Legislative checks give whoever wields them influence over policy making. It is argued in this article that this influence implies the ability not only to affect legislative content, but also to direct public resources toward private ends. Rational politicians should use access to checks to make themselves better off – for example, by biasing policy toward private interests or creating opportunities to draw directly from the public till. Disincentives exist only to the extent that those able to observe or block corruption do not themselves benefit from it. Political opponents thus can use checks to stymie each other, but legislative checks controlled by political allies create conditions for collusion and corruption. Testing this claim against data from a sample of 84 countries, the results presented in this article show strong support for the hypothesised relationship between institutional checks and corruption.

Recent research suggests that emotions are a central motivation for radical right voting. One emotion that has gained particular interest is nostalgia: Radical right politicians use nostalgic rhetoric, and feeling nostalgic is associated with radical right support. However, while nostalgia is widely and frequently experienced, previous work differentiates personal contents of nostalgia (e.g., childhood) from group‐based contents (e.g., traditions) and suggests that only the latter is related to the radical right. But why does nostalgia, and specifically its group‐based content, matter? In the present paper, I argue that nostalgia evokes implicit comparisons between the past and the present. Using relative deprivation theory, I posit that group‐based nostalgia makes people subjectively evaluate society's present as worse than its past. In turn, this temporal group‐based relative deprivation is associated with attempts to restore the past through radical right voting. Personal nostalgia, instead, does not evoke equivalent experiences of personal relative deprivation and is, therefore, unrelated to radical right support. In preregistered analyses of representative panel data from the Netherlands, I show that group‐based nostalgia is more consistently related to radical right support than personal nostalgia. In subsequent exploratory analyses, I test the relative deprivation argument and find that group‐based relative deprivation does indeed mediate the relationship between group‐based nostalgia and radical right voting: People who long for the group‐based past are more likely to feel dissatisfied with the government and, in turn, consider voting for the radical right. In studying this mechanism, I connect recent work on emotional and relative deprivation explanations to radical right voting.

Modern democracies are dependent on regular elections and citizens’ legitimacy beliefs. Studies have shown that repeated electoral defeats are associated with lower levels of satisfaction with democracy and political trust. However, previous studies have only considered one type of legitimacy belief at a time, never in comparison. What is more, all previous work is based on observational studies and has not been able to identify any causal effects of losing repeatedly. Building on previous work and classic theories of political legitimacy beliefs, we argue that repeatedly losing in elections represents a form of long‐term exclusion from democratic power that has additional negative effects on people's legitimacy beliefs because they lose faith in the system and start questioning its evenhandedness. We support our predictions using six‐wave panel data and test our hypothesis a total of 16 times within the same context. The findings show that repeated losers are never less satisfied with democracy but that an additional electoral loss leads to lower levels of political trust. The findings have important implications for the meaning of different indicators of legitimacy beliefs but also for electoral research and the underpinnings of stable democracies.

The income gradient in political participation is a widely accepted stylized fact. Based on nine panel datasets from six countries, this research note asks whether income changes trigger short‐term effects on political involvement. Irrespective of indicator, specification, and method (hybrid random effects models, fixed effects models with lags and leads, and error correction models), there are few significant short‐term effects of income changes. In conjunction with earlier research, this finding suggests that the income gradient in political participation is likely to reflect stable differences between rich and poor voters emerging early in the life course.

For decades, scholars have argued that low and declining political trust affect citizens’ support for democratic and undemocratic reform. While some theorized that low political trust induces alienation and support for non‐democratic decision making, others argued that it pushes critical citizens to support reforms aimed to reinvigorate democracy. Yet, empirical tests of these expectations remained sparse and inconclusive. This paper employs panel data from the Netherlands (covering 3 waves in 3 years) to test these diverging theories simultaneously. We employ the random effects within‐between (REWB) model to differentiate between the effects of structurally low and declining political trust. Our results suggest that low and declining trust both diminish support for representative democracy, enhance support for direct democratic decision making and do not affect support for authoritarianism. These findings cast doubt on the understanding of political distrust as a determinant of political alienation. Rather, they support theories of critical citizenship and stealth democracy.

