International Association of Agricultural Economists Conference, Beijing, China, 16-22-August-2009

1. Grain Markets and Large Social Transfers:
An Analysis of Productive Safety Net Program in Ethiopia
Shahidur Rashid1
International Food Policy Research Institute
Alemayehu Seyoum Taffesse
International Food Policy Research Institute
Contributed Paper prepared for presentation at the International Association of Agricultural
Economists Conference, Beijing, China, August 16-22, 2009
Copyright 2009 by the authors. All rights reserved. Readers may make verbatim copies of this document
for non-commercial purposes by any means, provided that this copyright notice appears on all such
copies.
1
E-mail addresses are: S.Rashid@cgiar.org and A.Seyoumtaffesse@cgiar.org, respectively.
1

2. Grain Markets and Large Social Transfers:
An Analysis of Productive Safety Net Program in Ethiopia
1. Introduction
It is almost universally agreed that providing access to food to the poor through social
transfer programs is a valid policy intervention, irrespective of economic ideology, functioning
of markets, or even the level of development of a given country. However, there is a long
standing debate as to whether these transfers should be in‐kind or in cash (Sen, 1990; Coate,
1989; Basu 1996). Four main arguments are made in favor of cash transfers are that they: (i)
avoid potential disincentive effects of food aid on domestic markets, (ii) can provide more
choices to the beneficiaries and hence relatively improve their well‐being, (iii) are more cost‐
effective than food transfers, as they entail food handling costs, and (iv) can boost consumer
market demand, which in turn can contribute towards market development (World Bank 2005).
A critical underlying assumption behind all these arguments is that the markets are integrated
and well‐functioning so that food is available in local markets at moderate prices, an
assumption which may not hold in many developing countries. This is one of the reasons why
both emergency assistance and safety net programs continue to be food‐based. 2
However, apart from situations of extreme civil conflict or war, it is unlikely that market
locations in all parts of a country will be isolated from major central markets. This implies that
in most cases, it should be possible to implement a mix of food and cash based safety net or
emergency assistance programs. Cash transfer programs could be implemented in more
developed geographic locations, where transactions costs are low and cash injection is likely to
create demand for local products, yet not raise food prices excessively. Food transfer programs
could be implemented in more remote places where markets are thin (not integrated with
other markets), so as to avoid possible surges in food prices in local markets from cash transfers
that would adversely affect not only the households receiving social transfers, but also poor
non‐beneficiaries (Basu 1996). Food transfers may also be easier to implement in more remote
areas if these areas also lack implementation capacity (e.g., non‐functioning or non‐existence of
financial institutions). Thus, from operational and cost effectiveness viewpoints, an optimum
policy option might be to combine both cash and food.
Ethiopia’s Productive Safety Net Program (PSNP), launched in January 2005, is one example
of large scale social transfer program with a mix of cash and in‐kind transfers. Introduction of
the PSNP was a strategic move on the part of the Ethiopian government towards reducing food
aid dependence, boosting domestic production, and fostering market development. The
country’s food aid imports did in fact declined from 861 thousand tons in 2004‐05 to 377
thousand tons in 2005‐06 and 447 thousand tons in 2006‐2007. Meanwhile, official estimates
2
Food aid donors’ desire to support their own domestic farmers and shippers is another major reason
for preference for transfers‐in‐kind. See Barrett and Maxwell (2005).
