dynamics of farm households on-farm and off-farm

DYNAMICS OF FARM HOUSEHOLDS ON-FARM AND OFF-FARM

DIVERSIFICATION: THE CASE OF COFFEE PRODUCING FARM

HOUSEHOLDS IN SOUTHERN ETHIOPIA

FIKADU MITIKU ABDISSA

http://www.wur.nl/NL/

II

Dynamics of Households On-farm and Off-farm Diversification: The

Case of Coffee Producing Farm Households in Southern Ethiopia

Fikadu Mitku Abdissa

Reg.No. 820522003080

MSc Thesis: DEC-80433

Supervisor

Dr. Kees Burger

A Thesis Submitted to Wageningen University and Research Center In Partial Fulfillment of the Requirements for the Degree of

Master of Science in International Development Studies

August 2011 Wageningen University and Research Center,

Wageningen

https://ssc.wur.nl/wpage8/xpage.aspx?xml=vak_xml.iread?Vak:Vak%20id=DEC80433$Ondeenheid:Gidsjaar=2010$Oplsrt:Oplsrt=M&xsl=/bois/xsl/vak.xsl&css=gids.css&lang=usa&app=bois

Acknowledgements First and for most I would like to thank the Almighty God for His Mercy and Courage throughout my

life and during this MSc study. Had he not been with me, all the ups and downs would have not been

passed and this thesis would have not been completed. I gratefully acknowledge Wageningen

University for admitting me to the MSc program in International Development studies, and the

Netherlands Organization for International Cooperation in Higher Education (Nuffic) for financially

assisting my study in the Netherlands. Had it not been because of the great roles they played in

bringing me to Wageningen University, I would have not enjoyed the great opportunity of working

with many international students. My special thanks also go to Dr. Sudha Loman, study advisor in the

program International Development Studies, for her encouragement, commitment and dedication to

solve any problem related to my study.

Next, I would like to extend my heartfelt gratitude to my supervisor Dr. Kees Burger who gave me

the data for this thesis work, guided me in close contact by investing a lot of his valuable time so that

I can put my idea on paper. His continuous advice and constructive comments helped me a lot to

organize this thesis to the level it is at final stage.

My special gratitude also goes to Mr. Mekonnen Bekele Wakeyo, PhD fellow at Wageningen

University, for his continuous encouragement, professional advices and provision of materials for

this study. I also thank Mrs. Tigest Eshetu for her moral support during my thesis work at

Wageningen University.

I also owed to thank many of my friends to state a few, Ashenafi Feyissa, Daniel Emana, Akalu

Dafissa, Zeleke Belay, Mosisa Chewaka, Lemessa Benti, Desalegn Obsi and Debela Deressa students

at Wageningen University for their moral support and sharing my tension during my thesis work.

Especially Daniel, with whom I shared interesting memory during my stay in Wageningen. I also

thank my friends, Dr. Feyissa Begna and Dr. Benti Deressa, the former for his taking care of my

salary at Jamma Unvirsity and dealing with all social issues related to my family back home and the

latter for his cooperation in signing my agreement with Jimma University. I would also like to thank

Jimma University for providing me leave of absence during my study abroad.

Last but not least, I would like to thank my parents particularly, my father Mr. Mitiku Abdissa, my

mother Mrs. Shitaye Diriba, both of my brothers and my only sister, for their moral encouragement

during the course of my study. I appreciate the contribution of my parents as they played a great

role in my life by sending me to school and keeping me there by providing necessary moral and

IV

material support even when I was apart from them for my schooling. Thanks you all who

contributed to my success in one or the other way!!

Fikadu Mitiku Abdissa

Wageningen, The Netherlands

V

List of tables Table 1:Three-way classification of activities: Sectoral, functional and spatial ..................................... 8

Table 2:Main sources of income and crop prices for coffee producing households over 1997 and

2004 ...................................................................................................................................................... 29

Table 3: Share of income from different income earning activities for pooled sample ....................... 30

Table 4: Share of income from different income earning activities only for households earning

positive income from off-farm self-employment ................................................................................. 31

Table 5: Share of income from different income earning activities only for households earning

positive income from off-farm wage employment ............................................................................... 32

Table 6: On-farm diversification between 1997 and 2004; as indicated in the share of farmers choice

for crop combinations ........................................................................................................................... 35

Table 7:Land allocation to different crop combinations....................................................................... 39

Table 8: Labor allocation to crop production ....................................................................................... 40

Table 9: Attractiveness of crops: net value product to labor allocated to crop production (in ETB) ... 42

Table 10: Household participation in off-farm self-employment by year ............................................ 43

Table 11: Frequencies of participation in off-farm wage employment by years .................................. 45

Table 12: Labor allocation to off-farm self-employment and off-farm wage employment for the

pooled sample ....................................................................................................................................... 47

Table 13: Definition and description of variables used in econometric estimation for pooled sample

of 1997 and 2004 .................................................................................................................................. 50

Table 14: Multinomial logit estimation result for the determinants of on farm diversification on the

pooled data of 1997 and 2004 .............................................................................................................. 56

Table 15: Estimation result of fixed effect logit model on determinants of diversification ................. 58

Table 16:Definitions and descriptive statistics of variables used in random tobit estimation ............. 60

Table 17:Random effect tobit model estimation results on the effect of coffee price and other

determinants of earning from off-farm self-employment (dependent variable= off-farm income) ... 63

Table 18 :Estimation result of random effect tobit model on the determinants of income from off-

farm wage employment ........................................................................................................................ 64

List of figures Figure 1: Factor proportions and specialization ................................................................................... 16

Figure 2: Households’ participation in off-farm self-employment by activities over 1997 and 2004 .. 44

Figure 3 :Households participation in off-farm wage employment by activities in 2004. .................... 46

VI

Abstract Using panel data from Ethiopian Rural Household Survey, this thesis analyzed the main

income earning strategies and how the contribution from each strategy had been changed

during coffee crises, and the patterns and determinants of households on-farm and off-farm

diversification among coffee producing farm households in southern Ethiopia. Reduced form

of standard farm household model has been adopted to create relationship between

diversification and its determinants. Multinomial logit model for households on-farm

diversification, fixed effect logit model for changes in on-farm diversification, and random

effect tobit model for the determinants of off-farm earnings has been specified. In total, 274

coffee producing farm households has been matched and included from the surveys of 1997

and 2004 from four villages in Southern Ethiopia. Five main income earning strategies have

been identified: income from crop sale, livestock and livestock product sale, off-farm self-

employment, off-farm wage employment and remittances. The share of income from off-

farm earning was significantly increased while the contribution of income from crop sale had

been significantly decreased between 1997 and 2004. Factors such as land ownership,

number of livestock, family size, access to road, and land quality are facilitating for

diversification from perennial to annual crops. Whereas, distance from market, distance of

plot from home and slope of land are constraining factors for on-farm diversification. The

main encouraging factors for households off-farm earning are livestock asset and household

valuables while the constraining factors are family size and age of head. Off-farm wage

employment was negatively influenced by land ownership and weakly and positively by

family size. Landless households enjoy better income from off-farm earnings than

landowners. Hence, any policy, program or project working for rural development of the

country had better focus on facilitating factors for off-farm earnings.

Key words: on-farm diversification, off-farm self-employment, off-farm wage employment,

determinants of diversification, coffee producing farm households, Southern Ethiopia

VII

Table of contents

Acknowledgements ................................................................................................................................ III

List of tables ............................................................................................................................................ V

List of figures ........................................................................................................................................... V

Abstract .................................................................................................................................................. VI

1. Introduction .................................................................................................................................... 1

1.1. Back ground ............................................................................................................................ 1

1.2. Statement of the problem ...................................................................................................... 3

1.3. Research questions ................................................................................................................. 6

1.4. Thesis out line ......................................................................................................................... 6

2. Conceptual and Theoretical Frame Work ...................................................................................... 7

2.1. Definitions of Diversification ................................................................................................... 7

2.2. Patterns of diversification ....................................................................................................... 8

2.3. Determinants of diversification .............................................................................................. 9

2.4. Coffee price fluctuation and diversification .......................................................................... 12

2.5. Theoretical Framework ......................................................................................................... 14

3. Research Methodology ................................................................................................................. 20

3.1. Data ....................................................................................................................................... 20

3.2. Econometric models ............................................................................................................ 23

3.2.1. Multinomial model for households on-farm diversification ......................................... 23

3.2.2. Fixed effect logit model ................................................................................................ 25

3.2.3. Random effect tobit model for diversification to off-farm earnings ............................ 27

4. Result and discussions .................................................................................................................. 28

4.1. The main income earning strategies and crop prices ........................................................... 28

4.2. Share of income by sources .................................................................................................. 28

4.3. Patterns of on-farm diversification, and land and labor allocation among crop

combinations. ................................................................................................................................... 33

4.3.1. Patterns of on-farm diversification at household level ................................................ 33

4.3.2. Land allocation among different crop combinations .................................................... 36

4.3.3. Labor allocation among different crop combinations .................................................. 37

4.3.4. Attractiveness of crop combinations ............................................................................ 41

4.4. Patterns of diversification to off-farm and off-farm income earning activities.................... 43

4.4.1. Households participation in off-farm self-employment by activities ........................... 43

4.4.2. Households participation in Off-farm wage employment ............................................ 44

VIII

4.4.3. Labor allocation to off-farm self-employment and off-farm wage employment ......... 46

4.5. Result of multinomial logit model for determinants of crop choice ................................... 53

4.6. Results of fixed effect logit model estimate for changes in diversification .......................... 58

4.7. Determinants of diversification to off-farm income earning and expectations ................... 59

4.8. Estimation result on the determinants of off-farm self-employment and off-farm wage

employment ...................................................................................................................................... 63

5. Conclusion ..................................................................................................................................... 70

References ............................................................................................................................................ 73

Annex 1: Summary of major crops and crop combinations.................................................................. 77

Annex 2: Factor intensities among crops .............................................................................................. 80

Annex 3: Models for dynamics of on-farm diversification.................................................................... 82

1. Introduction

1.1. Back ground

Agriculture is remaining a backbone of Ethiopian economy contributing more than 50% of

GDP, 80% of export earnings, and employing 85% of the population of the country. Coffee is

the main cash perennial crop in the country with having a large share of agricultural

commodity export. On estimation 15 million people of Ethiopia are dependent on coffee

sector (Petit, 2007). The southern, western, southwestern and eastern parts of the country

are the most common for producing well-known and internationally acceptable quality

coffee. However, the country is not using full potential of the land for coffee production.

While the country has 6 million ha of land suitable for coffee production, only 320.000 to

700.000 ha have been used (FAO, 1987; cited in Mekuria, 2004).

Coffee producing farm households are considered to be relatively better-off in cash earning

than non-coffee producing farm households in the areas. However, their earning depends

on seasons and price of coffee in international market. Households are affected differently

and arrange different coping strategies in response to fluctuating coffee incomes, whether

occasioned by declining international coffee prices or other factors (Petit, 2007). Farm

households try to reduce the effect through diversifying their activities, by allocating their

resources to different income earning activities like producing other perennial crops such as

chat, enset and others; annual crops such as field crops and annual horticultural crops. The

field crops are mainly cereals and pulses whereas the horticultural crops are sweet potato,

potato, vegetables and so on. Furthermore, they participate in off-farm activities to

generate additional income, reduce the use of other farm inputs, change their land use and

land management styles (Tucker et al., 2010) and migrate to other nearby villages or cities

to sustain their life (Lewis, 2005).

In countries like Ethiopia, such diversification is also constrained by factors such as absence

of infrastructure, absence of pro-poor credit service, shortage of land (fragmentation), lack

of appropriate technology, lack of education and absence of perfect market for resource

they have. There are policies, programs and projects that commit resources to rural

development of the country. Following the downfall of the Derg regime in 1991, reforms

such as currency devaluation, market liberalization and others have been made to ensure

2

food security. Farm households’ income diversification had also been getting attention at

the same time. However, the focus varies from region to region based on the relative

severity of poverty in the country. Literatures indicate that diversification is common in

every society (Barrett et al., 2001). Many comparative studies have been undertaken in the

area of livelihood diversification. The extent and effect of such diversification varies from

region to region and from household to household within the same vicinity (Escobal, 2001).

Other important factor for those households diversification is their resource endowment,

factor intensities of the activities, which are available for households to diversify to, and

returns from those activities. In the world of perfect market, households allocate their

factor of production to produce different crops or participate in different activities until

returns from each activity is equalized. This is, however, far to reach in the case of Ethiopia

where there is absence of market for factors of production. For instance, there is no formal

market transaction for land in Ethiopia, as land is not subjected to sell and/or exchange

according to current Ethiopian land policy. In this case, households might depend on their

own factor of production. This, according to Beven et al. (1989) has two effects on

household’s diversification.

The first is, because households differ in their initial quantity of factor endowment, only

factor abundant households can respond to exogenous change by allocating their factors of

production to other activities. The endowment-scarce households cannot participate in the

activity though endowment-abundant households enjoy the high return from it. The second

effect is the difference in households land-to-labor endowment results in difference in

return to each factor across households. Such reasoning makes its base on the Ricardo

model of international comparative advantage used as a base in the Heckscher-Ohlin trade

theory of cones of diversification.

Factor intensity of the activities also determines the pattern of diversification or

specialization. In the perfect market economy where price equal to cost of factor of

production, when the amount of one factor of production increases, the production of the

good which uses that particular factor of production intensively increases relative to the

increase in the factor of production (Rybczynski theorem). In our data set, households

produce different crops and participate in different off-farm activities as a result of decrease

3

in coffee price or to make use of all factor of production. Let us see the case of perennial

and annual crops and assume that perennial crops are land intensive and annual crops are

labor intensive, then the pattern of specialization or diversification is determined by the

quantity and ratio of factor endowment of those households. Therefore, only labor

abundant households can produce annual crops. The labor scarce households either remain

in perennial crop production or enter the on farm diversification and different activities

when their labor grows. The same holds for off-farm activities, as they are labor intensive.

The history is different when there is no perfect market for input and output as in the case

of many developing countries in general and in Ethiopia in particular. In such situation, the

decision factor will not only be exogenous market but also specific household characteristics

that affect production and consumption decision affects their pattern of diversification.

1.2. Statement of the problem

Farm households in developing countries are constrained in allocating their resources

among different activities. They neither have perfect information about market nor have

good infrastructure to access when the market exists. Because of this, they fail to integrate

themselves into the monetary market. In the case of coffee producing areas in Ethiopia,

farm households produce other food crops and cash crops to cope up with coffee price

fluctuation. Sometimes the price of those cash crops also goes down and the farm

households fall into shortage of cash to spend for input, children schooling and different

household expenditures. For instance, study by Forje (1998) shows that during the coffee

crises, Cameroon coffee farmers pulled their children out of school and started to produce

vegetable to sustain their life. This calls upon reallocating resource from coffee production

to other on-farm and off-farm activities such as annual crop production, wage employment,

self-employment and combination of many activities.

Such diversification is not only practiced in poor countries but also there in developed

economies. For instance, lbery (1991) studied farm diversification in UK, however,

diversification in developed economies referred to as “pluriactivity” (Evans and Ilbery, 1993)

which is different in nature and pattern of diversification in developing countries. Farm

households’ diversification to different income earning activities differs based on their

4

location, opportunities and constraints they have in their vicinity and the socio-economic

context of the country in which they live.

Many studies have been undertaken on farm households on-farm and off-farm

diversification, determinants of diversification, and causes and consequences of

diversification (Jansen et al., 2006, Ellis, 2000, Alemayehu et al., 2010, Escobal, 2001,

Mutenje et al., 2010, Corral and Reardon, 2001) especially their participation in off-farm

activities and the share of off-farm earning in the total income of farm households in

developing countries (Reardon, 1997, Barrett et al., 2001, Janvry and Sadoulet, 2001,

Fabusoro et al., 2010). Even a handful of studies have been done in different regions of

Ethiopia (Block and Webb, 2001). For instance, (Babulo et al., 2008, Woldehanna, 2000,

Woldenhanna and Oskam, 2001) studied the role of off-farm income in the livelihood of

farm households in Tigray regional state of Ethiopia. Likewise, Abebe, and Carswell studied

prevalence of livelihood diversification in Southern Ethiopia (Carswell, 2002, Abebe, 2007).

However, less attention has been paid to cash crop (coffee) producing areas of the country.

The researches in the cash crop areas focus on how farm households can specialize in cash

crops and benefit from the high income they are expected to earn from it (Abebe, 2007).

Other related risks get less attention. For instance, there are high weather, price and disease

risks for coffee producing farm households in Ethiopia; this emphasizes the importance of

focusing on other income earning possibilities that enable farmers to buffer the income loss

during coffee price recession.

Study by Abebe (2007) in Gedeo district, in the southern Regional state of Ethiopia showed

that, following the Structural Adjustment Program (SAP), farm households were encouraged

to focus on coffee production to increase the role of coffee in the economy of farmers as

well as the country at large. One case analyzed by the author in the district showed that,

farmers even destroyed enset (false banana) that was/is their staple food crop and

cultivated coffee. During the coffee price fall prior to 2002, however, they uprooted coffee

and re-established enset on their farm land (Abebe, 2007). Nevertheless, that could not be

an immediate solution for farmers as enset needs at least three to four years for maturity.

Farmers swung forth and back among many activities to sustain their income and

subsequently their livelihood.

