Mulling over Massachusetts: Health Insurance Mandates and Entrepreneurs

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Mulling overMassachusetts: HealthInsurance Mandatesand EntrepreneursScott Jackson

The author provides preliminary and provocative results regarding the impact of healthinsurance mandates on the propensity of entrepreneurs to start new organizations. Inkeeping with a well-observed propensity for individuals to adjust their economic calcula-tions in anticipation of future costs/benefits, the evidence suggests that when confrontedwith such mandates, potential entrepreneurs may either abandon entrepreneurial ambitionsor seek to minimize mandate costs through jurisdictional arbitrage with appreciable impli-cations for state and national level approaches to health care, health insurance provision,and workers.

Introduction

Does health care, and, by extension, public policy, affect the decision to become anentrepreneur? Many scholars have argued that policy establishes the framework in whichinnovation and economic growth take place, but little work has explored the specificimpact of individual policies. In recent years, a growing chorus of alarm has arisen overescalating health-care costs and their impact on businesses small and large. Ever increas-ing insurance premiums and health-care expenditures have fueled a lively political debateabout the future of the American health-care system and what is good for business andgood for America (Economist, 2007a; Maynard & Bloor, 2003). The system’s failureshave been largely ascribed to moral hazard in the insurance markets (Folland, Goodman,& Stano, 2004), and in 2006, the State of Massachusetts moved to mitigate this with aninsurance mandate. Empirically, however, it remains unclear if the current situation istransitory or a new equilibrium state, and whether the Massachusetts prescription is worsethan the disease. Given the increasing economic importance of small firms both to jobgrowth and innovation (Acs & Armington, 2006; Audretsch, 1995; Birch, 1987; Reynolds,1994), and the difficulty of changing policy once it has attracted a political following,policy prescriptions which by design or default have the potential to alter the fundamentalstructure of the economy must be evaluated in situ and ex post.

The current entrepreneurship literature has largely focused on the impact of individualattributes on opportunity identification (Minniti & Bygrave, 1999), the influence of themacroenvironment on aggregate rates of entrepreneurship (Shane, 1996, 2003), and on

Please send correspondence to: Scott Jackson, tel.: (703) 863-3316; e-mail: [email protected].

PTE &

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909September, 2010DOI: 10.1111/j.1540-6520.2009.00351.x

the impact of weak and strong social networks on new venture formation (Aldrich &Zimmer, 1986; Kim, Aldrich, & Keister, 2006). Beyond fiscal and tax policy, very littleattention has been paid to the impact of specific public policies and especially socialpolicies on the new venture landscape. The present theory and literature suffers from alack of explicit consideration of behavioral aspects, such as the impact of positive andnegative incentives on entrepreneurial activity (Minniti, 2004), and, with regard to healthinsurance, has considered only the direct impact of health insurance for the entrepreneurand their family on the decision to enter self-employment (Gurley-Calvez, 2006; Holtz-Eakin & Rosen, 2004). It has not considered the impact of health insurance on opportunityidentification, attractiveness, firm structure, or the viability of the enterprise (Shane), andthis research is the first to do so explicitly.

The present treatise draws from the literature in agglomeration economies and therates school of entrepreneurship (Shane, 1996), incorporates entrepreneurs possessingbounded rationality resulting from ambiguity, and attempts to provide empirical supportfor the integration of the Austrian and neoclassical economic schools posited by Minniti(2004). The purpose of the analysis is to determine whether the insurance mandate policyin Massachusetts has adversely affected entrepreneurial opportunity as evidenced by firmformation activity. The contributions of the research are (1) to open a field of inquiry intothe effects of indirect incentives on new venture formation activity; (2) to explore thepotential for benefit requirements to affect the new venture opportunity landscape andtherefore the opportunity identification matrix of entrepreneurs; and (3) to utilize physicalspace as a means of controlling variables in a policy analysis regime. It will do so byexamining a natural experiment created by the impact of Massachusetts’ health insurancemandate on the location preference for new organizations in the portion of the Bostonmetropolitan area, which lies astride the border between Massachusetts and New Hamp-shire. Subsequent tables and figures are prepared using the data from the analysis data setunless otherwise noted. The remainder of this paper will be comprised of a brief presen-tation of the legislative background sufficient to inform the analysis; a discussion of therelevant theory and the experimental hypothesis; and presentation of the methodology,including a discussion of the policy frontier. This will be followed by a presentation of theanalytical results, their implications for public policy, directions for future research, anda brief statement of conclusions.

Legislative Background: Health Care for All!On April 12, 2006, the Commonwealth of Massachusetts further expanded state

influence over health insurance and health-care markets when it passed a bill whose statedpurpose was “to expand access to health care for Massachusetts residents . . .” (mass.gov,2006a). This was not Massachusetts’ first foray into mandatory health insurance, butearlier attempts were repealed because of budgetary and economic pressure and loss oftheir political champions (Dukakis, 1994; Oliver, 2005). Massachusetts is not alone inthis, as Hawaii, Oregon, Washington, and California have attempted some form of healthinsurance market reform in recent decades; all have met significant legal and politicalchallenges (Oliver).

