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The design of a desirable, sustainable energy system needs to consider a broad range of technologies, the market landscape, and the preferences of the population. In order to elicit these preferences, both toward lifestyle factors and energy system design, stakeholder engagement is critical. One popular method of stakeholder engagement is the deployment and subsequent analysis of a survey. However, significant time and resources are required to design, test, implement and analyze surveys. In the age of high data availability, it is likely that innovative approaches such as machine learning might be applied to datasets to elicit factors which underpin preferences toward energy systems and the energy mix. This research seeks to test this hypothesis, utilizing multiple algorithms and survey datasets to elicit common factors which are influential toward energy system preferences and energy system design factors. Our research has identified that machine learning models can predict response ranges based on preferences, knowledge levels, behaviors, and demographics toward energy system design in terms of technology deployment and important socio-economic factors. By applying these findings to future energy survey research design, it is anticipated that the burdens associated with survey design and implementation, as well as the burdens on respondents, can be significantly reduced.

We compare the inflation expectations reported by consumers in a survey with their behavior in a financially incentivized investment experiment. The survey is found to be informative in the sense that the beliefs reported by the respondents are correlated with their choices in the experiment. More importantly, we find evidence that most respondents act on their inflation expectations showing patterns consistent with economic theory. Respondents whose behavior cannot be rationalized tend to have lower education and lower numeracy and financial literacy. These findings help confirm the relevance of inflation expectations surveys and provide support to the microfoundations of modern macroeconomic models.

The synthetic control method is an increasingly popular tool for analysis of program efficacy. Here, it is applied to a neighborhood-specific crime intervention in Roanoke, VA, and several novel contributions are made to the synthetic control toolkit. We examine high-dimensional data at a granular level (the treated area has several cases, a large number of untreated comparison cases, and multiple outcome measures). Calibration is used to develop weights that exactly match the synthetic control to the treated region across several outcomes and time periods. Further, we illustrate the importance of adjusting the estimated effect of treatment for the design effect implicit within the weights. A permutation procedure is proposed wherein countless placebo areas can be constructed, enabling estimation of p-values under a robust set of assumptions. An omnibus statistic is introduced that is used to jointly test for the presence of an intervention effect across multiple outcomes and post-intervention time periods. Analyses indicate that the Roanoke crime intervention did decrease crime levels, but the estimated effect of the intervention is not as statistically significant as it would have been had less rigorous approaches been used. Supplementary materials for this article are available online.

Survey experimenters routinely test for systematically varying treatment effects by using interaction terms between the treatment indicator and covariates. Parametric models, such as linear or logistic regression, are currently used to search for systematic treatment effect heterogeneity but suffer from several shortcomings; in particular, the potential for bias due to model misspecification and the large amount of discretion they introduce into the analysis of experimental data. Here, we explicate what we believe to be a better approach. Drawing on the statistical learning literature, we discuss Bayesian Additive Regression Trees (BART), a method for analyzing treatment effect heterogeneity. BART automates the detection of nonlinear relationships and interactions, thereby reducing researchers' discretion when analyzing experimental data. These features make BART an appealing \"off-the-shelf tool for survey experimenters who want to model systematic treatment effect heterogeneity in a flexible and robust manner. In order to illustrate how BART can be used to detect and model heterogeneous treatment effects, we reanalyze a well-known survey experiment on welfare attitudes from the General Social Survey.

This article scrutinizes the relationship between collective nostalgia and populism. Different populist figures utilize nostalgia by referring to their country's ‘good old’ glorious days and exploiting resentment of the elites and establishment. Populists instrumentalize nostalgia in order to create their populist heartland, which is a retrospectively constructed utopia based on an abandoned but undead past. Using two original datasets from Turkey, this study first analyzes whether collective nostalgia characterizes populist attitudes of the electorate. The results illustrate that collective nostalgia has a significantly positive relationship with populist attitudes even after controlling for various independent variables, including religiosity, partisanship, satisfaction with life and Euroscepticism. Secondly, the study tests whether nostalgic messages affect populist attitudes using an online survey experiment. The results indicate that Ottoman nostalgia helps increase populist attitudes. Kemalist nostalgia, however, has a weak direct effect on populist attitudes that disappears after controlling for party preference.

