Climate Change and Income Inequality: Panel Evidence from 34 Indonesian Provinces, 2009–2024

Climate Change and Income Inequality: Panel Evidence from 34 Indonesian Provinces, 2009–2024 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Climate Change and Income Inequality: Panel Evidence from 34 Indonesian Provinces, 2009–2024 Aulia Davetta Athif, Wisnu Setiadi Nugroho This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8646521/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Climate change is a global issue affecting nearly every aspect of modern society. In Indonesia, the impact of climate change is evident in rising average temperatures and greater precipitation variability. Although much attention has been given to its effects on economic growth, research on its impact on other socio-economic factors, such as income inequality, remains limited. Using a dataset of 34 Indonesian provinces spanning 16 years from 2009 to 2024 and employing panel data econometric techniques, this paper investigates the association between climate change and income inequality across these provinces. The findings indicate that rising average temperatures and increased precipitation are positively associated with the Gini indices of Indonesian provinces. Additionally, the results show that the role of agriculture is not straightforward in Indonesian provinces. Evidence suggests that agriculture is one of the pathways through which climate change can affect income inequality levels in Indonesian provinces. However, specific regional and socioeconomic characteristics also suggest that the agricultural pathway can potentially cushion the impact of climate change for low-income deciles, while being detrimental for upper-income deciles. Ultimately, these potential impacts highlight the importance of policies that address the climate crisis and also adaptation policies tailored to support and shield low-income communities from the adverse impacts of climate change. JEL Codes: Q54, Q56, O15. Climate change Climate resilience Climate vulnerability Income inequality Figures Figure 1 1. Introduction Climate change is one of the most pressing issues of our time. Global measurements have shown that in 2024, the average global surface temperature was 1.47°C warmer than the late 19th-century pre-industrial average (NASA, 2024). This trend of ever-increasing average global temperatures threatens the predetermined 1.5°C limit set in the Paris Agreement, as exceeding it would push the Earth’s climate past many tipping points that could lead to instability and increased variability in global weather. These adverse climate conditions can lead to disruptions in economic activities and cause lasting economic harm (Lenton et al., 2008 ; Steffen et al., 2018 ; Armstrong McKay et al., 2022 ). Studies have found that a 1-degree Celsius increase in global temperature could lower global GDP by 12 percent and lead to a present-value welfare loss of 31 percent. The social cost of carbon emissions is estimated to be US $ 1,056 per ton of carbon dioxide. Climate change would also lead to more frequent and intense extreme disasters, resulting in humanitarian crises that are projected to become more common as 3.3 to 3.6 billion people live in highly vulnerable areas and have limited adaptive and mitigation capabilities (IPCC, 2023 ; Bilal and Känzig, 2024 ). In Indonesia, the impact of climate change is evident in the rising average temperatures and increased variability in precipitation. Measurements show that, similar to the global increase in average temperature, average temperatures in Indonesia have been on an increasing trend since the 1980s. Mean surface air temperature in 2023 is recorded at 26.2°C, marking an increase of 0.73°C compared to the average levels from 1901 to 1910. Precipitation variability also began to increase after the 1960s relative to levels from 1901 to 1910, with significant fluctuations from one year to the next (World Bank, 2025 ). The combined effects of climate change can potentially reduce Indonesia’s GDP by 4 percent by mid-century and up to 39.5 percent if warming reaches 3.2°C. Among forty-eight countries in Asia, Indonesia is ranked as one of the most vulnerable as it is exposed to all physical risks of climate change, ranging from sea level rise where it is estimated that by the period of 2070–2100 around 4.2 million people living in coastal areas are exposed to permanent flooding, heat stresses, and extreme hydrometeorological weather conditions that could affect agricultural yields, labor productivity, and tourism favorability (Bappenas, 2021 ; World Bank, 2021 ). Climate change has the potential to exacerbate the problem of income inequality. Studies show that rising average temperatures and higher-than-normal precipitation are positively associated with income inequality, particularly in poorer countries and those located in equatorial regions (Diffenbaugh and Burke, 2019 ; Emmerling et al., 2024 ). The link between climate change and income inequality is believed to stem from the uneven impacts of climate-related disasters across income groups. Low-income populations tend to bear the greatest burden of climate impacts due to their low resilience and high vulnerability to such disasters. They are also more likely to work in sectors that are more exposed to environmental shocks, such as agriculture, forestry, or livestock rearing. Increased temperatures can decrease total productivity in these sectors through lower farm yields, reduced labor productivity, and a higher likelihood of failed harvests. Consequently, those working in agriculture, primarily low-income groups, face rising costs and shrinking incomes due to these impacts and typically possess few assets that could buffer against income losses from climate events. Their limited social capital further restricts their access to social protection systems that could serve as safety nets. Many of their assets are also physical and non-liquid, making them more vulnerable compared to higher-income groups whose assets are often more liquid and non-physical. Such combination increases income inequality as low-income groups lose their assets and income-generating abilities more than higher-income groups as a result of climate change impacts (Huynh and Phan, 2024 ; Alam et al., 2017 ; Hallegatte and Rozen, 2017; Hallegatte et al., 2014 ). Issues could then arise as this potential connection between climate change and income inequality can reverse gains made in reducing income inequality and exacerbate the already complex socio-economic impacts of climate change. Heightened income inequality could further reduce low-income groups' ability to weather the effects of climate change, creating a negative feedback loop. This is a problem because higher income inequality is associated with greater ecological footprint, lower economic growth, higher food insecurity, and adverse effects on social cohesion (Herzer & Vollmer, 2012 ; Jetten et al., 2021 ; Onatunji, 2025 ; Zeb et al., 2025 ). Therefore, it is crucial to examine the potential effects of climate change on income inequality levels, particularly in developing countries located in warmer latitudes, as most existing studies rely on country-level data and are conducted at a global scale. This study aims to fill that gap by analyzing the relationship between climate change and income inequality levels across Indonesian provinces. This study would then provide insights into the relationship between climate change and income inequality in developing countries of tropical regions. Such findings can help policymakers prioritize policies that can mitigate climate change while addressing the pre-existing problem of income inequality. The study is structured as follows. Section 2 examines the existing literature concerning the relationship between rising temperatures and precipitation in relation to income inequality and explores the channels through which climate change may influence income inequality. Section 3 presents the data and methodology employed in this research. Section 4 provides and discusses the results of the empirical analysis. Section 5 concludes the paper by summarizing the findings, outlining the policy implications, and highlighting the limitations of this study, as well as providing recommendations for future studies. 2. Literature Review Literature on the effects of climate change on inequality can be distilled into two main strands. One strand of study identifies a parabolic relationship between climate change and inequality, where an optimal point exists in either temperature or precipitation. In this optimal point, economic benefits are maximized, climate-related impacts are minimal, and climate change has a negligible effect on inequality. The other strand presents a linear relationship between climate change and inequality. In this line of theory, there is no optimal point; as temperature increases or precipitation becomes more volatile, climate-related impacts intensify, leading to greater economic damage and greater inequality. 2.1 Relationship between Climate Change and Inequality Studies that found a parabolic relationship between climate change and inequality has identified an optimal temperature range of 10–18°C, where climate-related impacts are minimized or even positive for countries with average annual temperatures below this range and decile income growth is maximized. In this scenario, colder and wealthier countries are expected to see a 20 to 40 percent increase in per capita GDP, whereas warmer equatorial countries may face a 20 to 30 percent decline in their per capita GDP. Consequently, the GDP gap between rich and poor countries is projected to increase by 25 percent compared to a scenario without climate change and has the possibility to continue widening, eliminating the prospect of economic convergence between low- and high-income countries (Diffenbaugh and Burke, 2019 ; Taconet et al., 2020 ). Countries in lower, and therefore warmer, latitudes, under a 3.6°C increase in average temperatures, are projected to increase Gini indices by up to 6 percentage points, with the poorest deciles affected the most (Gilli et al., 2024 ). It is also found that for precipitation, a one standard deviation (SD) increase is projected to maximize the bottom 50 percent income share, especially in countries with high agricultural intensity. When precipitation increases by more than 1.5 SD, the income share of the bottom 50 percent would start to decrease (Palagi et al., 2022 ). The strand of studies that found a linear relationship between climate change and inequality is mainly focused on inequality within countries. Most agree that higher temperatures are associated with worse inequality levels (Méjean et al., 2024 ). It is found that a 1 percent increase in temperature is associated with a 0.5 percentage point increase in the Gini index. Additionally, extreme climate variations in a given year are associated with a 0.1–0.2 percentage-point increase in the Gini index (Paglialunga et al., 2022 ). Studies with longer timeframes found that a 1-degree Celsius increase in average temperature over 50 years is associated with a 4.2 to 4.8-point increase in the Gini index, while a 1-degree Celsius increase over 5 years is associated with a 1.3-point increase in the Gini index. In a scenario where average temperatures rise over 2.5 to 3.1°C by 2100, the average Gini indices of countries will, on average, increase by about 1.4 points or by as much as 2.5 points in already warmer regions (Castells-Quintana and McDermott, 2023 ; Emmerling et al., 2024 ). In the case of Asian countries, an additional thousand deaths due to climate-related disasters is associated with a 0.17 to 0.37 increase in the Gini index (Huynh and Phan, 2024 ). 