Working Paper
No. 3/2004
Food insecurity and Poverty in
Evidence and Lessons from Wollo
EEA/Ethiopian Economic
Policy Research Institute (EEPRI
Tel. 251-1-234363
Fax 251-1-234362
E-mail: eea@telecom.net.et
Food insecurity and Poverty
in
Evidence and Lessons from Wollo[1]
By
Samuel Gebre-Selassie (Ph.D)
EEA/EEPRI
Working Paper No. 3/2004
EEA/Ethiopian
Economic Policy Research Institute
December 2004
Acknowledgment
The author
would like to thank all research staff of the EEA/Ethiopian Economic Policy
Research Institute for their comments. Special thanks go to the director of the
EEA/EEPRI, Dr. Assefa Admassie, for his insightful review of the draft
document.
Content
3. Data and methods of data analysis
4. Brief review of poverty and food security in Ethiopia
5. Major causes of poverty and poverty indicator
5.1 Causes of poverty and food
insecurity
5.1.1 Shortage of productive resources
5.1.2 Low access to non-farm and off-farm activities
5.1.3 Low level of productivity
5.1.4 Low income and food consumption
5.2 Livestock as indictor of
poverty trend
6. Agricultural production and food security
6.1 Analysis at household
level
6.2 Analysis at community
level
6.3 Resettlement program – a
speedy pathway to food security
7. Determinants of grain production and food insecurity
7.1 Determinants
of grain production
7.2 Determinants of food
security
8. Summary and recommendations
List of Tables
Table 1: Average ownership of productive resources
in South and North Wello
Table 2. Income from non-farm and off-farm activities in the
study areas
Table 3. Land productivity in cereals production during
normal year
Table 4: Estimated level of poverty in the study areas
Table 5: Change in the size of livestock in the past seven
years
Table 6: Production and consumption of grain (Food balance
sheet) in the study areas
Table 7: Food security situation during normal and drought
years
Table 8: The degree of food security (aid requirement) in
the study areas
Table 9: Summary Statistics of variables estimated in the
regression model
Annexes
Annex 1: Distribution of farmland among sampled farmers
Annex 2: Drought affected population
Annex 3: Crop production in the study areas
Annex 5:Partial correlation of land, labor and oxen owned
by sampled households
Total grain production in
Likewise, the number of people
vulnerable to drought has been increasing. The drought of 2002/03 has shown
that people in some parts of the country where drought or drought induced
problems were manageable at local or household levels increasingly need food
aid to prevent widespread famine. At the same time, people in areas where
transitory or weather induced food insecurity has been the predominant problem
increasingly suffer from chronic food insecurity[3],
which is related more to poverty rather than to temporary shocks. This worsening trend is manifested by government
statistics on the number of people affected by drought. For example, only about 1.5 million or not
more than 5% of the total population of the country suffered from drought
induced food insecurity problem during the imperial regime in the 1960’s or
early 1970’s; by mid 1984, the figure increased to 7 million or 17.4% of the
total population. In 2003, it increased to 14.5 million or 22% of the total
population that was estimated at 69 million (see Annex 2). About five million
of these people have been suffering from chronic food insecurity.
This trend has led to a higher level of food aid to save life and narrow the
ever growing gap between food supply and demand. However, the non-stopping food
aid program has also become increasingly controversial. In some parts of the
country where relief has been provided for more than three decades, the problem
of dependency syndrome has reached its highest level and emerged as one of the problems
that hinder personal or collective motivations to challenge poverty and to find
ways to get out of the prevailing despair situation. Despite saving life and
preventing widespread malnutrition, some say the non-stopping relief program
has also contributed for peasants to give up hope. The current debate on the
role of aid and how to relate it to development programs through safety-net
programs and the on-going effort to find the best alternative way to use relief
resources for development has partly emanated from the growing dependency
syndrome and other problems related to relief programs which have been carried
out for so long in some areas of Ethiopia like Wollo.
The justification for the
continuous food aid in
Food aid has also become
controversial even at international level. Its supporters claim that it plays a
major role in feeding the poor, prevents severe food insecurity and saves lives
when emergencies arise. Its delivery is justified by the view that it is a
valuable macro-economic resource to fill the gap between the demand and the local
supply of food and to improve the balance of payments and budgetary deficit.
However, an increasing number of critics argue that food aid has contributed to
dependency at the institutional and household levels. They point to the
disincentive effects of food aid on agricultural innovation, intensification
and diversification (Masefield, 1996).
The study tries to assess the
food security situation of farmers in South and
3.
Data and
methods of data analysis
Data collected for an impact
assessment study of a cash for relief (CfR) project were used in the study[4].
The CfR program emerged from the experience of using food both as relief resource
and incentive to undertake environmental rehabilitation programs and other
public works such as feeder road and water development. The CfR project was
carried out in 2002 by SC-UK in six Weredas of North and South Wollo Zones,
including Legeambo and Mekete Weredas, where the CfR was started in 2000 as a pilot
program.
The sample size considered in
this study relates to 646 households residing in 12 peasant associations of Legeambo
and Meket Weredas of South and
Descriptive methods were employed
to explain the level and extent of food insecurity and poverty in the area, while
regression was used to identify the contributing factors to food insecurity.
4. Brief review of poverty and food
security in
A significant proportion of the
Ethiopian population has been suffering from poverty and poverty related
problems like malnutrition and disease for a very long period of time. The
proportion of people who are absolutely poor (unable to meet their basic needs)
during the year 1999/00 was 44.2 percent (about 30 million people). In urban
areas, the proportion of poor people as defined by the national poverty line
was 47%, while it was 33% in rural areas (MOFED, 2002). In addition to these, the
progress so far achieved in reducing poverty is only marginal. Consumption poverty measured by the head
count ratio, for example, has witnessed only a 1.3 percentage point decline
(2.9 percent) between 1995/96 and 1999/00 (MOFED, 2002). Even this trend seems short-lived. The recent
report by the Ethiopian Economic Association on the performance of the
Ethiopian economy, for instance, indicates that real per capita income was on
average falling both in rural and urban areas in the last three years. Real GDP
growth averaged 1.7% during the 2000/01-2002/03 period. This translates into a
1.2% decline in per capita income (EEA, 2004). Then in the following fiscal
year, 2003/04, the performance of the economy has improved. The government and
the IMF reported that the Ethiopian economy grew by 11.6% during the 2003/04
year, which implies a growth rate of 8.7% in per capita terms when compared to the
preceding year, when per capita income declined by 6.6% (NBE, 2004).
In addition to GDP and
population growth, poverty level is affected by the gap in income inequality or
the distribution of newly created wealth among citizens. According to the
government the increased gap in income inequality continues to contribute for a
high and steady level of poverty in
The major
factor that hinders a sustained economic growth and poverty reduction in
Despite the
relative high share of agriculture in aggregate output (GDP), the per-capita
value added in the agricultural sector has been on the decline since the
1960’s. In other words, the additional labor force that comes into the sector
does not have a positive value added to output (Daniel, 2003). Getahun’s (2003)
estimates have indicated that unless the declining trend of the sector
achievement is arrested and reversed, the poverty situation in the country will
rapidly aggravate as a result of which, by 2015, close to about two-third of
the population will be in absolute poverty.
