Keywords: Poverty, Land tenure security, Market access, Land conservation investment, Random utility model, Multinomial logistic model.

Transcription

1 June, 16, 2014 Preferences for Forms of Land Conservation Investment in the Ethiopian Highlands: A Household Plot-Level Analysis of the Roles of Poverty, Tenure Security, and Market Incentives by Genanew Bekele Worku (Corresponding author) 1 Economics Department, University of Dubai College of Business Administration P.O.Box: 14143, Dubai, UAE Tel: gbekele@ud.ac.ae or gensich@yahoo.com and Friedrich Georg Schneider Economics Department, Johannes Kepler University of Linz P.O.Box: A 4040 Linz Auhof, Austria Tel: friedrich.schneider@jku.at Abstract Building its framework with the random utility model as a base, this study makes use of the multinomial logistic model to examine the factors leading to differences in farm-households preferences for various forms of land conservation measures. Using a survey of 4,795 household-plots in rural Ethiopia, the study demonstrates the inappropriateness of pooling different forms of land conservation investments in preference studies. The empirical results suggest that poverty drives farm-households towards land conservation measures which are more short-term and which entail the expenditure of less skill. While tenure security has a mixed effect on such preferences, market access seems not to matter for preference decisions. Further, farm-households consider the characteristics of the plot in their preference decisions, which also vary across villages. Overall, this study shows that a farm-household s preference is a complex decision. Major changes in conservation investments on the part of farm-households will require attention to many factors, as no single factor exerts enough control to be used on its own as a major policy leverage instrument. Keywords: Poverty, Land tenure security, Market access, Land conservation investment, Random utility model, Multinomial logistic model. JEL classification: D1, Q12 1 The data used in this study were collected by the department of economics at Addis Ababa University (Ethiopia) and the University of Gothenburg (Sweden) with the project support of Sida. The authors would like to acknowledge these institutions for allowing access to the data. In particular, we are grateful to the organizers of the project: Dr. Mahmud Yesuf, Dr. Alemu Mekonnen, Dr. Gunnar Kohlin and Dr. Tekie Alemu. 1

2 1. Introduction: A critical environmental issue facing governments of developing societies is land degradation, which is a crucial concern affecting, among other things, the well-being of their people. Hurni (Hurni, 1985) noted well over twenty years ago that Ethiopia was the most environmentally distressed country in the Sahel belt. Studies have confirmed that land degradation undermines agricultural productivity primarily in the highlands of Ethiopia (c.f. Genanew et al., 2012), where most (about 88%) of the country s population lives, and involves more than 43% of the country s area, 95% of the cultivated land, and affects 75% of Ethiopia s livestock. Estimates of the extent of land degradation differ, but they all highlight the importance of the problem. The Ethiopian Highland Reclamation Study (EHRS) estimated that by the mid-1980s about 50% of the highlands area (27 million hectares) was significantly eroded, while more than one quarter was seriously eroded (EHRS, 1986; cited in Gebremedhin et al., 2003). Hurni (Hurni, 1988) found that soil loss in cultivated areas averaged about 42 metric tons per hectare per year, which far exceeds the soil formation rate of 3-7 metric tons per hectare per year. More than twenty years ago, Stahl (Stahl, 1990) estimated that the amount of land incapable of supporting cultivation would reach 10 million hectares by the year Until recently, the magnitude of land degradation (and deforestation) has far exceeded the conservation activities being carried out 2. Indeed, it is only recently that public intervention in land conservation has become a priority in Ethiopia. Land degradation was largely neglected by policymakers until the 1970s and national conservation programs introduced since then have not had the opportunity to profit from much prior research (Shiferaw et al., 1999). Policies and programs were adopted based on incorrect assumptions and little understanding of the incentives and constraints related to land conservation, all of which could be misleading. 2 Gebremedhin and Swinton (2003), for instance, observed that the extent of land conservation structures was less than the average requirement of 700 meters per hectare of stone terraces or soil bunds to conserve one hectare of land and effectively reduce soil erosion in the typically sloped areas of northern Ethiopia. 2

