research

STRATEGY SUPPORT PROGRAM | WORKING PAPER 150 AUGUST 2020

Food marketing margins during the

COVID-19 pandemic

Evidence from

vegetables in

Ethiopia

Kalle Hirvonen, Belay Mohammed,

Bart Minten,

and Seneshaw

Tamru

CONTENTS

Abstract ............................................................................................................................................

1

1

3

3

4

1. Introduction2. Data

............................................................................................................................................

.................................................................................................................................

2.1. In-person survey in

January and February 2020 .................................................................

2.2. Phone survey in May

20203. Results

........................................................................................................................................

..................................................................................................

3.1. Value chain agents' views about changes due to

the pandemic ..........................................

3.2. Price

formation in

the vegetable value chain before and during the pandemic

4. Conclusions ..............................................................................................................................

....................

5

5

6

11

References .....................................................................................................................................11

Appendix.........................................................................................................................................13

TABLES

Table 2.1: Comparing respondent characteristics in

the February 2020 survey sample between

respondents that were and were

not included in

the May 2020 phone survey.............................4

Table 3.1: Stated income losses in the

past month and future plans among farmers

........................5

Table 3.2: Stated changes in

traders' businesses compared to

three months prior

...........................6

Table 3.3: Procurement locations and sales patterns of urban wholesalers before and after onset of

COVID-19 pandemic...................................................................................................................6

Table 3.4: Price regressions by vegetable type

.................................................................................8

Appendix Table A1: Means and standard deviations of vegetable prices

(birr/kg) by survey round. 13

Appendix Table A2: Accuracy of prices reported by traders............................................................13

FIGURES

Figure 3.1: Retail prices by vegetable type and survey round ...........................................................7

Figure 3.2: Retail tomato prices by quality and survey round ............................................................8

Figure 3.3: Vegetable price

structure before and during the

pandemic, by vegetable type................9

Figure 3.4: Share of transportation costs in

final retail price, by vegetable and survey round ..........10

ii

ABSTRACT

It is widely feared that the COVID-19 pandemic will lead

to a significant worsening of the food

security situation in

low and middle-income countries. One

reason for this is the

disruption of

food

marketing systems and subsequent changes in

farm and consumer prices. Based on primary data

in Ethiopia collected just before

the start and a few months

into the pandemic,

we assess changes

in farm and consumer prices

of four major vegetables and

the contribution of different segments of

the rural-urban value chain

in urban retail price formation.

We find large, but heterogeneous, price

changes for different vegetables with

relatively larger changes seen at the farm

level, compared to

the consumer level, leading

to winners and losers among local vegetable farmers due

to pandemic

related trade disruptions. We

further note that despite substantial hurdles in

domestic trade reported

by most value chain agents, increases

in marketing – and especially transportation – costs have not

been the

major contributor to

overall changes in

retail prices. Marketing margins even declined for

half of the

vegetables studied. The

relatively small changes in

marketing margins overall indicate

the resilience

of these domestic value chains during the pandemic

in Ethiopia.

JEL codes: Q13; Q18; O13

Keywords: COVID-19; Food security; Food systems; Value chain analysis; Africa

1. INTRODUCTION

It is feared that the COVID-19 pandemic

will lead

to widespread increases

in global poverty and

food insecurity and that these negative impacts will concentrate on

the most vulnerable segments of

the population in low-

and middle-income countries (Torero 2020; Swinnen and McDermott 2020;

Barrett 2020). Laborde et al. (2020) estimate that due to

the COVID-19 pandemic over 140 million

people – a large number of them in

sub-Saharan Africa – will fall

into extreme poverty and suffer

from food insecurity and hunger. A major contributor to the

increased food insecurity is the

reduction

of income among vulnerable populations.

In addition, disruptions

to food systems and changes

in

farm and consumer prices could also

turn out

to be major drivers of food insecurity. Changes

in food

and agricultural prices are an obvious concern to

policy makers in low

and middle-income countries

during this pandemic, given the importance of

agricultural prices for

the income of farmers and food

prices for the purchasing power of consumers and their

link to social unrest (Barrett 2020;

Bellemare 2015).

There are various factors that may cause food and agricultural prices to

change during this

pandemic. Because of reduced demand due to

the global recession, some researchers predict that

commodity prices will

decrease globally (Laborde et al.

2020). Meanwhile, others predict price

increases, at least in

the short run, due to

hoarding and changes in

purchase and storage patterns

(e.g., Lusk 2020; Reardon et al. 2020). Increased marketing costs – due to

complications in

logistics

linked to the

pandemic (such as

seen in meat packing plants

in the

US; Hahn 2020) – may further

widen the wedge between farm and consumer prices (Reardon et al. 2020, Narayan and Saha

2020). So far,

global staple food prices have been remarkably stable, possibly due to good harvests

in the previous season and sufficient storage available globally (Glauber et al. 2020). While

significant food price

movements have been seen in some cases, they

are seemingly context

specific (Akter 2020; de

Paulo Farias

and de Araújo 2020; Yu et al.

2020), with price

rises noted in

some cases – most often for perishables such as

meat, fish, and vegetables (e.g., Akter 2020;

Mogues 2020; Lele et al. 2020) – and declines in

others (e.g., Harris et al. 2020; Narayan and Saha

2020). However, few

authors have looked at what factors in

the food systems of poorer economies

have contributed to

these local food price changes and which farmers have been affected by these

1

changes during the

COVID-19 pandemic. This is an

important topic as these food prices matter

enormously for the livelihoods of people in

low-income countries. Thus, insights on

price

movements provide valuable inputs into the design of effective policies to

mitigate negative effects

of the crisis.

