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Zhang, J. L. & Johansson, P. (2019). A Comparison of Methods of Inference in Randomized Experiments from a Restricted Set of Allocations.
Open this publication in new window or tab >>A Comparison of Methods of Inference in Randomized Experiments from a Restricted Set of Allocations
2019 (English)Report (Other academic)
Abstract [en]

Rerandomization is a strategy of increasing eciency as compared to complete randomization. The idea with rerandomization is that of removing allocations with imbalance in the observed covariates and then randomizing within the set of allocations with balance in these covariates. Standard asymptotic inference based on mean dierence estimator is however conservative after rerandomization. Given a Mahalanobis distancecriterion for removing imbalanced allocations, Li et al. (2018) derived the asymptotic distribution of the mean dierence estimator and suggesteda consistent estimator of its variance. This paper discusses several alternative methods of inference under rerandomization, and compare theirperformance with that of the method in Li et al. (2018) through a large Monte Carlo simulation. We conclude that some of the methods work better for small or moderate sample sized experiments than the method in Li et al. (2018).

Publisher
p. 23
Series
Working paper / Department of Statistics, Uppsala University ; 5
National Category
Probability Theory and Statistics
Research subject
Statistics
Identifiers
urn:nbn:se:uu:diva-396457 (URN)
Available from: 2019-11-05 Created: 2019-11-05 Last updated: 2019-11-07Bibliographically approved
Schultzberg, M. & Johansson, P. (2019). Asymptotic Inference for Optimal Re-Randomization Designs. Uppsala universitet
Open this publication in new window or tab >>Asymptotic Inference for Optimal Re-Randomization Designs
2019 (English)Report (Other academic)
Abstract [en]

Recently an experimental design strategy, called rerandomization, has been proposed as acomplement to traditional blocked designs. The idea of rerandomization is to remove, from consideration, those allocations with large imbalances in observed covariates according to abalance criterion, and then randomize within the set of acceptable allocations. This paper clarifies the concept of an ‘optimal’ rerandomization design for inferences using the mean difference sample estimator, SATE. Based on the Mahalanobis distance criterion for balancing the covariates, we show that standard asymptotic inference to the population, from which the units in the sample are randomly drawn, is possible using only the set of best, or ‘optimal’, allocations.

Place, publisher, year, edition, pages
Uppsala universitet, 2019. p. 17
Series
Working paper / Department of Statistics, Uppsala University ; 2019:2
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:uu:diva-381384 (URN)
Note

Title of the first published version of the report was: Optimal designs and asymptotic inference. A revised version with a new title was published 2019-09-30.

Available from: 2019-04-09 Created: 2019-04-09 Last updated: 2019-09-30Bibliographically approved
Eliason, M., Johansson, P. & Nilsson, M. (2019). Forward-looking moral hazard in social insurance. Labour Economics, 60, 84-98
Open this publication in new window or tab >>Forward-looking moral hazard in social insurance
2019 (English)In: Labour Economics, ISSN 0927-5371, E-ISSN 1879-1034, Vol. 60, p. 84-98Article in journal (Refereed) Published
Abstract [en]

This study tests for forward-looking moral hazard in the sickness insurance system by exploiting a 1991 reform in Sweden. The replacement rate was reduced for short absences but not for long absences, which introduced a potential future cost of returning to work. Using this exogenous variation in the replacement rate and controlling for dynamic selection, we find that the potential future cost of returning to work decreased the outflow from long-term sickness absence. This finding suggests that long-term sickness absentees respond to economic incentives and are forward-looking, which highlights the importance of taking forward-looking behavior into account when designing and evaluating social insurance programs.

