nep-cmp New Economics Papers
on Computational Economics
Issue of 2022‒04‒11
twenty-six papers chosen by
Stan Miles
Thompson Rivers University

  1. Deep Regression Ensembles By Antoine Didisheim; Bryan T. Kelly; Semyon Malamud
  2. Solving Multi-Period Financial Planning Models: Combining Monte Carlo Tree Search and Neural Networks By Af\c{s}ar Onat Ayd{\i}nhan; Xiaoyue Li; John M. Mulvey
  3. Computing Black Scholes with Uncertain Volatility-A Machine Learning Approach By Kathrin Hellmuth; Christian Klingenberg
  4. Informal Loans in Thailand: Stylized Facts and Empirical Analysis By Pim Pinitjitsamut; Wisarut Suwanprasert
  5. Macroeconomic Predictions Using Payments Data and Machine Learning By James Chapman; Ajit Desai
  6. Reputation systems and recruitment in online labor markets: insights from an agent-based model By Lukac, Martin; Grow, André
  7. Charging the macroeconomy with an energy sector: an agent-based model By Ciola, Emanuele; Turco, Enrico; Gurgone, Andrea; Bazzana, Davide; Vergalli, Sergio; Menoncin, Francesco
  8. Using Past Violence and Current News to Predict Changes in Violence By Mueller, H.; Rauh, C.
  9. High-Dimensional Dynamic Stochastic Model Representation By Aryan Eftekhari; Simon Scheidegger
  10. Macro-economic Impacts of the COVID-19 Pandemic on Mongolia’s Economy: CGE Analysis with the GTAP 10a Data Base By Enkhbayar Shagdar
  11. Games of Artificial Intelligence: A Continuous-Time Approach By Martino Banchio; Giacomo Mantegazza
  12. A simulation model for evaluating the efficiency of robot-supported order picking warehouses. By Zhang, Minqi; Winkelhaus, Sven; Grosse, E. H.; Glock, C. H.
  13. Hidden hazards and Screening Policy : Predicting Undetected Lead Exposure in Illinois Using Machine Learning By Abbasi, Ali; Gazze, Ludovica; Pals, Bridget
  14. Unpacking the black box of ICO white papers: a topic modeling approach By Pastwa, Anna M.; Shrestha, Prabal; Thewissen, James; Torsin, Wouter
  15. Optimal market completion through financial derivatives with applications to volatility risk By Matt Davison; Marcos Escobar-Anel; Yichen Zhu
  16. Transport Connectivity in Northeast Asia: in the Context of Trans-Eurasian Transport By Ryuichi Shibasaki; Hirofumi Arai; Kentaro Nishimura; Takuya Yamaguchi
  17. The Future of Taxation in changing labour markets By Michael Christl; Ilias Livanos; Andrea Papini; Alberto Tumino
  18. The economy-wide effects of mandating private retirement incomes By George Kudrna
  19. Robust Design, Analysis and Evaluation of Variable Speed Limit Control in a Connected Environment with Uncertainties: Performance Evaluation and Environmental Benefits By Yuan, Tianchen; Alasiri, Faisal; Ioannou, Petros A.
  20. Assessing the impacts of COVID-19 on household incomes and poverty in Rwanda: A microsimulation approach By Diao, Xinshen; Rosenbach, Gracie; Spielman, David J.; Aragie, Emerta
