pwc-archive/papers-with-abstracts huggingface dataset:
Urban Housing Prices and Migration's Fertility Intentions: Based on the 2018 China Migrants' Dynamic Survey
Authors: Jingwen Tan, Shixi Kang
Date: 2021-12-14 00:00:00

While the size of China's mobile population continues to expand, the fertility rate is significantly lower than the stable generation replacement level of the population, and the structural imbalance of human resource supply has attracted widespread attention. This paper uses LPM and Probit models to estimate the impact of house prices on the fertility intentions of the mobile population based on data from the 2018 National Mobile Population Dynamics Monitoring Survey. The lagged land sales price is used as an instrumental variable of house price to mitigate the potential endogeneity problem. The results show that for every 100\% increase in the ratio of house price to household income of mobile population, the fertility intention of the female mobile population of working age at the inflow location will decrease by 4.42\%, and the marginal effect of relative house price on labor force fertility intention is EXP(-0.222); the sensitivity of mobile population fertility intention to house price is affected by the moderating effect of infrastructure construction at the inflow location. The willingness to have children in the inflow area is higher for female migrants of working age with lower age, smaller family size and higher education. Based on the above findings, the study attempts to provide a new practical perspective for the mainline institutional change and balanced economic development in China's economic transition phase.
Henderson--Chu model extended to two heterogeneous groups
Authors: Oliver Chiriac, Jonathan Hall
Date: 2021-11-27 00:00:00
Tasks: model, Vocal Bursts Valence Prediction

The goal of this paper is to revise the Henderson-Chu approach by dividing the commuters into two income-based groups: the `rich' and the `poor'. The rich are clearly more flexible in their arrival times and would rather have more schedule delay cost instead of travel delay. The poor are quite inflexible as they have to arrive at work at a specified time -- travel delay cost is preferred over schedule delay cost. We combined multiple models of peak-load bottleneck congestion with and without toll-pricing to generate a Pareto improvement in Lexus lanes.
Observability, Dominance, and Induction in Learning Models
Authors: Daniel Clark, Drew Fudenberg, Kevin He
Date: 2022-01-03 00:00:00
Tasks: Form

Learning models do not in general imply that weakly dominated strategies are irrelevant or justify the related concept of "forward induction," because rational agents may use dominated strategies as experiments to learn how opponents play, and may not have enough data to rule out a strategy that opponents never use. Learning models also do not support the idea that the selected equilibria should only depend on a game's normal form, even though two games with the same normal form present players with the same decision problems given fixed beliefs about how others play. However, playing the extensive form of a game is equivalent to playing the normal form augmented with the appropriate terminal node partitions so that two games are information equivalent, i.e., the players receive the same feedback about others' strategies.
A Taxonomy of Non-dictatorial Unidimensional Domains
Authors: Shurojit Chatterji, Huaxia Zeng
Date: 2022-01-03 00:00:00
Tasks: Classification

A preference domain is called a non-dictatorial domain if it allows the design of unanimous social choice functions (henceforth, rules) that are non-dictatorial and strategy-proof. We study a class of preference domains called unidimensional domains and establish that the unique seconds property (introduced by Aswal, Chatterji, and Sen (2003)) characterizes all non-dictatorial domains. The principal contribution is the subsequent exhaustive classification of all non-dictatorial, unidimensional domains and canonical strategy-proof rules on these domains, based on a simple property of two-voter rules called invariance. The preference domains that constitute the classification are semi-single-peaked domains (introduced by Chatterji, Sanver, and Sen (2013)) and semi-hybrid domains (introduced here) which are two appropriate weakenings of single-peaked domains and are shown to allow strategy-proof rules to depend on non-peak information of voters' preferences; the canonical rules for these domains are the projection rule and the hybrid rule respectively. As a refinement of the classification, single-peaked domains and hybrid domains emerge as the only unidimensional domains that force strategy-proof rules to be determined completely by voters' preference peaks.
Fuzzy Core Equivalence in Large Economies: A Role for the Infinite-Dimensional Lyapunov Theorem
Authors: M. Ali Khan, Nobusumi Sagara
Date: 2021-12-31 00:00:00

