TRIBE48 SCHRI49 SIKH50 SWEEPER (BHANGI)51 VISHWA KARMA52 VODA BHAVI53 VYSHAS54 WASHMAN
Figure 2a: Distribution of the Number of jatis per Village, 1999
0
2
4
6
8
10
12
14
16
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
0
2
4
6
8
10
12
14
1950-59 1960-69 1970-79 1980-89 1990-99
Rates of Out-Marriage (Hindus), by Decade, Rural India 1950-1999 (N=31,529)
0
2
4
6
8
10
12
14
1970-75 1975-79 1980-85 1985-90 1990-95 1995-2002
Rates of Out-Marriage (Hindus), by Quinquennia, Mumbai 1970-2002 (N=5,406)
HYV Yields (Rupees per acre) and Real Agricultural Wages, 1971-1999
0
2
4
6
8
10
12
14
16
18
1971 1982 1999
HYV Yield/100 (1971 rupees)
Agricultural Wage (1982 rupees)
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
Between Villages Between Jatis Within Villages Within Jatis
1982 1999
Figure 2: Changes in Gini Coefficients for Household Wealth, 1982-1999Between and Within Villages and Jatis
Our explanation for low mobility:
Mobility reduces ability of risk-sharing networks to function
Out-marriage: lower ability to sanction via family
Out-migration: lower ability to sanction individual who is away
Those who cannot be sanctioned are higher risks, and will be excluded from
networks (Grief)
So mobile individuals lose network services
Risk reduction a fundamental problem of rural areas in India - many studies
Such studies reject perfect insurance, but high degree of smoothing
But Indian studies ignore caste networks - village is relevant group
Imperfect commitment models imply quasi loans used to smooth rather than
transfers
But literature on rural credit focuses on local money lenders and banks,
ignoring caste loans implied by limited commitment models
We show using new data providing both sub-caste affiliation and loans by source and
purpose for national panel of rural Indian households
1. Caste loans are as important as money lender loans in portfolio.
2. Caste loans have superior terms - lower interest rates, lower collateral
requirements.
3. Using consumption data, importance of jati as the risk-sharing group.
4. Consistent with a limited commitment model of risk sharing with wealth
inequality indicating who benefits least from networks, we find:
A. Higher-wealth households within jatis pay lower interest rates on caste
loans, but not on bank loans or money-lender loans
B. Households with out-married or out-migrant men are significantly less
likely to receive caste loans - but this is not causal
C. Exogenous changes in relative wealth within the jati affect the probability
of mobility - relatively more wealthy always exit first (identification strategy)
D. Overall growth has little effect on mobility, given networks, but growth in
inequality within jatis lowers mobility - overall inequality does not matter
Data Sets
1. NCAER ARIS 3-year panel: 4,118 households in 259 villages in 17 states surveyed in each
of three crop years: 1968-69, 1969-70, 1970-71
A. Information on consumption, income, location, HYV use, village infrastructure
2. NCAER 1982 REDS: 4,979 households in same villages, except for Assam: 1971 panel
A. Information on consumption, income, location, HYV use, village infrastructure
B. Information on loans, by source and use: obtained in reference year and outstanding at
beginning of period
3. NCAER 1999 REDS: 7,578 households from the 1982 households, except for J&K state
A. Information on consumption, income, location, HYV use, village infrastructure
B. Information on loans, by source and use: obtained in reference year and outstanding at
beginning of period
C. Sub-caste (jati) identified for all immediate relatives of head and spouses
D. Marital and migration histories for all immediate relatives of heads
Figure 3Location of ARIS-REDS Villages and District Boundaries
Caste Networks and Consumption Smoothing: Loan Data
1. Are loans from caste members a significant part of loan portfolios? Yes
2. Are caste loans important for consumption smoothing? As important as moneylender
loans
Use 1982 loan information: loans by source (caste, bank, moneylender) and purpose:
investment, operating expenses, consumption, contingencies (illness, weddings)
