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PurposeThe general purpose of the paper is to examine the effect of financial globalization on income inequality. The specific purposes are: 1) To examine the effect of overall financial globalization on income inequality. 2) To analyze whether de facto and de jure financial globalization have differential effects on income inequality. 3) To scrutinize whether the effect of financial globalization on income inequality varies across countries of different income groups and quantiles of income inequality.Design/methodology/approachThe authors employed panel quantile regression using 73 countries over 2000–2016 to examine the effect of financial globalization on income inequality. The authors employed fixed effect and panel quantile regressions and classified the countries into income groups to compare differential effects of financial globalization across different income groups. Further, the authors unbundled financial globalization into de facto and de jure financial globalizations to investigate whether their effects on income inequality vary.FindingsOverall financial globalization raises income inequality more at lower quantiles of inequality. De jure financial globalization reduces income inequality in high-income countries. In high-income countries, de jure financial globalization has more favorable income distribution at lower quantiles of inequality. In contrast, de facto financial globalization raises inequality regardless of income classification of the countries.Originality/valueTo the best of the authors' knowledge, the authors for the first time employed panel quantile regression to analyze whether financial globalization affects income inequality across different quantiles. In addition to de facto globalization, the authors used the newly developed de jure financial globalization index to examine its impact on income inequality. The de jure dimension is largely neglected in the literature. The authors provide empirical evidence on how the different dimensions of financial globalization, de facto and de jure, impact inequality in high-income, middle-income and low-income countries.

This article challenges Fixed Effects (FE) modeling as the 'default' for time-series-cross-sectional and panel data. Understanding different within and between effects is crucial when choosing modeling strategies. The downside of Random Effects (RE) modeling—correlated lower-level covariates and higher-level residuals—is omitted-variable bias, solvable with Mundlak's (1978a) formulation. Consequently, RE can provide everything that FE promises and more, as confirmed by Monte-Carlo simulations, which additionally show problems with Plümper and Troeger's FE Vector Decomposition method when data are unbalanced. As well as incorporating time-invariant variables, RE models are readily extendable, with random coefficients, cross-level interactions and complex variance functions. We argue not simply for technical solutions to endogeneity, but for the substantive importance of context/heterogeneity, modeled using RE. The implications extend beyond political science to all multilevel datasets. However, omitted variables could still bias estimated higher-level variable effects; as with any model, care is required in interpretation.

