@article{Gardner2021,
author = {Gardner, John},
file = {:C\:/Users/ab542x/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Gardner - 2021 - Two-stage differences in differences.pdf:pdf},
journal = {Working Paper},
keywords = {c01,c10,c21,c22,c23,department of economics,di ff erences,di ff erences in,ects,edu,erogenous treatment e ff,het-,i thank,jel codes,jrgardne,misspecification,ms,olemiss,program evaluation,treatment e ff ects,university,university of mississippi},
number = {April},
title = {{Two-stage differences in differences}},
year = {2021}
}
@article{Callaway2021a,
abstract = {This paper analyzes difference-in-differences setups with a continuous
treatment. We show that treatment effect on the treated-type parameters can be
identified under a generalized parallel trends assumption that is similar to
the binary treatment setup. However, interpreting differences in these
parameters across different values of the treatment can be particularly
challenging due to treatment effect heterogeneity. We discuss alternative,
typically stronger, assumptions that alleviate these challenges. We also
provide a variety of treatment effect decomposition results, highlighting that
parameters associated with popular two-way fixed-effect specifications can be
hard to interpret, even when there are only two time periods. We introduce
alternative estimation strategies that do not suffer from these drawbacks. Our
results also cover cases where (i) there is no available untreated comparison
group and (ii) there are multiple periods and variation in treatment timing,
which are both common in empirical work.},
archivePrefix = {arXiv},
arxivId = {2107.02637},
author = {Callaway, Brantly and Goodman-Bacon, Andrew and Sant'Anna, Pedro H. C.},
eprint = {2107.02637},
file = {:C\:/Users/ab542x/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Callaway, Goodman-Bacon, Sant'Anna - 2021 - Difference-in-Differences with a Continuous Treatment.pdf:pdf},
keywords = {C21,C23 Keywords: Difference-in-Differences,Continuous Treatment,JEL Codes: C14,Multi-Valued Treatment,Multiple Periods,Parallel Trends,Treatment Effect Heterogeneity,Two-way fixed effects,Variation in Treatment Timing},
month = {jul},
title = {{Difference-in-Differences with a Continuous Treatment}},
url = {https://arxiv.org/abs/2107.02637v2},
year = {2021}
}
@article{Cheetham2019,
abstract = {Objectives To understand the impact of the roll-out of Universal Credit (UC) from the perspectives of claimants and staff supporting them in North East England.

Design Qualitative study comprising interviews and focus groups.

Setting Gateshead and Newcastle, two localities in North East England characterised by high levels of socioeconomic deprivation, where the roll-out of UC started in 2017 as a new way to deliver welfare benefits for the UK working age population.

Participants 33 UC claimants with complex needs, disabilities and health conditions and 37 staff from local government, housing, voluntary and community sector organisations.

Results Participants' accounts of the UC claims process and the consequences of managing on UC are reported; UC negatively impacts on material wellbeing, physical and mental health, social and family lives. UC claimants described the digital claims process as complicated, disorientating, impersonal, hostile and demeaning. Claimants reported being pushed into debt, rent arrears, housing insecurity, fuel and food poverty through UC. System failures, indifference and delays in receipt of UC entitlements exacerbated the difficulties of managing on a low income. The threat of punitive sanctions for failing to meet the enhanced conditionality requirements under UC added to claimant's vulnerabilities and distress. Staff reported concerns for claimants and additional pressures on health services, local government and voluntary and community sector organisations as a result of UC.