3. Do caste loans have superior terms compared with bank and money lender loans?
Use 1982 and 1999 loan information:
Average interest rates: lower than bank or moneylender
Percentage of loans with no interest charged: higher than bank or moneylender
Collateral requirements: smaller proportion for caste loans
Table 1: Loans Received by Source and Purpose, 1982
Loan source: Caste Bank Moneylender Other(1) (2) (3) (4)
Total loan value (%) 12.33 46.30 12.19 29.18
Loan value by purpose (%):
Investment 17.07 26.47 16.83 39.63
Operating expenses 6.08 53.47 1.82 38.63Contingencies 42.61 20.56 27.48 9.35Consumption 23.11 15.08 47.42 14.39
Note: N=1,423 loans received by the sampled households in 1982.Loan values sum up to 100 across the four sources in each row.Investment includes land, house, business, etc.Operating expenses are for agricultural production.Contingencies include marriage, illness, etc.
Table 2: Terms of Loans, by Source and Year
Year:Source: Caste Bank Moneylender Caste Bank Moneylender
(1) (2) (3) (4) (5) (6)
Interest rate 10.70 14.88 16.99 8.23 10.16 30.63(0.50) (0.47) (0.42) (0.91) (0.23) (2.30)
Percentage zero-interest loans 34.87 0.27 2.84 59.78 0.17 15.07
Percentage loans requiring collateral 16.23 48.95 18.99 1.31 83.21 24.78
Note: N=3,158 loans received by the sampled households in 1982 and 1999.Statistics are weighted by the value of the loan.Standard errors in parentheses.
1982 1999
Table ExtraTest of Perfect Insurance, Full Sample
Dependent variable: log own-consumption(1) (2)
Log own-income 0.321 0.321(0.032) (0.032)
Village log-consumption 0.783 0.784(0.035) (0.029)
District log-consumption -- 0.002(0.034)
R-squared 0.900 0.900
Number of observations 12,338 12,338
Note: regressions use three years of data 1969-71 for each household.All regressions include household fixed effects.Standard errors in parentheses are clustered at the state-year level.
Table 3: Tests of Full Risk-Sharing
-
-
-
Dependent variable: log own-consumption (All housholds) (1) (2) (3) (4)
Log own-income 0.184 0.174 0.171 0.172 0.168 (0.038) (0.040) (0.041) (0.041) (0.042)
Village log-consumption 0.735 0.725 0.635 0.576 0.638 (0.039) (0.041) (0.052) (0.057) (0.048)
Jati log-consumption - -- 0.232 0.216 0.239(0.038) (0.038) (0.041)
District log-consumption - -- -- 0.095 --(0.059)
Caste log-consumption - -- -- -- -0.024(0.080)
R-squared 0.883 0.824 0.826 0.826 0.825
Number of observations 5,394 3,543 3,543 3,543 3,387
Note: regressions use three years of data 1969-71 for each household.All regressions include household fixed effects.Standard errors in parentheses are clustered at the state-year level.Only jatis with more than 10 sample households are included in columns 1-4.Caste in column 4 measures broad hierarchical category in each state.
