{"id":24540,"date":"2026-03-01T13:44:25","date_gmt":"2026-03-01T08:14:25","guid":{"rendered":"http:\/\/www.sachdevajk.in\/?p=24540"},"modified":"2026-03-01T13:44:25","modified_gmt":"2026-03-01T08:14:25","slug":"income-inequality-review-of-literature-2","status":"publish","type":"post","link":"http:\/\/www.sachdevajk.in\/?p=24540","title":{"rendered":"Income Inequality (Review of Literature)"},"content":{"rendered":"

\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 <\/span>INCOME INEQUALITY<\/u><\/b><\/span><\/p>\n

\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 <\/span>Author: Prajwal Devadiga<\/span><\/b><\/p>\n

Review of Literature:<\/span><\/b><\/p>\n

1.\u00a0\u00a0\u00a0\u00a0 <\/span><\/span><\/span><\/b>Accounting for change in Income Inequality in Rural India, 1993-2011: a regression-based decomposition:<\/span><\/b><\/p>\n

The literature ‘<\/span>Accounting for change in Income Inequality in Rural India, 1993-2011: a regression-based decomposition’<\/span>\u00a0has majorly focused on understanding the drivers of rising disparities after the 1991 economic reforms. A prominent methodological contribution to this field is the regression-based decomposition approach developed by Gary S. Fields (2003), which helped researchers to estimate the contribution of variables to overall income inequality. In India, M. Azam and A. Shariff (2011) decomposed rural income inequality by income sources and found that income not related to farming and diversification to a great extent shaped inequality patterns. Later, M. Azam (2016) brought to light the declining share of farm income and the increase in the alternative income sources in explaining changes in inequality.<\/span> Overall, previous studies suggest that while household characteristics and income sources matter, dimensional and structural factors are also major drivers of inequality. The contemporary study develops itself on this literature by inculcating district and state-level fixed effects to provide better explanation on dimensional contributions to rural income inequality between 1993 and 2011.<\/span><\/p>\n

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2.\u00a0\u00a0\u00a0\u00a0 <\/span><\/span><\/span><\/b>The impact of migration experience of Rural labour on Income inequality:<\/span><\/b><\/p>\n

The literature ‘<\/span>The impact of migration experience of Rural labour on Income inequality’<\/span>\u00a0suggests that labour migration plays a major role in modelling of rural income distribution. Previous studies shows that migration advances to human capital accumulation, increased earnings, and working mobility, which can influence income inequality (Adda et al., 2022; Wahba, 2026). Migration empowers individuals to acquire skills through \u201clearning by doing,\u201d enhances financial literacy, and increases risk taking ability, thereby fostering wealth creation and entrepreneurial activities (Adda et al., 2022; Awis et al., 2016).<\/span> Many scholars say that migration has differing effects depending on migrants\u2019 peculiarities, circumstances, destinations, and objectives. Migration to commercially developed regions provides better learning opportunities, environments and stronger labour market indicating effects, contributing to greater income stability and decreased wage inequality. Conversely, returns from under developed regions may not significantly reduce inequality due to comparatively weaker human capital advantages.<\/span> Other than some excellent work discussed above, previous research has also been\u2002criticized for concentrating on one-way step migration (or income but not distributional impacts) despite their sizeable effects. Methodological developments, like re-centered influence\u2002function (RIF) regressions, now make it possible to study the impact of migration by income distribution and inequality indices in a more complete way. The current paper\u2002adds to this literature by exploring how various forms, temporal stages and regional paths of migration experience may influence income inequality in rural China.<\/span><\/p>\n

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3.\u00a0\u00a0\u00a0\u00a0 <\/span><\/span><\/span><\/b>Does Targeted Poverty Alleviation Program Reduce Income Inequality in China:<\/span><\/b><\/p>\n

Sun (2026) evaluates if the targeted poverty alleviation program worked in a way to reduce income inequality by examining the socio-economic landscape of China. The author defined inequality as the ratio between urban and rural net income, this research makes an attempt to investigate whether this specific policy introduction facilitated any meaningful changes in living standards of people living in china. The findings indicate that the TPA program successfully reduced the income ratio in designated poverty counties by 3.6% relative to non-poverty counties, showing a meaningful trend towards economic equalization and stabilization. The narrowing of this income gap is primarily credited to a significant increase in rural income per capita, alongside with\u00a0 <\/span>\u00a0<\/span>improved employment rates and increased government spending within rural sectors. Sun (2026) argues that the program\u2019s success results from its rural-focused implementation of policy, which prioritizes the resource allocation to underdeveloped regions. Furthermore, this study highlights that public expenditure and the administration of general economic programs were key growth drivers in mitigating rural-urban income inequality and improving overall income distribution.<\/span><\/p>\n

