Economics
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Showing new listings for Thursday, 13 March 2025
- [1] arXiv:2503.08761 [pdf, html, other]
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Title: Beyond the heat: The mental health toll of temperature and humidity in IndiaComments: 28 pages, 11 pages appendix, 6 Tables, 5 FiguresSubjects: General Economics (econ.GN)
Evidence on the heat-mental health nexus remains mixed. I show that this can be partly explained by previous studies focusing solely on temperature while neglecting temperature-humidity interactions. Using a measure that considers both indicators (wet bulb temperature), I assess the causal link between extreme heat and mental health, and its heterogeneity across socioeconomic indicators. I combine self-reported depression and anxiety levels from three Indian WHO-SAGE survey waves with climate data, leveraging quasi-random variation in heat exposure due to survey timing and location. The results reveal that extreme heat increases the risk of depression but not of anxiety. Importantly, these effects are consistently smaller when humidity is not considered. Finally, the study provides evidence that the District Mental Health Program plays a protective role in mitigating adverse mental health effects. The findings suggest that the costs induced by climate change need to account for the economic consequences of deteriorated mental health.
- [2] arXiv:2503.09083 [pdf, other]
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Title: Impact of Multi-Platform Social Media Strategy on Sales in E-CommerceSubjects: General Economics (econ.GN)
Over the past several decades, major social media platforms have become crucial channels for e-commerce retailers to connect with consumers, maintain engagement, and promote their offerings. While some retailers focus their efforts on a few key platforms, others choose a more diversified approach by spreading their efforts across multiple sites. Which strategy proves more effective and why? Drawing on a longitudinal dataset on e-commerce social media metrics and performance indicators, we find that, all else being equal, companies with a more diversified social media strategy outperform those focusing on fewer platforms, increasing total web sales by 2 to 5 percent. The key mechanism driving this finding appears to be the complementary effect of overlapping impressions across platforms. When followers are present on multiple platforms, repeated exposure to consistent messaging reinforces brand awareness and enhances purchase intent. Our findings highlight important managerial implications for diversifying social media efforts to reach potential customers more efficiently and ultimately boost sales.
- [3] arXiv:2503.09212 [pdf, other]
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Title: Generative AI Adoption and Higher Order SkillsComments: 14 pages, 1 table, 3 figuresSubjects: General Economics (econ.GN)
We study how Generative AI (GenAI) adoption is reshaping work. While prior studies show that GenAI enhances role-level productivity and task composition, its influence on skills - the fundamental enablers of task execution, and the ultimate basis for employability - is less understood. Using job postings from 378 US public firms that recruited explicitly for GenAI skills (2021-2023), we analyze how GenAI adoption shifts the demand for workers' skills. Our findings reveal that the advertised roles which explicitly rely on GenAI tools such as ChatGPT, Copilot, etc., have 36.7 percent higher requirements for cognitive skills. Further, a difference-in-differences analysis shows that the demand for social skills within GenAI roles increases by 5.2 percent post-ChatGPT launch. These emerging findings indicate the presence of a hierarchy of skills in organizations with GenAI adoption associated with roles that rely on cognitive skills and social skills.
- [4] arXiv:2503.09287 [pdf, html, other]
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Title: On the Wisdom of Crowds (of Economists)Subjects: Econometrics (econ.EM); Applications (stat.AP)
We study the properties of macroeconomic survey forecast response averages as the number of survey respondents grows. Such averages are "portfolios" of forecasts. We characterize the speed and pattern of the gains from diversification and their eventual decrease with portfolio size (the number of survey respondents) in both (1) the key real-world data-based environment of the U.S. Survey of Professional Forecasters (SPF), and (2) the theoretical model-based environment of equicorrelated forecast errors. We proceed by proposing and comparing various direct and model-based "crowd size signature plots," which summarize the forecasting performance of k-average forecasts as a function of k, where k is the number of forecasts in the average. We then estimate the equicorrelation model for growth and inflation forecast errors by choosing model parameters to minimize the divergence between direct and model-based signature plots. The results indicate near-perfect equicorrelation model fit for both growth and inflation, which we explicate by showing analytically that, under conditions, the direct and fitted equicorrelation model-based signature plots are identical at a particular model parameter configuration, which we characterize. We find that the gains from diversification are greater for inflation forecasts than for growth forecasts, but that both gains nevertheless decrease quite quickly, so that fewer SPF respondents than currently used may be adequate.
New submissions (showing 4 of 4 entries)
- [5] arXiv:2405.04468 (replaced) [pdf, html, other]
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Title: Turning the Ratchet: Dynamic Screening with Multiple AgentsSubjects: Theoretical Economics (econ.TH)
We study a dynamic contracting problem with multiple agents and limited commitment. A principal seeks to screen efficient agents using one-period contracts, but is tempted to revise contract terms upon knowing an agent's type. Alterations of contracts are observable and, hence, whenever past promises are broken future information revelation stops. We provide necessary and sufficient conditions under which information revelation can be fostered. For sufficiently patient players, private information is either never revealed or fully revealed in a sequential manner. Optimal contracts provide high-powered incentives upon initial disclosure of an agent's type, and rewards for information revelation vanish over time.