At the same time, it was shown that with little improvement in
agricultural productivity, poverty could be reduced substantially by 2015
(Getahun, 2003). The government needs to find ways of raising the productivity
of rural labor in general, and farm labor in particular (MOFED, 2002).
Food insecurity which currently
affects over 40% of the population is the major challenge for
Drought, high population growth
and environmental degradation on the one hand, and technological, policy and
institutional factors that lead to declining labor productivity, on the other
hand, have been the principal causes of poverty and food insecurity especially
in rural areas. The government has continued with its effort of tackling these
chronic problems. However, to-date achievement has been modest and
insignificant. Even though various initiatives and programs have emerged at
different times since the change of government in 1991, the strategic direction
to achieve food security and economic development has been the
Agriculture-Development-Led-Industrialization strategy which was developed in
the mid-1990s. The most recent initiatives by the government are the new Coalition
for Food Security and the Ethiopia Strategy Support Program (ESSP). Both the Coalition and ESSP aimed to bring
the commercialization of Ethiopian smallholder agriculture, improve farmers’
productivity and achieve other objectives stated in the ADLI strategy. The ESSP
which was developed by IFPRI and EDRI in response to the direct request by the
government seems to be a broader strategy that aims to generate policy research
results and knowledge that will inform policy makers on shortcomings and
opportunities on existing rural development strategies (IFPRI and EDRI, 2004).
5. Major causes of poverty and poverty
indicator
5.1 Causes of poverty and food insecurity
The food security situation in
the study areas has not improved in recent years. According to the DPPC office
of the Amhara region, for instance, about 1,306,976 people or 35% of the total
population of North and South Wollo Zones received food aid every year between
1997 and 2001. A recent study carried out in
Despite a considerable
improvement in the amount of food aid and development assistance in recent
years, so far its impact on the level of food security is very limited. The
massive food aid that has been coming to the country has been successful in preventing
famine and widespread malnutrition. The second objective of food aid is to
prevent asset depletion of households participating in relief programs and
contribute to the rehabilitation of natural resources through relief-resources-sponsored
environmental rehabilitation programs (e.g. food-for-work program). However,
the program has not been successful at least in the study areas where aid and
aid related environmental programs have been carried out for the last 3 or more
decades.
Hunger and poverty are closely
related. While the lack of sufficient income to purchase food is clearly a
major factor causing household food insecurity, hunger itself contributes to
poverty by lowering labor productivity, reducing resistance to disease and
depressing educational achievements (FAO, 2001, cited by Getahun, 2003). Poverty
in the study area exhibits itself in many forms but mainly in terms of lack of
access to sufficient food and high vulnerability even to minor weather related
shocks. Some of the main causes of poverty include lack of productive
resources, low productivity and low income.
5.1.1 Shortage of productive resources
As non-farm activities have an
insignificant role in the local economy, farming and farm resources have
important implications on the level of food security and poverty. Therefore, size
of farmland, labor and livestock and fertility of soil have important
implications on households’ food security status and poverty level, especially
during normal agricultural years. During drought years, livestock, a major asset
that can be easily liquidated, is more important in terms of implying better
access to food.
Table 1: Average ownership of productive resources in South and
_____________________________________________________________________
Variable | Obs Mean
Std. Dev. Min Max
--------------------+-------------------------------------------------
Land (ha./HH) | 644 0.68
0.30 0.13 2.00
Per
LABOR
(ME) |
644 1.84 1.30 0.40 6.10
HH
size (AE) | 644 3.01
1.52 0.60 8.98
Livestock
(TLU) | 646 1.33
1.41 0.00 10.53
OX
(No.) | 443 0.47
0.65 0.00 4.00
Percent of HHs having no
ox = 61%
Of the sampled households, 94% owned one hectare or less, while the average farm size was 0.68 hectare. On per capita basis, average farm size was as low as 0.23 ha. About 61% of the farmers reported that they have no ox, while average ox ownership was only 0.47. The problem is not only the shortage of these resources but also the technologies used to convert these inputs into farm outputs as evidenced by the low input-output ratio.
In general, available farm
resources are too small to provide adequate food and income for the average
household. A study conducted in the farming system of the study area, for
example, reported that households that owned less than 0.5 ha are unable to
meet basic needs and are labeled as destitute households. Households with farm
sizes in the range of 0.5 to 1 ha and above 1 hectare are classified as
vulnerable and viable, respectively (IDS and SC-UK, 2002). Based on this single indicator, data
collected for this study indicate that about 14.9% of sampled households are
destitute, 79% are vulnerable and 6.1% are poor farmers. Even though measuring
the level of food insecurity of different households based on a single factor
(land) has its own limitation[6],
it can roughly indicate the extent of poverty given the current level of
productivity and household consumption requirement.
5.1.2 Low access to non-farm and off-farm
activities
Population pressure and reduced farm size and soil fertility
has forced farmers in the study area to work harder without being able to
maintain their income or standard of living, measured in terms of food
consumption. However, sustainable
livelihood has not only been threatened by reduced farm size and productivity,
but also by undeveloped non-farm economy in the area and the environs. Low
access to non-farm and off-farm activities is, therefore, another reason why
people in the study area are very poor and food insecure. Almost every household
looks for non-farm employment to supplement family food requirements. Data
collected from farmers, however, indicate that not more than 50% of sampled
households could get employment opportunities in any year, including a year
when food-aid-induced employment opportunities are available. During the survey
year, about 44% of households had access to off-farm or non-farm employment.
The average annual gross income from these activities was Birr 550 and 239 for
valid cases (i.e. households having access to non farm activities) and an average
household, respectively. This income from non-farm and off-farm activities
constitutes only 11.3% of the total household income (see Table 2).
Table 2.
Income from non-farm and off-farm activities in Legeambo and Mekete
Weredas, during the
survey year
|
|
Legeambo |
Mekete |
|
|
Households having access to non-farm income sources |
180 (46%) |
103 (41%) |
|
|
Mean non-farm income (Birr/annum) |
for households with access |
624 |
475 |
|
for all sampled households |
284.40 |
194.90 |
|
|
Estimated mean gross farm income (Birr/annum) |
1897.7 |
2069.0 |
|
|
Share of non- and off-farm income in total household
income |
for households with access
|
24.7% |
18.7%
|
|
for all sampled households |
13% |
8.6% |
|
5.1.3
Low level of
productivity
As mentioned earlier, the low productivity
of land and labor could explain partly why people in the area are very poor and
food insecure. Data collected from sampled households indicate that about 20%
of the farmers produce only 3 or less quintals of wheat-equivalent grain on a
hectare of land during a year they consider normal. More than 60% of the
sampled farmers reported that they produce 9 quintals or less per hectare. This
level of productivity is very low even compared to the national average.
While land degradation and
declining soil fertility are the direct causes of the low return to labor and
land, extreme poverty which induces cultivation of marginal and degraded land plays
a part indirectly. It contributes both for low land productivity and
unsustainable farming system practiced by peasants in the study areas. This
situation coupled with high population pressure creates a big challenge to
government and non-governmental organizations working to improve the level of food
security in the study areas. Any development intervention demands an intergraded
program that encompasses environmental rehabilitation, voluntary migration, family
planning, resettlement and the development of the non-farm economy. Moreover,
institutional support in terms of agricultural marketing and extension should also
be strengthened to improve labor productivity and farm income.
