‘Escape from Poverty’ and Occupations...Paul Lambert, University of Stirling Paper presented to the Scottish Social Survey Network, Seminar 7, University of Stirling, 4 June 2009

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Survey Network 4 June 2009 1

‘Escape from Poverty’ and Occupations

Paul Lambert, University of Stirling

Paper presented to the Scottish Social Survey Network, Seminar 7, University of Stirling, 4 June 2009


‘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 work3) Some preliminary results {for Scotland}

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 measurements

• Sociology – primacy of the occupation

• 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 by socially significant links (e.g. household sharing; career; parents)

• ..this may not be the same as current objective conditions...


1) Why occupations matter

Some claims about occupations: i. Occupations matter more than other thingsii. Occupational inequality is mostly one-

dimensional iii. Occupational information resources are

under-used, and this causes bad science *Quote as highlighted in Coxon and Jones (1978: 10)

“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)*

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(i) Occupations matter more

‘Gissa job’; ‘I can do that’

From http://www.bbc.co.uk/liverpool/content/articles/2007/10/09/boys_from_the_blackstuff_feature.shtml

Very important

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8

7

6

5

4

3

2

Not important

male femaleSource: 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)

I'm going to read out a list of things that people value. For each one I'd like you to tell meon a scale of 1 to 10 how important each one is to you.

Importance of 'Having a fulfilling job'


(i) Occupations matter morea) We behave as if they dob) Define our lifestylesc) Define structures of social inequality

b) LifestylesA 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)

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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)


A specially selected table…

Source: BHPS 2007, currently employed adults, predictors of smoking (additional controls for age and gender)

legend: * p<0.05; ** p<0.01; *** p<0.001 ll -3681 -3724 -3685 -3537 -3735 -3719 N 7130 7130 7130 7130 7130 7130 bic 7407 7493 7467 7110 7515 7483 r2_p .03158 .02027 .03058 .01727 .02149 r2_a .05038 qfihhmn -.00014*** qfimn -.00018*** _Ins_sec_8 .8483* _Ins_sec_7 .5991 _Ins_sec_6 .558 _Ins_sec_5 .3827 _Ins_sec_4 -.00227 _Ins_sec_3 -.00575 _Ins_sec_2 -.5701 ns_sec_s -.5622*** mcam -.02872*** Variable CAM NS_2 NS_8 SOC90 PINC HHINC

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[Occupational not geographical inequality – cf. Burrows & Crow 2006]

Scotland

0-20% 21-40% 41-6061-80% 81-90% 91%+

Central Scotland

Source: CASWEB, Census 2001 Output areas.Points show percentile mean average CAMSIS score for males in work.

2001 CensusGeography of occupational advantage


ii) Occupational inequality is mostly one-dimensional

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15

14

13

12

11

10

9

8

7

6

5

4

Ving

tiles

male femalemaximum: 5799

frequencyCAMSIS

routine occupations

semi-routine occupations

lower supervisory and technical

small employers and own account workers

intermediate occupations

lower managerial and professional

higher managerial and professional

male femalemaximum: 9764

frequencyNS-SEC

Source: Labour Force Survey Jan-Mar 2008, current job of employed (18yrs+)

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• Histograms go here 20 40 60 80 100

(1) UK Males, 2001

20 40 60 80 100

(2) UK Females, 2001

20 40 60 80 100

(3) UK Adults, 2001

LabourersFarm workers

Farmers

N=16306

0 20 40 60 80 100

(4) UK: Father's of births 1666-1839

Labourers

N=103354

0 20 40 60 80 100

(5) UK: Fathers of births 1840-1939

Domesticservice

Dressmakers, milliners, etc

N=11075

0 20 40 60 80 100

(6) UK: Females born 1666-1900

N=335287

20 40 60 80 100

(7) USA: Males from 1960

N used=700000

0 20 40 60 80 100

(7) USA: Females from 2000

N=887800

20 40 60 80 100

(7) Romania: Males + Females, 2002

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).


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)

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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

Men's jobs (frequencies)

90 86

85 84

83 82

81 80

78 76

75 74

73 72

71 70

69 66

64 63

62 61

60 59

55 54

53 52

51 50

49 48

46 45

44 43

42 41

40 39

37 36

35 34

33 32

31 30

29 28

27 26

25 24

23 22

21 20

19 18

17 16

15 14

13 12

11 10

0

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

Women's jobs (frequencies)

Source: British Household Panel Survey, last reported current jobs of adults, waves 1-17, N Males = 10223; N Females=9934X-asis shows ISCO-88 Sub-Major group of job; Y-axis shows ISCO-88 3rd and 4th digit codes.