5

To deal with the income variability farm households’ even diversity to low return crops for

subsistence and\or participate in any off-farm income earning activity. It has been explained

in literatures that rural off-farm employment are tended to be, in most countries, even

much more important than migration and farm wage-labor income and often more

important than cash cropping as a source of income and liquidity (Davis et al., 2009).

Another gap in the body of literature on how farm households diversify their activity is

absence of intertemporal data to analyze the patterns of farm households income

diversification. Even though many empirical evidences come up with increasing contribution

and importance of off-farm incomes in rural livelihood of developing countries (see for

instance Reardon, 1997, Carswell, 2002), it was forwarded by Ellis (1998) that there is lack of

comparable intertemporal data to study the direction and the patterns of the change in

rural areas.

Despite of the importance of diversification, the potential off-farm and off-farm activities,

their relative contribution to the total income of coffee producing farm households during

coffee price crises and factors determining farm households on-farm and off-farm

diversification has not been well studied in the rural area of Southern Ethiopia.

Therefore, it can be hypothesized that, in attempt to respond to decrease in coffee prices

through diversification, households on-farm and off-farm diversification is influenced by

their land and labor endowments, and access to public assets. In particular farm size, land

ownership, family size and access to road and market are expected to positively influence

farm households on-farm diversification. While asset ownership such as household valuable,

number of livestock owned, family size and access to infrastructure such as road, and input

and output market positively influence households earning from off-farm self-employment.

Hence, the objectives of this thesis were to identify the main sources of income for coffee

producers and the dynamics of their contribution over time; analyze the patterns of farm

household’s on farm and off-farm diversification; and to analyze the determinants of such

diversification among coffee producing farm households in Southern Ethiopia over the years

1997 and 2004.

6

1.3. Research questions

What are the main income earning strategies and how the contributions from each strategy

changed over the years 1997 and 2004 among coffee producing farm households in

Southern Ethiopia?

What are the patterns and determinants of on-farm and off-farm diversification among

coffee producing farm households in southern Ethiopia over the years 1997 and 2004?

1.4. Thesis out line

The thesis is organized into five chapters with the remaining chapter two dealing with

conceptual and theoretical framework of diversification. The conceptual framework part

includes definition of diversification, patterns of diversification, determinants of

diversification, and coffee price fluctuation and diversification. The theoretical framework

deals with economic model (household model) from which the reduced form of the model

has been used to create relation between diversification and its determinants that in turn

leaded us to specifying the testable econometric models. Chapter three deal with

methodology: the data used in the thesis work, econometric models and estimation

procedures. In chapter four, the main findings have been discussed and chapter 5 concludes.

7

2. Conceptual and Theoretical Frame Work

2.1. Definitions of Diversification

Ilbery defined diversification as ‘the adoption of income-earning activities outside the range

of conventional crop and livestock enterprises associated with agriculture. It involves a

diversion of resources (land, labor and capital) which were previously committed to

conventional farming activities’ (Ilbery, 1991).

Barrett et al (2001) give other definition of diversification, as ‘form of self-insurance in which

people exchange some foregone expected earnings for reduced income variability achieved

by selecting a portfolio of assets and activities that have low or negative correlation of

incomes.

The distinction between off-farm self-employment and off-farm wage employment is also

important. Off-farm self-employment involves ownership of a firm that produces goods and

services, and buyers who do not give direct orders (Reardon, 1997) where as wage

employment is a temporary employment contract in which the employer gives a direct

order (Woldenhanna and Oskam, 2001).

There are two main types of diversification: income diversification and livelihood

diversification. Income diversification is diversifying earnings in cash or in kind that can be

valued at market prices (Ellis, 1998). According to Ellis, the cash earnings components of

income include items like crop or livestock sales, wages, rents, and remittances. The in-kind

component of income refers to consumption of own farm produce, payments in kind (for

example, in food), and transfers or exchanges of consumption items that occur between

households in rural communities. Whereas a livelihood diversification encompasses

diversifying income earning activities in cash as well as in kind, and the social institutions like

kin, family, compound and village which support a household to sustain a given standard of

living.

In all cases, diversification depends on the asset of households. For instance, households

have non-productive assets such as household valuables which do not generate earned

income and productive assets such as human capital, land, livestock which generate earned

income indirectly through allocating to different activities (Barrett et al., 2001). Those

8

activities are broadly categorised as farm, off-farm wage employment and off-farm self-

employment. The last two can be put under the same category as simply off-farm. However,

categorizing the household diversification activities only into two seems general. To avoid

such confusion among the activities, Barrett linked the classification of activities to the

macro-economic sectoral distinction of national accounting system in which the sectors are

classified as primary (agriculture), secondary (manufacturing) and tertiary (service) sectors.

Hence, he categorised as sectoral (farm vs off-farm activities), functional forms (wage vs

self-employment), and spatial (local vs migratory). The summary has been given in table 1.

Table 1:Three-way classification of activities: Sectoral, functional and spatial

Primary sector Secondary sector Tertiary

Agriculture Mining and other

extractive

Manufacturing

Service sector

Wage

employment

Local Migratory Local Migratory Local Migratory Local Migratory

Self-

employment

Local Migratory Local Migratory Local Migratory Local Migratory

Source: Barrett et al, 2001

The first row indicates the sectoral classification of activities, where secondary sector

includes mining and other extractive activities and manufacturing. The first column indicates

the functional classification of the activities. The rest of the columns indicate where (local or

migratory) the activities take place or where resource is allocated within the sectors and for

the two functions (see Barrett et al, 2001 for detail)

2.2. Patterns of diversification

Diversification among farm households has received attention as livelihood strategy of

households on the globe. However, the content, extent, nature and intensity of

diversification differ based on the socio-economic and bio-physical environments in which

households live. For instance, it is known as “pluractivity” or “multi-functionality” in

developed world, where farmers provide different recreational landscape services beside to

farm produce. However, it can also be considered as survival strategy when households shift

9

their land to providing relatively low input demanding landscape service rather than

producing input intensive agricultural products.

Many case studies have been undertaken in the developing world on farm household

diversification to off-farm activities as a means of livelihood. For instances, studies by

Deininger and Olinto (2001), in rural Colombia showed that the average share of off-farm

income in total rural household income was 45%. Likewise, other studies also showed that

off-farm income in Africa accounts for 40-45% of average rural household income and was

growing in importance (Reardon, 1997, Little et al., 2001). Reardon et al. (2001) also found

in Nicaragua that rural off-farm income constitutes 41% of the total income of farm

households. Households’ labor participation to off-farm employment had also been

increasing. Huang et al. (2009) studied the determinants of farm household diversification

in one province of china based on a panel data and came up with the increase in farm

households’ off-farm work participation from 36% in 2001 to 42% in 2006. In addition, Davis

et al. (2009) reviewed the findings and reviews of many authors and summarised that rural

off-farm income increased from small base in 1960s/70s to 40-60% of rural income in Latin

America, Asia and Africa.

2.3. Determinants of diversification

This begs the question of why, which and under what condition farm households diversify

on farm by growing more crops than one or few, and to off-farm and what influences their

decision to diversify. There are many studies in the body of literature on the determinants

of diversification (Ellis, 1998 2000, Jansen, 2006, Woldenhanna, 2001). As to the question of

what necessitates diversification, Barrett, for instance, explained the origin of

diversification as the response to diminishing or time varying returns to labor and/or land

from market failures, or frictions for mobility into high-return niches from ex ante risk

management, and from ex post coping with adverse shock (Barrett, 2001). He summarizes

them as “push” and “pull” factors.

Ellis (1998) identified some main determinants (push factors) of diversification as

seasonality, differentiated labor markets, risk strategies, coping behaviour, credit market

10

imperfections(Ellis, 1998). Seasonality is very important as employment in agriculture,

especially where there is no well-developed irrigation scheme, is highly seasonal. Not only in

production activities but also there is seasonality in input supply and output market in many

developing countries (Ellis, 2000). Most perennial crops such as coffee and fruits might have

only one and/or short harvesting season. In such short seasons, there will be high demand

for labor and other necessary capital for harvesting, but the resource will be idle during

slack time. Therefore, there is surplus resource, especially labor, in the factor market during

slack seasons. Hence, to absorb the surplus labor in the economy, households diversify on-

farm to relatively labor intensive crop production and\or to off-farm activities. Such

diversification is seasonally induced diversification.

According to Ellis (2000), in economics terms, such seasonal variation results in variation to

labor income earned per day or per week in both on-farm and off-farm activities. When

there is no well-developed rural labor market like in many African countries, labor migration

is a common phenomenon for labor smoothening. Labor smoothing as referred in Ellis (2000)

is one face of consumption smoothing. Seasonality is not only in production and income

generating process but also there is price and consumption seasonality in rural developing

countries (Morduch, 1995). Hence, due to the prevalence of risk and market failure,

consumption smoothing is severe among poorer households in developing countries. For

this reason, the important income diversification related to seasonality is to reduce seasonal

income variability. Therefore, searching for wage employment to work on farm of other

households in their locality, look for small-scale income generating off-farm self-

employment, or migrating to another area for search of job is common among labor in such

households.

Risk strategy, by many researchers is considered to be among the most important motives

for diversification (Little et al., 2001, Ellis, 1998). Ellis in his review of (2000) articulated such

strategy as another way of saying that families that are vulnerable to failure in their means

of survival “do not put all their eggs into one basket“. The quote is same with risk averse

farm households that grow many crops on small area of land to minimize production and\or

income loss as crops have different coping ability to weather variability, diseases and

marketability. Diversifying to different income earning activities based on the availability of

11

labor is also to reduce the likelihood of loss in income. Hence, households allocate labor to

complementary activities, so that if one of the activities fails either to exist or to pay the

amount that is needed for household expenditure, then the other activity compensates.

Market failure is one of the important determinants of diversification. When there is market

for all input and output, households allocate their resource to different activities until return

from each activity is equalized (Barrett et al., 2001, Davis et al., 2009, van den Berg, 2010).

However, in many developing countries including Ethiopia, there is incomplete input,

output and credit market (Woldenhanna and Oskam, 2001). Under such conditions,

households are limited to some kind of work they can access. For instance, if credit market

is not there or limited due to some barriers, poor households cannot participate in off-farm

self-employment as off-farm self-employment relatively requires more capital investment.

When there is no market for resources that a household has, then the comparative

advantage of specializing in some of the crops or activities will be limited. Barrett (2001)

gives a nice example, on how a skilled blacksmith behaves in the absence of land and labor

market. If the smith has land, and market for land is operating, then smith either lease or

sell his land out and specialize in smith. However, when there is absence of land market and

imperfect labor market the smith combine his blacksmith and farming by hiring labor from

outside.

The pull factors for diversification are factors such as asset holding of a household that even

enable the household to create entry barriers for the poor to enter and share from the

existing opportunities. Many studies have been done on such determinants of household

on-farm and off-farm diversification. For instance, Woldehanna and Oskam (2001) studied

the entry barriers in to off-farm wage employment and off-farm self-employment in Tigray

region of Ethiopia and found that off-farm wage employment increases with increase in

farm production and off-farm wage employment increase with family size and decrease with

number of dependents. This indicates that as family size increases, there might be a

possibility to have large number of labor and low land to labor ratio. Therefore, to absorb

the surplus labor hours, households allocate their labor to work for wage. The increase in

the off-farm self-employment as farm production increases is probably because of the

12

ability such households have to satisfy the financial demand for off-farm self-employment

from their farm income and/or their ability to access micro credit.

In review by Barrett (2001), households asset such as landholdings are positively correlated

to off-farm income. Barrett says that, households that are on the bottom in asset holding

are least likely to overcome the entry barrier, as they cannot meet the investment

requirement for remunerative off-farm work. The finding by Block and Webb (2001) in

Ethiopia confirms the summary by Barrett (2001) as they found that better-off households

who had diversified life in 1989 had more diversified life and income, and calorie intake in

1994. While the least diversified fight hard to have diversified life. Reardon also showed that

the off-farm income share is larger for better-off households than for the asset poor in rural

Africa (Reardon 1997).

Studies in many Latin American countries also showed the same pattern as what has been

pointed out by Reardon in case of rural Africa (for instance, Reardon et al, 2000, Elbers and

Lanjouw, 2001). Oumer and Neergaard also studied livelihood strategy-poverty link in the

central highlands of Ethiopia and found that households in the upper income quartiles tend

to engage more in regular off-farm work like grain trading, fattening, and livestock trading

while casual off-farm work was a key feature of the bottom income quartiles (Oumer and de

Neergaard, 2010).

2.4. Coffee price fluctuation and diversification

This section focus on evidences from previous studies on change in households resource

allocation with coffee price fluctuations. There are different literatures that can be used as

bench mark to conceptualize the dynamics of coffee price fluctuation and farm households

decision making as a response either to cope up with price shocks through different

diversification mechanisms or on how they allocate their resources during coffee income

rises.

When price of coffee rises, citrus paribus, farm households might allocate more resources to

coffee plantation, on farm management, harvesting and post-harvest management.

Furthermore, they might commit new plots to expand the area allocated for coffee or

13

intensify the management to enhance the efficiency of resource use. Thereby they can

generate more cash income from coffee and allocate less land resource to the production of

food crops (Duncan 1998). In this context, there will be increase in wage in agricultural

activities and consumer good prices temporarily (Bevan et al., 1989). Furthermore, they

might expend the income windfall to non-productive assets or invest on high return

activities such as improved varieties of crops and livestock and other remunerative off-farm

activities.

Contrarily, when coffee price falls, households reallocate their resources from coffee

production and management to even low return food crops or change their land use pattern.

For instance, Vedenov et al (2007) explained that if market changes depress the prices of

the main cash crop, households lacking easy access to financial and factor markets might

respond by shifting their resource to the production of food crops as a self insurance

mechanism. Policies might also change in response to the international coffee prices.

Evidences show that, during 1999-2003 coffee crises in the world market, policy makers of

coffee producing nations suggested coffee producers to shift land that is less suitable for

coffee production to other crops, and improve the quality of coffee through efficiently

operating on suitable lands for coffee production (Varangis et al., 2003; cited in Tuker et al.,

2010).

By implementing the recommendation, coffee producers changed their land use and land

cover in response to the coffee crises. Even though it is difficult for farmers to immediately

reallocate their land from coffee to other crops for risk management Tucker et al. (2010), in

study carried out by Mekuria et al., (2004), it has been explained that coffee producers

uprooted coffee and replaced by other cash crops like chat (Chata edulis) and teff

(Ergirostis tef) in the coffee producing areas of Ethiopia due to coffee crises.

Furthermore, farmers might also respond by leaving the coffee plantations unharvested

when their marginal revenue is less than the marginal cost they incur to harvest and/or

store the product. Empirical evidence by Mekuria et al., (2004) shows that in the

southwestern Ethiopia, one of the large state owned coffee plantations; namely ‘Limmu

Coffee Plantation’, left coffee un harvested because of the insolvency it faced due to low

local coffee prices. During that time the output of coffee has severely been declined not

14

only because of less physical production but also because of the price crises. The study on

coffee sector in Ethiopia by Petit (2007) shows that, during 2002, when coffee price reached

its lowest level in the century in the world market, coffee prices could not cover the

production costs.

Another response to coffee crisis is migration of member of coffee producing households to

surrounding villages, towns, cities or countries for survival. As it has been argued in

development economics that migration is one way of income diversification, during coffee

price failure, it can be the driving force for many coffee farmers to leave their residence and

look for other opportunities in other locations. Luwis (2005), in his study on migration as

strategy for survival in response to coffee crises found that coffee price failure accelerated

the emigration of Mexicans coffee producers to United States.

2.5. Theoretical Framework

The issue of diversification or specialization is analyzed rooting its base into the Neoclassical

Economics theory of comparative advantage by Ricardo. Heckscher-Ohlin builds on the

comparative advantage theory and use general equilibrium in international trade to analyze

the patterns of commerce and production based on the factor endowments of a trading

region. The basic element of the theory is that countries produce the products that uses

their abundant and cheap factors of production and import products that use their scarce

and expensive factor (s). In the model by Heckscher-Ohlin trade theory it simplifies to two-

countries, two-factors of production and two-commodities (2x2x2) as initial reasoning.

The model assumes that countries should use identical technology to produce the same

product, production output has constant return to scale, the technology used to produce

the two commodities differ, labor and capital mobility within countries is allowed, but there

is no capital mobility between countries, commodities have the same price everywhere and

there is perfect internal completion. Many useful theorems has been drawn from the model

to analyze the cones of diversification for countries having trade together. One of the

theorems is factor price equalization in which free and competitive trade will make factor

prices converge along with traded goods prices. In this situation, there may exist one or

more cones of diversification within which countries have similar factor proportions,

identical factor prices, and identical sets of products (Burkett, 2000). For countries falling

15

within a cone of diversification, producing as many goods as they have factors, the theory

predicts that net exports by commodity, as well as national income, are linear functions of

factor endowments. Another is the Rybczynski theorem which says, when an endowment of

a factor of production increases for a country, then that country produces goods that use

that factor of production intensively.

