Low fertility and long run growth in an economy with a large public sector

There is plenty of evidence that growth has a negative relation to fertility and dependency ratios. Recently it has been suggested that low fertility countries may be caught in a trap that is hard to get out of. One important mechanism in such a trap would be social interaction and its effect on the ideal family size. Such social interaction mechanisms are hard to capture in formal models, therefore we use an agent based simulation model to investigate the issue. In our experimental setup a stable growth and population path is provoked into a fertility trap by rising relative child costs linked to positive growth. Even rather large increases in child benefits are then insufficient to get out of the trap. However, the small number of children temporarily enables the economy to grow faster for several decades. Removing the adaptation of social norms turns out to disarm the trap.low fertility trap; social norms relative income; economic growth

IFSIM Handbook

This handbook explains the simulation model IFSIM. IFSIM is an agent based simulation model written in JAVA. The model is constructed for analyzing demographic and economic issues. The aim of the model is to include the main consumption and production patterns over the life-cycle and thus being able to test demo-economic interactions.agent-based modelling; simulation model; JAVA; demogrphy; economy; demo-economic interactions

Low fertility and long-run growth in an economy with a large public sector

Recently it has been suggested that low fertility countries may be caught in a trap that is hard to get out of. One important mechanism in such a trap would be social interaction and its effect on the ideal family size. Such social interaction mechanisms are hard to capture in formal models, therefore we use an agent-based simulation model to investigate the issue. In our experimental setup a stable growth and population path is calibrated to Swedish data using the Swedish social policy setup. The model is provoked into a fertility trap by increasing relative child costs linked to positive growth. Even rather large increases in child benefits are then insufficient to get out of the trap. However, the small number of children temporarily enables the economy to grow faster for several decades. Removing the adaptation of social norms turns out to disarm the trap.Il a été suggéré récemment que les pays à basse fécondité pourraient être victimes d’un piège dont ils auraient du mal à se dégager. Un mécanisme essentiel dans ce piège serait l’interaction sociale et son effet sur la taille idéale de famille. Des mécanismes de ce type sont difficiles à représenter dans un modèle formel, et c’est pourquoi nous avons eu recours à un modèle de simulation multi-agents pour explorer le processus. Dans notre dispositif expérimental, un modèle de croissance et de population stable est calibré aux données suédoises, en utilisant la configuration suédoise de politique sociale. Le modèle est entraîné dans un piège de fécondité en élevant les coûts relatifs de l’enfant en lien avec la croissance positive. Dans ce cas, même des augmentations importantes des prestations familiales sont insuffisantes pour sortir du piège. Toutefois, le petit nombre d’enfants permet temporairement à l’économie de croître plus rapidement pendant quelques décennies. L’arrêt de l’adaptation aux normes sociales conduit à une neutralisation du piège

Endogenous Fertility, Mortality and Economic Growth: Can a Malthusian Framework Account for the Conflicting Historical Trends in Population?

The 19th century economist, Thomas Robert Malthus, hypothesized that the long-run supply of labor is completely elastic at a fixed wage-income level because population growth tends to outstrip real output growth. Dynamic equilibrium with constant income and population is achieved through equilibrating adjustments in "positive checks" (mortality, starvation) and "preventive checks" (marriage, fertility). Developing economies since the Industrial Revolution, and more recently especially Asian economies, have experienced steady income growth accompanied by sharply falling fertility and mortality rates. We develop a dynamic model of endogenous fertility, longevity, and human capital formation within a Malthusian framework that allows for diminishing returns to labor but also for the role of human capital as an engine of growth. Our model accounts for economic stagnation with high fertility and mortality and constant population and income, as predicted by Malthus, but also for takeoffs to a growth regime and a demographic transition toward low fertility and mortality rates, and a persistent growth in per-capita income.

