pwc-archive/papers-with-abstracts huggingface dataset:
Un índice discreto sensible a la desigualdad
Authors: Francisco José Zamudio Sánchez, Javier Jiménez Machorro, Roxana Arana Ovalle, Hildegardo Martínez Silverio
Date: 2024-09-11 00:00:00
Tasks: Sensitivity

This paper introduces the Relative Inequality Index at the Maximum (IDRM), a novel and intuitive measure designed to capture inequality within a population, such as income inequality. The index is based on the idea that individuals experience varying levels of inequality depending on their position within the distribution, particularly with respect to those at the top. The key assumption is that for individuals in lower positions, inequalities referenced to the top positions have greater impact on their well-being and the inequality relative to maximum is the most critical. The IDRM fulfills desirable theoretical properties which were used for its evaluation and comparison against widely accepted measures in inequality literature. From this perspective, the IDRM is shown to be as robust as traditional measures and outperforms the Gini and Dalton indices by satisfying eight out of nine key properties, including decomposability across population subgroups. In a comparative analysis using income data from 58 countries and microdata from Mexico, with the Gini, Theil, and Atkinson indices as benchmarks, the IDRM demonstrates superior consistency, sensitivity to inequality, reduced bias in grouped data, and enhanced precision. This index reflects the varying forms of income distribution, showing heightened sensitivity to the magnitude of inequality.
Policy consequences of the new neuroeconomic framework
Authors: A. David Redish, Henri Scott Chastain, Carlisle Ford Runge, Brian M. Sweis, Scott E. Allen, Antara Haldar
Date: 2024-09-11 00:00:00
Tasks: Decision Making, Management

Current theories of decision making suggest that the neural circuits in mammalian brains (including humans) computationally combine representations of the past (memory), present (perception), and future (agentic goals) to take actions that achieve the needs of the agent. How information is represented within those neural circuits changes what computations are available to that system which changes how agents interact with their world to take those actions. We argue that the computational neuroscience of decision making provides a new microeconomic framework (neuroeconomics) that offers new opportunities to construct policies that interact with those decision-making systems to improve outcomes. After laying out the computational processes underlying decision making in mammalian brains, we present four applications of this logic with policy consequences: (1) precommitment to avoid falling into the trap of sunk costs, (2) media consequences for changes in housing prices after a disaster, (3) contingency management as a treatment for addiction, and (4) how social interactions underlie the success (and failure) of microfinance institutions.
Mechanisms for belief elicitation without ground truth
Authors: Niklas Valentin Lehmann
Date: 2024-09-11 00:00:00

This review article examines the challenge of eliciting truthful information from multiple individuals when such information cannot be verified, a problem known as information elicitation without verification (IEWV). This article reviews over 25 mechanisms designed to incentivize truth-telling in such scenarios and their effectiveness in empirical studies. Although many mechanisms theoretically ensure truthfulness as a Bayesian Nash Equilibrium, empirical evidence regarding the effects of mechanisms on truth-telling is limited and generally weak. Consequently, more empirical research is needed to validate mechanisms. However, empirical validaton is difficult because most mechanisms are very complex and cannot be easily conveyed to research subjects. This review suggests that simple and intuitive mechanisms may be easier to empirically test and apply.
The Mismeasure of Weather: Using Remotely Sensed Earth Observation Data in Economic Context
Authors: Anna Josephson, Jeffrey D. Michler, Talip Kilic, Siobhan Murray
Date: 2024-09-11 00:00:00
Tasks: Earth Observation

The availability of weather data from remotely sensed Earth observation (EO) data has reduced the cost of including weather variables in econometric models. Weather variables are common instrumental variables used to predict economic outcomes and serve as an input into modelling crop yields for rainfed agriculture. The use of EO data in econometric applications has only recently been met with a critical assessment of the suitability and quality of this data in economics. We quantify the significance and magnitude of the effect of measurement error in EO data in the context of smallholder agricultural productivity. We find that different measurement methods from different EO sources: findings are not robust to the choice of EO dataset and outcomes are not simply affine transformations of one another. This begs caution on the part of researchers using these data and suggests that robustness checks should include testing alternative sources of EO data.
Watts and Bots: The Energy Implications of AI Adoption
Authors: Anthony Harding, Juan Moreno-Cruz
Date: 2024-09-10 00:00:00

