Tenure Security, Sustainable Land Management and Poverty: Case Studies from Kenya Jane Kabubo-Mariara, Ph.D. School of Economics, University of Nairobi, Kenya Email:
[email protected]
Vincent Linderhof, Ph.D. Agricultural Economics Research Institute (LEI), the Hague
1. Introduction Agriculture continues to be the leading sector in the Kenyan economy in terms of contribution to GDP, employment generation, foreign exchange earnings and ensuring food security. The sector also provides important linkages with other sectors of the economy, including provision of raw materials to the industrial sector, purchase of inputs from the industrial sectors and exchange with the services sector (mostly banking, and insurance). The contribution of the agricultural sector to GDP has however recorded a downward trend for the last 3 decades. Rapidly expanding population, rapid urbanization and the shortage of high potential arable land cause occasional imbalances between the national demand for food and its supply. In addition, macro-economic policies biased against agriculture have not only failed to alleviate poverty, but also led to a deterioration of the natural resource base on which rural livelihoods depend. Kenya also has climate and ecological extremes, which are largely unfavourable for agricultural production. The mean annual rainfall ranges from less than 250mm in the arid and semi-arid areas to 2500mm in high potential areas. Kenya has a total area of 580,367 square kilometers, of which only 12% is considered high potential for farming or intensive livestock production. A further 5.5%, which is classified as medium potential, mainly supports livestock, especially sheep and goats. The other 82% of the total land in the country is classified as arid and semi-arid and is largely used for extensive livestock production, as well as being the habitat for wildlife both in and outside national parks and game reserves. Because of differences in soil, climate and hydrological factors, agricultural productivity and incomes are highest in the high and medium potential zones and lowest in the arid and semi-arid areas (Kabubo-Mariara, 2007). As in other Sub-Saharan African countries, soil erosion and land degradation have become major environmental concerns and present a formidable threat to food security and sustainability of agricultural production. For this reason, livelihoods in many resource poor farming and pastoral systems have been sustained by land management practices which have tended to perpetuate poverty, soil erosion and other forms of land degradation, thereby jeopardizing hopes of sustainable development. Available literature however concurs that sustainable development depends on the dimensions of ecological sustainability, economic feasibility and social acceptance (see Kabubo-Mariara et al., 2006). In any less-favoured rural area, at least one of the dimensions dominates. In practice, these dimensions are usually reflected in four development domain dimensions: agro-ecological potential, population density, market access, and institutional setting. The development dimensions have important implications for the poverty-
environment nexus and also on adoption and levels of land management practices. Less-favoured areas are typically characterized by a combination of low agricultural potential or poor market access, and often exist in an institutional setting that is not conducive to alternative viable development pathways. Adoption of land management practices can therefore not be seen in isolation from development domain dimensions that frame the livelihood strategies of households in a specific area. To address the above concerns, this chapter explores the impact of tenure security and poverty on adoption of land management practices in Kenya. The chapter tries to answer the following research questions: Are the poor able to adopt sustainable land management practices? How does tenure security affect adoption of land management practices? What is the impact of household wealth on adoption of land management practices? What other factors affect adoption of land management practices? To answer these research questions, evidence from two case studies from Kenya is presented. The first case study covered three districts, namely Narok, Murang’a and Maragua. The second case study covered Kajiado district. The survey of the first case study included more information on crucial determinants of the adoption of SWC investments, such as economic, ecological and institutional indicators. The rest of the chapter is organized as follows: section two presents the methodology. Section three describes the first case study. Section four presents the results for the second case study, while section five discusses the results and concludes.
2. Methodology 2.1 Conceptual framework and hypotheses The conceptual framework for the this chapter draws from the sustainable land management framework which draws on the theories of induced technical and institutional innovation in agriculture that explain changing management systems in terms of changing microeconomic incentives facing farmers as a result of changing relative factor endowments (Pender, Ehui and Place, 2006, Boserup 1965, Kabubo-Mariara, 2005). According to Boserup, as population grows, land and other natural resources become scarce relative to labour and access to markets improve. As a result, agricultural intensification occurs, relative prices change and food prices increase as demand for food rises. This process induces institutional innovations such as private property rights, which then facilitate adoption of better technologies that help to stave off the operation of diminishing returns in natural resource use1. The same premise is held by the evolutionary land rights theory –ELRT (Plateau 1996, 2000)2. The framework also also draws from the literature on land tenure security and investment incentives (Kabubo-Mariara et al., 2006, KabuboMariara, 2007, Pender et al., 2006). Following the literature, we hypothesize that adoption of land management practices may be influenced by a host of factors including the biophysical 1
Although Boserup’s hypothesis has been found to be consistent with certain empirical evidence from developing countries (Tiffen et al. 1994 among other studies), we note that it has also been contradicted by some studies. Instead the latter studies show that population growth is associated with increased environmental degradation and declining productivity (Place and Otsuka 2000; Reardon and Vosti 1995). 2
The evolutionary land rights theory contends that as land scarcity increases, people demand more land tenure security. As a result, private property rights in land tend to emerge and once established, to evolve towards greater measures of individualization and formalization.
