The price of an egg: oocyte donor compensation in the US fertility industry

Abstract

Market-based health services have arisen out of major transformations in US healthcare in the last several decades. This study addresses pricing in the human egg donation market – an under-explored topic despite substantial scholarship on commodification and financial coercion of donors. Through analysis of primary data collected from 276 US fertility clinics and egg donation agencies, I assess what impacts average donor compensation and likelihood of compensation being more than $5000. Drawing on theories of organizational behavior, I test whether organizational characteristics, ecological factors, or regulatory pressure have the greatest impact on donor compensation amounts. I find that compensation is influenced primarily by ecological/market factors. Furthermore, industry self-regulation (measured by professional affiliation) did not deter clinics and agencies from having higher donor compensation levels, despite American Society for Reproduction Medicine recommendations. I conclude by addressing the broader implications of these findings for medical market dynamics and the problem of industry self-regulation.

Keywords:

• bio-economy
• oocyte donation
• medical markets

In the US, new reproductive technologies, such as egg donation and in vitro fertilization (IVF), have largely been concentrated in a private medical market where access is contingent on the ability to pay (Conrad and Leiter 2004; Spar 2006; Holster 2008). Market-based health services have arisen out of major transformations in the US healthcare system in the last several decades with the increasing merger of business and medicine – what Relman (1980) labeled the “medical-industrial complex.”

In this study, I contribute to the literature on medical markets and the US bio-economy by analyzing human egg donor compensation. What affects the price of a donor egg? Why is there such variation across US fertility clinics and egg donation agencies (Covington and Gibbons 2007; Levine 2010; Almeling 2011)? Prior research has emphasized characteristics of the bio-commodity itself to explain variation in donor egg pricing (e.g. Holster 2008; Almeling 2011). I move the focus to the organizational level to more fully understand pricing as a function of the context in which bio-commodities are valued and exchanged.

The main professional association for reproductive medicine – the American Society for Reproduction Medicine (ASRM) – creates guidelines for medical practice (Duka and DeCherney 1994), including donor compensation (The Ethics Committee 2007), although their current stance is now changing due to a price-fixing lawsuit (Gershman 2016). I analyze data on advertised donor compensation collected in 2009–2010, when the ASRM recommended limiting compensation to less than $5000 and definitely no more than $10,000 (The Ethics Committee 2007). As such, this analysis also extends existing literature examining why some clinics and agencies follow best practice guidelines and others do not (Keehn et al. 2012, 2015; Alberta, Berry, and Levine 2013; Holwell et al. 2014). How effective was industry self-regulation over donor compensation?

Oocytes and the US bio-economy

Egg donation – the transfer of human eggs from a living, healthy, young, donor to a recipient woman – emerged in the early 1980s as one of several treatments for infertility. Although labeled as a donation, it frequently involves compensation. Indeed, human eggs are a dramatically growing sector of the US bio-economy (e.g. Spar 2006; Waldby 2008) and donors are currently given a much larger compensation (typically several thousand dollars per donation) than sperm donors (typically $50–$100 per donation) (Covington and Gibbons 2007; Almeling 2011; author’s own data collection). This is justified by injections and procedures (New York State Task Force on Life and the Law, n.d.), and is for donors’ “time, inconvenience, and discomfort,” but nominally not for the eggs themselves (The Ethics Committee 2007).

Much prior scholarship has addressed ethical concerns about a market in human oocytes, emphasizing particular issues about the financial coercion of donors and commodification of human life through payment for women’s eggs (Pennings 2000; Gurmankin 2001; Steinbock 2004; Waldby 2008). Yet, there is little research on the dynamics of pricing in this market. This is a surprising omission given that existing tensions are not simply about whether donors should be paid, but also how much.

Building on prior research that problematizes the lack of regulation and the issues of self-regulation in the US fertility industry, I focus on the role of fertility clinics and egg donation agencies in setting donor compensation prices. Prior research has shown that donor characteristics (prior donations, fertility history, race/ethnicity, educational attainment) and perceived scarcity of certain types of donors factor into compensation amounts (Covington and Gibbons 2007; Holster 2008; Levine 2010; Almeling 2011). These findings, although important to understand, focus primarily on the specific bio-commodity being exchanged. Overall, there has been relatively little research more systematically examining industry-level practices (although see Holwell et al. 2014 and Keehn et al. 2015). As Birch (2017) argued, value is not an inherent characteristic of a bio-commodity itself; rather organizations in the bio-economy actively create and manage such value.

What’s driving the price of eggs?

To examine the dynamics of oocyte donor pricing, I analyze clinics and agencies (henceforth organizations) as the primary locus of these decisions. Why might a college-educated egg donor in the Northeastern US command $8000 for her eggs, but only $1500 if she were donating in the Midwest? Furthermore, why would some organizations set prices higher than the $5000 recommended sum from the ASRM (The Ethics Committee 2007)?

I draw on the multi-disciplinary organizational literature to delineate three potential explanations: (1) organizational characteristics, (2) ecological factors, and (3) regulatory pressure. The first explanation attributes pricing differences to simple differences between organizations, such as their form, age, and model of gamete donation (traditional/medical versus consumer-oriented). The second explanation views donor compensation largely as a product of the organizational environment. Finally, the third explanation views donor compensation as an issue of how effective regulatory pressure is from governing professional bodies.

