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Estimation of a Heterogeneous Demand Function with Berkson Errors

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Richard Blundell, Joel Horowitz, Matthias Parey

Econometrics · Submitted 26 Nov 2018

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim Accounting for Berkson errors is found to be quantitatively important for estimating price effects and for welfare calculations.

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arXiv ID1811.10690 (version 2)
Pith IDTBUBR3EH, Pith's permanent record of this paper and its review
CitationJoel Horowitz, Matthias Parey, Richard Blundell. "Estimation of a Heterogeneous Demand Function with Berkson Errors." arXiv:1811.10690 (2018). https://pith.science/paper/TBUBR3EH
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The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 5,511 input tokens.

What the abstract itself states, as probabilities: claims first 0.04 · states limitation 0.26 · human participants 0.21 · controlled comparison 0.05 · randomized assignment 0.02 · states quantitative result 0.10 · compares with prior results 0.32 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v2. Results and the main claim are underlined; .

Berkson errors are commonplace in empirical microeconomics. In consumer demand this form of measurement error occurs when the price an individual pays is measured by the (weighted) average price paid by individuals in a specified group (e.g., a county), rather than the true transaction price. We show the importance of such measurement errors for the estimation of demand in a setting with nonseparable unobserved heterogeneity. We develop a consistent estimator using external information on the true distribution of prices. Examining the demand for gasoline in the U.S., we document substantial within-market price variability, and show that there are significant spatial differences in the magnitude of Berkson errors across regions of the U.S. Accounting for Berkson errors is found to be quantitatively important for estimating price effects and for welfare calculations. Imposing the Slutsky shape constraint greatly reduces the sensitivity to Berkson errors.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 Berkson errors are commonplace in empirical microeconomics. background 1.00 0.05 0.02 asserted 1.00
s1 In consumer demand this form of measurement error occurs when the price an individual pays is measured by the (weighted) average price paid by individuals in a specified group (e.g., a county), rather than the true transaction price. background 1.00 0.07 0.03 asserted 0.99
s2 We show the importance of such measurement errors for the estimation of demand in a setting with nonseparable unobserved heterogeneity. contribution 0.74 0.77 0.36 asserted 1.00
s3 We develop a consistent estimator using external information on the true distribution of prices. method 0.79 0.96 0.11 asserted 1.00
s4 Examining the demand for gasoline in the U.S., we document substantial within-market price variability, and show that there are significant spatial differences in the magnitude of Berkson errors across regions of the U.S. result 0.93 0.95 0.09 asserted 1.00
s5 Accounting for Berkson errors is found to be quantitatively important for estimating price effects and for welfare calculations. result 0.90 0.94 0.48 asserted 1.00
s6 Imposing the Slutsky shape constraint greatly reduces the sensitivity to Berkson errors. result 0.96 0.83 0.15 asserted 1.00

What kind of paper the classifier reports this is: method or tool 0.95 · evidence from analysis of existing data 0.99.

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Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

Berkson errors are commonplace in empirical microeconomics. In consumer demand this form of measurement error occurs when the price an individual pays is measured by the (weighted) average price paid by individuals in a specified group (e.g., a county), rather than the true transaction price. We show the importance of such measurement errors for the estimation of demand in a setting with nonseparable unobserved heterogeneity. We develop a consistent estimator using external information on the true distribution of prices. Examining the demand for gasoline in the U.S., we document substantial within-market price variability, and show that there are significant spatial differences in the magnitude of Berkson errors across regions of the U.S. Accounting for Berkson errors is found to be quantitatively important for estimating price effects and for welfare calculations. Imposing the Slutsky shape constraint greatly reduces the sensitivity to Berkson errors.

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