Table 3. Land
productivity in cereals production during normal year
(quintal of wheat equivalent[7] per hectare of cultivated land)
____________________________________________________
Productivity| ________Households_______________
(qt./ha)
| Freq. Percent Cum.
------------+---------------------------------------
< 3 | 128
19.97 19.97
3.00–6 |
150 23.40 43.37
6.01–9 |
109 17.00 60.37
9.01-12 |
87 13.57 73.94
12.01-16 |
62 9.67 83.61
16.01-20 |
39 6.08
89.69
>20 | 66 10.31 100.00
------------+----------------------------------------
Total
| 641 100.00_______________
5.1.4 Low income and food consumption
Poverty is mainly manifested in
terms of hunger, malnutrition and poor access to social services like health
and education. All of these manifestations of poverty, however, should be
measured in terms of a single indicator to compare the level of poverty in
different communities and to monitor the progress made to eradicate poverty. This
indicator is called the poverty line which is used as a threshold level of per
capita income or consumption, below which an individual is considered to be
poor and unable to satisfy the minimum food (energy) and non-food consumption
requirements. This minimum income level is usually computed based on an income
level that is agreed on (by the government of the FDRE) as sufficient for
minimum food and non-food consumption expenditure for an adult person.
Real per capita consumption
(food and non-food consumptions) expenditure for rural people was Birr 995 in
1995/96, which was taken by the government as the poverty line for rural
Table
4: Estimated level of poverty in south and north
|
|
Good (Normal) year |
|||
|
Legeambo |
Meket |
|||
|
Average HH size in AEa |
3.58 |
3.52 |
||
|
Average consumption expenditure[10] |
Per capita food and non-food consumption
expenditure (for year 1995/96 ) ( |
995 |
995 |
|
|
12 months moving average general price index in
2001 (1996=100) |
102.8 |
102.8 |
||
|
Per capita food and non-food expenditure after
adjusting inflation in rural prices (for the year 2001) |
1022.9 |
1022.9 |
||
|
HH minimum food and non-food consumption
expenditure In 2001 |
3662 |
3601 |
||
|
Average Production |
Income from food crops production |
Production (qt.) |
9.03 |
10.64 |
|
Weighted average price (Br./qt. food crop)[11] |
190 |
175 |
||
|
Estimated annual income from |
1715.7 |
1862.0 |
||
|
Average estimated income from off/non-farm activities
(Br./annum)[12] |
624.0 |
475.0 |
||
|
Average estimated income from livestock sales
(Br./annum) |
182.0 |
207.0 |
||
|
Total estimated income (Br./annum) |
2521.7 |
2544.0 |
||
|
Additional income required to meet minimum
expenditure (Birr/annum/household) |
1140.3 |
1057.0 |
||
|
Average household capacity to meet minimum
consumption requirement (%) |
68.9 |
70.6 |
||
|
Estimated percent of population living below
poverty line |
62.1% |
58.7% |
||
a Weighted average for beneficiary and non-beneficiary
households.
5.2 Livestock
as indictor of poverty trend
Poverty is a major economic and
social problem in
In the study areas, livestock
has served as buffer against hard times. Households who have opportunity to
save usually keep their money in the form of livestock. They produce or buy
livestock (particularly small ruminants) to sell and buy food grains during
years of drought or to fill the gap in food requirements towards the end of the
agricultural year when they fall short of food. Therefore, changes in the size
of livestock of an average household could be taken as a proxy to indicate the
dynamics of poverty and vulnerability to drought[13].
Table 5: Change in the size of livestock in the past
seven years
|
|
Legeambo |
Meket |
||
|
HHs reported change in their livestock size |
72.8% |
61.5% |
||
|
Reported
change in |
Increased (TLU/valid case) |
0.27 |
0.11 |
|
|
Decreased (TLU/valid case) |
3.06 |
2.30 |
||
|
Estimated
net change |
TLU/valid cases |
-2.79 |
-2.19 |
|
|
TLU/sampled HHs |
-1.95 |
-1.35 |
||
|
Ox-equivalent/sampled HHs |
-2.40 |
-1.69 |
||
|
Estimated
net change |
TLU/valid cases |
-0.28 |
-0.24 |
|
|
TLU/sampled HHs |
-0.20 |
-0.15 |
||
|
Ox-equivalent/sampled HHs |
-0.25 |
-0.19 |
||
|
Livestock size of an average HH in 2003 as % of what
it owned five years ago |
54.6%
(average of the two
Weredas) |
|||
|
N (Number of
sampled households) |
395 |
251 |
||
Accordingly,
farmers were asked to indicate the size and composition of their livestock
during the survey year and to compare it with what they owned some seven years
ago. Table 6 shows the change in the size of livestock during this period. Data
collected from farmers in Legeambo Wereda indicate that 395 sampled households
lost on average about 79 TLU (in terms of oxen-equivalent, about 99 oxen[14])
of different kinds of livestock every year during 1996 to 2003. In other words, during the survey year, on
average four farmers own one ox (or one ox-equivalent livestock) less than what
they owned some seven years ago. In Meket Wereda, the loss is a bit lower.
About 251 farmers lost various livestock that is equivalent to 38 TLU (i.e.
equivalent to 47 oxen) every year in the past seven years. This is equivalent
to a loss of 1 ox among every five farmers. In general, about 68.5% of
households residing in the two Weredas reported change in the size of their
livestock. And when compared to what they owned five years ago (in 1998), the average
size of livestock of a household during the survey year (2003) is only 54.6%. In
conclusion, the survey result indicates the worsening trend of poverty in the study
area. Other studies also indicate a similar trend in the size of livestock of
households owned in the study areas[15].
6. Agricultural
production and food security
As the non-farm economy is not
developed, poverty and food security are closely related to the performance of
the agricultural sector. Any analysis of poverty and food security, therefore, necessitates
a closer look at the performance of the farm both at household and local
economy (community) levels.
6.1 Analysis
at household level
Different types
of food crops, notably cereals and pulses, are produced in the study areas. Compared
to farmers in Meket Weredea, crop production is, however, less diversified in
Legeambo Wereda where crops like Teff, Maize and Sorghum are uncommon. Grain production
does not vary much between beneficiary and non-beneficiary households in Legeambo
Wereda where an average household produces about 9.3 quintals of various food
crops. However, in Meket Wereda, CfR beneficiary households produce on average
5 quintals less than what non-beneficiary households produce (Table 6). Data
collected from farmers also indicate that in drought years, production could
decline on average by as much as 62% in Legeambo Wereda and by about 58% in
Meket Wereda.
The level of food security at
household level was estimated considering this production data, income from non-farm
activities[16] or
remittances, and households’ minimum
food and non-food consumption requirement. The sampled households were categorized
into four groups based on their location and participation in the relief (CfR)
program. The food balance sheet (i.e. the level of food security) was computed
for average households from every group.
The average household that participated
in the relief program in both Weredas, and non-beneficiary household of
Legeambo Wereda could not satisfy their food requirement even in normal (good)
agricultural year. They suffer from food shortage for a period of 1 to 2 months
in a year that is considered normal by them. Only the average non-beneficiary
household from Meket Wereda could feed his/her household and have some surplus
that could supply for 4 to 5 extra months.
