pwc-archive/papers-with-abstracts huggingface dataset:
Big Data Analytics-Enabled Dynamic Capabilities and Market Performance: Examining the Roles of Marketing Ambidexterity and Competitor Pressure
Authors: Gulfam Haider, Laiba Zubair, Aman Saleem
Date: 2024-07-22 00:00:00
Tasks: Marketing, Navigate

This study, rooted in dynamic capability theory and the developing era of Big Data Analytics, explores the transformative effect of BDA EDCs on marketing. Ambidexterity and firms market performance in the textile sector of Pakistans cities. Specifically, focusing on the firms who directly deal with customers, investigates the nuanced role of BDA EDCs in textile retail firms potential to navigate market dynamics. Emphasizing the exploitation component of marketing ambidexterity, the study investigated the mediating function of marketing ambidexterity and the moderating influence of competitive pressure. Using a survey questionnaire, the study targets key choice makers in textile firms of Faisalabad, Chiniot and Lahore, Pakistan. The PLS-SEM model was employed as an analytical technique, allows for a full examination of the complicated relations between BDA EDCs, marketing ambidexterity, rival pressure, and market performance. The study Predicting a positive impact of Big Data on marketing ambidexterity, with a specific emphasis on exploitation. The study expects this exploitation-orientated marketing ambidexterity to significantly enhance the firms market performance. This research contributes to the existing literature on dynamic capabilities-based frameworks from the perspective of the retail segment of textile industry. The study emphasizes the role of BDA-EDCs in the retail sector, imparting insights into the direct and indirect results of BDA EDCs on market performance inside the retail area. The study s novelty lies in its contextualization of BDA-EDCs in the textile zone of Faisalabad, Lahore and Chiniot, providing a unique perspective on the effect of BDA on marketing ambidexterity and market performance in firms. Methodologically, the study uses numerous samples of retail sectors to make sure broader universality, contributing realistic insights.
Leveraging Uniformization and Sparsity for Computation of Continuous Time Dynamic Discrete Choice Games
Authors: Jason R. Blevins
Date: 2024-07-20 00:00:00
Tasks: Computational Efficiency

Continuous-time formulations of dynamic discrete choice games offer notable computational advantages, particularly in modeling strategic interactions in oligopolistic markets. This paper extends these benefits by addressing computational challenges in order to improve model solution and estimation. We first establish new results on the rates of convergence of the value iteration, policy evaluation, and relative value iteration operators in the model, holding fixed player beliefs. Next, we introduce a new representation of the value function in the model based on uniformization -- a technique used in the analysis of continuous time Markov chains -- which allows us to draw a direct analogy to discrete time models. Furthermore, we show that uniformization also leads to a stable method to compute the matrix exponential, an operator appearing in the model's log likelihood function when only discrete time "snapshot" data are available. We also develop a new algorithm that concurrently computes the matrix exponential and its derivatives with respect to model parameters, enhancing computational efficiency. By leveraging the inherent sparsity of the model's intensity matrix, combined with sparse matrix techniques and precomputed addresses, we show how to significantly speed up computations. These strategies allow researchers to estimate more sophisticated and realistic models of strategic interactions and policy impacts in empirical industrial organization.
Predicting the Distribution of Treatment Effects: A Covariate-Adjustment Approach
Authors: Bruno Fava
Date: 2024-07-19 00:00:00
Tasks: valid

Important questions for impact evaluation require knowledge not only of average effects, but of the distribution of treatment effects. What proportion of people are harmed? Does a policy help many by a little? Or a few by a lot? The inability to observe individual counterfactuals makes these empirical questions challenging. I propose an approach to inference on points of the distribution of treatment effects by incorporating predicted counterfactuals through covariate adjustment. I show that finite-sample inference is valid under weak assumptions, for example, when data come from a Randomized Controlled Trial (RCT), and that large-sample inference is asymptotically exact under suitable conditions. Finally, I revisit five RCTs in microcredit where average effects are not statistically significant and find evidence of both positive and negative treatment effects in household income. On average across studies, at least 13.6% of households benefited, and 12.5% were negatively affected.
Regression Adjustment for Estimating Distributional Treatment Effects in Randomized Controlled Trials
Authors: Tatsushi Oka, Shota Yasui, Yuta Hayakawa, Undral Byambadalai
Date: 2024-07-19 00:00:00
Tasks: regression

