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In This Article Expand or collapse the "in this article" section Field Experiments

Introduction, data sources.

• Experimental Method and Random Assignment
• Field Experiments
• Noncompliance
• Interference
• Transfers, Subsidies, and Vouchers
• Decentralization and Governance
• Representation and Public Administration
• Discrimination
• Ethical Considerations

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Field Experiments by Donald P. Green , Alexander Coppock LAST REVIEWED: 26 August 2013 LAST MODIFIED: 26 August 2013 DOI: 10.1093/obo/9780199756223-0092

Field experimentation is a method of inquiry that allows social science researchers to accomplish at least two tasks that are impossible through speculation or observation alone. The first is to establish causality. Causal claims abound in the social sciences, and experiments provide a means to test them convincingly. The second task is estimating the sign and magnitude of treatment effects in real-world settings. The precision produced by experiments allows for a realistic calculation of the costs and benefits of policies and programs. This annotated bibliography aims to lay out the methodological benefits of field experimentation, as well as some important limitations and how best to deal with them. The references cited in these sections, which include noteworthy applications and technical discussions, should give the interested reader a broad introduction to field experimentation. The references also provide a snapshot of how field experimentation is used in political science and policy research.

Yale University’s Institution for Social and Policy Studies Data Archive , the Dataverse Network Project , and the Inter-University Consortium for Political and Social Research (ICPSR) provide access to publicly available datasets from randomized experiments. The Poverty Action Lab , Field Experiment Bibliography , and the Randomized Social Experiments eJournal collect published experimental results.

Greenberg, David H., and Mark D. Shroder, eds. Randomized Social Experiments eJournal .

A digest of recent published and unpublished manuscripts reporting field experimental results or methodological commentary.

Field Experiments Bibliography .

John List’s directory of field experiments, with a focus on economics.

Institution for Social and Policy Studies Data Archive . Yale University.

This archive features replication data from dozens of field experiments conducted by Yale researchers, dating back to 2000.

Inter-university Consortium for Political and Social Research (ICPSR) .

The largest social science data archive in the world contains replication data from several prominent field experiments in domains such as criminology, education, and public health.

The Dataverse Network Project .

This vast archive contains a wide array of studies, including an increasing number of field experiments.

Poverty Action Lab .

Provides extensive information on the practical issues concerning randomized evaluations.

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5 Field Experiments and the Study of Political Behavior

Tiffany C. Davenport is a doctoral student in Political Science at Yale University.

Yale University

Donald P. Green (Ph.D., University of California at Berkeley) is Professor of Political Science at Columbia University. The author of four books and more than one hundred essays, Green's research interests span a wide array of topics: voting behavior, partisanship, campaign finance, hate crime, and research methods. Much of his current work uses field experimentation to study the ways in which political campaigns mobilize and persuade voters, but he has also conducted experimental research on the effects of the mass media, civic education classes, and criminal sentencing. With Alan Gerber, he recently co-authored a textbook on this research method titled Field Experiments: Design, Analysis, and Interpretation (W.W. Norton, 2012).

• Published: 02 May 2010
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This article presents an overview of field experiments and their contribution to the study of political behavior. First, it briefly outlines the history of field experimentation in political science and neighboring disciplines. It then summarizes a series of important and interrelated research literatures, exploring the influences of political campaigns, political communication, and political socialization. The review shows that the advent of field experimentation represents a significant departure from what had been a field dominated by survey and lab research. Furthermore, the ways in which field experimental studies shaped the research in political behavior are addressed. Field experimental studies have brought a new level of precision to the study of campaign communication. The principal argument for observational research is that it enables scholars to study a broad class of causal questions, not just those that are amenable to experimental manipulation.

Experiments are studies in which the units of observation—people, locations, organizations—are randomly assigned to different interventions. The purpose of an experiment is to isolate the causal influence of these interventions. In political science, the term “field experiment” refers to experiments that take place in real‐world settings. Field experimentation attempts to approximate the conditions under which a causal process occurs, the aim being to enhance the external validity, or applicability, of experimental findings. Four criteria are often used to assess the degree to which field experiments approximate real‐world conditions: whether the stimulus used in the study resembles the stimuli of interest in the political world, whether the participants resemble the actors who ordinarily encounter these stimuli, whether the context within which subjects receive the stimulus resembles the political context of interest, and whether the outcome measures resemble the actual political outcomes of theoretical or practical interest.

