Immigration and Labor Productivity: New Empirical Evidence for Spain
In: IZA Discussion Paper No. 7297
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In: IZA Discussion Paper No. 7297
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In: IZA Discussion Paper No. 3978
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In: Business research quarterly: BRQ, Band 25, Heft 3, S. 283-294
ISSN: 2340-9444
New challenges arise in data visualization when the research involves a sizable database. With many data points, classical scatterplots are non-informative due to the cluttering of points. On the contrary, simple plots, such as the boxplot that are of limited use in small samples, offer great potential to facilitate group comparison in the case of an extensive sample. This article presents exploratory data analysis methods useful for inspecting variation across groups in crucial variables and detecting heterogeneity. The exploratory data analysis methods (introduced by Tukey in his seminal book of 1977) encompass a set of statistical tools aimed to extract information from data using simple graphical tools. In this article, some of the exploratory data analysis methods like the boxplot and scatterplot are revisited and enhanced using modern graphical computational devices (as, for example, the heat-map) and their use illustrated with Spanish Social Security data. We explore how earnings vary across several factors like age, gender, type of occupation, and contract, and in particular, the gender gap in salaries is visualized in various dimensions relating to the type of occupation. The exploratory data analysis methods are also applied to assessing and refining competing regressions by plotting residuals-versus-fitted values. The methods discussed should be useful to researchers to assess heterogeneity in data, across-group variation, and classical diagnostic plots of residuals from alternative models fits. JEL CLASSIFICATION: C55; J01; J08; Y10; C80
In: IZA Discussion Paper No. 13459
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Working paper
In: IZA Discussion Paper No. 12510
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In: IZA Discussion Paper No. 6503
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In: IZA Discussion Paper No. 5571
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In: IZA Discussion Paper No. 3983
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In: IZA Discussion Paper No. 6312
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In: IZA Discussion Paper No. 11811
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Working paper
In: The developing economies: the journal of the Institute of Developing Economies, Tokyo, Japan, Band 53, Heft 2, S. 75-99
ISSN: 1746-1049
This paper analyzes the relationship between social networks and the probability of finding a job. We explore geographic closeness as the social interaction to explain the job search function. Using data from Colombia in 2009, we calculate how neighborhoods have an effect on the channel used to find a job (social network versus no social network). In addition, we study how wage premium relates to using a social network in finding a job, exploring the inequality that can arise using a different job search method. Our results show that neighborhood affects the individual's job search method and that referred workers earn less at the bottom of the wage distribution with respect to non‐referred workers. Colombia presents persistent high levels of informality and inequality with the existence of spatial clusters that impose important social and economic costs with strong informational asymmetries on the job market.
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We interpret social transfers broadly as a set of measures to reduce or relieve poverty, and study how well this purpose is served in the countries that participated in the European Union Statistics on Income and Living Conditions in 2007. Motivated by the findings, we characterise a social transfer system in a country by its potential and effectiveness, and compare the countries for a range of definitions of the poverty threshold. The methods are also applied to two subpopulations of household types.
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In: IZA Discussion Paper No. 5223
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In: IZA Discussion Paper No. 6309
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