We present the equivalence between the fuzzy core and the core under minimal assumptions. Due to the exact version of the Lyapunov convexity theorem in Banach spaces, we clarify that the additional structure of commodity spaces and preferences is unnecessary whenever the measure space of agents is "saturated". As a spin-off of the above equivalence, we obtain the coincidence of the core, the fuzzy core, and the Schmeidler's restricted core under minimal assumptions. The coincidence of the fuzzy core and the restricted core has not been articulated anywhere.
On the Equivalence of Two Competing Affirmative Actions in School Choice
Authors: Yun Liu
Date: 2021-12-28 00:00:00
Tasks: Vocal Bursts Valence Prediction

This note analyzes the outcome equivalence conditions of two popular affirmative action policies, majority quota and minority reserve, under the student optimal stable mechanism. These two affirmative actions generate an identical matching outcome, if the market either is effectively competitive or contains a sufficiently large number of schools.
Uniformly Self-Justified Equilibria
Authors: Felix Kubler, Simon Scheidegger
Date: 2021-12-28 00:00:00

We consider dynamic stochastic economies with heterogeneous agents and introduce the concept of uniformly self-justified equilibria (USJE) -- temporary equilibria for which forecasts are best uniform approximations to a selection of the equilibrium correspondence. In a USJE, individuals' forecasting functions for the next period's endogenous variables are assumed to lie in a compact, finite-dimensional set of functions, and the forecasts constitute the best approximation within this set. We show that USJE always exist and develop a simple algorithm to compute them. Therefore, they are more tractable than rational expectations equilibria that do not always exist. As an application, we discuss a stochastic overlapping generations exchange economy and provide numerical examples to illustrate the concept of USJE and the computational method.
Designing Direct Matching Mechanism for India with Comprehensive Affirmative Action
Authors: Orhan Aygün, Bertan Turhan
Date: 2020-04-28 00:00:00

Since 1950, India has been implementing the most comprehensive affirmative action program in the world. Vertical reservations are provided to members of historically discriminated Scheduled Castes (SC), Scheduled Tribes (ST), and Other Backward Classes (OBC). Horizontal reservations are provided for other disadvantaged groups, such as women and disabled people, within each vertical category. There is no well-defined procedure to implement horizontal reservations jointly with vertical reservation and OBC de-reservations. Sequential processes currently in use for OBC de-reservations and meritorious reserve candidates lead to severe shortcomings. Most importantly, indirect mechanisms currently used in practice do not allow reserve category applicants to fully express their preferences. To overcome these and other related issues, we design several different choice rules for institutions that take meritocracy, vertical and horizontal reservations, and OBC de-reservations into account. We propose a centralized mechanism to satisfactorily clear matching markets in India.
The Inflation Game
Authors: Wolfgang Kuhle
Date: 2021-12-26 00:00:00

We study a game where households convert paper assets, such as money, into consumption goods, to preempt inflation. The game features a unique equilibrium with high (low) inflation, if money supply is high (low). For intermediate levels of money supply, there exist multiple equilibria with either high or low inflation. Equilibria with moderate inflation, however, do not exist, and can thus not be targeted by a central bank. That is, depending on agents' equilibrium play, money supply is always either too high or too low for moderate inflation. We also show that inflation rates of long-lived goods, such as houses, cars, expensive watches, furniture, or paintings, are a leading indicator for broader, economy wide, inflation.
Identification of misreported beliefs
Authors: Elias Tsakas
Date: 2021-12-24 00:00:00