Political science field experiments take many forms. For example, Miller and Krosnick ( 2004 ) collaborated with an interest group in order to test the effectiveness of alternative fundraising appeals. The treatments were fundraising letters; the experiment was unobtrusive in the sense that recipients of the fundraising appeals were unaware that an experiment was being conducted; and the outcomes were financial donations. Similarly, Bergan ( 2009 ) teamed up with a grassroots lobbying organization in order to test whether constituents' email to state representatives influences roll call voting. The lobbying organization allowed Bergan to extract a random control group from its list of targeted legislators; otherwise, its lobbying campaign was conducted in the usual way, and outcomes were assessed based on the legislators' floor votes.

Not all field experiments are as realistic and unobtrusive as these examples. Sometimes the interventions that are deployed in the field are designed by researchers rather than by practitioners. For example, Gosnell ( 1927 ) and Eldersveld ( 1956 ) fashioned their own get‐out‐the‐vote campaigns in order to test whether they would cause registered voters to cast ballots. Sometimes treatments are administered and outcomes are measured in a way that notifies participants that they are being studied, as in Paluck's (2009) experimental investigation of radio's effects on inter‐group attitudes in Rwanda. Her study enlisted groups of Rwandan villagers to listen to recordings of radio programs on a monthly basis for a period of one year.

The distinction between field experiments and other types of experiments hinges on the causal question that the researcher has in mind. To the researcher who seeks to understand how college students behave when presented with dilemmas involving the distribution of small sums of money, laboratory experiments in which undergraduates vie for monetary payoffs may be regarded as field experiments. On the other hand, if one seeks to understand how members of the general public respond to political communication, the external validity of lab studies involving undergraduates has been questioned (Sears 1986 ), and similar objections have been levied against the artificial ways in which outcomes are often measured in the lab (Aronson, Wilson, and Brewer 1998 ). These kinds of external validity concerns may one day subside if research demonstrates that lab studies involving undergraduates consistently produce results that are corroborated by experimental studies outside the lab; for now, the degree of correspondence remains an open question.

The same may be said of survey experiments. By varying question wording and order, survey experiments may provide important insights into the factors that shape survey response, and they may also shed light on decisions that closely resemble survey responses, such as voting in elections (Glaser 2006 ) and approval of presidential performance in the wake of a foreign policy confrontation (Tomz 2007 ). Whether survey experiments provide externally valid insights about the effects of exposure to media messages or other campaign influences, however, remains unclear. What constitutes a field experiment therefore depends on how “the field” is defined. Early agricultural experiments were called field experiments because they were literally conducted in fields. But if the question were how to maximize agricultural productivity of greenhouses, the appropriate field experiment might be conducted indoors.

Studies classified as “natural experiments” or “quasi‐experiments” are related to field experiments in that they enable researchers to gauge the causal influence of real‐world interventions. Although usage varies, the term “natural experiment” typically denotes randomized experiments where the treatments are assigned by government agencies or non‐academic entities. For example, the Vietnam Draft Lottery (Angrist 1990 ), the random assignment of defendants to judges (Green and Winik 2010 ; Waldfogel 1994 ), the random audit of local municipalities (Ferraz and Finan 2008 ), the assignment of students to schools by means of school choice lotteries (Hastings et al. 2007 ), and state‐run Powerball lotteries (Doherty, Gerber, and Green 2006 ) are a few examples where randomization procedures were employed by non‐researchers, setting the stage for an experimental analysis.

Related are “quasi‐experiments” (Cook and Campbell 1979 ) in which near‐random processes cause places, groups, or individuals to receive different treatments. Scholars have used seemingly arbitrary breaks in jurisdiction (Huber and Gordon 2004 ), geography (Huber and Arceneaux 2007 ; Krasno and Green 2008 ), representation rules (Pettersson‐Lidbom 2004 ), and election thresholds (Lee 2007 ) to identify a wide array of different parameters. Huber and Arceneaux ( 2007 ), for example, use the arbitrary shapes of television markets and the incidental exposure of voters in non‐battleground states to presidential advertisements in order to test whether these ads shape voters' preferences. Pettersson‐Lidbom ( 2004 ) tests whether the number of municipal representatives affects the amount of government spending by seizing on the fact that certain Scandinavian countries require the number of local representatives to change with population size according to a rigid schedule. By looking at towns on either side of these population thresholds, Pettersson‐Lidbom is able to test whether budgets shift as otherwise similar towns experience a shift in representation. Because quasi‐experiments do not involve random assignment, the causal inferences they support are subject to greater uncertainty (Gerber et al. 2009). Nevertheless, we review field, natural, and quasi‐experiments together because they all reflect a style of analysis that places emphasis on generating secure causal inferences through carefully constructed comparisons of groups receiving different treatments.

