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In this paper, we present a critical survey of experiments on political clientelism and vote-buying. We claim that through randomization and control, field experiments represent an important tool for answering causal questions, whereas list experiments provide useful methods that improve the hard task of measuring clientelism. We show that existing experimental research gives answers to the questions of why clientelism is effective for getting votes and winning elections, who relies more on this strategy – incumbents or challengers – how much clientelism takes place, and who tend to be the favorite targets of clientelistic politicians. The relationship between clientelism and other illicit strategies for getting votes, such as electoral violence and fraud, has also been analyzed through experimental interventions. Experiments have also studied mechanisms and policies for overcoming clientelism. Finally, we show that external validity is a major source of concern that affects this burgeoning literature.

The chapter considers the ethical problems engendered by random assignment and privacy concerns in randomised controlled experiments and cluster randomised trials. The particular focus is on procedural, legislative and technical approaches to reducing or avoiding the problems. Examples are given from a variety of disciplines including health and education, though the main emphasis is on research in crime and delinquency.

Confounding is of central importance in epidemiologic studies. Its definition has been under wide debate over the past decades. The classical definition is straightforward and easy-to-implement. Nevertheless, it is data-driven and has drawbacks. The more recent counterfactual definition captures the essential roles a confounder plays in causal inference. It would be beneficial for researchers to grasp substantive knowledge in causal structure and broadly adopt the latter definition. There are various methods of handling confounding issues. The choice of one option over another depends on various factors, including the nature of the study, sample size and rarity of events.

Though we have recently witnessed the “exponential production of digital data to measure, analyze, and predict educational performance” (Salajan & Jules, this volume), there has not been sufficient attention given to the quantitative methods that are used to process and transform this data in order to arrive at findings related to “what works”. This chapter addresses this gap by discussing a range of constraints that affect the main methods used for this purpose, with these methods being known as “impact evaluation.” Specifically, this chapter addresses its purpose, first, by making explicit the methodological assumptions, technical weaknesses, and practical shortcomings of the two main forms of impact evaluation—regression analysis and randomized controlled trials. Although the idea of Big Data and the ability to process it is receiving more attention, the underlying point here is that these new initiatives and advances in data collection are still dependent on methods that have serious limitations. To that end, not only do proponents of Big Data avoid or downplay discussion of the methodological pitfalls of impact evaluation, they also fail to acknowledge the political and organizational dynamics that affect the collection of data. To the extent that such methods will increasingly be used to guide public policy around the globe, it is essential that stakeholders inside and outside education systems are informed about their weaknesses—methodologically and in terms of their inability to take the politics out of policymaking. While the promises of Big Data are seductive, they have not replaced the human element of decision making.

Single-party regimes increasingly use Subnational Performance Assessments (SPAs) – rankings of provinces and districts – to improve governance outcomes. SPAs assemble and publicize information on local government performance to facilitate monitoring and generate competition among officials. However, the evidence are sparse on their effects in this context. The authors argue that built-in incentive structures in centralized single-party regimes distort the positive impact of SPAs.

The staggered rollout of the Vietnam Provincial Governance and Public Administration Performance Index (PAPI) created a natural experiment. Due to 2010 budget constraints, the first iteration of the PAPI survey covered only 30 of Vietnam’s 63 provinces before covering all in 2011. The PAPI team used matching procedures to identify a statistical twin for each province before randomly selecting one from each pair. The authors use randomization inference to compare the outcomes of these control and treatment groups in 2011.

Exposure to PAPI helped improve almost all aspects of governance; however, significant evidence of prioritization bias exist. The positive effects only persisted for the dimension of administrative procedures, which was the one area of governance that was prioritized by the central government at the time. Other dimensions only registered short-term effects.

Our study provides an examination of the impact of SPAs in a single-party regime context. In addition, the authors leverage the natural experiment to identify information effects causally. The authors also look past short-term effects to compare outcomes for five years after the treatment occurred.

The purpose of this study is to present a systematic methodological review of the application of field experiments in the domain of marketing research. By performing this study, the authors seek to offer necessary advice and suggestions to marketing scholars interested in the application of field experiments and to promote the adoption of field experiments as a preferred methodological choice among scholars in this domain.

A total of 315 field experiments published in the ten leading marketing journals in the past five decades were analyzed in this systematic methodological review. This study examines various aspects of field experiments, including the research profile of existing research, different trends and topics related to field experiments, choice of research questions, methods of observations, unobtrusive data collection, types of interventions and outcome variables.