It is well-known that subjective beliefs cannot be identified with traditional choice data unless we impose the strong assumption that preferences are state-independent. This is seen as one of the biggest pitfalls of incentivized belief elicitation. The two common approaches are either to exogenously assume that preferences are state-independent, or to use intractable elicitation mechanisms that require an awful lot of hard-to-get non-traditional choice data. In this paper we use a third approach, introducing a novel methodology that retains the simplicity of standard elicitation mechanisms without imposing the awkward state-independence assumption. The cost is that instead of insisting on full identification of beliefs, we seek identification of misreporting. That is, we elicit beliefs with a standard simple elicitation mechanism, and then by means of a single additional observation we can tell whether the reported beliefs deviate from the actual beliefs, and if so, in which direction they do.
Bidding in Multi-Unit Auctions under Limited Information
Authors: Bernhard Kasberger, Kyle Woodward
Date: 2021-12-21 00:00:00

We study multi-unit auctions in which bidders have limited knowledge of opponent strategies and values. We characterize optimal prior-free bids; these bids minimize the maximal loss in expected utility resulting from uncertainty surrounding opponent behavior. Optimal bids are readily computable despite bidders having multi-dimensional private information, and in certain cases admit closed-form solutions. In the pay-as-bid auction the minimax-loss bid is unique; in the uniform-price auction the minimax-loss bid is unique if the bidder is allowed to determine the quantities for which they bid, as in many practical applications. We compare minimax-loss bids and auction outcomes across auction formats, and derive testable predictions.
Contextually Private Mechanisms
Authors: Andreas Haupt, Zoë Hitzig
Date: 2021-12-20 00:00:00

We introduce a framework for comparing the privacy of different mechanisms. A mechanism designer employs a dynamic protocol to elicit agents' private information. Protocols produce a set of contextual privacy violations -- information learned about agents that may be superfluous given the context. A protocol is maximally contextually private if there is no protocol that produces a subset of the violations it produces, while still implementing the choice rule. We show that selecting a maximally contextually private protocol involves a deliberate decision about whose privacy is most important to protect, and these protocols delay questions to those they aim to protect. Taking the second-price auction rule as an instructive example, we derive two novel designs that are maximally contextually private: the ascending-join and overdescending-join protocols.
A dynamic theory of spatial externalities
Authors: Raouf Boucekkine, Giorgio Fabbri, Salvatore Federico, Fausto Gozzi
Date: 2021-12-20 00:00:00

We characterize the shape of spatial externalities in a continuous time and space differential game with transboundary pollution. We posit a realistic spatiotemporal law of motion for pollution (diffusion and advection), and tackle spatiotemporal non-cooperative (and cooperative) differential games. Precisely, we consider a circle partitioned into several states where a local authority decides autonomously about its investment, production and depollution strategies over time knowing that investment/production generates pollution, and pollution is transboundary. The time horizon is infinite. We allow for a rich set of geographic heterogeneities across states. We solve analytically the induced non-cooperative differential game and characterize its long-term spatial distributions. In particular, we prove that there exist a Perfect Markov Equilibrium, unique among the class of the affine feedbacks. We further provide with a full exploration of the free riding problem and the associated border effect.
Algorithm Design: A Fairness-Accuracy Frontier
Authors: Annie Liang, Jay Lu, Xiaosheng Mu, Kyohei Okumura
Date: 2021-12-18 00:00:00
Tasks: Fairness

Algorithm designers increasingly optimize not only for accuracy, but also for the fairness of the algorithm across pre-defined groups. We study the tradeoff between fairness and accuracy for any given set of inputs to the algorithm. We propose and characterize a fairness-accuracy frontier, which consists of the optimal points across a broad range of preferences over fairness and accuracy. Our results identify a simple property of the inputs, group-balance, which qualitatively determines the shape of the frontier. We further study an information-design problem where the designer flexibly regulates the inputs (e.g., by coarsening an input or banning its use) but the algorithm is chosen by another agent. Whether it is optimal to ban an input generally depends on the designer's preferences. But when inputs are group-balanced, then excluding group identity is strictly suboptimal for all designers, and when the designer has access to group identity, then it is strictly suboptimal to exclude any informative input.
Distance Functions and Generalized Means: Duality and Taxonomy
Authors: Walter Briec
Date: 2021-12-17 00:00:00