This thesis uses a panel data to investigate the effects of eight macroeconomic variables on the evolution of growth rate of Gross Domestic Product per capita. The panel data consist of 23 years of observation for ten developed and ten developing countries. The years covered are from 1990 to 2012. The independent variables selected are: (i) initial GDP per capita (INIGDPPC) to account for the effect of convergence (ii) terms of trade (TOT), (iii) trade openness (OPEN), (iv) gross fixed capital formation (GFCF), (v) human capital (EDUC) measured as average years of schooling, (vi) inflation (INF), (vii) government size (GOVT) and (viii) population growth (POPUL). The thesis methodology is unique in combines cutting-edge data-driven models such as hybrid artificial neural network with genetic algorithm (ANN/GA) and fixed effect panel model. First, the impact of eight independent variables on growth is investigated and dominant variables are identified by using three data samples: developed countries only, developing countries only, and developed and developing countries together. Moreover the study uses three different data formatting for each sample: annual data, periodic data of 4 years overlapping and periodic data of 4 years non-overlapping. Second, two estimation methods are used to predict values of growth. This allows us to compare those forecasting methods with each other. The analysis indicates INIGDPPC, INF, GFCF, GOVT, EDUC, POPUL, TOT and OPEN variables have the statistically significant impact on growth in the panel regression. The INIGDPPC, POPUL, GOVT, and INF have negative and OPEN, EDUC and GFCF have positive statistically significant effects on the economic growth in developed and developing countries. Moreover, the results obtained from the study have shown that the power of the hybrid ANN/GA method (combined the artificial neural network method and genetic algorithm) is more than Panel fixed effect estimation method in predicting the economic growth. ; ÖZ: Bu tez, panel veri kullanarak, sekiz tane makroekonomik değişkenin kişi başı gayri safi yurt içi hasıla büyüme oranına etkisini inceler. Pnael very 23 yıldan; ve onu gelişmiş, onu da gelişmekte olan , toplam 20 ülkeden oluşmaktadır. Veri 1990 ile 2012 seneleri arasındaki yılları kapsamaktadır. Kullanılan 8 makroekonomik değişken şunlardır: (i) Kişi başı GSYİH başlangıç değeri (INIGDPPC), (ii) ticaret terimi (TOT), (iii) ticaret açıklığı (OPEN), (iv) yatırımlar (GFCF), (v) insan sermayesi (EDUC), (vi) enflasyon (INF), (vii) hükümet harcamaları büyüklüğü (GOVT), ve (viii) nüfus artış hızıdır (POPUL). Çalışma iki tane metodoloji kullanmaktadır: Biri genetic algoritma ile birleştirilmiş yapay neural network metotu, bir diğeri ise panel fixed effect metotudur. Calışma 3 ülke grubu ve 3 veri formatlaması kullanarak, toplamda 9 kez seçilen 8 makroekonomik değişkenin büyümeye etkisini inceledi. Ülke grupları: sadece gelişmiş ülkeler, sadece gelişmekte olan ülkeler, ve gelişmiş ve gelişmekte olan ükleler beraber olmak üzere 3 tane idi. Veri formatı ise yıllık veriler, 4 yıllık periyodik veri (yıllar örtüşüyor) ve 4 yıllık periyeodik veri (yıllar örtüşmüyor) şeklindeydi. Bu çalışma ayni zamanda ANN/GA metodu ile panel fıxed effect metodunu büyüme tahminleri alanındaki karşılaştırmasını yapmıştır. Sonuç olarak, INIGDPPC, INF, GFCF, GOVT, EDUC, POPUL, TOT and OPEN değişkenlerinin istatistiksel büyüme değerlerine etkisi olduğu görülmüştür. Hem gelişmiş hem de gelişmekte olan ülkelerde INIGDPPC, POPUL, GOVT, ve INF eksi bir etki, OPEN, EDUC ve GFCF ise artı bir etki yapmıştır. Sonuçlar ayrıva ANN/GA metodunun panel fıxed effet metoduna gore daha güçlü bir metot olduğunu göstermiştir. ; Doctor of Philosophy in Economics. Thesis (Ph.D.)--Eastern Mediterranean University, Faculty of Business and Economics, Dept. of Economics, 2017. Supervisor: Assoc. Prof. Dr. Çağay Coşkuner.

The opportunities for understanding how treatment effects vary across different segments of the population have led to a rise in the use of quantile regressions for identifying unconditional quantile treatment effects (QTEs). However, existing quantile regression models fall into two categories: those that are unsuitable for identifying unconditional QTEs, and those that often struggle with the complex data structures common in sociology and other social sciences. Therefore, existing methods to identify unconditional QTEs are incomplete: the propensity score framework of Firpo (2007) allows for only a binary treatment variable, and the generalized quantile regression model of Powell (2020) faces difficulties with large data sets and high-dimensional fixed effects. This paper introduces a two-step approach to estimating unconditional QTEs, which is easy to use and aligns with the needs of sociologists. First, the treatment variable is decomposed into a systematic and random part, and then, the random variation in the treatment status is used in a bivariate quantile regression model. Through a series of simulations and three empirical applications, we demonstrate that the RQR approach provides unbiased estimates of unconditional QTEs. Moreover, the RQR approach offers greater flexibility and enhances computational speed compared to existing models, and it can easily handle high-dimensional fixed effects. In sum, the RQR approach fills a pressing void in quantitative research methodology, offering a much-needed tool for studying treatment effect heterogeneity.