In this paper, we address the issue of estimating and inferring distributional treatment effects in randomized experiments. The distributional treatment effect provides a more comprehensive understanding of treatment heterogeneity compared to average treatment effects. We propose a regression adjustment method that utilizes distributional regression and pre-treatment information, establishing theoretical efficiency gains without imposing restrictive distributional assumptions. We develop a practical inferential framework and demonstrate its advantages through extensive simulations. Analyzing water conservation policies, our method reveals that behavioral nudges systematically shift consumption from high to moderate levels. Examining health insurance coverage, we show the treatment reduces the probability of zero doctor visits by 6.6 percentage points while increasing the likelihood of 3-6 visits. In both applications, our regression adjustment method substantially improves precision and identifies treatment effects that were statistically insignificant under conventional approaches.
Willingness to Pay for an Electricity Connection: A Choice Experiment Among Rural Households and Enterprises in Nigeria
Authors: Pouya Janghorban, Temilade Sesan, Muhammad-Kabir Salihu, Olayinka Ohunakin, Narges Chinichian
Date: 2024-07-22 00:00:00

Rural electrification initiatives worldwide frequently encounter financial planning challenges due to a lack of reliable market insights. This research delves into the preferences and marginal willingness to pay (mWTP) for upfront electricity connections in rural and peri-urban areas of Nigeria. We investigate discrete choice experiment data gathered from 3,599 households and 1,122 Small to Medium-sized Enterprises (SMEs) across three geopolitical zones of Nigeria, collected during the 2021 PeopleSuN project survey phase. Employing conditional logit modeling, we analyze this data to explore preferences and marginal willingness to pay for electricity connection. Our findings show that households prioritize nighttime electricity access, while SMEs place a higher value on daytime electricity. When comparing improvements in electricity capacity to medium or high-capacity, SMEs exhibit a sharp increase in willingness to pay for high-capacity, while households value the two options more evenly. Preferences for the electricity source vary among SMEs, but households display a reluctance towards diesel generators and a preference for the grid or solar solutions. Moreover, households with older heads express greater aversion to connection fees, and male-headed households show a stronger preference for nighttime electricity compared to their female-headed counterparts. The outcomes of this study yield pivotal insights to tailor electrification strategies for rural Nigeria, emphasizing the importance of considering the diverse preferences of households and SMEs.
Income, health, and spurious cointegration
Authors: José A. Tapia Granados, Edward L. Ionides
Date: 2024-07-22 00:00:00
Tasks: Time Series

Data for many nations show a long-run increase, over many decades, of income, indexed by GDP per capita, and population health, indexed by mortality or life expectancy at birth (LEB). However, the short-run and long-run relationships between these variables have been interpreted in different ways, and many controversies remain open. It has been claimed that population health and income are cointegrated, and that this demonstrates a positive long-run effect of income on population health. We show, however, that an empirically tested cointegration between LEB and GDP per capita is not a sound method to infer a causal link. For a given country it is easy to find computer-generated data or time series of real observations, related or unrelated to the country, that according to standard methods, are also cointegrated with the country's LEB. More generally, given a trending time series, it is easy to find other series, observational or artificial, that appear cointegrated with it. Thus, standard cointegration methodology, often used in empirical investigations, cannot distinguish whether cointegration relationships are spurious or causal.
The increasing share of low-value transactions in international trade
Authors: Raúl Mínguez, Asier Minondo
Date: 2024-07-22 00:00:00

This paper documents a new feature of international trade: the increase in the share of low-value transactions in the total volume of transactions. Using Spanish data, we show that the share of low-value transactions in the total number of transactions increased from 9% to 61% in exports and from 14% to 54% in imports between 1997 and 2023. The increase in the number of low-value trade transactions is explained by the rise of e-commerce and direct-to-customer sales facilitated by online retail platforms, and the fast-fashion strategy followed by clothing firms.
Deep Learning for Economists
Authors: Melissa Dell
Date: 2024-07-22 00:00:00
Tasks: Deep Learning

Deep learning provides powerful methods to impute structured information from large-scale, unstructured text and image datasets. For example, economists might wish to detect the presence of economic activity in satellite images, or to measure the topics or entities mentioned in social media, the congressional record, or firm filings. This review introduces deep neural networks, covering methods such as classifiers, regression models, generative AI, and embedding models. Applications include classification, document digitization, record linkage, and methods for data exploration in massive scale text and image corpora. When suitable methods are used, deep learning models can be cheap to tune and can scale affordably to problems involving millions or billions of data points.. The review is accompanied by a companion website, EconDL, with user-friendly demo notebooks, software resources, and a knowledge base that provides technical details and additional applications.
Industry Dynamics with Cartels: The Case of the Container Shipping Industry
Authors: Suguru Otani
Date: 2024-07-21 00:00:00
Tasks: counterfactual