3 The rural land administration and land use proclamation 3 no. 456/2005 of Ethiopia (the land certification in particular) has been a step towards land titling (Federal Democratic Republic of Ethiopia, 2005). Its legal ramifications are fundamental to the farm-household s decision to adopt land conservation investment strategies as well as to the intensification of conservation measures. The rural household survey used in this study shows that about 98% of farm-households would like to undertake soil conservation work on their plots if they were provided with a land certificate. But questions remain: which form of land conservation investment do farm-households prefer to undertake on their plot, what determines their preferences, and which factors have a relatively stronger influence on their preference decisions? Land conservation investment promotion policies and programs with little or no understanding of these questions can be deceptive and may in fact exacerbate the land degradation process. Objective and hypothesis Research into both the incentives as well as the constraints governing farm-households choices among the various forms of land conservation investment measures is of interest for land conservation policy-making. Understanding these incentives and constraints would fill an information gap and help program designers seeking policy instruments to encourage or discourage farmers use of one form of land conservation measure over another. This study may contribute to filling this gap of knowledge as it examines the factors which lead to differences in farm-households preferences among the various forms of land conservation investments. In particular, the study focuses on the role of poverty, land tenure security, and market access factors in influencing these preference decisions. 3 Land in Ethiopia is not private. The right to ownership of land is exclusively vested in the state and in the people: it cannot be sold. The land holder has only the right of use, with no time limit imposed [ Article 7(1)]. The landholder can lease the land [Article 8(1)], may (using his land use right) undertake development activity jointly with an investor [Article 8(3)], and has the right to transfer the right of use through inheritance to family members [Article 8(5)]. For these [Article 6(3)], the landholder is to be given a land-holding certificate that indicates, among other things, the landholder s rights and obligations [Article 10(1)]. Accordingly, the landholder is expected to protect the land, for otherwise, s[he] would lose these rights. 3

4 A farm-household s choice among the different forms of land conservation investment measures can be influenced by the characteristics of the household, the plot and its environment as well as issues related to the institutional set- up relevant to the farmhousehold. These include level of poverty, land tenure security attached to the plot, access to markets, and other variables, including physical incentives and village-level characteristics. This study makes the following three working hypotheses: H1: The poor, because of their higher rates of time preference (RTPs) and risk preference, tend to prefer short-term benefits and cheaper land conservation measures over long-term sustainable measures. (See related arguments in Hagos et al., 2006; Gebremedhin et al., 2003; Clay et al., 2002; Holden et al., 2002; Godoy et al., 2001 and Holden et al., 1998). H2: Farm-households cultivating more secured plots have a greater incentive to undertake long-term beneficial land conservation measures; this is in contrast to farmers with unsecured plots who may have an immediate need to protect their crops. This follows from the Neoclassical economic theory which suggests that, ceteris paribus, reduced risk and longer time planning horizons should enhance expected return and encourage investment. (c.f. Gebremedhin et al., 2003, Alemu, 1999, and Feder et al., 1988). H3: Farm-households with better access to markets and infrastructure development expect higher return from land conservation measures and hence tend to undertake long-term sustainable measures. It is our belief that this study makes a number of contributions to the existing literature on land conservation. The study is unique in the sense that it is - to the best of our knowledge - the first comprehensive plot-level assessment of un-pooled preferences of land conservation measures by the farm-household using a theoretical and conceptual framework designed with the random utility model as a base. Its contributions are also related to the empirical application of a model of landowner decision-making to a vast and comprehensive dataset that enables 4

5 investigation of several relevant aspects of the problem. In particular, its use of additional explanatory variables is what this study offers when compared with the previous literature. In contrast to other relevant studies, the richness of the data allowed us to incorporate a wider range of variables, including asset- and poverty-related factors as possible determinants of farm-households preferences among the different forms of land conservation investment measures. The balance of the paper is structured as follows: the conceptual framework used in this study is presented in Section 2, and Section 3 presents the methodology employed for this study. Data, results, and interpretation are presented in Section 4, while Section 5 summarizes and concludes. 2. Conceptual and Theoretical Framework The framework for the analysis in this study makes use of the random utility model as a basis. In this model, it is assumed that the decision-maker (the farm-household) will choose an outcome (i.e. a specific form of land conservation investment on a specific plot) which maximizes that household s utility. Since the household s utility is not observable - rather, only certain attributes of the alternatives as faced by the decision-maker are observable - the utility of the household is decomposed into a deterministic (or a systematic) component V ij and a random component ξ ij of utility (Greene, 2011): U = + ξ, for all j =1, 2,, J and i =1, 2,, N (1) ij V ij ij where U ij is the level of utility which decision-maker i obtains from choosing alternative j. Since the unknown component ξ 4 ij is not observable, the decision-maker s choice cannot be predicted exactly; instead, the probability of any particular outcome is derived. 4 The random component, ξ ij, is assumed to be independently and identically distributed with type I extreme value (Gumbel) distribution (Greene, 2011). 5

6 Thus the probability that decision-maker i chooses outcome j (i.e. P ij ) is equal to the probability that the utility of alternative outcome j is greater than the utilities of all other alternatives in the choice set: P ij = Pr( U > U ) = Pr( V + ξ > V + ξ ), for all k j (2) ij ik ij ij ik ik The above equation shows that the choice probability, P ij, depends only on the difference in utility, not on its absolute level. This implies that, in general, the only parameters that can be estimated or identified are those capturing differences across alternatives. The specific form of the above discrete choice model is determined by the assumed distribution of the random component, ξ ij, and the specification of the deterministic component, V ij. While the latter is often treated as a linear function of explanatory variables and unknown vector of underlying parameters Q, the expectation of the former [i.e. E( ξ ij ) ] is assumed to be zero in random utility models, implying that E ( U ij ) = Vij (Ivan et al., 2003). The specific forms of the assumptions related to V ij and ξ ij in the MNL model are described in the methodology section. Given these assumptions, the unknown parameter, Q, of the discrete choice model can be estimated to examine the way in which observed factors influence the choice of the decision-maker. The log-likelihood estimator can be used to estimate the parameters, for which the log-likelihood function to be maximized over parameters Q can be given by: N J ln L( Q) = y ij ln P ij (3) i= 1 j = 1 where y ij equals 1 if alternative j is chosen, and 0 for all other non-chosen alternatives. 3. Methodology This section begins by presenting the rationale for the appropriateness of the multinomial logit 6

