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Social Security Disability Applications : = Seasonality, Cross-Sectional Correlations, and the COVID-19 Pandemic.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Social Security Disability Applications :/
Reminder of title:	Seasonality, Cross-Sectional Correlations, and the COVID-19 Pandemic.
Author:	Yin, Yimeng.
Description:	1 online resource (121 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-11, Section: A.
Contained By:	Dissertations Abstracts International85-11A.
Subject:	Public policy. -
Online resource:	click for full text (PQDT)
ISBN:	9798382360836

Social Security Disability Applications : = Seasonality, Cross-Sectional Correlations, and the COVID-19 Pandemic.
Yin, Yimeng.

Social Security Disability Applications :Seasonality, Cross-Sectional Correlations, and the COVID-19 Pandemic. - 1 online resource (121 pages)

Source: Dissertations Abstracts International, Volume: 85-11, Section: A.

Thesis (Ph.D.)--State University of New York at Albany, 2024.

Includes bibliographical references

This dissertation investigates three important topics related to the time pattern and the inter-state heterogeneity of the application volumes of the two public disability programs run by the U.S. Social Security Administration (SSA) - Social Security Disability Insurance (SSDI) and Supplemental Security Income (SSI).First, we examine the seasonality in SSDI and SSI applications and show that the monthly disability applications exhibit a double-peak seasonal pattern which lags a similar seasonal pattern in unemployment and unemployment insurance initial claims by one to two months. The broad seasonal patterns in disability applications are remarkably similar across states but with significant heterogeneity in amplitudes, which seems to be associated with climatic factors. We utilize this inter-state heterogeneity to show that the seasonal patterns in disability applications and labor market conditions are correlated even after controlling for climatic effects. We also show that the seasonally in disability applications generated by the automatic settings of the widely used seasonal adjustment program X13 ARIMA-SEATS are distorted during the COVID-19 pandemic and the pattern of the distortion is similar to that in employment data.Second, we investigate the cross-sectional correlations in state-level disability benefit applications in the context of panel data models, which are typically assumed away or treated using fixed effects in prior studies. Prominent cross-sectional correlations are found in the error terms of conventional panel data models for state-level disability applications even after controlling for labor market conditions. To shed light on the nature of the observed cross-sectional correlations in disability applications, we first employ factors models and dynamic spatial models to separately capture the dependencies caused by common shocks and spatial effects, then utilize a modeling framework that incorporates both types of dependencies to jointly testing their existence. The results show that both types of dependencies are statistically significant in state-level disability applications especially when the models are estimated using quarterly data. Comparing the estimation results across alternative model specifications shows that explicitly accounting for cross-sectional correlations slightly reduces the estimated association between labor market conditions and disability benefit applications. Third, we identify drivers of the substantial inter-state variations in disability benefit applications during the COVID-19 pandemic, in terms of both the overall level shift and the dynamics over time. We show that greater decreases in disability applications at the state level over the first two years of the pandemic are associated with more generous economic supports provided through state governments, enhanced UI programs, and a greater share of population with disabilities. More prevalent access to high-speed internet, which may mitigate the impact of SSA office closures, seems to be a countervailing force for the decline of disability applications. Higher state poverty rates, which suggest greater demand for income support, are also associated with more disability applications during the pandemic. The state-level measures examined in this chapter can collectively explain about one third of the variations in the overall shift in disability applications across states in 2020 and 2021, but they can only explain a much less portion of the within-state variation over time.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798382360836Subjects--Topical Terms:

1002398
Public policy.
Subjects--Index Terms:

Cross-sectional correlationsIndex Terms--Genre/Form:

554714
Electronic books.

Social Security Disability Applications : = Seasonality, Cross-Sectional Correlations, and the COVID-19 Pandemic.
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504 $a Includes bibliographical references
520 $a This dissertation investigates three important topics related to the time pattern and the inter-state heterogeneity of the application volumes of the two public disability programs run by the U.S. Social Security Administration (SSA) - Social Security Disability Insurance (SSDI) and Supplemental Security Income (SSI).First, we examine the seasonality in SSDI and SSI applications and show that the monthly disability applications exhibit a double-peak seasonal pattern which lags a similar seasonal pattern in unemployment and unemployment insurance initial claims by one to two months. The broad seasonal patterns in disability applications are remarkably similar across states but with significant heterogeneity in amplitudes, which seems to be associated with climatic factors. We utilize this inter-state heterogeneity to show that the seasonal patterns in disability applications and labor market conditions are correlated even after controlling for climatic effects. We also show that the seasonally in disability applications generated by the automatic settings of the widely used seasonal adjustment program X13 ARIMA-SEATS are distorted during the COVID-19 pandemic and the pattern of the distortion is similar to that in employment data.Second, we investigate the cross-sectional correlations in state-level disability benefit applications in the context of panel data models, which are typically assumed away or treated using fixed effects in prior studies. Prominent cross-sectional correlations are found in the error terms of conventional panel data models for state-level disability applications even after controlling for labor market conditions. To shed light on the nature of the observed cross-sectional correlations in disability applications, we first employ factors models and dynamic spatial models to separately capture the dependencies caused by common shocks and spatial effects, then utilize a modeling framework that incorporates both types of dependencies to jointly testing their existence. The results show that both types of dependencies are statistically significant in state-level disability applications especially when the models are estimated using quarterly data. Comparing the estimation results across alternative model specifications shows that explicitly accounting for cross-sectional correlations slightly reduces the estimated association between labor market conditions and disability benefit applications. Third, we identify drivers of the substantial inter-state variations in disability benefit applications during the COVID-19 pandemic, in terms of both the overall level shift and the dynamics over time. We show that greater decreases in disability applications at the state level over the first two years of the pandemic are associated with more generous economic supports provided through state governments, enhanced UI programs, and a greater share of population with disabilities. More prevalent access to high-speed internet, which may mitigate the impact of SSA office closures, seems to be a countervailing force for the decline of disability applications. Higher state poverty rates, which suggest greater demand for income support, are also associated with more disability applications during the pandemic. The state-level measures examined in this chapter can collectively explain about one third of the variations in the overall shift in disability applications across states in 2020 and 2021, but they can only explain a much less portion of the within-state variation over time.
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