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Quality of education: Estimating the human capital output of formal schooling

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Abstract:

Quality of education: Estimating the human capital output of formal schooling

Authors: Carina Omoeva and Babette Wils, Education Policy and Data Center (EPDC)

This paper seeks to estimate the value of education output across 184 countries included in the 2011 cycle of the World Bank’s International Comparison Program, focusing in particular on the most challenging aspect of education: the quality of learning. Building on the recent literature on the measurement of and linking diverse quality measures (Hanushek and Kimko 2000; Hanushek and Woessmann 2009), we dramatically expand the number of countries for which proxy measures of quality become available, and significantly improve the consistency of estimates, as well as their conceptual relevance of as the value-added of education systems.
Methodology:
First, international student assessments (PISA, TIMSS, PIRLS, SACMEQ, SERCE) are used as direct measures of the level of learning outcomes. Conditional mean imputation methodology (Schafer 1997; Allison 2002) is used to unify available test measures, and to impute missing values for countries that have never participated in assessments. The imputation models exploit the relationship between the measures of student outcomes and an array of macro-level indicators such as the country’s economic, social, and demographic features, as well as education system indicators.
Further, we make adjustments to the final estimated quality measures, designed to take out the variation associated with two major factors that may bias the observed measures: 1) unequal levels of parent schooling, and 2) unequal student composition relative to the overall population, in countries with high repetition and dropout rates.
Finally, we propose a unified estimate of total output of human capital across the 184 developed and developing countries, based on estimated quality measures (see above) and observed pupil participation rates, and using the models specified in Woessmann (2000).

Results:
- Quality of learning estimates for 184 countries
- Estimates of associations between country-level indicators and predicted quality
- Interpretation of biases in observed learning scores
- Estimates of total human capital output.
Convention
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Association:
Name: 55th Annual Conference of the Comparative and International Education Society
URL:
http://www.cies.us


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URL: http://citation.allacademic.com/meta/p494071_index.html
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MLA Citation:

Omoeva, Carina. and Wils, Annababette. "Quality of education: Estimating the human capital output of formal schooling" Paper presented at the annual meeting of the 55th Annual Conference of the Comparative and International Education Society, Fairmont Le Reine Elizabeth, Montreal, Quebec, Canada, May 01, 2011 <Not Available>. 2014-11-26 <http://citation.allacademic.com/meta/p494071_index.html>

APA Citation:

Omoeva, C. and Wils, A. , 2011-05-01 "Quality of education: Estimating the human capital output of formal schooling" Paper presented at the annual meeting of the 55th Annual Conference of the Comparative and International Education Society, Fairmont Le Reine Elizabeth, Montreal, Quebec, Canada <Not Available>. 2014-11-26 from http://citation.allacademic.com/meta/p494071_index.html

Publication Type: Conference Paper/Unpublished Manuscript
Review Method: Peer Reviewed
Abstract: Quality of education: Estimating the human capital output of formal schooling

Authors: Carina Omoeva and Babette Wils, Education Policy and Data Center (EPDC)

This paper seeks to estimate the value of education output across 184 countries included in the 2011 cycle of the World Bank’s International Comparison Program, focusing in particular on the most challenging aspect of education: the quality of learning. Building on the recent literature on the measurement of and linking diverse quality measures (Hanushek and Kimko 2000; Hanushek and Woessmann 2009), we dramatically expand the number of countries for which proxy measures of quality become available, and significantly improve the consistency of estimates, as well as their conceptual relevance of as the value-added of education systems.
Methodology:
First, international student assessments (PISA, TIMSS, PIRLS, SACMEQ, SERCE) are used as direct measures of the level of learning outcomes. Conditional mean imputation methodology (Schafer 1997; Allison 2002) is used to unify available test measures, and to impute missing values for countries that have never participated in assessments. The imputation models exploit the relationship between the measures of student outcomes and an array of macro-level indicators such as the country’s economic, social, and demographic features, as well as education system indicators.
Further, we make adjustments to the final estimated quality measures, designed to take out the variation associated with two major factors that may bias the observed measures: 1) unequal levels of parent schooling, and 2) unequal student composition relative to the overall population, in countries with high repetition and dropout rates.
Finally, we propose a unified estimate of total output of human capital across the 184 developed and developing countries, based on estimated quality measures (see above) and observed pupil participation rates, and using the models specified in Woessmann (2000).

Results:
- Quality of learning estimates for 184 countries
- Estimates of associations between country-level indicators and predicted quality
- Interpretation of biases in observed learning scores
- Estimates of total human capital output.


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