nep-cmp New Economics Papers
on Computational Economics
Issue of 2009‒03‒07
five papers chosen by
Stan Miles
Thompson Rivers University

  1. Evaluating Alternative Basic Income Mechanisms. A Simulation for European Countries By Ugo Colombino
  2. The effect of restrictive policy instruments on Belgian fishing fleet dynamics By H. STOUTEN; A. HEENE; X. GELLYNCK; H. POLET
  3. La stratégie de croissance accélérée du Sénégal est t-elle pro-pauvre By François Joseph Cabral
  4. QMM. A Quarterly Macroeconomic Model of the Icelandic Economy By Ásgeir Daníelsson; Magnús F. Gudmundsson; Svava J. Haraldsdóttir; Thorvardur T. Ólafsson; Ásgerdur Ó. Pétursdóttir; Thórarinn G. Pétursson; Rósa Sveinsdóttir
  5. The great dissolution: organization capital and diverging volatility puzzle By Che, Natasha Xingyuan

  1. By: Ugo Colombino (Statistics Norway)
    Abstract: We develop and estimate a microeconometric model of household labour supply in five European countries representative of different economies and welfare policy regimes: Denmark, Italy, Norway, Portugal and United Kingdom. We then simulate, under the constraint of constant total net tax revenue, the effects of various hypothetical tax-transfer reforms which include alternative versions of a Basic Income mechanisms. We produce various indexes and criteria according to which the reforms can be ranked. The exercise can be considered as one of empirical optimal taxation, where the optimization problem is solved computationally rather than analytically.
    Keywords: Basic Income; Minimum Guaranteed Income; Models of Labour Supply; Tax Reforms; Welfare Evaluation; Optimal Taxation
    JEL: C25 H24 H31 I38
    Date: 2009–02
    Abstract: Even with the rapid changes in the level of complexity and the uncertainty of the environment in which Belgian sea fisheries operate, fisheries management in Belgium is still mainly based on restrictive policy instruments founded in the biological approach of fisheries management science. Since they will continue to play an important role, this paper evaluated changes in three restrictive policy instruments and their effect on future fleet performance and dynamics, i.e. maximum fishing days, total quota-restrictions and licences.<br><br>These effects are tested through scenarios in a microeconomic simulation model, including sensitivity analysis. This study opts for a dynamic simulation model based on a microeconomic approach of fleet dynamics using system dynamics as a modelling technique (operational base: Vensim®DSS).<br><br>The results indicated that changes in maximum fishing days and total quota resulted in higher fluctuations in fleet performance and dynamics compared to changes in licences. Furthermore, changes in maximum fishing days and total quota had a direct impact on fleet performance, though not always as expected, whereas licences only affected fleet performance indirectly since they only limit the entry of new vessels to the fleet and they can block the growth of successful sub fleets.<br><br>The outcomes of this study are translated into practical recommendations for improving fisheries management. Firstly, policy makers need to be more aware of misperceptions of feedback. Secondly, the results proved that altering only one type of restrictive policy instrument at a time often fails to meet desired outcomes. Therefore, policy makers need to find a balance in combining policy instruments. Finally, this paper opens the discussion on the future value of restrictive policy instruments in the rapidly changing, complex and uncertain fisheries environment. It suggests rethinking their use from “preserving a status quo and social peace” toward a driving factor in “stimulating fleet dynamics.”
    Keywords: Fisheries management, restrictive policy instruments, sensitivity simulation, system dynamics, fleet performance, fleet dynamics.
    Date: 2008–11
  3. By: François Joseph Cabral (Université Cheikh Anta DIOP, Université Polytechnique de Thiès, CRES et GREDI)
    Abstract: L’objectif principal de la Stratégie de croissance accélérée (SCA) du Sénégal est d’accroître le rythme de la croissance de l’économie en la rendant profitable aux pauvres. Or dans toute économie, il existe une relation entre la croissance économique, la réduction de la pauvreté et la distribution des revenus. Dans cet article, nous développons un modèle d’équilibre général calculable dynamique afin de simuler l’impact de la SCA et évaluons, à l’aide d’indices, la qualité de la croissance qu’elle génère. Les résultats montrent que la croissance issue de la SCA n’est pas pro-pauvre. Toutefois, comparée au sentier de croissance obtenu en l’absence de toute politique ou choc (« business as usual »), elle présente un profil plus bénéfique aux pauvres.
    Keywords: Dynamic CGE model, poverty, inequality
    JEL: D58 D31 F41 I32
    Date: 2009
  4. By: Ásgeir Daníelsson; Magnús F. Gudmundsson; Svava J. Haraldsdóttir; Thorvardur T. Ólafsson; Ásgerdur Ó. Pétursdóttir; Thórarinn G. Pétursson; Rósa Sveinsdóttir
    Abstract: This paper documents and describes Version 2.0 of the Quarterly Macroeconomic Model of the Central Bank of Iceland (QMM). QMM and the underlying quarterly database have been under construction since 2001 at the Research and Forecasting Division of the Economics Department at the Bank and was first implemented in the forecasting round for the Monetary Bulletin 2006.1 in March 2006. QMM is used by the Bank for forecasting and various policy simulations and therefore plays a key role as an organisational framework for viewing the medium-term future when formulating monetary policy at the Bank. This paper is mainly focused on the short and medium-term properties of QMM. Steady state properties of the model are documented in a paper by Daníelsson (2009).
    Date: 2009–02
  5. By: Che, Natasha Xingyuan
    Abstract: Most traditional explanations for the decreasing aggregate output volatility - so-called "Great Moderation" - fail to accommodate, or even directly contradict, another aspect of empirical data: the average sales volatility for publicly-traded US firms has been increasing during the same period. The paper aims to reconcile the opposite trends of firm-level and aggregate volatilities. I argue that the rise of organization capital, or firm-specific intangible capital, is the origin of the volatility divergence. Firms in the modern economy have been investing heavily in intangible and organizational assets, such as R&D, management processes, intellectual property, software, and brand name - the "soft" capitals that distinguish a firm from the sum of its physical properties. Most intangible assets are firm-specific, inseparable from the company that originally produced them, and difficult to trade on outside market. Investing in these organization-specific capitals insulates a firm from market-wide shocks, but introduces higher firm-specific risk that does not equally affect its peers. When value creation is increasingly relying on organization capital, the impact of idiosyncratic risk factor rises, while that of general risk factor declines. The former elevates firm-level volatility; the latter reduces aggregate volatility, mainly through weakening the positive co-movements among firms. Therefore, the decrease in aggregate output volatility is not because of less turbulent macro environment, but a result of more heterogeneity among production units. In this sense, the Great Moderation is rather a story of "Great Dissolution". It may indicate greater economic uncertainty faced by individual agents, instead of less. My empirical investigation found that, consistent with the paper's hypotheses, firm-level volatility increases with organizational investment, but general factors' impact on firm performance and a firm's correlation with others decrease with organizational investment. Simulations of the general equilibrium model featuring organization capital investment are capable of replicating the volatility trends at both aggregate and firm level for the past two decades.
    Keywords: organization capital; intangible capital; great moderation; firm volatility; business cycle; business investment
    JEL: D21 E22 D58 E10 E32 C23 E23 D24
    Date: 2009

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