4.\u00a0\u00a0\u00a0\u00a0 <\/span><\/span><\/span><\/b>Can digital training reduce income inequality? Evidence from rural China:<\/span><\/b><\/p>\n

Luo and Cai (2026) in their tried to make an attempt to find out whether digital training programs can effectively help in reduction of income inequality. They gathered the data from 3000 rural households across ten provinces in China, the researchers made use of a Conditional Mixed Process (CMP) model to address potential endogeneity in participation. The study sheds light on to a central “empowerment-inequality paradox,” questioning whether such training promotes inclusion or improvement by providing new skills or worsens and widens existing gaps by favoring households that already possess quality education and monetary resources. The findings suggests that digital training significantly helped household boosts their income while simultaneously reducing overall income inequality within rural families. This positive outcome is largely driven by the roles of human and social capital, which acts as critical mediating factors in how digital skills are translated into economic gains. By teaching residents with the ability to navigate online platforms and engage in e-commerce to expand their businesses, these programs enabled rural populations to better integrate into the digital economy and overcome traditional way of doing business by giving them wide access to market. Ultimately, the research suggests that targeted digital literacy interventions are essential tools for fostering more inclusive rural development and achieving broader sustainable development goals.<\/span><\/p>\n

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5.\u00a0\u00a0\u00a0\u00a0 <\/span><\/span><\/span><\/b>The role of the internet in moderating the effect of industrialization on income inequality in Africa:<\/span><\/b><\/p>\n

Dossou et al. (2025) investigated the complex relationship between technological advancement and economic disparity, These authors examined how penetration of internet moderated the impact of industrialization on income inequality throughout 44 African nations. They utilized a panel data set from the period between 2000 to 2020 and making use of the fully modified ordinary least squares (FMOLS) estimation technique, the researchers provide a sophisticated perspective on Africa\u2019s development paradox. Their analysis reveals that while increased internet penetration serves as a significant tool for reducing income inequality, industrialization, in its current state but it also exerted a positive and significant pressure that widens the income gap. The study further explains the intersection of these two elements, finding that the interaction between industrialization and internet penetration also contributes significantly to increased income inequality within the sampled countries. These findings highlighted a critical need to rework of policy for the continent, while digital connectivity is a powerful equalizer, the structural shifts which is associated with the industrial growth may unintendedly favor higher-income groups or urban centers. In Conclusion, the research puts emphasis that for African nations to achieve all-inclusive growth, industrial policies must be strategically integrated with digital infrastructure to ensure that all technological gains translate into broad-based economic benefits rather than reinforcing existing disparities.<\/span><\/p>\n

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6.\u00a0\u00a0\u00a0\u00a0 <\/span><\/span><\/span><\/b>Economic Growth and Income Inequality: The Challenge in Ex-Communist EU Countries:<\/span><\/b><\/p>\n

Pop (2026) studies the relationship between economic prosperity and income inequality across 11 Central and Eastern European (CEE) countries from period ranging from 1994 to 2020. By employing panel cointegration techniques, the researcher identifies a soporifically decreasing link between growth and inequality in the long run, a suprising finding that directly contradicts Kuznets\u2019 traditional theory based on emerging economies. The early phase of the switch to capitalism is showed by the research to have generated substantial widening of inequality, with a major\u2002role played there, above all, by labor market imperfections and learning lags in adapting education system to technological transformations. After this first round, reforms oriented towards more productive sectors and the EU entry allowed for a period of\u2002catching-up. Despite the prolonged downward trend, the study notes substantial short-run differences at the national level, where some countries showed statistically insignificant linear patterns. Conclusively, Pop (2026) suggests that the CEE experience reflects a unique developmental pattern where sustained growth has been proven very important in narrowing the income gap, even though the few specific outcomes remain highly dependent on country-specific macroeconomic backgrounds.<\/span><\/p>\n

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7.\u00a0\u00a0\u00a0\u00a0 <\/span><\/span><\/span><\/b>Climate change, adaptive strategies, and income inequality:<\/span><\/b><\/p>\n

