behavioral determinants of citizen involvement: evidence

Eric A. Coleman is assistant professor

in the Department of Political Science at

Florida State University. His research focuses

on the local participation and management

of common-pool resources.

E-mail: [email protected]

642 Public Administration Review • September | October 2014

Public Administration Review,

Vol. 74, Iss. 5, pp. 642–654. © 2014 by

The American Society for Public Administration.

DOI: 10.1111/puar.12249.

Eric A. ColemanFlorida State University

Th is article examines the robustness of citizen involvement in decentralized governance. It develops two behavioral theories of citizen involvement and examines their relative explanatory power with survey data collected from subsistence households in forest-dependent communities in Bolivia, Kenya, Mexico, and Uganda. Counterintuitively, the analysis fi nds that households that have been engaged with collective action the longest are the most likely to disengage from decentralized institu-tions once they confront crises. Th is result is interpreted in light of psychological self-licensing theory: people justify noninvolvement with decentralization precisely because of their past eff ort. Th is result implies that policies that rely on local involvement may be unsustainable insofar as they fail to address the underlying vulnerability of local users. In order to ensure that citizen involvement with decentralized governance is consistent and eff ective, policies need to address the structural factors that make users vulnerable to crises.

The question of why citizens become involved in the policy process has occupied public administration scholars for some time (Yang

and Pandey 2011). In particular, much research seeks to understand citizen involvement in systems with political and administrative decentralization (Devas and Grant 2003; Escobar-Lemmon and Ross 2014; Hart 1972). Robust citizen involvement with decen-tralized policy is important for two reasons (Yang and Pandey 2011). First, it is instrumental to ensure that local policy makers are accountable to citizens—a prerequisite to meeting other decentralization objec-tives, such as curtailing corruption, mitigating elite capture, reducing economic and social inequality, and improving sustainable resource use (Escobar-Lemmon and Ross 2014; Faguet 2014; Ribot, Agrawal, and Larson 2006). Second, citizen involvement is fundamentally an important—if not the primary— objective of decentralization itself (Agrawal, Chhatre, and Hardin 2008;

Andersson, Gibson, and Lehoucq 2004; Andersson and Van Laerhoven 2007): as citizens become involved, they develop local political networks that can “sever paternalistic relationships with the cen-tral state” and thus promote democracy from below (Taylor 1998, 129).

But why do citizens sacrifi ce their ongoing time and energy to engage with decentralized policy making? Citizen involvement poses a collective action dilemma: people, in general, will benefi t from the involvement of others, but it is individually costly to become involved oneself. Th us, each citizen has an incentive to free ride on the eff orts of others. As Olson (1965) demonstrates, few citizens become involved in the pol-icy-making process, and the little citizen involvement that does exist tends to be dominated by elites (Boone 2003; Dye, Schubert, and Zeigler 2012). Research in American politics, for example, consistently shows that citizens with high socioeconomic status are more likely to participate in politics (Leighley 1995), especially to the extent that those with status have the necessary time, resources, and civic skills (Brady, Verba, and Schlozman 1995). Despite this empirical regularity, many theorists see broad citizen involvement as neces-sary for sustainable local governance in complex policy environments (Agarwal 2010; Agrawal and Gupta 2005; Wester, Merrey, and de Lange 2003).

Institutional approaches to the problem of citizen involvement tend to emphasize that people will respond to material incentives; policy changes that alter these incentives can make involvement more instrumentally worthwhile for citizens (Hardin 1982;

Olson 1965; Ostrom 1990). Behavioral approaches to the problem, on the other hand, tend to emphasize the impor-tance of nonmaterial psychic incentives (Andreoni 1995; De Rooij, Green, and Gerber 2009; Ostrom 1998). It is fair to say that the extant evidence

Behavioral Determinants of Citizen Involvement: Evidence from Natural Resource Decentralization Policy

Citizen involvement poses a col-lective action dilemma: people, in general, will benefi t from the involvement of others, but it is individually costly to become

involved oneself.

Behavioral Determinants of Citizen Involvement: Evidence from Natural Resource Decentralization Policy 643

with the authority to make some management decisions about how the resource will be used. 1 Much of the literature evaluating resource decentralization thus examines the decisions made by local govern-ments and links them to outcomes such as ecological conditions and the livelihoods of citizens who depend on the resource for subsist-ence or commercial income (Larson and Soto 2008). Th e evidence from this research is decidedly mixed: decentralization sometimes improves ecological conditions and local livelihoods, and sometimes it fails to do so (Bartley et al. 2008; Coleman and Fleischman 2012; Larson and Soto 2008). In a literature review, Larson and Soto (2008, 221), however, fi nd that when local governments that receive power in the decentralization process are accountable to local popu-lations, positive eff ects are likely. Th e involvement of local citizens in local policy decisions is thus a crucial component to understand when decentralization is likely to be eff ective.

Despite the critical role of citizen involvement, most work on decentralized natural resource management does not try to explain citizen involvement with local policy (Faguet 2014). However, some evidence does suggest that socioeconomic status, other forms of political participation, and education are important factors (Agrawal and Gupta 2005). Other work emphasizes the role of institutions that systematically encourage or discourage participation among subgroups of resource users (Agarwal 2001).

Examining these questions is not merely a matter of academic concern. Resource decentralization policies are now ubiquitous throughout the developing world (Faguet 2014) and represent a large share of resource governance (Larson and Soto 2008). For example, Agrawal, Chhatre, and Hardin (2008) describe the vast—and growing—amount of locally managed forest commons through-out the world, while Basurto et al. (2012) report that 200 million to 250 million people participate in the management of local fi sheries worldwide. Th us, understanding why some people robustly engage with these institutions helps us understand political behavior for a large and growing segment of the world population in relation to the shared resources on which they critically depend.

Table 1 provides a summary of the context regarding forest decen-tralization policies and the degree of broad local engagement for each country in the present article. Coleman and Fleischman (2012) overview these policies in more detail; however, this article only briefl y outlines how each country has decentralized forest manage-ment and how local communities have responded.

Mexico has the longest history with legally recognized local man-agement of forest resources. Th e Mexican government formally recognized the rights of ejidos (communally held lands) to forest resources in 1917. Although not without confl ict, ejido members have actively participated in forest management since that time. Th us, forest institutions are well established, and there is wide participation from subsistence users in Mexico compared with the other states. On the opposite end of the spectrum, Kenyan decen-tralization is far more recent, and local forest users have limited legal authority to make decisions about forest use without approval from the Kenya Forest Service, a national bureaucracy.

Bolivia and Uganda represent intermediary cases in the degree of de jure authority of local people to make legally binding decisions

of citizen involvement in the policy process has been dominated by the institutional approach. Th is article provides a unique contribu-tion to the fi eld by examining behavioral theories to understand the problem of citizen involvement.