  21. Distributional consequences of wheat policy in Sudan: A simulation model analysis By Dorosh, Paul A.
  22. The Benefits and Costs of a U.S. Child Allowance By Irwin Garfinkel; Laurel Sariscsany; Elizabeth Ananat; Sophie M. Collyer; Robert Paul Hartley; Buyi Wang; Christopher Wimer
  23. Pricing vehicle emissions and congestion externalities using a dynamic traffic network simulator By Shaghayegh Vosough; André de Palma; Robin Lindsey
  24. A general equilibrium analysis of the economic impact of the post-2006 EU regulation in the services sector By Javier Barbero; Manol Bengyuzov; Martin Christensen; Andrea Conte; Simone Salotti; Aleksei Trofimov
  25. Analyzing EU-15 immigrants’ language acquisition using Twitter data By B. Sofia Gil-Clavel; André Grow; Maarten J. Bijlsma
  26. Streets That Fit: Re-allocating Space for Better Cities By ITF

  1. By: Antoine Didisheim (Swiss Finance Institute, UNIL); Bryan T. Kelly (Yale SOM; AQR Capital Management, LLC; National Bureau of Economic Research (NBER)); Semyon Malamud (Ecole Polytechnique Federale de Lausanne; Centre for Economic Policy Research (CEPR); Swiss Finance Institute)
    Abstract: We introduce a methodology for designing and training deep neural networks (DNN) that we call “Deep Regression Ensembles" (DRE). It bridges the gap between DNN and two-layer neural networks trained with random feature regression. Each layer of DRE has two components, randomly drawn input weights and output weights trained myopically (as if the final output layer) using linear ridge regression. Within a layer, each neuron uses a different subset of inputs and a different ridge penalty, constituting an ensemble of random feature ridge regressions. Our experiments show that a single DRE architecture is at par with or exceeds state-of-the-art DNN in many data sets. Yet, because DRE neural weights are either known in closed-form or randomly drawn, its computational cost is orders of magnitude smaller than DNN.
    Keywords: Deep learning, Neural network, Random features, Ensembles
    Date: 2022–03
  2. By: Af\c{s}ar Onat Ayd{\i}nhan; Xiaoyue Li; John M. Mulvey
    Abstract: This paper introduces the MCTS algorithm to the financial word and focuses on solving significant multi-period financial planning models by combining a Monte Carlo Tree Search algorithm with a deep neural network. The MCTS provides an advanced start for the neural network so that the combined method outperforms either approach alone, yielding competitive results. Several innovations improve the computations, including a variant of the upper confidence bound applied to trees (UTC) and a special lookup search. We compare the two-step algorithm with employing dynamic programs/neural networks. Both approaches solve regime switching models with 50-time steps and transaction costs with twelve asset categories. Heretofore, these problems have been outside the range of solvable optimization models via traditional algorithms.
    Date: 2022–02
  3. By: Kathrin Hellmuth; Christian Klingenberg
    Abstract: In financial mathematics, it is a typical approach to approximate financial markets operating in discrete time by continuous-time models such as the Black Scholes model. Fitting this model gives rise to difficulties due to the discrete nature of market data. We thus model the pricing process of financial derivatives by the Black Scholes equation, where the volatility is a function of a finite number of random variables. This reflects an influence of uncertain factors when determining volatility. The aim is to quantify the effect of this uncertainty when computing the price of derivatives. Our underlying method is the generalized Polynomial Chaos (gPC) method in order to numerically compute the uncertainty of the solution by the stochastic Galerkin approach and a finite difference method. We present an efficient numerical variation of this method, which is based on a machine learning technique, the so-called Bi-Fidelity approach. This is illustrated with numerical examples.
    Date: 2022–02
  4. By: Pim Pinitjitsamut; Wisarut Suwanprasert
    Abstract: This paper examines informal loans in Thailand using household survey data covering 4,800 individuals in 12 provinces across Thailand’s six regions. We proceed in three steps. First, we establish stylized facts about informal loans. Second, we estimate the effects of household characteristics on the decision to take out an informal loan and the amount of informal loan. We find that age, the number of household members, their savings, and the amount of existing formal loans are the main factors that drive the decision to take out an informal loan. The main determinations of the amount of informal loan are the interest rate, savings, the amount of existing formal loans, the number of household members, and personal income. Third, we train three machine learning models, namely K–Nearest Neighbors, Random Forest, and Gradient Boosting, to predict whether an individual will take out an informal loan and the amount an individual has borrowed through informal loans. We find that the Gradient Boosting technique with the top 15 most important features has the highest prediction rate of 76.46 percent, making it the best model for data classification. Generally, Random Forest outperforms the other two algorithms in both classifying data and predicting the amount of informal loans.
    Keywords: Informal Loans; Machine Learning; Shadow Economy; Thailand; Loan Sharks
    JEL: E26 G51 O16 O17
    Date: 2022–02
  5. By: James Chapman; Ajit Desai
    Abstract: Predicting the economy’s short-term dynamics—a vital input to economic agents’ decision-making process—often uses lagged indicators in linear models. This is typically sufficient during normal times but could prove inadequate during crisis periods such as COVID-19. This paper demonstrates: (a) that payments systems data which capture a variety of economic transactions can assist in estimating the state of the economy in real time and (b) that machine learning can provide a set of econometric tools to effectively handle a wide variety in payments data and capture sudden and large effects from a crisis. Further, we mitigate the interpretability and overfitting challenges of machine learning models by using the Shapley value-based approach to quantify the marginal contribution of payments data and by devising a novel cross-validation strategy tailored to macroeconomic prediction models.