Conclusions The findings add considerable detail to emerging evidence of the deleterious effects of UC on vulnerable claimants' health and wellbeing. Our evidence suggests that UC is undermining vulnerable claimants' mental health, increasing the risk of poverty, hardship, destitution and suicidality. Major, evidence-informed revisions are required to improve the design and implementation of UC to prevent further adverse effects before large numbers of people move on to UC, as planned by the UK government.},
author = {Cheetham, Mandy and Moffatt, Suzanne and Addison, Michelle and Wiseman, Alice},
doi = {10.1136/BMJOPEN-2019-029611},
file = {:C\:/Users/ab542x/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Cheetham et al. - 2019 - Impact of Universal Credit in North East England a qualitative study of claimants and support staff.pdf:pdf},
issn = {2044-6055},
journal = {BMJ Open},
keywords = {mental health,public health,qualitative research},
month = {jul},
number = {7},
pages = {e029611},
pmid = {31272984},
publisher = {British Medical Journal Publishing Group},
title = {{Impact of Universal Credit in North East England: a qualitative study of claimants and support staff}},
url = {https://bmjopen.bmj.com/content/9/7/e029611 https://bmjopen.bmj.com/content/9/7/e029611.abstract},
volume = {9},
year = {2019}
}
@article{Goodman-Bacon2021,
abstract = {The canonical difference-in-differences (DD) estimator contains two time periods, ”pre” and ”post”, and two groups, ”treatment” and ”control”. Most DD applications, however, exploit variation across groups of units that receive treatment at different times. This paper shows that the two-way fixed effects estimator equals a weighted average of all possible two-group/two-period DD estimators in the data. A causal interpretation of two-way fixed effects DD estimates requires both a parallel trends assumption and treatment effects that are constant over time. I show how to decompose the difference between two specifications, and provide a new analysis of models that include time-varying controls.},
author = {Goodman-Bacon, Andrew},
doi = {10.1016/J.JECONOM.2021.03.014},
file = {:C\:/Users/ab542x/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Goodman-Bacon - 2021 - Difference-in-differences with variation in treatment timing.pdf:pdf},
issn = {0304-4076},
journal = {Journal of Econometrics},
keywords = {Difference-in-differences,Treatment effect heterogeneity,Two-way fixed effects,Variation in treatment timing},
month = {dec},
number = {2},
pages = {254--277},
publisher = {North-Holland},
title = {{Difference-in-differences with variation in treatment timing}},
volume = {225},
year = {2021}
}
@techreport{DepartmentforWork&Pensions2023,
address = {London},
author = {{Department for Work & Pensions}},
title = {{Universal Credit statistics: background information and methodology}},
url = {https://www.gov.uk/government/publications/universal-credit-statistics-background-information-and-methodology/universal-credit-statistics-background-information-and-methodology},
year = {2023}
}
@article{Hernan2016,
abstract = {Ideally, questions about comparative effectiveness or safety would be answered using an appropriately designed and conducted randomized experiment. When we cannot conduct a randomized experiment, we analyze observational data. Causal inference from large observational databases (big data) can be viewed as an attempt to emulate a randomized experiment - the target experiment or target trial - that would answer the question of interest. When the goal is to guide decisions among several strategies, causal analyses of observational data need to be evaluated with respect to how well they emulate a particular target trial. We outline a framework for comparative effectiveness research using big data that makes the target trial explicit. This framework channels counterfactual theory for comparing the effects of sustained treatment strategies, organizes analytic approaches, provides a structured process for the criticism of observational studies, and helps avoid common methodologic pitfalls.},
author = {Hern{\'{a}}n, Miguel A. and Robins, James M.},
doi = {10.1093/aje/kwv254},
file = {:C\:/Users/ab542x/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Hern{\'{a}}n, Robins - 2016 - Using Big Data to Emulate a Target Trial When a Randomized Trial Is Not Available.pdf:pdf},
issn = {14766256},
journal = {American Journal of Epidemiology},
keywords = {big data,causal inference,comparative effectiveness research,target trial},
month = {apr},