Two opposing theoretical views are used to explain citizen involve-ment with decentralized governance, each of which critically depends on a citizen’s level of past collective action. Identity theory (IT) proposes that people become involved with local policy making because past collective action establishes norms of cooperation that become internalized during the process of cooperating with others (Gneezy et al. 2012). People with extensive histories of collec-tive action are thus more likely to internalize such norms and act collectively in the future. Furthermore, these norms continue to motivate costly collective action even when it becomes more costly to remain involved in the policy process. Alternately, self-licensing theory (SLT) proposes that people can justify noncooperation if they have a social history that demonstrates their cooperativeness in the past (Merritt, Eff ron, and Monin 2010). Past good behavior on their part provides a license to minimize cooperation when times are tough.

Th e analysis examines these questions by drawing on a unique data set that records the self-reported behavior of 1,433 surveyed house-holds aff ected by forest decentralization policies in Bolivia, Kenya, Mexico, and Uganda. Each of the four countries has decentralized resource management in the forestry sector. Statistical analysis shows that when citizens confront crises—as those in the developing world routinely do—they psychologically license themselves to withdraw their involvement from decentralized policy forums. Th at is, those who have engaged in collective action in the past are the most likely to forgo collective action when times are tough. Th is implies that policies that rely on local involvement may be unsustainable insofar as they fail to address the underlying vulnerability of local users. In order to ensure that citizen involvement with decentralized govern-ance is consistent and eff ective, policies need to address the struc-tural factors that make users vulnerable to crises.

The Context of Natural Resource DecentralizationOver the past three decades, many international donors and development scholars have become enamored with decentraliza-tion (see Treisman 2007), and nowhere is this enthusiasm more apparent than in eff orts to decentralize the governance of natural resources (Andersson, Gibson, and Lehoucq 2004). But even as central governments continue to decentralize rights and responsi-bilities for resource governance to local actors, there is still strong debate about the eff ectiveness of such strategies (Larson and Soto 2008). Optimists believe that decentralization can simultaneously promote democratic involvement in policy making and increase the accountability of government actors, all while increasing the sustain-ability of resource use (World Bank 1997). Pessimists, on the other hand, believe that decentralization is often employed to reinforce the power of the central state by providing patronage to local elites (Boone 2003; Ribot, Agrawal, and Larson 2006).

Broadly, decentralization “refers to a transfer of powers from central authorities to lower levels in a political-administrative and territorial hierarchy” (Larson and Soto 2008, 216). When states decentralize natural resource management, they imbue lower-level governments


positions—self-licensing theory and identity theory—argue that the eff ect of personal crises on present citizen involvement depends on their past collective action. Th e central empirical question in this article, then, is how does the marginal eff ect of personal crises change for those who have diff erent histories of collective action? From a statistical modeling perspective, this means the theoreti-cal interest lies in the interaction eff ect of personal crises with past collective action. Th e remainder of this section will explain how self-licensing theory and identity theory address this question.

Self-Licensing TheorySelf-licensing theory argues that past prosocial behaviors and attitudes license people to behave selfi shly in the present (Merritt, Eff ron, and Monin 2010). If people have established prosocial cre-dentials in the past, they feel unencumbered to engage in potentially suspect behavior in the present. In a recent survey, Merritt, Eff ron, and Monin (2010) explore numerous of examples of self-licensing theory in venues as diverse as political correctness, prosocial behav-ior, and consumer choice. For example, after subjects are allowed to establish nonracist credentials in an experiment, they are more likely to subsequently exhibit racist attitudes than those who were never allowed to establish nonracist credentials in the fi rst place (Eff ron, Cameron, and Monin 2009). Another popular example is when people donate less to charity if they recall a past time when they donated (Sachdeva, Iliev, and Medin 2009). Th us, those who have established prosocial credentials more easily justify subsequent antisocial behavior.

Crises exacerbate the temptation people have to act in their narrow self-interest and free ride in a collective action dilemma. Crises cause stress, reducing people’s capacity to resist the temptation to free ride (DeWall et al. 2008). Th e pernicious consequences of frequent stress caused by household crises have been demonstrated in a variety of development settings (Banerjee and Dufl o 2011, 198–200). In terms of collective action, some evidence suggests that stress subdues the guilt associated with antisocial behavior; once the guilt for behaving noncooperatively subsides, people more easily justify non-cooperation through self-licensing (Xu, Bègue, and Bushman 2012).

People may thus willingly cooperate when they do not face crises but then justify noncooperation when times are tough. Th e main tenet of self-licensing theory is that people can easily justify noncoopera-tion if given a plausible reason to excuse their behavior. Reviewing the literature in behavioral economics on self-licensing and prosocial behavior, Benabou and Tirole conclude that “it takes remarkably little to turn [prosocial] behaviors on or off . Th e slightest change in framing, the thinnest of veils as to the moral implications of their

regarding forest governance. Bolivian decentralization started around 1997 and gave indigenous communities rights to forest management where none had previously existed. Th is resulted in signifi cantly more participation in forest governance than prior to decentralization, although this appears to be mediated by municipal political institutions (Andersson and Gibson 2007). Ugandan policy regarding local governance is far more variegated: since decentrali-zation eff orts starting in about 1993, management has fl uctuated between centralized and decentralized control depending on the broader political climate (Bartley et al. 2008). Th is institutional instability appears to have led local elites to cautiously control local resources, although groups of forest users appear to have mobilized as well (Coleman and Fleischman 2012).

In each country—as well as in virtually all countries where resource decentralization occurs—policy goals explicitly state a desire for increased involvement in decision making by local users (Andersson, Gibson, and Lehoucq 2004). Furthermore, becoming involved in local governance institutions creates a collective action dilemma in which users are tempted to simply free ride on the eff orts of other users. Th us, these cases provide apposite data to examine the hypotheses of self-licensing theory and identity theory.

Self-Licensing, Identity, and the Robustness of Citizen InvolvementCitizen involvement poses a collective action dilemma and is thus costly and susceptible to free riding (De Rooij, Green, and Gerber 2009). An extremely promising area of research in behavioral theory emphasizes impulse control (Baumeister and Tierney 2011; McGonigal 2011). For consistent citizen involvement in public policy, people must be able to control the impulse to free ride on the involve-ment of others. Impulse control depends on a number of factors, but stress in particular is important. Frequent stress is an acute problem among households in the context of forest decentralization—subsist-ence users who tend to be “disproportionately vulnerable to loss of livelihood and assets, dislocation, hunger and famine” (Ribot 2010, 48). Th ese personal crises cause stress, which then reduces impulse control, making involvement in the policy process less likely. Th is mechanism can be alternatively framed as an increase in the oppor-tunity costs of participation: once households experience a crisis, it becomes relatively more expensive to be involved in public policy.

