Empirical Economics最新文献

Over the last two decades, social security reforms in several European countries have turned early retirement routes for older workers increasingly difficult. The size of the effects of these reforms on labour supply and social security transfers, and how these effects interact with workers’ characteristics have yet to be measured. This article sheds light on this issue by exploring the consequences of postponing access to an old-age unemployment programme—from age 58 to 60—in Belgium. The programme provides laid-off workers with a combination of unemployment benefits and a monthly supplement paid by the employer until the full retirement age. Exploiting register data on the universe of workers and using a difference-in-difference identification strategy, the authors find that UCS eligibility negatively affects employment participation but also mitigates older workers’ participation in other social security programmes.

We investigate the long-run impact of education on longevity using data for England and Wales from the Health and Lifestyle Survey. Longevity is modelled by survival analysis using a mixed proportional hazard model. For identification we propose a Regression Discontinuity Design implied by an increase in the minimum school leaving age in 1947 (from 14 to 15) combined with a principal stratification method for estimation of the mortality hazard rate. This method allows us to derive the causal effect of extended education on longevity. In line with earlier studies we do not find credible evidence of a causal impact of the additional years of schooling that were induced by the reform on longevity.

This study examines the characteristics of Japan’s dual labor market, which consists of standard and non-standard employment. I conduct labor stock and flow analyses using Japanese Labour Force Survey data from 2002 to 2022. The stock analysis suggests that, in the long run, non-standard employment improves labor market conditions, such as employment and unemployment rates. Changes in the composition of standard and non-standard employment reduce the average hours per worker in the long run but play a limited role over the business cycles. The flow analysis reveals that inflows and outflows involving non-standard employment have relatively significant effects on changes in employment and unemployment rates. This feature of non-standard employment is more pronounced for females and young individuals. The flow analysis also shows that within-employment reallocation, that is, transitions between standard and non-standard employment, primarily determines the changes in the share of non-standard employment. The sluggish movement between these two types of contracts leads to a persistently high level of non-standard employment in Japan.

Building on the GJR-GARCH model, this paper uses the mixed-data sampling (MIDAS) approach to link monthly realized volatility of EU carbon future prices and macroeconomic variables to the volatility of EU carbon futures market and proposes the GJR-GARCH-MIDAS model incorporating macroeconomic variables including the economic sentiment indicator of the EU, the harmonized index of consumer prices of the EU, the European economic policy uncertainty index and ECB’s marginal lending facility rate (GJR-GARCH-MIDAS-X models). An empirical analysis based on the monthly macroeconomic variables and daily EUA futures data shows that the above four low-frequency macroeconomic variables have significant positive or negative impacts on the long-term volatility of EUA future prices, respectively. The GJR-GARCH-MIDAS-X models significantly outperform other competing models, including the GJR-GARCH model, GARCH-MIDAS model and standard GJR-GARCH-MIDAS model, in terms of out-of-sample volatility forecasting, which suggests that macroeconomic variables contain important information for EUA future price volatility forecasts. In particular, the GJR-GARCH-MIDAS model with harmonized index of consumer prices (HICP) (GJR-GARCH-MIDAS-HICP model) performs best in out-of-sample volatility forecasting, and our findings are robust to different forecasting windows.

Abstract

The article presents a robust quantitative approach for determining significant economic factors for sex trafficking in the United States. The aim is to study monthly counts of sex trafficking-related convictions, and use a wide range of economic variables as covariates to investigate their effect on conviction counts. A count time series model is considered along with a regression setup to include economic time series as covariates (economic factors) to explain the counts on sex trafficking-related convictions. The statistical significance of these economic factors is investigated and the significant factors are ranked based on appropriate model selection methods. The inclusion of time-lagged versions of the economic factor time series in the regression model is also explored. Our findings indicate that economic factors relating to immigration policy, consumer price index and labor market regulations are the most significant in explaining sex trafficking convictions.

Using the Consumer Expectations Survey of the ECB, I estimate how individual expectations on core economic outcomes changed in France, Germany, Italy, and Spain right after the beginning of the Ukraine–Russia war. I find that individuals expected lower economic growth and higher inflation. The effect of the war was larger in the countries with a higher energy-imports dependency. Hence, the expectation formation process might have changed.

This study employs Chilean administrative data to investigate the impact of school starting age on the characteristics of students’ initial enrolled schools. Employing minimum age requirements and an RD design to mitigate endogeneity concerns, we identify benefits linked to commencing school at a later age. Our findings demonstrate that children starting school at an older age enroll in institutions with higher average scores in standardized tests and interact with older peers whose parents have higher education levels. Furthermore, they display a heightened likelihood of entering schools employing academic selection methods, a greater proportion of full-time teachers, and a larger percentage of instructors with a 4-year college degree. The analysis by level of education of the parents and gender reveals that most of our results are driven by parents with lower levels of education and girls. Subgroup analyses further reveal that many of our results are driven by parents with lower levels of education and parents of girls.

This paper studies whether forced increases in the residual working life, determined by a restrictive pension reform, induce additional training activities. By exploiting a sizable Italian pension reform, in a difference-in-differences setting, I find that a lengthening of the working horizon increases, through training, workers’ human capital. Additionally, I show that the response to the reform appears very heterogeneous and depends on gender, age, education, marital status, sector of employment and firm size. My estimates suggest, furthermore, that these individual positive effects are not attributable to employers’ sponsorship.

With mean instrumental variables regression, k-class estimators have the potential to reduce bias, which is larger with weak instruments. With instrumental variables quantile regression, weak instrument-robust estimation is even more important because there is less guidance for assessing instrument strength. Motivated by this, we introduce an analogous k-class of estimators for instrumental variables quantile regression. We show the first-order asymptotic distribution under strong instruments is equivalent for all conventional choices of k. We evaluate finite-sample median bias in simulations for a variety of k, including the k for the conventional k-class estimator corresponding to limited information maximum likelihood (LIML). Computation is fast for all k, and compared to the (k=1) benchmark estimator (analogous to 2SLS), using the LIML k reliably reduces median bias in a variety of data-generating processes, especially when the degree of overidentification is larger. We also revisit some empirical estimates of consumption Euler equations derived from quantile utility maximization. All code is provided online (https://kaplandm.github.io).