I investigate how explicit cartels, known as ``shipping conferences", in a global container shipping market facilitated the formation of one of the largest globally integrated markets through entry, exit, and shipbuilding investment of shipping firms. Using a novel data, I develop and construct a structural model and find that the cartels shifted shipping prices by 20-50\% and encouraged firms' entry and investment. In the counterfactual, I find that cartels would increase producer surplus while slightly decreasing consumer surplus, then may increase social welfare by encouraging firms' entry and shipbuilding investment. This would validate industry policies controlling prices and quantities in the early stage of the new industry, which may not be always harmful. Investigating hypothetical allocation rules supporting large or small firms, I find that the actual rule based on tonnage shares is the best to maximize social welfare.
Temptation: Immediacy and certainty
Authors: J. Lucas Reddinger
Date: 2024-07-20 00:00:00

Is an option especially tempting when it is both immediate and certain? I test the effect of risk on the present-bias factor given quasi-hyperbolic discounting. In my experiment workers allocate about thirty to fifty minutes of real-effort tasks between two weeks. I study dynamic consistency by comparing choices made two days in advance of the workday with choices made when work is imminent. My novel design permits estimation of present bias using a decision with a consequence that is both immediate and certain. I find greater present bias when the consequence is certain. This finding has implications for any economic decision involving a present-biased decision-maker, including labor contracting and consumer good pricing. I offer a methodological remedy for experimental economists.
Mapping the disaggregated economy in real-time: Using granular payment network data to complement national accounts
Authors: Kerstin Hötte
Date: 2024-07-20 00:00:00
Tasks: Time Series

In an era of rapid change, timely and disaggregated economic insights are crucial for effective policymaking. This study explores the potential of real-time payment data to complement traditional economic measurement. Using anonmysed UK business payments from 2015-2023, we analysed inter-industry financial flows at a granular 5-digit SIC level and compared them systematically with established economic indicators such as GDP and input-output tables (IOTs). Our findings show strong correlations with GDP and qualitative consistency with official IOTs, highlighting the value of the novel high-frequency data for real-time economic monitoring. We also benchmarked network statistics at the 5-digit level, showing how industry-specific payment structures align with stylised facts from the empirical economic network literature. While outlining methodological and interpretative challenges, we discuss the integration of such bottom-up data into national accounts. This work contributes to ongoing efforts to advance economic measurement and offers additional tools for tracking economic dynamics in real time.
Similarity of Information and Collective Action
Authors: Deepal Basak, Joyee Deb, Aditya Kuvalekar
Date: 2024-07-20 00:00:00
Tasks: Diversity

We study a canonical collective action game with incomplete information. Individuals attempt to coordinate to achieve a shared goal, while also facing a temptation to free-ride. Consuming more similar information about the fundamentals can help them coordinate, but it can also exacerbate free-riding. Our main result shows that more similar information facilitates (impedes) achieving a common goal when achieving the goal is sufficiently challenging (easy). We apply this insight to show why insufficiently powerful authoritarian governments may face larger protests when attempting to restrict press freedom, and why informational diversity in committees is beneficial when each vote carries more weight.
Quantifying the Blockchain Trilemma: A Comparative Analysis of Algorand, Ethereum 2.0, and Beyond
Authors: Yihang Fu, Mingwei Jing, Jiaolun Zhou, Peilin Wu, Ye Wang, Luyao Zhang, Chuang Hu
Date: 2024-07-19 00:00:00
Tasks: POS

Blockchain technology is essential for the digital economy and metaverse, supporting applications from decentralized finance to virtual assets. However, its potential is constrained by the "Blockchain Trilemma," which necessitates balancing decentralization, security, and scalability. This study evaluates and compares two leading proof-of-stake (PoS) systems, Algorand and Ethereum 2.0, against these critical metrics. Our research interprets existing indices to measure decentralization, evaluates scalability through transactional data, and assesses security by identifying potential vulnerabilities. Utilizing real-world data, we analyze each platform's strategies in a structured manner to understand their effectiveness in addressing trilemma challenges. The findings highlight each platform's strengths and propose general methodologies for evaluating key blockchain characteristics applicable to other systems. This research advances the understanding of blockchain technologies and their implications for the future digital economy. Data and code are available on GitHub as open source.
Why to DAO: a narrative analysis of the drivers of tokenized Exit to Community
Authors: Tara Merk
Date: 2024-07-19 00:00:00

This paper asks why startups in the blockchain industry are exiting to Decentralized Autonomous Organizations (DAOs), an outstanding phenomena in the wider digital economy which has tended to retain centralized ownership and governance rights of many platforms, products and protocols. Drawing on a narrative analysis of three case studies, I find three possible drivers: (1) exit to DAO is motivated by both financial and stewardship goals which it simultaneously promises to realize via the issuance of tokens; (2) exit to DAO adds an additional layer of ownership and governance rights via tokens, without requiring existing rights to be relinquished, thus making it a lucrative strategy; and (3) markets, laws and social norms underpinning the broader environment in which exits to DAO occur, seem to play an important role in driving the decision. This paper contributes to the academic literature by situating DAOs as a hybrid (and perhaps incomplete) entrepreneurial exit strategy and identifying plausible drivers of the phenomenon which warrant further dedicated research.
The spatial evolution of economic activities: from theory to estimation
Authors: Davide Fiaschi, Angela Parenti, Cristiano Ricci
Date: 2024-07-19 00:00:00