This study identified various trends and topics, categories of manipulations, types of limitations and important considerations in designing field experiments and offered necessary advice on the future of field experiments in marketing research.

This study provides a complete roadmap for future marketing scholars to adopt field studies in their research plans. The systematic summary of limitations and the checklist will be helpful for the researchers to design and execute field studies more effectively and efficiently.

This review study offers a complete roadmap for marketing scholars who are interested in adopting field experiments in their research projects. The discussion of trends and topics, manipulations, limitations, design considerations and checklist items for field experiments offers relevant insights to marketing scholars and may help them design and execute field experiments more effectively and efficiently.

To the best of the authors’ knowledge, this study is the first of its kind to provide a comprehensive methodological review of field experiments published in leading marketing journals throughout the past five decades. This study makes novel and unique contributions to both theory and literature on field experiments in the marketing discipline.

The need for randomised controlled trials (RCTs) to support evidence‐based services to improve outcomes for children is increasingly recognised by researchers and policy‐makers. However, this brings a pressing requirement to build research capacity for conducting RCTs and to address the concerns of practitioners who may be suspicious about the method. This article reviews a variety of texts on the subject, ranging from analyses of the historical and political context of RCTs, to concise introductions of the key methodological and practical issues, to more in‐depth discussions of complex designs and statistics. The article seeks to help readers navigate these resources by focusing on seven questions that seem particularly salient for those considering whether and how to commission, undertake, participate in or use results from RCTs.

Meat consumption has a variety of implications in society. While various types of meat-restricted diets exist to address this, not enough is known about how the average meat consumer views different avenues to lessening their intake. In response, this study aims to assess US omnivores’ impressions of three meat-restricted diets.

An online survey was administered to a cross-sectional, census-balanced sample from Nielsen of 30,000+ US adults. Omnivores (N = 928) were randomized into one of the three conditions where they were asked about their perceptions of a vegetarian diet, a reduced-meat diet or a chicken-free diet as individuals not currently following that dietary pattern.

The findings showed that omnivores had a more favorable perception of a reduced-meat diet on a greater number of study variables as compared to the vegetarian or chicken-free diets. The research also demonstrated that a majority of omnivores were in the precontemplation stage of cognitive change, suggesting that most American omnivores are not actively demonstrating a readiness to alter their meat consumption in the ways presented.

This research is the first to examine the comparative trends around these three diets among omnivores. It also speaks to how the desirability of meat restriction varies by type of approach, i.e. elimination or reduction, and if the latter, what type. The findings may be of relevance for efforts to reduce global meat consumption for ethical, health, or environmental reasons.

By using the central limit theorem, it is possible to consider the Fourier’s transform of a stochastic process with independent increments as a Gaussian random variable of which the mathematical expectation and the variance are respectively integrals of the mean and the variance of this process. One can then use this result to analyze the statistical properties of linear feedback systems in the frequency domain, that is to say in terms of transfer functions. The advantage of this approach is that one can deal with linear systems subject to squared white noises, in a very simple manner.

While the main purpose of reporting – e.g. reporting for taxes – is to gauge the economic state of a company, the fact that reporting is done at pre-determined dates distorts the reporting results. For example, to create a larger impression of their productivity, companies fire temporary workers before the reporting date and re-hire then right away. The purpose of this study is to decide how to avoid such distortion.

This study aims to come up with a solution which is applicable for all possible reasonable optimality criteria. Thus, a general formalism for describing and analyzing all such criteria is used.

This study shows that most distortion problems will disappear if the fixed pre-determined reporting dates are replaced with individualized random reporting dates. This study also shows that for all reasonable optimality criteria, the optimal way to assign reporting dates is to do it uniformly.

This study shows that for all reasonable optimality criteria, the optimal way to assign reporting dates is to do it uniformly.

It is found that the individualized random tax reporting dates would be beneficial for economy.

It is found that the individualized random tax reporting dates would be beneficial for society as a whole.

This study proposes a new idea of replacing the fixed pre-determining reporting dates with randomized ones. On the informal level, this idea may have been proposed earlier, but what is completely new is our analysis of which randomization of reporting dates is the best for economy: it turns out that under all reasonable optimality criteria, uniform randomization works the best.