Empirical Economics最新文献

Using a structural vector autoregression (SVAR) model, we demonstrate that the response of consumer prices in oil-producing Canadian provinces to oil market shocks is similar to that of consumer prices in non-oil-producing provinces, though the magnitudes and durations vary. In particular, the effects of oil supply and oil-specific demand shocks lead to significant consumer price increases while unanticipated expansions in global demand only lead to modest increases in consumer prices across both sets of provinces. Our results suggest that oil market shocks are transmitted from consumer prices in oil-producing provinces to non-oil-producing ones largely through changes in the labor market. Further, we find that roughly 41% of the long-run variation in consumer price inflation in oil-producing provinces is attributable to oil market shocks while it is about 46% in non-oil-producing provinces on average. Finally, we show that historically, oil market shocks have contributed to fluctuations in consumer price inflation in each province with different signs at different points in time. Consequently, our results suggest that policymakers should pay close attention to the effects of oil market shocks on subnational consumer prices in order to curb the impacts of adverse supply shocks and to mediate demand-side forces.

We examine the effect of auditing on dividends in small private firms. We hypothesize that auditing can constrain dividends by way of promoting accounting conservatism. We use register data on private Norwegian firms and random variation induced by the introduction of a policy allowing small private firms to forgo the use of an auditor to estimate the effect of auditing on dividend payout. Identification is obtained by a regression discontinuity around the arbitrary thresholds for the policy. Propensity score matching is used to create a balanced synthetic control. We consistently find that forgoing auditing led to a significant increase in dividends in small private firms.

We examine an extension of the latent class stochastic frontier model (LCSFM) to productivity estimation and the decomposition of productivity change into technical change, output-oriented technical efficiency change, and scale change. We base our productivity estimation on a Multi-class Grifell-Tatjé, Lovell & Orea Malmquist (GLOM) index. An advantage of this new productivity index is to account for classes' posterior probabilities to derive individual farm parameters. In addition, we extend our analysis to estimate a metafrontier GLOM productivity index to explore potentialities when all firms use the best available technologies. An empirical application to a sample of French sheep and goat farms observed between 2002 and 2021 confirms the necessity to account for technological heterogeneity when measuring productivity change. Among the two classes of farms identified by the LCSFM, the intensive class experiences TFP gains, while the extensive class sees its TFP worsening. However, the gap between intensive and extensive technologies seems to reduce over time. Finally, the multi-class GLOM reveals technical change as the primary driver of productivity for French goat and sheep farms.

In this paper, we study the asymmetric effects of different types of capital-embodied technological change, as proxied by tangible and intangible assets, on relative wages (high- to medium-skilled, high- to low-skilled and medium- to low-skilled workers), relying upon the technology-skill complementarity and polarization of the labor force frameworks. We also consider two additional major channels that contribute to shaping wage differentials: globalization (in terms of trade openness and global value chains participation) and labor market institutions. The empirical analysis is carried out using a panel dataset comprising 17 mostly advanced European economies and 5 industries, with annual observations spanning the period 2008–2017. Our findings suggest that software and databases—as a proxy for intangible technologies—exert downward pressure on low-skilled wages, while robotics is associated with a polarization of the wage distribution at the expense of middle-skilled labor. Additionally, less-skilled workers’ relative wages are negatively affected by trade openness and global value chain participation, but positively influenced by sector-specific labor market regulations.

The study of Ellwanger and Snudden (J Bank Financ 154:106962, 2023) discovers a new and remarkable finding regarding the ability of the random-walk model using the end-of-month price of crude oil to forecast future monthly average crude oil prices out-of-sample. The magnitude and nature of the relative predictive gains lead the authors to question whether any other model can “beat” the end-of-month price random-walk out-of-sample. I make an attempt to do so by relying on plain end-of-month crude oil price autoregressive fractionally integrated moving average (ARFIMA) models. These models are more nuanced and at the same time comprehensively account for one of the most salient features of the price of crude oil, namely, its persistence. Consequently, a forecaster is inclined to believe that they might “beat” the end-of-month random-walk model. However, out-of-sample results demonstrate that a uniform (definitive) conclusion cannot be drawn. On the contrary, conclusions depend heavily on the definition of “beating”, i.e. population-level versus finite-sample relative predictability, the forecast horizon, state of the business cycle and the choice of the crude oil price series itself. The decisions, judgments and dilemmas faced by the forecaster are presented and elaborated.

This paper examines whether or not public support for climate change mitigation policy can be affected by salient events such as natural disasters. We test this hypothesis using detailed, county-level data from the 2018 Yale Climate Opinion Maps, which documents both the degree to which residents of a county support climate change policy. We show that while natural disasters lead to statistically significant increases in both the share of a county’s population that support climate change mitigation policy and/or believe that climate change is happening, the magnitude of these estimated effects are economically small and perhaps not robust to hidden bias. As a result, and even assuming our results are in fact causal, the magnitude of our findings suggest that support as a policy objective by targeting agent’s beliefs about the risks climate change poses may ultimately be an ineffectual approach at achieving policymakers’ goals.

Do different tasks shield differently from the scarring effects of job loss? This study examines how the effects of job loss depend on task usage. We use Finnish linked employer–employee data from 2001 to 2016, representative survey data on task usage, and plant closures to identify individuals who involuntarily lose their jobs. We find that heterogeneity in the cost of job loss is linked to task usage. Workers in more social task-intensive origin jobs have smaller employment and earnings losses, whereas workers in routine jobs face larger wage losses. The probability of being employed is 8.3 pp higher (3.9 pp lower) per one standard deviation higher than mean social (routine) task usage 1 year after the job loss event. We also find that workers with longer tenure face larger losses and that task usage contributes more to their losses. The results show that the costs of job loss depend on task usage in the origin job. Public policy measures should be targeted at employees in routine-intensive jobs, since they face the largest losses.

The study set out to investigate whether transparency can mitigate the negative effects volatile capital flows have on growth using cross-section panel data from 21 sub-Saharan African countries from 2000 to 2019. Using the IVQR model, the study finds that at 75th quantile, poor growth performance in SSA is explained mostly by the volatility in debt net inflows compared to other categories of capital, while portfolio net inflow contributes most significantly to the low-level growth for low and medium income countries. Focusing on the interaction between transparency and capital net inflows, the study finds evidence that transparency reduces most of the negative effects of the volatility in debt net inflow compared to other categories of capital inflow. Thus, the study provides evidence that transparency can reduce the negative effects of volatile capital inflows on growth by a significant amount, which varies depending on the type of capital inflow. The implication is that the extent transparency dampens the negative impact of volatile capital flows depend on both the capital type and the level of income of the country concerned. Regarding FDI and FPI, transparency is most effective in reducing volatility of the flow for low income countries, while for debt flows transparency penalizes the volatility of flows for high income countries. On this basis, it recommends that central banks should adopt transparency as a policy tool, particularly in SSA economies with probably low initial transparency to help mitigate the harmful effects of large and volatile capital inflows.