This paper introduces in production theory a large class of efficiency measures that can be derived from the notion of utility function. This article also establishes a relation between these distance functions and Stone-Geary utility functions. More specifically, the paper focusses on new distance function that generalizes several existing efficiency measures. The new distance function is inspired from the Atkinson inequality index and maximizes the sum of the netput expansions required to reach an efficient point. A generalized duality theorem is proved and a duality result linking the new distance functions and the profit function is obtained. For all feasible production vectors, it includes as special cases most of the dual correspondences previously established in the literature. Finally, we identify a large class of measures for which these duality results can be obtained without convexity.
Taxes and Market Power: A Principal Components Approach
Authors: Andrea Galeotti, Benjamin Golub, Sanjeev Goyal, Eduard Talamàs, Omer Tamuz
Date: 2021-12-15 00:00:00

Suppliers of differentiated goods make simultaneous pricing decisions, which are strategically linked. Because of market power, the equilibrium is inefficient. We study how a policymaker should target a budget-balanced tax-and-subsidy policy to increase welfare. A key tool is a certain basis for the goods space, determined by the network of interactions among suppliers. It consists of eigenbundles -- orthogonal in the sense that a tax on any eigenbundle passes through only to its own price -- with pass-through coefficients determined by associated eigenvalues. Our basis permits a simple characterization of optimal interventions. A planner maximizing consumer surplus should tax eigenbundles with low pass-through and subsidize ones with high pass-through. The Pigouvian leverage of the system -- the gain in consumer surplus achievable by an optimal tax scheme -- depends only on the dispersion of the eigenvalues of the matrix of strategic interactions. We interpret these results in terms of the network structure of the market.
TFCN: Temporal-Frequential Convolutional Network for Single-Channel Speech Enhancement
Authors: Xupeng Jia, Dongmei Li
Date: 2022-01-03 00:00:00
Tasks: Speech Enhancement

Deep learning based single-channel speech enhancement tries to train a neural network model for the prediction of clean speech signal. There are a variety of popular network structures for single-channel speech enhancement, such as TCNN, UNet, WaveNet, etc. However, these structures usually contain millions of parameters, which is an obstacle for mobile applications. In this work, we proposed a light weight neural network for speech enhancement named TFCN. It is a temporal-frequential convolutional network constructed of dilated convolutions and depth-separable convolutions. We evaluate the performance of TFCN in terms of Short-Time Objective Intelligibility (STOI), perceptual evaluation of speech quality (PESQ) and a series of composite metrics named Csig, Cbak and Covl. Experimental results show that compared with TCN and several other state-of-the-art algorithms, the proposed structure achieves a comparable performance with only 93,000 parameters. Further improvement can be achieved at the cost of more parameters, by introducing dense connections and depth-separable convolutions with normal ones. Experiments also show that the proposed structure can work well both in causal and non-causal situations.
Task-specific Optimization of Virtual Channel Linear Prediction-based Speech Dereverberation Front-End for Far-Field Speaker Verification
Authors: Joon-Young Yang, Joon-Hyuk Chang
Date: 2021-12-27 00:00:00
Tasks: Denoising, Speaker Verification, Speech Denoising, Speech Dereverberation

Developing a single-microphone speech denoising or dereverberation front-end for robust automatic speaker verification (ASV) in noisy far-field speaking scenarios is challenging. To address this problem, we present a novel front-end design that involves a recently proposed extension of the weighted prediction error (WPE) speech dereverberation algorithm, the virtual acoustic channel expansion (VACE)-WPE. It is demonstrated experimentally in this study that unlike the conventional WPE algorithm, the VACE-WPE can be explicitly trained to cancel out both late reverberation and background noise. To build the front-end, the VACE-WPE is first independently (pre)trained to produce "noisy" dereverberated signals. Subsequently, given a pretrained speaker embedding model, the VACE-WPE is additionally fine-tuned within a task-specific optimization (TSO) framework, causing the speaker embedding extracted from the processed signal to be similar to that extracted from the "noise-free" target signal. Moreover, to extend the application of the proposed front-end to more general, unconstrained "in-the-wild" ASV scenarios beyond controlled far-field conditions, we propose a distortion regularization method for the VACE-WPE within the TSO framework. The effectiveness of the proposed approach is verified on both far-field and in-the-wild ASV benchmarks, demonstrating its superiority over fully neural front-ends and other TSO methods in various cases.
DPCCN: Densely-Connected Pyramid Complex Convolutional Network for Robust Speech Separation And Extraction
Authors: Jiangyu Han, Yanhua Long, Lukas Burget, Jan Cernocky
Date: 2021-12-27 00:00:00
Tasks: Speech Extraction, Speech Separation