Organizational characteristics

The various organizations involved in egg donation constitute an organizational field “organized around a common substantive interest” (Aldrich and Ruef 2006, 40). Within this field, fertility clinics and egg donation agencies comprise two distinct, but interrelated organizational forms (Romanelli 1991; Holster 2008). Clinics are staffed and directed by reproductive endocrinologists and other medical professionals such as nurses and andrologists; egg donation is one medical service among many provided to infertility patients. Agencies are staffed and directed by a range of personnel, including previous donors, nurses, reproductive lawyers, family practitioners, and even former modeling agency CEOs (author’s own data collection). As “intimate intermediaries” (Spar 2006, 44), agencies create arrangements between donors and recipients and are considered more responsive to client desires (Treiser, n.d.). Agencies are typically privately owned, stand-alone businesses, whereas clinics can be private practices or based on larger institutional settings such as universities or hospitals. As such, they may be accountable to different authority structures with differing organizational goals (Aldrich and Ruef 2006; Keehn et al. 2015).

Organizational age has been important for understanding organizational behavior: younger organizations may suffer from a “liability of newness,” where they cannot compete against older, more established organizations because they do not have the same reputation yet (Stinchcombe 1965). Related to this is the notion that organizations change slower than their environments (Aldrich and Ruef 2006) – although Reich (2014) argues that it is not necessarily lag, but a conscious holding-on of “founding values.” One major change in the fertility industry has been the purported shift from the traditional “medical model” of gamete donation (Braverman 2010) to a more consumer-oriented model (Holster 2008; Almeling 2011), echoing broader shifts in US healthcare (Relman 1980; Conrad and Leiter 2004). While the former preserved strict boundaries between donors and recipients (Braverman 2010), the latter allows for increasingly open arrangements, including exchange of photographs, and in-person meetings (Johnson 2011).

If younger organizations are susceptible to the “liability of newness,” we might expect them to be more compliant with ASRM guidelines, for fear of damaging their developing reputation. On the other hand, newer organizations trying to succeed in an increasingly saturated market (Spar 2006) may use more aggressive tactics in recruiting and promoting donors to gain a competitive edge. This may include both driving up and driving down donor compensation prices.¹ Older organizations may be more steadfast about compliance with professional guidelines because they have not yet transitioned to the more consumer-oriented models of gamete donation. Alternatively, they may feel less pressure to meet current market trends because they have greater legitimacy in the industry and can attract clients through their history and reputation – i.e. their founding values (Reich 2014).

Ecological factors

Fertility clinics and egg donation agencies operate within specific local, state, and regional contexts that are important considerations. For example, Levine (2010) found that advertised donor compensation rates were higher on college campuses with higher average SAT scores: this speaks to both the potential “supply” of eggs (i.e. availability of college-educated women) and the “quality” of eggs (i.e. smart/educated donors). Hamilton and McManus (2005b) noted several relevant fertility market characteristics, including the total population, median household income, and proportion of women who were college educated, which approximate women’s demand for and ability to access reproductive services.

The degree of competition also affects organizational behavior. When organizations are competing for resources or clientele within the same environment, they may find niches of specialization to differentiate themselves (Barman 2002; Aldrich and Ruef 2006). Some speculate that competition may influence organizations to act more aggressively, disregarding professional guidelines (Henne and Bundorf 2010). Because clinics and agencies are simultaneously buyers and sellers of egg donation services, competition and pricing can be looked at from two perspectives: gaining consumers versus gaining donors. As buyers, organizations may seek to maximize their profits by lowering donor compensation. At the same time, organizations do not want to go too low because donors can simply go to the highest bidder. Yet, organizations are not true buyers in this market. They facilitate the transaction, but donors are only paid when matched with a client: donor compensation is bundled into the client’s payment for all reproductive services rendered. As sellers, organizations may have incentives to raise donor compensation to increase profits from a smaller number of (wealthier) consumers or lower donor compensation to increase widespread demand among customers of varying socioeconomic statuses. Still, these organizations are not true sellers because they pay the collected donor compensation to the donor and make their profits primarily from selling other fertility-related services, such as IVF, patient consults, attorney services, and psychological services. Because clinics and agencies are neither true buyers nor sellers in this scenario, it is currently unclear how competition may affect donor compensation.

Beyond local market dynamics, organizations may be differentially impacted by state and regional contexts. Particular areas of the US have been prone to extreme industry growth. Both the East and West Coast have a higher concentration of clinics and agencies compared to other regions of the US (author’s analysis of geocoded data from SART and CDC reports). This might explain why Covington and Gibbons (2007) found higher average donor compensation for the Eastern/Northeast and Western US regions. California is also specifically known for being a very “congested” fertility market (Spar 2006; personal communication, organizational staff).

