Association Between Receipt of Unemployment Insurance and Food Insecurity Among People Who Lost Employment During the COVID-19 Pandemic in the United States
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SciScore for 10.1101/2020.07.28.20163618: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization CESR randomly assigned panel members to be invited to respond on a pre-assigned day of a two-week period, so that the full sample was invited to participate over each 14-day period. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
Software and Algorithms Sentences Resources We also adjusted for several time varying, self-reported covariates, including receiving a federal stimulus payment, receiving Supplemental Nutrition Assistance Program (SNAP) benefits in the month prior to survey, and employment status at the time of survey. Nutrition Assistance Programsuggested: NoneEconomic … SciScore for 10.1101/2020.07.28.20163618: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization CESR randomly assigned panel members to be invited to respond on a pre-assigned day of a two-week period, so that the full sample was invited to participate over each 14-day period. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
Software and Algorithms Sentences Resources We also adjusted for several time varying, self-reported covariates, including receiving a federal stimulus payment, receiving Supplemental Nutrition Assistance Program (SNAP) benefits in the month prior to survey, and employment status at the time of survey. Nutrition Assistance Programsuggested: NoneEconomic stimulus funds.” \ We coded SNAP benefits as 1 or 0 for each wave based on the respondent’s answer of “yes” or “no” to the question, “In the past month, did you or anyone in your household receive any of the following government benefits? SNAPsuggested: (SNAP, RRID:SCR_007936)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Limitations: As with all difference-in-differences analyses, particularly those conducted in the rapidly changing policy context of COVID-19,27 our study has clear limitations. Unemployment insurance, stimulus payments, and SNAP were often delivered in close temporal proximity to each other, making it difficult to fully distinguish the effects of each, even after covariate adjustment. We were also unable to distinguish the effect of the additional $600 in federal unemployment benefits from the benefits of receiving any unemployment insurance because the supplement was in place for the entire duration of the study. In addition to these statistical concerns, measures of both the outcome and exposure rely on self-report, which may be prone to bias. We investigated why the estimates of the proportion of people who were employed in February who lost their jobs are greater than the 11.1% unemployment estimates reflected in the Bureau of Labor Statistics Employment Situation Summary Report.28 The BLS estimates may be underestimates due to misclassification bias,29 and our estimates are consistent with the Federal Reserve Bank’s estimates that 20% of all those who were employed in February 2020 and 39% of those with household incomes of less than $40,000 were unemployed in March 2020.30
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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SciScore for 10.1101/2020.07.28.20163618: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization CESR randomly assigned panel members to be invited to respond on a pre-assigned day of a two-week period, so that the full sample was invited to participate over each 14-day period. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
Experimental Models: Organisms/Strains Sentences Resources Most (53%) were non-Hispanic White, 24% were Hispanic, 11% were non-Hispanic Black, and the remainder were of other races and ethnicities (Table 1, column 4). …non-Hispanic Whitesuggested: NoneSciScore for 10.1101/2020.07.28.20163618: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization CESR randomly assigned panel members to be invited to respond on a pre-assigned day of a two-week period, so that the full sample was invited to participate over each 14-day period. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
Experimental Models: Organisms/Strains Sentences Resources Most (53%) were non-Hispanic White, 24% were Hispanic, 11% were non-Hispanic Black, and the remainder were of other races and ethnicities (Table 1, column 4). non-Hispanic Whitesuggested: NoneSoftware and Algorithms Sentences Resources We also adjusted for several time varying, self-reported covariates, including receiving a federal stimulus payment, receiving Supplemental Nutrition Assistance Program (SNAP) benefits in the month prior to survey, and employment status at the time of survey. Nutrition Assistance Programsuggested: NoneSNAPsuggested: (SNAP, RRID:SCR_007936)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
Limitations As with all difference-in-differences analyses, particularly those conducted in the rapidly changing policy context of COVID-19,27 our study has clear limitations. Unemployment insurance, stimulus payments, and SNAP were often delivered in close temporal proximity to each other, making it difficult to fully distinguish the effects of each, even after covariate adjustment. We were also unable to distinguish the effect of the additional $600 in federal unemployment benefits from the benefits of receiving any unemployment insurance because the supplement was in place for the entire duration of the study. In addition to these statistical concerns, measures of both the outcome and exposure rely on self-report, which may be prone to bias. We investigated why the estimates of the proportion of people who were employed in February who lost their jobs are greater than the 11.1% unemployment estimates reflected in the Bureau of Labor Statistics Employment Situation Summary Report.28 The BLS estimates may be underestimates due to misclassification bias,29 and our estimates are consistent with the Federal Reserve Bank’s estimates that 20% of all those who were employed in February 2020 and 39% of those with household incomes of less than $40,000 were unemployed in March 2020.30 Conclusion More than 40% of people with household incomes less than $75,000 who were employed in February 2020 lost their jobs during the COVID-19 pandemic. Among those who lost their jobs, 31% reported food insecurity and 33% reported eating less due to financial constraints between April 1 and July 8, 2020. During the period when the CARES Act provided a $600 supplement to state unemployment insurance, receiving unemployment insurance was associated with a 30% reduction in reporting any food insecurity and a 42% decline in eating less. Policymakers may wish to consider continued investment in unemployment insurance as an approach to reducing food insecurity in the context of high unemployment during the continuing COVID-19 pandemic.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
About SciScore
SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore is not a substitute for expert review. SciScore checks for the presence and correctness of RRIDs (research resource identifiers) in the manuscript, and detects sentences that appear to be missing RRIDs. SciScore also checks to make sure that rigor criteria are addressed by authors. It does this by detecting sentences that discuss criteria such as blinding or power analysis. SciScore does not guarantee that the rigor criteria that it detects are appropriate for the particular study. Instead it assists authors, editors, and reviewers by drawing attention to sections of the manuscript that contain or should contain various rigor criteria and key resources. For details on the results shown here, including references cited, please follow this link.