7 (MNL) model for our study, and goes on to describe the MNL model and the methods of interpretation related to it Model appropriateness There are three reasons for the MNL model s desirability for this study. The first is related to the appropriateness of the MNL model for the analysis of characteristics of individuals. This study examines how household plot-level characteristics affect a household s preference when choosing among different forms of land conservation investments. For a given individual plot, the values of the independent variables are the same for all outcomes. For instance, the poverty level of the household does not change with the choice of a certain form of land conservation. The data used in this study consists of such specific individual characteristics and the MNL model is well suited to analyzing the characteristics of individuals (Long, 1997). The second reason relates to the assumption of independence of irrelevant alternatives or simply IIA: the alternatives are distinct and independent of one another (Long et al., 2006). This assumption can only be empirically tested 5 when certain respondents have different choice sets. That is, the IIA assumption is not a serious problem when everyone in the sample is presented with the same choice (Allison, 1999; sited in Han et al., 2004). In this study, the alternatives are presented to all respondents; hence, the IIA assumption holds in this study. Moreover, the MNL model is enormously useful for the analysis of nominal variables and is easy to estimate, even for a large number of alternatives (Long, 1997 and Borsch-Supan, 1990). Finally, the MNL model is appealing for this study given its practical appropriateness compared to alternative models such as the multinomial probit model (c.f. Long, 1997) and the nested logit model. In particular, in 5 The two most common tests of IIA, the Hausman-McFadden test and the Small-Hsiao test, often provide conflicting information on whether IIA has been violated. Neither of these tests is recommended for making decisions concerning IIA assumptions. See Cheng et al., 2005; Amemiya, 1981 and McFadden, The latter two respectively suggested the use of the MNL model in cases where the alternatives can plausibly be assumed to be distinct and weighted independently in the eyes of each decision-maker as well as when the alternatives are 7

8 studies like ours where many independent variables are considered, the MNL model seems most appropriate Econometric Modeling: The Multinomial Logit Model This section formally presents the multinomial logit model as it fits into our study. Proceeding from the random utility model developed earlier, the utility derived from choosing a land conservation investment type j by a farm-household i on its particular plot of land l is U ilj = µ + ξ, f or all j (4) ilj ilj where µ ilj is the average utility associated with choice j (among the J alternatives) for farm-household i on its particular plot l and ξ ilj is the random error associated with that choice - the probability of this choice is the probability that the utility obtained from alternative j exceeds that of any other alternative; for example, k : Pr( y il = j) = Pr = Pr( U > U ) = Pr( ξ < µ µ + ξ ), for all j (5) ilj ilj ilk ilk ilj ilk ilj The specific form of the discrete choice model is determined by the assumed distribution of ξ and the specification of how obtained by letting (Long, 1997) µ j is related to measured variables. The MNL model is µ j be a linear combination of household-plot level characteristics of the farm-household (i.e. µ = X β ) and by assuming (see McFadden, ilj il j 1974) each random component ξ ilj in equation (4) to be independently distributed and to have identical type I variance π 2 /6: dissimilar (Long et al., 2006). In this study, the alternative forms of land conservation investments are assumed to be distinct and dissimilar, a situation which would suggest use of the MNL model. 6 It is generally recommended that seven variables plus or minus two are appropriate in nested logit models (George Institute Technology, sited in Han et al., 2004), whereas more than 20 independent variables are considered in this study. 8

9 f ξ ξ ilj ilj e ( ξ ) = e e and ilj F ξilj e ( ξilj ) = e (6) where f (.) is the density and F (.) is the cumulative distribution. Combining equation (4) and the probability distribution for the random components ξ ilj in equation (6), and assuming independence among the random components of the alternatives, P ilj [equation (5)] is given by: P ilj = Pr( U ilj > U ilk ) = F( µ ilj µ ilk + ξilj ) f ( ξilj ) dilj, for k j (7) k j Equation (7) has a closed-form solution and, after manipulating the logit probabilities with µ = X β, becomes (Ivan et al., 2003): ilk il j P ilj = exp( X k il exp( X β ) il j β ) k (8) Since the MNL model is a model where independent variables do not vary over choices, coefficients are estimated for any choice. Moreover, the model requires identification. To identify the model, the common method is to treat one of the preferences, say j, as the category (i.e. to impose constraint on β j to equal 0). The log-likelihood function will, therefore, be: exp( X β ) ln L( β ) = (9) N J il j yilj ln i= 1 k= 1 exp( X j ilβk ) where the right-hand side of equation (8) is substituted for P ij in equation (3). Equation (9) can then be maximized to estimate the parameters β that are consistent, asymptotically normal and efficient. Thus, under conditions that are likely to apply in practice, the likelihood function is globally concave, ensuring the uniqueness of the MNL model (Amemiya, 1985). The MNL as an Odds Model 9

