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دانلود رایگان مقاله انگلیسی در مورد اثرات نوسان درآمد بر توزیع درآمد: شواهدی نامتقارن از داده های سطح ایالتی در آمریکا به همراه ترجمه فارسی
عنوان فارسی مقاله | در مورد اثرات نوسان درآمد بر توزیع درآمد: شواهدی نامتقارن از داده های سطح ایالتی در آمریکا |
عنوان انگلیسی مقاله | On the Effects of Income Volatility on Income Distribution: Asymmetric Evidence from State Level Data in the U.S. |
رشته های مرتبط | اقتصاد، حسابداری و مدیریت، مدیریت مالی، اقتصاد مالی و حسابداری مالی |
کلمات کلیدی | توزیع درآمد، بی ثباتی درآمد، عدم تقارن، داده های سطح ایالتی، ایالات متحده آمریکا |
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کیفیت ترجمه | کیفیت ترجمه این مقاله متوسط میباشد |
نشریه | الزویر – Elsevier |
مجله | تحقیق در اقتصاد – Research in Economics |
سال انتشار | 2018 |
کد محصول | F645 |
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فهرست مقاله: چکیده |
بخشی از ترجمه فارسی مقاله: ۱. مقدمه |
بخشی از مقاله انگلیسی: I. Introduction The inverted-U hypothesis, introduced by Kuznets (1955), basically identifies the level of economic activity as the main determinant of income inequality. More precisely, it asserts that at the early stages of economic growth, income inequality worsens and it only improves at the later stages. Empirical support for the hypothesis is rather mixed, mostly rejecting the hypothesis.1 Another strand of the literature, however, argues that income or output volatility as a measure of uncertainty can worsen income inequality. Hausmann and Gavin (1997) is perhaps the first study that alludes us to the adverse effects of income volatility on income distribution by arguing that poorer members of society are not well equipped to absorb economic shocks or uncertainties relative to richer members. Using cross-sectional data from 56 countries in Latin America and industrial economies, they found that while neither GDP growth nor inflation had any significant effects on income inequality, the volatility of real GDP had significantly adverse effects on income inequality. The same is supported by Caroli and Garcia-Penalosa (2001), who looked at the effects of volatility of wages on wage differentials between low skilled and high skilled workers. Similar arguments are extended to the distribution of human capital rather than distribution of income by Checchi and Garcia-Penalosa (2004) who develop a theoretical model, showing that aggregate production risk determines the average level of education and its distribution. The higher the production risk, the higher the educational inequality. Other cross-sectional studies that support the adverse impact of output volatility on income distribution are Breen and Garcia-Penalosa (2005) and Laursen and Mahajan (2005). While the above studies have used cross-sectional data from different countries, two studies have used panel data across countries and over time. Calderon and Yeyati (2009) uses data from 75 countries over the 1970-2005 (5-year period observations) to show that even in a panel model, output volatility has adverse effects on income inequality measured by GINI coefficient. Their findings do not seem to be sensitive to different measures of volatility, nor to different measures of income inequality. They also assess asymmetric effects of output fluctuations by assigning dummy variables to output drops and output jumps to show that output volatility has asymmetric effects on income distribution. Finally, Huang et al. (2015) criticize all of the above studies for not using recent advances in error-correction modelling techniques and employ a panel error-correction approach instead of the conventional method of using cross-sectional data. Their panel data is different than that of Calderon and Yeyati (2009) in that they use annual data from the 48 states of the continental U.S. from 1945 to 2004 which forms a balanced panel set with N = 48 and T = 60.2 Their findings are no different than any of the previous studies, in that they also find that volatility of income has an adverse effect on income distribution in the U.S. and this conclusion is not sensitive to different measures of income inequality, nor to different measures of volatility. The panel studies reviewed above do suffer from aggregation bias in that what is true in one cross-sectional unit, may not necessarily be true in another cross-sectional unit. To resolve the issue, we adhere to time-series modelling only and reconsider the relation between income volatility and income inequality in each state of the U.S. This is now possible, since Frank (2009) has extended his data set through 2013, providing 68 annual observations for each state. Since the two variables could be stationary or non-stationary, the appropriate approach will be the linear ARDL approach of Pesaran et al. (2001). Within time-series framework, we will take an additional step and assess the asymmetric effects of volatility on income distribution by using the nonlinear ARDL approach of Shin et al. (2014) which also allows us to detect asymmetric causality. This is a plausible inquiry since the rate at which income inequality responds to an increase in income volatility could be different than the rate at which it responds to a decline. Indeed, if poorer members of the society cannot absorb economic shocks or uncertainties as easily as richer members, both group will react differently to increased uncertainty compared to decreased uncertainty, hence asymmetric response. The rest of the paper is organized in the following manner. We outline our models and methods in Section II and present our empirical results in Section III. Section IV provides a summary and an Appendix reveals definition of variables and sources of the data. |