factors determining agricultural potential, population density, and access to markets and infrastructure, which determine the comparative advantage of a location by affecting the costs and risks of producing different commodities, the costs and constraints to marketing, local commodity and factor prices. Another important factor influencing land management decisions is access to programs and services, such as government or nongovernmental organization (NGO) technical assistance and micro–finance institutions, through increasing access to technologies and information, and therefore expanding households’ available production and marketing possibilities. Other factors include households’ endowments of physical assets (e.g., livestock and equipment), “human capital” (assets embodied in people’s knowledge and abilities, such as education, experience, and training), “social capital” (assets embodied in social relationships, such as through participation in organizations or informal networks), “financial capital” (access to liquid assets, including credit and savings), and natural capital (quantity and quality of land). Four groups of factors are hypothesized to be the key drivers of adoption of land management practices. These include: market access, population density, development domain access to programs and services and property right institutions. Market access is hypothesized to have a direct effect on adoption of land management practices, though the impact may be ambiguous. Better access to markets and roads is expected to increase the use of purchased inputs and the capital intensity of agriculture by increasing the profitability and availability of such inputs and increasing farmers’ access to credit. Market-driven intensification may contribute to land degradation by leading to reduced fallowing, which will contribute to declining soil fertility and increased erosion unless sufficiently offset by adoption of more intensive soil fertility management and soil management practices. Market-driven intensification may however also lead to reduced erosion and improved soil fertility management as a result of the increased incentive to invest in land improvements, given the rising value of land relative to labor (Tiffen, Mortimore, and Gichuki 1994, see also Pender et al., 2006, Kabubo-Mariara et al., 2006). Population pressure may cause households to expand agricultural production into areas less suited to agriculture, contributing to natural resource degradation. But population pressure may also cause households to intensify their use of labor and other inputs on the land and may also induce innovations in technology, markets, and institutions or investments in infrastructure, thus possibly mitigating or outweighing such negative Malthusian effects (Pender et al., 2006, Kabubo-Mariara 2007, 2006, Boserup, 1965, Tiffen, Mortimore, and Gichuki 1994). The interaction of agricultural potential, market access, and population pressure define the development domains of a locality. The interactions may contribute to self-reinforcing patterns of development, while some of the relationships may cause offsetting tendencies leading to land degradation. Consequently, the potentials for different land management practices, and the effects of policies influencing these decisions are likely to vary across such development domains (Pender, Place, and Ehui 1999). Access to Programs and Services and other institutional endowment affects land management practices adopted by farmers by affecting their access to information about technologies, their capacities to effectively use technologies or to organize collective action, and their financial or other constraints (Bromley, 2006; Pender et al., 2006). Three types of programs and organizations may be expected to have significant influence on land management: technical
assistance programs and organizations, such as agricultural and natural resource management extension groups; credit programs, which may enable farmers to purchase inputs, hire labour or acquire physical capital, thus contributing to technology adoption, increased capital, input and labour intensity in agriculture; human capital services, such as education. Property right institutions can have substantial effects on land management by regulating land use and land management decisions, by facilitating or inhibiting collective action, and by affecting households’ incentive and ability to invest in land management practices. The set of rights associated with the different tenure systems are more important for land management than security of tenure. For instance, freehold lands have complete rights to use, lease, sell, bequeath, and mortgage, but owners or occupants of lands under other tenure systems may have more restricted rights. Even in the presence of private land rights, customary land tenure institutions determine what land use rights and land management obligations farmers have, how secure those rights are, whether those rights may be transferred or used as collateral and how conflicts are resolved, and other questions. Acquisition of land may also influence farmers’ tenure security and incentives to invest in land management (Pender et al., 2006; Besley 1995; Boserup, 1965, Besley 1995; Brasselle, Gaspart and Platteau 2002, Jacoby, Li and Lozelle 2002).
2.2 Empirical Specification From the conceptual framework and the literature, the decision concerning adoption of land management practices (LMP3) is specified as: LMP = f ( H c , Z , Nc, T , Sc, Vc, ε ) Where Hc is a vector of household human capital and wealth (education, family size and composition, age, and gender of household head); Z is a vector of household’s endowments of physical capital and wealth (land, livestock and other assets including previous soil management structures still on plot), Nc is a vector of plot characteristics (soil quality, topography, which capture agricultural potential); T is a vector of tenure characteristics of the plot (mode of acquisition, use rights, bequest rights e.t.c.); Sc is social capital (participation in village level institutions); Vc is a vector of village level characteristics (presence of social institutions, market access, population density e.t.c.), ε is a random error term The institutional factors such as the presence of special interest groups and extension agencies, and the choice of soil and water conservation (SWC) investments are (partly) endogenous, and they have to be explained themselves. In particular, the presence of interest groups and the willingness to listen to extension agents affect the willingness to invest. In order to disentangle partly endogenous effects we use a step-wise estimation approach (see Kabubo-Mariara et al., 2006 for more details). To capture the impact of the membership of the household in special interest groups/village institutions, we specify probit models for three different interest groups: membership in income generating groups, loans groups and benevolent groups. In addition, we are interested in analyzing the impact of extension services on adoption of land management 3
In this chapter land management practices (LMP) are used interchangeably with soil and water conservation (SWC) investments.
practices. We specify probit models for the willingness to listen to extension services in general and the willingness to listen to extension in natural resource management. We then specify the willingness to invest in natural resource management at household level. The willingness to invest is defined as investments made up to five years ago and depends on the willingness to listen to natural resource management (NRM) extension, and the willingness to listen to extension in general as well as the awareness of the presence and membership of NRM and other special interest groups. However, since these variables are endogenous, we apply a nested methodology for capturing these variables. We then use the residual terms of each of the probit equations as explanatory variables in the willingness to invest equation. The reason for using these residual terms for estimating the endogenous variables is that the terms are orthogonal to other independent variables in the equation at hand4. The truly independent variables still capture both the direct and indirect effects, while the residual terms capture the effect of the endogenous variable (Kabubo-Mariara et al., 2006). Due to our approach, the residuals are a set of identical equations, explained by the same set of factors. In principal, membership in special interest groups and willingness to listen to extension services (including extension in NRM) are related and therefore we correct for correlation of variances. We do this by applying factor analysis on the residuals of each of the probit results for each equation to find a common variance factor(s).These factors are then included in the final estimation model of the household’s willingness to invest in SWC (Kabubo-Mariara et al., 2006). Factors derived from the factor analysis are then used in the final estimating model of the willingness to invest. We however acknowledge that the approach used does not fully resolve the endogeneity issue. Lack of proper instruments hinders us from using the instrumental variable method to correct for any possible endogenity bias. Use of predicted values and residuals provides a quick alternative in the face of instrumentation constraints (see for instance Kabubo-Mariara et al., 2006, Pender et al., 2006). The relationships between village level institutions and adoption of land management practices discussed above is illustrated in figure 1.
4
Note that the predicted residuals are not necessarily independent from the error term of the equation in which the endogenous variable appears as explanatory variable. If they are dependent this estimation approach will yield biased estimators, although the bias will be small.
Figure 1: Link between village institutions and investment in SWC
Households/villages with special interest groups
Households’ willingness to listen to
Awareness
Extension agents in general
Membership
Extension agents on NRM
Willingness to invest in NRM
Willingness to invest in NRM (plot level)
Actual investments (plot level)
Intensity of SWC (plot level)
Type of investments (plot level)
3.