number = {8},
pages = {758--764},
pmid = {26994063},
publisher = {Oxford University Press},
title = {{Using Big Data to Emulate a Target Trial When a Randomized Trial Is Not Available}},
url = {https://pubmed.ncbi.nlm.nih.gov/26994063/},
volume = {183},
year = {2016}
}
@techreport{NationalAuditOffice2018,
address = {London},
author = {{National Audit Office}},
file = {:C\:/Users/ab542x/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Department for Work & Pensions - 2018 - Rolling out Universal Credit.pdf:pdf},
institution = {National Audit Office},
title = {{Rolling out Universal Credit}},
url = {https://www.nao.org.uk/wp-content/uploads/2018/06/Rolling-out-Universal-Credit.pdf},
year = {2018}
}
@article{Roth2022,
abstract = {This paper synthesizes recent advances in the econometrics of difference-in-differences (DiD) and provides concrete recommendations for practitioners. We begin by articulating a simple set of "canonical" assumptions under which the econometrics of DiD are well-understood. We then argue that recent advances in DiD methods can be broadly classified as relaxing some components of the canonical DiD setup, with a focus on $(i)$ multiple periods and variation in treatment timing, $(ii)$ potential violations of parallel trends, or $(iii)$ alternative frameworks for inference. Our discussion highlights the different ways that the DiD literature has advanced beyond the canonical model, and helps to clarify when each of the papers will be relevant for empirical work. We conclude by discussing some promising areas for future research.},
archivePrefix = {arXiv},
arxivId = {2009.01963},
author = {Roth, Jonathan and Sant'Anna, Pedro H. C. and Bilinski, Alyssa and Poe, John},
doi = {10.1086/711509},
eprint = {2009.01963},
file = {:C\:/Users/ab542x/Downloads/2201.01194.pdf:pdf},
issn = {2333-5955},
journal = {Papers},
month = {mar},
number = {2},
pages = {235--275},
publisher = {arXiv.org},
title = {{What's Trending in Difference-in-Differences? A Synthesis of the Recent Econometrics Literature}},
url = {https://ideas.repec.org/p/arx/papers/2201.01194.html},
volume = {8},
year = {2022}
}
@techreport{Brewer2022,
abstract = {The UK Universal Credit (UC) welfare reform simplified the benefits system whilst strongly incentivising a return to sustainable employment. Exploiting a staggered roll-out, we estimate the differential effect of entering unemployment under UC versus the former system on mental health. Groups with fewer insurance possibilities - single adults and lone parents – experience a mental health deterioration of 8.4-13.9\% sd. For couples, UC partially or fully mitigates mental health consequences of unemployment. Exploring mechanisms, for single adults and lone parents, reduced benefit income and strict job search requirements dominate any positive welfare effects of the reduced administrative burden of claiming benefits.},
author = {Brewer, Mike and Dang, Thang and Tominey, Emma},
file = {:C\:/Users/ab542x/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Brewer, Dang, Tominey - 2022 - Universal Credit Welfare Reform and Mental Health.pdf:pdf},
institution = {Institute of Labor Economics (IZA)},
keywords = {decomposition,mediation,mental health,universal credit,welfare reform},
month = {mar},
number = {15178},
title = {{Universal Credit: Welfare Reform and Mental Health}},
type = {IZA Discussion Papers},
url = {https://ideas.repec.org/p/iza/izadps/dp15178.html},
year = {2022}
}
@article{Baxter2022,
author = {Baxter, Andrew and Wickham, Sophie and Cheetham, Mandy and Coombes, Emma and Taylor-Robinson, David and Munford, Luke and Xiang, Huasheng and Richiardi, Mattheo and Brown, Heather and Sutton, Matthew},
doi = {10.17605/OSF.IO/KNAJB},
journal = {OSF},
publisher = {OSF},
title = {{Difference-in-difference effect estimates of the mental health impacts of the implementation of Universal Credit using Annual Population Survey data (2013-2018 rollout period) - Protocol and documentation}},
url = {https://osf.io/knajb/},
year = {2022}
}
@article{Craig2022,
abstract = {Introduction The UK social security system is being transformed by the implementation of Universal Credit (UC), which combines six existing benefits and tax credits into a single payment for low-income households. Despite extensive reports of hardship associated with the introduction of UC, no previous studies have comprehensively evaluated its impact on mental health. Because payments are targeted at low-income households, impacts on mental health will have important consequences for health inequalities.