On the other hand, none of the
average households could feed themselves in drought years. They face food
shortage for a period of 6 to 8 months. If one assumes that households could
get cash for their non-food requirement by working outside their farm or
totally abandon their expenditure on non-food consumption during drought
period, the period of food shortage could narrow marginally to 5 to 6 months
(in both Weredas).
Table
6: Production and consumption (Food balance sheet) of an average
household in the study area
|
Wereda |
|
Normal agricultural year |
Drought year |
|||
|
CfR participant |
Non-participant |
CfR Participant |
Non-participant |
|||
|
Legambo |
Annual food (energy) production in wheat
equivalent (qt.) |
8.94 |
9.32 |
3.5 |
3.6 |
|
|
Annual food (energy) requirement in wheat
equivalent (qt.) |
8.76 |
10.21 |
8.76 |
10.21 |
||
|
Annual food (energy) balance from own
production |
Shortage/surplus in wheat equivalent |
0.18 qt. |
-0.95 qt. |
-5.26 qt. |
-6.67 qt. |
|
|
Shortage/surplus in months |
0.25 |
-1.11 |
-7.17 |
-7.76 |
||
|
Annual cash expenditures (food and non-food
purposes)[17] |
Birr/annum |
931 |
931 |
372.4 |
372.4 |
|
|
In Wheat equivalent (qt./annum)[18] |
5.6 |
5.6 |
2.3 |
2.3 |
||
|
Estimated cash income from off-farm/non-farm
business (Birr/annum)[19]
|
591 (3.58 qt. wheat) |
734 (4.49 qt. wheat) |
354.6 |
440.4 |
||
|
Estimated income from livestock sales (Quintal of
wheat equivalent/HH) [20] |
0.40 |
1.3 |
0.40 |
1.3 |
||
|
Annual food (energy) balance from own production |
Shortage/surplus in wheat equivalent (qt.) |
-1.44 |
-0.7 |
-5.01 |
-6.21 |
|
|
Shortage/surplus in months |
-1.96 |
-0.81 |
-6.9 |
-5.83 |
||
|
N |
303 |
92 |
303 |
92 |
||
|
Meket |
Annual food (energy) production in wheat
equivalent (qt.) |
9.62 |
14.04 |
3.8 |
4.16 |
|
|
Annual food (energy) requirement in wheat
equivalent (qt.) |
7.79 |
10.15 |
7.79 |
10.15 |
||
|
Annual food (energy) balance from own production |
Shortage/surplus in wheat equivalent (qt.) |
1.83 qt. |
3.89 qt. |
-3.99 qt. |
-6.01 qt. |
|
|
Shortage/surplus in months |
2.8 |
4.6 |
-6.23 |
-7.01 |
||
|
Annual cash expenditures (for food & non-food
purposes) |
Birr/annum |
931 |
931 |
372.4 |
372.4 |
|
|
In Wheat equivalent (qt./annum)) |
5.6 |
5.6 |
2.3 |
2.3 |
||
|
Estimated cash income from off-farm/non-farm
business (Birr/annum) |
421 (2.6 qt. wheat) |
654 |
252.6 (1.53 qt. wheat) |
392.4 (2.38 qt. wheat) |
||
|
Estimated income from livestock sales (Quintal of
wheat equivalent/HH) |
0.48 |
1.6 |
0.48 |
1.6 |
||
|
Annual food (energy) balance from own production |
Shortage/surplus in wheat equivalent (qt.) |
-0.69 |
3.85 |
-4.28 |
-4.31 |
|
|
Shortage/surplus in months |
-1.06 |
4.62 |
-6.6 |
-5.1 |
||
|
N |
193 |
58 |
193 |
58 |
||
6.2 Analysis
at community level
The level of food security at community
level (i.e. taking into account each and every household who participated in
the survey which constitutes 5% of households in the community) is estimated
based on information obtained from sampled farmers. Information on farm production
and income including remittance and non-farm and off-farm income, and secondary
data on food and cash requirement for non-food purposes are collected. Similar
to the analysis made above for the average household, some adjustment is made
on the data to account for the reduction of non-food expenditures and cash
income during drought years. Accordingly, non-farm income and cash expenditures
are assumed to decline by 40% during drought year (when compared to their level
during normal years).
Table 7: Food
security situation during normal and drought years
(percent of sampled farmers)
|
|
Normal (good) year |
Drought (Bad) year |
||||
|
All |
Legeambo |
Meket |
All |
Legeambo |
Meket |
|
|
Food secured HHs[21] |
54.66 |
53.16 |
57.03 |
19.72 |
19.75 |
19.68 |
|
Food insecured HHs |
45.34 |
46.84 |
42.97 |
80.28 |
80.25 |
80.32 |
|
N |
644 |
395 |
249 |
644 |
395 |
249 |
As shown in Tables 7 and 8 lack of access to
adequate food which is the worst manifestation of poverty is a widespread phenomenon
in the community. During a year
considered normal and drought, about 10% and 39% of the sampled households,
respectively, satisfy only 25% or less of their food requirement. This implies
that they require food aid for 9 or more months. The percentage of chronic food
insecure people (or households which could not meet part of their family food
requirement from own source during a normal year) is 45%, while during drought
year about 80% of the people depend on food aid for a long period of time (of
which, more than 60% need assistance for a period of 6 to 12 months). In
addition to the 45% chronically food insecure households, 35% of the households
will join this group during drought years.
In conclusion, both analyses made at
household and community levels indicate that food insecurity is very rampant in
the community that is hit by drought too frequently[22]. The
study also indicates the need for long-term comprehensive development
interventions. Government and non-government organizations working in the area
should not limit themselves to food aid and relief-related development efforts
which usually lack long-term financial and non-financial commitments. In this regard, the resettlement program initiated
recently by the government is a step in the right direction as it may ease the
population pressure in the areas. However, settlement, if well planned and
implemented, should be considered only as one of the ingredients that could
help to achieve sustainable livelihood. It should also not be considered as an
end by itself.
Table 8: The degree
of food security (aid requirement) in the study areas
|
Food security situation |
Normal year |
Drought year |
|||
|
Food security level in percent |
Food aid requirement |
Number |
Percent |
Number |
Percent |
|
25% or less |
9 or more
months |
61 |
9.47 |
253 |
39.29 |
|
26 – 50% |
6 up to 9
months |
85 |
13.20 |
135 |
20.96 |
|
51 – 75% |
3 up to 6
months |
88 |
13.66 |
82 |
12.73 |
|
76 – 100 |
3 or less
months |
58 |
9.01 |
47 |
7.30 |
|
Satisfy
100%+ from (Food secured HHs) |
No need |
352 |
54.6 |
127 |
19.7 |
|
Chronically food insecure households |
45.4% |
||||
|
Households vulnerable to drought |
34.9% |
||||
|
N |
644 |
||||
6.3
Resettlement program – a speedy pathway to food security
Resettlement program of moving people who suffered
from drought and shortage of productive land to relatively land and water
(rain) abundant parts of the country is one of the recent initiatives taken by
the government to relax the problem of shortage of farmland and fix chronic
food insecurity problems in a short period of time. This is in addition to the
effort that has been carried out since the early 1990s to improve the
productivity of existing cultivated land using modern farm inputs like
inorganic fertilizers and improved seeds. The resettlement program has been
pursued both by the present (EPRDF-led) and the previous (Derg) governments.