    Keywords: Business fluctuations and cycles; Econometric and statistical methods; Payment clearing and settlement systems
    JEL: C53 C55 E37 E42 E52
    Date: 2022–03
  6. By: Lukac, Martin; Grow, André
    Abstract: Online labor markets—freelance marketplaces, where digital labor is distributed via a web-based platform—commonly use reputation systems to overcome uncertainties in the hiring process, that can arise from a lack of objective information about employees’ abilities. Research shows, however, that reputation systems tend to create winner-takes-all dynamics, in which differences in candidates’ reputations become disconnected from differences in their objective abilities. In this paper, we use an empirically validated agent-based computational model to investigate the extent to which reputation systems can create segmented hiring patterns that are biased toward freelancers with good reputation. We explore how jobs and earnings become distributed on a stylized platform, under different contextual conditions of information asymmetry. Our results suggest that information asymmetry influences the extent to which reputation systems may lead to inequality between freelancers, but contrary to our expectations, lower levels of information asymmetry can facilitate higher inequality in outcomes.
    Keywords: agent-based modeling; economic sociology; gig economy; inequality; online labor markets; reputation systems
    JEL: R14 J01
    Date: 2020–08–02
  7. By: Ciola, Emanuele; Turco, Enrico; Gurgone, Andrea; Bazzana, Davide; Vergalli, Sergio; Menoncin, Francesco
    Abstract: The global energy crisis that began in fall 2021 and the following spike in energy price constitute a major challenge for the world economy which risks undermining the post-COVID-19 recovery. In this paper, we develop and validate a new macroeconomic agent-based model with an endogenous energy sector to analyse the role of energy in the functioning of a complex adaptive system and assess the effects of energy shocks on the economic dynamics. The economic system is populated by heterogeneous agents, i.e., households, firms and banks, who take optimal decision rules and interact in decentralized markets characterized by limited information. After calibrating the model on US quarterly macroeconomic data, we investigate the economic and distributional effects of different types of energy shocks, that is an exogenous increase in the price of natural resources such as oil or gas and a decrease in the energy firms' productivity. We find that whereas the two energy shocks entail similar effects at the aggreagate level, the distribution of gains and losses across sectors is largely driven by the subsequent impact on the relative energy price, which varies depending on the type of shock. Our results suggest that, in order to design effective measures in response to energy crises, policymakers need to carefully take into account the nature of energy shocks and the resulting distributional effects.
    Keywords: Political Economy, Production Economics, Research Methods/ Statistical Methods, Resource /Energy Economics and Policy
    Date: 2022–03–07
  8. By: Mueller, H.; Rauh, C.
    Abstract: This article proposes a new method for predicting escalations and de†escalations of violence using a model which relies on conflict history and text features. The text features are generated from over 3.5 million newspaper articles using a so†called topic†model. We show that the combined model relies to a large extent on conflict dynamics, but that text is able to contribute meaningfully to the prediction of rare outbreaks of violence in previously peaceful countries. Given the very powerful dynamics of the conflict trap these cases are particularly important for prevention efforts.
    Keywords: Conflict, prediction, machine learning, LDA, topic model, battle deaths, ViEWS prediction competition, random forest
    JEL: F21 C53 C55
    Date: 2022–03–22
  9. By: Aryan Eftekhari; Simon Scheidegger
    Abstract: We propose a scalable method for computing global solutions of nonlinear, high-dimensional dynamic stochastic economic models. First, within a time iteration framework, we approximate economic policy functions using an adaptive, high-dimensional model representation scheme, combined with adaptive sparse grids to address the ubiquitous challenge of the curse of dimensionality. Moreover, the adaptivity within the individual component functions increases sparsity since grid points are added only where they are most needed, that is, in regions with steep gradients or at nondifferentiabilities. Second, we introduce a performant vectorization scheme for the interpolation compute kernel. Third, the algorithm is hybrid parallelized, leveraging both distributed- and shared-memory architectures. We observe significant speedups over the state-of-the-art techniques, and almost ideal strong scaling up to at least $1,000$ compute nodes of a Cray XC$50$ system at the Swiss National Supercomputing Center. Finally, to demonstrate our method's broad applicability, we compute global solutions to two variates of a high-dimensional international real business cycle model up to $300$ continuous state variables. In addition, we highlight a complementary advantage of the framework, which allows for a priori analysis of the model complexity.