Regulation can affect how an industry develops and can also affect consumer access and demand (Hamilton and McManus 2005a). Currently, 15 states have insurance mandates for infertility coverage (NCSL 2014). While these differ in various respects, they mediate the relationship between consumer resources and acquisition of reproductive goods and services, reflecting a mediated medical market (Conrad and Leiter 2004). These markets may “dampen” consumer demand because third parties limit the type or amount of treatment covered (Conrad and Leiter 2004). However, by easing consumer cost, they may also open up access, thereby increasing demand (Hamilton and McManus 2005a; Schmidt 2007; Holster 2008). This might drive up donor compensation even as insurers cap treatment costs because donor services are typically not covered in state-mandated insurance (NCSL 2014).

While ecological factors are important in shaping organizational behavior more generally (Aldrich and Ruef 2006), one important consideration here is that the egg donation market is not necessarily geographically bound. There has been significant growth in both domestic and international reproductive tourism (Martin 2009). As Martin (2009) describes, the laissez-faire approach that governs the US fertility industry means that if consumers cannot find what they are looking for in one state, they can go to others and find a different set of regulations in place. Thus, consumer demand is not wholly locally constrained. The increasingly virtual character of the egg donation market (Holster 2008; Braverman 2010) also changes how supply is constructed. Donors may apply at a local clinic or agency, but match with a recipient out of state.

However, there are limits to the mobility of supply and demand in the egg donation market. First, donors are more likely to be asked about willingness to travel when they apply at agencies compared to fertility clinics.² Second, donor travel costs are typically borne by the recipient, which may steer them toward choosing locally.³ Third, insurance requirements, such as preferred provider/in-network coverage, may keep consumers in-state and within their local provider network to lessen the financial burden of fertility procedures. This may be especially the case for recipients in states with mandated coverage. For example, Arkansas mandates insurance coverage of IVF, but the procedure must be performed in a facility licensed by the Arkansas Department of Health (NCSL 2014). Thus, we should address local and state-level factors, although it is unclear how supply and demand dynamics operate given the potential for reproductive travel.

Regulatory pressure

Many point to the industry itself as one that pushes ethical boundaries through the interaction of largely “experimental” science, profit motivation, and “desperate” infertile men and women (Clarke 1990; Spar 2006). Indeed, there have been many calls for increased regulation, labeling the US the “wild west” of the global fertility industry (Yoshino 2009). While there is a definite lack of comprehensive oversight (Spar 2006; Martin 2009; Levine 2010; Alberta, Berry, and Levine 2013), there are still some guidelines provided from the ASRM – the major professional association in reproductive medicine (Duka and DeCherney 1994). Nearly a decade ago, the ASRM Ethics Committee defended donor compensation but recommended that sums over $5000 required justification and anything over $10,000 was deemed “inappropriate” (The Ethics Committee 2007), thus attempting to restrain marketization. Under the auspices of the ASRM is SART – the Society for Assisted Reproductive Technology – which acts as a trade organization (personal communication, Chief Advocacy and Policy Officer, ASRM Public Affairs). These professional associations offer some surveillance and sanctioning: egg donation agencies unwilling to comply with compensation guidelines were threatened with removal from a SART-published list for prospective consumers (Covington and Gibbons 2007).

Because the regulatory framework in the US fertility industry is relatively sparse, the primary means of regulation lies with professional associations, which potentially deploy three acknowledged mechanisms (coercive, mimetic, and normative) to influence organizational behavior (DiMaggio and Powell 1983). Coercive refers to external pressure to adopt or change behaviors. Mimetic suggests that organizations model their behavior on others in times of “uncertainty.” Finally, normative forces operate through “diffusion of values” (King and Lenox 2000, 701), such as forming a professional association.

Regarding coercive pressure, SART lists egg donation agencies who have agreed to comply with guidelines on donor compensation and threatens removal if they do not (SART 2007; The Ethics Committee 2007). This signals to consumers which agencies are acting in line with professional standards and are “safe” choices. In terms of mimetic pressure, the ASRM explicitly creates and disseminates best practices guidelines (Duka and DeCherney 1994). Finally, the very formations of ASRM and SART indicate normative pressure through participation in these associations.

One key issue, however, is that the industry is largely self-regulated (Levine 2010; Alberta, Berry, and Levine 2013; Keehn et al. 2015). There exists substantial debate on whether self-regulation is actually effective or merely provides a symbolic public relations “face” (Gunningham and Rees 1997). While there is ample evidence for both, research indicates that self-regulation without adequate sanctioning is more the issue than self-regulation per se (King and Lenox 2000). Because there are minimal sanctions for violating ASRM and SART guidelines, we might expect that professional affiliation will have little effect on donor compensation.

Methods

Data

Data came from a quantitative content analysis of organizational materials from US fertility clinics and egg donation agencies. This included websites, brochures, donor applications/profiles, informational packets, other donation forms, and written or phone communication with physicians and staff regarding organizational practices. Data collection occurred in 2009–2010 as part of a larger study on the US fertility industry. Websites were the first point of data collection and a major data source: more than 90% of organizations have an online presence. Three attempts were also made to directly contact organizations for additional information. Of the initial 702 organizations identified from the larger study, 15% were closed or did not have updated contact information, 5.5% refused, and 55% did not respond. In 2010, a research assistant made follow-up phone calls to organizations with missing data (non-responses and partial responses). She identified herself as a potential egg donor⁴ to increase the likelihood of response and made contact with 285 distinct organizations. No organizations that had initially refused were re-contacted.

