The Structure, Conduct, and Performance of Maize Markets in Malawi. A presentation by D. Ochieng and R. Botha, IFPRI Malawi, May 2, 2019

1. THE STRUCTURE, CONDUCT AND PERFORMANCE
OF MAIZE MARKETS IN MALAWI
Findings from main harvest and lean seasons
Dennis Ochieng and Rosemary Botha
IFPRI Malawi
IFPRI-Lilongwe | 05/02/2019
05/02/2019 1

2. Background
Maize is the main staple crop in Malawi (Sitko et al., 2017)
Maize production is largely by smallholder farmers –on 75%
of plots (IHS4)
 Maize marketing is key to food and nutrition security,
poverty reduction
Malawi’s food security is defined in terms of availability of
and access to maize (Derlagen, 2012)
Maize-contributes 66% of calorie in typical diets
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3. Background cont…
Trends in prod and consumption
• Per capita consumption growth from 129kg
(years 1998-2008)-146kg/year (by 2017)
• Production increased significantly over the
years
Challenges:
• Unpredictable maize marketing environment
• Price volatility and seasonality, production
seasonality
Research gap:
• Seasonal analysis of the structure,
conduct and performance of maize market
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-
500,000
1,000,000
1,500,000
2,000,000
2,500,000
3,000,000
3,500,000
4,000,000
4,500,000
1961
1964
1967
1970
1973
1976
1979
1982
1985
1988
1991
1994
1997
2000
2003
2006
2009
2012
2015
Quantity(Tonnes)
Imports Exports Maize production
Source: FAOSTAT
Figure 1: Trend in Maize availability in Malawi

4. Objectives of the SCP Study
1. To describe the structure of the maize market by season: i.e., maize
marketing channels
2. To examine the conduct of maize value chain actors and how it
influences maize marketing across seasons
3. To analyze performance of the maize market by season
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5. Data and Methods
A mixed method approach was employed, with
semi-structured interviews with traders during main
marketing (555) and lean (605) seasons.
• A pure panel of 408 traders,
• 28 focus group discussions (FGDS) with maize
farmers
The fieldwork covered:
• 7 districts and 14 Extension Planning Areas
(EPAs) from Mulanje in the South to Chitipa in the
North
• 2 EPAs per district, 2 Focus Group Discussions
(FGDs) per EPA, 15 farmers per FGD (of mixed
gender and ages)
• 62 markets covered
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Figure 2. Map of the study area

6. Data and Methods (cont…)
Data sources
• Primary data: trader survey and FGDs
• Secondary data: prices (IFPRI, WFP, FAOGIEWS, FAOSTAT)
Analytical approaches
• Descriptive statistics – market structure and conduct -concentration, price movements,
price setting mechanism, traders’ profile, seasonal trade patterns,
access to information
• Quantitative analysis – market performance –price volatility, spatial market integration
(using a threshold error-correction model with transfer
costs) between 16 market pairs
• Qualitative analysis – transcripts of 28 FGDs (analyzed using Nvivo software)
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7. Findings
• Demographics:
 Most traders were men (over
76%)
 Majority of traders are between
36-50yrs (51%)
 8 years of trading maize on
average
 Maize trade is the main
occupation
• Legal status and market reach
 Most businesses are not
registered
 Trading is mostly within the
village based
 Limited trading outside the region
based or outside Malawi
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Profile of traders
Variable
Assembler
(n=45)
Broker
(n=10)
Retailer
(n=239)
Wholesaler
(n=114)
All
(408)
Gender (%men) 77.8 100 72.8 81.6 76.5
Age (average years) 34.8 38.4 38.7 38.5 38.2
Years of schooling 8.5 8.8 8.8 9.2 8.9
Experience in maize business (years) 7.0 7.8 8.9 7.7 8.4
Trading as primary occupation (% yes) 84.4 50.0 80.8 68.4 77.0
Legally registered for business 28.9 30.0 15.5 37.7 23.5
Member of Commodity marketing
association (%yes) 2.2 0 0.8 5.3 2.2
Within village based 82.2 60.0 63.2 50.9 61.8
Within town but outside village based 68.9 50.0 40.6 43.9 44.9
Within district but outside town based 42.2 30.0 36.4 46.5 39.7
Within region but outside district based 4.4 10.0 22.2 30.7 22.3
Within Malawi but outside region based 0.0 10.0 5.9 14.9 7.8
Table 1: Profile of traders –panel analysis

