Empirical Economics最新文献

We analyze the impact of COVID-19 diagnoses on student grades, retention, and on-time graduation at a large public university in the USA. Even though COVID-19 rarely causes major health complications for university students, diagnosis and quarantine may cause non-trivial disruptions to learning. Using event study analysis, we find that a COVID-19 diagnosis decreased a student’s term grade point average (GPA) modestly by 0.09 standard deviations in the semester of diagnosis without significant effects afterward. The results were more pronounced for male students, individuals with face-to-face instruction, and those with higher GPAs before the pandemic. We do not find a significant increase in the incidence of failing or withdrawing from a course due to diagnosis. In addition, we find no general evidence that the diagnoses delayed graduation or significantly altered first-year retention. However, the University experienced significant grade inflation during the pandemic, like other institutions, which exceeded the estimated effects of any COVID-19 diagnoses.

The COVID-19 pandemic has triggered an unprecedented shock to global stock markets, exceeding the economic impacts of prior pandemics. This paper examines the pandemic’s impact on global stock markets across 34 countries, focusing on the relationship between the pandemic’s severity, government policy responses, and economic stimuli. Panel data regressions reveal that increased daily COVID-19 cases initially negatively impacted stock returns and increased volatility. Stringent government measures positively influenced market returns but also heightened volatility. The research challenges previous assumptions about the influence of geographical and economic factors on market reactions. By segregating the sample period by investor sentiment, the study finds a consistent pattern of negative lagged returns, indicating stronger mean reversion during high VIX periods. During low market volatility, government stringency measures are perceived as harmful to economic activity, negatively impacting stock returns. The insights from the COVID-19 pandemic can inform responses to future market disruptions from health crises, geopolitical tensions, environmental disasters, or other systemic shocks.

This paper introduces a novel semiparametric econometric framework for policy evaluation and estimates a green knowledge production function for a large, 30-year panel dataset of high-income countries. Due to the substantial uncertainty in the data-generating process and the potential presence of nonlinearities and latent common factors, the paper explores semiparametric panel specifications that go beyond interactive fixed effects fully parametric models. The findings suggest that (i) the semiparametric additive specification with individual time trends is the preferred model, (ii) threshold effects and nonlinearities are salient features of the data that parametric specifications fail to capture, and (iii) the impact of environmental policy is noteworthy and exhibits clear heterogeneity when modelled as a nonparametric function of specific knowledge inputs. The evidence reveals a significant nonlinear policy inducement effect stemming from R&D investments.

There is growing rigorous evidence on the schooling impacts of unconditional cash transfers, but only few have systematically reviewed the literature. This paper fills the gap through applying a meta-regression analysis to 38 studies of 22 programmes in 18 countries. We find that unconditional cash transfers improve both student enrolment and attendance, and the result is robust to the exclusion of studies with a high risk of bias. We also find statistically significant heterogeneity in effect sizes across studies. The effect on enrolment is larger for a setting where average monthly labour income in the economy is lower and for secondary school students. However, we do not find other moderators in this paper, namely transfer size, whether the programme is pilot, and poverty head headcount ratio, explain the variation in effect sizes. Our paper highlights the need of more evaluations on the schooling impacts of unconditional cash transfers and how tweaks in programme design could make a difference.

This work probes the dynamic co-movement between the Climate Policy Uncertainty Index (CPU) and the Renewable Energy and Clean Technology Index (RECT) employing the novel wavelet power spectrum (WPS) and wavelet coherence (WC) approaches for monthly data between 2013 and 2022. Using the wavelet approach enables us to observe the causality direction from both time and frequency dimensions and also to help detect the causal linkage in the short-medium and long-term horizons. This is the first study aiming to perform this relationship from both time and frequency dimensions. Remarkably, findings reveal that: i) CPU seems only volatile in 2019 and 2021 in the short run; (ii) there was significant volatility in the RECT in the short and long terms (SLT) between 2018 and 2022; (iii) RECT significantly caused the CPU between 2014 and 2018; iv) after 2019, CPU started to cause RECT in the short and medium terms (SMT).

