RURAL POVERTY, PROPERTY RIGHTS AND ENVIRONMENTAL RESOURCE MANAGEMENT IN KENYA JANE KABUBO-MARIARA

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1 RURAL POVERTY, PROPERTY RIGHTS AND ENVIRONMENTAL RESOURCE MANAGEMENT IN KENYA JANE KABUBO-MARIARA DEPARTMENT OF ECONOMICS UNIVERSITY OF NAIROBI P.O BOX NAIROBI Telephone Office Ext House FAX Jmariara@uonbi.ac.ke Jkmariara@yahoo.com Paper Prepared for the Beijer International Institute of Ecological Economics Research Workshop, Durban, South Africa, May

2 Abstract This study investigates the relationship between rural poverty, property rights, and environmental resource management in a semi-arid region of Kenya using survey data. We hypothesize that reduced environmental degradation will increase agricultural productivity, which translates into lower levels of poverty as incomes and consumption expenditures rise; and that the quality of the environment and thus productivity and poverty are unaffected by property right regimes. We use a combination of probit and robust regression methods to derive the parameter estimates of our models. The results imply that poverty is associated with higher levels of environmental degradation, wellspecified property rights are associated with higher productivity and lower poverty and that environmental conservation is an important mechanism for escaping poverty. The study recommends a need to speed up privatization of the remaining group ranches so that the community can enjoy the benefits of private property. If privatization is not feasible, the existing common rights systems should be strengthen in-order to boost productivity and reduce poverty. Also we recommend encouragement of environmental conservation and preservation of biomass in-order to improve productivity and reduce poverty. Lastly we recommend the need to address factors governing the link between poverty, environmental conservation and productivity, especially development of markets, infrastructure and reduced family size Key words: Poverty, property rights, environmental conservation 1 Introduction and Research Problem At independence, the Kenyan government identified poverty, ignorance and disease as the major constraints to socio-economic development. Although the commitment to growth and poverty reduction has remained unchanged over the years, the poverty situation in the country has worsened (Republic of Kenya, 2000). Previous studies indicate that poverty is most prevalent and severe in rural areas where the bulk of the population (85%) reside deriving its livelihood from agriculture and related activities. Household budget surveys suggest that the major cause for low incomes in rural areas has been stagnating agricultural production. This is consistent with Barbier s (1997) argument that in developing countries, poor rural households often live in marginal agricultural areas where land productivity and thus household income is stagnant or declining. Furthermore, the poorest groups in rural Kenya are concentrated on lowpotential lands (defined as resource- poor or marginal agricultural lands) where inadequate or unreliable rainfall, adverse soil conditions, fertility and topography limit agricultural productivity and increase the risk of chronic land degradation. Given this 1

3 scenario, resource management by poor rural households is a crucial issue in solving the development and poverty problems facing Kenya. Since the incidence of poverty and squalor is most severe in the rain-fed semi-arid regions, where the land is environmentally vulnerable/degraded, reversing the life of the poor demands for a regeneration of the environment in which they live, improved productivity of land and provision of drinking and irrigation water. Moreover, since the poor reside primarily in rural areas of developing countries and are dependent on the land for their livelihood, rural poverty and environmental degradation are closely related. Through their agricultural activities, poor people seek to husband the available soil, water and biotic resources so as to harvest a livelihood for themselves. Poor people in their struggle to survive are driven to doing environmental damage with long-term consequences. Their herds overgraze, their shortening fallows on steep slopes and fragile soils induce erosion, their need for off-season incomes drives them to cut trees and sell firewood, they are forced to cultivate and degrade their land. On the other hand, environmental degradation depresses the poor s ability to generate income requiring them to divert an increasing share of their labor to routine household tasks such as fuelwood collection; and also by decreasing the productivity of those natural resources from which the poor wrest their livelihood (Mink 1993). The links between poverty and the environment are conditioned by the interaction of economic, social, demographic and even climatic factors: These include the existence, structure and performance of markets (including institutions such as property rights); production and resource conservation technologies; relative input prices, output prices, wages and interest rates and community wealth. Other factors include physical assets, rural infrastructure and human capital (Reardon and Vosti 1995; World Bank 1997; Kabubo-Mariara 2000). For instance limited access to physical assets limits a farmer s ability to engage in improved land use practices such as terracing, planting of trees and resistant vegetation and blocking of soil erosion outlets, which could enhance the productivity of farmers. In other words, these factors act as a disincentive for 2

