Is Compassion a Good Career Move?: Nonprofit Earnings Differentials from Job Changes

Andrew C. Johnston; Carla Johnston

doi:10.3368/jhr.56.4.0319-10120R1

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Figure 1 Event Study—Job Changes from For-Profit Employers
Notes: We plot the event–time dummies for workers who changed jobs between 2003 and 2012 and held the previous and new job for at least six quarters (18 months). After t = 6, the results derive from an unbalanced panel. Controls include a full set of event–time dummies, year–quarter dummies, and event-specific dummies, a refinement of worker fix effects.
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Figure 2 Nonprofit Influence on the Income Distribution
Notes: Figure shows the coefficients on the nonprofit indicator from Equation 2 for several pre-treatment income groups. To determine pre-treatment wage groups, we residualize log quarterly earnings from the pretreatment year on event and industry fixed effects. We use this residualized log quarterly earnings to partition workers into pre-treatment wage quantiles. The data are from administrative unemployment insurance records for the universe of Florida workers, 2003–2012.
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Figure 3 Classic Nonprofit Event-Study Figures
Notes: Figure shows the coefficients α_j^q in Equation 1 for two event types: moves from a for-profit to a nonprofit (which we refer to as the treatment group) and moves from a for-profit to another for-profit firm (the control group) for various three-digit NAICS industries. The dependent variable is log quarterly earnings. The event–time dummy at t = −1 is omitted. To generate the control event for religious, civic organization, and social advocacy industries, we identify the three three-digit NAICS codes that most commonly transition to that particular nonprofit type and identify workers transitioning between for-profit jobs in those industries. The gray shaded areas bounding each line represent the 95 percent confidence interval.
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Figure 4 Commercial Nonprofit Event-Study Figures
Notes: Figure shows the coefficients α_j^q in Equation 1 for two event types: moves from a for-profitto a nonprofit (which we refer to as the treatment group) and moves from a for-profit to another for-profit firm (the control group) for various industries. The dependent variable is log quarterly earnings. The event-time dummy at t = −1 is omitted. All industries are determined by three-digit NAICS codes, except for doctors’ offices, which corresponds to a six-digit NAICS code. The gray shaded areas bounding each line represent the 95 percent confidence interval.
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Figure 5 Nonprofit Differential and Nonprofit Penalty over Time
Notes: The circle-dotted line represents the cross-sectional nonprofit differential in each year. The triangle-dotted line presents the nonprofit penalty in each year, which accounts for worker-specific differences using individual fixed effects.
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Figure 6 Worker Type over the Business Cycle
Notes: Figure shows the worker fixed effects plotted over time. Worker fixed effects are estimated from an Abowd–Kramarz–Margolis worker–firm fixed-effects model with year and quarter controls. All sample restrictions described in Section III are imposed.

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Table 1

Measurement Error in Nonprofit Status

	Within Industry Percent Nonprofit
	ACS	Admin	Percentage Point Error	% of Total Nonprofit Workers
	(1)	(2)	(3)	(4)
Hospitals	43	72	-29	40
Educational services	43	49	-6	19
Ambulatory healthcare services	12	14	-2	10
Social assistance	49	55	-6	8
Nursing and residential care facilities	25	27	-2	7
Religious and civic organizations	100	42	58	4
Recreation industries	5	12	-7	2
Credit and banking	15	7	7	2
Scientific and technical services	2	2	0	2
Utilities	8	12	-4	1

Notes: The first column is the percentage of reported nonprofit workers in each industry from the ACS Florida sample in 2010. The second column is the percentage of recorded nonprofit workers from the universe of Florida’s UI records in 2010. The third column is the percentage point difference between Columns 1 and 2. The fourth column is the industry’s share of all nonprofit workers according to UI records.

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Table 2

Industry Composition & Nonprofit Earnings Differences

	Average Quarterly Earnings ($)
	Overall	For-Profits	Nonprofits	Nonprofit Differential	Share Nonprofit
Industry	(1)	(2)	(3)	(4)	(5)
All industries	11,394	11,476	10,633	0.07	0.10
Health and human services
Outpatient healthcare (621)	13,103	13,449	11,739	0.13	0.20
Doctor’s offices (621,111)	16,256	16,086	17,618	-0.10	0.11
Hospitals (622)	11,352	11,206	11,413	-0.02	0.71
Nursing facilities (623)	7,824	7,740	7,900	-0.02	0.53
Social services (624)	7,321	6,969	7,505	-0.08	0.66
Childcare (62,441)	5,873	5,438	6,652	-0.22	0.36
Education (611)	10,522	10,380	10,646	-0.03	0.53
Finance and management
Law offices (54,111)	16,173	16,235	9,781	0.40	0.01
Banking & credit (522)	13,225	13,443	8,751	0.35	0.05
Investments (523)	25,460	24,567	40,427	-0.65	0.06
Insurance (524)	12,936	12,938	11,058	0.15	0.00
Administration (561)	9,995	9,993	10,853	-0.09	0.00
Utilities (221)	18,545	18,837	11,164	0.41	0.04
Classic charities
Religious organizations (8,131)	8,541	7,939	8,659	-0.09	0.84
Grant-making foundations (8,132)	10,194	11,084	10,157	0.08	0.96
Social advocacy (8,133)	8,605	9,687	8,403	0.13	0.84
Civic organizations (8,134)	9,220	9,766	8,998	0.08	0.71

Notes: Summary statistics are calculated using the sample which includes all workers for the years 2003–2012. No sample restrictions are imposed. “Share nonprofit” indicates the share of industry workers which are employed by a nonprofit firm.

