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Trade Costs and Household Specialization: Evidence
from Indian Farmers
Nicholas Li (University of Toronto)
Stanford IGC Nov. 14th 2014
Subsistence farmers
Uganda 2009 Timor 2000 India 1993 India 2009 Peru 1994 Guatemala 2000
Home share food expenditure 0.53 0.43 0.45 0.36 0.39 0.30
Home share food varieties 0.34 0.36 0.17 0.11 0.26 0.12
Agricultural VA per worker 221 508 473 600 1095 1945
1 Schulz (1953) “food problem”: low productivity, food a necessity
2 Dispersed production, comparative advantage from growing
conditions, high trade costs/frictions
Consumption and production decisions linked for farmers
Indian agriculture: quantities
1987-2010: Indian aggregate home share declines from 14% to 7%
(22% to 14% for food)
1987-88 2009-10
Farmer Ag.laborer Rural other Urban Farmer Ag.laborer Rural other Urban
Share of households 0.27 0.23 0.23 0.26 0.22 0.19 0.29 0.29
Real PCE 179 133 189 269 205 156 223 364
Land (ha) 2.6 0.39 0.57 0.24 1.7 0.24 0.27 0.12
Home share 0.29 0.06 0.07 0.02 0.2 0.04 0.04 0.01
Food share 0.67 0.67 0.65 0.62 0.56 0.57 0.55 0.5
Home food share 0.43 0.09 0.11 0.03 0.36 0.07 0.08 0.01
Indian agriculture: prices
Table : District average price paid vs. farm-gate price
1987-88 2009-10
Farmer Ag.laborer Rural other Urban Farmer Ag.laborer Rural other Urban
Rice 1.01 0.98 1.04 1.14 0.95 0.76 0.91 1.11
Wheat 1.02 1.03 1.05 1.12 0.96 0.87 0.96 1.09
Chickpea 1.1 1.16 1.16 1.2 1.14 1.18 1.18 1.21
Pigeon Pea 1.1 1.12 1.16 1.22 1.13 1.15 1.17 1.21
Potato 1.3 1.29 1.33 1.36 1.29 1.3 1.3 1.33
Onions 1.26 1.27 1.28 1.26 1.38 1.42 1.4 1.39
This project: Farmers and trade costs
1 Document household-level trade patterns, specialization, consumption
frictions for Indian farmers
2 Model where internal marketing frictions and trade costs affect
non-farmer vs. farmer choice and farmer specialization
3 Estimate drivers of decline in home share and farmer-level welfare
effects of trade
Literature and contributions
1 Trade: welfare gains proportional to trade share (Arkolakis, Costinot,
Rodriguez-Clare, 2012), Ricardian models applied to agriculture in India
(Donaldson, 2013), US (Costinot and Donaldson, 2014), Peru (Sotelo, 2014) →
individual farm households
2 Macro: misallocation across production units (Adamopolous and Restuccia, 2013),
sectoral models (Lagakos and Waugh (2013), Gollin and Rogerson (2014), Adam
et al. (2012), Restuccia, Yang and Zhu (2008))→ specialization and
trade/consumption frictions
3 Development: frictions generate non-separability of consumption/production
decisions (de Janvry and Sadoulet, 2003 review article) → big picture, welfare
Focus on India
Internal policy barriers to trade: state tariffs, district-level entry taxes,
licensing for traders, barriers to external trade (Atkin, 2013)
“Natural” barriers to trade: poor roads and low productivity in
distribution/retail (Lagakos, 2013) and/or markups (Atkin and
Donaldson, 2013)
Government intervention in marketing: Food Corporation of India and
Public Distribution System
India’s Public Distribution System
Central government (Food Corporation of India): procurement (15% of rice/wheat
output), transport, storage, allotted to states at “economic cost”
State governments: distribution through 478,000 Fair Price (“ration”) shops
1997 reform: “Targeted’ PDS offering 20KG at 50% economic cost to BPL
(below poverty line) households, subsidy value doubles 0.5% to 1% of GDP
Diversion and leakage: estimated 60% of grains do not reach intended BPL
households (identified with “ration card”)
Vast differences across states in implementation (Bihar vs. Tamil Nadu)
Outline
1 Stylized facts
2 Model
3 Estimation and welfare
Outline
1 Stylized facts
2 Model
3 Estimation and welfare
Data
NSS consumption and employment data (1987-2010)
Approx. 100,000 hh per survey round (10-12 per village)
Match districts over time district (about 300, median district 7500 sq.km, 116th ranked
MSA in the US)
Farmer=main household income is “self-employed in agriculture”
Household-level recall of 30-day consumption at item level (18 grains, 12 pulses, 53 fruits
and vegetables)
Record quantities and value and source (gives unit value 6= price)
Home-producible goods (77% of food exp.): value imputed at farm-gate price (exclusive
of distribution costs)
ICRISAT VDSA “meso” (district-level) data
Harvest prices, output and area sown for 11 major commodities (50% of food exp.)
