Government-initiated community resource management and local resource extraction from Nepal's forests
Introduction
Recent widespread concern about the destruction of the world's forests has elevated forest management issues to the forefront of development policy discussions. A consensus has emerged that local communities should be involved in managing their forests, and nearly every country in the world is experimenting with some form of “Community Forestry”.1 However, the capacity for governments to initiate and organize local communities to manage and protect their forests has not been established, especially not on a national scale. This study exploits an unusual policy experiment in Nepal to consider the impact of a nationwide community forestry program on resource extraction from local forests.
Forests have always been of tremendous policy importance in Nepal. Ninety-four percent of rural households in the hill and mountain areas of Nepal rely on fuelwood as their primary fuel for heating and cooking.2 In 1957, in order to protect local forests, the government of Nepal nationalized all forests in hill or mountain areas on greater than 1.25 ha of land (Bromley and Chapagain, 1984). Subsequent to nationalization, the government of Nepal created an administrative structure to protect and manage national forests, the Department of Forests. Deforestation and degradation appears to have accelerated under national management (Palit, 1996). Consequently, in 1993, the government of Nepal abandoned national management, passing the Forest Act of 1993 that transferred all accessible forestland from the central government to local communities through the creation of “Forest User Groups”.
The Forest Act redirects the field staff (“foresters”) of the Department of Forests from protecting national forests towards building forest user groups to manage all of the nation's forestland without interference from outside of the community. In a climate of excessive degradation, the first action of newly created forest groups is to restrict local resource extraction.3 Forest groups created under the Forest Act receive a set of guidelines describing how to accomplish this. They can ration access to the group's forest and tax anything removed from the forest. Most groups receive funding and assistance to hire guards and build fences around the forestland, both of which make rationing and taxation enforceable. In addition, transferring land to local users may cause users to internalize any externalities associated with extraction (as with privatization), or it may spur users to cooperate and coordinate their forest use (as in Runge, 1981). While forest groups have the capacity to affect reductions in resource extraction, it is an empirical question as to whether government-initiated community institutions actually function and constrain extraction after they have been created.
This paper takes advantage of the fact that building forest user groups for every forest in Nepal is a long, time-consuming process. By 1996, foresters have built more than 4000 new forest user groups, but these forest groups cover less than 10% of Nepal's forestland (Edmonds, 2000). Using household survey data from 1995/1996 merged with an administrative census of forest groups in the Arun Valley of eastern Nepal, this study compares resource extraction in areas with and without forest groups. In the cross-sectional (unconditional) mean, I find resource extraction to be 14% lower in areas with forest groups.
However, forest groups are not randomly placed. Thus, in this paper, I discuss forest group formation (in Section 2) and use this institutional detail in three distinct approaches to evaluate the robustness of this 14% difference. First (in Section 3), I include a variety of sets of control variables to control for observable differences in the placement of forest groups. I consider parametric, semi-parametric, and nonparametric functional forms. With this conditioning on observables approach, I am unable to reject the 14% difference in resource extraction observable in the raw means. The obvious criticism of conditioning on observables is that there is no way to test for whether one is controlling for all the differences associated with forest group placement. In my second estimation approach (Section 4.1), I compare resource extraction by households in communities that receive forest groups immediately before the household survey to extraction by households that receive forest groups immediately after the survey. This comparison identifies the effect of forest groups under the assumption that communities receiving forest groups at a similar point in time are most comparable. The results of this switching communities approach suggest larger reductions in resource extraction than the conditioning on observables approach, but the two estimates are not statistically different from the 14% sample mean.
Finally (in Section 4.2), I treat the placement of forest groups as an endogenous variable that depends on the accessibility of a community to foresters. In this endogenous programs approach, I use a set of variables describing the accessibility of a community to foresters as instruments to predict the location of forest groups in the first stage of an instrumental variables model. The results of the endogenous programs approach are of a similar magnitude to the switching communities findings and within a 99% confidence interval of the conditioning on observables results. As I discuss in Section 5, the evidence in this paper is consistent with government-initiated community institutions having the capacity to operate without extensive external oversight to reduce local resource extraction relative to the resource management regime that exists in the absence of forest user groups.
Section snippets
Background information
In this paper, I use data from the Arun Valley of Nepal.4
Controlling for observable heterogeneity by conditioning on observables
I first attempt to control for differences between areas induced by the placement rule (3) using various controls for the determinants of household fuelwood collection and for the placement of forest groups. Let X denote this vector of controls. The impact of forest groups is identified, if conditional on the control variables X, the error in estimating Y0i is the same in areas with and without user groups: E[ε0∣X,v>v*]=E[ε0∣X,v<v*]. Moreover, the identification of the impact of forest groups
Modeling omitted heterogeneity
The identification of the effect of forest user groups on fuelwood collection in the previous section relies on two assumptions. First, conditional on the control variables, the assignment of forest user groups is independent of firewood collection. Second, there must be overlapping support of the control variables. In this section, I estimate the effect of forest groups on fuelwood by exploiting the institutional detail of the user group formation process described in Section 2. First, in the
Conclusion
This study uses a single cross-sectional dataset to assess the impact of forest groups on the collection of wood for fuel. The evidence in this paper is consistent with forest user groups reducing household extraction of fuelwood from the forest. Point estimates of the magnitude of this effect vary across estimation methods, but all results are within a 99% confidence interval of the 14% reduction in wood extraction found in the raw sample mean.
The principal econometric problem in this paper is
Acknowledgements
I received financial support on this paper from the National Science Foundation, the John D. and Catherine T. MacArthur Foundation, and the Andrew W. Mellon Foundation. I have benefited from the insights and assistance of Marianne Bertrand, Kristin Butcher, Anne Case, Angus Deaton, Karla Hoff, Bo Honore, Keshav Karmacharya, Douglas Miller, Jonathan Morduch, Nina Pavcnik, Chris Paxson, Giovanna Prennushi, Yam Raya, K.B. Shrestha, Rajendra Shrestha, Jonathan Skinner, Sanjay Verma, and two
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