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Table 4 Baseline results Oaxaca-Blinder decomposition at the mean

From: Same degree but different outcomes: an analysis of labour market outcomes for native and international PhD students in Australia

 

Pooled: Natives

Native ESB vs

 

vs. Foreigners

Native NESB

Foreign ESB

Foreign NESB

Difference in ln hourly wage:\({\Delta }_{t}\)

.004

(.009)

.002

(.010)

.038

(.024)

.005

(.014)

Nr observations

19,087

16,945

14,043

15,091

Decomposition

    

Explained (E)

 − .045***

(.009)

 − .022***

(.004)

 − .062***

(.018)

 − .063***

(.010)

Unexplained (U)

.037***

(.010)

.031***

(.009)

.074***

(.024)

.058***

(.013)

Interaction

.011

(.010)

-.007*

(004)

.028

(.018)

.010

(.010)

E contributors

    

Gender

 − .009***

(.002)

 − .003***

(.001)

 − .002

(.002)

 − .013***

(.003)

Age

 − .004

(− .015)

 − .004

(.006)

 − .008

(.058)

 − .003

(.008)

NESB

.008

(.007)

–

–

–

Go8

.0001

(.0001)

 − .0001

(.0001)

 − .0001

(.0001)

.0001

(.0001)

Work part-time

.024***

(.002)

.007***

(.001)

.019***

(.005)

.029***

(.003)

  1. All observations with complete information. Mean wage decompositions are carried out using Oaxaca-Blinder method (Stata command: oaxaca). The reference group is the natives ESB. The covariates used in the model are human capital controls (gender, age, age square, if disable, if Aboriginal, if English is main language spoken at home, if graduated from Go8 university, if worked in last year of study, mode of attendance, share of foreign students in same field of education and university), institutional and labour market controls (lagged average wage and lagged unemployment rate by field of study and year), and dummy variables for the survey year and the geographical location of the employer. Adjustment is made for selection into emigration. Standard errors are bootstrapped (50 draws) and clustered by university. The signs *, **, and *** indicate p-values of < .1, < .05, and < .01, respectively