Income inequality in the face of climate change: an empirical investigation on unequal nations, vulnerable regions and India

SenGupta, Swapnanil; Atal, Aakansha Article — Published Version Income inequality in the face of climate change: an empirical investigation on unequal nations, vulnerable regions and India SN Business & Economics Provided in Cooperation with: Springer Nature Suggested Citation: SenGupta, Swapnanil; Atal, Aakansha (2024) : Income inequality in the face of climate change: an empirical investigation on unequal nations, vulnerable regions and India, SN Business & Economics, ISSN 2662-9399, Springer International Publishing, Cham, Vol. 4, Iss. 8, https://doi.org/10.1007/s43546-024-00685-8 This Version is available at: https://hdl.handle.net/10419/316968 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. http://creativecommons.org/licenses/by-nc-nd/4.0/ Vol.:(0123456789) SN Bus Econ (2024) 4:87 https://doi.org/10.1007/s43546-024-00685-8 ORIGINAL ARTICLE Income inequality intheface ofclimate change: anempirical investigation onunequal nations, vulnerable regions andIndia SwapnanilSenGupta1,2 · AakanshaAtal3 Received: 5 February 2024 / Accepted: 3 July 2024 / Published online: 17 July 2024 © The Author(s) 2024 Abstract Climate change is the paramount challenge of our era. While considerable attention has been given to its impact on financial stability and economic productivity, there is limited focus on its effects on income inequality. We introduce a unique criterion for sample selection. We aim to provide an empirical analysis of how climate change impacts income inequality in 43 most climate-vulnerable countries, 39 most unequal countries in terms of income distribution, and India separately (1971–2021). We select the most vulnerable countries to investigate the magnitude of income inequality caused by climate change. Given that climate hazards enhance inequality in countries with prevailing socioeconomic inequalities, we choose unequal countries to investigate whether climate change is a significant determinant of their existing condition. We also analyse the relationship for India because India is one of the most climate-vulnerable and unequal countries. We use three climate change indices—vulnerability to climate change, annual surface temperature change, and frequency of climate-related disasters—to check the effects of particular aspects of climate change. We deploy a standard panel regression analysis followed by a series of robustness checks and an autoregressive distributed lag (ARDL) bounds test approach to analyse the link for India. We find that climate change has adverse impacts in both groups and in India, both in the long and short term. Our findings suggest that policymakers in developing countries may need to equally place importance on developmental and climate change abatement goals as well as expedite their environmental targets. Keywords Climate change· Income inequality· Panel data· India· Fixed effects regressions· ARDL Bounds Test * Swapnanil SenGupta [email protected] 1 The University ofManchester, Manchester, UK 2 University ofTübingen, Tübingen, Germany 3 Centre forInternational Trade andDevelopment, Jawaharlal Nehru University, NewDelhi, India SN Bus Econ (2024) 4:87 87 Page 2 of 33 JEL Classification D31· O10· O15· Q54 Introduction “On climate change, we often don’t fully appreciate that it is a problem. We think it is a problem waiting to happen.” - Kofi Annan. (Former UN Secretary-General, Nobel Peace Prize Recipient). Background The onset of the Industrial Revolution marked a pivotal moment in human history, ushering in unprecedented global progress. This remarkable development, however, has come at a cost, most notably reflected in the paramount challenge of our era: climate change. Climate change, characterised by complicated and ever-evolving dynamics, represents a shift in environmental conditions towards extremes, affecting everything from temperature to a range of interconnected factors. An extensive body of research highlights the urgency of addressing climate change. The National Oceanic and Atmospheric Administration (NOAA) reports that the earth’s temperature has risen by an average of 0.08°C per decade since 1880.1 Ongoing temperature analysis at NASA’s Goddard Institute for Space Studies (GISS) reveals that since 1880, the average global temperature has increased by at least 1.1°C in total, with the majority of the global warming occurring since 1975 (at a rate of approximately 0.15–0.20°C per decade).2 Not limited to temperature, numerous other factors linked to climate change have registered worsening trends over the decades (Fig. 1). These have heightened the occurrence and magnitude of climate-related shocks on a global scale. As per the IPCC (2018), crossing the threshold of 1.5 °C above pre-industrial temperatures could trigger catastrophic outcomes, and the prospects become even graver with temperature rises surpassing 2°C. The concerns become intensified as these extreme weather events and related consequences are likely to exacerbate. There are projections of a 66% likelihood of annual average near-surface global temperature remaining more than 1.5°C above pre-industrial levels for at least one year between 2023 and 2027 (WMO 2023), and global mean temperature increasing by 4°C over the next century (IPCC 2021). The implications of climate change extend beyond the environment to cover economic aspects, including financial stability, fiscal health, long-term growth potential, and income distribution. The consequences of climate change have already caused global losses of approximately US$ 3.64 trillion between 1970 and 2019, according to the estimates of WMO (2021). Furthermore, research by the Notre Dame Global Adaptation Initiative (ND-GAIN) reveals varying degrees of vulnerability to climate change impacts among countries (see Fig.2), influenced by factors like economic 1 See Lindsey and Dahlman (2023). 