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- Eriksson, Stefan, et al.
(författare)
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What is the right profile for getting a job? : A stated choice experiment of the recruitment process
- 2012
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- We study the recruitment behavior of Swedish employers using data from a stated choice experiment. In the experiment, the employers are first asked to describe an employee who recently and voluntarily left the firm, and then to choose between two hypothetical applicants to invite to a job interview or to hire as a replacement for their previous employee. The two applicants differ with respect to characteristics such as gender, age, education, experience, ethnicity, religious beliefs, family situation, weight, and health. Our results show that employers discriminate against applicants who are old, non-European, Muslim, Jewish, obese, have several children, or have a history of sickness absence. Expressed in wage terms, this discrimination corresponds to a wage reduction of up to 50 percent. Moreover, increasing the firms’ cost of uncertainty in hiring – through more firm co-payment in the sickness benefit system – may reduce hiring, but does not affect the degree of discrimination. Also, there are only small differences in the degree of discrimination between different types of recruiters and firms. Overall, our results suggest that the discrimination, at least partially, should reflect statistical discrimination.
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| 2. |
- Johansson, Per, et al.
(författare)
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Replik till Åke Dahlberg
- 2008
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Ingår i: Arbetsmarknad & Arbetsliv. - Karlstad : Karlstads universitet. - 1400-9692 .- 2002-343X. ; 14:4, s. 75-79
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- Replik till Åke Dahlbergs kritik mot utvärderingen av "Arbetstorget för erfarna" (publicerad i Arbetsmarknad & Arbetsliv 2008, vol 14, nr 3, s 67-78)
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| 5. |
- Langenskiöld, Sophie
(författare)
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Analyzing the results using Rubin's Causal Model (Part II) : Peer effects and smoking roommates at Harvard College
- 2005
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- With this document, we conclude our demonstration of how Rubin's Causal Model (RCM) can be used to draw causal inferences in a two-step procedure. In the first step, we designed a study to evaluate if Harvard freshmen were more prone to start smoking when sharing a suite with at least one smoker than they would have been when sharing a suite with only non-smokers. Treated students were matched with control students, and models for the outcome analyses were specified. In this second step, we fit these models and evaluate the treatment effects. We also discuss how robust the effects are to various assumptions, as demonstrated by the variation in the effects across the different models. Our main result is that our effect of treatment is small and insignificant when we fit our statistical models on a well-balanced study. Also, this result is robust to the assumptions we make both with regard to the missing potential outcomes and to the various covariate adjustments. Our secondary result is that we would have found peer effects had we instead fitted a model on a less balanced sample, as has been done previously in the peer effect literature, using the traditional approach of causal inferences. However, this secondary result is not robust to the covariate adjustments we make. This exercise illustrates that it is difficult to replicate the results we find when we evaluate peer effects using a well-balanced study (RCM) when we evaluate peer effects using a less-balanced study (traditional approach). The result is reminiscent of the classic results of LaLonde (1986).
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| 6. |
- Langenskiöld, Sophie
(författare)
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Causal inference according to the traditional approach : Peer effects and snl.oking among husbands and wives
- 2005
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- We contribute to the literature on peer effects of smoking by studying a husband's decision to quit smoking as a function of his wife's smoking. We relax some of the strong assumptions made in previous studies. For example, we study a known peer group and allow for observed or unobserved time invariant exogenous characteristics that may influence both the agent's and his peers' smoking habits. Using data from the Panel Survey of Income Dynamics (PSID), we find a significant peer group effect. The point estimate suggests that the probability that a husband quits smoking increases by 24 percentage units if his wife quits smoking. Because we still make some strong assumptions, this estimate should be interpreted with caution.
