research

We estimate the causal effect of FEMA aid and SBA loans (federal aid) on economic recovery post disaster in the United States from 2003-2021. For the identification, we use a novel instrument based on the political competition within county and it’s effect on the volume of federal aid distribution, especially when disaster occurs closer to the election. Due to the winner-takes-all nature of the US election, we argue that in non-swing states, politicians in the swing counties have no incentive to perform any better, and thus political competitiveness affects GDP only through federal aid. We find that a 1% increase in disaster aid results in a 0.03% increase in local GDP for the next year. With respect to the literature on political competition, we found evidence that political competition within a county is positively associated with 1) higher volume of disaster aid in non-swing states and 2) higher GDP growth rate in swing states.

We examine the effect of affordable high-speed mobile internet on children’s educational outcomes using the nationwide rollout of Reliance Jio’s 4G network in India in 2016 as a natural experiment. Jio’s entry led to an abrupt and unanticipated decline in mobile data prices and a rapid expansion of 4G coverage. Exploiting cross-district variation in early exposure to Jio’s network, we implement a difference-in-differences design using district-level test score data from the Annual Status of Education Report (ASER). We find that districts with greater initial access to low-cost 4G internet experienced statistically significant improvements of approximately 0.18 standard deviations in reading scores and 0.15 standard deviations in mathematics scores between 2016 and 2018. These effects are robust to alternative treatment definitions, a continuous treatment-intensity specification, and controls for local economic activity and demographic composition. Our results suggest that widespread access to affordable, mobile-first internet can complement traditional education inputs and improve learning outcomes in developing-country contexts.

We examine whether the European Union’s Carbon Border Adjustment Mechanism (CBAM) generated anticipatory trade responses prior to implementation. Using bilateral monthly trade data from EU COMEXT between 2018 and 2023, we estimate an event-study specification comparing CBAM-covered sectors to non-CBAM-covered sectors across advanced and emerging market exporters to the EU. We find evidence of export frontloading following the announcement of CBAM, particularly among emerging market economies, with exports in CBAM-covered sectors rising by approximately 42–49 percent relative to non-CBAM-covered sectors within three quarters of the announcement. No comparable response is detected for advanced economies. The results suggest that climate-linked trade policy can influence international trade flows well before formal enforcement begins.

This paper examines whether India’s adoption of flexible inflation targeting (FIT) in 2016 altered the dynamic relationship between capital flows and foreign exchange intervention. Using a structural vector autoregressive model on quarterly data from 2002Q1 to 2025Q3, capital flow shocks are identified as the dominant driver of reserve dynamics, explaining over 60% of forecast error variance at the twenty-quarter horizon. The reserve response to capital inflows is positive and persistent while the response to exchange rate depreciation is negligible, confirming asymmetric intervention. A split-sample analysis reveals two structural shifts post-FIT: the capital flow response to interest rate differential shocks reverses sign, consistent with monetary tightening being reinterpreted as a signal of domestic stress rather than a carry opportunity, and the reserve accumulation response to capital flow shocks attenuates markedly, becoming statistically insignificant by the fourth quarter compared to ten quarters pre-FIT. These findings suggest that FIT altered both the external signalling channel of monetary policy and the intensity of reserve intervention in India, consistent with a gradual transition toward greater exchange rate flexibility as the framework has matured.

We investigate whether the mean-variance efficient frontier for Indian equities shifted meaningfully between the pre-COVID period (January 2015 – January 2020) and the post-COVID period (July 2021 – December 2024), and identify the structural forces that drive this shift. Using a universe of 45 NIFTY-50 constituents, Ledoit-Wolf shrinkage covariance estimation, and long-only mean-variance optimization, we trace frontiers at both daily and weekly rebalancing frequencies. The post-COVID frontier lies above and to the right of the pre-COVID frontier at every point: expected returns increased by approximately +5 percentage points while volatility rose by only +3 percentage points, improving Sharpe ratios by +6–7% at the tangency portfolio.