2 See https:// earth obser vatory. nasa. gov/ worldofchange/ globaltempe ratur es. SN Bus Econ (2024) 4:87 Page 3 of 33 87 structure, institutional strength, and the capacity for climate mitigation and adaptation. Unchecked climate change could jeopardise poverty eradication efforts by pushing up to 130 million people into poverty in the coming years (Nishio 2021), disproportionately hit the most underdeveloped regions, and exacerbate income inequality within countries (World Bank 2020). While considerable attention has been dedicated to examining the impact of climate change on financial stability and economic productivity, there has been comparatively less emphasis on exploring the income inequality impacts of climate change. This gains particular significance in light of the global increase in income inequality experienced over the last four decades across most countries. The widely recognised theoretical framework of the climate change-income inequality nexus states that inequality exacerbates the vulnerability of disadvantaged societal groups to the impacts of climate change in three significant ways: (i) rise in vulnerability to climate hazards; (ii) heightened sensitivity to the impact of climate hazards; (iii) diminished capacity to manage and recover from the damage. Due to the collective impact of the three channels mentioned, a vicious cycle ensues in which climate 421.6 300 320 340 360 380 400 420 440 1963 1967 1971 1975 1979 1983 1987 1991 1995 1999 2003 2007 2011 2015 2019 2023* 84.3 -40 -20 0 20 40 60 80 100 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020 2022 31.2 30 31 31 32 32 33 33 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020 1.4 -1 0 1 1 2 2 1962 1966 1970 1974 1978 1982 1986 1990 1994 1998 2002 2006 2010 2014 2018 2022 A. Annual Average of Global Atmospheric CO2 Concentration (Parts per million) B. Change in Mean Sea Level(millimetres) C. Share of Forest Area D. Change in Annual Surface Temperature (°C) Fig. 1 Global Climate Change Indicator Trends. A Annual Average of Global Atmospheric CO2 Concentration (Parts per million). B Change in Mean Sea Level (millimetres). C Share of Forest Area. D Change in Annual Surface Temperature (°C). Source: National Oceanic and Atmospheric Administration. Source: FAO, UN. *Annual Average for Global Atmospheric CO2 Concentration for 2023 is taken until May 2023. For creating Fig.1B, we take the final recording of each year SN Bus Econ (2024) 4:87 87 Page 4 of 33 change hazards worsen existing inequalities.3 Diffenbaugh and Burke’s (2019) empirical evidence attests to the concept suggesting that many wealthy countries have benefited disproportionately from the activities that have caused global warming and have been made even more opulent, while many poor nations have become poorer. Figure3 provides an overview of the existing income inequality in the world. Research goals andmethodology The objective of this paper is to provide a detailed empirical analysis of how climate change impacts income inequality in 43 countries most vulnerable to climate change,4 39 most unequal countries in terms of income distribution,5 and India separately, spanning from 1971 to 2021 (check Online Appendix I for the list of countries). We select the most vulnerable countries to climate change because we want to investigate the magnitude of income inequality caused by climate change and, hence, form an idea about how worse the situation can be should climate change go unchecked. UNDESA (2016) mentions that initial socioeconomic inequalities determine the disproportionate adverse effects of climate hazards on people at a disadvantage, and the effects of climate hazards in turn result in greater inequality. Following this line of thought, we choose unequal countries to empirically Fig. 2 ND-GAIN Vulnerability Index, 2021. Source: Notre Dame Global Adaptation Initiative; Authors’ calculations 4 List compiled using reports from Germanwatch’s Global Climate Risk Index 2021, INFORM Climate Change Risk Index and IPCC 2022. 5 List compiled from World Inequality Lab’s Top 10% Income data, and SWIID database. 3 See Islam and Winkel (2017) for details. SN Bus Econ (2024) 4:87 Page 5 of 33 87 investigate whether climate change is a significant determinant of their existing condition. We also take motivation from Cevik and Jalles (2023) who recommend replication of their results in different contexts such as geographical areas and time periods. The research utilises different climate change datasets to check the effects of particular aspects of climate change. We deploy a standard panel regression analysis followed by a series of robustness checks to confirm the soundness of the results and an autoregressive distributed lag (ARDL) bounds test approach to analyse the link for India. Novelty andcontribution We contribute to the existing literature in the following ways: (i) We utilise the longest available data period. This extended time frame allows us to account for longer periods of climate change, providing a more comprehensive understanding of its impact on income inequality. Such an approach yields more insightful results and establishes a solid foundation for assessing the long-term consequences of climate change on income distribution. (ii) We introduce a unique criterion for the selection of countries. By focusing on 43 of the most climate-vulnerable countries and 39 of the most unequal countries in terms of income distribution, we investigate the magnitude of income inequality caused by climate change and its implications. This distinctive approach facilitates a nuanced analysis, offering insights that were previously lacking in the literature. (iii) Our study addresses a significant gap in Fig. 3 Average Disposable Gini (2007–2020). Source: Standardized World Income Inequality Database (SWIID); Authors’ calculations SN Bus Econ (2024) 4:87 87 Page 6 of 33 the existing research by examining the link between climate change and income inequality in India. To the best of our knowledge, there is a scarcity of studies that explore this relationship in the Indian context.6 By confirming climate change as a determinant of income inequality in India, our research not only paves the way for further investigation but also holds direct policy implications for a country with a rapidly evolving climate and economic landscape. In terms of its relevance, our study bears importance on multiple fronts. From an academic perspective, it extends current knowledge on the relationship between climate change and income inequality. By examining the issue and providing detailed insights into specific groups of countries, our research adds depth and nuance to the existing discourse. From a policymaking standpoint, our findings shed light on the magnitude of climate change’s impact on income distribution, providing policymakers with data-driven insights. This information can help in prioritising and tailoring interventions for countries facing varying levels of vulnerability and inequality. Notably, our findings offer valuable insights into the ongoing debate about the priorities of developing and emerging nations—whether to focus on tackling climate change or achieving development goals. Our research concentrates on these specific economies, revealing a substantial impact of climate change on income inequality in these regions. While the common stance of prioritising developmental goals over climate change abatement in the developing world (e.g., Tongia 2022) is understandable, our study introduces a critical consideration. Many developing and emerging economies, despite being proactive in climate change abatement, have set longterm goals. For example, India aims for net-zero carbon emissions by 2070 (Ministry of Environment Forest and Climate Change 2022). This prompts us to question whether there is a need to expedite the process. Waiting for such an extended period might not only slow down but also potentially reverse development progress, working against their primary goals instead of supporting them. Therefore, our research contributes to informed decision-making and effective policy responses in the global effort to balance both immediate needs and long-term sustainability in these developing and emerging economies. The rest of the paper is structured as follows: Section "Theoretical framework and literature review" provides the theoretical framework and literature review; Section "Research gap" presents the research gap; Section "Methodology" presents the empirical analyses; Section "Conclusion" concludes the study; Section "Policy implications" sheds light on the policy recommendations; and Section "Limitations and recommendations" presents the limitations of this study and further recommendations. 6 The only existing study on a related subject matter is by Aggarwal (2021) who analyses the impacts of climate shocks on real monthly per capita consumption expenditure in India. He uses micro-level data and sheds light on consumption inequality. Hence, we believe that this study on India features unprecedented characteristics. SN Bus Econ (2024) 4:87 Page 7 of 33 87 Theoretical framework andliterature review The discussion surrounding the impacts of climate change is consistently deliberated within the multi-dimensional framework of poverty and socio-economic inequality. However, the relationship between climate change and within-country income inequality has been given relatively less attention. In this section, we mainly focus on the relationship between our chosen indices (annual surface temperature, climate disasters or hazards, and vulnerability) of climate change and income inequality. The existing body of literature studying the climate change and income inequality relationship can be categorised into two primary themes: (i) investigating the influence of climate change on income disparity among distinct socioeconomic segments within a given nation; and (ii) analysing the impact of climate change on withincountry income inequality across diverse countries, considering economic development, exposure to long-term climate changes, and related factors. In this review section, we emphasise both (i) and (ii), aiming to offer a comprehensive conceptual and empirical insight into the discussed themes. Surface temperature Higher temperatures can lead to more frequent and severe weather events, such as floods, droughts, wildfires, etc. These events often disproportionately affect poor communities, resulting in economic disruptions. Low-income populations often rely more on sectors like agriculture and fisheries, which are highly sensitive to climate variations. Precipitation variability can affect agricultural productivity, leading to crop failures and shortages. The loss of livelihoods in these specific sectors could increase poverty and within-country income inequality. Precipitation variability can also drive food prices, impacting the budgets of lower-income households. Agriculture relies mainly on rainfall, with minimal irrigation coverage, making it vulnerable to changes in rainfall patterns that impact both agricultural output and food security (Di Falco and Chavas 2009). Wealthier households may have more resources to adapt to changing food availability and rising prices, potentially leading to a widening gap in food security between different income groups. The increase in temperatures is expected to increase within-country income inequality through various events discussed below (Hallegatte etal. 2016): 1. Warmer climates in the drylands of the Middle East and Northern Africa would shift vegetation to the north, triggering desertification and soil salinization. These long-term climate shocks can result in potential livelihood impacts for poor people by increasing water stress, affecting livestock and crop production, and having negative impacts on smallholding farmers and herders. 