10 An alternative way to express the MNL model is in terms of odds. The odds of outcome j versus outcome k, given X, are defined as (Long, 1997): Pr( yil = j X il ) Ω ( X il ) = (10) j k Pr( y = k X ) Placing equation (8) into equation (10) and taking logs yields: il il Ω X ) = X ( β β ) (11) ( j k il il j k This result demonstrates that the MNL model is linear in the logit Interpretation of Parameters The number of parameters in the MNL model is often very large. As a result, their signs and magnitudes are not very directly informative. There are several ways to interpret the MNL model estimation results, including predicted probabilities and their changes, and the odds ratio (Long, 1997 and Long et al., 2006). The factor change in the odds for change in an independent variable x k, unlike the marginal and discrete changes, does not depend on the value of x k or on the value of any other variable. This is the advantage of the odds model: interpretation of coefficients is simple. On the other hand, it is unsatisfactory, since it is difficult to convey the substantive meaning of a change in the log of the odds. The difficulty in interpreting odds ratios using the MNL model is that the coefficients for comparisons among all pairs of outcomes need to be examined to understand the effect of a given variable. In this respect, measures of discrete change in probabilities are effective methods of interpretation for both continuous and dummy independent variables. However, they do not indicate the dynamics among the dependent outcomes, and discrete changes are different for different values of the variables. Both of these disadvantages of discrete changes become advantages when the odds model is used. All in all, there are certain advantages to either way of interpreting the MNL model results, as each method provides useful information that the other does not. In this study, 10

11 therefore, interpretation of parameters of the model is based on a discrete change in predicted probabilities as well as the odds ratio. Moreover, discrete change plots and odds ratio plots are also used for summarizing results and discerning patterns Test Equations The survey used in this study solicited farm-households preferences among the different forms of land conservation investments, which the authors recorded as seven alternative categories. These mutually exclusive and exhaustive alternatives include traditional terraces with rocks (TTR); modern terraces with rocks (MTR); traditional terraces with soil (TTS); modern terraces with soil (MTS); ditch digging (DD); Kitir works (KW) 7 ; and Other types such as grass cover, contour farming, and forestation. These categories are used as outcomes of the dependent variable in the MNL model. Five vector-independent variables (see Table 1) are considered: poverty, land tenure security, market access, plot characteristics (i.e. physical incentives), and residence in a certain village. Past land investment intensity is also considered as an independent variable. Table 1 about here We assumed that the following test equations fit our MNL model: ln Ω TTR Other ( X ) = γ γ 0, TTR Others 4, TTR Others + γ 1, TTR Others Plot + γ Poverty + γ 5, TTR Others Village + γ 2, TTR Others 6, TTR Others Tenure + γ 3, TTR Others Past int ensity Market + Similar specifications are made for the remaining preferences (i.e. MTR, TTS, MTS, DD and KW), and the alternative other types (others) is set as the base category (i.e. the comparison group). These other types are generally short-term measures incurring less investment time, labor, and money than the other six land conservation investment alternatives. 7 It is a barrier of stone, wood or branches of trees across a gully to reclaim gully lands to productive lands by controlling the rate of runoff and trapping the soil. 11

12 Based on the hypotheses set forth earlier in this study, we test whether: H1: The poor tend to prefer short-term beneficial but cheaper land conservation measu res over long-term, sustainable measures. i e. γ < 0, for m TTR, MTR, TTR, MTS, DD KW. =, 1, m Others H2: Farm-households cultivating more secured plots have greater incentives to undertake long-term beneficial land conservation measures in contrast to farmers with unsecured plots. i e. γ > 0, for m TTR, MTR, TTR, MTS, DD KW. =, 2, m Others H3: Farm-households with better access to market and infrastructure development tend to undertake long-term, sustainable measures. i e. γ > 0, for m TTR, MTR, TTR, MTS, DD KW. =, 3, m Others 4. Data, Results, and Interpretation 4.1. Data and Descriptive Statistics This study utilized a primary data collected through interview of rural farm-households in the Ethiopian highlands of East-Gojam and South-Wello zones of the Amhara region, with the general objective of studying farm-household behavior in relation to sustainable land use. The data gathering is a part of a collaborative research project between the department of economics at Addis Ababa University (Ethiopia) and Göteborg University (Sweden) with financial support from the department for Research Co-operation of the Swedish International Development Agency (Sida/SAREC). Households who were interviewed in the first round (year 2000) were re-interviewed in the second round (year 2002) using the same set of questions. A total of 1520 households from 12 sites/kebeles (with a minimum of 120 from each site) were interviewed in each of the two rounds. Two more sites (i.e. 318 households) and some new questions were included in the third round. This made a total of 1838 households in 14 sites (villages or kebeles ) to be interviewed in the third round in 2004/5. The selection of the sites was purposive to ensure variation in the characteristics of the sites including agro-ecology and vegetative cover. Households from each 12