Case study One – Narok, Murang’a and Maragua districts
3.1 Study site and sampling This case study in this chapter is based on data collected from a self-weighting probability sample of 457 households in November and December 2004. The National Sample Survey and Evaluation Programme (NASSEP5) IV of the Central Bureau of Statistics, Ministry of Planning and National Development was used as the sampling frame for the field survey. The sample survey utilized a multi-stage sample design. A mixture of purposive, stratified and random sampling methods were employed to arrive at the final sample. The first stage in the sampling procedure involved selecting study districts, based on differences in poverty, population density, terrain and tenure security issues. The second stage involved selecting administrative divisions within each of the three districts, based on agro-ecological diversity. The third stage involved selection of locations and sub-locations, which were also based on agro-ecological diversity. The fourth stage involved selection of sample points (clusters) from the NASSEP frame, which was based on the total number of clusters within a sub location and the number of households in each cluster. In the final stage, the desired number of households were selected from each cluster. To arrive at the total number of households actually visited, we took a self weighting probability sample from each cluster in a district making a total of 457 households from the three districts (151, 188 and 151 from Murang’a, Maragua and Narok districts respectively). In addition to the household survey, a community questionnaire was administered to selected key informants in each cluster (village). The village survey collected information on product and input prices, market access and village infrastructure and was meant to supplement information collected from households. Full details on sampling, data and all descriptive statistics are presented in Kabubo-Mariara et al., (2006). 3.2 Sample statistics In this subsection, we only briefly explore the sample statistics of the key variables of interest in order to save on space. Of special interest are tenure security factors, soil quality and topography (indicators of agricultural potential), market access and institutional factor. Factor Analysis (FA) was used to derive key variables for econometric analysis. In the case of tenure security we applied a factor analysis at plot level to obtain the key elements of tenure security, while for soil quality, we used an FA to derive key elements of soil quality based on qualitative responses on farmer’s perception of the quality of the soil of their plots. In this way, we derive a number of key aspects of soil quality. For market access and institutional factors, we used community and household level data to derive final indicators (see Kabubo-Mariara et al., 2006 for full details of the factor analysis). The factors derived from factor analysis are orthogonal which is a very convenient property when including the factors in econometric analysis. Tenure security To capture all aspects of tenure security, data was collected on the mode of acquisition and expected land rights on all plots owned, used or rented/lent out by the household. The mode of 5
The NASSEP frame has a two-stage stratified cluster design for the whole country. First, enumeration areas are selected using the national census records, with the probability proportional to size of expected clusters. The number of expected clusters is obtained by dividing each primary sampling unit into 100 households. The clusters are then selected randomly and all the households enumerated.
acquisition probed on how, when and for how long the plot has been in the household. The expected land rights probed on the perception on land rights in terms of tenure security (whether land is shared, whether it can be taken away from household and by whom etc). In addition, we probed nature of rental arrangements and land rights on rented out and lent out land. A summary of the tenure security variables from the survey is presented in Table 1. The data shows that households owned (and often used) 71% of the plots, rented in 22% of the plots, and rented out 7% of the plots. More than half of the plots are inherited, and the duration of ownership is more than 18 years on average. In addition, 5% of the plots are owned for more than 50 years. In 46% of the cases the plots are registered to the household management team (head or spouse) while 31% of the plots are registered to relatives (like father or mother of head of the household). ****** Table 1 here****** In the literature on land tenure systems in developing countries, there is consensus on the fact that they have many different aspects, such as ownership, right to use, right to access, right to sell, rental arrangement and so forth. Though the choice of a specific indicator is more or less arbitrary, the inclusion of all aspects of tenure security is cumbersome. Differences in the definitions of land rights and in the methodological approaches employed are a major reason for explaining the differences in findings of the impact of tenure security in the literature. In terms of definition, most studies focus on security of tenure rather than transferability. Other studies use binary dummies to capture security in terms of having a land title (see for instance KabuboMariara, 2006; Pinckney and Kimuyu, 1994; Migot-Adholla et al., 1994; Shiferaw and Holden, 1999; Place and Otsuka, 2002), while Gebremedhin and Swinton (2003) take a continuum of expected rights. Other studies focus on the mode of land acquisition, such as purchase, borrow, or gift (Brasselle et al. 2002; Otsuka et al., 2003 and Gavian and Fafchamps, 1996). To overcome this arbitrariness in choice of indicators of tenure security, we use factor analysis to derive measures of tenure arrangements from the existing information on various aspects of security, such as the mode of acquisition and expected land rights from the plot level data. In the case of ownership duration, the extreme values might influence the outcomes of the FA significantly. Therefore, we truncate the ownership duration at 50 years, and we add a dummy variable indicating whether the ownership duration exceeds 50 years or not. The factor analysis yields five factors. These five factors explain almost 80% of the variance in the tenure security data. The rotated factor loadings led to the selection of the five factors. The first factor is referred to as farmland ranging from full ownership to indefinite rental arrangements. Farmland is the land owned or rented that is used for agricultural purposes. These plots are registered to what we refer to as the household management unit (either head or spouse). The second factor reflects plots owned (i.e. own use or rented out) by the family, or family land. The third factor presents plots (land for sale) for which other relatives have to give permission for selling or bequeath, and the fourth factor covers the possibly specific aspects of plots rented out (land rented out). The final factor reflects rental conditions of plots that are either rented or lent with or without permission (rental land). Soil quality and topography The collected data on soil quality on all plots included soil type, workability, soil texture, depth of soil, as well as the perceptions regarding soil fertility. Table 2 shows the summary statistics on
soil characteristics of plots. In the survey, 42% of the plots have red soils, while 23% have black soils and 25% have a mixture of red and black soils. Two-third of the plots were slightly to steeply sloped, while 22% of the plots were flat. The workability in 60% of the plots was easy, and the soil texture was fine in almost half of the plots. Only 14% of the plots had coarse soil textures. The average soil depth of plots was estimated at 22.5 cm. For 12% of the plots, household judged the fertility as poor or very poor, 37% of the plots had the fertility judged as average, while for a similar share of plots, the fertility was judged as good. In 11% of the cases, the soil of plot was classified as very fertile. ****** Table 2 here****** Eleven factors related to soil quality and topography were selected from the factor analysis. The first two factors reflect the dimensions of texture (from fine to intermediate) and fertility (modest to average fertility). The third and fourth factors are dominated by one single variable. The fifth factor reflects the steepness of the slope of the plot (from flat to slightly sloped). The rest of the factors included very fertile soil, poor soil, coarse soil, red vs. black soil, undulated slope and moderate slope. Institutional factors Institutional variables capture both institutional presence and membership in institutions. To derive the final variables, the factor analysis was based on the number (count) of institutions in each community, 27 dummy variables for type of organization (village, men’s groups, women groups and other) combined with purpose (investments in livestock and agriculture, burial and illness, income generation, household investments, non-economic purposes, NRM management). The factor analysis yielded 8 of which five were found useful for empirical analysis. The factors reflected the presence of groups in general, presence of men's group, the presence of income generating groups, presence of village institution presence and the presence of safety nets groups and groups for investments in natural resource management. Market access Market access was based on information on distance, mode, travel time and expenses from the village to particular destinations like markets and roads amongst others collected using a community questionnaire. The factor analysis for market access was limited to four facilities, namely distance to local market, all-weather roads, public transport (matatu) and main town (market). The factor analysis yielded only one factor reflecting the travel expenses per kilometre as the best measure of market access. Land management practices The three most frequently observed land management investments in the survey were tree planting (28%), terracing with grass strips (26%), and grass strips (23%). Most investments (namely 72%) are permanent investments (Table 3). Of all investments 317 (31%) investments had been made last year. One-third of the investments were made more than five years ago. In 47 cases (5%), the investments had been abandoned. The average period of abandonment was more than 3 years. There were 254 plots with terracing with grass strips (37%). One-third of all plots (229 plots) had previous land management investments.
