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Student Publications
Author: Felix Mmboyi
Title:
Poverty
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RELATIONSHIP BETWEEN RURAL POVERTY AND
AGRICULTURAL
PRODUCTIVITY IN KENYA: AN ANALYTICAL APPROACH
1.0 Introduction
The CBS in the Economic Survey for year 2006, GoK (2006b) note that
poverty is multi-dimensional
and that its definition clearly depends on the perception of the
society involved. Sarlo (2001) provide
dictionary definitions of poverty as "without or lack of basic
necessities or deprived of basic needs or
simply without luxuries" but proceeds to question these very
definitions. He observes that it is indeed
difficult to specify poverty for practical distinction, for instance
in the case of attempting to locate
poverty in the continuum of living standards or quantifying the
magnitude of deprivation.
The Kenya Participatory Impact Monitoring (KEPIM) (2005) provides
definitions from various
communities that include lack of access to productive assets, lack
of access to social services,
dependency and inability to participate and lack of access to basic
infrastructure. Whether one
perceives poverty as lack of necessities or as a particularly low
position in the distribution of income,
it is evident that poverty condition is known but difficult to
generalize across societies or even
countries. A more concise definition of poverty therefore accounts
for the given society and the lack of
basic necessities considered minimally decent.
The pursuance of Structural Adjustment Programmes (SAPS) in recent
years has awakened most
Governments, including Kenya's, to the increasing prevalence of
rural poverty in their countries. In
2000, more than 45 percent of sub-Saharan Africa's population was
estimated to be in poverty, and
this situation has not improved in at least the last 15 years (World
Bank, 2000). While efforts have
been made to track poverty levels through standard welfare
monitoring surveys and the computation of
statistics on poverty prevalence, depth and severity, such
information rarely provides insights for the
design of specific anti-poverty programmes.
Rising poverty levels have prompted the international community to
develop and seek consensus on
internationally agreed development goals to be pursued by
governments. This has led to the adoption
of the International Development Goals and consequently the United
Nations endorsed Millennium
Development Goals (MDGs).
At the same time, multilateral lending agencies also developed their
own version of development goals
that focus on poverty alleviation strategies. As a result, loan
recipient governments have been required
to develop Interim Poverty Reduction Strategy Papers (IPRSP) as a
prelude to more elaborate Poverty
Reduction Strategy Papers (PRSP) that together with other
requirements form The CBS in the
Economic Survey for year 2006 GoK, (2006b) note that poverty is
multi-dimensional and that its
definition clearly depends on the perception of the society
involved.
In order to fill this void, the Ministry of Finance through the PRSP
secretariat and the Central Bureau
of Statistics (CBS) have devised innovative systems to capture
information pertinent to monitoring
poverty over time. This system involves the development of
monitoring and evaluation protocols and
poverty mapping tools in areas experiencing high and severe poverty
and the associated socio-
4
economic groups. The poverty mapping concept has
been applied in the current welfare monitoring
survey in Kenya but was limited to Nairobi and its environs.
The objectives of this paper are threefold: First, we measure the
prevalence of rural poverty in 1997
and 2005. Second, we categorize households according to whether they
were above the poverty line in
both 1997 and 2005, entered into poverty or exited from poverty
between 1997 and 2005, or were
above the poverty line in both years. Third, the paper identifies
the household-level and community-
level factors associated with rural poverty through econometric
analysis. Lastly, we consider the
implications of these results for the design of appropriate poverty
reduction strategies.
2.0 POVERTY AND WELFARE MEASUREMENT IN KENYA
Efforts to measure poverty and welfare in Kenya were initiated as
early as 1972 by FAO using the
Food Balance Sheet Studies. This was followed closely by the
Integrated Rural Survey. Crawford
and Thorbecke, (1975) which was the first documented attempt to
estimate rural poverty in Kenya.
The core programme on poverty and welfare measurement has been the
Welfare Monitoring Survey
(WMS) series that were prompted by pursuit of Structural Adjustment
Programmes (SAPs) reforms at
the behest of the World Bank and International Monetary Fund in the
late 1980's.
The WMS was an initiative to monitor the socio-economic effects of
the SAPS and was designed to
provide indications of the poverty levels within the country (GoK,
2006a).
From the absolute poverty line, other measures such as food poverty
and "hard-core" poverty lines are
derived and expressed in the same currency. Food poverty lines for
rural and urban areas are obtained
using a specific food basket of goods consumed per month per adult
equivalence. Hard-core poverty, by
contrast, refers to those households whose total incomes cannot
cover their basic food requirements.
Food Poverty lines are shown in Table 1 below.
Table 1: Food Poverty Lines, in Kenya Shillings (Ksh) per person
1992 WMS I
1994 WMS II
1997 WMS III
Rural
404.66
702.9
927.08
Urban
514.25
874.27
1253.9
Source: GoK, (2006b): Economic Survey 2006
2.1 Limitations of Poverty Estimation Methods
The standard critique of poverty estimation methods emanates from
the use of household consumption
expenditure and income as the basis of computation.
Household consumption expenditure based methods are more favoured
due to the common argument
that households generally smoothen their consumption and
consequently it is less susceptible to
fluctuations. It is also argued that consumption expenditures are
easier to track and therefore it is more
precise as a measurement.
5
However, it is recognized that there are major
problems as to the composition of the basket of goods
and the pricing of those goods. This basket of goods can be based on
WHO defined adult equivalent
nutritional requirements but the issue of which commodities to
include will still vary from country to
country and even within countries.
Income based poverty measures are considered less precise because it
is known that income values are
generally not exact considering suspicions that respondents express.
Income based poverty measures
are therefore difficult to estimate and are most likely to bias
poverty levels upwards i.e. overstate
poverty incidences.
A more general critique is derived from the approach taken by the
World Bank of establishing
international poverty lines. Since these poverty measures are based
on purchasing power parity
exchange rates and country poverty lines which form the background
to the now common $1/day
poverty line, they are extremely vulnerable to exchange rate
variations. Deaton, (2001).
Poverty lines and the corresponding poverty incidences are further
criticized owing to their static nature.
The contention that these statistics are derived from household
surveys, which are basically cross-
sectional, implies that the statistics are less useful in measuring
changes in household welfare over time.
The inadequacy of household based poverty measures implies that the
efforts of governments and other
development agents in addressing poverty reduction cannot be easily
validated i.e. in the absence of a
dynamic poverty measurement tool, it is difficult to state or
measure the impact of PRSP however short-
term.
