nep-cmp New Economics Papers
on Computational Economics
Issue of 2021‒11‒08
nine papers chosen by
Stan Miles
Thompson Rivers University

  1. Understanding the Sources of Earnings Losses After Job Displacement: A Machine-Learning Approach By Pytka, Krzysztof; Gulyas, Andreas
  2. The Role of (non-)Topological Features as Drivers of Systemic Risk: a machine learning approach By Michel Alexandre; Thiago Christiano Silva; Colm Connaughton; Francisco A. Rodrigues
  3. An agent-based model of trickle-up growth and income inequality By Elisa Palagi; Mauro Napoletano; Andrea Roventini; Jean-Luc Gaffard
  4. Do patents really foster innovation in the pharmaceutical sector? Results from an evolutionary, agent-based model By Giovanni Dosi; Elisa Palagi; Andrea Roventini; Emanuele Russo
  5. Partial Dominance in Branch-Price-and-Cut for the Basic Multi-Compartment Vehicle-Routing Problem By Katrin Heßler; Stefan Irnich
  6. Assessing the economic impact of lockdowns in Italy: a computational input-output approach By Severin Reissl; Alessandro Caiani; Francesco Lamperti; Mattia Guerini; Fabio Vanni; Giorgio Fagiolo; Tommaso Ferraresi; Leonardo Ghezzi; Mauro Napoletano; Andrea Roventini
  7. The Impact of Research Independence on PhD Students' Careers: Large-scale Evidence from France By Sofia Patsali; Michele Pezzoni; Fabiana Visentin
  8. The Effects of US-China Rivalry on Latin America and Their Implications By Hong, Sungwoo; Yoon, Yeo Joon; Kim, Jino; Rim, Jeewoon; Nam, Jimin
  9. Towards a dynamic spatial microsimulation model for projecting Auckland’s spatial distribution of ethnic groups By Mohana Mondal; Michael P. Cameron; Jacques Poot

  1. By: Pytka, Krzysztof; Gulyas, Andreas
    JEL: J01 J3
    Date: 2021
  2. By: Michel Alexandre; Thiago Christiano Silva; Colm Connaughton; Francisco A. Rodrigues
    Abstract: The purpose of this paper is to assess the role of financial and topological variables as determinants of systemic risk (SR). The SR, for different levels of the initial shock, is computed for institutions in the Brazilian interbank market by applying the differential DebtRank methodology. The financial institution(FI)-specific determinants of SR are evaluated through two machine learning techniques: XGBoost and random forest. Shapley values analysis provided a better interpretability for our results. Furthermore, we performed this analysis separately for banks and credit unions. We have found the importance of a given feature in driving SR varies with i) the level of the initial shock, ii) the type of FI, and iii) the dimension of the risk which is being assessed – i.e., potential loss caused by (systemic impact) or imputed to (systemic vulnerability) the FI. Systemic impact is mainly driven by topological features for both types of FIs. However, while the importance of topological features to the prediction of systemic impact of banks increases with the level of the initial shock, it decreases for credit unions. Concerning systemic vulnerability, this is mainly determined by financial features, whose importance increases with the initial shock level for both types of FIs.
    Date: 2021–10
  3. By: Elisa Palagi; Mauro Napoletano (OFCE - Observatoire français des conjonctures économiques - Sciences Po - Sciences Po); Andrea Roventini (OFCE - Observatoire français des conjonctures économiques - Sciences Po - Sciences Po); Jean-Luc Gaffard (OFCE - Observatoire français des conjonctures économiques - Sciences Po - Sciences Po)
    Abstract: We build an agent-based model to study how coordination failures, credit constraints and unequal access to investment opportunities affect inequality and aggregate income dynamics. The economy is populated by households who can invest in alternative projects associated with different productivity growth rates. Access to investment projects also depends on credit availability. The income of each house- hold is determined by the output of the project but also by aggregate demand conditions. We show that aggregate dynamics is affected by income distribution. Moreover, we show that the model features a trickle-up growth dynamics. Redistribution towards poorer households raises aggregate demand and is beneficial for the income growth of all agents in the economy. Extensive numerical simulations show that our model is able to reproduce several stylized facts concerning income inequality and social mobility. Finally, we test the impact of redistributive fiscal policies, showing that fiscal policies facilitating access to investment opportunities by poor households have the largest impact in terms of raising long-run aggregate income and decreasing income inequality. Moreover, policy timing is important: fiscal policies that are implemented too late may have no significant effects on inequality.