Bevan et al. (1989) developed on neoclassical Economics theory for the case of developing

countries with small, open economies, with weak domestic financial markets, subject to a

variety of government controls and liable to periodic temporary shocks in their terms of

trade. They applied Controlled Open Economies model in which access to some activities,

especially off-farm earning is rationed by factors such as skill, education, past experience

and social networks. They applied the model to analyze the effect of coffee boom in Kenya

over 1976-79, and the economic collapse of Tanzania over 1978-84. Furthermore, the basic

of international trade theory has been used to analyze how households diversify or

specialize when there is perfect market and in the case of absence or imperfect market for

factors of production as in the case of many developing countries in general and in Ethiopia

in particular as I introduced the issue of land market, for instance.

To make the reasoning concrete, I use the (2x2x2) model of Heckscher-Ohlin for households

pattern of diversification or specialization. Let us assume that there are two households, A

and B, two activities X and Y and two factors of production land and labor. Let us further

assume that X is labor intensive and Y is land intensive. In addition, I initially assume that

there is perfect market where there is constant return to scale and no consideration for risk.

In this situation, households production choice relies on their comparative advantage which

in turn is determined not only by quantity of land and labor endowment but also by the

ratio of land and labor these households own. The assumption of perfect market allows

hiring labor and renting and/or selling land among the households until the prices of factors

of production is equalized for both households in producing X and Y.

If this holds, then household A and B have comparative advantage in specializing in X and Y

respectively. If labor increase for household B, but still less labor-to-land ratio than

household A, then it hires out its labor and remain in producing Y.

16

However, there has been severe imperfection in land, labor and credit markets in rural

Ethiopia. In effect, households might only use in production those factors which they own.

This, according to Bevan et al. (1989), has two effects on households resource allocation.

First, only factor abundant households can respond to any exogenous change in their

activity choice. For example, if (X) becomes economies of scale activity or the price of this

output increases, then only household A can enjoy high return from it. Second, because the

households differ in the ratio of land-to-labor endowments, the return to each factor differ

across households. Household B starts to produce X if and only if its labor endowment

increases. However, there will be threshold for the household to enter economies of scale

activity (X). Figure 2 gives the summary of how households allocate their resource over time

in the situation of constant returns to scale in both X and Y with the ratio of factor

endowments determining the pattern of specialization.

Figure 1: Factor proportions and specialization

Source: Bevan et al. (1989): 77

In our data, perennial crops are relatively land intensive and annual crops are relatively

labor intensive. Therefore, when family size increases for households, then they are

expected to have more labor and diversify to produce more of annual crops (Rybczynski

theorem, refer to table IV in annex 2 for factor intensities of crops). The model is useful to

capture how endowments affect households activity choice, to test whether there is

increasing return in some of the activities and to estimate production and consumption

17

equations. However, since the main objective of this thesis is to look at the determinants of

households on-farm and off-farm diversification, figuring out the overall factors affecting

households resource allocation is more convenient than focusing only on their endowments.

When there is no perfect market, households resource allocation is not only determined by

their labor and land endowment and other direct inputs for production but also affected by

other exogenous as well as endogenous (household specific) factors. In her PhD thesis,

Kuiper writes how rural households strive to achieve their objective under different

constraints;

“Within the limits set by its biophysical and socio-economic environment, the household

matches its resources and objectives through selection of on-farm and off-farm activities.

These activities generate income (possibly in-kind, like food crops) that is divided between

consumption, saving and investment. The household interacts with its biophysical

environment by affecting natural resource (for example through erosion and nutrient

depletion), and interacts with its socio-economic environment through participation in

markets and non-market institutions.” Kuiper, 2005

The bio-physical environment can be the natural resources that households own, such as

land attributes (including area, land quality, slope... etc.), water, climate and other

environmental elements that directly or indirectly, temporarily or permanently, as well as

negatively or positively affect their decision to produce and consume. The socio-economic

environment includes markets, non-market institutions such as social institutions, policies

and regulations such as property rights and infrastructures such as availability of road,

communication services, electricity and so on.

Households are considered to be both producers and consumers. If there is no market for

input and output, then their production decision depends on their consumption decision. In

such situation, non-separable farm households model first developed by Singh et al. (1986)

is appropriate. Households maximize utility subjected to cash constraints, production

technology to own-farming and off-farm employment activities, exogenous effective prices

for tradable; an equilibrium condition for self-sufficiency of farm production; and an

equilibrium condition for family labor or total time endowment (Escobal, 2001). They

maximize utility from consumption of good and leisure.

18

They consume food commodities, non-food commodities and leisure (Burger, 1994). Leisure

according to Burger (1994) is defined as “the total time available minus the time spent on

economic activities”. The full cash is earned from on-farm work, off-farm work and

remittance. The cash from farm work includes the sale of cash crops (coffee and other cash

crop outputs), that is assumed to be sales equals production (in fact households use at least

some portion of cash crops for home consumption), sale of food crops that is the difference

between production and consumption minus the food bought from market, and sale of

livestock and livestock products. Cash income from off-farm work includes income from off-

farm wage employment and off-farm self-employment. The final source of income is

remittance sent by household member, relatives or friends who emigrated from one village

to another, the nearby town or city, or to another country. Since the main objective is

neither to estimate production nor consumption functions rather to analyze the

determinants of on-farm and off-farm diversification, then there is no need of specifying the

series of utility and production functions.

When production and consumption decisions of households are not separable, then prices

remain endogenous to the farm households and affected by the costs of market

transactions. In that case, the surplus cannot be sold for the good that is not traded. The

shadow price (ρ & ω) that governs the choices of the household for output and input is

determined by the internal equation of demand and supply for the output and input (Benin

et al., 2004). Hence, the specific characteristics of farm households and markets influence

the magnitude of transactions costs involved in market exchanges and, through the shadow

prices the household choices (Escobal, 2001).

In such situation, reduced form of the model can be used to create relationship between

diversification and endowments, household asset, farm characteristics, household

characteristics and other public assets. The advantage of using reduced form household

model is its ability to suffice to specify the variables that enter the equations and

postulating the general conditions on the functional form (H.Kuiper, 2005). In that case, it

might not be necessary to specify the series of utility and production functions. First order

conditions can be derived which are then rewritten to establish relation between the

19

exogenous variables and endogenous variables of interest. The resulting reduced-form

equation is then estimated econometrically.

Many authors used reduced form of non-separable farm household model to study

determinants of on-farm and off-farm diversification. To state few of them Benin et al.

(2004), for instance, used reduced form of farm household model to study the economic

determinants of cereal crop diversity in the highlands of Ethiopia. Escobal (2001) also used

reduced form of farm household model to study determinants of off-farm income

diversification in rural Peru.

In this situation, the household’s optimal choice for activities and/or crops and crop

combinations can be expressed as a reduced form as follows;

Where D is crop choice or earning from off-farm self-employment and off-farm wage

employment; A= assets such as farm size, livestock and other household valuables;

R=exogenous income, Zk=financial assets that facilitates access to credit, zh=household

characteristics, Zf = farm characteristics such as land quality, slope, distance of plot from

home, and indicators for climate characteristics such as timeliness of rainfall, sufficiency of

rain fall; Zm = market characteristics as proxied by distance of market from home and road

availability in the villages.

The above model is a base to derive econometric models for the examination of the

determinants of household on-farm and off-farm diversification through choice for crops

combinations, and their earning from off-farm self-employment and off-farm wage

employment.

20

3. Research Methodology

3.1. Data

The data used in this thesis is part of the panel data from Ethiopian Rural Households Survey

(ERHS) collected by Economics Department of Addis Ababa University (EDAAU) in

collaboration with the Centre for the Study of African Economies at Oxford University

(CSAEOU) and the International Food Policy Research Institute IFPRI). They started

collecting these data in 1989 where only 450 households were included in the survey to

study mainly the level of poverty after the 1984’s famine. Hence, the setup of questionnaire

was different in 1989. Three other sites were added to include areas in the northern part of

the country that could not be reached because of military force on the area in 1989. In the

second round of the 1994 survey, six other zones had been added to include the different

agro climatic and farming systems of the richer parts of the country. Total of 1447

households had been surveyed in the years 1994 (two rounds), 1995, 1997, 1999 and 2004

and the data is publicly available.

Stratified sampling system was used to assure the inclusion of all agro ecological parts,

female headed households and landless households. In total 18 peasant associations

(villages) had been included (Lim et al., 2007). The survey gives sufficient information

regarding the income, asset, farming systems, activities in which households participate,

crop production; input use and output supply. Since the aim of this thesis to study the

dynamics of diversification in the coffee producing farm households in southern Ethiopia, 4

villages were chosen for this purpose. The villages are Imdibir, Azedeboa, Adado and

Garagodo. These villages are environmentally similar, but different in social profiles.

Different major ethnic groups (90%) inhabitate in each village (Gurage, Kambata, Gedeo and

Walaita) respectively. Coffee is main cash crop while enset is main food crop in the area. In

total 360 households were included from these villages of which 91% grew coffee. After

matching, 274 households in each survey years were included in the analytical data analysis.

For this thesis, we used two round surveys: 1997 and 2004. In these surveys, the details of

crops grown by households as well as other variables have been sufficiently found. Not only

the availability of information but also the time gap between the two survey years enables

us to get insight into household’s diversification over different crop combinations (including

21

perennial crops) as well as off-farm activities. In addition, there was coffee crisis in the years

1999 to 2003. The dynamics can show us how farm households diversify/specialize in these

years and whether it is possible to draw lessons about diversification and coffee price based

on the findings.

Though coffee and enset are main perennial crops in the villages, farmers not only grow

these crops but also grow different perennial as well as field and horticultural crops. The

cropping patterns were complex (i.e. that they grow many crops on the same plot or small

size of land in such a way that it is very difficult to separately calculate either area allocated

or other input used for each crop). For instance, 129 types of crop combinations have been

found in 1997 and 72 crop combinations had been found in 2004 at plot level.

The difference in crop combinations might be attributed to difference in recording crops

grown by households on a plot. Households were asked to tell up to five crops on the same

plot for the year 1997, but they were asked to tell only three crops in the year 2004. The

major crops and crop combinations are given in annex (1) tables (I), (II) and (III). Table (I)

contains the major (first) crop grown on the plots over the two survey years excluding the

intercropped ones, the price of coffee and prices of other crops. The other two tables

contain all major crops and crop combinations grown on the plots either in intercropped or

cropped side by side.

Accordingly, in the first table, the main perennial crops grown by households were, coffee

(Coffee Arabica), enset (Enset ventricosum), chat (Chata edulis) , eucalyptus, avocado, gesho

and sugarcane. The major cereals are teff (Ergirostis tef), maize (Zea mays ) and wheat

(Tritium sativum ) whereas the only pulse is horse beans. The major horticultural crops were,

potato (Solanum tuberosum), sweet potato (Ipomea batatas), adenguare, godere (Taro),

and other vegetables. The latter two tables show slightly different patterns in farm

household’s crop choice. The most frequent perennial crops in year 1997 (table II) are coffee,

enset, chat, coffee with enset, coffee with chat, coffee with chat and enset, Eucalyptus,

Eucalyptus with grass, banana, coffee with enset and eucalyptus, coffee with enset and

gesho, coffee with enset and sugarcane, and other perennial crop combinations.

22

The cereals grown in the year 1997 were; maize and teff whereas the horticultural crop

grown alone on more than 10 plots was sweet potato. The cross combination among the

three crop categories (perennial, field and horticultural) were; coffee with enset and maize,

enset with maize, chat and coffee, and others.

In the year 2004 (table III), the crop combinations were different. The perennial crop

combinations had been decreased from that of 1997 and the other crop combinations had

been increased. The major perennial crops and perennial crop combinations were, coffee,

enset, chat, enset with coffee, enset with chat and eucalyptus. Whereas the major cereal

crops were; maize, teff and wheat. The horticultural crops were; sweet potato and

adenguare (garden beans). The combinations of cereals and horticultural crops had been

increased to maize with adenguare, maize with sweet potato, teff with sweet potato, and

others.

Hence, the patterns and determinants of diversification can better be studied by

categorizing the households into those who grow only perennial crops; perennial and field

crops; perennial and horticultural crops and the combination of the three crop categories

(perennial, field crops and horticultural crops). Since perennial crops are the major crops

grown by households in the area of study, households either diversify to field crops,

horticultural crops or combination of the two crops. The on-farm diversification in the crop

sector can be captured by identifying whether households specialize in some of these crop

combinations or change such cropping pattern over that time and the factors determine

their choice.

In addition to crop production and livestock husbandry, households participate in different

off-farm activities. Hence, the main sources of income for the households were categorized

into five: namely; income from crop sale, livestock and livestock product sales, off-farm self-

employment, off-farm wage employment and remittances. The off-farm self-employments

are activities such as trade in crops, trade in livestock and livestock products, handicrafts

including pottery, traditional healers and others. Figure 2 below summarizes the major off-

farm self-employment activities in which a member or members of households participate.

Whereas off-farm wage employments include activities such as working on others farm and

23

in community development activities such as food for work and others. Figure 3 below gives

the off-farm wage employment activities in which households participate.

The off-farm wage employment activities were not recorded in the 1997 survey, but the

income from this category was given. However, both the activities and income from off-

farm wage employment has been given in the 2004 survey. For off-farm self-employment,

the activities as well as the income had been given in both survey times. Therefore, the

activity level analysis for off-farm wage employment was made only for 2004 (figure 3). The

absence of the activities for off-farm wage employment in 1997 survey has no effect on the

findings of this thesis since our aim is on the determinants of the income from the activities

than the activities themselves.

3.2. Econometric models

3.2.1. Multinomial model for households on-farm diversification

Here we estimate the probabilities of crops combinations to be chosen by households over

time and the determinants of those choices.

As explained under (3.1) households’ crop choice has been categorized into four: namely,

perennials (G1), perennial and field crops (G2), perennial and horticultural crops (G3), and

the combination of perennial, field and horticultural crops (G4). The term perennial is used

together with other crops because all households grow this crop category irrespective of

whether they grow other annual crops or not. In other words, perennial and field crops

means households who diversify from growing only perennial crops to grow field crops as

well. However, G3 was not included in the multinomial choice model because of low

number of households growing this crop category, but included in descriptive statistics for

the patterns of diversification. Therefore, only the first two and the last categories have

been included in the multinomial crop choice model. The crop categorization was made

based on the difference in resource requirement of the crops such as factor intensity; their

time requirement for maturity; and their natural resistance to whether and other stresses.

In our data, perennial crops are relatively land intensive than the other crop combinations

(annual crops).

24

Furthermore, within annual crops grown with perennial crops, horticultural crops are less

labor intensive than field crops, and the factor intensity for the combination of the two

crops is between the two annual crops categories grown with perennial crops (See

explanations and tables in Annex under factor intensities for crop combinations). The

farmers choose from those crop combinations based on their land and labor endowments

and other determinants. The panel nature of the data enables us to get insight how their

choice for crops changed over the survey times. Since we have two times observation with

sufficient time gap to account for the change in cropping patterns, it allows us to include

perennial crops as a choice.

For the choices, log-linear model can be used in which the choices of crops can be computed

as a function of other explanatory variables. In principle, the probability of crops grown in

combination to be chosen is the product of the probability of each crop when chosen alone.

Here, since we have all crops in combinations with other crops, then the combinations are

treated as independent crop. In that case, multinomial logit model can be used. The

advantage of multinomial model is its ease of calculation and facility of including

explanatory variables (Burger, 1994). One important point to note here is, even though

households in the four villages face almost the same climatic condition that was shown by

the suitability of the environment for coffee, enset and other crops as explained in the data

set, difference was observed in their cropping pattern. Therefore, analysis was also made at

village level though focus is given to the total (pooled) sample because of sample size.

One important assumption in using logit model is that the error terms of the utility that the

household attach to each choice follows a sech2 distribution, from these results the optimal

choice is distributed as a logistic statistic. However, multinomial logit model is not free of

weakness. It assumes that the choices are independent while in reality one choice might

depend on another one (Hausman and McFadden, 1984).

To capture for the dynamics of crop choices, it is recommended to use conditional

maximum likelihood multinomial logit model as specified by (Chamberlain, 1980).

Chamberlain specifies a situation in which the choice is panel and multinomial. The model

can be specified as given in Annex 3. Stata program called Generalized Linear Latent and

25

Mixed Models (GLLAMM) had been applied. However, it could not maximize and

subsequently could not provide solutions. Hence, I pooled the data for a multinomial choice

to account for within time heterogeneity among farm households’ crop choices and their

determinants. The model used for the pooled data is specified as follows;

Where Yij is utility a household I attach to choice j. The choices j=0 is when the household

grows only perennial crops, j=1 when the household grows perennials with field crops and

j=2 when the household grows perennial with field and horticultural crops. For perennial

crop is the base choice, the above can be rewritten as;

The explanatory variables have been given in table (13) with corresponding explanation on

the expectation of the direction of their influence on households crop choice and the result

was given in table (14).

3.2.2. Fixed effect logit model

In the multinomial logit case we cannot account for change in households crop choice

because of inability of the program to converge. However, the determinants of households

diversification over time is our central question. Particularly, the issue of initial condition of

households in their factor endowment and other assets was found to be the interest of the

this thesis. Hence, to see how these initial conditions affect change in households on farm

diversification I regressed the change in choice on explanatory variables of 1997. However,

the model itself was not significant (refer table V in annex 3). i.e. the change in households

crop choice was not predicted by the initial condition of the households.

Alternative econometric model used to account for status change is fixed effect logit model.