8. Structure
Figure 3. Maize marketing channels
Notes
1. Marketing channels (and trader types) changed
between the seasons.
2. Government distributing relief maize through
ADMARC
3. ADMARC began selling maize from late August
2018
4. Limited sales through structured markets i.e.
commodity exchanges (commex) –0.9% are
aware about commex.
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9. Market concentration
• No significant differences in
inequality of sales revenue
across the seasons (Figure 4)
• Higher variation in sales
income within groups (Table 2)
10/9/2018 9
Theil's T Between Within
Harvest vs Lean
1.06
0.11(10.3) 0.96(89.7)
Trader type 0.14(13.0) 0.93(86.9)
Urban vs Rural 0.00(0.0) 1.06(100.0)
Table 2: Theil's T decomposition
Figure 4: Gini coefficients (Harvest vs lean season)
Structure cont…

10. Market Concentration
Inequality in revenues among traders
• High inequality among all trader types – greater
deviations of the sales incomes from the line of
equality in both main harvest and lean seasons
• Significant differences in levels of inequality
across trader types
o Wholesaler-Assembler
o Wholesaler-Retailer
Structure cont…
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Assembler Broker/Agent Retailer ANOVA
Broker/Agent 0.2038
F=195.96
P=0.000
Retailer 0.0078 -0.1960
Wholesaler 0.5552*** 0.3514 0.5474***
Table 3. Bonferroni multiple-comparison test

11. Conduct
Variable
Assembler
(n=45)
Broker/agent
(n=10)
Retailer
(n=239)
Wholesaler
(n=114)
All
(408)
Collusion 31.6 10 32.6 17.4 27.2
Cost-plus pricing 44.7 60 45.4 63 50.9
Demand driven 2.6 10 7 5.4 6.1
Follow-leader 21.1 20 12.8 11.9 13.8
Predatory 0 0 1.2 1.1 1
Based on quality 0 0 1.2 1.1 1
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Table 4. Price setting mechanism
Price setting mechanism
• Overall, price was mainly set
based on cost
• Price was limitedly based on
quality
Seasonal switch between trader
types
• Assemblers switching to retailers
• Wholesalers switching to retailers

12. Conduct cont…
• Purchases:
 Highest purchases in
the lean season for all
trader types
• Sales
 Highest volumes traded
by wholesalers
 Highest volumes traded
in the lean season
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Seasonality in trade
Table 5. Seasonality of maize trade (MT/month)
Variable
Assembler
(n=45)
Broker
(n=10)
Retailer
(n=239)
Wholesaler
(n=114)
All
(408)
Purchases
April 8.4 3 8 22 11.7
May 5.6 16.8 10.6 24.2 13.9
June 5.4 25.3 7.5 22.7 11.9
October 14 27.4 11.4 35.1 18.7
November 10.8 18.4 11.1 25.6 15.3
December 13.1 27.0 12.1 27.2 16.8
Sales
April 3.1 3 7.8 23.7 11.6
May 4 14.4 9 25.5 13.2
June 4.7 23.1 5.7 18.7 9.7
October 12.7 23.9 10.6 32.2 17.2
November 12.1 19.4 9.6 26.9 15
December 14 13.1 10.1 29.5 16

13. Conduct cont…
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Source: Author's calculations from SCP survey data (Jun/Jul 2018 and Jan/Feb 2019)
Notes: *,**,***, Mann-Whitney test of differences in proportions are significant at the 10%,5% and 1% level, respectively
Table 6 Sources of purchases and salesSources of purchases and
sales
• Significantly lower purchases from
SSF
• Increases in purchases from LF, Other
traders, ADMARC