13 site were then selected using simple random sampling. Most of the variables of interest of our study were not included in the first two rounds and hence our study focused only on analyzing the data gathered in the third round with the inclusion of poverty and asset related variables from the second round. A sample comprising responses from 4,795 household-plots was examined, after dropping the remaining plots due to missing variables and values. The data gathered a host of household-related variables as well as plot-level data, including land conservation and input practices as well as questions pertaining to household poverty, land tenure security and market incentives. Farm-households were asked to state which form of land conservation investment they preferred to undertake on their specific plot. Their response became the observed dependent variable in the multinomial logit model used in this study. The responses were distributed among seven mutually exclusive and exhaustive alternative preferences: traditional terraces with rocks, TTR (40 percent of households chose this alternative); modern terraces with rocks, MTR (6 percent); traditional terraces with soil, TTS (20 percent); modern terraces with soil, MTS (3 percent); ditch digging, DD (19 percent); Kitir works, KW (6 percent); and Other types (7 percent), such as grass cover, contour farming, and forestation. To explain the farm-household s preference among these alternatives, a range of explanatory variables (measuring the effects of poverty, land tenure security, access to market, physical incentives and village characteristics) were used. The definition and specification of the independent variables used in the analysis are described in Table Testing Model Parameters The MNL model was estimated (see Appendix A) and model tests were carried out. The latter included testing the effect of the independent variables (Table 2), testing the multiple independent of a set of explanatory variables (Table 3), and tests for combining outcomes of the dependence variable (Table 4). The Wald test result suggests that the hypothesis (H1) stating that the effects of 13

14 variables involving poverty are simultaneously equal to 0 can be rejected at the 0.01 significance level. The test also shows similar results for the effects of the variables addressing land tenure security, market access, and physical incentives. Moreover, the test confirms that any two alternative outcomes of the dependent variable are distinguishable 8, suggesting that it is inappropriate to pool the different forms of land conservation investments in preference studies. Table 2, 3, and 4 about here 4.3. Determinants of Preferences for Forms of Land Conservation Variations in Predicted Probabilities: Table 5 presents the distribution of the predicted probabilities for each outcome of the dependent variable within the sample. It is worth noting that the range of the predicted probabilities is substantively large for each alternative outcome, suggesting that there is sufficient variation in each outcome category of the dependent variable to justify further analysis. Table 5 about here Discrete Changes in Probability: Table 6 presents estimates of the marginal and discrete changes in predicted probabilities. Setting the values of all the explanatory variables at their mean (see bottom of Table 6), the probability of preferring TTR is the highest (56 percent), followed by that of TTS (26 percent) and DD (at 9 percent). Finally, MTS is least preferred by farm-households. Table 6 about here Factors Involving Poverty: Holding all other explanatory variables at their means, having access to credit increases the probability of preferring traditional rock terracing by 0.04, modern rock tracing by a margin of 0.001, and ditch digging by The tendency of the 8 For Anderson, 1984, two alternative outcomes of the dependent variable in a MNL model (say outcomes TTR and MTR) are said to be indistinguishable with respect to the covariates in the model if none of the independent variables significantly affects the odds of outcome TTR versus outcome MTR. In the case where the two outcomes are indistinguishable, then combining them yields more efficient estimates. 14

15 effect of farm size is to impede preference probabilities, with the exception of ditch digging and kitir works, which have greater impact on the probability of preferring traditional rock terracing (0.02) and kitir works (0.03). If a household contains an additional adult female, this decreases the probability of preferring kitir works by 0.002, while a household with more dependent members shows less preference for traditional soil tracing. Having access to extension services increases the probability of preferring modern rock terracing by 0.03, modern soil terracing by 0.01, other types by 0.02, and decreases the probability of ditch digging by In general, access to soil conservation advice produces effects on preference probabilities which are contrary to those produced by access to extension services, with stronger changes in preference probabilities for the former. It is also demonstrated that having access to soil conservation advice increases the probability of preferring ditch digging by As can be noted from above, the large number of coefficients to interpret makes it a challenge to sort out all the relevant information. Plotting the discrete-change coefficients may help to discern patterns. Figure 1 summarizes the discrete changes in predicted probabilities for the statistically significant covariates. In order to allow comparison of the magnitude of the effect of independent variables, Panels A through D are drawn at the same scale. Interpretations have been made while holding control variables at their mean. Figure 1 about here In Figure 1, Panel A clearly shows how a one-unit increase (for the dummy independent variables) or an increase in standard deviation (for the continuous independent variables) in each of the covariates affects the probability that each land conservation type will be preferred. Accordingly, the impact of credit access on the probability of preferring traditional rock terracing or ditch digging is the largest: households with access to credit are more likely to prefer rock terracing and ditch digging than households without access. 15

