Strengthening storage, credit, and food security linkages: The role and potential impact of...
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Strengthening storage, credit, and food security linkages
The role and potential impact of warehouse receipt systems in Malawi
Karl Pauw (IFPRI) – [email protected] with Brent Edelman, Hak Lim Lee, Athur Mabiso & Valerie Muller
Prepared for the Inaugural ECAMA Research Symposium, Lilongwe, Malawi8–10 October 2014
Introduction
• Warehouse receipt systems (WRSs) are a relatively new concept in Malawi
• Offers owners of commodities certified storage as alternative to traditional storage
• Warehouse receipt – guarantees depositor specified grade, quantity,
and security of stored commodity– may be used as collateral for loan
• Overall objective to enhance market efficiency
Introduction (continued)• Price seasonality at heart of storage demand models
opportunity to engage in temporal arbitrage:– demand for storage as long as expected future price is high
enough to compensate for opportunity cost of credit, storage cost, normal profit margin, and a risk premium
• Law of supply and demand: as more agents store, price seasonality is reduced and arbitrage profits decline
• Storage demand also depends on – Risk, e.g. unpredictable future prices; potential postharvest
losses – Access to credit
• WRS combines aspects of storage, access to credit, reduced postharvest losses … great model for grain traders, but what about smallholder farmers?
Outline
• Smallholder maize production and marketing in Malawi
• Price volatility: seasonality versus unpredictability
• Storage options and post-harvest losses• Access to credit• Summary
Smallholder maize production
APES estimates: avg. 2008/09 to 2013/14
IHS3 estimates (2008/09 and 2009/10)
Prod.
(mt/year)Prod.
share (%)Surplus (deficit)
Avg. prod.
/farmer (kg)
No. of maize
farmers
No. of “large” farmers (3mt+)
North 451,944 13.7 30,027 777 307,711 8,945 Central 1,830,134 55.7 488,955 1,400 1,059,936 104,352
Kasungu 890,615 27.1 396,347 1,966 386,293 61,637 Lilongwe 797,946 24.3 112,671 1,114 552,866 37,445
South 1,005,301 30.6 (341,687) 529 1,075,672 9,567 National 3,287,378 100.0 177,295 938 2,443,319 122,864
Source: Agricultural Production Estimates Survey (APES) 2008/09 to 2013/14; IHS3 (2010/11).
Nkhotakakota: 1,750mt (1)
Mchinji: 500mt (1)
Dowa: 300mt (1)
Lilongwe: 57,120mt (7)
Dedza: 300mt (1)
Balaka: 460mt (1)
Machinga: 5,000mt (1)
Zomba: 2,500mt (1)
Blantyre: 67,700mt (5)
Warehouses
Deposit points
ACE warehouse and deposit point locations
Farmers’ market transactions
8.5%
55.3%
3.6%
4.7%
0.5%
27.4%Sold maize/did not buy Bought maize and/or maize meal/did not sellBought and sold – net seller Bought and sold – net buyer Bought and sold – net zero Neither bought nor sold – autarkic
Source: Jayne et al. (2009) based on 2006/07 to 2007/09 selling seasons
Timing of maize sales
0%
5%
10%
15%
20%
25%
30%
8090100110120130140150160170
5.5%
14.4%
19.5%
25.7%
13.9%12.4%
3.5%1.7% 0.8% 0.4% 0.6% 1.7%
Share of farmers selling most of their cropAverage market price maize (2007/08 - 2013/14)
Perc
enta
ge o
f far
mer
s (%
)
Pric
e in
dex
(Mar
ch =
100
)
Source: Integrated Household Survey (2010/11) & Agricultural Market Information System (AMIS) 20072014
Price volatility: two components
Seasonal component: regular and repeated price fluctuations over time predictable
– Linked to market power along value chain; high transaction or transport costs; credit constraints (e.g. farmers forced to sell-low/buy-high) (Kaminski et al. 2014)
Unpredictable component: difference between what we expected at time the price would be at time +1, i.e., and the actual price
Real maize prices 20072014
Mar/07 Mar/08 Mar/09 Mar/10 Mar/11 Mar/12 Mar/13 Mar/1410
20
30
40
50
60
70
80
90
100
110
0.00
0.10
0.20
0.30
0.40
0.50
0.60
Harvest period (low prices) Hunger season (peak prices)Real MWK price Nominal USD price
Mal
awi K
wac
ha (M
WK)
US
Dol
lars
(USD
)
Source: Agricultural Market Information System (AMIS) Market Prices (2007 to 2014)
Real maize price index 20072014
Source: Agricultural Market Information System (AMIS) Market Prices (2007 to 2014)
Wee
k 20
Wee
k 23
Wee
k 26
Wee
k 29
Wee
k 32
Wee
k 35
Wee
k 38
Wee
k 41
Wee
k 44
Wee
k 47
Wee
k 50
Wee
k 1
Wee
k 4
Wee
k 7
Wee
k 10
Wee
k 13
Wee
k 16
Wee
k 19
50
100
150
200
250
2007/08
2008/092009/10
2010/11
2011/12
2012/13
2013/14
Average
Pric
e in
dex
(wee
k 20
= 1
00 fo
r all
year
s)
Seasonality and unpredictability
Source: Agricultural Market Information System (AMIS) Market Prices (2007 to 2014)
Wee
k 20
Wee
k 23
Wee
k 26
Wee
k 29
Wee
k 32
Wee
k 35
Wee
k 38
Wee
k 41
Wee
k 44
Wee
k 47
Wee
k 50
Wee
k 1
Wee
k 4
Wee
k 7
Wee
k 10
Wee
k 13
Wee
k 16
Wee
k 19
50
100
150
200
250
Average
Upper
Lower
Pric
e in
dex
(wee
k 20
= 1
00 fo
r all
year
s)
Seasonal: on average peak prices (mid-March) are 69%
above trough (mid-May)
Unpredictable: 60% of overall price volatility (Kaminski et al.
