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Randomized Evaluation of Institutions: Theory with Applications

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Randomized Evaluation of Institutions: Theory with Applications Randomized Evaluation of Institutions: Theory with Applications to Voting and Deliberation Experiments Yves Atchadey and Leonard Wantchekonz April 23, 2009 Abstract We study causal inference in randomized experiments where the treatment is a decision making process or an institution such as voting, deliberation or decentralized governance. We provide a statistical framework for the estimation of the intrinsic e¤ect of the institution. The proposed framework builds on a standard set-up for estimating causal e¤ects in randomized experiments with noncompliance (Hirano-Imbens-Rubin-Zhou [2000]). We use the model to re- analyze the e¤ect of deliberation on voting for programmatic platforms in Benin (Wantchekon [2008]), and the e¤ect of direct democracy on public goods provision in Indonesia (Olken [2008]). 1 Introduction Randomized experiments are a widely accepted approach to infer causal relations in statis- tics and social sciences. The idea dates back at least to Neyman (1923) and Fisher (1935) and has been extended by D. Rubin and coauthors (Rubin (1974), Rubin (1978), Rosenbaum and Rubin (1983)) to observational studies and other more general experimental designs. In this approach, causality is de…ned in terms of potential outcomes. The causal e¤ect of a treatment, say Treatment 1 (compared to another treatment, Treatment 0) on the variable Y and on the statistical unit i is de…ned as Y i(1) Y i(0) (or its expected value) where Y i(j) is the value we would observe on unit i if it receives Treatment j. The estimation of this e¤ect is problematic because unit i cannot be given both Treatment 1 and Treatment 0. Randomizing the assignment of units to treatments allows us to overcome this di¢ culty. To estimate the causal e¤ect of a treatment, two random samples of units are selected, the …rst group is assigned to Treatment 0 and the second group to Very preliminary and incomplete. yAssistant Professor of Statistics, University of Michigan. zProfessor of Politics and Economics, New York University. 1 Treatment 1. The di¤erence in the sample means of Y (or some other statistic of interest) over the two groups is used as an estimate of the causal e¤ect of the treatment. The main idea is that randomization eliminates (at least in theory) any systematic di¤erence between the two samples. 1 The past ten years have seen a sharp increase in the use of randomized experiments in development economics and political science. Researchers and policy makers became increasingly concerned about identi…cation of the e¤ects of programmes in face of complex and multiple channels of causality (Banerjee and Du‡o [2008]. p. 2). Some early experiments in economics were interested in identifying the causal e¤ects of various education inputs such as textbooks, and the student-teacher ratio on learning; Others looked at the e¤ect of the treatment of intestinal worms on various measures education outcomes. Randomized …eld experiments in political science have primarily focused on studying the way in which various techniques of voter mobilization (mail, canvass, telephone) a¤ect voter turnout.2 More recent work covers a very wide range of topics such a women leadership, corruption, conditional cash transfer programmes, clientelist and programmatic politics. They also use increasingly re…ned and reliable identi…cation strategies. (See Du‡o (2008) and Gerber and Green (2007) for a survey). In most previous research, the treatment is conceived and designed by the experimenter and assigned to an individual or a group of individuals. There might be compliance problems, i.e. individuals in active treatment groups might ex-post choose to enter the control group or vice-versa (see Imbens and Rubin (1997) and Angrist, Imbens and Rubin (1996)). It might also not be legally feasible to assign individuals to treatment or control groups, so the experimenter simply encourages individuals to take treatment 1 (and individuals so encouraged comprise the treatment group) (Hirano, Imbens, Rubin and Zhou (2000)). The policy to be evaluated might lack clarity or its implementation might be imperfect (Harrison, Lau and Rutström (2005)). In all these cases, there is a di¤erence between the treatment assigned and the treatment received and this has been dealt with in a variety of ways by the encouragement design, non-compliance and treatment uncertainty literature. Now assume that the treatment or the policy to be evaluated is an unknown outcome of a well speci…ed process. That is, groups of individuals are randomly assigned to decision-making processes that allows them to pick the treatment they will eventually receive. For instance, instead of assigning schools to textbooks, ‡ip charts or deworming treatments, we assign them a decision-making process over these three possible treatments,whereby parents and teachers use simple majority rule to decide whether all the classrooms should receive textbooks or ‡ip charts, 1 See Holland (1986) among others for a review. 