‘Escape from Poverty’ and
Occupations
Paul Lambert, Univ. Stirling
Vernon Gayle, Univ. Stirling & ISER, Univ. Essex
Paper presented to the BHPS Research Conference
9-11 July 2009, University of Essex
Lambert: BHPS conf July 2009
1
‘Escape from Poverty’ and Occupations
Or, ‘Occupational disadvantage and its
relation to poverty and poverty transitions’
1) Why occupations matter
2) How an occupational approach to measuring
poverty could work
3) Some preliminary results
Lambert: BHPS conf July 2009
2
Some background
• Research on stratification, inequality, poverty
• Whole distribution, cf the most disadvantaged
– E.g. Poverty as < 50% median; ‘Underclass’ as lacking assets; etc
• Direct v’s indirect measures of poverty (e.g. Gordon, 2000, 2006)
• Absolute or relative measures
• Sociology – primacy of the occupation
• Economics, Social Policy: primacy of income and work-based
income (e.g. UK ‘welfare to work’ policies influenced by income
considerations)
 Notion of a latent, underlying, socially embedded
occupation as an indirect measure of poverty...?
Lambert: BHPS conf July 2009
3
1) Why occupations matter
“Nothing stamps a man as much as his occupation. Daily work
determines the mode of life.. It constrains our ideas, feelings and
tastes” (Goblot, 1961)*
Some claims about occupations:
i. Occupations matter more than other things
ii. Occupational inequality is mostly onedimensional
iii. Occupational information resources are
under-used, and this causes bad science
*Quote as highlighted in Coxon and Jones (1978: 10)
Lambert: BHPS conf July 2009
4
(i) Occupations matter more
Importance of 'Having a fulfilling job'
I'm going to read out a list of things that people value. For each one I'd like you to tell me
on a scale of 1 to 10 how important each one is to you.
Not important
2
3
4
5
6
7
8
9
Very important
male
female
Source: BHPS wave M (2003), Scottish respondents, valid N=2733, variables 'mlfimp*'.
Other options (mean): Health (9.5); money (6.5); children (7.7); job (7.9);
independence (8.7); own own home (7.7); good partnership (8.9); good friends (9.3)
‘Gissa job’; ‘I can do that’
From http://www.bbc.co.uk/liverpool/content/articles/2007/10/09/boys_from_the_blackstuff_feature.shtml
Lambert: BHPS conf July 2009
5
(i) Occupations matter more
a) We behave as if they do
b) Define our lifestyles
c) Define structures of social inequality
b) Lifestyles
A large body of sociological evidence on the social meaning of
occupations – define friendships, marriage, leisure, consumption,
and social reproduction itself (e.g. Devine 2004, Pettinger et al.
2005; Guveli et al. 2007; Archer 2007; Bottero et al. 2009)
“A man’s work is as good a clue as any to the course of his life
and to his social being and identity” (Hughes, 1958)
Lambert: BHPS conf July 2009
6
A specially selected table…
Source: BHPS 2007, currently employed adults, predictors of smoking
(additional controls for age and gender)
Variable
mcam
ns_sec_s
_Ins_sec_2
_Ins_sec_3
_Ins_sec_4
_Ins_sec_5
_Ins_sec_6
_Ins_sec_7
_Ins_sec_8
qfimn
qfihhmn
r2_a
r2_p
bic
N
ll
CAM
NS_2
NS_8
SOC90
PINC
HHINC
-.02872***
-.5622***
-.5701
-.00575
-.00227
.3827
.558
.5991
.8483*
-.00018***
-.00014***
.05038
.03158
7407
7130
-3681
.02027
7493
7130
-3724
.03058
7467
7130
-3685
7110
7130
-3537
.01727
7515
7130
-3735
.02149
7483
7130
-3719
legend: * p<0.05; ** p<0.01; *** p<0.001
Lambert: BHPS conf July 2009
7
.05
Health status in last 6 months
Current
own job
(N~7700)
All
valid
(N~11700)
.01
.02
.03
.04
Recent
own job
(N~10500)
HH dominance
current/recent/par
job
Recent own
(N~11340)
or parents' job
(N~10900)
HH dominance
current or recent job
(N~11310)
HH dominance
current job
(N~9220)
Nulls
NS-SEC2
HH Income
CAMSIS
SOC90
Pers. Educ12
Source: BHPS Wave 17 (2007), adult interviews (Britain), unweighted
Lambert: BHPS conf July 2009
NS-SEC8
Pers. income
HH Educ12
8
5 6
.15
7
.06
.04
4
5 6
7
Smokes
1 2 3 4 5 6
7
0
1
2 3 4 5 6 7
0
0
0
.05
.05
Saves money at present
1 2 3 4 5 6 7
.01 .02.03 .04
Father's occupation
1 2 3 4 5 6 7
.1
Own or buying home
1 2 3 4 5 6 7
.15
.1
.15
0
0
1 2 3
.01.02.03.04.05.06
4
.05
1 2 3
Expenditure on leisure
.1
.01
GHQ well-being
Has a tumble drier
.02
Satisfaction with life
1 2 3 4 5 6 7
0
Subjective health
1 2 3 4 5 6 7
.005 .015 .025
.01
.02
.01.02 .03.04 .05
R-2 Gains with occupation-based measures
Source: BHPS Wave 17 adult interviews (Britain). Unweighted N varies by subsamples used.