Mutual Insurance Models
Assume two identical (=wealth) individuals and two iid payoffs H (high) and L (low)
HH LL HL, LHA. 4 states of the world: P , P , P P
HH LL HL LH B. P + P + P + P = 1
HL LH C. P = P (equal wealth condition)
1. Perfect Insurance (Rejected in Townsend test, importance of loans for consumption
smoothing):
Consumption in any period = (H + L)/2 obtained via transfers of (H - L)/2
Ratio of marginal utilities equal across all states
2. No commitment (Coate and Ravallion, 1993):
In H,L state, incentive to quit network for person with H based on comparison of
gain from deviating H - (H + L)/2 versus future permanent loss of insurance
Transfers < (H - L)/2
3. Limited commitment (Ligon, Thomas and Worrall, 2002):
In H,L state, H person given promise of future compensatory transfers in all future
periods with equal payoffs by L, until new H,L or L,H state occurs
Expectation of future transfers in are such that in H,L state H does not deviate
Note: transfers are like loans in that they imply future payoffs, although state-contingent
What happens if partners are unequal in wealth? Two characterizations
1. Mean-preserving spread in wealth via change in probabilities
HL LH HL LHAssume P > P , with (ÄP = - ÄP ) so mean-preserving spread
Rationale: irrigation for some
A. Less wealthy individual is now more likely to be a net borrower
And, to maintain participation in the H,L state for the wealthier individual:
B. Future compensatory transfers must be higher when states equal (worse
loan terms for low-wealth L): interest rates on loans out higher
C. Transfers to wealthy when he is an L (borrower) have lower compensatory
transfers (better loan terms for high-wealth H): borr. interest rates are lower
2. Mean-preserving spread in wealth via change in payoffs in H states
Rationale: HYV availability for some
A. Wealthier agent better off compared to before (for given transfers) but
given declining risk aversion, as before benefits less from insurance in future
L state
So ambiguous results for loan position and interest rates on loans out
and in (note in first case declining risk aversion reinforces)
Now, introduce social sanctions imposed by network:
1. Raises cost of reneging, so improves loan terms for all
2. Ensures that those who leave the network will be less able to obtain loans
on favorable terms because those with lower possibility of sanctions are
undesirable insurance partners (Greif): cannot participate
Implications for Network Exit and Stability
1. Implication is that an increase in inequality requires that transfers be responsive so that
participation constraint is met for the wealthiest
2. Limits on responsiveness: A. Subsistence: consumption floor
B. Norms of redistribution: customary obligations of wealthy
3. If rigidity, then increase in inequality increases “tax” on wealthy - who are net lenders
If there is network exit, then will be by the wealthiest
Then scope for redistribution lowered for poorest, network gains less for them
4. Mobility, given sanctions, is associated with loss of network benefits - exit=mobility
A. Out-marriage: lowers capacity to sanction via families
B. Migration: lowers capacity to sanction individual who is away
Does mobility lead to loss of insurance network benefits?
1. Estimate “effect’ of exiting on benefits: caste loans, consumption variability
Correlation should exist between ability to obtain caste loans and exit, but
could be because those who are not able to get caste loans more likely to exit
2. Assess implications of model that incorporates loss of benefits:
those with least to lose (most to gain) exit (stay)
A. For given average jati wealth, those with greater own wealth more likely to
leave (relative wealth, more likely net lender) if adjustment imperfect, but:
1. Higher wealth households may have more outside options in “open”
marriage market too (reinforce)
2. Higher-wealth can afford to invest in mobility; opportunity cost
higher (oppose)
B. For given own wealth, increase in average jati wealth should decrease exit
Also show: higher average jati wealth, for given own wealth, increases jati loan access
Are the data on loan terms consistent with the model?
Wealthy within caste group should give and receive caste loans on terms that are more
favorable to them compared to the less wealthy for caste loans:
interest rates lower for loans obtained, higher for loans given out
Sample divided into two groups based on whether household wealth was
above or below the median within each caste group in survey period
Compare terms by within-jati wealth group also for bank and money lender
loans
Results:
1. Caste loans: Wealthy pay lower interest rates, lend at higher rates
2. Bank loans: Wealthy pay same rates as less wealthy
3. Money lender loans: Wealthy pay higher rates (monopsonistic
discrimination?)
Table 5: Interest Rates by Source and Household Wealth
Loan source:Wealth category: High Low High Low High Low
(1) (2) (3) (4) (5) (6)
Borrowing 10.08 11.98 12.09 12.08 28.78 14.22(0.83) (0.69) (0.25) (0.26) (2.04) (0.66)
Lending 10.83 9.20 -- -- -- --(1.82) (2.16)
Note: the household is the unit of observation.Interest rates are computed by pooling loans in 1982 and 1999.Standard errors are in parentheses.The cut-off separating low and high wealth is the median wealth level within the jati in each year.
