pwc-archive/papers-with-abstracts huggingface dataset:
Quantifying age-specific household contacts in Aotearoa New Zealand for infectious disease modelling
Authors: Caleb Sullivan, Pubudu Senanayake, Michael J. Plank
Date: 2024-04-04 00:00:00

Accounting for population age structure and age-specific contact patterns is crucial for accurate modelling of human infectious disease dynamics and impact. A common approach is to use contact matrices, which estimate the number of contacts between individuals of different ages. These contact matrices are frequently based on data collected from populations with very different demographic and socioeconomic characteristics from the population of interest. Here we use a comprehensive household composition dataset based on Aotearoa New Zealand census and administrative data to construct a household contact matrix and a synthetic population that can be used for modelling. We investigate the behaviour of a compartment-based and an agent-based epidemic model parameterised using this data, compared to a commonly used contact matrix that was constructed by projecting international data onto New Zealand's population. We find that using the New Zealand household data, either in a compartment-based model or in an agent-based model, leads to lower attack rates in older age groups compared to using the projected contact matrix. This difference becomes larger when household transmission is more dominant relative to non-household transmission. We provide electronic versions of the synthetic population and household contact matrix for other researchers to use in infectious disease models.
Efficient Quantum Circuits for Machine Learning Activation Functions including Constant T-depth ReLU
Authors: Wei Zi, Siyi Wang, Hyunji Kim, Xiaoming Sun, Anupam Chattopadhyay, Patrick Rebentrost
Date: 2024-04-09 00:00:00
Tasks: Quantum Machine Learning

In recent years, Quantum Machine Learning (QML) has increasingly captured the interest of researchers. Among the components in this domain, activation functions hold a fundamental and indispensable role. Our research focuses on the development of activation functions quantum circuits for integration into fault-tolerant quantum computing architectures, with an emphasis on minimizing $T$-depth. Specifically, we present novel implementations of ReLU and leaky ReLU activation functions, achieving constant $T$-depths of 4 and 8, respectively. Leveraging quantum lookup tables, we extend our exploration to other activation functions such as the sigmoid. This approach enables us to customize precision and $T$-depth by adjusting the number of qubits, making our results more adaptable to various application scenarios. This study represents a significant advancement towards enhancing the practicality and application of quantum machine learning.
Regression Discontinuity Design with Spillovers
Authors: Eric Auerbach, Yong Cai, Ahnaf Rafi
Date: 2024-04-09 00:00:00
Tasks: regression

Researchers who estimate treatment effects using a regression discontinuity design (RDD) typically assume that there are no spillovers between the treated and control units. This may be unrealistic. We characterize the estimand of RDD in a setting where spillovers occur between units that are close in their values of the running variable. Under the assumption that spillovers are linear-in-means, we show that the estimand depends on the ratio of two terms: (1) the radius over which spillovers occur and (2) the choice of bandwidth used for the local linear regression. Specifically, RDD estimates direct treatment effect when radius is of larger order than the bandwidth, and total treatment effect when radius is of smaller order than the bandwidth. In the more realistic regime where radius is of similar order as the bandwidth, the RDD estimand is a mix of the above effects. To recover direct and spillover effects, we propose incorporating estimated spillover terms into local linear regression -- the local analog of peer effects regression. We also clarify the settings under which the donut-hole RD is able to eliminate the effects of spillovers.
Common Trends and Long-Run Identification in Nonlinear Structural VARs
Authors: James A. Duffy, Sophocles Mavroeidis
Date: 2024-04-08 00:00:00
Tasks: Time Series

While it is widely recognised that linear (structural) VARs may fail to capture important aspects of economic time series, the use of nonlinear SVARs has to date been almost entirely confined to the modelling of stationary time series, because of a lack of understanding as to how common stochastic trends may be accommodated within nonlinear models. This has unfortunately circumscribed the range of series to which such models can be applied -- and/or required that these series be first transformed to stationarity, a potential source of misspecification -- and prevented the use of long-run identifying restrictions in these models. To address these problems, we develop a flexible class of additively time-separable nonlinear SVARs, which subsume models with threshold-type endogenous regime switching, both of the piecewise linear and smooth transition varieties. We extend the Granger--Johansen representation theorem to this class of models, obtaining conditions that specialise exactly to the usual ones when the model is linear. We further show that, as a corollary, these models are capable of supporting the same kinds of long-run identifying restrictions as are available in linearly cointegrated SVARs.
Estimating granular house price distributions in the Australian market using Gaussian mixtures
Authors: Willem P Sijp, Anastasios Panagiotelis
Date: 2024-04-08 00:00:00