2.2. Pathway of Climate Change on Affecting Income Inequality Current literature indicates that the effects of climate change are unequally distributed across income groups; lower-income groups bear the brunt of climate change impacts due to their higher vulnerability. They are mostly dependent on labor for their income, and many are employed in the primary sector, which is more affected by climate change. Higher income groups, on the other hand, are more dependent on capital that is less affected by climate change, for the time being (Chancel et al., 2024; Méjean et al., 2024 ). Low-income groups are also disproportionately affected because their assets and livelihoods are more vulnerable to climate impacts, leading to greater asset and productivity losses compared to other income groups as some studies have noted the positive association between climate vulnerability and multidimensional poverty (IPCC, 2015; Ahmad et al., 2024). In Nigeria, the poorest 20 percent are 50% more likely to be affected by floods, 130% more likely to be affected by droughts, and 80% more likely to be affected by heatwaves. This can result in a negative feedback loop, where losses in assets and productivity reduction lower their ability to cope with future disasters, resulting in even greater losses. Greater exposure to climate impacts also hampers human capital accumulation, as asset losses hinder the ability to attain education or healthcare services. Low-income households living in climate-prone regions are found to have their enrollment rates reduced by 20 percent, and children starting school are delayed on average by 3.7 months. Combined, this would result in lower income-generating capabilities for low-income groups (Hallegatte et al., 2014 ; Hallegatte and Rozen, 2017), and it has been found that the incomes of lower-income quintiles are more sensitive to temperature changes than higher-income quintiles, with the sensitivity of income towards temperature changes decreasing as the income distribution moves toward higher-income quintiles (Gilli et al., 2024 ). Climate change then presents more barriers to wealth for low-income groups, as they have greater vulnerability and lower resilience towards climate change relative to higher-income groups. Figure 1 below summarizes the pathways or mechanisms by which climate change can affect income inequality levels. [Figure 1. Climate Change Pathway towards Income Inequality ] The agricultural sector is also one of the pathways through which climate change can affect income inequality levels. Higher temperatures can result in lower crop yields and quality; it also lowers farmers’ productivity through a higher incidence of heat-related fatigue. Regions most affected by climate change are also the regions where agricultural productivity is reduced the most (Amare and Balana, 2023 ; Dasgupta et al., 2023 ; Méjean et al., 2024 ). A one percent increase in temperature is projected to reduce current paddy yields by 3.44 percent, while a one percent increase in precipitation is projected to reduce yields by 0.12 percent (Alam et al., 2017 ; Huynh and Phan, 2024 ). Higher average temperatures are also associated with lower growth rates, lower gross output, and lower value added in the agricultural sector. A 1°C increase in average temperatures is associated with a 0.025 to 0.032 percent decrease in the growth rate of the primary sector, and a 0.88 percent decrease in value added in the agricultural sector (Li et al., 2020 ; Castells-Quintana and McDermott, 2023 ). A higher share of employment in the agricultural sector is also associated with a higher positive association between average temperatures and the Gini index. A 1°C increase in average temperatures is associated with an increase of 0.664 percentage points in the Gini indices of counties with medium rural populations, while counties with high rural populations are associated with an increase of 2.1 percentage points. A 1.5 increase in the standard deviation of average precipitation is also associated with a 35 times stronger impact for the bottom 50 percent of income shares in countries with a high agricultural employment share relative to countries with a low agricultural employment share (Palagi et al., 2022 ; Paglialunga et al., 2022 ). These compounding effects lead to lower incomes for individuals working in the agricultural sector, many of whom are low-income and/or rural individuals. 3. Data and Methodology 3.1. Data The empirical analysis in this paper covers all 34 Indonesian provinces from 2009 to 2024, using a 16-year panel dataset. This data combines climate variables from various weather stations across Indonesia with income inequality indicators derived from national workforce surveys, resulting in 544 observations. The dependent variables are four measures of income inequality. The first is the annual Gini index for each province, provided by the Indonesian Statistics Agency (Badan Statistik Nasional), which ranges from 0 to 100, where 100 indicates perfect inequality and 0 signifies perfect equality in income distribution. The second is the annual Theil index, which takes a value of 0 if there is perfect income equality and ln N if there is perfect inequality. The third measure is the 90:10 income ratio that compares the income of the top 10 percent to the bottom 10 percent. The fourth measure is the decile income share, showing the percentage of total income held by each decile, from the bottom 10 percent to the top 10 percent. Different income inequality measures are used to cover for the deficiencies of each measure, as the Gini index is more sensitive to changes in the middle part of the income distribution, while the Theil index is more sensitive to changes at the top of the income distribution. The 90:10 income ratio sees extreme differences, and income deciles see the in-depth picture (De Maio, 2007 ). National Labor Force Surveys (Survei Angkatan Kerja Nasional) from 2009 to 2024 are used to calculate all income inequality indicators except for the Gini index. Individual income data from the survey is compiled and grouped by province and year and all indicators calculated from the survey are based on unweighted individual data or samples from each province rather than weighted data. These indicators allow us to observe how income distribution shifts within each province. Climate change-related variables are captured through the annual averages of daily temperatures and monthly precipitation, both of which are significantly affected by climate change. Using these variables enables us to assess the impact of climate change, as changes in these parameters result from exogenous processes within the meteorological cycle that are currently influenced by climate change (Hansen et al., 2006 ; Ingram, 2016 ). Data for these variables are obtained from the Meteorology, Climatology, and Geophysics Agency (Badan Meteorologi, Klimatologi, dan Geofisika or BMKG). Daily average temperatures in degrees Celsius are collected from weather stations within each province and average annually. Monthly precipitation data, measured in millimeters per day, are also collected from weather stations across provinces and averaged each year using monthly records. Controlling for independent variables involves accounting for the determinants of inequality, enabling us to consider other factors that influence inequality in each province. The socioeconomic determinants include GDP per capita and the squared GDP per capita for each province. GDP per capita acts as an indicator of economic growth, one of the determinants of inequality. The squared GDP per capita helps capture the potential presence of a Kuznets inverted-U curve within our provinces. Additionally, the unemployment rate and the shares of employment in agriculture, manufacturing, and services are included because these variables are believed to influence inequality levels (Schaffner, 2014 ; Todaro and Smith, 2015 ; Cevik and Jalles, 2023 ; Paglialunga et al., 2024). These variables are collected annually from the Indonesian Statistics Agency. [Table 1. Variables used with measurements and data types] Technological determinants are controlled by the percentage of homes using electricity and the proportion of households that have used the internet in the past three months in each province. The institutional determinant is represented by the average years of schooling in each province. These data are also sourced from the Indonesian Statistics Agency. The share of homes using electricity and the internet indicates the proportion of households with access to these services, with higher ratios reflecting greater penetration of the internet and electricity usage. The average years of schooling reflect the educational attainment of individuals in the province, with a higher mean indicating a more educated population. The summary of all variables used are shown in Table 1 above. 3.2. Empirical Model and Variables This paper will explore the relationship between climate change and inequality while controlling for socioeconomic, technological, and institutional determinants of inequality in a panel setting. To measure the effect of climate change on income inequality, a panel regression model with fixed effects will be used. A linear model is chosen because average temperatures in Indonesia are already higher than the optimal temperatures highlighted by previous studies. Another reason is that the independent variables of climate change, the average annual temperatures and monthly precipitation, are exogenous, and fixed effects are used to control time-invariant regional and yearly effects (Wooldridge, 2016 ). This model will then allow us to observe how income distribution is impacted by provincial variations in average temperature and precipitation after controlling provincial-level determinants of income inequality. Two main regression models are used: a main baseline model and an agricultural interaction term model. For the baseline model, the following regression model is estimated: Inequality pt = β 0 + β 1 Climate pt + β k X k,pt + r p + y t + \(\:{ϵ}_{vt}\) (1) Where \(\:Inequalit{y}_{pt}\) are the income inequality measures such as the Gini index, the Theil index, 90:10 income ratio, and the decile income shares of province p at year t ; \(\:Climat{e}_{pt}\) represents the measures of climate change, annual average temperatures and monthly precipitation, of province p at time t ; \(\:{X}_{k,pt}\) are a set of socio-economic, technological, and institutional control variables that consists of the provincial GDP per capita, the squared GDP per capita, unemployment rate, the shares of employment in agriculture, manufacturing, and services, electrification ratio, internet penetration, and the average years of schooling of the population in each province p at time t ; \(\:{r}_{p}\) represents the regional-fixed effects in the form of a dummy variable for each province; \(\:{y}_{t}\) represents the year-fixed effects in the form of a dummy variable for the year 2009 to 2024; and \(\:{ϵ}_{vt}\) is the error term. The estimate of interest is \(\:{\beta\:}_{1}\) as it highlights the potential association between climate change variables and income inequality. To examine the potential relationship between climate change, the agricultural sector, and income inequality, Eq. 2 uses interaction terms between climate variables and the employment share in the agricultural sector in each province. Interaction terms enable us to examine the partial effect of an independent variable on a dependent variable, in this case, the climate variables, relative to another explanatory variable, namely the agricultural share in employment (Wooldridge, 2016 ). Based on this, the following model would be estimated: Inequality pt = β 0 + β 1 Climate pt + β 2 Climate pt × Agri pt + β k X k,pt + r p + y t + \(\:{ϵ}_{vt}\) (2) where every variable is the same as Eq. 1 and \(\:Agr{i}_{pt}\) represents the percentage share of employment in the agricultural sector for province p at time t. The estimate of interest is \(\:{\beta\:}_{2}\) as it is the estimate of the interaction terms between the climate variables and the share of employment in the agricultural sector. To address the issue of serial correlation and heteroskedasticity that may be present in the panel dataset, all regression models are estimated using clustered standard errors, as this approach can account for both heteroskedasticity and spatial correlation (Wooldridge, 2016 ). As past temperatures and precipitation may influence current income inequality levels, the study used a lagged model observe the effect of past temperatures and precipitation on income inequality in each province. Robustness checks are also performed by estimating some of the models with random effects and log models to see if the results are robust across different regression models. The results of the lagged model, robustness checks, and full regression tables of selected regression models are shown in the Supplementary Information (SI). 