Graph shows gain in R2 due to occupation-based measures over and above regression with gender and quadratic age controls.
(unshaded columns show R2 due to gender and quadratic age only).
Populations analysed are individuals with:
1. Current own job, N~7700; 2. Recent own job, N~10500; 3. Recent own job or parents job, N~10900;
4. HH Dominance (1), N~9220; 5. HH dominance (2), N~11310; 6. HH Dominance (3), N~11340; 7. All valid individuals, N ~11700
Lambert: BHPS conf July 2009
9
c) Occupations define structures of
social inequality
– Occupations are convenient markers of major social inequalities
– Occupations (division of labour) are the primary driver of the
structure of social inequality
Empirical evidence..
• Reaffirms economic significance of jobs
• (McGovern et al, 2007; )
• Rejects thesis of diminished structural significance of
occupations in modern society
• (Blossfeld et al., 2006)
• Highlights centrality of occupations in contours of other
social divisions
• (e.g. immigration - Waldinger and Lichter, 2003)
Lambert: BHPS conf July 2009
10
[Occupational not geographical inequality – cf. Burrows & Crow 2006]
Geography of occupational advantage
2001 Census
Scotland
Central Scotland
0-20%
21-40%
41-60
61-80%
81-90%
91%+
Source: CASWEB, Census 2001 Output areas.
Points show percentile mean average CAMSIS
scoreBHPS
for males
work.
Lambert:
confinJuly
2009
11
Occupations ‘stamp’ the life-course…
(i) Stability of measures over time: Design effects for income and occupations
BHPS annual panel, waves 9-17
England
Wales
Scotland
Personal income [fimn]
4.71
12157; 5.5
3.91
4356; 5.4
4.49
4691; 5.4
Household inc. [fihhmn]
4.09
12339; 5.6
3.37
4457; 5.4
4.00
4766; 5.4
Current job CAMSIS
5.51
8956; 5.0
5.40
2874; 4.8
5.36
3341; 4.9
Current / recent job
5.51
11748; 5.8
5.39
4092; 5.6
5.36
4525; 5.6
HH dom. current job
5.06
10386; 5.2
4.83
3499; 4.9
4.99
3864; 5.0
HH dom current/recent
5.07
12442; 5.6
4.83
4411; 5.4
4.99
4766; 5.4
Cells show DEFF statistics (Kish 1965, via Stata’s svymean), & N indvs; mean(#responses/#indvs)
Lambert: BHPS conf July 2009
12
..occupations and life-time lifestyles..
0
.5
1
1.5
2
R2 gains in repeated measures predictors of smoking
CAMSIS job
NS2 job
CAMSIS HH
PA panel model
NS2 HH
Indv Income HH Income
PA panel plus lag effect
Source: BHPS waves 9-17, adults from Britain. [A somewhat contrived outcome]
Graph shows 100*pseudo-R2 increments adding explanatory vars to basic model with
controls for gender and quadratic age (population average logit model).