Other measures of welfare have been developed but they present even
greater challenges in terms of
measurement because some of the components are not amenable to
quantification. In recent years,
prominence has been given to these welfare measures such as Human
Development Index (HDI) and
Participatory Poverty Assessments which attempt to incorporate key
aspects of human well being in the
measurement yardstick.
These controversies over poverty measurements not withstanding, we
have chosen to utilize the WMS
poverty line as the basis of our analysis in modeling poverty
dynamics. The Absolute Poverty line is
used to estimate poverty incidence depth and severity through the
methodology developed by Forster,
Green and Thorbecke (FGT), Forster et al (1984).
Another welfare measurement of interest is the Gini Coefficient.
Gini coefficients which shows the level
of inequality in the distribution of resources within a population,
and range from zero (complete equality
of income across all households) to one (extremely concentrated
distribution of income). These
computations are, in most cases static and do not therefore reveal
issues that are inherently dynamic. It is
consequently, expected that the examination of changes in the level
of inequality as measured by income
Gini Coefficients should be more informative.
2.2 Poverty Dynamics
It has become increasingly evident that the poor are indeed
heterogeneous and that some element of
dynamics does exist (Barrett, 2003). These developments have led to
a scrutiny of poverty as determined
by the duration spent under poverty. Further enquiries have been
made to establish the determinants of
exit or entry into poverty. Stevens, 1995; Davis and Stampini,
(2002).
6
These developments have resulted in further
categorization of poverty into chronic and transitory.
Chronic poverty is considered to be the component of total poverty
that is static and transitory poverty is
the component that is attributable to intertemporal variability.
Jallan and Ravallion, (1996). The isolation
of the process underlying chronic and transitory poverty is
considered essential in understanding the
extent to which each poverty type may obscure the other or even
distort the effects of government anti-
poverty programmes.
Aliber (2001) emphasizes that chronic poverty exists when a
household's or individual's poverty
condition endures over a given duration. The specific duration that
defines chronic poverty varies and
depends on the available data, Researchers and analytical tools
employed. The concept of chronic
poverty has been expanded to include households/ individuals unable
to emerge from poverty or
lacking opportunities to improve their circumstances. Okidi (2002).
Bird and Shepherd (2003) extend
chronic poverty analysis by pursuing the relationship that exists
between poverty and remote rural
areas especially the effects of political exclusion. Stevens (1995)
examine the persistence of poverty
over individuals lifetimes through a hazard rate (spells) approach
and a variance component model.
These approaches are considered an improvement over the Bane and
Ellwood (1986) study since they
take into consideration multiple spells of poverty rather than
focusing on a single spell.
Bigsten et al (2003) and Hadad and Ahmed (2003) provide an
insight into transitory poverty by
examining the characteristics of households that exit or enter
poverty. Similarly, the pathways out of
poverty were studied by Davis and Stampini (2002) and Krishna et
al (2003).
McCulloch and Baulch (2000) provide a simulation of the impact of
policy upon chronic and
transitory poverty although they utilize the squared poverty gap
measure which is more suited to
severity rather than poverty levels. They conclude that different
anti-poverty interventions may be
needed to address chronic and transitory poverty.
It is evident that the analysis of poverty dynamic constitutes a
significant aspect in understanding the
persistence of poverty by providing the defining characteristics of
those who remain persistently poor.
This distinction and characterization is particularly useful in
developing/designing government anti-
poverty programmes.
3.0 PANEL DATA DESCRIPTION
The panel data used in the analysis was obtained through rural
household surveys conducted in 1997
and 2006. These surveys covered 1441 households in both 1997 and
2006.
4.0 ESTABLISHING POVERTY CATEGORIES
Incomes from farm and non-farm sources were computed from the 1997
and 2006 rural household
survey data. The 1997 poverty line was then inflated to 2006 levels
to compute a new poverty line for
2006.
The WMS poverty line for 1997 and the 2006 computed poverty line
were utilized to establish rural
households below and above the poverty line for 1997 and 2006
respectively. The rural income poverty
7
incidence for 1997 was found to be 58% while
that for 2006 was 61%. The computed rural income
poverty incidences appear to be consistent with the widely held
perception that poverty levels in the
country have been increasing during the study period particularly in
view of the loss of non-farm income
from retrenchment programmes in the civil service and parastatals.
The private sector also shrunk at the
time due to capital flight, reduced capital inflows and relocation
of investors attributed to the
unfavourable economic and political climate.
Poverty categories were developed from the panel sample of rural
households using a modified spell in
poverty approach and defined as follows:
i)
Chronically poor refers in this study to those households
that fell below the poverty line in
both 1997 and 2006. Our use of the term here does not imply that
these households are
necessarily consistently poor year in and year out, as we lack the
multiple years of panel data
required to determine this.
ii)
Transitorily poor refer to those households that fell below
the poverty line in either 1997 or
2006 but in not in both periods.
iii)
Non-poor characterize those households that did not fall
below the poverty line in either year
(1997 and 2006).
The foregoing categorization produced the results indicated in table
2 below.
Table 2: Poverty Categories
Frequency Percent
Non poor
470
33.7
Transitory poor
433
30.1
Chronic poor
535
37.2
Total
1438
100.0
On the whole it appears the income poor constitute a very large
proportion of the rural households.
The chronic poor form the largest proportion of the rural households
at 37% compared to the other
categories. This is in contrast to other developing countries with
similar economic status. Hadad
and Ahmed, (2003); Dercon and Krishnan, (2002).
However, our understanding of "poverty dynamics", e.g., the extent
to which poor households in
one year remain poor in subsequent years as opposed to moving out of
poverty, has not received
commensurate attention from either the PRSP secretariat or the CBS.
This can partly be attributed
to the lack of appropriate panel data that tracks the poverty status
of rural households over time in
Kenya. This has also inhibited the ability to understand the reasons
why some households that are
below the poverty line in one period are able to climb out of
poverty in subsequent periods, while
others remain chronically mired in poverty. It should be noted that
this problem is not peculiar to
Kenya and is exhibited in a number of countries. Even the World
Bank, which is renowned for its
8
eminent work in the area of poverty dynamics,
has little relevant information on Kenya. The PRSP
monitoring and evaluation exercise and the CBS poverty mapping
process can be complemented
by rigorous analysis of panel data to provide gainful insights into
the dynamics of poverty in
Kenya through the analytical methods utilized in this study.
As stated earlier, the objectives of this paper are threefold:
First, i measure the prevalence of rural
poverty in 1997 and 2006, based on the nationwide survey. Second, i
categorize households
according to whether they were above the poverty line in both 1997
and 2006, entered into poverty
or exited from poverty between 1997 and 2006, or were above the
poverty line in both years.