We know from previous research that an exclusionary reaction in public opinion is likely following a sudden and large‐scale influx of refugees of the sort experienced in many European countries in 2015. Yet, we know much less about the scope of these expected reactions. This article makes a conceptual and empirical contribution to the analysis of the scope of exclusionary reactions following a refugee crisis. Conceptually, we distinguish between three scope dimensions: substantive reach, duration and politicization. Empirically, we evaluate each of the scope dimensions using seven‐wave panel‐data collected before, during and after the large‐scale influx of refugees to Norway. We find that the expected exclusionary reaction (a) spilled over to opinion about immigration broadly speaking; (b) endured in that it lasted long after the situation in Norway had been brought under control; (c) encompassed voters of all political stripes. Nevertheless, we also document an important limitation to the scope of the reaction: The sudden influx of refugees to Norway did not cause a permanent shift in public opinion. Approximately two years after the situation had been brought under control, opinion about both refugee rights and immigration generally had reverted back to pre‐crisis baseline levels. Interestingly, the conceptual and empirical analysis suggests that public opinion dynamics following a sudden and large‐scale influx of refugees is similar to that found in response to other forms of large national or international crises.

We provide improved evidence on effects that fund-raising, government support, and program revenue of U.S. higher education, hospital, and scientific research nonprofit organizations (NPOs) have on donations to those NPOs and provide improved estimates of price elasticities of donations to, and donor demand for output of, those NPOs. Applying econometric tests, we find the best-specified model is two-way fixed effects, which controls for organization-specific and time- specific factors. Results suggest that U.S. higher education, hospital, and scientific research NPOs fund-raise to the point where the marginal fund-raising dollar brings in zero dollars of donations, donor demand for output of hospitals and scientific research NPOs is price inelastic and price elastic, respectively, and results are not sensitive to specification of price.

Studying interest groups in the European Union (EU) over time has proven difficult because of the lack of the critical data necessary for studying changes in interest group population dynamics and behaviour. In this article, I explain how data has been gathered, coded, and combined to create a data set worthy of population ecology studies vis-à-vis interest groups in the EU. I provide details of both the data-mining experience and some of the theoretical bases for coding decisions. In addition, I explain what new contributions this data set makes to the established data sets in the field and offer ways in which it enhances the potential for more valid research in the future while also discussing its shortcomings and possible methods of improvement.

Low political trust disengages citizens from mainstream politics, stimulating anti-establishment voting and even electoral abstention. However, existing scholarship has largely overlooked the temporal dynamics of political trust. Next to high versus low trust, our study identifies two additional components of political trust: its long-term variability and its short-term variation. We employ fifteen waves of the Dutch LISS panel (2008–2023) to systematically test the impact of these three components of political trust on electoral behavior. We find that there are systematic and meaningful differences between stable and variable (dis)trusters. While trust levels are the strongest predictor of both support for anti-establishment and abstention, trust variability has an additional effect on electoral behavior. Short-term declines in political trust increase the chances of anti-establishment voting and abstention, independent of individuals’ overall trust levels and variability. These findings have important implications for our understanding of democratic alienation and critical citizenship.

This paper analyzes the relationship between microfinance, competition, and growth in a sample of 119 countries over the period 1999–2018. Our results are fourfold. First, we show that microfinance increases economic growth. Second, we identify investment and consumption as the main channels explaining the positive effect of microfinance on growth. Third, our study highlights that the conventional financial sector and microfinance are substitutes and not complements in emerging and developing countries. Finally, we show that competitive microfinance markets allow increasing the positive effect of microfinance on growth.

Research has found that asset accumulation is associated with vote preferences, with those with a high number and value of assets being more likely to vote for centre-right parties. Yet the bulk of this literature often falls short of accounting for alternative mechanisms that could be driving this relationship. In this letter, we investigate the association between patrimony and the vote longitudinally, assessing the effects of within-person changes in patrimony on party support. Drawing on an 11-year panel from Britain, our results indicate that patrimony, whether measured by the number of assets one owns or the total value of these assets, is unrelated to support for the Conservative Party. This finding is solid against several robustness tests. Our data analysis suggests that patrimonial voting in Britain – as identified in prior research – may be driven primarily by pre-existing differences between asset owners and non-owners rather than the assets themselves.

We estimate the effect of temperature on the economic activity of Mexico utilizing 42 years of quarterly panel data of economic growth at the state level. Our findings elicit a concave relationship between economic growth and temperature that is maximized at around 20°C. Temperatures below or above this level are associated with lower growth rates. Temperature affects aggregate economic activity mainly through the effect it has on the growth of the primary and secondary sectors. In addition, the estimated sensitivity of economic growth to temperature has not decreased within our sample period which indicates that adaptation to climate change has been limited. When combining our panel estimates with temperature projections by the year 2100, our results suggest that quarterly economic growth might be reduced by 0.4 percentage points, on average, under an intermediate scenario of climate change with reductions as large as 1.0 percentage point during the spring and summer quarters.

