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

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

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 1.How do the effects of social capital differ across occupational prestige levels? 2.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 with recorded occupation – Used country weights to create large sample that included participants in 21 countries All analysis done in STATA

7 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

8 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)

9 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( )

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


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