Third, the paper identifies the household-level and community-level
factors associated with rural
poverty through econometric analysis. Lastly, i consider the
implications of these results for the
design of appropriate poverty reduction strategies.
Results, displayed in Tables 3a and 3b, indicate a high degree of
correlation among all indicators,
all of which are significantly related at the 1 percent level of
significance. However, as might be
expected, the three income-based measures show a particularly high
degree of correlation, whereas
the Spearman correlation coefficient between the household asset
variable and the household
income variables are in the range of 0.44 to 0.45. For comparability
with previous studies in
Kenya, our analysis proceeds on the basis of the income measures,
keeping in mind the partial
degree of correlation between these measures and asset levels.
Table 3a. Correlation Coefficients of Indicators of Household
Welfare, 1996/97 Season
Total household
Per capita
Household cash
income
household income
income
Per capita household income
.916
Household cash income
.985
.908
Total value of household assets
.553
.513
.515
Source: derived from the CBS household surveys, 1996/97, and 1999.06
Table 3b. Correlation Coefficients of Indicators of Household
Welfare, 1999/06 Season
Total household
Per capita household
Household cash
income
income
income
Per capita household income
.877
Household cash income
.977
.866
Total value of household assets
.530
.460
.530
Source: derived from the CBS household surveys, 1996/97, and 1999.06
4.1 Spatial Distribution of Poverty Categories
9
Since the categorization of poverty into
"chronic," transitory and non-poor as above was
performed without reference to either agro-ecological zones or the
administrative districts, it is
imperative to examine their distribution within these locations. The
spatial distribution of poverty
by agro-ecological zones is therefore shown in Table 4 below.
Table 4: Distribution of Poverty Categories within Agro-ecological
zones.
Zone
Non
Transitory Chronic Group
poor
poor
poor
Total
Coastal Lowlands
Count
12
40
27
79
Percent within zone
15.2
50.6
34.2
100.0
Eastern Lowlands
Count
57
55
44
156
Percent within zone
36.5
35.3
28.2
100.0
Western Lowlands
Count
16
39
120
175
Percent within zone
9.1
22.3
68.6
100.0
Western Transitional
Count
37
69
59
165
Percent within zone
22.4
41.8
35.8
100.0
High Potential Maize Zone Count
151
94
140
385
Percent within zone
39.2
24.4
36.4
100.0
Western Highlands
Count
15
43
81
139
Percent within zone
10.8
30.9
58.3
100.0
Central Highlands
Count
166
61
31
258
Percent within zone
64.3
23.6
12.0
100.0
Marginal Rain Shadow
Count
11
21
16
48
Percent within zone
22.9
43.8
33.3
100.0
Group Total
Count
465
422
518
1405
Percent within zone
33.1
30.0
36.9
100.0
Except for Central Highlands, all the other zones record chronic
poverty levels well above 25%,
which implies that chronic poverty is predominant in the country.
Western lowlands, , Western
Lowlands and Western highlands record the highest levels of chronic
poverty whereas transitory
10
poverty is spread out over all the zones. The
observation here is that poverty is not confined to
specific zones irrespective of the agricultural potential of the
area (zone).
To examine the spatial pattern of income poverty, we regress per
capita incomes on geographic
categorical variables of varying size. This is equivalent to an
ANOVA test measuring the extent of
inter-zone vs. intra-zone variation. When provincial-level dummy
variables are used, the R2 of
these models is 0.06, indicating that roughly 94% of the variation
in per capita incomes across
these 1,400 rural households is explained by differences within the
provinces rather than between
them. When smaller geographic variables (districts) are used, the R2
of these models only rises to
the range of 0.14. And when using the smallest administrative unit
available in the data set
(villages), the R2 of these models indicates 23.5% of the variation
in per capita incomes across the
sample can be explained by differences between villages. By far the
most important factors
associated with the variation in per capita incomes across the
households in the sample are not
related to village-specific factors such as rainfall, soil types,
market access, etc. We believe that
this is an important finding that is somewhat in conflict with
conventional wisdom. There are
indeed significant regional differences in incomes as shown in Table
6. But despite such regional
differences, the largest source of variation in household incomes is
to be found within villages, i.e.,
poverty is primarily an intra-village phenomenon which demands
strategies that identify and take
into account household-level resources and characteristics.
The presence of both transitory and chronic poverty in all areas of
the country also implies that
successful poverty reduction strategies must be developed to account
for these two different types
of poverty.
11
5.0 INCOME INEQUALITY
To examine the income distribution more carefully, we present
various Gini coefficient estimates
from the household data. According to Deininger and Squire (1996),
the average income Gini
coefficient in Sub-Saharan Africa, based on 40 surveys that passed
their data-quality criteria, is 0.45,
while it is 0.50 in Latin America, where income inequalities are
generally considered to be relatively
severe. We find Gini coefficients of 0.52 for Kenya in 1997 and 0.55
in 2006. This is considerably
higher than the 0.37 Gini coefficient reported for Kenya's rural
areas by Haggblade and Hazell
(1988) in the 1970s. Moreover, the current Gini estimates from our
sample are also generally higher
than Haggblade and Hazell's estimates for rural Asia from the 1960s
and 1970s. This might be
considered especially surprising given that our sample is confined
to the small-scale farming sector
and does not even count the large-scale farming sector. From these
comparisons, it appears that the
distribution of rural income appears to have widened over the past
two decades, although differences
in survey design and samples warrant caution in these comparisons.
But at least there is prima facie
evidence that income distribution may be worsening in these
countries over time, and that rural
income distribution is actually worse in Kenya in the late 1990s
than in most of Asia at the time of
the green revolution there.
We next examine income inequality within each agro-ecological zone
studied as measured by the
Gini Coefficients. The gini coefficients for each year are shown in
Figure 1:
Figure 1: Gini coefficient for agro-ecological zones
0.7
0.6
tnei 0.5
cif 0.4
eoC 0.3
ini 0.2
G 0.1
0.0
id
ands
ands
ands
ghlands
ghlands
rthern Ar
aize Zone
No
M
Coastal Lowl
Eastern Lowl
estern Lowl
W
estern Transitional
HP
estern Hi
Central Hi
W
W
arginal Rain Shadow
M
Zone
1997
2000
The zone Gini coefficients are lower than that for the nationwide
sample except in western lowlands
and Hp maize zones. This is because some of the income variation
across zones is eliminated with
examining inequality only within a given zone. Yet the level of
income inequality within zones still
appears to be quite high. The lowest Gini coefficient in 1997 is
recorded by the Marginal Rain
Shadow at 0.40 and Central Highlands at 0.44. Both years show high
levels of income inequality
with the highest level in 1997 and 2006 being in Western
Transitional and High Potential Maize
Zone respectively.
