Empirical Economics最新文献

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.

This paper examines the short- and medium-run effects of U.S. federal personal income and corporate income tax cuts on a wide array of economic policy variables in a data-rich environment. Using a panel of U.S. macroeconomic data set, made up of 132 quarterly macroeconomic series for 1959–2018, we estimate factor-augmented vector autoregression (FAVARs) models where an extended narrative tax changes dataset combined with unobserved factors. The narrative approach classifies if tax changes are exogenous or endogenous. This paper identifies narrative tax shocks in the vector autoregression model using the sign restrictions with the Uhlig's (J Monet Econ 52(2):381–419, 2005. https://doi.org/10.1016/j.jmoneco.2004.05.007) penalty function. Empirical findings show a significant expansionary effect of tax cuts on the macroeconomic variables. Cuts in personal and corporate income taxes cause a rise in output, investment, employment, and consumption; however, the effects of corporate tax cuts have relatively smaller effects on output and consumption but show immediate and higher effects on fixed investment and price levels. We validate the model's specification and the identification of tax shocks through a reliability test based on the Median-Target method. Additionally, sensitivity analysis employing the local projection vector autoregression model, number of iterations of the algorithm, and incorporating diverse factor specifications reaffirms tax cuts' persistent and expansionary effects. Our contribution to the narrative tax literature lies in providing empirical evidence that aligns with the notion that reductions in personal taxes demonstrate a higher efficacy as a fiscal policy tool when compared to reductions in corporate income taxes.

The debate about the impact of robots on employment has been lively. In this paper, I examine the effect of robots on local labor demand in South Korea, one of the most technologically advanced countries in terms of robotics. Using the regional variation in robot exposure constructed from national industry-level robot adoption data and the initial distribution of industrial employment in cities, I find that robots did not reduce local labor demand. However, I estimate declines in labor demand in the manufacturing sector and routine jobs. An increase in one robot per 1000 workers in terms of exposure to robots is correlated with a decline in the job vacancy growth rate of 2.6%p in the manufacturing sector and of 2.5%p in routine jobs. No significant relationship is found between robot exposure and labor demand in the service sector or non-routine jobs.

After Japan’s bubble economy collapsed in 1991, household values diversified, and, gradually, the budget shares on intangible services exceeded that on tangible goods, leading to a shift from demand for goods to services. In this study, we focus on the increased budget allocation to services in Japanese household expenditure and verify whether service-related items can be aggregated into a service group using Lewbel’s (Am Econ Rev 86(3):524–543, 1996) generalized composite commodity theorem (GCCT). We first accurately reclassify into all 51 items consisting of goods and services and verify whether these aggregations are justified. Next, we verify whether these 51 items can be sub-aggregated into goods or service groups. In testing the GCCT, we incorporate panel time-series analysis with cross-sectional dependence, unlike traditional GCCT tests with time-series data. We also conduct a nonparametric revealed preference test for weak separability as a benchmark against the GCCT test results. Our findings demonstrate that the utility function can be rationalized even when the data set is reclassified into 51 items, justifying the aggregation into service groups. This suggests that in the future, specifying the functional form of a service group can be developed into a traditional demand analysis, such as calculating estimates and elasticities.