2

3. of production of the four major cereals (teff, wheat, maize and sorghum) showed a 40 percent
increase from 8.3 million tons in 2004‐05 to 11.7 million tons in 2006‐07.3
The PSNP spans up to 262 woredas4 that had been regular recipients of food aid between
2002 and 2004. It operates as a safety net, targeting transfers to poor households in two ways,
through public works (PW) schemes and direct support (DS). Public works, the larger of the two
programs, pays selected beneficiaries 6 Birr per day, raised to 8 Birr per day in December, as
payments for their labor on labor‐intensive projects designed to build community assets.5
Direct support is provided to labor‐scarce households including those whose primary income
earners are elderly or disabled. This component thus aims to maintain the safety net for the
poorest households who cannot participate in public works.6
The main objective of this paper is to analyze whether PSNP is linked with this unusual
price trend. The analysis is based on a large data set collected by the Central Statistical Agency
(CSA) of Ethiopia, which contains Peasant Association (smallest administrative unit) level data
on prices, production, yields, and marketing of all major cereals. Two sets of analyses are
conducted, with first set focusing on targeting characteristics and overall price trends and the
other on the price relationships between PSNP and non‐PSNP areas using co‐integration
methods.
2. PSNP Transfers and Grain Market Linkages
The effects of PSNP transfers on the prices in any given woreda will depend on: 1) whether
or not the woreda’s cereal markets are integrated with the national or larger regional market;
and 2) whether the transfers are delivered in cash or in‐kind. If cereal markets are integrated
both before and after a cash or food transfer, the transfer effectively increases the supply (in
the case of a food transfer) and demand (in the case of both cash and food transfers) for the
entire integrated market. In this case, the price effect will generally be small, although
comparing PSNP to a counterfactual of no food transfers whatsoever, PSNP actually increases
national wheat supply by about 10 percent.
In addition to market integration, a critical assumption is whether or not market in the
PSNP regions is large enough to influence prices in the non‐PSNP regions.7 Given almost half of
the woreda are covered by the program, which includes woreda close to PSNP, it is realistic to
assume that PSNP can influence prices in the non‐PSNP regions. Based on these assumptions,
price effects of food or cash transfers are characterized in Table 1, which forms the conceptual
basis of most of the analyses carried out in this paper.
3
However, prices of major cereals rose rapidly between 2006 and 2008, despite consecutive years of good harvest,
suggesting that production increases may have been over‐estimated.
4
Ethiopia has about 500 woredas (an administrative unit below region and zone.
5
These fall between US$0.75‐US$0.85 reflecting exchange rate differences.
6
Further details on the nature and interim impact of the PSNP can be found in Gilligan et al. (2007, 2008).
7
This is similar to standard small / large country assumptions in trade literature
3

4.
Table 1: Price effects of food versus cash transfers
Forms of Market Integration Effects on prices and price dynamics
Transfers Status Direction of Price Change Changes in Price relatives
(Convergence /Divergence)
Markets are Prices in both markets No convergence or
Food Integrated decline proportionately divergence.
Markets are not Prices in A decline Leads to convergence if
Integrated Prices in B remain the same transfers do not trigger
if transfers do not trigger interregional trade
trade.
Markets are Prices in both markets No convergence or
Cash Integrated increase proportionately divergence
Markets are not 1. Prices in A increase only 3. Divergence if transfers do
Integrated if transfers do not trigger not trigger trade;
trade 4. Higher price differentials if
2. Prices in both A and B transfers trigger trade
increase
Source: Authors’ compilation
The more interesting cases are for PSNP woredas that are autarkic (non‐integrated). As is
shown in section 3 below, prices in PSNP woredas are in general above those in non‐PSNP
woredas. A cash transfer could raise cereal demand and local market prices enough so that
trade from non‐PSNP woredas to a PSNP woreda becomes profitable, thus potentially
integrating the two markets and increasing the price differential between PSNP and non‐PSNP
woredas. Alternatively, a food transfer could lower prices in a PSNP woreda, reducing the price
differential between PSNP and non‐PSNP woredas.
