Environmental shocks and sustainability in a basic economy-environment model

We study a stochastic, discrete-time, economy-environment integrated model, where human activity affects the evolution of pollution over time. We assume that exogenous i.i.d. environmental shocks determine the rate of pollution transfer. We show that the pollution to capital ratio dynamics can be read as an iterated function system converging to an invariant distribution supported on a (asymmetric) Cantor set, and that human intervention aiming at offsetting the environmental impact of economic activities is needed to ensure sustainability

Growth maximizing government size, social capital, and corruption

Our paper intersects two topics in growth theory: the growth maximizing government size and the role of Social Capital in development. We modify a simple overlapping generations framework by introducing two key features: a production function \ue0 la Barro\ua0together with the possibility that public officials steal a fraction of public resources under their own control. As underlined by the literature on corruption, Social Capital affects public officials' accountability through many channels which also affect the probability of being caught for embezzlement and misappropriation of public resources. Therefore, in our endogenous growth model such probability is taken as a proxy of Social Capital. We find that maximum growth rates are compatible with Big Government size, measured both in terms of expenditures and public officials, when associated with high levels of Social Capital

Self-similar measures in multi-sector endogenous growth models

We analyze two types of stochastic discrete time multi-sector endogenous growth models, namely a basic Uzawa–Lucas (1965, 1988) model and an extended three-sector version as in La Torre and Marsiglio (2010). As in the case of sustained growth the optimal dynamics of the state variables are not stationary, we focus on the dynamics of the capital ratio variables, and we show that, through appropriate log-transformations, they can be converted into affine iterated function systems converging to an invariant distribution supported on some (possibly fractal) compact set. This proves that also the steady state of endogenous growth models—i.e., the stochastic balanced growth path equilibrium—might have a fractal nature. We also provide some sufficient conditions under which the associated self-similar measures turn out to be either singular or absolutely continuous (for the three-sector model we only consider the singularity)

Self-Similar Measures in Multi-Sector Endogenous Growth Models

We analyze two types of stochastic discrete time multi-sector endogenous growth models, namely a basic Uzawa-Lucas (1965, 1988) model and an extended three-sector version as in La Torre and Marsiglio (2010). As in the case of sustained growth the optimal dynamics of the state variables are not stationary, we focus on the dynamics of the capital ratio variables, and we show that, through appropriate log-transformations, they can be converted into affine iterated function systems converging to an invariant distribution supported on some (possibly fractal) compact set. This proves that also the steady state of endogenous growth models-i.e., the stochastic balanced growth path equilibrium-might have a fractal nature. We also provide some sufficient conditions under which the associated self-similar measures turn out to be either singular or absolutely continuous (for the three-sector model we only consider the singularity)