Oxford Bulletin of Economics and Statistics最新文献

We study gender inequality in conference acceptance using data from the Irish Economic Association annual conference from 2016 to 2022, exploiting the introduction of anonymized submission in 2021 to study the effect of blinding. While no gender gap is observed in organizers' acceptance decisions, there is an indication of gender difference favouring the in-group at the reviewer stage. In particular, male reviewers persistently give higher scores to papers with an increasing share of male authors. Evidence suggests that the difference stems from unconscious stereotyping against lesser known female authors. Anonymization eliminates the gender gap of male reviewers, but introduces a gender gap in favour of male authors for female reviewers. We explore differential selection as an alternative explanation, finding that reviewer experience postblinding could potentially account for our results.

We re-examined the impact of rising imports from China on intra-firm productivity growth in the European Union during the period 2005–16. In contrast to previous studies, we found that an increasing share of Chinese imports in total imports has slowed productivity growth over the observation period. This unfolded especially after the 2008/09 financial crisis and was more pronounced for firms with lower productivity growth. On average, the net effect of China's increasing import intensity on productivity growth has been negative for firms in the European Union since 2010. At the beginning of the sample, firms growing at the median rate experienced a modest growth-enhancing effect of 0.02 percentage points, which turned slightly negative at −0.06 percentage points in the last observation year. The effect was muted for high-growth multinational firms, which experienced a productivity growth premium from Chinese import competition at higher growth rates. Compared with the United States, the negative impact of Chinese import competition on the performance of EU firms is visible with a time lag.

We apply a structural vectorautoregressive analysis to decompose fluctuations in the growth rate of industrial production and inflation precipitated by the COVID-19 pandemic in the USA into aggregate demand, aggregate supply, and uncertainty shocks. While all three types of shocks contributed to output and inflation dynamics, the surge in economic uncertainty contributed to the decline in output more strongly than aggregate demand or aggregate supply disruptions. In 2021, the decline in uncertainty and adverse aggregate supply shocks emerged to be similarly important in spurring inflation.

The task specialization literature suggests that migrant workers concentrate on physically intensive occupations, pushing natives towards less-risky jobs. What is the effect of immigration on the severity of work-related injuries? By matching administrative data on work-related injuries and residence registries in Italy, this paper shows that migrant inflows lead to a reduction of physical impairment and injury-related paid sick leave for native workers, independently from occupation and sector transitions. The effect is largest in manufacturing and construction and among the eldest employees. The analysis exploits spatial and temporal variation in foreign-born residents' province shares and an instrumental variable strategy based on historical co-national local settlements. To rationalize the underlying mechanism, we show that migrant workers sort into risky occupations and we study workforce composition effects. We rule out that the effect is due to higher unemployment among natives with lower education, more exposed to injury risks, or to native workers' local migration. Longitudinal worker-level data from the Labor Force Survey show that native workers' transitions between sectors and occupations in response to immigration are not significant. Hence, our results suggest that the reduction in injury severity may result from a reallocation of riskier tasks from native workers, especially of older ages, to migrant workers, even when occupational transitions do not occur.

We introduce firm-specific returns to experience and tenure into a standard two-way fixed effects model, show that they are separately identified under the standard exogenous mobility assumption and with sufficient between firm mobility, and provide a new evidence on heterogeneity of returns to experience and tenure across firms using the administrative data from Brazil over the years 1999–2014. We document that (1) returns to tenure are not strongly related to firm wage premia, (2) returns to experience are strongly negatively correlated with firm wage premia, (3) the relationship between firm wage premium and return to experience is stronger for ‘blue collar’ firms.

Kaldor called the constancy of certain ratios stylized facts, Klein and Kosobud called them great ratios. While they often appear in theoretical models, the empirical literature finds little evidence for them, perhaps because the procedures used cannot deal with lack of co-integration, two-way causality, and cross-country error dependence. We propose a new system pooled mean group estimator that can deal with these features. Monte Carlo results show it performs well compared with other estimators, and using it on a dataset over 150 years and 17 countries, we find support for five of the seven ratios considered.

This paper considers transformations of nonlinear semi-parametric mean functions that yield moment conditions for estimation. Such transformations are said to be information equivalent if they yield the same asymptotic efficiency bound. I derive a unified theory of algebraic equivalence for moment conditions created by a given linear transformation. The main equivalence result states that under standard regularity conditions, transformations that create conditional moment restrictions in a given empirical setting need only to have an equal rank to reach the same efficiency bound. I compare feasible and infeasible transformations of both nonlinear models with multiplicative heterogeneity and linear models with unobserved factor structures.

In this paper we provide a general two-step framework for linear projection estimators of impulse responses in structural vector autoregressions (SVARs). This framework is particularly useful for situations when structural shocks are identified from information outside the VAR (e.g. narrative shocks). We provide asymptotic results for statistical inference and discuss situations when standard inference is valid without adjustment for generated regressors, autocorrelated errors or non-stationary variables. We illustrate how various popular SVAR models fit into our framework. Furthermore, we provide a local projection framework for invertible SVAR models that are estimated by instrumental variables (IV). This class of models results in a set of quadratic moment conditions used to obtain the asymptotic distribution of the estimator. Moreover, we analyse generalized least squares (GLS) versions of the projections to improve the efficiency of the projection estimators. We also compare the finite sample properties of various estimators in simulations. Two highlights of the Monte Carlo results are (i) for invertible VARs our two-step IV projection estimator is more efficient compared to existing projection estimators and (ii) using the GLS projection variant with residual augmentation leads to substantial efficiency gains relative to standard OLS/IV projection estimators.

We study the effect of exposure to older, more experienced, classroom peers resulting from the widespread use of multi-grade classes in Scottish primary schools. For identification, we exploit that a class-planning algorithm quasi-randomly assigns groups of pupils to multi-grade classes. We find that school-starters benefit from exposure to second-graders in measures of numeracy and literacy. We do not find any evidence that these gains are driven by smaller class sizes or more parental input. While short-lived, these benefits accrue independent of socioeconomic background, to boys and girls alike, and our results provide no evidence that they come at the expense of older peers from the preceding cohort.

We combine a model of symmetric information with selfish and office-motivated politicians and a Regression Discontinuity Design analysis based on close municipal elections to study partisan bias in the allocation of drought aid relief in Brazil. We identify a novel pattern of distributive politics whereby partisan bias materialises only before municipal elections, while it disappears before presidential elections. Furthermore, before mayoral elections, it fades for extreme (high or low) aridity levels while persisting for moderate levels. Our empirical results show that in this case alignment increases the probability of receiving aid relief by a factor of two (equivalent to 18.1 percentage points).