In this paper,

we provide a careful study

of farm and consumer prices and marketing margins

during the

COVID-19 pandemic in

Ethiopia, the second most populous country in

Africa. After the

first COVID-19

case was confirmed

in Ethiopia

in mid-March 2020,

the government closed schools,

banned all

public gatherings, and recommended social distancing. Other measures to

contain the

spread of the virus

soon followed. Travelers from abroad were initially put into a 14-day mandatory

quarantine and travel through land borders was prohibited. Several regional governments banned

all public transportation and imposed restrictions on other vehicle movements between cities and

rural areas. In

Addis Ababa, public and private transportation, such as minibus taxis, were ordered

to work at half capacity

(with half

the load

of people) and charge double the

price. Private cars could

only be driven

every other day (a

restriction that was lifted after a few weeks). Moreover, the

wholesale market for fruits

and vegetables was

relocated from crowded quarters (Atikilit Tera) to an

open space (Jan Meda) to

facilitate social distancing between customers and traders. While these

actions were

expected to

slow the

spread of the virus,

they may have had substantial unintended

effects on the

functioning of

food value chains.

We study rural-urban vegetable value chains from the country’s most important commercial

horticultural cluster in

the East Shewa zone in the

Central Rift

Valley, which supplies approximately

200 million USD worth of vegetables annually (Minten et al.

2020) to

Addis Ababa, the

largest city

of

the country.

To assess the changes

in the vegetable marketing system,

we rely on unique large

scale price data (more than 10,000 observations)

at different levels of the value chain, including

rural farmers, wholesale markets, wet

markets, and urban retailers, that were collected for major

vegetables just

before the onset of the

pandemic (February 2020) and three months into the

pandemic (May 2020). We also

collected data from

value chain participants – farmers, wholesalers,

and retailers – on adjustments and disruptions in

their marketing activities linked to

the pandemic.

Value chain agents indicated that their businesses were seriously affected by the

COVID-19

pandemic. Most agents reported a decrease in demand, turnover, and clients; increased losses;

less competition; higher transport costs; and changes

in procurement areas.

We further find that

producer prices changed significantly over this

period, with

increases over a three-month period up

to 64 percent (tomato) and decreases as high

as 67 percent (green pepper). Changes

at the retail

level were

however relatively much smaller, between 19

percent increases (tomato) and 29 percent

decreases (green pepper). We

find that despite significant hurdles in

domestic trade, changes in

marketing costs have not been the

major contributor to

increases in

retail prices. Rather, changes in

retail prices have mostly been linked to

changes in

farm gate prices. Marketing margins even

declined in

the case of two of the four vegetables studied.1 Moreover, increases in

transportation

costs, which were seen in

Ethiopia as well

as in

other countries during the

pandemic (Narayan and

Saha 2020), might have been less

of a driving factor of overall price changes in

these value chains

of perishable products. Such products

are typically traded over relatively short distances,

so

transportation costs are relatively less

important as a component of their retail prices, in

contrast

with other crops

(Dillon and Barrett 2016).

These findings suggest that a number of other factors outside the

studied domestic value chains

have contributed to a larger extent

to farm and consumer price changes. First,

the ban on

international trade and disruptions to

inter-regional trade led to

a more localized marketing system.

1

Changes in

marketing margins between the two

periods reduced the change in

absolute terms of vegetable retail

prices compared to

farm prices, i.e., in

the case of increases of

farm gate prices, smaller marketing costs reduced that increase at the retail

level and vice

versa.

2

It seems that this wider market access before the pandemic seemingly played a price-stabilizing role

for some

vegetables, leading to large

price swings during the pandemic. These effects have then

created winners and losers among farmers, as

has been seen during previous trade policy shocks

(Aksoy and Beghin 2004; Headey 2011). Second, urban demand fell due to

income losses and the

widespread fear that eating raw

vegetables would increase the

likelihood of

contracting the virus

(Hirvonen et al. 2020).

2. DATA

2.1. In-person survey

in January and February 2020

This study builds

on a large-scale vegetable value chain survey conducted

in January and February

2020 in

Ethiopia. Focusing on the main

value chain supplying vegetables to

Addis Ababa, we

fielded primary surveys at different levels of the value chain, going from rural producers to

urban

retailers.2

The sampling strategy varied depending on

the respondent type. First,

four major vegetable

producing woredas (districts) in

East Shewa zone in the

Oromia region (Adami Tulu, Bora, Dugda,

and Lume) were purposely selected for this

study. From these woredas, we selected kebeles (sub

districts) that had at least 100 hectares of irrigated land. A total of 37

kebeles were identified

(Dugda: 12

kebeles; Adami Tulu: 12; Bora: 7; Lume: 6).

Then within each kebele, we

categorized all

farmers as

either investor or

smallholders depending on the

amount of

land they were

renting in.

Farmers that rented in less than 0.5 hectares of land were considered to be smallholders, while

those renting in more than

0.5 hectares

were considered

to be investors (see Minten et

al. 2020).

One-quarter of the farmers interviewed were

randomly selected from the

investor farmers list;

three

quarters from the

smallholder list. In

each kebele, a community questionnaire was

fielded as

well. A

total of 810 vegetable farmers were

interviewed in

January and February, of

which 634 were

smallholders and 176 were medium-scale investors.3

Second, 56

urban wholesalers operating in

Addis Ababa were interviewed. These wholesalers

were randomly selected from the

group of wholesalers that dealt with

one of the five major

vegetable crops focused on in

the study: onion, tomato, green pepper, cabbage, and Ethiopian kale.

