This paper examines the gender wage gap as well as male and female earnings as a function of within-firm tenure in the Czech Republic during 2004-2008. Using segmented regression, the wage gap and tenure were found positively correlated for approximately the first five years of within-firm employment, after which the correlation was near zero. Returns on tenure were greater for men than women, with a statistically significant breakpoint at the seven-year mark for men. These findings confirmed the outcome of other researchers who attributes the gender wage gap to the life and work cycles of women. The model of deferred compensation, in which women are more likely to select out of companies that defer compensation, may help explain the results.
ABSTRACT: It is widely held that smart contracts on a blockchain possess several unique properties, including immutability, disintermediation, and enhanced security, that can be advantageous to organizations. In particular, having these properties can enable smart contracts to benefit human resources departments in a number of ways, including applicant verification, tracking employee skills and tasks, and facilitating compensation. However, it is also reported that effectively implementing smart contracts involves a number of challenges to HR managers. To address these challenges, it would be valuable to establish criteria to help HR managers employ smart contracts successfully. The purpose of this paper is to develop a set of such criteria. The paper first provides an overview of the nature of blockchain and smart contracts and then, based on a review of relevant literature, describes how implementation of blockchain-enabled smart contracts in an HR department may benefit an organization by producing transaction cost savings through expediting processes, enhancing security, and reducing intermediaries. The paper then focuses on various challenges that have been identified in the literature to the successful use of smart contracts. These include issues regarding smart contract integrity, immutability, and security, as well as potential problems associated with a variety of legal issues. Synthesizing this information, the paper develops a set of best practice guidelines to help HR managers determine whether and how to employ smart contracts successfully for HR-related processes. The guidelines emphasize the importance of initial understanding and testing of planned smart contracts, protecting security by ensuring that only permissioned people can access smart contract data, and guaranteeing the integrity of smart contracts by paying very close attention to the translation of natural to programming language and establishing robust reviews of programmed contracts. Policy implications of the guidelines include the importance of HR departments ensuring that all employees who are involved in implementing the technology have a good understanding of the nature and capabilities of smart contracts, that robust methods be implemented to guarantee that the contracts are correctly programmed, and that HR managers keep abreast of legislative environment related to legal issues that may affect their department's use of smart contracts.
Theoretical notions in the literature suggest the existence of a negative relationship between earnings inequality and social capital as measured by interpersonal trust. In a number of crosscountry studies this negative relationship has been confirmed. However, in later cross country-studies that control for per capita wealth and several characteristics of the population and analyse one year or shifts over longer periods find no relationship between inequality and trust. In our study we take into account that socio-cultural factors may obscure this relationship. Our main focus is on the role of a country's welfare regime. Four regimes are distinguished: social democratic regime, conservative regime, liberal regime and transition country. We further examine the impact on interpersonal trust of per capita GDP and the societal indicators percent of residents with tertiary education, percent urbanization, government spending as a percent of GDP. We use data over four points of time from 17 European countries. Besides cross-section OLS regression models using averaged data, fixed-effects models are estimated to control for unobserved time-invariant factors. We find the hypothesised negative relationship between earnings inequality and interpersonal trust not confirmed by our data. Our findings do suggest associations between interpersonal trust and welfare regime. With regard to the welfare regime, we found in particular that a conservative regime, a liberal regime and a transition regime are all associated with a much lower level of interpersonal trust than a social democratic regime. The difference in the level of interpersonal trust from countries with a social democratic regime is largest in countries in transition and smallest in countries with a liberal regime. For the development of institutions that can raise the transition countries to higher levels of social capital and economic development, those from the social democratic welfare regime appear to offer the best perspective. The social democratic welfare regime not only pays more attention to the socially vulnerable in society by paying their benefits, but – in addition to that – by investing in strengthening their productive capabilities to improve their chances on the labour market.
ABSTRACT: Medical tourism is considered nowadays as a multi-billion industry which can promote a country's economic growth. Therefore, forecasting the scheduled tourism demand for medical services is of great importance for policy makers. Doing so, however, is not an easy task due to the following reasons: Data on medical tourism are (i) not easily accessible; (ii) not typically distinguished from tourists' non-scheduled (unintentional) use of a country's medical services; and (iii) usually not publicly available for long time periods. In this paper, we present a novel way to forecast tourism demand (intentional and unintentional) foro medical services —a rough but informative proxy of medical tourism— using limited data. To perform the analysis, we use data on the percentage of hospital discharges of non-residents for 17 European countries over the period 2008-2019 retrieved from Eurostat. Our methodological approach is based on a forecasting technique recently developed by Kyriazi, Thomakos and Guerard ; the adaptive learning forecasting. According to this method, MSE (Mean Squared Error)-performance enhancements can be achieved using any forecast as input —as long as that input is not a 'perfect' forecast— by learning from past forecast errors. Within this context, even the most basic forecast, the no-change or naïve forecast, can be used as input to the adaptive learning procedure. Kyriazi, Thomakos and Guerard approach is very well suited to our research question because (i) the no-change forecast is a natural candidate in a short time series where models cannot be estimated with sufficient accuracy, (ii) the no-change forecast is obviously far from being the 'perfect' forecast, and (iii) the adaptive learning process can be initialized by the no-change forecast and then learn by its own past forecast errors. Our results show that adaptive learning forecasting leads to performance enhancements that range from the 5% to more than 20% relative to the no-change benchmark. This finding indicates the efficiency of the adaptive learning method in forecasting medical tourism demand; an important subcategory of tourism demand for which data are not easily accessible and freely available historical data are existing for short time periods.
Abstract University admission mechanisms are often quite complex. This paper examines one effect of their design on the students' incentives to exert effort in preparation for the test. We adapt a multi-unit all-pay model of auction to draw the conclusion that abler students work harder: this conclusion is in line with the behaviour of a sample of students who apply for admission to the Greek university system with the complex rules newly introduced in 2013.