Lambert: BHPS conf July 2009
13
9 year average occupation/income, by housing tenure
2
0
-2
-4
-4
-2
0
2
4
HH dominance current/recent CAMSIS
4
Current job CAMSIS
0
1000
2000
3000
0
2000
3000
Social housing
Private renting
10
Buying home
Own home
Buying home
Social housing
Private renting
0
0
5
Own home
15
Household income (to 97th ptile)
5
10
15
Personal income (to 97th ptile)
1000
0
1000
2000
3000
0
1000
2000
Source: BHPS, waves 9-17, unweighted. Ever present always employed adults (N=3193).
Graph shows within 9-year mean andLambert:
min-maxBHPS
rangeconf
for July
each2009
adult
3000
14
ii) Occupational inequality is mostly onedimensional
CAMSIS
NS-SEC
frequency
frequency
4
5
higher managerial and professional
6
7
lower managerial and professional
8
Vingtiles
9
intermediate occupations
10
11
small employers and own account workers
12
13
lower supervisory and technical
14
15
semi-routine occupations
16
17
18
routine occupations
male
female
maximum: 5799
male female
maximum: 9764
Source: Labour Force Survey Jan-Mar 2008, current job of employed (18yrs+)
Lambert: BHPS conf July 2009
15
(1) UK Males, 2001
(2) UK Females, 2001
• Histograms go here
20
40
60
80
100
(4) UK: Father's of births 1666-1839
Labourers
Farm workers
20
40
60
80
(3) UK Adults, 2001
100
(5) UK: Fathers of births 1840-1939
Farmers
20
40
60
80
100
(6) UK: Females born 1666-1900
N=103354
N=11075
Domestic
service
Dressmakers, milliners, etc
N=16306
0
20
40
60
80
100
Labourers
0
(7) USA: Males from 1960
20
40
60
80
100
(7) USA: Females from 2000
0
20
40
60
80
100
(7) Romania: Males + Females, 2002
N used=700000
N=335287
N=887800
20
40
60
80
100
0
20
40
60
80
100
20
40
60
80
100
CAMSIS scales have mean 50, sd 15 for derivation population. Histogram bins=2 points. Kdensity width=15.
Source: (1),(2),(3): UK Census Samples of Annonymised Records, Individual samples, aggregated occupational minor groups;
(4),(5),(6): Family History Study (Prandy & Bottero, 1998);
(7),(8),(9): IPUMS-International (Minnesota Population Center, 2008).
Lambert: BHPS conf July 2009
16
iii) Occupational information resources are
under-used, and this causes bad science
• Detailed occupational data is important
• e.g. Weeden & Grusky (2005)
• Handling of detailed occupational data is
generally poor
– Re-coding to simplified categorisations
– Ignoring complex data (e.g. careers; gender seg.)
• For more and more (and more) on this see
– www.dames.org.uk
– Lambert et al (2007)
Lambert: BHPS conf July 2009
17
Men's jobs (frequencies)
1 11 12 13 20 21 22 23 24 31 32 33 34 41 42 51 52 61 71 72 73 74 81 82 83 91 92 93
maximum: 335
0
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
39
40
41
42
43
44
45
46
48
49
50
51
52
53
54
55
59
60
61
62
63
64
66
69
70
71
72
73
74
75
76
78
80
81
82
83
84
85
86
90
Women's jobs (frequencies)
1 11 12 13 20 21 22 23 24 31 32 33 34 41 42 51 52 61 71 72 73 74 81 82 83 91 92 93
maximum: 895
Source: British Household Panel Survey, last reported current jobs of adults, waves 1-17, N Males = 10223; N Females=9934
X-asis shows ISCO-88 Sub-Major group of job; Y-axisLambert:
shows ISCO-88
3rdconf
and 4th
digit
codes.
BHPS
July
2009
18
2) How an occupational approach to
measuring poverty could work
• Notion of a latent, underlying, socially embedded
occupation as an indirect measure of poverty...?
• CAMSIS scales: relative social advantage typically associated with
incumbents of occupational positions over lifetime (Stewart et al,
1980)
• Non-working have ‘latent’ occupations easily measured by socially
significant links (e.g. household sharing; career; parents)
• ..this may not be the same as current objective conditions...
