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on Investment |
| By: | Chiswick, Barry R. (George Washington University); Corman, Hope (Rider University); Dave, Dhaval M. (Bentley University); Reichman, Nancy E. (Rutgers University) |
| Abstract: | This study analyzes, for the first time, the effect of increases in the minimum wage on the labor market outcomes of working age adults with cognitive disabilities, a vulnerable and low-skilled sector of the actual and potential labor pool. Using data from the American Community Survey (2008-2023), we estimated effects of the minimum wage on employment, labor force participation, weeks worked, and hours worked among working age individuals with cognitive disabilities using a generalized difference-in-differences research design. We found that a higher effective minimum wage leads to reduced employment and labor force participation among individuals with cognitive disabilities but has no significant effect on labor supply at the intensive margin for this group. Adverse impacts were particularly pronounced for those with lower educational attainment. In contrast, we found no significant labor market effects of an increase in the minimum wage for individuals with physical disabilities or in the non-disabled population. |
| Keywords: | american community survey, labor market outcomes, employment, cognitive disability, minimum wage |
| JEL: | J14 J2 |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp18021 |
| By: | Oguzhan Akgun; Alain Pirotte; Giovanni Urga; Zhenlin Yang |
| Abstract: | This paper proposes a selective inference procedure for testing equal predictive ability in panel data settings with unknown heterogeneity. The framework allows predictive performance to vary across unobserved clusters and accounts for the data-driven selection of these clusters using the Panel Kmeans Algorithm. A post-selection Wald-type statistic is constructed, and valid $p$-values are derived under general forms of autocorrelation and cross-sectional dependence in forecast loss differentials. The method accommodates conditioning on covariates or common factors and permits both strong and weak dependence across units. Simulations demonstrate the finite-sample validity of the procedure and show that it has very high power. An empirical application to exchange rate forecasting using machine learning methods illustrates the practical relevance of accounting for unknown clusters in forecast evaluation. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.14621 |
| By: | María José Arteaga Garavito; Riccardo Colacito; Mariano Max Croce; Biao Yang |
| Abstract: | We develop novel high-frequency indices that measure climate attention across a wide range of developed and emerging economies. By analyzing the text of over 23 million Tweets published by leading national newspapers, we find that a country experiencing more severe climate news shocks tends to see both an inflow of capital and an appreciation of its currency. In addition, brown stocks experience large and persistent negative returns after a global climate news shock if located in highly exposed countries. A risk-sharing model in which investors price climate news shocks and trade consumption and investment goods in global markets rationalizes these findings. |
| JEL: | F3 F4 G1 |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34084 |
| By: | Mahdi Goldani |
| Abstract: | In statistical modeling, prediction and explanation are two fundamental objectives. When the primary goal is forecasting, it is important to account for the inherent uncertainty associated with estimating unknown outcomes. Traditionally, confidence intervals constructed using standard deviations have served as a formal means to quantify this uncertainty and evaluate the closeness of predicted values to their true counterparts. This approach reflects an implicit aim to capture the behavioral similarity between observed and estimated values. However, advances in similarity based approaches present promising alternatives to conventional variance based techniques, particularly in contexts characterized by large datasets or a high number of explanatory variables. This study aims to investigate which methods either traditional or similarity based are capable of producing narrower confidence intervals under comparable conditions, thereby offering more precise and informative intervals. The dataset utilized in this study consists of U.S. mega cap companies, comprising 42 firms. Due to the high number of features, interdependencies among predictors are common, therefore, Ridge Regression is applied to address this issue. The research findings indicate that variance based method and LCSS exhibit the highest coverage among the analyzed methods, although they produce broader intervals. Conversely, DTW, Hausdorff, and TWED deliver narrower intervals, positioning them as the most accurate methods, despite their medium coverage rates. Ultimately, the trade off between interval width and coverage underscores the necessity for context aware decision making when selecting similarity based methods for confidence interval estimation in time series analysis. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.16655 |
