Institutional Change and Embeddedness: Caste and Gender in Financial Cooperatives in Rural India

ABSTRACT

This article examines the interaction between institutional change and caste and gender embeddedness. It develops a framework for understanding how this interaction takes place. The article presents data on the results of an effort by a non-profit development organization, the Cooperative Development Foundation (CDF), to promote women's cooperatives, grounded in the ideas of mutual self-help and equitable access to services, in rural Andhra Pradesh in India. Using a simple theoretical framework that highlights the multi-level nature of institutional change, and the potential for interactions with embeddedness at one level to affect interactions at other levels, the article presents data showing how particular institutional rules of the cooperatives interacted with caste and gender to shape the overall governance and service delivery practices of these new organizations. In particular, after failed attempts to integrate existing, male-dominated cooperatives with women, the CDF created gender-segregated cooperatives. Data from the women's cooperatives show the creation of mixed-caste organizations, with boards of directors that were fairly representative of their membership, but with presidents that were more likely to be from the higher-castes. Access to financial services shows little caste bias, though lending is through caste-based peer groups. The data suggest that the interaction of embeddedness and institutional change is a process contingent on: a) the strategy taken by the development organization towards embeddedness; b) the nature of the institutional change itself relative to the existing social structure; and c) the effects the interactions between institutional change and embeddedness at different levels have on each other.     

Using the Results from Rigorous Multisite Evaluations to Inform Local Policy Decisions

Evidence‐based policy at the local level requires predicting the impact of an intervention to inform whether it should be adopted. Increasingly, local policymakers have access to published research evaluating the effectiveness of policy interventions from national research clearinghouses that review and disseminate evidence from program evaluations. Through these evaluations, local policymakers have a wealth of evidence describing what works, but not necessarily where. Multisite evaluations may produce unbiased estimates of the average impact of an intervention in the study sample and still produce inaccurate predictions of the impact for localities outside the sample for two reasons: (1) the impact of the intervention may vary across localities, and (2) the evaluation estimate is subject to sampling error. Unfortunately, there is relatively little evidence on how much the impacts of policy interventions vary from one locality to another and almost no evidence on the implications of this variation for the accuracy with which the local impact of adopting an intervention can be predicted using findings from an evaluation in other localities. In this paper, we present a set of methods for quantifying the accuracy of the local predictions that can be obtained using the results of multisite randomized trials and for assessing the likelihood that prediction errors will lead to errors in local policy decisions. We demonstrate these methods using three evaluations of educational interventions, providing the first empirical evidence of the ability to use multisite evaluations to predict impacts in individual localities—i.e., the ability of “evidence‐based policy” to improve local policy.     

Mass Media and Electoral Preferences During the 2016 US Presidential Race

Political Behavior - This paper uses analyses of commercial polls alongside content-analytic measures of sentiment in the content of nine newspapers to explore the relationship between voter...     

Policy attribute framing: A comparison between three policy instruments for personal emissions reduction

A comparative experiment in the UK examined people's willingness to change energy consumption behavior under three different policy framings: energy tax, carbon tax, and personal carbon allowances (PCA). PCA is a downstream cap‐and‐trade policy proposed in the UK, in which emission rights are allocated to individuals. We hypothesized that due to economic, pro‐environmental and mental accounting drivers PCA would have greater potential to deliver emissions reduction than taxation. Participants (n = 1,096) received one version of a survey with the same energy‐behavior–related questions and identical incurred costs under one of the following framings: energy tax (where carbon was not mentioned), carbon tax, and PCA. Results suggest that policies that draw people's attention to carbon (PCA and carbon taxation) could have greater impact on their stated willingness to reduce energy consumption, and on the reduction amounts prompted, than would a non‐overt price signal (energy tax). There is mixed evidence, however, as to whether PCA or carbon taxation would produce the largest energy demand reductions. Some indication was found for a spillover toward wider carbon conservation under the PCA framing. © 2011 by the Association for Public Policy Analysis and Management.     

Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference

Kosuke Imai Department of Politics, Princeton University, Princeton, NJ 08544 e-mail: kimai{at}princeton.edu Gary King Department of Government, Harvard University, 1737 Cambridge Street, Cambridge, MA 02138 Elizabeth A. Stuart Departments of Mental Health and Biostatistics, Johns Hopkins Bloomberg School of Public Health, 624 North Broadway, Room 804, Baltimore, MD 21205 e-mail: estuart{at}jhsph.edu e-mail: king{at}harvard.edu (corresponding author) Although published works rarely include causal estimates frommore than a few model specifications, authors usually choosethe presented estimates from numerous trial runs readers neversee. Given the often large variation in estimates across choicesof control variables, functional forms, and other modeling assumptions,how can researchers ensure that the few estimates presentedare accurate or representative? How do readers know that publicationsare not merely demonstrations that it is possible to find aspecification that fits the author's favorite hypothesis? Andhow do we evaluate or even define statistical properties likeunbiasedness or mean squared error when no unique model or estimatoreven exists? Matching methods, which offer the promise of causalinference with fewer assumptions, constitute one possible wayforward, but crucial results in this fast-growing methodologicalliterature are often grossly misinterpreted. We explain howto avoid these misinterpretations and propose a unified approachthat makes it possible for researchers to preprocess data withmatching (such as with the easy-to-use software we offer) andthen to apply the best parametric techniques they would haveused anyway. This procedure makes parametric models producemore accurate and considerably less model-dependent causal inferences. Authors' note: Our thanks to Dan Carpenter and Jeff Koch fordata; Alberto Abadie, Neal Beck, Sam Cook, Alexis Diamond, BenHansen, Guido Imbens, Olivia Lau, Gabe Lenz, Paul Rosenbaum,Don Rubin, and Jas Sekhon for many helpful comments; and theNational Institutes of Aging (P01 AG17625-01), the NationalInstitute of Mental Health (MH066247), the National ScienceFoundation (SES-0318275, IIS-9874747, SES-0550873), and thePrinceton University Committee on Research in the Humanitiesand Social Sciences for research support. Software to implementthe methods in this paper is available at http://GKing.Harvard.edu/matchitand a replication data file is available as Ho et al. (2006).