Transcript THE ROLE OF RURAL FACTOR MARKETS IN REDUCING …

THE ROLE OF RURAL FACTOR MARKETS IN
REDUCING POVERTY, RISKS AND
VULNERABILITY IN RURAL KENYA: Evidence from
Kakamega and Vihiga Districts.
BY
Joseph Karugia
W. Oluoch-Kosura
Rose Nyikal
Michael Odumbe
Paswel Marenya.
Study objective
• Overall objective was to determine the role
of factor markets in reducing poverty, risks
and vulnerability in study areas.
Study areas
• Shirugu location in Kakamega District- medium
agricultural potential, good market access,
relatively abundant landholding, recent
resettlement patterns.
• Central Maragoli location in Vihiga District- poor
market access, small land parcels, good
agricultural potential.
• The two Districts provided unique data sets:
poverty counts of 57.71% and 61.97%
respectively compared to national average of 56%.
Study methodology
• Examines all aspects of earning a living in
the rural areas.
• Focus was on rural factor markets (land,
capital and labour markets).
• Considered assets owned (human capital,
physical and natural capital, and financial
assets), activities engaged in and outcomes
in terms of contributing to income.
• Households visited in one-round survey
conducted in months of May and June 2004.
• Collected both quantitative and qualitative
data.
• Quantitative data: semi- structured
questionnaire.
• Qualitative data: interviews with key
informants and groups.
Data of interest
• Farm sizes, Family size, Income sources
and levels, main occupation of household
head, gender of household head, assets,
household members with skilled
employment, land tenure, credit situation,
labour markets and causes of poverty.
• Also collected- indicators of vulnerability
status at the household level.
Findings
• Figure 1. Distribution of selected household assets by
study location.
education years
8
6
4
livestock units
2
household size
0
equipment
maragoli location
land size
shirugu location
• Marked differences in land holdings and livestock
ownership(CEUs) between the two locations:
Shirugu better placed.
• Mean Land ownership was 1.97 ha and 0.37 ha,
respectively.
• Mean livestock units were 3.24 and 1.51,
respectively.
• However, comparison of human capital revealed
that a higher % of households with above
secondary education in Central Maragoli as
compared to Shirugu location (20%, 13.4).
• Partly explained by the small farm sizes that
free labour and also limits expected returns
to agricultural livelihoods and consequently
increase the returns to investment in human
capital/non-farm livelihoods activities.
• Examination of different income sources
share in total incomes indicates how
different asset endowments affect livelihood
strategies that households engage in. These
are illustrated in tables 1 and 2 below;
Table 1. Share of income sources by income
quintiles (Shirugu)
Per capita
Incomes
Salary
Busines Transfers
incomes s
Informal
incomes
Total offfarm
income
Crop
incomes
Livestock
incomes
34, 885
36
17
7
0
60
35
5
10, 951
8.4
18
7.6
12
46
46
8
5, 825
0
14
8
13
35
47
18
4, 584
0
10
8
13
31
61
8
1, 640
0
5
12
14
31
53
16
11, 275
23
15
7
7
50
43
7
Table 2. Share of income sources by income
quintiles (Maragoli)
Per capita
Incomes
Salary
Busines Transfers
incomes s
Informal Total offincomes farm
income
Crop
incomes
Livestock
incomes
24, 455
34
9
20
9
72
16
12
9, 207
22
10
12
14
58
20
22
5, 895
4.4
2.1
9.34
14
30
50
20
3, 659
0
2.9
4.8
21
29
58
13
1, 883
2
1
9
11
23
71
6
9, 419
23
8
15
14
60
26
14
• Share of off-farm incomes are highest for
the high income groups in both locations.
• Largest share of off-farm income for the top
quintiles accrue salaried income
• Low income groups receive their off-farm
incomes predominately from participation
in informal wage opportunities.
• Poorer households in these regions rely on
farming and seasonal labour activities as
their main source of income
• Suggests high levels of vulnerability
• Additionally off-farm income particularly from
the formal wage and salaried sector offers higher
returns in both areas.
• Underscores the need to equip poor households
with skills necessary to tap into off-farm
opportunities.
• Shows that with low landholdings and increase in
population, farming activities can only offer a very
modest basis to secure livelihoods.
• Becoming less reliant on farming is part of the
process of climbing out of poverty.
• Non-farm income opportunities (formal labor
markets) seem to offer a pathway out of poverty.