This chapter provides an overview of field experiments and their contribution to the study of political behavior. We begin by briefly summarizing the history of field experimentation in political science and neighboring disciplines. We then review a series of important and interrelated research literatures examining the effects of political campaigns, political communication, and political socialization. Our review suggests that the advent of field experimentation represents a significant departure from what had been a field dominated by survey and lab research. The net effect of this development has been to focus new attention on causal inference and to challenge key claims that had heretofore rested on observational or laboratory data.

History and Revival of Field Experimentation

The history of field experimentation may be traced to early attempts to conduct controlled interventions in the context of political campaigns. An early example of such work was Harold Gosnell's ( 1927 ) study of voter registration and turnout in Chicago prior to the 1924 and 1925 elections. Gosnell divided neighborhoods into blocks, assigning certain blocks to the treatment condition of his experiment, which consisted of a letter urging adults to register to vote. Tabulating the registration and voting rates in his treatment and control group, Gosnell found his intervention produced a small increase in voting in 1924 but a large increase in voting in the mayoral election of 1925. In 1935 George Hartmann conducted a controlled experiment in which he distributed 10,000 leaflets bearing either “rational” or “emotional” appeals for the Socialist party. Examining ballot returns, Hartmann ( 1936 –37) found Socialist voting to be somewhat more common in wards that received emotional leaflets.

These early studies might be characterized as controlled field experiments, as distinct from randomized field experiments. Although Gosnell and Hartmann determined which blocks or wards were to receive their solicitations, they did not assign observations to treatment and control conditions on a random basis. As the statistical insights of Fisher ( 1935 ) took root in social science, experimentation became synonymous with randomized experimentation. For example, Hovland, Lumsdaine, and Sheffield ( 1949 ), working in the Experimental Section of the Research Division of the War Department during the Second World War, conducted a series of randomized experiments examining the effectiveness of various training films designed to indoctrinate Army personnel. Eldersveld's ( 1956 ) classic study of voter mobilization in the Ann Arbor elections of 1953 and 1954 built randomization into the basic design of the Gosnell study. Assigning voters to receive phone calls, mail, or personal contact prior to election day, Eldersveld examined the marginal effects of different types of appeals, both separately and in combination with one another.

Although Eldersveld's research was admired, the research methodology that it introduced did not catch on in political science. New developments in data analysis, sampling theory, and computing seemed to make non‐experimental research more promising and experimentation less necessary. Surveys offered an inexpensive means by which to gather information from nationally representative samples; they could inquire whether the respondent had been contacted by parties or campaigns; indeed, they could examine the psychological mechanisms that might explain why canvassing leads to higher rates of political participation. Moreover, survey data could be mined again and again by researchers interested in an array of different questions, not just the causal question that animated a particular experiment. Surveys seemed not only superior as instruments of measurement and description, but also as vehicles for causal analysis.

Overshadowed by survey‐based investigations, field experimentation had trouble taking root as a method for studying mass political behavior. The Handbook of Political Science devoted a chapter to “Experimentation and Simulation” (Brody and Brownstein 1975 ). Although the authors praised field experiments, they could point to few examples. In Donald Kinder and Thomas Palfrey's edited volume Experimental Foundations of Political Science ( 1993 ), only one of the twenty research essays may be considered a field experiment, Cover and Brumberg ( 1982 ). No field experiments were published in leading political science journals during the 1990s.

The turning point in field experimentation may be traced to economics, which saw during the 1980s and 1990s increasing debate about the accuracy of inferences derived from observational research. In a memorable essay, Lalonde ( 1986 ) argued that regression models—even technically sophisticated ones—may fail to extract accurate parameter estimates from survey data. LaLonde's demonstration involved comparing survey results to estimates generated by randomized field experiments. The exercise provided an unusually clear demonstration of the biases of non‐experimental methods. Especially disconcerting was the fact that observational estimates remained inaccurate even when state‐of‐the‐art statistical methods, such as propensity score matching or Heckman selection models, were used to correct for threats of bias (Glazerman, Levy, and Myers 2003 ). In short, despite the growing sophistication of statistical techniques, they remain unable to reliably address the threat of bias, and they remain unable to properly quantify the uncertainty of the estimates they generate.