Caste Bank Moneylender
Loan Access and Network Exit (Mobility)
Do those households in which immediate relatives have married out or migrated (men) receive
less caste loans?
Exit causes access to drop
Low access lowers cost of exiting
Out-marriage household: any immediate relative of the head (sibling, child) married someone
from another jati prior to the survey
Out-migrant household: any brother or son of the head left the village prior to the survey
Results:
Out-marriage household 30% less likely to receive a caste loan
Out-migrant household 20% less likely to receive a caste loan
Does exit cause loss of network services? Indirect tests
Table 6: Out-Marriage, Out-Migration, and Access to Network Loans
Reported statistic:Network exit: No Yes
(1) (2)
Measures of exit:
Married outside jati 6.17 4.76(0.25) (0.66)
Migrated outside village 6.30 5.27(0.28) (0.43)
Standard errors in parentheses.
Percent households receiving a caste loan
Specification and Estimation: Model implies that both own and jati-level wealth affect the
household’s equilibrium loan position (net lender):
it it t i itb = "w + $W + f + , "<0, $>0
itwhere b = household i’s loan position at t
itw = own wealth at t
tW = average wealth in the jati at t
if = household fixed effect
We estimate using two surveys (1982 and 1999) aggregating households at the dynasty level:
it it t it)b = ")w + $)W + ),
it it it itNote: cov()w ,), ) � 0 cov()W ,), ) � 0
So, use instruments to predict wealth change:
1. Land (acreage) inherited by the household prior to 1982
2. Initial (1971) conditions characterizing state of village HYV availability and use
Table 6: Descriptive Statistics, Panel Sample
Year: 1982 1999(1) (2)
Panel A: Loan Value by Source
Caste loans-in minus loans-out 44.21 41.34(31.55) (13.83)
Caste loans 71.42 81.72(11.43) (10.78)
Bank loans 393.96 235.39(89.54) (35.03)
Moneylender loans 47.77 46.13(7.61) (10.42)
Panel B: Marriage and Migration
Out marriage 0.07 0.09(0.01) (0.01)
Migration 0.10 0.06(0.01) (0.01)
Panel C: Wealth and Access to Banks
Household wealth 4831.91 20311.48(163.98) (1408.72)
Jati wealth 4609.11 20103.21(81.18) (1182.78)
Bank in village 0.19 0.36
Standard errors in parentheses. Statistics are computed using households in the 1982-1999 panel.Statistics computed using jatis with at least 10 sample households.