Before going to the detail of the model let me define how diversification is measured and

fitted to this model. Accordingly, diversification can be accounted, for instance, if a

household growing only perennial crops in the year 1997, change its status to either of the

three crop combinations: perennial and field crops, perennial and horticultural crops, or the

combination of perennial, field crops and horticultural crops, then the household will be

assigned a value of 1. In other words, if the household starts to grow either field crops,

horticultural crops or the combination of the two crops categories instead of growing only

26

perennial crops, then value 1 is assigned for such household. In addition, if a household

growing either perennial and field crop or perennial and horticultural crops in the year 1997,

starts to grow the combinations of perennial, field crops and horticultural crops, then the

household is said to be diversified and assigned a value of 1.

If the household goes down the hierarchy from the previous status, say from growing

perennial and field crops in the year 1997 to grow only perennial crops in 2004, then the

household is said to be relatively specialized or de-diversified. In this case, a value of 1 is

assigned in the previous year and a value of 0 is assigned in the final year. Likewise, if the

household de-diversify from growing perennial, field and horticultural crops to grow only

perennial and field crops or perennial and horticultural crops, then the household has de-

diversified and the above reasoning holds. If the household remains in producing the same

crop combinations, then the household does not have status change and value of 0 is

assigned in both survey seasons.

For such choice model, the conditional fixed effect logit model can be specified as follows:

Since we have T=2, we are interested in the situation in which the sum of ,

that enables us to define the change in status as if

The conditional probability density

Hence,

and

It does not depend on incidental parameter ( . can be obtained using a standard ML

binary logit program (Chamberlain, 1980, Verbeek, 2008)

Explanatory variables used for the fixed effect logit model were part of the variables used

for the multinomial crop choice model without including the variables that are time

invariant. Hence, land ownership, farm size, number of plots, number of livestock, oxen

ownership, off-farm job participation and off-farm income. The descriptive statistics has

been provided in table 13.

27

3.2.3. Random effect tobit model for diversification to off-farm earnings

The descriptive analysis with mean comparisons showed that participation to off-farm self-

employment and off-farm wage employment work as well as income share from off-farm

self-employment has been increased over the survey years. Therefore, to study

determinants of households’ income from off-farm works and test our hypothesis we use

random effect tobit model. The rationale behind using tobit model is to account for the

censured nature of the data. The income is positive and continuous for those who

participate and earn positive income and zero for non-participants. In that case, OLS gives

biased estimates. Such a model had been used by authors such as Woldenhanna and Oskam

(2001) to study the diversification to off-farm self-employment and off-farm wage

employment in Tigray region, Ethiopia. The model can be specified as;

;

While;

For the function can be specified as;

and for it is given as;

=

Where is latent earning from off-farm self-employment and off-farm wage

employment, column vectors of explanatory variables, row vectors of parameters.

Unlike Linear models, tobit model has two parts, one for the situation of which

resembles the likelihood in the case of probit model and the other is which is for

the continuous part of the observations and same with OLS regression model. If OLS is

applied on such data, then the parameters will be biased downward. We make the usual

random effect assumption that and are i.i.d. normally distributed independent of

with zero means and variances and

respectively. In the situation that

these assumptions hold, using random effect tobit model gives us most efficient as it

considers both within and between variation among the variables of interest over time.

The explanatory variables has been given in table 16 and their expected signs has been

explained as indicated in theories and previous literatures.

28

4. Result and discussions

4.1. The main income earning strategies and crop prices

The main income sources for the households in the villages were categorized in to five:

namely, income from sale of crops including coffee, livestock and livestock products, off-

farm self-employment, off-farm wage employment and income from transfer payment

(remittances). Despite of constant average price of crops, and significant decrease in price of

coffee, households’ income has significantly increased from 1997 to 2004. The change in

income is attributed to the increase in income from off-farm self-employment. The within

year mean income shows that, the average income for households who earn positive

income from off-farm self-employment and/or off-farm wage employment (3rd row in table

2) was greater than the mean for the total sample (2nd raw in the same table) over both

survey years. Income from off-farm self-employment and remittance had been significantly

increased between over that time.

Although there was indication of lower aggregate household level price in 2004 than in 1997,

the change was not significant. However, the price of coffee showed significant decrease

between 1997 and 2004. The trend in village level shows that the price of coffee decreased

between the two years of data collection in all villages except the village Azedeboa, in which

the price per kilo has slightly been increased from 12.0 ETB to 12.60 ETB ( refer to table (I) in

annex). This has the implication that even in 2004 the price of coffee did not recover to the

level it was before the coffee crises which started in 1999.

4.2. Share of income by sources

The above summary gives the absolute contribution of each source of income to the total

income of the households. However, it is subjected to weaknesses such as inflation.

Therefore, to minimize the impact of such problems, the share of these incomes earning

strategies are given in tables (3), (4) and (5) for total sample, subsamples: only participants

in off-farm self-employment and off-farm wage employment respectively. The comparison

for the change in income from crop sale, off-farm self-employment and off-farm wage

employment has been given in table (3).

29

Table 2:Main sources of income and crop prices for coffee producing households over 1997 and 2004

Income categories Mean income Mean difference

1997 2004 Mean (2004) – mean (1997)

Total income for full sample in ETB 835.28 (68.65) N=274

1142.6 (82.64) N=274

307.27*** (101.67)

Total income for those who earn positive off and/or non farm income in ETB

1020.30 (113.06) N=122

1420.8 (106.02) N=168

478.68*** (178.59)

Income from crop (coffee included) in ETB 469.77 (52.24) N=274

489.63 (52.25) N=273

22.36 (70.10)

Income from livestock and livestock product in ETB

191.46 (35.79) N=274

130.80 (18.66) N=274

-60.65 (40.61)

Income from off-farm self-employment in ETB

89.35 (20.90) N=274

266.54 (30.77) N=274

177.19*** (37.21)

Income from off-farm wage employment in ETB

53.77 (14.79) N=274

57.58 (17.31) N=274

3.81 (20.87)

Income from remittances in ETB 29.22 (5.34) N=274

96.45 (28.96) N=274

67.23** (29.36)

Average prices of crops per kilogram Aggregate in ETB

4.33 (0.07) N=272

4.30 (0.10) N=274

-0.027 (0.12)

Coffee price per Kilogram in ETB 10.68 (0.12) N=274

8.88 (0.15) N=274

-1.80*** (0.08) N=274

Author’s calculation from ERHS 1997 and 2004

Notes: standard errors are given in parenthesis

*, ** and *** indicates the differences are significant at 10%, 5% and 1% significance level

respectively. ETB is Ethiopian currency called Ethiopian Birr.

Accordingly, the share of income from crop sale decreased from 59.2% in 1997 to 53.16% in

year 2004 for the pooled sample and this change was significant at 5% significance level.

Whereas the share of income from off-farm self-employment was significantly increased by

10.67% and income share from off-farm wage employment was significantly decreased by

3.67%. The coefficients for the difference in income shares for off-farm self-employment

and off-farm wage employment are significantly different from 0 at 1% significance level.

30

Share of income from livestock and livestock product was also significantly decreased from

17.38% in 1997 to 12.21% in 2004.

Table 3: Share of income from different income earning activities for pooled sample

Share of Income from

N Means Mean Difference

overall 1997 2004 overall 1997 2004 (2004-1997)

off-farm self-

employment

548 274 274 14.80

(1.08)

9.46

(1.32)

20.13

(1.64)

10.67***

(1.99)

off-farm

wage

employment

548 274 274 5.17

(0.63)

7.01

(1.14)

3.34

(0.51)

-3,67***

(1.23)

remittance 547 274 273 6.39

(0.63)

6.24

(0.85)

6.52

(0.92)

0.25

(1.09)

crop sale 548 274 274 56.18

(1.45)

59.20

(2.01)

53.16

(2.09)

-6,04**

(0.027)

livestock

&livestock

product sale

547 273 274 14.80

(1.03)

17.38

(1.57)

12.21

(1.37)

-5,01**

(2.06)

Source: Author calculation from ERHS 1997 and 2004 Note: standard errors are in parenthesis *, **, *** indicates that means of share income from different activities are significantly different between 1997 and 2004 at 10%, 5% and 1% significance level respectively. Overall: indicates the total sample in both survey times (i.e. stands for the share of income from different income earning activities over that time). The mean difference is calculated based on share of income from the activities in the two survey times for the total households included in the survey.

To look at the importance of off-farm self-employment and off-farm wage employment I

further restricted the sample only to those households who earn positive income from these

activities. The result for the change in share of income from different activities for

households who earn positive income from off-farm self-employment and off-farm wage

employment has been given in tables 4 and 5 respectively. The result shows that from 1997

to 2004 the income share from off-farm self-employment significantly increased while

income share from off-farm wage employment, crop sale and, livestock and livestock

product were significantly decreased for both sub samples.

The change in share of income from remittance was not significantly different from zero,

but the coefficient is negative for sub sample households who earn positive income from

off-farm self-employment. However, it was significantly increased for households who earn

31

positive income from off-farm wage employment. This implies that households who

participate in off-farm wage employment were the relatively poor households that were

more dependent on transfers from other sources. One study in Ethiopia on transfers

supports that transfers in the country are targeted the poor (Pan, 2007).

Table 4: Share of income from different income earning activities only for households earning positive income from off-farm self-employment


N Means Mean difference

overall 1997 2004 overall 1997 2004 (2004-1997)

off-farm self-

employment 220 76 144 36.86

(1.88)

34.11

(3.43)

38.31

(2.22)

16.61***

(3.01)

off-farm

wage

employment

220 76 144 2.91

(0.54)

2.15

(1.00)

3.31

(0.64)

-3.61**

(1.65)

remittance 219 76 143 5.57

(0.91)

4.09

(1.30)

6.36

(1.21)

-0.75

(1.32)

crop sale 220 76 144 40.76

(1.95)

47.53

(3.56)

37.19

(2.26)

-11.25***

(3.63)

livestock

&livestock

product sale

220 76 144 13.27

(1.47)

16.01

(2.83)

11.82

(1.66)

-5.32**

(2.60)

Source: Author’s calculation from ERHS 1997 and 2004

Note: standard errors are in parenthesis

*, **, *** indicates the mean share of income from different activities are significantly

different between 1997 and 2004 at 10%, 5% and 1% significance level respectively.

Overall: indicates the subsample of households earning positive income from off-farm self-

employment in the total surveys 1997 and 2004 (i.e. it stands for the mean share of income

from different income earning activities for those who earn positive income from off-farm

self-employment over that time).

The mean difference was calculated based on share of income from the activities in the two

survey times for households earning positive income from off-farm self-employment in either

of the two years.

32

Table 5: Share of income from different income earning activities only for households earning positive income from off-farm wage employment


N Means Mean differences

Overall 1997 2004 overall 1997 2004 2004-1997

off-farm self-

employment

124 54 70 11.92

(1.83)

3.92

(1.64)

18.08

(2.77)

14.16***

(2.94)

off-farm

wage

employment

124 54 70 22.85

( 2.13)

35.54

(.299)

13.07

(1.50)

-9.13***

(3.00)

remittance 123 54 69 8.63

(1.50)

5.95

(1.86)

10.73

(2.21)

3.68**

(1.77)

crop sale 124 54 70 41.53

(2.66)

54.29

(4.12)

31.69

(3.00)

-16.18***

(3.87)

Sale of

livestock

&livestock

product

124 54 70

12.28

(1.68)

8.86

(2.02)

14.92

(2.51)

-4.09 (2.99)

Source: Author’s calculation from ERHS 1997 and 2004 Note: standard errors are in parenthesis *, **, *** indicates the mean share of income from different activities are significantly different between 1997 and 2004 at 10%, 5% and 1% significance level respectively. Overall: indicates the subsample of households earning positive income from off-farm wage employment in the total surveys of 1997 and 2004(stands for the share of income from different income earning activities for those who earn positive income from off-farm wage employment over that time. The mean difference is calculated based on share of income from the activities in the two survey times for households earning positive income from off-farm wage employment in

either of the two years. The positive change in income share from off-farm self-employment and the negative

change in income share from other income earning activities, especially crop sale can be

explained by two possible reasons. The first is attributed to direct effect of decrease in

coffee price on farmers income. It is obvious that when coffee price decreases the same

quantity of coffee fetch less income. The second goes with the indirect effect of coffee price,

since coffee is main cash crop in the area, the effect of coffee price decrease can also result

in low price of other crops in the market. coffee producers might diversify to food crops and

the supply of food crops increase in the market, then it results in low income from those

other crops. In addition, when coffee prices decrease, households diversify their livelihood

to off-farm income earning activities to compensate the income loss from coffee. The

33

change in coffee price also has effect on demand of labor in other activities. For instance,

most of the off-farm wage employments are in coffee sector, when coffee price decreases,

those coffee farmers either not able to pay attractive wage that can reasonably increase the

off-farm income share of households or the income increase from other sectors, in the total

income, might be more than that of off-farm wage employment.

4.3. Patterns of on-farm diversification, and land and labor allocation among crop combinations.

In this part, the descriptive statistics of crop choice has been discussed. The pattern of

diversification has been presented at household level for the period of 1997 to 2004. Land

and labor allocation to crop production has been given.

4.3.1. Patterns of on-farm diversification at household level

Households’ choice for crops and its change over time among the villages included for the

study has been presented in table 6. Accordingly, in the pooled sample of 1997 most

households grew perennial and field crops and only perennial crops in order of their weight,

39.4% and 37.6%, respectively. The least crop combination grown in that year was

perennial and horticultural crops with only 2.9% of farmers growing it. The highest hierarchy

for diversification in this case is the combination of perennial, field and horticultural crops.

Only 20% of farm households were growing this crop combination in 1997. Hence, the

pattern of crop choice in 1997 shows that farmers were less diversified in their crop choice

in that year.

When decomposed to village level, the pattern of crop choice varied among villages; while

large share of farm households in Imdibir and Azedeboa grew perennial and field crops with

share of 72% and 71.2% respectively, it was the second largest choice for Garagodo next to

perennial, field and horticultural crops. The pattern was completely different for farm

households in Adado in which 85.6 % of the households depend on only perennial crops

with only 7.7% growing perennial and field crop and 1.9% growing perennial, field and

horticultural crops. Hence, during 1997, Adado was more specialized in perennial crops than

other crops or less diversified to field and horticultural crops.

34

The pattern was almost similar for perennial crops in the year 2004 with 38% of the

households still growing perennial crops with one individual falling back into growing only

perennial crops. There was change in the pattern of choice for perennial and field crops, and

perennial, field and horticultural crops with comparable decrease and increase in the former

and the latter respectively. The share of farmers growing perennial and field crops in 1997

was 39.4, which were decreased by half to 20.4% in 2004. Contrarily the share for perennial,

field and horticultural crops had been increased with the same proportion from 20.1% to

39.1%. This clearly indicate that the increase in diversification was between perennial and

field crops and perennial, field and horticultural crops.

The pattern of diversification was similar for villages Azedeboa and Garagodo with dramatic

increase in the share of farmers growing the combination of perennial, field and

horticultural crops. It was increased from 18.6% to 67.8% for Azedeboa and from 52.9 to

89.9% for Garagodo. The pattern of those two villages was similar in their choice of

perennial and field crops combinations. It was decreased from 71.2% to 20.35% in the

village Azedeboa and from 39.7% to 7.2% in Garagoda between 1997 and 2004. A slight

difference between the two villages was that the share of farmers growing perennial and

horticultural crops increased in Azedeboa from 10.2%, while it was null in the case of

Garagodo.

The choice for perennial crop combinations was doubled in the village Imdibir while the

share of farmers growing the combination of perennial and field crops, and perennial, field

and horticultural crops comparably decreased. Hence, there was relative specialization into

perennial crop combinations in Imdibir. The pattern was almost the same in the village

Adado over the time with more than 85% of the households growing only perennial crop

combinations. The choice for other crop combinations remained almost the same.

To sum up, based on the pattern of households crop choice over 1997 and 2004,

diversification had been increased in the region while difference in diversification had been

noted among villages. While villages Azedeboa and garagodo, while it was decreased in

Imdibir and not changed in Adado.

35

Table 6: On-farm diversification between 1997 and 2004; as indicated in the share of farmers choice for crop combinations

Crop combinations Share of households growing in numbers and in percentage

1997 2004

Pooled Imdibir Azedeboa Adado Garagodo Pooled Imdibir Azedeboa

Adado Garagodo

Perennials 103 (37.6)

6 (14) 6 (10.2) 89 (85.6) 2 (2.9) 104 (38) 12 (28) 1 (1.7) 89 (86) 2 (2.9)

Perennial &field crops

108 (39.4)

31 (72) 41 (71.2)

8 (7.7) 27 (39.7) 56 (20.4) 27 (63) 12 (20.3) 12 (12) 5 (7.2)

Perennial & horticultural crops

8 (2.9) - - 5 (4.8) 3 (4.4) 7 (2.6) - 6 (10.2) 1 (1) -

Perennial, field crops & horticultural crops

55 (20.1)

6 (14) 11(18.6)

2 (1.9) 36 (52.9) 107 (39.1) 4 (9) 40 (67.8) 1 (1) 62 (89.9)

Total 274 (100)

43 (100) 59 (100) 104 (100) 68 (100) 274 (100) 43 (100)

59 (100) 103 (100) 69 (100)

Source: Author’s computation from ERHS 1997 and 2004 Note:- percentage shares of farmers growing the respective crop combinations has been given in parenthesis

“ – “ indicates the respective crop combination was not grown by any of the households in the respective villages in respective year.