Growing skepticism about the accuracy of conclusions rooted in observational research helped pave the way for a series of ambitious randomized experiments in a variety of social science disciplines. Criminologists launched a series of remarkable studies to assess the deterrent effects of arresting perpetrators of domestic violence (Sherman and Berk 1984 ) or police raids of drug distribution centers (Sherman and Rogan 1995 ). Sociologists and economists collaborated to study the effects of randomly moving residents of economically depressed housing projects to nearby neighborhoods with lower poverty and crime rates (Kling, Liebman, and Katz 2007 ). Economists working in developing countries introduced field experimentation on a grand scale to study the effectiveness of anti‐poverty measures ranging from public health interventions to schooling to loans (Banerjee and Duflo 2006 ; Kremer 2003 ). The net effect was a rapidly expanding network of scholars working in different substantive areas but using similar methodological tools.

Amid these intellectual currents, field experimentation resurfaced in political science. Gerber and Green ( 2000 ) conducted a randomized experiment in New Haven, Connecticut in 1998, testing the extent to which door‐to‐door canvassing, calls from a commercial phone bank, and direct mail increased voter turnout. The study was in many ways reminiscent of earlier work by Eldersveld ( 1956 ) but differed in two important respects. First, the scale of the Gerber and Green ( 2000 ) study was approximately one hundred times larger than Eldersveld's. The number of observations in the New Haven study for the first time enabled researchers to gauge reliably the relative effectiveness and cost‐efficiency of different get‐out‐the‐vote tactics. Second, Gerber and Green took advantage of statistical advances that had occurred in the decades since the publication of Eldersveld's path‐breaking study. Their statistical analysis addressed the problems that arise in field experiments where some of those who are assigned to receive phone calls or visits turn out to be unreachable. Whereas Eldersveld and other scholars classified the unreachable subjects together with the control group, Gerber and Green preserved the original randomly assigned categories and showed how they could be used to estimate properly the effects of the phone calls and visits. This study sparked interest in field experimentation, and during the next decade dozens of articles employing field experimentation appeared in political science journals and edited volumes. The next section considers the ways in which field experimental studies shaped the research in political behavior.

The Effects of Political Campaigns and Political Communication

Before the recent advent of field experimentation, the literature on campaign effects consisted of three main lines of research. The first relied on surveys conducted over the course of a political campaign or series of political campaigns. Many of the most influential studies of public opinion and voting behavior draw on surveys that measured respondents' choices and the kinds of environmental influences that may have shaped them. For example, Rosenstone and Hansen ( 1993 ) used a series of American National Election Studies to demonstrate that voter mobilization efforts conducted by political parties and campaigns increase voter turnout. Regression analysis indicated that, controlling for other factors that may influence turnout, those who report having been called or visited by campaign workers are more likely to report having voted. The problem with this type of analysis is that it is prone to bias. Campaigns do not target voters at random. Rather, they tend to target frequent voters. Thus, even if campaign contact had no causal effect on turnout, a regression analysis might indicate that those who were contacted by campaigns tend to vote at higher rates. The threat of bias is compounded by survey error. Survey respondents are notoriously inaccurate in their reports of campaign exposure, and there appears to be a tendency for self‐reported voters to exaggerate the amount of campaign contact to which they are exposed. Similar problems afflict efforts to estimate the persuasive effects of campaign messages or communication within one's social network.

The second line of research on campaigns grew out of the work of Jacobson ( 1978 ), who pioneered the study of campaign spending and its effects on congressional election outcomes. In contrast to the survey‐based studies mentioned above, Jacobson's approach involved assembling a database in which each House or Senate election comprised an observation. The independent variables of central interest in his analysis were expenditures by challengers and incumbents; by estimating the translation of money into votes, Jacobson's analysis addressed policy debates about campaign finance reform while at the same time speaking to the broader social science question of how voters form preferences about candidates. This line of research, however, became deadlocked over the question of whether reliable inferences could be drawn. Green and Krasno ( 1988 ), Levitt ( 1994 ), and Erikson and Palfrey ( 1998 ) each advanced critiques suggesting that regression analysis could be biased if the amount that candidates spend is a marker for unobserved factors that affect election outcomes. Incumbents, for example, may raise and spend more money when faced with a talented and well‐connected challenger. If incumbents in general spend more when unmeasured factors make them more vulnerable, the analyst might underestimate the causal influence of incumbent spending on the incumbent's vote share. Each of the aforementioned authors advances a different statistical approach for grappling with this issue, but each model imposes its own set of assumptions. This literature came to a standstill when it became clear that different assumptions generated contradictory results: some authors found challenger spending to be influential; some found challenger and incumbent spending to be influential; and some found neither to be influential.