Along with three research assistants, I coded all organizational materials. This study relied on manifest content (i.e. explicit discussions of practices), so coder subjectivity was minimal. To bolster reliability (Lombard, Snyder-Duch, and Campanella Bracken 2002), coding decisions were reviewed until an absolute agreement was reached.

Sample selection

Multiple sources were used to assess the population of all known, operating clinics and agencies in the US. The 1992 Fertility Clinic Success Rate and Certification Act (CDC, n.d.) requires clinics to report annually to the CDC and SART. These reports are publicly available, with a lag between reporting and dissemination. I pooled together the most recent years available at the time of data collection (2005–2007) and then removed duplicates, organizations that had merged, and clinic name changes between years. I then used three additional criteria: (1) clinics had to have in-house donor recruitment; (2) clinics could not solely work with known donors (i.e. friends or family members as donors); and (3) for networked clinics, I selected only the main branch to avoid repeat information. Of the initial listings (n = 516), 227 clinics met the criteria.

For egg donation agencies, I began with a list of those that agreed to comply with compensation guidelines (SART 2007). I also sought listings from Resolve and the American Fertility Association – two major infertility patient advocacy organizations – as well as referrals from individual clinic websites. Of the initial 130 listings, 101 were in business at the time of data collection.

In total, there were 328 organizations currently operating that recruited egg donors. After accounting for missing data, the final analytic sample contained 276. In analyses not shown, the analytic sample was compared with select variables from the 328 organizations, to gauge the impact of missing data. The variables were quite similar across the two samples, suggesting that the analytic sample adequately represented the population of US clinics and agencies at the time of data collection.

Dependent variables

I created two measures for donor compensation. Starting compensation indicated the dollar amount given for one donation cycle. Some organizations reported a range as opposed to a single value. For these, I took the highest publicly stated amount. Compensation tiers indicated whether compensation was less than or equal to $5000 (=0) (Tier 1) or greater than $5000 (=1) (Tier 2). This reflects the first major cutoff stated in the ASRM guidelines (The Ethics Committee 2007). Only two organizations in the sample had compensation greater than $10,000 ($10,500 and $15,000), so I could not feasibly examine a Tier 3 distinction.

Independent variables

Organizational characteristics

Organizational form distinguished whether an organization was an agency (=1) or clinic (=0). I coded all materials for how many years the organization had been in business. If the year of founding was missing, I used the earliest year mentioned, such as when a senior staff member had joined. If this information was not available, I used the first year that a clinic reported success rates to SART (1995–2007). Because agencies do not report to SART, I used the year their website domain was first registered using utrace.org – a website that allows users to geographically and temporally locate IP addresses and domain names. This final measure is not ideal, but provides an approximate timeframe for when an organization was founded.

Three variables served as proxies for what type of gamete donation model an organization used (traditional/medical versus consumer oriented): (1) whether donation arrangements were anonymous (=1) or if non-anonymous options were available (=0); whether donor photographs were available to clients (=1) or not (=0); and whether an organization used selective recruitment (=1) or not (=0). Selective recruitment included any of the following (1 = yes to any; 0 = no to all): (1) requiring or preferring applicants to be college educated (achieved or in progress); (2) advertising “exceptional,” or “premier” donors at a higher compensation level; and (3) specializing in donors with a particular racial/ethnic background, such as African American, Asian, Jewish, or Indian donors.

Ecological measures

Consumer demand was assessed with three measures using data from the 2000 Census: relative income of the local population, proportion of highly educated women in the local population, and total population. Hamilton and McManus (2005b) defined fertility clinic market areas by Metropolitan Statistical Areas (MSAs); however, not all organizations were located in MSAs. As an alternative, I used Census places, which include towns, cities, and villages (U.S. Census Bureau, n.d.). I created relative income from a ratio of the median Census place income to the median state income in order to standardize across states. A higher value indicated a relatively wealthier Census place within the state. I created an indicator for the proportion of highly educated women by summing the number of women over 25 with a Master’s or professional degree, or Ph.D., in a given Census place and dividing by the total female population over 25. I logged total Census place population after a preliminary analysis showed a highly skewed distribution.

Donor supply was indicated by the number of four-year colleges or universities within a five-mile radius from the organization, given the aggressive recruitment of college women as egg donors. This was created via the College Navigator zipcode searches from the National Center for Education Statistics website (http://nces.ed.gov/collegenavigator/). The five-mile radius was the minimal distance available for searching. This radius is also practically salient because many students would likely not donate eggs if a clinic or agency was a substantial distance away from campus due to barriers such as traveling time and access to transportation.

Market structure was measured by five separate variables: the degree of local market competition; whether the organization was in a mediated medical market (yes = 1; no = 0) as indicated by state insurance mandates; whether an organization was located in California (yes = 1; no = 0); whether an organization was in the West (yes = 1; no = 0); and whether an organization was in the Northeast (yes = 1; no = 0).

To gauge competition, I geo-coded each organization and computed a distance matrix (Ersts, n.d.). I then created count variables of the number of clinics and agencies within a 20-mile radius of each organization. Following Henne and Bundorf (2010), I created dummy variables for no competition (no organizations within 20 miles), low-medium competition (1–10 within 20 miles), and high competition (11+ within 20 miles).

