Zeitschrift fur Arbeitsmarktforschung

Abstract Unemployment rates provide information on the economic development of countries. Unemployment is not only an economic problem but also a social one. As such, unemployment rates are important for governments and policy makers. Therefore, researchers around the world are constantly developing new forecasting models to successfully predict the unemployment rate. This article presents a new model that combines two deep learning methodologies used for time series forecasting to find the future state of the unemployment rate. The model, created by combining LSTM and GRU layers, has been used to forecast unemployment rates in the United States, United Kingdom, France and Italy. Monthly unemployment rate data was used as the dataset between January 1983 and May 2022. The model’s performance was evaluated using RMSE, MAPE, and MAE values and compared to a stand-alone LSTM and GRU model. Results indicate that the hybrid model performed better for the four countries, except for Italy where the GRU model yielded better results.

Mustafa Yurtsever

Journal for Labour Market Research

Unemployment rate forecasting: LSTM-GRU hybrid approach

失业率预测：LSTM-GRU混合方法

Abstract Previous research shows that female workers are less sensitive to wages in their decision to switch jobs than male workers, and that this could explain a substantial part of the gender wage gap. This paper studies to what extent gender differences in preferences and personality traits explain the gender gap in the wage-elasticity of job-to-job transitions in the labor market. Using a novel decomposition approach in the context of mediated moderation and using German Socio-Economic Panel Survey (G-SOEP) data for the period 2005–2019, we find that gender differences in risk preferences, patience, trust, reciprocity, altruism, conscientiousness, ambition and self-esteem explain about 25% of the gender gap in wage-elasticities of job separations. A detailed decomposition suggests that risk preferences, trust and ambition contribute most to this gender gap in wage-elasticity.

Céline Detilleux

Nick Deschacht

The gender gap in the wage sensitivity of job transitions: a decomposition analysis

工作转换中工资敏感性的性别差异：一项分解分析

Abstract I study the association between task content of jobs and mothers’ employment transitions after the first birth in Germany. I construct measures of task content of jobs using data from the Employment Survey conducted by the German Federal Institute for Vocational Education and Training (BiBB). These indicators illustrate the career cost of children and how it is impacted by the technology- and globalization-driven labour market change. The measures are then linked to high-quality individual register data from the German Pension Fund (FDZ-RV) covering the years 2012–2020. Utilizing competing risk models, I show that women engaged in occupations with analytic and interactive task content, which are in high demand and incompatible with maternity-related employment breaks, are the most likely to transition to employment after their first birth. Conversely, women with occupations intense in routine tasks, which are more susceptible to automation or trade competition, are more likely to experience unemployment.

Honorata Bogusz

Task content of jobs and mothers’ employment transitions in Germany

德国工作任务内容与母亲就业转变

Abstract I estimate the effect of tightness on wages in Hungary and Slovakia. The Mortensen Pissarides model predicts a positive relationship but the empirical evidence is scarce. I instrument tightness by the distance of a district from the Austrian border, interacted with a dummy that marks the opening of the Austrian labour market to these countries in 2011. I find a positive effect of tightness on wages, which is in line with the conclusion of the models. If tightness increase by 1 per cent than ceteris paribus wages increase by roughly 0.2 per cent both in Hungary and Slovakia as well.

Lajos Tamás Szabó

The effect of labour tightness on wages at the regional level in Central Europe

中欧地区劳动力紧张对工资的区域性影响

Abstract Non-take-up of welfare schemes is a key concern of policy effectiveness. Building on studies that have shown the low take-up of minimum income schemes, our case study of Ireland’s Working Family Payment is the first to analyse non-take-up of an in-work benefit and its determinants with a special focus on labour market factors. Based on EU-SILC (2014–2019) we estimate a non-take-up rate between 63 and 76%, which poses a major obstacle for effective poverty prevention. Moreover, we stress that non-take-up of in-work benefits differs to minimum income schemes. We provide new evidence on how labour market characteristics play an important role in explaining non-take-up, especially self-employment and the interaction with unemployment benefits. Benefit erosion is a key factor in declining eligibility, which should be addressed by indexing wages and prices. Furthermore, we propose policy reforms around automatic enrolment or tax credits to mitigate non-take-up and alleviate in-work poverty.

Diego Muñoz-Higueras

Stephan Köppe

Rafael Granell

Amadeo Fuenmayor

Non-take-up of in-work benefits: determinants, benefit erosion and indexing

在职福利的未领取：决定因素、福利侵蚀与指数化

Abstract This paper investigates whether the adoption of E-learning by firms contributed to offset the decrease in firm-sponsored training during the COVID-19 pandemic. Using a novel firm level survey linked with administrative data on the universe of workers within those firms, I study the role of E-learning in firms’ training activities during the crisis. I find that the COVID-19 pandemic substantially decreased firm-sponsored training by up to 11 percentage points. However, firms’ ability to use E-learning nullifies this negative effect. Furthermore, the differential capabilities of firms to apply E-learning might exacerbate already prevalent inequalities in training opportunities.

Christoph Müller

The COVID-19 pandemic and firms’ E-learning use: implications for inequality in training opportunities

COVID-19 大流行与企业电子学习使用：对培训机会不平等的影响

短名	J Labour Market Res
Journal Impact	1.76
国际分区	INDUSTRIAL RELATIONS & LABOR(Q2)

ISSN	1614-3485, 1867-8343, 2510-5019, 2510-5027
h-index	26

出版信息	出版商: Springer Science and Business Media Deutschland GmbH，出版周期: ，期刊类型: journal，开源期刊: 否
基本数据	创刊年份: 2009，原创研究文献占比: ，自引率:， Gold OA占比:

Zeitschrift fur Arbeitsmarktforschung

期刊引文格式

有 1 位以上作者的期刊

有 2 位作者的期刊

有 3 位作者的期刊

有 5 位以上作者的期刊

书籍引用格式

学位论文引用格式

网页引用格式

专利引用格式

手工熬夜修改参考文献？研飞自动匹配期刊，一键轻松成稿，支持 Word/WPS

投稿经验分享

Unemployment rate forecasting: LSTM-GRU hybrid approach

The gender gap in the wage sensitivity of job transitions: a decomposition analysis

Task content of jobs and mothers’ employment transitions in Germany

The effect of labour tightness on wages at the regional level in Central Europe

Non-take-up of in-work benefits: determinants, benefit erosion and indexing

The gender gap in the wage sensitivity of job transitions: a decomposition analysis

Task content of jobs and mothers’ employment transitions in Germany

The effect of labour tightness on wages at the regional level in Central Europe

The COVID-19 pandemic and firms’ E-learning use: implications for inequality in training opportunities

Non-take-up of in-work benefits: determinants, benefit erosion and indexing