2. In Europe and Central Asia, arid areas are expanding and droughts are becoming more frequent and intense, resulting in desertification, which leads to a reduction of areas available for rain-fed crop production and puts pressure on livestock, negatively impacting smaller agricultural producers. SN Bus Econ (2024) 4:87 87 Page 8 of 33 3. The shift of boreal and temperate forests with heat waves, water stress, forest fires, and tree mortality in boreal forests in Russia could contribute to a decline in timber harvest, endangering the jobs of workers in the forestry sector. 4. Latin America and the Caribbean’s dry season length, extreme drought, and forest fires can cause tropical forest degradation, resulting in reduced availability of forest resources for indigenous people, forest dwellers, and communities living on the forest fringe. 5. In South Asia, the melting of glaciers in the Himalayas and Hindu Kush leads to increased seasonal variability of water flows in rivers, which may negatively affect food production for smallholder farmers in the river basins. 6. The retreat of glaciers in Europe and Central Asia is causing a rise in seasonal water variability, leading to a significant water shortage in the long run. This could result in increased water stress, affect irrigated agriculture, and have negative impacts on workers on commercial farms. 7. In Latin America and the Caribbean, negative impacts on water supply due to similar climate shocks can reduce crop production, affecting smallholder farmers and indigenous communities in the Andes. 8. In South Asia, changes in monsoonal precipitation increase river floods, the number of dry days, the severity of droughts, and the reduction of groundwater resources could result in increased water stress affecting crop production with negative impacts for smallholder farmers. 9. In South Asia, alterations in monsoonal precipitation patterns lead to an increase in river floods, a higher number of dry days, severe droughts, and a reduction of groundwater resources. These changes cause an increase in water stress, affecting crop production and negatively impacting smallholder farmers. A few major studies examine the empirical relationship of the effects of increasing surface temperatures on within-country income inequality. A comprehensive study conducted by Paglialunga etal. (2020) , estimates pooled OLS and Fixed Effects estimation techniques on a panel dataset covering 150 nations over the period from 2003 to 2017. They reveal that the increase in temperatures has exacerbated income distribution, exerting a significant influence on the escalation of within-country income disparities. Specifically, rising temperatures and irregular precipitation patterns were found to have adverse and significant effects on within-country inequality, particularly in nations characterized by a larger proportion of their population residing in rural areas and a substantial labour force engaged in agriculture. In their analysis using a global dataset covering subnational poverty in 134 countries, Dang etal. (2023) conclude that a one-degree Celsius increase in temperature correlates with a 1.4 percent rise in the Gini inequality index in a panel of 134 countries. Notably, while climate change disproportionately affects poorer nations, particularly those in South Asia and SubSaharan Africa, the research suggests that household adaptation measures may have partially mitigated some of the long-term adverse effects. Chisadza etal. (2023) examine the effects of rising temperatures on income inequality in 50 U.S. states between 1940 and 2015 by computing impulse response functions (IRFs) from the local projections’ method. They find an immediate temporary SN Bus Econ (2024) 4:87 Page 15 of 33 87 Unemployment, a key driver of income inequality (e.g., Saunders 2002), is included as a control for labor market policies and conditions. Unemployment forces labours out of income sources, and consequently, increases economic inequality. Population size is an important factor in income inequality and can have varying effects depending on factors like the level of development, composition, policies, resource availability, and social norms. For instance, in larger populations, there may be opportunities for economies of scale in production and distribution, potentially leading to lower prices for goods and services. This could benefit lower-income individuals by improving their purchasing power and reducing relative income inequality. Larger populations may also offer a broader and more diverse labour force. This can lead to greater wage disparities as competition for certain skilled jobs intensifies, potentially increasing income inequality. Technological advancements, such as artificial intelligence and advanced machinery, can boost productivity, reduce production costs, and decrease dependence on human labour, potentially widening income disparities. Conversely, technological progress can create new industries and employment opportunities, reducing income inequality. A recent study by Poliquin (2021) illustrates how technology, including broadband adoption, affects wages, leading to disparities between different job positions. The research investigates the introduction of something as relatively mundane as broadband in Brazil between 2000 and 2009. It is revealed that the technology coincided with an increase in wages across the labour market. The average employee saw a wage rise of just 2.3%, whereas those in managerial positions and boardrooms saw a 9% and 19% boost in income, respectively. It