14. Price volatility
 Maize availability and affordability
facilitates a well performing market
 Retail maize prices were higher in the
southern than in central and northern
markets
 Retail maize prices were generally more
volatile in the main marketing than in the
lean season
 The differences in volatility between the
seasons are statistically significant for all
markets, except Rumphi and Chikwawa.
 61% of traders perceived that prices were
most volatile during main harvest season
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Findings: Performance
Market
Average prices Volatility
Difference in
volatility (c-d)
Harvest
(a)
Lean Harvest Lean
(b) (c) (d)
North
Chitipa 88.1 103.4 0.034 0.02 0.013***
Karonga 111.1 119.6 0.018 0.01 0.008***
Rumphi 113.4 129.5 0.016 0.017 -0.001
Mzuzu 106.8 128.3 0.025 0.016 0.009***
Mzimba 93.9 114.5 0.027 0.014 0.013***
Center
Salima 119.9 163.8 0.026 0.018 0.008***
Mchinji 109.9 140.7 0.029 0.009 0.020***
Mitundu 116.1 147.8 0.027 0.008 0.019***
Chimbiya 108.6 141.4 0.025 0.015 0.011***
South
Lunzu 128.2 162.5 0.017 0.009 0.009***
Mwanza 129.9 179.9 0.021 0.011 0.009***
Liwonde 125.4 159.4 0.022 0.011 0.012***
Luchenza 120.7 161.9 0.035 0.012 0.023***
Mulanje 117.9 162.9 0.034 0.01 0.024***
Chikwawa 122.9 160.9 0.016 0.015 0.001
Nsanje 118.1 159.5 0.035 0.02 0.015***
Average 114.1 145.1 0.009 0.006 0.003***

15. Spatial Market integration (threshold error-correction model)
Findings: Performance (cont …)
Medium distance market pairs (150-300km)
 Integrated market pairs (5): Mwanza-Mulanje; Mchinji-Chimbiya; Chimbiya-Liwonde; Mzimba-
Mchinji; Mzimba-Chimbiya
(1-19 days)
Short distance market pairs (<150km)
 Integrated markets pairs (4): Lunzu-Mulanje; Mzimba-Mzuzu; Mchinji-Mitundu; Nsanje-Chikwawa
 Even within the same region, markets that were near to each other were not well-integrated i.e.. Mchinji-
Mitundu
(2- 8 days)
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Long distance market pairs (>300km)
 Integrated market pairs (4): Karonga-Mzimba; Chimbiya-Lunzu; Mchinji-Lunzu; Mchinji-Mulanje
(1-47 days)

16.  Policy environment:
i) Trade restrictions (i.e. export bans, other restrictions) create
market uncertainties that stifle growth of maize sector,
ii) Trade restrictions expand informal maize imports/exports.
Iii) Trade restrictions create maize volume and price volatility
 Maize market:
 Unpredictable marketing environment
 Limited quality and weights standardization
 limited market opportunities beyond large
traders and ADMARC
 Inaccurate market scales lead to quantity
losses of about 10kg per 50 kg bag
 ADMARC:
 Delays in opening depots costs
traders/farmers a lot
 Higher quantities requirement exclude
small traders from supplying ADMARC
 ADMARC market scales perceived
‘inaccurate’.
Findings: Qualitative analysis
05/02/2019 16
 Large private traders
 Set prices in both rural and urban markets
 Contract small traders to aggregate maize during the
lean seasons
 ‘Depress’ prices during harvest periods and inflate prices
in the lean seasons
 Beneficiaries of ADMARC market and NFRA tenders

17. Summary (1/2)
Structure and conduct:
 Maize market is pyramidical in structure: many actors at lower levels but few towards
is upper levels
 There is a switch between trader types across seasons i.e wholesalers/assemblers
becoming retailers
 Farmers perceive maize as thinly traded, exposing them to ‘exploitation’
 Small farmers’ and traders’ access to structured markets is limited
 Farmers and small traders perceive ADMARC as serving the interests of large traders
and influential businessmen
 Heavy regulation by GOM with export bans and local trade restrictions=> widely
perceived as disincentive to maize trading
05/02/2019 17

18. Summary (2/2)
Performance
 Maize market is imperfect with high price and volume seasonality/ price volatility
 The maize market is characterized by intense competition at lower levels but
minimal competition at higher levels
 The maize market is not transparent enough to facilitate planning of maize
marketing to stabilize volumes and prices
 There is widespread lack of maize quality and weights standardization
 Maize markets in Malawi are poorly spatially integrated =>slow price
transmission between markets
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19. Policy Implications
 Agricultural commercialization should be accelerated to expand maize
productivity and marketed surpluses, coupled with effective post-harvest
management practices to improve quality
 Business skills of small farmers should be enhanced to view maize farming as an
agribusiness
 Small farmers and traders need to be educated about existing structured trade
opportunities
 Discretionary policy interventions that restrict and undermine incentives in the
maize trade should be minimized (if not eliminated)
 Upgrading road and telecommunication infrastructure in remote areas, as well as
better warehousing, will facilitate timely and cheaper access to markets and
market information
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20. Acknowledgements
This research was made possible with funding from the United States
Agency for International Development (USAID Malawi) and the UK
Department for International Development (DFID Malawi)
05/02/2019 20
END