Trade costs: road density, retail/wholesale/transport sector employment (NSS)
Production inputs, technology
Stylized facts
1 Home shares of farmers and districts: variable, declining over time
Dispersion/decline in food home shares
District mean farmer home shares
Stylized facts
1 Home shares of farmers and districts: variable, declining over time,
and correlated
2 Specialization: farmers and districts becoming more specialized
Farmer and district speciaization rising
Stylized facts
1 Home shares of farmers and districts: variable, declining over time,
and correlated
2 Margins of import shares and specialization: extensive margin
(variety) specialization and relative quantities important
3 Farm-gate vs. market prices and geographic dispersion: market/farm
wedge variable and declining, geographic market price dispersion not
declining
Within-district market/home wedge dispersion/decline
Across-district rural market prices
Stylized facts
1 Home shares of farmers and districts: variable, declining over time,
and correlated
2 Margins of import shares and specialization: extensive margin
(variety) specialization and relative quantities important
3 Farm-gate vs. market prices and geographic dispersion: market/farm
wedge variable and declining (big role for PDS), large relative to
geographic market price dispersion (not declining)
4 Home shares positively correlated with farmer size, negatively
correlated with trade costs
Farmer characteristics and home share
District trade costs and home share
Outline
1 Stylized facts
2 Model
3 Estimation and welfare
Ingredients
Lagakos and Waugh (2013): heterogeneous ability in farm vs. market
(urban) sector (within location)
Eaton and Kortum (2002): comparative advantage across “locations”
for farm goods
Conventional trade frictions between locations, marketing friction
within “location”
Highlights role of local “marketing costs” in allocation across sectors (farm
vs. non-farm) and specialization within farming
Preferences
Continuum of households with measure 1, Stone-Geary preferences over
agriculture (necessity) and market good (luxury)
U = (qa)α(qm + γ)1−α (1)
subject to Paqa + pmqm ≤ I .
Agricultural consumption is CES aggregate of varieties:
qa ≡(∫ 1
0qσ−1σ
i di
) σσ−1
(2)
with∫ 10 qaipaidi ≤ Paqa, CES price index Pa
Production
Fraction of households in agriculture denoted by F ; households choose between
agriculture or market production
Households have market productivity drawn from distribution S
Average and marginal market productivity given proportion of farmers denoted
S(F ) and S∗(F ); both are increasing functions of F .
Agricultural productivity for each variety (Ai ) is same for households in a location but
varies across locations.∫ 1
0yi/Aidi ≤ 1
Drawn from Frechet distribution with parameters T and θ (Pr(Ai < z) = e−Tz−θ )
Frictions
For simplicity assume N symmetric locations
Iceberg trade cost across locations for agriculture varieties: d
Iceberg “marketing cost” within location paid for non-home produced
agricultural varieties: τ
This gives agric. price index for agric. households:
Paa = T 1/θw
(1−θ + N(dτ)−θ
)−1/θ(3)
and for non-agric. households:
Pma = T 1/θw
(τ−θ + N(dτ)−θ
)−1/θ(4)
Non-farmer/farmer price wedge: W ≡ Pma
Paa> 1
Equilibrium
Welfare of marginal worker equalized between sectors Ua(F ) = Um(F ):(PaaT
1/θ
(∑
d−θi )−1/θ+ γ
)W α = (S∗(F ) + γ) (5)
m good market clears:
(1 − α)
(F
PaaT
1/θ
(∑
d−θi )−1/θ+ (1 − F )S(F )
)− γα = (1 − F )S(F ) (6)
Numeraire pm = 1, two equations in two unknowns: F and Paa .