Table A: First-Stage Estimates
Dependent variable: HH wealth change
Jati wealth change
HH wealth change
Jati wealth change
(1) (2) (3) (4)
Inherited land 13.84 0.02(2.56) (1.47)
Inherited land (jati avg.) 47.98 77.81(15.56) (25.09)
Inherited unirrigated land -- -- 14.66 -0.44(4.20) (1.77)
Inherited irrigated land -- -- 13.63 3.61(6.13) (6.61)
Inherited unirrigated land (jati) -- -- 26.27 55.32(9.91) (19.13)
Inherited irrigated land (jati) -- -- 87.04 117.48(14.92) (45.97)
HYV in the village in 1971 x 10 1.66 -1.85 1.09 -2.78(2.80) (1.73) (2.61) (1.81)
HYV in the village in 1971 x 10 18.36 29.96 14.74 26.35(7.44) (11.92) (5.92) (10.77)
IAADP district x 103 5.72 11.56 3.42 8.92(3.84) (4.89) (3.30) (4.22)
Village bank in 1971 x 103 -0.33 -2.91 -0.65 -3.33(2.71) (2.98) (3.29) (2.89)
Table A: First-Stage Estimates
Bank change (1982-1999) x 103 -0.27 -5.00 -1.49 -6.20(3.79) (4.20) (3.56) (4.69)
F statistic 7.79 3.24 32.97 3.68
p-value 0.0008 0.0328 0.0000 0.0146
R-squared 0.087 0.198 0.100 0.219
Number of observations 2094 2094 2,094 2,094
Standard errors in parentheses are robust to clustering at the state level.Dependent variables are computed as the change between 1982 and 1999.All variables in the regression are excluded from the second stage except bank change (1982-99).Regressions restricted to jatis with at least 10 households in sample and households with heads at least age 35 in 1982
Table 7: FE-IV Loan Estimates
Loan source: Bank Moneylender(2) (3) (4)
Household wealth -0.009 -0.060 0.004(0.004) (0.031) (0.004)
Jati wealth 0.007 0.020 -0.006(0.003) (0.026) (0.004)
Bank in village 141.654 453.976 31.762(68.575) (340.690) (96.710)
Constant -73.021 219.995 12.409(75.098) (124.592) (68.736)
F-statistic (over-id) 1.46 1.39 2.14p-value 0.26 0.28 0.11
N 2,094 2,094 2,094
Standard errors in parentheses are robust to clustering at the state level.
Caste
Table 8: FE-IV Out-Marriage and Out-Migration Estimates
Instrument set:Specification:Dependent variable: Out marriage Migration Out marriage Migration Out marriage Migration
(1) (2) (3) (4) (5) (6)
Own wealth x 10-6 0.62 1.24 0.63 1.41 1.84 5.06(0.34) (0.55) (0.31) (0.57) (1.22) (2.32)
Own wealth* -- -- -- -- -1.24 -3.70Above-mean x 10-6 (0.95) (1.86)Jati wealth x 10-6 -0.93 -0.64 -0.88 -0.91 -1.19 -1.68
(0.36) (0.48) (0.34) (0.45) (0.61) (0.97)Bank dummy x 10-2 -0.70 -0.25 -0.69 -0.35 -0.72 0.15
(0.85) (1.71) (0.84) (1.73) (0.85) (1.63)Constant x 10-2 2.94 3.03 2.81 3.14 2.94 2.87
(0.83) (2.14) (0.86) (2.10) (0.71) (2.02)F-statistic (over-id) 0.56 0.25 2.23 1.07 0.93 0.79p-value 0.76 0.95 0.09 0.44 0.54 0.69
N 896 925 896 925 896 925
Standard errors in parentheses are robust to clustering at the state level.Regression use 1982 and 1999 data and are run using differenced variables.Instruments include inherited land, initial HYV adoption in the village in 1971, bank in 1971.
RestrictedSymmetric wealth effects Asymmetric wealth effects
Full
Implications of the Point Estimates
1. What is the effect of average wealth on mobility? " + $
Fourfold increase in wealth 1982-99:
Out-marriage: -0.4 percentage points
Out-migration: +0.75 percentage points
2. What is the effect of a within-jati wealth increase (hh)? "
Median standard deviation of within-jati wealth in 1999: 13,318 rupees
Out-marriage: 0.8 percentage points (9 percent)
Out-migration: 1.9 percentage points (32 percent)
3. What is the effect of a mean-preserving increase in within-jati inequality?
Transfer 13,318 rupees from below- to above-mean wealth households
Need responses to differ by own wealth: re-estimate
Out-marriage: -1.7 percentage points (19 percent decline)
Out-migration: -5.0 percentage points (84 percent decline)
4. Seeds of self destruction? Will selective exit, which reduces average jati wealth, lead to
more exit? $
Discard top 10% of households in jati with average wealth at the mean of all jatis
(20,445 Rupees) - reduction in average jati wealth by 8,852 Rupees
Out-marriage: 0.8 percentage points
Out-migration: 0.8 percentage points