4 environmental conservation and often result in a fundamental process of cumulative causation of poverty, environmental degradation and underdevelopment (Barbier 1997). These arguments not withstanding, studies on poverty in Kenya have ignored the links between the environment and poverty. Furthermore, existing policy result in marginal groups (poor) being relegated to fragile economic environments due to inability to access productive land and lack of technology to improve the productivity of their land. This calls for the need to understand and empirically investigate these links as well as to assess the impact of the conditioning factors. Our study aims at filling this research gap. The study addresses the following questions: What factors condition the link between environment and poverty? What factors determine environmental conservation practices. What factors determine poverty? Are grazing pastures held in common more degraded than those held under other regimes? What property right regime would be most appropriate for semi-arid regions in-terms of conserving the environment, increasing productivity and reducing poverty? 2 The Setting Kajiado district is one of the arid and semi arid districts in Kenya and lies to the southern of the Rift Valley province. The district covers an area of 21,105 square kilometers. Plains and occasional hills characterize the general topography. The district has a bimodal rainfall pattern with the short rains falling between October and December and the long rains between March and May. The rainfall is however quite unreliable. In-terms of vegetation, 80% of the district can be classified as low savanna and arid/semi-arid (ranching zones). The soils are of low to moderate fertility and make the ecosystem fragile and easily degradable. In bio-diversity terms any increase in range stock and /or wildlife could lead to acute land degradation and rapid destruction of fauna and flora habitats. The land spans between agro-ecological zones III (semi-humid climate mixed agriculture, 1.2%), IV (arable semi-humid/semi-arid climate, 6.5%), V and VI (arid climate ranching, pastoral land, 92.3%). Economic activities are therefore largely dependent on livestock and wildlife. 3

5 The total population of the district was estimated at about 406,054 at the onset of the survey (1999), with 52% males and 48% females, but was expected to rise to about 500,000 by the year 2001, given a growth rate of 5.54%. Given the total land area in the district, it is apparent that if not for aridity, scarcity of land is not a problem in the district. There are three categories of land tenure in the district. First there is trustland on which most of the game reserves and national parks (Tsavo and Amboseli) are found. Most of the trust land is often used as open access by herders, often leading to humanwildlife conflicts. Two, group ranches (common property resources) are found in most parts of the districts, where the main economic activity is livestock keeping. Finally, private ownership is mainly in Loitoktok and Ngong divisions, where the main economic activity is small-scale farming and livestock production (Kabubo-Mariara 2002). Government Welfare surveys indicate that about 22% of all individuals in the district were poor in absolute terms in 1994, compared to 30% in 1997, a change in the incidence of poverty by 8% in three years. In the same period, the incidence of food poverty rose from 22.74% to 25.36%, while the depth and severity of poverty declined by 4% each. However, the incidence, depth and severity of hard core poverty increased by 7%, 2% and 0.18% respectively. Participatory poverty assessments also show that over this period, only 3% were ranked as rich, compared to 62% ranked as poor and 36% as very poor. Furthermore, only 5% indicated that their status of poverty had improved between the three years, 8% felt that their status had not changed while the rest 87% felt that their status had worsened. The assessments also indicated that famine/drought was the single most important problem in the district (44%), followed by scarcity of water (39%). On the other hand, responses show that agricultural services were either not available (90%) or not affordable (93%), (Republic of Kenya, 2000). Though the Government survey data implies that Kajiado district is not one of the poorest districts in the country, it is evident that the status of poverty has not improved over time. One way of making such improvements is through boosting agricultural productivity. This could be through ensuring affordability and accessibility of agricultural services and inputs, availability of water and environmental conservation. Our paper seeks to address 4

6 such policy concerns focusing specifically on relationship between environmental conservation, productivity and poverty. 3. ANALYTICAL AND EMPIRICAL FRAMEWORK Introduction. To achieve the objectives of the study, we estimate poverty, environmental conservation and productivity functions. This section presents the derivation of these functions, measurement of variables and the estimation procedures. We specify probit models relating the status of poverty on one hand and environmental conservation on the other to a range of determinants and then proceed to specify the productivity function. 3.1 Links between Poverty and the Environment Different framework have been proposed and utilized to analyze the poverty-environment nexus. The main arguments found in the literature indicate that poverty leads to environmental degradation; while other studies argue that environmental degradation leads to low productivity and therefore increases poverty. However, the general consensus in the literature is that poverty acts as a constraint on incentives to control land degradation. Emerging literature however argue that more complex set of variables govern the link between poverty and environmental degradation. This study adopts the conceptual framework developed by Jalal (1993) and Reardon and Vosti (1995) to link poverty and the environment. Reardon and Vosti (1995) relate four blocks of variables: categories of assets of the rural poor; household behavior pertinent to environment-poverty links (income generation, investment and land use); categories of natural resources (soils, water, e.t.c); conditioning variables (market conditions e.t.c). The asset categories of poverty affect household and village behavior, which in-turn affects the quality and quantity of natural resources as well as household/village assets. The conditioning variables influence the links between the types of poverty and behavior as well as the links between behavior and natural resources. 5