is hypothesised that the new technology allowed the more productive workers to be further productive, thus widening the income gap. On the other hand, in a study of country panel data for 29 countries, it is demonstrated that innovation activities reduce personal income inequality by matching patents from the European Patent Office with their inventors (Benos and Tsiachtsiras 2019). Following Baek and Shi (2016), this study introduces internet users as a proxy for technological innovation, a critical factor for both exacerbating and mitigating income inequality. Wan et al. (2022) note that income inequality in Less Developed Countries (LDCs) is significantly associated with the urban–rural gap. Urbanization may exacerbate income inequality due to higher urban wages compared to rural areas. However, extensive urbanisation can mitigate income inequality through trickle-down effects (Ha etal. 2019). Lastly, we incorporate institutional quality. Good governance can reduce income inequality via several channels, such as ensuring equitable access to resources, labour market regulations, focusing on progressive taxation and social welfare policies, ensuring political stability and rule of law that can create an environment conducive to prosperity. However, empirical evidence does not find such a straightforward effect. Asamoah (2021), when institutional quality is measured using the World Governance Indicators, finds a quadratic effect for advanced countries but a monotonic negative impact for developing countries. When institutional quality measure derived from the International Country Risk Guide is used as the threshold variable, an inverted U-shaped Kuznets relationship between institutions and income SN Bus Econ (2024) 4:87 87 Page 16 of 33 inequality in both advanced and developing countries is identified. Regardless, institutional quality indeed determines income inequality levels. For panel data, basic estimation methods such as OLS are prone to inefficiency because they do not discriminate between various cross-sectional units and, thus, may be blamed for camouflaging the heterogeneity existing within each crosssectional unit. Hence, more sophisticated estimators like the Fixed Effects (FE) or Random Effects (RE) estimators are suitable. The idea behind the RE model is that the variation across the entities is assumed to be random and uncorrelated with the covariates used in the model ( Cov( 𝛼 i ,Covariates it) = 0 ). However, this assumption is potentially impractical in many applications. On the other hand, in the FE model, the intercept varies across individuals (countries in this case) and hence, it relies on the variation within individuals and not between them. It is assumed that, if not controlled for, something with the individual may bias the outcome variables, which is the justification of the assumption of the correlation between the predictor variables and the entity’s error term. In simpler terms, the consistent estimation in the FE model does not impose that the unobserved heterogeneity is uncorrelated with the explanatory variables ( Cov( 𝛼 i ,Covariates it) ≠ 0 ). We run the Hausman test to decide between fixed or random effects model. The null hypothesis of the Hausman test is that the preferred model is random effects. It tests whether the unique errors are correlated with the regressors and the null hypothesis is they are not. We cluster robust standard errors at the country level to control for heteroskedasticity, cross-sectional dependence, and any potential correlation of standard errors among countries.7 We also test for suitability of time effects using the testparm i.year command. If the test statistic if significant, we reject the null that the coefficients for all years are jointly equal to zero, therefore time fixed effects are necessary. Since the climate change vulnerability index tends to be strongly correlated with macroeconomic variables, such as GDP per capita, we adjust the index for the level of income. India model specification We model the climate change and income inequality relationship for the Indian context in the following form: where Z is a set of income inequality determinants of India consisting of government consumption, PCA of strongly correlated fertility rate and age dependency ratio to quantify population characteristics, consumer price inflation, gross secondary school enrollment, globalization index, log of GDP per capita, and square of the log of GDP per capita. We only use annual surface temperature change as a climate change indicator for India because data for other indicators are available for shorter periods, which would make the number of observations even smaller and that would consequently make having meaningful and reliable inferences tougher. Due to the (2) NetGini =f(Surface Temperature Change,Z) 7 We also checked with Heterescedasticity and Autocorrelation Consistent (HAC) standard errors and the results are same. SN Bus Econ (2024) 4:87 Page 17 of 33 87 strong correlation between the climate change variable and other regressors, we create a GDP per capita adjusted index following the same steps as earlier. We only include net Gini in this model because, attributing the complex Indian tax structures to a significantly large informal economy, disposable income is more intuitive and informative for the income distribution context. Additionally, in the related literature, net Gini is most preferred as it accounts for the fiscal policies implemented in a given year. We also linearly interpolate the few missing observations for the secondary school enrollment variable. As a measure of fiscal policy, we include government consumption because it is a crucial determinant of income inequality in India (Munir and Sultan 2017). It can have both positive and negative impacts. For instance, if the government chooses to