1/L The Impact of a Temporary Help Job: An Analysis of Outcomes for Participants in Three Missouri...

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The Impact of a Temporary Help Job: An Analysis of Outcomes for Participants in

Three Missouri Programs

Carolyn J. HeinrichLaFollette School of Public AffairsUniversity of Wisconsin-Madison

Peter R. MueserUniversity of Missouri-Columbia

Department of Economics

Kenneth R. TroskeUniversity of Kentucky

Department of Economicsand

Center for Business and Economic Research

October 2006

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Introduction

• Employment in temporary help service (THS) firms increased from less than 0.5% in 1982 to over 2.5% by 2004

• Growth was even more dramatic among the most disadvantaged

• Increasingly used as a tool to help those facing difficulties obtaining employment

– Complementary with “work first” programs

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• Temporary help service provider hires worker

• It then contracts with firm for firm to “use” worker

• Worker activity occurs at firm site

• Temporary help service receives payment from firm

• Temp help service provider pays wages, taxes, benefits, etc.

• Firm has no legal employment relationship with worker

• THS is classified as an industry even though work site is in other industry

Introduction (continued) Definition of THS

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• Two views of temp help

– Previously dominant view of temp help

• less job stability

• fewer fringe benefits

• lower wages

– Alternative view

• path to permanent and stable employment

• access to informal training and screening

• consistent with “work-first” strategy

Introduction (continued)

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Introduction (continued)• Our analysis looks at effects of holding a THS job for

those entering three federal programs in Missouri in two different years—1997 and 2001:

– Temporary Assistance for Needy Families (TANF): Single mothers with very low incomes

– Job Training—Job Training Partnership Act (JTPA) in 1997 and Workforce Investment Act (WIA) in 2001: Low income adults & displaced workers

– Employment Exchange (“job service”) Anyone seeking a job

• For each program, individuals are likely to be facing employment difficulties

• But level of job skills differs across program

• As does the severity of the employment shock

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Literature

• Empirical studies confirm that temp help services jobs

– pay lower wages

– offer fewer work hours

– shorter in tenure

– less likely to provide fringe benefits (e.g. health insurance, pensions)

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Literature (continued)

• Causal impact?

– Most studies that look at impact find small or no negative effects of holding a temp help service job on eventual employment success (Lane et al. 2002; Heinrich et al. 2005; Anderson et al. 2002; Segal and Sullivan 1997; Booth et al. 2000)

– One exception is Autor and Houseman (2005) who find that working in temp help does not lead to eventual employment success

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Our contribution

• We examine whether any other industry serves a similar role as THS

• We look at 3 classes of workers who differ by their level of market disadvantage: Do effects differ?

• We look at 7 industry groups: How do other specific industries compare with temporary help?

• We look at how temp help workers succeed: Role of job changes in helping temp help workers succeed?

• We look at whether the effect of temp help varies across the business cycle

• We perform diagnostics to test whether results are likely spurious

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Our findings• Temporary help industry serves a unique role

as a transitional industry• Earnings are lower than in most other

industries• Within 2 years, earnings have largely caught

up• Still, those with initial jobs in some other

industries are doing better (often manufacturing)

• The catch up for temp help workers depends on moving to a better job

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Our findings (continued)

• Results strikingly similar for participants in different programs and for men and women

• Benefits of a job in an alternative industry are slightly larger during a downturn, but basic patterns are similar (2001 vs 1997)

• Effect estimates are not likely to be spurious

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Data

• Participants entering program in calendar year 1997, and 2001

– Focus much of the discussion on 1997 results

• Age at least 18 but less than 65

• Program info from Missouri state administrative sources

• Earnings/employment from the Unemployment Insurance (UI) “wage record data” for both Missouri and Kansas

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Missouri

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• Population: 5.70 m (2003)

• Land area: 178,415 sq km

• Cities:– Kansas City

metro area: 1.12 m

– St. Louis metro area: 2.05 m

– 4 smaller metro areas

Switzerland population: 7.17 m

Portugal population: 10.05 m

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• Population: 5.70 m (2003)

• Land area: 178,415 sq km

• Cities:– Kansas City

metro area: 1.12 m

– St. Louis metro area: 2.05 m

– Columbia metro area 149,000

Missouri is a very “typical” of US states in terms of income, industry, age, race, politics.