In recent years, a number of time-domain speech separation methods have been proposed. However, most of them are very sensitive to the environments and wide domain coverage tasks. In this paper, from the time-frequency domain perspective, we propose a densely-connected pyramid complex convolutional network, termed DPCCN, to improve the robustness of speech separation under complicated conditions. Furthermore, we generalize the DPCCN to target speech extraction (TSE) by integrating a new specially designed speaker encoder. Moreover, we also investigate the robustness of DPCCN to unsupervised cross-domain TSE tasks. A Mixture-Remix approach is proposed to adapt the target domain acoustic characteristics for fine-tuning the source model. We evaluate the proposed methods not only under noisy and reverberant in-domain condition, but also in clean but cross-domain conditions. Results show that for both speech separation and extraction, the DPCCN-based systems achieve significantly better performance and robustness than the currently dominating time-domain methods, especially for the cross-domain tasks. Particularly, we find that the Mixture-Remix fine-tuning with DPCCN significantly outperforms the TD-SpeakerBeam for unsupervised cross-domain TSE, with around 3.5 dB SISNR improvement on target domain test set, without any source domain performance degradation.
Noisy Speech Based Temporal Decomposition to Improve Fundamental Frequency Estimation
Authors: A. Queiroz, R. Coelho
Date: 2021-12-18 00:00:00

This paper introduces a novel method to separate noisy speech into low or high frequency frames, in order to improve fundamental frequency (F0) estimation accuracy. In this proposal, the target signal is analyzed by means of the ensemble empirical mode decomposition. Next, the pitch information is extracted from the first decomposition modes. This feature indicates the frequency region where the F0 of speech should be located, thus separating the frames into low-frequency (LF) or high-frequency (HF). The separation is applied to correct candidates extracted from a conventional fundamental frequency detection method, and hence improving the accuracy of F0 estimate. The proposed method is evaluated in experiments with CSTR and TIMIT databases, considering six acoustic noises under various signal-to-noise ratios. A pitch enhancement algorithm is adopted as baseline in the evaluation analysis considering three conventional estimators. Results show that the proposed method outperforms the competing strategies, in terms of low/high frequency separation accuracy. Moreover, the performance metrics of the F0 estimation techniques show that the novel solution is able to better improve F0 detection accuracy when compared to competitive approaches under different noisy conditions.
RawNeXt: Speaker verification system for variable-duration utterances with deep layer aggregation and extended dynamic scaling policies
Authors: Ju-ho Kim, Hye-jin Shim, Jungwoo Heo, Ha-Jin Yu
Date: 2021-12-15 00:00:00
Tasks: Speaker Verification

Despite achieving satisfactory performance in speaker verification using deep neural networks, variable-duration utterances remain a challenge that threatens the robustness of systems. To deal with this issue, we propose a speaker verification system called RawNeXt that can handle input raw waveforms of arbitrary length by employing the following two components: (1) A deep layer aggregation strategy enhances speaker information by iteratively and hierarchically aggregating features of various time scales and spectral channels output from blocks. (2) An extended dynamic scaling policy flexibly processes features according to the length of the utterance by selectively merging the activations of different resolution branches in each block. Owing to these two components, our proposed model can extract speaker embeddings rich in time-spectral information and operate dynamically on length variations. Experimental results on the VoxCeleb1 test set consisting of various duration utterances demonstrate that RawNeXt achieves state-of-the-art performance compared to the recently proposed systems. Our code and trained model weights are available at https://github.com/wngh1187/RawNeXt.