With the rapid expansion of Artificial Intelligence, there are expectations for a proportional expansion of economic activity due to increased productivity, and with it energy consumption and its associated environmental consequences like carbon dioxide emissions. Here, we combine data on economic activity, with early estimates of likely adoption of AI across occupations and industries, to estimate the increase in energy use and carbon dioxide emissions at the industry level and in aggregate for the US economy. At the industry level, energy use can increase between 0 and 12 PJ per year, while emissions increase between 47 tCO$_2$ and 272 ktCO$_2$. Aggregating across industries in the US economy, this totals an increase in energy consumption of 28 PJ per year, or around 0.03% of energy use per year in the US. We find this translates to an increase in carbon dioxide emissions of 896 ktCO$_2$ per year, or around 0.02% of the CO$_2$ emissions per year in the US.
Evidence gathering under competitive and noncompetitive rewards
Authors: Philip Brookins, Jennifer Brown, Dmitry Ryvkin
Date: 2024-09-10 00:00:00

Reward schemes may affect not only agents' effort, but also their incentives to gather information to reduce the riskiness of the productive activity. In a laboratory experiment using a novel task, we find that the relationship between incentives and evidence gathering depends critically on the availability of information about peers' strategies and outcomes. When no peer information is available, competitive rewards can be associated with more evidence gathering than noncompetitive rewards. In contrast, when decision-makers know what or how their peers are doing, competitive rewards schemes are associated with less active evidence gathering than noncompetitive schemes. The nature of the feedback -- whether subjects receive information about peers' strategies, outcomes, or both -- also affects subjects' incentives to engage in evidence gathering. Specifically, only combined feedback about peers' strategies and performance -- from which subjects may assess the overall relationship between evidence gathering, riskiness, and success -- is associated with less evidence gathering when rewards are based on relative performance; we find no similar effect for noncompetitive rewards.
Contests with sequential moves: An experimental study
Authors: Arthur B. Nelson, Dmitry Ryvkin
Date: 2024-09-10 00:00:00

We study experimentally contests in which players make investment decisions sequentially, and information on prior investments is revealed between stages. Using a between-subject design, we consider all possible sequences in contests of three players and test two major comparative statics of the subgame-perfect Nash equilibrium: The positive effect of the number of stages on aggregate investment and earlier mover advantage. The former prediction is decidedly rejected, as we observe a reduction in aggregate investment when more sequential information disclosure stages are added to the contest. The evidence on earlier mover advantage is mixed but mostly does not support theory as well. Both predictions rely critically on large preemptive investment by first movers and accommodation by later movers, which does not materialize. Instead, later movers respond aggressively, and reciprocally, to first movers' investments, while first movers learn to accommodate those responses.
Optimal In-Kind Redistribution
Authors: Zi Yang Kang, Mitchell Watt
Date: 2024-09-09 00:00:00

This paper develops a model of in-kind redistribution where consumers participate in either a private market or a government-designed program, but not both. We characterize when a social planner, seeking to maximize weighted total surplus, can strictly improve upon the laissez-faire outcome. We show that the optimal mechanism consists of three components: a public option, nonlinear subsidies, and laissez-faire consumption. We quantify the resulting distortions and relate them to the correlation between consumer demand and welfare weights. Our findings reveal that while private market access constrains the social planner's ability to redistribute, it also strengthens the rationale for non-market allocations.
Patterns of Medical Care Cost by Service Type Associated with Lung Cancer Screening
Authors: Kris Wain, Mahesh Maiyani, Nikki M. Carroll, Rafael Meza, Robert T. Greenlee, Christine Neslund-Dudas, Michelle R. Odelberg, Caryn Oshiro, Debra P. Ritzwoller
Date: 2024-09-09 00:00:00