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Basic Model

Reference quarter

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Control Variables

• Background: – age, age2

– years of education, high school, college– nonwhite– St. Louis central area– Kansas City central area– suburban, small metro, nonmetro

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• Prior labor market experience– proportion of previous 8 quarters working – working all previous 8 quarters– no work in previous 8 quarters– total earnings in prior year– total earnings two years prior– prior industry

• Quarter of entry (1997:1-1997:4 or 2001:1-2001:4)

• Unemployment in county in outcome quarter

Control Variables (continued)

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Industry code

• One industry in quarter– temporary help services (THS)– manufacturing– retail trade– service (but not THS)– other

• Multiple industries– including THS– not including THS

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Dependent variables

• Basic model– Earnings in outcome quarter (quarter 9)

• includes zeros

• Difference-in-difference(quarter 9 earnings) – (quarter -9 earnings)

• OLS• Interpretation is as prediction of “expected

earnings”

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Implicit Assumptions of the Analysis

• We assume that, conditional on the control variables, industry in reference period is not associated with outcome earnings

• Is this reasonable?• We think so:

– Extensive list of control variables including prior work history and prior industry

– Previous paper (Heinrich et al., 2005) controlled for selection and it didn’t matter

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Implicit Assumptions of the Analysis

• Also:– Determinants of industry choice from logit model

reveals very little difference in type of industry– Very similar results with very different samples– Diagnostics suggest that effects estimates are not

likely to be spurious

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Table 1: Distribution of Employment Across Industries Prior and Subsequent to Program Entry in 1997

4 Quarters before Entry

1 Quarter after Entry





No Job 47.49 52.68 36.60 40.75 36.26 33.39

3-digit industry581 Eating and drinking places 12.34 9.09 5.18 4.38 8.90 8.32

736 Personnel supply services 6.11 8.77 5.42 9.46 4.08 9.03805 Nursing and personal care

facilities 6.56 5.76 3.86 4.58 3.95 4.26

806 Hospitals 1.67 1.32 5.40 2.26 2.49 2.34

4-digit industry

5810 Eating and drinking places 11.98 8.90 5.10 4.38 8.71 8.18

7363 THS 5.86 8.55 5.18 9.11 3.87 8.73

8051 Skilled nursing care facilities 5.32 4.51 3.17 3.67 3.04 3.338062 General medical and surgical

hospitals 1.51 1.18 5.21 2.10 2.22 2.09

Employment ExchangeTANF Job Training

Females

Employment in Industries 1997: Females

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No Job 33.36 34.74 35.25 32.053-digit industry

581 Eating and drinking places 4.43 3.57 6.64 6.30736 Personnel supply services 6.70 11.46 4.19 8.98805 Nursing and personal care

facilities 1.22 1.09 0.86 0.87

806 Hospitals 1.55 0.69 0.82 0.774-digit industry5810 Eating and drinking places 4.33 3.50 6.42 6.157363 THS 6.54 11.10 4.08 8.788051 Skilled nursing care facilities 0.96 0.79 0.68 0.698062 General medical and surgical

hospitals 1.39 0.56 0.69 0.64

MalesJob Training Employment

Table 1: Distribution of Employment Across Industries Prior and Subsequent to Program Entry in 1997

Employment in Industries 1997: Males

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Who Gets a THS job?

• MNL predicting industry in reference quarter (quarter 1 after program entry)

• Dependent variable: THS, THS and other, other industry, no job (excluded)

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Who Gets a THS Jobs?

• Nonwhites• Those living in metro areas

“Race and place matter”

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Who Gets Temporary Help Jobs?