The third line of research uses laboratory experiments to assess the influence of campaign communications. Laboratory settings permit the researcher to randomly assign different kinds of campaign stimuli. Because randomly assigned campaign exposure is statistically independent of factors that might affect vote choice or behavior, laboratory experiments overcome one of the key impediments preventing survey researchers from drawing sound inferences about the causal influence of commercials and other forms of communication. Laboratory studies such as Ansolabehere and Iyengar ( 1995 ), Brader ( 2006 ), Iyengar, Peters, and Kinder ( 1982 ), Mendelberg ( 1997 ), and Mutz ( 1998 ) are outstanding examples of research that demonstrated the causal influence that televised news and commercials could have on public opinion and voting behavior. The limitations of lab research have to do with the extent to which results from the lab can be generalized to real world settings. Often, the subjects in laboratory experiments are drawn from the ranks of undergraduate classrooms; subjects are presented with the experimental communication in an unusual context (often a simulated living room on a college campus); and outcomes measures are survey responses gathered shortly after the stimulus is presented to the subject. These concerns raise the question of what laboratory results suggest about the effectiveness of an actual advertising campaign, in which the general population may be exposed to ads days before an election takes place.

Field experimental studies have brought a new level of precision to the study of campaign communication. In contrast to survey research, which typically categorizes campaign activity in very broad terms (“Did anyone from one of the political parties call you up or come around and talk to you about the campaign?”), experimental control enables the research to know what the communication was, who delivered it, and when. More importantly, the campaign intervention is randomly assigned and therefore unrelated to attributes that might predict the receiver's political attitudes and behavior. Although this virtue is also applicable to lab work, most field experiments have the advantage of measuring outcomes in an unobtrusive fashion so that participants remain unaware of the connection between the intervention and the outcome. When surveys are used to assess the effectiveness of campaigns, they are conducted so that neither the treatment nor control group is aware of the connection between the survey and the experimental intervention.

Interpersonal influence is another topic of broad concern to students of political behavior that has in recent years seen renewed use of experimental methods. Building on the earlier socialization studies of Jennings and Niemi ( 1974 , 1981 ), Huckfeldt and Sprague ( 1987 , 1995 ) pioneered the survey‐based investigation of how friends and family members influence each other's political views. Their methodological approach was to survey respondents and people with whom these respondents discussed politics, a methodology that rapidly spread to other studies of interpersonal influence (Mutz 1998 ). Huckfeldt and Sprague found that the correlation between discussion‐mates' political views persisted even after controlling for their background characteristics. These authors inferred that discussion itself caused the transmission of political views. However, the discussions themselves were neither observed nor randomly manipulated. Field experimental studies that have taken the Huckfeldt and Sprague findings as their point of departure have sought to test whether the correlation observed in these surveys reflects the causal influence of interpersonal communication per se , as opposed to unmeasured similarities between discussion‐mates.

What has been learned through field experimental investigation? Let us preface the answer by defining what we mean by learning. We take a Bayesian perspective and envision a learning process by which prior beliefs about causal parameters are updated in the wake of new findings in order to form posterior beliefs (see Gerber, Green, and Kaplan 2004 ). These beliefs may be described as distributions, so that at any point in time a researcher's beliefs may be characterized in terms of their location and dispersion. The mean of this distribution indicates what the researcher takes to be the expected value of the causal parameter; the wider the dispersion of these beliefs, the greater the researcher's uncertainty about the causal parameter. From this vantage point, learning is a matter of reducing uncertainty. Even if the mean of the prior and posterior distribution remains unchanged in the wake of new data, the narrowing of the distribution indicates that learning has occurred. Thus, we must be careful not to disparage experimental results as merely “telling us what we already know” if they substantially reduce our uncertainty.

Collective Action

With that in mind, the field experimental literature has shed light on four important propositions about the effects of campaigns on voting behavior and candidate choice. The first concerns collective action. The basic theoretical insight from formal analysis of collective action problems is this: pursuit of a collective goal will falter in situations where individuals can enjoy a collective outcome without incurring the private costs of bringing it about. In the context of an election, for example, a voter may strongly prefer to see a Republican presidential candidate elected; however, the chances of influencing the outcome are infinitesimal, and the success of the GOP candidate can be enjoyed regardless of whether the voter cast a ballot. Thus, the voter abstains rather than expend time and effort to cast a ballot. Unless people find voting intrinsically rewarding, only a small portion of the electorate casts ballots or, for that matter, engages in any kind of collective political action (Olson 1965 ).