12
Figure 1 also reveals a worrying trend in that
the income inequality for the agro-ecological zones
shows an upward trend from 1997 to 2006. Except for Northern Arid
and Western Transitional zones
all the other zones record higher Gini coefficients in 2000 compared
to 1997.
Western lowland zone did not experience change in Ginni coefficient.
Though the coefficient
remained high in both years, it was the same for both 1997 and 2000.
6.0 POVERTY AND ACCESS TO RESOURCES
6.1 Poverty and Access to Human Capital (Education)
Human capital in the form of education and skills contribute to
poverty reduction efforts by
providing the tools to identify and exploit economic opportunities.
Bruno et al, (1998); World Bank
(2000). Marenya et al (2003) also find a strong relationship
between education, non-farm income
and farm investments that over a long period of time contribute to
significant reduction in poverty
levels in western Kenya. It is however noted that the effects of
investments in human resource
development on poverty is manifested only in the long term, and thus
should be viewed as a
potential means to alleviate chronic poverty. Transitory poverty
alleviation requires other types of
public policy interventions.
The relationship between poverty and education distinctly emerges
from the CBS household survey
data as shown in Table 5. The relationship between chronic poverty
categories and years of
education of the most highly educated adult member of the household
is strongly inversely
correlated. For example, over 60% of the households whose household
head had no primary school
education were below the poverty line in both 1997 and 2006. By
contrast, less than 20% of the
households that had household head with education beyond Form 4 were
chronically poor. The
major turning point at which education levels are associated with
sharp reductions in chronic poverty
occurs at fourth form level. It is instructive that this
relationship is exhibited in both the 1997 and
2006 data.
Table 5: Comparison of Poverty Categories by Education in 1997
NON
TRANSITORY
CHRONIC
TOTAL
POOR
POOR
POOR
None
Count
6
8
15
29
Percentage
20.7
27.6
51.7
100.0
Primary unfinished
Count
51
69
161
281
Percentage
18.1
24.6
57.3
100.0
Finished primary
Count
96
109
158
363
Percentage
26.4
30
43.5
100.0
Some Secondary
Count
66
79
95
240
Percentage
27.5
32.9
39.6
100.0
13
Form 4
Count
177
131
90
398
Percentage
44.5
32.9
22.6
100.0
Form 6 / Post secondary
Count
63
32
14
109
Percentage
57.8
29.4
12.8
100.0
1st degree and above
Count
11
5
1
17
Percentage
64.7
29.4
5.9
100.0
Total
Count
470
433
534
1437
Percentage
32.7
30.1
37.2
100.0
Source: CBS Household Surveys, 1997.
6.2 Poverty and Access to Land
Emerging evidence suggests that the poverty reducing effects of
economic growth are influenced
by the initial distribution of assets and the more general issues of
inequality. For example,
Ravallion and Datt (2002) found that the initial percentage of
landless households significantly
affected the elasticity of poverty to non-farm output in India. In a
sample of 69 countries. Gugerty
and Timmer, (1999) found that, in countries with an initial "good"
distribution of assets, both
agricultural and non-agricultural growth benefited the poorest
households slightly more in
percentage terms. In countries with a "bad" distribution of assets,
however, economic growth was
skewed toward wealthier households, causing the gap between rich and
poor to widen. It is
especially noteworthy that in this latter group of countries,
agricultural growth was associated with
greater increases in inequality than was non-agricultural growth.
This reverses what has been
considered the more typical pattern, wherein agricultural growth is
seen to contribute more to
poverty reduction than growth outside the agricultural sector. These
findings reinforce the idea that
where access to land is highly concentrated and where a sizable part
of the rural population lack
sufficient land or education to earn a livelihood, then special
measures will be necessary to tackle
the problem of persistent poverty. Ravallion, (1997).
An examination of access to land by the different poverty categories
in Kenya indicates that the
area of land cultivated is strongly associated with household per
capita income. Figure 2 shows
that in both 1997 and 2006 the chronic poor cultivated less land.
Figure 2: Poverty Categories by Cultivated Area
14
Poverty categories by cultivated area
8.0
edativ 6.0
tulc
1997
4.0
area
2000
2
2.0
ean M 0.0
Non poor
Transitory poor
Chronic poor
Poverty Category
Source: Tegemeo Household Surveys, 1997 and 2006.
It is well recognized that severe land inequalities persist between
Kenya's small-scale and large-
scale farms. Yet the smallholder farm sector is typically
characterized as small but relatively
"unimodal" and equitably distributed land holdings situated within a
"bi-modal" distribution of
land between large-scale and small-scale farming sectors. Redressing
these inequalities is likely to
be an important element of an effective rural poverty reduction
strategy in countries such as
Zimbabwe and Kenya. Yet despite widespread acceptance that
"pro-poor" agricultural growth is
strongly associated with equitable asset distribution, little
attention has been devoted to quantifying
land distribution patterns within Kenya's small-scale farming
sector.
Nevertheless, it is possible that the bottom land quartile may
contain mostly "Sunday farmers"
who are engaged primarily in off-farm activities for their
livelihoods. To examine this possibility,
we compute income shares from crop production, animal and
animal-derived production, and off-
farm income for each land quartile (Table 5). As expected, off-farm
income shares are highest for
the bottom land quartile and decline as landholding size rises.
However, households in the bottom
land quartile earn 50% of their total income, on average, from
agriculture, despite their very small
farms. The Ginis are comparable to those estimated for much of Asia
during the 1960s and 1970s.
Haggblade and Hazell, (1988). If land is allocated according to
household size or labor availability,
we should find more equal land distribution in household per
capita or per adult land holdings
than per household land holdings. This would imply that the
Gini coefficients of land holding by
per capita and per adult measures should be smaller than those of
landholding per household.
However, this is not the case, as can be seen in Table 8. The Gini
coefficients of landholding size
are virtually unchanged after accounting for family size in the
estimates of land distribution
inequality.
Our point in highlighting the low explanatory power of these models
is to show that most of the
variation in household per capita landholding size within the
smallholder farm sector must be
contained in factors other than village-level differences and
observed household level differences
in assets and socio-demographic characteristics. Research in other
disciplines has highlighted the
importance of the period of the clan's settlement in a particular
area in determining land allocated
to the clan, which is subdivided among families within the clan
Kajoba, (1994); Block and Foltz,
(1999). Late migrants into an area typically are eligible for
relatively small tracts of land for
15
subdivision within the areas controlled by
their clans. Marrule (1998) argues that kinship ties and
power relationships within traditional governance structures also
partially explain the observed
disparities in land allocation, variables that are not included in
these models. These hypothesized
processes are related to the recently emerging literature on kinship
ties, trust, and social capital
(e.g., Fafchamps, (1992); Platteau, (1994); Gabre-Madhin, (2001). In
an attempt to test these
hypotheses, These emerging findings lead us to speculate that, more
generally, there may be
important institutional and governance factors operating within
local systems for allocating land
that may be accounting for at least some of the unexplained
variation in per capita landholding size
within the smallholder farm sector.