Keywords
Dynamic incentives, Forward-looking behavior, Moral hazard, Sickness absence, Sickness insurance
National Category
Economics
Identifiers
urn:nbn:se:uu:diva-399004 (URN)10.1016/j.labeco.2019.06.003 (DOI)000491685300007 ()
Available from: 2019-12-16 Created: 2019-12-16 Last updated: 2019-12-16Bibliographically approved
Johansson, P., Schultzberg, M. & Rubin, D. (2019). On optimal re-randomization designs.
Open this publication in new window or tab >>On optimal re-randomization designs
2019 (English)Report (Other academic)
Abstract [en]

Blocking is commonly used in randomized experiments to increase efficiency of estimation. A generalization of blocking is to remove allocations with imbalance in covariates between treated and control units, and thenrandomize within the set of allocations with balance in these covariates. This idea of rerandomization was formalized by [5], who suggested using the affinely invariant Mahalanobis distance between treated and control covariate means as the criterion for removing unbalanced allocations. [3] proposed reducing the set of balanced allocations to the minimum. Here we discuss the implication of such an ‘optimal’ rerandomization design for inferences to the units inthe sample and to the population from which the units in the sample were randomly drawn. We argue that, in general, it is a bad idea to seak the optimal design for an inference to the population because that inference typically only reflects uncertainty from the usually hypothetical random sampling, and not the randomization of treatment versus control.

Publisher
p. 20
Series
Working paper / Department of Statistics, Uppsala University ; 2019:3
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:uu:diva-381381 (URN)
Available from: 2019-04-09 Created: 2019-04-09 Last updated: 2019-09-27Bibliographically approved
Angelov, N., Johansson, P. & Lee, M.-j. (2019). Practical causal analysis for the treatment timing effect on doubly censored duration: effect of fertility on work span. Journal of the Royal Statistical Society: Series A (Statistics in Society), 182(4), 1561-1585
Open this publication in new window or tab >>Practical causal analysis for the treatment timing effect on doubly censored duration: effect of fertility on work span
2019 (English)In: Journal of the Royal Statistical Society: Series A (Statistics in Society), ISSN 0964-1998, E-ISSN 1467-985X, Vol. 182, no 4, p. 1561-1585Article in journal (Refereed) Published
Abstract [en]

We present a practical causal framework to estimate the effects of a treatment and its timing on a doubly censored response. We then apply the methodology to find the effect of fertility on work duration where, not just fertility itself, but the timing of fertility should matter greatly. Since fertility and its decision of timing are chosen by the individual, it is likely to be endogenous. We use a populationwide data set over mothers with two children to address the endogeneity issue by using the first two children's same-sex instrument in a 'control function' setting. We find that having a third child reduces the average labour market work duration, and that the magnitude of the effect increases monotonically with the waiting time between the second and third children. Moreover, the negative effect varies substantially over education and second-birth age, being stronger for mothers with higher education and lower second-birth age.

Place, publisher, year, edition, pages
WILEY, 2019
Keywords
Doubly censored response, Fertility, Labour supply, Treatment timing
National Category
Economics
Identifiers
urn:nbn:se:uu:diva-396960 (URN)10.1111/rssa.12474 (DOI)000492420800021 ()
Available from: 2019-11-13 Created: 2019-11-13 Last updated: 2019-11-13Bibliographically approved
Schultzberg, M. & Johansson, P. (2019). Re-randomization: A complement or substitute for stratification in randomized experiments?. Uppsala University
Open this publication in new window or tab >>Re-randomization: A complement or substitute for stratification in randomized experiments?
2019 (English)Report (Other academic)
Abstract [en]

Rerandomization is a strategy for improving balance on observed covariates in randomized control trails. It has been proposed as a complement to traditional stratied (blocked) designs. However, the relationship and differences between stratification, rerandomization, and the combination of the two have not been previouslyinvestigated. In this paper, we show that stratified designs can be recreated by rerandomization and explain why, in most cases, stratification on binary covariates followed by rerandomization on continuous covariates is more efficient than rerandomization on all covariates at the same time.