7 The authors postulates that a household s objective is to maximize food security and other livelihood objectives subject to access to a set of natural resources, human capital and on-farm and off-farm physical and financial capital as well as a set of external conditioning factors (prices, policies, technologies, institutions, community assets).the household allocates its labour, land and capital to income-earning activities and investments in two sectors, agriculture and non-agriculture, which have environmental consequences. The activities and investments, plus the environmental consequences alter the household s access to resources and capital, thus the household has a new stock of assets as it enters period two. Following this approach and Deininger and Minten (1999), we hypothesis that poverty is a function of environmental conservation practices (investment strategies) along side other control variables such as human capital and property rights. The probability that a household will be poor can be specified as: P = F( ) = (1) 1+ e α βxi ( α + βx i ) where P 1 is the probability that the i th household will be poor given x i, where x is a vector of explanatory variables and e is the natural logarithm. Equation (1) can be written as P ( + βxi ) [ 1 + e ] 1 1 = = Where α + βx i = log P1 and 1- P1 α... (2) P1 1 - P a household is poor. The model to be estimated is specified as 1 is the odds ratio, whose log gives the odds that Log P1 = α + β 0 W i + β 1 TEC ij +β 2 PPR i +β 3 NCO i +β 4 H i +β 5 X i +β 6 TR i +β 7 POP i 1- P1 + β 8 VH ij + β 9 FAMI i + β 10 ENVCP i +β 11 BIO i +β 12 CROP i.... (3) Where W i is the total wages paid to labour, TEC ij is technology, (j= 1,2 for fixed and variable capital), PPR i is property right regimes, NCO i is the number of cattle owned, H i is human capital variables such as education, age and sex e.t.c, X i is the total land owned, 6

8 TR i is transfers, POP i is the population, VH ij is village level infrastructure and j=1,2 for availability of water and distance to markets, FAMI i is non-farm income. ENVCP i is a dummy for environmental conservation equal to 1 if herder migrates, otherwise zero, BIO i is availability of biomass, CROP i is a dummy to capture whether herder also grows crops or not. Equation (3) is a logit model but could also be estimated as a probit model. The difference between the two is that the assumptions on the error terms differ with the error term for the former following a logistic distribution while that for the probit is normally distributed. Our paper adopts the later approach. To test for the robustness of the determinants of poverty, we also estimate equation (3) with log per capita expenditure as the dependent variable Predicting Environmental Conservation Environmental conservation is an indicator variable assigned the value of one if the herder migrates in search of pasture and water, otherwise zero for not migrating. We extend the probit framework to model the probability that the herder will migrate as Log P1 = α + β 0 PERCAP i + β 1 X i + β 2 W i + β 3 TR i + β 4 FAMI i + β 5 TEC ij + β 6 POP i 1- P1 +β 7 H i + β 8 NCO i + β 9 RSG+ β 10 Θ i + β 11 VH ij +β 12 BIO i + β 13 PPR i.... (4) Where PERCAP i is per capita expenditure, RSG i is the ratio of sheep and goats to cattle, Θ i is the perceived effect of migration on productivity and all other variables are as defined earlier. 1 There are two approaches to the analysis of poverty determinants. In one approach, probabilities of being poor are estimated using logit or probit procedures. In the other approach, household welfare functions (proxied by household expenditure functions) are estimated using least squares methods (Ravallion 1996). 7

9 3.3 Modeling Productivity To model the impact of property rights and environmental conservation on productivity, our study estimates an average revenue function using the production function approach 2. Some studies have used productivity as a proxy for environmental degradation (Stevenson 1991), although increased productivity is also argued to increase consumption and therefore to reduce poverty (Lopez 1997, 1998, Barbier and Lopez 1998). The innovation is to follow the standard revenue/consumption maximization problem but to constrain it by degradation of the resource stock. For instance, Stevenson (1991), introduces a simple model to compare the productivity of private and common property as: Y= γ 1 R + β 1 X 1 +µ 1. (5) where, Y is average milk production (liters/cow/day), R is a dummy variable for property right regimes equal to 1 if private, otherwise equal to 0, X 1 is a vector of exogenous variables other than the rights system that might affect productivity; γ 1 and β 1 are unknown coefficients and µ 1 is a stochastic disturbance term. The author argues that milk production is a proxy for overgrazing. Following this approach Lopez (1997, 1998), we specify a productivity model as REV i = α + β 0 W i + β 1 X i + β 2 TEC ij + β 3 Pc ij + β 4 VH ij +β 5 H i + β 6 BIO i + β 7 ENVCP i + β 8 PPR i (6) where REV i is revenue per acre for herder i, Pc ij is a vector of livestock prices and all other variables are as defined above. Constraining revenue by the quality of the environment is well documented by Lopez (1997,1998) who focuses on productivity under shifting cultivation and Ahuja (1998) among other studies. Our study takes into account the fact that the basic constraints in an 2 Average revenue (revenue per acre) is taken as a proxy for productivity as an increase in output with prices and land held constant translates to an increase in average revenue resulting from increased 8