36

4.3.2. Land allocation among different crop combinations

Land is important input for any farm activity. Table 7 gives the descriptive statistics of land

allocated for different crop combinations between 1997 and 2004 for the households in the

pooled sample and at village level. Accordingly, the total area of land allocated for crop

production was almost doubled with only 442.96 hectares during 1997; and was increased

to 870.85 hectares in the year 2004. There are two possibilities to explain this dramatic

increase in land under cultivation.

First, there might be commitment of new plots of land that was previously used for grazing,

forest or marginalized land for crop production. Such conversion of land to crop cultivation

is highly prevalent in many developing countries because of high population growth and loss

of soil fertility of land under continuous cultivation (Soini, 2005). Second, there might be

households who invested in coffee production on large area of land despite the coffee price

recession; this could also be possible by reallocating land from other uses. The higher

number of plots allocated to crop production in 2004 confirmed the increment in land area

for crop production in 2004 than in 1997. The number of plots allocated for crop production

was 1305 (4.84 on average) in 2004 compared to only 1024 (3.86 on average) in 1997 (for

the total numbers of plots please refer to tables II and III in annex 1). The increase in area of

land allocated for crop production over the time was further confirmed by low land

fragmentation in 2004 than in 1997. Land fragmentation is calculated by dividing number of

plots to total holding of farm households. Accordingly, the figure was 16.56 and 8.04 in the

years 1997 and 2004 respectively.

Holding the above situation in place, when share of land allocated for different crop

combinations is concerned, the percentage of land allocated for perennial, perennial and

field crops, and perennial and horticultural crops production respectively decreased from

52.95% to 28.26%, 32.32% to 6.47% and 3.41% to 0.64% where as the area of land allocated

for the combination of the three crop combinations (perennial, field crops and horticultural

crops) had been increased from 11.32% to 64.63%.

37

The share of land allocated to different crop combinations at village level followed similar

pattern as in share of households' crop choice. In the villages Azedeboa and Garagodo, there

was ‘reallocation’ of land from growing only perennial and field crops to grow perennial,

field and horticultural crops. In Garagodo, the land allocated to the three crop combinations

has been increased by more than 100 times. The change in land allocation for different crop

combinations clearly showed the upward change in diversification between 1997 and 2004.

The diversification was not arbitrary to analyze its determinants for coffee producing farm

households in southern Ethiopia.

4.3.3. Labor allocation among different crop combinations

Labor is important for any economic activity in general and agriculture production of

developing countries in particular. Overall crop cultivation is labor intensive agricultural

activity although the level of intensity varies from one crop to another (Kasem and Thapa,

2011). Activities of labor in crop production can mainly be categorized in to three: land

preparation/planting, general cultivation and harvesting. The land preparation includes

activities such as land clearing, ploughing, up to planting; the general cultivation includes

activities such as, manual weeding, spraying herbicides, protecting from insects and pests

while the crops are on the field; and harvesting includes activities such as cutting, picking,

trashing, up to transporting to stores. Though the survey of 1997 contained the above

categories, the survey for 2004 did not contain this detail, therefore, the average man-days

worked in different crop combinations for the total activities by villages and years was given

in table 8.

Accordingly, the average days worked per hectare for all crop combinations were higher in

1997 than that of 2004. When within year labor allocation to different crops is considered,

the highest labor man-day was allocated to perennial crop production and the least to

perennial and field crops production in 1997. Whereas, it was the highest for perennial and

field crops and slightly the same for the other crops combinations in the year 2004. Overall,

between the years, the pattern of labor allocation was not similar in general, but slightly for

the combination of perennial and field crops. It was higher in year 1997 than 2004 for all

crop combinations. For instance, 377.49 man-days worked for perennial crop production on

one hectare in the year 1997, whereas it was only 67.86 in the year 2004. However, such

38

large difference was not observed in the case of perennial and field crops. It was 188.83 in

1997 and 141.01 in 2004.

The relative large labor time allocated for perennial and field crop in the year 2004 than

other crop combinations can be explained by the relative length and frequency of labor time

required by field crops from the stage of land preparation to harvesting. For instance, teff

requires more often ploughing and other land preparations than other cereals, pulses and

any of the annual horticultural crops. Maize, on the other hand, needs much more time for

maturity than any other cereal and horticultural crops recorded in the survey. For this

prolonged time, maize needs much labor time for on farm management than other crops.

Another possible explanation can be the change in farmers land management because of

low coffee price.

Village level labor allocation shows that the large labor allocated for working in perennial

crop production was in Adado with 446.39 labor man-days worked per hectare on average.

This number inflated the figures for overall labor allocated to perennial crop production in

1997. The same had been observed in the case of Garagodo in 2004, but its influence was

not as high as in the case of Adado because few farmers grew perennial crops in Garagodo.

39

Table 7:Land allocation to different crop combinations

Crop types Area of land allocated for crop combinations (in hectares and shares)

1997 2004

Pooled Imdibir Azedeboa Adado Garagodo Pooled Imdibir Azedeboa Adado Garagodo

Perennials 234.56

(52.95)

6.54

(21)

2.83

(3)

224.5

(88)

0.69

(1.4)

246.10

(28.26)

5.98

(25)

0.38

(0.5)

238.4

(88.4)

1.31

(0.26)

Perennial &field 143.18

(32.32)

20.51

(66)

91.38

(87)

12.06

(5)

19.23

(37.3)

56.33

(6.47)

14.51

(61)

8.88

(11)

30.06

(11.1)

2.88

(0.58)

Perennial &

horticultural crops

15.09

(3.41)

- - 14.03

(5)

1.06

(2)

5.6

(0.64)

- 5.25

(7)

0.38

(0.11)

-

Perennial, field crops

& horticultural crops

50.13

(11.32)

4.2

(13)

10.52

(10)

4.84

(2)

30.56

(59.3)

562.82

(64.63)

3.42

(13)

63.56

(81)

0.8

(0.3)

495.04

(99.16)

Total 442.96

(100)

31.25

(100)

104.73

(100)

255.4

(100)

51.54

(100)

870.85

(100)

23.91

(100)

78.07

(100)

269.67

(100)

499.23

(100)

Source: Author computation from ERHS 1997 and 2004

Percentage share of land allocated for the respective crop combinations is given in parenthesis

“-“indicates no land was allocated for the production the respective crop combination

40

Table 8: Labor allocation to crop production

Crop combinations

Average days worked per hectare (1997) Average days worked per hectare (2004)

Pooled Imdibir Azedeboa Adado Garagodo Pooled Imdibir Azedeboa Adado Garagodo

Perennial crops 377.45

(118.10)

56.38

(17.91)

161.32

(138.43)

446.39 (145.18)

24.33

(2.33)

67.86

(8.41)

142.83 (33.78)

72 (.)

51.72 (6.86)

334.00 (54.00)

Perennial and field crops

188.83

(50.47)

66.43

(27.37)

23.72

(4.19)

454.13 (157.28)

507.59

(178.68)

141.01

(52.47)

225.00 (106.24)

80.68 (35.63)

36.91 (5.22)

82.11 (9.09)

Perennial and horticultural crops

336.14

(206.73)

-

-

522.83 (310.54)

24.99

(2.89)

99.88

(70.37)

- 109.19 (82.53)

44 (.)

-

Perennial, field and horticultural crops

241.88

(54.70)

74.21

(28.75)

39.95

(7.77)

3.04 (0.20)

344.80

(78.40)

100.58

(16.13)

41.15 (10.33)

80.50 (31.16)

22.18 (.)

118.64 (19.08)

Source: Author’s computation from ERHS 1997 and 2004

Note :- “ – “ indicates the crop combination was not grown in a respective village in a respective year -Standard error of the means are given in parenthesis

41

The large labor time decrease in 2004 can be explained by reallocation of labor from on

farm to off-farm activities over the data collection time. The participation and labor

allocation of households to off-farm income earning activities was higher in 2004 than that

of 1997 (look at labor allocation under off-farm self-employment table (12). This might be

because of the persistently low coffee price in international market over the years 1999 to

2003 that resulted in lower coffee price and subsequently lower coffee income in the year

2004 than 1997 at household level.

4.3.4. Attractiveness of crop combinations

Attractiveness of crop combinations has been analyzed in terms of quasi profit or average

net value product of labor. It was quasi profit because it is net of variable cost that was

captured as agricultural expenditure, which includes cost of fertilizer, seeds, herbicide,

pesticide and wage for hired labor if any. Therefore, the value specified here as average net

value product includes cost of family labor, shadow price of land, rent for capital used by

households from its own, seed used from own stock and other fixed costs.

Table 9 summarizes the average net value product of labor. As it can be seen, when

compared within year, it was the highest for labor worked on combinations of perennials

and field crops in both years. The least rewarding was the combination of perennial, field

and horticultural crops in the year 1997 whereas it was the combination of perennial and

horticultural crops in 2004.

The diversification (change in crop choice) was not in parallel with the net value product

that households earn from those crops. This was shown by the increase in the choice of the

combination of perennial, field and horticultural crops between the years despite of its least

rewarding in 1997. This implies that attractiveness of crops (the average net value product)

of labor does not influence diversification. In other words, on-farm diversification was not

by choice but by necessity. This flushes light that the lower coffee price in 2004 than 1997

has accelerated diversification together with other factors.

42

Table 9: Attractiveness of crops: net value product to labor allocated to crop production (in ETB)

Crop combinations Average net value product of labor

(1997) (2004)

Pooled Imdibir Azedeboa Adado Garagodo pooled Imdibir Azedeboa Adado Garagodo

Perennial 161.82

(21.14)

40.15

(16.89)

228.95

(144.48)

166.47

(22.39)

118.18

(65.90)

101.87

(21.16)

200.03

(172.46)

3.33

(.)

89.75

(9.46)

101.80

(98.09)

Perennial and field

crops

515.62

(185.30)

1504.5

(615.48)

169.28

(28.95)

69.07

(20.82)

51.47

(17.15)

119.98

(31.93)

91.51

(24.19)

232.91

(190.10)

97.08

(49.18)

46.41

(16.15)

Perennial and

horticultural crops

187.86

(83.55)

-

-

227.77

(130.65)

121.34

(68.69)

96.94

(41.70)

- 103.17

(48.79)

59.55

(.)

-

Perennial, field and

horticultural crops

123.14

(30.31)

68.71

(17.02)

209.35

(64.20)

258.40

(96.34)

98.35

(40.72)

102.52

(13.98)

134.70

(80.49)

164.86

(30.07)

33.72

(.)

61.32

(10.99)


Notes: Standard errors are given in parenthesis

“-“indicates that no household had grown the respective crop in the respective villages in the respective year

(.) Indicates missing standard error

43

4.4. Patterns of diversification to off-farm and off-farm income earning activities

In this section, the descriptive statistics of households’ participation into off-farm self-

employment and off-farm wage employment; the share of income from different income

earning strategies for the pooled sample and for only those who earn positive income from off-

farm self-employment and off-farm wage employment is given. Furthermore, labor allocation

to off-farm self-employment and off-farm wage employment, and the types of activities in

which a member or members of households can get access to work have also been presented.

Table 10: Household participation in off-farm self-employment by year

Response categories Frequencies by year

1997 2004 Total

No

196

(71.5)

124

(45.3)

320

(58.4)

Yes 78

(28.5)

150

(54.7)

228

(41.6)

Total 274 274 548

(100.0) (100.0) (100.0)

Pearson Chi2=38.937, prob chi2=000


Percentages are given in parenthesis

4.4.1. Households participation in off-farm self-employment by activities

When households participation in off-farm self-employment is categorized by activities over the

time of observation, there are three major activities in which households participate. The first is

trade in grain, sales of banana, red peppers, honey, and …etc followed by trade in livestock and

livestock products and handcraft including pottery as a second and third respectively. Other

activities such as weaving/spinning, milling, salt trade, colleting and selling of firewood, dung

cake, and charcoal; traditional hairdressing and traditional hillers absorb some labor force and

contribute to some extent.

Among the above major activities, there was a significant change in participation into trade in

grain, and handicraft including pottery over the time of observation. Participation in both

44

activities had almost been doubled, while participation in trade in livestock and livestock

products was almost the same.

Figure 2: Households’ participation in off-farm self-employment by activities over 1997 and 2004


4.4.2. Households participation in Off-farm wage employment

As has been indicated, households also participate in off-farm wage employments such as food-

for-work, farm work for pay, traditional labor sharing, unskilled off-farm work and others.

Participation of households in off-farm wage employment was not as many as off-farm self-

employment. When it was considered over the years of observation, participation in off-farm

wage employment had been increased from only 19.7% in 1997 to 24.8% in 2004 but this

difference was not significant (table 11).

45

Table 11: Frequencies of participation in off-farm wage employment by years

Response categories Frequencies by years

1997 2004 Total

No

220 206 426

(80.3) (75.2) (77.7) Yes

54 68 122

(19.7) (24.8) (22.3)

Total

274 274 548

(100.0) (100.0) (100.0)

Pearson Chi2(1) =2.067, prob Chi2=0.151


Off-farm wage employment activities had not been given in 1997, but the distribution in 2004

shows that most of the off-farm wage employment is attributed to food for work followed by

farm worker for pay and traditional labor sharing as second and third activities respectively

(Figure 4). However, labor sharing is not a paid work as the household compensates for the

time worked by member of other household. In general, the low level of households’

participation in off-farm wage employment can be explained by two possible reasons.

First, the low coffee price might constrain the cash income of better-off households (better-off

is a relative term used to indicate those who produce relatively large quantity of coffee on large

area of land and better in other asset holding) from employing the relatively poor. Since off-

farm wage employments are mostly in coffee sector, when coffee price was too low to cover

the cost of harvesting, coffee producers might have left coffee unharvested (Mekuria, 2004). In

that case, the opportunity of labor to work on others farm for pay will be low. The second can

be attributed to the custom of labor migration from non-coffee producing parts of the country

to coffee producing areas during peak time for coffee harvest. If this holds, more off-farm wage

employment might be given to those migrants as they might be willing to work for lower wage

than labor in the locality. However, figure for hired labor was not recorded in the survey of

1997 and it was few for the year 2004.

46

Figure 3 :Households participation in off-farm wage employment by activities in 2004.

Source: Author’s computation from ERHS 2004

4.4.3. Labor allocation to off-farm self-employment and off-farm wage employment

Labor allocation to off-farm self-employment and off-farm wage employment has been given in

table 12. The overall labor allocation to off-farm self-employment and off-farm wage

employment showed that there was much more labor time allocated to off-farm self-

employment than off-farm wage employment. When compared within years, even though the

labor time allocation to both activities was similar in 1997, more labor time was allocated to

off-farm self-employment than off-farm wage employment in the year 2004. The between

years labor time allocated for off-farm wage employment does not significantly differ over the

two years, however, labor time allocated to off-farm self-employment work was significantly

increased from 1997 to 2004.

47

Table 12: Labor allocation to off-farm self-employment and off-farm wage employment for the pooled sample

Activities N Mean of man-days worked in the activities by years

Mean difference (2004-1997)

Pooled 1997 2004 Pooled 1997 2004

Off-farm self-

employment

546 273 273 77.06 (6.58)

10.82 (1.54)

143.30 (11.79)

130.76*** (11.83)

Off-farm wage-

employment

548 274 274 11.65 (1.64)

11.72 (2.07)

11.57 (1.55)

-0.15 (3.14)


Note: Standard errors are given in parentheses

*, ** and *** indicates the mean difference in labor allocation to off-farm self-employment and

off-farm wage employment are different from 0 at 10%, 5% and 1% significance level

respectively.

Determinants of on-farm diversification and expectations

As discussed in literature part on the determinants of diversification, on-farm diversification is

highly dependent on factors such as farm assets such as land ownership, farm size, number of

plots, oxen ownership and number of livestock the households owns. Labor endowment is

crucial factor especially for on-farm diversification. Most on-farm activities are seasonal and

hardly possible to postpone. During peak time for land preparation, cultivation and harvesting,

a day makes difference in productivity of factor of production.

The number of livestock can be competing with or complementary to crop production. Increase

in livestock needs more grazing land that necessitates reallocation of land from crop production

to pasture. Kurosaki and Fafchamps (2002) found supportive evidence in Pakistan as

households with more livestock devote a larger share of their cultivable acreage to fodder than

grain. However, if households use straw of crops for animal feed, then household’s number of

livestock can increase with increase in crop production.

On the other hand, livestock contribute to crop production in the form of drought power and

nutrient recycling through manure production (AMEDE and DELVE, 2008). In that case, as

48

number of livestock increases, the productivity of land, other inputs, and the cost of production

decreases as expenditure on chemical fertilizer is waived. Hence, since it is difficult to decide at

this end, livestock will have an ambiguous effect on farm diversification. However, oxen

ownership, in particular, is expected to positively influence on-farm diversification to field and

horticultural crops. Drought power, for which households mainly depend on oxen, and which is

not important for most or all of perennial crops production, is crucial especially for field crop

production.

The descriptive statistics in table 13 shows that average number of livestock owned by

households is the highest for the village Azedeboa and the least for Adado. Oxen ownership

was slightly the same for Azedeboa and Garagodo with only 3.4% and 3.7% of households

owning oxen in the two villages respectively. The least oxen owning village was Adado in which

only 0.5% of households own oxen. It is not surprising that few households own oxen since the

villages were highly dependent on perennial crops for which drought power is not as important

as in the case of other annual crops.

