Working Paper - CDE Working paper No. 345, Slides

Abstract:-This study examines how rising temperatures affect India’s long-term economic growth, both at aggregate and disaggregated levels across regions, sectors, and seasons. Existing research has predominantly relied on pooled estimation with panel data that inadequately address heterogeneity, temperature dynamics, and underlying transmission mechanisms, particularly at disaggregated levels. A cross-sectionally augmented auto-regressive distributed lag model (CS-ARDL) is estimated using panel data that capture state-specific weather-output relationships, long-term impacts, temperature persistence, intra-annual variability, unobserved heterogeneity, and cross-regional spillovers. The findings reveal that, on average, a 1 °C annual temperature variation reduces economic growth by 3.89%, nearly twice the previous estimate. Second, results suggest that annual temperature variation diminishes growth by damaging productivity through reduced ecosystem services, labour, and capital efficiency. Third, state capacity plays a moderating role in shaping regional vulnerability to temperature shocks. Fourth, impacts are heterogeneous, varying by season (greater in winter), region (higher in southern and hotter regions), and across sectors.

Abstract:-This paper examines the understudied relationship between anthropogenic global warming and wealth inequality, two defining challenges of the 21st century, by focusing on the impact of temperature on subnational wealth inequality across 1,000 regions worldwide, using data from the Global Data Lab spanning the period from 1992 to 2021. Building on earlier climate-economy studies, this paper estimates heterogeneous parameter models under a common factor framework, which addresses econometric challenges of heterogeneous slopes, cross-sectional spillover, unobserved common factors, and explicitly allows the temperature effect on wealth to differ across sub-national regions. Our preferred specification estimates provide suggestive evidence that a 1°C rise in temperature is associated with a modest increase in wealth inequality, measured by Gini coefficients, approximately 0.54 units. The effect of precipitation on wealth inequality remains unclear. Second, the results suggest that poorer and hotter regions, predominantly located in the Global South, are adversely affected by temperature-induced wealth inequality. Third, we empirically identify two key plausible channels among others through which temperature worsens wealth inequality: (i) health and education-induced reduction in labour productivity, (ii) worsening gender equality. Our findings are consistently robust across alternative specifications, datasets, and estimation strategies. The evidence suggests that climate change will significantly shape the trajectory of future global inequality and poses serious challenges for sustainable development under business-as-usual emission scenarios.

This study, based on panel fixed effects models applied to subnational-level panel data from 1,500 regions across 130 countries, reveals that global warming has worsened gender equality. First, the impact of global warming on gender equality is predominantly negative, with considerable heterogeneity across income groups and climatic regions. Second, these adverse effects are driven by mechanisms such as relative productivity loss, primarily caused by health and education-related skill disruptions, which disproportionately impact females. Third, poorer areas, regions with extreme climates, lower income quintiles, and areas within the global south experience more severe impacts of rising temperatures on gender equality. Finally, gender norms, wealth, and state capacity moderate the effects, highlighting the importance of gender-sensitive climate policies in advancing the United Nations’ Sustainable Development Goals (SDGs).

Abstract : This chapter reviews empirical methodologies studying the impact of global warming on economic indicators in India. We begin with an analysis of global climate dynamics, which includes definitional debate of level and growth effects, using climate econometrics to highlight the methodological issue in temperature-GDP econometrics, and summarising methodological discussion on differentiation between weather and climate change. The chapter then evaluates India’s vulnerability to climate shocks, detailing macroeconomic impacts on critical sectors such as agriculture, health, and productivity, and reviews the evolution of India’s environmental policies. We further review the role of fiscal, regulatory, and monetary policies in facilitating climate adaptation and mitigation. Additionally, we explore diverse econometric approaches for estimating climate change impacts, including cross-sections and panel methods. This review highlights the urgency of coordinated international action and the need for India to implement robust climate policies to ensure sustainable economic growth and fulfil its emission reduction targets.
JEL Code: Q54, Q50, Q58

Keywords: Temperature, India, Climate Change, Adaptation, Mitigation

Published Version. Working Paper Version - CDE Working paper No. 342 , Slides  SAU , Seminar Video(ISID)

Mentions- UN REPORT, PRODUCTIVITY INSTITUTE REPORT

Abstract :-Emerging economies (EMEs) often ignore effective mitigation strategies for climate risks to prioritise growth acceleration. This paper shows that EMEs cannot sustain their economic growth trajectory due to the adverse impact of climate change on total factor productivity (TFP). Using a standard growth model, it demonstrates how temperature rise and variation from growing industrial emissions reduce capital productivity along with the damage to ecosystem services and labour productivity, adversely impacting total factor productivity (TFP). A cross-sectional augmented auto-regressive distributed lag model (CS-ARDL), which addresses the issues of endogeneity and cross-sectional dependence with stochastic trends, has been applied to 21 EMEs over the period from 1990 to 2018 and reveals a strong negative impact of temperature rise on total factor productivity. Although EMEs have heterogeneous impacts across the countries depending upon their climatic zones and income levels, a one-degree increase in temperature, on average, decreases the TFP by approximately 3 per cent. It is much higher in the extreme climatic zones and less developed EMEs.

Mentions - NITI Aayog report,JSGP Working Paper, SSRN Working paper ,Techiexpert.com, YouTube, Slides 

This paper attempts to estimate long-run forecasting of Indian GDP for the post-COVID period using the factor error correction model (FECM). The model builds on a dynamic factor model that directly and indirectly captures many dimensions affecting the cycles of GDP. The availability of big data enables the extraction of a few common factors from large dimensions, which essentially produces better precision in forecasting estimates. We first extract leading factors and then add proxy policy variables to establish their long-run relationship with the GDP which produces insignificant in-sample bias. The estimated long-run relationship has been employed to predict GDP for 2022–35. We found three major dynamic factors that capture 80% of variations from 56 quarterly variables of the Indian economy. These three factors with their four lags and four exogenous policy instruments have been included in the FECM model for forecasting estimation. We find that the real GDP is expected to grow at 4–8% annually, depending upon the actual realisation of external shocks and policies. The expected rise in temperature and oil prices seems to be dampening the growth. Whereas, the institutional reforms for making more effective public investment and the currency digitalisation that reduces cash requirements could play an expansionary role. If the oil price and the temperature remain at the current level, the growth rate can go closer to approximately 8%.

This paper employs the structural VAR methodology to empirically analyse how a domestic monetary policy shock affects foreign direct investment (FDI) to India. The paper uses two variables, interest rate differential and domestic money supply growth, as measures of domestic monetary policy shock to assess its impact on FDI flows. Empirical results reveal that interest rate differential is not a significant determinant of FDI flows to India, indicating that FDI flows are mainly driven by domestic fundamentals and the economy's growth potential. The results further reveal that domestic money supply growth has a positive and statistically significant impact on FDI flows. This suggests that while FDI flows to India are not affected by a change in interest rate differential, the central bank, using monetary policy, can influence FDI flows by managing domestic money supply growth. In particular, the central bank can attract greater FDI flows by increasing domestic money supply growth, which has a positive impact on ongoing domestic economic growth that creates an expectation of future GDP growth. Among other factors, domestic output growth is found to be the most important and significant determinant of FDI flows to India, followed by domestic infrastructure, domestic creditworthiness and domestic macroeconomic instability.