– Apparently straightforward decision to make defining
a threshold level of the average social advantage
typically associated with incumbents of the
occupational position
Lambert: BHPS conf July 2009
19
MCAMSIS - BHPS adults' most recent job
0
20
40
50% Med
60
60% Med
80
100
Mean - (1SD - Skew)
MCAMSIS - BHPS adults' fathers
0
20
40
50% Med
60
60% Med
Lambert: BHPS conf July 2009
80
100
Mean - (1SD - Skew)
20
occupation (soc): current main job
mean(mcam)
N(mcam)
401. Local government clerical officers
440. Stores despatch production control
441. Storekeepers, warehousemen/women
500. Bricklayers, masons fixer
501. Roofers, slaters, tilers, sheeters,
507. Painters and decorators
509. Other construction trades n.e.c. bu
537. Welding trades
553. Sewing machinists, menders, darners
554. Coach trimmers, upholsterers and ma
555. Shoe repairers, leather cutters and
569. Other printing and related trades n
570. Carpenters and joiners
581. Butchers, meat cutters
590. Glass product and ceramics makers
594. Gardeners, groundsmen/women
596. Coach painters, other spray painter
615. Security guards and related occupat
622. Bar staff
721. Retail cash desk and check-out oper
722. Petrol pump forecourt attendants
800. Bakery confectionery process hand f
809. Other food, drink and tobacco proce
820. Chemical, gas and petroleum process
825. Plastic process operatives, moulder
839. Other metal making treating process
840. Machine tool operatives (inc. CNC m
850. Assemblers/lineworkers (electrical/
851. Assemblers/lineworkers vehicles met
859. Other assemblers/lineworkers poppy
862. Packers, bottlers, canners, fillers
872. Drivers of road goods vehicles
873. Bus and coach drivers
882. Rail drivers railways second
887. Fork lift and mechanical truck driv
896. Construction and related operatives
899. Other plant and machine operatives
900. Farm workers livestock hand
902. All other occupations in farming an
929. Other building and civil engineerin
931. Goods porters
952. Kitchen porters, hands
954. Shelf fillers
958. Cleaners, domestics
990. All other labourers and related wor
38
35.4
36.5
34.3
32.3
31.3
34.7
32.7
31.2
36.8
37.9
38.1
37.9
33.6
29.6
33.4
34.4
38.1
36.1
35
37.3
30.7
28.3
36.3
28.5
28.2
36.5
36.1
36.3
19.6
29.2
33.1
34.5
36.5
28.9
35.7
35.1
30.8
34.4
28.1
32.5
37.9
34.4
34.5
28
96
142
1,259
417
234
206
99
357
338
104
91
178
524
195
118
287
90
468
928
1,034
99
116
363
233
147
97
162
428
209
98
857
1,788
444
101
267
108
581
280
139
250
125
373
416
2,575
162
All jobs, male
scale:
threshold=38.51
Occupational unit
groups with > 90 in
BHPS sample
Remember
that these
jobs’ scores
are crossclassified by
employment
status
21
FCAMSIS - BHPS females' most recent job
20
40
50% Med
60
60% Med
Lambert: BHPS conf July 2009
80
100
Mean - (1SD - Skew)
22
Female jobs, female scale: threshold = 38.45
Occupational unit groups with > 50 females in BHPS sample
occupation (soc): current main job
mean(fcam)
N(fcam)
441. Storekeepers, warehousemen/women
553. Sewing machinists, menders, darners
555. Shoe repairers, leather cutters and
569. Other printing and related trades n
591. Glass product and ceramics finisher
599. Other craft and related occupations
619. Other security protective service o
620. Chefs, cooks hotel supervisor
622. Bar staff
641. Hospital ward assistants
644. Care assistants and attendants old
671. Housekeepers (non-domestic)
672. Caretakers school
673. Launderers, dry cleaners, pressers
722. Petrol pump forecourt attendants
800. Bakery confectionery process hand f
809. Other food, drink and tobacco proce
825. Plastic process operatives, moulder
850. Assemblers/lineworkers (electrical/
851. Assemblers/lineworkers vehicles met
859. Other assemblers/lineworkers poppy
861. Inspectors viewers testers examiner
862. Packers, bottlers, canners, fillers
899. Other plant and machine operatives
900. Farm workers livestock hand
940. Postal workers, mail sorters
941. Messengers, couriers
952. Kitchen porters, hands
953. Counterhands, catering assistants h
954. Shelf fillers
958. Cleaners, domestics
35.6
27.2
30.3
34
30.9
29.4
30.2
37.2
36.4
32.1
36.7
33.7
27
26.3
38
28
27.3
29.4
32
28.4
29.6
32.1
28.9
28.3
35.3
38.1
36.9
33.9
35.3
37.9
26.9
260
354
61
89
55
44
69
139
654
16
1,758
142
100
114
64
74
139
74
217
64
64
98
576
86
104
91
78
410
813
205
2,374
23
Assigning occupations to all:
parsimonious cross-sectional strategy..?
