On the welfare costs of business-cycle fluctuations and economic-growth variation in the 20th century

Lucas (1987) has shown a surprising result in business-cycle research: the welfare cost of business cycles are very small. Our paper has several original contributions. First, in computing welfare costs, we propose a novel setup that separates the effects of uncertainty stemming from business-cycle fluctuations and economic-growth variation. Second, we extend the sample from which to compute the moments of consumption: the whole of the literature chose primarily to work with post-WWII data. For this period, actual consumption is already a result of counter-cyclical policies, and is potentially smoother than what it otherwise have been in their absence. So, we employ also pre-WWII data. Third, we take an econometric approach and compute explicitly the asymptotic standard deviation of welfare costs using the Delta Method. Estimates of welfare costs show major differences for the pre-WWII and the post-WWII era. They can reach up to 15 times for reasonable parameter values -β=0.985, and ∅=5. For example, in the pre-WWII period (1901-1941), welfare cost estimates are 0.31% of consumption if we consider only permanent shocks and 0.61% of consumption if we consider only transitory shocks. In comparison, the post-WWII era is much quieter: welfare costs of economic growth are 0.11% and welfare costs of business cycles are 0.037% - the latter being very close to the estimate in Lucas (0.040%). Estimates of marginal welfare costs are roughly twice the size of the total welfare costs. For the pre-WWII era, marginal welfare costs of economic-growth and business- cycle fluctuations are respectively 0.63% and 1.17% of per-capita consumption. The same figures for the post-WWII era are, respectively, 0.21% and 0.07% of per-capita consumption.

On the welfare costs of business-cycle fluctuations and economic-growth variation in the 20th century

Lucas(1987) has shown a surprising result in business-cycle research: the welfare cost of business cycles are very small. Our paper has several original contributions. First, in computing welfare costs, we propose a novel setup that separates the effects of uncertainty stemming from business-cycle uctuations and economic-growth variation. Second, we extend the sample from which to compute the moments of consumption: the whole of the literature chose primarily to work with post-WWII data. For this period, actual consumption is already a result of counter-cyclical policies, and is potentially smoother than what it otherwise have been in their absence. So, we employ also pre-WWII data. Third, we take an econometric approach and compute explicitly the asymptotic standard deviation of welfare costs using the Delta Method. Estimates of welfare costs show major diferences for the pre-WWII and the post-WWII era. They can reach up to 15 times for reasonable parameter values = 0:985, and = 5. For example, in the pre-WWII period (1901-1941), welfare cost estimates are 0.31% of consumption if we consider only permanent shocks and 0.61% of consumption if we consider only transitory shocks. In comparison, the post-WWII era is much quieter: welfare costs of economic growth are 0.11% and welfare costs of business cycles are 0.037% the latter being very close to the estimate in Lucas (0.040%). Estimates of marginal welfare costs are roughly twice the size of the total welfare costs. For the pre-WWII era, marginal welfare costs of economic-growth and business-cycle uctuations are respectively 0.63% and 1.17% of per-capita consumption. The same gures for the post-WWII era are, respectively, 0.21% and 0.07% of per-capita consumption.

A monthly indicator of Brazilian GDP. The Brazilian business cycle and growth cycle

This paper constructs an indicator of Brazilian GDP at the monthly ftequency. The peculiar instability and abrupt changes of regimes in the dynamic behavior of the Brazilian business cycle were explicitly modeled within nonlinear ftameworks. In particular, a Markov switching dynarnic factor model was used to combine several macroeconomic variables that display simultaneous comovements with aggregate economic activity. The model generates as output a monthly indicator of the Brazilian GDP and real time probabilities of the current phase of the Brazilian business cycle. The monthly indicator shows a remarkable historical conformity with cyclical movements of GDP. In addition, the estimated filtered probabilities predict ali recessions in sample and out-of-sample. The ability of the indicator in linear forecasting growth rates of GDP is also examined. The estimated indicator displays a better in-sample and out-of-sample predictive performance in forecasting growth rates of real GDP, compared to a linear autoregressive model for GDP. These results suggest that the estimated monthly indicator can be used to forecast GDP and to monitor the state of the Brazilian economy in real time.

Monopolies life cycle, bureaucratization, and Schumpeterian growth

This paper analyzes the links between the internaI organization of firms and macroeconomic growth. We present a Schumpeterian growth model in which firms face dynamic agency costs. These agency costs are due to the formation of vertical collusions within the organization. To respond to the opportunity of internaI collusion, firms go through a whole life cycle, getting more bureaucratized and Iess efficient over time. vVeak creative destruction in the economy facilitates informal collusion inside firms and exacerbates bureaucratization. As bureaucratization affects the firms' profitability and the return to innovation, stationary equilibrium growth depends in turn on the efficiency of collusive side-contracts within firms.

Life Cycle Models, Heterogeneity of Initial Assets,and Wealth Inequality

Life cycle general equilibrium models with heterogeneous agents have a very hard time reproducing the American wealth distribution. A common assumption made in this literature is that all young adults enter the economy with no initial assets. In this article, we relax this assumption – not supported by the data - and evaluate the ability of an otherwise standard life cycle model to account for the U.S. wealth inequality. The new feature of the model is that agents enter the economy with assets drawn from an initial distribution of assets, which is estimated using a non-parametric method applied to data from the Survey of Consumer Finances. We found that heterogeneity with respect to initial wealth is key for this class of models to replicate the data. According to our results, American inequality can be explained almost entirely by the fact that some individuals are lucky enough to be born into wealth, while others are born with few or no assets.