****** Table 3 here****** 3.3 Determinants of adoption of land management practices: Econometric results The empirical analysis concentrates on adoption of terraces, though we also present results for the probability of adopting any land management practice. Analysis of other forms of land improvements is presented in Kabubo-Mariara et al. (2006). The results (Table 4) suggest that most household characteristics do not matter much in influencing the adoption decision. While this result is not uncommon in the literature (see for instance, Gebremedhin and Swinton, 2003; Kabubo-Mariara et al., 2006, Kabubo-Mariara 2007), it could also be due to the fact that our models capture both direct and indirect effects of the explanatory factors. One of the exceptions is the number of children aged between 6 and 16 years, which is positively correlated with adoption of all types of terraces. This suggests that children provide additional labour inputs for adoption of land management technologies in labour constrained households. The number of very young children (under 5 years of age) is negatively correlated with adoption of tearrcing. Given that terracing is labour intensive, the result implies that presence of young children diverts labour from conservation activities to childcare. Market access and population density are positively correlated with adoption of soil terraces and terracing in general. This suggests the importance of development domain dimensions in adoption of land management investments. The two district dummies (Murang’a and Narok) suggest that location is an important determinant of the decision to adopt land management technologies. Specifically, there is a higher probability of adoption if a household is located in Murang’a relative to Maragua district, more so for all permanent investments. The likelihood of adoption is lower in Narok district but the coefficients are insignificant. Given the diversity of agro-ecology in the three districts, we conclude that the impact of regional dummies reflect the unobserved relative importance of different development domains (Kabubo-Mariara et al., 2006)
****** Table 4 here****** The impact of tenure security factors is captured by the five variables discussed in the previous section: owned plots (which are often inherited); family land that can be sold or bequeathed with or without permission; land registered in family name; rented out and the right to rent out land without permission. In this case, the first three rights represent the strongest rights to land. The coefficients for the first three variables exhibit positive coefficients (though not all are significant) suggesting the importance of tenure security in adoption of land management investments. The negative and mostly significant coefficients of the last two variables show that weak security of tenure will discourage adoption of long term investments in land management technologies. We do not uncover an important impact of plot size on adoption. Distance to plot is however inversely and significantly correlated with adoption of all land management technologies, implying that technologies are more likely to be adopted on home plots rather than distant plots.
Agricultural potential is captured by soil quality and topography. Generally, soil quality seems to matter for soil terraces but not for grass stripped terraces. Soil texture, fertility and depth particularly encourage adoption of soil terraces and adoption of land management technologies in general. Course and very fertile soils encourage adoption of terracing, while there is less likelihood of investment in SWC on fertile soils compared to average fertile soils. Red vs. black soils are also inversely related with adoption of SWC investments. Moderate slopes and undulating terrain favour adoption of all forms of terracing and land management technologies in general. Conversely, steep slopes lower probability of adoption of terracing and all technologies in general. There is also less likelihood of adoption of land management technologies on flat land. Household assets are captured by lagged values of farm equipment and livestock wealth, plot size and existing land improvements (assets- permanent investments not made in the past year but still in function). Farm equipment and livestock wealth is positively correlated to adoption of land management technologies. Existing soil and water conservation assets on a plot are inversely related to the probability of making new investments. This implies that additional investments are more likely to be made on plots without any prior land improvements than plots with existing investments. The analysis does not uncover much impact of the presence of village institutions on adoption of SWC investments. The analysis included three error correction residuals in the determination of the adoption of land improvement technologies. We test the impact of listening to agricultural extension services in general, membership in village institutions and the willingness to invest in land improvements. This result suggests that there is no need to put a lot of effort into extension and helping local organizations for SWC because they have very little impact. The residuals for listening to extension services and of the willingness to invest are positively correlated with adoption of land management technologies. The residual of membership in village institutions is negative suggesting that such institutions may not necessarily boost adoption of land management technologies. 4. Case study Two 4.1 Study area and sampling The second case study is based on data collected from Kajiado district located on the southern part of the Rift Valley province of Kenya. The district has a bimodal rainfall pattern with the short rains falling between October and December and the long rains between March and May, but the rainfall is quite unreliable and influenced by altitude. The soils are of low to moderate fertility and make the ecosystem fragile and easily degradable. The district spans three agroecological zones (based on differences in soil quality, rainfall variability, altitude and vegetation): a semi-humid climate that supports mixed agriculture, an arable semi-humid/semiarid climate and an arid climate, favourable mainly for ranching and pastoral activities. This study focuses on crop farming households in the district. Data was also collected using the NASSEP frame, but in 3 different phases between 1999 and 2000. The analysis was based on pooled data from the 3 phases, making a total of 1600 observations (see Kabubo-Mariara, 2006 for full details of the data set).