    Date: 2022–02
  10. By: Enkhbayar Shagdar (Economic Research Institute for Northeast Asia (ERINA))
    Abstract: This paper evaluates macro-economic impacts of the various policy measures implemented by governments in response to the worldwide COVID-19 pandemic by employing the standard GTAP Model and Data Base 10a, with Mongolia as the focus area. The simulation results demonstrate that Mongolia’s real economy would witness a 4.9% contraction, which is relatively compatible with the actual rate of -4.6% in 2020. All components of the welfare indicator are negative, and the country’s total welfare losses would equal $772.2 million. Most welfare deficits are associated with productivity drops followed by the terms of trade in goods and services and allocative efficiency losses. Also, both merchandise exports and imports decline along with worsening of the terms of trade. The pandemic triggers output drops for almost all sectors in Mongolia, including its major industry—the extractive sector. A few industries, such as textiles, other foods, and apparel, would experience output growths despite the pandemic shocks. However, the low self-sufficiency rates of these industries would undermine their output expansions during a prolonged pandemic, such as COVID-19. The Mongolian government’s stimulus packages to minimize the negative impacts of the pandemic on its economy have had positive effects on households by supporting consumption. However, unskilled labor has been the most vulnerable group during the pandemic, so it is desirable to implement targeted programs over universal stimuluses.
    Keywords: COVID-19 impacts, Economic growth, Welfare Impacts, CGE analysis
    JEL: E01 F17 C68
    Date: 2022–03
  11. By: Martino Banchio; Giacomo Mantegazza
    Abstract: This paper studies the strategic interaction of algorithms in economic games. We analyze games where learning algorithms play against each other while searching for the best strategy. We first establish a fluid approximation technique that enables us to characterize the learning outcomes in continuous time. This tool allows to identify the equilibria of games played by Artificial Intelligence algorithms and perform comparative statics analysis. Thus, our results bridge a gap between traditional learning theory and applied models, allowing quantitative analysis of traditionally experimental systems. We describe the outcomes of a social dilemma, and we provide analytical guidance for the design of pricing algorithms in a Bertrand game. We uncover a new phenomenon, the coordination bias, which explains how algorithms may fail to learn dominant strategies.
    Date: 2022–02
  12. By: Zhang, Minqi; Winkelhaus, Sven; Grosse, E. H.; Glock, C. H.
    Date: 2021–07–12
  13. By: Abbasi, Ali (Department of Surgery, University of California San Francisco); Gazze, Ludovica (Department of Economics, University of Warwick); Pals, Bridget (School of Law, New York University)
    Abstract: Lead exposure remains a significant threat to children’s health despite decades of policies aimed at getting the lead out of homes and neighborhoods. Generally, lead hazards are identified through inspections triggered by high blood lead levels (BLLs) in children. Yet, it is unclear how best to screen children for lead exposure to balance the costs of screening and the potential benefits of early detection, treatment, and lead hazard removal. While some states require universal screening, others employ a targeted approach, but no regime achieves 100% compliance. We estimate the extent and geographic distribution of undetected lead poisoning in Illinois. We then compare the estimated detection rate of a universal screening program to the current targeted screening policy under different compliance levels. To do so, we link 2010-2016 Illinois lead test records to 2010-2014 birth records, demographics, and housing data. We train a random forest classifier that predicts the likelihood a child has a BLL above 5µg/dL. We estimate that 10,613 untested children had a BLL≥5µg/dL in addition to the 18,115 detected cases. Due to the unequal spatial distribution of lead hazards, 60% of these undetected cases should have been screened under the current policy, suggesting limited benefits from universal screening.