A new methodology is proposed to approximate the time-dependent house price distribution at a fine regional scale using Gaussian mixtures. The means, variances and weights of the mixture components are related to time, location and dwelling type through a non linear function trained by a deep functional approximator. Price indices are derived as means, medians, quantiles or other functions of the estimated distributions. Price densities for larger regions, such as a city, are calculated via a weighted sum of the component density functions. The method is applied to a data set covering all of Australia at a fine spatial and temporal resolution. In addition to enabling a detailed exploration of the data, the proposed index yields lower prediction errors in the practical task of individual dwelling price projection from previous sales values within the three major Australian cities. The estimated quantiles are also found to be well calibrated empirically, capturing the complexity of house price distributions.
Context-dependent Causality (the Non-Nonotonic Case)
Authors: Nir Billfeld, Moshe Kim
Date: 2024-04-07 00:00:00
Tasks: Causal Inference

We develop a novel identification strategy as well as a new estimator for context-dependent causal inference in non-parametric triangular models with non-separable disturbances. Departing from the common practice, our analysis does not rely on the strict monotonicity assumption. Our key contribution lies in leveraging on diffusion models to formulate the structural equations as a system evolving from noise accumulation to account for the influence of the latent context (confounder) on the outcome. Our identifiability strategy involves a system of Fredholm integral equations expressing the distributional relationship between a latent context variable and a vector of observables. These integral equations involve an unknown kernel and are governed by a set of structural form functions, inducing a non-monotonic inverse problem. We prove that if the kernel density can be represented as an infinite mixture of Gaussians, then there exists a unique solution for the unknown function. This is a significant result, as it shows that it is possible to solve a non-monotonic inverse problem even when the kernel is unknown. On the methodological front we leverage on a novel and enriched Contaminated Generative Adversarial (Neural) Networks (CONGAN) which we provide as a solution to the non-monotonic inverse problem.
Towards a generalized accessibility measure for transportation equity and efficiency
Authors: Rajat Verma, Mithun Debnath, Shagun Mittal, Satish V. Ukkusuri
Date: 2024-04-07 00:00:00

Locational measures of accessibility are widely used in urban and transportation planning to understand the impact of the transportation system on influencing people's access to places. However, there is a considerable lack of measurement standards and publicly available data. We propose a generalized measure of locational accessibility that has a comprehensible form for transportation planning analysis. This metric combines the cumulative opportunities approach with gravity-based measures and is capable of catering to multiple trip purposes, travel modes, cost thresholds, and scales of analysis. Using data from multiple publicly available datasets, this metric is computed by trip purpose and travel time threshold for all block groups in the United States, and the data is made publicly accessible. Further, case studies of three large metropolitan areas reveal substantial inefficiencies in transportation infrastructure, with the most inefficiency observed in sprawling and non-core urban areas, especially for bicycling. Subsequently, it is shown that targeted investment in facilities can contribute to a more equitable distribution of accessibility to essential shopping and service facilities. By assigning greater weights to socioeconomically disadvantaged neighborhoods, the proposed metric formally incorporates equity considerations into transportation planning, contributing to a more equitable distribution of accessibility to essential services and facilities.
Stratifying on Treatment Status
Authors: Jinyong Hahn, John Ham, Geert Ridder, Shuyang Sheng
Date: 2024-04-06 00:00:00

We study the estimation of treatment effects using samples stratified by treatment status. Standard estimators of the average treatment effect and the local average treatment effect are inconsistent in this setting. We propose consistent estimators and characterize their asymptotic distributions.
Absolute Technical Efficiency Indices
Authors: Montacer Ben Cheikh Larbi, Sina Belkhiria
Date: 2024-04-06 00:00:00

Technical efficiency indices (TEIs) can be estimated using the traditional stochastic frontier analysis approach, which yields relative indices that do not allow self-interpretations. In this paper, we introduce a single-step estimation procedure for TEIs that eliminates the need to identify best practices and avoids imposing restrictive hypotheses on the error term. The resulting indices are absolute and allow for individual interpretation. In our model, we estimate a distance function using the inverse coefficient of resource utilization, rather than treating it as unobservable. We employ a Tobit model with a translog distance function as our econometric framework. Applying this model to a sample of 19 airline companies from 2012 to 2021, we find that: (1) Absolute technical efficiency varies considerably between companies with medium-haul European airlines being technically the most efficient, while Asian airlines are the least efficient; (2) Our estimated TEIs are consistent with the observed data with a decline in efficiency especially during the Covid-19 crisis and Brexit period; (3) All airlines contained in our sample would be able to increase their average technical efficiency by 0.209% if they reduced their average kerosene consumption by 1%; (4) Total factor productivity (TFP) growth slowed between 2013 and 2019 due to a decrease in Disembodied Technical Change (DTC) and a small effect from Scale Economies (SE). Toward the end of our study period, TFP growth seemed increasingly driven by the SE effect, with a sharp decline in 2020 followed by an equally sharp recovery in 2021 for most airlines.
Fast and simple inner-loop algorithms of static / dynamic BLP estimations
Authors: Takeshi Fukasawa
Date: 2024-04-06 00:00:00

