New digital safety net or just more ‘friendfunding’? Institutional analysis of medical crowdfunding in the United States

Figures & data

Table 1. Institutions and key players in healthcare provision and finance.

	Institutions	Key organizational forms & actors	Provision/Financing Healthcare
Formal public & private safety nets	Government	Central government Local government State	Federal/state/local government healthcare service providers/hospitals Public health insurance
	The market	Firms Workplace organizations	For profit healthcare service providers/hospitals Private health insurance (employer-based or individual)
Informal safety nets	Voluntary sector	Charities Foundations Religious organizations Support groups	Non-profit healthcare providers/ hospitals/community health centers Fundraising for medical causes Volunteering in health-related services
	Kinship	Families Friends Neighbors Personal network	Home-based care Interfamilial financial support; gift giving Exchange of money, caregiving, and/or information
-	Medical Crowdfunding	Platforms Users / participants	Monetary donations Emotional support Information

Source: Conventional institutions are based on Gilbert and Terrell (2002), Glennerster (2003), and Narayan (1999).

Figure 1. Pathways of financing healthcare for households. Source: Conventional income earners, reallocators, and beneficiaries are adapted from Glennerster (2003) and List (2011).

Table 2. Descriptive statistics of main variables scraped from Gofundme.

Campaign variables	N	Mean	SE	Min	Max
Funds raised (USD)	914	47278.05	29026.68	26600.00	317351.00
Funding goal (USD)	914	80332.62	123752.96	1000.00	2000000.00
Duration (days)	914	97.93	49.57	1.00	183.00
No. of photos	914	8.53	12.54	1.00	179.00
Text (word count)	914	2570.62	1994.59	136	15137
No. of updates	914	4.57	7.15	.00	102.00
On behalf of someone else	914	Yes = 72% (n = 661)
Size of online social network (No. of Facebook Friends)	523	822.32	794.90	2.00	4999.00
Times shared in social media	914	1384.64	1668.68	2.00	20000.00

Note. Facebook reports larger numbers of shares in units of 1k, hence the round max figure.

Table 3. Logistic regression model to predict the appearance of campaigns.

Independent variables	B	SE	Wald	Exp(B)
Socioeconomic factors
Population (Ln)	.748	.434	2.967	2.113
Income (Ln)	1.981	.823	5.787	7.247*
Income ratio (Ln)	−.230	1.005	.052	.795
Education (Ln)	1.538	.533	8.340	4.655**
Broadband subscriptions (Ln)	1.871	1.628	1.321	6.494
Information industry (Ln)	−.675	.427	2.504	.509
Finance and insurance industry (Ln)	−.527	.357	2.182	.590
Healthcare and social service industry (Ln)	−.263	.582	.204	.769
Formal health-specific safety nets
Private health insurance coverage rate (Ln)	−4.596	1.374	11.194	.010**
Public health insurance coverage rate (Ln)	−1.081	.610	3.135	.339
Uninsured rate (Ln)	−.111	.300	.138	.895
Formal non-health specific safety nets
Social security income (Ln)	1.194	1.423	.704	3.301
Supplemental security income (Ln)	.033	.144	.053	1.034
Public cash assistance (Ln)	.055	.190	.085	1.057
Informal safety nets
Social association (Ln)	.188	.306	.379	1.207
Culture of giving (Ln)	.385	.346	1.242	1.470
Health problems (Ln)	−.166	.077	4.612	.847*
Constant	−32.112	15.100	4.522	.000*
Model Chi squared (df = 19)	1,045.408***
Nagelkerke R^2	.561
No. of observations	3141

***p < .001, **p < .01, *p < .05.

Table 4. Multiple regression models to predict the number and success of campaigns.

