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Simultaneous Quantile Regression

Simultaneous Quantile Regression. William Smith EPSSA Methods Workshop 4/11/13. Introduction to Research Project. The Non-linear effects of social capital on occupational prestige. Social capital is important in occupational attainment. Hints of non-linearity

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Simultaneous Quantile Regression

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  1. Simultaneous Quantile Regression William Smith EPSSA Methods Workshop 4/11/13

  2. Introduction to Research Project The Non-linear effects of social capital on occupational prestige. • Social capital is important in occupational attainment. • Hints of non-linearity • Informal channels are more effective early in careers (Flap & Boxman 2000). • ‘Ceiling effect’ of weak ties (Lin 1999). • Females are limited by overreliance on strong ties (Moore 1990).

  3. Research Questions • How do the effects of social capital differ across occupational prestige levels? • How do the effects of social capital differ by gender?

  4. Method Selection & Appropriateness • Needed to test non-linearity • Simultaneous Quantile Regression • Allow you to identify quantiles (percentiles) along a continuum. • Provide linear projections for each quantile. • Different projections at different points along the continuum. • Can test for significant differences between projections (coefficients)

  5. How is it different from OLS? • Ordinary Least Square (OLS) and Ordinal Logistic Regression both provide a mean projection. • Constant slope • Acts like a linear relationship • Since both are linear projections you can compare OLS with Simultaneous Quantile Regression coefficients.

  6. Data • 2001 International Social Survey Programme • Focused on social relationships in 27 countries • Sample • Limited to ages 25-64 with recorded occupation • Used country weights to create large sample that included participants in 21 countries • All analysis done in STATA

  7. Variable Preparation gen siop=0 replace siop=63 if isco88==1141| isco88==1142|isco88==1143| isco88==1220 • Occupational Prestige • Available Social Capital • Strong & Weak Ties • Interaction Terms gen scjob=. replace scjob=1 if v46==1| v46==2| v46==3 replace scjob=2 if v46==4 replace scjob=0 if v46==5| v46==6| v46==7| v46==8| v46==9| v46==10 ASC = gen scnumb = v4r + v8r + v23r + v24r + v25r gen scstrength = v7 + v11 + v13 + v15 + v17 + v18 + v19 + v20 + v21 + v28 gen sctotal = scnumb + scstrength gen femwtie=female*wtiejob

  8. OLS Regression Syntax and Output • Full Regression Model • OLS Output • See handout regsiopi.country female age35_44 age45_54 age55_64 married educyrssctotal /// primary secondary higher wtiejobstiejobfemwtiefemstie [pw=weight], cluster (country)

  9. Simultaneous Quantile Regression Syntax • Full Simultaneous Quantile Regression Model • Simultaneous Quantile Regression Output • See handout sqregsiopaustraliagermanygreatbritainhungarynorwayczechreppolandrussia /// newzealandcanadaphillipinesjapanspainlatviacypruschiledenmarkswitzerland brazil /// finland female age35_44 age45_54 age55_64 married educyrssctotal primary secondary /// higher wtiejobstiejobfemwtiefemstie, q(.1 .3 .5 .7 .9)

  10. Comparison Table

  11. Available Social Capital

  12. Job Search Channel - Female

  13. Comparing Sexes

  14. Checking for Significant Differences • Check for non-linearity • Is the difference between the high and low point (coefficient) statistically different than zero? test [q30]sctotal=[q90]sctotal test [q50]stiejob=[q90]stiejob test [q30]wtiejob=[q90]wtiejob test [q50]femstie=[q90]femstie test [q50]femwtie=[q90]femwtie

  15. Questions? Collaborations? William C. Smith Education Theory and Policy Comparative International Education wcs152@psu.edu

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