4. Results and Discussion 4.1. Baseline Model Results from the baseline model, as seen in Table 2, found a positive and statistically significant association between rising average temperatures and the Gini index of Indonesian provinces. A lower magnitude of increase is also seen from the coefficient of the Theil index and the 90:10 ratio, but both are statistically insignificant. In economic terms, the magnitude of change is only 2.3 percent of the average Gini index of 35.48. However, comparing it to the movement of the national Gini index, which have declined by 3.96 points in 2024 from its 16-year maximum point in 2012, this increase of 0.801 points in the Gini index represents a 20 percent reduction in the gains made on reducing income inequality (BPS, 2025). Exceeding the 1.5 degrees Celsius limit set by the Paris Agreement would potentially increase the Gini index by 1.2 points in Indonesia, reversing 30 percent of the gains made from 2012. For precipitation, it is observed that there is a negative and statistically significant association between rising monthly precipitation and the Gini index of Indonesian provinces. Similar to temperature, the coefficients for the Theil index and the 90:10 income ratio are statistically insignificant. The Gini index coefficient is small enough that it can be considered that the effect is minimal; a rise of at least 100 millimetres in monthly precipitation will result in a noticeable decrease in the Gini index. Economically, a 100-millimeter increase in precipitation would potentially lower the Gini index by 0.46 points, adding 11.6 percent to the gains made since 2012. In the case of income deciles, as seen in Table 3, rising temperature has no effect except for the 4th decile where a 1-degree increase in average annual temperature is associated with a 0.12 percentage point lower income share for that decile. The mean income share of the 4th decile is 5.37 percent of total income, and so this equates to a 2.2 percent reduction in the income share of the 4th decile. For precipitation, rising monthly precipitation has no effect except for the 9th decile where a 10-millimeter increase in monthly precipitation is associated with a 0.0125 percentage point increase in their income share. This is a minuscule increase compared to the mean income share of the 9th decile, around 0.075 percent. These results suggest that the impacts of rising temperatures and precipitation on income inequality are not straightforward. From the three income inequality indicators, there is evidence that rising average temperatures have a positive association with the Gini index of Indonesian provinces while there is no discernible association with the other inequality indicators. This suggests that in Indonesian provinces, temperature increases affect the income distribution of the bottom and middle decile more than the upper decile, as the Gini index is more sensitive towards changes in the middle part of the income distribution, whereas the Theil index is more sensitive towards changes in the top part of the income distribution (De Maio, 2007 ). Furthermore, the estimates for the Gini index are higher than estimates from studies that use Asian countries, but lower than those from studies that use a global dataset. It is also different from studies that found positive and statistically significant associations between temperature and the Theil index (Paglialunga et al., 2022 ; Castells-Quintana and McDermott, 2023 ; Gilli et al, 2024 ; SenGupta and Atal, 2024 ; Huynh and Hoang, 2025 ). These results suggest that Indonesia's socioeconomic structure has greater vulnerabilities and lower resilience to climate change, especially for lower- and middle-income deciles, relative to other Asian countries; however, it is still comparatively better than several global regions, such as Sub-Saharan Africa (Cevik and Jalles, 2023 ). The negative association between the income share of the 4th decile and rising temperatures also supports the suggestion that climate change has unequal impacts, where bottom income deciles are disproportionately affected relative to upper income deciles. This study also finds that there is an inverse association between average monthly precipitation and the Gini index, where higher precipitation would lead to a miniscule decrease in the Gini index of provinces. This could indicate that for Indonesian provinces, short-term increases in precipitation could enhance agricultural productivity and generate additional income for low-income deciles who predominantly work in the agricultural sector. This is supported by studies that found that in specific cases, short-term small increases in precipitation could increase net crop income (Coromaldi, 2020 ; Otrachshenko and Popova, 2022 ). Moreover, the baseline decile model shows that precipitation is positively associated with the income share of the 9th decile; suggesting that top income deciles also benefit from increases in monthly precipitation. [Table 2. Effect of temperature and precipitation on income inequality indicators] [Table 3. Effect of temperature and precipitation on income decile shares] 4.2. Agricultural Pathway Model The results from the agricultural interaction model found that there is a positive and statistically significant association between rising temperatures and precipitation, share of employment in agriculture, and the Gini index of Indonesian provinces. In the case of temperature, shown in Table 4, for every 1-degree Celsius increase in average temperatures, an extra 1 percentage point share of employment in the agricultural sector would result in an additional 0.0294-point increase in the Gini index. With the average share of employment in agriculture for Indonesian provinces at 37.27 percent, this would mean that, on average, a 1-degree Celsius increase in temperature can lead to an additional a 1.1-point increase in the Gini index of Indonesian provinces. It is also the case for precipitation, shown in Table 5, where a 10-millimetre increase in average monthly precipitation, an extra 1 percentage point share of employment in agriculture would result in an additional 0.00243-point increase in the Gini index. But this is a small increase and taking into account the average share of employment in agriculture as mentioned above, a 10-milimetre increase in the average province can lead to an additional 0.09-point increase in the Gini index. For income deciles, the results are surprising. As seen in Table 6, there is a positive association between the temperature-agriculture interaction term and the income shares of the lower- and middle-income deciles. Conversely, there is a negative association between the same temperature-agriculture interaction term and the income shares of upper income deciles. For the 1st, 2nd, 3rd, 4th, and 5th income deciles, there is a cushioning effect where a higher provincial agricultural employment share reduces the adverse association of rising temperatures on their income shares. The reverse is happening for the 8th income decile; a higher agricultural share reduces the positive association between increasing temperatures and their income shares. It must also be noted that in Table 6, rising temperatures has a negative and statistically significant association with the income shares of the lower- and middle-deciles, and a positive association with the 8th decile. This further supports the results we see from Eq. 1 that rising temperatures is rising income inequality levels in Indonesian provinces. In the case of precipitation, there is no association as all the estimates are statistically insignificant. The results partially support that there is a positive association between climate change, the agricultural sector, and income inequality. Evidence suggests that in Indonesian provinces, the agricultural pathway plays a puzzling role. On one hand, there is evidence that the agricultural pathway is present in Indonesian provinces, as seen in Tables 4 and 4.4. On the other hand, there is also evidence that it can soften the effects of rising temperatures on the income shares of lower- and middle-income deciles, and strengthening that effect for higher-income deciles. There are several possible explanations, the first is that this may suggest that there is a differential effect of rising temperatures in urban versus rural areas. The impact of rising temperatures on urban low-income groups is larger than the impact on rural low-income groups, as provinces with low agricultural employment share are usually more urbanized, and vice versa. The interaction term picks up this phenomenon, resulting in the dual effect mentioned above. Studies have shown that heat stress from climate change is higher in urban areas because of the urban heat island phenomenon, and it, combined with the specific vulnerabilities of low-income urban groups, such as the lack of access to necessary infrastructure and services, causes them to face and suffer higher risks from warming, with some studies showing the higher mortality rates of low-income urban groups because of climate-related impacts (Fischer et al., 2012 ; Sera et al., 2019 ; Grasham et al., 2019 ). Another possible reason is that climate shocks can reduce agricultural productivity, lowering agricultural yield, resulting in higher food prices from climate-related agricultural shocks that are beneficial for low-income rural households, while being detrimental for upper-income rural households (Wilts et al., 2021 ). This can explain the dual effect, as most low-income groups work in the agricultural sector, while upper-income groups are mostly employed in non-agricultural sectors. In spite of the mixed results of the interaction term, the estimates confirms that there is a positive association between average temperatures and income inequality indicators, as seen with the estimates of low-income deciles that are negative and upper-income deciles that are mostly positive, and both are statistically significant. The estimates then corroborate the notion that disadvantaged groups, in this case low-income deciles, have socioeconomic characteristics that make them more vulnerable and less resilient towards climate-related impacts. Low-income deciles suffer disproportionately from climate change impacts as they are more exposed to climate change impacts, more susceptible to climate-related damages, and have a lower ability to recover from such events (Islam and Winkel, 2017 ; Hallegatte and Rozen, 2017; Hallegatte et al., 2014 ). The positive coefficient for the upper-income decile may suggests that upper-income groups have higher adaptation capabilities, as they have more capital to adapt, insulating their productivity and sources of income from climate-related impacts. Upper-income groups also have diversified income sources and capital to invest in climate projects that can further augment their incomes (Chancel et al., 2022 ; Davis et al., 2021 ). [Table 4. Interactional effect of temperature and agricultural share on income inequality indicators] [Table 5. Interactional effect of precipitation and agricultural share on income inequality indicators] [Table 6. Interactional effect of temperature and agricultural share on income decile shares] 5. Conclusion Climate change is one of the most pressing issues of our time, with its impact far-reaching and multidimensional. The effects of climate change on economic growth have received significant attention in the literature, and there is consensus that climate change, through rising temperatures and precipitation anomalies, can incur substantial economic damages that are felt globally. However, climate change has the ability to affect our socio-economic systems in many ways beyond economic losses, and the discussion around its impact on other socio-economic aspects, such as income inequality, is still new and limited. There are gaps in the literature, especially in economically developing tropical countries, as most studies focus on the global and broad-based impacts of climate change on income inequality. This paper explores the connection between climate change, through its indicators of rising average temperatures and monthly precipitation, on income inequality, through the Gini index, the Theil index, the 90:10 income ratio, and decile income shares. To investigate the potential connection, a dataset was constructed combining average temperature and precipitation data from weather stations across Indonesian provinces with various income inequality measures for each province, resulting in a dataset of 34 Indonesian provinces spanning a 16-year timeframe from 2009 to 2024. The study then uses panel data econometric techniques, exploiting the exogenous characteristics of the climate indicators, and also fixed effects to control for time and regional-invariant effects. The findings of this study suggest three main conclusions. First, the study confirms that rising average temperatures and increased precipitation have a considerable positive association with the Gini indices of Indonesian provinces. Furthermore, the magnitude of the effect is stronger than in other Asian countries and has the possibility of reversing 30 percent of the gains made in reducing the provinces’ income inequality levels since 2012. Second, rising temperatures is the main driver in the change in income inequality levels as the potential effects is far greater than rising precipitation. Third, evidence shows that the agricultural pathway is present but it may play a more complex role due to the diverse socioeconomic characteristics of Indonesian provinces. Estimates suggest that for Indonesian provinces, agricultural employment can potentially cushion the adverse impact of climate change on the income shares of low-income deciles, while being strengthening the impacts for the income shares of the upper-income deciles. 