Nonlinear trend in inequality of educational opportunity in the netherlands 1930 1989
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Nonlinear Trend in Inequality of Educational Opportunity in the Netherlands 1930-1989. Maarten L. Buis Harry B.G. Ganzeboom. Outline. Main results Model selection Continuous or discrete education and father’s status Importance mother’s education relative to father’s education

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Nonlinear trend in inequality of educational opportunity in the netherlands 1930 1989

Nonlinear Trend in Inequality of Educational Opportunity in the Netherlands 1930-1989

Maarten L. Buis

Harry B.G. Ganzeboom


Outline
Outline the Netherlands 1930-1989

  • Main results

  • Model selection

    • Continuous or discrete education and father’s status

    • Importance mother’s education relative to father’s education

    • Difference in effect between sons and daughters

  • Non-linearity in trend in effects: identify periods of negative, positive, and no trend.


Main results
Main results the Netherlands 1930-1989

  • Model selection

    • distinction between highest and lowest educated parent is more important than distinction between father and mother, or same-sex-parent.

    • Effects of parental education and father’s occupational status is the same for sons and daughters

  • Non-linearity in trend

    • Effect of father’s status decreases non-linearly over time, slowing down significantly around 1970

    • parental education decreases most likely linearly.


Data the Netherlands 1930-1989

  • International Stratification and Mobility File (ISMF)

  • 49 surveys held between 1958 and 2003 with information on cohorts 1930-1989.

  • 80,000 observations, of which 66,000 have complete information on child's, father’s and mother’s education and father's status.

  • Number of cases are unequally distributed over cohorts.


Model 1 linear regression
Model 1: linear regression the Netherlands 1930-1989

  • Dependent variable is years of education and treated as continuous.

  • Parental education is either entered as father’s and/or mother’s education, highest and/or lowest educated parent, or education of same sex parent

  • Father’s occupational status is measured in ISEI scores

  • Trend in effects are measured as third order orthogonal polynomials or lowess curves.


Two objections against linear education
Two objections against linear education the Netherlands 1930-1989

  • Regression coefficient is effected by both ‘real’ effects of parental characteristics on probabilities of making transitions and educational expansion

    • True, if education is studied as a process

    • False, if education is studied as an outcome

  • education is discrete

    • this does not have to be a problem if there is no concentration in the lowest or highest category.


Model 2 stereotype ordered regression sor
Model 2:Stereotype Ordered Regression (SOR) the Netherlands 1930-1989

  • SOR allows for ordered dependent variable

  • SOR will estimate (sequentially) an optimal scaling of education and the effect of independent variables on this scaled education.


Model 3 row collumn model ii rc2
Model 3: Row Collumn Model II (RC2) the Netherlands 1930-1989

  • Objection against use of ISEI:

    • Effect of father’s occupation is better represented by small number of discrete classes, rather than on continuous scale.

  • Classes used are EGP classification.

  • RC2 is extension of SOR that also estimates an optimal scaling for EGP


Father s and mother s education
Father’s and mother’s education the Netherlands 1930-1989

  • Conventional model: Only father matters

  • Individual model: Both mother and father matter

  • Joint model: Effect of father and mother are equal

  • Dominance model: Highest educated parent matter

  • Modified Dominance model: Highest and lowest educated parent matter

  • Sex Role model: Same sex parent matters


BICs the Netherlands 1930-1989


Scaling of father s status
Scaling of father’s status the Netherlands 1930-1989


Scaling of education
Scaling of education the Netherlands 1930-1989



Identifying periods with significant trend
Identifying periods with significant trend the Netherlands 1930-1989

  • A negative slope means a negative trend.

  • A positive slope mean a positive trend.

  • A zero slope means no trend.


Identifying periods with significant change in trend
Identifying periods with significant the Netherlands 1930-1989change in trend

  • An accelerating trend means that a negative trend becomes more negative, so a negative change in slope.

  • A decelerating trend means that a negative trend becomes less negative, so a positive change in slope.

  • A constant trend means no change in slope.


Data the Netherlands 1930-1989

  • The ISMF dataset is converted into a new dataset, containing estimated IEO for 60 annual cohorts.

  • The precision of the estimates (the standard error) is used to weigh the cohorts.


Lowess
Lowess the Netherlands 1930-1989

  • We have a dataset consisting of estimates of IEO for each annual cohort which used only information from that cohort

  • If we think that IEO develops like a smooth curve over time, than nearby estimates also contain relevant information.

  • The lowess curve creates an improved estimate of the IEO for each cohort using information from nearby cohorts.

  • It results in a smooth line by connecting the lowess estimates.

  • Estimates of the trend and change in trend at each cohort can also be obtained from this curve.


Lowess curve in 1949
Lowess curve in 1949 the Netherlands 1930-1989

  • Point on lowess curve in 1949

  • Select closest 60% of the points.

  • Give larger weights to nearby points.

  • Adjust weights for precision of estimated IEO.

  • Normal regression of IEO on time, time squared and time cubed on weighted points.

  • Predicted value in 1949, is smoothed value of 1949.

  • First derivative in 1949 is trend in 1949.

  • Second derivative in 1949 is change in trend in 1949.

  • Repeat for all cohorts and connect the dots.


Selecting spans
Selecting spans the Netherlands 1930-1989

  • Percentage closest points (span) determines the smoothness of the lowess curve.

  • Trade-off between smoothness and goodness of fit.

  • Can be judged visually by comparing lowess curves with different spans.

  • Numerical representations of this trade-off are Generalize Cross Validation, and Akaike Information Criterion.

  • Lower values mean a better trade-off.


Bootstrap confidence intervals
Bootstrap confidence intervals the Netherlands 1930-1989

  • Confidence interval gives the range of results that could plausibly occur just through sampling error.

  • Make many `datasets' that could have occurred just by sampling error.

  • Fit lowess curves through each `dataset'.

  • The area containing 90% of the curves is the 90% confidence interval.

  • The estimates of IEO are regression coefficient with standard errors.

  • The standard error gives information about what values of IEO could plausibly occur in `new' dataset.


OLS the Netherlands 1930-1989

SOR

RC2


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