Does Internet use help to achieve sustainable food consumption? Evidence from rural China

Internet use is widely studied as an important socio-economic factor influencing agricultural productivity, income, the adoption of sustainable agricultural practices, and farmer welfare, but scant attention is given to its influence on sustainable food consumption. Using longitudinal data from the China Health and Nutrition Survey (CHNS), this study seeks to better understand the causal effect of Internet use on sustainable food consumption measured by food carbon and food water footprints and shed light on its underlying channels. The instrumental variable estimation is used to solve the endogeneity problem of Internet use and the propensity score matching (PSM) method is used for robustness check. The results show that Internet use significantly decreases food carbon and food water footprints by 18.1 % and 10.6 %, respectively. Internet use promotes the development of sustainable food consumption mainly by reducing the consumption of animal-based food such as pork and eggs. Further heterogeneity analysis results indicate that Internet use mainly affects the sustainable food consumption of young and high-income individuals. Policy implications for reducing food carbon and food water footprints and achieving a win-win situation for consumption and the environment are also discussed

Rational inattention during an RCT

We introduce an information provision experiment into a standard dynamic rational inattention model. We derive analytical results about how the treatment effect varies with characteristics of the environment and the individual. We use these results to discuss findings in the empirical literature on information provision experiments that can be explained by rational inattention of survey respondents and what this interpretation implies about behavior outside the survey

Using Cross-Survey Imputation to Estimate Poverty for Venezuelan Refugees in Colombia

Household consumption or income surveys do not typically cover refugee populations. In the rare cases where refugees are included, inconsistencies between different data sources could interfere with comparable poverty estimates. We test the performance of a recently developed cross-survey imputation method to estimate poverty for a sample of refugees in Colombia, combining household income surveys collected by the Government of Colombia and administrative (ProGres) data collected by the United Nations High Commissioner for Refugees in 2019 and 2022. We find that certain variable transformation methods can help resolve these inconsistencies. Estimation results with our preferred variable standardization method are robust to different imputation methods, including the normal linear regression method, the empirical distribution of the errors method, and the probit and logit methods. Several common machine learning techniques generally perform worse than our proposed imputation methods. We also find that we can reasonably impute poverty rates using an older household income survey and a more recent ProGres dataset for most of the poverty lines. These results provide relevant inputs into designing better surveys and administrative datasets on refugees in various country settings

Forward guidance and fiscal rules in HANK

I show that in a canonical HANK model, under a balanced budget fiscal rule, the effect of a nominal interest rate peg is much larger than in a representative agent (RA) model. By contrast, under a standard fiscal rule where tax revenue responds gradually to deviations of the debt-to-GDP ratio from steady-state and depends on economic activity, the effect of forward guidance is much weaker than in the RA model, and becomes linear in the length of the peg. This result is robust to allowing for countercyclical inequality and income risk, and carries over to a quantitative model with capital

Replication Report of "Belief Elicitation and Behavioral Incentive Compatibility" by Danz, Vesterlund, and Wilson (2022)

This study replicates and extends the analysis of belief elicitation methods conducted by Danz et al. (2022). The original paper scrutinizes the binarized scoring rule (BSR) method and its effectiveness in incentivizing truthful reporting. Using data from the original controlled laboratory experiments conducted at the Pittsburgh Experimental Economics Laboratory (PEEL), this replication investigates the impact of varying levels of information about incentives on belief reporting accuracy. The findings of the replication confirm systematic biases in belief reporting, particularly a center-bias effect, challenging the behavioral incentive compatibility of the BSR method. Robustness checks further confirm the generalizability of these results across different settings and belief elicitation tasks. These findings underscore the need for improved methodologies that ensure both theoretical and behavioral incentive compatibility in belief elicitation

Should states allow early school enrollment? An analysis of individuals’ long-term labor market effects