Third, prices of vegetables

were followed daily

in the wholesale market and

in four large

wet

markets in Addis Ababa.

The value chain survey covered different types of urban retail outlets in five

sub-cities (out of 10)

in Addis Ababa.

In these sub-cities,

we visited

all supermarkets and minimarkets, as well as all the

Ethiopian Fruit and Vegetable Marketing Share Company (ET-FRUIT) outlets. We

then randomly

selected four kebeles and visited 10 randomly selected local fruit

and vegetable shops in each

kebele. Within each kebele, two

ketenas (neighborhoods) were

randomly selected and all micro

sellers of the five

vegetables were listed. From this list,

three micro-sellers were

randomly selected.

The five vegetables examined

are the most important vegetables grown in this area,

with 33 and

31 percent of the irrigated land allocated

to tomatoes and onions, respectively,

while cabbage,

green pepper, and Ethiopian kale are estimated to be

grown on 8, 8,

and 9 percent of the

land,

respectively. Other crops make up

the remaining 12 percent.

2 This type of detailed value chain data, collected

in a cascading manner,

is vital

for developing a complete

picture of the value chain.

Other approaches come with

a number of

methodological weaknesses. For example, the common practice in

food value

chain analysis is

that it often only uses anecdotal or qualitative evidence and

it does not

rely on reliable and representative surveys (e.g., Webber and

Labaste 2009; World Bank 2009; Nang’ole, Mithöfer, and Franzel 2011). Moreover, household surveys based on

random sampling have

the disadvantage that the selected farmers might be of relatively less importance in major

food supply areas and thus might not present a

representative picture of the

farmers who effectively participate in

value chains of specific food crops (World Bank 2009).

3 We

also interviewed 169 beneficiaries of SNV's

Horti-Life project but did not attempt to

re-contact these farmers in the

phone survey.

3

2.2. Phone

survey in May 2020

To understand how

the COVID-19 pandemic

is affecting the vegetable value chain,

we conducted a

phone survey with the

farmers as well

as the wholesale and retail outlets that took part in

the in

person survey in

early 2020. In the

phone survey, we

planned to

re-contact half of

the vegetable

farmers as well

as half of the wholesale and retail outlets. If the respondent could not be

reached,

refused to

take part in

the survey, or was no longer active in

the vegetable sector, he

or she was

replaced with

another respondent from the in-person survey sample. All

phone survey respondents

were randomly selected from the

pool of

previous survey respondents. The final phone survey

sample included 433 farmers,4 30

wholesale outlets, and 235 retail outlets.

An average phone interview took approximately 30 minutes.

The farmer survey instruments

focused on access to

inputs, marketing and income, behavioral responses to

COVID-19, and their

plans for the next cropping season. The

wholesale instrument asked about the

trading activities and

changes in

operations in the

past three

months. The retail instrument asked questions about

procurement and sales and changes in

operations in the

past three months. All

three instruments

asked about vegetable prices at the time

of the survey, permitting us to compare prices at different

levels of

the value chain in the

same period. We

also used that same price instrument to

daily follow

prices at the wholesale market and four major wet markets

in Addis Ababa, as

we did in the first

survey round

in early 2020.

Table 2.1: Comparing respondent characteristics in the

February 2020 survey sample

between respondents that were

and were not

included in the

May 2020 phone

survey

Included in Not included

Observations and variables

phone in phone

survey survey

Difference p-value

Farmers

Male headed households (%)

96.1 92.9 3.2 0.04

Level of education of

respondent (years) 6.6 5.1 1.5 0.00

Vegetable business experience

of respondent

(years) 10.1 9.6 0.5 0.25

Observations: 433 546

Urban wholesalers

Male respondent (%) 93.3 100.0 -6.7 0.18

Level of education of

respondent (years) 9.2 9.5 -0.3 0.68

Vegetable business experience of respondent (years) 11.3 10.4 0.9 0.63

Observations: 30 26

Urban retailers

Supermarket (%)

19.2 12.8 6.4 0.49

Fruit & vegetable grocery

shops (%) 46.4 57.8 -11.4 0.08

Fruit & vegetable

micro-sellers (%) 28.5 23.7 4.8 0.56

ET-FRUIT shops (%) 6.0 5.7 0.3 0.98

Male respondent (%) 45.1 49.5 -4.4 0.53

Level of education of respondent (years) 7.5 7.0 0.5 0.25

Vegetable business experience

of respondent

(years) 7.8 7.9 -0.1 1.00

Observations: 235 211

Source: February 2020 and May 2020 survey rounds.

Note: Difference in means between

the groups tested

with a t-test (null-hypothesis: difference

in means =

0).

In Table 2.1

we use

the data collected

in February

to compare

the farmer, wholesale and retail

outlet characteristics between those who

took part in the

phone survey and those who did

not. We

see that the

two sub-samples are generally well balanced.

The differences

in means are largely not

4 The survey team attempted to

call a total of 570 farmers. Out of these, 433 were

successfully interviewed, 51 experienced phone

problems (no network or

wrong number), 14 farmers refused to

take part in the

phone survey, and 72

farmers did not sell or

grow one of

the five

studied vegetables in

the previous month. Out of the

433 farmers in the

final phone survey sample, 299 were smallholder farmers

and 134 investor farmers. However, we do not disaggregate our findings along this dimension in this

paper.

4

statistically different from zero. The exception is

that farmers

that took part in the

phone survey

were, on

average, somewhat more

educated and more likely

to originate from

male-headed

households. In

addition, among the retail outlets, fruit

and vegetable shops were

less represented in

the phone survey compared to

the in-person survey conducted before the

pandemic.