5. The basic analyses of PSNP
This section presents some basic statistical results on program characteristics and, to a
limited extent, operational performance of the PSNP. Specifically, we carry out some simple
statistical tests on remoteness and agricultural developments, examine welfare implications of
cash versus food transfers since the launching of the program, and provide descriptive and
simple statistical test results on price relationships between PSNP and non‐PSNP areas.
a. The program characteristics
The PSNP program implementation manual provides detail descriptions of the process by
which to determine the form of transfers in a given locality. The manual states that “….Food
Security Task Force (FSTF) makes a request for specific types of resources (cash and/or food as
the means of transfers to households), for each kebele considered chronically food insecure, to
the Regional Food Security Steering Committee. The Regional Food Security Steering
Committee will then reconcile these requests with resource availability from the federal
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5. allocation and allocate resources to each woreda. After approval by the Regional Council, the
overall request for resources will be sent to the Federal level as part of the Regional Safety Net
budget plan”. The key determining factors are (i) community preferences regarding food versus
cash, (ii) food availability at the community level, (iii) market access, and (iv) institutional
capacity of a given region.
To examine to what extent these criteria are met, we have carried out some simple mean
difference tests between non‐PSNP and PSNP areas in terms of agricultural development and
remoteness indicators. The results are presented in Table 1, which presents two sets of tests
results; one testing the equality of variance and the other testing the equality of mean.
b. Welfare implications for cash versus food transfers
When a social program combines both food and cash, a critical challenge is making sure
that the values of transfers remain the same for both types of beneficiaries. It becomes
particularly difficult in high inflation macroeconomic environment. When PSNP was launched,
low inflation was a hallmark of Ethiopian economy, which has dramatically changed since 2006.
The food component of the national consumer price index has increased from about eight
percent in 2003 to 19 percent in 2006, with an average annual increase of about 13 percent
(World Bank, 2007). Inflation continued at approximately 20 percent in 2007, but has since then
further accelerated, with total inflation approaching 100 percent for calendar year 2008. In
spite of this high inflation rate, however, the amount of the cash transfer (ETB 6 or US$0.70)
remained the same until December 2007, causing severe erosion of benefits to the households
receiving cash transfers.
Figure 1, constructed with PSNP woreda level data, illustrates this fact. It plots wheat
equivalent of cash transfers; nominal daily agriculture wage (represented by the right axis);
wheat equivalent of daily nominal wages; and food transfers (3kgs of wheat per day)
represented by the horizontal line.
c. The price relationships
As the previous section has demonstrated, the starting point for analyzing the linkage
between PSNP and non‐PSNP price relationships should be examining whether prices in the
PSNP regions are indeed larger than the prices in the non‐PSNP regions. Thus, we begin our
analysis by plotting averages of monthly prices of the cereals in PSNP and non‐PSNP areas in
three areas, (data for maize are shown in Figure 2).
As shown in Table 2, prices in PSNP areas are indeed higher than prices in non‐PSNP areas.
Statistical analysis of mean differences (not shown in Table 2) indicate that mean prices in PSNP
woredas are statistically significantly higher than mean prices in non‐PSNP woredas. Mean
prices in PSNP woredas with cash transfers only are statistically significantly higher than mean
prices in PSNP woredas with food transfers only.
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6. Notice that the convergence of prices between PSNP and non‐PSNP began in the 2002‐05
period and then continued in the 2005‐08 period. The convergence from 2002 to 2005 is most
likely attributable to improvement in road and communication networks. Indeed, available
data show that there was a large increase in public expenditure on roads since 2000.
To further examine these trends, we further examined the differences between prices
disaggregated by three sub‐periods. Tests of the mean‐differences in growth rates of prices in
PSNP versus non‐PSNP woredas showed that these differences were statistically significant
during the 2005‐08 period. Growth rates of prices of PSNP cash woredas were not statistically
significantly different from growth rates of prices in non‐PSNP woredas, however. The same
lack of a statistically significant difference was found in comparing growth rates for PSNP food
transfer and non‐PSNP woredas. The lack of a statistically significant difference may be due in
part to the small sample size of the cash and food transfer woredas.