The importance of these findings for rural growth and poverty
alleviation strategies depends in part
on the degree to which land allocation patterns influence household
income and poverty. If non-
farm activities are able to compensate for small landholdings and
provide land-poor households
with adequate alternative income sources, then disparities in land
ownership should not necessarily
be a policy problem. However, as we will examine in more detail
later, the relationship between
households' off-farm income, total income, and landholding size is
very strong.
16
Table 6. Smallholder Land Distribution in Kenya , 1997 and 2000.
(a)
(b)
(c)
(d)
(e)
Ave.
Household Per Capita Land Access
RELGA
Gini Coefficients
landholding
P1
sample
size including
size
rented land
Ave.
Quartile 1
Land per Land per Land per
househol
capita
adult
d
1
2
3
4
ha
ha
ha
1997
1,380
2.65
0.41
0.08
0.17
0.29
0.73
1.59
0.55
0.56
0.54
2000
1,345
2.59
0.40
0.07
0.16
0.27
0.76
1.73
0.56
0.57
0.55
1 RELGAP is the difference in mean land size between the first and
fourth quartiles divided by the mean.
Source: Tegemeo Household Surveys, 1997 and 2000.
17
6.3Education and cultivated area
The area cultivated by level of education of the household head is
shown in Table 7. The overall trend
seems to suggest that the area cultivated increases with the level
of education attained by the household
head. For now, we can only speculate about the direction of
causality: do more educated households
cultivate more land because they are more entrepreneurial and
skilled, or are wealthier households with
relatively large farms able to educate their members better? While
the direction of causality is likely to
flow in both directions, ongoing research is attempting to examine
the entry points for poverty
alleviation policy. At this stage, we simply note that education
seems to be positively correlated with
several important indicators of household welfare, and that raising
poor children's' access to education
is likely to have beneficial effects on poverty alleviation and
income distribution over the long run.
Table 7. Household Head Education by Mean Cultivated Area
1997
2000
Acres
Acres
None
4.70
4.69
Primary unfinished
3.81
5.09
Finished primary
4.24
5.39
Some Secondary
3.88
5.11
Form 4
4.49
5.11
Form 6 / Post secondary
5.95
6.44
1st degree and above
6.69
6.90
Table Total
4.82
5.54
Additional insights can be obtained by examining income levels
disaggregated by the type of income, by
education and landholding size categories. We rank all households in
the sample by education of the
most highly educated adult member, and by landholding size, and then
create three education categories
(low, medium and high) and four landholding size categories. The
mean years of education of the three
education terciles are 0.4? or0.26? years, 5.4 years, and 10.8
years. The mean landholding sizes of the
four land quartiles are 0.08, 0.17, 0.29, and 0.73 hectares per
capita.
Table 10 shows the income levels (by source of income) for each of
the 12 groups. Within each
landholding size quartile, we find that mean per capita incomes are
substantially higher for households
in the highest education tercile than those in the first education
tercile. This strengthens our earlier
observations about the contribution of education to poverty
alleviation, because these results persist even
after holding landholding size relatively constant.
18
The results in Table 8 also show that per
capita incomes rise substantially with each landholding
size quartile. Households with highly educated member (mean 10.4? or
10.8?
19
Table 8. Income Levels and Sources of Rural
Households in Kenya, by Education and Landholding Size Category,
1997 and 2000 pooled
Quartiles of Households Ranked by Landholding Size Per Capita
Smallest (mean 0.08 ha per
Second Quartile (mean 0.17 ha
Third Quartile (mean 0.29 ha
Highest (mean 0.73 ha per
capita)
per capita)
per capita)
capita)
Education Group* (1=lowest;
3=highest):
1
2
3
1
2
3
1
2
3
1
2
3
sample size (n)
130
153
97
101
135
144
100
129
149
124
122
133
land access (average for 97 and 00)
0.41
0.55
0.58
1.06
1.17
1.17
1.49
1.90
1.87
3.73
3.77
3.78
land access per capita
0.06
0.08
0.08
0.17
0.17
0.17
0.29
0.29
0.29
0.81
0.68
0.72
female headed households (%)
12.31
13.07
7.22
30.69
13.33
5.56
17.00
17.83
7.38
25.81
17.21
3.76
Per capita income
162.02
142.56
234.63
158.90
238.70
281.17
285.77
258.38
362.61
363.25
466.76
468.50
crop income share (%)
27.60
31.14
28.06
35.64
39.04
32.24
31.44
42.71
36.62
43.22
45.56
35.11
livestock prod income share (%)
30.79
17.41
14.74
18.76
18.80
16.56
22.63
15.79
14.48
25.66
22.42
21.76
off-farm income share (%)
41.61
51.45
57.20
45.60
42.16
51.20
45.93
41.50
48.91
31.12
32.02
43.13
Of which: remittances
4.52
4.27
2.08
5.50
3.45
4.83
5.58
4.50
3.95
4.44
5.51
4.26
business income
15.37
16.66
18.42
16.13
18.56
13.57
16.53
15.80
12.01
14.45
13.71
10.30
non-ag wage labour
19.49
28.27
34.78
22.78
18.46
32.23
22.81
20.45
32.62
11.59
11.55
28.23
ag-wage labour
2.23
2.25
1.93
1.19
1.68
0.56
1.00
0.75
0.34
0.65
1.25
0.33
Crop income per hectare (US$)
554.99
597.99
820.67
345.00
560.44
551.35
304.90
386.89
467.79
211.75
322.04
260.81
20
data.
Note: Mean years of education of the most highly educated adult
member: Group 1 (0.26? or 0.4? years); Group 2 (5.4 years); Group 3
(10.8?
10.4? years).
Source: Tegemeo Household Surveys, 1997 and 2000.
21
years of education) had lower per capita
incomes as a group than households with adults possessing less
than one year of education but in the third or fourth landholding
size quartiles. In short, the greater land
resources of these households allowed them to out-earn the
land-constrained households with adults
possessing 10 more years of education. These results show the
predominant association between
constrained landholding size and rural poverty.
