The “invisible middle” of common bean value chains: Evidence from peri-urban markets in Uganda and Kenya Francesconi, N, E. Wilkus, E. Birachi, N.Mango, H.Ngala, C. Chege, M. Lungaho, D.Birungi, and M.Jagger

Context

The “invisible middle” and the role of brokers among small and medium enterprises Invisible middle – The poorly characterized set of individuals and organizational mechanisms that facilitate informal transactions between shopkeepers, wholesalers and traders Brokers - intermediaries, who do not generally deal with physical commodities - i.e. they do not purchase, store, transport or sell commodities - but only connect suppliers to buyers. Small and medium enterprises (SMEs) - SMEs link farmers to consumers across Africa. They tend to employ fewer than 1000; but varies from country to country

Key findings • Survey findings counter conventional wisdom: • “traders or middlemen” did not exploit farmers but rather improved SME financial performance and quality assurance • product losses along African value chain are not substantial • women are not less likely to participate in and benefit from agribusiness activities • Survey findings provide empirical evidence of networks, alliances and horizontal relationships among similar SMEs within each and every segment of the chain. The data confirms that that value chains are not linear.

Methods

Methods • Surveyed SME operating in and around the four major urban areas of Uganda and Kenya • “Stacked” survey sampling techniques used • Collected data on 350 interconnected SMEs, mostly represented by retailers, wholesalers and traders

Uganda and Kenya 21% 45% 34%

"Tier 0" Shopkeepers "Tier 1" Wholesalers "Tier2" Traders

Methods • Retailer (shopkeeper or “Tier 0” SMEs): sell most of their bean products directly to consumers) located in particularly poor urban neighborhoods. Shopkeepers were surveyed first. • Wholesalers (or “Tier 1” SMEs): supply beans to previously surveyed shopkeepers located in open urban markets. • Traders (or “Tier 2” SMEs): supply beans to previously surveyed wholesalers.

The average bean supply chain (Uganda and Kenya)

Market Trends

Broker Participation Used brokers Mean (SD)

Did not use brokers Mean (SD)

Probability of procuring beans through brokers

Typology (shopkeeper- 1, wholesaler-2, trader-3)

2.49 (0.69)

1.58 (0.69)

0.47 (0.17) **

Age (years) Proprietor

38.62 (10.50)

40.08 (10.32)

-0.03 (0.01)**

Gender (male dummy)

0.66 (0.48)

0.44 (0.50)

-0.36 (0.26)

Education (years)

11.23 (3.54)

11.42 (3.44)

-0.00 (0.04)

IT

Cell phones provide market information (dummy)

0.97 (0.17)

0.85 (0.36)

0.87 (0.59)*

Travel time to closest city (hours)

1.68 (6.03)

2.04 (7.38)

-0.00 (0.02)

Distance to closest feed point (km)

7.17 (59.32)

1.94 (29.98)

0.97 (0.65)*

SME location in market area (dummy)

0.82 (0.39)

0.78 (0.41)

-0.39 (0.29)*

Electricity from national grid system (dummy)

0.73 (0.45)

0.64 (0.48)

-0.27 (0.34)

McFadden Pseudo R-squared

0.24

Value of building facilities (USD)

Log likelihood 21145.85

-73.51 0.00 (0.00)

1.0

0.8

0.50 0.43

0.4 0.23 0.2

0.17

0.0

Procured through a broker Sold through a broker

SME

0.57

Negotiating power

0.6

0.71

Assets

Proportion of SMEs that used brokers

Procurement through brokers in Kenya and Uganda, 2015

Value of transportation (USD)

N. of (42535.5) observations

43624.71 (68384.43)

13028.54 (37549.2) 13483.12 (17991.75)

350

0.00 (0.00)**

SME Performance: Profit Margin Profit margins among bean SMEs in Kenya and Uganda, 2015

3.0

Profit margins (USD)

2.7

21% 53%

13% 13%

Farmer (inferred)

1.1 1.0

0.5

0.5

0.1 0.0

Negotiating power

IT

1.5

No broker for Broker for No broker for Broker for sales sales sales sales No broker for procurement Broker for procurement

Assets

Profit margin (USD)

2.0

Shopkeeper, Retailer (Tier 0, n=140) Wholesaler (Tier1, n=84) Trader (Tier 2, n=63)

Proprietor SME

2.5

Typology (shopkeeper- 1, wholesaler-2, trader3)

0.54 (0.52)

Age (years)

-0.05 (0.04)

Gender (male dummy)

1.09 (0.76)

Education (years)

0.10 (0.10)

Cell phone for market information (dummy)

0.31 (1.05)

Travel time to closest city (hours)

-0.06 (0.05)

Distance to closest feed point (km)

5.15 (1.59)**

SME location in market area (dummy)

0.79 (0.90)

Electricity from national grid system (dummy)

-2.15 (1.01)**

Value of building facilities (USD)

0.00 (0.00)

Value of transportation (USD)

0.00 (0.00)

R-squared

0.12

Adjusted R-squared

0.06

N. of observations

350

SME Performance: Quality • Bean supplies are seldom uniform • Chemical residues/ contamination as well as losses from germination or mold in the bean supply are low 1.0 0.85

0.90 0.77

Proportion of SMEs

0.8 0.59

0.6

0.4 0.24 0.2

0.0

Bean supply is not uniform

Reject bean supply

0.20

Bean supply has chemical residues/contamination

No broker for bean procurement

0.22 0.14

Bean supply is moldy

Broker for bean procurement

0.18 0.17

Bean supply is partially germinated

SME Performance: Quality SMEs reject bean supply in Kenya and Uganda, 2015 Did not reject bean supply Mean (SD)

Probability of procuring beans through brokers

Typology (shopkeeper- 1, wholesaler-2, trader-3)

1.80 (0.79)

1.70 (0.77)

0.07 (0.16)

Age (years)

39.09 (9.75)

40.90 (11.23)

-0.03 (0.01)**

Gender (male dummy)

1.50 (0.50)

1.53 (0.50)

-0.01 (0.23)

Education (years)

11.23 (3.43)

11.64 (3.5)

-0.06 (0.03)*

Cell phones provide market information (dummy)

0.89 (0.31)

0.84 (0.36)

-0.27 (0.48)

Travel time to closest city (hours)

1.72 (5.94)

2.39 (8.77)

0.01 (0.01)

Distance to closest feed point (km)

0.15 (0.25)

0.13 (0.26)

0.10 (0.55)

SME location in market area (dummy)

0.79 (0.41)

0.81 (0.41)

0.15 (0.30)

0.2

Electricity from national grid system (dummy)

0.69 (0.47)

0.61 (0.49)

0.16 (0.30)

0.0

Value of building facilities (USD)

15189.13 (35914.92)

14327.32 (43666.39)

0.00 (0.00)

Value of transportation (USD)

24465.96 (44527.3)

16907.42 (35077.69)

0.00 (0.00)

Reject bean supply 0.77 0.59

0.4

No broker for Broker for bean bean procurement procurement

Negotiating power

0.6

IT

0.8

Assets

Proportion of SMEs

1.0

Proprietor

SME

Rejected bean supply Mean (SD)

McFadden Pseudo R-squared

0.06

Log likelihood

-90.16

N. of observations

350

Practitioners Perspective

Potential interventions • SME-broker linkages can be utilized as a method to promote: • Economic performance • Quality control • Efforts to establish these linkages can target SMEs that are likely to utilize and benefit from brokers namely, SMEs that are: • Located far away from feed points • Managed by young proprietors

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