There are some differences and similarities between the two programs. The
present resettlement program is said to be voluntary (i.e. based on the
willingness of the settlers)[23]. The other difference between the previous and
the present programs is that the latter is carried out within a given region
which may minimize problems that may arise due to cultural and language
differences between the recipient and sender communities. Moreover, the present
program seems better in terms of government support and commitment as it has
been carried out during a period of peace and increased international financial
support.
There are also similarities between the present and
the previous resettlement programs. Like
the 1984/85 resettlement program, the current program has been initiated following
a big drought that threatened the lives of 14 million Ethiopians. This fact may
indicate that the current program, as its predecessor, is a spontaneous attempt
that emerged when the problem reached a critical and desperate level which left
no option for other alternatives that could bear fruit gradually but on a
sustainable manner. On the other hand,
many observers in aid/donor organizations and civic societies worry about the
pace of the current program which plans to relocate about 2 million people
within 3 to 5 years. They fear that this
condition, coupled with shortage of social and economic infrastructures in an
environment tough for human settlement, could lead to social problems in the
short-run and challenge the success of the program in the long-run.
The issue of sustainable development that maintains
the balance between environment, agriculture and population is the other point
that worries some observers of the resettlement program. They fear that
relocating peasants with low knowledge of and experience in sustainable
agriculture, low technologies and high fertility rate to environmentally
fragile areas is a difficult task unless supported differently. Still others say that Ethiopia’s potential to
feed itself should primarily rely on increasing yield on existing farmlands and
providing employment in the non-farm sectors that could strengthen the
performance of the rural sector and its interface with the urban sectors which will
ultimately lead to the commercialization of Ethiopian subsistence agriculture
(EEA, 2004). In conclusion, the efficacy
of resettlement program to relax the constraint of nonrenewable resources like farmland
is short-lived, unless it is effectively supported by other long-term
interventions that enhance labor productivity in the farming sector and improved
employment opportunities in the non-farm sectors.
7. Determinants of grain production and food
insecurity
7.1
Determinants
of grain production
A regression model is estimated to verify some of the
results obtained through the descriptive analysis and establish whether basic economic
relationships assumed to exist in a production process is supported by
empirical evidence. Accordingly, a non-linear (Cobb-Douglas) production
function was estimated using Tobit regression model to identify the
determinants of farm output. Estimates derived from regression models and
inferences made based on those estimates are valid under certain conditions – conditions
that amount to the regression model being “well-specified”. One of the major factors
that determine the specification of the model is the type of the production
function adopted for regression. In this regard it is important to consider the
peculiar production feature of the study area where many farm households
produce positive amount, while there are also other households who produce
nothing because of lack of productive resources, mainly fertile land[24].
Tobit model is an appropriate technique to run a regression
of dependent variable that is essentially continuous over a range of values but
also takes on zero (the threshold value) with positive probability over a
number of explanatory variables. The
model fits dependent variable on
independent variables where the censoring values are fixed (Hog and Lunde,
2002). The shorter version of the functional form adopted and estimated is:
Y=f(X,Z) and in log form
LnYi = ∑βjlnXij +
∑αjlnZij + γ + µi
Where: Y is
quantity of output (cereal production)
X is a vector of physical inputs including
land, labor and oxen
Z is a vector of other factors that affect the
operation of a farmer like age, sex,
engagement in land
rental market, off farm activities, etc.
γ and µ are
constant and error terms, respectively.
The independent variables that are supposed to affect the
level of production are broadly classified into four groups:
i.
Physical inputs
– land, labor and ox. Capital has little contribution in subsistence mode of
production. Measuring any capital stock used in the production process is also
difficult. However, cultivated land and ox/oxen used in the production process could
be used as a proxy measure for capital stock. Due to lack of data, fertilizer
was not incorporated in the regression model.
The size of land cultivated and
owned by sampled households was also entered into the
model. Labor (excluding children) was measured and entered into the model in
terms of man-equivalent labor to reflect variations attributed to age and sex.
The number of oxen owned by a household was considered to estimate the impact
of available drought power on production.
ii.
The characteristics
of farm manager (household head) – age, sex and level of education of
household head were considered. While age was a continuous variable, sex and
education level (being able to read and write) were entered into the model as
dummy variables.
iii. Factor (land) market – this refers to
the existence and farmers’ financial ability to command scarce resource through
the market. Specifically, the amount of land rented-in and land shared-in were
considered.
iv. Non-farm income – this indicates remittance
and participation in off-farm activities (a dummy variable). The result could
indicate the degree of linkage between farm and non-farm sectors.
Land productivity was not considered in the model due to
lack of data and some irregularities in the collected data.
Table 9: Summary Statistics of variables estimated in the
regression model
-------------------------------------------------------------------------
Variable | Obs
unit Mean
Std. Dev. Min Max
-------------------+-----------------------------------------------------
Land
| 646
ha 0.68 0.30
0.13 2.00
Family labor
| 646
AE 1.84 1.30
0.40 6.10
HH members
| 646
No. 3.66
2.11 1.00
11.00
Children < 7 years | 646 No.
1.19 1.03
0.00 5.00
Children b/n 7&14 | 646
No. 1.08
1.04 0.00
6.00
Children b/n 14&50 | 646 No.
1.32 1.07
0.00 5.00
HH members > 50
| 646 No.
0.08
0.32 0.00
2.00
OX ownership
| 443 No. 0.47 0.65
0.00 4.00
Livestock own.
| 443 TLU 1.33 1.41
0.00 10.53
HH head Age
| 646 year 56.00 23.00
17.00 71.00
HH head Sex
| 646 percent male 74.00
43.98
0 100.00
HH head education | 646
percent able
to read&write 50.50 39.12 0
100.00
HHs’ share-in land | 620 percent of HH 11.94 32.44 0
100.00 HHs’ rent-in land | 632
percent of HH 18.98 39.25
0 100.00
HHs’ engage in off
farm activities |
645 percent of HH 45.74 49.85 0
100.00
Amount of income
from off farm act. | 285 Birr
515.00 1141.14
0
6384.49
HHs having
remittance income | 629
percent of HH 12.3 22.33
0 100.00
-------------------------------------------------------------------------
The result of the regression model of the Cobb-Douglas (CD) production function is reported in Table 10. Land and oxen which could also be used as proxies for capital stock are found important to explain existing variation in the level of production among sampled households. The coefficient for land is statistically significant at 1%. However, the coefficient for oxen is relatively high but significant only at 5% level. The contribution of labor is statistically insignificant to explain observed differences in output among sampled households.