16 Likewise, having access to soil conservation advice has a considerable negative effect on the probability of preferring traditional soil terracing. All in all, Panel A shows that the effect of poverty on the probability of preferring a particular form of land conservation investment is not quantitatively substantial. Land Tenure Security Factors: Holding all other variables constant at their mean, farmhouseholds that believe they are going to cultivate their plot in 5 years increases their probability of preferring traditional rock terracing by 0.07, and the probability of traditional soil terracing by 0.05; while it decreases the probability of opting for ditch digging by For the farm-household whose land is private (allocated or inherited), the probability of preferring modern rock tracing decreases by a margin of In contrast to land ownership type, belief in ownership triggers greater changes in preference probabilities. For the farm-household that believes that the land is its own, the probabilities of preferring traditional rock terracing, traditional soil terracing and ditch digging increase by 0.03, 0.01 and 0.05 respectively, while the probabilities of opting for kitir works as well as other types decrease by 0.02 and 0.02 respectively. Land fragmentation seems to have a considerable impact on land conservation preferences. A standard deviation increase (centered around the mean) in the land fragmentation index decreases the probabilities of choosing traditional soil terracing, ditch digging, and kitir works by 0.03, 0.01 and 0.01, respectively, yet it increases the probability of traditional rock terracing and other types by 0.04 and On the other hand, plot age only has a marginal effect on preference. Panel B (see Figure 1) summarizes the effect of land tenure security on preferences among the types of land conservation investment. Market Access: The expectation of households concerning their return on their land conservation investment turned out to be the only viable market access variable to affect their preferences. If households expect their return from their land investment will reduce or 16

17 bring about no change in productivity, this decreases the probability of preferring traditional soil terracing by 0.08, of preferring kitir works by 0.02, and of other types by This negative expectation also decreases the probability of traditional soil terracing by about Panel C (see Figure 1) summarizes the effect of market access factors on preferences among the forms of land conservation investment. Physical Incentives (Plot Characteristics): Holding all other variables constant at their mean, the fact that a household has its plot in the highlands decreases that household s probability of preferring traditional soil terracing by 0.24, and increases the probability of preferring kitir works by Having a plot situated on steep uphill ( dagetama ) slopes decreases the probability of choosing traditional soil terracing by 0.12 and decreases that of choosing ditch digging by Further, if a plot has access to irrigation, the probability of opting for modern rock terracing is decreased, while for plots used primarily for grazing (as opposed to plots being farmed or fallowed), the probability of preferring traditional soil terracing is reduced by The above results concerning the effect of physical incentives on the probability of land conservation preference are summarized in Panel D (see Figure 1). Looking at Panels A through D, preferences for modern terracing with both rock and soil seem not to be influenced by poverty, land tenure security, market access, or physical incentive factors. Moreover, market access does not explain the probability of preferring ditch digging, kitir works and other types. All in all, the effects of physical incentive factors (i.e. plot characteristics) on preferences are the greatest, the impact of market access factors on preferences are about average, while the least influence on preferences is due to factors of poverty and land tenure security. Village Characteristics: The results seem to suggest that village characteristic variables (or community-level factors) have effects on farm-households preferences as to which type of 17

18 land conservation investment the particular household wishes to undertake. In relative terms, the effect of village characteristics is greater on the probability of choosing ditch digging and traditional soil terracing and these factors change probabilities by more than On the other hand, village characteristics exert the weakest effects on the probability of choosing modern terracing with both rock and soil Dynamism among the dependent outcomes: Interpreting the odds ratio The dynamic interrelationship among the dependent outcomes is not immediately visible from the measures of discrete change discussed above. This dynamism is better explained by interpretations using an odds ratio. Table 7 presents the factor changes in the odds for change in the independent variables relative to the other types. Table 7 about here Poverty: Households with access to credit are more likely to prefer traditional rock terracing, modern rock terracing and ditch digging than other types. The estimated odds for households with access to credit for preferring traditional rock terracing, modern rock terracing and ditch digging are respectively 2.30, 2.77 and 3.71 times higher than for preferring other types. The odds ratio for farm size is 1.74, 1.78, 1.92 and 2.80 for preferring traditional rock terracing, traditional soil terracing, ditch digging and kitir works respectively. This indicates that as household s total land holding increases, the odds of preferring traditional rock terracing, traditional soil terracing, ditch digging and kitir works increases by a multiplicative factor of 1.74, 1.78, 1.92 and 2.80, respectively. For the farm-household with access to extension services, the odds of preferring traditional rock terracing, modern rock terracing, traditional soil terracing, modern soil terracing and ditch digging are respectively 4.01, 3.37, 4.40, 11.81, and 4.42 times larger than of preferring other types. The odds ratios that a household with access to soil conservation advice prefers traditional rock terracing, traditional soil terracing and 18