| By: | Filip Tokarski |
| Abstract: | A welfare-maximizing designer allocates two kinds of goods using two wasteful screening instruments: ordeals, which enter agents' utilities additively, and damages, which harm agents in proportion to their values for the goods. If agents have common valuations for one of the goods, damages always lead to Pareto-dominated mechanisms: any allocation using damages can also be implemented with ordeals alone, while also leaving greater rents to inframarginal types. However, using damages can be optimal when agents' valuations for both goods are heterogeneous: with multidimensional types, the two devices differ in how they sort agents into available options, with the optimal sorting sometimes requiring the use of damages. I nevertheless identify distributional conditions under which using damages is not optimal. In those cases, the optimal mechanism produces an efficient allocation by posting "market-clearing" ordeals for each type of good. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.04456 |
| By: | André Luiz Ferreira (Universidade Federal do Pará); André Luis Squarize Chagas (Departmento de Economia, FEA-USP); Carlos Roberto Azzoni (Departmento de Economia, FEA-USP) |
| Abstract: | This paper develops a spatial stochastic frontier framework for panel data that jointly accounts for spatial dependence and heteroskedastic technical inefficiency. Inefficiency and noise components are parameterized using scaling functions, while spatial dependence is modeled through both a spatial lag (SF-SLM) and a spatial Durbin specification (SF-SDM). Maximum likelihood estimation is implemented by explicitly incorporating the spatial autoregressive process into the log-likelihood function. A key innovation of this study is the use of the spatial multiplier to decompose estimated technical inefficiency into three components: (i) own inefficiency, (ii) spill-in effects (feedback of a unit’s inefficiency on itself through spatial interactions), and (iii) spillover effects (inefficiency transmitted from neighboring regions). This approach extends the stochastic frontier literature by showing that inefficiency is not purely local but can propagate across space. The method is applied to the Brazilian food manufacturing industry (2007–2018). Likelihood ratio tests confirm that spatial models outperform the nonspatial specification, with SF-SDM providing the best fit and more stable inefficiency estimates. Results reveal that, for an average region, approximately 9% of inefficiency is due to spillovers from neighbors, while 0.2% is explained by spill-in effects. Ignoring spatial structure would therefore overestimate region-specific inefficiency and underestimate the role of interregional linkages. The proposed framework offers a flexible tool for analyzing productive efficiency in spatially interconnected settings and provides new insights for regional policy and future research. |
| Keywords: | Spatial stochastic frontier; Maximum likelihood estimator; Technical inefficiency; spatial spillover |
| JEL: | C23 C51 R12 R15 L66 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ris:nereus:021487 |
| By: | Britos, Braulio; Hernandez, Manuel A.; Trupkin, Danilo R. |
| Abstract: | International migration is a recurrent phenomenon that has grown rapidly over the past two decades. This paper examines the role of agricultural distortions in shaping emigration patterns and influencing productivity and welfare in developing countries, using Guatemala as a case study. We develop a theoretical framework where household members can work in agriculture, non-agriculture, or emigrate, and calibrate the model combining detailed micro and aggregate data. Our model identifies two key channels through which agricultural distortions affect migration and productivity: a first channel where distortions increase emigration among more productive agents, reducing aggregate productivity, and a second channel where distortions drive factor misallocation, lowering incomes and increasing overall emigration. |
| Keywords: | Agricultura, Migración, |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:dbl:dblwop:2502 |
| By: | Huan Qing; Xiaofei Xu |
