Oxford Bulletin of Economics and Statistics最新文献

We illustrate the use of Pearl's (1995) front-door criterion with observational data with an application in which the assumptions for point identification hold. For identification, the front-door criterion leverages exogenous mediator variables on the causal path. After a preliminary discussion of the identification assumptions behind and the estimation framework used for the front-door criterion, we present an empirical application. In our application, we look at the effect of deciding to share an Uber or Lyft ride on tipping by exploiting the algorithm-driven exogenous variation in whether one actually shares a ride conditional on authorizing sharing, the full fare paid, and origin–destination fixed effects interacted with two-hour interval fixed effects. We find that most of the observed negative relationship between choosing to share a ride and tipping is driven by customer selection into sharing rather than by sharing itself. In the Appendix, we explore the consequences of violating the identification assumptions for the front-door criterion.

This paper presents a sample selection model with spatial correlation in the selection and outcome variables and studies the maximum likelihood method of estimation. Consistency and asymptotic normality of the maximum likelihood estimator are established by the spatial near-epoch dependent properties of the variables. Monte Carlo simulations show its good finite-sample performance. This model is used to examine the impact of climate change on cereal yields in Southeast Asia and projects that climate change may cause a reduction in cereal yields by $��7�\%�$ ($��31�\%�$) in the minimum-change (maximum-change) scenario.

I study the impact of stress resulting from the unexpected death of a child on parents' mortality risk. Using a flexible approach and allowing for time-varying treatment effects, I find no impact on the short-run mortality risk. However, I estimate a substantial increase in the long-run, particularly among men. I provide evidence that this group likely resorts to risky health behaviours such as heavy smoking and drinking as coping mechanisms. Assessing the possible protective effects of mental health support, I find evidence that it lowers the mortality risk for women. Mental health support has a smaller impact for men. I show that this is likely due to underutilization by those who would benefit the most. Finally, I present robustness of my results to specific departure from my identifying assumptions.

This paper examines the impact of environmental news coverage in US newspapers on stock markets. Using textual analysis with a dictionary-based approach, we obtain several measures of attention, tonality and uncertainty in the coverage of environmental news in major US newspapers. We consider different weighting schemes to account for the visibility and relevance of the text sources, and several sets of newspapers to measure the possible impact of their editorial line. Our results show that greater attention to environmental news in the US media reduced the excess returns of carbon-intensive stocks and increased their volatility over the last decade, especially when the coverage was uncertain. The opposite result holds for the most virtuous green assets. Restricting the corpus of texts to conservative newspapers mitigates the impact of coverage. Overall, our results illustrate how rising environmental concerns lead investors to change their asset allocation.

We develop a non-parametric technique framework for estimating firm-level Total Factor Productivity (TFP). Our paper has two major novelties: first, we propose a modelling of productivity with both firm-idiosyncratic factors and aggregate shocks. Second, we apply the Bayesian Markov Chain Monte Carlo (MCMC) technique that offers a numerical integration of productivity outside the posterior overcoming the restrictive assumptions about the relationship between productivity and variable production inputs. We implement our methodology in a group of 4,286 manufacturing firms from France, Germany, Italy, and the UK (2001–14). The results show that: (i) aggregate shocks matter for firm TFP evolution. The global financial crisis of 2008 caused severe, albeit short, adverse effects on TFP; (ii) there is substantial heterogeneity across countries in the way firms react to changes in R&D and taxation. German and UK firms are more sensitive to fiscal changes than R&D, while the opposite is true for Italian firms. R&D and taxation effects are symmetrical for French firms; (iii) the UK productivity handicap continues for years after the financial crisis; and (iv) there are substantial knowledge spillovers among German and Italian firms.

International financial integration exposes countries to external shocks. This paper identifies the impact and transmission of global financial risk (GFR) shocks to emerging market economies (EMEs). Heightened GFR significantly raises EME borrowing costs and lowers equity returns, reducing domestic economic activity. We document a novel transmission channel of GFR shocks to EMEs via international capital flows. Countries experiencing larger capital inflows are more affected by GFR fluctuations. Exploring the transmission through capital flows, GFR shocks affect EMEs mainly through their effect on equity returns, instead of country spreads. We show that equity returns contain more information about EME macroeconomic fluctuations than sovereign and corporate bond spreads.

With the global rise in populism over the last decade, there has been an increase in political commentaries (PC) by leaders that criticize their central banks and argue for lower interest rates. We analyse the effects of PCs on exchange rates, bond yields, and the risk premium for six countries that are subject to political pressures. Utilizing a specification with time-varying parameters, we show that PCs affect the level and the volatility of exchange rates, bond yields and the risk premium in Turkey. The response increases over time. In other countries, there is a significant impact on exchange rate volatility.

This paper quantifies global demand and supply conditions and compares two major global recessions: the 2009 Great Recession and the COVID-19 pandemic. First, we compute demand and supply sentiment by applying Natural Language Processing techniques on earnings call transcripts. Second, we corroborate our sentiment measure by identifying demand and supply shocks using a structural Bayesian vector autoregression model. The results highlight sharp contrast in the size of supply and demand conditions over time and across sectors. While the Great Recession was characterized by weak demand, COVID-19 caused sizable disruptions to both demand and supply, with varying relative importance across major sectors. Furthermore, certain sub-sectors, such as professional and business services, internet retail, and grocery/department stores, fared better than others during the pandemic.

Despite their high predictive performance, random forest and gradient boosting are often considered as black boxes which has raised concerns from practitioners and regulators. As an alternative, we suggest using partial linear models that are inherently interpretable. Specifically, we propose to combine parametric and non-parametric functions to accurately capture linearities and non-linearities prevailing between dependent and explanatory variables, and a variable selection procedure to control for overfitting issues. Estimation relies on a two-step procedure building upon the double residual method. We illustrate the predictive performance and interpretability of our approach on a regression problem.

Employers use various proxies to predict the future labour productivity levels of the job applicants. Success in school, especially in high-level coursework, is among the most widely used proxies to screen entry-level candidates. We estimate the causal effect of graduating with honours (i.e. with a grade point average of 3.00 and above out of 4.00) on the starting wages of economics majors in Türkiye. Using comprehensive micro data on all economics majors between 2014 and 2018, matched with administrative records about their first jobs, we implement a regression discontinuity analysis to investigate whether there is any statistically significant jump in the starting wages at the honours-degree cutoff. We find that graduating with honours increases the wages of males, while there is no impact on females. We further document that the impact on males is almost entirely driven by the graduates of non-elite universities. In particular, graduating with an honours degree increases the entry wages of males from non-elite universities by about 4%, on average. We provide an explanation for these patterns using the theory of statistical discrimination. We discuss the potential reasons behind the heterogeneous signal value of graduating with honours between males vs. females and elite versus non-elite university graduates.