We test whether forecast bias affects individual investors' stock trading by combining bias measures from laboratory experiments with administrative trade data. Forecast bias is positively associated with past excess returns of purchased stocks: Compared to contrarians, extrapolators purchase...

Improper quality adjustment has long been identified as a source of potential measurement bias in consumer price indices (CPIs). However, as no benchmark for quality adjustment exists, it has always been difficult to estimate a potential bias. For this paper, we develop a corridor in which...

The EU Artificial Intelligence Act (Regulation (EU) 2024/1689) designates AI systems used in life and health insurance underwriting as high-risk systems, imposing rigorous requirements for bias testing, technical documentation, and post-deployment monitoring. Leveraging 12.4 million...

Quantitative research relies heavily on coding, and coding errors are relatively common even in published research. In this paper, we examine whether individuals are more or less likely to check their code depending on the results they obtain. We test this hypothesis in a randomized experiment...

Begum et al. (2018) study gender bias in parental attitudes using an experimental approach in rural Bangladesh. Households are reported as randomly assigned to treatment conditions in a lab-in-the-field allocation task. We show that the group assignment was inherited from Islam (2019), a...

This work highlights a previously overlooked factor that contributes to bias in private inflation forecasts-ignorance of confidential monetary rules. Additionally, it examines how this ignorance indirectly affects policy rate settings. The model proposed reconciles biases in two key forecast...

We investigate the (a:b) Maker-Breaker subgraph game played on the edge set of the complete graph Kn, where n,a,b∈N, and Maker's objective is to construct a member of a prescribed family of graphs H, while Breaker aims to prevent this. In our study, Breaker moves first, and H is taken to be...

This article introduces a novel approach to decomposing the Blinder-Oaxaca gender wage gap using pseudo-panel data. In many developing countries, panel data are not available; however, understanding the evolution of the gender wage gap over time requires tracking individuals longitudinally. When...

Data is an essential fuel for artificial intelligence (AI) to power the underlying machine learning (ML) algorithms. Missing data is common in most real-world datasets due to measurement errors, non-responses, and human errors during the data collection, which can ultimately lead to reduced...

This paper investigates the trade elasticity, a key parameter in international economics, and examines biases in its estimation arising from dynamic treatment effects and staggered adoption. Leveraging the 2018 US trade war tariffs as a natural experiment, I apply a local projections...

We analyse potential mismeasurement of the Harmonised Index of Consumer Prices (HICP) at the upper level of aggregation, focusing on two sources of measurement error: the choice of index formula (representativity component) and the reliability of weights (data vintage component). The...

Behavioral interventions on citizens are often promoted as a low-cost route to induce environmentally friendly behavior, yet published estimates of their effectiveness are highly variable and prone to selective reporting. We reanalyzed the evidence of behavioral interventions on citizens. We...

Decisions informed by past events can be distorted when outcomes partially determined by chance are misinterpreted as purely skill-based. This can lead to outcome bias, where decisions are evaluated based on results rather than the quality of the performance that produced them. Outcome bias is...

Expectations about future events can be influenced by prior outcomes of similar events, even when those outcomes were partly determined by chance. When such randomness is overlooked in decision-making, outcome bias may arise. This paper investigates the presence of outcome bias in consumer...

Many studies exploit the random placement of individuals into groups such as schools or regions to estimate the effects of group-level variables on these individuals. Assuming a simple data generating process, we show that the typical estimate contains three components: the causal effect of...

Exploiting exogenous natural variation to study the impact of hard-to-randomize policies based on instrumental variables (IVs) is a widespread research design in economics. The key identification assumption underlying this design is the exclusion restriction, requiring the IV to affect the...

While content moderation began as a solution to online abuse, it has increasingly been framed as a source of abuse by a diverse coalition of users, civil society organisations, and politicians concerned with platform bias. The resulting crisis of legitimacy has motivated interest in more...

We investigate a bracketing property that purports to yield upper- and lower bounds on the treatment effects obtained from a fixed effects- and lagged dependent variable model. Referencing both analytical results and a Monte Carlo simulation, we explore the conditions under which the bracketing...

In this article, new ratio regression type estimators with imputation have been proposed as means to overcome the problem of missing data relating to a studied variable in a sample survey. It has been shown that the suggested estimators are more efficient than the mean method of imputation, the...