Wu and Liu (2025) examine the cross-section of how changes in environment and economic unevenness by reviewing how climate-influenced precipitation anomalies affect income inequality among farm households in China. Using a fixed-effects model, the researchers demonstrate that heterodox precipitation creates disturbance during the growing season lead to a 1.65% increase in income inequality. This broadening gap is primarily accounted to the sporadic impact of rainfall scarcity on different household income levels which results in to long-term shifts in agricultural production decisions. This study lists out several methods to lessen these negative effects, Precisely highlighting out of farm employment opportunities, the improvisation of agricultural infrastructure, and increased regional mechanization. The findings of study indicates that without some targeted policy to intervene in order to support these progressive measures, the climate change will continue to serve as key driver in wealth concentration and economic instability in rural regions.<\/span><\/p>\n

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8.\u00a0\u00a0\u00a0\u00a0 <\/span><\/span><\/span><\/b>Impacts of ICT and digital finance on poverty and income inequality: a sub-national study from India:<\/span><\/b><\/p>\n

In this study Das and Chatterjee (2023) investigates the dual sided impacts of information and communication technology (ICT) and digital finance on poverty and income inequality. Using some estimation methods researchers were able to find that ICT diffusion directly contributes to reduction of poverty in both the case of urban and rural regions but its relationship with income inequality is more complex. The researchers mentioned that while financial inclusion generally influences inequality, the introduction of ICT banking can be the bridge that narrows the widening of urban inequality but study finds no such explanation for rural inequality Ultimately, Das and Chatterjee (2023) explains that for digital tools to effectively solve socioeconomic disparities, policy interventions must prioritize a uniform spread of digital financial infrastructure. This ensuring that marginalized populations in both rural and urban sectors can make adequate use of these technologies to improve their economic environment rather than being left behind between widening digital divide.<\/span><\/p>\n

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9.\u00a0\u00a0\u00a0\u00a0 <\/span><\/span><\/span><\/b>Reducing income inequalities through government expenditures on education Evidence for European countries<\/span><\/b><\/p>\n

P\u0142atkowski and Lechman (2025) dig deep in to how government\u2019s expenditure plays its part in influencing income inequality across 30 European countries. Ny employing dynamic panel regression on datasets from 1995 to 2018 the found that increasing public expenditure on education can help significantly in narrowing wealth gap. The study emphasizes that this impact is more visible in less developed economies and nations with level of income disparity. This shows that effectiveness of educational spending is contingent upon a country’s baseline economic conditions. This study shows that targeted funding for secondary and tertiary contributes effectively in easing the income gap but educational spending is still foundational in narrowing the gap. In conclusion strategic increase in educational spending is essential easing income inequality in Europe.<\/span><\/p>\n

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10.\u00a0 <\/span><\/span><\/span><\/b>Minimum wage and income inequality among the migrant population in China.<\/span><\/b><\/p>\n

The researchers Li, Qi, and Wu (2025) analyzed city level data and china migrant survey from 2011 to 2018. These researchers found that minimum wage serves as important tool for reducing the wealth gap within this group. This study also reveals that positive impact is closely tied to the regional implementation of these policies. These results provide critical evidence for the role of labor market institutions in promoting social equity and suggest that further optimization of the minimum wage system is a viable path toward achieving China\u2019s broader goal of common prosperity. <\/span><\/p>\n

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11.\u00a0 <\/span><\/span><\/span><\/b>Conclusion:<\/span><\/b><\/p>\n

All 10 studies revolves around inequality and various complexities surrounding the given topic. There are various factors influencing income inequality and which is differing across various countries like india, Africa and Europe. Digital connectivity and information technologies have potential to narrow wealth gaps is contingent upon uniform infrastructure and targeted training to prevent a widening digital divide. Government intervention s such as minimum wage protections, targeted poverty alleviation programs, and sustained public investment in education serve as critical redistributive mechanisms that safeguard vulnerable populations and promote long-term economic growth. Ultimately, for growth to sustainable development it must be supported by a robust social infrastructure and policy frameworks.<\/span><\/p>\n

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\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 <\/span>REFERENCES:<\/span><\/b><\/p>\n

Azam, M. (2026). Accounting for change in income inequality in rural India, 1993\u20132011: a regression-based decomposition. Applied Economics Letters<\/i>, 33<\/i>(2), 287\u2013292. <\/span>https:\/\/doi.org\/10.1080\/13504851.2024.2364021<\/span><\/a><\/p>\n