Regulatory measures

Three measures assessed organizational conformity to regulatory pressure: whether the minimum age for donors was less than 21 (yes = 1; no = 0); whether an organization was professionally affiliated (yes = 1; no = 0); and whether the organization was in a state with parentage laws (yes = 1; no = 0).

All donors need to be 18, but the ASRM recommends 21 because of the medical requirements and for emotional and social maturity (ASRM 2006). Setting the minimum age below 21 may indicate a willingness to more aggressively recruit young donors or engage in other practices that do not entirely adhere to professional guidelines.

Data sources on professional affiliation differed for clinics and agencies. For clinics, I relied on documentation of the medical director on the current web-published ASRM membership list (www.asrm.org). For agencies, I relied on whether or not they were listed in the SART publication for compensation compliance (SART 2007).

At the time of data collection, eight states had specific parentage laws addressing egg donation (Crockin and Jones 2010). Organizations were coded as residing in a state with laws present (=1) or absent (=0). While parentage laws do not directly impact donor compensation and recruitment practices, they indicate regulatory climate.

Analytic strategy

In the first model, I examined starting donor compensation (dollars). This was a continuous variable, so I used linear regression. In the second model, I examined what predicted whether an organization had Tier 1 (less than or equal to $5000) versus Tier 2 (more than $5000) compensation. This was a dichotomous variable, so I used logistic regression.

Results

 Table 1 shows descriptive sample statistics. Average donor compensation was $5890. However, there was a substantial variation, ranging from $1300 to $15,000 per cycle. Nearly half (47.1%) of organizations set their starting compensation above $5000. Around one-third of the organizations were agencies, with the remaining two-thirds being fertility clinics. Average time in business was 13.9 years, but ranged from 1 to 45 years. Most organizations (71.4%) had anonymous-only arrangements; however, most (79.7%) did allow photographs of the donor to be shared with recipients. Just over one-fifth had selective donor recruitment criteria, such as requiring a college degree or recruiting donors of a specific race/ethnicity.

Table 1. Descriptive statistics (n = 276).

		M or %	SD	Range
	Compensation ($)	$5890.0	$2357.9	$1300 to $15k
	Compensation >$5000	47.1	0.5
Organizational	Agency	34.1	0.5
	Years in business	13.9	8.0	1–45
	Medical vs. consumer model
	Anonymous only	71.4	0.5
	Photographs	79.7	0.4
	Selective recruitment	21.7	0.4
Ecological	Consumer demand
	Relative income	1.2	0.5	0.55–3.25
	Prop. educated women	0.1	0.1	0.01–0.55
	Total pop (logged)	11.9	2.0	6.73–15.90
	Donor supply
	Nearby colleges/univ.	6.0	7.8	0–40
	Market structure
	No competition (within 20 mi)	11.6	0.3
	High competition (within 20 mi)	15.6	0.4
	Mediated	53.6	0.5
	California	21.4	0.4
	West	35.1	0.5
	Northeast	22.8	0.4
Regulatory	Min age < 21	35.5	0.5
	ASRM affiliate	78.6	0.4
	Parentage laws	22.1	0.4

In terms of ecological variables, most organizations were located in Census places with higher average income relative to the state average (1.2; a value of “1” indicates equality and less than one is lower average income than the state average). This suggests that clinics and agencies are located in somewhat higher socioeconomic areas, on average; although this ranged substantially within the sample (0.55–3.25). The average proportion of highly educated women in the surrounding area was 0.10, but this also ranged from 0.01 to 0.55. Average logged population was 11.9, which translates to approximately 150,000 people. Regarding donor supply, most organizations had an average of six four-year colleges/universities within five miles, but this ranged from 0 to 40. For market structure, 11.6% of organizations had no other competition within 20 miles; 15.6% had high competition, with 11 or more other clinics/agencies within a 20-mile radius. More than half of organizations (53.6%) were in a mediated infertility market (i.e. in a state with an infertility insurance mandate). Just over one-fifth of all organizations were located in California. More than one-third of organizations were located in the Western US and an additional 22.8% were located in the Northeast, collectively comprising over half of all organizations in the sample.

Finally, regarding regulatory variables, more than one-third of organizations recruited donors younger than 21. More than three-quarters were ASRM affiliated or on the SART-compliance list (78.6%). Twenty-two percent were located in states that had parentage laws.

Regression results

 Table 2 displays the linear and logistic regression results predicting average starting donor compensation and the likelihood of donor compensation being greater than $5000, respectively. Organizational form (agency vs. clinic) was significantly associated with both higher average donor compensation and having Tier 2 donor compensation rates (>$5000). Average agency compensation was $1595 more than fertility clinics, controlling for all other variables in the model. This is intuitive because agencies are considered more responsive to consumer demands by offering “hard-to-find” donors (Treiser, n.d.) and providing recipients with their ideal “match” or “perfect” donor (Spar 2006). Findings from the second model also confirm that agencies were more likely than clinics to set compensation levels over $5000. This is in line with prior work indicating that agencies have been viewed as somewhat more problematic in terms of abidance by ASRM standards (e.g. Keehn et al. 2015). It is important to reiterate, however, that only two organizations in the sample explicitly set their compensation at rates that violated the ASRM guidelines: one was a clinic ($10,500) and one was an agency ($15,000).