spend more on welfare benefits, that would most certainly reduce the level of income inequalities since the workforce gets a shot at improving their skills. However, unrestrained government expenditure is known to crowd out private investments, which might stagnate employment conditions and thus, increase income disparity. Also, if government expenditure is concentrated on fields like the military, that might repel foreign investors because they deem such settings hostile (Wisniewski and Pathan 2014). We carefully chose the control variables and kept their numbers limited to avoid overfitting and reducing degrees of freedom. For estimating the relationship in the Indian context, we adopt the ARDL bounds testing approach. The ARDL approach to cointegration was first developed by Pesaran and Shin (1997). Pesaran et al. (2001) subsequently redeveloped the bounds testing approach. We chose this particular model due to its five comparative advantages over earlier and more cointegration techniques and simple regression techniques. First, the ARDL model does not require all variables to be in the same order of integration; it can be deployed even when the included series have both I(0) and I(1) integrations. Second, ARDL circumvents the assumption of equal lag length, unlike the traditional cointegration models. Third, one of the main advantages of using the ARDL approach to cointegration is that the power of this test does not suffer in finite samples when invalid restrictions are imposed. For its finite sample properties, the ARDL bounds test approach to cointegration is robust and performs better even in smaller samples (Mah 2000; Pattichis 1999; Tang and Nair 2002). Fourth, the ARDL approach can generate non-biased long-run model estimates (Harris and Sollis 2003). Fifth, a bound testing approach is possible even when the explanatory variables are endogenous (Alam and Quazi 2003; Tang 2004). In accordance with Pesaran etal. (2001), the ARDL model of this study is specified as follows: SN Bus Econ (2024) 4:87 87 Page 18 of 33 where Δ is the first difference operator, n is the lag length, 𝛼1 - 𝛼9 denote the dynamic short-run coefficients, 𝛽1 - 𝛽9 are the long-run multipliers and 𝜀t is the error term. To carry out the ARDL bounds testing procedure, there are two stages. The first stage involves testing for a cointegrating relationship to establish whether there is a linear combination for nonstationary processes and determines the long-run level relationship between variables. From Eq.(3), the null hypothesis for testing no cointegration is given by: The f-statistic of the joint significance test (Wald test) is used to determine whether the lagged level of the variables is significant and cointegrated in the Conditional Error Correction model. The F-statistic is compared with the two asymptotical critical value sets developed by Pesaran etal. (2001), suitable for large sample studies, and further developed by Narayan (2004, 2005) to accommodate smaller sample studies ranging between 30 and 80 observations. The first set assumes that all the variables are I(0), while the other assumes all the variables are I(1). The decision to reject the null hypothesis of no cointegration depends on whether the F-statistic is greater than the I(1) bound. If the F-statistic falls above the I(1) bounds, a long-run level relationship exists; if it falls below the I(0) bound, there is no existence of a long-run cointegrating relationship; the outcome remains inconclusive if the F-statistic falls between the bounds. It is to be noted that before testing for the cointegrating relationship, the variables are to be first tested for the presence of unit roots to determine stationarity. This is necessary to confirm whether all series are either I(0) or I(1). I(2) series may lead to biased F-test results. The second stage of ARDL modelling deals with the estimation of the coefficients of the long-run relationships as well as interpreting the values of the estimated coefficients. Using the Akaike Information Criterion (AIC), the optimal lag length of the ARDL model is determined. On confirmation of cointegration, the error correction model (ECM) is specified as follows: (3) Δ gini_disp =𝛼0+ n ∑ i=1 𝛼1iΔgini_dispt−i+ n ∑ i=1 𝛼2iΔannual_surface_tempt−i+ n ∑ i=1 𝛼3iΔpop_statt− i + n ∑ i=1 𝛼4iΔgov_cont−i+ n ∑ i=1 𝛼5iΔinft−i + n ∑ i=1 𝛼6iΔsec_schoolt−i+ n ∑ i=1 𝛼7iΔkoft−i+ n ∑ i=1 𝛼8iΔlgdppct−i + n ∑ i=1 𝛼9iΔlgdppc_sqt−i+𝛽1gini_dispt−1 +𝛽2annual_surface_tempt−1+𝛽3pop_statt−1+𝛽4gov_cont−1+𝛽5inft−1 +𝛽6sec_schoolt−1+𝛽7koft−1+𝛽8lgdppct−1 + 𝛽9lgdppc _ sqt−1 + 𝜀t H0 ∶𝛽 1 =𝛽 2 =𝛽 3 =𝛽 4 =𝛽 5 =𝛽 6 =𝛽 7 =𝛽 8 =𝛽 9 = 0 SN Bus Econ (2024) 4:87 Page 19 of 33 87 In Eq.(4), 𝜃1−𝜃9 are the short-run coefficients, 𝜑 is the coefficient of the error correction term ( ECT ) which captures the long-run dynamics and 𝜇t is the residual error term. The validity of the error correction mechanism depends on the size and sign of the coefficient representing the speed of adjustment. 𝜑 is expected to have a negative value and be statistically significant. Data type andsource The study involves annual data ranging from 1971 to 2021. We incorporate two different panel datasets (unbalanced): (i) 43 countries most vulnerable to climate change (compiled using reports from Germanwatch’s Global Climate Risk Index 2021, INFORM Climate Change Risk Index, and IPCC 2022), and (ii) 39 most unequal countries in terms of income (compiled from World Inequality Lab’s Top 10% Income data and SWIID database). For India, we prepared a time series dataset. We collect data from various sources, namely, UN World Population Prospects, World Development Indicators-World Bank, Worldwide Governance Indicators-World Bank, ND-GAIN, Emergency Events Database (EM-DAT) World Bank Education Statistics, Food and Agriculture Organization (FAO), SWIID, KOF, and International Financial Statistics (IFS) by the IMF. For detailed variable information, summary statistics, and source links, refer to Online Appendix II, III and XXII. Results anddiscussion Panel analysis Tables1 and 2 display our baseline estimation results of Eq.