Place, publisher, year, edition, pages
Uppsala University, 2019. p. 36
Series
Working paper / Department of Statistics, Uppsala University ; 2019:4
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:uu:diva-382225 (URN)
Note

A revised version was published 2019-09-30.

Available from: 2019-04-23 Created: 2019-04-23 Last updated: 2019-09-30Bibliographically approved
Johansson, P. & Schultzberg, M. (2019). Re-randomization strategies for balancing covariates using pre-experimental longitudinal data. Department of Statistics, Uppsala University
Open this publication in new window or tab >>Re-randomization strategies for balancing covariates using pre-experimental longitudinal data
2019 (English)Report (Other academic)
Abstract [en]

This paper considers experimental design based on the strategy of rerandomization to increase the effciency in experiments. Two aspects of rerandomization are addressed. First, we propose a two-stage allocation sample scheme for randomization inference to the units of the experiments in balanced experiments that guarantees that the difference-in-mean estimator is an unbiased estimator of SATE for any experiment, conserves the exactness of randomization inference, and halves the time consumption of the rerandomization design. Second, we propose a rank-based covariate balance measure which can take into account the estimated relative weight of each covariate. Several strategies for estimating these weights using pre-experimental data are proposed. Using Monte Carlo simulations, the proposed strategies are compared to complete randomization and Mahalanobis-based rerandomization. An empirical example is given where the power of a mean difference test of the electricity consumption of 54 households is increased by 99%, in comparison to complete randomization, using one of the proposed designs based on high frequency longitudinal electricity consumption data.

Place, publisher, year, edition, pages
Department of Statistics, Uppsala University, 2019. p. 38
Series
Working paper / Department of Statistics, Uppsala University ; 2018:4
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:uu:diva-364864 (URN)
Note

Title of the first published version of the report was: Experimental design using longitudinal data

A revised version with a new title was published 2019-01-25.

A second revised version was published 2019-09-30.

Available from: 2018-11-05 Created: 2018-11-05 Last updated: 2019-09-30Bibliographically approved
Avdic, D., Hägglund, P., Lindahl, B. & Johansson, P. (2019). Sex differences in sickness absence and the morbidity-mortality paradox: a longitudinal study using Swedish administrative registers.. BMJ Open, 9(8), Article ID e024098.
Open this publication in new window or tab >>Sex differences in sickness absence and the morbidity-mortality paradox: a longitudinal study using Swedish administrative registers.
2019 (English)In: BMJ Open, ISSN 2044-6055, E-ISSN 2044-6055, Vol. 9, no 8, article id e024098Article in journal (Refereed) Published
Abstract [en]

OBJECTIVE: To analyse whether gender-specific health behaviour can be an explanation for why women outlive men, while having worse morbidity outcomes, known as the morbidity-mortality or gender paradox.

SETTING: The working population in Sweden.

PARTICIPANTS: Thirty per cent random sample of Swedish women and men aged 40-59 with a hospital admission in the 1993-2004 period were included. The sample for analysis consists of 233 274 individuals (115 430 men and 117 844 women) and in total 1 867 013 observations on sickness absence.

INTERVENTION: Hospital admission across 18 disease categories.

MAIN OUTCOME MEASURES: The main outcome measures were sickness absence (morbidity) and mortality. Longitudinal data at the individual level allow us to study how sickness absence changed after a hospital admission in men and women using a difference-in-differences regression analysis. Cox regression models are used to study differences in mortality after the admission.

RESULTS: Women increased their sickness absence after a hospital admission by around five more days per year than men (95% CI 5.25 to 6.22). At the same time, men had higher mortality in the 18 diagnosis categories analysed. The pattern of more sickness absence in women was the same across 17 different diagnosis categories. For neoplasm, with a 57% higher risk of death for men (54.18%-59.89%), the results depended on the imputation method of sickness for those deceased. By using the premortality means of sickness absence, men had an additional 14.47 (-16.30- -12.64) days of absence, but with zero imputation women had an additional 1.6 days of absence (0.05-3.20). Analyses with or without covariates revealed a coherent picture.

