How long is the shadow? The relationships of family background to selected adult outcomes: results from PIAAC

Braun, Henry

doi:10.1186/s40536-018-0058-x

Cited by 7 publications

(13 citation statements)

References 35 publications

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“…taking PIAAC as an example, one topic of interest is the nature of the statistical patterns related to adult incomes. In all countries, even after adjusting for family background, age, cognitive skills, educational attainment, full-time employment, and occupational category, women are less likely (and in some countries markedly so) than men to achieve incomes that place them in the top quartile of the national income distribution (Braun 2018). the consistency of the findings across twentyone OECD countries is striking, but still only suggestive.…”

Section: Technical Issuesmentioning

confidence: 82%

“…that information would come from auxiliary sources such as longitudinal studies or substantively grounded analyses of underlying mechanisms. For example, estimates of gender-related differences in incomes (Braun 2018) should not be interpreted directly as measures of bias in the workplace. Other factors, such as choices people make in trading income for time flexibility, as well as differences in employment histories (e.g., the extent of part-time work or time out of the workforce), may account for some of the observed differences in incomes.…”

Section: Technical Issuesmentioning

confidence: 99%

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Assessment for Monitoring of Education Systems: International Comparisons

Braun¹,

Singer²

2019

The ANNALS of the American Academy of Political and Social Scie

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Over the last two decades, with the increase in both numbers of participating jurisdictions and media attention, international large-scale assessments (ILSAs) have come to play a more salient role in global education policies than they once did. This has led to calls for greater transparency with regard to instrument development and closer scrutiny of the use of instruments in education policy. We begin with a brief review of the history of ILSAs and describe the requirements and constraints that shape ILSA design, implementation, and analysis. We then evaluate the rationales of employing ILSA results for different purposes, ranging from those we argue are most appropriate (comparative description) to least appropriate (causal inference). We cite examples of ILSA usage from different countries, with particular attention to the widespread misinterpretations and misuses of country rankings based on average scores on an assessment (e.g., literacy or numeracy). Looking forward, we offer suggestions on how to enhance the constructive roles that ILSAs play in informing education policy.

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Section: Technical Issuesmentioning

confidence: 82%

Section: Technical Issuesmentioning

confidence: 99%

Assessment for Monitoring of Education Systems: International Comparisons

Braun¹,

Singer²

2019

The ANNALS of the American Academy of Political and Social Scie

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“…ILSA results are also used for secondary analyses that can uncover patterns of relationships that either directly inform policy or suggest promising directions for further study. Using PIAAC data for OECD countries, Braun (2018) showed that women working fulltime were much less likely than men working full-time to have incomes in the top quartile of the national income distribution, even after controlling for family background, measured cognitive skills, educational attainment, and occupational sector. The degree of disadvantage ranged from modest (United States) to very substantial (Japan, Netherlands).…”

Section: Policy Utilitymentioning

confidence: 99%

Changing times, changing needs: enhancing the utility of international large-scale assessments

Kirsch

Braun

2020

Large-scale Assess Educ

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Some say that technology changes everything. Both in the workplace and in day-today activities, individuals are increasingly required to navigate, critically analyze, and problem solve in data-intensive, complex digital environments. Beyond the loss of millions of jobs in the United States and elsewhere, many have warned about the larger impact artificial intelligence (AI) and robotics will likely have on both labor markets and everyday life (Brynjolfsson and McAfee 2014). And, according to a recent report from the Metropolitan Policy Program at the Brookings Institution (Muro et al. 2019), automation and

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“…A practical strategy for conducting analyses that take into account these challenges is illustrated here with a subset of the findings reported by Braun (2018). 1 Braun employed data from an international assessment of adult literacy, the Programme for the International Assessment of Adult Competencies (PIAAC Cycle 1 -Round 1).…”

Section: Introductionmentioning

confidence: 99%

“…The focal outcome that concerns us here is the individual's location in the national income distribution. Braun (2018) investigated the extent to which the strength of the associations between temporally earlier variables were attenuated as more proximal variables were entered. Specifically, interest centered on quantifying the 'long shadow' of demographic characteristics and family background on an individual's labor market success-both as the sole set of predictors and in the presence of more proximal measures of cognitive skills and educational attainment.…”

Section: Introductionmentioning

confidence: 99%

Defensible inferences from a nested sequence of logistic regressions: a guide for the perplexed

Gurkan

Benjamini

Braun

2021

Large-scale Assess Educ

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Employing nested sequences of models is a common practice when exploring the extent to which one set of variables mediates the impact of another set. Such an analysis in the context of logistic regression models confronts two challenges: (i) direct comparisons of coefficients across models are generally biased due to the changes in scale that accompany the changes in the set of explanatory variables, (ii) conducting a large number of tests induces a problem of multiplicity that can lead to spurious findings of significance if not heeded. This article aims to illustrate a practical strategy for conducting analyses in the face of these challenges. The challenges—and how to address them—are illustrated using a subset of the findings reported by Braun (Large-scale Assess Educ 6(4):1–52, 2018. 10.1186/s40536-018-0058-x), drawn from the Programme for the International Assessment of Adult Competencies (PIAAC), an international, large-scale assessment of adults. For each country in the dataset, a nested pair of logistic regression models was fit in order to investigate the role of Educational Attainment and Cognitive Skills in mediating the impact of family background and demographic characteristics on the location of an individual’s annual income in the national income distribution. A modified version of the Karlson–Holm–Breen (KHB) method was employed to obtain an unbiased estimate of the true differences in the coefficients between nested logistic models. In order to address the issue of multiplicity, a recent generalization of the Benjamini–Hochberg (BH) False Discovery Rate (FDR)-controlling procedure to hierarchically structured hypotheses was employed and compared to two conventional methods. The differences between the changes in coefficients calculated conventionally and with the KHB adjustment varied from negligible to very substantial. When combined with the actual magnitudes of the coefficients, we concluded that the more proximal factors indeed act as strong mediators for the background factors, but less so for Age, and hardly at all for Gender. With respect to multiplicity, applying the FDR-controlling procedure yielded results very similar to those obtained by applying a standard per-comparison procedure, but quite a few more discoveries in comparison to the Bonferroni procedure. The KHB methodology illustrated here can be applied wherever there is interest in comparing nested logistic regressions. Modifications to account for probability sampling are practicable. The categorization of variables and the order of entry should be determined by substantive considerations. On the other hand, the BH procedure is perfectly general and can be implemented to address multiplicity issues in a broad range of settings.

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How long is the shadow? The relationships of family background to selected adult outcomes: results from PIAAC

Cited by 7 publications

References 35 publications

Assessment for Monitoring of Education Systems: International Comparisons

Assessment for Monitoring of Education Systems: International Comparisons

Changing times, changing needs: enhancing the utility of international large-scale assessments

Defensible inferences from a nested sequence of logistic regressions: a guide for the perplexed

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