2) How an occupational approach to measuring poverty could work (part A)

– Apparently straightforward decision to make defining a threshold level…

– ..of the average social advantage typically associated with incumbents of the occupational position

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0 20 40 60 80 100

50% Med 60% Med Mean - (1SD - Skew)

MCAMSIS - BHPS adults' most recent job

0 20 40 60 80 100


MCAMSIS - BHPS adults' fathers

16

All jobs, male scale:

threshold=38.51Occupational unit

groups with > 90 in BHPS sample

Remember that these jobs’ scores are cross-classified by employment status

990. All other labourers and related wor 28 162 958. Cleaners, domestics 34.5 2,575 954. Shelf fillers 34.4 416 952. Kitchen porters, hands 37.9 373 931. Goods porters 32.5 125929. Other building and civil engineerin 28.1 250902. All other occupations in farming an 34.4 139 900. Farm workers livestock hand 30.8 280899. Other plant and machine operatives 35.1 581896. Construction and related operatives 35.7 108887. Fork lift and mechanical truck driv 28.9 267 882. Rail drivers railways second 36.5 101 873. Bus and coach drivers 34.5 444 872. Drivers of road goods vehicles 33.1 1,788862. Packers, bottlers, canners, fillers 29.2 857 859. Other assemblers/lineworkers poppy 19.6 98851. Assemblers/lineworkers vehicles met 36.3 209850. Assemblers/lineworkers (electrical/ 36.1 428840. Machine tool operatives (inc. CNC m 36.5 162839. Other metal making treating process 28.2 97825. Plastic process operatives, moulder 28.5 147820. Chemical, gas and petroleum process 36.3 233809. Other food, drink and tobacco proce 28.3 363800. Bakery confectionery process hand f 30.7 116 722. Petrol pump forecourt attendants 37.3 99721. Retail cash desk and check-out oper 35 1,034 622. Bar staff 36.1 928615. Security guards and related occupat 38.1 468596. Coach painters, other spray painter 34.4 90 594. Gardeners, groundsmen/women 33.4 287 590. Glass product and ceramics makers 29.6 118 581. Butchers, meat cutters 33.6 195 570. Carpenters and joiners 37.9 524569. Other printing and related trades n 38.1 178555. Shoe repairers, leather cutters and 37.9 91554. Coach trimmers, upholsterers and ma 36.8 104553. Sewing machinists, menders, darners 31.2 338 537. Welding trades 32.7 357509. Other construction trades n.e.c. bu 34.7 99 507. Painters and decorators 31.3 206501. Roofers, slaters, tilers, sheeters, 32.3 234 500. Bricklayers, masons fixer 34.3 417 441. Storekeepers, warehousemen/women 36.5 1,259440. Stores despatch production control 35.4 142401. Local government clerical officers 38 96 occupation (soc): current main job mean(mcam) N(mcam)

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20 40 60 80 100


FCAMSIS - BHPS females' most recent job

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Female jobs, female scale: threshold = 38.45Occupational unit groups with > 50 females in BHPS sample

958. Cleaners, domestics 26.9 2,374 954. Shelf fillers 37.9 205953. Counterhands, catering assistants h 35.3 813 952. Kitchen porters, hands 33.9 410 941. Messengers, couriers 36.9 78 940. Postal workers, mail sorters 38.1 91 900. Farm workers livestock hand 35.3 104899. Other plant and machine operatives 28.3 86862. Packers, bottlers, canners, fillers 28.9 576861. Inspectors viewers testers examiner 32.1 98 859. Other assemblers/lineworkers poppy 29.6 64851. Assemblers/lineworkers vehicles met 28.4 64850. Assemblers/lineworkers (electrical/ 32 217825. Plastic process operatives, moulder 29.4 74809. Other food, drink and tobacco proce 27.3 139800. Bakery confectionery process hand f 28 74 722. Petrol pump forecourt attendants 38 64 673. Launderers, dry cleaners, pressers 26.3 114 672. Caretakers school 27 100 671. Housekeepers (non-domestic) 33.7 142 644. Care assistants and attendants old 36.7 1,758 641. Hospital ward assistants 32.1 16 622. Bar staff 36.4 654 620. Chefs, cooks hotel supervisor 37.2 139619. Other security protective service o 30.2 69599. Other craft and related occupations 29.4 44591. Glass product and ceramics finisher 30.9 55569. Other printing and related trades n 34 89555. Shoe repairers, leather cutters and 30.3 61553. Sewing machinists, menders, darners 27.2 354 441. Storekeepers, warehousemen/women 35.6 260 occupation (soc): current main job mean(fcam) N(fcam)

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2) How an occupational approach to measuring poverty could work (part B)

• ‘For most individuals, their income and social status is derived directly through their employment; when children, through their parents’employment; for some adults, through their partner’s employment; for retired people, through their and/or their partner’s work histories’(McKnight, 2009: 91)

• An occupational approach to measuring poverty would need to address three critical issues

How to define occupation-based advantage/povertyThe currently non-workingThe weighting of occupations linked to people


A 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 used • For those in household without job..