The results in Table 10 also indicate how the sources of income
change as education varies within each
landholding size category. As education increases (from education
group 1 to group 3), the income share
of crops remains roughly constant, and the income share of livestock
products declines. Among the most
land constrained landholding quartile, the biggest difference
between the most and least educated
households is the share and magnitude of off-farm income
non-agricultural wage labour in particular.
These results suggest that for households with inadequate access to
land to earn a livelihood from
agriculture, education is a major pathway out off poverty. Although
it is a pathway that pays off only in
the long-term, increased public investment now is likely to reap
tangible benefits for poverty reduction
10-20 years down the road and for Kenya's long term development
prospects.
6.4 Poverty and gender
Table 10. Mean Household Incomes by Gender of Household Head
1997
2006
Male
130,526.5 164,892.6
Female
94,963.9 108,103.0
Differences in land access and education appear to be accounting for
part of the income disparity
between male-headed and female headed households. Jayne et al.,
(2003) found that female-headed
(unmarried) households in Kenya have, on average, 1.03 hectares less
land than male-headed
households, which is a huge relative difference considering that
mean farm size for the entire sample is
2.65 hectares. Female-headed households in which a male partner
resides off-farm also tend to have less
land than male-headed households, although the effect is weaker than
for female-headed unmarried
households. We also see in Table 10 that a much higher percentage of
female-headed households fall
into the lowest education category in every landholding size group.
6.5 Poverty and Land Tenure
As shown in Table 11, the proportion of households owning land with
title deeds is inversely related to
poverty, and the proportion of households owning land without title
deed is positively related to poverty.
The more common reason for this phenomenon is that the cost of
processing land titles is prohibitively
high and consequently inhibits the participation of the poor in land
registration.
22
Table 11 Poverty Categories By Land Tenure in
1997
Non-poor
Transitory
Chronic poor
Total
poor
Freq
Col%
Freq
Col%
Freq
Col%
Tot Row Tot Row%
Owned with title deed
249
39.3
183
28.9
202
31.9
634
100.0
Owned without title deed
163
28.7
174
30.7
230
40.6
567
100.0
Rented
6
31.6
10
52.6
3
15.8
19
100.0
Owned by parent/relative
50
25.9
58
30.1
85
44.0
193
100.0
Government/communal land/others
1
4.8
8
38.1
12
57.1
21
100.0
Total
469
32.7
433
30.2
532
37.1
1434
100.0
It is generally acknowledged that the easing of land title
processing presents a dilemma for it can either
result in reduced poverty levels or increased destitution. Where the
proceeds from land sales are invested
well the result could be reduction in levels of poverty but where it
is not then the poverty levels are
aggravated. The common observation is that the later case often
prevails.
6.6. Poverty and Agricultural Credit
A larger percentage of the non-poor (42%) received agricultural
credit compared to the transitory poor
(27%) and chronic poor (16%) in 1997. The same trend was repeated in
2000. There is however a slight
increase in those who receive agricultural credit within each
category as shown in Table 12 below.
Table 12. Poverty categories by agricultural credit
1997
2006
Number
Percent
Number
Percent
Non poor
Received credit
195
41.5
261
56.1
No credit
275
58.5
204
43.9
470
100.0
465
100.0
Transitory poor
Received credit
123
28.4
218
51.1
No credit
310
71.6
209
48.9
433
100.0
427
100.0
Chronic poor
Received credit
86
16.1
214
40.1
No credit
448
83.9
320
59.9
534
100.0
534
100.0
23
The pattern exhibited above brings to the fore
the need to restructure the agricultural credit system to
be more responsive to the needs of the rural poor. Only about 20
percent of the chronic poor -- who
probably need credit the most -- are able to access it. This
suggests that the existing agricultural credit
system is unfavourable to the poor, and that efforts to develop
financial products that suit the needs of
relatively poor small-scale farmers may have higher payoffs both in
terms of poverty alleviation and
rural equity. However, this will need to be done in a way that does
not erode the incentives to lenders.
Suppliers of loan money base their lending decisions on the expected
returns and risks of potential
clients. The poor generally represent greater risk of default
because they have less residual assets to
draw on if weather vagaries make it difficult to repay loans through
the sale of crop/livestock
production. There is potentially a useful role for the public sector
to provide loans to farmers who
meet certain poverty-based criteria, but the main challenge here is
how to ensure high loan repayment
and avoid strategic default to maintain the sustainability of the
system.
A further disaggregation of those who received agricultural credit
by agro-ecological zone and poverty
category shows that the majority of those who received agricultural
credit among both the non-poor
and the transitory poor are located in the Central Highlands and
High Potential Maize Zones the
most productive agricultural areas of the country. Among the chronic
poor, Western Highlands had the
highest percentage of those receiving agricultural credit (Table
13). The Coastal and Western
Lowlands have the lowest percentage of those receiving agricultural
credit within the zone.
Table 13: Received credit by zone and poverty categories in 1997
Non poor Transitory Chronic Total
poor
poor
% of households receiving ag. credit
Coastal Lowlands
0.5
3.3
2.3
1.7
Eastern Lowlands
6.7
8.9
11.6
8.4
Western Lowlands
1.5
1.6
4.7
2.2
Western Transitional
8.7
19.5
10.5
12.4
High Potential Maize Zone
15.4
16.3
15.1
15.6
Western Highlands
4.6
16.3
36.0
14.9
Central Highlands
62.1
33.3
19.8
44.3
Marginal Rain Shadow
0.5
0.8
0.5
Total
100.0
100.0
100.0
100.0
Source: Tegemeo Household Surveys, 1997
Poverty and Nominal Crop Land Productivity
24
Crop land productivity was computed using crop
income and area cultivated for each of the poverty
categories for 1997 and 2000.
Table 14: Poverty Category by Mean Crop Land Productivity
Poverty Categories
Mean Crop Land Productivity (Kshs)
1997
2000
Non-Poor
105,422
142,941
Transitory Poor
43,992
79,684
Chronic Poor
20,314
29,525
The above figures suggest that there is some potential for poverty
reduction through improved crop
productivity.
Nominal mean crop land productivity was also computed for the
different agro-ecological zone and
is shown in the table 15 below.
Table 15: Mean Crop Land Productivity in Kshs.
1997
2000
Coastal Lowlands
14,475.35
41,041.49
Eastern Lowlands
30,533.61
70,085.56
Western Lowlands
16,544.12
24,791.10
Western Transitional
53,324.17
110,807.30
High Potential Maize Zone
94,187.68
93,609.26
Western Highlands
25,400.79
65,781.65
Central Highlands
80,916.79
125,373.2
Marginal Rain Shadow
19,808.99
15,864.96
The crop productivity figures appear to correspond to the poverty
levels experienced in the agro-
ecological zone. High Potential Zone, Central Highlands and Western
Transitional have the highest
crop land productivity and also have the lowest chronic poverty as
compared to Western Lowlands,
Eastern Lowlands and Marginal Rain Shadow.