1) Modified Household ‘dominance’ approach
• Use the most advantaged occupation within the
household, prioritising ft work (e.g. Erikson, 1984),
and recognising gender of occupation-holder
• For students, parental jobs may be used
•
For those in household without job..
2) Retrospective questions on last main job
3) Parental jobs used for those aged < 30
4) ?Possible weighting factor for unemployment dur.?
Lambert: BHPS conf July 2009
24
Valid data on occupations (BHPS wave 17, excluding NI)
Using male CAMSIS threshold=36.0, female threshold=38.5
% poor
N men
N fem
m
f
5695
6793
Income based [HH equiv.]
5424
6385
9.1
10.6
(1) [cji]
Current job, indv
3869
3832
15.1
11.6
(2) [rji]
Current or recent job, indv.
4968
5610
18.9
17.8
(3) [pji]
(2) + parents job if <30 &
missing, PT or student
5123
5767
18.3
10.4
(4) [cjd]
Current Hld dom job
4393
4835
9.2
7.5
(5) [rjd]
Current/recent Hhld dom job
5294
6053
10.6
9.3
(6) [pjd]
(5) + parent’s job if < 30 &
missing, PT or student
5306
6068
11.0
9.8
(93%)
(89%)
(6) + [hhp] if (6) is missing
5671
6729
11.3
10.6
(99%)
(99%)
All valid respondents
[hhp]
(7) [pad]
25
No occupational data - BHPS wave 17 (2007)
All adults
1900
1920
1940
1960
doby: year of birth
1 [cji]
1980
2000
1900
1920
2 [rji]
1900
1920
1940
1960
doby: year of birth
1920
1940
1960
doby: year of birth
1980
2000
1900
1920
1920
1940
1960
doby: year of birth
2000
1940
1960
doby: year of birth
1980
2000
1980
2000
5 [rjd]
1980
2000
1900
1920
6 [pjd]
1900
1980
3 [pji]
4 [cjd]
1900
1940
1960
doby: year of birth
1940
1960
doby: year of birth
7 [pad]
1980
2000
1920
Lambert: BHPS conf July 2009
1940
1960
doby: year of birth
1980
2000
26
Correlations between measures, BHPS, w17 individuals.
These low correlations reflect people making 1 poverty threshold and not another
Lambert: BHPS conf July 2009
27
Selected correlations with binary poverty indicators
BHPS wave 17 excluding NI, N=12448
Correlations*100
qAge
Smokes
Health
Tenure
Leisure
expend.
Tumble
drier
[cji]
current job (n=7701)
6
13
5
15
-6
-1
[rji]
recent job (n=10578)
7
16
13
23
-11
-1
(almost) all adults..
[rjd]
recent / hhld
8
12
9
20
-10
-4
[pjd]
recent / hhld/parent
8
12
9
20
-10
-3
[pad]
[pjd] + [eqinc] if miss
10
11
9
18
-11
-5
[pinc]
Pers. Income
23
1
4
9
-16
1
[hinc]
HHld income
35
3
14
29
-20
-18
[eqinc]
Equiv. hhld income
14
7
8
22
-13
-7
28
3) Some preliminary results
Who are the ‘poor’ in Britain?
% classed as ‘poor’, with significance of correlation (cf. Tsakloglou & Papadopoulos, 2003)
[rji]
recent job (n=10551) (18%)
Retired
Sick
Young
adults
Single
parents
Migrants
in year
24*
44*
19
22*
15*
(almost) all adults..