Figure 2: Income distribution across land ownership
categories (pooled sample)
100000
Amount of income (Kshs)
90000
80000
70000
crop incomes
60000
livestock income
50000
off-farm income
40000
total income
30000
20000
10000
0
2 acres and below
2.1 acres and above
land ownership category
• Households who own (37.5%) above 2 acres of land have
higher values of crop, livestock and off-farm incomes as
compared to those who own 2 acres and below.
• Off-farm income has the largest share in total incomes in
both land ownership categories.
• Shows that as far sizes continue to shrink, there is need for
equal policy focus on facilitating access to off-farm
income opportunities.
• 66.2% of female respondents fall in the lower land
ownership category as compared 60% of their male
counterparts.
• Off-farm income is used to finance on-farm investments in
both locations.
Figure 3:Distribution of incomes across education level
categories (pooled sample).
100000
90000
Incomes (Kshs)
80000
70000
crop incomes
60000
livestock incomes
50000
off-farm incomes
40000
total incomes
30000
20000
10000
0
Below secondary
secondary and
above
Education level category
• About 61% of respondents had upto primary level
of education, 39% had secondary and above.
• The figure above shows marked differences in
incomes among the two categories, with those
having secondary education being better placed.
• Suggest a strong education-non-farm and on-farm
association in study areas.
• However, poor households face financial entry
barriers posed by high cost of education beyond
the primary level. The result is that they remain
trapped in lower category.
Exploring correlates of incomes
• Semi-log analysis of household per capita incomes
revealed that the co-efficients of land holding size,
education level of household head, non-land based
assets,value of livestock holding are positive and
significant in inflencing household incomes.
• On the other hand only education attainment of
household head positively influenced amount of
off-farm income received by households in study
areas.
Credit access
• Respondents in both locations noted lack of
financial capital as one of the major bottlenecks
hindering improved productivity.
• Using observed borrowing, the study estimated a
logit model for access to credit in each location.
• Education level of household head and land size
influenced credit access positively in Maragoli
while amount of off-farm income influenced it
negatively.
• In Shirugu, the education level of household head
and amount of off-farm income influenced access
positively.
• Better educated farm household heads are likely to
be more aware and can take advantage of existing
credit resources.
• In Maragoli, average land holding size is 0.37 ha,
which means that those with relatively smaller
land parcels for whom increase in productivity
may be necessary do not have access to credit.
• Duplication of credit programs in different regions
is likely to be ineffective in reaching poor
households. Need to adapt these to local
conditions.
Challenges
• Only 15% of households sampled
completed secondary school. The rest have
no prospects for being absorbed into
remunerative off-farm activities.
• Employment opportunities are limited to
teaching in primary and secondary schools,
other jobs in local government offices.
• Private sector opportunities are also limited
e.g. matatu drivers.
• In Maragoli , the small farm sector cannot
generate sufficient income despite farming
being the main source of livelihood.
• Unskilled labour is the only alternative
beyond farming that is available to these
households yet opportunities are also
limited
• 70% of sampled households stated farming
as their main occupation.
• The poor in both locations seem to be
locked in a trap characterized by low
education, subsistence farming, and
unskilled informal labour activities.
• Investment in natural capital to improve its
productivity is evidently lacking in study
sites.
• Only 50% of total sample used fertilizers
and hybrid seeds.
Conclusions
• Access to land is still an important source of
livelihoods even where land is scarce.
• Immediate course of action must lie in
improving the productivity of the limited
natural capital base.
• Nevertheless, a burgeoning population and
diminishing land sizes imply that access to
land alone may not guarantee households
sufficient incomes to escape poverty.
Conclusions Cont’d
• More of the rural population must
necessarily be absorbed in the off-farm
sector.
• Skill acquisition as well as rural
industrialization programs will be key.
Policy Implications
• The study advocates for a more integrated
approach to rural development by:
• expanding educational services especially
secondary education to build human capital
• Investments to provide skills for off-farm
activities
Policy Implications Cont’d
• Creation of remunerative employment in the
rural areas through rural industrialization.
• Improving farm productivity by adequate
provision of inputs such as fertilizers and
high yielding varieties and developing
product markets through investments in
infrastructure.
Acknowledgements
• USAID for providing the funds for the
study
• Cornell & Clark-Universities for the SAGA
initiative
• SAGA Kenya collaborators
• Farmers & key informants for sharing data
and their knowledge
THANK YOU ALL