10 arid land household unit differs from those in the community Lopez focuses on. While farmers under shifting cultivation attempt to maximize productivity subject to the growth rate of village biomass, the producers in an arid land setting seek to maximize the amount of food and fodder for their livestock subject to the prevailing environmental conditions and the technological constraints facing them. Nomadic pastoralism (which involves movement with livestock in search of pasture and water) is synonymous to shifting cultivation. The movement with cattle allows for regeneration of natural vegetation. If such movements are too short, they lead to insufficient growth of natural vegetation and consequently to low soil fertility and soil instability. The vegetation that grows in the fallow period is a form of capital that accumulates and is eventually used when the pastrolists return home with their livestock. If a herder does not move with his livestock to avoid overgrazing, the natural vegetation is reduced. Thus, overgrazing has a direct short-term output-increasing effect at the cost of reducing the natural capital and thus reducing productivity Lopez (1997,1998). 3.4 Definition and Measurement of Variables. Dependent Variables. Revenue (REV i ) per acre is measured as the total value of output divided by the amount of land owned. Poverty: Two measures are used: a dummy variable equal to 1 if household falls below the poverty line, otherwise equal to zero 3. The other measure (PERCAP i ) is proxied by the logarithm of household per capita expenditure. Although the poor are conventionally defined as the population that fall below a certain poverty line, it is assumed that even individuals above the poverty line may suffer from investment poverty (Reardon and Vosti 1995). productivity. 3 The only poverty line available is the provincial poverty line of 830 Kshs per month (Mwabu et. al. 2000). 9

11 Environmental conservation practices (ENVCP i ) is a dummy variable equal to 1 if a household migrates in search of pasture and water, zero otherwise. Such a practice is expected to increase productivity and consumption and therefore reduce poverty. Independent Variables Total land owned by the herder (X i ) is expected to reduce migration, poverty but to raise productivity. For herders under group ranches, total land owned was estimated by dividing the total ranch land by the number of members. W i is a vector of wage rates, which is estimated by taking the total value of payments to hired labour for the last season. Wages are expected to encourage conservation but to have an indeterminate effect on productivity and poverty as the impact will depend on productivity of labour. However it is expected that a herder would only hire more labour if he expects it to boost productivity and therefore reduce poverty. TR i represents the amount of transfers (remittances), which are expected to have a negative impact on productivity. The assumption here is that households that receive transfers may lack the incentive to struggle as hard as those who do not assuming that the nature of their utility function is one that seeks to maximize a target level of satisfaction. However, in some cases, transfers received may be spent on productive inputs and therefore boost productivity. Transfers are expected to be a mechanism for escaping poverty. The total value of non- farm income (FAMI i ) is expected to have a similar effect as transfers. Technology (TEC i ) is measured by the current market value for capital flows (drugs, animal feeds e.t.c.), while fixed capital (machinery) is valued at the purchase price. Physical capital is expected to have a positive impact on productivity, and therefore to reduce poverty. The impact on migration is indeterminate. Livestock prices (Pc ij ), are expected to encourage conservation, boost productivity and reduce poverty. This variable is measured in terms of the current market price per cow and the average market price per sheep and goat. 10

12 POP i measures the total family labor available to a herder. It is expected that the more the number of laborers, the higher the output other factors held constant. Availability of labor is also expected to facilitate migration with livestock, leading to higher productivity and lower poverty. Household size is used as a proxy for family labour. Larger households are in theory expected to be poorer than their smaller counterparts due to higher expenditures. The number of cattle (NCO i ) is measured as the standard cattle units and is expected to encourage conservation, increase productivity and also to have a positive impact on poverty reduction. Ratio of sheep and goats to cattle (RSG i ) is expected to discourage migration. The perceived impact of migration on productivity (Θ i ) is expected to encourage migration. This variable is proxied by a dummy variable equal to one if herder believes that migration will increase productivity, otherwise equal to zero. H i measures household characteristics (human capital variables), which are expected to have different impacts on productivity. Age and education are hypothesized to have a direct impact on productivity (and therefore to reduce poverty) due to the experience of the household head. Three dummy variables are used to capture the impact of education: primary school education, secondary school and post secondary education. Gender (sex) is measured in-terms of a dummy variable equal to 1 for males, otherwise equal to 0. The expected impact of this variable is indeterminate. Marital status is also a dummy equal to 1 if herder is married and zero otherwise. Its impact is also indeterminate. VH i measures village level infrastructure (availability of water and markets) and is included to depict the village environment, which facilitates decision making at the household level in-terms of migration or participation in commons. It is expected that availability of such infrastructure will augment productivity and reduce poverty. Availability of infrastructure is measure by the distance of these facilities from the homestead. CROP i is a dummy variable equal to 1 if herder also cultivates crops, otherwise equal to zero. Farming is expected to reduce poverty through increased consumption and reduced spending on food. 11

