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

## Presentation on theme: "Simultaneous Quantile Regression William Smith EPSSA Methods Workshop 4/11/13."— Presentation transcript:

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 – 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).

Research Questions 1.How do the effects of social capital differ across occupational prestige levels? 2.How do the effects of social capital differ by gender?

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)

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.

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

Variable Preparation Occupational Prestige Available Social Capital Strong & Weak Ties Interaction Terms gen siop=0 replace siop=63 if isco88==1141| isco88==1142| isco88==1143| isco88==1220 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

OLS Regression Syntax and Output Full Regression Model OLS Output – See handout reg siop i.country female age35_44 age45_54 age55_64 married educyrs sctotal /// primary secondary higher wtiejob stiejob femwtie femstie [pw=weight], cluster (country)

Simultaneous Quantile Regression Syntax Full Simultaneous Quantile Regression Model Simultaneous Quantile Regression Output – See handout sqreg siop australia germany greatbritain hungary norway czechrep poland russia /// newzealand canada phillipines japan spain latvia cyprus chile denmark switzerland brazil /// finland female age35_44 age45_54 age55_64 married educyrs sctotal primary secondary /// higher wtiejob stiejob femwtie femstie, q(.1.3.5.7.9)

Comparison Table

Available Social Capital

Job Search Channel - Female

Comparing Sexes

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

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