Poverty Traps

no abstract given.Poverty

Ageing and Export Dependency

The primary manifestation of the demographic transition in a modern economic context is through ageing and the primary transmission from ageing to the macro economy is through its effect on saving and investment behavior. These two effects taken together suggest a strong impact from the continuing process of ageing on international capital flows and global macroeconomic imbalances. This paper explores the potential relationship between ageing on a macroeconomic level and the reliance, or outright dependency, on exports and foreign asset income to achieve economic growth. The paper’s argument is both theoretical and empirical. Using a standard overlapping generation framework (OLG) in an open economy context this paper discusses whether the proposed relationship between a transition into old age and dissaving is feasible and desirable (or even optimal?). Finally, an empirical analysis is presented on Germany and Japan to show how these two economies, as the oldest in the world, may exactly be in a state of export dependency.demographics, international capital flows, open economy macroeconomics, ageing, intertemporal choice,

Risk, Institutions and Growth: Why England and Not China?

We analyze the role of risk-sharing institutions in transitions to modern economies. Transitions requires individual-level risk-taking in pursuing productivity-enhancing activities including using and developing new knowledge. Individual-level, idiosyncratic risk implies that distinct risk-sharing institutions – even those providing the same level of insurance – can lead to different growth trajectories if they differently motivate risk-taking. Historically, risk sharing institutions were selected based on their cultural and institutional compatibility and not their unforeseen growth implications. We simulate our growth model incorporating England’s and China’s distinct pre-modern risk-sharing institutions. The model predicts a transition in England and not China even with equal levels of risk sharing. Under the clan-based Chinese institution, the relatively risk-averse elders had more control over technological choices implying lower risk-taking. Focusing on non-market institutions expands on previous growth-theoretic models to highlight that transitions can transpire even in the absence of exogenous productivity shocks or time-dependent state variables. Recognizing the role of non-market institutions in the growth process bridges the view that transitions are due to luck and the view that transitions are inevitable. Transitions transpire when ‘luck’ creates the conditions under which economic agents find it beneficial to make the choices leading to positive rates of technological change. Luck came in the form of historical processes leading to risk-sharing institutions whose unintended consequences encouraged productivity-enhancing risk-taking.institutions, risk, growth, development

Data Informed Health Simulation Modeling

Combining reliable data with dynamic models can enhance the understanding of health-related phenomena. Smartphone sensor data characterizing discrete states is often suitable for analysis with machine learning classifiers. For dynamic models with continuous states, high-velocity data also serves an important role in model parameterization and calibration. Particle filtering (PF), combined with dynamic models, can support accurate recurrent estimation of continuous system state. This thesis explored these and related ideas with several case studies. The first employed multivariate Hidden Markov models (HMMs) to identify smoking intervals, using time-series of smartphone-based sensor data. Findings demonstrated that multivariate HMMs can achieve notable accuracy in classifying smoking state, with performance being strongly elevated by appropriate data conditioning. Reflecting the advantages of dynamic simulation models, this thesis has contributed two applications of articulated dynamic models: An agent-based model (ABM) of smoking and E-Cigarette use and a hybrid multi-scale model of diabetes in pregnancy (DIP). The ABM of smoking and E-Cigarette use, informed by cross-sectional data, supports investigations of smoking behavior change in light of the influence of social networks and E-Cigarette use. The DIP model was evidenced by both longitudinal and cross-sectional data, and is notable for its use of interwoven ABM, system dynamics (SD), and discrete event simulation elements to explore the interaction of risk factors, coupled dynamics of glycemia regulation, and intervention tradeoffs to address the growing incidence of DIP in the Australia Capital Territory. The final study applied PF with an SD model of mosquito development to estimate the underlying Culex mosquito population using various direct observations, including time series of weather-related factors and mosquito trap counts. The results demonstrate the effectiveness of PF in regrounding the states and evolving model parameters based on incoming observations. Using PF in the context of automated model calibration allows optimization of the values of parameters to markedly reduce model discrepancy. Collectively, the thesis demonstrates how characteristics and availability of data can influence model structure and scope, how dynamic model structure directly affects the ways that data can be used, and how advanced analysis methods for calibration and filtering can enhance model accuracy and versatility