From this theoretical vantage point, voter mobilization campaigns are predicted to influence voter turnout when they change the benefit–cost ratio. Merely imploring strangers to vote should have little effect on whether they cast ballots. And yet it does. Door‐to‐door canvassing has a powerful effect on voter turnout (Gerber and Green 2000 ; Green, Gerber, and Nickerson 2003 ; Michelson 2005 ). One interpretation is that mobilization by canvassers imparts information to voters about when and where to cast ballots. But contrary to this hypothesis, reminders to vote by professional phone banks on average have little effect on turnout (Gerber and Green 2005 ; Green and Gerber 2008 ; McNulty 2005 ; but see Nickerson 2007 ). The overall pattern of results is inconsistent with the standard logic of collective action insofar as turnout is stimulated by factors having nothing to do with the direct personal costs and benefits of casting a ballot.

Source Credibility and Persuasion

The persuasive influence of credible sources is a second major proposition that field experiments address. The extensive laboratory and survey literature on persuasion has long suggested that people readily take cues from trusted individuals and groups (Lupia 2000 ; Petty and Wegener 1998 ), particularly in the context of low‐salience elections in which information about the candidates is scarce. This proposition has recently been tested in various field contexts using campaign messages from party leaders and issue‐advocacy groups. The results suggest that such communications have a surprisingly limited role in shaping vote choice (Arceneaux and Kolodny 2009 ). Perhaps the most striking result comes from a field experiment in which the popular Republican governor of a solidly Republican state recorded phone messages directed at Republican voters targeted by the campaign as the governor's likely supporters. The messages endorsed a sitting Supreme Court justice who had been appointed by the governor, urging Republicans to vote for him in the upcoming GOP primary on the basis of his record as a “true conservative.” The experimental results, however, suggested that these messages had minimal effects on both vote choice and voter turnout (Gerber et al. 2007a ). Field experimental results, in other words, seem to underscore the difficulty of changing voters' minds through direct appeals, whereas studies conducted in laboratory settings and eliciting immediate survey responses from participants tend to find much stronger interactions between messages and credibility (Iyengar and Valentino 2000 ; Yoon, Pinkleton, and Ko 2005 ). The more muted effects observed in field experiments are consistent with the observation that aggregate public opinion seldom shows marked and enduring shifts in the wake of persuasive communications (Gaines, Kuklinski, and Quirk 2007 ).

The Effects of Campaign Spending on Election Outcomes

The first article to draw the link between field experimentation and the literature on campaign finance was Gerber ( 2004 ), which attempted to gauge the cost‐per‐vote from experiments and compare it to the cost‐per‐vote implied by previous observational studies. Gerber's own experiments looked at the cost‐effectiveness of partisan direct mail in congressional, state legislative, and mayoral contests, using voter turnout records to assess mobilization effects and post‐election surveys to measure persuasive effects on vote choice. Although these studies were limited to direct mail, they were conducted with both Democratic and Republican candidates and both incumbents and challengers. These studies suggest that campaign spending is much more effective for challengers than incumbents, consistent with the original claims of Jacobson ( 1978 ).

Why do challengers enjoy greater marginal returns from their campaign spending? One long‐standing hypothesis (Mann and Wolfinger 1980 ) is that challengers suffer from low name recognition, and voters are reluctant to vote for candidates whose names they cannot recognize. Through campaign expenditures, challengers can make rapid gains in name recognition, but incumbents cannot. The name‐recognition hypothesis has a number of testable implications, one being that publicizing the names of both incumbents and challengers in low‐salience elections has the net effect of benefiting challengers. Panagopoulos and Green ( 2008 ) tested this proposition using a series of radio advertisements. In randomly selected cities and towns where mayoral contests were being conducted in 2005 and 2006, Panagopoulos and Green placed radio advertisements that encouraged voter participation in the upcoming elections and mentioned the names of the leading candidates (without endorsing any of them). In both years, they found that towns in which these ads aired gave higher vote percentages to mayoral challengers. Like the Gerber ( 2004 ) findings, the Panagopoulos and Green ( 2008 ) results merit further experimentation, for it would be interesting to know how much of the effectiveness of campaign communication on behalf of challengers is due to augmented name recognition and how much is due to other factors, such as communicating the challenger's qualifications, policy stances, or endorsements. Nevertheless, field experiments can be credited with breathing new life into important research literatures.