13 Availability of village level biomass (BIO i ) is expected to have a direct impact on yield per acre. If biomass is available at the village level, herders may be in a position to take their cattle for communal grazing and thereby conserve their own pastures, leading to higher productivity. Biomass is measured in-terms of Kilograms per acre and is sourced from secondary data. Well-specified property right institutions (PPR i ) are expected to affect the decision to migrate with livestock as well as govern incentives for use of resources. For all herders combined, property right regimes are proxied by a dummy variable =1 for private ownership, otherwise = Estimation Procedures The probit regression method is used to model the status of poverty and environmental conservation. The identifying factors for the poverty equation over productivity are the number of cattle owned, transfers, non-farm income, age squared and crop farming. The identifying factors for productivity equation are livestock prices. The identifying factors for migration over productivity are per capita expenditure, transfers, non-farm income, perceived effect of migration and livestock ownership. However, we note that our results may suffer from the well-known endogeneity problem and that some of the instruments turn out to be insignificant and therefore we may not have completely identified our equations. Robust regressions are used to estimate the determinants of per capita expenditure and productivity. Robust regression provides an alternative that is somewhat safer than OLS when the latter is applied without diagnostics in three respects: First, with robust regressions, outliers automatically get lower weights, which lessen their influence. Outliers affect the slope, standard errors, hypothesis tests, R-squared and other statistics. Second, robust methods have better statistical properties than OLS and their advantage include efficiency and more accurate confidence intervals and tests, since they resist 12

14 extreme values and do not assume normality. Third, robust regressions provide a model that better fits most of the data 4. Another estimation issue considered here is the simultaneity problem, due to the feed back effects between conservation, poverty and productivity. To take care of this problem, we use the three-stage least squares method to estimate the three equations simultaneously. The results are presented in the appendix table A1. The results show that other than for the revenue per acre equation, the results compare closely with those of the single equation models. For this equation, livestock prices and biomass have the unexpected negative significant impact, while all education dummies have the expected positive impact. However, we need to caution that due to the difficulties of finding good instruments, this approach may not necessarily yield more consistent estimates than the single equation approaches. 4. THE DATA This paper is based on both primary and secondary data. The primary data was collected from a cross section of households in Kajiado district, in three phases. In phase one, data was collected for the long rains (March-May 1999), phase two for short rains (October- December 1999) and phase three for the long rains (March- May 2000). The data was collected using the National Sample Survey and Evaluation Program (NASSEP III) frame. A sample of 220 households was visited with a response rate of 202, 192 and 176 households in phases one, two and three respectively, making a pooled sample of 570 observations. A detailed questionnaire was used to collect the requisite data (see Kabubo- Mariara 2000 for research instruments). STATA software was used to clean and analyze the primary data. The secondary data was sourced from Remote sensing data based on satellite images and vegetation indices collected by the National Oceanic and Atmospheric Administration (NOAA) and translated into biomass in kilograms per hectare by the Department of Resource Surveys and Remote Sensing (DRSRS) using Geographical Information Systems (GIS). 4 See Gujarati 1995 for a discussion of the identification problem and Hamilton L (1992) for a discussion on the derivation and properties of robust regression. 13

15 The main variables used in this paper are summarized in table 4.1. In general, the data indicates that 69% (393) of the observed households hold land under private property, as compared to 31% (177) who hold land under common property. Of the total, 58% (327) were found to fall below the prevailing poverty line and were therefore classified as poor. Surprisingly, equal percentages of the poor and the non-poor were found under common (58%) and private (57%) property. Table 4.1 Means and Standard Deviations of Sample Variables Private Property Common Property All Herders Variable Mean Std. Dev. Mean Std. Dev. Mean Std. Dev. Age of household head Age of household head squared Sex of household head Marital status of household head Household size Number of years in school Highest grade attained Can read and write Total land owned (acres) Total value of output (Kshs. 000)* Per capita expenditure (Kshs. 000) Wages (Kshs. 000) Transfers (Kshs. 000) Non-farm income (Kshs. 000) Value of equipment (Kshs. 000) Value of inputs (Kshs. 000) Number of cattle owned Ratio of sheep/goats to cattle Distance to markets (Km)** Distance to source of water (Km) Household migrates Distance migrated (Km) Perceived effect of migration Biomass ( 000 Kg per acre) Number of observations * Kshs: Kenyan shillings; ** Km: Kilometers 14