This study investigates computationally efficient inner-loop algorithms for estimating static/dynamic BLP models. It provides the following ideas for reducing the number of inner-loop iterations: (1). Add a term relating to the outside option share in the BLP contraction mapping; (2). Analytically represent the mean product utilities as a function of value functions and solve for value functions (for dynamic BLP); (3). Combine an acceleration method of fixed-point iterations, especially the Anderson acceleration. They are independent and easy to implement. This study shows the good performance of these methods using numerical experiments.
Challenges in Finding Stable Price Zones in European Electricity Markets: Aiming to Square the Circle?
Authors: Teodora Dobos, Martin Bichler, Johannes Knörr
Date: 2024-04-09 00:00:00
Tasks: Clustering

The European day-ahead electricity market is split into multiple bidding zones with a uniform price. The increase in renewables leads to a growing number of interventions in the generation of energy sources and increasing redispatch costs. To ensure efficient congestion management, the EU Commission mandated a Bidding Zone Review (BZR) to reevaluate the configuration of European bidding zones. An integral part of this process was a locational marginal pricing study. Based on these prices, alternative bidding zone configurations were proposed. These bidding zones shall be stable and robust over time. For Germany, four configurations were suggested. We analyzed the proposed configurations considering different clustering algorithms and periods based on the publicly released data set in the context of the BZR, and found that the configurations do not reduce the price standard deviations within zones much, and the average prices across zones are similar. Other configurations identified based on clustering the prices lead to lower price variance but they are not geographically coherent. Independent of the clustering features and algorithms used, the resulting clusters are not stable over time. Interestingly, the effect of a split on prices would be low based on an analysis of the BZR data set.
High-skilled Human Workers in Non-Routine Jobs are Susceptible to AI Automation but Wage Benefits Differ between Occupations
Authors: Pelin Ozgul, Marie-Christine Fregin, Michael Stops, Simon Janssen, Mark Levels
Date: 2024-04-09 00:00:00

Artificial Intelligence (AI) will change human work by taking over specific job tasks, but there is a debate which tasks are susceptible to automation, and whether AI will augment or replace workers and affect wages. By combining data on job tasks with a measure of AI susceptibility, we show that more highly skilled workers are more susceptible to AI automation, and that analytical non-routine tasks are at risk to be impacted by AI. Moreover, we observe that wage growth premiums for the lowest and the highest required skill level appear unrelated to AI susceptibility and that workers in occupations with many routine tasks saw higher wage growth if their work was more strongly susceptible to AI. Our findings imply that AI has the potential to affect human workers differently than canonical economic theories about the impact of technology on work these theories predict.
Towards a representative social cost of carbon
Authors: Jinchi Dong, Richard S. J. Tol, Fangzhi Wang
Date: 2024-04-07 00:00:00

The majority of estimates of the social cost of carbon use preference parameters calibrated to data for North America and Europe. We here use representative data for attitudes to time and risk across the world. The social cost of carbon is substantially higher in the global north than in the south. The difference is more pronounced if we count people rather than countries.
Two-Sided Flexibility in Platforms
Authors: Daniel Freund, Sébastien Martin, Jiayu Kamessi Zhao
Date: 2024-04-06 00:00:00

Flexibility is a cornerstone of operations management, crucial to hedge stochasticity in product demands, service requirements, and resource allocation. In two-sided platforms, flexibility is also two-sided and can be viewed as the compatibility of agents on one side with agents on the other side. Platform actions often influence the flexibility on either the demand or the supply side. But how should flexibility be jointly allocated across different sides? Whereas the literature has traditionally focused on only one side at a time, our work initiates the study of two-sided flexibility in matching platforms. We propose a parsimonious matching model in random graphs and identify the flexibility allocation that optimizes the expected size of a maximum matching. Our findings reveal that flexibility allocation is a first-order issue: for a given flexibility budget, the resulting matching size can vary greatly depending on how the budget is allocated. Moreover, even in the simple and symmetric settings we study, the quest for the optimal allocation is complicated. In particular, easy and costly mistakes can be made if the flexibility decisions on the demand and supply side are optimized independently (e.g., by two different teams in the company), rather than jointly. To guide the search for optimal flexibility allocation, we uncover two effects, flexibility cannibalization, and flexibility abundance, that govern when the optimal design places the flexibility budget only on one side or equally on both sides. In doing so we identify the study of two-sided flexibility as a significant aspect of platform efficiency.
Breaking New Ground, Reinforcing Old Gaps: Gender Disparities in Access to Emerging Research Frontiers
Authors: Carolina Biliotti, Luca Verginer, Massimo Riccaboni
Date: 2024-04-06 00:00:00
Tasks: Epidemiology