Though covering more observations, the data for this case study were not as rich as for the first case study in terms of information on economic, ecologic and institutional indicators. However, the case study presents good support of the literature that tenure security matters for adoption of land management practices, though some results suggest ambiguous impact of land tenure security on land improvements (see for instance Brasselle et al. 2002). The case study also presents evidence that poverty may hinder adoption of land improvements, depending on the nature of improvements in question. The case study explores the relationship between land property rights and adoption of land management practices on the one hand and the relationship between poverty and adoption of land management practices on the other. Both descriptive and econometric results are presented. In the fist subsection, we use descriptive analysis to explore two questions: (i) Do land property rights affect adoption of land management practices? (ii) Are the poor able to adopt sustainable land management practices? Due to limitation of data used for this case study, we are unable to explore the full details of tenure security as in the previous case study. Instead, the analysis is based on five dummy variables representing households’ rights to: sell land, bequeath land, full ownership right, land belonging to a group ranch (scheme) and tenancy rights. The first three dummies can be seen as indicators of longer term tenure security, while the last two rights are measures of short-term tenure security. Analysis is also carried out based on a binary dummy variable for formal land rights (private versus common property rights). 4.2 Sample statistics and results Land property rights and adoption of sustainable land management practices To test whether land property rights affect adoption of land management practices, sample mean tests for differences in adoption of land management practices by private and common property rights are carried out. The results (Table 5) show that except for soil bunds, farmers with more secure land rights are more likely to adopt land improvements than their counterparts with less secure rights. The differences are statistically significant at all conventional levels of significance. This result is also supported by an analysis of the statistical significance of means for adoption of practices under a continuum of rights (Table 6). Table 5 shows that farmers under group ranches (scheme) and tenancy rights are less likely to construct soil bunds, though the difference for tenancy is not statistically significant. These two categories of ownership are also associated with a lower likelihood of adoption of terracing. Scheme members are also less likely to plant drought resistant vegetation and trees and to invest in any land improvement. The results therefore confirm that tenure security is important for investment in land improvements. ****** Table 5 here****** ****** Table 6 here****** Land Management Practices and Poverty The question whether the poor are able to adopt sustainable land management practices is partially answered by an analysis of the differences in sample means of farmers adopting various land management practices by their poverty status6. Our analysis shows that except for terracing, there is no significant difference between the likelihood of the poor and non-poor adopting land 6
We define the poor as those households whose mean per capita expenditures fell below the official poverty line of Kshs 1239 based on the Welfare Monitoring Survey estimates (Republic of Kenya, 2003).
management practices (Table 7). This implies that the poverty status as proxied by per capita expenditure may not be an important determinant of adoption of land management practices. ****** Table 7 here****** The above result is further supported by analysis of adoption of land management practices by various measures of welfare, which suggests absence of any clear pattern of adoption of land management practices across welfare groups. First we divide our farmers into per capita expenditure quintiles and use these groups to investigate the adoption of land management practices. The results, presented in Table 8 suggest that middle income families (namely 2nd and 3rd quintiles) are more likely to adopt land management practices than the poorest 20% and the richest 40%. ****** Table 8 here****** To analyze the impact of assets on adoption of land management practices, we construct a simple asset index using the principal component analysis. Due to paucity of asset variables in our data, we only use land, total livestock units and years of education. First we note that the mean asset index is negative implying that the average household in the sample is depleting its assets. These results (Table 9) strongly support the conclusion from Table 8: except for soil bunds and terracing, adoption of land management practices decline with assets. Furthermore, the poorest 40% are more likely to plant trees and to adopt any practice than farmers with more assets. This could be explained by the fact that this index is constructed from number of total livestock units owned and acres of land owned. Farmers with more non-income wealth are less likely to invest in crop land improvements probably due to alternative income earning opportunities outside crop farming (say from herding). The results are supported by further analysis based on land and total livestock unit quintiles (see tables 10 and 11). ****** Table 9 here****** ****** Table 10 here****** ****** Table 11 here****** The likelihood ratio test results presented in the last row of tables 13-16 indicate statistical independence and significant differences in the probability of adoption of land management practices across wealth categories. Further, the above descriptive analysis suggests that though we do not uncover a clear relationship between the status of poverty and adoption of management methods, assets are indeed important for adoption of some practices, suggesting that adoption of some practices may require more resources than others. In the next subsection, we present a brief overview of the results of multivariate econometric analysis of the impact of land rights and poverty measures on adoption of land management practices.
4.3 Impact of poverty on adoption of land management practices: econometric results
The results in table 12 illustrate the impact of property rights and wealth (measured by household assets on adoption of land management practices. The coefficients for asset variables show mixed results. Rent income, transfers, investment in farm capital and amount of biomass available at the village level are associated with a higher probability of adoption of practically all practices. Number of adults in a household is inversely related to adoption of some land management practices. Although there are mixed results for some practices, overall, the regression results show that poverty in assets could hinder adoption of land management practices. The asset index has significant positive impacts on adoption of soil bunds and terracing, though a negative impact on planting drought resistant vegetation/trees and adoption of all practices combined. The impact of asset index on adoption of soil bunds and terracing suggest that poverty in assets will discourage adoption of soil bunds and terracing, which supports findings in earlier studies (Shiferaw and Holden; 1999, 2001; Li et al., 1998 and KabuboMariara, 2004). ****** Table 12 here****** The results for land right variables suggest that security of land rights are important for adoption of some land management practices. Except for terracing, results with continuum of rights show that the likelihood of adoption of land management practices is higher where land rights are more secure and not under common property resources. There is however some ambiguity of the impact, though in most cases the unexpected impact is insignificant. These results are consistent with regressions with a binary dummy for land rights (last row of table 12) and also support the descriptive results.
5. Discussion and conclusions This chapter investigates the impact of household wealth on the decision to adopt land management practices. We present evidence from two case studies in rural Kenya. Both case studies differed in period, information and methodology and, but from both studies we analyzed the impact of household wealth and tenure security among other factors on the adoption of land management practices. Obviously, the relationship between household wealth and tenure security on adoption of land management practices is not straightforward, and there might be indirect impacts through institutional arrangements and development domains. These aspects also affect household wealth directly, and thus the adoption of land management practices directly and indirectly. Therefore, the first case study is particularly relevant as it focused on the sum of direct and indirect effects of the relationship between household wealth and adoption of land management practices. Results from both studies emphasize the importance of tenure security in adoption of land management practices. The more secure farmers are about their land rights, the more likely it is that farmers will adopt land management practices. The decision to invest depends on other relevant aspects as well. If there are already soil and water conservation structures present on the land, the probability of adopting new land management practices declines. Moreover, the agroecological (or agricultural) potential of land (soil quality and topography) has a positive impact on the investment decision. So, if the agro-ecological conditions are poor, farmers have less
incentives to adopt land management practices. In this respect, the question remains who owns or uses the land with poor agro-ecological potential. Market access and population density both have a positive impact on adopting land management practices. So, if farmers have more opportunities to off-set their agricultural products at local or regional, they are more willing to invest in land management practices. The results further suggest that village institutions or extension services for SWC investments have no impact on adopt of land management practices. Obviously, the willingness to invest measured by investments done in the past (more than 5 years ago) have a positive impact on actual investment decisions. Finally, in terms of asset wealth, farmers are more likely to adopt land management practices when they are wealthier especially in the case of terracing. This result is observed in the case of farm equipment assets in the first case study and for incomes (short term wealth indicator) and assets indicator (structural wealth indicator) in the second case study. Livestock wealth in the first case study has a positive significant impact, although it is insignificant. Note however that this does not necessarily imply that livestock assets do not affect the adoption decision. Merely, the net effect (i.e. the direct and indirect effects via tenure security for instance) of livestock asset on the adoption decision is ignorable, although the absolute values of the direct and the indirect effect might be significant. For other types of land management practices, the positive impact of wealth indicators is less obvious, such as soil bunds or tree planting. Based on the analysis, it remains difficult to drive a clear pattern on the impact of wealth indicators on the adoption decision. More research is required to get insight on the direct and indirect effects separately. Though the results in this chapter suggest that there is a positive impact of household wealth on terracing, this is not necessarily the case for other types of land management practices. Moreover, the adoption decision is rather complex, and there are many relevant indicators (economic, institutional and ecological) that have to be taken into account. However, since all these indicators affect the adoption decision directly, they might also show indirect effects through livestock indicators. In fact, the consequence of this complexity of relationships is that the explanation of adoption decision cannot be analyzed separately from the explanation of household wealth. This is one of the major challenges for future research on the relationship between household wealth on land management practices.