3. RESULTS

3.1. Value chain agents' views about changes due to the pandemic

In the phone survey,

we asked our respondents how their operations had changed during the

pandemic. Nearly 60 percent of the smallholder farmers and more than 60 percent of the investors

reported that they received less

income than usual in

the past 30 days (Table 3.1). Others indicated,

however, same or

even higher incomes. Despite the

larger share reporting lower incomes, the

majority of smallholders and investors reported to

plan to

continue growing vegetables in the

next

season – most signaled that they would be

expanding their operations by

renting in more land

in the

near future (Table 3.1).

Table 3.1: Stated income losses in the

past month

and future

plans among

farmers

Smallholders

(%)

Investors

(%)

Income changes:

"In the past

30 days

would you say that your household

received more or less

income compared to the

income you usually receive at this time of the year?"

Much less

Less

Same

8.7

50.8

10.5

53.0

26.1

9.7

28.1

11.0

1.3 0.8

77.5 88.8

94.6 88.1

More

Much more

Future plans:

Plan to grow vegetables

in next rainy season

Plan to grow vegetables

in next irrigation season

Intention of farmers on land rental in next

irrigation season:

No change

Rent in more

Rent in less

Rent out more

Rent out less

Do not know yet

Source: May 2020 survey round. Observations: 433 farmers.

42.6 20.9

43.0 59.0

3.02.4

1.0 1.5

0.3 0.0

10.7 15.7

We asked wholesale and retail traders

to compare the situation at the

time of the survey

to the

situation three

months earlier, i.e., before the

pandemic began. Most wholesalers and retailers

reported that there was less choice when it comes to transportation and that related costs had

substantially increased (Table 3.2). Moreover, both the

demand and the

quantity of vegetables sold

had decreased and the

share of vegetables that could not be sold

had increased (Table 3.2).

5

Table 3.2: Stated changes in

traders' businesses compared to

three months prior

Remained

Decreased same Increased

Wholesalers (%) who believe

that…

… the choice in

transporters going to rural areas

… 63.3 36.7 0.0

… the cost of transport from

rural areas to

Addis Ababa … 0.0 6.7 93.3

… the

number of clients that

they sell to … 83.3 0.0 16.7

… turnover (quantity of

vegetables sold) … 86.7 0.0 13.3

… losses … 3.3 20.0 76.7

Retailers (%) who

believe that…

… the choice in

transporters from wholesale markets

… 55.7 37.5 6.8

… the cost of transport from Addis wholesale markets to

retail shops … 1.3 24.7 74.0

… the number of clients that they sell to … 82.1 9.4 8.5

… turnover (quantity of

vegetables sold) … 80.4 10.2 9.4

… losses … 11.5 26.4 62.1

Source: May 2020 survey round. Observations: 30 wholesalers; 235 retailers.

At the

time of the phone interview

in May, the wholesale traders further reported that, compared

to the period before

the pandemic, more of the vegetables they sold originated from

the East Shewa

zone – where the

interviewed farmers were

located (Table 3.3). We also

observe important shifts in

the clientele of the wholesale traders. The role of micro-sellers and public institutions (schools,

hospitals, etc.) declined, while a larger share of the produce was sold to

supermarkets, grocery

stores, and restaurants.

Table 3.3: Procurement locations and sales patterns of urban wholesalers before and after

onset of COVID-19 pandemic

3 months Difference

before (%) Now (%) (%-point)

Origin of vegetables:

East Shewa 44.7 60.0 15.3

Other areas 55.3 40.0 -15.3

Clients sold to:

Other wholesalers 19.4 16.7 -2.7

Consumers 2.3 0.0 -2.3

Institutions (schools,

universities, jails, army, hospitals,

etc.) 6.4 1.2 -5.2

Restaurants 6.5 11.3 4.8

Supermarkets 8.7 12.8 4.1

Micro fruit and vegetable sellers 40.5 24.8 -15.7

Fruit & vegetable grocery

shops 17.4 33.2 15.8

Other clients 0.8 0.0 -0.8

Source: May 2020 survey round. Observations: 30 wholesalers.

3.2. Price formation in the vegetable value chain before and during the

pandemic

The survey instruments fielded

in both survey rounds

were carefully designed

to collect price data at

different levels of the

value chain. We

asked farmers to

estimate the price of the

vegetables with

different quality characteristics in

their kebele at the time of

the survey. We

asked wholesalers and

retailers to

quote the

prices for all

qualities of vegetables that they were selling the day of the

interview. We have a total of 11,665 price observations for tomatoes, onions, green pepper, and

6

cabbage. 5 Table A1 in

the Appendix provides summary statistics for these price data. As all

surveys

were conducted at

the same time,

we can analyze vegetable price formation along the value chain.

A concern with the

price data collected from the traders is

that they may

be subject to

reporting

bias. To

explore this issue, in

the February survey round we

also asked price quotes from

customers visiting

the same wholesale and retail outlets.6 A comparison of the prices quoted by the

traders and the

buyers shows negligible and not statistically significant differences, indicating that

the traders were reporting prices truthfully and accurately (Table

A2 in Appendix).

Figure 3.1: Retail prices

by vegetable

type and survey

round

Source: February 2020 and May 2020 survey rounds.

Figure 3.1 shows a box and whiskers diagram of

the retail (consumer) price

changes and

variations across the four vegetable types. The size

of the box marks the

difference between the

25th percentile (the left-hand side of

the box) and the 75th

percentile (the right-hand side of the

box)

of the retail price distribution for a given vegetable and survey round.