    Keywords: Lead Poisoning ; Environmental Health ; Screening
    Date: 2022
  14. By: Pastwa, Anna M.; Shrestha, Prabal; Thewissen, James (Université catholique de Louvain, LIDAM/LFIN, Belgium); Torsin, Wouter
    Abstract: We apply a novel topic modeling method to map Initial Coin Offerings’ (ICOs’) white paper thematic content to analyze its information value to investors. Using a sentence-based topic modeling algorithm, we determine and empirically quantify 30 topics in an extensive collection of 5,210 ICO white papers between 2015 and 2021. We find that the algorithm produces a semantically meaningful set of topics, which significantly improves the model performance in identifying successful projects. The most value-relevant topics concern the technical features of the ICO. However, we find that white paper’s informativeness substantially diminishes after the token is listed. Moreover, we show that credibility-enhancing mechanisms (i.e., regulations and ICO analysts) reinforce the information value of ICO white papers. Overall, our results suggest that the topics discussed in white papers and the attention devoted to each topic are useful ICO performance indicators.
    Keywords: ICOs ; White paper informativeness ; Topic modeling ; ICO regulation ; ICO analysts
    JEL: G15 M13 L26 D80
    Date: 2021–01–01
  15. By: Matt Davison; Marcos Escobar-Anel; Yichen Zhu
    Abstract: This paper investigates the optimal choices of financial derivatives to complete a financial market in the framework of stochastic volatility (SV) models. We introduce an efficient and accurate simulation-based method, applicable to generalized diffusion models, to approximate the optimal derivatives-based portfolio strategy. We build upon the double optimization approach (i.e. expected utility maximization and risk exposure minimization) proposed in Escobar-Anel et al. (2022); demonstrating that strangle options are the best choices for market completion within equity options. Furthermore, we explore the benefit of using volatility index derivatives and conclude that they could be more convenient substitutes when only long-term maturity equity options are available.
    Date: 2022–02
  16. By: Ryuichi Shibasaki (School of Engineering, the University of Tokyo); Hirofumi Arai (Economic Research Institute for Northeast Asia (ERINA)); Kentaro Nishimura (Graduate School of Engineering, the University of Tokyo); Takuya Yamaguchi (Graduate School of Engineering, the University of Tokyo)
    Abstract: As China has promoted the Belt and Road Initiative (BRI) since 2014 and some CIS countries including Russia established the Eurasian Economic Union (EAEU) in 2015, trans-Eurasian land transport has gained attention. Under this background, this paper examines two questions. The first question is how significantly the recent strategic policies such as BRI and EAEU could shift container cargo from maritime shipping to land transport. The other is how much the shift could affect individual countries and regions in Northeast Asia. To answer these questions, the authors estimate their impacts on cargo volume using the intermodal network simulation model. The simulation results indicate that the cargo volume shifted would be about 10 percent of the total container flows between Asia and Europe, under our assumptions. Although land transport can potentially increase cargo volume several times its current level, maritime shipping will remain the dominant mode in intercontinental cargo transport. In addition, the simulation reveals possible negative impacts on the Primorye region of Russia and Mongolia, while the shift will advance.
    Date: 2021–12
  17. By: Michael Christl (European Commission - JRC); Ilias Livanos (European Centre for the Development of Vocational Training (CEDEFOP)); Andrea Papini (European Commission - JRC); Alberto Tumino (European Commission - JRC)
    Abstract: This paper provides a first assessment of the fiscal and distributional consequences of the ongoing structural changes in the labour markets of EU Member States, mostly driven by technological progress and ageing. Cedefop 2020 Skill forecasts, EUROSTAT population projections and the forecast on pension expenditures from the 2021 Ageing Report depict a scenario of an ageing population, an inverted U-shaped unemployment trend and potentially polarising labour markets, the latter mostly driven by a surge in high-skill occupations. This analysis makes use of the microsimulation model EUROMOD and reweighting techniques to analyse the fiscal and distributional impacts of these trends, given the current tax-benefit policies. The results suggest that the macro trends will increase pressure on government budgets. The analysis also shows evidence of the capacity of the current tax-benefit systems to counterbalance the increases in income inequality and poverty risks triggered by the expected future labour markets developments.