Methods and analysis We will conduct a mixed methods study. Work package (WP) 1 will compare health outcomes for new recipients of UC with outcomes for legacy benefit recipients in two large population surveys, using the phased rollout of UC as a natural experiment. We will also analyse the relationship between the proportion of UC claimants in small areas and a composite measure of mental health. WP2 will use data collected by Citizen's Advice to explore the sociodemographic and health characteristics of people who seek advice when claiming UC and identify features of the claim process that prompt advice-seeking. WP3 will conduct longitudinal in-depth interviews with up to 80 UC claimants in England and Scotland to explore reasons for claiming and experiences of the claim process. Up to 30 staff supporting claimants will also be interviewed. WP4 will use a dynamic microsimulation model to simulate the long-term health impacts of different implementation scenarios. WP5 will undertake cost–consequence analysis of the potential costs and outcomes of introducing UC and cost–benefit analyses of mitigating actions.

Ethics and dissemination We obtained ethical approval for the primary data gathering from the University of Glasgow, College of Social Sciences Research Ethics Committee, application number 400200244. We will use our networks to actively disseminate findings to UC claimants, the public, practitioners and policy-makers, using a range of methods and formats.

Trial registration number The study is registered with the Research Registry: researchregistry6697.},
author = {Craig, Peter and Barr, Benjamin and Baxter, Andrew J. and Brown, Heather and Cheetham, Mandy and Gibson, Marcia and Katikireddi, Srinivasa Vittal and Moffatt, Suzanne and Morris, Steph and Munford, Luke Aaron and Richiardi, Matteo and Sutton, Matt and Taylor-Robinson, David and Wickham, Sophie and Xiang, Huasheng and Bambra, Clare},
doi = {10.1136/BMJOPEN-2022-061340},
file = {:C\:/Users/ab542x/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Craig et al. - 2022 - Evaluation of the mental health impacts of Universal Credit protocol for a mixed methods study.pdf:pdf},
issn = {2044-6055},
journal = {BMJ Open},
keywords = {HEALTH ECONOMICS,MENTAL HEALTH,PUBLIC HEALTH,QUALITATIVE RESEARCH},
month = {apr},
number = {4},
pages = {e061340},
pmid = {35396318},
publisher = {British Medical Journal Publishing Group},
title = {{Evaluation of the mental health impacts of Universal Credit: protocol for a mixed methods study}},
url = {https://bmjopen.bmj.com/content/12/4/e061340 https://bmjopen.bmj.com/content/12/4/e061340.abstract},
volume = {12},
year = {2022}
}
@misc{OfficeforNationalStatistics2018,
author = {{Office for National Statistics}},
title = {{Personal well-being user guidance}},
url = {https://www.ons.gov.uk/peoplepopulationandcommunity/wellbeing/methodologies/personalwellbeingsurveyuserguide},
urldate = {2023-03-17},
year = {2018}
}
@misc{DepartmentforWork&Pensions2022,
author = {{Department for Work & Pensions}},
booktitle = {GOV.UK Guidance},
month = {apr},
title = {{Universal Credit and your claimant commitment}},
url = {https://www.gov.uk/government/publications/universal-credit-and-your-claimant-commitment-quick-guide/universal-credit-and-your-claimant-commitment},
urldate = {2022-12-21},
year = {2022}
}
@article{Callaway2021,
abstract = {In this article, we consider identification, estimation, and inference procedures for treatment effect parameters using Difference-in-Differences (DiD) with (i) multiple time periods, (ii) variation in treatment timing, and (iii) when the “parallel trends assumption” holds potentially only after conditioning on observed covariates. We show that a family of causal effect parameters are identified in staggered DiD setups, even if differences in observed characteristics create non-parallel outcome dynamics between groups. Our identification results allow one to use outcome regression, inverse probability weighting, or doubly-robust estimands. We also propose different aggregation schemes that can be used to highlight treatment effect heterogeneity across different dimensions as well as to summarize the overall effect of participating in the treatment. We establish the asymptotic properties of the proposed estimators and prove the validity of a computationally convenient bootstrap procedure to conduct asymptotically valid simultaneous (instead of pointwise) inference. Finally, we illustrate the relevance of our proposed tools by analyzing the effect of the minimum wage on teen employment from 2001–2007. Open-source software is available for implementing the proposed methods.},