| Abstract: | Traditional threshold-based stock networks suffer from subjective parameter selection and inherent limitations: they constrain relationships to binary representations, failing to capture both correlation strength and negative dependencies. To address this, we introduce statistically validated correlation networks that retain only statistically significant correlations via a rigorous t-test of Pearson coefficients. We then propose a novel structure termed the largest strong-correlation balanced module (LSCBM), defined as the maximum-size group of stocks with structural balance (i.e., positive edge-ign products for all triplets) and strong pairwise correlations. This balance condition ensures stable relationships, thus facilitating potential hedging opportunities through negative edges. Theoretically, within a random signed graph model, we establish LSCBM's asymptotic existence, size scaling, and multiplicity under various parameter regimes. To detect LSCBM efficiently, we develop MaxBalanceCore, a heuristic algorithm that leverages network sparsity. Simulations validate its efficiency, demonstrating scalability to networks of up to 10, 000 nodes within tens of seconds. Empirical analysis demonstrates that LSCBM identifies core market subsystems that dynamically reorganize in response to economic shifts and crises. In the Chinese stock market (2013-2024), LSCBM's size surges during high-stress periods (e.g., the 2015 crash) and contracts during stable or fragmented regimes, while its composition rotates annually across dominant sectors (e.g., Industrials and Financials). |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.04970 |
| By: | Del Frari, Elisa; Aassve, Arnstein; Melegaro, Alessia (Bocconi University) |
| Abstract: | Ongoing demographic changes driven by increased life expectancy and declining fertility rates are starting to exert pressure on Pay-As-You-Go pension schemes, which depend on the transfer of resources from the employed population to the retired one. Existing research presents mixed conclusions on the effectiveness of various policy measures designed to ensure the long-term fiscal sustainability of pension systems. This paper contributes to the literature by employing a tailored Agent-Based Model (ABM) for Italy, which integrates demographic and pension dynamics. The model evaluates the impact of policies aimed at increasing labor market participation, specifically reducing the number of NEETs, boosting female labor force participation, introducing more flexible retirement options, increasing immigration and raising fertility rates. Projections extending to 2070 indicate that the aging process will persist, leading to a continued deterioration in the fiscal balance of the Italian pension system, despite the automatic adjustments to the retirement age linked to variations in life expectancy. The results indicate that promoting labour participation significantly enhances the sustainability of the pension system. In particular, policies aimed at increasing female participation emerge as the most effective individual intervention. However, no single measure, nor any combination of the simulated policies, is sufficient to place the Italian pension system to a fully sustainable trajectory. |
| Date: | 2025–08–11 |
| URL: | https://d.repec.org/n?u=RePEc:osf:osfxxx:g2xqt_v1 |
| By: | Mountacer Bourjila (UIT - Université Ibn Tofaïl); Ayoub El Bahi (UIT - Université Ibn Tofaïl); Fahima Charef |
| Abstract: | In an increasingly unstable economic environment—marked by successive health, geopolitical, and energy crises—small and medium-sized enterprises (SMEs) are facing a growing imperative for organizational resilience. Constrained by structural limitations—limited resources, dependency on key actors, and low procedural formalization—SMEs must nonetheless demonstrate agility, operational continuity, and the ability to adapt rapidly. This article explores the role of Enterprise Resource Planning (ERP) systems in this context: can they evolve from being perceived as a technological constraint to becoming a strategic lever for resilience? Drawing on a multidisciplinary literature review—at the crossroads of information systems, organizational management, and resilience theory—this work first examines the historical and cultural barriers to ERP adoption in SMEs, such as high initial costs, perceived rigidity, and resistance to change. It then highlights how, under certain conditions, ERP systems can be transformed into infrastructures for organizational learning, provided they are contextually adapted, flexibly configured, and integrated into a reimagined governance framework. By structuring information, automating key processes, and offering real-time cross-functional visibility, ERP systems support rapid decision-making and enable the agile reconfiguration of operations. They thus become a technological foundation for dynamic resilience. The article also sheds light on the inherent tension between technological standardization and the operational flexibility that SMEs require. Its main contribution lies in reframing the role of ERP systems—not as imposed or static tools, but as catalysts for agility, robustness, and collective intelligence in environments shaped by uncertainty. |