    Keywords: income inequality,social mobility,credit constraints,coordination failures,effective demand,trickle-up growth,fiscal policy
    Date: 2021
  4. By: Giovanni Dosi; Elisa Palagi; Andrea Roventini; Emanuele Russo
    Abstract: The role of the patent system in the pharmaceutical sector is highly debated also due to its strong public health implications. In this paper we develop an evolutionary, agent-based model of the pharmaceutical industry to explore the impact of different configurations of the patent system upon innovation and competition. The model is able to replicate the main stylized facts of the drug industry as emergent properties. We perform policy experiments to assess the impact of different IPR regimes changing the breadth and length of patents. Results suggest that enlarging the extent and duration of patents yields adverse effects in terms of innovation outcomes, as well as of market competition and consumer welfare. Such general conclusions hold even if one takes into account the possible positive effects on R&D intensity and information disclosure triggered by patents.
    Keywords: Innovation; Intellectual property rights; Market power; Pharmaceutical sector; Agent-based models.
    Date: 2021–10–28
  5. By: Katrin Heßler (Johannes Gutenberg University Mainz); Stefan Irnich (Johannes Gutenberg University Mainz)
    Abstract: We consider the exact solution of the basic version of the multiple-compartment vehicle-routing problem, i.e., a problem consisting of clustering customers into groups, routing a vehicle for each group, and packing demands of the visited customer uniquely into one of the vehicle’s compartments. Compartments have a fixed size, and there are no incompatibilities between the transported items or between items and compartments. The objective is to minimize the total distance of all vehicle routes such that all customers are visited. We study the shortest-path subproblem that arises when exactly solving the problem with a branch-price-andcut algorithm. For this subproblem, we compare a standard dynamic-programming labeling approach with a new one that utilizes a partial dominance. While the algorithm with standard labeling already struggles with relatively small instances, the one with partial dominance can cope with much larger instances.
    Keywords: vehicle routing, packing, shortest-path problem with resource constraints, dynamic-programming labeling, partial dominance
    Date: 2021–02–11
  6. By: Severin Reissl; Alessandro Caiani; Francesco Lamperti; Mattia Guerini; Fabio Vanni (OFCE - Observatoire français des conjonctures économiques - Sciences Po - Sciences Po); Giorgio Fagiolo; Tommaso Ferraresi; Leonardo Ghezzi; Mauro Napoletano (OFCE - Observatoire français des conjonctures économiques - Sciences Po - Sciences Po); Andrea Roventini (OFCE - Observatoire français des conjonctures économiques - Sciences Po - Sciences Po)
    Abstract: We build a novel computational input-output model to estimate the economic impact of lockdowns in Italy. The key advantage of our framework is to integrate the regional and sectoral dimensions of economic production in a very parsimonious numerical simulation framework. Lockdowns are treated as shocks to available labor supply and they are calibrated on regional and sectoral employment data coupled with the prescriptions of government decrees. We show that when estimated on data from the first "hard" lockdown, our model closely reproduces the observed economic dynamics during spring 2020. In addition, we show that the model delivers a good out-of-sample forecasting performance. We also analyze the effects of the second "mild" lockdown in fall of 2020 which delivered a much more moderate negative impact on production compared to both the spring 2020 lockdown and to a hypothetical second "hard" lockdown.