With regard to land ownership and farm size, all households in Azedeboa and Garagodo owned

land while the least land ownership was in Imdibir, in which only 50% of households own land.

Farm size is proportional with the landownership among the villages. It was the largest for

Garagodo with 2.20 hectares per household, compared to only 0.641 hectare for households in

Imdibir. This can make difference among villages in patterns of diversification. Land ownership

is important for diversification, especially to horticultural crops, as some horticultural crops

such as spices are mostly grown on garden of small holders and it is not appropriate to operate

on other person’s garden, on one hand, and absence of competition of interest on crops to be

grown as in the case of contractual farming, on the other hand. Therefore, land ownership is

expected to positively influence on-farm diversification.

There is mixed effect of farm size in diversification literatures. Study in Thailand, for instance,

showed that small farms were more diversified than large farms (Kasem and Thapa, 2011).

Whereas, study by Weiss and Briglauer on determinants and dynamics of farm diversification in

Australia validated that farm size positively influenced on farm diversification (Weiss and

49

Briglauer, 2000). Benin et al. (2004) also found positive effect of farm size on farm

diversification in highlands of Tigray and Amhara regions of Ethiopia. Since farm households in

Southern Ethiopia are small farms, we expect a positive influence of farm size on-farm

diversification.

Number of plots of households also positively influence on farm diversification. Farms with

large number of plots are characterized by heterogeneous land conditions. As heterogeneity in

land condition increases, on farm diversification increase (e.g., Marshal and Brown, 1975 as

cited in Benin et al. 2004). In table 13, it has been shown that the number of plots was the

highest for Imdibir and the least for Garagodo with corresponding average numbers of plots of

6.67 and 2.98 respectively.

Household characteristics such as family size, education level, age and sex of household head

are influential factors in decision making to diversify. Family size is important for diversification,

when family size increases more labor can be available to work on farm so that labor can easily

be allocated to labor-intensive crops.

In economic terms households allocate the surplus labor to equalize the factor of production

with economic activities. Hence, households diversify to different crops and\or income earning

activities. Kasem and Thapa (2011) studied farm households’ crop diversification in Thailand

and found that households with larger family size were more diversified from rice mono-

cropping. It was also explained in Benin et al. (2004) that family size positively influences

diversification through preference, heterogeneity and labor capacity.

The descriptive statistics shows that the average family size was similar for all villages with

almost the same with pooled sample mean, but slightly higher and lower for households in the

villages Gargodo and Adado respectively.

50

Table 13: Definition and description of variables used in econometric estimation for pooled sample of 1997 and 2004

Variables description Mean

Pooled Imdibir Azedeboa Adado Garagodo

Farm assets

Nolivest- number of livestock 4.196 (0.161)

5.398

(0.403)

6.627

(0.383)

2.953

(0.179)

3.390

(0.220)

Oxen = dummy for oxen ownership (1 if

household has oxen and 0

otherwise)

0.022 (0.006)

0.012

(0.116)

0.034

(0.017)

0.005

(0.005)

0.037

(0.016)

Land = dummy for land ownership (1 if

household has land and 0

otherwise

0.863 (0.015)

0.500

(0.054)

1.000

(0.000)

0.845

(0.025)

1.000

(0.000)

Farmsize = total farm size in hectare 1.662 (0.405)

0.641

(0.056)

1.557

(0.327)

1.651

(0.359)

2.197

(1.367)

Noplots = plots owned by household in

number

4.379 (0.109)

6.686

(0.365)

5.390

(0.184)

2.976

(0.121)

4.073

(0.115)

Household characteristics

Hhsize = family size of the household

in number

9.302 (0.166)

9.954

(0.420)

9.797

(0.267)

8.106

(0.245)

10.314

(0.350)

Sex = sex of household head, 1 if male

and 0 otherwise

0.868 (0.015)

0.900

(0.037)

0.870

(0.031)

0.920

(0.019)

0.770

(0.036)

Age =age of household head 50.437 (0.666)

55.564

(1.497)

50.560

(1.318)

49.362

(1.108)

49.461

(1.149)

Education

Illitrat= 1 if head is illiterate and 0

otherwise

0.617 (0.022)

0.640

(0.052)

0.449

(0.046)

0.609

(0.034)

0.766

(0.036)

Literate = 1 if household head is literate 0.383 (0.021)

0.36

(0.052)

0.551

(0.046 )

0.391

(0.034)

0.234

(0.036)

Labor use for on-farm work

Labur= total labor worked on farm

(in ’00 of man-days)

0.842 (0.091)

0.456

(0.067)

0.401

(0.055)

0.662

(0.152)

1.736

(0.264)

Labor allocation to off-farm activities,

and off farm income

Offfarm = Dummy for whether

household worked off-farm

12 months, 1 if worked and 0 if

not

0.415 (0.022)

0.560

(0.054)

0.440

(0.046)

0.360

(0.033)

0.390

(0.042)

Non_off = non-fam and off-farm income

( in ’00 ETB)

2.336

(0.217)

3.668

(0.614)

2.388

(0.521)

2.643

(0.399)

0.994

(0.154)

51

Source: Authors computation from ERHS 1997 and 2004

-standard errors are given in parenthesis

Since diversification requires either innovation or adoption of new crops, livestock and/or

activities, education is important variable that is expected to positively influence diversification.

Not only innovation or adoption, but also it enables households to better understand the

necessary training and/or demonstration for successful crop management. Studies showed that

education increased households’ on-farm and off-farm diversification (Ibrahim, 2009). However,

more than 60% of household heads in the study area were illiterate. The figure was comparable

among the villages with the largest number of illiterates in Garagodo with 76.6% as compared

to the relatively small number in Azedeboa in which only 45% were illiterate.

Daysworked_off = number of days

worked off-farm (in ’00 of man-

days).

0.888 (0.067)

1.381

(0.209)

0.767

(0.144)

0.794

(0.094)

0.822

(0.137)

Infrastructure

Dismarkt= distance from market in

minutes

40.133 (0.981)

30.357

(1.585)

58.116

(1.403)

40.297

(1.536)

30.400

(1.816)

Road = dummy for access to road, 1 if

household has access and 0 if not

0.395 (0.022)

1.000

(0.000)

1.000

(0.000)

0.000

(0.000)

0.000

(0.000)

Farm characteristics

Landqual= land quality, 1 =good,

2=medium, 3 =bad

1.466 (0.025)

1.712

(0.067)

1.733

(0.059)

1.254

(0.030)

1.346

(0.034)

Slope = land slope, 1 =plain/flat, 2=steep,

3 =very steep

1.411 (0.021)

1.350

(0.047)

1.158

(0.031)

1.736

(0.034)

1.220

(0.028)

Displot = average distance of plot from

home in minutes

6.067 (0.336)

3.129

(0.226)

1.650

(0.192)

13.591

(0.647)

2.212

(0.144)

Climate variables

timely_r = whether the first summer rain

come on time, 1=on time, 2 = too

late, 3= too early

1.360 (0.026)

1.800

(0.079)

1.470

(0.051)

1.13

(0.029)

1.290

(0.042)

enough_r = whether there was enough

rain at the beginning of the

rainy season, 1=enough, 2= too

much, 3 = too little

1.445 (0.034)

1.860

(0.099)

1.790

(0.086)

1.130

(0.025)

1.310

(0.054)

Numbers of observations 548 86 118 207 134

52

Age of households’ head is expected to have negative relationship with crop diversification as

younger households are expected to better try new crops and activities than older households

(Benin et al., 2004). Sex of household head is difficult to predict a priori.

Off-farm work participation might encourage or discourage on farm diversification. It

encourages as households get extra income they can have more purchasing power for inputs

that are important for improved variety production and subsequently increase the productivity

of land and labor. On the other hand, it competes with labor time of households for on-farm

work. Hence, the effect of participation in off-farm activities and the level of income from the

activities are ambiguous, whereas the amount of time worked by households in off-farm

activities is expected to negatively influence on-farm diversification.

The descriptive statistics for households to off-farm activity participation has been given in

table (13). The summary showed that on average, 41.5% of households participated in off-farm

self-employment activities. The highest participation rate was in Imdibir (56%) and the least

was in Adado (36%). The average income from off-farm and off-farm activities was 233.6 ETB

with the highest off-farm and off-farm income attributed to the households in the village

Imdibir (366.8 ETB) and the least to the households in the village Garagodo (99.4 ETB). The days

worked by households in the off-farm and off-farm activities also showed the same pattern.

While it was comparable for all the three villages with relatively less than the total average, the

number of man-days worked by households in Imdibir was higher than average. The large

number of participants, higher income and larger labor time allocated to off-farm and off-farm

activities in the village Imdibir shows the relative importance of off-farm income for households

in the village.

Access to infrastructures such as road is expected to positively influence diversification whereas

distance from market is expected to influence negatively. The more the households can have

access to all weather roads, and the lesser the distance from market, the lesser the transaction

53

costs of buying input and selling output, as a result, the more they diversify to high value crops

and high return activities.

The finding by Ibrahim (2009) supports this hypothesis while other authors, for instance, (Smale

et al., 2001) argued that the more the transaction costs faced by households in the community

the more they allocate land to diverisfied crops to satisfy their consumtion requiremnt. The

dummy for access to road by villages showed that households in Imdibir and Azedeboa had

access to road during the survey times, whereas the households in the villages Adado and

Garagodo had not. Distance from market was a continuous variable in minutes and Imdibir (30

minutes on average) was near to market than other villages. The furthest village was Azedeboa

with average time of an hour for households to travel to the nearby market.

Farm characteristics such as land quality, slope and distance of plot from home can have

influence on households’ on-farm diversification. Studies showed that land quality enable

farmers to grow many crops because of its ability to provide crops with necessary nutrients

(Benin et al., 2004). Slope of land and distance of plots from home are expected to negatively

influence on-farm diversification. Steep sloped land is characterized by relatively high soil

erosion and less nutrient holding capacity. On steep sloped lands, farmers rather grow

perennial crops such as enset and fruits to protect it from erosion. As distance of plots from

home increases, the transaction cost of growing and managing multi-crops will be higher (Benin

et al., 2004). Hence, distance of plot from home is expected to negatively influence on farm

diversification.

4.5. Result of multinomial logit model for determinants of crop choice

The multinomial logit estimation results on pooled data showed that households on-farm

diversification to field crops relative to growing only perennial crops was positively influenced

by number of livestock the household owns, land ownership, number of plots, access to road

and land quality. Whereas negatively influenced by distance from market, slope of the land and

distance from plot.

54

Diversification to both field and horticultural crops relative to growing only perennial crops was

positively influenced by number of livestock owned by the households, land ownership, number

of plots, household size, land quality, and timeliness of rainfall. Whereas negatively influenced

by age of household head, distance from market, distance from plots and slope of the land. The

positive influence of land ownership, number of plots, family size, access to road and land

quality and the negative influence of age of household head, distance from nearby market,

distance from plot and slope of the land are according to our expectation and supports the

findings of many authors.

To state few of the authors, Kasem and Thapa (2011), studied household on-farm diversification

in Thailand and found positive relation between family size and on-farm diversification. Benin

et al. (2004) also found positive influence of family size and number of plots on households on

farm diversification in highlands of Ethiopia. The positive influence of number of livestock on

the diversification to field and horticultural crops can be attributed to two reasons: first manure

requirement by households for crop production, second, households might rely more on

livestock and annual crop production as a means of insurance because of fluctuating coffee

price. The positive influence of access to road and negative influence of distance from market

on households diversification implies that households diversification is not mainly to reduce

income variability, rather it is a shift to high return activities as defined in Ilbery (1991). If the

ultimate goal of households had been to reduce income variability by reallocating resource to

activities that has negative relation with income, their diversification would have increased as

they become in remote areas (Smale et al., 2001).

The signs and effects of number of plots, land quality, slope of land and distance of plots from

home on households on-farm diversification are as we expected and according to the findings

different authors. The number of plots positively influenced households on-farm diversification

from growing only perennial crop to grow field and/or horticultural crops. Different plots with

different land quality were given to households during land redistribution in 1975 and after in

55

the areas. The heterogeneity in their land quality enable households to grow different crops.

Hence, farm households having more number of plots were diversified more than those who

has low number of plots. Benin et al. (2004) also found the same effect in the highlands of

Ethiopia. The same reasoning can hold for the positive effect of land quality as it is easy for

lands with good quality to provide different crops with necessary nutrients. In that case, it

allows households to intercrop different crops on the same plot.

The negative effect of slope on on-farm diversification is attributed to the less suitability of land

for crop production as its slopes increase. This is partly because of its inappropriateness to

manage crops on the steep sloped land and partly to the nutrient and water holding capacity of

such land. On the steep sloped lands households, most of the time, grow perennial crops to

decrease the effect of run-off. The negative effect of distance of plots from home on

households on-farm diversification is attributed to the transaction cost of managing more than

one crop on the same plot. The effect of distance of plot is in line with the finding of Benin et al.

(2004).

56

Table 14: Multinomial logit estimation result for the determinants of on farm diversification on the pooled data of 1997 and 2004

Explanatory variables

pooled sample Imdibir Adado

Perennial & field crops

Perennial, field & horticultural crops



Coefficient Coefficient Coefficient Coefficient

Nolivest 0.107** (0.055)

0.140** (0.059)

0.549** (0.220)

0.154 (0.114)

Oxen 1.299 (1.265)

0.598 (1.281)

31.800 (.)

-40.588 (.)

Land 1.778*** (0.553)

4.828*** (0.846)

2.613** (1.284)

1.164 (1.139)

Farmsize -0.022 (0.034)

0.001 (0.019)

0.647 (0.996)

-0.012 (0.054)

noplots 0.133* (0.080)

0.368*** (0.092)

-0.032 (0.164)

0.123 (0.144)

hhsize -0.016 (0.043)

0.111** (0.047)

-0.168 (0.168)

-0.086 (0.085)

sex 0.418 (0.424)

-0.404 (0.432)

1.561 (1.135)

19.580*** (2.368)

Age -0.007 (0.011)

-0.036*** (0.013)

0.011 (0.036)

0.024 (0.020)

Litrate -0.158 (0.325)

-0.077 (0.360)

0.695 (1.004)

0.363 (0.669)

Offfarm -0.090 (0.363)

0.511 (0.394)

2.316 (1.481)

1.182 (0.787)

Non_off 0.011 (0.033)

-0.064 (0.043)

-0.082 (0.109)

0.045 (0.045)

Daysworked_

off

-0.024 (0.120)

0.120 (0.135)

0.449 (0.448)

-0.375 (0.251)

dismarkt -0.016** (0.007)

-0.015** (0.008)

-0.045 (0.031)

-0.010 (0.013)

Road 1.582*** (0.439)

-0.690 (0.0497)

landqual 0.538* (0.305)

0.891*** (0.341)

-1.048 (0.749)

-0.185 (0.723)

slope -0.909*** (0.319)

-1.519*** (0.383)

-0.215 (1.041)

-0.327 (0.586

Displot -0.105*** (0.028)

-0.262*** (0.045)

-0.551 (0.289)

-0.014 (0.033)

timely_r -0.079 (0.306)

0.746** (0.330)

-1.846 (0.806)

0.701 (0.593)

enough_r 0.331 (0.241)

0.433 (0.268)

0.934 (0.621)

0.555 (0.632)

57

Source: Author’s computation from ERHS 1997 and 2004 Note: Standard errors are in parenthesis *, ** and *** indicates that the coefficients are significant at of 10%, 5% and 1% significance level respectively.

Another point is the effect timeliness of rainfall on households diversification. When rain comes

either early or late, then households diversify more to horticultural crops then field crops. This

is an important implication for short time required by horticultural crops for maturity. When

rain come early households grow annual horticultural crops first, then after harvesting it, they

cultivate other crops. When the rain is late, then households grow horticultural crops that have

short life span so that they immediately reach for consumption.

Village level multinomial logit estimation result shows different histories. For instance, number

of livestock owned by the household positively and significantly influenced households

diversification to field crops relative to perennial crops in the village Imdibir whereas it is not

significant in the case of Adado. Diversification to field and horticultural crops was not

captured by the model for villages Imdibir and Adado. For villages Azedeboa and Garagodo, the

model cannot maximize the determinants of diversification to both crop categories the reason

is unclear. Someone can speculate the reason as small observation for the households in the

categories, but this cannot hold as the number of households in both villages is higher than that

of Imdibir. Timeliness of rainfall and distance from plots negatively influenced households

diversification to field crops in the village Imdibir though the latter was weakly significant.

cons -1.456 (1.228)

-4.468*** (1.525)

2.273 (3.288)

-24.701 (.)