6. Econometric analysis of price dynamics
a. A brief note on analytical method
The analyses in the previous section suggest that cereal prices between PSNP and non‐
PSNP areas are converging. However, this convergence can be driven by factors other than
launching of PSNP. To explore any possible linkage with PSNP, we carry our further tests within
Johansen’s (1988) and Johansen and Juselius’s (1990) co‐integration framework. In
implementing the method, all preliminary tests on time series properties and model
specifications are conducted before estimating the long run relationships among prices. This
includes tests for non‐stationarity, lag length determination, inclusion of deterministic
components into the cointegarion space, and misspecification tests on residuals. For the sake of
brevity, these results are not presented here, but are available upon request from
corresponding author. Once unit root tests confirm that all prices are I(1), Johansen’s trace
tests are performed to determine co‐integrating relationship between prices of three major
cereals (wheat, maize, and teff) in PSNP and non‐PSNP areas. After determining co‐integration
rank, normality and auto‐correlation tests are performed on the saved residuals.
Three specific sets of tests are conducted on co‐integrating relationships: (1) tests for price
convergence, (2) tests for Granger causality, and (3) the analyses of shocks using generalized
impulse response, proposed in Pesaran and Shin (1995). The intuition behind the test for
convergence follows from the very meaning of it—that is, a decline prices between PSNP and
non‐PSNP over time, suggesting non‐stationarity of Pt A − PtB . This restriction implies that,
β′= α
i ( A
)
, − α B , ∗ , ∗ … … … … … … (4)
where α , and − α are the long run coefficients of the prices in PSNP and non‐PSNP prices and
A B
the asterisks mean that the other coefficients are left unrestricted. Following Johansen and
Juselius (1992), the null hypothesis can be formulated as,
R ′β = 0, … … … … … … (5 )
6

7. Where R′= [1 1 0 0] . The hypothesis is tested using a Likelihood Ratio test, in which
eigenvalues of the full model are compared with the eigenvalues of the restricted model.
The Granger causality test follows the method proposed by Hall and Milne (). The test
relies on imposing zero restrictions on the loading coefficients to the long run cointegarion
relationship. The intuition behind the test can be illustrated by the following equation:
⎡ΔPtA ⎤ ⎡μA ⎤ k−1 ⎡Γi,AA ⎡ A ⎤ ⎡αA ⎤ ⎡PA ⎤
Γi,AB⎤ ΔP − i
⎢ t ⎥ ⎡A B ⎤ ⎢ t − k ⎥ ⎡εAt ⎤
⎢ B⎥ = ⎢ ⎥+ ∑ ⎢ ⎥⎢ +⎢ ⎥ β
⎥ ⎢ B⎥ ⎢
β
⎥ ⎢ B ⎥ ⎢εBt ⎥
+ ……………… (6)
⎢ΔPt ⎥ ⎢μB ⎥ i=1 ⎣Γi,BA
⎣ ⎦ ⎣ ⎦ Γi,BB
⎦ ⎢ΔPB ⎥ ⎣α ⎦ ⎣ ⎦
⎢Pt − k ⎥ ⎣ ⎦
⎣ t − i⎦ ⎣ ⎦
Where A and B represent PSNP and Non‐PSNP areas, Γ are 2×2 matrices of coefficients;
Δ = (I−L) and L is the lag operator; k is lag length; μ is a vector of constants, α' s and β' s are
loading and long run coefficients, respectively. There are three possible cases of causality
testing: ii) αA = 0, αB ≠ 0; ii) αA ≠ 0, αB = 0; and iii) αA ≠ 0, αB ≠ 0, where the first two cases imply
unidirectional causality and the third case suggest feedback between PtA and PtB . To see how
causality implications are drawn, suppose that α A = 0 . This implies that the error correction
term, i.e., the third term on the RHS, is eliminated and the long run solution to P A t will be
unaffected by the deviations from the equilibrium path defined by the co‐integrating vector.
b. Econometric Results
Co‐integration results are presented in Table 3. Following Johansen (1992), three different
models are considered. The first model restricts all deterministic components to a constant in
the cointegration relation; the second model allows a constant plus a deterministic trend in
level; and the third model accounts for a constant in the cointegrating relation, a trend in level,
and a trend in cointegrating relations. Note that for r=0 the null is clearly rejected for all three
models and for all three commodities. The first time the null hypothesis is accepted at 5% level
of significance is when r=1 under the first model for all three commodities. Thus, based upon
these results, we conclude that the model that restricts all deterministic components into a
constant is the appropriate model; all pairs of PSNP and non‐PSNP prices are have unique
cointegrating vector.