| Abstract: | Dans un contexte économique instable, marqué par l'enchaînement de crises sanitaires, géopolitiques et énergétiques, les PME se trouvent confrontées à un impératif de résilience organisationnelle. Soumises à des contraintes structurelles fortes - ressources limitées, dépendance aux acteurs clés, faible formalisme - elles doivent néanmoins faire preuve d'agilité, de continuité opérationnelle et de capacité d'adaptation rapide. Cet article propose d'interroger la place des ERP (Enterprise Resource Planning) dans cette dynamique : peuvent-ils passer du statut de contrainte technologique à celui de levier stratégique de résilience ? Fondé sur une revue de littérature pluridisciplinaire, croisant les apports des systèmes d'information, du management des organisations et de la théorie de la résilience, ce travail examine dans un premier temps les freins historiques et culturels à l'appropriation des ERP dans les PME : coûts initiaux élevés, rigidité perçue, résistance au changement. Il met en évidence que ces systèmes, lorsqu'ils sont contextualisés, paramétrés avec souplesse, et intégrés à une gouvernance renouvelée, peuvent devenir des infrastructures d'apprentissage organisationnel. En structurant l'information, en automatisant les processus clés, et en offrant une visibilité transverse en temps réel, l'ERP soutient la prise de décision rapide et favorise une reconfiguration agile des opérations. Il devient ainsi un socle technologique de résilience dynamique. L'article souligne les tensions inhérentes entre la logique de standardisation technique et le besoin de flexibilité opérationnelle propre aux PME. La contribution principale consiste à requalifier l'ERP : non plus perçu comme un système imposé ou figé, mais comme un catalyseur d'agilité, de robustesse et d'intelligence collective dans des environnements marqués par l'incertitude. |
| Keywords: | organizational resilience, strategic adaptation, information systems, digital transformation, governance, adaptation stratégique, systèmes d'information, transformation numérique, gouvernance, résilience organisationnelle, PME, ERP |
| Date: | 2025–07–27 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05203147 |
| By: | Khalil, Makram; Rouillard, Pierre; Strobel, Felix |
| Abstract: | We find evidence that US manufacturing sectors experience US import tariffs either as supply-side or demand-side shocks, depending on the location of the sector and the affected products in the US production network. Using local projections in a panel of US manufacturing sectors, we find that US import tariffs – in particular including the 2018-19 tariff hikes – led to sectoral output contractions via two different channels: (1) Tariff increases act as negative supply shocks for sectors that use the affected goods as input in production and thus face rising input costs. (2) Tariff increases act as negative demand shocks for sectors whose customers experience the tariff increase as a negative supply shock and reduce their production. Though the aim of tariffs often is to protect local industries, we find only limited evidence of such a protective effect. Overall, our finding suggests that tariffs markedly reduce US manufacturing production and that the role of input-output linkages is key for understanding the transmission of import tariff shocks. |
| Keywords: | Import tariffs, sectoral production and prices, input-output-tables, production networks, United States. |
| JEL: | E22 E32 F13 |
| Date: | 2025–08–09 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:125698 |
| By: | Wanting He; Wenyuan Li; Yunran Wei |
| Abstract: | This paper introduces an innovative framework for the periodic evaluation of defined-contribution pension funds. The performance of the pension fund is evaluated not only at retirement, but also within the interim periods. In contrast to the traditional literature, we set the dynamic risk measure as the criterion and manage the tail risk of the pension fund dynamically. To effectively interact with the stochastic environment, a model-free reinforcement learning algorithm is proposed to search for optimal investment and insurance strategies. Using U.S. data, we calibrate pension members' mortality rates and enhance mortality projections through a Lee-Carter model. Our numerical results indicate that periodic evaluations lead to more risk-averse strategies, while mortality improvements encourage more risk-seeking behaviors. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.05241 |
| By: | Bonilla-Mejía, Leonardo (Banco de la República de Colombia); Bracco, Jessica (CEDLAS-UNLP); Ham Gonzalez, Andres (Department of Economics, Universidad de los Andes); Peñaloza-Pacheco, Leonardo (Cornell University) |
| Abstract: | We study how drug-related violence affects emigration from Central America, a region with rapidly rising migration to the United States. Using multiple data sources, we apply an instrumental variables strategy based on proximity to drug-trafficking routes and coca production in Colombia. We find that violence significantly increases intentions, plans, and preparations to emigrate—especially to the U.S.—with stronger effects among young and high-skilled individuals. Mediation analysis suggests this response is driven by declining economic activity and, more importantly, deteriorating labor market conditions caused by escalating violence. |