[cjd]
Current / hhld (8%)
13*
18*
7
9
10
[rjd]
recent / hhld (10%)
14*
23*
9
12*
11
[pjd]
recent / hhld/ parent (10%)
14*
24*
12
13*
11
[pad]
[pjd] + [eqinc] (11%)
14*
24*
12
14*
11
[pinc]
Pers. Income (23%)
28
32*
50*
24
25
[hinc]
HHld income (14%)
37*
30*
10*
14
8
[eqinc]
Equiv. hhld income (10%)
15*
21*
14*
15*
16*
Lambert: BHPS conf July 2009
29
..preliminary results..
Determinants of being ‘poor’?
Correlations (and significance) with poverty indicators
(‘Ethnicity’ = effect proportional scaling of ethgp, ranked by parental CAMSIS)
[rji]
recent job (n=9379)
Parental
CAMSIS (r)
Own
Educ. (r2)
Ethnicity
(r; UK)
Hhld fam
type (r2)
-20*
11*
-1
1*
(almost) all adults..
[cjd]
Current / hhld
-13*
7*
-3*
0*
[rjd]
recent / hhld
-15*
8*
-3*
1*
[pjd]
recent / hhld/ parent
-18*
8*
-3*
1*
[pad]
[pjd]+[eqinc]
-16*
8*
-2*
1*
[pinc]
Pers. Income
0
5*
3*
1*
[hinc]
HHld income
-11*
9*
-2*
27*
[eqinc]
Equiv. hhld income
-4*
4*
0
3*
Lambert: BHPS conf July 2009
30
Logit predictors of being in poverty by alternate measures
Variable
fem
pa_mcam
fedhi_c
cohab
wave
retir
sick
singpar
yadult
migr
N
r2_p
ll
CJI
RJI
CJD
RJD
PJD
PAD
FI_POV
HH_POV
HE_POV
-.21
-4.25
-.024
-12.7
-.14
-27.3
-.056
-.879
-.035
-5.94
-.092
-.374
.51
2.33
.19
2.29
.21
1.9
-.028
-.61
-.1
-2.47
-.023
-15
-.15
-34
-.23
-4.76
-.017
-4.07
.042
.605
.72
7.85
.1
1.55
.065
.736
.09
2.53
-.22
-4.31
-.024
-12.3
-.12
-22.3
.0047
.0635
-.03
-4.64
-.13
-1.01
.31
2.62
.089
.961
-.22
-2.05
.17
3.63
-.18
-4
-.024
-13.8
-.12
-26.2
-.51
-9.27
-.01
-1.94
-.11
-1.37
.45
4.99
-.15
-1.88
-.49
-4.86
.2
4.7
-.16
-3.63
-.031
-17
-.12
-25.4
-.52
-9.57
-.014
-2.66
-.11
-1.41
.44
4.86
-.12
-1.54
-.1
-1.1
.14
3.44
-.14
-3.26
-.028
-16.4
-.11
-25.3
-.5
-9.54
-.015
-2.98
-.12
-1.57
.44
4.96
-.11
-1.43
-.12
-1.25
.13
3.33
1.1
33.9
.0048
4.27
-.075
-22
.63
14.6
-.039
-9.85
.83
14
.82
11.8
-.27
-4.54
2.2
26.5
-.058
-1.55
.3
7.56
-.0034
-2.44
-.066
-17.4
-1.9
-41.7
.033
6.5
.93
14.6
1.1
16.4
-.82
-10.4
-.13
-1.33
.84
19.4
.16
4.4
-.0015
-1.15
-.064
-17
-.36
-7.84
-.017
-3.24
.57
8.51
.94
14.3
.43
7
.26
3.06
.62
15.9
61290
.11
-23042
92577
.13
-40770
73167
.082
-21402
95207
.1
-30084
95322
.11
-30712
97728
.1
-32063
95457
.14
-44784
94560
.27
-28542
94560
.071
-29187
legend: b/
(Model shows coefficient B and t statistics for logit models predicting whether individuals are in poverty by
alternative measures. BHPS waves 9-17 with HW standard error adjustment. Additional controls for age, gender).