Given cointegrating relationships, tests for convergence of prices are carried out using
equations (4) and (5). The Likelihood Ratio Test statistics, which follows χ2(1), are calculated as
10.42 for wheat, 16.15 for maize, and 15.57 for teff. This implies that the differences between
the pairs of prices are non‐stationary. This further strengthens the earlier of price convergence
between PSNP and non‐PSNP regions.
The long run Granger causality test results, presented in Table 4, suggest that both in
absolute numerical terms and in terms of statistical significance, the main direction of long run
causality of prices flows into the non‐PSNP. So this result strongly argues that PSNP prices
7

8. Granger cause the non‐PSNP prices. However, in interpreting these results one has to keep in
mind that the term 'causality' refers to some variant on 'Granger causality', that is X Granger
causes Y if a change in X generally predates a change in Y. In this sense, the results mean that
changes in PSNP predate changes in price in no‐PSNP.
7. Summary and policy implications
In 2005, Ethiopia implemented a major new social transfer program, the Productive Safety
Net Program (PSNP), that involved some form of work requirement in exchange for either cash
or in‐kind transfers (or a mix of the two), with the composition of the transfers administratively
set to be uniform throughout the administrative region (woreda). In this paper, we analyze
monthly data on cereal prices over 12 years in areas, comparing price movements for areas
included in the PSNP with those outside the program. We find that prices have converged
between PSNP and non‐PSNP woredas over time, but that this convergence began well before
the introduction of the program.
This result suggests that the impact of cash transfers in non‐integrated PSNP (which would
tend to produce divergence of prices for woredas that are not integrated with non‐PSNP
woredas) is not the dominant driver of these price movements. Rather, the observed
convergence in prices suggests either that the effect of in‐kind transfers dominates (and that
PSNP and non‐PSNP markets are not integrated) or that the convergence is caused by other
factors (such as improved road infrastructure). Given that we also find that the markets (on
average) are co‐integrated, the implication is that on average the convergence is caused by
other factors (most likely, infrastructure improvements).
To arrive at more definitive conclusions will require further disaggregated analysis involving
distinguishing between the levels of transfers, the size of woreda markets and their locations.
Such analysis, provided that it can updated at regular intervals, may be able to provide valuable
inputs into operational decisions on where to use cash and where to use in‐kind transfers in the
PSNP program in rural Ethiopia.
REFERENCES
Barrett, Christopher B. and Daniel G. Maxell (2005). Food Aid After Fifty Years: Recasting Its
Role. Routledge: New York.
Basu, K., (1996) Relief Programs: When it May be Better to Give Food Instead of Cash, World
Development, Vol.24, No.1, pp.91‐96, 1996;
Central Statistical Agency (CSA) of Ethiopia
Coate, Stephen. (1989). Cash versus Direct Food Relief, Journal of Development Economics,
.30(2), pp. 199‐224
Gilligan, D., J. Hoddinott, A. S. Taffesse, S. Dejene, N. Tefera, and Y. Yohannes. 2007. Ethiopia
Food Security Programme: Report on 2006 Baseline Survey. International Food Policy
Research Institute, Washington, D.C. Photocopy.
8

9. Gilligan, D., J. Hoddinott, A. S. Taffesse. 2008. “The Impact of Ethiopia’s Productive Safety Net
Programme and its Linkages,” Journal of Development Studies, forthcoming.
Hall, S. G. and Milne, A. (1994), “The Relevance of P‐Star Analysis to UK Monetary Policy,” The
Economic Journal, 104, 597‐604.