CONCLUSIONS: The pattern of increased sickness absence (morbidity) and lower mortality in women provides evidence on the more proactive and preventive behaviour of women than of men, which could thus explain the morbidity-mortality paradox.

Keywords
difference-in-difference design, health, sex differences, mortality, population register data, sick leave
National Category
Cardiac and Cardiovascular Systems
Identifiers
urn:nbn:se:uu:diva-401252 (URN)10.1136/bmjopen-2018-024098 (DOI)000502537200011 ()31481361 (PubMedID)
Funder
Forte, Swedish Research Council for Health, Working Life and Welfare
Available from: 2020-01-07 Created: 2020-01-07 Last updated: 2020-01-17Bibliographically approved
Johansson, P., Karimi, A. & Nilsson, J. P. (2019). Worker absenteeism: peer influences, monitoring, and job flexibility. Journal of the Royal Statistical Society: Series A (Statistics in Society), 182(2), 605-621
Open this publication in new window or tab >>Worker absenteeism: peer influences, monitoring, and job flexibility
2019 (English)In: Journal of the Royal Statistical Society: Series A (Statistics in Society), ISSN 0964-1998, E-ISSN 1467-985X, Vol. 182, no 2, p. 605-621Article in journal (Refereed) Published
Abstract [en]

We study the presence of other-regarding preferences in the workplace by exploitinga randomized experiment that changed the monitoring of workers’ health during sick leave. We show that workers’ response to an increase in co-worker shirking, induced by the experiment,is much stronger than the response to a decrease in co-worker shirking. The asymmetric spillover effects are consistent with evidence of fairness concerns documented in laboratory experiments. Moreover, we find that the spillover effect is driven by workers with highly flexible and autonomous jobs, suggesting that co-worker monitoring may be at least as important as formal monitoring in alleviating shirking.

Place, publisher, year, edition, pages
John Wiley & Sons, 2019
National Category
Economics
Identifiers
urn:nbn:se:uu:diva-375107 (URN)10.1111/rssa.12406 (DOI)000456275600012 ()
Funder
The Jan Wallander and Tom Hedelius Foundation
Available from: 2019-01-26 Created: 2019-01-26 Last updated: 2019-02-11Bibliographically approved
Jans, J., Johansson, P. & Nilsson, P. (2018). Economic status, air quality, and child health: Evidence from inversion episodes. Journal of Health Economics, 61, 220-232
Open this publication in new window or tab >>Economic status, air quality, and child health: Evidence from inversion episodes
2018 (English)In: Journal of Health Economics, ISSN 0167-6296, E-ISSN 1879-1646, Vol. 61, p. 220-232Article in journal (Refereed) Published
Abstract [en]

Normally, the temperature decreases with altitude, allowing air pollutants to rise and disperse. During inversion episodes, warmer air at higher altitude traps air pollutants at the ground. By merging vertical temperature profile data from NASA with pollution monitors and health care records, we show that inversions increase the PM10 levels by 25% and children's respiratory health problems by 5.5%. Low-income children are particularly affected, and differences in baseline health seem to be a key mediating factor behind the effect of pollution on the SES health gap. Policies that improve dissemination of information on inversion status may hence improve child health, either through private action or via policies that curb emissions during inversion episodes.

Keywords
Air pollution, Inversions, Environmental policy, Nonparametric estimation, Socioeconomic gradient, Inequality, Labor supply
National Category
Health Care Service and Management, Health Policy and Services and Health Economy
Identifiers
urn:nbn:se:uu:diva-369006 (URN)10.1016/j.jhealeco.2018.08.002 (DOI)000447106100015 ()30193188 (PubMedID)
Available from: 2018-12-14 Created: 2018-12-14 Last updated: 2018-12-14Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-6140-9123

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