This study provides a policy evaluation of laws allowing early school enrollment of children, i.e., enrollment before the official school starting age. It investigates the effects of early enrollment on educational attainment, wages and employment. While the school starting age is usually determined by children’s date of birth and legal cutoffs, some German states allowed early enrollment in some years. Exploiting state and cohort variation, the results show that male early enrollees attain fewer years of schooling, enter the labor market earlier and have a larger labor market attachment at around age 16. Positive wage effects persist until approximately age 35. Results for women roughly resemble those for men, but they are less convincingly estimated

Not yet Trump-proof: An evaluation of the European Commission's emerging policy platform

The economic strategy being defined by the 2024-2029 European Commission seems to follow the prescriptions on innovation and single market reform, and the expansive approach to industrial policy, set out by Mario Draghi in his September 2024 report on European Union competitiveness, with two important differences. First, the Commission stops short of calling for World Trade Organisation-prohibited subsidies - this is welcome. Second, the Commission proposes a new state aid framework for national industrial policy rather than expansion of EU-level public investment funding. This runs the risk of weakening the single market and harming competition, with the unintended consequence of protecting incumbents and inhibiting structural change. In terms of specific policies, on defence, the Commission is right to face up to the challenge of defining an EU procurement mechanism that offers sufficient speed and cost advantages to justify large-scale funding. On economic security and international partnerships, the Commission is right to take a broader approach than a foreign economic policy focused only on supply chains. What is lacking is a much greater commitment to providing support for climate mitigation in developing countries. The second Trump presidency creates risks for the Commission strategy. President Trump has gone further than expected in threatening territorial expansion and with the speed, aggression and disregard for the rule of law with which he has started to implement his policies. These factors will complicate the EU-United States relationship. The best defence against both Trump and the competitive and security threats posed by China is to accelerate policies that address the EU's structural weaknesses: raising productivity growth, defence capacity and economic security. Economic security, in turn, requires more resilient trade relationships, less financial dependence on the US and an improved standing with emerging market and developing economies. The EU should also seize the opportunity offered by the shift in US policy from subsidies to deregulation. While the EU should not race Trump to the bottom on environmental or financial deregulation, it should rapidly improve its own regulatory framework while building on its core strengths: human capital and the rule of law. Unlike tariff wars or discriminatory subsidies, a competition to provide a good business environment is not a zero-sum game

Utilizing managerial beliefs for set identification of price elasticities of demand

Abstract Data-driven decision-making is increasingly prevalent but can clash with managerial beliefs, risking biased decisions. A prime example is pricing strategy optimization, where traditional methods for estimating price elasticities of demand often lead to counter-intuitive results due to model misspecification and the reliance on single-point estimates. To address this, we propose utilizing structural vector-autoregressions (SVARs) to generate identified sets of elasticities, integrating managerial beliefs into the analysis to improve decision-making processes. Using weak restrictions about the directional effects of supply and demand shocks on sales and prices, and assumptions about the functioning of in-store promotions effectively sharpens the identified sets. Specifically, we analyze the demand for beer at a large scale for 1,953 stores in the US. For many stores (i.e., at least 40%), both recent endogeneity-robust single-equation methods and alternative identification strategies for SVARs used in marketing studies yield positive price elasticity estimates that oppose behavioral fundamentals. Hence, these are hardly informative for designing pricing strategies. Instead, the suggested approach to set identification yields elasticity estimates that are sufficiently precise to improve the design of retail pricing strategies and offer insights into customer’s distinct price sensitivities in grocery and drug stores. Overall, our approach emphasizes the importance of combining data-driven analysis with managerial insights for evidence-based decision-making

Losses from natural disasters: County-level data on damages, injuries, and fatalities

We introduce the first comprehensive publicly available dataset on county-level damages, injuries, and fatalities from natural disasters in the U.S. and present a few facts on the economic and human costs of extreme climate events. Our source is the National Oceanic and Atmospheric Administration's Storm Events Database, which reports losses for geographic areas largely defined based on meteorological science. We map these areas to counties using geographic tools together with the spatial distribution of population, housing stock, and economic activity. Our estimates are particularly accurate for severe disasters. The Losses from Natural Disasters dataset is regularly updated at https://newyorkfed.org/research/policy/natural-disaster-losses