5.1. Policy Implication Ultimately, these potential impacts highlight the importance of policies that addresses the climate crisis and also policies tailored towards supporting low-income communities adapt to the already existing impacts of climate change. Economically developing tropical countries and Indonesian in particular, would then benefit from several policies outlined. First, reducing greenhouse gas emissions by increasing emission reduction targets stated in Indonesia’s Enhanced Nationally Determined Contribution 2022 and recommitting to plans stated in LTS-LCCR 2050, so large parts of the economy can transition from fossil fuels and decarbonize to limit the rise in global temperatures and precipitation (IPCC, 2023 ). Second, instituting a mixture of policies tailored towards increasing the climate resilience of low-income rural groups such as a combination of disbursing climate-resilient seeds, instituting agricultural insurance schemes by expanding and bolstering Asuransi Usaha Tani Padi to areas prone to climate disasters, and pushing small-scale mechanization that require little capital to maintain but can reverse productivity losses from climate-related impacts such as expanding the Agricultural Ministry’s Bantuan Alsintan to climate-prone areas (Milhorance, 2022; IPCC, 2023 ). Third, urban-specific policy mixture that increase the overall climate resilience of urban areas and also the resilience of low-income individuals that live in it. This can be done by upgrading urban infrastructure systems, expanding social safety nets, conditional cash transfers, and increasing access to essential services (Tyler and Moench, 2012 ; Zakir and Ashiq, 2018). In the case of Indonesia, these can be in the form of expanding the cash transfer program, Program Keluarga Harapan towards low-income urban households and ensuring universal healthcare access through Jaminan Kesehatan Nasional. 5.2. Limitations and Recommendations Overall, study has its limitations and remains an exploratory analysis into climate change and income inequality. Using average temperature and precipitation overlooks the impact of extreme weather anomalies or disasters, which are increasing in frequency due to climate change. Data collected from weather stations and national surveys are also susceptible to measurement errors. There is also a possibility of omitted variable biases, as the determinants of income inequality are many, and some are not included in this study because of constraints in data availability. Several results also show unique pathways and interactions that are still little discussed in the literature. Future studies may benefit from; (1) employing different climate indicators that measure extreme weather events, (2) collecting climate and income inequality data from other sources with longer timeframes and of better quality, and (3) analyzing deeper into the interplay between agricultural employment, regional socioeconomic factors, climate change, and income inequality in Indonesia. Declarations Competing Interests The authors also have no competing interests to declare that are relevant to the content of this article. Ethics Approval and Consent to Participate Not applicable. Consent for Publication Not applicable. Funding The authors did not receive support or funding from any organization for the submitted work. Author Contributions The study conception and design were done by Aulia Davetta Athif with guidance from Wisnu Setiadi Nugroho. Material preparation, data collection and analysis was performed by Aulia Davetta Athif. The first draft of the manuscript was written by Aulia Davetta Athif and all authors commented and edited previous versions of the manuscript. All authors read and approved the final manuscript. Availability of Data and Materials The data that support the findings of this study are not publicly available due to the usage of the Indonesian National Labor Force Survey (SAKERNAS) that are prohibited to be redistributed toward third parties. Researchers may request access directly from BPS via the survey data request portal at https://silastik.bps.go.id/ . Access may be subjected to data access policies and approval and/or associated fees. Data Availability Statement The data that support the findings of this study are not publicly available due to the usage of the Indonesian National Labor Force Survey (SAKERNAS) that are prohibited to be redistributed toward third parties. 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Socioeconomic determinants of ecological footprints: Bridging the gap between developed and developing nations. Environment, Development and Sustainability . https://doi.org/10.1007/s10668-025-06081-y Tables Tables 1 to 6 are available in the Supplementary Files section. Supplementary Files SI1LaggedModelResultsandDiscussion.pdf SI2Appendices.pdf TableS1.1.xlsx TableS1.2.xlsx TableS1.3.xlsx TableS1.4.xlsx TableS2.1.xlsx TableS2.10.xlsx Table1Variablesusedwithmeasurementsanddatatypes.xlsx TableS2.11.xlsx Table2Climatevariablesoninequalityindicators.xlsx TableS2.12.xlsx Table3Climateindicatorsonincomedecileshares.xlsx TableS2.13.xlsx Table4InteractionalTempxAgIID.xlsx TableS2.2.xlsx Table5InteractionalPrecxAgIID.xlsx TableS2.3.xlsx Table6InteractionalTempxAgDIS.xlsx TableS2.4.xlsx TableS2.5.xlsx TableS2.6.xlsx TableS2.7.xlsx TableS2.8.xlsx TableS2.9.xlsx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 26 Apr, 2026 Reviewers invited by journal 24 Apr, 2026 Editor assigned by journal 23 Jan, 2026 First submitted to journal 22 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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10:05:50","extension":"xlsx","order_by":13,"title":"","display":"","copyAsset":false,"role":"supplement","size":10663,"visible":true,"origin":"","legend":"","description":"","filename":"Table3Climateindicatorsonincomedecileshares.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8646521/v1/2358422fbcc00653df155b76.xlsx"},{"id":108494605,"identity":"e692de1d-b780-4f2b-bbcd-97c756315280","added_by":"auto","created_at":"2026-05-05 10:05:56","extension":"xlsx","order_by":14,"title":"","display":"","copyAsset":false,"role":"supplement","size":10560,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2.13.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8646521/v1/30d32f0b1697fe829464564e.xlsx"},{"id":108494658,"identity":"8a65968e-7360-4f63-b3ff-25fdc770cfc3","added_by":"auto","created_at":"2026-05-05 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10:10:03","extension":"xlsx","order_by":17,"title":"","display":"","copyAsset":false,"role":"supplement","size":10595,"visible":true,"origin":"","legend":"","description":"","filename":"Table5InteractionalPrecxAgIID.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8646521/v1/4c03f8f62c25b82ef97117c1.xlsx"},{"id":108494214,"identity":"e6c6b345-423e-44db-aab4-c1ead3e8ec8f","added_by":"auto","created_at":"2026-05-05 10:03:08","extension":"xlsx","order_by":18,"title":"","display":"","copyAsset":false,"role":"supplement","size":10662,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2.3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8646521/v1/cf080044f01026c598f7be45.xlsx"},{"id":108494604,"identity":"08aa7853-63d7-49c1-840c-a5b1926722d4","added_by":"auto","created_at":"2026-05-05 10:05:55","extension":"xlsx","order_by":19,"title":"","display":"","copyAsset":false,"role":"supplement","size":11176,"visible":true,"origin":"","legend":"","description":"","filename":"Table6InteractionalTempxAgDIS.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8646521/v1/a8ab12b52ec49560d159bcea.xlsx"},{"id":108494699,"identity":"2485cc18-37de-4a12-81be-e124afa509db","added_by":"auto","created_at":"2026-05-05 10:06:41","extension":"xlsx","order_by":20,"title":"","display":"","copyAsset":false,"role":"supplement","size":12121,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2.4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8646521/v1/2b8a995a062a229a1fb4d2aa.xlsx"},{"id":108494704,"identity":"ea849b87-93e2-4983-a74c-18b8606c21e4","added_by":"auto","created_at":"2026-05-05 10:06:42","extension":"xlsx","order_by":21,"title":"","display":"","copyAsset":false,"role":"supplement","size":12168,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2.5.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8646521/v1/b9dafdced61d1bd0a180c543.xlsx"},{"id":108494612,"identity":"3de79b90-ed22-4771-be27-52d7f524fcf2","added_by":"auto","created_at":"2026-05-05 10:06:02","extension":"xlsx","order_by":22,"title":"","display":"","copyAsset":false,"role":"supplement","size":12104,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2.6.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8646521/v1/a04b549723d3501d045cf23d.xlsx"},{"id":108804560,"identity":"231a7309-45c1-4b4f-9631-88e536cccedb","added_by":"auto","created_at":"2026-05-08 15:21:39","extension":"xlsx","order_by":23,"title":"","display":"","copyAsset":false,"role":"supplement","size":10637,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2.7.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8646521/v1/b773cddcd57a2d620d90196c.xlsx"},{"id":108804004,"identity":"2f77c268-9dd8-4a57-af78-c2efde4d2fc6","added_by":"auto","created_at":"2026-05-08 15:14:17","extension":"xlsx","order_by":24,"title":"","display":"","copyAsset":false,"role":"supplement","size":10693,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2.8.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8646521/v1/14becca0774256e4042ce15d.xlsx"},{"id":108803939,"identity":"7187721c-6958-48aa-a88a-9007605976ff","added_by":"auto","created_at":"2026-05-08 15:12:05","extension":"xlsx","order_by":25,"title":"","display":"","copyAsset":false,"role":"supplement","size":10653,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2.9.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8646521/v1/5500edc14b638535c5a7474b.xlsx"}],"financialInterests":"","formattedTitle":"Climate Change and Income Inequality: Panel Evidence from 34 Indonesian Provinces, 2009–2024","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eClimate change is one of the most pressing issues of our time. Global measurements have shown that in 2024, the average global surface temperature was 1.47\u0026deg;C warmer than the late 19th-century pre-industrial average (NASA, 2024). This trend of ever-increasing average global temperatures threatens the predetermined 1.5\u0026deg;C limit set in the Paris Agreement, as exceeding it would push the Earth\u0026rsquo;s climate past many tipping points that could lead to instability and increased variability in global weather. These adverse climate conditions can lead to disruptions in economic activities and cause lasting economic harm (Lenton et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Steffen et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Armstrong McKay et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Studies have found that a 1-degree Celsius increase in global temperature could lower global GDP by 12 percent and lead to a present-value welfare loss of 31 percent. The social cost of carbon emissions is estimated to be US\u003cspan\u003e$\u003c/span\u003e1,056 per ton of carbon dioxide. Climate change would also lead to more frequent and intense extreme disasters, resulting in humanitarian crises that are projected to become more common as 3.3 to 3.6\u0026nbsp;billion people live in highly vulnerable areas and have limited adaptive and mitigation capabilities (IPCC, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Bilal and K\u0026auml;nzig, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn Indonesia, the impact of climate change is evident in the rising average temperatures and increased variability in precipitation. Measurements show that, similar to the global increase in average