The bottom and top rule

indicate the

bottom 5th and top 5th percentiles of

the full distribution. Focusing on the vertical bar rule

inside the

box that marks the

median, we

see that retail price trends were

quite heterogeneous

during the

pandemic. Median tomato and onion prices increased by

33 and 20

percent, respectively,

while the median prices

of green pepper and cabbage went down by 13 and 12 percent,

respectively. Moreover, the sizes

of the

boxes in

the diagram reveal considerable variation in

retail

prices, particularly for green pepper.

These within-product price

variations are to

a large extent driven by

quality differences. This is

illustrated in

Figure 3.2, which shows how the

tomato prices vary across different quality indicators,

such as the trader's self-assessment of the

overall quality and the length of the product.

5

6

We dropped

the analysis of

price formation

for Ethiopian

kale given the

use of units

in markets that were

difficult to convert

to kilograms,

such as small bunches, large bunches, and bags.

Enumerators were

told to

stay around for

a few

minutes after

completing their interview, to

wait for a buyer at

that particular wholesale or

retail outlet.

If a buyer appeared, they asked

the price directly

from the buyer,

if not, they moved

to the next interview. This explains

the

lower number of observations in Table A1.

7

Figure 3.2: Retail tomato prices

by quality and survey

round

a. Trader's self-assessment of product quality b. Length of the product

Source: February 2020 and May 2020 survey rounds.

We then analyze

price formation before and during

the pandemic using a regression approach.

Specifically, we

regress the price

of the

vegetable on

binary variables for each level of the

value

chain and then interact these with

a binary variable capturing the

survey round (=

1 if May

survey,

zero otherwise). We

also control for differences in

vegetable quality and origin (place of

production).7 Table 3.4 reports the regression results separately for

each vegetable type. The

bottom row shows the farm gate price measured in birr/kg for each vegetable in

February 2020. The

coefficient on the 'May

survey' variable shows the change in

the farm gate price in May 2020.

We

see that

the farm gate prices for tomatoes and onions increased considerably over the three-month

period, while the opposite was

true for green pepper and cabbage.

9.01*** 23.06***

Table 3.4: Price regressions by

vegetable type

(1) (2) (3) (4)

Tomato Onion Green pepper Cabbage

Urban wholesale 1.74*** 4.02*** -0.26 0.41*

(0.25) (0.58) (0.89) (0.22)

Urban retail 7.80*** 5.64***

(0.20) (0.46) (0.69) (0.18)

Urban wholesale x May

survey 0.72*** -0.80 0.97 1.11***

(0.28) (0.52) (0.96) (0.18)

Urban retail x May survey

-1.13*** -2.02*** 2.17** 1.18***

(0.23) (0.55) (0.85) (0.21)

May survey 4.77*** 5.34*** -11.08*** -2.11***

(0.12) (0.22) (0.53) (0.11)

Quality and origin controls? Yes Yes Yes Yes

Observations 3,230 3,491 2,646 2,266

R2 0.697 0.398 0.623 0.647

Farm gate price in February

(birr/kg) 5.73 13.67 22.05 5.44

Source: February 2020 and May 2020 survey rounds.

Note: Heteroskedasticity robust standard errors

in parentheses. * p < 0.10, p < 0.05, p < 0.01. Quality controls for tomato are:

overall quality, ripeness, length, form, and origin; for

onion: overall quality, length, and origin; for

green pepper: overall quality, length,

thickness, color, and origin; and for cabbage: overall quality, size, and origin.

** ***

The coefficients on the non-interacted value chain level variables quantify the gross marketing

margins before the

pandemic in

February 2020. The gross

margins at wholesale level are

generally

small relative to

farm gate prices

and to

the gross margins in the

urban retail sector. In the

case of

7 The quality indicators vary across vegetable types. A subjective quality indicator (best; medium; low) is

used for all

vegetables, ripeness

for tomato (green; ripe; overripe; semi-rotten; rotten), length (in cm)

for tomato, onion, green pepper, form (circle; oval; mixed) for

tomato,

thickness and color for green pepper and size for

cabbage (large >1.5 kg; medium 1-1.5 kg; small <1

kg).

8

green pepper, the

gross margin estimate is

not statistically different from zero in

both rounds. The

margins were highest for

onions in

February (18

percent). While brokers and traders in

wholesale

markets are

often seen as exploitative and overly powerful (Gebreamlak 2020), these results

suggest that even if there

is market power and one could address it,

it seemingly

will not reduce

urban retail prices nor increase producer prices very much given the relatively small

contribution of

these margins to

final prices.8

The coefficients on

the interacted terms inform us how

the marketing margins changed during

the pandemic. In

the case of onion, the

difference in

wholesale margin between the two

rounds is

negligible and not statistically different from zero. For tomatoes and cabbage, the

wholesale margin

on average increased 0.7 and 1.1 birr per kg,

respectively. Meanwhile, urban retail margins declined

significantly in

the case of tomatoes and onions and increased for green pepper and cabbage, the

two products that

saw price decreases between February and

May (see Figure 3.1).

We use these regression results

to show the average price composition along the value chain,

i.e., from farmer

to consumer,

in both periods.9 Figure 3.3 shows the predicted gross margins

in birr

terms as

well as percentages

of the final retail price. Strikingly, the observed changes

in retail

(consumer) prices during the pandemic are largely driven by

increases or decreases in

farm gate

prices and not by

wholesale or

retail cost margins. It seems then that the various disruptions

associated with the

pandemic have not led to

substantial increases in

marketing margins.

Figure 3.3: Vegetable price structure

before and

during the pandemic,

by vegetable

type

a. Prices and margins in birr/kg

b. Margins as

percentages of the final price

Note: These graphs are based on the

estimated coefficients reported in

Table 3.4.