Observation 529 86 200

Log likelihood -356.43 -34.42 -52.93

LR Chi2 (38)= 442.14 76.19 54.70

Prob Chi2 0.000 0.000 0.024

Pseudo R2 0.3828 0.53 0.34

58

4.6. Results of fixed effect logit model estimate for changes in diversification

Table 15: Estimation result of fixed effect logit model on determinants of diversification

Explanatory variables

Total sample Imdibir Azedeboa Coefficient Coefficient Coefficient

nolivest 0.010 (0.037)

0.260 (0.1620

-0.044 (0.078)

farmsize -0.017 (0.023)

2.045* (1.181)

-0.008 (0.063)

noplots -0.109* (0.059)

-0.274* (0.161)

0.090 (0.156)

hhsize -0.053 (0.063)

0.339 (0.378)

0.837*** (0.301)

age -0.025** (0.011)

0.032 (0.040)

-0.029 (0.032)

offfarm 0.148 (0.240)

0.500 (0.500)

0.148 (0.508)

non_off -0.055** (0.025)

-0.223** (0.101)

-0.140** (0.065)

landqual 0.340 (0.231)

0.270 (0.727)

0.363 (0.442)

timely_r 0.444* (0.228)

-0.485 (0.662)

0.774 (0.779)

enough_r 0.103 (0.169)

0.230 (0.552)

-0.725 (0.078)

Observations 378 78 112 Groups 139 39 56 LR chi2(11) 32.41 24.81 25.76 Prob > chi2 0.001 0.006 0.004 Log likelihood -114.80 -14.63 -25.935

Source: Authors computation from ERHS 1997 and 2004 Standard errors are given in parenthesis *, ** and *** indicates significance at 10%, 5% and 1% levels respectively.

The essence of using fixed effect logit model is to account for the effect of within change in the

value of explanatory variables on the change in diversification. The result of this model is

interpreted as how diversification change when the difference in the two times observation of

the independent variables change over its mean. Accordingly, the model tells us different

histories for on-farm diversification. The null hypothesis for the model has not been rejected

for villages Adado and Garagodo with p-value=0.213 and p-value =0.093 respectively. In the

fixed effect logit model, many variables are not significant. Only exogenous income and age of

head negatively and significantly influence households on-farm diversification. This is might be

59

because of the competition for labor between on-farm diversification and off-farm earning on

one hand and they might earn enough income from off-farm to compensate the income loss

from coffee crises and purchase food rather than producing on their farm. The negative sign of

head with diversification is according to our expectation, and the possible reasoning that when

age of households increase, they become risk averse to try new crops. Therefore, their choice

can be explained as “state dependent” rather than trying to change their crop choice.

The village level analysis showed that exogenous income negatively influenced on-farm

diversification in both Imdibir and Azedeboa, but its influence is weak for Imdibir. Family size

has positive and significant influence only for farm households residing in the village Azedeboa.

Other factors such as number of livestock, farm size and number of plots are not significant

either in the overall sample or village level samples.

4.7. Determinants of diversification to off-farm income earning and expectations

Table (15) gives summary of the variables used in the random effect Tobit model for the

determinants of households to off-farm and off-farm income diversification. Factors such as

asset holding are expected to influence households’ diversification to off-farm self-employment

and off-farm wage employment. Assets such as household valuables are expected to positively

influence off-farm self-employment. Household valuables includes fixed assets such as house

and equipments that are not directly productive, but facilitate access to other sources of

income such as informal credit from families or friends or formal credit from credit institutions

as they can be used as collateral.

Empirical researches such as by Weldehanna and Oskam (2001) found that households asset

holding positively influenced household diversification to off-farm self-employment. However,

its effect on off-farm wage employment is difficult to determine a priori. There are two reasons

for this: first because of its fixed nature, household valuables cannot solve immediate cash

problem of households and in that case households can work for casual labor irrespective of

60

their fixed asset. Second, households who are considered better-off in the community are less

likely to work off-farm wage employment. Hence, it is difficult to determine the direction of the

influence of households valuable on off-farm wage employment.

As far as they are indicators of wealth, assets such as farm size (Barrett, 2001), land ownership,

number of livestock and farm production (Woldehanna and Oskam, 2001) are expected to

positively influence farm households diversification to off-farm self-employment. Démurger et

al. (2010) in their study on determinants of diversification in northern China found that

hosuehold wealth strongly influenced household participation in off-farm self-employment that

needs initial investment. However, they are expected to negatively influence income from off-

farm wage employment. The richer the household the less the member of the households work

for casual labor, food for work or other related activities, except skilled labor work such as

teachers and rural administrators. Escobal (2001) found in rural Peru that ownership of fixed

agricultural asset enable household to increase the share of their income from farm activities

and discourage to work for wage either on-farm or off-farm.

Table 16:Definitions and descriptive statistics of variables used in random tobit estimation

Variables Description Total

sample

Imdibir Azedeboa Adado Garagod

o

Off-farm

income

Income from off-farm

self-employment in ETB

177.94

(18.965)

276.60

(54.278)

134.51

(30.678)

227.15

(39.639)

79.08

(13.461)

Off-farm

income

Income from off-farm

wage employment in

ETB

55.68

(11.372)

90.16

(32.894)

104.32

(44.037)

35.65

(7.675)

22.40

(7.158)

Nolivest Number of livestock in

number

4.196

(0.161)

5.398

(0.403)

6.627

(0.383)

2.953

(0.179)

3.390

(0.220)

Hhvaluable Asset house, equipment

and stocks in excluding

value of livestock in

100’s ETB

8.16

(0.671)

10.84

(1.684)

10.02

(1.282)

8.80

(1.374)

3.91

(0.636)

Land Land ownership, 1 if

household has land and

0 otherwise

0.863

(0.015)

0.500

(0.054)

1.000

(0.000)

0.845

(0.025)

1.000

(0.000)

Farm size Farm size in hectare 1.662

(0.405)

0.641

(0.056)

1.557

(0.327)

1.651

(0.359)

2.197

(1.367)

61

Credit_t Amount of credit in ETB 157.71

(17.7360

198.60

(39.5890

301.20

(54.144)

79.90

(18.022)

125.32

(36.326)

Hhsize Family size in number 9.302

(0.166)

9.954

(0.420)

9.797

(0.267)

8.106

(0.245)

10.314

(0.350)

Sex Dummy for Sex of

household head, 1 if

male and 0 otherwise

0.868

(0.015)

0.900

(0.037)

0.870

(0.031)

0.920

(0.019)

0.770

(0.036)

Age Age of household head

in years

50.437

(0.666)

55.564

(1.497)

50.560

(1.318)

49.362

(1.108)

49.461

(1.149)

Illiterate Dummy for illiterate, 1 if

household is illiterate

and 0 otherwise

0.617

(0.022)

0.640

(0.052)

0.449

(0.046)

0.609

(0.034)

0.766

(0.036)

Literate Dummy for literacy, 1 if

household head is

literate and zero

otherwise

0.383 (0.021)

0.36

(0.052)

0.551

(0.046 )

0.391

(0.034)

0.234

(0.036)

Netvalue Mean net value product

of labor (farm quasi-

profit) in ’00 ETB

2.005

(0.379)

6.128

(2.33)

1.763

(0.214)

1.269

(0.116)

0.713

(0.124)

Dismarket Distance from nearest

market in minutes

40.133

(0.981)

30.357

(1.585)

58.116

(1.403)

40.297

(1.536)

30.400

(1.816)

Road Dummy for access to all

weather road, 1 if has

access and 0 otherwise

0.395

(0.022)

1.000

(0.000)

1.000

(0.000)

0.000

(0.000)

0.000

(0.000)

Year Year dummy, 1 if year

=2004 and 0 otherwise

0.50

(0.021)

0.50

(0.054)

0.50

(0.046)

0.50

(0.035)

0.50

(0.043)

Observations 545 86 117 207 134


Standard errors are given in parenthesis

ETB = Ethiopian currency known as Ethiopian Birr

Households characteristics also plays major role in households diversification decision to off-

farm self-employment and off-farm wage employment. Family size is expected to positively

influence off-farm diversification in general and off-farm wage employment in particular.

Woldehanna and Oskam, (2001), for instance, found that family size positively influenced

household’s diversification to off-farm wage employment. Reardon (1997) also indicated that

family size affects the ability of a household to supply labor to the nonfarm sector. When

households have larger family size, they can easily satisfy on-farm labor demand and supply the

62

rest to off-farm activities. Household size is expected to positively influence household’s off-

farm earning (Démurger et al., 2010).

Household head education is also expected to positively influence households’ diversification to

off-farm self-employment. Education is believed to better stimulates households’

entrepreneurial activity and enhances productivity (Fabusoro et al., 2010). Gordon and Craig

(2001) have identified the significance of education on off-farm diversification. They explained

that education increase skill level and training processes that increase confidence, establish

useful networks, or contribute to productive investment. Escobal (2001) also found in rural

Peru that the higher is the education of household head, the more the household is diversified

to off-farm income earning activities or the lesser the household supplies labor to on-farm

activities. Therefore, we expect positive influence of education on off-farm income

diversification.

Age of household head is expected to negatively influence households’ diversification to off-

farm activities. As most off-farm activities are done out of door and has investment

characteristics, the older the household head the slower the decision to diversify. Hence, age of

household head is expected to negatively influence diversification to off-farm income earning.

Access to credit is expected to positively influence household's diversification to off-farm self-

employment. Escobal (2001) explains that credit is a key determinant for household income

earning activity diversification. Credit is principal for activities that need startup capital, without

credit households remain in low return activities (Fabusoro et al., 2010, Gordon and Craig,

2001 ). However, credit is expected to be endogenous with off-farm income. For such variables

instruments can be used to correct for the effect of endogeneity. In this case, strong

instrument cannot be found for the level of credit households get access to. The variable has

been dropped from estimation model as I believed that its effect is captured by household

asset and other variables. This is one advantage of random effect model. Because of the

63

inclusion of unit specific effects ( in the model, it faces less effect of potential missing

variables.

Other variables such as farm income are also important determinants for off-farm farm

earnings. Woldehanna and Oskam (2001) found that farm income positively influenced

households’ diversification to off-farm income earning activities. On the other hand, Escobal

argue that when income from farm increase, participation in off-farm income earning activities,

especially wage employment decreases. Therefore, the influence of net value product of labor

in farm activities is ambiguous. However, this variable was also dropped because of fear of the

problem of endogeneity.

Village level variables like infrastructure development such as road availability and access to

market are expected to positively influence farm households diversification to off-farm self-

employment and off-farm wage employment.

4.8. Estimation result on the determinants of off-farm self-employment and off-farm wage employment

Table 17:Random effect tobit model estimation results on the effect of coffee price and other determinants of earning from off-farm self-employment (dependent variable= off-farm income)

Variables Pooled sample Imdibir Azedeboa Adado Garagodo Coefficient Coefficient Coefficient Coefficient Coefficient

Nolivest 46.970*** (12.650)

39.443* (23.514)

28.686* (15.821)

96.033** (39.615)

23.432* (12.793)

hhvaluable 8.524*** (2.267)

-0.844 (5.598)

4.733 (4.187)

11.925*** (4.299)

-1.593 (4.152)

Land -259.944** (111.846)

-955.129*** (182.101)

- 583.416** (310.881)

-

Farmsize -2.873 (6.084)

-48.033 (211.533)

-7.900 (22.504)

7.899 (19.307)

-2.820 (3.878)

Hhsize -23.350** (11.763)

-69.752** (28.382)

-6.515 (22.97)

-42.639 (30.370)

4.058 (8.261)

Sex -120.151 (112.398)

369.007 (304.537)

-29.414 (187.050)

-294.135 (320.578)

-20.894* (67.491)

Age -11.960*** (3.095)

-2.920 (7.509)

-12.009** (4.935)

-30.294*** (7.839)

0.181 (2.380)

Literate

-1.287 (87.635)

-80.774 (202.689)

-101.566 (146.162)

154.122 (197.555)

2.069 (73.363)

64

Dismarket -2.368 (1.841)

4.850 (6.102)

-8.706** (4.369)

1.153 (3.879)

-0.650 (1.424)

Road

-1.885 (92.040)

- - -

year 503.994** (80.498)

- 245.100* (130.580)

255.164 (204.071)

325.610*** (63.242)

_cons 440.056* (225.508)

622.668 (571.076)

725.184 (470.129)

439.913 (536.151)

-278.588** (139.604)

Observation 548 86 117 207 134 Groups 274 43 59 104 69 Wald chi2 (11) 94.69 Wald Chi2(9)

33.6 Wald chi2(9)

17.20 Wald chi2(10) 46.10

Wald chi2(13)=36.50

Prob chi2 0.000 0.000 0.046 0.000 0.000 Loglikelihood -1923.10 -360.37 -418.92 -662.10 -406.89 Left censured observations

328 43 67 134 84

Uncensored observations

220 43 50 73 53

Source: Author computation from ERHS 1997 and 2004 Notes: Standard errors are given in parenthesis *, **, *** indicates the coefficients are significant at 10%, 5% and 1% significance level respectively Table 18 :Estimation result of random effect tobit model on the determinants of income from off-farm wage employment

Variables Pooled sample Coefficient

nolivest -18.184

(13.16) hhvaluable 3.725

(2.44) land -275.145**

(122.75) farmsize -30.982

(28.015) hhsize 20.417*

(11.970) sex 145.360

(126.405) age -0.214

(3.074) litrate 54.724

(90.089) dismarkt -1.002

65

(2.12) year 18.90

(77.49) imdibir 189.351

(152.12) azedeboa 432.96***

(139.18) Adado 29.42

(121.34) _cons -685.74***

(254.19) Observation 547 Groups 274 Wald chi2 (13) 31.07 Prob chi2 0.0033 Loglikelihood -1123.28 Left censured observation

423

Uncensored observation

124

Source: Author computation from ERHS 1997 and 2004 Notes: standard errors are given in parenthesis *, ** and *** indicates significance at 10%, 5% and 1% significance level respectively

The random effect tobit model estimation result for the determinants of earning from off-farm

self-employment and off-farm wage employment are given in tables (16) and (17) respectively.

Table 16 shows that asset of the households such as number of livestock and household

valuables positively and significantly influence households earning from off-farm self-

employment.

These effects are according to our hypothesis and in line with the findings of many authors. For

instance, Woldehanna and Oskam (2001) found positive influence of number of livestock and

farm production on households’ diversification to off-farm self-employment. Escobal (2001)

also found, in rural Peru, that households’ wealth positively influenced their diversification to

off-farm income earning and those households in the bottom with their asset holding hardly

overcome investment requirement for remunerative off-farm work. Block and Webb (2001)

also found that better-off households were more diversified than asset poor households in rural

66

Ethiopia. Reardon (1997) also found the same influence of asset on household’s diversification

to off-farm income earning activities in rural Africa.

Household asset not only solve cash problem through facilitating access to credit but also

serves as self insurance for risk of business failure so that households even try risky businesses.

On the other hand, it enables households to erect entry barrier for asset poor households and

create monopoly power for the rich to enjoy the advantage of remunerative income alone. The

positive effect of number of livestock can be attributed to the fact that households rent out

oxen, they might buy and resale livestock, they can buy for fattening, which is part of off-farm

earning and also rent out transport animals such as donkey. This result might be criticized of

the reverse causality between the two variables (i.e. households can buy more livestock as their

off-farm income increases). This what I have not checked for.

Access to infrastructure, such as road is also important for households off-farm diversification.

It facilitates access to input and output market and other service sectors. For instance, Escobal

(2001) concludes that better infrastructure and denser population reduces transaction cost

and subsequently eases investment in farm and off-farm sectors. Particularly, most of off-

farm activities in the Southern Ethiopia are directly linked to market. Trades in crops, livestock

and livestock products is impossible without getting access to nearby market. But access to

road is not significant in our case of off-farm income earning in southern Ethiopia.

Unlike the finding of many authors, family size negatively influenced households diversification

to off-farm self-employment in the coffee producing areas of Southern Ethiopia. Reardon (1997)

explained that larger family size can ease labor supply to off-farm income earning activities; I

think it needs to differentiate between off-farm self-employment and off-farm wage

employment. In developing countries, most of the time, large family sizes are known for high

income constraint because of more food and non-food expenditures than household with small

family size. In that case they will be constrained with capital to invest in off-farm self-

employment and supply their labor to off-farm wage employment. In study by Woldehanna and

67

Oskam (2001) in Tigray region Ethiopia, it has been shown that off-farm wage employment was

positively influenced by family size than off-farm self-employment. Though weakly significant,

our finding also shows similar trend. We found positive influence of family size on off-farm

wage employment. The effect of age of household head on diversification to off-farm self-

employment income is negative and significant. This is according to our expectation, the older

the head the lesser are households expected to diversify their livelihood.

Age of household head has negative influence on households earning from off farm self-

employment. This is according to our expectation, it can be attributed to the slow decision

making process (state dependence) of older household heads and the outdoor nature of off-

farm self-employment in the areas of study. Most of the off-farm self-employments are in trade

in crop, and livestock and livestock product. This requires moving from one market to another

with the commodity to be traded, which is less convenient for older households.

There is mixed effect of land ownership on off-farm earning. While its effect is negative and

significant for total sample and Imdibir, but is positive and significant for the village Adado. The

negative effect of land ownership on off-farm earning can be explained by the higher possibility

for on-farm diversification for land holders than landless. Hence, when coffee price decrease,

land owners can diversify to different crops to compensate the income loss, this is not possible

for the landless. The landless households must look for either off-farm self-employment or off-

farm wage employment. Another explanation is the competition among activities for labor,

more labor will be allocated to on-farm diversification than off-farm activities.

We have not found sex differential in diversification to off-farm self-employment, except in the

village Gargodo where male headed households earn less off-farm income than female headed

households. This can be attributed to the nature of off-farm activities in which households

participate for their income generation. Most off-farm self-employment activities in the village

are trade in crop (19.7%) followed by handicraft including pottery (6.6%). Many handicrafts in

general and pottery in particular is mainly undertaken by female than by male. In addition, in

68

the village, the proportion of female headed households is the highest (23% look at table 15)

from all villages included in the analysis.