Table 2. Linear and logistic regression results predicting donor compensation (n = 276).

		Starting $		>$5000
		B	S.E.	B	S.E.	OR
Organizational	Agency	1595.26***	347.53	0.95*	0.44	2.59
	Years in business	−6.26	16.27	0.00	0.02	1.00
	Medical vs. consumer model
	Anonymous only	−215.39	329.30	−0.95*	0.42	0.39
	Photographs	58.84	304.24	−0.09	0.40	0.91
	Selective recruitment	30.40	299.22	−0.21	0.39	0.81
Ecological	Consumer demand
	Relative income	73.93	374.32	0.04	0.48	1.04
	Prop. educated women	−1541.55	2472.75	−1.16	3.16	0.31
	Total pop (logged)	−202.30*	90.72	−0.16	0.12	0.85
	Donor supply
	Nearby colleges/univ.	66.05**	20.86	0.06*	0.03	1.07
	Market structure
	No competition (within 20 mi)	−1052.52**	391.28	−1.95**	0.69	0.14
	High competition (within 20 mi)	229.83	387.55	0.94	0.52	2.55
	Mediated	1127.16***	341.07	1.23**	0.42	3.42
	California	681.16	419.30	0.29	0.51	1.34
	West	247.83	385.02	0.09	0.54	1.10
	Northeast	801.73*	376.90	0.43	0.47	1.54
Regulatory	Min age < 21	−106.39	260.66	−0.31	0.34	0.73
	ASRM affiliate	−53.43	291.35	0.35	0.38	1.42
	Parentage laws	314.40	321.32	0.65	0.42	1.91
	Constant	6764.03***	1358.17	0.82	1.73
	R^2/Pseudo R^2	0.40		0.27

*p < .05.

**p < .01.

***p < .001.

Organizations with anonymous-only donations were significantly less likely to set compensation above $5000. This finding is similar to sperm donation where willing-to-be-known donors command a substantially higher compensation (author’s own data collection). No other organizational characteristics were significantly related to donor compensation.

Ecological factors had the largest number of significant predictors for both models. Most of the variables measuring consumer demand were not significant; however, the total population was negatively associated with donor compensation. Areas with higher population had lower compensation by approximately $200 on average, controlling for all other variables. Increased donor supply was positively and significantly associated with donor compensation; organizations with a greater number of nearby colleges/universities within five miles had both higher average donor compensation and a greater likelihood of having donor compensation above $5000. Traditional microeconomic theory asserts that the price of a good or service responds to supply and demand (Propp, Krubert, and Sasson 2003) and that a greater supply should theoretically lower the price. Yet the increased supply of potential donors predicts higher donor compensation here. This reaffirms previous assertions that medical markets do not necessarily operate in expected ways (Light 2000; Spar 2006; Almeling 2011), especially Almeling’s (2011) observation that the basics of the supply–demand equilibrium do not function as expected in the US gamete market. Yet, why would greater potential supply drive up donor compensation? It is possible that greater potential supply is evidence of a more active market area. Organizations may also be able to generate “supplier-induced demand” (Propp, Krubert, and Sasson 2003) because of the concentration of a more “attractive” donor supply. By having more donor options, organizations can offer more consumer choice. Having ample supply is also low risk for organizations because they do not pay donor compensation until a client is matched; the largely virtual marketplace for donors (Holster 2008) means that organizations have low overhead costs for maintaining their “supply” of donors.

Market structure was also significantly associated with donor compensation. Intriguingly, while traditional microeconomic theory suggests that competition should influence price by relying on the laws of supply and demand to set the market price, having high levels of nearby competition did not affect donor compensation. Additionally, having no competition within a 20-mile radius significantly decreased both average donor compensation amounts and the likelihood that donor compensation was greater than $5000. Organizations with no nearby competition have both a monopoly and a monopsony (Pauly 1988) – they operate simultaneously as both the only seller and buyer of donor eggs. Being the only organization in a 20-mile radius could mean that a clinic or agency is trying to establish service in a frontier area, or an area with both low demand and low supply. In this instance, it could be a higher risk for an organization to charge more for donor compensation because it would further dampen demand from potential fertility clients. Additionally, given that they are the only donor egg “buyer” in the area, the lower donor compensation would likely not deter potential donors because they have no nearby alternatives.

Being in a mediated medical market (state-mandated insurance coverage of infertility services) was significantly associated with organizations having higher average donor compensation and a greater likelihood of donor compensation rates of more than $5000. Prior research has noted that a mediated market might either dampen demand because of caps placed on insurance payments or that it might increase demand by opening up access to a wider range of customers who could not afford to pay fully out of pocket (Conrad and Leiter 2004; Holster 2008). The fact that mediated markets drive up the price of donor eggs indirectly suggests that demand for donor eggs is also increased in these areas. This might indicate a market response to increased consumer ability to pay for, and therefore demand, reproductive services because costs of IVF treatment, which is part of the egg donation process, may at least be partially covered by health insurance (Hamilton and McManus 2005a; NCSL 2014).

