Sustained efforts and investments in different sectors are essential for the overall development of a region. Various studies around the globe underscore the importance of investment in road transport infrastructure in many developing countries to achieve their development targets. The...

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...

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...

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...

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...

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...

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...

This study investigates the dynamic relationship between interest and noninterest income at Azer Turk Bank using quarterly data from 2016Q1-2024Q3. Unit root tests including Augmented Dickey-Fuller (ADF), Kwiatkowski-Phillips-Schmidt-Shin (KPSS), and Fourier-KPSS indicate that both variables are...

This study provides new evidence on the impact of the EU Cohesion Policy on income growth in less developed regions. Our panel includes data from all European regions for the years 1989-2020. Using a fuzzy Regression Discontinuity Design, we model treatment dynamics by applying a random effects...