Has the Economic Structure Optimization in China’s Supply-Side Structural Reform Improved the Inclusive Green Total Factor Productivity?

One of the goals of China’s supply-side structural reform is to improve total factor productivity (TFP). Considering the problems of environmental deterioration and income disparity faced by China, this paper first incorporates environmental pollution and income disparity as undesirable outputs into the TFP analysis framework, and extends the concept of inclusive green TFP (henceforth IGTFP).We measure and analyze the IGTFP in China’s provinces from 1995 to 2017 using the Malmquist–Luenberger index, and then examine, for the first time, the impacts of economic structural optimization in the supply-side structural reform on the IGTFP. The results are shown as follows. First, China’s national IGTFP index is significantly smaller than the traditional TFP index. That is, the traditional TFP without the constraints of environmental pollution and income disparity overestimates China’s real TFP. Second, there are significant regional differences in China’s IGTFP, the average annual growth rate of IGTFP shows a gradual downward trend from east to west. This would further exacerbate the regional imbalance in China’s economic development. Third, among the structural factors in economic structure optimization, industrial structure and energy structure are negatively correlated with the IGTFP, while factor structure, labor structure and urban–rural structure are all positively correlated with the IGTFP. These results imply that the economic structure optimization driven by supply-side structural reform will improve China’s IGTFP.

Attention to social stratification in the public discourse: An empirical study based on big data of books (1949–2008)

Using the Chinese corpus of Google Books Ngram in line with other macro-level socioeconomic data, this paper examines and analyzes the trend of change in public discourse about social structure in China from 1949 to 2008, as well as the mechanism that influences this trend. We find that since the reform and opening-up, the official discourse on “class,” as constructed by the official ideology, has gradually declined, while the importance of a “stratum” discourse oriented toward the mass population has increased. Using principal component analysis, we generate an index for public attention on social strata and run it through a Granger causality test along with time-series data such as macro-level economic and political indicators. The results show that since the reform and opening-up, public attention to stratum has been influenced by the general trend of the economy, income disparity, and level of political participation. Income disparity influences public attention on stratum-related topics more than macro-level economic indicators do. Official intervention on public opinions does not affect public attention on the stratum, but the former is affected by the latter.

Impact of Aging and Underemployment on Income Disparity between Agricultural and Non-Agricultural Households

This paper examines how aging and underemployment affect household income and household income disparity between agricultural and non-agricultural sectors. Our study uses household panel data from South Korea for the period 2009–2016, which include, on average, 6721 representative households each year. A three-step regression analysis was conducted to estimate the aging and underemployment effects on household income and the income disparity between agricultural and non-agricultural households. First, we estimate aging and underemployment effects on household income from all households using a year fixed-effect longitudinal model. Second, our study investigates whether the marginal effect of aging and underemployment on household income differs between agricultural and non-agricultural sectors. Finally, we simulate the estimated model to illustrate how government policies could help reduce the income disparity. Our results show that aging and underemployment affect household income negatively overall. The negative marginal effect of the two factors was greater in the agricultural sector than in the non-agricultural sector. Results from policy simulations suggest that the implementation of proper government policies to address aging and underemployment problems in agricultural households could significantly reduce the income disparity between agricultural and non-agricultural sectors.

CORRUPTION, DEVELOPMENT, AND INCOME DISPARITY IN PAKISTAN

Corruption and income disparity have positive correlation and negative impact on Sustainable growth and development. Corruption is the root cause of income inequality. Corruption of all kinds are deeply incorporated in every sector of Pakistani Institutions. Corruption leads to many discriminations in a society where wealthy individuals get wealthier while the marginalized people remain poor. Previous Research also reveal that Income inequality may rise in democratic regime than in Authoritative one. Current study shows the direct relationship between the corruption and inequality and its impact on the Overall Growth and Development of Pakistan. In order to achieve inclusive growth, it is recommended that effective policies should be adopted so that corruption and income disparity could be eradicated.

Determinants Comparative Advantage of Non-Oil Export 34 Provinces in Indonesia

In spite of the country’s export activities constantly increase every year, based on the latest report Indonesia still sits at number 28th in the world's top export countries, consider Indonesia as one of the most populated nations and its rich natural resources. There is so much research in the literature about this issue, but to the author’s knowledge, there is still a lack of studies that analyze the performance of non-oil export comparative advantage between provinces in Indonesia instead of between its commodities. The purpose of this research is (1st) to compare non-oil exports comparative advantage between 34 provinces in Indonesia and (2nd) to prove the effect of chosen factors which are a foreign direct investment, local direct investment, inflation, interest rate, exchange rate, population, labor, minimum wage, education, income disparity, regional GDP, government expenditure, and GDP of importing country toward provinces comparative advantage of non-oil export. This research using provinces panel data years 2010-2019. The method of this study is Revealed Comparative Advantage (RCA) index and Trade Balance Index (TBI) to unveil non-oil export comparative advantage between 34 provinces in Indonesia and panel data regression to estimate the impact of determinant factors. The result of comparative advantage index estimation shows that 24 provinces have a comparative advantage based on the RCA index approach and 32 provinces have a comparative advantage based on the TBI approach on non-oil export activities year 2010-2019. The result of panel data regression found that 9 out of 13 determinant variables had a significant effect on the RCA index namely foreign direct investment, local direct investment, exchange rate, population, labor, minimum wage, income disparity, regional GDP, and government expenditure. Then 6 out of 13 determinant variables had a significant effect on TBI which are a local direct investment, interest rate, exchange rate, education, income disparity, and regional GDP.