Th e key theoretical advance of the present article, however, is not that crises decrease the probability of citizen involvement. Instead, the theoretical reasoning outlined here discusses how crises aff ect citizen involvement diff erently for those who have been involved in the past compared to those who have not. Two opposing theoretical

Table 1 Summary of Forest Decentralization Policies

Country Forest Decentralization Policy Local Governance

Bolivia (n = 153) Decentralization starting in 1997 gave property rights to indigenous groups where there were none before.

Local groups are engaged in more rulemaking and have greater investment in forests than before decentralization.

Kenya (n = 337) Decentralization started in 2005, but most power to control forest policy remains centralized.

Local groups are not as active in governance as the other countries.

Mexico (n = 236) There is a long history of decentralized forest management at the ejido level (legally recognized communities).

Well-organized, formal, and active local governance authorities.

Uganda (n = 707) The degree of de jure rights of local groups to actively manage forests has fl uctuated widely since initial efforts at decentralization in 1993.

High variability in local group organization and participation; trends toward increased rulemaking and forest investments since 2003 reforms.

Note: The number of household surveys in parentheses.


Identity theory does not require that people are completely unaff ected by personal crises, just that those who have a past history of collective action are relatively less aff ected than those without such a history. Th e logic of this argument is expressed in the following hypothesis:

IT Hypothesis: Past collective action decreases the capacity of crises to reduce present citizen involvement.

In a statistical modeling framework, the IT hypothesis implies that the interaction between past collective action and crises is positive.

Survey Research DesignTo examine the hypothesis derived from these theories, data were collected from users residing near forests with decentralized manage-ment in Bolivia, Kenya, Mexico, and Uganda. Th e data come from the broader International Forestry Resources and Institutions (IFRI) research program (see Wollenberg et al. 2007 for details). IFRI maintains a data repository from fi eld research conducted in vari-ous villages and forests in these four states, as well as in other states around the world. Both forest institutional data (data on the rules used in the forests) and biological data are collected at each research site under the same set of IFRI data-gathering protocols. Th ese protocols demand that researchers spend time in each village coding a common set of variables that are then integrated into the broader database. Social and institutional data are collected through eth-nographic research methods, including semistructured interviews, rapid rural appraisal, and key informant interviews. Biological data from the forest are also collected at each site.

In 2008, a household survey was conducted to augment the main IFRI data within each of the four states. Team leaders drew ran-dom samples of households in IFRI sites in the following manner: When there were more than 30 households at the research site, the researchers randomly chose between 30 and 200 households, approaching every third dwelling for an interview. When there were fewer than 30 households at the research site, the researchers sur-veyed all households. Th e interview asked approximately 100 ques-tions and took approximately one hour to complete. Households were asked to provide detailed descriptions of their assets, envi-ronmental preferences, demographic information, forest use, and participation in local forest institutions. In each forest, there is an organized user group that participates in the local policy-making process governing forest use; the survey also collected data on each household’s participation in these groups. Finally, the survey col-lected information about external stressors—personal crises—faced by each household in the past year.

Motivating Citizen InvolvementTh is section briefl y examines the reasons that are traditionally cited

for citizen involvement with decentralized governance and then examines their preva-lence in the present data. First, Mancur Olson (1965) emphasizes that collective action can be achieved if it is instrumentally rational for someone to contribute to the group. He suggests that selective material incentives can induce citizens to become involved by provid-ing some benefi ts that are exclusive to only group members who actively participate. For

choices suffi ces for many people to revert to self-interest” (2011, 809). Crises dramatically change circumstances and provide ample reason to justify noncooperation. Th us, self-licensing theory posits that crises reduce the propensity to behave cooperatively in a collec-tive action dilemma for those who have a history of collective action.

Self-licensing theory requires that two important elements are satis-fi ed before citizens become uninvolved with the local policy process: fi rst, that the citizen has exhibited a prosocial tendency in the past, and second, that he or she has suffi cient reason to justify noncoop-eration in the present. Crises provide such a reason. Th us, those with a past history of collective action will license noncooperation when confronted with crises. Th e following hypothesis expresses this logic:

SLT Hypothesis: Past collective action increases the capacity of crises to reduce present citizen involvement.

In a statistical modeling framework, the SLT hypothesis implies that the interaction between past collective action and crises is negative.

Identity TheoryIdentity theory posits that people have latent identities that moti-vate their behavior and that these identities reinforce norms of coop-eration in times of crisis. Th ere is a long literature in the sociology of behavior demonstrating that people behave according to the roles they construct around a situation-specifi c identity (e.g., Akerlof and Kranton 2000; Stryker and Burke 2000; Turner 1956). People want to retain internal consistency in their behavior and behave according to a “logic of appropriateness” that they construct according to the type of person they believe themselves to be in a particular environ-ment (March and Olsen 1989). If people self-identify as being the type of person who acts collectively—that is, by being involved in the policy process—then their behavior is likely to conform to their beliefs about this identity (Benabou and Tirole 2011). Gneezy et al. (2012), for example, fi nd that when people engage in an initially costly collective action, they self-identify as the type of person who cooperates. Th is costly initial cooperation causes norms of coop-eration to be internalized; this, in turn, motivates them to behave according to type so that they engage in subsequent collective action, thus avoiding the self-license to be noncooperative.

People who have internalized norms of cooperation—and thus identify as cooperators—fi nd it diffi cult to justify noncooperation and remain consonant with their self-identity (Gneezy et al. 2012). Th us, identity theory makes diff erent predictions about the eff ect of crises on citizen involvement than self-licensing theory. Identity theory antici-pates that, on the margin, those with a history of past collective action are less sensitive to the stress caused by personal crises than those without a history of collective action. Put diff erently, those without a history of collective action are less likely to self-identify as a cooperator; thus, when confronted with crises, they reduce their collective action without threatening their identity. On the other hand, those with a history of collective action fi nd such a decision more costly: noncoopera-tion confl icts with their identity both in terms of their internal self-image and in terms of the external reputation they have built within the group (Frank 1988).

Th ose without a history of collective action are less likely to self-identify as a cooperator;

thus, when confronted with crises, they reduce their collec-tive action without threatening

their identity.


forest management groups. Of the 1,433 surveyed households, 452 indicated that they participate in these forest management groups in some way. Each household ranked the top three, out of a possible seven, reasons for their participation; the data in fi gure 1 report their answers. Th e most frequently cited reason for participating was a moral norm—the duty to protect the forest for the future. Households also highly ranked selective material incentives in the form of increased access to forest products, future forest benefi ts, and access to government and nongovernmental organization benefi ts. Households ranked social reputation—in terms of being respected—less highly, but they ranked the social aspect of working together with others as important. Households rarely indicated that they felt forced to participate in these institutions by the govern-ment, chiefs, or neighbors. Th us, while there are diverse reasons households participate with forest management groups, no particu-lar reason seems to dominate the others.