19 ditch digging over other types are 0.25, 0.15, and 0.18, respectively. This implies that these options are less preferred when compared to the choice of other types. While interpretation of the odds ratio as discussed above (as can be noted by examining the results in Table 7) is possible and can be done for all of the independent variables, the larger number of coefficients (and hence the larger number of comparisons) makes it difficult to see patterns. An odds-ratio plot (see Figure 2) not only makes it simpler to see patterns in the coefficients, but also helps visualizing the effect of the independent variables on the preference of one outcome relative to each of the other outcomes. Figure 2 about here Figure 2 summarizes the odds ratio for the statistically significant (at least at 0.10 level) covariates. To allow comparison of the magnitudes of the effects of the covariates of poverty, land tenure security, market access and physical incentives, Panels I through IV are drawn with the same logit coefficient scale. In Panel I 9, credit access orders land conservation preferences from modern soil terracing to other types to traditional soil terracing to kitir works to traditional rock terracing to modern rock terracing to ditch digging, but none of the adjacent outcome categories (shown by the connecting lines) are significantly differentiated by access to credit. Accordingly, having access to credit increases the odds of preferring kitir works, traditional soil terracing, or other types relative to preferring modern soil terracing, but the effects are not statistically significant. On the other hand, having access to credit significantly increases the odds of preferring traditional rock terracing, modern rock terracing, and ditch digging relative to preferring modern soil terracing. Compared to its impact on the other outcomes, the impact of credit access on the 9 Panel I to Panel IV plot the coefficients relative to the base category outcome (i.e. Other types ), which is located on the factor change scale (top) at 1 and the logit coefficient scale (bottom) at 0. The relative magnitude of the effects for each variable is shown by the distance between this comparison outcome [i.e. 7 ( Other type )] and the respective outcome. If an outcome is to the right of another outcome, then increases in the covariate make the outcome to the right more likely. The connecting lines show whether or not the preference of one versus another type is differentiated by a particular independent variable. 19

20 preference for ditch digging is the largest. This can be seen by the greater spread in the scatterplot in Panel I. Compared to other indicators of poverty, the effects of a change in the standard deviation for farm size are positive and exert their greatest effect on the preference for kitir works. The effect of a change in the standard deviation of the number of adult female labor per hectare is generally much weaker. Overall, the effects on the preferences of access to extension are positive and those of access to soil conservation advice are negative, with both effects being greater than those previously mentioned. Exceptionally, access to extension assistance has the largest effect. Land tenure security: The results in Table 7 demonstrate that the odds of preferring traditional rock terracing and traditional soil terracing relative to other types are respectively 1.93 and 2.11 times greater for households who expect to cultivate the plot in 5 years than those who do not. On the other hand, the odds of preferring modern soil terracing relative to other types is 0.39 times lower for households who expect to cultivate the plot in 5 years than those who do not. The odds of preferring modern rock terracing over other types are 0.39 times lower for privately-owned plots than for plots that are not private. Furthermore, the odds of preferring traditional rock terracing, modern rock terracing, traditional soil terracing, modern soil terracing and ditch digging relative to other types are 2.17, 3.29, 2.42, 4.46, and 3.90 times greater among households who believe they own the land than those who do not. In terms of land fragmentation, the study reveals that the more household plots are fragmented, the less conservation investment will be undertaken. The graph in Panel II of Figure 2 summarizes the effect of the land security factors discussed above. Market access: The study reveals that the further households reside away from town, the less likely they will be to prefer traditional soil terracing and ditch digging than other types. The odds are respectively 0.98 and 0.99 times lower for preferring traditional soil terracing and ditch digging than other types. These results indicate that the odds of households, who 20

21 expect returns from land investment to decrease or to have no effect on productivity, preferring modern rock terracing are 0.42 times smaller than the odds of preferring other types. The graph in Panel III of Figure 2 summarizes the effect of market access factors discussed above. Physical incentives: The odds that traditional soil terracing will be preferred relative to other types are 0.31 times lower on plots situated in the highlands than plots situated elsewhere. On the other hand, the odds that kitir works will be preferred relative to other types are 6.46 times greater for plots situated in the highlands than plots located elsewhere. The odds that modern rock terracing, traditional soil terracing and ditch digging are preferred relative to other types are 0.34, 0.37, 0.45 times lower on plots situated on steep uphill ( dagetama ) slopes than on plots situated in other locations. The odds of plots with access to irrigation to be preferred for modern rock terracing are 0.13 times smaller than those for other types. Plots primarily used for grazing are less likely to be preferred for traditional soil terracing than other types. Finally, the odds of plots used primarily for grazing to be preferred for traditional soil terracing are 0.35 times smaller than for other types. The graph in Panel IV of Figure 2 summarizes the effect of the physical incentive factors discussed above. All in all, a comparison of Panels I through IV of Figure 2 reveals that the effect of market access on preference among the various forms of land conservation investments appears to be the weakest when compared to the impact of poverty, land tenure security or physical incentives. Among the poverty factors, female labor per hectare seems to have the weakest effect on a household s preference. With regard to land tenure security factors, the impacts of plot age and land fragmentation on preference are the smallest. Among the market access factors, distance of residence from the nearest car-road (accommodating car and truck traffic) and market place has the smallest impact on household preference. With regard to the physical incentive variables, the effects on preference of plot area and plot access to irrigation 21