This paper specifies the panel data experimental design condition under which ordinary least squares, fixed effects, and random effects estimators yield identical estimates of treatment effects. This condition is relevant to the large body of laboratory experimental research that generates panel data. Although the point estimates and the true standard errors of the estimated average treatment effects are identical across the three estimators, the estimated standard errors differ. A standard F test as well as asymptotic reasoning guide the choice of which estimated standard errors are the appropriate ones to use for statistical inference.

An IRT model with a parameter-driven process for change is proposed. Quantitative differences between persons are taken into account by a continuous latent variable, as in common IRT models. In addition, qualitative interindividual differences and autodependencies are accounted for by assuming within-subject variability with respect to the parameters of the IRT model. In particular, the parameters of the IRT model are governed by an unobserved or “hidden'” homogeneous Markov process. The model includes the mixture linear logistic test model (Mislevy & Verhelst, 1990), the mixture Rasch model (Rost, 1990), and the Saltus model (Wilson, 1989) as specific instances. The model is applied to a longitudinal experiment on discontinuity in conservation acquisition (van der Maas, 1993).

Researchers in the field of network psychometrics often focus on the estimation of Gaussian graphical models (GGMs)—an undirected network model of partial correlations—between observed variables of cross-sectional data or single-subject time-series data. This assumes that all variables are measured without measurement error, which may be implausible. In addition, cross-sectional data cannot distinguish between within-subject and between-subject effects. This paper provides a general framework that extends GGM modeling with latent variables, including relationships over time. These relationships can be estimated from time-series data or panel data featuring at least three waves of measurement. The model takes the form of a graphical vector-autoregression model between latent variables and is termed the ts-lvgvar when estimated from time-series data and the panel-lvgvar when estimated from panel data. These methods have been implemented in the software package psychonetrics, which is exemplified in two empirical examples, one using time-series data and one using panel data, and evaluated in two large-scale simulation studies. The paper concludes with a discussion on ergodicity and generalizability. Although within-subject effects may in principle be separated from between-subject effects, the interpretation of these results rests on the intensity and the time interval of measurement and on the plausibility of the assumption of stationarity.

Stochastic actor-oriented models (SAOMs) can be used to analyse dynamic network data, collected by observing a network and a behaviour in a panel design. The parameters of SAOMs are usually estimated by the method of moments (MoM) implemented by a stochastic approximation algorithm, where statistics defining the moment conditions correspond in a natural way to the parameters. Here, we propose to apply the generalized method of moments (GMoM), using more statistics than parameters. We concentrate on statistics depending jointly on the network and the behaviour, because of the importance of their interdependence, and propose to add contemporaneous statistics to the usual cross-lagged statistics. We describe the stochastic algorithm developed to approximate the GMoM solution. A small simulation study supports the greater statistical efficiency of the GMoM estimator compared to the MoM.

Changes in dichotomous data caused by treatments can be analyzed by means of the so-called linear logistic model with relaxed assumptions (LLRA). The LLRA does not require observable criteria representing a single underlying latent trait, but it postulates the generalizability of the treatment effects over criteria and subjects. To test this latter crucial assumption, the mixture LLRA was proposed that allows directly unobservable types of subjects to have different treatment effects. As the earlier methods for estimating the parameters of the mixture LLRA have specific drawbacks, a further method based on the conditional maximum likelihood principle will be presented here. In contrast to the earlier conditional methods, it uses all of the dichotomous change data while having fewer parameters. Further, its goodness-of-fit tests become more sensitive to a falsely specified number of change-types even though the treatment effects are biased. For typically occurring small to moderate sample sizes, however, parametric bootstrapping of the distributions of the fit statistics is recommended for performing hypotheses tests. Finally, three applications of the new method to empirical data are described: first, about the effect of the so-called Trager psychophysical integration, second, about the effect of autogenic therapy on patients with psychosomatic symptoms, and, third, about the effect of religious education on the attitude towards sects.

A new Bayesian multinomial probit model is proposed for the analysis of panel choice data. Using a parameter expansion technique, we are able to devise a Markov Chain Monte Carlo algorithm to compute our Bayesian estimates efficiently. We also show that the proposed procedure enables the estimation of individual level coefficients for the single-period multinomial probit model even when the available prior information is vague. We apply our new procedure to consumer purchase data and reanalyze a well-known scanner panel dataset that reveals new substantive insights. In addition, we delineate a number of advantageous features of our proposed procedure over several benchmark models. Finally, through a simulation analysis employing a fractional factorial design, we demonstrate that the results from our proposed model are quite robust with respect to differing factors across various conditions.