Integrating the Management of Ruaha Landscape of Tanzania with Local Needs and Preferences

Abstract

Sustainable management of landscapes with multiple competing demands such as the Ruaha Landscape is complex due to the diverse preferences and needs of stakeholder groups involved. This study uses conjoint analysis to assess the preferences of representatives from three stakeholder groups—local communities, district government officials, and non-governmental organizations—toward potential solutions of conservation and development tradeoffs facing local communities in the Ruaha Landscape of Tanzania. Results demonstrate that there is little consensus among stakeholders about the best development strategies for the Ruaha region. This analysis suggests a need for incorporating issues deemed important by these various groups into a development strategy that aims to promote conservation of the Ruaha Landscape and improve the livelihood of local communities.

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Acknowledgments

This publication was made possible through support provided to the Global Livestock Collaborative Research Support Program by the Office of Agriculture, Bureau for Economic Growth, Agriculture and Trade, United States Agency for International Development under terms of Grant No. PCE-G-00-98-00036-00, support from a Borlaug LEAP Fellowship and Jim Ellis Award (MM), and the Gund Institute for Ecological Economics at the University of Vermont. The opinions expressed herein are those of the author(s) and do not necessarily reflect the views of the USAID.

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Correspondence to Michel Masozera.

Appendix: Conjoint Model Specification

Appendix: Conjoint Model Specification

A random utility model is used to explain local stakeholder preferences toward various environmental, economic and social aspects associated with designation and management of protected areas. When presented with a set of alternatives, individuals are assumed to make choices that maximize their utility or satisfaction. The utility that the ith individual derives from the choice of the jth alternative (U ij ) can be represented as:

$$U_{ij} = \overline{{U_{ij} }} + e_{ij} = X^{\prime}_{ij} \beta + e_{ij}$$
(2)

where X ij is a vector of variables which may include transformations of variables that represent values for each attribute of the jth alternative to the ith individual; β is a vector of unknown parameters; and e ij is a random disturbance, which may reflect unobserved attributes of the alternatives, random choice behavior, or measurement error. In the empirical study under consideration, a respondent’s utility level (U ij ) for each of the J alternatives is not observed but a ranking (r j ) is observed that corresponds to the order of his or her underlying utilities. For example, the probability of alternative 1 being ranked above other alternatives is:

$$\begin{aligned} P_{i1} & = \Pr (U_{i1} > U_{i2} \,{\text{and}}\,U_{i1} > U_{i3} \ldots \,{\text{and}}\,U_{i1} > U_{ij} ) \\ & = \Pr \left[ {(e_{i2} } \right. - e_{i1} ) < (X^{\prime}_{i1} \beta - X^{\prime}_{i2} \beta )\,{\text{and}}\,\left. {(e_{ij} - e_{i1} ) < (X^{\prime}_{i1} \beta - X^{\prime}_{ij} \beta )} \right] \\ \end{aligned}$$
(3)

Similar expressions hold for each of the remaining alternatives being chosen next in the choice set, and the P ij values become well-defined probabilities once a joint density function is chosen for the e ij (Judge et al. 1985).

McKelvey and Zavoina (1975) developed a polychotomous probit model to analyze ordinal level dependent variables. They assume that the e ij values are distributed normally with mean 0 (the variance is standardized to unity), and that the observed variable (Y ij , the ranks for the J alternatives) is related to the true unobserved utilities (U ij ) in the following way:

$$Y_{ij} = 0\quad \,{\text{if}}\quad \,U_{ij} \le \mu_{i1} ,Y_{ij} = 1\,\quad {\text{if}}\quad \,\mu_{i1} < U_{ij} \le \mu_{i2} , \ldots Y_{ij} = J - 1\,\quad {\text{if}}\,\quad U_{ij} > \mu_{ij - 1}$$
(4)

The μ ik values define the boundaries of the intervals for the unobserved utilities that correspond to the observed ordinal response. Since the μ are free parameters, there is no significance to the unit distance between the set of observed values of Y; they merely provide the ranking.

Estimates are obtained by maximum likelihood, and the probabilities entering the log-likelihood function are the probabilities that the observed ranks (Y ij values) fall within the J ranges defined by J + 1 μ values. The parameters to be estimated are J − 2 μ values plus the β vector; μ 0 and μ J are assumed to be negative and positive infinity, respectively, and μ 1 is normalized to 0. Mckelvey and Zavoina (1975) describe the model and maximum likelihood estimators in greater detail.

In the polychotomus probit model the estimated value \((X'_{ij} \beta )\) for an observation determines the position of the mean of the distribution of response categories over underlying scale. The \(\mu 's\) delineate ranges of the unobserved underlying variable (utility) that correspond to the observed response categories. The estimated probability that a response falls in each category or rank in the case under consideration is measured by the area under the normal standard density curve \(\left[ {f(X'_{ij} \beta )} \right]\) and bounded by the respective μs. These probabilities can be computed using the estimated model parameters:

$$\Pr (Y_{j} = k - 1) = \Pr (U_{j} \,{\text{is in the kth range}}) = F(\mu_{k} - X'_{j} \beta ) - F(\mu_{k - 1} - X'_{j} \beta )$$
(5)

where k indexes the rankings and F(…) is the cumulative distribution function, assumed normal for the probit specification. Thus, the effect of a discrete change in the level of the nth independent variable (x nj ) on the estimated probability that a response will fall within each of the categories (ranks) can be calculated by substituting the estimated parameters (β and μ values) into Eq. (5). The magnitude of that change will depend on the values for all the estimated parameters and associated variables, as indicated by Eq. (5).

The probit formulation appears to offer the most theoretically sound technique, primarily because it does not exhibit the characteristic of independence of irrelevant alternatives (IIA). For this reason, it was chosen as the primary procedure for estimating the conjoint model.

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Masozera, M., Erickson, J.D., Clifford, D. et al. Integrating the Management of Ruaha Landscape of Tanzania with Local Needs and Preferences. Environmental Management 52, 1533–1546 (2013). https://doi.org/10.1007/s00267-013-0175-9

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Keywords

  • Africa
  • Community-based conservation
  • Community wildlife management associations
  • Conjoint analysis
  • National parks
  • Ruaha
  • Tanzania