Johansen, S. (1992), “Determination of Cointegration Rank in the Presence of a Linear Trend”,
Oxford Bulletin of Economics and Statistics, 54(3), 383‐397.
_______ and Juselius, K. (1990), “Maximum Likelihood Estimation and Inference on
Cointegration with applications to the Demand for Money”, Oxford Bulletin of
Economics and Statistics, 52(2), 169‐210.
_______ and Juselius, K. (1992), “Testing Structural Hypotheses in a Multivariate Cointegration
Analysis of the PPP and the UIP for UK”, Journal of Econometrics, 53, 211‐244.
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10. Figure 1: Rural Agricultural Wages and Real Value of Cash Transfers, Jan 2005 to Feb 2008
Figure 2: Comparison of Maize Price Trends in PSNP and non-PSNP Prices, Sep 1996 to Feb 2008
10

11. Table 1: Agricultural Development and Remoteness Indicators between PSNP and non-PSNP
Test for Equality of Mean Tests of Equality of
Variances difference Means**
Ag development and between non‐
remoteness Indicators* Test Stats P‐Values PSNP and PSNP Test Stats P‐Values
(F‐Value) (t‐Values)
Per capita cereal production 7.511 0.007 15.26 2.84 0.005
Yield per hectare 12.309 0.001 37.66 2.31 0.022
Travel time of Addis Ababa 3.444 0.065 ‐4.51 ‐4.07 0.00
Cereal sales as % of production 0.743 0.39 2.75 2.86 0.005
Travel times to the nearest
town of 20,000 people 1.38 0.242 ‐0.77 ‐1.127 0.261
*Cereal production and marketing data are from CSA; and remoteness measures are from Chamberlin et
al. 2006.
Table 2: Comparison PNSP non-PSNP differences in price and price growth
% difference between PSNP and Non-PSNP
Commodities/Period prices growth rates
Mean price Price Growth
Pre-period I (1996-2001)
Wheat white 8.66 -10.28
Maize 24.48 9.38
Teff white 14.19 90.38
Barely 15.13 133.54
Pre-PSNP period II (2002-2005)
Wheat white 4.49 -56.61
Maize 8.22 -3.59
Teff white 3.79 -10.17
Barely 5.46 -15.10
Post-PSNP period (2005-2008)
Wheat white 3.14 -12.98
Maize 7.25 -4.78
Teff white 2.68 -10.10
Barely 1.65 13.40
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12. Table 3: Johansen’s Cointegration Rank Test and Model Selection for PSNP and Non-PSNP Price
Relationships
Null Model 2 Model 3 Model 4
Commodities
Hypotheses
Trace 95 % Trace 95 % Trace 95 %
Test Critical Test Critical Test Critical
Value Value Value
r=0 23.94 19.96 25.78 15.41 31.12 25.32
Maize
r≤1 2.04 9.25 2.04 3.76 2.19 12.25
r=0 31.35 19.96 31.29 15.41 35.62 25.32
Wheat
r≤1 6.04 9.24 5.99 3.76 6.47 12.25
r=0 23.3 19.96 22.91 15.41 42 25.32
Teff
r≤1 5.62 9.24 5.26 3.76 9.34 12.25
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13. Table 4: Long Run Granger Causality Test Results
Granger Causality*
Commodity Prices Estimated Loading H0: αi = 0
2
weights (αi) Statistics χ (1) P-value
Maize
Prices in PSNP areas (lmsn) -0.022 0.090 0.770
Prices in Non-PSNP areas (lmnsn) 0.194 3.900 0.050
Wheat
Prices in PSNP areas (lwsn) -0.050 0.120 0.730
Prices in Non-PSNP areas (lwnsn) 0.389 9.210 0.000
Teff
Prices in PSNP areas (ltsn) 0.210 3.761 0.050
Prices in Non-PSNP areas (ltnsn) -0.101 0.530 0.470
13