Organizations located in the Northeast (compared to the South and the Midwest) had significantly higher average donor compensation levels, although they were not more likely start compensation above $5000. Notably, while much of the focus in investigating the fertility industry has been in California and the Northwest (Spar 2006; Almeling 2011), neither of these were significant predictors here of average compensation or likelihood of compensation greater than $5000.

Finally, there were no significant associations between any of the regulatory pressure variables (ASRM affiliation, parentage laws, donor minimum age practices) and donor compensation amounts. This lack of findings is quite intuitive, given the minimal regulatory oversight in the US and the lack of sanctions from industry self-regulation (King and Lenox 2000; SART 2007). Most notably, professional affiliation specifically was not associated with donor compensation amount or whether compensation exceeded $5000. This affirms prior work by Keehn et al. (2012) that professional affiliation does not guarantee adherence to best practice guidelines. Rather, in the absence of more effective sanctioning, organizational form (agency vs. clinic), donor supply, and market structure are more powerful in shaping prices (Covington and Gibbons 2007; Levine 2010).

Discussion and conclusion

This study aimed to empirically contribute to the growing literature on medical markets and the US bio-economy, focusing on the human egg donation market. I examined variability in egg donor compensation across the US. Although prior work has focused primarily on issues of commodification and coercion in the medical market for human eggs (Gurmankin 2001; Steinbock 2004), there has been little systematic analysis of actual pricing dynamics in the industry. Furthermore, the research that has been done on pricing has tended to focus on donor characteristics (Holster 2008; Almeling 2011), overlooking the larger organizational, market, and regulatory context in which these bio-commodities are valued and exchanged.

This study tested three primary explanations for what affects the price of donor eggs, drawn from the multi-disciplinary literature on organizational behavior: (1) organizational characteristics, (2) ecological factors, and (3) regulatory pressure. Organizational form (agency vs. clinic) was a consistent factor in predicting donor compensation. However, ecological factors are a main driving force in donor compensation. In particular, compensation levels were most responsive to potential donor supply as well as market structure (the degree of competition, mediated market). This reaffirms Spar’s (2006) assertion that the “baby business” needs to be seriously recognized as an industry in order to fully understand how it operates. Two broader implications of this analysis are addressed below: (1) the failure of regulatory pressure to produce organizational compliance and (2) the seemingly counterintuitive findings of donor supply and competition.

Failure of regulatory pressure?

The fact that ASRM affiliates were not significantly different from non-affiliates suggests that industry guidelines (The Ethics Committee 2007) were not being effectively translated into practice. Other than professional guidelines and industry self-sanctioning, there are no other regulatory mechanisms for the US fertility industry (Spar 2006; Levine 2010; Keehn et al. 2015). Although previous scholars have focused primarily on consumer preferences in shaping how biotechnologies will be used (King 1999; Silver 1999; Pennings 2000), this analysis suggests that we need to ask whether and how self-regulation works in mediated or fully private medical markets.

The ASRM effectively operates as a boundary organization (Miller 2001), attempting to strike a balance between the ethics and practice of reproductive medicine. In its earlier decision on donor compensation, the ASRM Ethics Committee explicitly recognized the clinical labor of donors – “the processes in which subjects give clinics and commercial biomedical institutions aces to their in vivo and in vitro biology” (Mitchell and Waldby 2010, 339) – by allowing payment for time and potential risk of the procedure. Yet, it simultaneously denied the biovalue of the product itself (Mitchell and Waldby 2010) by making it clear that payment was explicitly not for the eggs themselves, thereby attempting to downplay marketization. The fact that the recent price-fixing lawsuit (Gershman 2016) sided against the ASRM indicates that as a boundary organization it was not successfully managing the interface of ethics and practice. How might this affect or reflect on their ability to regulate other practices in the industry? Future research should explore this important question in more detail, especially engaging through observational or interview studies with practitioners making decisions “on-the-ground” in comparison to best practice guidelines.

Illogical medical market dynamics?

Scholars have already pointed out that medical markets do not necessary behave as theoretically expected (Light 2000; Almeling 2011). In particular, Spar (2006, xvi) has observed that the “baby market” defies laws of traditionally theorized markets: “differential prices that make little sense; scale economies that don’t bring lower costs; and customers who will literally pay whatever they possibly can.” In particular, we see this in the findings that increased potential supply raises donor compensation and organizations with no competition have lower donor compensation levels.

Regarding donor supply, it is important to note here that commodities in the “baby market” are not wholly fungible (Waldby 2008). Fertility clinics and agencies often use language that emphasizes finding a “perfect” or “ideal” match between egg donors and prospective parents (Johnson 2011, 2013), suggesting that this is a search for a specific donor with specific qualities (Holster 2008) as opposed to a search for the best deal one can get on an egg donor. Therefore, having a greater supply of possible donors increases the likelihood that a consumer will find their perfect match. This, combined with the low overhead cost associated with virtual marketplaces (Holster 2008), leads to a finding that on the surface appears to violate the supply–demand equilibrium, but is actually quite logical given the terms of the egg donation market.