Lambert: BHPS conf July 2009
31
Logit predictors of escape from poverty (given in poverty last year)
Variable
fem
pa_mcam
fedhi_c
owner
lcohab
cohab
N
r2_p
ll
e_CJI
e_RJI
e_CJD
e_RJD
e_PJD
e_PAD
e_FI_~V
e_HH_~V
e_HE_~V
-.24
-2.62
.0069
2.06
.047
4.56
.26
2.85
-.091
-.547
.07
.411
-.31
-4.06
.0099
3.59
.076
8.91
.56
7.53
-.15
-1.16
.077
.586
.19
2.4
.0051
1.74
.034
3.89
.21
2.63
.01
.0687
.1
.684
.15
2.03
.0069
2.47
.04
4.7
.46
6.09
-.14
-1.08
.49
3.74
.076
1.02
.012
4.11
.031
3.74
.34
4.65
-.021
-.159
.54
4.14
.15
2.03
.015
5.47
.042
5.03
.46
6.21
-.038
-.295
.49
3.8
-.99
-16.6
-.0037
-1.97
.083
14.6
-.51
-8.97
.25
2.02
-.74
-5.78
-.12
-1.92
.0023
1.08
.03
4.94
.54
8.39
-.39
-3.6
1.8
17
-.11
-1.78
.00015
.0728
.036
5.76
.26
4.15
-.16
-1.39
.4
3.39
7921
16644
6161
9418
9667
10132
18992
12516
8638
-3693
-5705
-3632
-4704
-4797
-5261
-10162
-6623
-5754
legend: b/t
(Model shows coefficient B and t statistics for random effects logit models (xtlogit) predicting whether individuals
leave poverty given they were in the previous year, by alternative measures. BHPS waves 9-17. Additional controls
for age, gender).
Lambert: BHPS conf July 2009
32
Poverty profiles 1999-2007: BHPS, 2007
0 1 2 3 4
Never in poverty 1999-2007, N= 1-2k
CJI RJI CJD RJD PJD
FI
HH HE PAD
% Female
Age/100
Fath. CAMSIS
% Smokers
% Own/buy home
Leis. exp/5
Mostly in poverty 1999-2007, N=40-307
0
0
.2
.5
.4
1
.6
1.5
.8
2
In and out of poverty 1999-2007, N=500-1000
CJI RJI CJD RJD PJD
FI
HH HE PAD
CJI RJI CJD RJD PJD FI
Lambert: BHPS conf July 2009
HH HE PAD
33
4) Conclusions
‘Escape from social disadvantage’ and occupations
• Occupational measures as feasible indirect indicators of
relative poverty/disadvantage for the whole population
• Reduce demographic/life-stage influence cf. income measures
• Measurement challenges
• Reflecting current circumstances [vulnerability to poverty, Gordon 2006]
• Other alternative measures (e.g. using Unemployment data)
• The concept of poverty
• Implicitly absolute concept?
• Implicitly longitudinal (a thing to escape), but is this over-optimistic?
• Measurement & social science disciplines [Grusky & Kanbur 2007]
• What determines social disadvantage/poverty?
•
•
•
•
Disadvantage is more stable than income-based measures show
Education and social background matters more than is recognised
Family status / demographics matter less
‘Welfare to work’ is flawed?
Lambert: BHPS conf July 2009
34
The bottom line…
 There are a core of people who experience social
disadvantage which is often longer term & fairly stable
 We ought to identify and provide welfare support to the
disadvantaged
– If we use income based poverty indicators we often identify the
wrong people
• ..and make poor policy decisions..
• E.g. of the UK’s ‘Working Families Tax Credits’
– Occupational data might be parsimoniously used as an
alternative indicator of social disadvantage
Lambert: BHPS conf July 2009
35
Data sources
•
University of Essex, & Institute for Social and Economic Research. (2009). British
Household Panel Survey: Waves 1-17, 1991-2008 [computer file], 5th Edition.
Colchester, Essex: UK Data Archive [distributor], March 2009, SN 5151.
•
General Register Office for Scotland, 2001 Census: Standard Area Statistics
(Scotland) [computer file]. ESRC/JISC Census Programme, Census Dissemination
Unit, Mimas (University of Manchester)
Halpin, B. (2006). British Household Panel Survey Combined Work-Life History Data,
1990-2005 [computer file]. 5th ed. Colchester, Essex: Institute for Social and
Economic Research, [original data producer(s)]. UK Data Archive [distributor],
November 2006. SN: 3954. .
