IAOS-OECD 2018 conference, 19-21 September 2018, Paris. More information at: http://www.oecd.org/iaos2018/

1. Estimating fertility differentials by
occupation
Eunkoo Lee
September, 2018

2. 2
Motivation
1. S.Korea’s rapid fertility decline
TFR (1960s) 6.00 TFR (1983): 2.01 TFR (2017): 1.05
0.00
1.00
2.00
3.00
4.00
5.00
6.00
1950 1960 1970 1980 1990 2000 2010 2020
Korea Japan USA UK
TFR
Total Fertility Rate

3. 3
Introduction(1)
1. TFR is the most widely used fertility measure (simple, easy to
understand, age-standardized measure)
2. But limited in providing multidimensional aspects of fertility
3. Have to Pool multiple sources of data to estimate socio-
economic specific TFR (difficult to prone and error)
4. Census has many socio-economic variables and fertility
history in a single data set
5. Use Own-Children method to estimate a socio-economic
specific TFR

4. 4
Introduction(2)
6. Past studies have tried to build models to explore causal
relationship between occupation and its impact on fertility
* Swedish “power couples” working in the public sector have
higher probability to continue childbearing (Dribe and
Stanfors, 2012)
* But Begall and Mills did not find a significant difference in
the Netherlands context
* “Greater autonomy”, “earning potential” and “working
conditions” favorable to childbearing are linked to variations
in fertility levels by occupation

5. 5
Objective
1. Estimate period TFRs by occupational
categories enumerated in the census
2. Use two different census periods to validate
the consistency of the OWCH estimates
(fluctuations due to change in jobs or data
quality issues?)

6. 6
Data
1. 2010 (10%) and 2015 (20%) censuses
2. Use the Korean Standard Classification of
Occupation to create 6 data sets
Code Categories
1 Managers
2 Professionals, technicians and associate professionals
3 Clerks, service and sales workers,
4 Crafts and related trade workers
5 Plant and machine operators and assemblers
6 Skilled agricultural, forestry and fishery workers

7. 7
Method (OWCH)
1967 1970 (Census)
B24
W24
C3, 27
W27
F24(1967) =
B24
W24
Reference : Cho et al. (1986)
Household#, age, sex, relation to head of household

8. 8
Results(1)
1. TFR by OWCH VS Vital Statistics (all women)
1.00
1.10
1.20
1.30
1.40
1.50
1.60
1.70
1.80
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
TotalFertilityRate
Vital statistics Own-children 2010 (10% sample)
Own-children 2000 (2% sample) Own-children 2005 (2% sample)
Source: Lee (2018)

9. 9
Results(2)
2. TFR by occupation (OWCH)
0.30
0.80
1.30
1.80
2.30
1996 1998 2000 2002 2004 2006 2008 2010
Managers
Professionals, technicians and associate
professionals
Clerks, service and sales workers
Crafts and related trade workers
Plant and machine operators and assemblers
Skilled agricultural, forestry and fishery
workers
0.30
0.80
1.30
1.80
2.30
2001 2003 2005 2007 2009 2011 2013 2015
Managers
Professionals, technicians and associate
professionals
Clerks, service and sales workers
Crafts and related trade workers
Plant and machine operators and assemblers
Skilled agricultural, forestry and fishery
workers
<2010 census sample (10%)> <2015 census sample (20%)>

10. 10
Results(3)
2. TFR by occupation (2010 VS 2015)
0.30
0.80
1.30
1.80
2.30
1996
1998
2000
2002
2004
2006
2008
2010
2012
2014
Managers (2010)
Managers (2015)
0.30
0.80
1.30
1.80
2.30
1996
1998
2000
2002
2004
2006
2008
2010
2012
2014
Professionals, technicians and associate
professionals (2010)
Professionals, technicians and associate
professionals (2015)
0.30
0.80
1.30
1.80
2.30
1996
1998
2000
2002
2004
2006
2008
2010
2012
2014
Clerks, service and sales workers (2010)
Clerks, service and sales workers (2015)
0.30
0.80
1.30
1.80
2.30
1996
1998
2000
2002
2004
2006
2008
2010
2012
2014
Crafts and related trade workers (2010)
Crafts and related trade workers (2015)

11. 11
Results(4)
2. TFR by occupation (2010 VS 2015)
0.30
0.80
1.30
1.80
2.30
1996
1998
2000
2002
2004
2006
2008
2010
2012
2014
Plant and machine operators and assemblers
(2010)
Plant and machine operators and assemblers
(2015)
0.30
0.80
1.30
1.80
2.30
1996
1998
2000
2002
2004
2006
2008
2010
2012
2014
Skilled agricultural, forestry and fishery workers
(2010)
Skilled agricultural, forestry and fishery workers
(2015)

12. 12
Conclusion
1. OWCH is useful in estimating period TFR from a census or
large scale survey(given age, relationship to HofH are
accurate)
2. Accuracy, validity, and other close observations on levels and
trends of OWCH TFR estimates can be drawn by comparing
estimates from multiple censuses
3. TFR of “Skilled agricultural , forestry and fishery workers”
is rapidly declining and converging
4. Managerial group(higher occupational status) seems to be
immune to external shocks (for example economic crisis)

13. 13
Conclusion
Thank you!