2014)
Confidence interval
What causes price unpredictability?
• Country typology A– Limited market intervention– Invest in infrastructure– Encourage crop diversification– Strong financial markets
• Country typology B– Only partially liberalized– Intervene in food markets
(e.g. marketing board activities) – Discretionary trade policies– Engage in trade or stock release
Low degree of price unpredictability (e.g. Uganda & Mozambique)
High degree of price unpredictability (e.g. Malawi & Zambia)
Source: Chapoto & Jayne (2009)
Price volatility & food security
• Many African countries experience major departures from the normal seasonal price patterns imparts major risks to seasonal storage (Chapoto & Jayne 2009)
• Household food consumption inversely tracks food prices; seasonality continues to be pervasive in African food markets challenging for households to smooth consumption (Kaminski et al. 2014)
• Food price increases [not related to productivity changes] raise poverty in the short run because the majority farmers are net consumers do not benefit sufficiently from higher selling prices to offset the negative impacts of higher consumer prices (Ivanic & Martin 2014)
3.7%
74.4%
20.6%
1.3%
HeapedBaggedTraditional granaryImproved granary or other
Storage choices
Source: Integrated Household Survey (2010/11)Notes: (1) Figure shows storage choice for 54% of maize farmers that reportedly stored crops; (2) When asked why people store, 99% answered “food for household”, and the rest “to sell at higher price”, “saved seed for planting”, or “other”; (3) Despite this, about one-in-six reportedly sell at least some of their crop
Links in the postharvest chain
Source: http://www.aphlis.net/
Typical weight loss ranges for links in the postharvest chain for cereal grains in Sub-Saharan Africa
PHL estimates diverge
• Golop et al. (2014) estimates for Malawi 2010/11 range from 11% (rapid loss assessment) to 15.7% (conventional)
• FAO (2011) estimates cereals PHL for SSA at 20.5% of which 6% during harvesting and 8% during post-harvest handling and storage
• World Bank (2011) & De Groote (2013) estimates handling & storage losses of 5 to 15% (cited in Kaminski & Christiaensen 2014)
• Kaminski and Christiaensen (2014) use representative IHS3 data : 7% report losses & lose 21% on average national average only 1.4%
More analysis required, esp. on storage choice and losses; evidence suggests losses during postharvest handling & storage not that significant
Malawi access to credit
• Only 13% of households had access to loans (IHS3)
Relatives or neighbors
Merchants, moneylenders or employers
Credit bureaus or banks
NGO, religious institutions & other
50%
14%
16%
20%
• Only 16% of those loans from “formal” credit institutions only 2% of households
• Access to formal collateralized loans considered one of the main benefits of WRS
Source: Integrated Household Survey (2010/11)
Benefits of credit access• Stephens & Barrett (2011): liquidity constraints force many
farmers to use maize market as “high-interest lender of last resort” (Kenya)
• Burke (2014): timely access to affordable credit influences timing of sales and enables farmers to engage in temporal arbitrage (Kenya)
• Sun et al. (2013): households with formal and informal sources of debt sell maize much earlier (lower prices) than debt-free households (China)
• Fink et al. (2014): cash/food constraints may force households to sell ganyu labor; leads to under-investment in own plot yields and production drop in a vicious cycle (Zambia); however, evidence mixed (Dimowa et al. 2010; Orr et al. 2009)
Summary
• WRS appealing to traders as long as prices are predictable. Can smallholder farmers benefit?
• Three-quarters of smallholders are either autarkic or buy rather than sell maize: – PHLs alone not high enough to justify full cost of WRS– Access to formal credit a major advantage; more analysis required
to value the benefit– Alternative storage models based on more accessible village-level
facilities (e.g. warrantage schemes) may be more appropriate • For those smallholder farmers that trade:
– Outright sellers can achieve better prices; access credit in the interim to cover necessary expenses
– Buyers/sellers can avoid sell-low/buy-high trap (or may choose to become autarkic themselves)
Summary (continued)
• Net-consuming farmers or non-farmers – Price seasonality is associated with significant fluctuations
in consumption levels food security implications– Increased storage reduces seasonal price fluctuations
• Policy issues– Price seasonality best addressed through investing in
storage capacity, road infrastructure (market linkages), & improved credit market efficiency
– Unpredictable prices imply significant temporal arbitrage risks; stable policy environment & liberalized markets crucial