References
Besley, T. (1995). Property Rights and Investment Incentives: Theory and Evidence from Ghana. Journal of Political Economics 103(5), 903-937. Boserup, E. (1965). The Conditions of Agricultural Growth: The Economics of Agrarian Change under Population Pressure. New York: Aldine Press. Brasselle, A.S., Gaspart, F. & Platteau, J-P. (2002). Land Tenure Security and Investment Incentives: Puzzling Evidence from Burkina Faso, Journal of Development Economics 67, 373418. Gavian S. & Fafchamps, M. (1996). Land Tenure and Allocative Efficiency in Niger. American Journal of Agricultural Economics 78, 460-471. Gebremedhin B. & Swinton, S.M. (2003). Investment in Soil Conservation in Northern Ethiopia: The Role of Land Tenure Security and Public Programs. Agricultural Economics 29, 69-84. Jacoby H.G., Li, G. & Rozelle, S. (2002). Hazards of Expropriation: Tenure Insecurity and Investment in Rural China. American Economic Review 92(5), 1420-1477. Kabubo-Mariara, J. (2007). Land Conservation and Tenure Security in Kenya: Hypothesis Revisited”. Ecological Economics, doi:10.1016/j.ecolecon.2007.06.007
Boserup’s
Kabubo-Mariara, J. (2006). Land Conservation in Kenya: The Role of Property Rights. African Economic Research Consortium, Research Paper Series. Research Paper 153, March 2006. Kabubo-Mariara, J. (2005). Herders Response to Acute Land Pressure under Changing Property Rights: Some Insights from Kenya. Environment and Development Economics 10(1), 67-85. Kabubo-Mariara, J. (2004). Poverty, Property Rights and Socio-economic incentives for Land Conservation: The case for Kenya. African Journal of Economic Policy 11(1), 35-68. Kabubo-Mariara, J. and F. Karanja (2007). The Economic Impact of Climate Change on Kenyan Crop Agriculture: A Ricardian Approach. Global and Planetary Change 57:319-330, 2007 Kabubo-Mariara J., Linderhof V., Kruseman G., Atieno R., and Mwabu G. (2006). Household Welfare, Investment in Soil and Water Conservation and Tenure Security: Evidence from Kenya. Poverty Reduction and Environmental Management (PREM): Working paper 06-06. Li G., Rozelle, S. & Brandt, L. (1998). Tenure, Land Rights, and Farmer Investment Incentives in China. Agricultural Economics 19, 63-71
Migot-Adholla, S.E., Place, F. & Oluoch-Kosura, W. (1994). Security of Tenure and Land Productivity in Kenya. In: Bruce, J.W. & Migot-Adholla, S.E. (eds.), Searching for Land Tenure Security in Africa. Dubuque, Iowa: Kendall/Hunt Publishing Cy. 119-40. Pender J., S. Ehui and F. Place (2006) . Strategies for Sustainable Land Management in the East African Highlands. International Food Policy Research Institute. Washington D.C. Pinckney C. & Kimuyu, P.K. (1994). Land Tenure Reform in East Africa: Good, Bad or Unimportant? Journal of African Economies 3(1), 1-28. Place F. & Otsuka, K. (2002). Land Tenure Systems and their Impacts on Productivity in Uganda. Journal of Development Studies 38(6), 105-128. Platteau J.P. (2000), Institutions, Social Norms, and Economic Development. Amsterdam: Harwood Academic Publishers. Platteau, J.P. (1996), The Evolutionary Theory of Land Rights As Applied to Sub-Saharan Africa: A Critical Assessment. Development and Change 27(1), 29-86. Reardon, T. & Vosti, S.A. (1995). Links Between Rural Poverty and the Environment in Developing Countries: Asset Categories and Investment Poverty. World Development 23(9), 1495-1506. Republic of Kenya (2003) Geographic Dimensions of Well-Being in Kenya: Where are the Poor. Vol 1. Central Bureau of Statistics. Ministry of Planning and National Development. Shiferaw B. & Holden, S. (2001). Farm-level Benefits to Investment for Mitigating land Degradation: Empirical Evidence from Ethiopia. Environment and Development Economics 6, 335-358. Shiferaw B. & Holden, S. (1999). Soil Erosion and Smallholders’ Conservation Decisions in the Highlands of Ethiopia. World Development 27(4), 739-752. Singh, I., Squire, L. & Strauss, J. (1986). Agricultural household models: extensions, applications, and policy. Baltimore: The Johns Hopkins University Press. Tiffen M., Mortimore, M. & Gichuki, F. (1994). More People, Less Erosion: Environmental Recovery in Kenya. Chichester: John Wiley and Sons.
Appendix Table 1:
Summary statistics of tenure security variables
Description Acreage Distance from homestead Ownership Own plot Rented plot Plot rented out Acquisition Purchased plot Gifted plot Inherited plot Ownership duration Ownership duration in years up to 50 years Ownership duration 50 years or more (dummy) Registration Plot registered to head or spouse Plot registered to another relative Permission Sell without permission Sell or bequeath with permission Bequeath without permission Rent or lent with(out) permission Permission of a relative Arrangement Rental arrangement Indefinite arrangement Payment per acre for land rented in or out
Mean 4.75 2.34
Std. dev. 11.52 21.24
Min. 0 0
Max 100 500
0.71 0.22 0.07
0.46 0.42 0.26
0 0 0
1 1 1
0.10 0.05 0.62
0.30 0.22 0.49
0 0 0
1 1 1
18.1 0.05
14.7 0.21
0.5 0
50 1
0.46 0.31
0.50 0.46
0 0
1 1
0.36 0.09 0.21 0.12 0.10
0.48 0.28 0.41 0.33 0.30
0 0 0 0 0
1 1 1 1 1
0.14 0.26 525.0
0.34 0.44 1221.4
0 0 0
1 1 10,000
Note that there is no type of acquisition available for plots rented in, because the respondents are not the owners of those plots.