Table 10: Determinants of Tobit estimates:
Dependent variable: Output
|
|
Tobit estimates: Dependent
variable - Output |
|||
|
Coefficient |
t-value |
Coefficient |
t-value |
|
|
Constant |
15.82 |
3.87 |
12.82 |
3.05 |
|
ln
(Land) |
2.18 |
1.77* |
1.93 |
1.55 |
|
ln
(labor) |
2.67 |
1.88 |
3.26 |
2.22** |
|
ln
(Oxen) |
5.27 |
2.47** |
3.63 |
1.56 |
|
Age |
0.08 |
1.23 |
0.01 |
1.34 |
|
Sex
|
1.64 |
0.59 |
1.54 |
0.56 |
|
Head
education |
-0.79 |
1.99** |
-0.52 |
1.29 |
|
Land
rent-in |
|
|
0.03 |
0.02 |
|
Land
share-in |
|
|
4.17 |
2.06** |
|
Remittance |
|
|
7.41 |
2.27** |
|
Participation
in off farm-activities |
|
|
0.77 |
0.57 |
|
|
Number of obs
168
LR chi2(6) = 18.12
Prob > chi2= 0.0059 Log likelihood= -582.6723 |
Number of obs = 155
LR chi2(11) = 22.95
Prob > chi2= 0.0180 Log likelihood = -533.3385 |
||
*,
**, *** indicate significance level at 10%, 5% and 1% respectively.
Age and sex of household head are also found statistically insignificant
to explain observed variations in output. The neutrality of being male-headed
or female-headed household on grain production reinforces the abovementioned
result on labor and indicates the existence of surplus male labor in the study
areas. The coefficient for household heads’ education level (i.e. by and large
refer to the ability to read and write) is found negative and significant at 5%
level. Although difficult to interpret from theoretical point of view, this is because
of the negative correlation between literacy level and ownership of productive
resources which are more important than the level of education to explain existing
variation in output (see Annex 4).
The impact of improved access to land on grain production is
analyzed by incorporating the amount of land rented-in and shared-in into the
model. These two forms of acquiring land, however, have different implications
in terms of efficiency in resource utilization and production risk which is
very common in the farming system of the study areas. Land sharing which
involves cultivating someone’s land for sharing some portion of the final
output also implies the sharing of production risk like crop failure. This type
of production arrangement may lead to suboptimal utilization of resources when
compared to renting land in which all cost and benefit associated to the use of
someone’s land is exclusively related to a person that rented the land.
The regression model reveals the positive impact of improved
access to land through land sharing arrangement on grain production (at
community level). The coefficient is high and statistically significant at 5%
level. Moreover, the coefficient for labor becomes significant when access to
farmland is enhanced. This is not a surprising result as land and labor have a
negative correlation (Annex 5). Land sharing compensates some of the negative
impacts of mismatched ownership of productive resources on grain production. In
other words, it substitutes to some degree the weakness of factor market in the
study areas. Policy makers should deal with the problems that lead to
disproportionate ownership of productive resources among households in the
study areas and find ways that facilitate the functioning of factor market. Contrary
to what was expected, land rented-in is found statistically insignificant to
affect grain production.
The fact that land sharing becomes less preferable but more
important in terms of improving grain production could also provide some
empirical evidence on the behavior of peasants working in a risky production
environment. It signifies the existence
of production risks that provide the economic rationale for land sharing. However,
the popularity of land rental market vis-ŕ-vis land sharing makes this argument
a delicate issue. Why does the less popular alternative (land sharing) become
more rewarding? Possible explanations
could include lack of financial capacity to rent land and purchase other
resources simultaneously or those who engage in land sharing arrangement could
be wealthier but more risk averse than those who rented land. This question, however, should be explicitly answered
by future researches.
Nevertheless the result indicates the necessity to provide
institutional support to overcome existing problems of production risks and/or
shortage of financial resources.
Whenever they design development projects, local government and
non-government institutions should, therefore, be able to provide institutional
support, for example, in the form of insurance against potential production
risks. The support could take different
forms. For instance, rainfall insurance or fertilizer subsidy could encourage
farmers to use fertilizers at the level recommended by extension institutions. Such
intervention will make farmers risk takers and allow them to push their
existing production function to a higher level or to the point where the law of
diminishing returns setoff.
Compared to households that did not get remittance, households that have external income in the form of remittance have a higher probability of producing more cereals.
The result, therefore, indicates the existence of linkage between farm and non-farm economy especially if remittances are originated from non-farm activities. Moreover, it could be considered as the positive contribution of labor that migrated outside their village.
7.2
Determinants of food security (household level analysis)
Despite high correlation between grain production and food security, they could not be affected by the same factors. A logit regression model was formulated, therefore, to know the determinants of food security and whether the two were affected by the same variables or not.
Accordingly, different variables were hypothesized to
determine food security at household level. As independent variable household
size and its composition, non-agricultural income in the form of remittance and
off-farm income, age, sex and level of education of the household head and size
of livestock owned by a household were entered into the model. The level of own food production was not
considered due to the problem of endogenity. Due to lack of appropriate
variable, instrumental variable was also not used to substitute it. The
dependent variable is the level of food security of a household which is expressed
as a dummy variable where 0 represents households that could not fulfill the
food requirement of their members and 1 otherwise.
Regression result indicates that household size which is
measured in terms of a standard consumption unit increases the chance of
falling into food poverty. The result is positive and significant even after family
members were grouped into different age groups to see the impact of dependency
related to age differences[25].
The probability of becoming food secure is not affected by
households’ participation in off-farm activities and the amount of remittances
they got. On the other hand, as the age of household head increases the
probability of falling into food poverty increases. Even though the coefficient
is very low, it is significant at 10% level. The result implies that the
probability of falling into food poverty is higher for households headed by
seniors (old age) than those headed by youngsters. Sex and level of education (being
able to read and write) of the household head were found statistically
insignificant to explain variations in the probability of being food insecure.
Livestock size which is measured in terms of tropical
livestock unit (TLU)[26]
has no effect on the probability of being food secure. The coefficient is very
small and also statistically insignificant. Even though this is contrary to
what was expected, it has indicated that variation in the size of livestock
(i.e. asset accumulation) during the survey year is too little to cause
differences in food security among different households.
Table 11: Logit estimates: Dependent variable: Food
insecurity during normal year
|
|
Logit estimates |
|
|
Coefficient |
z-value |
|
|
Constant |
-0.851 |
2.37 |
|
Number of children less than 7 years old |
0.540 |
3.55*** |
|
Number of persons between 7 - 14 years |
0.428 |
2.93*** |
|
Number of persons between 15 – 50 years |
0.521 |
3.26*** |
|
Number of persons above 50 years |
0.417 |
0.80 |
|
Remittance |
-0.310 |
-0.41 |
|
Off-farm income |
-0.000 |
-0.19 |
|
Age of hh head |
0.003 |
1.86* |
|
Sex of hh head |
-0.216 |
-0.59 |
|
Education of hh head |
-0.000 |
-0.72 |
|
Size of livestock |
-0.001 |
-0.78 |
|
|
Number of obs= 277
LR chi2(11)= 74.88
Prob > chi2=0.0000 Log likelihood = - 150.889 |
|
*,
**, *** indicate significance level at 10%, 5% and 1% respectively
8. Summary and recommendations
The level of poverty in
Result from the regression model shows that access to productive farm resources principally to land and ox significantly affects the level of grain production. On the other hand, labor is not a constraint given the current farm size.
The probability of becoming food insecure varies indirectly
with household size. In addition to production enhancing interventions, the new
food security strategy of the government should, therefore, incorporate family
planning as one of its priority areas in the fight against food insecurity in
the study areas.
Due to problems related to uneven distribution of factors of
production and weak factor market, development programs should be sufficiently
flexible to deal with problems of different farmers differently. For instance,
farmers who lack sufficient land should be supported differently from farmers who
encounter labor shortage like female-headed households. Land shortage could be
compensated to some extent by providing incentive and support to farmers to
grow high-value crops using small scale irrigation or through the use of land
saving technologies like chemical fertilizers that will increase the return per
unit area. On the other hand, labor saving technologies or alternative
livelihoods are more important for labor scarce families.