Introduction: Lung cancer screening (LCS) increases early-stage cancer detection which may reduce cancer treatment costs. Little is known about how receipt of LCS affects healthcare costs in real-world clinical settings. Methods: This retrospective study analyzed utilization and cost data from the Population-based Research to Optimize the Screening Process Lung Consortium. We included individuals who met age and smoking LCS eligibility criteria and were engaged within four healthcare systems between February 5, 2015, and December 31, 2021. Generalized linear models estimated healthcare costs from the payer perspective during 12-months prior and 12-months post baseline LCS. We compared these costs to eligible individuals who did not receive LCS. Sensitivity analyses expanded our sample to age-eligible individuals with any smoking history noted in the electronic health record. Secondary analyses examined costs among a sample diagnosed with lung cancer. We reported mean predicted costs with average values for all other explanatory variables. Results: We identified 10,049 eligible individuals who received baseline LCS and 15,233 who did not receive baseline LCS. Receipt of baseline LCS was associated with additional costs of $3,698 compared to individuals not receiving LCS. Secondary analyses showed suggestive evidence that LCS prior to cancer diagnosis decreased healthcare costs compared to cancer diagnosed without screening. Conclusion: These findings suggest LCS increases healthcare costs in the year following screening. However, LCS also improves early-stage cancer detection and may reduce treatment costs following diagnosis. These results can inform future simulation models to guide LCS recommendations, and aid health policy decision makers on resource allocation.
Microfinance in Thailand: Navigating Challenges and Unlocking Opportunities
Authors: Worrawoot Jumlongnark
Date: 2024-09-05 00:00:00
Tasks: Fairness

This review article explores the challenges and opportunities faced by the Bank for Agriculture and Agricultural Cooperatives (BAAC) in Thailand from a microfinance perspective. It examines the role of BAAC as a specialized financial institution in assisting underprivileged households and small businesses in accessing financial services. The study emphasizes the challenges and opportunities faced by BAAC in promoting sustainable development. It also explores BAAC's role in advancing the BCG Model policy, which fosters sustainability in the agricultural sector through Bio Economy Credit, Circular Economy Credit, and Green Credit. These initiatives support investments in biotechnology, waste reduction (Zero Waste), organic farming, and safe food production, all aimed at enhancing farmers' quality of life, stimulating growth in agriculture, and preserving the environment. Moreover, BAAC remains committed to upholding transparency, fairness, and operational standards.
Digital Platform Consolidation and Offline Expansion: Strategic Convergence and Market Welfare in China's Second-hand Real Estate Market
Authors: Guoying Deng, Xuyuan Zhang
Date: 2024-09-06 00:00:00
Tasks: Clustering

This study analyzes the impact of offline expansion and online platform consolidation in China's second-hand real estate market. Using micro-level transaction data and difference-in-differences estimations, we find offline store entry significantly boosts transaction volumes (9-10\%) and reduces price concessions (1\%) initially, though effects diminish over time. Platform consolidation via Lianjia's Agent Cooperation Network yields delayed yet persistent transaction volume increases (5-6\%), particularly in less concentrated markets, and consistently lowers price concessions. These strategies sustainably enhance brokerage competitiveness, bargaining power, and market welfare, despite increased market concentration, ultimately benefiting sellers and improving overall efficiency.
Deep Learning for Multi-Country GDP Prediction: A Study of Model Performance and Data Impact
Authors: Huaqing Xie, Xingcheng Xu, Fangjia Yan, Xun Qian, Yanqing Yang
Date: 2024-09-04 00:00:00
Tasks: Deep Learning, Prediction, regression