Crop land productivity also increases with increasing levels of
education of the household head
particularly for the year 2006 where a clear picture emerges as
shown in table 16.
25
Table 16: Productivity by Household Head
Education
1997
2000
None
55,621.27
58,380.94
Primary unfinished
47,352.37
76,524.18
Finished primary
50,993.84
85,066.58
Some Secondary
49,286.54
83,292.34
Form 4
69,215.65
81,328.08
Form 6 / Post secondary
87,484.81
107,407.90
1st degree and above
109,320.10
135,995.00
7.0 RURAL POVERTY DYNAMICS
To gain an insight into rural poverty dynamics, the transitory poor
are further disaggregated into those
entering poverty and those exiting poverty. This sub-categories of
poverty are isolated by identifying
those who were above the poverty line in 1997 but fell below the
poverty line in 2006 (entry) and
those who were below the poverty line in 1997 but were above it in
2006 (exits).
In order to provide a complete perspective of poverty dynamics, the
distribution of all the categories
and sub-categories within the agro-ecological zones is shown in
table 17.
Table 17: Poverty Dynamics by Zone
Non-poor in Exit from Entry
Chronic Poor
Total
both years
poverty
into
poverty
Coastal Lowlands
Count
12
13
27
27
79
Percentage
15.2
16.5
34.2
34.2
100.0
Eastern Lowlands
Count
57
30
25
44
156
Percentage
36.5
19.2
16
28.2
100.0
Western Lowlands
Count
16
18
21
120
175
Percentage
9.1
10.3
12.0
68.6
100.0
Western Transitional
Count
37
58
11
59
165
Percentage
22.4
35.2
6.7
35.8
100.0
High Potential Maize
Count
151
59
35
140
385
26
Zone
Percentage
39.2
15.3
9.1
36.4
100.0
Western Highlands
Count
15
33
10
81
139
Percentage
10.8
23.7
7.2
58.3
100.0
Central Highlands
Count
166
42
19
31
258
Percentage
64.3
16.3
7.4
12.0
100.0
Marginal Rain Shadow
Count
11
14
7
16
48
Percentage
22.9
29.2
14.6
33.3
100.0
Total
Count
465
267
155
518
1405
Percentage
33.1
19.0
11.0
36.9
100.0
A comparative analysis of the poverty entry and exit columns shows
that the majority of the agro-
ecological zones registered more entries into poverty than exits
from poverty. This may explain why
the incidence of poverty increased between 1997 and 2000.
Western Transitional Zone has the largest proportion of households
(35%) exiting poverty. 58
households in this zone climbed over the poverty line between 1997
and 2000, while only 11
households in this zone descended into poverty in 2000 after having
been above the poverty line in
1997. The Western Highlands Zone also registered a decline in
transitory poverty, 23.7% exited
poverty while only 7.2% entered into poverty. But several zones
recorded an alarming increase in
poverty between 1997 and 2000, in particular Coastal Lowlands,
Eastern Lowlands, Western
Lowlands, and the High-Potential Maize Zone. Among the districts,
Kakamega, Nyeri, Bungoma
and Kisii have the largest proportion exiting poverty while Nakuru,
Uasin Gishu, Kisumu and
Makueni have the largest proportions entering poverty.
7.1 Changes in Poverty and Cultivated Area.
The area under cultivation by the different poverty categories
generally increased in 2006.
Table 18: Mean Area Cultivated by Change in Poverty
Mean Cultivated Area
Poverty Categories
1997
2006
Non-Poor
7.43
5.89
Exits
5.07
3.85
Entries
5.65
5.19
27
Chronic Poor
3.67
2.91
It would appear that exiting poverty is not directly related to the
acreage under cultivation. The
computations above indicates that those exiting poverty had a lower
mean cultivated area a fact that
seems to point towards productivity changes.
7.2 Crop Land Productivity by Changes in Poverty
Poverty dynamics is closely related to crop land productivity as
indicated in Table 19.
Table 19: Crop Land Productivity by Poverty Dynamics
Mean Crop Productivity (Nominal)
Poverty Categories
1997
2000
Non-Poor
105,422.50
142,941.80
Exits
29,255.53
104,225.30
Entries
69,687.28
37,637.90
Chronic Poor
20,314.79
29,525.78
The transitory poor who exited poverty attained a much higher crop
land productivity against their
counterparts who entered poverty in 2006.
The converse is also true in that those who exited poverty had a
lower crop land productivity in
relation to those who entered poverty in 1997.
The foregoing observation implies that productivity is a major
determinant in exiting or entering
poverty or remaining chronically poor.
8.0 DETERMINANTS OF RURAL CHRONIC POVERTY
To establish the factors that influence rural chronic poverty, a
Probit model is used for analysis. In this
case the dependent variable takes on a value of one if Chronic poor
and zero otherwise.
9.0 Probit Model Estimation Results
The estimation results as indicated in table 20 indicate that
initial assets, the number of female and
male household members aged between 17 and 39, the number of
household members aged over 40,
the total acreage cultivated, the distance to a tarmac road and the
highest education of male household
members are negatively related to chronic poverty. These variables
therefore reduce the probability of
28
being chronic poor in the rural households. The
change over from a female household head to a male
household head also appear to reduce the chances of chronic poverty.
In terms of resource endowments, initial assets, total acreage
cultivated and highest level of education
of male household members are found to significantly influence a
household's poverty category by
reducing the probability of chronic poverty. These factors should
provide avenues for intervention
through anti-poverty programmes.
Changes in these same variables also significantly influence Chronic
Poverty.
Table 20: Probit Model Estimation Results
Probit Estimates
Number of obs =
1338.00000
LR chi2(43) =
483.73000
Prob > chi2 =
0.00000
Log Likelihood = -676.452
Pseudo R2 =
0.26340
Pov
|
Coef.