- [6] arXiv:2408.12577 (replaced) [pdf, other]
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Title: A nested nonparametric logit model for microtransit revenue management supplemented with citywide synthetic dataSubjects: Econometrics (econ.EM)
As an IT-enabled multi-passenger mobility service, microtransit can improve accessibility, reduce congestion, and enhance flexibility. However, its heterogeneous impacts across travelers necessitate better tools for microtransit forecasting and revenue management, especially when actual usage data are limited. We propose a nested nonparametric model for joint travel mode and ride pass subscription choice, estimated using marginal subscription data and synthetic populations. The model improves microtransit choice modeling by (1) leveraging citywide synthetic data for greater spatiotemporal granularity, (2) employing an agent-based estimation approach to capture heterogeneous user preferences, and (3) integrating mode choice parameters into subscription choice modeling. We apply our methodology to a case study in Arlington, TX, using synthetic data from Replica Inc. and microtransit data from Via. Our model accurately predicts the number of subscribers in the upper branch and achieves a high McFadden R2 in the lower branch (0.603 for weekday trips and 0.576 for weekend trips), while also retrieving interpretable elasticities and consumer surplus. We further integrate the model into a simulation-based framework for microtransit revenue management. For the ride pass pricing policy, our simulation results show that reducing the price of the weekly pass ($25 -> $18.9) and monthly pass ($80 -> $71.5) would surprisingly increase total revenue by $127 per day. For the subsidy policy, our simulation results show that a 100% fare discount would reduce 61 car trips to AT&T Stadium for a game event, and increase 82 microtransit trips to Medical City Arlington, but require subsidies of $533 per event and $483 per day, respectively.
- [7] arXiv:2409.13674 (replaced) [pdf, html, other]
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Title: Topological Components in a Community Currency NetworkSubjects: General Economics (econ.GN); Physics and Society (physics.soc-ph)
Transaction data from digital payment systems can be used to study economic processes in such detail that was not possible previously. Here, data from the Sarafu token network, a Community Inclusion Currency in Kenya, is analysed. During the COVID-19 emergency, Sarafu was distributed as part of a humanitarian aid project. In this work, the transactions are analysed using network science. A topological categorisation is defined to identify cyclic and acyclic components. Furthermore, temporal aspects of the circulation that takes place within these components are considered. The significant presence of different types of strongly connected components compared to randomised null models shows the importance of cycles in this economic network. Especially, indicating their key role in currency recirculation. In some acyclic components, the most significant triad suggests the presence of a group of users collecting currency from accounts that are active only once, hinting at a possible misuse of the system. In some other acyclic components, small isolated groups of users were active only once, suggesting the presence of users only interested in trying the system out. The methods used in this paper can answer specific questions related to user activities, currency design, and assessment of monetary interventions. The methodology provides a general quantitative tool to analyse the behaviour of users in a currency network.
- [8] arXiv:2503.07876 (replaced) [pdf, html, other]
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Title: Impact of the Pandemic on Currency Circulation in Brazil: Projections using the SARIMA ModelSubjects: General Economics (econ.GN)
This study analyzes the impact of the COVID-19 pandemic on currency circulation in Brazil by comparing actual data from 2000 to 2023 with counterfactual projections using the \textbf{SARIMA(3,1,1)(3,1,4)\textsubscript{12}} model. The model was selected based on an extensive parameter search, balancing accuracy and simplicity, and validated through the metrics MAPE, RMSE, and AIC. The results indicate a significant deviation between projected and observed values, with an average difference of R\$ 47.57 billion (13.95\%). This suggests that the pandemic, emergency policies, and the introduction of \textit{Pix} had a substantial impact on currency circulation. The robustness of the SARIMA model was confirmed, effectively capturing historical trends and seasonality, though findings emphasize the importance of considering exogenous variables, such as interest rates and macroeconomic policies, in future analyses. Future research should explore multivariate models incorporating economic indicators, long-term analysis of post-pandemic currency circulation trends, and studies on public cash-holding behavior. The results reinforce the need for continuous monitoring and econometric modeling to support decision-making in uncertain economic contexts.
- [9] arXiv:2405.11284 (replaced) [pdf, html, other]
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Title: The Logic of Counterfactuals and the Epistemology of Causal InferenceSubjects: Artificial Intelligence (cs.AI); Econometrics (econ.EM); Methodology (stat.ME); Other Statistics (stat.OT)
The 2021 Nobel Prize in Economics recognized an epistemology of causal inference based on the Rubin causal model (Rubin 1974), which merits broader attention in philosophy. This model, in fact, presupposes a logical principle of counterfactuals, Conditional Excluded Middle (CEM), the locus of a pivotal debate between Stalnaker (1968) and Lewis (1973) on the semantics of counterfactuals. Proponents of CEM should recognize that this connection points to a new argument for CEM -- a Quine-Putnam indispensability argument grounded in the Nobel-winning applications of the Rubin model in health and social sciences. To advance the dialectic, I challenge this argument with an updated Rubin causal model that retains its successes while dispensing with CEM. This novel approach combines the strengths of the Rubin causal model and a causal model familiar in philosophy, the causal Bayes net. The takeaway: deductive logic and inductive inference, often studied in isolation, are deeply interconnected.