Minnesota Population Center. (2008). Integrated Public Use Microdata Series International: Version 4.0. Minneapolis: University of Minnesota.
Office for National Statistics. Social and Vital Statistics Division and Northern Ireland
Statistics and Research Agency. Central Survey Unit, Quarterly Labour Force Survey,
January - March, 2008 [computer file]. 2nd Edition. Colchester, Essex: UK Data
Archive [distributor], October 2008. SN: 5851.
Prandy, K., & Bottero, W. (1998). The use of marriage data to measure the social
order in nineteenth-century Britain. Sociological Research Online, 3(1), U43-U54.
•
•
•
•
Lambert: BHPS conf July 2009
36
References
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
Archer, M. S. (2007). Making Our Way Through the World: Human Reflexivity and Social Mobility. Cambridge:
Cambridge University Press.
Blossfeld, H. P., Mills, M., & Bernardi, F. (Eds.). (2006). Globalization, Uncertainty and Men's Careers: An
International Comparison. Cheltenham: Edward Elgar.
Bottero, W., Lambert, P. S., Prandy, K., & McTaggart, S. (2009). Occupational Structures: The Stratification Space
of Social Interaction. In K. Robson & C. Sanders (Eds.), Quantifying Theory: Pierre Bourdieu (pp. 141-150).
Amsterdam: Springer Netherlands.
Burrows, R., & Crow, G. (2006). Geodemographics, Software and Class. Sociology, 40(5), 793-812.
Coxon, A. P. M., & Jones, C. L. (1978). The Images of Occupational Prestige: A Study in Social Cognition. London:
MacMillan Press.
Devine, F. (2004). Class Practices: How parents help their children get good jobs. Cambridge: Cambridge
University Press.
Erikson, R. (1984). Social Class of Men, Women and Families. Sociology, 18(4), 500-514.
Goblot, E. (1961). Class and Occupation. In T. Parsons (Ed.), Theories of Society. New York: Free Press.
Gordon, D., Pantazis, C., & Townsend, P. (2000). Absolute and overall poverty: A European history and proposal
for measurement. In D. Gordon & P. Townsend (Eds.), Breadline Europe : The measurement of poverty (pp. 79106). Bristol: The Policy Press.
Gordon, D. (2006). The concept and measurement of poverty. In C. Pantazis, D. Gordon & R. Levitas (Eds.),
Poverty and Social Exclusion in Britain: The Millenium Survey. Bristol: The Policy Press.
Guveli, A., Need, A., & De Graaf, N. D. (2007). Socio-political, cultural and economic preferences and behaviour of
the social and cultural specialists and the technocrats. Social class or education? . Social Indicators Research,
81(3), 597-631.
Hughes, E. C. (1958). Men and their Work. Glencoe, Ill.: Free Press.
Lambert, P. S., Tan, K. L. L., Turner, K. J., Gayle, V., Prandy, K., & Sinnott, R. O. (2007). Data Curation Standards
and Social Science Occupational Information Resources. International Journal of Digital Curation, 2(1), 73-91.
McGovern, P., Hill, S., Mills, C., & White, M. (2007). Market, Class and Employment. Oxford: Oxford University
Press.
Pettinger, L., Parry, J., Taylor, R., & Glucksmann, M. (Eds.). (2005). A New Sociology of Work? London::
Blackwell.
Stewart, A., Prandy, K., & Blackburn, R. M. (1980). Social Stratification and Occupations. London: MacMillan.
Tsakloglou, P., & Papadopoulos, F. (2003). Poverty, material deprivation and multi-dimensional disadvantage
during four life stages: Evidence from the ECHP. In M. Barnes, C. Heady, S. Middleton, J. Millar, F. Papadopoulos,
G. Room & P. Tsakloglou (Eds.), Poverty and Social Exclusion in Europe. Cheltenham: Edward Elgar.
Waldinger, R., & Lichter, M. I. (2003). How the Other Half Works: Immigration and the Social Organization of
Labor. Berkeley: University of California Press.
Weeden, K. A., & Grusky, D. B. (2005). The Case for a New Class Map. American Journal of Sociology, 111(1),
141-212.
37