22 are the weakest. 5. Summery and Conclusions This study examines the factors leading to differences in farm-households preferences for various forms of land conservation investment measures. It focuses on the role of poverty, land tenure security, market access and physical incentives in making this choice. The study made use of a rural household-plot level survey of farm-household behavior involving sustainable land conservation use in the Ethiopian highlands. Taking the random utility model as a basis, this study made use of the multinomial logistic model. The estimates of parameters were obtained using maximum likelihood. In addition, the Wald tests were carried out to test the model parameters. The results demonstrate that variables pertinent to poverty, land tenure security, market access, and physical incentives jointly influence farm-households preferences. They also confirmed the inappropriateness of pooling the different forms of land conservation investments, suggesting the need for separate treatment of each form in studying land conservation investment preferences. In general, the message learned from this study is that a farm-household s preference for undertaking a particular conservation measure on its specific plot is influenced by many variables. Specifically, the empirical evidence in this study shows that: o Poverty seems to drive farm-households towards short-term land conservation measures (such as grass cover, contour farming and forestation) which are less expensive (in terms of labor and money) and which entail less skill than long-term land conservation measures (such as rock or soil terracing, ditch digging and kitir works). An increase in farm size encourages farm-households to prefer traditional rock and soil terracing, ditch digging and kitir works. Farm-households with more adult female members tend to prefer kitir works. Soil or rock terracing (both traditional and modern) and ditch 22

23 digging are more likely to be preferred by farm-households with access to extension services than those without access to them. The effect of access to soil conservation advice, in contrast to what might be expected, is to detract from a farm-household s preference for traditional terracing (with rock or soil) and ditch digging. This may be due to the fact that advice on soil conservation might have focused on discouraging traditional land conservation measures. o Land tenure security has a mixed effect on a farm-household s preferences (in terms of short-term versus long-term measures). Expecting to operate the plot for the following 5 years is associated with a greater preference for traditional terracing with rock or soil, and a lesser preference for modern soil terracing. Privately-owned (allocated by a peasant association or inheritance) plots are less likely to be preferred for modern rock terracing. Farm-households who believe the land belongs to them tend to prefer long-term land conservation investment measures (terracing, either traditional or modern, with rock or soil). Farmers cultivating more fragmented plots are less likely to prefer either traditional or modern rock terracing, traditional soil terracing, ditch digging or kitir works. A plot acquired (by the farm-household) for a long period is associated with a lesser preference for traditional terracing (with rock or soil), ditch digging, and kitir works. o A farm-household s access to market, in general, seems not to matter in regard to that household s decision to preference a particular form of land conservation investment. While the distance of a farm-household s residence from town diminishes the household s preference for traditional soil terracing and ditch digging, a farm-household that expects an increasing return on land investment is more likely to prefer traditional soil terracing. o A farm-household also considers the characteristics (physical incentives) of the plot when deciding which land conservation investment to choose. Plots situated in the highlands are 23

24 less likely to be opted for traditional soil terracing and are more likely to be chosen for kitir works than plots situated elsewhere. Plots situated on steep uphill ( dagetama ) slopes are less likely to be preferred for traditional soil terracing and ditch digging. Moreover, farm plots with access to irrigation are less likely to be preferred for modern rock terracing, while plots primarily used for grazing are less likely to be preferred for traditional soil terracing. All in all, this study has demonstrated that a farm-household s preference as to which form of land conservation investment to undertake on its particular plot is a complex decision. This can be noted from the large number of statistically significant covariates in the model, each of which contributes marginally to the overall decision process. 6. Policy and Research implications: Poverty: The results in this study suggest the importance of disaggregating the different components of poverty in assessing its effect on farm-household s decision as to which type of land conservation to prefer. Policies and programs that do not consider which type of poverty (and how) affects farm-households' preference decisions may lead to erroneous conclusions. All in all, poverty seems to drive farm-households towards short-term land conservation measures (such as grass cover, contour farming and forestation) which are less expensive (in terms of labor and money) and which entail less skill than long-term land conservation measures (such as rock or soil terracing, ditch digging and kitir works). This study confirms that long-term land conservation investments such as rock terracing (both traditional and modern) and ditch digging are likely to be preferred among farm-households with access to credit. This important role of access to credit on farm-household s preference decision needs to be recognized by land conservation policy makers. This study also suggests the important role of access to extension services and soil conservation advices in creating awareness and knowledge among the farm-households in their preference decisions. The 24