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

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 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

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

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

Disability Costs and Equivalence Scales in the Older Population in Great Britain

We use a standard of living (SoL) approach to estimate older people's disability costs, using data on 8000 individuals from the U.K. Family Resources Survey. We extend previous research in two ways. First, by allowing for a more flexible relationship between SoL and income, the structure of the estimated disability cost and equivalence scale is not dictated by a restrictive functional form assumption. Second, we allow for the latent nature of disability and SoL, addressing measurement error in the disability and SoL indicators in surveys. We find that disability costs are strongly related to severity of disability, and vary with income in absolute and proportionate terms. Older people above the median disability level require an extra �99 per week (2007 prices) on average to reach the SoL of an otherwise similar person at the median. Costs faced by older people in the highest decile of disability average �180

Nonparametric errors in variables models with measurement errors on both sides of the equation

Measurement errors are often correlated, as in surveys where respondent's biases or tendencies to err affect multiple reported variables. We extend Schennach (2007) to identify moments of the conditional distribution of a true Y given a true X when both are measured with error, the measurement errors in Y and X are correlated, and the true unknown model of Y given X has nonseparable model errors. After showing nonparametric identification, we provide a sieve generalized method of moments based estimator of the model, and apply it to nonparametric Engel curve estimation. In our application measurement errors on the expenditures of a good Y are by construction correlated with measurement errors in total expenditures X. This problem, which is present in many consumption data sets, has been ignored in most demand applications. We find that accounting for this problem casts doubt on Hildenbrand's (1994) "increasing dispersion" assumption

Imputation Rules to Improve the Education Variable in the IAB Employment Subsample

The education variable in the IAB employment subsample has two shortcomings: missing values and inconsistencies with the reporting rule. We propose several deductive imputation procedures to improve the variable. They mainly use the multiple education information available in the data because the employees' education is reported at least once a year. We compare the improved data from the different procedures and the original data in typical applications in labor economics: educational composition of employment, wage inequality, and wage regression. We find, that correcting the education variable: (i) shows the educational attainment of the male labor force to be higher than measured with the original data, (ii) gives different values for some measures of wage inequality, and (iii) does not change the estimates in wage regressions much

A simple framework for analysing the impact of economic growth on non-communicable diseases

YesNon-communicable diseases (NCDs) are currently the leading cause of death worldwide. In this paper, we examine the channels through which economic growth affects NCDs’ epidemiology. Following a production function approach, we develop a basic technique to break up the impact of economic growth on NCDs into three fundamental components: (1) a resource effect; (2) a behaviour effect; and (3) a knowledge effect. We demonstrate that each of these effects can be measured as the product of two elasticities, the output and income elasticity of the three leading factors influencing the frequency of NCDs in any population: health care, healthrelated behaviours and lifestyle, and medical knowledge