A large literature documents that women are different from men in their choices and preferences, but little is known about gender differences in the boardroom. If women must be like men to break the glass ceiling, we might expect gender differences to disappear among directors. Using a large survey of directors, we show that female and male directors differ systematically in their core values and risk attitudes, but in ways that differ from gender differences in the general population. These results are robust to controlling for differences in observable characteristics. Consistent with findings for the population, female directors are more benevolent and universally concerned but less power oriented than male directors. However, in contrast to findings for the population, they are less tradition and security oriented than their male counterparts. They are also more risk loving than male directors. Thus, having a woman on the board need not lead to more risk-averse decision making. This paper was accepted by Brad Barber, Teck Ho, and Terrance Odean, special issue editors.

This review provides a critical survey of psychology-and-economics (\"behavioraleconomics\") research in contract theory. First, I introduce the theories of individual decision making most frequently used in behavioral contract theory, and formally illustrate some of their implications in contracting settings. Second, I provide a more comprehensive (but informal) survey of the psychology-and-economics work on classical contract-theoretic topics: moral hazard, screening, mechanism design, and incomplete contracts. I also summarize research on a new topic spawned by psychology and economics, exploitative contracting, that studies contracts designed primarily to take advantage of agent mistakes.

Surveys usually yield rates of voting in elections that are higher than official turnout figures, a phenomenon often attributed to intentional misrepresentation by respondents who did not vote and would be embarrassed to admit that. The experiments reported here tested the social desirability response bias hypothesis directly by implementing a technique that allowed respondents to report secretly whether they voted: the \"item count technique.\" The item count technique significantly reduced turnout reports in a national telephone survey relative to direct self-reports, suggesting that social desirability response bias influenced direct self-reports in that survey. But in eight national surveys of American adults conducted via the Internet, the item count technique did not significantly reduce turnout reports. This mode difference is consistent with other evidence that the Internet survey mode may be less susceptible to social desirability response bias because of self-administration.

COVID-19 pandemic led to major life changes. We assessed the psychological impact of COVID-19 on dental academics globally and on changes in their behaviors. We invited dental academics to complete a cross-sectional, online survey from March to May 2020. The survey was based on the Theory of Planned Behavior (TPB). The survey collected data on participants' stress levels (using the Impact of Event Scale), attitude (fears, and worries because of COVID-19 extracted by Principal Component Analysis (PCA), perceived control (resulting from training on public health emergencies), norms (country-level COVID-19 fatality rate), and personal and professional backgrounds. We used multilevel regression models to assess the association between the study outcome variables (frequent handwashing and avoidance of crowded places) and explanatory variables (stress, attitude, perceived control and norms). 1862 academics from 28 countries participated in the survey (response rate = 11.3%). Of those, 53.4% were female, 32.9% were <46 years old and 9.9% had severe stress. PCA extracted three main factors: fear of infection, worries because of professional responsibilities, and worries because of restricted mobility. These factors had significant dose-dependent association with stress and were significantly associated with more frequent handwashing by dental academics (B = 0.56, 0.33, and 0.34) and avoiding crowded places (B = 0.55, 0.30, and 0.28). Low country fatality rates were significantly associated with more handwashing (B = -2.82) and avoiding crowded places (B = -6.61). Training on public health emergencies was not significantly associated with behavior change (B = -0.01 and -0.11). COVID-19 had a considerable psychological impact on dental academics. There was a direct, dose-dependent association between change in behaviors and worries but no association between these changes and training on public health emergencies. More change in behaviors was associated with lower country COVID-19 fatality rates. Fears and stresses were associated with greater adoption of preventive measures against the pandemic.

Extreme right-wing parties are increasing in polls around Europe, largely fueled by an anti-migrant rhetoric. Political economy literature points to, on average, net positive effects that migrants bring to the economy, but the balance on the political market is more worrisome. For a small open economy, overly dependent on tourism, whose population reduced by more than 1 million in the last 30 years, the question of successful integration of migrants represents a first order condition of public policy. Thus the research question set in this paper is how to change attitudes on immigration among students in the Croatian society. Our approach is based on an experiment within a survey and it is tested on a sample of 1,450 students from five university cities in Croatia (Osijek, Pula, Rijeka, Split and Zagreb). Results indicate that there is a sizeable and statistically significant effect for the treated groups vis-a-vis their attitudes on the effect that migrants have on the labour market, social security system, overall safety and the economic development of Croatia.