Interpersonal Influence

The experimental study of interpersonal influence dates back to the 1950s, when Katz ( 1957 ) studied the diffusion of rumors. In recent years, research has used three experimental paradigms. The first was pioneered by Sacerdote ( 2001 ), who studied the effects of randomly assigned college roommates on grades and other academic outcomes. This line of research was extended to the effects of randomly assigned racial contact or racial priming cues on racial policy attitudes (Newswanger 1996 ; Valentino, Traugott, and Hutchings 2002 ; Van Laar et al. 2005 ). Nickerson ( 2004 ) extended this analysis still further, looking at the transmission of political attitudes among randomly‐assigned roommates and suitemates. Nickerson's findings challenge both the conclusions and methodology of the Huckfeldt and Sprague studies. The roommate study finds little or no transmission of political orientations or policy opinions, except for those topics that are especially salient to undergraduates, such as legalization of marijuana or the statutory drinking age. Nickerson also shows that the statistical analysis used by Huckfeldt and Sprague, when applied to the experimental roommate data, severely exaggerates the interpersonal transmission of attitudes.

A second line of interpersonal influence research was also pioneered by Nickerson, this time in the context of voter mobilization. Nickerson ( 2008 ), in what probably ranks as the most elegant experimental design used to date, studied the effects of a canvassing campaign in which canvassers alternated between a get‐out‐the‐vote script and a please‐recycle script. In both cases, canvassers targeted two‐voter households and communicated their script to the first person who came to the door. In keeping with prior turnout experiments, Nickerson finds that the get‐out‐the‐vote script raised turnout substantially (those who came to the door and were treated with the voter mobilization script were approximately 9 percentage‐points more likely to vote than those who came to the door and were encouraged to recycle). More importantly, Nickerson finds that the untreated voter in the households who were assigned to the voter mobilization script were substantially more likely to vote than their counterparts in the households assigned to the recycle condition. This result implies that those who heard the get‐out‐the‐vote appeal somehow transmitted its influence to their housemate.

A third approach to the study of social influence is to communicate varying messages, each representing somewhat different social psychological forces. One such study is Gerber, Green, and Larimer's ( 2008 ) study of the role of social pressure on voting. Gerber et al. test the hypothesis that the likelihood of voting increases when people are reminded of the norm of voting and convinced that others are monitoring whether they comply with this norm. Their experiment consists of five randomly assigned groups, a control group comprising 100,000 households and four treatment groups of 20,000 households apiece, each of which received one mailing. The first treatment group received mail encouraging them to vote on the grounds that voting is a civic duty. The next treatment group was encouraged to vote using a similar appeal but was further informed that researchers were monitoring whether they voted for academic purposes. This treatment was designed to measure the Hawthorne effect of openly studying a person's turnout. More social pressure was exerted in the third treatment group, which encouraged recipients to do their civic duty and vote but also presented household members with official records of whether they had voted in two prior elections. The treatment exerting maximal social pressure presented not only the household's official voting records but also the voting records of several neighbors. In the last two conditions, recipients were told that they would receive a follow‐up mailing indicating whether they (and their neighbors) voted. The results show a steady and dramatic increase in the effectiveness of each treatment as social pressure increases. The maximal social pressure condition raised turnout from 29.7 percent in the control group to 37.8 percent in the treatment group, an effect that rivals the effect of contact with a door‐to‐door canvasser. The finding that a single mailing can have explosive effects suggests the importance of surveillance to the enforcement of norms. The implication is that social pressure to conform to norms is one of the factors sustaining collective action.

Prospects and Limitations of Field Experimental Research

The recent turn toward experiments and quasi‐experiments reflects a growing sense among students of political behavior that the field had begun to reach the limits of what could be learned from observational research. Although observational studies supplied crucial descriptive information about the contours of public opinion, rates of political participation, subgroup differences, and changes over time, they meet with increasing skepticism from a social science community that has grown wary of inferences premised on untestable assumptions.

This pessimistic assessment of observational research methods has met resistance from those who believe that sophisticated statistical methods can resuscitate observational research. In recent years, scholars in economics (Dehejia and Wahba 1999 ) and political science (Imai 2005 ) have contended that methods such as propensity score matching enable observational researchers to closely approximate experimental results, thus sidestepping the practical and ethical challenges of experimentation. This argument set in motion a surge of observational research employing matching on topics such as the effects of deliberation on opinion change (Barabas 2004 ). However, the claim that matching provides reliable estimates of causal effects has drawn criticism outside of political science (Glazerman, Levy, and Myers 2003 ) and, increasingly, within it (Gerber and Green 2005 ). For example, Arceneaux, Gerber, and Green ( 2006 ) demonstrate that matching grossly misestimates the mobilizing effects of non‐partisan phone calls on voter turnout. Their field experiment involving over one million registered voters showed these calls to have negligible effects on voter turnout; reanalyzing the same data using matching suggested that the calls profoundly increased turnout.

