New articles on Economics


[1] 2407.14623

Fair allocation of riparian water rights

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.


[2] 2407.14635

Predicting the Distribution of Treatment Effects: A Covariate-Adjustment Approach

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.


[3] 2407.14773

Similarity of Information and Collective Action

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.


[4] 2407.14776

National accounting from the bottom up using large-scale financial transactions data: An application to input-output tables

Technical advances enabled real-time data collection at a large scale, but lacking standards hamper their economic interpretation. Here, we benchmark a new monthly time series of inter-industrial flows of funds, constructed from aggregated and anonymised real-time payments between UK businesses, covering 5-digit SIC codes industries for the period 08/2015 to 12/2023, against established economic indicators, including GDP, input-output tables (IOTs), and stylised facts of granular firm- and industry-level production networks. We supplement the quantitative analyses with conceptual discussions, explaining the caveats of bottom-up collected payment data and their differences to national account tables. The results reveal strong GDP correlations, some qualitative consistency with official IOTs and stylised facts. We guide on the interpretation of the data and areas that require special attention for reliable quantitative research.


[5] 2407.14914

Leveraging Uniformization and Sparsity for Computation of Continuous Time Dynamic Discrete Choice Games

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.


[6] 2407.14955

Temptation: Immediacy and certainty

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. My experimental subjects 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. I offer a methodological remedy for experimental economists.


[7] 2407.14959

(Non-)Commutative Aggregation

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.


[8] 2407.15147

Industry Dynamics with Cartels: The Case of the Container Shipping Industry

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.


[9] 2407.15339

Deep Learning for Economists

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.


[10] 2407.15509

The increase in the number of low-value transactions in international trade

This paper documents a new feature of international trade: the increase in the number of low-value 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 related to the rise in e-commerce and direct-to-customer sales facilitated by online retail platforms. In the case of exports, the increase in the number of low-value transactions is also explained by the fast-fashion strategy followed by clothing firms.


[11] 2407.15522

Big Data Analytics-Enabled Dynamic Capabilities and Market Performance: Examining the Roles of Marketing Ambidexterity and Competitor Pressure

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.


[12] 2407.15755

Income, health, and cointegration

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 are still open. Some authors have claimed that the causal relationships between population health and income can be discovered using cointegration models. We show, however, that empirically testing a cointegration relation between LEB and GDP per capita is not a sound method to infer a causal link between health and income. 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 cannot distinguish whether cointegration relationships are spurious or causal.


[13] 2407.15757

Willingness to Pay for an Electricity Connection: A Choice Experiment Among Rural Households and Enterprises in Nigeria

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.


[14] 2407.15016

Rethinking Digitalization and Climate: Don't Predict, Mitigate

Digitalization is a core component of the green transition. Today's focus is on quantifying and pre-dicting the climate effects of digitalization through various life-cycle assessments and baseline sce-nario methodologies. Here we argue that this is a mistake. Most attempts at prediction are based on three implicit assumptions: (a) the digital carbon footprint can be quantified, (b) business-as-usual with episodic change leading to a new era of stability, and (c) investments in digitalization will be delivered within the cost, timeframe, and benefits described in their business cases. We problema-tize each assumption within the context of digitalization and argue that the digital carbon footprint is inherently unpredictable. We build on uncertainty literature to show that even if you cannot predict, you can still mitigate. On that basis, we propose to rethink practice on the digital carbon footprint from prediction to mitigation.


[15] 2407.15256

Weak-instrument-robust subvector inference in instrumental variables regression: A subvector Lagrange multiplier test and properties of subvector Anderson-Rubin confidence sets

We propose a weak-instrument-robust subvector Lagrange multiplier test for instrumental variables regression. We show that it is asymptotically size-correct under a technical condition. This is the first weak-instrument-robust subvector test for instrumental variables regression to recover the degrees of freedom of the commonly used Wald test, which is not robust to weak instruments. Additionally, we provide a closed-form solution for subvector confidence sets obtained by inverting the subvector Anderson-Rubin test. We show that they are centered around a k-class estimator. Also, we show that the subvector confidence sets for single coefficients of the causal parameter are jointly bounded if and only if Anderson's likelihood-ratio test rejects the hypothesis that the first-stage regression parameter is of reduced rank, that is, that the causal parameter is not identified. Finally, we show that if a confidence set obtained by inverting the Anderson-Rubin test is bounded and nonempty, it is equal to a Wald-based confidence set with a data-dependent confidence level. We explicitly compute this Wald-based confidence test.


[16] 2407.15276

Nonlinear Binscatter Methods

Binned scatter plots are a powerful statistical tool for empirical work in the social, behavioral, and biomedical sciences. Available methods rely on a quantile-based partitioning estimator of the conditional mean regression function to primarily construct flexible yet interpretable visualization methods, but they can also be used to estimate treatment effects, assess uncertainty, and test substantive domain-specific hypotheses. This paper introduces novel binscatter methods based on nonlinear, possibly nonsmooth M-estimation methods, covering generalized linear, robust, and quantile regression models. We provide a host of theoretical results and practical tools for local constant estimation along with piecewise polynomial and spline approximations, including (i) optimal tuning parameter (number of bins) selection, (ii) confidence bands, and (iii) formal statistical tests regarding functional form or shape restrictions. Our main results rely on novel strong approximations for general partitioning-based estimators covering random, data-driven partitions, which may be of independent interest. We demonstrate our methods with an empirical application studying the relation between the percentage of individuals without health insurance and per capita income at the zip-code level. We provide general-purpose software packages implementing our methods in Python, R, and Stata.


[17] 2407.15715

Cryptoeconomics and Tokenomics as Economics: A Survey with Opinions

This paper surveys products and studies on cryptoeconomics and tokenomics from an economic perspective, as these terms are still (i) ill-defined and (ii) disconnected from economic disciplines. We first suggest that they can be novel when integrated; we then conduct a literature review and case study following consensus-building for decentralization and token value for autonomy. Integration requires simultaneous consideration of strategic behavior, spamming, Sybil attacks, free-riding, marginal cost, marginal utility and stabilizers. This survey is the first systematization of knowledge on cryptoeconomics and tokenomics, aiming to bridge the contexts of economics and blockchain.


[18] 2407.15801

Selection pressure/Noise driven cooperative behaviour in the thermodynamic limit of repeated games

Consider the scenario where an infinite number of players (i.e., the \textit{thermodynamic} limit) find themselves in a Prisoner's dilemma type situation, in a \textit{repeated} setting. Is it reasonable to anticipate that, in these circumstances, cooperation will emerge? This paper addresses this question by examining the emergence of cooperative behaviour, in the presence of \textit{noise} (or, under \textit{selection pressure}), in repeated Prisoner's Dilemma games, involving strategies such as \textit{Tit-for-Tat}, \textit{Always Defect}, \textit{GRIM}, \textit{Win-Stay, Lose-Shift}, and others. To analyze these games, we employ a numerical Agent-Based Model (ABM) and compare it with the analytical Nash Equilibrium Mapping (NEM) technique, both based on the \textit{1D}-Ising chain. We use \textit{game magnetization} as an indicator of cooperative behaviour. A significant finding is that for some repeated games, a discontinuity in the game magnetization indicates a \textit{first}-order \textit{selection pressure/noise}-driven phase transition. The phase transition is particular to strategies where players do not severely punish a single defection. We also observe that in these particular cases, the phase transition critically depends on the number of \textit{rounds} the game is played in the thermodynamic limit. For all five games, we find that both ABM and NEM, in conjunction with game magnetization, provide crucial inputs on how cooperative behaviour can emerge in an infinite-player repeated Prisoner's dilemma game.