Why is consumption more log normal than income? Gibrat’s Law revisited

Significant departures from log normality are observed in income data, in violation of Gibrat’s law. We identify a new empirical regularity, which is that the distribution of consumption expenditures across households is, within cohorts, closer to log normal than the distribution of income. We explain these empirical results by showing that the logic of Gibrat’s law applies not to total income, but to permanent income and to maginal utility. These findings have important implications for welfare and inequality measurement, aggregation, and econometric model analysis

Sparse demand systems: corners and complements

We propose a demand model where consumers simultaneously choose a few different goods from a large menu of available goods, and choose how much to consume of each good. The model nests multinomial discrete choice and continuous demand systems as special cases. Goods can be substitutes or complements. Random coefficients are employed to capture the wide variation in the composition of consumption baskets. Non-negativity constraints produce corners that account for different consumers purchasing different numbers of types of goods. We show semiparametric identification of the model. We apply the model to the demand for fruit in the United Kingdom. We estimate the model’s parameters using UK scanner data for 2008 from the Kantar World Panel. Using our parameter estimates, we estimate a matrix of demand elasticities for 27 categories of fruit and analyze a range of tax and policy change scenarios

Nonparametric Euler Equation Identification andEstimation

We consider nonparametric identification and estimation of pricing kernels, or equivalently of marginal utility functions up to scale, in consumption based asset pricing Euler equations.Ours is the first paper to prove nonparametric identification of Euler equations under low level conditions (without imposing functional restrictions or just assuming completeness). We also propose a novel nonparametric estimator based on our identification analysis, which combines standard kernel estimation with the computation of a matrix eigenvector problem. Our esti-mator avoids the ill-posed inverse issues associated with existing nonparametric instrumental variables based Euler equation estimators. We derive limiting distributions for our estimator and for relevant associated functionals. We provide a Monte Carlo analysis and an empirical application to US household-level consumption data.nonparametric identificatio

Nonparametric Euler Equation Identi cation and Estimation

We consider nonparametric identification and estimation of pricing kernels, or equivalently of marginal utility functions up to scale, in consumption based asset pricing Euler equations. Ours is the first paper to prove nonparametric identification of Eu

Estimating the Binary Endogenous Effect of Insurance on Doctor Visits by Copula-Based Regression Additive Models

This paper seeks to estimate the causal effect of having health insurance on health care utilization, while accounting for potential endogeneity bias. The topic has impor- tant policy implications, because health insurance reforms implemented in U.S. in recent decades have focused on extending coverage to the previously uninsured. Consequently, understanding the effects of those reforms requires an accurate estimate of the causal effect of insurance on utilization. However, obtaining such an estimate is complicated by the discreteness inherent in common measures of health care usage. This paper presents a flexible estimation approach, based on copula functions, that consistently estimates the coefficient of a binary endogenous regressor in count data settings. The relevant numeri- cal computations can be easily carried out using the freely available GJRM R package. The empirical results find significant evidence of favorable selection into insurance. Ignoring such selection, insurance appears to increase doctor visit usage by 62%, but adjusting for it, the effect increases to 134%

Crop supply dynamics and the illusion of partial adjustment

We use field-level data to estimate the response of corn and soybean acreage to price shocks. Our sample contains more than eight million observations derived from satellite imagery and includes every field in Iowa, Illinois, and Indiana. We estimate that aggregate crop acreage responds more to price shocks in the short run than in the long run, and we show theoretically how the benefits of crop rotation generate this response pattern. In essence, farmers who change crops due to a price shock have an incentive to switch back to the previous crop to capture the benefits of crop rotation. Our result contradicts the long-held belief that agricultural supply responds gradually to price shocks through partial adjustment. We would not have obtained this result had we used county-level panel data. Standard econometric methods applied to county-level data produce estimates consistent with partial adjustment. We show that this apparent partial adjustment is illusory, and we demonstrate how it arises from the fact that fields in the same county are more similar to each other than to fields in other counties. This result underscores the importance of using models with appropriate micro-foundations and cautions against inferring micro-level rigidities from inertia in aggregate panel data. Our preferred estimate of the own-price long-run elasticity of corn acreage is 0.29 and the cross-price elasticity is -0.22. The corresponding elasticities for soybean acreage are 0.26 and -0.33. Our estimated short-run elasticities are 37 percent larger than their long-run counterparts