As indicated earlier, the reductions in both international trade and domestic trade between sub

national regions (e.g., between the Oromia region, which

Addis Ababa is surrounded

by, and

the

Amhara region) seem to

have increased the

reliance of Addis Ababa on vegetables produced in

the

Central Rift

Valley. This reduced trade may have contributed to the

unusually large

changes in

agricultural prices – confirming earlier findings that regional and international trade can

play an

important role in

stabilizing food price

volatility (e.g., Minot 2004). This reduction in

trade over longer

distances has also led to

winners and losers, as is typically

seen after trade policy reforms or trade

related shocks (Aksoy and Beghin 2004; Headey 2011).

In Ethiopia, the pandemic disrupted trade between neighboring countries and sub-national

regions. Therefore, those farmers in

the Central Rift

Valley that faced international and regional

competition in

marketing their produce before the pandemic seemingly benefited as

the trade

8 The

price difference between

farm and urban wholesale also accounts for the transportation costs

to bring vegetables from rural

production areas to the

city.

In the case of green pepper, the estimate on the wholesale margin

in February

is negative, though not statistically different

from zero.

We set this margin

to zero

in Figure 3.3.

9

9

disruptions reduced supply, leading to price

increases.10 Onions provide a good example of a crop

that was widely imported before the

pandemic.

11 In

contrast, those farmers in

the Central Rift Valley

that were producing crops that were exported to

other sub-national regions (e.g., to

Amhara) lost

out due to

decrease in demand, which subsequently led

to an oversupply and declining

prices. In

our case, such

dynamics are

observed for green peppers. Moreover, it seems that especially

farmers – more than consumers – were

exposed to large

price volatility because of these domestic

and international trade disruptions.

Finally, transportation costs are often a major determinant of

food prices (Dillon and Barrett 2016;

World Bank 2009; Minten et al.

2016). However, their importance depends on trade distances and

the final value of produce.

It seems that changes

in transportation costs might

be less of an issue

in

the case of value chains

with perishable products that are typically traded over relatively short

distances. Most traders that we

interviewed reported that transportation costs increased

considerably between the two

periods. The median increase in

transportation costs reported by

wholesalers who transported vegetables was about 25 percent, while

transportation costs in

the city

changed similarly.

Interestingly, transport costs in the city

over relatively small distances are as high

as transport costs from rural areas

to the capital. The relatively high costs

in the city are due

to the

small quantities that are bought daily by

retailers and that often need to be transported

on small

mini-bus taxis, while

transport from

rural areas is done on

large and more cost-efficient trucks that

specialize in

the transport of vegetables.

It is important

to note that transportation costs form a negligible part of

the total retail

prices of

the vegetables studied here and stayed

so even

with the inflated transport costs during

the

pandemic. Using the

data collected in

the February survey, we

estimate that transportation costs

from rural

areas to

Addis Ababa as well as transport within the city

were each about 0.66 birr/kg,

amounting to a total transport cost of 1.3 birr/kg, a relatively

small share of the final retail

price

(Figure 3.4). Figure 3.4 further indicates that the increase in

these transportation costs in May – by

0.3 birr per

kg – has not been a major contributor

to changes

in the final retail price of these

vegetables.

Figure 3.4: Share

of transportation costs

in final retail price,

by vegetable

and survey

round

Source: February 2020 and May 2020 survey rounds.

10

Minten et al.

(2020) show, however, that input costs, such as wages, went up as

well for a number of these farmers, but that these

changes were

less than output price

changes.

11 The

increase of onion prices is

seemingly linked to the

closure of the land borders with

Sudan due to the COVID-19

pandemic. In a

typical year, Ethiopia imports a significant tonnage of onions from Sudan – e.g.

16.1 million USD in

2018 (data downloaded from

https://comtrade.un.org/data/). But, as borders have been closed, onion imports from Sudan dried

up.

10

4. CONCLUSIONS

Based on unique large-scale price data from different segments in

rural-urban vegetable value

chains in

Ethiopia, we

study the

evolution of farm and retail prices and marketing margins during the

first three months of

the COVID-19 pandemic.

To slow

the spread of

the virus, the Government of

Ethiopia banned travel through land borders while some regional states imposed restrictions on

border crossings. The evidence provided here suggests that this led to

substantial changes in

vegetable prices. In

particular, we

see large price changes for farmers, but the effects are

heterogenous: farmers who faced less competition from other areas (locally or

internationally)

benefited through higher output prices from the

imposed pandemic trade restrictions, while those

that could no longer export to

other areas in the

country lost out. Overall changes in

wholesale and

retail marketing margins have been relatively less important, despite the reduced turnover, higher

losses, and higher transportation costs reported by agricultural traders and retailers. We take

this as

evidence of notable resilience in the local marketing systems.

Our findings have important implications for policy. First,

close monitoring

of price movements

and the factors contributing to

those movements is paramount, especially during this

crisis period.

Changes in consumer prices

are often claimed

to be linked

to predatory behavior among traders,

motivating government intervention to

curb trading activity, as has already been witnessed during

the COVID-19 pandemic (Resnick 2020; Wegerif 2020; Gebreamlak 2020). However, the earlier

evidence on such predatory behavior is

limited (Sitko and Jayne 2014, Minten et al.

2017) and the

findings reported here indicate that the

price changes during this pandemic have not been driven by

large increases

in marketing margins. Second, quantitative assessments on

the relative importance

of different segments

in the value chains are useful for setting priorities

to reduce farm-retail

spreads in

order to

achieve higher prices for

producers and lower prices for consumers. Our data in

particular illustrate the

lower importance of transportation costs and the large contribution of urban

distribution costs in

the final retail prices of vegetables. More focus on

addressing potential

inefficiencies in

these urban distribution systems is

therefore called for.