    Keywords: income distribution, budget, deficit, job polarisation, population ageing
    JEL: J11 J21 H68
    Date: 2022–03
  18. By: George Kudrna
    Abstract: This paper investigates the economy-wide effects of mandating private (employment-related) pensions. It draws on the Australian experience with its Superannuation Guarantee legislation which mandates contributions to private retirement (superannuation) accounts. Our key objective is to quantify the long-run implications of alternative mandatory superannuation contribution rates for household economic decisions over the life cycle, household welfare, and macroeconomic and fiscal aggregates. To that end, we develop a stochastic, overlapping generations (OLG) model with labor choice and endogenous retirement, which distinguishes between (i) ordinary private (liquid) assets and (ii) superannuation (illiquid) assets. The benchmark model is calibrated to the Australian economy, fitted to Australian demographic, household survey and macroeconomic data, and accounting for a detailed representation of Australia’s government policy, including its mandatory superannuation system. The model is then applied to simulate the effects of alternative mandatory superannuation contribution rates, with a specific focus on the counterfactual of a legislated future rate of 12% of gross wages. Based on the model simulations, we show that in the long run, this increased mandate generates larger average household wealth, output and consumption per capita and (rational) household welfare across income distribution.
    Keywords: Private Pension, Social Security, Income Taxation, Labor Supply, Endogenous Retirement, Stochastic General Equilibrium
    JEL: J32 H55 H31 J22 J26 C68
    Date: 2022–03
  19. By: Yuan, Tianchen; Alasiri, Faisal; Ioannou, Petros A.
    Abstract: Connectivity between vehicles and infrastructure allows the efficient flow of information in a dynamic traffic environment. This information can be used to provide recommendations to vehicles in order to alleviate traffic congestion, improve mobility with considerable benefits to the environment. The traffic flow environment however is very complex and involves many uncertainties that include inaccurate measurements, missing data, etc. Any approach to manage or control traffic should be able to handle such uncertainties in a robust way. This project focusses on variable speed limit (VSL) control as an approach to reduce congestion at bottlenecks despite the presence of uncertainties. Numerous research efforts have been made over the years in the field of VSL control in order to resolve bottleneck congestion and improve traffic mobility. Nevertheless, few of them have looked into the issue of robustness with respect to measurement or model uncertainties. In this project, a robust VSL controller is designed based on a modified multi-section cell transmission model (CTM) to alleviate freeway traffic congestion and reject uncertainties. The proposed VSL controller computes the speed limit recommendations using measured flows and densities and communicates them to the upstream vehicles. The optimum location where the speed limit recommendation should be communicated to vehicles is another control variable addressed in the project in order to maximize performance and benefits to the environment. The proposed VSL controller is integrated with ramp metering (RM) controllers and lane change (LC) recommendations to maximize performance. The effectiveness of the integrated control scheme is demonstrated using extensive Monte Carlo microscopic simulations under several traffic demand scenarios and different types and levels of uncertainties. The microscopic simulations are carried out using the commercial traffic software VISSIM. Real data are used to validate the traffic simulator. The benefits in terms of mobility, safety and emissions are quantified. View the NCST Project Webpage
    Keywords: Engineering, Variable speed limit, Uncertainty, Sign distance, Integrated control
    Date: 2022–03–01
  20. By: Diao, Xinshen; Rosenbach, Gracie; Spielman, David J.; Aragie, Emerta
    Abstract: In Rwanda, as in other countries, different types of households will experience the economic effects of the COVID-19 pandemic differently. We use a microsimulation approach to highlight the importance of these differences and to draw attention to the diversified livelihood strategies of Rwandan households in order to fully understand COVID-19’s impacts on their income and poverty status. Our approach complements macro-level assessments of COVID-19’s economic impacts, focusing on the contribution of the income sources, asset holdings, and location (urban/rural) of households to understanding these differential effects.
    Keywords: RWANDA, CENTRAL AFRICA, AFRICA SOUTH OF SAHARA, AFRICA, Coronavirus, coronavirus disease, Coronavirinae, COVID-19, economic impact, agrifood systems, poverty, policies, modelling, households, household income, lockdown, Social Accounting Matrix (SAM)
