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1. The Effect of Macroeconomic Uncertainty on Household Spending

   https://doi.org/10.1257/aer.20221167  

   Coibion, Olivier; Georgarakos, Dimitris; Gorodnichenko, Yuriy; Kenny, Geoff; Weber, Michael (March 2024, American Economic Review)

   We use randomized treatments that provide different types of information about the first and/or second moments of future economic growth to generate exogenous changes in the perceived macroeconomic uncertainty of treated households. The effects on their spending decisions relative to an untreated control group are measured in follow-up surveys. Our results indicate that, after taking into account first moments, higher macroeconomic uncertainty induces households to significantly and persistently reduce their total monthly spending in subsequent months. Changes in spending are broad based across spending categories and apply to larger durable good purchases as well. (JEL D12, D81, D84, E21, E23, G51) 

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2. Scrutinizing countries’ climate commitments: Insights from recovery spending

   https://doi.org/10.1016/j.oneear.2023.08.022  

   Egli, Florian; Schmid, Nicolas; Fransen, Taryn; Stünzi, Anna; Isah, Abdulrasheed; Pineda, Adrianna; Beaton, Christopher; Schmidt, Tobias S. (September 2023, One Earth)

   The Global Stocktake of the Paris Agreement on Climate Change assesses countries’ collective progress toward holding warming to well below 2 C, or 1.5 C. In this commentary, we argue that governments’ fiscal responses to global economic shocks provide an important opportunity to investigate the credibility of climate pledges and progress. 

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3. Do Government Spending Multipliers Depend on the Sign of the Shock?

   https://doi.org/10.1257/pandp.20231071  

   Ben Zeev, Nadav; Ramey, Valerie A.; Zubairy, Sarah (May 2023, AEA Papers and Proceedings)

   We analyze whether government spending multipliers differ by the sign of the shock. Using aggregate historical US data, we apply Ben Zeev's (2020) nonlinear diagnostic tests and find evidence of nonlinearities in the impulse response functions of both government spending and GDP. We then extend Ramey and Zubairy's (2018) framework to allow for asymmetric effects as a type of state dependence to estimate multipliers. While we find differences in the impulse response functions, the resulting multipliers do not differ by sign of the shock. Thus, declines in government spending do not have different effects on economic activity than rises in spending. 

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4. Policing the California Outercity: Drivers of Police Spending in a Changing Metropolis

   https://doi.org/10.1093/socpro/spad050  

   Ross, Ángel Mendiola (October 2023, Social Problems)

   Abstract This paper explores the intersection of two major trends in the United States over the last forty years: a substantial investment in local law enforcement and the diversification of suburbia. While previous research on police spending has focused almost exclusively on large central cities, this study broadens this perspective to assess how these dynamics play out in outer-ring suburbs. I construct a unique panel dataset of over 200 California municipalities and find that the drivers of police spending vary across the metropolis in significant ways. Fixed-effects models that control for unobserved heterogeneity across place suggest that suburbs with growing shares of renters spend more on police. Elaborating on the concept of renter threat, I show how increases in renter households are associated with increases in police expenditures across a range of model specifications in suburbia. I point to suburban homeowner concerns about crime and property values as well as the history of racial exclusion in suburbia that is often couched in economic terms as potential explanations for these findings. Results point to the enduring role of police as a contemporary mechanism of both social control and inequality in California suburbs. 

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5. Packet Pruning: Finding Better Energy Spending Policies for Batteryless Human Activity Recognition

   https://doi.org/10.1109/BSN63547.2024.10780463  

   Cooper, Geffen; Marculescu, Radu (October 2024, IEEE)

   Batteryless sensing devices which rely on energy harvesting can enable more sustainable and long-lasting Internet of Things (IoT) based wearables. While it has become feasible to implement energy-harvesting based wearables for digital health applications, it remains challenging to integrate such devices and the data they collect into machine learning pipelines for tasks such as human activity recognition (HAR). A key obstacle is uncertainty in the data acquisition process. Given the discontinuous and uncertain availability of harvested energy, when should a sensor spend energy to sample and transmit data packets for processing? A common approach is to spend energy opportunistically by sending packets whenever sufficient energy is available. However, when considering a specific task, namely HAR with kinetic energy harvesting based sensors, this approach unfairly prioritizes data from activities where more energy can be harvested (e.g., running). In this work, we improve the opportunistic energy spending policy by pruning redundant packets to reallocate energy towards activities where less energy is harvested. Our approach results in an increase in the F1-score of ‘lower energy’ activities while having a minimal impact on the F1-score of ‘higher energy’ activities. 

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