Figure 2 compares motivations across the four states in this study. Th e fi gure shows the proportion of households that indicated each reason was one of their top three reasons for participating, as a fraction of all households that participated in some way. Th ere is some diff erence in motivations across the four states. For example, in Bolivia, there is a more even distribution in the number of people who cited each reason, while in Kenya, being respected and being

social are not as important as selective incen-tives and a sense of duty to protect the forest. In Bolivia, Kenya, and Mexico, a sense of duty was frequently cited, while in Uganda, it seems less important. In Uganda, on the other hand, the social aspect of participation seems far more important than in the other states.

Th ese data provide important information about the self-reported motivation for collective action and highlight diff erences in motiva-tion across the states. In particular, the data show that nonmaterial incentives such as social norms and a sense of duty are important factors motivating involvement for many people. Because many people are motivated by nonmaterial incentives, behavioral theories that explore this motivation will be appropriate to explain their involvement. For example, in times of crisis, people may reassess

example, in the realm of common-pool resource decentralization, those in local governance can provide legal access rights to appropri-ate for those resource users who contribute to the group. Although some people may appropriate illegally from the resource even if they do not become involved in local governance, the selective incentive of access rights can alleviate the risk of doing so.

Second, people may participate because of moral norms—that is, they participate in decentralized institutions because they believe it is the right thing to do (Bicchieri 2006; Elster 2007). For those who participate because of strong moral norms, the material costs and benefi ts of participating are much less salient than the deontological considerations of fulfi lling a moral imperative to act collectively for a just cause (Devall 1988). Hardin (1982, 103–12), for example, argues that some people act collectively from either a Kantian sense of moral duty or out of a sense that their participation enhances group solidarity for a cause they deem important. If people have a strong sense of community or environmental preferences, they may participate in decentralized common-pool resource governance even if it is not instrumentally rational for them to do so.

Th ird, people may participate because of social norms—that is, they participate because of group expectations that they act collectively and because others can observe and perhaps punish their behavior if they do not (Elster 2007). Th ere are two types of social norms: social pressure and social rep-utation. Social pressure promotes cooperation out of a desire to conform to the group, even in situations in which it may not be in one’s self-interest to conform (Asch 1955; Banerjee 1992). On the other hand, people may also be motivated to attain a reputation as the type of person who cooper-ates in the community of users. Work in sociology by Willer (2009) indicates that people who cooperate in collective action dilemmas gain status among the group, and such status has large reputation eff ects—it increases the likelihood of future cooperation and gift exchange from people in other domains.

To examine these motivations, each household in the survey indicated whether and why they participate in the decentralized

Note: Th e fi gure reports the number of households indicating that each reason was the top-ranked, second-ranked, or third-ranked most important reason motivating collective action in a decentralized forest management group.

Figure 1 Top Three Reasons for Participating in Decentralized Resource Governance

4712

4919823

3012722

31271144

46110143

26180123

5611642

0 50 100 150 200

Number of Households

Forced by govt, chiefs, or neighbors

Social aspect (working together with others)

Being respected and regarded as responsible

My duty to protect the forest for future

Access to govt or NGO benefits

Better management and more benefits in future

Increased access to forest products

Rank-Order

Motivations for Collective Action

Top Ranked 2nd Ranked 3rd Ranked

Th ere are two types of social norms: social pressure and social

reputation.


Independent VariablesTh e number of personal crises faced by the household and the household’s history of collective action are the key independent variables for the analysis. Th e variable Crises measures the number of crises faced by the household in the past year. Each household indicated in the survey whether they had experienced a crisis of any of the following types: serious crop failure, serious illness in family, death of a productive age-group adult, land loss (e.g., from expro-priation), major livestock loss (e.g., from fi re, theft, or fl ood), wed-ding, funeral, or other. Th e variable Crises adds the number of crises across the nine types of crisis. Th e variable ranges from zero to nine, with zero indicating that the household did not experience a crisis in the past year and nine indicating that the household experienced a crisis of each type.

Years in Local Organizations measures the past participation of each household in local collection action organizations. In the data set, some households did not participate in local organizations at all, and at least one household has been involved in local organizations for 64 years. Th ese organizations are sometimes related to forest management activities and sometimes not. Th is variable is meant as a general measure of self-identity: those who have long histories of collective action are those most likely to have established identities and reputations as cooperators. Households that have participated in local organizations for many years might react in two plausi-ble ways if they face a crisis in the present: they could justify not donating labor or attending meetings because of their long history of cooperation, or they could continue participating at a higher rate than those with a short history of collective action, thus acting in accordance with their previously revealed preferences.

Control VariablesTh e subsequent analysis includes a variety of control variables that could simultaneously aff ect both the key independent and depend-ent variables. Th e fi rst control variable is household dependence on forest products. Th is variable is the most obvious threat to estimating a spurious eff ect between the key independent variables and Labor Days. For example, those who heavily depend on the forest are likely

whether social norms still hold sway. Th is provides some justifi ca-tion for examining the explanatory power of the behavioral theories in the current article. However, this analysis does have some major shortcomings. First, people may not be forthcoming with their motivations in a survey. Second, the data fail to explain why some people act collectively while others do not, as only those who are involved answered these questions. Th ird, and fi nally, the data fail to speak to the question of robustness to crises. Th e analysis in the next section addresses these questions.

Analysis of Citizen Involvement with DecentralizationTh is section describes the statistical model used to test the self-licensing theory and identity theory hypotheses. Th e goal is to esti-mate the interaction eff ect between past collective action and crises on present citizen involvement in decentralized resource governance. Th is section fi rst describes the variables in the analysis and then turns to the statistical model used to test the hypotheses.

Dependent VariableTo test the hypotheses, the proposed statistical model uses a dependent variable that represents ongoing costly citizen involve-ment in decentralized governance by the household instead of the usual measures of political participation such as voting that only occur irregularly (Leighley 1995). Th e dependent variable, Labor Days, is the self-reported number of full working days each house-hold spent on group activities (i.e., attending meetings, polic-ing, and other joint work) over the past 12 months in the forest management group.2 Th ese activities are important predictors for successful decentralized common-pool resource governance. For example, although meeting attendance is a minimal bar for citizen involvement, it provides some indication of the level of coopera-tion with the group over and above those who do not participate in any meaningful way (Agarwal 2009). Policing and other joint work, more costly forms of involvement, have been directly linked to policy relevant outcomes: policing—that is, monitoring the use of others—has been correlated in previous work with sustainable forest outcomes (Coleman 2009; Ostrom and Nagendra 2006) and sustainable livelihoods (Chhatre and Agrawal 2009).