Existing labor in the area is surplus given current farm
size. Any additional labor, therefore, does not have a positive value added to
output. On the other hand, model statistics indicate that households who received
remittance have a higher probability of high grain production. As most of these
remittances have originated from non-farm activities, there may be a positive
return from policy interventions that encourage migration and alternative
livelihood. Government should, therefore, provide various incentives for farmers
in the area to encourage migration in search of alternative livelihoods. This
could be implemented by providing long-term tenure security to farmers
migrating to other areas in search of employment[27].
For farmers who own uneconomical holding or cultivate very fragile and degraded
land, the government could go to the extent of even paying some money to
encourage voluntary abandoning of farming occupation on such lands[28]. In parallel, concerted effort should be made
to create labor-intensive employments in the non-farm sector (either in rural
or urban areas) to reduce existing population pressure in rural areas and allow
farming not to be a reservoir of unproductive labor that damages the
sustainability of the system.
Annex 1: Distribution of
farmland among sampled farmers
______________________________________________________________
Land
in ha. | Freq. Percent Cum.
--------------+------------------------------------
<=0.13
| 12 1.86 1.86
0.19
| 1 0.16 2.02
0.25
| 70 10.87 12.89
0.38
| 13 2.02 14.91
0.50
| 192 29.81 44.72
0.63
| 7 1.09 45.81
0.68
| 40 6.21 52.02
0.75
| 150 23.29 75.31
0.88
| 1 0.16 75.47
1.00
| 119 18.48 93.94
1.13
| 1 0.16
94.10
1.25
| 20 3.11 97.20
1.38
| 1 0.16 97.36
1.50
| 14 2.17 99.53
1.75
| 2 0.31 99.84
>= 2.00
| 1 0.16
100.00
--------------+--------------------------------------
Total
| 644
___100________________
Annex 2: Drought affected
population
|
Year |
Disaster/drought affected
population (million) |
Proportion affected |
|
1980/81 |
2.82 |
7.7 |
|
1981/82 |
3.70 |
9.8 |
|
1982/83 |
3.30 |
8.5 |
|
1983/84 |
4.21 |
10.5 |
|
1984/85 |
6.99 |
17.0 |
|
1985/86 |
6.14 |
14.5 |
|
1986/87 |
2.53 |
5.8 |
|
1987/88 |
4.16 |
9.3 |
|
1988/89 |
5.35 |
11.6 |
|
1989/90 |
3.21 |
6.8 |
|
1990/91 |
7.22 |
14.8 |
|
1991/92 |
7.85 |
15.6 |
|
1992/93 |
4.97 |
9.6 |
|
1993/94 |
6.70 |
12.6 |
|
1994/95 |
3.99 |
7.3 |
|
1995/96 |
2.78 |
4.9 |
|
1996/97 |
3.36 |
5.8 |
|
1997/98 |
4.10 |
6.8 |
|
1998/99 |
7.19 |
11.7 |
|
1999/00 |
10.56 |
16.6 |
|
2000/01 |
6.24 |
9.6 |
|
Average |
5.37 |
10.3 |
|
2002/2003* |
14.5 |
21.0 |
*
Estimated.
Source:
Mulat Demeke (2003)
Annex
3: Crop production in the
study areas (qt./ average household)
|
Wereda |
Crops |
Normal agricultural year |
Drought year |
|||
|
Beneficiary |
Non-beneficiary |
Beneficiary |
Non-beneficiary |
|||
|
Legeambo |
Cereals |
All cereals |
7.03 |
7.01 |
2.83 |
2.62 |
|
Barley |
6.29 |
6.06 |
2.47 |
2.27 |
||
|
Wheat |
0.74 |
0.86 |
0.36 |
0.34 |
||
|
Sorghum |
0 |
0.09 |
0 |
0 |
||
|
Maize |
0 |
0 |
0 |
0.01 |
||
|
Pulses |
All pulses |
1.92 |
2.32 |
0.68 |
0.91 |
|
|
Lentil |
0.89 |
1.28 |
0.31 |
0.51 |
||
|
Peas |
0.69 |
0.79 |
0.25 |
0.30 |
||
|
Beans |
0.31 |
0.21 |
0.11 |
0.08 |
||
|
Vetch |
0.03 |
0.04 |
0.01 |
0.02 |
||
|
Oil crops |
Flax |
0.31 |
0.05 |
0.001 |
0.03 |
|
|
All Food
crops |
9.26 |
9.38 |
3.51 |
3.56 |
||
|
Share of Belg in total production (%) |
51.8% |
56.5% |
45.8% |
49.7% |
||
|
N |
303 |
92 |
303 |
92 |
||
|
Meket |
Cereals |
All cereals |
6.71 |
10.91 |
3.20 |
4.06 |
|
Barley |
2.44 |
4.06 |
0.81 |
1.38 |
||
|
Wheat |
1.26 |
2.30 |
0.50 |
0.70 |
||
|
Teff |
1.55 |
2.26 |
1.05 |
1.40 |
||
|
Sorghum |
1.31 |
2.27 |
0.79 |
0.50 |
||
|
Maize |
0.14 |
0.02 |
0.05 |
0.08 |
||
|
Oat (Aja) |
0.01 |
0 |
0.005 |
0 |
||
|
Pulses |
All pulses |
1.78 |
2.56 |
0.79 |
0.76 |
|
|
Lentil |
0.20 |
0.49 |
0.04 |
0.21 |
||
|
Peas |
0.52 |
0.77 |
0.16 |
0.18 |
||
|
Beans |
0.93 |
1.08 |
0.28 |
0.28 |
||
|
Vetch |
0.13 |
0.22 |
0.31 |
0.09 |
||
|
Oil crops |
Flax |
0.01 |
0 |
0.08 |
0.01 |
|
|
All Food
crops |
8.50 |
13.47 |
4.07 |
4.83 |
||
|
Share of Belg in total production (%) |
17.5% |
16.3% |
13.2% |
10.8% |
||
|
N |
193 |
58 |
193 |
58 |
||
Annex 4: Partial correlation of
level of education of HH head and ownership of productive
resources (Land and
oxen) (N=443)
-------------------------------------------------
| HH Head Edu. Land
Oxen
-------------+-----------------------------------
HH Head edu. |
1.0000
Land
| -0.0135 1.0000
Oxen
| -0.0481 -0.0719
1.0000
-------------------------------------------------
Annex 5:Partial correlation of land, labor and oxen
owned by sampled
households (N=443)
-------------------------------------------
| Land Labor
Oxen
-------------+-----------------------------
Land
| 1.0000
Labor
| -0.1327 1.0000
Oxen
| -0.0719 0.2828
1.0000
-------------------------------------------
Bibliography
Addis Fortune (2004). A weekly Newspaper, Volume 5,
No. 221, Addis Abeba.
Austrian Development Co-operation
(2003):
Bekele Shiferaw (1998). Peasant Agriculture and
Sustainable Land Use in
Daniel Assefa (2003).Family Planning Services: an
Important Front in the
De Graaff, Jan (1996). The Price of
Soil Erosion: An economic evaluation of soil conservation and watershed
development. Wageningen Agricultural University, Published Ph.D Thesis.