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Table 3

Nonprofit Differential Estimates

	Log Earnings
	Cross-Sectional Difference			Within-Worker Estimates
	(1)	(2)	(3)	(4)	(5)	(6)
Nonprofit	-0.069***	-0.053***	-0.055***	-0.012***	-0.010***	-0.009***
	(0.001)	(0.001)	(0.001)	(0.001)	(0.001)	(0.001)
Year–quarter FE	X	X	X	X	X	X
County FE		X	X	X	X	X
Event–time FE			X	X	X	X
Worker FE				X
Event FE					X	X
Industry FE						X
R²	0.01	0.02	0.03	0.81	0.82	0.82
Observations	26,919,859	26,919,859	26,919,859	26,919,859	26,919,859	26,919,859

Notes: Table is based on the estimation of Equation 2 where the dependent variable is log quarterly earnings. All sample restrictions described in Section III are imposed. Columns 1–3 provide estimates without controlling for person fixed effects (FE). Columns 4 includes a worker fixed effect, while Columns 5 and 6 include fixed effects for events, allowing a worker with multiple events a separate fixed effects for each event. Industries are grouped by three-digit NAICS codes. This table leverages 1,336,205 unique workers and 1,568,483 unique job-change events. Significance: *p < 0.05, **p < 0.01, ***p < 0.001.

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Table 4

Nonprofit Differential Estimates by Industry

	Nonprofit Differential Estimates			Difference Decomposition
	Cross-Sectional Difference	Within-Worker Estimate	Gen-Diff-in-Diff Estimate	Percent Demand	Percent Supply
Industry	(1)	(2)	(3)	(5)	(6)
All industries	-0.053***	-0.012***	-0.010***	19	81
	(0.001)	(0.001)	(0.001)
	26,919,859	26,919,859	26,919,859
Health and human services
Outpatient healthcare (621)	0.068***	0.035***	0.035***	51	49
	(0.010)	(0.004)	(0.004)
	905,031	1,030,102	905,031
Doctor’s offices (621,111)	0.227***	0.025**	0.026**	11	89
	(0.026)	(0.010)	(0.010)
	360,097	383,801	360,097
Hospitals (622)	0.008	0.000	-0.002	-26	126
	(0.005)	(0.003)	(0.003)
	381,971	757,031	381,971
Nursing facilities (623)	0.005	-0.002	-0.001	-16	116
	(0.011)	(0.006)	(0.006)
	110,191	183,575	110,191
Family services (624)	0.128***	0.033***	0.034***	26	74
	(0.013)	(0.007)	(0.008)
	65,926	152,957	65,926
Childcare (62,441)	0.049***	0.054***	0.050***	102	-2
	(0.016)	(0.011)	(0.011)
	42,360	54,401	42,360
Education (611)	-0.049***	-0.023***	-0.025***	50	50
	(0.011)	(0.008)	(0.008)
	104,528	165,407	104,528
Finance and management
Banking & credit (522)	-0.270***	-0.027***	-0.026***	10	90
	(0.010)	(0.007)	(0.007)
	976,814	988,174	976,814
Investments (523)	0.345***	-0.026	-0.023	-7	107
	(0.037)	(0.018)	(0.018)
	80,185	80,349	80,185
Insurance (524)	-0.068	-0.038	-0.038	55	45
	(0.107)	(0.085)	(0.082)
	567,213	567,270	567,213
Administration (561)	0.096***	0.039**	0.039**	40	60
	(0.024)	(0.014)	(0.013)
	2,279,915	2,280,313	2,279,915
Utilities (221)	0.118	0.012	0.101	86	14
	(0.251)	(0.188)	(0.199)
	161,157	161,853	161,157
Classic charities
Legal aid (54,111)	-0.256***	-0.131***	-0.129***	51	49
	(0.036)	(0.036)	(0.036)
	270,984	271,996	270,984
Religious organizations (8131)	-0.264***	-0.099***	-0.098***	37	63
	(0.014)	(0.013)	(0.013)
	4,003,999	4,012,994	4,003,999
Grant-making foundations (8,132)	-0.123***	-0.020*	-0.017	14	86
	(0.012)	(0.009)	(0.009)
	3,943,822	3,971,955	3,943,822
Social advocacy (8,133)	-0.243***	-0.024***	-0.021*	9	91
	(0.012)	(0.009)	(0.009)
	3,949,345	3,974,332	3,949,345
Civic organizations (8,134)	-0.081***	-0.051***	-0.050***	62	38
	(0.015)	(0.011)	(0.011)
	2,581,211	2,593,224	2,581,211

Notes: Table is based on the estimation of Equation 2 estimated for various industries. Column 1 reflects the cross-sectional earning difference between nonprofit and for-profit workers in each category. Column 2 estimates the nonprofit penalty by adding worker fixed effects to the estimation of Equation 1. Column 3 implements a generalized difference-in-difference that adds to the specification in Column 2 event–time fixed effects to account for general dynamics surrounding job changes. Column 4 reflects an estimate of how much of the nonprofit differential arises from demand-side forces, calculated by dividing the value in Column 3 with the value in Column 1. Column 5 reflects the share of the nonprofit differential arising from supply differences, which is the remaining nonprofit differential unexplained by demand. The third row for each estimate provides the relevant N. Significance: *p < 0.05, **p < 0.01, ***p < 0.001.