Note: The fi gure shows the proportion of households for which each motivation was in their top three reasons for participating in decentralized resource governance. The proportion is the fraction of households indicating the given reason out of all those who participated in some way. Households could indicate up to three reasons for participating, so each reason is not mutually exclusive.

Figure 2 Comparing Participatory Motivations

0 .2 .4 .6 .8

Proportion of Households

Uganda

Mexico

Kenya

Bolivia

Increased access to forest products

Better management and more benefits in future

Access to govt or NGO benefits

My duty to protect the forest for future

Being respected and regarded as responsible

Social aspect (working together with others)

Forced by govt, chiefs, or neighbors


majority ethnic group, controlling for socioeconomic status, also are likely to feel more pressure to participate. Th e model thus includes these two variables—Distance, a measure in kilometers from the household to the nearest village center, and Largest Ethnic Group, a binary indicator of whether the household belongs to the largest ethnic group (coded 1) or not (coded 0). Finally, the model includes the log of the Age of the household head in years and the Household Size, measured as the log of the number of people in the household as additional controls. Table A1 in the appendix reports the sum-mary statistics for all of these variables.

Statistical AnalysisTh e dependent variable in the analysis, Labor Days, is a continuous variable that has three complicating factors regarding its distribution (fi gure A1 in the appendix shows a histogram for Labor Days). First, the distribution of Labor Days is highly skewed to the right. Second, there is a high number of zero values. Th ird, the data were collected in a multilevel format, coming from 23 villages. Th e statistical model—a random-intercept Tobit corner-solution model—corrects for these complications. Th e Tobit corner-solution model corrects for the high number of excess zeroes, and, to address skewness, the model uses the natural log of Labor Days as the dependent variable (Wooldridge 2002, chap. 16).3 Th e corner-solution model concep-tualizes the decision to donate labor to the collective action group as each household solving an optimization problem of allocating their time among various activities. For many households, they will choose to abstain completely from resource governance; thus, an excess of zeroes builds at that point. Th e Tobit model corrects for this buildup of zeroes by modeling the distribution of the dependent variable as if the dependent variable were censored at zero. Finally, the model incorporates a random intercept (with random intercepts at the village level) to correct for the multilevel nature of the data. Th e fi nal estimated model is thus a random-intercept Tobit model in which 23 random intercepts are estimated from an assumed normal distribution of random intercepts across the villages.

All of the models reported in table 2 come from estimating random-intercept Tobit models when using the (natural log of ) Labor Days as the dependent variable. Model 1 reports the estimated eff ects of the independent and control variables, but it does not include an interaction eff ect between the key independent variables. Model 2 includes this interaction, but it does not include the control variables. Model 3 includes the controls, the independent variables, and the interaction between the key independent variables.

All of the estimated models fi nd that the longer a household has participated in local organization, the more labor it donates to the forest management group. Th e fi nding that past participation is signifi cantly and positively correlated with present levels of par-ticipation is common in many areas of political behavior research (Leighley 1995, 181–209). Model 1 in table 2 indicates that, on average, there is an insignifi cant relationship between crises and col-lective action, after controlling for the other variables.

Models 2 and 3 show that past collective action signifi cantly moder-ates the relationship between crises and the number of labor days the household contributes to the forest management group. In particular, the model estimates a signifi cant and negative interaction eff ect between crises and the number of years in local organizations.

to be more vulnerable to crises while simultaneously having stronger material incentives to participate in decentralized resource govern-ance. A novel method measures forest dependence and controls for this potential threat. Th e variable Forest Dependence is a continuous measure of how dependent the household is on raw and processed forest products. Th e variable Forest Dependence is calculated by fi rst having each household rank the importance of forest products in terms of subsistence and cash income relative to nine other sources of income (such as labor and farm income). Once the household has ranked forest income relative to other sources, they allocate 50 wooden tokens (provided by the survey administrator) that the respondent uses to further weight the importance of forest income; they do this by allocating these tokens into diff erent income-source boxes laid out on a sheet of paper. Th e fi nal variable for Forest Dependence is the percentage of total income that is derived from all forest products using both the weights and ranks of income sources. It is scaled from zero to 100, where 100 indicates that forest income is the most important income source and all 50 wooden tokens were placed on forest-income boxes; zero indicates that forest income is not important and no wooden tokens were placed on those boxes. Th is variable captures the fact that the households that are most reliant on the forest are also expected to contribute the most to forest management (after controlling for other factors).

Th e model also includes variables that measure the traditional explanations of collective action found in the literature and explored in the self-stated reasons examined in the previous section. Th ese include two variables to measure core beliefs and awareness of poli-cies surrounding resource management issues as outlined in the Advocacy Coalition Framework (Sabatier and Jenkins-Smith 1999) and applied in numerous settings to examine environmental policy attitudes and behavior (Lubell 2007; Lubell, Henry, and McCoy 2010). Th ese variables indicate the degree to which each household has norms that might motivate them to participate in forest manage-ment institutions. Forest Purpose is a binary indicator of whether the household perceives the forests primarily as an economic (coded 0) or an environmental (coded 1) resource. Policy Changes is a self-reported binary indicator of whether the household is aware of the policy changes regarding forest use. Th e literature on environmental preferences would anticipate that households with strong environ-mental attitudes and awareness of environmental politics are more likely to participate in decentralized resource institutions than house-holds that have weaker attitudes or are unaware of such policies.

Th e model also includes variables that measure each household’s socio-economic status to proxy the material incentives that drive collective action (Humphreys and Weinstein 2008). Th e following variables measure socioeconomic status: Farmer is a binary indictor of whether the household head is a farmer (coded 1) or not (coded 0); Cropland is the log of hectares of cropland owned by the household; and Percent Food Grown measures the percentage of food that the household con-sumed in the last year. Households that are self-suffi cient for subsist-ence sources are less likely to benefi t from collective action in the forest than those that must rely on forest products for their subsistence.

Finally, the model includes measures of social pressure. Household that live near the city center—and thus farther away from the for-est—face less pressure to become involved in forest management activities than those who live close to the forest. Households in the


experience crisis. However, for those who have been involved in local organizations for many years, the number of labor days they devote to local forest governance depends critically on whether they have faced crises. Panel (a) shows that households that have faced major crises and have been involved in local organizations for many years donate fewer labor days relative to households that have simi-lar histories of collective action but have experienced no crises.