Wageningen, The
DPPC (2000). National Food Aid
Targeting Guidelines.
EDRI and IFPRI (2004).
Ethiopian Economic Association (1999).
The First Annual Report on the Ethiopian Economy. Vol. I. 1999/2000.
_________(2002). Land Tenure and
Agricultural Development in
________ (2004). Report on the
Ethiopian Economy. Volume III 2003/04.
FAO (2001). The State of
Federal Democratic
Francois Bourguignon (2004). The
Poverty-Growth-Inequality Triangle. A Paper Presented at the Indian Council for
Research on International Economic Relations,
Getahun Tafesse (2003).The Roles and
Externalities of Agricultural Growth to Poverty Reduction in
Holden, S.T. and Bekele Shiferaw
(1999). Incentives for Sustainable Land Management in Peasant Agriculture in
the Ethiopian Highlands. In: Sanders, D.W., Huszar, P.C., Sombatpanit, S. and
Enters, T. (eds). Incentives in Soil Conservation From Theory to Practice.
World Association of Soil and Water Conservation.
Hog and Lunde (2002). Application of Tobit
Model. Department of Econometrics, NYU,
IDS and SC-UK (2002). Destitution in
the
Keddeman, W. (1992). An Economic
Analysis of Soil Conservation Projects in
Julius Holt and Dessalegn Rahmato
(1999). Study Report: Sustainable Livelihoods in
Julius Holt and Mark Lawrence (1993). Making
Ends Meet: A Survey of the Food Economy of the Ethiopian North-east Highlands.
Mary datchelor House,
Leisinger, K.M., Schmitt, K. and ISNAR
(eds). (1995). Survival in the
Masefield, A. (1996). The Great Grain or Cash Debate:
Food for work versus cash for work
in the context of
employment based safety net policy in
MOFED (2002).
Promoting
Development and Poverty Reduction.
Mulat Demeke (2003): An Overview of
Agricultural Production and Land Resource Management in
NBE (2004). Quarterly Bulletin, Volume
19, No. 4, Second Quarter, 2003/2004. Addis Abeba.
Save the Children
Save
the Children
Save the Children
Save the Children
[1] This paper is a modified version of the impact assessment study carried out to evaluate the Cash-for-Relief project of SC-UK.
[2] This estimate
is based on the assumption that all energy requirement comes from grain
(cereals, pulses and oil crops) consumption which largely reflects the reality
especially in most parts of central and northern
[3] Some Weredas in western Haraghie and Arsi became
vulnerable to drought in 2002/03 for the first time, while about 35% of the
Wollo population received food aid annually between 1997 and 2001.
[4] Interested readers could get a copy of the main report of the CfR impact assessment study from the EEA/EEPRI.
[5] However, recent data indicate that population growth has started to decline gradually.
[6] Normally an index should be developed to encompass the various factors involved in determining the level of food security of a given household. Compared to other factors, however, the size of farmland is believed to be the single most important factor that determines the level of food security.
[7] The various
food crops produced in the area were expressed in terms of their wheat energy
equivalent.
The major food crops of the area are
barley, wheat and maize, sorghum, lentil, peas, beans, vetch and
flax.
[8] Still, there is
a two year gap between our data on the poverty line which is computed for 2001
and the actual
income collected from framers in 2003.
However, this has little effect to change the analysis or the conclusion
derived from it.
[9] The level of poverty is
calculated based on minimum consumption level for rural
[10] Data on per capita and
average household minimum expenditure for food and non-food consumption was
taken from a document prepared for the SDPRP by the Ministry of Finance and
Economic Development of the FDRE; and on price index from EEA database.
[11] Average weighted price calculated based on price and
production data collected from the household survey. The share of the various
crops in total production was taken into account in the calculation of the
average weighted price.
[12] Data on income from off-farm
and non-farm activities for normal/good agricultural year was obtained from the
survey
result. But for drought year and
income from these activities is assumed to decline by 40% as the general
economic activities
in the area (except for activities
related to food/cash aid operations) could weaken.
[13] Due to
lack of baseline data, the study chooses an indirect way of measuring the
dynamics of poverty
in the study areas.
[14] 1 TLU=1
camel, or 0.7 ox or cow, or 10 sheep or goats, or 0.5 Donkey or bull, or 0.45
heifer or calf,
or
0.7 mule, or 0.8 horse or 100 chicken (Hans E.Jahnke, 1982, quoted from
EEA, 2002).
[15] Although the data
is a bit old, a study by SC-UK reported that the size of
livestock in 1992 increased for
14% of the farmers, remained stable
for 15%, decreased for 42% and much decreased for 29% when
compared to the trend level (Julius
Holt and Mark Lawrence,1993).
[16] Income
from livestock sales and off/non-farm activities were also considered.
[17] Average household cash
expenditures for food and non-food purpose were calculated based on data
collected from baseline study conducted in South Wollo Highland Belg FEZ by
SC-UK. The report classified the community according to their wealth status
into better off, middle, poor and very
poor households who constitute 20%, 35%, 25% and 20% of the population
respectively. Their annual cash expenditure is 1550
[18] Based on various survey
conducted in the study area, the price for one quintal of wheat is taken as 165
[19] Farmers’ income from
off-farm and non-farm activities was recorded for an average (normal)
year. Income from these activities
during bad or drought year is assumed to be 60% of the average year.
[20] According to a study made in
the study areas, livestock sales could produce an average income that could
purchase 1.3 and 1.6 quintal of grain in S.Wollo and N.Wollo respectively. But
income from livestock sales is concentrated in a few hands. Roughly 60% of
income from the sale of oxen and cattle and 90% of income from sales of shoats
goes to the 20% of households with the largest holdings of these animals
(SC-UK, 1993). Based on this information and survey results from this study,
assumptions made that income from livestock sale of beneficiary households is
only 30% of non-beneficiary households’ income from livestock sale.
[21] Food secured households are households who can produce
sufficient food from their own farm to feed their family. Similarly food
insecured households are households who could not meet part of their family
food requirement from own production.
[22] Farmers covered by the survey reported that drought
occur every 2 to 3 years.
[23] However, many question whether this massive relocation program is voluntary or not. For instance, Benjamin Joffe-Walt (cited by the weekly Addis Fortune, Volume 5, No. 221) quoted some farmers who said that they were moved by force.
[24] Even though environmental degradation and drought are
the principal causes of food insecurity, lack of productive resources has
increasingly led many households into chronic food insecurity problems. There
are many households in the study areas that depend on food aid even in normal
year.
[25] Regression results on household size measured in
terms of adult equivalent was not reported.
[26] TLU is
an index number that aggregates the different types of livestock a household
owned to a single
number. Even though different
livestock which implies different degree of liquidity are aggregated into an
index, the total livestock size still
could broadly indicate existing differences in households’ vulnerability
to drought. Households in the area
normally keep any of their savings in livestock which could be
liquidated any time the household
face food shortage.
[27] In this regard, the recent attempt by the government to provide farmers with land (user) certificate could relax the problem. But this depends on the type and clarity of the rights the certificate could guarantee farmers and the efficiency of the law enforcing agencies to enforce the law in case of potential disputes.
[28] This is not a hypothetical recommendation. Some farmers in the study area have already considered their farmland as something that could not support their current and future livelihood. This makes them too careless to conserve or develop their farmlands.