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SciScore for 10.1101/2020.07.28.20163618: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization CESR randomly assigned panel members to be invited to respond on a pre-assigned day of a two-week period, so that the full sample was invited to participate over each 14-day period. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
Experimental Models: Organisms/Strains Sentences Resources Most (53%) were non-Hispanic White, 24% were Hispanic, 11% were non-Hispanic Black, and the remainder were of other races and ethnicities (Table 1, column 4). …non-Hispanic Whitesuggested: NoneSciScore for 10.1101/2020.07.28.20163618: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization CESR randomly assigned panel members to be invited to respond on a pre-assigned day of a two-week period, so that the full sample was invited to participate over each 14-day period. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
Experimental Models: Organisms/Strains Sentences Resources Most (53%) were non-Hispanic White, 24% were Hispanic, 11% were non-Hispanic Black, and the remainder were of other races and ethnicities (Table 1, column 4). non-Hispanic Whitesuggested: NoneSoftware and Algorithms Sentences Resources We also adjusted for several time varying, self-reported covariates, including receiving a federal stimulus payment, receiving Supplemental Nutrition Assistance Program (SNAP) benefits in the month prior to survey, and employment status at the time of survey. Nutrition Assistance Programsuggested: NoneSNAPsuggested: (SNAP, RRID:SCR_007936)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
Limitations As with all difference-in-differences analyses, particularly those conducted in the rapidly changing policy context of COVID-19,27 our study has clear limitations. Unemployment insurance, stimulus payments, and SNAP were often delivered in close temporal proximity to each other, making it difficult to fully distinguish the effects of each, even after covariate adjustment. We were also unable to distinguish the effect of the additional $600 in federal unemployment benefits from the benefits of receiving any unemployment insurance because the supplement was in place for the entire duration of the study. In addition to these statistical concerns, measures of both the outcome and exposure rely on self-report, which may be prone to bias. We investigated why the estimates of the proportion of people who were employed in February who lost their jobs are greater than the 11.1% unemployment estimates reflected in the Bureau of Labor Statistics Employment Situation Summary Report.28 The BLS estimates may be underestimates due to misclassification bias,29 and our estimates are consistent with the Federal Reserve Bank’s estimates that 20% of all those who were employed in February 2020 and 39% of those with household incomes of less than $40,000 were unemployed in March 2020.30 Conclusion More than 40% of people with household incomes less than $75,000 who were employed in February 2020 lost their jobs during the COVID-19 pandemic. Among those who lost their jobs, 31% reported food insecurity and 33% reported eating less due to financial constraints between April 1 and July 8, 2020. During the period when the CARES Act provided a $600 supplement to state unemployment insurance, receiving unemployment insurance was associated with a 30% reduction in reporting any food insecurity and a 42% decline in eating less. Policymakers may wish to consider continued investment in unemployment insurance as an approach to reducing food insecurity in the context of high unemployment during the continuing COVID-19 pandemic.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
About SciScore
SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore is not a substitute for expert review. SciScore checks for the presence and correctness of RRIDs (research resource identifiers) in the manuscript, and detects sentences that appear to be missing RRIDs. SciScore also checks to make sure that rigor criteria are addressed by authors. It does this by detecting sentences that discuss criteria such as blinding or power analysis. SciScore does not guarantee that the rigor criteria that it detects are appropriate for the particular study. Instead it assists authors, editors, and reviewers by drawing attention to sections of the manuscript that contain or should contain various rigor criteria and key resources. For details on the results shown here, including references cited, please follow this link.
-
SciScore for 10.1101/2020.07.28.20163618: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization CESR randomly assigned panel members to be invited to respond on a pre-assigned day of a two-week period, so that the full sample was invited to participate over each 14-day period. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
Software and Algorithms Sentences Resources We also adjusted for several time varying, self-reported covariates, including receiving a federal stimulus payment, receiving Supplemental Nutrition Assistance Program (SNAP) benefits in the month prior to survey, and employment status at the time of survey. Nutrition …SciScore for 10.1101/2020.07.28.20163618: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization CESR randomly assigned panel members to be invited to respond on a pre-assigned day of a two-week period, so that the full sample was invited to participate over each 14-day period. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
Software and Algorithms Sentences Resources We also adjusted for several time varying, self-reported covariates, including receiving a federal stimulus payment, receiving Supplemental Nutrition Assistance Program (SNAP) benefits in the month prior to survey, and employment status at the time of survey. Nutrition Assistance Programsuggested: NoneOur statistical model is presented in Equation 1, where FIit is a binary indicator for food insecurity, UIit is a binary indicator that switches from 0 to 1 if the individual begins receiving unemployment insurance, Sit is a binary indicator that switches from 0 to 1 if the individual receives the stimulus payment, SNAPit is a binary indicator for receiving SNAP benefits in the past month, Ii is individual fixed effects, and tt is period fixed effects. SNAPsuggested: (SNAP, SCR_007936)Data from additional tools added to each annotation on a weekly basis.
About SciScore
SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore is not a substitute for expert review. SciScore checks for the presence and correctness of RRIDs (research resource identifiers) in the manuscript, and detects sentences that appear to be missing RRIDs. SciScore also checks to make sure that rigor criteria are addressed by authors. It does this by detecting sentences that discuss criteria such as blinding or power analysis. SciScore does not guarantee that the rigor criteria that it detects are appropriate for the particular study. Instead it assists authors, editors, and reviewers by drawing attention to sections of the manuscript that contain or should contain various rigor criteria and key resources. For details on the results shown here, including references cited, please follow this link.
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