Incomplete Credit Markets and
Commodity Marketing Behavior
Emma Stephens (Pitzer College)
and
Chris Barrett (Cornell University)
May 1, 2009 seminar
Lafayette College
Motivation
The “Sell Low, Buy High” Puzzle
• In developing countries, sharp seasonal
grain price fluctuations are common.
• Few farmers take advantage of the
resulting arbitrage opportunity.
• Indeed, many agricultural households
do the opposite: “sell low and buy high”.
… Why?
Hypothesis
Hypothesis:
Puzzling commodity marketing patterns
arise due to a ‘displaced distortion’ from
missing credit markets (Barrett 2007).
Liquidity-constrained farmers use
commodity markets as if they were a
source of seasonal credit. The resulting
terms of trade losses are akin to an
interest rate on a seasonal loan.
Hypothesis
Competing explanations:
Need to rule out alternative explanations:
– Impatience /inferior returns to grain storage
• Unlikely. Bank deposit rate 5%, seasonal maize
price increase 44%.
– High cost of storage and social taxation
• Unlikely. Inexpensive storage technologies
widely used commonly limit losses to 1-2% …
improving storage would yield far higher returns
than “sell low, buy high”.
– Price risk
• But price risk aversion should generally lead to
precautionary storage as hedging behavior.
Conceptual model
Conceptual behavioral model:
Representative agricultural household both
produces and consumes basic grain.
Chooses consumption, production and storage
so as to maximize intertemporal utility, defined
over seasons, with
(i) time-varying grain prices,
(ii) transactions costs to market participation,
(iii) possible liquidity constraints
(plus the usual time and budget constraints).
Conceptual model
Seasonal Household Market Participation
In the absence of binding liquidity constraints:
- Grain supply and demand functions in each season are a
function of current and expected future prices.
- Transactions costs generate a price band and householdspecific shadow price … some households buy, others sell,
others autarkic.
- Given exogenous seasonality in prices, demand increases
(decreases) in the post-harvest (hungry) period due to low
(high) prices.
- With constant transactions costs, if household participates
in market, it should be canonical arbitrage: buy low (postharvest period) and/or sell high (lean period), smoothing
consumption and maximizing intertemporal welfare.
Conceptual model
Seasonal Household Market Participation
With binding liquidity constraints post-harvest:
- Post-harvest grain demand falls.
- Yet can no longer smooth consumption across seasons …
kinked Euler function means household optimally stocks
out in post-harvest. Consumption no longer a function of
expected future prices, just current. More likely to sell,
even with low prices.
- Having stocked out post-harvest, have to buy in lean
season to survive, in spite of higher prices.
- Result: sell low, buy high behavior. When liquidity
constraints bind, expected future prices and income no
longer condition current choices. Arbitrage disrupted even
though households recognize price seasonality.
Conceptual model
Behavioral predictions:
Standard:
- Sales (purchases) increase (decrease) with
market prices.
- Sales are increasing in productive assets (land,
education)
Novel:
- Greater income and credit access reduces
(increases) the probability of harvest period
grain sales (purchases) and of lean season
purchases (sales).
Estimation strategy
Challenges:
- Transactions costs and shadow prices unobserved.
- Market participation behaviors correlated within
and across seasons for a given household. Need
systems estimator allowing for different parameters
for season and market participation regime.
- Transaction volume decisions not independent of
(discrete) household market participation choice.
Potential for sample selection bias.
- Transaction volume censored.
Estimation strategy
Approach:
Use Yen’s (2005 AJAE) multivariate sample
selection model (MSSM), a switching estimator
for censored demand systems:
- system of 4 (binary) market participation and 4
(censored) marketed quantity equations: one
each for harvest/lean season purchase/sale
Marketed quantity
Discrete market participation
Log(qs,ni) = xsn,i’βsn+υsn,i
if zsn,i’αsn+μsn,i > 0
=0
if zsn,i’αsn+μsn,i ≤ 0
where n = season, n = sale or purchase, μ, υ ~ MVN(0, ∑)
with ∑ the 8x8 covariance matrix
Estimation strategy
Key independent variables:
Operationalize liquidity constraints using:
- Credit access … instrumented due to endogeneity
using a probit identified using distance measures
- Off-farm cash income
