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It is widely believed that rents from windfall revenue undermine accountability. An enduring explanation is that windfalls free leaders from the need to tax, producing a quiescent population. Yet, there is little direct evidence of how windfalls and taxes affect citizen political action. I use novel revenue and information experiments to examine whether and why windfalls (compared to taxes) affect how citizens participate in politics. The experiments were embedded in a public awareness campaign conducted with 1,863 citizens in Indonesia. The results—from an original survey and postcard campaign—indicate that the tax treatment increased monitoring and anti-incumbent political action. Yet, when given spending information, citizens in the windfall treatment cared just as much about misused revenue as those in the tax treatment. The findings have important implications for understanding not only how revenue affects citizen political behavior but also how people acquire and process information on government spending.

This article examines the significance of the Legislation Law (lifa fa), passed by the National People's Congress (NPC) in March 2000. The Legislation Law represents an attempt by the NPC to rationalize China's legal system, establish a uniform legislative hierarchy and consolidate its authority over other important lawmaking institutions. The politics behind the Law's development therefore offer insight into the balance of power in China's lawmaking arena, revealing how key institutions – the NPC, the State Council and local people's congresses – engaged in bureaucratic bargaining over fundamental questions of their existence and authority within an evolving system. While the promulgated Law reveals the mixed results of this complex process, it also makes possible a more open and consultative legislative process by sanctioning the emergence of public legislative hearings. Now gaining currency around China, hearings are a new development and could be an important step in institutionalizing more meaningful citizen participation in the legislative process.

AbstractWhile recent evidence suggests that women exhibit a high capacity to cooperate in all-women groups, existing research focuses on how women cooperate among themselves versus in mixed-gender situations. We still know little, however, about how social differences among women affect their collective action capacity. We examine this by implementing a public goods experiment in Lebanon in which 713 women and men were randomly assigned to play in same-gender groups that were either homogeneous or heterogeneous in their class and sectarian compositions. We show that women contribute significantly less in mixed-class groups while men contribute more, reinforcing that this pattern is unique to women. We also demonstrate that class differences can undermine women's cooperation more than sectarian differences. These findings highlight how social differences – and class differences in particular – can impede women's collective action capacity, revealing the potential barriers to building broad, gender-based coalitions to advance women's rights and interests.

While ex-combatant reintegration is vital to successful transitions from war to peace, some former fighters turn to crime following demobilization. Such criminality undermines the consolidation of political order. Leading theories of crime participation emphasize the role of both individual economic opportunities and factors related to social ties. Yet, we still know little about the social logic of ex-combatant criminality and how social and economic factors relate as drivers of crime participation. This article presents a theory of how wartime social ties – namely, vertical ties to former commanders and horizontal ties to ex-combatant peers – influence ex-combatant crime on their own and via their relationship to economic opportunity costs. We use the theory to derive predictions in the context of Colombia, and then test them with a combination of administrative data and high-quality original survey data. We find that both vertical and horizontal wartime ties are powerful drivers of ex-combatant criminality. Our evidence indicates that wartime ties mitigate the risks of criminal behavior by facilitating the transmission of criminal capabilities and pro-crime social norms. We do not find that economic conditions moderate the effect of wartime times nor do we find any indication that economic opportunity costs, on their own, predict criminality. These findings underscore the importance of wartime ties – both vertical and horizontal – to understanding post-conflict transitions and designing reintegration interventions.

A central challenge in development involves ensuring that aid reaches those in greatest need. Aid agencies typically try to achieve this by targeting aid to vulnerable individuals or groups. Despite the prevalence of targeting, we know little about its effects on distributional outcomes and social cohesion in communities where some are intended to benefit and others are excluded. We investigate this by formalizing targeting as a bargaining game with coalition formation involving three players—the target group, the elite, and an excluded group. Our approach yields the counter-intuitive insight that the target group will actually benefit more in communities where elites and the excluded group compete to capture aid. We provide support for predictions using a regression discontinuity design and original survey data from an aid program implemented in Aceh, Indonesia. This article demonstrates the importance of understanding the role of community dynamics in shaping the economic and social outcomes of targeted aid programs.

We present new methods to estimate causal effects retrospectively from micro data with the assistance of a machine learning ensemble. This approach overcomes two important limitations in conventional methods like regression modeling or matching: (i) ambiguity about the pertinent retrospective counterfactuals and (ii) potential misspecification, overfitting, and otherwise bias-prone or inefficient use of a large identifying covariate set in the estimation of causal effects. Our method targets the analysis toward a well-defined "retrospective intervention effect" based on hypothetical population interventions and applies a machine learning ensemble that allows data to guide us, in a controlled fashion, on how to use a large identifying covariate set. We illustrate with an analysis of policy options for reducing ex-combatant recidivism in Colombia.

ABSTRACTThis article introduces and demonstrates the utility of a new event dataset on democratic erosion around the world. Through case studies of Turkey and Brazil, we show that our Democratic Erosion Event Dataset (DEED) can help to resolve debates about the extent to which democracy is backsliding based on prominent cross-national indicators, focusing in particular on the Varieties of Democracy (V-Dem) and Little and Meng (L&M) indices. V-Dem suggests that democracies are deteriorating worldwide; L&M argue that this may be an artifact of subjectivity and coder bias and that more "objective" indicators reveal little to no global democratic backsliding in recent years. Using DEED, we show that—at least in these cases—objective indices may underestimate the extent of democratic erosion whereas subjective indices may overestimate it. Our analyses illustrate the ways in which DEED can complement existing indices by illuminating the nature and dynamics of democratic erosion as it occurs on the ground.