This paper studies the evolution of economic activities using a continuous time-space aggregation-diffusion model, which encompasses competing effects of agglomeration and congestion. To bring the model to the real data, a novel discretization technique over time and space is introduced. This technique effectively disentangles spatial effects into pure topography, agglomeration, repulsion, and diffusion forces, which is crucial for developing robust econometric methods in spatial economics. Our empirical analysis of personal income across Italian municipalities from 2008 to 2019 validates the model's primary predictions and demonstrates superior performance compared to the most common spatial econometric models in the literature.
Rational Bubbles: A Clarification
Authors: Tomohiro Hirano, Alexis Akira Toda
Date: 2024-07-19 00:00:00

"Rational bubble", as introduced by the famous paper on money by Samuelson (1958), means speculation backed by nothing. The large subsequent rational bubble literature has identified attaching bubbles to dividend-paying assets in a natural way as an important but challenging question. Miao and Wang (2018) claim to "provide a theory of rational stock price bubbles". Contrary to their claim, the present comment proves the nonexistence of rational bubbles in the model of Miao and Wang (2018). We also clarify the precise mathematical definition and the economic meaning of "rational bubble" in an accessible way to the general audience.
Factor-Biased Efficiency Gains from Exporting: Evidence from Colombia
Authors: Joonkyo Hong, Davide Luparello
Date: 2024-07-19 00:00:00

This study examines whether exporting enhances efficiency and favors specific inputs. We develop a production function model within a dynamic exporting and investment framework, capturing factor-biased technical changes. Using Kalman filtering to address measurement error and propensity score matching to control for self-selection into exporting, we analyze Colombia's manufacturing sectors from 1981 to 1991. New exporters achieve a 4% annual increase in labor-augmenting and unskilled labor relative productivity, with no change in Hicks-neutral productivity. Unskilled labor-augmenting productivity grows by 8% annually, aligning with machinery asset expansion, while TFP rises by 3% per year.
(Non-)Commutative Aggregation
Authors: Yuzhao Yang
Date: 2024-07-20 00:00:00
Tasks: Decision Making, Decision Making Under Uncertainty

Commutativity is a normative criterion of aggregation and updating stating that the aggregation of expert posteriors should be identical to the update of the aggregated priors. I propose a thought experiment that raises questions about the normative appeal of Commutativity. I propose a weakened version of Commutativity and show how that assumption plays central roles in the characterization of linear belief aggregation, multiple-weight aggregation, and an aggregation rule which can be viewed as the outcome of a game played by "dual-selves," Pessimism and Optimism. Under suitable conditions, I establish equivalences between various relaxations of Commutativity and classic axioms for decision-making under uncertainty, including Independence, C-Independence, and Ambiguity Aversion.
Fair allocation of riparian water rights
Authors: Ricardo Martinez, Juan D. Moreno-Ternero
Date: 2024-07-19 00:00:00

We take an axiomatic approach to the allocation of riparian water rights. We formalize ethical or structural properties as axioms of allocation rules. We show that several combinations of these axioms characterize focal rules implementing the principle of Territorial Integration of all Basin States in various forms. One of them connects to the Shapley value, the long-standing centerpiece of cooperative game theory. The others offer natural compromises between the polar principles of Absolute Territorial Sovereignty and Unlimited Territorial Integrity. We complete our study with an empirical application to the allocation of riparian water rights in the Nile River.
Justified Fairness in House Allocation Problems: two Characterizations of Strategy-proof Mechanisms
Authors: Di Feng, Jacob Coreno
Date: 2024-07-19 00:00:00
Tasks: Fairness

We consider the house allocation problems with strict preferences, where monetary transfers are not allowed. We propose two properties in the spirit of justified fairness. Interestingly, together with other well-studied properties (strategy-proofness and non-bossiness), our two new properties identify serial dictatorships and sequential dictatorships, respectively.
Cohesion, Ideology, and Tolerance
Authors: Patrick Allmis
Date: 2024-07-19 00:00:00

Agents with different ideologies often form alliances to achieve their goals. Paradoxically, ideologically similar agents are often opponents. In this paper, ideologically heterogeneous agents choose the ideological composition of their neighborhood, their tolerance, and invest into connections. The resulting weighted network describes allies, opponents, and strengths. Disputes with opponents determine benefits, which increase in an agent's strength and cohesion. Cohesive agents have fewer mutual allies with opponents. In equilibrium, the network is segregated when cohesion is effective enough and some agents tolerate ideologically distant types to oppose closer ones. Subsidizing connections dampens polarization in societies on the verge of segregation.