Table 2:
Summary statistics on the soil characteristics (plot level)
Characteristics Soil type Red Mixed Black Rocky White Position Flat Weak undulation Slightly sloped Moderately sloped Steeply sloped Workability Easy Moderate Difficult Soil texture of the plot Coarse Intermediate Fine Perceived soil quality Very fertile Fertile Average Not fertile Very poor Soil depth in centimetres
Number of plots
Share of plots
287 174 157 51 15
0.42 0.25 0.23 0.07 0.02
150 78 256 101 99
0.22 0.11 0.37 0.15 0.14
409 186 89
0.60 0.27 0.13
98 257 327
0.14 0.38 0.48
76 266 255 66 16 Mean 22.5
0.11 0.39 0.37 0.10 0.02 St.dev. 28.1
Table 3:
Number of SWC investments per plot
Number of investments per plot No investment One investment Two investments Three investments Four investments Five investments Total
Number of plots Total Number
159 206 172 123 3 1 684
Share (%)
23 30 25 18 3 0.1 100
Period of implementation Last year
455 160 51 17 1 684
Last five years
495 132 43 13 1 684
More than five years ago
Already on land upon acquisition
481 114 51 35 3
620 39 16 9
684
684
Table 4: Determinants of adoption of land management practices Grass strip terraces Household characteristics Child less than 5 years old in a household Children 6 to 16 years old in a household Number of adults in a household Household head years of schooling Village Characteristics Number of institutions present Presence of men’s groups Presence of income generating groups Presence of village committees/groups Presence of safety net and NRM groups
Population density Market access Murang’a district dummy Narok district dummy Tenure security and related factors Land registered in house hold head or spouse Family land registered in extended family Right to sell family land with permission Rented out land Lent out land Soil quality & Topography Moderate vs. fine texture Very fertile soils Fertile to average fertile
Soil terraces
All Terraces
All investments
0.0272 [0.25] 0.0405 [0.65] -0.0927 [1.03] -0.0155 [0.64]
-0.2993 [1.11] 0.4373 [2.39]** 0.1331 [0.76] 0.0702 [1.11]
-0.2084 [2.02]** 0.0914 [1.61] 0.0431 [0.59] 0.0037 [0.17]
-0.0255 [0.36] 0.0857 [2.01]** 0.0652 [1.19] 0.0205 [1.28]
-0.2059 [0.28] -0.1157 [0.64] -0.177 [0.39] 0.0578 [0.27] 0.2855 [0.36]
-0.6928 [0.43] -0.0025 [0.01] -0.6883 [0.68] -0.8814 [1.93]* 0.4848 [0.41]
-2.1674 [0.65] -0.5177 [0.64] -1.4526 [0.68] -0.5841 [0.63] 0.1316 [0.11]
-0.138 [0.42] 0.0217 [0.24] -0.1065 [0.51] 0.0154 [0.15] -0.0067 [0.02]
0.0011 [1.12] -0.057 [0.16] 0.0675 [0.26] -3.1432 [1.21]
0.0067 [1.90]* 0.446 [0.92] 0.6821 [1.03] 1.3896 [0.24]
0.0021 [2.26]** 0.5216 [1.75]* 0.4007 [1.70]* -6.1944 [0.65]
0.0003 [0.41] -0.0213 [0.12] 0.4674 [2.46]** -1.3943 [1.17]
0.1591 [1.78]* 0.0281 [0.36] 0.0776 [0.90] -0.1653 [1.24] -0.2365 [2.06]**
0.5042 [1.88]* 0.0772 [0.37] 0.1313 [0.88] -0.3862 [1.05] 0.0108 [0.05]
0.3387 [4.05]*** 0.0701 [0.96] 0.2638 [3.95]*** -0.3376 [2.88]*** -0.1459 [1.58]
0.3431 [5.41]*** 0.0335 [0.60] 0.1807 [3.07]*** -0.3121 [3.92]*** -0.1849 [2.79]***
-0.0095 [0.10] 0.0684 [0.77] -0.2188
0.3784 [1.74]* -0.8843 [2.10]** 0.5233
0.0131 [0.15] -0.0972 [1.13] 0.11
-0.2758 [4.09]*** -0.1194 [1.84]* 0.0264
Coarse soils Red vs. black soils Poor soils Soil depth Steep slope Moderate slope Undulating terrain Flat slope Assets Plot area (farm size) Distance to plot Lagged value of livestock (log) Lagged value of farm equipment (log) Previous soil conservation structures Error correction terms (residuals) Listened to extension services Membership in village institutions Willingness to invest in SWC Constant Observations
[1.34] [2.43]** -0.0713 0.5735 [0.60] [2.82]*** -0.1527 -0.3655 [1.48] [1.53] -0.0659 0.6699 [0.75] [2.80]*** -0.0023 0.0494 [0.15] [1.43] 0.0568 0.276 [0.71] [1.14] 0.3105 0.5621 [3.58]*** [2.25]** -0.1203 0.3463 [1.19] [1.66]* -0.0078 -0.7073 [0.09] [1.78]*
[1.23] 0.0927 [1.04] -0.0912 [1.02] -0.0147 [0.19] 0.0209 [1.49] -0.0097 [0.12] 0.2981 [3.63]*** 0.1512 [2.13]** -0.222 [2.60]***
[0.39] 0.0249 [0.37] -0.1467 [2.03]** 0.0567 [0.96] 0.0301 [2.89]*** -0.0864 [1.41] 0.3009 [4.78]*** 0.1344 [2.31]** -0.1424 [2.28]**
0.0028 -0.0883 [0.14] [1.47] -0.0108 -0.0079 [1.47] [0.42] 0.107 0.0459 [1.10] [0.19] 0.0619 0.3269 [0.89] [1.70]* -0.6552 -0.8502 [4.87]*** [2.71]***
-0.0005 [0.05] -0.0255 [2.61]*** 0.0344 [0.40] 0.1075 [1.68]* -1.1342 [7.64]***
0.0094 [1.45] -0.0239 [2.81]*** 0.157 [2.43]** 0.0508 [1.04] -0.8348 [9.72]***