• Nonwhites• Those living in metro areas

Why?• Employers can screen nonwhites cheaply• Temp help jobs require labor market scale

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Table 3: Predicted Earnings and Impact by Industry of Employment in Quarter Following Program Entry in 1997

No Job THSManufac-

turing Retail Trade

Service (excluding

THS) Other

THS and Any Other Industry

Any Industry Not THS

TANF 0 1,131 1,763 1,188 1,547 2,147 1,632 1,766

(0) (35) (52) (19) (22) (60) (38) (42)

1,008 1,818 1,831 1,597 1,953 2,449 2,060 1,922(14) (58) (75) (31) (34) (81) (67) (57)

1,164 1,585 1,848 1,556 1,747 2,051 1,737 1,730(15) (51) (62) (31) (29) (58) (55) (49)

0 421 684 393 584 887 574 566(0) (54) (64) (35) (33) (61) (57) (52)

0 525 682 491 678 949 655 614

(0) (62) (74) (40) (38) (70) (66) (60)

5. Impact on earnings based on difference-in-difference

1. Initial mean earnings

2. Mean earnings 8 quarters later

3. Mean earnings 8 quarters later controlling characteristics

4. Impact on earnings, relative to no job category

One Industry Multiple Industries

Panel A - Females

TANF Females

Predicting Quarterly Earnings 1997

Reference quarter earnings

Reference quarter industry

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Table 3: Predicted Earnings and Impact by Industry of Employment in Quarter Following Program Entry in 1997

No Job THSManufac-

turing Retail Trade

Service (excluding

THS) Other



Job Training1. Initial mean earnings 0 1,529 2,748 1,727 2,968 3,315 2,494 2,849

(0) (95) (122) (100) (98) (147) (148) (148)

1,941 2,838 2,968 2,657 3,464 3,300 3,140 3,352(52) (172) (138) (139) (101) (130) (172) (174)

2,193 2,789 2,909 2,882 3,057 3,063 3,040 3,214(55) (159) (137) (123) (80) (116) (166) (140)

0 596 716 689 864 870 847 1,021(0) (169) (147) (135) (99) (129) (176) (152)

0 610 244 490 712 736 787 959(0) (407) (353) (324) (239) (310) (425) (366)

Employment Exchange

0 1,027 1,608 872 1,205 1,614 1,408 1,443(0) (37) (31) (25) (22) (30) (36) (29)

One Industry Multiple Industries

Panel A - Females






Training & Employment Exchange Females



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No Job THSManufac-

turing Retail Trade

Service (excluding

THS) Other



Job Training

0 1,661 3,795 2,412 4,739 5,557 3,014 5,648(0) (158) (172) (233) (230) (516) (190) (676)

2,402 2,590 4,603 2,894 4,774 5,004 3,822 4,484(113) (249) (202) (226) (269) (243) (302) (260)

2,574 3,458 4,386 3,490 4,340 4,526 4,216 4,283(121) (303) (210) (272) (202) (182) (293) (252)

0 884 1,812 915 1,766 1,952 1,642 1,708(0) (329) (243) (301) (238) (222) (322) (284)

0 802 1,191 553 1,338 1,897 1,318 1,572(0) (868) (639) (791) (625) (581) (847) (746)

Employment Exchange

0 915 2,316 1,254 1,499 2,081 1,360 1,986(0) (47) (34) (36) (36) (29) (45) (37)

Panel B - Males


1. Initial mean earnings





Training & Employment Exchange Males


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Predicting Employment Probability

• For both men and women employment in any sector in the reference period is strongly positively associated with the probability of employment eight quarters later relative to not having a job.

• Once we control for characteristics there is very little difference between workers in the temp help sector and other sectors in the probability of employment in the future.