archivePrefix = {arXiv},
arxivId = {1803.09015},
author = {Callaway, Brantly and Sant'Anna, Pedro H. C.},
doi = {10.1016/j.jeconom.2020.12.001},
eprint = {1803.09015},
file = {:C\:/Users/ab542x/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Callaway, Sant'Anna - 2021 - Difference-in-Differences with multiple time periods.pdf:pdf},
issn = {18726895},
journal = {Journal of Econometrics},
keywords = {Difference-in-Differences,Doubly robust,Dynamic treatment effects,Event study,Semi-parametric,Treatment effect heterogeneity,Variation in treatment timing},
number = {2},
pages = {200--230},
publisher = {Elsevier B.V.},
title = {{Difference-in-Differences with multiple time periods}},
url = {https://doi.org/10.1016/j.jeconom.2020.12.001},
volume = {225},
year = {2021}
}
@misc{OfficeforNationalStatistics-SocialSurveyDivision2022,
author = {{Office for National Statistics - Social Survey Division}},
doi = {10.5255/UKDA-SN-6721-22},
edition = {23rd Editi},
publisher = {UK Data Service},
title = {{Annual Population Survey, 2004-2021: Secure Access}},
url = {http://doi.org/10.5255/UKDA-SN-6721-22},
year = {2022}
}
@article{Butts2021a,
abstract = {Recent work has highlighted the difficulties of estimating difference-in-differences models when treatment timing occurs at different times for different units. This article introduces the R package did2s which implements the estimator introduced in Gardner (2021). The article provides an approachable review of the underlying econometric theory and introduces the syntax for the function did2s. Further, the package introduces a function, event_study, that provides a common syntax for all the modern event-study estimators and plot_event_study to plot the results of each estimator.},
archivePrefix = {arXiv},
arxivId = {2109.05913},
author = {Butts, Kyle and Gardner, John},
eprint = {2109.05913},
file = {:C\:/Users/ab542x/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Butts, Gardner - 2021 - did2s Two-Stage Difference-in-Differences.pdf:pdf},
journal = {arXiv},
pages = {1--13},
title = {{did2s: Two-Stage Difference-in-Differences}},
url = {http://arxiv.org/abs/2109.05913},
year = {2021}
}
@techreport{Craig2019,
abstract = {This document provides guidance on the development, evaluation and implementation of complex interventions to improve health. It updates the advice provided in the 2000 MRC Framework for the Development and Evaluation of RCTs for Complex Interventions to Improve Health, taking account of the valuable experience that has accumulated since then, and extending the coverage in the guidance of non-experimental methods, and of complex interventions outside the health service. It is intended to help researchers to choose appropriate methods, research funders to understand the constraints on evaluation design, and users of evaluation to weigh up the available evidence in the light of these methodological and practical constraints. Box 1 summarises the main elements of the process, and the key questions that researchers should ask themselves as they work through it.},
author = {Craig, Peter and Dieppe, Paul and Macintyre, Sally and Michie, Susan and Nazareth, Irwin and Petticrew, Mark},
file = {:C\:/Users/ab542x/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Craig et al. - 2019 - Developing and evaluating complex interventions.pdf:pdf},
publisher = {Medical Research Council},
title = {{Developing and evaluating complex interventions: new guidance}},
url = {www.mrc.ac.uk/complexinterventionsguidance},
year = {2019}
}
@article{Matthews2022,
abstract = {The randomised trial is the preferred study design for evaluating the effectiveness and safety of interventions. Yet such trials can be prohibitively expensive, unethical, or take too long. When it is not possible to carry out a randomised trial, observational data can be used to answer similar questions. Here, we describe the process of using observational data to emulate a target trial, which applies the study design principles of randomised trials to observational studies that aim to estimate the causal effect of an intervention. The target trial provides a formal framework to help avoid self-inflicted biases common to observational studies.