Panel (b) of fi gure 3 shows that the marginal eff ect of Crises on donated labor days is signifi cantly decreasing in Years in Local Organizations. Th ose who have been active in local organizations for many years are more likely to reduce their collective action in response to crises than those who have not been active in local organizations for many years: people use their past collective action to self-license and decrease the labor they donate to the group when they confront crises. Substantively, the marginal eff ects plotted in panel (b) of fi gure 3 represent a proportion change in labor days for a one unit change in crises at various levels of years in local organizations. For those with about 10 or fewer years in local organizations, the marginal eff ect is estimated as positive, although this is not signifi cantly diff erent from zero. For those with more than about 10 years in local organizations, the eff ect is negative and increasingly negative the longer one has spent in local organizations. For example, the average individual who has spent 35 years in local organizations reduces his or her collective action if faced with an additional crisis by about 10 percent (i.e., the estimated marginal eff ect ≈ 0.10). Again, the overall results from fi gure 3 provide evi-dence in favor of self-licensing theory.

Table 2 also shows the results from estimating the eff ects of the control variables traditionally associated with collective action. Surprisingly, Forest Dependence does not appear to signifi cantly explain collective action after controlling for other factors. However, environmental views (Forest Purpose) and knowledge about envi-ronmental issues (Policy Changes) do appear to signifi cantly and positively correlate with collective action in the estimated models, as anticipated. Large households, those whose household head is more educated, farmers, those who grow a large portion of their own food, and households located in South America are signifi cantly more likely to engage in collective action as well. Th e remaining control variables are insignifi cant at the .10 level.

Model fi t statistics favor the multilevel random-intercept specifi ca-tion reported in model 3. Th e estimated variance of the random intercept (at the village level) is signifi cant in all models and approximately equal in size to the residual variance (the variance at the household level); thus, the intraclass correlation coeffi cient (the proportion of variance explained by the village level vari-ance) is about .5 in all models. Th ese results imply the need to account for the multilevel structure of the data. Furthermore, the log- likelihood, chi-squared statistic for joint signifi cance of all explanatory variables, and AIC (Akaike information criterion) statistic all favor model 3.

Discussion and ConclusionTh e main result of this article is that households with a long history of collective action are the most likely to self-license and disengage with decentralized institutions when they confront crises. Th is result is perhaps counterintuitive to those who conceive of collective action

Th is fi nding provides evidence in favor of self-licensing theory and against identity theory. Th at is, past collective action increases the capacity of crises to reduce present collective action. Figure 3 presents the substantive eff ects of this interaction.

Panel (a) of fi gure 3 plots the predicted number of Labor Days for those who have not experienced any crises and for those who have experienced nine crises, across the range of values for Years in Local Organizations. Panel (b) plots the estimated marginal eff ect of Crises on ln(Labor Days) across the range of values for Years in Local Organizations. Th us, this fi gure reports how past collective action changes the capacity of crises to aff ect current labor days devoted to collective action in decentralized forest governance. Panel (a) shows that those who have not participated in local organizations in the past are unlikely to devote many labor days to decentralized forest governance institutions in the past year, regardless of whether they

Tab le 2 Tobit Random-Intercept Models for ln(Labor Days)

Model 1 Model 2 Model 3

Crises 0.001 0.073 0.081(0.05) (0.05) (0.05)

Crises x Years in Local Organizations

–0.009*** –0.008***(0.00) (0.00)

Years in Local Organizations

0.051*** 0.101*** 0.089***(0.01) (0.01) (0.01)

Institutional Perceptions 0.122 0.124(0.13) (0.13)

Forest Dependence 0.002 0.002(0.00) (0.00)

Forest Purpose 0.516*** 0.517***(0.17) (0.17)

Policy Changes 0.703*** 0.703***(0.19) (0.18)

Largest Ethnic Group 0.067 0.058(0.23) (0.23)

Distance 0.021 0.028(0.10) (0.10)

ln(Household Size) 0.447*** 0.386**(0.16) (0.16)

Cropland –0.055 –0.049(0.07) (0.07)

ln(Age) –0.014 –0.026(0.27) (0.27)

Education 0.104*** 0.108***(0.02) (0.02)

Farmer 0.490** 0.487**(0.20) (0.20)

Percent Food Grown 0.012*** 0.012***(0.00) (0.00)

Africa –3.390*** –3.216***(0.97) (0.96)

Constant –2.432* –1.639*** –2.623**(1.33) (0.58) (1.32)

σu2.141*** 2.542*** 2.133***

(0.41) (0.46) (0.41)σe 2.075*** 2.218*** 2.049***

(0.08) (0.09) (0.08)Chi-squared 123.057*** 59.182*** 138.688***Log-likelihood –1217.802 –1355.389 –1209.744N 1,255 1,433 1,255AIC 2471.603 2722.778 2457.489BIC 2564.031 2754.383 2555.052

Notes: Random intercepts at village level (23 villages). The parameter σu is the standard deviation of the random intercept, and σz is the standard deviation of the residual error. Parameter standard errors are in parentheses. Two-tailed hypothesis tests: ***p < .01; **p < .05; *p < .10.


involvement, the results show that there may be serious behavioral factors that reduce the capacity of local actors to robustly engage with these institutions. Th us, institutional remedies to increase local engagement with decentralization may be insuffi cient insofar as they only create venues for participation and engagement without addressing the structural

and behavioral barriers to eff ective citizen involvement. Th is fi nding highlights how important it is to reduce the vulnerability of local users to crises in order to ensure that their involvement with decen-tralized governance is consistent and eff ective.

Th e results may point to even more pernicious eff ects: even well-intentioned development policies that try to encourage citizen involvement can leave communities less resilient to crises. Th is problem is likely to be even more acute among subsistence users

of natural resources, who will be especially vulnerable to the disturbances inherent in climate change (Ribot 2010). Th is is not to say that development projects should exclude local people from policy mak-ing; rather, involvement in policy needs to be accompanied by addressing the more structural problems faced by citizens in the developing world. For example, insur-ance against personal crises could go a long way in encouraging robust citizen engage-ment thereby increasing accountability in decentralization policy.

in terms of identity theory, in which people are motivated by norms that activate relatively more participation when people confront cri-ses. Th e important contribution of the current study is that crises moderate the eff ects of past participation in the opposite way: those who confront crises license themselves to disengage with politics when times are tough. Because crises systematically decrease involvement in decentralized institu-tions for those who have the longest history of collective action, the group composition of those engaged in decentralization forums will also change as crises come and go. Th is disrupts the ability of the group to cohesively and eff ectively act collectively (Ostrom 2000) and to provide the accountability so essential for eff ective decentrali-zation (Escobar-Lemmon and Ross 2014).