temperature, average temperatures in Indonesia have been on an increasing trend since the 1980s. Mean surface air temperature in 2023 is recorded at 26.2\u0026deg;C, marking an increase of 0.73\u0026deg;C compared to the average levels from 1901 to 1910. Precipitation variability also began to increase after the 1960s relative to levels from 1901 to 1910, with significant fluctuations from one year to the next (World Bank, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The combined effects of climate change can potentially reduce Indonesia\u0026rsquo;s GDP by 4 percent by mid-century and up to 39.5 percent if warming reaches 3.2\u0026deg;C. Among forty-eight countries in Asia, Indonesia is ranked as one of the most vulnerable as it is exposed to all physical risks of climate change, ranging from sea level rise where it is estimated that by the period of 2070\u0026ndash;2100 around 4.2\u0026nbsp;million people living in coastal areas are exposed to permanent flooding, heat stresses, and extreme hydrometeorological weather conditions that could affect agricultural yields, labor productivity, and tourism favorability (Bappenas, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; World Bank, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eClimate change has the potential to exacerbate the problem of income inequality. Studies show that rising average temperatures and higher-than-normal precipitation are positively associated with income inequality, particularly in poorer countries and those located in equatorial regions (Diffenbaugh and Burke, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Emmerling et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The link between climate change and income inequality is believed to stem from the uneven impacts of climate-related disasters across income groups. Low-income populations tend to bear the greatest burden of climate impacts due to their low resilience and high vulnerability to such disasters. They are also more likely to work in sectors that are more exposed to environmental shocks, such as agriculture, forestry, or livestock rearing. Increased temperatures can decrease total productivity in these sectors through lower farm yields, reduced labor productivity, and a higher likelihood of failed harvests. Consequently, those working in agriculture, primarily low-income groups, face rising costs and shrinking incomes due to these impacts and typically possess few assets that could buffer against income losses from climate events. Their limited social capital further restricts their access to social protection systems that could serve as safety nets. Many of their assets are also physical and non-liquid, making them more vulnerable compared to higher-income groups whose assets are often more liquid and non-physical. Such combination increases income inequality as low-income groups lose their assets and income-generating abilities more than higher-income groups as a result of climate change impacts (Huynh and Phan, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Alam et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Hallegatte and Rozen, 2017; Hallegatte et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIssues could then arise as this potential connection between climate change and income inequality can reverse gains made in reducing income inequality and exacerbate the already complex socio-economic impacts of climate change. Heightened income inequality could further reduce low-income groups' ability to weather the effects of climate change, creating a negative feedback loop. This is a problem because higher income inequality is associated with greater ecological footprint, lower economic growth, higher food insecurity, and adverse effects on social cohesion (Herzer \u0026amp; Vollmer, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Jetten et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Onatunji, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Zeb et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Therefore, it is crucial to examine the potential effects of climate change on income inequality levels, particularly in developing countries located in warmer latitudes, as most existing studies rely on country-level data and are conducted at a global scale. This study aims to fill that gap by analyzing the relationship between climate change and income inequality levels across Indonesian provinces. This study would then provide insights into the relationship between climate change and income inequality in developing countries of tropical regions. Such findings can help policymakers prioritize policies that can mitigate climate change while addressing the pre-existing problem of income inequality.\u003c/p\u003e \u003cp\u003eThe study is structured as follows. Section 2 examines the existing literature concerning the relationship between rising temperatures and precipitation in relation to income inequality and explores the channels through which climate change may influence income inequality. Section 3 presents the data and methodology employed in this research. Section 4 provides and discusses the results of the empirical analysis. Section 5 concludes the paper by summarizing the findings, outlining the policy implications, and highlighting the limitations of this study, as well as providing recommendations for future studies.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cp\u003eLiterature on the effects of climate change on inequality can be distilled into two main strands. One strand of study identifies a parabolic relationship between climate change and inequality, where an optimal point exists in either temperature or precipitation. In this optimal point, economic benefits are maximized, climate-related impacts are minimal, and climate change has a negligible effect on inequality. The other strand presents a linear relationship between climate change and inequality. In this line of theory, there is no optimal point; as temperature increases or precipitation becomes more volatile, climate-related impacts intensify, leading to greater economic damage and greater inequality.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Relationship between Climate Change and Inequality\u003c/h2\u003e \u003cp\u003eStudies that found a parabolic relationship between climate change and inequality has identified an optimal temperature range of 10\u0026ndash;18\u0026deg;C, where climate-related impacts are minimized or even positive for countries with average annual temperatures below this range and decile income growth is maximized. In this scenario, colder and wealthier countries are expected to see a 20 to 40 percent increase in per capita GDP, whereas warmer equatorial countries may face a 20 to 30 percent decline in their per capita GDP. Consequently, the GDP gap between rich and poor countries is projected to increase by 25 percent compared to a scenario without climate change and has the possibility to continue widening, eliminating the prospect of economic convergence between low- and high-income countries (Diffenbaugh and Burke, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Taconet et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Countries in lower, and therefore warmer, latitudes, under a 3.6\u0026deg;C increase in average temperatures, are projected to increase Gini indices by up to 6 percentage points, with the poorest deciles affected the most (Gilli et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). It is also found that for precipitation, a one standard deviation (SD) increase is projected to maximize the bottom 50 percent income share, especially in countries with high agricultural intensity. When precipitation increases by more than 1.5 SD, the income share of the bottom 50 percent would start to decrease (Palagi et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe strand of studies that found a linear relationship between climate change and inequality is mainly focused on inequality within countries. Most agree that higher temperatures are associated with worse inequality levels (M\u0026eacute;jean et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). It is found that a 1 percent increase in temperature is associated with a 0.5 percentage point increase in the Gini index. Additionally, extreme climate variations in a given year are associated with a 0.1\u0026ndash;0.2 percentage-point increase in the Gini index (Paglialunga et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Studies with longer timeframes found that a 1-degree Celsius increase in average temperature over 50 years is associated with a 4.2 to 4.8-point increase in the Gini index, while a 1-degree Celsius increase over 5 years is associated with a 1.3-point increase in the Gini index. In a scenario where average temperatures rise over 2.5 to 3.1\u0026deg;C by 2100, the average Gini indices of countries will, on average, increase by about 1.4 points or by as much as 2.5 points in already warmer regions (Castells-Quintana and McDermott, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Emmerling et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In the case of Asian countries, an additional thousand deaths due to climate-related disasters is associated with a 0.17 to 0.37 increase in the Gini index (Huynh and Phan, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Pathway of Climate Change on Affecting Income Inequality\u003c/h2\u003e \u003cp\u003eCurrent literature indicates that the effects of climate change are unequally distributed across income groups; lower-income groups bear the brunt of climate change impacts due to their higher vulnerability. They are mostly dependent on labor for their income, and many are employed in the primary sector, which is more affected by climate change. Higher income groups, on the other hand, are more dependent on capital that is less affected by climate change, for the time being (Chancel et al., 2024; M\u0026eacute;jean et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Low-income groups are also disproportionately affected because their assets and livelihoods are more vulnerable to climate impacts, leading to greater asset and productivity losses compared to other income groups as some studies have noted the positive association between climate vulnerability and multidimensional poverty (IPCC, 2015; Ahmad et al., 2024). In Nigeria, the poorest 20 percent are 50% more likely to be affected by floods, 130% more likely to be affected by droughts, and 80% more likely to be affected by heatwaves. This can result in a negative feedback loop, where losses in assets and productivity reduction lower their ability to cope with future disasters, resulting in even greater losses. Greater exposure to climate impacts also hampers human capital accumulation, as asset losses hinder the ability to attain education or healthcare services. Low-income households living in climate-prone regions are found to have their enrollment rates reduced by 20 percent, and children starting school are delayed on average by 3.7 months. Combined, this would result in lower income-generating capabilities for low-income groups (Hallegatte et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Hallegatte and Rozen, 2017), and it has been found that the incomes of lower-income quintiles are more sensitive to temperature changes than higher-income quintiles, with the sensitivity of income towards temperature changes decreasing as the income distribution moves toward higher-income quintiles (Gilli et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Climate change then presents more barriers to wealth for low-income groups, as they have greater vulnerability and lower resilience towards climate change relative to higher-income groups. Figure\u0026nbsp;1 below summarizes the pathways or mechanisms by which climate change can affect income inequality levels.