Observational studies can provide evidence on the effectiveness of interventions when randomised trials are not feasible because they are expensive, unethical, or take too long. Causal inference using observational data is, however, challenging; not only are observational studies prone to confounding bias due to the lack of randomisation, but incorrect study design choices (such as the specification of the start of follow-up) can also cause self-inflicted biases.1 Such study design flaws can be overcome by first designing a hypothetical randomised trial—the target trial—that would answer the question of interest, then emulating this target trial using the available observational data and appropriate methodology.23

The first step is to specify the protocol of the trial that ideally would have been conducted, within the constraints of the available observational data. Several elements are considered at this stage245: eligibility criteria, treatment strategies, assignment procedures, outcome(s), follow-up, causal contrasts of interest (eg, {\ldots}},
author = {Matthews, Anthony A. and Danaei, Goodarz and Islam, Nazrul and Kurth, Tobias},
doi = {10.1136/BMJ-2022-071108},
file = {:C\:/Users/ab542x/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Matthews et al. - 2022 - Target trial emulation applying principles of randomised trials to observational studies.pdf:pdf},
isbn = {0b013e3181875e61},
issn = {1756-1833},
journal = {BMJ},
month = {aug},
pmid = {36041749},
publisher = {British Medical Journal Publishing Group},
title = {{Target trial emulation: applying principles of randomised trials to observational studies}},
url = {https://www.bmj.com/content/378/bmj-2022-071108 https://www.bmj.com/content/378/bmj-2022-071108.abstract},
volume = {378},
year = {2022}
}
@article{Wickham2020,
abstract = {Background: Universal Credit, a welfare benefit reform in the UK, began to replace six existing benefit schemes in April, 2013, starting with the income-based Job Seekers Allowance. We aimed to determine the effects on mental health of the introduction of Universal Credit. Methods: In this longitudinal controlled stuWickham, S. et al. (2020) ‘Effects on mental health of a UK welfare reform, Universal Credit: a longitudinal controlled study', The Lancet Public Health, 5(3), pp. e157–e164. doi: 10.1016/S2468-2667(20)30026-8.dy, we linked 197 111 observations from 52 187 individuals of working age (16–64 years) in England, Wales, and Scotland who participated in the Understanding Society UK Longitudinal Household Panel Study between 2009 and 2018 with administrative data on the month when Universal Credit was introduced into the area in which each respondent lived. We included participants who had data on employment status, local authority area of residence, psychological distress, and confounding variables. We excluded individuals from Northern Ireland and people out of work with a disability. We used difference-in-differences analysis of this nationally representative, longitudinal, household survey and separated respondents into two groups: unemployed people who were eligible for Universal Credit (intervention group) and people who were not unemployed and therefore would not have generally been eligible for Universal Credit (comparison group). Using the phased roll-out of Universal Credit, we compared the change in psychological distress (self-reported via General Health Questionnaire-12) between the intervention group and the comparison group over time as the reform was introduced in the area in which each respondent lived. We defined clinically significant psychological distress as a score of greater than 3 on the General Health Questionnaire-12. We tested whether there were differential effects across subgroups (age, sex, and education). Findings: The prevalence of psychological distress increased in the intervention group by 6{\textperiodcentered}57 percentage points (95% CI 1{\textperiodcentered}69–11{\textperiodcentered}42) after the introduction of Universal Credit relative to the comparison group, after accounting for potential confounders. We estimate that between April 29, 2013, and Dec 31, 2018, an additional 63 674 (95% CI 10 042–117 307) unemployed people will have experienced levels of psychological distress that are clinically significant due to the introduction of Universal Credit; 21 760 of these individuals might reach the diagnostic threshold for depression. Interpretation: Our findings suggest that the introduction of Universal Credit led to an increase in psychological distress, a measure of mental health difficulties, among those affected by the policy. Future changes to government welfare systems should be evaluated not only on a fiscal basis but on their potential to affect health and wellbeing. Funding: Wellcome Trust, UK National Institute for Health Research, and Medical Research Council.},
