On Binscatter

Cattaneo, Matias D.; Crump, Richard K.; Farrell, Max H.; Feng, Yan

doi:10.48550/arxiv.1902.09608

Cited by 23 publications

(31 citation statements)

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“…For a given L * , Cattaneo et al (2019a) show the large-sample asymptotics of the estimates ∆Y j and provide robust standard errors for the conditional means that account for the additional randomness due to quantile estimation. Since our estimator is a difference in means, standard errors on our estimate τj are given by σ 2 j + σ 2 L , where σ j is the standard error recommended by Cattaneo et al (2019a). These standard errors are produced by the Stata/R package binsreg.…”

Section: Countiesmentioning

confidence: 99%

“…The choice of L in finite samples is not entirely clear. Cattaneo et al (2019a) derive the IMSE-optimal choice of L which is a completely data-driven choice. The optimal L is driven by two competing terms in the IMSE formula.…”

Section: Countiesmentioning

confidence: 99%

“…11. The number of intervals are decided based on a different IMSE condition described in Cattaneo et al (2019a) and the quantiles are calculated using the distribution of distances in both periods.…”

Section: B1 Nonparametric Estimationmentioning

confidence: 99%

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Difference-in-Differences with Geocoded Microdata

Butts¹

2021

Preprint

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This paper formalizes a common approach for estimating effects of treatment at a specific location using geocoded microdata. This estimator compares units immediately next to treatment (an inner-ring) to units just slightly further away (an outer-ring). I introduce intuitive assumptions needed to identify the average treatment effect among the affected units and illustrates pitfalls that occur when these assumptions fail. Since one of these assumptions requires knowledge of exactly how far treatment effects are experienced, I propose a new method that relaxes this assumption and allows for nonparametric estimation using partitioning-based least squares developed in Cattaneo et al. (2019a,b). Since treatment effects typically decay/change over distance, this estimator improves analysis by estimating a treatment effect curve as a function of distance from treatment. This is contrast to the traditional method which, at best, identifies the average effect of treatment. To illustrate the advantages of this method, I show that Linden and Rockoff (2008) under estimate the effects of increased crime risk on home values closest to the treatment and overestimate how far the effects extend by selecting a treatment ring that is too wide.

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Section: Countiesmentioning

confidence: 99%

Section: Countiesmentioning

confidence: 99%

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Difference-in-Differences with Geocoded Microdata

Butts¹

2021

Preprint

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“…Before proceeding with the formal analysis it is worth analyzing the relation between OSS and redistribution preferences at different levels of OUR, looking at raw data after partialling out the effect of control variables (Hainmueller, Mummolo, and Xu, 2019). Since this is a large data set I display the relation using binscatter plots (Cattaneo et al, 2019). Basically, the binscatter consists of splitting the explanatory variable in bins and displaying the average level of the dependent variable for each bin.…”

Section: Data Sources and Operationalizationmentioning

confidence: 99%

Occupation-Specific Skills, Labor Market Context and Preferences for Redistribution

Serrano-Serrat¹

2022

Preprint

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Different strands in social sciences have analyzed the role of specific skills on distinct political stances, however, a consensus has not been reached yet. This is so because some scholars stress the fact that specific skills tend to shelter workers from job loss (replaceability thesis), while others that it decreases re-employment potential (portability thesis). To tackle these contradictory results, in the current paper I argue that OSS are a key element in shaping labor market prospects, and for studying this relation scholars should move beyond theories about firm-specific skills (FSS). The main difference outlined is that, contrary to FSS, occupational context moderates how OSS shape labor market prospects. I claim that OSS affect two key elements that allow to explain redistribution preferences: social mobility and social risks. Both of these effects are moderated by occupational context. After a discussion about how OSS shape labor market prospects, a simple formal model of redistribution preferences is presented. I show the relevance of this new approach using different rounds from the ESS and the ISSP.

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“…Panel A plots state-level estimates. Panel B plots CZ-level estimates, where the dark points are binned averages constructed using the binsreg command fromCattaneo et al (2019). The horizontal axes are the estimated effect on the insurance rate at age 65 by locality.…”

mentioning

confidence: 99%

The Great Equalizer: Medicare and the Geography of Consumer Financial Strain

Goldsmith-Pinkham¹,

Pinkovskiy²,

Wallace³

2021

Preprint

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We use a five percent sample of Americans' credit bureau data, combined with a regression discontinuity approach, to estimate the effect of universal health insurance at age 65-when most Americans become eligible for Medicare-at the national, state, and local level. We find a 30 percent reduction in debt collections-and a two-thirds reduction in the geographic variation in collections-with limited effects on other financial outcomes. The areas that experienced larger reductions in collections debt at age 65 were concentrated in the Southern United States, and had higher shares of black residents, people with disabilities, and for-profit hospitals.

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On Binscatter

Cited by 23 publications

References 0 publications

Difference-in-Differences with Geocoded Microdata

Difference-in-Differences with Geocoded Microdata

Occupation-Specific Skills, Labor Market Context and Preferences for Redistribution

The Great Equalizer: Medicare and the Geography of Consumer Financial Strain

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