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Table 5: Transition between Sectors Over Eight Quarters: Program Entry, 1997

No Job

Service (including

THS)Manufac-

turingRetail trade Other

Multiple sectors Total

Panel A - FemalesEmployment Exchange No job 58.3 17.7 4.0 8.5 6.9 4.5 100.0One THS 30.2 27.6 8.9 8.0 14.0 11.3 100.0Sector Manufacturing 22.5 9.6 49.6 5.3 5.8 7.3 100.0

Retail trade 29.2 15.1 4.0 35.0 7.0 9.7 100.0Service (excluding THS) 25.1 51.5 2.8 5.9 6.4 8.4 100.0Other 21.3 13.6 3.3 6.1 47.0 8.6 100.0

Multiple THS and any other industry 21.4 28.4 10.0 9.3 15.0 15.8 100.0Sectors Any industry not THS 20.1 24.9 9.5 13.4 15.7 16.5 100.1

Employment Eight Quarters Later (Percent)

Reference Quarter Employment

Transitions between sectors 1997

Transitions between sectors: Temporary help is easy to leave.

Temporary help jobs often lead to manufacturing jobs

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Analysis for 2001

• We redo our analysis for individuals entering the three programs in 2001

• 1997 was a period of growth– Unemployment around 3-4 percent in 1997-1998.– Between 1997-1999 employment grew by 4.4

percent

• 2001 was a period of contraction– Unemployment was over 5.5 percent– Between 2001-2004 employment declined by 1.5

percent.

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Analysis for 2001

• Did the role of THS change? • No:

– THS is still unique: THS employment increases with program entry more than any other industry

• Growth in temp help is somewhat less strong, however, especially for TANF participants

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No Job THSManufac-

turingRetail Trade

Service (excluding

THS) Other



TANF 1997 0 1,131 1,763 1,188 1,547 2,147 1,632 1,766

1. Initial mean earnings (0) (35) (52) (19) (22) (60) (38) (42)

2001 0 1,158 2,046 1,213 1,671 2,367 1,897 1,777(0) (43) (79) (18) (23) (68) (55) (54)

4. Impact on earnings 1997 0 421 684 393 584 887 574 566

vs no job category (0) (54) (64) (35) (33) (61) (57) (52)

2001 0 453 803 360 645 886 764 614(0) (56) (77) (32) (30) (59) (61) (52)

Job Training4. Impact on earnings 1997 0 596 716 689 864 870 847 1,021 vs no job category (0) (169) (147) (135) (99) (129) (176) (152)

2001 0 596 1,137 447 930 1,379 1,014 1,120(0) (162) (135) (115) (87) (123) (153) (130)

Employment Exchange4. Impact on earnings 1997 0 1,027 1,608 872 1,205 1,614 1,408 1,443 vs no job category (0) (37) (31) (25) (22) (30) (36) (29)

2001 0 1,286 2,140 1,121 1,579 1,980 1,690 1,788(0) (60) (45) (35) (30) (38) (60) (41)

Panel A - Females

Predicted Quarterly Earnings 8 Quarters Later: Program Entry in 1997 and 2001 Females

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2001 Results

• Results for Men are similar

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2001 Results

• Temporary help still unique in its role as a transitional industry

• Earnings, employment and transition results from 2001 follow a strikingly similar pattern to the 1997 result

• Impact of Temporary Help jobs is slightly less beneficial during a recession

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Correlated Errors: Robustness Check

• What if unmeasured factors are correlated with reference quarter industry and outcome earnings?

• Altonji, Elder and Taber (2005) suggest using measured controls to suggest how large the bias of unmeasured factors may be