Das, S., & Chatterjee, A. (2023). Impacts of ICT and digital finance on poverty and income inequality: a sub-national study from India. Information Technology for Development<\/i>, 29<\/i>(2-3), 378\u2013405. <\/span>https:\/\/doi.org\/10.1080\/02681102.2022.2154385<\/span><\/a><\/p>\n

Dossou, T. A. M., Asongu, S. A., Dossou, K. P., & Alinsato, A. S. (2025). The role of the internet in moderating the effect of industrialization on income inequality in Africa. Information Technology for Development<\/i>, 31<\/i>(4), 764\u2013791. <\/span>https:\/\/doi.org\/10.1080\/02681102.2024.2414404<\/span><\/a><\/p>\n

Li, Y., Qi, J., & Wu, S. (2025). Minimum wage and income inequality among the migrant population in China. Labor History<\/i>, 66<\/i>(2), 175\u2013189. <\/span>https:\/\/doi.org\/10.1080\/0023656X.2024.2371126<\/span><\/a><\/p>\n

Liu, L., Han, Y., Sun, Z., & Li, S. (2026). The impact of migration experience of rural labour on income inequality. Applied Economics<\/i>. Advance online publication. <\/span>https:\/\/doi.org\/10.1080\/00036846.2026.2617602<\/span><\/a><\/p>\n

Luo, Y., & Cai, Y. (2025). Can digital training reduce income inequality? Evidence from rural China. Applied Economics<\/i>. Advance online publication. <\/span>https:\/\/doi.org\/10.1080\/00036846.2025.2597475<\/span><\/a><\/p>\n

P\u0142atkowski, P., & Lechman, E. (2025). Reducing income inequalities through government expenditures on education: Evidence for European countries. Applied Economics Letters<\/i>, 32<\/i>(20), 3014\u20133020. <\/span>https:\/\/doi.org\/10.1080\/13504851.2024.2360655<\/span><\/a><\/p>\n

Pop, T.-M. (2026). Economic growth and income inequality: The challenge in ex-communist EU countries. Eastern European Economics<\/i>, 64<\/i>(1), 29\u201358. <\/span>https:\/\/doi.org\/10.1080\/00128775.2024.2370350<\/span><\/a><\/p>\n

Sun, P. (2026). Does the Targeted Poverty Alleviation program reduce income inequality in China? Review of Development Economics<\/i>, 30<\/i>(1), 484\u2013502. <\/span>https:\/\/doi.org\/10.1111\/rode.70012<\/span><\/a><\/p>\n

Wu, Y., & Liu, L. (2025). Climate change, adaptive strategies, and income inequality. Applied Economics<\/i>. Advance online publication. <\/span>https:\/\/doi.org\/10.1080\/00036846.2025.2569766<\/span><\/a><\/p>\n

\u00a0<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 INCOME INEQUALITY \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Author: Prajwal Devadiga Review of Literature: 1.\u00a0\u00a0\u00a0\u00a0 Accounting for change in Income Inequality in Rural India, 1993-2011: a regression-based decomposition: The literature ‘Accounting for change in Income Inequality in Rural India, 1993-2011: a regression-based decomposition’\u00a0has majorly focused on understanding the drivers of rising disparities after the 1991 economic reforms. A prominent… Continue reading Income Inequality (Review of Literature)<\/span><\/a><\/p>\n","protected":false},"author":140104,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-24540","post","type-post","status-publish","format-standard","hentry","category-uncategorized","entry"],"_links":{"self":[{"href":"http:\/\/www.sachdevajk.in\/index.php?rest_route=\/wp\/v2\/posts\/24540","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/www.sachdevajk.in\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/www.sachdevajk.in\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/www.sachdevajk.in\/index.php?rest_route=\/wp\/v2\/users\/140104"}],"replies":[{"embeddable":true,"href":"http:\/\/www.sachdevajk.in\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=24540"}],"version-history":[{"count":1,"href":"http:\/\/www.sachdevajk.in\/index.php?rest_route=\/wp\/v2\/posts\/24540\/revisions"}],"predecessor-version":[{"id":24541,"href":"http:\/\/www.sachdevajk.in\/index.php?rest_route=\/wp\/v2\/posts\/24540\/revisions\/24541"}],"wp:attachment":[{"href":"http:\/\/www.sachdevajk.in\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=24540"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/www.sachdevajk.in\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=24540"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/www.sachdevajk.in\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=24540"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}