Recently it has been hypothesized that climate change will affect total factor productivity growth. Given the importance of TFP for long-run economic growth, if true this would entail a substantial upward revision of current impact estimates. Using macro TFP data from a recently developed dataset in Penn World Tables, we test this hypothesis by directly examining the nature of the relationship between annual temperature shocks and TFP growth rates in the last decades. The results show a negative relationship only in poor countries. While statistically significant, the estimate upper bound is a reduction of TFP growth is less than 0.1%, i.e., climate change will decelerate but not reverse economic growth. This finding increases concerns over the distributional issues of future impacts, and restates the case for complementarity between climate policy and poverty reduction.

Using the LSMS-ISA Tanzania National Panel Survey by the World Bank, we study the relationship between rural household consumption growth and temperature shocks over the period 2008-2013. Temperature shocks have a negative and significant impact on household growth if their initial consumption lies below a critical threshold. As such, temperature shocks slow income convergence among households, at least in the short run. Crop yields and total factor productivity in agriculture are the main transmission channels. Extrapolating from short-term elasticities to long-run phenomena, these findings support the Schelling Conjecture: economic development would help poor farming households to reduce the impacts of climate change. Hence, closing the yield gap, modernizing agriculture and favouring the structural transformation of the economy are all crucial issues for adaptation of farmers to the negative effects of global warming.

The empirical literature on the impacts of weather shocks on agricultural prices typically explores post-harvest price dynamics rather than pre-harvest ones. Inspired by the intra-annual competitive storage theory, we empirically investigate the role of weather news in traders' anticipations on pre-harvest price fluctuations in India's local markets. Using a panel of district-level monthly wholesale food prices from 2004 to 2017, we leverage the time lag between a weather anomaly and the corresponding supply shock to isolate price reactions caused by changes in expectations. We find that drought conditions significantly increase food prices during the growing period, that is before any harvest failure has materialized. These results suggest that markets respond immediately to expected supply shortfalls by updating their beliefs and adapting accordingly and that the expectation channel accounts for a substantial share of supply-side food price shocks. A direct comparison with the effects of the same weather anomalies on the prices of the first harvest month reveals that expectations anticipate more than 80% of the total price impact.

Estimates of the real death toll of the COVID-19 pandemic have proven to be problematic in many countries, Italy being no exception. Mortality estimates at the local level are even more uncertain as they require stringent conditions, such as granularity and accuracy of the data at hand, which are rarely met. The “official” approach adopted by public institutions to estimate the “excess mortality” during the pandemic draws on a comparison between observed all-cause mortality data for 2020 and averages of mortality figures in the past years for the same period. In this paper, we apply the recently developed machine learning control method to build a more realistic counterfactual scenario of mortality in the absence of COVID-19. We demonstrate that supervised machine learning techniques outperform the official method by substantially improving the prediction accuracy of the local mortality in “ordinary” years, especially in small- and medium-sized municipalities. We then apply the best-performing algorithms to derive estimates of local excess mortality for the period between February and September 2020. Such estimates allow us to provide insights about the demographic evolution of the first wave of the pandemic throughout the country. To help improve diagnostic and monitoring efforts, our dataset is freely available to the research community.

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