• We implemented their methods in 3 ways

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Program Entry THS

Manufac-turing

Retail Trade

Service (not THS) Other

THS and Any Other

Industry


TANF 1997 421 685 393 584 888 574 566 ▲

▲ ▲ ▲ ▲

2001 453 804 360 645 887 765 615▲ ▲ ▲ ▲ ▲

Job Training1997 597 717 690 865 871 849 1,023 ▲▲

▲ ▲▲ ▲ ▲

2001 597 1,139 447 931 1,381 1,016 1,122▲▲ ▲▲▲ ▲▲▲ ▲▲▲ ▲ ▲▲▲ ▲▲

Employment Exchange ▲▲▲1997 1,027 1,608 872 1,205 1,614 1,408 1,443

▲▲▲ ▲ ▲▲▲ ▲▲ ▲

2001 1,286 2,140 1,121 1,579 1,980 1,690 1,788▲▲ ▲ ▲▲▲ ▲▲ ▲▲ ▲▲

Job Training1997 429 1,170 96 1,369 1,628 1,224 1,118

▲▲▲ ▲▲ ▲▲▲ ▲ ▲▲ ▲

2001 390 1,705 771 955 1,527 1,307 1,567▲▲ ▲ ▲▲▲ ▲▲ ▲▲▲ ▲▲▲ ▲▲▲

1997 915 2,317 1,254 1,499 2,081 1,360 1,986▲▲▲ ▲ ▲ ▲ ▲▲▲

2001 1,049 2,806 1,644 2,010 2,425 1,509 2,296▲▲▲ ▲ ▲▲▲ ▲▲ ▲ ▲▲▲ ▲▲▲

1 ratio outside range 0-1.2

2 ratios outside range 0-1.2


Dependent Variable: Earnings 2 Years After Reference Quarter Estimated Impact

of Industry on

Earnings Relative

to No J ob

Employment Exchange

Multiple Industries

Females

Males

Robustness Checks: Summary

Coefficientsin red notstatistically significant

▲ One test implies coefficient is not spurious

▲▲ Two tests imply coefficient is not spurious

▲▲▲ Three tests imply coefficient is not spurious

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Program Entry THS

Manufac-turing

Retail Trade

Service (not THS) Other

THS and Any Other

Industry


TANF 1997 421 685 393 584 888 574 566 ▲

▲ ▲ ▲ ▲

2001 453 804 360 645 887 765 615▲ ▲ ▲ ▲ ▲

Job Training1997 597 717 690 865 871 849 1,023 ▲▲

▲ ▲▲ ▲ ▲

2001 597 1,139 447 931 1,381 1,016 1,122▲▲ ▲▲▲ ▲▲▲ ▲▲▲ ▲ ▲▲▲ ▲▲

Employment Exchange ▲▲▲1997 1,027 1,608 872 1,205 1,614 1,408 1,443

▲▲▲ ▲ ▲▲▲ ▲▲ ▲

2001 1,286 2,140 1,121 1,579 1,980 1,690 1,788▲▲ ▲ ▲▲▲ ▲▲ ▲▲ ▲▲

Job Training1997 429 1,170 96 1,369 1,628 1,224 1,118

▲▲▲ ▲▲ ▲▲▲ ▲ ▲▲ ▲

2001 390 1,705 771 955 1,527 1,307 1,567▲▲ ▲ ▲▲▲ ▲▲ ▲▲▲ ▲▲▲ ▲▲▲

1997 915 2,317 1,254 1,499 2,081 1,360 1,986▲▲▲ ▲ ▲ ▲ ▲▲▲

2001 1,049 2,806 1,644 2,010 2,425 1,509 2,296▲▲▲ ▲ ▲▲▲ ▲▲ ▲ ▲▲▲ ▲▲▲

1 ratio outside range 0-1.2



Dependent Variable: Earnings 2 Years After Reference Quarter Estimated Impact

of Industry on

Earnings Relative

to No J ob

Employment Exchange

Multiple Industries

Females

Males


Coefficientsin red notstatistically significant

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• In most cases, in order for estimated coefficient to be spurious – error term needs to be related to THS employment

in a very different way than observed controls

• This seems implausible• All estimated impacts are unlikely to be

spurious

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Conclusions

• We have investigated temp help jobs obtained following an employment “crisis”

• Temporary help industry serves a unique role as a transitional industry

• Earnings are lower than in most other industries

• Within 2 years, earnings have largely caught up

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Conclusions

• Still, those with initial jobs in some other industries are doing better (often manufacturing)

• The catch up for temp help workers depends on moving to a better job

• Results strikingly similar for participants in different programs and for men and women

• Benefits of a job in an alternative industry are slightly larger during a downturn, but basic patterns are similar

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Conclusions

• Obtaining a temporary help job is clearly better than having no job– We see no evidence that a strategy of waiting for

a “better” job yields any benefits.

• These results do not differ across our three programs– Heterogeneity of our sample suggests that our

results are general.

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