The finding that organizations with no nearby competition have lower donor compensation levels, and that high competition does not affect donor compensation, violates the notions of how competition might affect price. Conventional understandings of “perfect competition” (Propp, Krubert, and Sasson 2003), however, clearly do not apply to the egg donation market. Yet, how do we interpret the counterintuitive effect that no nearby competition had on lowering donor compensation? I argue that this finding is linked to the fact that: (1) fertility organizations are simultaneously buyers and sellers of donor eggs and (2) areas where there is little industry growth may indicate lessened demand or infrastructure for the industry to develop if demand is dampened in these areas, organizations as sellers are incentivized to keep donor compensation prices low to attract consumers. Organizations as buyers also have no need to raise compensation to attract donors: they have a monopsony (Pauly 1988). Additionally, some agencies and clinics may actually cooperate rather than compete; because clinics offer a variety of other infertility services beyond egg donation, they may have a smaller in-house donor pool and rely on nearby agencies to meet client demands for specific donors (personal communication, organizational staff). Clinics would still be involved in the medical aspects of the donation, thereby not wholly losing out on profits from outsourcing donor services. Thus, examining network ties between organizations is an important avenue to pursue.

Overall, it is clear that the egg donation market does not mimic the theorized dynamics of traditional markets because it violates many of the basic assumptions upon which this ideal type is constructed (Light 2000). Rather than asking why the egg donation market is behaving in apparently irrational ways, a more productive question would be to ask: what is the internal logic of the egg donation market? What are basic assumptions that medical markets more broadly might operate under?

The study does have some limitations. First, data were collected only at one time point in 2009–2010. A more holistic analysis would be useful to show compensation rates over time, perhaps connected to founding and dissolution of organizations, shifts in nearby competition, and before/after the recent price-fixing lawsuit. Given the difficulty collecting data on this population, however, this offers a useful baseline analysis.

This study also had missing data on 52 organizations, resulting in a final analytic sample of 276 from a possible 328. It is possible, given the limited sample size, that certain effects were prone to a Type II statistical error (i.e. false negative results) or that the sample itself was biased in findings. Regarding the former, the only true remedy is to collect additional data to increase sample size. Comparative analysis with the full sample did not differ across certain characteristics that were available on all organizations, however, suggesting that the latter was not likely an issue.

The lack of findings for consumer demand, an important part of the market equation, is likely due to difficulty in conceptualizing demand here. I used a geographically linked measure of demand, but demand is not wholly geographically constrained because of the growth of the virtual marketplace and reproductive tourism (Holster 2008; Martin 2009; Braverman 2010). Therefore, demand may be more nebulous to adequately measure and understand in connection with spatially constructed measures of fertility markets. How can we address this in an increasingly virtual marketplace? One area where there is a significant gap in our knowledge is the question of how many donors and recipients travel to different locales for reproductive matches and services, and how far they travel (another city, another state, another country, etc.). Future research needs to more explicitly address this mobility to better understand how fertility market dynamics are shaped vis-à-vis reproductive tourism.

Finally, another fruitful area of research would be to examine regional or state-specific locations where the industry appears to be growing dramatically to better understand the fertility market. What predicts the growth in these areas? How might this affect how these technologies are used? As prior research has addressed (Conrad and Leiter 2004; Spar 2006), there are many ways to understand growth, including expanding potential consumers and potential uses of a technology, to normalizing a technology by moving it from experimental to mainstream, and achieving greater success rates due to new developments. Each of these potentially affects the dynamics of what is currently a highly contentious, but steadily growing medical market in the US.

Acknowledgements

I would like to thank Pat Rafail and David Johnson for reading and commenting on drafts of this paper. An earlier version, titled “Problematic Practices? Compensation and Selection in the Egg Donation Market” was presented at the 2011 ASA Meetings in Las Vegas, NV.

Disclosure statement

No potential conflict of interest was reported by the author.

Additional information

Funding

This material is based upon work supported by the National Science Foundation, Directorate for Social, Behavioral and Economic Sciences [grant number 0927836] and The Pennsylvania State University College of Liberal Arts RGSO Dissertation Support Grant.

Notes

1. By driving up donor compensation prices, they may be able to more aggressively recruit donors. By driving down donor prices, they may be able to more aggressively attract potential consumers. Either practice might be different from what older organizational practices are.

2. Currently, there are no studies addressing how many or how far donors and recipients travel in the US. To gauge organizational expectations, I drew a stratified, random sample of 30 organizations (15 clinics, 15 agencies) from my larger sample, described below, and coded donor/recipient intake forms. Sixty percent of agency in-take forms asked if a donor was willing to travel compared to 13% of fertility clinic forms. While this is not definitive, it strongly suggests that agencies are more likely to have donors travel to match with recipients compared to clinics.

3. One agency broke down travel costs in their recipient booklet for a non-local donor and a companion to accompany her. This included flights, hotel, daily meal allowance, phone/entertainment allowance, rental car, miscellaneous expenditures, and travel for medical/psychological screening, totaling around $5000 in addition to the $6000–$8000 donor compensation fee. This is likely prohibitive for many recipients or strongly figures into their choice.

4. While “covert research” can produce ethical conflicts in human subjects research, this study was focused on organizational policies: covert tactics used did not do harm to any individuals. Furthermore, the data being requested was innocuous information that a prospective donor would have been given over the phone: e.g. How much is donor compensation? Do I need parental consent to donate?