21. Findings: Structure and conduct (cont …)
• Storage facilities:
 Most traders have access to long term
storage facilities (66%)
 Less than half had own warehouses
 Limited capacities of warehouses
• Quality improving assets
 Limited ownership of quality improving
equipment
 Limited ownership of processing
equipment (mill)
 Traders least invested in moisture metres
 Widespread ownership of weighing scales
yet traders use pails especially in the North
05/02/2019 21
Table 7. Facilities and assets by trader type
Variable
Assembler
(n=45)
Broker
(n=10)
Retailer
(n=239)
Wholesaler
(n=114)
All
(408)
Access to long term storage
facility (%yes) 71.1 40 63.6 70.2 65.7
Own warehouse (%yes) 37.5 50 34.2 50 39.6
Own warehouse capacity (in
MT) 33.4 100 12.8 65.1 36.5
Rented warehouse (%yes) 49.9 25 52.6 43.8 48.9
Owned buildings (excluding
storage) (%yes) 23.3 20 30.1 52.6 36.5
Owned weighing scales 66.7 90 64.9 79.8 70
Owned cleaning/drying
equipment 13.3 10 13.4 13.2 14
Owned bagging equipment 8.9 0 14.2 13.2 13
Owned vehicles (%) 4.4 0 5 24.6 10.3
Owned ox-carts 4.4 10 5.4 7 5.9
Owned maize mills (%) 4.4 0 1.3 5.3 2.7
Owned moisture meters 2.2 0 0.8 2.6 1.5
Number of workers 8 8 8 8 8
Number of paid workers 4 4 4 4 4
Source: Author's calculations from SCP survey data (Jun/Jul 2018 and Jan/Feb 2019)

22. Findings: Performance (cont …)
Seasonal trade pattern
 The greatest proportion of maize
purchased was from small-scale
farmers and other traders
 Greater proportion of purchases from
large farmers in the lean season
 Most sales made to individuals and
other traders
 Minimal sales through ADMARC and
certified warehouses/ Commex
 Trading with ADMARC reported only
in the lean season
Table 9 Maize purchases and sales through various channels
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Main harvest season (Apr-Jul 2018) Lean season (Oct 2018-Feb 2019)
Variable Asse
mble
r
(n=4
5)
Broker
/agent
(n=10)
Retaile
r
(n=239
)
Whole
saler
(n=114
)
All
(408)
Asse
mble
r
(n=45
)
Broker/
agent
(n=10)
Retailer
(n=239)
Wholesal
er
(n=114)
All (408)
Purchase market
Share of maize purchases
Small scale
farmers (%)
65.4 68.0 46.2 51.1 50.3 34.9 46.0 26.6 34.3 30.2
Large farmers (%) 2.1 1.4 1.5 2.3 1.8 5.5 50.2 10.0 12.1 11.1
Other traders (%) 23.6 3.9 44.8 30.5 37.5 56.2 2.4 61.6 49.8 56.2
ADMARC (%) 0.0 0.0 0.0 0.0 0.0 1.1 1.4 0.4 2.1 1.0
Certified
warehouses (%)
0.1 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Sales market
Share of maize sales
Individuals (%) 60.3 20 72.1 32 58.3 61.2 29.6 66.8 45.6 59.3
Retail stores (%) 1.2 0.0 1.6 2.4 1.7 0.0 0.0 0.2 0.3 0.2
Processors (%) 0 0.0 2.7 3.5 2.6 2.6 2.4 2.3 1.5 2.1
Other traders (%) 21.4 46.7 13.2 36.5 21.5 15.8 51.6 10.4 31.5 17.9
ADMARC (%) 0.0 0.0 0.0 0.0 0.0 0.1 0.4 0.3 0.1 0.2
Warehouses (%) 0.0 3.3 0.5 1.3 0.8 0.0 2 0.4 1.7 0.8