Std. Err.
z
P>|z|
[95% Conf. Interval]
agehh97
|
0.42686
0.47163
0.91000
0.36500
-0.49752
1.35124
ageh97sq
|
-0.10034
0.14630
-0.69000
0.49300
-0.38709
0.18641
femhhd97
|
0.19280
0.12658
1.52000
0.12800
-0.05529
0.44089
asset97
|
-0.00001
0.00000
-7.04000
0.00000
-0.00001
-0.00001
F1739_97
|
-0.54836
0.24987
-2.19000
0.02800
-1.03809
-0.05862
m1739_97
|
-0.04170
0.12167
-0.34000
0.73200
-0.28017
0.19677
ov40_97
|
-0.34923
0.22223
-1.57000
0.11600
-0.78478
0.08633
un16_97
|
0.04823
0.11666
0.41000
0.67900
-0.18042
0.27688
feduc97
|
0.07597
0.07094
1.07000
0.28400
-0.06308
0.21502
meduc97
|
-0.07747
0.04388
-1.77000
0.07700
-0.16348
0.00853
tacr97
|
-0.08768
0.01727
-5.08000
0.00000
-0.12154
-0.05383
Deathml
|
0.25500
0.32655
0.78000
0.43500
-0.38503
0.89503
Deathfl
|
0.25038
0.42181
0.59000
0.55300
-0.57636
1.07711
Dtmroad
|
-0.00874
0.00686
-1.27000
0.20300
-0.02219
0.00472
Kilifi
|
-0.03156
0.27840
-0.11000
0.91000
-0.57722
0.51410
Kwale
|
-1.03206
0.43241
-2.39000
0.01700
-1.87957
-0.18455
29
Taita
|
0.61239
0.56575
1.08000
0.27900
-0.49646
1.72123
Kitui
|
2.09886
0.51706
4.06000
0.00000
1.08543
3.11228
Mach
|
1.06442
0.36591
2.91000
0.00400
0.34724
1.78159
Mak
|
-0.34878
0.27028
-1.29000
0.19700
-0.87852
0.18097
Meru
|
-1.86400
0.37458
-4.98000
0.00000
-2.59817
-1.12983
Mwing
|
0.99015
0.33461
2.96000
0.00300
0.33432
1.64598
Kisii
|
0.51682
0.25930
1.99000
0.04600
0.00860
1.02504
Kisum
|
0.45327
0.25411
1.78000
0.07400
-0.04477
0.95131
Siaya
|
0.61336
0.26201
2.34000
0.01900
0.09984
1.12688
Bungoma
|
-0.06349
0.25058
-0.25000
0.80000
-0.55462
0.42763
Kkmega
|
0.13518
0.22829
0.59000
0.55400
-0.31227
0.58262
Vihiga
|
0.18830
0.27754
0.68000
0.49700
-0.35566
0.73227
Muranga
|
-0.32138
0.25866
-1.24000
0.21400
-0.82834
0.18558
Nyeri
|
-0.96906
0.25848
-3.75000
0.00000
-1.47568
-0.46244
Bomet
|
-0.03292
0.30685
-0.11000
0.91500
-0.63432
0.56849
Nakuru
|
0.19100
0.24756
0.77000
0.44000
-0.29421
0.67621
Narok
|
1.20922
0.44936
2.69000
0.00700
0.32850
2.08995
Tnzoia
|
-0.02366
0.27108
-0.09000
0.93000
-0.55497
0.50765
Ugishu
|
0.01672
0.25034
0.07000
0.94700
-0.47394
0.50737
ast0097
|
0.00000
0.00000
-3.29000
0.00100
0.00000
0.00000
f1739097
|
0.12871
0.04860
2.65000
0.00800
0.03346
0.22397
m1739097
|
0.07932
0.04621
1.72000
0.08600
-0.01125
0.16990
ov400097
|
-0.09980
0.08905
-1.12000
0.26200
-0.27434
0.07473
un160097
|
-0.05199
0.05049
-1.03000
0.30300
-0.15095
0.04696
tacr0097
|
-0.03493
0.00971
-3.60000
0.00000
-0.05396
-0.01590
fem_2_ml
|
-0.22612
0.15328
-1.48000
0.14000
-0.52654
0.07429
ml_2_fem
|
0.10107
0.26758
0.38000
0.70600
-0.42337
0.62551
_cons
|
0.21597
0.42250
0.51000
0.60900
-0.61212
1.04406
Note: 12 failures and 0 success completely determined
30
10.0 CONCLUSIONS AND IMPLICATIONS FOR POLICY
The study offers the following lessons from a Policy perspective:
Poverty reduction cannot be attained in the absence of a strong and
sustained pro-poor economic growth.
The country's poor and non-poor are closely associated with
agriculture and the greatest gains on
poverty reduction can be achieved through stimulating an efficient
agricultural sector. This is further
reinforced by the observation that the periods of highest economic
growth in Kenya coincided with the
periods when agriculture was most vibrant.
The country has a large population under chronic poverty as opposed
to other regions of the world. This
observation in itself implies that for anti-poverty programmes to
achieve the intended, they have to be
designed and implemented in a manner that takes into account the
large presence of the chronic poor.
Anti-poverty programmes that favour the chronic poor require
programmes that address mean income
growth as opposed to transitory poverty that requires programmes
that smooth mean incomes over time.
Generic anti-poverty programmes are likely to benefit the transitory
poor more than the chronic poor.
However, a blend of anti-poverty programmes that provide for both
chronic and transitory poverty is
imperative.
1. The poor are generally distributed all over the country to the
extent that even areas thought to be
exclusively non-poor still show elements of chronic poverty. It
would therefore be prudent to
recognize that poverty in Kenya is an intra-village phenomenon
rather than an inter-village issue.
This implies that poverty traps take on a rather different dimension
from the conventional which
seem to associate poverty to spatial location.
2. The design and implementation of anti-poverty programmes'
Monitoring and Evaluation tools can
substantially benefit from the categorization and characterization
of poverty levels and the
corresponding analytical tools. The process of examining poverty
dynamics can enrich the PRSP's
M&E initiative in the short term and the Poverty Eradication Plan in
the Long term especially through
the development of sustained and consistent data bases that can
elicit the desired information. This
also calls for the strengthening of Poverty dynamics analytical
capacity in the various Government
organs vested with the responsibility of monitoring poverty levels
and evaluating anti-poverty
programmes.
3. Effective anti-poverty programmes have to account for the
following which have significant effects on
chronic poverty and transitory (exit or entry) into poverty:
i)
Anti-poverty measures directed towards improving Agricultural
productivity are likely
to reduce chronic poverty and influence movement out of poverty
ii)
Education, in as far as it influences agricultural productivity
plays a significant role in
poverty reduction. It should, however be noted that there exists a
turning point in the
effect of education on agricultural productivity and consequently
poverty reduction. It
appears that completion of secondary school education has the
closest association with
maximum poverty reduction and exit from poverty.
31
iii)
The agricultural credit system requires restructuring to be
accessible to the
poor. Credit has also been shown to be closely associated with high
agricultural productivity and movement out of poverty.
32
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