I study a model of legislative policymaking with interest groups. To lobby, groups must have access. Access provides opportunities to lobby particular legislators when they control the agenda. In equilibrium, persistent access creates a tradeoff. It changes legislature-wide expectations, thereby affecting which policies pass today. Thus, access to particular legislators can indirectly affect proposals by other legislators. These endogenous spillovers encourage access to some legislators but discourage access to others.
This paper provides new evidence on how criminal skills exported from the US affect gang development in El Salvador and child migration to the US. In 1996, the US Illegal Immigration Responsibility Act drastically increased the number of criminal deportations. In particular, the members of large Salvadoran gangs that developed in Los Angeles were sent back to El Salvador.
I analyze the evolution of organizations that allow free entry and exit of members, such as cities, trade unions, sports clubs and cooperatives. Current members choose a policy for the organization, but this, in turn, may lead to new agents joining or dissatisfied members leaving, yielding a new set of policymakers tomorrow. The resulting feedback effects may take the organization down a “slippery slope”, which agents may allow in equilibrium despite being forward-looking and patient, a result that contrasts with existing models of elite clubs.
We present results from laboratory experiments studying the impacts of affirmative- action policies. We induce statistical discrimination in simple labor-market interactions between firms and workers. We then introduce affirmative-action policies that vary in the size and duration of a subsidy firms receive for hiring discriminated-against workers. These different affirmative-action policies have nearly the same effect and practically eliminate discriminatory hiring practices. However, once lifted, few positive effects remain and discrimination reverts to its initial levels.
Integrating information from multiple sources plays a key role in social science research. However, when a unique identifier that unambiguously links records is not available, merging datasets can be a difficult and error-prone endeavor. In “Active Learning for Probabilistic Record Linkage”, I propose an active learning algorithm which efficiently incorporates human judgement to produce a probabilistic estimate for the unobserved matching status across records. I show that the algorithm significantly improves accuracy at the cost of manually labeling a small number of records.
Human pro-sociality towards non-kin is ubiquitous and almost unique in the animal kingdom. It remains poorly understood, though a proliferation of theories has arisen to explain it. We present evidence from survey data and from laboratory treatment of experimental subjects that is consistent with a set of theories based on group level selection of cultural norms favoring pro-sociality.