0.1585 0.5548 [1.14] [1.77]* -0.0891 -0.0786 [0.83] [0.32] 0.3149 0.6325 [1.90]* [1.48] -0.1179 -10.1602 [0.11] [2.71]*** 684 684
0.0989 [0.80] -0.1698 [1.70]* 0.5675 [3.73]*** -0.9295 [0.35] 684
0.0053 [0.05] -0.1509 [2.01]** 0.774 [6.78]*** -1.2862 [2.05]** 684
Absolute value of z statistics in brackets * significant at 10%; ** significant at 5%; *** significant at 1%
Table 5: Land Management Practices by Property Right Regime Practice Private Common ‘t’ Property** Property Statistic Construction of soil bunds 8.0 (0.27) 9.1 (0.29) 0.71
0.4783
Terracing
9.4 (0.29)
2.6 (0.16)
4.62
0.0000
Planting vegetation and trees
23 (0.42)
7.9 (27.1)
6.73
0.0000
All practices
40 (0.49)
20 (0.40)
7.74
0.0000
Pr>’t’
Source: Kabubo-Mariara (2006) **Standard deviations in parenthesis
Table 6: Land Management Practices by Land Security Measure Right \Practice
Sell Yes
No
Soil Bunds
0.01
Terracing
Bequeath Yes
Own
No
Yes
No
0.08*** 0.09
0.07
0.07
0.10
0.04*** 0.09
Vegetation & 0.21 Trees All practices 0.38
Scheme Yes
Tenant
No
Yes
No
0.11*** 0.08
0.08
0.08
0.08
0.04*** 0.01
0.04*** 0.02
0.09*** 0.08
0.04
0.15*** 0.22
0.12*** 0.21
0.14*** 0.09
0.21*** 0.23
0.18*
0.29*** 0.40
0.22*** 0.39
0.29*** 0.19
0.39
0.34
0.35
Source: Kabubo-Mariara (2006)
***;*: Significant at 1% and 10% level respectively
Table 7: Adoption of Land Management Practices by Poverty Status Poor (%)** Practice type Non- Poor (%) ‘t’ Statistic
Pr>’t’
Soil Bunds
10 (0.29)
7
(0.26)
1.57
0.0588
Terracing
3 (0.18)
11 (0.31)
5.73
0.0000
Planting vegetation & trees
20 (0.40)
18 (0.38)
1.18
0.2389
All practices
33 (0.47)
36 (0.49)
1.29
0.1966
Source: Kabubo-Mariara (2006) **Standard deviations in parenthesis
Table 8: Land Management by Per-capita Income Quintiles (%) Quintiles Soil Bunds Terracing Planting vegetation and trees 1st 9.63 (0.29) 2.80 (0.17) 15.84 (0.37)
All practices 28.26 (0.45)
2nd
10.00 (0.30)
2.81
(0.17)
24.06 (0.43)
36.88 (0.48)
3rd
9.69
(0.29)
12.50 (0.33)
18.44 (0.39)
40.63 (0.49)
4th
7.81
(0.27)
6.25
(0.24)
18.75 (0.30)
32.81 (0.47)
5th
4.38
(0.20)
13.75
(0.34)
16.25 (0.37)
34.38 (0.48)
(51.58) (0.00)
8.80 (0.07)
12.11 (0.02)
Lr Chi2(4)
10.45 (0.00)
Source: Kabubo-Mariara (2006) **Standard deviations in parenthesis Table 9: Land Management Practices by Asset Index Quintiles (%) Quintiles Soil Bunds Terracing Planting vegetation and trees 1st 12.65 (0.33) 2.78 (0.16) 32.41 (0.47) 2nd 15.09 (0.36) 2.52 (0.15) 27.67 (0.45) 3rd 5.63 (0.23) 6.56 (0.25) 15.0 (0.36) th 2.81 (0.17) 14.37 (0.35) 9.69 (0.30) 4 5th 5.31 (0.22) 11.88 (0.32) 8.44 (0.28) Lr Chi2(4) 47.18 (0.00) 54.07 (0.00) 99.50 (0.00)
All practices 47.84 45.28 27.19 26.88 25.62 68.26
(0.50) (0.49) (0.45) (0.44) (0.43) (0.00)
Source: Kabubo-Mariara (2006) **Standard deviations in parenthesis
Table 10: Land Management Practices by Land Ownership Quintiles (%) Quintiles Soil Bunds Terracing Planting vegetation All practices and trees 1st 18.46 (0.39) 2.46 (0.16) 20.92 (0.41) 41.85 (0.49) nd 2 7.48 (0.26) 2.80 (0.17) 36.14 (0.48) 46.42 (0.50) rd 3 8.18 (0.27) 6.60 (0.25) 13.52 (0.34) 28.30 (0.45) th 4 2.81 (0.17) 17.19 (0.38) 15.63 (0.36) 35.63 (0.48) th 5 4.40 (0.21) 9.12 (0.29) 6.92 (0.25) 20.44 (0.40) Lr Chi2(4) 58.21 (0.00) 63.06 (0.00) 99.41 (0.00) 62.98 (0.00) Source: Kabubo-Mariara (2006) **Standard deviations in parenthesis
Table 11: Land Management Practices by Total Livestock Owned Quintiles (%) Quintiles Soil Bunds Terracing Planting vegetation All practices and trees 10.7 (0.31) 3.06 (0.17) 29.66 (0.46) 43.43 (0.50) 1st nd 17.3 (0.38) 5.35 (0.23) 26.73 (0.44) 49.37 (0.50) 2 4.33 (0.20) 8.36 (0.28 16.72 (0.37) 29.41 (0.46) 3rd
4th 5th Lr Chi2(4)
5.41 (0.23) 3.75 (0.19)
9.55 (0.29) 11.88 (0.32)
10.83 (0.31) 9.06 (0.29)
25.80 24.69
(0.44) (0.43)
51.36 (0.00)
23.81 (0.00)
73.79 (0.00)
70.00 (0.00)
Source: Kabubo-Mariara (2006) **Standard deviations in parenthesis
Table 12: Impact of Household Assets and Land Rights on Adoption of Land Management Practices Soil Bunds
Terracing
Tree Planting
All practices
+
-***
-
-*
+***
+***
-**
+*
+
+
-***
+
+*
+***
+***
+***
+**
+***
-***
-
+** Has right to bequeath land Has land in Scheme +* -*** Has own plot + + Has tenancy right Has right to bequeath land -** +*** Property rights regime dummy (1=private, 0= common) ***;**;*: Significant at 1%, 5% and 10% level respectively
+*
+*
-
-
-
+
-
+
+***
+***
Variable Household Assets Log number of adults Log rent income Log transfer income received Log value of farm equipment Asset index Land Rights
