Abstract
In order to shed light on the patterns of economic development characterized by sustained growth and persistent wealth inequality, we develop an agent-based model in which generations overlap and parents leave bequests to offspring. The model shows how neighborhood effects influence the long-run trajectory of human capital investments and wealth distribution. The analyses proceed in three steps. First, we implement the agent-based version of an overlapping generations model, enabling validation of our baseline simulations. Second, we introduce direct, local interactions to demonstrate how human capital investments and inequality respond to neighborhood effects. As far as we know, our study is among the first to explore the impact of local interactions on wealth inequality in an agent-based model with intergenerational transfers. Finally, we show how local interactions can influence the efficacy of policies that aim to generate an equitable distribution of wealth. The main results indicate that human capital is highest when local interactions are moderate, that is, neither too weak nor too strong. At the optimal level, either higher- or lower-intensity interactions would reduce aggregate human capital and therefore output. The distributional impact exhibits a similar pattern albeit with very different ramifications. Specifically, the moderate interactions that maximize output also generate the most unequal distribution of wealth. An exogenous shock leading to a more cohesive community lowers output but produces a more equitable distribution. By contrast, weak interactions precipitate a mild output contraction and, at the same time, significantly lower wealth inequality. The agent-based simulations thus suggest diametrically opposed responses of output and equity to changes in the intensity of local interactions. The agent-based model developed for this study offers the potential to improve our understanding of the complex interplay between human capital, intergenerational transfers, and neighborhood effects.
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Notes
- 1.
See the extensive survey in Card (1999).
- 2.
ABM is also known as Agent-based Computational Economics (ACE) (Tesfatsion 2005). Tesfatsion stressed the importance of ACE in dealing with complicated micro-behavior in real world (e.g., asymmetric information, imperfect information, and multiple equilibria).
- 3.
The formulation (known as Cobb-Douglas) of the production function belongs to the general class of concave functions that GZ use in a more abstract setting. Because our model is computational, we need to specify a functional form so that the algorithm can compute the numerical values of output. The Cobb-Douglas formulation is chosen because it remains the most ubiquitous form of production function in today’s theoretical and empirical analyses of output growth (Felipe and Adams 2005).
- 4.
GZ assume endogenous wages for unskilled workers for their purely mathematical treatment. For a numerical model like ours, fixing unskilled wages while permitting skilled ones to vary as a “free” variable allows the simulations to be initialized much more efficiently.
- 5.
We use Repast library version 3.1, developed by North et al. (2006). Repast is a free open source toolkit and available at Repast homepage at https://repast.sourceforge.net/repast_3/index.html (Road 2004).
- 6.
This restriction is necessary in order to prevent the economy from spiraling out of control.
- 7.
See Sen (1997) for historical perspectives and mathematical treatment.
- 8.
It turns out that results of the subsidy scenario analysis are qualitatively very similar under Setups 2 and 3.
- 9.
Results not reported here due to space considerations.
- 10.
The total expenditures would be ex-post identical only in the trivial case where every agent invests in human capital because education has been rendered affordable to all agents.
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Wicaksono, G., Mansury, Y. (2020). An Agent-Based Model of Wealth Inequality with Overlapping Generations, Local Interactions, and Intergenerational Transfers. In: Thill, JC. (eds) Innovations in Urban and Regional Systems. Springer, Cham. https://doi.org/10.1007/978-3-030-43694-0_10
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