(1) where fixed effectsregression is estimated. The highly significant test statistics confirm the suitability of the fixed effects model, including time fixed-effects across all specifications. We find that an increase in all three climate change indices worsens gross income inequality in unequal countries (Table1). The impact of climate change vulnerability is most profound on income distribution. A one-unit increase in vulnerability, surface temperature, and climate-related disasters leads to approximately 59, 0.67, and 0.05 unit increases in gross income inequality, respectively, and the coefficients are significant (4) Δgini_disp =𝜃0+ n ∑ i=1 𝜃1iΔgini_dispt−i+ n ∑ i=1 𝜃2iΔannual_surface_tempt− i + n ∑ i=1 𝜃3iΔpop_statt−i+ n ∑ i=1 𝜃4iΔgov_cont−i+ n ∑ i=1 𝜃5iΔinft−i + n ∑ i=1 𝜃6iΔsec_schoolt−i+ n ∑ i=1 𝜃7iΔkoft−i + n ∑ i=1 𝜃8iΔlgdppct−i+ n ∑ i=1 𝜃9iΔlgdppc_sqt−i+𝜑ECTt−1 +𝜇t SN Bus Econ (2024) 4:87 87 Page 20 of 33 Table 1 Fixed effects estimation (Climate Change and Gross Gini) ***, **, and * represent significance at 1%, 5%, and 10% levels, respectively Robust standard errors are mentioned in the parenthesis Dependent Variable: gini _ mkt (1) (2) (3) (4) (5) (6) Unequal Countries Vulnerable Countries vulnerability 58.563** (26.142) 29.617 (17.666) annual_surface_temp 0.667** (0.243) 0.274 (0.171) climate_disaster 0.050** (0.024) – 0.002 (0.024) lgdppc – 2.146 (5.085) 2.828 (4.644) 2.592 (5.046) 3.733 (2.918) 3.924 (3.232) 6.563** (2.997) lgdppc_sq 0.056 (0.273) – 0.088 (0.287) – 0.089 (0.298) – 0.425** (0.190) – 0.383* (0.207) – 0.565*** (0.178) age_dep – 0.010 (0.078) 0.004 (0.068) 0.013 (0.085) 0.063 (0.058) 0.068 (0.063) 0.074 (0.064) gdp_growth – 0.017 (0.020) – 0.042* (0.024) – 0.018 (0.029) – 0.009 (0.013) – 0.009 (0.015) 0.005 (0.016) inf 0.012* (0.007) 0.012* (0.007) 0.010* (0.006) 0.002 (0.002) 0.001 (0.002) 0.003 (0.002) trade 0.000 (0.015) – 0.008 (0.013) – 0.001 (0.015) – 0.007 (0.012) – 0.008 (0.012) – 0.012 (0.014) unemployment – 0.001 (0.116) – 0.133 (0.125) – 0.059 (0.137) – 0.030 (0.041) – 0.043 (0.046) – 0.050 (0.050) pop_gr 0.031 (0.561) 0.319 (0.527) 0.489 (0.623) – 0.251 (0.259) – 0.086 (0.314) – 0.307 (0.343) urb 0.239 (0.123) 0.231* (0.130) 0.309** (0.136) – 0.074 (0.074) – 0.077 (0.079) – 0.039 (0.082) internet – 0.049 (0.031) – 0.055* (0.031) – 0.040 (0.033) 0.002 (0.018) – 0.001 (0.020) 0.002 (0.018) inst_qual 1.057 (0.989) 1.220 (1.002) 1.094 (1.116) 1.373* (0.694) 1.499** (0.713) 1.529** (0.686) Observations 778 736 638 660 629 532 R20.4771 0.5027 0.4561 0.5643 0.5601 0.5717 Hausman Test statistic 57.02*** 81.99*** 64.53*** 105.20*** 99.13*** 82.42*** testparm i.year 2.97*** 6.17*** 4.32*** 3.02*** 3.15*** 8.67*** SN Bus Econ (2024) 4:87 Page 21 of 33 87 Table 2 Fixed effects estimation (Climate Change and Net Gini) ***, **, and * represent significance at 1%, 5%, and 10% levels, respectively Robust standard errors are mentioned in the parenthesis Dependent Variable: gini _ disp (1) (2) (3) (4) (5) (6) Unequal Countries Vulnerable Countries vulnerability 51.251* (25.404) 33.934* (17.614) annual_surface_temp 0.736*** (0.213) 0.324* (0.162) climate_disaster 0.056* (0.031) – 0.018 (0.025) lgdppc – 1.071 (5.205) 3.603 (4.702) 3.289 (4.905) 3.703 (2.851) 3.551 (3.156) 6.733** (2.899) lgdppc_sq – 0.040 (0.279) – 0.188 (0.286) – 0.187 (0.295) – 0.423** (0.186) – 0.349* (0.204) – 0.563*** (0.176) age_dep 0.007 (0.076) 0.022 (0.065) 0.040 (0.079) 0.051 (0.054) 0.060 (0.058) 0.062 (0.059) gdp_growth 0.004 (0.019) – 0.018 (0.021) 0.007 (0.026) – 0.011 (0.014) – 0.008 (0.015) 0.002 (0.018) inf 0.011* (0.006) 0.012* (0.006) 0.010** (0.005) 0.002 (0.002) 0.002 (0.002) 0.003 (0.002) trade – 0.009 (0.013) – 0.016 (0.013) – 0.009 (0.012) – 0.005 (0.011) – 0.007 (0.012) – 0.012 (0.014) unemployment – 0.008 (0.114) – 0.141 (0.119) – 0.065 (0.132) – 0.000 (0.052) – 0.007 (0.055) – 0.021 (0.057) pop_gr – 0.327 (0.579) – 0.071 (0.561) 0.567 (0.636) – 0.319 (0.280) – 0.168 (0.343) – 0.385 (0.349) urb 0.217** (0.107) 0.203* (0.102) 0.281** (0.110) – 0.068 (0.078) – 0.069 (0.084) – 0.027 (0.085) internet – 0.063** (0.031) – 0.068** (0.030) – 0.055* (0.032) – 0.016 (0.018) – 0.021 (0.020) – 0.018 (0.020) inst_qual 1.307 (1.022) 1.454 (1.034) 1.336 (1.127) 1.236 (0.752) 1.346* (0.782) 1.430* (0.776) Observations 778 736 638 660 629 532 R20.5317 0.5636 0.5231 0.5829 0.5802 0.5974 Hausman Test statistic 60.95*** 96.05*** 75.27*** 64.86*** 60.24*** 49.10*** testparm i.year 1.94** 9.03*** 5.57*** 3.62*** 7.47*** 4.45*** SN Bus Econ (2024) 4:87 87 Page 22 of 33 at a 5% level. For vulnerable nations, though we find positive and economically significant coefficients for vulnerability and surface temperature change, they are statistically insignificant. We find no impact of climate-related disasters on gross income inequality in vulnerable countries. This is possible if disasters affect middle-income groups, thereby leaving income inequality levels unaltered (Pleninger 2022). We find negative and statistically significant coefficients of the squared log of GDP per capita in the panel of vulnerable countries, which suggests that gross income inequality decreases with prolonged economic growth. In Table2, we plot the empirical outcomes of the climate change and net income inequality relationship. We find that the coefficient of climate change indices is positive and significant across all specifications, except for specification (6). A unit increase in vulnerability worsens income inequality in both unequal and vulnerable countries by approximately 51 and 34 units, respectively. Surface temperature change by a unit leads to an