REFERENCES

Aksoy, M.A., and

J.C. Beghin.

2004. Global agricultural

trade and developing countries.

Washington, DC: World Bank.

Akter, S. 2020. “The impact of

COVID-19 related ‘stay-at-home’ restrictions

on food prices

in Europe: findings from a

preliminary analysis.” Food Security, forthcoming (https://doi.org/10.1007/s12571-020-01082-3).

Barrett, C.B. 2020. “Actions now

can curb food systems fallout from

COVID-19.” Nature Food: 1-2.

Bellemare, M.F. 2015. “Rising food prices, food price volatility, and social unrest.” American Journal of Agricultural

Economics 97 (1):

1-21.

de Paulo Farias,

D. and F.F. de

Araújo. 2020. Will COVID-19 affect food supply

in distribution

centers of Brazilian

regions

affected by the

pandemic? Trends in

Food Science & Technology.

Dillon, B.M., and C.B. Barrett. 2016. “Global oil prices and local food prices:

Evidence from East

Africa.” American Journal

of Agricultural Economics

98 (1): 154-171.

Gebreamlak, H. 2020.

“Onion tears.” Addis Fortune June 20

(21): 1051. Downloaded

on 08/03/2020

from

https://addisfortune.news/onion-tears/.

Glauber, J., D. Laborde,

W. Martin, and R. Vos. 2020.

“COVID-19: Trade

restrictions are worst possible response

to

safeguard food security.”

In COVID-19 and Global Food Security,

edited by J. Swinnen,

and J. McDermott, 66-68.

Washington DC: IFPRI.

Hahn, W. 2020. Livestock, Dairy, and Poultry Outlook, LDP-M-312. U.S. Department of Agriculture, Economic Research

Service.

Harris, J., L. Depenbusch, A.A.

Pal, R.M. Nair, and S. Ramasamy.

2020. “Food system disruption:

initial livelihood and

dietary effects

of COVID-19 on vegetable

producers in India.” Food Security:

1-11.

Headey, D. 2011.

“Rethinking the global food crisis: The role of trade shocks.”

Food Policy

36 (2):

136-146.

11

Hirvonen, K., G. Abate, and

A. de Brauw. 2020. Food

and nutrition security

in Addis Ababa during COVID-19 pandemic:

May 2020 report.

ESSP Working Paper 143. Washington,

DC and Addis Ababa, Ethiopia:

International Food Policy

Research Institute (IFPRI) and Policy Studies Institute (PSI).

Laborde, D., W. Martin, J. Swinnen, and R.

Vos. 2020.

“Covid-19 risks to global food security.” Science 369 (6503): 500

502.

Lele, U., S. Bansal, and J.V. Meenakshi. 2020. “Health and Nutrition of India’s Labour Force and COVID-19

Challenges.” Economic & Political Weekly 55 (21): 13.

Lusk, J.

2020. Meat and egg

prices following the COVID‐19

outbreak. Blogpost. Retrieved

from http://jaysonlusk.com/blog/2020/4/5/food-sales-and-prices-following-covid-19-outbreak

Minot, N. 2014. “Food

price volatility in sub-Saharan Africa: Has it really increased?” Food Policy 45:

45-56.

Minten, B., E. Tamru, E. Engida, and K. Tadesse.

2016. “Feeding

Africa's cities: The case of the supply chain of teff

to

Addis Ababa.” Economic Development

and Cultural

Change 64 (2):

265-297.

Minten, B., T. Assefa, and

K. Hirvonen. 2017.

“Can agricultural

traders be trusted? Evidence

from coffee in Ethiopia.”

World Development

90: 77-88.

Minten, B., B. Mohammed, and

S. Tamru. 2020.

Emerging medium-scale

tenant farming, gig economies,

and the COVID

19 disruption: The case

of commercial

vegetable clusters in Ethiopia. ESSP Working Paper 149, Washington,

DC and

Addis Ababa, Ethiopia: International Food Policy

Research Institute (IFPRI) and Policy Studies

Institute (PSI).

Mogues, T.

2020. Food Markets During COVID-19.

Special Series on Covid-19.

Washington DC: International

Monetary

Fund. Downloaded

on 08/03/2020 from https://www.imf.org/~/media/Files/Publications/covid19-special-notes/en

special-series-on-covid-19-food-markets-during-covid-19.ashx.

Narayan, S., and S. Saha.

2020. One step behind: The

government of Indian

and agricultural policy

during the COVID-19

lockdown. Review of Agrarian Studies,

forthcoming.

Nang’ole, E.M., D. Mithöfer,

and S.

Franzel. 2011.

Review of guidelines and manuals for

value chain analysis for

agricultural and

forest products. ICRAF

Occasional Paper 17. Nairobi: World

Agroforestry Centre (ICRAF).

Reardon, T., M.F. Bellemare,

and D. Zilberman. 2020.

“How COVID-19 may disrupt food supply

chains in developing

countries.” In COVID-19

and Global Food Security,

edited by J. Swinnen,

and J.

McDermott, 78-80. Washington DC:

IFPRI.

Resnick, D. 2020. “COVID-19 lockdowns threaten Africa’s vital informal urban food trade.” In COVID-19 and Global Food

Security, edited by

J. Swinnen,

and J. McDermott, 73-74. Washington DC: IFPRI.