GDP is a vital measure of a country's economic health, reflecting the total value of goods and services produced. Forecasting GDP growth is essential for economic planning, as it helps governments, businesses, and investors anticipate trends, make informed decisions, and promote stability and growth. While most previous works focus on the prediction of the GDP growth rate for a single country or by machine learning methods, in this paper we give a comprehensive study on the GDP growth forecasting in the multi-country scenario by deep learning algorithms. For the prediction of the GDP growth where only GDP growth values are used, linear regression is generally better than deep learning algorithms. However, for the regression and the prediction of the GDP growth with selected economic indicators, deep learning algorithms could be superior to linear regression. We also investigate the influence of the novel data -- the light intensity data on the prediction of the GDP growth, and numerical experiments indicate that they do not necessarily improve the prediction performance. Code is provided at https://github.com/Sariel2018/Multi-Country-GDP-Prediction.git.
Coping or Hoping? Livelihood Diversification and Food Insecurity in the COVID-19 Pandemic
Authors: Ann M. Furbush, Anna Josephson, Talip Kilic, Jeffrey D. Michler
Date: 2024-09-03 00:00:00

We examine the impact of livelihood diversification on food insecurity amid the COVID-19 pandemic. Our analysis uses household panel data from Ethiopia, Malawi, and Nigeria in which the first round was collected immediately prior to the pandemic and extends through multiple rounds of monthly data collection during the pandemic. Using this pre- and post-outbreak data, and guided by a pre-analysis plan, we estimate the causal effect of livelihood diversification on food insecurity. Our results do not support the hypothesis that livelihood diversification boosts household resilience. Though income diversification may serve as an effective coping mechanism for small-scale shocks, we find that for a disaster on the scale of the pandemic this strategy is not effective. Policymakers looking to prepare for the increased occurrence of large-scale disasters will need to grapple with the fact that coping strategies that gave people hope in the past may fail them as they try to cope with the future.
Impact Evaluations in Data Poor Settings: The Case of Stress-Tolerant Rice Varieties in Bangladesh
Authors: Jeffrey D. Michler, Dewan Abdullah Al Rafi, Jonathan Giezendanner, Anna Josephson, Valerien O. Pede, Elizabeth Tellman
Date: 2024-09-03 00:00:00
Tasks: Earth Observation

Impact evaluations of new technologies are critical to assessing and improving investment in national and international development goals. Yet many of these technologies are introduced and promoted at times and in places that lack the necessary data to conduct a strongly identified impact evaluation. We present a new method that combines remotely sensed Earth observation (EO) data, recent advances in machine learning, and socioeconomic survey data so as to allow researchers to conduct impact evaluations of a certain class of technologies when traditional economic data is missing. To demonstrate our approach, we study stress tolerant rice varieties (STRVs) that were introduced in Bangladesh more than a decade ago. Using 20 years of EO data on rice production and flooding, we fail to replicate existing RCT and field trial evidence of STRV effectiveness. We validate this failure to replicate with administrative and household panel data as well as conduct Monte Carlo simulations to test the sensitivity to mismeasurement of past evidence on the effectiveness of STRVs. Our findings speak to conducting large scale, long-term impact evaluations to verify external validity of small scale experimental data while also laying out a path for researchers to conduct similar evaluations in other data poor settings.
Inventor Mobility After the Fall of the Berlin Wall
Authors: Paul Hünermund, Ann Hipp
Date: 2024-09-03 00:00:00

This study examines the inter-organizational and spatial mobility patterns of East German inventors following the fall of the Berlin Wall. Existing research often overlooks the role of informal institutions in the mobility decisions of inventors, particularly regarding access to and transfer of knowledge. To address this gap, we investigate the unique circumstances surrounding the dissolution of the German Democratic Republic, which caused a significant shock to establishment closures and prompted many inventors to change their jobs and locations. Our sample comprises over 25,000 East German inventors, whose patenting careers in reunified Germany post-1990 are traced using a novel disambiguation and matching procedure. Our findings reveal that East German inventors in technological fields where access to Western knowledge was facilitated by industrial espionage were more likely to pursue inter-organizational mobility and continue their inventive activities in reunified Germany. Additionally, inventors from communities with strong political support for the ruling socialist party encountered difficulties in sourcing knowledge through weak ties, resulting in a lower likelihood of continuing to patent. However, those who overcame these obstacles and continued to produce inventions were more likely to relocate to West Germany, leaving their original social contexts behind.
Reinterpreting economic complexity in multiple dimensions
Authors: Önder Nomaler, Bart Verspagen
Date: 2024-09-03 00:00:00

