Strengthening storage, credit, and food security linkages

The role and potential impact of warehouse receipt systems in Malawi

Karl Pauw (IFPRI) – k.pauw@cgiar.org 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