16 The data further indicates that out of the 570 observations, 54% migrated in search of pasture and water. Of those who migrated, 71% were engaged in long distance migration while the remaining 21% commuted daily with livestock. These households further reported that the migrants were mainly children (34%), household heads (25%), workers (19%) and other relatives (12%). Though the mean household size was found to be 7 with a standard deviation of 4, some households were very large, with 84% of all households reporting household sizes of less than 10 members and 4% reporting more than 25 members. 5. Results 5.1 Participation in Environmental Conservation Herders conserve the environment through movement (migration) with livestock in search of pasture and water. We estimate probits for participation for herders holding land under common property and private property separately and then proceed to estimate a probit model for participation for all herders with a dummy for property right regimes. The results are presented in table 5.1. We correct the models for heteroscedasticity using White s Method (Greene 1997). The log likelihood ratio statistics {Chi-square (19)} for each of the three specifications imply that the model fits the data significantly better than the model with the intercept only, and that the variables are jointly significant at the 1% level. The results indicate that log per capita expenditure, which is a proxy for poverty has a positive significant coefficient for herders holding land under private property and for all herders combined, implying that poverty (low per capita expenditure) will tend to discourage migration for these two groups. However, the opposite is observed for herders holding land under common property, implying that non-poor herders holding land under common property are less likely to migrate than their poor counterparts. This result is interesting as in theory, migration could result from poverty and could therefore be a mechanism adopted by households in order to escape poverty. 15

17 Land ownership increases the likelihood of migration in search of pasture and water, irrespective of whether an individual holds land under private or common property arrangements. The results however imply that the impact is stronger for common than for private ownership, judging from the coefficients. This is probably because it is only in the more arid zones where individuals own very large tracts of land. Such herders are therefore more likely to have deficiencies of pasture as compared to those with less, but more productive land. Table 5.1 Probit Estimates for Environmental Conservation (Migration) Private property Common property All property rights Variable Par. Estimates Par. Estimates Par. Estimates Log per capita expenditure 0.306* (0.089) * (0.162) 0.116*** (0.072) Log total land owned 0.210* (0.058) 0.223* (0.078) 0.172* (0.040) Log wages ** (0.024) *** (0.051) ** (0.020) Log transfers ** (0.024) (0.045) * (0.020) Log non-farm income (0.022) (0.036) (0.017) Log value of equipment * (0.063) (0.117) *** (0.063) Log value of inputs 0.077** (0.042) 0.200* (0.055) 0.110* (0.033) Log household size (0.475) (0.811) (0.369) Age *** (0.008) * (0.016) * (0.007) Sex / gender (0.314) (0.507) (0.241) Marital status 0.526** (0.312) (0.424) (0.247) Primary school education (0.247) * (0.456) * (0.207) Secondary sch. Education * (0.287) (0.539) * (0.253) Post secondary education * (0.475) (0.677) * (0.336) Log number of cattle owned 0.889* (0.181) 1.203* (0.384) 0.811* (0.147) Ratio of sheep/goats to cattle (0.014) (0.019) (0.012) Perceived effect of migration (0.192) (0.342) (0.149) Log distance to markets (0.110) (0.185) (0.088) Log distance to source of water 0.264* (0.111) (0.204) 0.183* (0.091) Log of biomass per acre (0.281) 1.068* (0.379) (0.218) Property right regimes * (0.160) Constant (2.382) (2.975) (1.753) Number of observations

18 Wald chi2(20) * 88.99* Chi2(21) =226.28* Log likelihood Z values in parenthesis *, **, *** Significant at 1%, 5% and 10% respectively The coefficient for wages is negative and significant for all three cases. The effect is however stronger for herders holding land under private property than under common property. The results here imply that herders who pay a lot to hire labour are less likely to migrate than those who pay less. This could probably be explained by the fact that for the more nomadic type of herding, little labour is required as there seem to be lots of economies of scale in grazing/migration where one or two morans can take care of a very large herd. On the other hand, wage for migratory labour is not competitive as the morans may be paid as little as one goat for migrating with cattle for a whole dry season. Transfer incomes also have negative coefficients, which are significant for private property and for all herders, but insignificant for those holding land under common property. The implication here is that private property owners who also have other sources of income are less likely to have the incentive to migrate in search of pasture and water, probably because they do not have to rely on income from livestock. Non-farm income also has a negative but insignificant coefficient in all the three cases. These results contradict findings by Chopra and Gulati (1998), who found a positive impact of non- farm income on migration. Value of equipment has negative significant coefficients, except for common property holders. This implies that households who invest in a lot of physical capital face less odds of migrating with cattle than those who invest less, if they hold land under private property. This could be explained by the fact that those who make such investments are the more sedentary herders who also keep mixed and grade cattle, for which they at times purchase fodder and thus may have a lower propensity to migrate. The coefficient for those holding land under common property is insignificant. Value of livestock inputs has a positive and significant effect except for those holding land under private property, implying that those who invest more in livestock inputs face better odds of migrating. 17

