This paper proposes a new resilience index, CLARE (Causal machine Learning Approach to Resilience Estimation), which is rooted in an impact evaluation framework and causal machine learning algorithms applied to longitudinal household survey data. The indicator is model-agnostic, data-driven,...

We investigate the long-run relationship between organized crime and corruption using data from 20 Italian regions over 30 years. As Rose-Ackerman and Palifka (2018) argue, corruption and organized crime often go together. Our study contributes to the literature in several ways in terms of...

We estimate monetary wage returns to academic-track education, Germany’s elite secondary school type granting university entrance. Because academic-track attendance and subsequent university education are institutionally linked, we disentangle their contributions using a causal mediation...

Global warming remains one of the greatest threats to the sustainability of the planet. The literature is replete with studies investigating the economic effects of global warming with very little attention being paid to the financial ramifications. To this end, the present study attempts to...

Many instrumental variables applications specify a single Bernoulli treatment. But instruments may change outcomes through multiple pathways or by varying treatment intensity. Lottery instruments that boost charter school enrollment, for instance, may affect outcomes by lengthening time enrolled...

Banks often assume that higher credit limits increase customer default risk because greater exposure appears to imply greater vulnerability. This reasoning, however, conflates correlation with causation. Whether increasing a customer's credit limit truly raises the likelihood of default remains...

We study firms' financial and real decisions after an exogenous change in their interest rate beliefs induced by a survey experiment with an information treatment. Firms revise their expectations downward after learning about the European Central Bank's policy rate. Moreover, we find a reduction...

Researchers typically collect experimental data sequentially, allowing early outcome observations and adaptive treatment assignment to reduce exposure to inferior treatments. This article reviews multiarmed-bandit adaptive experimental designs that balance exploration and exploitation. Because...

This paper provides an introduction to Double/Debiased Machine Learning (DML). DML is a general approach to performing inference about a target parameter in the presence of nuisance functions: objects that are needed to identify the target parameter but are not of primary interest. Nuisance...

Researchers typically collect experimental data sequentially, allowing early outcome observations and adaptive treatment assignment to reduce exposure to inferior treatments. This article reviews multi-armed-bandit adaptive experimental designs that balance exploration and exploitation. Because...

We approximate the finite-sample distribution of impulse response function (IRF) estimators that are just-identified with a weak instrument using the conventional local-to-zero asymptotic framework. Since the distribution lacks a mean, we assess bias using the mode and conclude that researchers...

In emerging markets, stock markets play a crucial role in supporting long-term growth. This study explores the causal relationship between stock market development and economic growth in the Fragile Five countries-Brazil, India, Indonesia, South Africa, and Turkey-covering the period from 2001...

This paper focuses on an empirical analysis of the relationship between investing and saving, taking into account various economic regions. The economic aggregates are selected following the International Monetary Fund (IMF) standard classification. The investigation is developed within the...

This paper presents a novel method for estimating treatment effects in cases where prior knowledge of the exogeneity of the treatment variable is limited. We employ a machine learning technique, double selection via Lasso, to identify a robust set of control variables without requiring prior...

Applied researchers commonly interpret coefficient movements across OLS and difference-in-differences specifications with varying controls as evidence of bias reduction or improved identification. This note shows that such interpretations generally fail under heterogeneous treatment effects. In...

This study addresses a fundamental challenge in the empirical application of the Capability Approach: the measurement of the "capability set" as an opportunity set. Unlike standard utility-based measures that focus solely on achieved outcomes, measuring capability requires assessing the welfare...

The Common Agricultural Policy (CAP) is a cornerstone policy of the European Union, increasingly focused on promoting environmentally sustainable practices. In 2014, the CAP introduced Greening payments and a crop diversification requirement to enhance soil resilience and mitigate ecosystem...

When estimating the effects of treatments defined by complex formulas, researchers often use simple functions of exogenous shocks as instruments. A leading example is "simulated instruments" for public policy eligibility, which capture variation in state-level policy generosity. We show how more...

This study proposes a regression-based estimation method in difference-in-differences settings in the presence of time-varying covariates - a scenario commonly encountered in applications. We impose only a conditional parallel trends assumption with time-varying covariates and plausible...

The COVID-19 pandemic and the implementation of strict lockdown measures have significantly impacted various dimensions of the global economy. This study examines the impact of COVID-19 and lockdown stringency on exchange rate volatility in India using three core variables, i.e., COVID-19 cases,...

This study investigates the effect of the recent Ghanaian banking crisis on the country's ongoing sovereign debt crisis. An Autoregressive Distributed Lag Model (ARDL) is adopted to estimate the immediate effects of Ghana's banking crisis on public debt levels using secondary data spanning the...

This study revisits the macroeconomic determinants of non-performing loans using a deep neural network (DNN). We present the proposed DNN as a methodological framework that combines deep learning techniques and causal inference methods. We employ a rigorous triple-validation methodology that...

Difference-in-differences (DiD) is one of the most popular approaches for empirical research in economics, political science, and beyond. Identification in these models is based on the conditional parallel trends assumption: In the absence of treatment, the average outcome of the treated and...