This paper investigates the relationship between annual disbursements of Brazil’s largest development bank, BNDES, and mayors political affiliation. We explore a set of Difference-in-Difference (DiD) designs to evaluate causal effects of policy interventions. Using data of Brazilian municipalities, we find that municipalities with mayors belonging to the coalition that supports the Federal government get higher disbursements than the ones with mayors that are out of the coalition. There is strong evidence that firms located in allied municipalities receive average higher loans. Our findings support the view that political bias may distort the credit allocation of state-owned banks.

This paper estimates the impact of family ancestry on the educational outcomes of a cohort of Brazilian students. Based on longitudinal data with student identification, we apply an algorithm of surname classification that assigns the student, based on the surnames of his/her parents and grandparents, to one of the following ancestry groups: Iberian, Japanese, Italian, German, Eastern European and Syrian-Lebanese. Our identification strategy relies on the epidemiological approach, controlling for individual trajectory since birth and the persistence of local institutions established during the Era of Mass Immigration to Brazil in the 19th and 20th centuries. We show that, despite slight or absent differences in preschool attendance rate, students with non-Iberian ancestry obtain statistically and substantively higher promotion rates and scores on 3^rd and 5^th grade nationwide standardized tests.

In this paper, we propose the use of a conditional nonparametric robust estimator to evaluate countries pre-vaccine responses to the outburst of COVID-19 pandemic. We collect data for 105 countries (comprehending the initial period of the pandemic through the end of May 2021), with variables regarding the death toll, economic indicators, demographic characteristics and non-pharmaceutical interventions. We create a novel empirical framework for estimating efficiency of countries responses in more general terms than simply evaluating healthcare system performance. We use two distinct well-known second-stage approaches: regressing the conditional efficiency scores on the environmental factors, in order to compute measures of managerial efficiency to rank responses; and regressing the ratio of conditional and unconditional scores on conditioning factors, seeking to explore the relationship between non-pharmaceutical interventions and estimated efficiencies. Our results indicate which countries and regions stood out for presenting efficient/inefficient responses and point to an expected conclusion: The environmental factor elderly population has a significant and unfavorable effect on a country efficiency. Furthermore, the factors median stringency index and median retail and recreation visitors change show no significant effect on efficiency.

This paper characterises the Laffer curve of each individual taxpayer in a schedular multi-rate income tax with income shifting. Analytical expressions for the revenue-maximising tax rate and the revenue-maximising elasticity are provided for the individual taxpayer and the aggregate population, as well as new estimates of the Elasticity of Taxable Income. Applying these to the Spanish income tax demonstrates that 44.72% (58.49%) of the taxpaying population in the non-savings tax base (savings tax base) is on the “normal” side of the Laffer curve. On average, these taxpayers are 6.59 points (24.73 points) above (below) the maximum of the Laffer curve. The fraction of total tax revenue lost through behavioural responses amounts to 53.77%. However, this fraction varies by population subgroup and decreases when we account for income-shifting responses, suggesting the presence of fiscal externalities in the Spanish PIT.

This paper introduces a threshold mixed data sampling logit (TM-logit) model, which allows for a threshold effect of independent variables sampled at different frequencies on the log-odds of dependent variable. We propose model estimation procedure and develop test statistics for relevance of high-frequency predictors, threshold effect, and equal weighting scheme. We also suggest a test statistic for the difference in forecasting accuracy between two competing models. We then extend the model to the framework with a covariate-dependent threshold (CDTM-logit) and propose estimation procedure and test statistic for threshold constancy. Monte Carlo simulations are conducted to assess the finite sample performance of the proposed estimation procedure and test statistics. The simulation results show that the estimation procedure performs well and test statistics have good size and power properties in finite samples. We apply the proposed model to predict US bank failures, and the empirical results indicate that the TM-logit and CDTM-logit models have good forecasting performance.