This paper provides robust estimates of the impact of both product and labor market regulations on unemployment using data from 24 European countries over the period 1998–2013. Controlling for country fixed effects, endogeneity, and a large set of covariates, results show that product market deregulation overall reduces the unemployment rate. This finding is robust across all specifications and in line with theoretical predictions. However, not all types of reforms have the same effect: deregulation of state controls and in particular involvement in business operations tend to push up the unemployment rate. Labor market deregulation, proxied by the employment protection legislation index, is detrimental to unemployment in the short run, while a positive impact (i.e., a reduction in the unemployment rate) occurs only in the long run. Analysis by sub-indicators shows that reducing protection against collective dismissals helps in reducing the unemployment rate. The unemployment rate equation is also estimated for different categories of workers. Although men and women are equally affected by product and labor market deregulations, workers distinguished by age and educational attainment are affected differently. In terms of employment protection, young workers are almost twice as strongly affected as older workers. Regarding product market deregulation, highly educated individuals are less impacted than low- and middle-educated workers.
Pablo de Pedraza, Stefano Visintin, Kea Tijdens and Gábor Kismihók
This paper studies the relationship between a vacancy population obtained from web crawling and vacancies in the economy inferred by a National Statistics Office (NSO) using a traditional method. We compare the time series properties of samples obtained between 2007 and 2014 by Statistics Netherlands and by a web scraping company. We find that the web and NSO vacancy data present similar time series properties, suggesting that both time series are generated by the same underlying phenomenon: the real number of new vacancies in the economy. We conclude that, in our case study, web-sourced data are able to capture aggregate economic activity in the labor market.
Yukun Zheng, Yiqun Liu, Zhen Fan, Cheng Luo, Qingyao Ai, Min Zhang and Shaoping Ma
A number of deep neural networks have been proposed to improve the performance of document ranking in information retrieval studies. However, the training processes of these models usually need a large scale of labeled data, leading to data shortage becoming a major hindrance to the improvement of neural ranking models’ performances. Recently, several weakly supervised methods have been proposed to address this challenge with the help of heuristics or users’ interaction in the Search Engine Result Pages (SERPs) to generate weak relevance labels. In this work, we adopt two kinds of weakly supervised relevance, BM25-based relevance and click model-based relevance, and make a deep investigation into their differences in the training of neural ranking models. Experimental results show that BM25-based relevance helps models capture more exact matching signals, while click model-based relevance enhances the rankings of documents that may be preferred by users. We further proposed a cascade ranking framework to combine the two weakly supervised relevance, which significantly promotes the ranking performance of neural ranking models and outperforms the best result in the last NTCIR-13 We Want Web (WWW) task. This work reveals the potential of constructing better document retrieval systems based on multiple kinds of weak relevance signals.