Local and Regional Procurement
of Food Aid:
Preliminary Findings from 2010-11
US Programs
Christopher B. Barrett and Erin C. Lentz, Cornell University
LRP Learning Alliance
Local And Regional Procurement Learning and Knowledge Workshop
Sponsored by TOPS, funded by USAID Food for Peace
Washington DC, Nov 14, 2011
Introduction
The LRP Learning Alliance
A group of PVOs working together and with Cornell University
to establish a common, rigorous framework for M&E of local
and regional procurement (LRP) of food aid under the USDA
LRP pilot program and the USAID Emergency Food Security
Program. Materials on Learning Alliance web site at
https://sites.google.com/site/lrplearningalliance/home.
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The Global Framework
Motivation for Global Framework
Motivation for Framework
 Integrate reporting requirements of USDA and USAID
 Gather data needed to generate rigorous evaluation of LRP
performance along multiple dimensions: timeliness, cost
effectiveness, price/price volatility impacts, recipient
satisfaction, smallholder supplier impacts
 Enable direct comparison across LRP project modalities and
regions and with other forms of food aid (e.g., traditional
transoceanic food aid) to inform policy deliberations
 Common database to manage data across projects
 Foster improved PVO market monitoring and analysis
The Global Framework
Data Collection and Analysis Tools
 Constructed eight forms to collect data systematically
 Data collected to analyze evaluation topics
 USDA required - Historic supply, demand and price movements;
do no harm; reasonable market rate; timeliness; product quality
and safety; cost; government interference
 Additional topics - Producer price stimulus; supplier behavioral
change; volumes; and food production shocks
 Trained PVO personnel on price data collection methods
and basic price analysis techniques. Materials available on
Learning Alliance web site.
 Can help establish when/where/whether LRP makes sense
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and what to monitor and key impacts on which to focus.
Impacts:
Preliminary Findings
Preliminary Findings on LRP Impacts
Evaluation Focus Areas
• Timeliness
• Costliness
• Recipient Satisfaction
• Impacts on Smallholder Suppliers
• Impacts on Price Levels
• Impacts on Price Volatility
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Impacts:
Preliminary Findings
Preliminary Findings on LRP Impacts
To date, we have sufficient data to do analyses on some
dimensions for nine different programs:
1) Bangladesh (USDA LRPPP cereal bars from chickpeas, peanuts,
puffed rice, sesame seeds, etc.)
2) Burkina Faso (USDA LRPPP cowpeas, millet, veg oil)
3) Guatemala (USDA LRPPP beans, CSB, white maize)
4) Kenya (USDA LRPPP maize, beans, CSB, vegetable oil, salt
5) Kyrgyzstan (USAID EFSP cash transfer)
6) Mali (USDA LRPPP cowpeas, millet, rice)
7) Niger (USDA LRPPP cowpeas, maize, millet and vouchers for salt and
veg oil)
8) Uganda (USDA LRPPP vouchers)
9) Zambia (USDA LRPPP beans, CSB, maize, meal, veg oil)
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Impacts:
Preliminary Findings
Timeliness of Deliveries
Method
 Compare event histories of LRP and transoceanic (USAID or
USDA) deliveries to the same country up to 6 months before
or after an LRP purchase.
 Compare the time it takes from initiating procurement (IFB,
tender release, etc.) until delivery to terminal warehouses for
shipments from US vs. LRP.
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Impacts:
Preliminary Findings
Timeliness of Deliveries (weeks)
Burkina Faso (n=5/63)
Zambia (n=6/2)
Niger (n=10/12)
Kyrgyzstan (n=1/7)
Mean difference:
13.4 weeks (59%)
Mali (n= 23/2)
Kenya (n=23/25)
Uganda (n=1/13)
Guatemala (n=7/5)
Bangladesh (n=24/9)
0
5
LRP
10
15
20
25
30
35
40
Transoceanic
Huge, statistically significant gains in timeliness
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Impacts:
Preliminary Findings
Delivered Commodity Cost
Method
 Same comparison group as timeliness: LRP and
transoceanic (USAID or USDA) deliveries to the same
country up to 6 months before or after an LRP purchase.
Now we match by commodity.
 Compare the cost of commodity, ocean freight and ITSH of
LRP and transoceanic USAID or USDA shipments.