Sitko, N.J., and T.S. Jayne. 2014. “Exploitative briefcase businessmen, parasites, and

other myths and legends: assembly

traders and the performance of

maize markets in eastern

and southern

Africa.” World Development 54: 56-67.

Swinnen, J., and J. McDermott. 2020.

COVID-19 and

Global Food Security.

Washington DC: IFPRI

Torero, M. 2020.

“Without food, there can

be no exit from the

pandemic.” Nature, 580: 588-589.

Webber, M., and P. Labaste.

2009. Building competitiveness

in Africa’s agriculture: A

guide to value chain concepts

and

applications. Washington, DC: World Bank.

Wegerif, M. 2020.

“Informal” food traders and food security: experiences

from the Covid-19

response in South

Africa." Food Security 2020:

1-4.

World Bank. 2009. “Eastern Africa: A Study of the

Regional Maize

Market and

Marketing Costs.” Report no.

49831-ET,

World Bank, Washington, DC.

Yu, X., C. Liu, H. Wang, and J.H. Feil. 2020. The impact of COVID-19 on food prices in China: evidence of four major food

products from Beijing, Shandong and Hubei Provinces. China

Agricultural Economic Review.

12

APPENDIX

Appendix Table A1: Means and standard deviations of

vegetable prices (birr/kg) by

survey

round

May 2020

Observ

ationsSD Mean SD

2.3 10.5 2.8

3.0 4.7

610

602

337

348

10.0

18.9

12.0

3.3

7.0

2.2 1.4

2.0 97 2.8

February 2020

Observ

ations Mean

Farm gate

Tomato 1,123 5.7

Onion 1,266 13.7

Green pepper 807 22.0

Cabbage 823 5.4

Urban wholesale

Tomato 176 5.9

Onion 191 16.0

Green pepper 189 21.2

Cabbage 80 6.9

Urban retail

Tomato 783 12.4

Onion 838 22.6

Green pepper 801 42.9

Cabbage 594 10.9

Source: February 2020 and May 2020 survey rounds.

Note: SD = Standard deviation.

12.1

22.12.8 74 2.9

6.4 81 11.0 4.5

0.8 65 5.6 1.0

460 16.5 3.43.8

11.6

12.8

25.5 9.5524

436 12.236.1

10.53.3 360 2.7

16

18

Appendix Table A2: Accuracy of

prices reported by

traders

Price quoted by

traders(birr/kg) Price quoted by

buyers

Observ (birr/kg)

Vegetable type

t-test

ations Mean Median SD Mean Median SD t-value p-val

Wholesale price

Tomato 24 5.98 6 1.49 5.94 6 1.55 0.09 0.925

Onion 37 16.00 16 1.45 15.97 1.48 0.08 0.937

Green pepper 27 21.15 18 6.61 21.26 6.46 -0.06 0.950

Head cabbage 22 6.77 7 0.86 6.75 6.75 0.86 0.09 0.930

Retail price

Tomato 363 11.63 12 3.03 11.41 12 3.22 0.96 0.337

Onion 400 23.62 18 14.46 23.62 14.39 0.00 0.997

Green pepper 453 40.13 40 12.68 38.98 40 13.41 1.32 0.186

Head cabbage 263 12.25 13 2.66 11.95 12 2.52 1.34 0.182

Source: February 2020 survey round.

Note: SD

= Standard deviation. Difference in means between

the groups tested

with a t-test (null-hypothesis: difference

in means = 0).

18

13

ABOUT THE AUTHORS

Kalle Hirvonen is a Senior Research Fellow in

the Development Strategy and Governance Division

(DSGD) of the International Food Policy Research Institute (IFPRI), based

in Addis Ababa. Belay

Mohammed is a Research Officer at the Ethiopia Strategy Support Program of IFPRI, Addis Ababa.

Bart Minten is a Senior Research Fellow in DSGD of IFPRI, based in Myanmar. Seneshaw Tamru

is a Collaborator of DSGD of IFPRI, based in Addis Ababa.

INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

1201 Eye St, NW

| Washington, DC 20005 USA

T. +1-202-862-5600 | F. +1-202-862-5606

Email: [email protected] | www.ifpri.org | www.ifpri.info

POLICY STUDIES INSTITUTE

P.O. Box 2479, Addis Ababa, Ethiopia

T. +251.11-550-6066; +251-11-553-8633 | F. +251-11-550-5588

http://psi.gov.et/

IFPRI–ESSP ADDIS ABABA

P.O. Box 5689, Addis Ababa, Ethiopia

T. +251-11-617-2000 | F. +251-11-667-6923

Email: [email protected] | http://essp.ifpri.info

The Ethiopia Strategy Support Program (ESSP) is

managed by the

International Food Policy Research Institute (IFPRI); is

jointly implemented

with the Policy Studies Institute (PSI); and

is financially supported by

the United States Agency

for International Development (USAID), the

Department for International Development (DFID)

of the government of the

United Kingdom, and the European Union (EU).

The research

presented here was conducted as

part of

the CGIAR

Research Program on Policies, Institutions, and Markets (PIM), which is

led by IFPRI.

This publication has been prepared as

an output of ESSP

and has not been independently peer reviewed. Any opinions expressed here belong

to the author(s) and are not necessarily representative of or endorsed by

IFPRI, PSI, USAID,

DFID, EU, PIM, or CGIAR.

© 2020, Copyright remains with

the author(s). This publication is

licensed for

use under a Creative Commons Attribution 4.0 International License

(CC BY 4.0). To view this license,

visit https://creativecommons.org/licenses/by/4.0.

1

IFPRI is a CGIAR Research Center | A

world free of hunger

and malnutrition