This finding and others like it do not demonstrate that all matching exercises are doomed to fail, but they do call attention to a fundamental problem that besets all observational studies, even those that rely on sophisticated statistical techniques: it is extremely difficult to assess ex ante the adequacy of an observational study's key assumptions. A researcher can argue forcefully that a statistical technique eliminates bias only to find one's conclusions upended by an experiment. As Gerber, Green, and Kaplan ( 2004 ) demonstrate, it is the threat of bias that fundamentally alters the statistical precision of an observational study. The nominal standard errors that are reported by statistical software packages ignore altogether this source of uncertainty. Indeed, even if the sample sizes of an observational study were infinite, the uncertainty associated with its conclusions would not be reduced to zero because there remains uncertainty about whether unmeasured differences between those who received different treatments invalidate the conclusions. In sum, the advantages of experiments are not easily approximated via observational research and statistical technique.

The principal argument for observational research is that it enables scholars to study a broad class of causal questions, not just those that are amenable to experimental manipulation. The question is how to balance the magnitude of the research question against the difficulty of extracting reliable answers. One approach is to look for quasi‐experiments that speak more convincingly to broad‐ranging questions than a conventional observational study. Gentzkow's ( 2006 ) study of television's consequences for electoral participation is an outstanding example of this style of research. Taking advantage of the stop‐and‐start way in which broadcast licenses were granted over time, Gentzkow shows that as broadcast television entered different geographic areas, it caused a decline in radio and newspaper consumption, which in turn produced a decline in voter turnout that was especially pronounced in off‐year elections. Another approach is to conduct more ambitious field experiments. The scale and scope of field experimentation continues to grow. Until recently, it would have been unthinkable to experimentally manipulate a set of competing parties' campaign appeals (Wantchekon 2003 ) or millions of dollars in television ads aired on behalf of an incumbent governor (Gerber et al. 2007b ) or a nationwide campaign to hold legislators accountable to their constituents (Humphreys and Weinstein 2007 ).

One way to make field experiments more ambitious is to sharpen their theoretical focus. For example, research in social psychology has long argued that people conform to the views and behaviors of others, an argument of special relevance given recent interest in deliberative democracy (Fishkin 1997 ). The pressures of conformity could induce members of a minority to succumb to opinions expressed by a majority, regardless of the merit of those opinions (Mendelberg 2002 ). By randomly assigning individuals to discussion groups, Farrar et al. ( 2009 ) tested whether people assigned to more conservative or liberal discussion groups emerged with more conservative or liberal views and found that they did not. Findings such as these serve two functions. They speak to long‐standing propositions about political behavior in the context of small groups and present a challenge to laboratory experiments on conformity that have tended to rely on undergraduate subjects and have largely focused on non‐political decisions. A wide array of core propositions from social and cognitive psychology awaits experimental testing in political settings.

Another unexplored opportunity for field experimental research is the interaction between political elites and the mass public. Consider, for example, the domain of constituency service, which is said to bolster the incumbent's “personal vote” (Cain, Ferejohn, and Fiorina 1987 ). Apart from one relatively small field experiment looking at the effectiveness of franked mail (Cover and Brumberg 1982 ), scholars have not seized on opportunities to evaluate the impact of various facets of interaction between elected officials and their constituents. The number and timing of trips to the home district, actions on behalf of constituents who raise questions or request assistance, and initiatives designed to raise the visibility of a political issue all represent experimentally manipulable interventions that speak to the general question of whether constituents notice and reward legislative effort. Conversely, from the standpoint of the constituent one could evaluate the relationship between the attributes of those who request assistance or information from government officials and the nature of their response. Just as experiments are used to gauge discrimination by employers against those with putatively African American names (Bertrand and Mullainathan 2004 ), they could be used to assess whether constituents with different ethnic, age, class, gender, or partisan attributes receive different treatment.

The history of science suggests that significant advances often occur in the wake of new measurement technologies, such as the telescope or microscope. The experimental method represents this type of generative technology in the social sciences. Druckman et al. ( 2006 ) document the steep rise in experimental methods since the 1970s and the recent advent of field experimentation, noting the important substantive contributions that have been made during a relatively short period of time. But the benefits of the experimental method extend well beyond the specific research conducted to date. Experimental design forces researchers to state their hypotheses with greater clarity and often allows them to analyze the results more simply and transparently. Experimental procedures enable all researchers, not just those with a flair for statistical analysis, to draw unbiased inferences from data. And experiments conducted in the field draw the social scientist into the world of practitioners, where questions of cause and effect are of both immediate and scientific significance. Just as the challenges of civil engineering informed physics, the rigorous study of political interventions informs political science by providing a reliable empirical foundation from which theoretical propositions can be derived.

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