| Keywords: | drug trafficking, violence, economic activity, labor markets, migration |
| JEL: | J61 O15 N96 |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp18028 |
| By: | Boris Podobnik; Dorian Wild; Dejan Kovac |
| Abstract: | We show that the amount of foreign exchange reserves (FER) in the world in a given currency is highly correlated with the GDP and military spending of that country for a set of western economies during the last 20 years. Taking into account multicollinearity, Ridge and Lasso regressions reveal that the Foreign Exchange Reserve is better explained by military spending than GDP for seven western currencies. For each year shown, military spending is statistically significant more than the monetary instrument M2. Comparing the currency of the second world economy, the Chinese renminbi, is well beyond the western FER equilibrium, but yearly analysis shows that there is a steady trend towards a new FER balance. Next, we define a complex geopolitical network model in which the probability of switching to an alternative FER currency depends both on economic and political factors. Military spending is introduced into the model as an average share of GDP observed within the data. As the GDP of a particular country grows, so does the military power of a country. The nature of the creation of new currency networks initially depends only on geopolitical allegiance. As the volume of trade with a particular country changes over a designated threshold, a country switches to the currency of that country due to increased trade. If the current steady trend continues within the same geopolitical setting as in the past twenty years, we extrapolate that the RMB and Western currencies could reach a new FER balance within 15 to 40 years, depending on the model setup. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.05856 |
| By: | Oriol Saguillo; Vahid Ghafouri; Lucianna Kiffer; Guillermo Suarez-Tangil |
| Abstract: | Polymarket is a prediction market platform where users can speculate on future events by trading shares tied to specific outcomes, known as conditions. Each market is associated with a set of one or more such conditions. To ensure proper market resolution, the condition set must be exhaustive -- collectively accounting for all possible outcomes -- and mutually exclusive -- only one condition may resolve as true. Thus, the collective prices of all related outcomes should be \$1, representing a combined probability of 1 of any outcome. Despite this design, Polymarket exhibits cases where dependent assets are mispriced, allowing for purchasing (or selling) a certain outcome for less than (or more than) \$1, guaranteeing profit. This phenomenon, known as arbitrage, could enable sophisticated participants to exploit such inconsistencies. In this paper, we conduct an empirical arbitrage analysis on Polymarket data to answer three key questions: (Q1) What conditions give rise to arbitrage (Q2) Does arbitrage actually occur on Polymarket and (Q3) Has anyone exploited these opportunities. A major challenge in analyzing arbitrage between related markets lies in the scalability of comparisons across a large number of markets and conditions, with a naive analysis requiring $O(2^{n+m})$ comparisons. To overcome this, we employ a heuristic-driven reduction strategy based on timeliness, topical similarity, and combinatorial relationships, further validated by expert input. Our study reveals two distinct forms of arbitrage on Polymarket: Market Rebalancing Arbitrage, which occurs within a single market or condition, and Combinatorial Arbitrage, which spans across multiple markets. We use on-chain historical order book data to analyze when these types of arbitrage opportunities have existed, and when they have been executed by users. We find a realized estimate of 40 million USD of profit extracted. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.03474 |
| By: | Yong Li; Sushanta K. Mallick; Tao Zeng; Junxing Zhang |
| Abstract: | Optimal data detection in massive multiple-input multiple-output (MIMO) systems often requires prohibitively high computational complexity. A variety of detection algorithms have been proposed in the literature, offering different trade-offs between complexity and detection performance. In recent years, Variational Bayes (VB) has emerged as a widely used method for addressing statistical inference in the context of massive data. This study focuses on misspecified models and examines the risk functions associated with predictive distributions derived from variational posterior distributions. These risk functions, defined as the expectation of the Kullback-Leibler (KL) divergence between the true data-generating density and the variational predictive distributions, provide a framework for assessing predictive performance. We propose two novel information criteria for predictive model comparison based on these risk functions. Under certain regularity conditions, we demonstrate that the proposed information criteria are asymptotically unbiased estimators of their respective risk functions. Through comprehensive numerical simulations and empirical applications in economics and finance, we demonstrate the effectiveness of these information criteria in comparing misspecified models in the context of massive data. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.00763 |