Variables such as farm size, education, distance from market are not significantly influenced

household’s diversification to off-farm income earning activities among coffee producing farm

households in Southern Ethiopia in general. However, the village level analysis shows that the

effect of distance from market is negative and significant in the case of village Azedeboa. As can

be seen from the descriptive statistics it was the furthest village from nearby market (on

average households walk 58.12 minutes to the nearest market compared to only 30.36 minutes

in Imdibir). This is interesting indicator of the role of transaction costs that households incur to

access market on their activity choice. It is according to our hypothesis and in line with the

findings of many previous authors. For instance, Ibrahim (2009) in Nigeria found that as

distance from market increases household’s income diversification decreases. Escobal (2001)

also found the same effect of distance from market on off-farm self-employment in rural Peru.

We included year dummy to account for the year effect. The result shows that the income from

off-farm self-employment was significantly higher in 2004 than 1997. It confirmed the results in

the descriptive statistics part of this paper. This finding is also in line with the finding of many

authors on the share of off-farm sector in the income of rural households. To state few of them,

The tobit estimation result on the off-farm wage employment shows that, access to land

negatively influences household’s income from off-farm wage employment whereas coffee

price and households size have positive influence though the influence of the latter is weak (i.e.

households size is significant only at 10% significance level). Access to land and family size are

according to our expectation and in line with earlier findings. But, it is possible when any

household member can work for skilled labor, such as local school teacher, village level

administration, builder and mason. In our pattern of diversification to off-farm and off-farm

income earning activities, in figure (4) it has been shown that there were households working

69

for skilled labor. Since skilled labor work is rationed by some skill that basically depend on level

of education, then the better educated the person, the better paid she or he is.

Being in any of the villages does not make difference for household’s diversification to off-farm

wage employment, but households in the village Imdibir earn less off-farm income than others

with weak significance level. Year dummy has no significant effect on household’s

diversification to off-farm wage employment and consequently it does not have effect on

household’s off-farm income.

70

5. Conclusion Unlike the world in perfect market where labor allocation is derived by relative wages in

different sectors or activities, farm households in developing countries allocate their resource

under many constraints. Price crises are common in cash crop areas of developing countries in

general and less favored regions like Ethiopia in particular. Coffee producing farm households in

Ethiopia faces such price crises related to international coffee market. To cope up with such

fluctuations farm households diversify their livelihood either to produce more of food crops

and/or diversify to off-farm income earning activities to compensate the cash lost due to low

coffee income. However, their diversification is again constrained by many exogenous and

endogenous factors.

This thesis attempted to answer two questions: how farm households income earning

strategies and their contribution had been changed for coffee producing households during

coffee crises between 1997 to 2004? And how the patterns of farm households on-farm and

off-farm diversification changed and what determines the change in diversification over the

course of time?

Reduced form of farm household model has been used to create relationship between

diversification and endowments such as land and labor endowments, other household asset

and infrastructures. Multinomial logit model was applied on the pooled data to identify the

factors that determine households on farm diversification from perennial crops to field,

horticultural or the combinations of the two crops categories. Farther more, fixed effect logit

model has been implemented to analyze factors that determine households change in choice

for crops or crop combinations over the course of time. For the determinants of earning from

off-farm self-employment and off-farm wage employment, random effect tobit model was used.

From the result of the study it can be concluded that during coffee crises of the 1999 to 2003,

which even did not recovered in 2004, households earning from off-farm self-employment has

been significantly increased while the contribution of crop sector to total income of households

71

had been significantly decreased. Furthermore, it has been identified that better off households

better earn from off-farm self-employment than the poor. This has been seen as the total

income of households those who earn positive off-farm income was statistically significantly

higher than those who earn nothing from the sector.

From the multinomial logit model estimate result, it can be concluded that land ownership,

number of livestock and access to infrastructure like road and market are the main influential

factors for households diversification to annual crops from growing only perennial crops. There

was also indication that labor endowment as indicated by family size encourages households

diversification to field and horticultural crops. From the fixed effect logit model, the conclusion

follows that on-farm diversification and off-farm earnings are opposite to each other. The

competition for labor among the activities surpass the contribution of off-farm earning to the

input purchasing capacity of farm households for field and horticultural crop production. In

addition, those households who own land earn less off-farm income than those who are

landless.

Factors such as household valuables and number of livestock are the main determinants in

diversification to off-farm self-employment. This can lead to the conclusion that households

asset that facilitate access to credit play a great role for households off-farm earning. The

conclusion subsequently results in what many authors concluded in previous studies that is

“better-off households better benefit from off-farm self-employment”. Though infrastructures

are expected to link farm households to the nearest market, village dummies like access to road

are not influential for households off-farm earning. The finding showed that there was no effect

of education on off-farm self-employment. Off-farm wage employment was not as important as

off-farm self-employment, the change in the contribution of this sector to the income of

households is not significant. When coffee prices decrease, income from off-farm wage

employment decrease, since most wage employments are in coffee sector.

72

Landless households having other household valuables and livestock assets earn higher off-farm

income than land owners. Therefore, policies, programs and projects that might be designed to

improve the income of the landless class of the society had better focus on improving the

inhibiting factors for off-farm earnings.

Finally, the negative influence of being in remote area, as indicated by distance from market, on

households on-farm diversification and the absence of influence of infrastructure on

households off-farm earning implies that households diversification is not mainly to

compensate the income variability as defined by Barret et al. (2001), but it supports the

definition given by Ilbery (1991) that households diversify to better income earning activities

than remaining in conventional agriculture.

73

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77

Annex 1: Summary of major crops and crop combinations


Note: number of plots are given in parenthesis

Table (I): Summary of major crop grown by households over 1997 and 2004

Villages 1997 2004

Crop types Prices Crop types prices

Coffee (53) 13.4 Enset (242) 2.0

Enset (50) 0.5 Chat (75) 10.0

Imdibir Chat (48) 14.8 Coffee (36) 10.50

Banana (48) 2.0

Eucalyptus (31) 3.0

Gesho (27) 1.0

Azedeboa Coffee (67) 12.00 Coffee (69) 12.60

Teff (41) 1.78 Maize (60) 1.30

Banana (24) 2.00 Enset (48) 5.0

Chat (21) 5.67 Sweet potato (41) 0.65

Grass (17) 0.2 Teff (36) 2.80

Wheat (15) 1.67 Wheat (28) 2.30

Horse beans (10) 1.53 Potato (140 1.30

Eucalyptus (10) 3.0 Avocado (10) 2.36

Coffee (125) 8.17 Coffee (268) 6

Enset (94) 0.85 Enset (83) 0.8

Gesho (30) 1.0 Maize (19) 1.27

Chat (16) 7

Adado Sugarcane (15) 2.0

Eucalyptus (15) 3

Teff (88) 1.71 Maize (96) 1.12

Coffee (78) 11.67 Teff (74) 2.2

Enset (45) 0.41 Coffee (61) 9.0

Sweet potato(37) 2.0 Enset (33) 3.0

Gara godo Banana (23) 2.0 Sweet potato (31) 0.65

Adenguare (16) 3.0

Godere (15) 1.5

78

Table (II) Summary of major crop combinations in 1997-frequency of plots allocated to

different crops and crop combinations

Crop combinations Frequencies

Pooled

sample

Imdibir Azedeboa Adado Garagodo

coffee 66 (6.4) 6 (2.9) 25 (8.7) 10 (3.3) 25 (10.8)

enset 85 (8.3) 9 (4.4) 29 (10.1) 36( 12.0) 11 (4.7)

chat 18 (1.8) 12 (5.8) 5 (1.7) - 1 (0.4)

maize 32 (3.1) - 26 (9.1) 2 (0.7) 4 (1.7)

coffee+enset 161 (15.1) 3 (1.5) 13 (4.5) 136 (45.5) 9 (3.9)

Enset+maize 14 (1.4) 8 (3.9) 5 (1.7) - 1 (0.4)

coffee+chat 14 (1.4) 7 (3.4) 6 (2.1) 1 (0.3) -

coffee+chat+enset 26 (2.5) 8 (3.9) 3 (1) 14 (4.7) 1 (0.4)

Eucalyptus 70 (6.8) 35 (17) 24 (8.4) 6 (2.0) 5 (2.2)

Teff 133 (13) 2 (1) 58 (17.7) 1 (0.3) 79 (31.5)

sweet potato 32 (3.1) - 8 (2.8) - 24 (10.3)

grass 62 (6.1) 42 (20.1) 16 (5.6) - 4 (1.7)

enset+maize+coffee 13 (1.3) 11 (5.3) 1 (0.3) 1 (0.3) -

Eucalyptus+grass 19 (1.9) 10 (4.9) 8 (2.8) - 1 (0.4)

Banana 14 (1.4) 1 (0.5) 8 (2.8) - 5 (2.2)

coffee+enset+banana 12 (1.2) 1 (0.5) 3 (1) 4 (1.3) 4 (1.7)

coffee+Enset +Eucalyptus 14 (1.4) - 1 (0.3) 13 (4.3) -

enset+coffee+gesho 25 (2.4) 2 (1) 1 (0.3) 20 (6.7) 2 (0.9)

coffee+enset+sugarcane 9 (0.9) - - 9 (3.0) -

Enset+Maize+chat+coffee 11 (1.1) 11 (5.3) - - -

Others 181 (17.7) 39 (18.9) 47 (16.4) 45 (15.1) 56 (24.1)

Total 1024 (100) 206 (100) 287 (100) 299 (100) 232 (100)

Source: Authors computation from ERHS 1997

Note: percentages are given in parenthesis

79

Table (III)Summary of major crop combinations in 2004-frequency of plots allocated to different crops and crop combinations.

Crop combinations Frequencies

Pooled sample Imdibir Azedeboa Adado Garagodo

Coffee 99 (7.6) 22 (6.0) 28 (8.5) 43 (13.5) 6 (2)

Enset 178 (13.6) 102 (28) 27 (8.2) 44 (13.8) 5 (1.7)

Chat 46 (3.5) 39 (10.7) 2 (0.6) 5 (1.6) -

Maize 71 (5.4) 2 (0.5) 35 (10.3) 11 (3.4) 23 (7.8)

Enset+coffee 249 (19.1) 22 (6.0) 15 (4.4) 169 (53.0) 43 (14.7)

enset+ maize 47 (3.6) 41 (11.3) 3 (0.9) 2 (0.6) 1 (0.3)

enset+chat 15 (1.1) 14 (3.8) 2 (0.6) - -

ecualyptus 91 (7.0) 44 (12.1) 24 (7.3) 1 (0.3) 22 (7.5)

Teff 66 (5.0) - 33 (9.8) - 33 (11.3)

Wheat 11 (0.8) - 10 (3) - 1 (0.3)

Sweet potato 47 (3.6) - 29 (8.8) 1 (0.3) 17 (5.8)

Grass 64 (4.9) 42 (11.5) 8 (2.4) - 14 (4.8)

Adenguare 10 (0.8) - 5 (1.5) - 5 (1.7)

eucalyptus+grass 22 (1.7) - 15 (4.6) - 4 (1.4)

Maize+ adenguare 14 (1.1) - 3 (0.9) - 11 (3.8)

Maize+S.Potato 33 (2.5) - 6 (1.8) - 27 (9.2)

teff +S.potato 24 (1.8) - 3 (0.9) - 21 (7.2)

Others 218 (16.7) 45 (12.4) 89 (27.1) 43 (13.5) 60 (20.5)

Total 1305 (100) 364 (100) 329 (100) 319 (100) 293 (100)

Source: Authors computation from ERHS 2004

Note: percentages are given in parenthesis

80

Annex 2: Factor intensities among crops The factor intensity is computed from the total labor in man-days allocated by households for some

crops or crops combinations over the areas allocated for the crops.

Mathematically,

where is area allocated by household to produce crop , where i= 1,...n,

j=1, ...,m, is the coefficient for marginal change in labor as a result a hectare change in land

and is error term with mean zero and constant variance. Dependent variable, Li is total

labor (in man-days) allocated by households i to produce crops or crop combinations. Since I do

not have plot level labor for crops, I pooled the crop combinations at household level. OLS was

applied to estimate the coefficients. Letter A in front of the crops and crop combinations in

table IV indicates area allocated for that crop or crop combinations. For example, (Acoffee)

represents area allocated for coffee production.

The result shows that perennial crops are relatively land intensive and annual crops are

relatively labor intensive. In the case of perennial crops and perennial crop combinations, a

marginal increase in land requires less increase in labor man-days. The following table (IV) gives

the estimation result of the above linear model. For instance, a hectare increase in land

requires only 0.324 man-days of labor for coffee production and when land increase by 1

hectare, it requires only 6.97 man-days of labor for enset production. Whereas, it is the highest

for cereal crops such as teff. A hectare increase in land requires 246.85 man-days of labor for

teff production. This is logical as teff requires more often ploughing and intensive management

than other cereals.

Horticultural crops are between field and perennial crops. It is more labor intensive than

perennials, but less labor intensive than field crops. For instance, a hectare increase in land

requires 32.94 man-days of labor for adenguare (garden beans) production. Adenguare is a

leguminous crop grown mostly in gardens and considered as horticultural crop. Labor intensity

of the combination of field and horticultural crops is between the labor intensity of annual

horticultural and field crops from lower and upper respectively. For instance, an hectare

increase in land for of combination of maize and sweet potato production requires 81.36 man-

81

days of labor. This is by far more than what was required by horticultural crops, but very less

than what is required by teff (cereal).

When cereals are grown with perennials, the labor intensity is much more than that of growing

only perennial crops. For instance when maize is grown with enset, it requires 197.316 man-

days of labor for a hectare increase in land.

In all of the above cases we can see that perennial crops are relatively land intensive than field

and horticultural crops. Therefore, diversification to annual crops in general requires more

labor than producing only perennial crops.

Table IV OLS estimation result of factor intensity (dependent variable is total household labor worked on-farm during 1997 and 2004)

Variables coefficient Std. error t P-value Acoffee 0.324 1.465 0.22 0.825 Aenset 6.970 11.039 0.63 0.528 Ateff 246.845 35.173 7.02 0.000 Aadenguare 32.936 30.787 1.07 0.285 Aenset_coffee 0.896 1.075 0.83 0.405 Aenset_maize 197.316 175.614 1.12 0.262 Amaize_sweetpotato 81.364 91.640 0.89 0.375 Acoffee_enset_sugarcane 2.332 13.515 0.17 0.863 Aenset_maize_coffee 124.591 218.959 0.57 0.570 Aenset_maize_chat_coffee 107.324 243.026 0.44 0.659 N= 514, F (12, 502) =5.15 Prob>F=0.000 R2 =0.11


82

Annex 3: Models for dynamics of on-farm diversification To capture the effect of both time and heterogeneity for crop choices (dynamics of crop

choices), it is recommended to use conditional maximum likelihood multinomial logit model as

specified by (Chamberlain, 1980). Chamberlain specifies a situation in which the choice is panel

and multinomial. If the observation within a group and between groups is independent, then

the distribution of the probabilities for the choice follows as;

(j=1… N), where N =3 in this case,

Where is vector of parameters and are vectors of explanatory variables for household ,

choice at time .

In general case of T independent observations on each group with taking on J values, it is

possible to define when and 0 otherwise.

By conditioning on j=1, . . ., J, gives the following log-likelihood function.

,

Where

This is a log likelihood function can be maximized using standard multinomial logit programs

(Chamberlain, 1980). For the computation, we tried to use stata program known as Generalized

Linear Latent and Mixed Models (GLLAMM). However, the program could not maximize and

give solution.

The role of initial endowment on on-farm diversification

Another option to account for the dynamics in diversification is capturing how the initial situation of

households influence change in diversification from perennial crops to annual crops. I applied the

change in choices of crops between 1997 and 2004 over 1997’s observations of explanatory variables.

The model has been specified as follows;

83

With ,

Where is the change in diversification between 1997 and 2004. column vectors of

explanatory variables observed in 1997, β's are row vectors of parameters and is randomly and

independently distributed error term with constant variance.

The estimation results is given in the table (V) below. The model itself is not significant with LR chi2

(19)=23.66 and Prob > chi2 =0.310. This implies that the initial condition alone cannot predict the

change in on-farm diversification. Therefore, fixed effect logit model has been used to account for

household’s status change as has been specified in under econometric models.

Table V: Estimation result of probit model on changes is diversification over households initial

condition (dependent variable: change in diversification between 1997 and 2004)

Variables Probit model

(coefficients)

Nolivestock -0.046

(0.063)

oxen -

Land -0.571

(0.505)

Farm size -0.048

(0.072)

Noplots -0.044

(0.097)

Hhsize -0.004

(0.056)

Sex 0.092

(0.514)

Age 0.027**

(0.014)

Offfarm 0.396

84

(0.420)

Non_off 0.015

(0.048)

Daysworked off_farm -0.232

(0.502)

Labour 0.019

(0.011)

Dismarkt 0.010

(0.009)

Road 0.481*

(0.533)

Landqual -0.551**

(0.393)

Slope 0.238**

(0.397)

Displot 0.051**

(0.022)

Timely_r 0.604*

(0.023)

Enough_r 0.245

(0.425)

Constant -0.571

(0.378)

N=272

LR chi2 (21)=23.66

Prob > chi2 =0.310 = 0.3097


Note: Standard errors are given in parenthesis

dynamics of farm households on-farm and off-farm

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