approximately 0.74 unit increase in the net Gini in the unequal countries, and the coefficient is significant at a 1% level whereas it has a comparatively smaller impact for the vulnerable countries. Climate-related disaster is found to slightly increase net income inequality, with a statistically significant relationship at a 10% level for the unequal countries, while no relationship is found for the vulnerable nations. Technical innovation (proxied by share of internet users in our case) reduces net income inequality in the group of unequal countries, and the coefficients are significant at 5 and 10 percent level. In general, our findings in Tables1 and 2 are in line with existing literature that unanimously agrees on the aggravating effects of climate change on income inequality (for instance, see, Cevik and Jalles 2023; Paglialunga etal. 2020). Table3 presents the diagnostic test results for heteroskedasticity and slope heterogeneity (Pesaran and Yamagata 2008) for all the specifications. The significant Table 3 Heteroskedasticity and slope hetereogeneity tests ***, **, and * represent significance at 1%, 5%, and 10% levels, respectively Modified Wald Test for group-wise Heteroskedasticity Slope Heterogeneity Test Delta Adj. Delta Table1 (1) 43,386.6*** 7.64*** 12.87*** Table1 (2) 23,981.5*** 6.92*** 11.65*** Table1 (3) 0.0*** 3.43*** 7.49*** Table1 (4) 12,703.2*** 2.11** 4.55*** Table1 (5) 6618.5*** 1.68* 3.56*** Table1 (6) 54,228.0*** – 1.99** – 8.66** Table2 (1) 42,487.6*** 6.73*** 11.34*** Table2 (2) 11,748.9*** 5.97*** 10.05*** Table2 (3) 0.0*** 2.71*** 5.91*** Table2 (4) 24,691.7*** 2.67*** 5.76*** Table2 (5) 13,386.2*** 2.42*** 5.14*** Table2 (6) 21,042.3*** – 0.95 – 4.14*** SN Bus Econ (2024) 4:87 Page 23 of 33 87 test statistics indicate the presence of heteroskedaticity and slope heterogeneity. In Table4, we show the VIF to examine multicollinearity issues in our specifications. The values are all below 10, with the means less than 4. Hence, we can safely conclude there are no multicollinearity issues. In Table5, the test statistics of the Table 4 Multicollinearity Tests Unequal countries Vulnerable countries Corresponding Equations (For both Table1 and 2) → (1) (2) (3) (4) (5) (6) vulnerability 1.68 2.47 annual_surface_temp 1.33 1.22 climate_disaster 1.75 1.33 lgdppc 6.85 5.53 5.77 9.45 6.77 7.52 age_dep 6.34 6.07 7.91 4.44 4.37 4.83 gdp_growth 1.13 1.16 1.16 1.15 1.18 1.23 inf 1.10 1.10 1.09 1.09 1.08 1.10 trade 1.29 1.34 1.51 1.18 1.20 1.17 unemployment 1.41 1.42 1.32 1.52 1.57 1.38 pop_gr 4.20 4.07 4.78 3.30 3.39 3.62 urb 3.86 3.14 3.63 7.45 5.65 5.87 internet 2.31 2.66 2.27 2.30 2.61 2.48 inst_qual 1.40 1.38 1.40 1.72 1.69 1.76 Mean VIF 2.87 2.65 2.96 3.28 2.79 2.94 Table 5 Pesaran (2015) Crosssectional dependence test ***, **, and * represent significance at 1%, 5%, and 10% levels, respectively Unequal countries Vulnerable countries gini_mkt 86.89*** 71.85*** gini_disp 86.09*** 72.26*** vulnerability 53.01*** 30.94*** annual_surface_temp 91.22*** 79.12*** climate_disaster 42.66*** 40.99*** lgdppc 106.34*** 89.40*** age_dep 33.48*** 44.91*** gdp _ growth 37.68*** 28.34*** inf 22.37*** 28.22*** trade 98.35*** 79.25*** unemployment 20.17*** 32.14*** pop_gr 26.60*** 27.70*** urb 107.63*** 93.23*** internet 71.63*** 42.00*** inst_qual 3.28*** 3.88*** SN Bus Econ (2024) 4:87 87 Page 24 of 33 Pesaran (2015) cross-sectional dependence test are highly significant at 1% level, which confirms the presence of strong cross-sectional dependence for all variables.8 India analysis We start our analysis for India by performing the Augmented Dickey-Fuller and the Phillips-Perron tests to ensure that no variables included in our model are integrated into order 2. Based on the results in Table6, we confirm that none of the series are I(2). Subsequently, we perform the ARDL bounds test for cointegration. We find that the F-statistic is higher than the critical value bounds at all significance levels. Consequently, the null hypothesis of no cointegration is rejected, and we conclude that there is a long-term relationship among the variables (see Table7). After confirming cointegration, we estimate the ARDL ECM model. The optimal lag length is determined using the AIC criterion. The AIC selected ARDL (1, 1, 2, 2, 2, 3, 2, 0, 2) for the model. The shortand long-run estimations are reported in Table8. We find that in the long-run, a unit-positive change in annual surface temperature leads to a 1.57 unit increase in income inequality in India. The coefficient is statistically significant at the 10% level. In the short run, we find an economically and statistically significant (5% level) coefficient of climate change that indicates an income inequality worsening impact of climate change. This finding is similar to Aggarwal (2021) who concludes that climate shocks are responsible for worsening consumption inequality among Indian sectors. Most importantly, the coefficient of the lagged error correction term is highly significant at a 1% level and negative, Table 6 Unit root tests ***, **, and * represent significance at 1%, 5%, and 10% levels, respectively H0: Unit Root Augmented Dickey-Fuller Phillips-Perron Decision Variables Stationarity in Levels Stationarity in First Difference Stationarity in Levels Stationarity in First Difference I(d) gini_disp – 1.782 – 4.001*** – 1.345 – 3.765*** I(1) annual_surface_temp – 5.746*** – – 5.703*** – I(0) pop_stat – 4.132** – – 4.132** – I(0) gov_con – 2.851 – 5.109*** – 1.986 – 4.992*** I(1) inf – 3.504** – – 4.969*** – I(0) sec_school – 2.382 – 3.545** – 1.915 – 6.329*** I(1) kof 2.072 – 1.814* 2.947 – 2.563** I(1) lgdppc – 1.402 – 6.048*** – 1.615 – 6.089*** I(1) lgdppc_sq – 1.179 – 5.924*** – 1.361 – 5.965*** I(1) 8 The cross-sectional dependence test has been performed prior to the estimations. 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