\u003c/p\u003e \u003cp\u003e[Figure 1. \u003cem\u003eClimate Change Pathway towards Income Inequality\u003c/em\u003e]\u003c/p\u003e \u003cp\u003eThe agricultural sector is also one of the pathways through which climate change can affect income inequality levels. Higher temperatures can result in lower crop yields and quality; it also lowers farmers\u0026rsquo; productivity through a higher incidence of heat-related fatigue. Regions most affected by climate change are also the regions where agricultural productivity is reduced the most (Amare and Balana, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Dasgupta et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; M\u0026eacute;jean et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). A one percent increase in temperature is projected to reduce current paddy yields by 3.44 percent, while a one percent increase in precipitation is projected to reduce yields by 0.12 percent (Alam et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Huynh and Phan, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Higher average temperatures are also associated with lower growth rates, lower gross output, and lower value added in the agricultural sector. A 1\u0026deg;C increase in average temperatures is associated with a 0.025 to 0.032 percent decrease in the growth rate of the primary sector, and a 0.88 percent decrease in value added in the agricultural sector (Li et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Castells-Quintana and McDermott, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). A higher share of employment in the agricultural sector is also associated with a higher positive association between average temperatures and the Gini index. A 1\u0026deg;C increase in average temperatures is associated with an increase of 0.664 percentage points in the Gini indices of counties with medium rural populations, while counties with high rural populations are associated with an increase of 2.1 percentage points. A 1.5 increase in the standard deviation of average precipitation is also associated with a 35 times stronger impact for the bottom 50 percent of income shares in countries with a high agricultural employment share relative to countries with a low agricultural employment share (Palagi et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Paglialunga et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These compounding effects lead to lower incomes for individuals working in the agricultural sector, many of whom are low-income and/or rural individuals.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Data and Methodology","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Data\u003c/h2\u003e \u003cp\u003eThe empirical analysis in this paper covers all 34 Indonesian provinces from 2009 to 2024, using a 16-year panel dataset. This data combines climate variables from various weather stations across Indonesia with income inequality indicators derived from national workforce surveys, resulting in 544 observations. The dependent variables are four measures of income inequality. The first is the annual Gini index for each province, provided by the Indonesian Statistics Agency (Badan Statistik Nasional), which ranges from 0 to 100, where 100 indicates perfect inequality and 0 signifies perfect equality in income distribution. The second is the annual Theil index, which takes a value of 0 if there is perfect income equality and ln N if there is perfect inequality. The third measure is the 90:10 income ratio that compares the income of the top 10 percent to the bottom 10 percent. The fourth measure is the decile income share, showing the percentage of total income held by each decile, from the bottom 10 percent to the top 10 percent. Different income inequality measures are used to cover for the deficiencies of each measure, as the Gini index is more sensitive to changes in the middle part of the income distribution, while the Theil index is more sensitive to changes at the top of the income distribution. The 90:10 income ratio sees extreme differences, and income deciles see the in-depth picture (De Maio, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). National Labor Force Surveys (Survei Angkatan Kerja Nasional) from 2009 to 2024 are used to calculate all income inequality indicators except for the Gini index. Individual income data from the survey is compiled and grouped by province and year and all indicators calculated from the survey are based on unweighted individual data or samples from each province rather than weighted data. These indicators allow us to observe how income distribution shifts within each province. Climate change-related variables are captured through the annual averages of daily temperatures and monthly precipitation, both of which are significantly affected by climate change. Using these variables enables us to assess the impact of climate change, as changes in these parameters result from exogenous processes within the meteorological cycle that are currently influenced by climate change (Hansen et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Ingram, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Data for these variables are obtained from the Meteorology, Climatology, and Geophysics Agency (Badan Meteorologi, Klimatologi, dan Geofisika or BMKG). Daily average temperatures in degrees Celsius are collected from weather stations within each province and average annually. Monthly precipitation data, measured in millimeters per day, are also collected from weather stations across provinces and averaged each year using monthly records. Controlling for independent variables involves accounting for the determinants of inequality, enabling us to consider other factors that influence inequality in each province. The socioeconomic determinants include GDP per capita and the squared GDP per capita for each province. GDP per capita acts as an indicator of economic growth, one of the determinants of inequality. The squared GDP per capita helps capture the potential presence of a Kuznets inverted-U curve within our provinces. Additionally, the unemployment rate and the shares of employment in agriculture, manufacturing, and services are included because these variables are believed to influence inequality levels (Schaffner, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Todaro and Smith, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Cevik and Jalles, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Paglialunga et al., 2024). These variables are collected annually from the Indonesian Statistics Agency.\u003c/p\u003e \u003cp\u003e[Table\u0026nbsp;1. Variables used with measurements and data types]\u003c/p\u003e \u003cp\u003eTechnological determinants are controlled by the percentage of homes using electricity and the proportion of households that have used the internet in the past three months in each province. The institutional determinant is represented by the average years of schooling in each province. These data are also sourced from the Indonesian Statistics Agency. The share of homes using electricity and the internet indicates the proportion of households with access to these services, with higher ratios reflecting greater penetration of the internet and electricity usage. The average years of schooling reflect the educational attainment of individuals in the province, with a higher mean indicating a more educated population. The summary of all variables used are shown in Table\u0026nbsp;1 above.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Empirical Model and Variables\u003c/h2\u003e \u003cp\u003eThis paper will explore the relationship between climate change and inequality while controlling for socioeconomic, technological, and institutional determinants of inequality in a panel setting. To measure the effect of climate change on income inequality, a panel regression model with fixed effects will be used. A linear model is chosen because average temperatures in Indonesia are already higher than the optimal temperatures highlighted by previous studies. Another reason is that the independent variables of climate change, the average annual temperatures and monthly precipitation, are exogenous, and fixed effects are used to control time-invariant regional and yearly effects (Wooldridge, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). This model will then allow us to observe how income distribution is impacted by provincial variations in average temperature and precipitation after controlling provincial-level determinants of income inequality. Two main regression models are used: a main baseline model and an agricultural interaction term model. For the baseline model, the following regression model is estimated:\u003c/p\u003e \u003cp\u003eInequality\u003csub\u003ept\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;β\u003csub\u003e0\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β\u003csub\u003e1\u003c/sub\u003eClimate\u003csub\u003ept\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β\u003csub\u003ek\u003c/sub\u003eX\u003csub\u003ek,pt\u003c/sub\u003e + r\u003csub\u003ep\u003c/sub\u003e + y\u003csub\u003et\u003c/sub\u003e + \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{ϵ}_{vt}\\)\u003c/span\u003e\u003c/span\u003e (1)\u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Inequalit{y}_{pt}\\)\u003c/span\u003e\u003c/span\u003e are the income inequality measures such as the Gini index, the Theil index, 90:10 income ratio, and the decile income shares of province \u003cem\u003ep\u003c/em\u003e at year \u003cem\u003et\u003c/em\u003e; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Climat{e}_{pt}\\)\u003c/span\u003e\u003c/span\u003e represents the measures of climate change, annual average temperatures and monthly precipitation, of province \u003cem\u003ep\u003c/em\u003e at time \u003cem\u003et\u003c/em\u003e; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{X}_{k,pt}\\)\u003c/span\u003e\u003c/span\u003e are a set of socio-economic, technological, and institutional control variables that consists of the provincial GDP per capita, the squared GDP per capita, unemployment rate, the shares of employment in agriculture, manufacturing, and services, electrification ratio, internet penetration, and the average years of schooling of the population in each province \u003cem\u003ep\u003c/em\u003e at time \u003cem\u003et\u003c/em\u003e; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{r}_{p}\\)\u003c/span\u003e\u003c/span\u003e represents the regional-fixed effects in the form of a dummy variable for each province; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{y}_{t}\\)\u003c/span\u003e\u003c/span\u003e represents the year-fixed effects in the form of a dummy variable for the year 2009 to 2024; and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{ϵ}_{vt}\\)\u003c/span\u003e\u003c/span\u003e is the error term. The estimate of interest is \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{1}\\)\u003c/span\u003e\u003c/span\u003e as it highlights the potential association between climate change variables and income inequality.\u003c/p\u003e \u003cp\u003eTo examine the potential relationship between climate change, the agricultural sector, and income inequality, Eq.\u0026nbsp;2 uses interaction terms between climate variables and the employment share in the agricultural sector in each province. Interaction terms enable us to examine the partial effect of an independent variable on a dependent variable, in this case, the climate variables, relative to another explanatory variable, namely the agricultural share in employment (Wooldridge, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Based on this, the following model would be estimated:\u003c/p\u003e \u003cp\u003eInequality\u003csub\u003ept\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;β\u003csub\u003e0\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β\u003csub\u003e1\u003c/sub\u003eClimate\u003csub\u003ept\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β\u003csub\u003e2\u003c/sub\u003eClimate\u003csub\u003ept\u003c/sub\u003e \u0026times; Agri\u003csub\u003ept\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β\u003csub\u003ek\u003c/sub\u003eX\u003csub\u003ek,pt\u003c/sub\u003e + r\u003csub\u003ep\u003c/sub\u003e + y\u003csub\u003et\u003c/sub\u003e + \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{ϵ}_{vt}\\)\u003c/span\u003e\u003c/span\u003e (2)\u003c/p\u003e \u003cp\u003ewhere every variable is the same as Eq.\u0026nbsp;1 and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Agr{i}_{pt}\\)\u003c/span\u003e\u003c/span\u003e represents the percentage share of employment in the agricultural sector for province \u003cem\u003ep\u003c/em\u003e at time \u003cem\u003et.\u003c/em\u003e The estimate of interest is \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{2}\\)\u003c/span\u003e\u003c/span\u003e as it is the estimate of the interaction terms between the climate variables and the share of employment in the agricultural sector.