    Date: 2021
  21. By: Dorosh, Paul A.
    Abstract: Despite reforms in early 2021, including a devaluation of the currency and a liberalization of imports, there remain significant distortions in Sudan’s wheat value chain, especially related to subsidized sales prices of flatbread. This flatbread subsidy, a key component of wheat policy, is not well-targeted. Calculations based on 2009 national household survey data and current 2021 prices and wheat supply show that urban poor households annually receive slightly less from this subsidy than urban non-poor households (18,900 and 20,800 SDG/capita). Rural poor households receive only 2,700 SDG/capita. This paper presents the results of several simulations of a partial equilibrium model of Sudan’s wheat economy that are designed to analyze the impacts of recent shocks and various policy options. Model simulations show that increased wheat imports, such as those financed by food aid, add to supplies for processing into wheat flour, flatbread, and other wheat products, resulting in lower prices for consumers and increased consumption, but also disincentives for production. A 300,000 ton increase in wheat imports, as occurred in early 2021, results in an 8 percent increase in wheat consumption and a 35 percent decline in the market price of non-flatbread wheat products. Production falls by 12 percent. Since flatbread prices are unchanged, wheat consumption of the urban poor, for whom flatbread is the major wheat product consumed, increases by only 4 percent. Raising flatbread prices by 30 percent to reduce the size of the fiscal subsidy reduces total consumption of flatbread by 17 percent and sharply reduces wheat consumption and real incomes of the urban poor. All households suffer a loss of 41 to 45 percent in the value of flatbread subsidies received. The urban poor experience the largest decline in total consumption of wheat (14 percent) and in total income (11 percent). (The average total income loss for all households is only 3 percent.) Reducing the flatbread subsidy without a compensating income transfer would significantly reduce the welfare of the urban poor and likely threaten political stability. Our results suggest that a combination of key wheat policies involving high levels of imports – including injection of food aid wheat into the economy in late 2020 – and subsidized flatbread will significantly benefit urban poor households. Nonetheless, the are important data gaps on several aspects of the wheat sector, including no recent nationally representative household expenditure survey data. In addition, greater transparency, including publication of quantities and prices of government purchases, sales of wheat and wheat flour, and quantities and prices of subsidized flatbread across the country has the potential to significantly increase the efficiency of the entire wheat sector. As shown in this paper, Sudan’s wheat policies in recent years, such as increased wheat imports, price subsidies in the wheat value chain, and low prices of flatbread, have in general favored consumers, to the detriment of producers. These interventions in the wheat value chain, especially those related to subsidies on flatbread, have especially large effects on the welfare of urban households, making these policies particularly politically sensitive. However, they have entailed high fiscal costs, threatening macro-economic stability and crowding out other possible investments to promote growth and poverty reduction. Careful policy analysis and ongoing monitoring of outcomes and new developments will be needed to help guide the important choices ahead.
    Keywords: REPUBLIC OF THE SUDAN, EAST AFRICA, AFRICA SOUTH OF SAHARA, AFRICA, wheat, wheat flour, bread, food prices, value chains, trade, models, wheat value chain, model simulation
    Date: 2021
  22. By: Irwin Garfinkel; Laurel Sariscsany; Elizabeth Ananat; Sophie M. Collyer; Robert Paul Hartley; Buyi Wang; Christopher Wimer
    Abstract: We conduct a benefit-cost analysis of a U.S. child allowance, based on a systematic literature review of the highest quality available causal evidence on the short- and long-term effects of cash and near-cash transfers. In contrast to the previous studies we synthesize, which tend to measure a subset of benefits and costs available in a particular dataset, we establish a comprehensive accounting of potential effects and secure estimates of each. We produce core estimates of the benefits and costs per child and per adult of increasing household income by $1,000 in one year; these can be applied to value any cash or near-cash program that increases household income. Using microsimulation, we then apply these estimates to determine net aggregate benefits of three child allowance policies, including the expanded Child Tax Credit as enacted for the year 2021 in the American Rescue Plan (ARP). Our estimates indicate that making that expansion permanent would cost $97 billion per year and generate social benefits with net present value of $982 billion per year. Sensitivity analyses indicate that our estimates are robust to alternative assumptions and that all three child allowance policies we evaluate produce very high net returns for the U.S. population.
    JEL: H2 H23 I38 J18
    Date: 2022–03
  23. By: Shaghayegh Vosough; André de Palma; Robin Lindsey (Université de Cergy-Pontoise, THEMA)
    Abstract: Road traffic is a major contributor to air pollution which is a serious problem in many large cities. Experience in London, Milan, and Stockholm indicates that road pricing can be useful in reducing vehicle emissions as well as congestion. This study uses a dynamic traffic network simulator that models choices of mode, departure time, and route to investigate the effectiveness of tolls to target emissions and congestion externalities on a stylized urban road network during a morning commuting period. The spatial distribution of four pollutants is calculated using a Gaussian dispersion model that accounts for wind speed and direction. Single and double cordon tolls are evaluated, as well as flat tolls that do not change during the simulation period and step tolls that change at half-hourly intervals. The presence of emissions externalities raises optimal toll levels, and substantially increases the welfare gains from tolling, although the proportional advantage of step tolls over flat tolls is lower than if congestion is the only externality. The individual welfare-distributional effects of tolling vary strongly with residential and workplace locations relative to the cordon, and also differ for the upwind and downwind sides of the city.