| By: | Seeger, Lukas; Burr, Wolfgang |
| Abstract: | The use of automated facial recognition in retail offers a wide range of potential for increasing efficiency, improving service quality and optimising customer targeting. This article, based on an empirical survey of 101 participants, examines factors that determine the acceptance and use of facial recognition technology among retail customers in Germany. However, the results show a strong negative influence of data protection and security issues on the willingness to adopt facial recognition technology among customers of German retailers. This article broadens the understanding of the acceptance and willingness to use digital technologies in public places going hand in hand with problems regarding privacy and security. The results of our study confirm the TAM model and the correlations it contains between the perceived usefulness, the perceived ease of use and the intention to use. The study also confirms the Technology Acceptance Model formulated by Davis in the special context of German retail business. The TAM model is extended in this paper by two new factors, privacy and security as mediators between privacy concerns and intention to use. |
| Keywords: | Facial recognition, retail, digital technology, user acceptance, service quality, data protection regulation in Germany |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:stuist:323955 |
| By: | Gardiner, Hannah; Pettinger, Clare; Haslam-Lucas, Amanda; Diouri, Barbara; Ruminska, Joanna; Dunn, Laura; Ashton, Yve; Hunt, Louise; Hickson1, Mary |
| Abstract: | For complex challenges like food systems transformation, some scholars suggest co-production involving multiple actors including citizens is essential. Additionally, some argue the desirability of moving towards ‘food democracy’, aligning with participatory approaches gaining popularity more broadly. Urban food policy initiatives are examples of innovation in food democracy and food citizenship, but questions around delivery of participation and engagement remain. For example, delivering authentic participation is an ongoing challenge and the impacts on those engaged have rarely been studied. Furthermore, personal transformations are essential for collective action on urban food system transformation and similarly receive minimal focus. The need for development in these areas is reflected in the Milan Urban Food Policy Pact (MUFPP) goals including enhancing stakeholder participation (action 2) and enhancing food knowledge and action through participatory education, training and research (action 19). We report the experiences of 12 individuals engaged as community food researchers (CFRs) within transdisciplinary food system research in UK urban settings (FoodSEqual). Creative methods were used, including participatory mapping, collage, and poetic inquiry; alongside utilising assemblage theory concepts. We found CFRs developed relationships within and beyond their communities, expanded their food system knowledge and hope for change, and gained advocacy-related skills and beliefs. Our unique contribution demonstrates how personal outcomes from engagement in participatory research could support urban food systems transformation by creating conditions and capacities for active food citizenship and food democracy alongside personal transformations. This also suggests the CFR model could contribute to delivery of MUFPP goals, particularly actions 2 and 19 (described above). |
| Date: | 2025–08–01 |
| URL: | https://d.repec.org/n?u=RePEc:osf:osfxxx:skr48_v1 |
| By: | Andrianady, Josué R. |
| Abstract: | This study tests the Environmental Kuznets Curve (EKC) hypothesis in Madagascar using time-series data from 1990 to 2015. Employing the autoregressive distributed lag (ARDL) approach and Granger causality tests, we analyze the nexus between CO$_2$ emissions, economic growth, agricultural production, and trade openness. Results confirm a U-shaped EKC, with economic growth initially reducing emissions before increasing at higher income levels. Trade openness marginally reduces emissions, while agricultural production has no significant impact. Granger causality tests indicate that economic growth drives emissions. Policy recommendations include promoting trade in environmentally friendly goods and investing in clean energy to mitigate emissions. |
| Keywords: | EKC Hypothesis, Carbon Dioxide Emissions, Economic Growth, ARDL, Granger Causality, Madagascar |
| JEL: | A1 Q4 Q5 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:125625 |