author = {Wickham, Sophie and Bentley, Lee and Rose, Tanith and Whitehead, Margaret and Taylor-Robinson, David and Barr, Ben},
doi = {10.1016/S2468-2667(20)30026-8},
file = {:C\:/Users/ab542x/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Wickham et al. - 2020 - Effects on mental health of a UK welfare reform, Universal Credit a longitudinal controlled study.pdf:pdf;:C\:/Users/ab542x/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Wickham et al. - 2020 - Effects on mental health of a UK welfare reform, Universal Credit a longitudinal controlled study(2).pdf:pdf},
issn = {24682667},
journal = {The Lancet Public Health},
month = {mar},
number = {3},
pages = {e157--e164},
pmid = {32113519},
publisher = {Elsevier Ltd},
title = {{Effects on mental health of a UK welfare reform, Universal Credit: a longitudinal controlled study}},
url = {www.thelancet.com/},
volume = {5},
year = {2020}
}
@article{Sun2021,
abstract = {To estimate the dynamic effects of an absorbing treatment, researchers often use two-way fixed effects regressions that include leads and lags of the treatment. We show that in settings with variation in treatment timing across units, the coefficient on a given lead or lag can be contaminated by effects from other periods, and apparent pretrends can arise solely from treatment effects heterogeneity. We propose an alternative estimator that is free of contamination, and illustrate the relative shortcomings of two-way fixed effects regressions with leads and lags through an empirical application.},
archivePrefix = {arXiv},
arxivId = {1804.05785},
author = {Sun, Liyang and Abraham, Sarah},
doi = {10.1016/J.JECONOM.2020.09.006},
eprint = {1804.05785},
file = {:C\:/Users/ab542x/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Sun, Abraham - 2021 - Estimating dynamic treatment effects in event studies with heterogeneous treatment effects.pdf:pdf},
issn = {0304-4076},
journal = {Journal of Econometrics},
keywords = {Difference-in-differences,Pretrend test,Two-way fixed effects},
month = {dec},
number = {2},
pages = {175--199},
publisher = {North-Holland},
title = {{Estimating dynamic treatment effects in event studies with heterogeneous treatment effects}},
volume = {225},
year = {2021}
}
@misc{OfficeforNationalStatistics2012,
author = {{Office for National Statistics}},
month = {sep},
title = {{Annual population survey (APS) QMI}},
url = {https://www.ons.gov.uk/employmentandlabourmarket/peopleinwork/employmentandemployeetypes/methodologies/annualpopulationsurveyapsqmi},
urldate = {2022-02-11},
year = {2012}
}
@techreport{DepartmentforWork&Pensions2018,
abstract = {Research Report 958.},
author = {{Department for Work & Pensions}},
file = {:C\:/Users/ab542x/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Department for Work & Pensions - 2018 - Universal Credit Full Service Claimant Survey.pdf:pdf},
institution = {Department for Work and Pensions},
isbn = {978-1-5286-0490-1},
month = {jun},
title = {{Universal Credit Full Service Survey}},
url = {https://www.gov.uk/government/publications/universal-credit-full-service-claimant-survey},
year = {2018}
}
@techreport{DepartmentforWork&Pensions2022a,
address = {London},
author = {{Department for Work & Pensions}},
month = {jun},
title = {{Completing the move to Universal Credit}},
url = {https://www.gov.uk/government/publications/completing-the-move-to-universal-credit/completing-the-move-to-universal-credit--2},
year = {2022}
}
@misc{DepartmentforWork&Pensions2018a,
annote = {archived at https://web.archive.org/web/20220924014730/https://www.gov.uk/government/publications/universal-credit-transition-to-full-service/universal-credit-transition-rollout-schedule-march-2018-to-december-2018},
author = {{Department for Work & Pensions}},
month = {dec},
title = {{Universal Credit transition rollout schedule March 2018 to December 2018}},
url = {https://www.gov.uk/government/publications/universal-credit-transition-to-full-service/universal-credit-transition-rollout-schedule-march-2018-to-december-2018},
urldate = {2022-02-16},
year = {2018}
}