Identification:
We identify the discrete participation (selection)
equations using fixed transactions costs
Data
Survey data
2005 survey by Tegemeo Institute
N=1682 households in western Kenya
Choice-based sample (corrections made)
Monthly purchase and sale volumes and prices,
July 2004-June 2005. We discretize this into
two seasons: harvest (July-January) and lean
(February-June).
Credit use and standard household data.
Data
Maize is staple crop.
Rudimentary,
rainfed cultivation
of, on average, only
2.3 acres.
Simple at-home storage, average value KSh859
(~US$12). Yet 87% report no maize storage losses
and mean loss for rest is <8%. 80% of households
had no stored maize at the time of survey.
Data
Buy low/sell high is most common pattern (49.7%) for
those with seasonal net sales … average loss: 29.3%.
Canonical intertemporal grain arbitrage rare (2% )
Marketing Regime
(Harvest-Lean)
Net Buyer-Net Buyer
Autarkic-Net Buyer
Net Seller-Net Buyer
Autarkic-Autarkic
Net Seller-Autarkic
Autarkic-Net Seller
Net Seller-Net Seller
Net Buyer-Net Seller
Net Buyer-Autarkic
Frequency
550
327
300
165
114
79
73
38
36
% of sample
(non-weighted)
33
19
18
10
7
5
4
2
2
N=1682
100
Data
Liquidity
Binary indicator of credit obtained, whether for nonagricultural (typically consumption) purposes or for
agricultural inputs. Access highly limited, only 28.5% get it.
Credit use is a highly imperfect proxy, but the only one
available in these data. Supplement with off-farm cash
earnings (salary and self-employment).
Instrument for credit access using distance measures to local
markets and services. Simple first-stage probit to predict
credit access. Standard results: credit increases with income,
education, longevity in village, etc., decreases with distance.
Results
With respect to the liquidity variables of interest, effects
generally consistent with our core hypothesis.
- Credit use associated with reduced likelihood of harvest
season sales and lean season purchases and with increased
likelihood of hungry season purchases.
- Credit use and off-farm income associated with increased
purchase volumes (conditional on purchasing) in both
seasons.
-Magnitude of credit/off-farm effects generally similar.
- Only odd result: off-farm income associated with reduced
likelihood of harvest season purchases.
Results
Corrected standard errors in parentheses.
Results
With respect to conventional explanatory variables, effects
are as one would predict:
- Purchase volumes decreasing in price, sales volumes
increasing in price.
- Probability of maize sales (purchases) increasing
(decreasing) in land owned and sales volumes increasing in
land holdings.
- Value of storage facilities has no statistically significant
effects on marketing patterns.
Results
Cross-equation correlations also intuitive:
- Correlation of entry and purchase decisions for same
season and marketing position – analogous to an inverse
Mills ratio – are strongly positive. Households in the
market transact more than a randomly selected household.
- Maize purchase volumes strongly negatively correlated
with likelihood of sales in either season.
- Strongly positive interseasonal correlation in both sales
and purchases volumes. No significant correlations
between purchase and sales volumes.
Conclusions
- Yen’s MSSM works well in tackling this complex
market participation behavior estimation problem.
- Liquidity constraints and seasonal “quasiborrowing” seem the bestexplanation of the “sell
low, buy high” puzzle. Inferior returns, high storage
costs and price risk seem implausible causes.
- Financial market failures spill over into
commodity markets by inducing stock-outs and
breaking of standard intertemporal arbitrage
conditions. Damages small farmers’ ability to
accumulate agricultural profits and invest.
Thank you for your time,
interest and support!
21
Harvest Season Market Participation
35
Purchases region (p*>mp+t.c.)
30
qt ( p*t , E t p*t 1 )  latent supply
Market Price (Ksh/kg)
25
20
mp+t.c.
 region
Autarkic
shadow
prices, p*
market
price
(mp)
mp-t.c.
15
10
c ( pt ,Yt ( ))
=liquidity constrained demand
c( p*t , E t p*t 1 ,Yt , E tYt 1 )  latent demand
5

Sales region
(p*<mp-t.c.)

0
5
10
15
20
Quantity (kg)
25
30
35
Lean Season Market Participation
35
q (p )
L L
=liquidity constrained supply
Purchases region (p*>mp+t.c.)
30

qt ( p*t , E t p*t 1 )  latent supply
Market Price (Ksh/kg)
25
20
mp+t.c.
 region
Autarkic
shadow
prices, p*
market
price
(mp)
mp-t.c.
15
10
Sales region (p*<mp-t.c.)
5
c( p*t , E t p*t 1 ,Yt , E tYt 1 )  latent demand

0
5
10
15
20
Quantity (kg)
25
30
35