تأثیر پیچیدگی اقتصادی بر نابرابری درآمد با تأکید بر نقش شاخص توسعه انسانی در اقتصاد ایران با رویکرد ARDL بوت استرپ (مقاله علمی وزارت علوم)
درجه علمی: نشریه علمی (وزارت علوم)
آرشیو
چکیده
هدف: هدف مطالعه حاضر بررسی رابطه بین پیچیدگی اقتصادی با در نظر گرفتن واسطه هایی نظیر سرمایه انسانی، مخارج دولت و شاخص باز بودن تجارت است. روش: از آزمون (ADF) استفاده می کنیم و برای بررسی قدرت، از آزمون فیلیپس پرون (PP) و همچنین آزمون زیوت اندریوز با لحاظ یک شکست ساختاری نیز استفاده می کنیم. به منظور بررسی استحکام تجزیه وتحلیل ریشه واحد، ما از آزمون ریشه واحد SOR نیز استفاده کرده ایم که شکست های تند و هموار را در برمی گیرد. برای بررسی رابطه هم انباشتگی بین متغیرها، از روش هم انباشتگی ARDL بوت استرپ مک نون و همکاران (2018) استفاده می شود. یافته ها: نتایج نشان می دهد پیچیدگی اقتصادی به طور قابل توجهی با نابرابری درآمد بالاتر مرتبط است. همچنین، باز بودن تجارت و مخارج دولت در بلندمدت باعث افزایش نابرابری درآمد می شود. سرمایه انسانی، باعث کاهش نابرابری درآمد می شود، اما با در نظر گرفتن اثر افزایشی پیچیدگی اقتصادی بر نابرابری درآمد تأثیری بر بهبود این رابطه ندارد. نتیجه گیری: برای بهبود رابطه بین پیچیدگی اقتصادی و نابرابری درآمد لازم است سیاست هایی اتخاذ شود که اقتصاد ایران از شرایط صادرات تک محصولی خارج شود و تنوع صادراتی ایجاد شود.The Impact of Economic Complexity on Income Inequality with Emphasis on the Role of Human Development Index in Iran's Economy with ARDL Bootstrap Approach
Objective: As economic complexity changes, rewards to different skills and knowledge levels change, thus directly affecting wage differentials. In fact, with the increase in economic complexity, the demand for knowledge and therefore the demand for skilled labor increases, which results in an increase in the wage gap between skilled and unskilled workers. On the other hand, after reaching a certain level of economic complexity, the inequality between the wages of skilled and unskilled workers decreases. The purpose of this study is to investigate the relationship between economic complexity by considering mediators such as human capital, government spending and trade openness index.Method: To check the integration characteristics of the studied variables, the unit root test of sharp and smooth structural breaks provided by Shahbaz, Omay and Roubaud (SOR, 2018) is used. Although there are various unit root tests to check the significance of variables, these tests provide biased experimental results due to their low explanatory power (Shehbaz et al., 2018). The uniqueness of SOR is that the nonlinear unit root test considers sharp and smooth breaks in a time series. To examine the co-accumulation relationship between variables, the bootstrap ARDL co-accumulation method of McNown et al. (2018) is used. The originality of the bootstrap ARDL approach is its ability to deal with the properties of size and weak power that exist in the conventional ARDL approach of Pesaran et al. (2001). In addition, in order to increase the power of t and F test, this approach has the ability to integrate a new cointegration test during design and add to the cointegration framework of the conventional ARDL bounds test approach. Pesaran et al. (2001) found two conditions necessary to identify clustering. First, the coefficients of the error correction term must be statistically significant. Second, the coefficients of the interrupted explanatory variables should also be statistically significant. Pesaran et al. (2001) suggest that for the second case, critical boundaries (upper and lower bounds) should be used, but for the first case, there is no test of critical boundaries or boundaries. In the first situation, when the coefficients of the error correction term are statistically significant, if all the variables of the model are accumulated from the first degree, the test can be used; Therefore, the conventional unit root test is inappropriate due to their power characteristics and low explanatory power as shown by Goh et al. (2017). This problem can be solved using the ARDL band test presented by McNown et al. (2018).Findings: We use the (ADF) test and to check the power, we also use the Phillips-Perron (PP) test and also the Zivot-Andrews test in terms of a structural break. The results of both tests show that the variables are I(1), but in the Zivot-Andrew test, the trade openness variable is at a significant level. Also, none of the variables are accumulated with the second-order difference or I(2), so it also fulfills an important condition to perform the ARDL method. In order to check the robustness of the unit root analysis, we have also used the SOR unit root test, which includes sharp and smooth failures. The experimental results of the SOR unit root test show that the null hypothesis of the unit root is not rejected for the variables used in the study, the experimental results show that the variables include unit root processes. The ARDL bootstrap regression results show that economic complexity increases income inequality in the long term and has no effect on income inequality in the short term. This case can be because the complexity in Iran is not at a high level in general and it cannot improve the income distribution. In addition, human capital reduces income inequality in the short and long term. Therefore, improving the quality of human capital can strengthen economic structures and reduce income inequality. In the long run, per capita income reduces income inequality and the square of per capita income increases income inequality. In fact, the mode is U-shaped rather than U-shaped. This could be because economic growth in Iran is dependent on oil revenue rather than industry. Although, on a temporary basis and with an increase in oil income, it can reduce income inequality, but in the long run, considering that this income is not invested in industries with export capability and productivity is not paid much attention, it has no effect on reducing income inequality in the long term, and even the effects The negative side of unbalanced growth also increases income inequality. Government spending has no effect on income inequality in the short and long term. In fact, the government's consumption expenditure in Iran constitutes a large part of the government's expenditure and does not have much efficiency and productivity that can improve income distribution. The openness of trade in the short and long term increases income inequality, which can actually be the reason that there is very little export diversity in Iran, and therefore the openness of trade cannot have much effect on the improvement of people's conditions on a large scale.Conclusion: According to the results, economic development has not been able to reduce income inequality in Iran. In fact, reducing income inequality requires a combination of several development policies. Improvements in productive capabilities that lead to higher economic complexity must be implemented along with improvements in institutions, government spending, human capital, and trade liberalization. Although human capital has reduced income inequality, it still has little effect on improving the relationship between economic complexity and income inequality. Therefore, there is a need to reform the educational system and provide appropriate training to the characteristics of Iran's labor market to facilitate the entry of people into the labor market and improve human capital and increase productivity, which can lead to the improvement of production capabilities and the improvement of production capabilities and the strengthening of economic structures. It can increase economic complexity and consequently reduce income inequality.
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