\u003c/p\u003e \u003cp\u003eTo address the issue of serial correlation and heteroskedasticity that may be present in the panel dataset, all regression models are estimated using clustered standard errors, as this approach can account for both heteroskedasticity and spatial correlation (Wooldridge, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). As past temperatures and precipitation may influence current income inequality levels, the study used a lagged model observe the effect of past temperatures and precipitation on income inequality in each province. Robustness checks are also performed by estimating some of the models with random effects and log models to see if the results are robust across different regression models. The results of the lagged model, robustness checks, and full regression tables of selected regression models are shown in the Supplementary Information (SI).\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Results and Discussion","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Baseline Model\u003c/h2\u003e \u003cp\u003eResults from the baseline model, as seen in Table\u0026nbsp;2, found a positive and statistically significant association between rising average temperatures and the Gini index of Indonesian provinces. A lower magnitude of increase is also seen from the coefficient of the Theil index and the 90:10 ratio, but both are statistically insignificant. In economic terms, the magnitude of change is only 2.3 percent of the average Gini index of 35.48. However, comparing it to the movement of the national Gini index, which have declined by 3.96 points in 2024 from its 16-year maximum point in 2012, this increase of 0.801 points in the Gini index represents a 20 percent reduction in the gains made on reducing income inequality (BPS, 2025). Exceeding the 1.5 degrees Celsius limit set by the Paris Agreement would potentially increase the Gini index by 1.2 points in Indonesia, reversing 30 percent of the gains made from 2012. For precipitation, it is observed that there is a negative and statistically significant association between rising monthly precipitation and the Gini index of Indonesian provinces. Similar to temperature, the coefficients for the Theil index and the 90:10 income ratio are statistically insignificant. The Gini index coefficient is small enough that it can be considered that the effect is minimal; a rise of at least 100 millimetres in monthly precipitation will result in a noticeable decrease in the Gini index. Economically, a 100-millimeter increase in precipitation would potentially lower the Gini index by 0.46 points, adding 11.6 percent to the gains made since 2012. In the case of income deciles, as seen in Table\u0026nbsp;3, rising temperature has no effect except for the 4th decile where a 1-degree increase in average annual temperature is associated with a 0.12 percentage point lower income share for that decile. The mean income share of the 4th decile is 5.37 percent of total income, and so this equates to a 2.2 percent reduction in the income share of the 4th decile. For precipitation, rising monthly precipitation has no effect except for the 9th decile where a 10-millimeter increase in monthly precipitation is associated with a 0.0125 percentage point increase in their income share. This is a minuscule increase compared to the mean income share of the 9th decile, around 0.075 percent.\u003c/p\u003e \u003cp\u003eThese results suggest that the impacts of rising temperatures and precipitation on income inequality are not straightforward. From the three income inequality indicators, there is evidence that rising average temperatures have a positive association with the Gini index of Indonesian provinces while there is no discernible association with the other inequality indicators. This suggests that in Indonesian provinces, temperature increases affect the income distribution of the bottom and middle decile more than the upper decile, as the Gini index is more sensitive towards changes in the middle part of the income distribution, whereas the Theil index is more sensitive towards changes in the top part of the income distribution (De Maio, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Furthermore, the estimates for the Gini index are higher than estimates from studies that use Asian countries, but lower than those from studies that use a global dataset. It is also different from studies that found positive and statistically significant associations between temperature and the Theil index (Paglialunga et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Castells-Quintana and McDermott, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Gilli et al, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; SenGupta and Atal, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Huynh and Hoang, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). These results suggest that Indonesia's socioeconomic structure has greater vulnerabilities and lower resilience to climate change, especially for lower- and middle-income deciles, relative to other Asian countries; however, it is still comparatively better than several global regions, such as Sub-Saharan Africa (Cevik and Jalles, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The negative association between the income share of the 4th decile and rising temperatures also supports the suggestion that climate change has unequal impacts, where bottom income deciles are disproportionately affected relative to upper income deciles. This study also finds that there is an inverse association between average monthly precipitation and the Gini index, where higher precipitation would lead to a miniscule decrease in the Gini index of provinces. This could indicate that for Indonesian provinces, short-term increases in precipitation could enhance agricultural productivity and generate additional income for low-income deciles who predominantly work in the agricultural sector. This is supported by studies that found that in specific cases, short-term small increases in precipitation could increase net crop income (Coromaldi, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Otrachshenko and Popova, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Moreover, the baseline decile model shows that precipitation is positively associated with the income share of the 9th decile; suggesting that top income deciles also benefit from increases in monthly precipitation.\u003c/p\u003e \u003cp\u003e[Table\u0026nbsp;2. Effect of temperature and precipitation on income inequality indicators]\u003c/p\u003e \u003cp\u003e[Table\u0026nbsp;3. Effect of temperature and precipitation on income decile shares]\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Agricultural Pathway Model\u003c/h2\u003e \u003cp\u003eThe results from the agricultural interaction model found that there is a positive and statistically significant association between rising temperatures and precipitation, share of employment in agriculture, and the Gini index of Indonesian provinces. In the case of temperature, shown in Table\u0026nbsp;4, for every 1-degree Celsius increase in average temperatures, an extra 1 percentage point share of employment in the agricultural sector would result in an additional 0.0294-point increase in the Gini index. With the average share of employment in agriculture for Indonesian provinces at 37.27 percent, this would mean that, on average, a 1-degree Celsius increase in temperature can lead to an additional a 1.1-point increase in the Gini index of Indonesian provinces. It is also the case for precipitation, shown in Table\u0026nbsp;5, where a 10-millimetre increase in average monthly precipitation, an extra 1 percentage point share of employment in agriculture would result in an additional 0.00243-point increase in the Gini index. But this is a small increase and taking into account the average share of employment in agriculture as mentioned above, a 10-milimetre increase in the average province can lead to an additional 0.09-point increase in the Gini index. For income deciles, the results are surprising. As seen in Table\u0026nbsp;6, there is a positive association between the temperature-agriculture interaction term and the income shares of the lower- and middle-income deciles. Conversely, there is a negative association between the same temperature-agriculture interaction term and the income shares of upper income deciles. For the 1st, 2nd, 3rd, 4th, and 5th income deciles, there is a cushioning effect where a higher provincial agricultural employment share reduces the adverse association of rising temperatures on their income shares. The reverse is happening for the 8th income decile; a higher agricultural share reduces the positive association between increasing temperatures and their income shares. It must also be noted that in Table\u0026nbsp;6, rising temperatures has a negative and statistically significant association with the income shares of the lower- and middle-deciles, and a positive association with the 8th decile. This further supports the results we see from Eq.\u0026nbsp;1 that rising temperatures is rising income inequality levels in Indonesian provinces. In the case of precipitation, there is no association as all the estimates are statistically insignificant.\u003c/p\u003e \u003cp\u003eThe results partially support that there is a positive association between climate change, the agricultural sector, and income inequality. Evidence suggests that in Indonesian provinces, the agricultural pathway plays a puzzling role. On one hand, there is evidence that the agricultural pathway is present in Indonesian provinces, as seen in Tables\u0026nbsp;4 and 4.4. On the other hand, there is also evidence that it can soften the effects of rising temperatures on the income shares of lower- and middle-income deciles, and strengthening that effect for higher-income deciles. There are several possible explanations, the first is that this may suggest that there is a differential effect of rising temperatures in urban versus rural areas. The impact of rising temperatures on urban low-income groups is larger than the impact on rural low-income groups, as provinces with low agricultural employment share are usually more urbanized, and vice versa. The interaction term picks up this phenomenon, resulting in the dual effect mentioned above. Studies have shown that heat stress from climate change is higher in urban areas because of the urban heat island phenomenon, and it, combined with the specific vulnerabilities of low-income urban groups, such as the lack of access to necessary infrastructure and services, causes them to face and suffer higher risks from warming, with some studies showing the higher mortality rates of low-income urban groups because of climate-related impacts (Fischer et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Sera et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Grasham et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Another possible reason is that climate shocks can reduce agricultural productivity, lowering agricultural yield, resulting in higher food prices from climate-related agricultural shocks that are beneficial for low-income rural households, while being detrimental for upper-income rural households (Wilts et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This can explain the dual effect, as most low-income groups work in the agricultural sector, while upper-income groups are mostly employed in non-agricultural sectors. In spite of the mixed results of the interaction term, the estimates confirms that there is a positive association between average temperatures and income inequality indicators, as seen with the estimates of low-income deciles that are negative and upper-income deciles that are mostly positive, and both are statistically significant. The estimates then corroborate the notion that disadvantaged groups, in this case low-income deciles, have socioeconomic characteristics that make them more vulnerable and less resilient towards climate-related impacts. Low-income deciles suffer disproportionately from climate change impacts as they are more exposed to climate change impacts, more susceptible to climate-related damages, and have a lower ability to recover from such events (Islam and Winkel, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Hallegatte and Rozen, 2017; Hallegatte et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The positive coefficient for the upper-income decile may suggests that upper-income groups have higher adaptation capabilities, as they have more capital to adapt, insulating their productivity and sources of income from climate-related impacts. Upper-income groups also have diversified income sources and capital to invest in climate projects that can further augment their incomes (Chancel et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Davis