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| 7. |
- Langenskiöld, Sophie
(författare)
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Designing a study using Rubin's Causal Model (Part I) : Peer effects and smoking roommates at Harvard College
- 2005
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- In quasi-experimental and observational studies, the causal effect of treatment cannot be estimated without controlling for the systematic differences between treated and control subjects. In a sequence of two papers, we suggest an approach for these studies that first, without having access to outcome data, balances these differences and defines appropriate models for the key outcome analyses. Then, when access to outcome data is gained, these models can be fitted without repeated attempts. In this document, we design a hypothetical randomized experiment, studying peer effects related to smoking, that is close to an actual quasi-experimental study. We find that the balance of the observed covariates in the designed study is better than would be expected in a randomized experiment. Furthermore, we define the models of the key outcornes concerning smoking behavior that we commit to run in our next document. Finally, we gain an understanding of which treatment effects we can expect to be real and important in the real outcome analyses by conducting practice analyses on intermediate outcomes using our design.
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| 9. |
- Langenskiöld, Sophie, et al.
(författare)
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Outcome-free Design of Observational Studies : Peer Influence on Smoking
- 2008
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Ingår i: Annales d'économie et de statistique (Annals of Economics and Statistics). - : JSTOR. - 0769-489X. ; :91/92, s. 107-125
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- For estimating causal effects of treatments, randomized experiments are appropriately considered the gold standard, although they are often infeasible for a variety of reasons. Nevertheless, nonrandomized studies can and should be designed to approximate randomized experiments by using only background information to create subgroups of similar treated and control units, where "similar" here refers to their distributions of background variables. This activity should be conducted without access to any outcome data to assure the objectivity of the design. In many situations, these goals can be accomplished using propensity score methods, as illustrated here in the context of a study on whether nonsmoking Harvard freshmen are influenced by their smoking peers. In that study, propensity score methods were used to create matched groups of treated units (rooming with at least one smoker) and control units (rooming with only non-smokers) who are at least as similar with respect to their distributions of observed background characteristics as if they had been randomized, thereby approximating a randomized experiment with respect to the observed covariates.
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| 10. |
- Langenskiöld, Sophie
(författare)
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Peer influence on smoking : causation or correlation?
- 2005
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- In this thesis, we explore two different approaches to causal inferences. The traditional approach models the theoretical relationship between the outcome variables and their explanatory variables, i.e., the science, at the same time as the systematic differences between treated and control subjects are modeled, i.e., the assignment mechanism. The alternative approach, based on Rubin's Causal Model (RCM), makes it possible to model the science and the assignment mechanism separately in a two-step procedure. In the first step, no outcome variables are used when the assignment mechanism is modeled, the treated students are matched with similar control students using this mechanism, and the models for the science are determined. Outcome variables are only used in the second step when these pre-specified models for the science are fitted. In the first paper, we use the traditional approach to evaluate whether a husband is more prone to quit smoking when his wife quits smoking than he would have been had his wife not quit. We find evidence that this is the case, but that our analysis must rely on restrictive assumptions. In the subsequent two papers, we use the alternative RCM approach to evaluate if a Harvard freshman who does not smoke (observed potential outcome) is more prone to start smoking when he shares a suite with at least one smoker, than he would have been had he shared a suite with only smokers (missing potential outcomes). We do not find evidence that this is the case, and the small and insignificant treatment effect is robust against various assumptions that we make regarding covariate adjustments and missing potential outcomes. In contrast, we do find such evidence when we use the traditional approach previously used in the literature to evaluate peer effects relating to smoking, but the treatment effect is not robust against the assumptions that we make regarding covariate adjustments. These contrasting results in the two latter papers allow us to conclude that there are a number of advantages with the alternative RCM approach over the traditional approaches previously used to evaluate peer effects relating to smoking. Because the RCM does not use the outcome variables when the assignment mechanism is modeled, it can be re-fit repeatedly without biasing the models for the science. The assignment mechanism can then often be modeled to fit the data better and, because the models for the science can consequently better control for the assignment mechanism, they can be fit with less restrictive assumptions. Moreover, because the RCM models two distinct processes separately, the implications of the assumptions that are made on these processes become more transparent. Finally, the RCM can derive the two potential outcomes needed for drawing causal inferences explicitly, which enhances the transparency of the assumptions made with regard to the missing potential outcomes.
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