Capitalism and Freedom

We explain the proliferation of panel data studies in terms of (i) data availability, (ii) the more heightened capacity for modeling the complexity of human behavior than a single cross-section or time series data can possibly allow, and (iii) challenging methodology. Advantages and issues of panel data modeling are also discussed.

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Panel data analysis—advantages and challenges

: One use of spectral analysis of time series is to determine if two different time series are realizations from the same process This thesis develops the theory behind Krishnaiah and Schuurmann's theoretical work reported in their report Approximations to the Distributions of the Traces of Complex Multivariate Beta and F Matrices We take the trace of a test statistic calculated from the spectral density matrices of the time series and test it The thesis applies the theory to two small sample simulations (Author)

Spectral Analysis of Time Series.

Será que a desigualdade de rendimentos aumentou durante os últimos tempos? Quem ganhou e quem perdeu neste processo? Este processo afectou todos os países da OCDE uniformemente? Em que medida é que maiores desigualdades de rendimentos são a consequência de maiores diferenças nos rendimentos dos trabalhadores e até que ponto são afectados por outros factores? Finalmente, como é que a redistribuição governamental através do sistema de benefícios fiscais afecta estas tendências?

Book Review: OECD, Growing Unequal? Income Distribution and Poverty in OECD Countries:

In their widely cited study, Shavit and Blossfeld report stability of socioeconomic inequalities in educational attainment over much of the 20th century in 11 out of 13 countries. This article outlines reasons why one might expect to find declining class inequalities in educational attainment, and, using a large data set, the authors analyze educational inequality among cohorts born in the first two-thirds of the 20th century in eight European countries. They find, as expected, a widespread decline in educational inequality between students coming from different social origins. Their results are robust to other possible choices of method and variables, and the authors offer some explanations of why their findings contradict Shavit and Blossfeld's conclusions.

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Nonpersistent inequality in educational attainment: evidence from eight European countries.

Estimation and Inference for Multi-dimensional Heterogeneous Panel Datasets with Hierarchical Multi-factor Error Structure

Following recent contributions on migration flows, we contribute to the literature by relaxing restrictions on how multilateral resistance to migration (MRM) may affect province-pair-specific migration flows. We follow recent advancements in the three-dimensional (3D) panel data models with a hierarchical multifactor structure and develop the more flexible specification for MRM. In addition to including unobserved global (country) factors with province-pair-specific coefficients, we can control for local origin (destination)-specific factors that have heterogeneous effects on destinations (origins). We apply the 3DCCE estimator advanced by Kapetanios et al. (J Econom, 2020) to an analysis of the determinants of interprovincial migration flows in Canada from 1976 to 2014. In particular, we find that the recent rise in the internal migration flows, registered in Canada from 2009 onwards, is more likely to be associated with the relative income inequality and network presence rather than the conventional long-run determinants such as income and unemployment differentials.

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Gravity models of interprovincial migration flows in Canada with hierarchical multifactor structure

This paper analyses the return plans of irregular migrants by stressing the role of individual skills and network effects. We propose a simple two-period life-cycle model that we test using individual-level data on irregular migrants in Italy and on undocumented Mexicans in the USA. Our evidence shows that highly skilled clandestine migrants are more likely to return home than migrants with low or no skills. We argue this result is due to constraints imposed by the irregular status on migrants' ability to fully use human capital in the destination country. However, the presence of strong social networks may lessen this effect.

https://papers.ssrn.com/sol3/Delivery.cfm/j_1467-9914_2010_00500_x_wml.pdf?abstractid=1732445&mirid=3

Return Decisions of Undocumented Migrants: Do Network Effects Help the High‐skilled Overstay?

In this paper we propose a de_nition of fairness in education which is based onthe theory of equality of opportunity developed in the last decades in the philosophical and economic literature (Roemer,1998; Fleurbaey, 2008), we derive opportunity inequality measures based on such conceptual framework, and we use these measures to evaluate the 1999 reform of the Italian university system (the so called “3+2" reform). Looking at 1995-2004 college graduates data our estimates show an improvement in the equality of opportunity in the access to university. However, the aggregated data available for the 2005-2008 suggest that such a positive e_ect may vanish in the medium run.

Inequality of educational opportunity in Italy:how fair is the “3+2" reform?

Undocumented migration is a pervasive and increasingly relevant phenomenon in modern societies. In this paper we shed some lights on the factors affecting the return plans of irregular migrants and in particular on the role of individual skills and abilities. We show that highly skilled clandestine migrants are more likely to return home than migrants with low or no skills; we argue that this result is due to constraints imposed by the irregular status on migrants' ability to fully employ human capital in the destination country ("skill waste"). We present this idea in a simple life-cycle framework where illegality is modeled as a tax on skills that reduces the opportunity cost of returning home, particularly for the highly skilled. This proposition is tested using individuallevel data on irregular migrants in two OECD countries Italy and the US. The two data sources offer two very distinct situations - in terms of densities of migrants networks, duration of the migration spell, country of origin and destinations etc. - on which to test the implications of the model. Empirical evidence confirms that the intention to return to the home country is more likely for highly skilled illegal immigrants. The effects are weaker when migration takes place within consolidated networks of already established migrants, as for the case of Mexicans in the US. In general the results of this paper suggest that when a large proportion of immigration flows takes places outside the legal system, the out-migration of irregular migrants is likely to reinforce the negative self-selection at entry (those with relatively higher skills are more likely to return in the home countries).

/pdf/clandestine-migrants-do-the-high-skilled-return-home-first-bq1g43buds.pdf

Laura Serlenga

Papers

Estimation and Inference for Multi-dimensional Heterogeneous Panel Datasets with Hierarchical Multi-factor Error Structure

Gravity models of interprovincial migration flows in Canada with hierarchical multifactor structure

Return Decisions of Undocumented Migrants: Do Network Effects Help the High‐skilled Overstay?

Inequality of educational opportunity in Italy:how fair is the “3+2" reform?

Clandestine Migrants: Do the High-Skilled Return Home First?