2 Gosnell (1927), Elderveld (1956), Adams and Smith (1980), Miller, Bositis and Baer (1981) and more recently Green and Gerber (2000). 2 or all the students should be treated with deworming drugs. Instead of majority rule, the decision- making process could be a strict proportionality rule: if percent of the parents and teachers prefer X, then a proportion of the school budget should be spent on X. This type of experiment would help identify the causal e¤ect of either education input, when they are endogenously selected by parents and teachers. It could also help identify the intrinsic e¤ect of majority or proportionality rule, and this result would have implications for evaluating not only education policies, but any other public policies. The study would also contribute to empirical studies of institutions by providing a rigorous test of the causal e¤ect of majority and proportionality rule on a variety of outcomes.3 There are at least two recent papers that explicitly integrate institutions or decision-making processes in …eld experiments. Olken (2008) provides experimental evidence from Indonesia on the e¤ect of direct democracy on support for public goods provision. The experiment involves 49 vil- lages that were assigned to select development projects either through direct elections or meetings of local leaders. The author …nds that direct participation has a positive e¤ect on satisfaction among villagers, knowledge about the project and willingness to contribute, but …nds no signi…cant di¤er- ence between direct democracy and representative-based meetings in terms of the project picked. In a paper using similar approach, Wantchekon (2008) provides experimental evidence on the com- bined e¤ect of "informed" platforms and public deliberation on electoral support for programmatic, non-clientelist platforms. The experiment takes place in Benin and involves 5 candidates running in the …rst round of the 2006 presidential elections. The treatment to be evaluated is a two-stage public deliberation process. In the …rst stage, policy experts helped candidates design electoral platforms that are speci…c and transparent in terms policy promises. In the second stage ( during the elections), there were town meetings in treatment villages, while there were rallies in control villages. The author …nds that the treatment (speci…c platforms and town meetings) has a positive e¤ect on voter information about policies and candidates. He …nds that both turnout and electoral support for the candidate running the experiment was higher in treatment areas than in control areas (even though the turnout result was more signi…cant than electoral support result). One important limitation of these two papers is that they could only identify ITT type e¤ects and did not really isolate the intrinsic e¤ect of the institutions from the e¤ect of the se- lected policy. For instance, in Olken (2008), satisfaction is higher under direct democracy than under representative meetings, but it is unclear if the result was driven by democracy or by the outcome of democracy (i.e. the project that was picked by plebiscite). In fact, they are number of cases where the projects selected under democracy were di¤erent from the ones 3 The study would also be of great interest for policy-makers since it incorporates political economy considerations in the impact evaluation of education inputs on learning. 3 selected under representative meetings. Thus, the di¤erence in satisfaction could well be driven at least in part by di¤erences in policy selected under the two political mechanisms. In addition, since the selection of policy is endogenous, a simple comparison of groups that have selected the same policy under direct democracy and representative-based meeting can lead to a selection bias. As for Wantchekon (2008), treatment groups have town meetings where speci…c policies were dis- cussed as opposed to control villages where rallies where held and mostly clientelist platforms were presented. The paper did not investigate wether the e¤ect of the treatment was driven by the information content of the electoral platforms or by the institution of the town meetings. The goal of this paper is to provide a statistical model that disentangle these two e¤ects thereby helping to identify the intrinsic e¤ect of the institution. In the next section, we will present the statistical framework. We then apply it to the town meeting experiment in Benin and the democracy experiment in Indonesia. Finally we provide general guidelines for the conduct of randomized evaluation of institutions. 2 The Model 2.1 De…ning the causal e¤ects Suppose we have two decision-making processes or institutions denoted 0 and 1. The processes are assigned to communities, i.e. groups of individuals. For simplicity, we assume that Process 0 is the status-quo or the control, which consists of applying a clearly de…ned policy (called Treatment 0) to the community; whereas in Process 1, the community is given the possibility of choosing through some decision-making process (e.g.
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