et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e[Table\u0026nbsp;4. Interactional effect of temperature and agricultural share on income inequality indicators]\u003c/p\u003e \u003cp\u003e[Table\u0026nbsp;5. Interactional effect of precipitation and agricultural share on income inequality indicators]\u003c/p\u003e \u003cp\u003e[Table\u0026nbsp;6. Interactional effect of temperature and agricultural share on income decile shares]\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eClimate change is one of the most pressing issues of our time, with its impact far-reaching and multidimensional. The effects of climate change on economic growth have received significant attention in the literature, and there is consensus that climate change, through rising temperatures and precipitation anomalies, can incur substantial economic damages that are felt globally. However, climate change has the ability to affect our socio-economic systems in many ways beyond economic losses, and the discussion around its impact on other socio-economic aspects, such as income inequality, is still new and limited. There are gaps in the literature, especially in economically developing tropical countries, as most studies focus on the global and broad-based impacts of climate change on income inequality. This paper explores the connection between climate change, through its indicators of rising average temperatures and monthly precipitation, on income inequality, through the Gini index, the Theil index, the 90:10 income ratio, and decile income shares. To investigate the potential connection, a dataset was constructed combining average temperature and precipitation data from weather stations across Indonesian provinces with various income inequality measures for each province, resulting in a dataset of 34 Indonesian provinces spanning a 16-year timeframe from 2009 to 2024. The study then uses panel data econometric techniques, exploiting the exogenous characteristics of the climate indicators, and also fixed effects to control for time and regional-invariant effects.\u003c/p\u003e \u003cp\u003eThe findings of this study suggest three main conclusions. First, the study confirms that rising average temperatures and increased precipitation have a considerable positive association with the Gini indices of Indonesian provinces. Furthermore, the magnitude of the effect is stronger than in other Asian countries and has the possibility of reversing 30 percent of the gains made in reducing the provinces\u0026rsquo; income inequality levels since 2012. Second, rising temperatures is the main driver in the change in income inequality levels as the potential effects is far greater than rising precipitation. Third, evidence shows that the agricultural pathway is present but it may play a more complex role due to the diverse socioeconomic characteristics of Indonesian provinces. Estimates suggest that for Indonesian provinces, agricultural employment can potentially cushion the adverse impact of climate change on the income shares of low-income deciles, while being strengthening the impacts for the income shares of the upper-income deciles.\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e5.1. Policy Implication\u003c/h2\u003e \u003cp\u003eUltimately, these potential impacts highlight the importance of policies that addresses the climate crisis and also policies tailored towards supporting low-income communities adapt to the already existing impacts of climate change. Economically developing tropical countries and Indonesian in particular, would then benefit from several policies outlined. First, reducing greenhouse gas emissions by increasing emission reduction targets stated in Indonesia\u0026rsquo;s Enhanced Nationally Determined Contribution 2022 and recommitting to plans stated in LTS-LCCR 2050, so large parts of the economy can transition from fossil fuels and decarbonize to limit the rise in global temperatures and precipitation (IPCC, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Second, instituting a mixture of policies tailored towards increasing the climate resilience of low-income rural groups such as a combination of disbursing climate-resilient seeds, instituting agricultural insurance schemes by expanding and bolstering Asuransi Usaha Tani Padi to areas prone to climate disasters, and pushing small-scale mechanization that require little capital to maintain but can reverse productivity losses from climate-related impacts such as expanding the Agricultural Ministry\u0026rsquo;s Bantuan Alsintan to climate-prone areas (Milhorance, 2022; IPCC, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Third, urban-specific policy mixture that increase the overall climate resilience of urban areas and also the resilience of low-income individuals that live in it. This can be done by upgrading urban infrastructure systems, expanding social safety nets, conditional cash transfers, and increasing access to essential services (Tyler and Moench, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Zakir and Ashiq, 2018). In the case of Indonesia, these can be in the form of expanding the cash transfer program, Program Keluarga Harapan towards low-income urban households and ensuring universal healthcare access through Jaminan Kesehatan Nasional.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e5.2. Limitations and Recommendations\u003c/h2\u003e \u003cp\u003eOverall, study has its limitations and remains an exploratory analysis into climate change and income inequality. Using average temperature and precipitation overlooks the impact of extreme weather anomalies or disasters, which are increasing in frequency due to climate change. Data collected from weather stations and national surveys are also susceptible to measurement errors. There is also a possibility of omitted variable biases, as the determinants of income inequality are many, and some are not included in this study because of constraints in data availability. Several results also show unique pathways and interactions that are still little discussed in the literature. Future studies may benefit from; (1) employing different climate indicators that measure extreme weather events, (2) collecting climate and income inequality data from other sources with longer timeframes and of better quality, and (3) analyzing deeper into the interplay between agricultural employment, regional socioeconomic factors, climate change, and income inequality in Indonesia.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting Interests\u003c/h2\u003e \u003cp\u003eThe authors also have no competing interests to declare that are relevant to the content of this article.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eEthics Approval and Consent to Participate\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for Publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThe authors did not receive support or funding from any organization for the submitted work.\u003c/p\u003e\u003ch2\u003eAuthor Contributions\u003c/h2\u003e \u003cp\u003eThe study conception and design were done by Aulia Davetta Athif with guidance from Wisnu Setiadi Nugroho. Material preparation, data collection and analysis was performed by Aulia Davetta Athif. The first draft of the manuscript was written by Aulia Davetta Athif and all authors commented and edited previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAvailability of Data and Materials\u003c/h2\u003e \u003cp\u003eThe data that support the findings of this study are not publicly available due to the usage of the Indonesian National Labor Force Survey (SAKERNAS) that are prohibited to be redistributed toward third parties. Researchers may request access directly from BPS via the survey data request portal at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://silastik.bps.go.id/\u003c/span\u003e\u003cspan address=\"https://silastik.bps.go.id/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Access may be subjected to data access policies and approval and/or associated fees.\u003c/p\u003e\u003ch2\u003eData Availability Statement\u003c/h2\u003e \u003cp\u003eThe data that support the findings of this study are not publicly available due to the usage of the Indonesian National Labor Force Survey (SAKERNAS) that are prohibited to be redistributed toward third parties. Researchers may request access directly from BPS via the survey data request portal at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://silastik.bps.go.id/\u003c/span\u003e\u003cspan address=\"https://silastik.bps.go.id/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Access may be subjected to data access policies and approval and/or associated fees.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAhmad, D., Khurshid, S., \u0026amp; Afzal, M. (2023). 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Socioeconomic determinants of ecological footprints: Bridging the gap between developed and developing nations. \u003cem\u003eEnvironment, Development and Sustainability\u003c/em\u003e. https://doi.org/10.1007/s10668-025-06081-y\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 6 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"climatic-change","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"clim","sideBox":"Learn more about [Climatic Change](https://www.springer.com/journal/10584)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/clim/default.aspx","title":"Climatic Change","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Climate change, Climate resilience, Climate vulnerability, Income inequality","lastPublishedDoi":"10.21203/rs.3.rs-8646521/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8646521/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eClimate change is a global issue affecting nearly every aspect of modern society. In Indonesia, the impact of climate change is evident in rising average temperatures and greater precipitation variability. Although much attention has been given to its effects on economic growth, research on its impact on other socio-economic factors, such as income inequality, remains limited. Using a dataset of 34 Indonesian provinces spanning 16 years from 2009 to 2024 and employing panel data econometric techniques, this paper investigates the association between climate change and income inequality across these provinces. The findings indicate that rising average temperatures and increased precipitation are positively associated with the Gini indices of Indonesian provinces. Additionally, the results show that the role of agriculture is not straightforward in Indonesian provinces. Evidence suggests that agriculture is one of the pathways through which climate change can affect income inequality levels in Indonesian provinces. However, specific regional and socioeconomic characteristics also suggest that the agricultural pathway can potentially cushion the impact of climate change for low-income deciles, while being detrimental for upper-income deciles. Ultimately, these potential impacts highlight the importance of policies that address the climate crisis and also adaptation policies tailored to support and shield low-income communities from the adverse impacts of climate change.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJEL Codes: \u003c/strong\u003eQ54, Q56, O15.\u003c/p\u003e","manuscriptTitle":"Climate Change and Income Inequality: Panel Evidence from 34 Indonesian Provinces, 2009–2024","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-05 08:31:22","doi":"10.21203/rs.3.rs-8646521/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2026-04-26T18:05:11+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-24T10:08:23+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-23T09:04:22+00:00","index":"","fulltext":""},{"type":"submitted","content":"Climatic Change","date":"2026-01-22T05:59:32+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"climatic-change","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"clim","sideBox":"Learn more about [Climatic Change](https://www.springer.com/journal/10584)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/clim/default.aspx","title":"Climatic Change","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"e9f69ef4-bfbc-4a5f-981b-e21530f86fea","owner":[],"postedDate":"May 5th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-05T08:31:23+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-05 08:31:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8646521","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8646521","identity":"rs-8646521","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}