19 This could be explained by claims by some herders that migration is associated with spread of livestock diseases and therefore, a migratory herder has to spend a lot of funds on drugs (preventive and curative). The fact that the impact is stronger for those holding land under common property implies that such herders may have to spend more on inputs as their herds come into contact with others much more than for private property herders, thereby exposing such herds to livestock diseases. Household characteristics were also apriori expected to influence the decision to migrate with livestock. The results indicate that household size has contradicting but insignificant impacts on migration, implying that family labour is not important in determining the decision to migrate. Age has a negative but significant coefficient implying that elderly herders face less odds of migrating than their younger counterparts. The impact is stronger for common than for private property holders. The impact of gender is negative but insignificant for private property holders, but positive and significant for common property owners, implying that men are more likely to migrate than women, especially if they hold land under common property, which conforms to our apriori expectations as in reality migration is a male affair and women only migrate if they have to accompany their spouses. Marital status has a positive impact for private property holders implying that herders who are married are more likely to migrate than those who are not. The impact for common property holders is negative but insignificant. All education dummies have negative coefficients, which are significant except for primary schooling under private property and secondary and post secondary for common property. The results implying that those who have some level of education face less odds of migrating than those without any education, probably because they are more aware of the benefits of own farm development, the dangers of migration (mostly spread of livestock diseases), or they may also be engaged in other non-herding activities. The results further indicate that the number of cattle owned has a positive significant impact in all cases, implying that the more the number of cattle, the higher the probability 18

20 that the household will migrate in search of pasture and water (Chopra and Gulati, 1998), irrespective of the property right arrangements. The coefficients imply that the impact is much stronger for common property owners than for their private owner counterpart. This could be explained by the fact that having more cattle depletes pastures faster, forcing the herders to migrate in search of more. Households with smaller herds are also better placed to send cattle to relatives/friends for keeping during the dry season, so that the household does not have to migrate. For all cases, the ratio of sheep and goats to cattle, perceived effect of migration on output and distance to livestock markets all have insignificant effects. The distance traveled to watering point has the expected positive and significant impact for private property holders and all herders combined, implying that lack of water is one of the most important reasons why herders migrate as the traditional cattle can feed on dry grass so long as water is available. So if no water is available, the herder has to migrate with his livestock. The coefficient for common property holders is negative but insignificant which is contrary to expectations. Availability of biomass at the village level would be expected to reduce the likelihood of migration as it would imply that there is no deficiency of pasture. However, the coefficient of biomass has the unexpected positive effect, which is only significant for common property holders, implying that in general, environmental conservation, which may preserve biomass, may not necessarily reduce the likelihood of migration. This may however imply that herders will commute with livestock (open grazing) around the village rather than migrate for long distances in search of pasture. For all herders, combine, a dummy is included to capture the general impact of property right regimes. The results indicate that property right regimes negatively and significantly influence the decision to migrate in search of pasture and water. This implies that as expected those who hold land under private property arrangements face less odds of migrating than those who hold land under common property ownership. 19

21 In summary, the results for participation in environmental conservation imply that poverty, wages, transfers, technology and education discourage migration in search of pasture for private property owners, while assets (land and livestock) and lack of water encourage migration. The same is observed for common property holders except that poverty encourages migration for this group. The joint impact of the variables also seem to be much more important for private property owners. In general common property owners are more likely to migrate than their private holding counterparts. 5.2 Determinants of Productivity This section presents and discusses the regression estimates for productivity. We hypothesis that environmental conservation leads to higher productivity and hence whether land is degraded or not can be inferred from the productivity level (Stevenson 1991). We therefore test the impact of property rights and migration among other factors on the environment through the impact on productivity. However, we also use biomass (which is an indicator of the quality of the environment) as an explanatory variable (Lopez 1997,1998 and Abuja, 1998). The results are presented in table 5.2. The Chow tests (F statistics) for all specifications confirm the goodness of fit of the model and also confirm the stability of the coefficients to changes in specifications. In general, the results for those holding land under private property lead to similar conclusions with those of all herders combined, in-terms of signs and significance of coefficients. We therefore discuss the results for private property and common property holders. For instance, the results indicate that wages have a positive impact on productivity for all herders and for those holding land under private property though the coefficients are insignificant. The coefficient for those holding land under common property is negative and also insignificant. These results imply that wages are not an important determinant of productivity and confirm field observations, which indicated that there are lots of economies of scale in grazing, such that the herder doesn t have to employ a lot of laborers for larger herds. The results for common property agree with findings by Lopez (1998) and Ahuja (1998) who found a negative impact of wages on 20

