4 edition of Measurement error in education and growth regressions found in the catalog.
Measurement error in education and growth regressions
|Statement||by Miguel Portela, Rob Alessie, Coen Teulings.|
|Series||Discussion paper ;, no. 1165, Discussion paper (Forschungsinstitut zur Zukunft der Arbeit : Online) ;, no. 1165|
|Contributions||Teulings, C. N., Alessie, Rob.|
|The Physical Object|
|LC Control Number||2005619079|
and the growth rate of the terms of trade (export prices relative to import prices). B. Education Data The education variable contained in the baseline regression system is one that I found previously had significant explanatory power for economic growth. This variableFile Size: KB. the regression coefficient when measurement errors are absent. When the measurement errors are present in the data, the same OLSE becomes biased as well as inconsistent estimator of regression File Size: KB.
Robert L. Linn is a Distinguished Professor emeritus of Education at the University of Colorado at Boulder. He is a member of the National Academy of Education and Lifetime National associate of the National Academies. He is a former President of the Division of Evaluation and Measurement of the American Psychological Association, former President of the National Council on Measurement in /5(22). 1) Gross Errors. Gross errors are caused by mistake in using instruments or meters, calculating measurement and recording data results. The best example of these errors is a person or operator reading pressure gage N/m2 as N/m2.
The question whether a gender gap in education is associated with economic growth is assessed in two common ways in our sample. As shown in Table 1, half of the studies, and estimates, are based on gender-disaggregated measures for education (i.e. one measuring a country’s male and one measuring a country’s female education), which are included separately in the by: 2. If the dependent variable in a regression is measured with error, regression analysis and associated hypothesis testing are unaffected, except that the R 2 will be lower than it would be with perfect measurement.
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The use of the perpetual inventory method for the construction of education data per country leads to systematic measurement error. This paper analyzes its effect on growth regressions. We suggest a methodology for correcting this error Cited by: Measurement error in education and growth regressions an underestimation of the growth of education during the period.1 Classical errors in variables would lead to an underestimation of the coefficient for education in a growth regression.
The opposite holds in this case, because. The perpetual inventory method used for the construction of education data per country leads to systematic measurement error. This paper analyses the effect of this measurement error on GDP regressions.
There is a systematic difference in the education level between census data and observations constructed from enrolment data. However, research aimed at validating the inclusion of education measures in growth regressions has yet to reach a consensus, often finding that the sign and significance of education depends on.
The perpetual inventory method used for the construction of education data per country leads to systematic measurement error. This paper analyses the effect of this measurement error on GDP regressions. There is a systematic difference in the education Cited by: Portela, M., R.J.M.
Alessie & C.N. Teulings (), Measurement Error in Education and Growth Regressions, The Scandinavian Journal of Economics, vol(3), p. () Discussion Paper Series / Tjalling C. Koopmans Research Institute, vol is pp. 1 - Measurement error in education is widely recognized as an important source of bias in growth regressions, see for example Krueger and Lindahl ().
Barro and Lee (, ) constructed education data from census information where available, and for miss-ing information used enrolment data and the perpetual inventory method for updating.
Measurement error in education is widely recognized has an important source of bias in growth regressions; see for example Krueger and Lindahl (). This paper shows that the way Barro and Lee () constructed the education data yields a systematic error.
Peter Foldvari & Bas van Leeuwen, "An alternative interpretation of 'average years of education' in growth regressions," Applied Economics Letters, Taylor & Francis Journals, vol.
16(9), pages Tiago Sequeira & Elsa Martins, "Education public financing and economic growth: an endogenous growth model versus evidence," Empirical Economics, Springer, vol.
35(2), pages. DANS is an institute of KNAW and NWO. Driven by data. Go to page top Go back to contents Go back to site navigationCited by: is a platform for academics to share research papers.
The use of the perpetual inventory method for the construction of education data per country leads to systematic measurement error. This paper analyses its effect on growth regressions.
We suggest a methodology for correcting this error. CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): Any opinions expressed here are those of the author(s) and not those of the institute. Research disseminated by IZA may include views on policy, but the institute itself takes no institutional policy positions.
The Institute for the Study of Labor (IZA) in Bonn is a local and virtual international research center and. By Miguel Portela, Rob Alessie and C. Teulings; Abstract: The perpetual inventory method used for the construction of education data per country leads to systematic measurementCited by: COVID Resources.
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The perpetual inventory method used for the construction of education data per country leads to systematic measurement error. This paper analyses the effect of this measurement error on GDP regressions. There is a systematic difference in the education.
Education and Economic Growth: A Meta-Regression Analysis Nikos Benosa,b* and Stefania Zotou a** University of Ioannina, Ioannina, Greece *Corresponding author: Nikos Benos, Tel: +, Fax: +, E-mail: [email protected] ** E-mail: [email protected] a Department of Economics, University of Ioannina, University Campus, P.O.
BoxFile Size: KB. In order to study the effect of alternative economic growth measures on the reported findings, we include one dummy variable in our meta-regression model which equals one, if the study uses the real GDP growth as a proxy for economic growth and zero if real per capita growth measures (GDP growth per-capita, per worker, or per labor force-aged person) are by: Di⁄erence this to eliminate the –xed e⁄ect i.
y it y it 1 = (x it x it 1)+ it it 1 As before we only observe ex it = x it +u our results from above plim b = ˙2 xFile Size: KB. Downloadable! This paper studies the puzzling lack of correlation between income and schooling in macro regressions. It is argued that the root of the puzzle is threefold.
First, there is a problem of a proper definition of the way in which years of schooling should enter into a production function. Second, collinearity between physical and human capital stocks seriously undermines the ability.This paper surveys the literature which examines the effect of education on economic growth.
Specifically, we apply meta-regression analysis to 57 studies with estimates and show that there is.MEASUREMENT ERROR MODELS XIAOHONG CHEN and HAN HONG and DENIS NEKIPELOV1 Key words: Linear or nonlinear errors-in-variables models, classical or nonclassical measurement errors, attenuation bias, instrumental variables, double measurements, deconvolution, auxiliary sample JEL Classiﬁcation: C1, C3 1 IntroductionFile Size: KB.