We build on the interpretation of the Economic Complexity method as Correspondence Analysis (CA), and propose that the Canonical form of CA (CCA), which originated in the ecology literature, can be used to calculate multi-dimensional economic complexity. The traditional (CA) way of calculating economic complexity includes no "external" information such as countries' development characteristics to facilitate interpretation of "complexity". This has led to a wide range of fairly ad hoc interpretations of economic complexity on the basis of ex-post correlation to a long list of other variables. By the ex-ante inclusion of a number of country variables in the construction of the complexity indicators, CCA enables better interpretation, also in the case of multi-dimensional indicators. The analysis is further facilitated by another element of the ecologists' toolbox, the so-called biplots, which are CCA-based graph embeddings that represent a lower-dimensional product-space in which products and countries are positioned together, in mutual correspondence to each other. We show that in this way, CCA provides a richer account of development in many of its aspects, especially economic growth.
Shrouded Sin Taxes
Authors: Johannes Kasinger
Date: 2024-09-02 00:00:00

Strategic shrouding of taxes by profit-maximizing firms can impair the effectiveness of corrective taxes. This paper explores tax shrouding and its consequences after the introduction of a digital sin tax designed to discourage harmful overconsumption of online sports betting in Germany. In response to the tax reform, most firms strategically shroud the tax, i.e., exclude tax surcharges from posted prices. Using an extensive novel panel data set on online betting odds, I causally estimate the effect of the tax on consumer betting prices. Consumers bear, on average, 76% of the tax burden. There is considerable and long-lasting heterogeneity in effects conditional on shrouding practices. Firms that shroud taxes can pass 90% of the tax onto consumers, while the pass-through rate is 16% for firms that directly post tax-inclusive prices. To understand the results' underlying mechanisms and policy implications, I propose an optimal corrective taxation model where oligopolistic firms compete on base prices and can shroud additive taxes. Tax shrouding is only attainable in equilibrium if (some) consumers underreact to shrouded attributes. According to the theoretical predictions, the empirically identified heterogeneity suggests that strategic tax shrouding significantly attenuates the positive corrective welfare effects of the tax. The results prompt regulating shrouding practices in the context of corrective taxation.
Estimating Heterogenous Treatment Effects for Survival Data with Doubly Doubly Robust Estimator
Authors: Guanghui Pan
Date: 2024-09-02 00:00:00
Tasks: Causal Inference, Survival Analysis

In this paper, we introduce a doubly doubly robust estimator for the average and heterogeneous treatment effect for left-truncated-right-censored (LTRC) survival data. In causal inference for survival functions in LTRC survival data, two missing data issues are noteworthy: one is the missing data of counterfactuals for causal inference, and the other is the missing data due to truncation and censoring. Based on previous research on non-parametric deep learning estimation in survival analysis, this paper proposes an algorithm to obtain an efficient estimate of the average and heterogeneous causal effect. We simulate the data and compare our methods with the marginal hazard ratio estimation, the naive plug-in estimation, and the doubly robust causal with Cox Proportional Hazard estimation and illustrate the advantages and disadvantages of the model application.
Price effects and pass-through of a VAT increase on restaurants in Germany: causal evidence for the first months and a mega sports event
Authors: Matthias Firgo
Date: 2024-09-02 00:00:00
Tasks: counterfactual

This paper analyses the price effects and tax pass-through of a VAT increase from 7% to 19% on restaurant services in Germany as of January 1, 2024. The Synthetic Control Method (SCM) is used to identify the causal effects of this reform using prices of goods and services unaffected by the tax change as a counterfactual for restaurant prices. Immediately in January, 31% of the tax increase was passed on to consumer prices. Pass-through increased to 58% in the following six months, which corresponds to a causal consumer price increase of about 6.5%. The presumed increase in demand for gastronomy services due to hosting the UEFA Euro 2024 tournament did not alter the path of price adjustments compared to previous months.