In situations in which crises are rare, the eff ects of self-licensing may not be particularly problematic; how-ever, for the states in the present study and in developing states more generally, crises are unfortunately frequent among those most aff ected by resource decentralization policies (Banerjee and Dufl o 2011; Ribot, Agrawal, and Larson 2006). Th e results of this article make an important contribution for those who think critically about whether decentrali-zation in the developing world can engender meaningful citizen involvement in the policy process (Faguet 2014). Rather than examining the potential institutional barriers to eff ective

23

45

E[L

abor

Day

s]

0 5 10 15 20 25 30 35 40 45 50 55 60 65

Years in Local O rganizations

No Crises

9 Crises

(a) Predicted Labor Days

-.5

-.4

-.3

-.2

-.1

0

Mar

gina

l Effe

ct o

f Cris

es

0 5 10 15 20 25 30 35 40 45 50 55 60 65

Years in Local O rganizations

(b) Marginal Effects

Panel (a): T he predicted number of Labor Days for a household that faces no crises (the minimum) or nine crises (the maximum) across the range of Years in Local Organizations. The expected labor days are calculated on the full censored, exponentiated dependent variable (so that the scale is on actual labor days rather than log labor days) from the estimated model 3 in table 2 while holding all variables at their mean and setting the random intercepts to zero. Panel (b): Marginal effects on the natural log of Labor Days—interpreted as the proportion change in labor days for a one-unit change in Crises. The estimated magni-tude of these marginal effects is shown by the solid dark line, while 95 percent confi dence intervals for the estimated marginal effects are shown by the dashed lines. The estimated marginal effects are calculated on the full censored dependent variable from the estimated model 3 in table 2 while holding all variables at their mean and setting the random intercepts to zero. The estimated confi dence intervals are calculated using the delta method.

Figure 3 T he Effect of Crises on Citizen Involvement with Decentralized Institutions across the Range of Years in Local Organizations

Households with a long history of collective action are the most

likely to self-license and disengage with decentralized institutions

when they confront crises.

Institutional remedies to increase local engagement with decentralization may be insuffi -cient insofar as they only create

venues for participation and engagement without address-

ing the structural and behavio-ral barriers to eff ective citizen

involvement.


Th e present study does have a number of shortcomings. First, the data used in this study do not come from a random sample of for-est communities in the world, nor do they come from a random sample of forest communities in the particular states being studied. Unfortunately, the sampling frame that would permit random selec-tion of sites is ill defi ned because many communities of potential users exist. Scholars of forest commons have acknowledged this problem for some time; the data gathered through the IFRI program, however, represent perhaps the most complete data collection eff ort to look at local forest governance institutions worldwide (Chhatre and Agrawal 2010). Within the IFRI framework, the survey ran-domly selected household within the predefi ned forest communities.

Second, using observational data, with the attendant statistical modeling assumptions, poses a challenge to test the study’s hypoth-eses—these include decisions about which control variables to use, the functional form of the estimated eff ects, and the specifi c probability model that was estimated. Th e results presented here, however, are robust to a variety of model specifi cations, including alternate models of the multilevel structure of the data and to using alternative dependent variables.4

Over all, the results presented here challenge scholars of decentraliza-tion, and citizen involvement more generally, to critically examine the factors that challenge robust involvement with decentralized public policy, especially in situations in which citizens frequently confront crises. If citizen involvement is fragile—that is, if people easily license themselves to disengage from politics because of their past behavior after they face a crisis—then this may have profound implications for political decentralization, engagement with democratic and collabora-tive decision making more generally, and accountability.

Notes1. It is important to note, however, that central governments often give little actual

discretion to sublevel governments and thus thwart the implementation of politi-cal decentralization (Ribot, Agrawal, and Larson 2006).

2. Th e variable for Labor Days comes from asking the following question: “How many person days (= full working days) did household members spend on group activities (meetings, policing, joint work, etc.) over the past 12 months?” Th at is, we only have a single measure of the number of total labor days. To check the robustness of our results to this question, we replicated this analysis in a number of ways, reported in the appendix in table A4. Model 1 in table A4 codes the dependent variable as binary (1 if the household contributed any labor days and 0 otherwise); model 2 reports the results when using a binary variable indicating whether the household attends meetings, in particular (1 if attended meetings, 0 otherwise); and model 3 uses a binary variable of whether the household is a formal member of the forest user group (1 if yes, 0 if no) involved in the policy process. Th e results from each of these models substantiate the main fi ndings of the article (a signifi cant negative interaction eff ect, although the magnitude is diff erent because of the scale of the dependent variable).

3. Th e analysis follows the convention outlined in Cameron and Trivedi (2009, 532–33). When taking the natural logs of zeroes, the model treats all logs of zero values as censored and identifi es the censored value of the distribution as a very small negative value.

4. Appendix table A2 reports results when estimating the model separately for each country. Th e main result (interaction eff ect) is signifi cant in three of the four countries, with Kenya being the lone exception. Even in Kenya, however, the result is still of the expected sign (negative). Second, appendix table A3

reports two additional models. Model 1 uses fi xed eff ects (dummy variables) for each village (this subsumes the country-level fi xed eff ects as well). Model 2 uses a random intercept for each of the four countries and the 23 villages. Th ese models show very similar estimates to the results reported in the text. We prefer the results reported in the text, however, because (1) a multilevel model with only four higher-level random intercepts is a bit iff y, and (2) the random-eff ects model (as opposed to the fi xed-eff ects model) allows for some across-village vari-ance to identify the parameters. See note 2 for a discussion of the robustness of the results to alternate outcome variables.

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Table A1 Summa ry Statistics

Mean SD Min. Max. Obs.

Labor Days 10.422 29.880 0.000 365.000 1,433

Crises 2.257 2.335 0.000 9.000 1,433

Institutional Perceptions 1.844 0.716 1.000 3.000 1,278

Forest Dependence 51.804 34.365 0.000 100.000 1,430

Forest Purpose 0.592 0.492 0.000 1.000 1,430

Policy Changes 0.299 0.458 0.000 1.000 1,431

Largest Ethnic Group 0.696 0.460 0.000 1.000 1,433

Distance 1.098 1.020 0.000 5.000 1,431

ln(Household Size) 1.508 0.541 0.000 2.996 1,433

Cropland 2.392 1.467 1.000 12.801 1,430

ln(Age) 3.790 0.352 2.708 4.543 1,421

Education 5.998 4.000 0.000 20.000 1,431

Farmer 0.757 0.429 0.000 1.000 1,433

Percent Food Grown 56.943 29.893 0.000 100.000 1,433

Africa 0.729 0.445 0.000 1.000 1,433

Table A2 Tobit Random-Intercept Models for ln(Labor Days) by Country

Bolivia Kenya Mexico Uganda

Crises 0.343 –0.005 –0.013 0.417** (0.28) (0.13) (0.05) (0.18)

Crises x Years in Local Organizations –0.030** –0.011 –0.003** –0.219***(0.02) (0.02) (0.00) (0.08)

Years in Local Organizations 0.266*** 0.377*** 0.024*** 1.433***(0.07) (0.10) (0.01) (0.18)

Institutional Perceptions –0.012 0.152 –0.151 0.409* (0.42) (0.37) (0.15) (0.22)