Analisis disparitas pendapatan di Indonesia tahun 2015-2019: analisis regresi data panel

Income disparity is a problem in many countries that can cause economic inefficiency, weaken social stability and solidarity, and inequality in long-term well-being. This study describes the income disparity in the provinces in Indonesia using the Williamson Index. The purpose of this study is to find out how the conditions of income disparity in Indonesia are and what are the variables that influence it. This study analyzes panel data regression from 32 provinces in Indonesia for 5 years through Eviews10. From the Williamson index calculation, East Java Province has the highest level of disparity while Gorontalo Province has the lowest disparity. Economic growth, investment, APBD, and Unemployment have a positive effect on income disparity while HDI has a negative effect. Variables that have a significant effect on the 5 percent alpha level are HDI, Government Spending, and Unemployment. While the variable of economic growth has a prob of 0.1558 so that it has a significant effect on income disparities at the alpha level of 0.2 or 20 percent, and investment has a prob of 0.4570 so that it has a significant effect on the alpha level of 0.5 or 50 percent. Abstrak Disparitas pendapatan merupakan suatu permasalahan di banyak negara yang dapat menyebabkan inefisiensi ekonomi, melemahkan stabilitas sosial dan solidaritas, serta ketidakadilan dalam kesejahteraan dalam jangka panjang. Penelitian ini menggambarkan disparitas pendapatan di provinsi-provinsi di Indonesia dengan Indeks Williamson. Tujuan dari penelitian ini adalah untuk mengetahui bagaimana kondisi disparitas pendapatan di Indonesia dan apa saja variabel-variabel yang mempengaruhinya. Penelitian ini menganalisis regresi data panel dari 32 provinsi di Indonesia selama 5 tahun melalui Eviews10. Dari perhitungan indeks williamson, Provinsi jawa timur memiliki tingkat disparitas tertinggi sementara Provinsi Gorontalo memiliki disparitas terendah. Pertumbuhan ekonomi, investasi, APBD, dan TPT berpengaruh positif terhadap disparitas pendapatan sedangkan IPM berpengaruh negatif. Variabel yang berpengaruh signifikan pada tingkat alpha 5 persen yakni IPM, APBD, dan TPT. Sedangkan variabel pertumbuhan ekonomi memiliki prob sebesar 0.1558 sehingga berpengaruh signifikan terhadap disparitas pendapatan pada tingkat alpha 0.2 atau 20 persen, dan investasi memiliki prob sebesar 0.4570 sehingga berpengaruh signifikan pada tingkat alpha 0.5 atau 50 persen.

Analysis of Income Disparity of Coastal and Non-Coastal Communities in Banten Province, Indonesia

This study aims to analyze the large income disparity in coastal and non-coastal areas in Banten Province and to analyze whether the fisheries sub-sector has become the economic base in Banten Province in 2015 – 2019. January until May 2021. The method is carried out by survey method then analyzed quantitatively and presented descriptively. The data of this study were sourced from secondary data and primary data. Data were obtained from BPS Banten Province, BPS West Java Province, Marine and Fisheries Service Banten Province, Marine and Fisheries Service West Java Province and through questionnaires from coastal and non-coastal communities. Data analysis includes analysis of Williamson index, Gini coefficient and Location Quotient (LQ). based on the results of research conducted, it can be concluded that the income disparity in the coastal areas of Banten Province is greater than in the non-coastal areas. The Gini ratio analysis shows that the value of the Gini ratio has decreased from year to year, which means that income distribution is more evenly distributed until in 2019 it reaches 0. The fisheries sector in Banten Province is included in the non-base sector because it has a value of 0.5671. Production of catch commodities that get superior commodities: small pelagic fish group (mean LQ=1.81) and demersal fish group (mean LQ=0.84). The fisheries sector in Banten Province is included in the non-base sector because it has a value of 0.5671. Production of catch commodities that get superior commodities: small pelagic fish group (mean LQ=1.81) and demersal fish group (mean LQ=0.84). The fisheries sector in Banten Province is included in the non-base sector because it has a value of 0.5671. Production of catch commodities that get superior commodities: small pelagic fish group (mean LQ=1.81) and demersal fish group (mean LQ=0.84).