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Journal for Labour Market Research

Table 6 Main results Firpo-Fortin-Lemieux decomposition along the wage distribution

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

 

Pooled Natives-Foreigners

Natives ESB-NESB

ESB Natives-Foreigners

ESB Nat.-NESB Foreigners

 

25q

50q

75q

25q

50q

75q

25q

50q

75q

25q

50q

75q

STEM

            

\({\Delta }_{t}, {\Delta }_{t}\left(\tau \right)\)

 − .067***

(.022)

 − .077***

(.015)

 − .019

(.012)

.0001

(.018)

 − .030**

(.014)

 − .031**

(.015)

 − .046

(.044)

 − .034

(.038)

.021

(.021)

 − .077***

(.025)

 − .077***

(.018)

 − .032***

(.013)

Explained (E)

 − .073***

(.008)

 − .089***

(.007)

 − .072***

(.007)

 − .017

(.014)

 − .048***

(.008)

 − .043***

(.010)

 − .080***

(.009)

 − .100***

(.009)

 − .092***

(.011)

 − .063***

(.019)

 − .085***

(.014)

 − .051***

(.010)

Unexplained (U)

.005

(.021)

.012

(.014)

.052***

(.012)

 − .018

(.012)

.018*

(.011)

.011

(.010)

.034

(.045)

.066*

(.039)

.113***

(.022)

 − .014

(.015)

.008

(.015)

.018**

(.008)

Humanities

\({\Delta }_{t}, {\Delta }_{t}\left(\tau \right)\) − adj. for selection

.004

(.051)

.046*

(.025)

.042

(.028)

 − .026

(.030)

 − .028

(.027)

.035*

(.021)

.010

(.102)

.042

(.116)

.146

(.094)

.058

(.082)

.075

(.050)

.026

(.051)

Explained (E)

 − .035***

(.013)

 − .068***

(.012)

 − .040***

(.012)

 − .020

(.018)

 − .034**

(.016)

.016

(.017)

 − .023*

(.013)

 − .046***

(.015)

 − .023

(.015)

.071

(.044)

.016

(.029)

.031

(.040)

Unexplained (U)

.039

(.051)

.113***

(.027)

.082***

(.027)

 − .006

(.023)

.006

(.020)

.019

(.016)

.033

(.098)

.088

(.117)

.169*

(.090)

 − .013

(.062)

.059

(.044)

 − .005

(.044)

Medicine and Health

\({\Delta }_{t}, {\Delta }_{t}\left(\tau \right)\)

.115

(.091)

.028

(.052)

.033

(.058)

.045

(.037)

.057**

(.029)

.031

(.032)

 − .518

(4.72)

 − .417

(2.32)

 − 1.28

(3.59)

.114

(.167)

.042

(.092)

.062

(.076)

Explained (E)

 − .010

(.020)

 − .007

(.015)

.012

(.015)

.020

(.024)

.023

(.020)

.0003

(.020)

 − .009

(.025)

.006

(.021)

.031

(.023)

.039

(.038)

.046

(.034)

.064*

(.037)

Unexplained (U)

.125

(.093)

.035

(.052)

.021

(.059)

.024

(.029)

.034*

(.018)

.031

(.024)

 − .510

(4.73)

 − .422

(2.32)

 − 1.31

(3.60)

.076

(.158)

 − .004

(.080)

 − .001

(.070)

  1. Quantile wage gap decompositions using the two-step method proposed by Firpo, Fortin, and Lemieux (2011). 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
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