    Keywords: input-output,Covid-19,lockdown,Italy
    Date: 2021
  7. By: Sofia Patsali (Université Côte d'Azur, France; CNRS, GREDEG); Michele Pezzoni (Université Côte d'Azur, France; CNRS, GREDEG); Fabiana Visentin (Maastricht University; UNU-MERIT)
    Abstract: This study investigates the effect of research independence during the PhD period on students' career outcomes. We use a unique and detailed dataset on the French population of STEM PhD students who graduated between 1995 and 2013. To measure research independence, we compare the PhD thesis content with the supervisor's research. We employ advanced neural network text analysis techniques evaluating the similarity between student's thesis abstract and supervisor's publications during the PhD period. After exploring which characteristics of the PhD training experience and supervisor explain the level of research similarity, we estimate how similarity associates with the likelihood of pursuing a research career. We find that the student thesis's similarity with her supervisor's research work is negatively associated with starting a career in academia and patenting probability. Increasing the PhD-supervisor similarity score by one standard deviation is associated with a 2.1 percentage point decrease in the probability of obtaining an academic position and a 0.57 percentage point decrease in the probability of patenting. However, conditional on starting an academic career, PhD-supervisor similarity is associated with a higher student's productivity after graduation as measured by citations received, network size, and probability of moving to a foreign or US-based affiliation.
    Keywords: Research independence, Early career researchers, Scientific career outcomes, Neural network text analysis
    JEL: D22 O30 O33 O38
    Date: 2021–10
    Abstract: The conflict between the United States and China may be the issue of most importance as well as interest to the world, prior to COVID-19. This conflict between the two countries is appearing not only in the economic sector, but also in various field such as politics, diplomacy, and military affairs. Such competition between the two countries is likely to escalate further as multilateral systems such as the WTO are threatened and protectionism intensifies in the post-COVID-19 world. Even within Latin America, the competition between the two countries frequently appears in a variety of forms. Conflicts between the United States and China in Latin America tend to occur mainly in the infrastructure sectors. Furthermore, the United States pressured Latin American countries to choose between the United States and China, with the results of this pressure depending on the political orientation of the ruling government. In order to investigate the impact of retaliatory tariffs between the two countries on Latin American countries’ exports and welfare, we employ an event analysis for exports and computational general equilibrium (CGE) model for welfare, with Argentina, Brazil, Mexico, and Chile as the subject of our analysis. Based on the outcome of the event study, Brazil’s exports to the United States moderately increased due to the tariff imposition, and such an effect persisted for short term. Its exports to China rose considerably immediately after the tariff imposition, and then the impact tended to decrease over time. By contrast, it is difficult to conclude that the tariff imposition had a statistically significant and lasting effect on the exports of the remaining three countries to the United States and China. As a result of the analysis using the CGE model, meanwhile, the tariffs imposed between the United States and China trivially increased the welfare of Latin American countries.
    Keywords: US-China; Latin America; rivalry; conflict
    Date: 2021–02–10
  9. By: Mohana Mondal (University of Waikato); Michael P. Cameron (University of Waikato); Jacques Poot (University of Waikato and Vrije Universiteit Amsterdam)
    Abstract: In this paper we describe the development, calibration and validation of a dynamic spatial microsimulation model for projecting small area (area unit) ethnic populations in Auckland, New Zealand. The key elements of the microsimulation model are a module that projects spatial mobility (migration) within Auckland and between Auckland and the rest of the world, and a module that projects ethnic mobility. The model is developed and calibrated using 1996-2001 New Zealand Linked Census (i.e. longitudinal) data, and then projected forward to 2006. We then compare the results with the actual 2006 population. We find that in terms of indexes of overall residential sorting and ethnic diversity, our projected values are very close to the actual values. At a more disaggregated spatial scale, the model performs well in terms of the simulated normalised entropy measure of ethnic diversity for area units, but performs less well in terms of projecting residential sorting for each individual ethnic group.
    Keywords: dynamic microsimulation model; ethnic identity; location transition; ethnic transition
    JEL: J11 R10 R15
    Date: 2021–10–30

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