2) Retrospective questions on last main job 3) Parental jobs used for those aged < 30

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Valid data on occupations (BHPS wave 17, excluding NI)

%poor

16.4

14.8

14.8

11.1

26.5

22.4

m

67935695(1) All

15.966865623(5) + parent’s job if missing or student

(7) [pjd2]

13.562165295(5) + parent’s job if < 30 and missing or student

(6) [pjd]

13.562105293Current/recent Hhld dom job(5) [rjd]

9.246364250Current Hld dom job(4) [cjd]

16.949584414Current or recent job, indv(3) [rji]

11.638323869Current job, indv(2) [cji]

f N femN men


No occupational data - BHPS wave 17 (2007)

1900 1920 1940 1960 1980 2000year of birth

1

1900 1920 1940 1960 1980 2000year of birth

2

1900 1920 1940 1960 1980 2000year of birth

3

1900 1920 1940 1960 1980 2000year of birth

4

1900 1920 1940 1960 1980 2000year of birth

5

1900 1920 1940 1960 1980 2000year of birth

6

1920 1940 1960 1980 2000year of birth

7

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Correlations between measures, BHPS, w17 individuals.These low correlations reflect people making 1 threshold and not another

7256 8905 8417 10900 10907 11746 11809 he_pov -0.0060 0.0960* 0.0251* 0.1238* 0.1216* 0.3565* 0.5857* 7256 8905 8417 10900 10907 11746 11809 hh_pov 0.0096 0.0905* 0.0414* 0.1764* 0.1723* 0.1841* 1.0000 7209 8858 8365 10838 10845 11746 fi_pov 0.0029 0.0716* 0.0477* 0.0610* 0.0591* 1.0000 7701 9372 8886 11503 11511 pjd_p 0.6334* 0.6051* 0.9926* 0.9917* 1.0000 7701 9372 8886 11503 rjd_p 0.6333* 0.6063* 1.0000* 1.0000 7470 8263 8886 cjd_p 0.6261* 0.5391* 1.0000 7701 9372 rji_p 0.9639* 1.0000 7701 cji_p 1.0000 cji_p rji_p cjd_p rjd_p pjd_p fi_pov hh_pov


Selected correlations with binary poverty indicatorsBHPS wave 17 excluding NI, N=12448

3

29

-10

10

10

2

-1

Age

(almost) all adults..

Correlations*100

22

29

9

25

25

23

17

Tenure

-13

-20

-16

-12

-12

-10

-4

Leisure expend.

7

3

1

13

13

17

13

Smokes

-78Equiv. hhld income[eqinc]

-1814HHld income[hinc]

14Pers. Income[pinc]

-614recent / hhld/parent[pjd]

-614recent / hhld[rjd]

-113recent job (n=9379)[rji]

-15current job (n=7700)[cji]

Tumble drier

Health

13


Methodological issues• Handling categorical data (on occupations)

– Scoring occupations: supports standardisation approach for longitudinal data and other comparability issues

– Categorisation of occupations: could be harder to establish in terms of relative occupational advantage over time/place

• Change over time (escape from poverty) – Long term and short term change? – Macro-economic shocks?– Current measures ignore current situation, such as unemployment

• Further occupational measures– Average of occupations over the previous career? – Average of occupations within household (rather than dominance) – Consider weighting occupation score by working status / unemployment?


3) Some preliminary results for Scotland

-2409*9*Current / hhld[cjd]

Who are the ‘poor’ in Scotland?Correlations with poverty indicators (cf. Tsakloglou & Papadopoulos, 2003)

7*

-6*

28*

-1

-1

7*

Young adults

6*

34*

4

16*

16*

10*

Retired


3

-2

-2

0

0

-1

Migrants in year

11*

12*

7*

13*

13*

15*

Sick

9*Equiv. hhld income[eqinc]

-2HHld income[hinc]

2Pers. Income[pinc]

2recent / hhld/ parent[pjd]

2recent / hhld[rjd]

2recent job (n=9379)[rji]

Single parents

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..preliminary results for Scotland..