This study exploits COVID-19 as an exogenous shock in biomedical research to show how the emergence of an unexpected new research topic exacerbates gender bias in key authorship positions of scientific publications relevant to new research topics (e.g. Vaccines, Epidemiology). We determine author's gender based on the names listed on their scientific publications and analyze the changes in the composition of the scientific teams after the COVID-19 outbreak. Using a Difference-in-Differences approach, we find that although the share of female authorship has increased overall, women are less likely to be first or last authors (the most prestigious positions) on COVID-19-related research papers and more likely to be found in middle author positions. Stay-at-home mandates, the journal importance and funding opportunities do not fully account for the decline of women in key author positions. The main difference in first authorship is due to the composition of the team and the experience of the lead authors in COVID-19 related research. First authorship by women declined after teams of novices emerged, where lead authors have no prior experience in COVID-related research. Discretionality in first-author appointments for newcomers, combined with high pressure to publish quickly, may have led to discriminatory biases. Conversely, there may also be differences in risk-taking attitudes in doing research in unfamiliar domains. Monitoring gender inequality in scientific production is crucial for reducing gender inequalities and for implementing timely policies that ensure equal access to emerging research topics.
Early Adoption of Generative AI by Global Business Leaders: Insights from an INSEAD Alumni Survey
Authors: Jason P Davis, Jian Bai Li
Date: 2024-04-06 00:00:00

How are new technologies like generative AI quickly adopted and used by executive and managerial leaders to create value in organizations? A survey of INSEAD's global alumni base revealed several intriguing insights into perceptions and engagements with generative AI across a broad spectrum of demographics, industries, and geographies. Notably, there's a prevailing optimism about the role of generative AI in enhancing productivity and innovation, as evidenced by the 90% of respondents being excited about its time-saving and efficiency benefits. Analysis revealed different attitudes about adoption and use across demographic variables. Younger respondents are significantly more excited about generative AI and more likely to be using it at work and in personal life than older participants. Those in Europe have a somewhat more distant view of generative AI than those in North America in Asia, in that they see the gains more likely to be captured by organizations than individuals, and are less likely to be using it in professional and personal contexts than those in North America and Asia. This may also be related to the fact that those in Europe are more likely to be working in Financial Services and less likely to be working in Information Technology industries than those in North America and Asia. Despite this, those in Europe are more likely to see AGI happening faster than those in North America, although this may reflect less interaction with generative AI in personal and professional contexts. These findings collectively underscore the complex and multifaceted perceptions of generative AI's role in society, pointing to both its promising potential and the challenges it presents.
Super Apps and the Digital Markets Act
Authors: Simonetta Vezzoso
Date: 2024-04-06 00:00:00
Tasks: Fairness

The Digital Markets Act (DMA) aims to ensure contestability and fairness in digital markets, particularly focusing on regulating Big Tech companies. The paper explores the DMA's capacity to address both current and future challenges in digital market contestability and fairness, spotlighting the trend towards platform integration and the potential rise of "super-apps" akin to WeChat and KakaoTalk. Specifically, it investigates WhatsApp, owned by Meta, as a gatekeeper that might expand its service offerings, integrating additional functionalities like AI and metaverse technologies. The paper discusses whether the DMA's obligations, such as mandated interoperability and data portability, can mitigate the emergent risks to market fairness and contestability from such integrations. Despite recognizing that the DMA has the potential to address many issues arising from platform integration, it suggests the necessity for adaptability and a complementary relationship with traditional antitrust law to ensure sustained contestability and fairness in evolving digital markets.
Estimating Contagion Mechanism in Global Equity Market with Time-Zone Effect
Authors: Boyao Wu, Difang Huang, Muzi Chen
Date: 2024-04-05 00:00:00

This paper proposes a time-zone vector autoregression (VAR) model to investigate comovements in the global financial market. Analyzing daily data from 36 national equity markets, we explore the subprime and European debt crises using static analysis and the COVID-19 crisis through a rolling window method. Our study of comovements using VAR coefficients reveals a resonance effect in the global system. Findings on densities and assortativities suggest the existence of the transmission mechanism in all periods and abnormal structural changes during the crises. Strength analysis uncovers the information transmission mechanism across continents over normal and turmoil periods and emphasizes specific stock markets' unique roles. We examine dynamic continent strengths to demonstrate the contagion mechanism in the global equity market over an extended period. Incorporating the time-zone effect significantly enhances the VAR model's interpretability. Signed networks provide more information on global equity markets and better identifies critical contagion patterns than unsigned networks.