22 productivity, implying that higher wages will reduce productivity through lower profits (resulting from extra spending). Total land owned has a negative and significant impact for private property holders, implying that herders owning large tracks of land are likely to have lower productivity. This agrees with the earlier results on migration which implied that those with large tracks of land are in the more arid zones and therefore, their land is unproductive. However, the coefficient is positive and significant for common property holders, implying that more land ownership could lead to higher productivity for those under group ranches/communally owned land. Table 5.2: Robust Regression Estimates for Revenue per Acre (Productivity) Private property Common property All property rights Variable Par. Estimates Par. Estimates Par. Estimates Log of wages (1.28) (-0.09) (1.29) Log total land owned * (-21.9) 0.372* (2.69) * (-30.2) Log value of equipment 0.129* (3.23) (1.33) 0.161* (4.80) Log value of livestock inputs 0.293* (13.8) 0.368* (3.19) 0.322* (17.1) Log price per cow (-0.48) (-0.95) (0.18) Log price per sheep/goats (0.66) * (-4.66) (0.87) Log distance to source of Water 0.184* (2.57) (-0.19) 0.249* (4.04) Log distance to livestock market -0.14*** (-1.81) (0.73) ** (-2.16) Log household size 1.029* (3.95) (0.86) 0.797* (3.53) Age (0.86) *** (-1.69) (0.30) Gender (1.05) (-0.41) (0.20) Marital status (-0.60) (0.63) (1.07) Primary school * (-2.28) (0.72) * (-2.54) Secondary school (-0.50) (0.29) (-0.67) Post secondary (0.93) (1.21) (-1.29) If migrate 0.704* (4.72) (0.58) 0.727* (5.51) Log biomass per acre 0.680* (3.82) * (-2.37) 0.603* (4.08) Property right regime *** (1.68) Constant (0.65) (5.40) (0.29) Number of observations F- statistic (17,375) = 46.56* (17,159) = 5.65* (18, 551)= 85.78* T values in parenthesis 21

23 *, **, *** Significant at 1%, 5% and 10% respectively The coefficients for technology have the expected positive impact for those holding land under private property, implying that investing in productivity enhancing technology increases livestock productivity. For common property holders, the two coefficients are positive but that of value of equipment is insignificant, implying that technology is a much more important determinant of productivity for private property holders than for common property holders. Although such investment would be expected to reduce profits due to expenditure incurred, it has a positive impact on productivity through increased output, all other factors held constant. The results for technology agree with findings by Lopez (1998) and Ahuja (1998). Prices of livestock do not seem to be important determinants of productivity for all herders as implied by the conflicting and generally insignificant coefficients. Prices of sheep/goats seem to reduce productivity for common property holders. This could be explained by the fact that livestock markets in the district are uncompetitive, since the culture of the Maasai does not encourage trade in livestock. In essence, the Maasai would be more unlikely to sell livestock the higher the prices, as cattle ownership is a sign of wealth /prestige. Needless to say, availability of well functioning factor and commodity markets play a critical role in enhancing productivity and reducing rural poverty (Dasgupta 2001, Mäler 1997, Dasgupta and Mäler 1995, Reardon and Vosti 1995, Mink 1993, Barbier and Burges 1992). The impact of distance to source of water is positive and significant for private property holders implying that contrary to expectations, the further the distance to source of water, the higher the productivity. However, the coefficient for common property holders has the expected negative but insignificant impact. The results further indicate that as expected, distance to livestock markets seems to reduce productivity for private property holders. This implies that well developed/functioning markets will enhance productivity. However, for common property holders, the impact is positive but insignificant, which 22

24 could be interpreted as reflecting the uncompetitive/underdeveloped livestock market in the district. Household size has a positive impact on productivity, which implies that the larger the household, the more the labor (population) available for boosting productivity (Ahuja 1998, Boserup 1965). This however contradicts Mäler (1997), Deininger and Minten (1999) and Reardon and Vosti (1995) who argue that population pressure can result in an extended period of land over-use resulting to unsustainable shortening of fallow periods, deforestation, cultivation and grazing on marginal lands and thus lower productivity. However, the coefficient for common property holders is insignificant implying that household size is a more important determinant of productivity for private property holders, probably due to pooling of labour and economies of scale in grazing under common property resources. The coefficient for age is positive but insignificant for private property holders, but negative and significant for common property holders, implying that younger herders record higher productivity than their older counterparts. This is contrary to expectations, as we would expect older herders to report higher productivity due to more experience and accumulated wealth. The impacts of gender and marital status are conflicting but insignificant, implying that they are not important determinants of productivity irrespective of whether one holds land under private or common property. Contrary to expectations, education seems to have a negative impact for private property holders, though the impact is only significant for primary schooling. For common property holders, all education dummies have positive coefficients, which are not significantly different from zero. The general implication is that education does not seem to be an important determinant of productivity in our sample. The coefficient for migration is positive, implying that herders who migrate are likely to report higher productivity than those who do not since they are in a better position to get more pasture/water for their livestock, especially for those under private property. This could be due to the fact that in the district under study, it is possible for private property 23