Trade and home shares
Implications for home production shares and trade shares
Farmer’s home share in agriculture: λ = 11+N(dτ)−θ
Fraction farmer sells to non-farmer in own location: τ−θ
τ−θ+N(dτ)−θ
Fraction farmer sells to non-farmer in other location: (dτ)−θ
τ−θ+N(dτ)−θ
Fraction farmer sells to farmer in other location: (dτ)−θ
1+N(dτ)−θ
Location-level “local share” of agriculture
With heterogeneous farm productivity, standard trade result (“bigger”
farmers have higher home shares)
Role of trade costs
Equating marginal utilities:
(paT
1/θ
(∑
d−θi )−1/θ+ γ
)W α = (S∗(F ) + γ)
Consumption advantage of farmers W :
W ≡(τ−θ + N(dτ)−θ
1−θ + N(dτ)−θ
)−1/θ
> 1 (7)
W is increasing in internal trade cost τ and external trade cost d (provided τ > 1)
Decline in W leads to marginal household entering market sector
F and Pa decline, market/farmer income gap falls in nominal and real terms
Non-homotheticity (γ > 0) increases impact of changes in W but not essential
Reduction in τ could increase local trade share with no change in external trade
costs/price gaps (d)
Extensions
Elasticity of substitution < 1 between farm/market sector amplifies
effects of marketing/trade costs, neutral and agr. productivity (T )
determine size of farm sector
Farmer heterogeneity: income distribution effects, different abilities,
land
Three sectors? Rural farmer vs. rural non-farmer (small income/price
gaps) vs. urban (larger gaps)
Calibration and simulation
Calibration Data Baseline No mkt cost No trade cost No frictions
Farmer share 0.22-0.73 0.585 0.575 0.580 0.575
Non-farmer/farmer income 1.3-1.7 1.288 1.257 1.271 1.257
Food share farmer 0.56 0.567 0.559 0.563 0.559
Food share non-farmer 0.5 0.474 0.475 0.475 0.475
Price index for A 10.663 9.574 7.953 6.854
Non-farmer/farmer price 1-1.13 1.057 1 1.025 1
Home share farmers 0.2 0.200 0.143 0.093 0.062
Home food share farmers 0.35 0.352 0.256 0.165 0.111
Whole pop. Home share 0.07 0.104 0.074 0.048 0.032
Aggregate local food share 0.7 0.311 0.256 0.141 0.111
Baseline calibration uses θ = 2.5, N = 9, d = 1.5, τ = 1.2, γ = 45, α = 0.15, S distributed U[5, 25]
Outline
1 Stylized facts
2 Model
3 Estimation and welfare
Estimated effect of trade cost variables (1987-2010)
Farmer-level District-level
(1) (2) (3) (4)
Home share Home varieties Frac. farmers Home share
Roads -0.0345*** -0.0478*** -0.0145*** -0.0336
(0.0125) (0.0180) (0.00333) (0.0555)
Frac. PDS -0.215*** -0.00784 -0.0105** 0.233*
(0.0667) (0.0774) (0.00471) (0.124)
Irrigation 0.211* -0.135 -0.00941 0.533***
(0.117) (0.116) (0.0115) (0.193)
Fertilizer 0.000509 -0.000558 -1.27e-05 -0.000723
(0.000328) (0.000356) (2.77e-05) (0.000533)
Population 0.0309 0.00340 -0.00129 -0.450***
(0.0417) (0.0542) (0.00142) (0.0900)
District FE YES YES YES YES
Year FE YES YES YES YES
Observations 84,688 84,693 1,302 1,292
R-squared 0.223 0.273 0.975 0.848
Standard errors in parentheses (clustered by district), *** p<0.01, ** p<0.05, * p<0.1
Farmer-level regressions include quadratics in land, household size and real expenditure.