    Keywords: congestion, dynamic traffic simulation, emissions, pollution dispersion, tolls
    JEL: R4 K32
    Date: 2022
  24. By: Javier Barbero (European Commission - JRC); Manol Bengyuzov (European Commission - DG GROW); Martin Christensen (European Commission - JRC); Andrea Conte (European Commission - JRC); Simone Salotti (European Commission - JRC); Aleksei Trofimov (European Commission - DG GROW)
    Abstract: This study uses both econometric and modelling techniques to quantify the macroeconomic impact of regulatory reforms removing barriers in the European Single Market for services that have taken place in the European Union between 2006 and 2017. It also provides scenario analyses of the impact of a number of hypothetical additional reforms aimed at further reducing regulatory restrictions. The results of the modelling simulations indicate that the regulatory reforms implemented between 2006 and 2017 will result in discounted cumulative gains of 2.1% of GDP by the year 2027. Furthermore, ambitious additional reforms from 2017 onwards would generate an additional growth potential of 2.5% of GDP by 2027. Combining the realised and potential gains would result in a cumulative gain in GDP of 4.65% and a rise in employment of more than 300,000 full time equivalents by 2027. More conservative hypotheses on the additional reforms from 2017 onwards would lead to a GDP cumulative gain of 3.22% by 2027.
    Keywords: rhomolo, region, growth, Services regulation, general equilibrium modelling, Single Market
    JEL: C68 R13
    Date: 2022–02
  25. By: B. Sofia Gil-Clavel (Max Planck Institute for Demographic Research, Rostock, Germany); André Grow (Max Planck Institute for Demographic Research, Rostock, Germany); Maarten J. Bijlsma (Max Planck Institute for Demographic Research, Rostock, Germany)
    Abstract: The increasingly complex and heterogeneous immigrant communities settling in Europe have led European countries to adopt civic-integration measures. Among these, measures that aim to facilitate language acquisition are often considered crucial for integration and cooperation between immigrants and natives. Simultaneously, the rapid expansion of the use of online social networks is believed to change the factors that affect immigrants’ language acquisition. However, so far, few studies have analyzed whether this is the case. This article uses a novel longitudinal data source derived from Twitter to: (1) analyze differences between destination-countries in the pace of immigrants’ language acquisition depending on the citizenship and civic-integration policies of those countries; and (2) study how the relative size of migrant groups in the destination-country, and the linguistic and geographical distance between origin- and destination countries, are associated with language acquisition. We focus on immigrants whose destination countries were in the EU-15 between 2012 and 2016. We study time until a user mostly tweets in the language of the destination-country for one month as a proxy of language acquisition using survival analysis. Results show that immigrants who live in countries with strict requirements for immigrants’ language acquisition and low levels of liberal citizenship policies have the highest median times of language acquisition. Furthermore, on social media such as Twitter, language acquisition is associated with classic explanatory variables, such as size of the immigrant group in the destination country, linguistic distance between origin- and destination-language, and geographical distance between origin- and destination-country.
    Keywords: European Union, computational social science, culture, immigration policy, international migration, languages
    JEL: J1 Z0
    Date: 2022
  26. By: ITF
    Abstract: Street space in cities is a rare resource. Much of it is currently allocated to highly space-consuming transport modes without taking into account that demands for that space vary over time. This report looks at how street space has typically been allocated in the past, examines the rationale for street space allocation and describes how to measure space consumption for mobility purposes. The study also explores by way of a simulation how new mobility services and travel modes interact when a limited, dynamic and demand-responsive re-allocation of street space is introduced in a mid-sized city.
    Date: 2022–02–17

This nep-cmp issue is ©2022 by Stan Miles. It is provided as is without any express or implied warranty. It may be freely redistributed in whole or in part for any purpose. If distributed in part, please include this notice.
General information on the NEP project can be found at For comments please write to the director of NEP, Marco Novarese at <>. Put “NEP” in the subject, otherwise your mail may be rejected.
NEP’s infrastructure is sponsored by the School of Economics and Finance of Massey University in New Zealand.