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Impacts:
Preliminary Findings
Total Costs (Commodity + Ocean Freight + ITSH)
Uganda
Commodity
(LRP / TO)
Chickpea / Lentils
Chickpea / Split Pea
Veg Oil / Veg Oil
Millet / Bulgur
Cowpeas / Lentils
Beans / Beans
Incaparina / CSB
Beans / Yellow Peas
CSB / CSB
Veg Oil / Veg Oil
Cash* / Wheat Flour
Cowpeas / Split Peas
Millet / Bulgur
Millet / Bulgur
Millet / Sorghum
Beans** / Yellow Peas
LRP
Transoceanic
$601.81
$781.26
$601.81
$1,031.52
$2,065.01
$1,941.88
$334.83
$837.48
$566.07
$1,060.90
$1,085.20
$999.30
$1,919.03
$867.23
$711.94
$976.51
$800.01
$985.02
$1,689.00
$1,727.83
$470.04
$1,081.27
$543.45
$970.36
$300.59
$714.31
$399.02
$867.36
$399.02
$715.76
$662.95
$853.94
Zambia
Maize flour**/ Cornmeal
Beans / Beans
$457.52
$1,148.00
Country
Bangladesh
Burkina Faso
Guatemala
Kenya
Kyrgyzstan
Mali
Niger
$934.26
$1,158.54
*Cash converted to wheat at prevailing market price in distribution zones
**Vouchers converted to beans and maize at prevailing market price in distribution zones
Total
Savings
23%
42%**
-6%
60%***
47%***
-9%
-121%***
27%**
19%***
2%
57%***
44%
58%
54%***
44%
22%
51%***
1%
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Impacts:
Preliminary Findings
Delivered Commodity Cost
For processed products and beans, often little or no cost
savings from LRP.
Slight variations by region (e.g., veg oil in Kenya and Burkina,
CSB in Kenya and Guatemala)
But for cereals and some pulses very large (and stat. sig.
savings).
Pulses: Simple average savings = 34% over comparable
commodities shipped from US to same (or neighboring) country
during same half year.
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Cereals: Simple average savings = 54%
Impacts:
Preliminary Findings
Recipient Satisfaction
 In Burkina Faso, Guatemala and Zambia, in addition to the LRP
program, there existed a nearby MYAP region delivering similar
products during the same period.
 We ran household surveys to assess recipients’ satisfaction
with food aid commodities received along various dimensions
and costs of meal preparation.
 Rated preferences on specific attributes of the commodities they
received on a scale of 1 (low) to 5 (high)
 Stated preparation needs from 1 (much more) to 5 (much less)
 Comparing LRP recipients vs. MYAP recipients gives us
insights on preferences and perceptions of LRP recipients
relative to those getting US-sourced commodities (i.e., MYAP).
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Impacts:
Preliminary Findings
Recipient Satisfaction
Estimated multivariate ordered logit models to control for
potentially confounding factors. Results very similar to
straight bivariate comparisons of LRP vs. MYAP.
Core results:
- Almost all food aid recipients satisfied with products on
all dimensions.
- But LRP recipients consistently most satisfied. This
holds across countries and commodities.
- But recipients’ preparation costs of LRP commodities
often higher ... partly due to commodity differences?
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Impacts:
Preliminary Findings
Recipient Satisfaction
Sample multivariate ordered logit regression results
Beans
Guatemala
Maize/Rice
Incaparina/
CSB
Burkina Faso
Grain
Legume
(millet /
(cowpeas /
bulgur wheat)
lentils)
N/A
N/A
N/A
N/A
LEAP
LEAP**
LEAP
LEAP***
LEAP*
LEAP***
LEAP**
LEAP***
MYAP**
LEAP
LEAP***
LEAP
MYAP***
MYAP***
Quality
LRP**
LRP**
LRP***
Quantity
LRP
MYAP
LRP*
Ration Size
N/A
N/A
N/A
Taste
LRP***
LRP
LRP**
Texture
LRP**
LRP***
LRP*
Appearance LRP**
LRP***
LRP*
Cleanliness
N/A
N/A
N/A
Storability
LRP***
LRP***
LRP***
Nutritional
LRP**
MYAP*
LRP
content
General
LRP**
LRP
LRP
LEAP
Satisfaction
***, **, * Statistically significant at 1%,5% and 10% levels, respectively
LEAP***
Zambia
ZLRP/CFAARM rations
Rations / Market
(vs. market options)
Options
ZLRP**
N/A
N/A
ZLRP**
ZLRP***
ZLRP***
ZLRP*
MYAP
ZLRP**
ZLRP**
Note: Zambia method measures relative to equiv. commodity
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available in local markets. Others use absolute measures.
Impacts:
Preliminary Findings
Recipient Satisfaction
Sample multivariate ordered logit regression results
Guatemala
Beans
Time
Effort
Cost
Fuel Use
Water Use
Cooking Oil Use
LRP***
LRP**
LRP
LRP***
LRP***
MYAP
Burkina Faso
Maize/
Rice
Incaparina
/CSB
LRP*
LRP
MYAP
LRP
MYAP
N/A
MYAP*
LRP
MYAP
MYAP
LRP
N/A
Grain
(millet /
bulgur wheat)
MYAP***
MYAP***
MYAP*
MYAP***
MYAP***
LEAP*
Legume
(cowpeas /
lentils)
MYAP***
MYAP***
MYAP
MYAP***
MYAP
MYAP
Zambia
ZLRP/CFAARM
rations
(vs. market
options)
MYAP*
MYAP
MYAP
MYAP*
MYAP***
ZLRP***
Note: Zambia method measures relative to equiv. commodity
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available in local markets. Others use absolute measures.