District-level regressions include fraction of farmers in 4 land size categories.
Gains from trade
Within agriculture sector Arkolakis, Costinot, Rodriguez-Clare
(2012) insights apply
Gains from trade: W = (λ′/λ)1/ε
Domestic (home production) shares of consumption: λ′(=1 under
autarky) and λ
Trade elasticity ε: -5 to -10 range in trade (Anderson and Van
Wincoop, 2003), but for individuals?
Estimating home share elasticity
ln farmerimportsidt = αd + γt + ε ln τdt + ΩXidt + uidt
Think of each farmer i trading with a single entity – “the market”
Farmers in a district face the same “marketization cost” τdt ,
measured using the market/home wedge index
Farmer productivity, trade costs with other districts, unobserved trade
costs might also affect farmer import share
Estimation Challenges
ln farmerimportsidt = αd + γt + ε ln τdt + ΩXidt + uidt
Measurement error in τdt
Omitted variables
Instruments for τdt :
1 Fraction of “market” rice and wheat sold through TPDS (=0 before
1992)
2 Road construction: national 4-lane highways 200km (1988) to
10,000km (today), rural road program
Controls: District and round fixed effects, quadratics in hh size, real
expenditure and land, district irrigation and fertilizer use, pop. density
Farmer trade elasticity
(1) (2) (3) (4)
OLS IV PDS IV Roads IV both
Major commodities
Elasticity -0.04 −2.62∗∗∗ −2.52∗∗ −2.44∗∗∗
(0.06) (0.75) (1.07) (0.73)
Obs. 112190 110869 103909 103908
First-stage F-stat 14.07 7.91 7.99
OverID test p-value 0.94
All food
Elasticity −0.26∗∗∗ −1.64∗∗ −3.75∗ −1.84∗∗
(0.09) (0.74) (2.10) (0.91)
Obs. 112739 112548 105593 105593
First-stage F-stat 11.05 4.7 6.01
OverID test p-value 0.16
Standard errors in parentheses (clustered by district), *** p<0.01, ** p<0.05, * p<0.1
Regressions include quadratics in land, household size and real expenditure, district and round FE.
Similar results using state by year FE.
Welfare gains for farmers
Major comm. All food
ACR(2012) ingredients
Elasticity 2.5 1.8
1993 Home share 0.53 0.54
2009 Home share 0.41 0.43
Welfare (food only)
Gains 2009 vs. autarky 0.30 0.37
Gains 2009 vs. 1993 0.10 0.11
Changes in trade costs 1993-2009
Observed % decline in τ 0.06 0.05
Removing TPDS (through τ) 0.04 0.04
∆ roads (through τ) 0.01 0.01
Implied % decline in τd 0.15 0.18
Policy Implications
Public Distribution System
Usually think about nutritional and poverty effects (Tarozzi (2005),
Dreze and Khera (2013))
PDS reforms in the 1990s had major effects on farmer specialization
and market/home price wedges
Partly counteract “distortion” that keeps households in farming and
producing for self instead of market
One the other hand, national-level farmer support prices are having the
opposite effect
Summary
1 Documented large decline in home production shares in India, interpreted through
the lens of trade and local “marketing” costs
2 Model relating home production and farmer/non-farmer occupation choice to
farmer/non-farmer wedge and usual trade forces
3 Despite minimal liberalization of domestic trade, substantial (10%) welfare gains
to Indian farmers through “trade channels” (PDS and local roads)
Empirical: spatial price differences and seasonality, detailed farmer production data
Theory: farmer vs. rural non-farmer vs. urban, adding farmer heterogeneity and land,
better calibration
Appendix: farmer trade costs (Gollin and Rogerson, 2014)
Appendix: VDSA harvest price vs. NSS “farm gate price”
Appendix: Rice prices 2009
Appendix: Wheat prices 2009
Appendix: PDS share 2009