	Dependent variable
Independent variables	No. of campaigns (Ln)		No. of campaigns that met their goal (Ln)
	β	VIF	β	VIF
Socioeconomic factors
Population (Ln)	.532***	2.740	0.443***	2.383
Income (Ln)	.341***	4.568	0.331**	5.381
Income ratio (Ln)	.068***	1.701	−0.079	2.871
Education (Ln)	.031	4.159	−0.056	6.563
Broadband subscriptions (1 = High)	.017	2.165	−0.038	1.987
Information industry (Ln)	.054***	1.290	0.145*	2.492
Finance and insurance industry (Ln)	.028	1.557	0.077	2.154
Healthcare and social service industry (Ln)	−.020	1.588	0.039	2.064
Formal health-specific safety nets
Private health insurance coverage rate (Ln)	−.203***	4.994	−0.297*	8.146
Public health insurance coverage rate (Ln)	−.046	3.030	−0.022	4.568
Uninsured rate (Ln)	−.019	2.037	−0.050	3.464
Formal non-health specific safety nets
Social security income (Ln)	−.062**	2.250	−0.055	3.121
Supplemental security income (Ln)	−.006	1.323	0.007	1.109
Public cash assistance (Ln)	−.058**	1.623	−0.019	2.249
Informal safety nets
Social association – Low (bottom 25%)	−	−	−	−
Social association – Mid-low	−.072***	1.823	0.054	1.916
Social association – Mid-high	−.108***	2.103	0.054	2.276
Social association – High (top 25%)	−.131***	2.977	0.152*	2.850
Culture of giving (Ln)	−.043*	2.351	−0.078	4.298
Health problems (Ln)	−.134***	1.882	−0.161**	1.309
Model F	126.578***		8.930***
df	19		19
R^2	.435		.338
No. of observations	3141		353

***p < .001, **p < .01, *p < .05.

Table 5. Hierarchical multiple regression model to predict the amount of funds raised by campaign.

		Funds raised by campaign (Ln)
Independent variables		R1 (β)	R2 (β)	R3 (β)	VIF^a
Campaign-level factors	Campaign elements
	Funding goal (Ln)	.379***	.364***	.362***	1.100
	Duration (0= a month and less)	–	–	–	–
	Duration – 2 months	−0.022	−0.024	−0.027	1.519
	Duration – 3 months	0.014	0.013	0.013	1.532
	Duration – 4 months	0.012	0.017	0.017	1.507
	Duration – 5 and 6 months	0.029	0.030	0.032	1.562
	Message attributes
	Photos (Ln)	0.105**	0.105**	0.102**	1.397
	Detailed message (Ln)	0.069*	0.066*	0.066*	1.019
	Updates (0= less than once a month)	–	–	–	–
	Updates – once a month	0.031	0.037	0.040	1.700
	Updates – 2–3 times a month	0.089*	0.089*	0.091*	1.951
	Updates – more than 3 times a month	0.076	0.079	0.076	2.430
	Controlled – no update among less than 1-month-old campaigns	0.033	0.038	0.035	1.349
	Fundraiser attributes
	On behalf of someone else (1=yes)	.051	.051	.057	1.913
	Online social network size (0= no account)	–	–	–	–
	Online social network size – Below 25%	−0.115**	−0.118**	−0.116	1.482
	Online social network size – 25-50%	−0.063	−0.057	−0.056	1.805
	Online social network size – 50-75%	−0.075	−0.077	−0.077	2.401
	Online social network size – Top 25%	−0.058	−0.052	−0.050	3.104
	Online social network size (sqrt) * On behalf	−0.013	−0.014	−0.022	4.160
	Times shared in social media (Ln)	.143***	.171***	.105*	2.258
County-level factors	County socioeconomic factors
	Population (Ln)		0.060	0.053	6.241
	Income (Ln)		0.110*	0.106*	3.254
	Income ratio (Ln)		−0.033	−0.031	2.524
	Education (Ln)		0.029	0.029	4.688
	Broadband subscriptions (Ln)		−0.029	−0.033	4.183
	Information industry (Ln)		0.039	0.037	2.625
	County informal safety nets
	Social association (0= Low: Bottom 25%)	–	–	–
	Social association – Mid-low		−0.013	−0.013	1.766
	Social association – Mid-high		0.023	0.023	2.299
	Social association – High (top 25%)		−0.044	−0.050	3.102
	Culture of giving (Ln)		−0.056	−0.051	3.149
	No. of campaigns in the county (Ln)		−0.005	−0.001	4.760
Interaction	Times shared (1=more than the median)* County income (Ln)			.098*	2.079
R^2			.239	.259	.264
Change in R^2			-	.020*	.005*
df			18	29	30
F			15.656***	10.680***	10.561***
No. of observations			914	914	914

***p<.001, **p<.01, *p<.05.

^a VIF is based on the entire model.

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