Impacts:
Preliminary Findings
Impacts on Smallholder Suppliers
 In Burkina Faso, used same matched MYAP/LRP zone
technique to survey smallholder cowpea producers, comparing
those supplying the LRP with otherwise identical ones in MYAP
zone selling just into regular market system.
 Assess impacts relative to control group (LRP cowpea suppliers
vs. cowpea farmers in MYAP region)
 Behavioral impacts – investments, improved storage, management
practices (e.g. use of improved seed), use of credit
 Profitability impacts - self-reported improvements in profitability,
farmgate price, transaction costs, time and distance travelled
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Impacts:
Preliminary Findings
Impacts on Smallholder Suppliers
Relative to previous year (intended and/or actual) participants:
• had a better understanding of quality standards for cowpeas.
• decreased travel time and distance traveled selling cowpeas by
(stat. sig.) average margins of 52% and 91%, respectively.
• received 49% higher cowpea prices and 41% higher revenue,
on average.
• enjoyed greater profitability in cowpea sales
• no more likely to use improved farm management practices
• direct LRP suppliers adopted improved storage practices (such
as storing cowpeas in double- or triple-lined bags) due to their
involvement in the program.
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Impacts:
Preliminary Findings
Impacts on Food Price Levels
 Does LRP drive up food prices for farmers and/or consumers?
 Developed a statistical model to estimate the effect of LRP on
food prices in local markets, controlling for a range of other
factors that influence prices: inflation, climate (temp/precip)
shocks, transport costs, seasonality, world market prices, WFP
LRP activities in subject and neighboring countries, etc.
 Not strictly causal estimates due to potential for omitted
relevant variables (e.g., government policies). But the best
feasible means of estimate LRP’s effects on market prices.
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Impacts:
Preliminary Findings
Impacts on Food Price Levels
Estimated % Change Price Level
Regression estimates of LRP’s market price impacts
15.0
10.0
Contemporaneous Price Impacts
BFaso
Kenya
Guate
Niger
Kyrgyz
Uganda
Zambia
5.0
0.0
-5.0
-10.0
-13.90
-15.0
Beans
-20.0
Roller
Meal
Mz flour
B'fast
Meal
-13.51
-9.34
-16.04
Red indicates statistically significant coefficient estimates
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Impacts:
Preliminary Findings
Impacts on Food Price Levels
• For most commodities and countries, there is no economically
or statistically significant impact on prices.
• For the few statistically significant point estimates, the values
are negative and implausibly large, suggesting unobserved
parallel (gov’t?).
• The possibility of significant induced price effects underscores
the importance of market monitoring.
• The relative infrequency of such effects suggests that LRP can
be undertaken effectively when well designed and monitored.
• Any price effects typically short lived; vanish within two months.
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Impacts:
Preliminary Findings
Impacts on Food Price Volatility
 Does LRP increase food price volatility in recipient country
markets?
 Used the same statistical model to estimate the effect of LRP
on food price volatility, measured as the standard deviation of
local market prices, controlling for same other factors.
 Again, not strictly causal estimates due to potential for omitted
relevant variables (e.g., government policies). But pretty good.
 Result: Only 1/18 point estimates statistically significantly
different from zero. 15/18 magnitude of point estimate 3% or
less. No apparent effect of LRP on food price volatility.
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Impacts:
Preliminary Findings
Summary of Preliminary Findings
• Local purchase offers big gains in timeliness, at least 59%
(13 weeks) quicker delivery than shipments from the US.
• In cereals and pulses, there are considerable cost savings
(~54% for cereals, 34% for pulses). Locally purchased
processed products (e.g., CSB, vegetable oil), however, are
often more expensive or near parity.
• Recipients routinely prefer locally purchased commodities
along any of multiple dimensions, although preparation time
and costs of LP commonly greater.
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Impacts:
Preliminary Findings
Summary of Preliminary Findings (2)
• In Burkina Faso, smallholder suppliers enjoyed high prices
and revenues and lower transactions costs.
• For most commodities/countries, we find no economically or
statistically significant impact on prices. But it can happen,
which underscores the importance of market monitoring.
• No market price volatility impacts of LRP.
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Conclusions
 Overall, US PVOs’ LRP programs appear to substantially
improve timeliness and reduce costs of food aid distribution,
while generating increased recipient satisfaction with rations
and some evidence of gains to smallholder suppliers, all
without any consistent evidence of causing harm via
significant price or price volatility effects.
 LRP not justified in all cases, but successful on most counts
so good reason to push for it to become a broader option.
 Couple an expanded LRP option with response analysis to
choose the right tool for the task and some sort of consortium
M&E platform to reduce costs of high quality of M&E.
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Thank you for
your time,
attention and
comments!
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