Forest Dependence 0.025** 0.000 0.002 0.002 (0.01) (0.01) (0.00) (0.01)

Forest Purpose 0.869 1.121** 0.081 0.459 (0.57) (0.56) (0.19) (0.29)

Policy Changes 0.311 2.100*** 0.146 0.272 (0.65) (0.58) (0.22) (0.29)

Largest Ethnic Group –1.066* 1.980 0.348 0.014 (0.63) (1.37) (0.46) (0.30)

Distance –0.149 0.149 –0.463 0.003 (0.30) (0.22) (0.40) (0.17)

ln(Household Size) –0.651 0.348 0.723*** 0.099 (0.61) (0.54) (0.17) (0.27)

Cropland –0.281 0.003 –0.004 –0.032 (0.19) (0.24) (0.08) (0.11)

ln(Age) –0.715 0.739 –0.532 0.368 (0.91) (0.99) (0.34) (0.43)

Education 0.110 0.124** 0.042 0.075* (0.08) (0.06) (0.03) (0.04)

Farmer 0.266 0.993 0.437** –0.366 (0.71) (0.65) (0.21) (0.37)

Percent Food Grown 0.016 0.002 0.015*** 0.015** (0.01) (0.01) (0.00) (0.01)

Constant –0.485 –12.454*** 2.692* –11.297***(3.75) (4.82) (1.53) (2.88)

σu1.867** 1.751** 0.816** 6.639***

(0.82) (0.71) (0.33) (2.27) σe 2.254*** 2.686*** 1.121*** 1.922***

(0.26) (0.29) (0.06) (0.15) Chi-squared 32.375*** 60.450*** 93.563*** 114.926***Log-likelihood –149.532 –197.244 –351.372 –330.372 N 139 330 232 554 AIC 335.063 430.487 738.745 696.745 BIC 387.884 498.871 800.786 774.454

Notes: Random intercepts at village level. The parameter σu is the standard deviation of the random intercept, and σε is the standard deviation of the residual error. Parameter standard errors are in parentheses. Two-tailed hypothesis tests: ***p < .01; **p < .05; *p < .10

Appendix

Figure A1. Hi stogram of Labor Days

0

500

1000

Fre

que

ncy

0 100 200 300 400

Labor Days


Table A4 Logit Random-Intercept Models

(1)Labor

(2)Meetings

(3)Member

Crises 0.116** 0.123** 0.133** (0.06) (0.06) (0.06)


–0.036*** –0.029*** –0.025***(0.00) (0.00) (0.01)

Years in Local Organizations

0.318*** 0.259*** 0.301***(0.04) (0.04) (0.04)

Institutional Perceptions 0.160 0.204 0.201 (0.14) (0.14) (0.14)

Forest Dependence 0.001 0.003 0.003 (0.00) (0.00) (0.00)

Forest Purpose 0.623*** 0.572*** 0.575***(0.19) (0.19) (0.19)

Policy Changes 0.740*** 0.850*** 0.567***(0.21) (0.21) (0.21)

Largest Ethnic Group 0.030 0.064 –0.139 (0.23) (0.24) (0.23)

Distance 0.003 0.055 0.053 (0.10) (0.10) (0.10)

ln(Household Size) 0.363** 0.430** 0.353** (0.18) (0.18) (0.18)

Cropland –0.073 –0.088 –0.119 (0.08) (0.08) (0.08)

ln(Age) –0.233 –0.257 –0.337 (0.29) (0.30) (0.29)

Education 0.080*** 0.079*** 0.067***(0.03) (0.03) (0.03)

Farmer 0.449* 0.373 0.307 (0.23) (0.23) (0.23)

Percent Food Grown 0.010*** 0.012*** 0.008** (0.00) (0.00) (0.00)

Africa –2.939** –3.254*** –2.705** (1.22) (1.12) (1.09)

Constant –2.318 –2.270 –1.585 (1.51) (1.47) (1.44)

σu1.951*** 1.776*** 1.724***(0.44) (0.42) (0.40)

Chi-squared 111.303*** 106.656*** 108.158***Log-likelihood –434.235 –433.187 –429.009 N 1,255 1,255 1,255 AIC 904.470 902.374 894.018 BIC 996.898 994.802 986.446

Notes: Model 1 codes the dependent variable as binary (1 if the household contributed any labor days, and 0 otherwise); model 2 reports the results when using a binary variable indicating whether the household attends meetings, in particular (1 if attended meetings, 0 otherwise); and model 3 uses a binary vari-able of whether the household is a formal member of the forest user group (1 if yes, 0 if no) involved in the policy process. The results from each of these models substantiate the main fi ndings of the article (a signifi cant negative interaction effect, although the magnitude is different because of the scale of the depend-ent variable). Random intercepts at village level. The parameter σu is the standard deviation of the random intercept. Parameter standard errors are in parentheses. Two-tailed hypothesis tests: ***p < .01; **p < .05; *p < .10.

Table A3 Multilevel Tobit Random-Intercept Models for ln (Labor Days)

(1)Fixed Effects

(2)Multilevel

Crises 0.087* 0.116***(0.05) (0.04)


–0.008*** –0.008***(0.00) (0.00)

Years in Local Organizations 0.088*** 0.102***(0.01) (0.01)

Institutional Perceptions 0.143 0.067(0.13) (0.12)

Forest Dependence 0.001 0.002(0.00) (0.00)

Forest Purpose 0.506*** 0.564***(0.17) (0.17)

Policy Changes 0.710*** 0.742***(0.18) (0.17)

Largest Ethnic Group 0.098 0.192(0.22) (0.19)

Distance 0.039 –0.083(0.10) (0.08)

ln(Household Size) 0.390** 0.324**(0.16) (0.16)

Cropland –0.036 –0.072(0.07) (0.06)

ln(Age) –0.053 –0.168(0.27) (0.26)

Education 0.107*** 0.097***(0.02) (0.02)

Farmer 0.490** 0.528***(0.20) (0.20)

Percent Food Grown 0.012*** 0.009***(0.00) (0.00)

Constant –2.541** –2.873***(1.15) (1.09)

σe 2.019*** 0.726***(0.08) (0.04)

σu1.644***

(0.09)σj 0.000

(0.11)Chi-squared 882.356***Log-likelihood –1161.687 –1213.466N 1,255 1,255AIC 2393.373 2464.933

BIC 2573.095 2562.496

Notes: Model 1: Fixed effects at village level (23 villages). Model 2: Random inter-cepts at village level (23 villages) and country level (four countries). The parameter σu is the standard deviation of the random intercept for villages, σϕ is the standard deviation of the random intercept for countries, and σε is the standard deviation of the residual error. Parameter standard errors are in parentheses. Two-tailed hypothesis tests: ***p < .01; **p < 0.5; *p < .10

behavioral determinants of citizen involvement: evidence

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