Determinants of being ‘poor’ in Scotland?Correlations with poverty indicators (‘Ethnicity’ = effect proportional scaling of ethgp, ranked by parental CAMSIS)

2*-2*9*-15*Current / hhld[cjd]

2

-2*

3*

-3*

-3*

-1

Ethnicity (r; UK)

-6*

-16*

-6*

-19*

-18*

-21*

Parental CAMSIS (r)


7*

12*

7*

13*

13*

12*

Own Educ. (r2)

5*Equiv. hhld income[eqinc]

27*HHld income[hinc]

2*Pers. Income[pinc]

5*recent / hhld/ parent[pjd]

5*recent / hhld[rjd]

1*recent job (n=9379)[rji]

Hhld famtype (r2)


(Additional controls for age, gender)

legend: b/t ll -21381 -36127 -18976 -30485 -30490 -37162 -25275 -25529 r2_p .14 .16 .11 .15 .15 .13 .29 .075 N 52581 78623 59378 83301 83383 83491 82674 82674 -1.69 .146 2.03 3.87 3.7 4.44 8.67 8.25 migr -.083 .0058 .11 .17 .16 .18 .43 .37 .615 .926 -.985 -1.65 -.731 13.3 1.27 2.77 yadult .1 .13 -.16 -.25 -.11 1.8 .19 .35 .0505 .0116 -.544 -1.79 -1.61 -2.64 -12.5 5.56 singpar .0051 .00092 -.056 -.15 -.13 -.21 -1.1 .4 2.32 7.42 5.15 9.13 9.11 11.8 16 14 sick .54 .71 .6 .77 .77 .86 1.1 .94 -.417 .148 1.86 2.84 2.84 13.5 14.6 8.7 retir -.1 .011 .22 .21 .21 .79 .95 .59 -4.4 -3.89 -4.56 -3.34 -3.55 -3.76 4.92 -3.38 wave -.026 -.017 -.031 -.017 -.018 -.016 .027 -.018 -3.38 -6.6 -7.43 -15.8 -15.7 17.7 -43.2 -7.78 cohab -.23 -.33 -.57 -.86 -.85 .92 -2 -.38 -28 -34.9 -24.4 -30 -29.9 -20.2 -17.2 -16.7 fedhi_c -.16 -.17 -.14 -.15 -.15 -.076 -.072 -.069 -13.2 -15 -10.9 -13.7 -14.4 2.96 -2.65 -.534 pa_mcam -.027 -.024 -.022 -.024 -.025 .0037 -.004 -.00077 -12.6 -12.2 -4.33 -3.93 -3.92 34.7 6.74 3.96 fem -.69 -.53 -.24 -.18 -.18 1.4 .29 .16 Variable CJI RJI CJD RJD PJD FI_POV HH_POV HE_POV

Logit predictors of being in poverty by alternate measures

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(Additional controls for age, gender)

legend: b/t ll -3533 -4964 -3276 -4849 -4843 -8542 -6071 -5122 r2_p N 8432 16499 6026 11387 11417 15573 11701 7705 1.07 1.13 3.4 8.07 8.37 -5.99 17.5 4.09 cohab .2 .17 .57 1.1 1.1 -.86 2.1 .53 -.556 -.183 -.418 -3.15 -3.12 .86 -4.42 -2.06 lcohab -.1 -.028 -.07 -.43 -.42 .12 -.52 -.27 2.11 4.88 5.24 8.08 7.72 -5.08 3.83 1.01 owner .21 .41 .47 .63 .6 -.32 .26 .068 5.57 9.23 3.49 6.64 6.62 11.6 6.17 6.61 fedhi_c .063 .091 .035 .061 .06 .07 .042 .045 3.32 5.16 2.03 4.12 4.13 -1.27 1.2 -.504 pa_mcam .012 .016 .0065 .012 .012 -.0026 .0027 -.0011 1.19 -.187 .667 1.38 1.1 -18 -1.58 -1.8 fem .12 -.016 .058 .11 .086 -1.2 -.11 -.12 Variable e_CJI e_RJI e_CJD e_RJD e_PJD e_FI_~V e_HH_~V e_HE_~V

Logit predictors of escape from poverty (given in poverty last year)


.2.4

.6.8

1

CJI RJI CJD RJD PJD FI HH HE

% Female Age/100Fath. CAMSIS % Smokers% Own/buy home Leis. exp/5

Never in poverty 1999-2007, N= 1-2k

.2.4

.6.8

1


In and out of poverty 1999-2007, N=500-1000

0.2

.4.6

.8


Mostly in poverty 1999-2007, N=40-307

Poverty profiles 1999-2007: BHPS, Scotland 2007

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5) 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]

• The concept of poverty• Implicitly absolute concept? • Implicitly longitudinal (a thing to escape), but is this over-optimistic?

• 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


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.

• Minnesota Population Center. (2008). Integrated Public Use MicrodataSeries - International: Version 4.0. Minneapolis: University of Minnesota.

• 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.

• 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.

• General Register Office for Scotland, 2001 Census: Standard AreaStatistics (Scotland) [computer file]. ESRC/JISC Census Programme, Census Dissemination Unit, Mimas (University of Manchester)

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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

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• 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.

Documents

‘Escape from Poverty’ and Occupations...Paul Lambert, University of Stirling Paper presented to the Scottish Social Survey Network, Seminar 7, University of Stirling, 4 June 2009