In brief, the Massachusetts legislation requires employers with 11 or more employeesto provide a Section 125 compliant, cafeteria-style health plan that can be purchased withpretax dollars. Individuals who are either self-employed or employed by employers withless than 11 employees are required to purchase insurance in the individual insurancemarket. The law provides penalties for noncompliance at the individual and employer level,and makes considerable attempts at mitigating some of the costs for smaller employers

910 ENTREPRENEURSHIP THEORY and PRACTICE

(mass.gov, 2006b, 2006c). The formal implementation date for employer coverage was July1, 2007, whereas individuals had until December 2007 to obtain coverage.

The economic justification for the law has been shrinking resources in the free caresystem, where funding is derived from an insurance surcharge. It is assumed the dropin funding is the result of individuals who use free care because either their employers donot provide insurance, they are inadequately self-insured, or they do not carry individualinsurance and consequently free ride on the system (mass.gov, 2006b). Achieving univer-sal insurance coverage would therefore more adequately fund the insurance risk pools inthe state, and individual and employer mandates were required to ensure participationin the insurance surcharge system.

At the bill’s passage, approximately 77% of employers in the Massachusetts portionof the study region had fewer than 11 employees; thus, the potential for a significantimpact to resident small businesses was substantial (see Table 1). To address this potential,the state created a bureaucracy charged with providing a low-cost insurance option andmonitoring the quality of insurance products in the state to ensure they met a minimumstandard. While such regulation can create substantial hardship on small businesses andtheir employees (Damberg, 1996), the current study is not concerned with the law’spotential to create footloose firms or encourage labor migration within a metropolitanstatistical area (MSA), but instead to explore the potential for the creation of barriers toentry or mobility by exacting a higher relative cost on new entrants than their predecessorsin the same industry had to bear (Caves & Porter, 1977).

If entrepreneurs possess bounded rationality, then the actual or absolute costs of theregulation are not as important as the perception of undue burden. Under these conditions,mandates may present barriers to entry and alter the overall firm formation ecology withpotentially significant long-term implications (Minniti, 2004, 2005). With few exceptions,the entrepreneurship literature has not attempted to assess the direct impact of individualpublic policies on new firm formation activity. Baumol (1990) points out that publicpolicy’s impact on entrepreneurship is the creation of the incentive structures that make

Table 1

Establishments by Size Grouping

Size group(employees)

Study region Private only Excluding orgs of 0 or 50+ MA only

Freq. % Freq. % Freq. % Freq. %

<20 39,588 78.19 38,565 78.07 38,480 95.420 86 0.17 50 0.191 6,188 12.22 3,267 12.72<11 36,058 71.22 19,891 77.4411–19 2,947 5.82 1,579 6.15�20 11,625 22.96 4,217 16.4220–49 1,977 3.9 1,847 3.74 1,847 4.58�50 9,065 17.9 8,986 18.19Total (n) 50,630 49,398 25,687

Source: InfoUSA/SalesGenie.MA, Massachusetts.

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certain activities either productive, unproductive, or destructive. The policy makes failingto provide health insurance illegal with penalties for noncompliance; penalties, whichalong with potential nonpecuniary costs (e.g., loss of prestige) for noncompliance, aredirect costs to the firm and create an opportunity to arbitrage the policy. Policy may alsoset up a self-reinforcing condition of x-efficiency, which is a technical inefficiency wheretotal costs fail to be minimized because output is lower than the level possible givenavailable inputs (Leibenstein, 1978). In this case, it would create a powerful disincentiveto new firm formation and an incentive to arbitrage opportunities across a relevantjurisdictional boundary. This study puts forward evidence that explicitly connects thepassage of this legislation with a notable downward shift in firm formation activity.

Theory and HypothesesFor many years, the entrepreneur was largely an implied element in economic theory

and empirical research (Baumol, 1968, 1993; Minniti, 2004). Alvarez and Barney (2007)have discussed two theories concerning the identification of entrepreneurial opportunities:the discovery theory and the creation theory. In the creation theory, the pool of opportunitiesis evolutionary in nature, subject to change by the action of economic agents. Theseopportunities are characterized by uncertainty and ambiguity as evolutionary processescreate, grow, and destroy them. Therefore, entrepreneurs assume ambiguity and uncertaintyin their efforts at exploitation, consistent with the neoclassical assumption of boundedrationality and the description of entrepreneurship as a complex system (Minniti).

In neoclassical theory, opportunities are resident in and part of a local milieu, whichin some measure determines the propensity toward productive, unproductive, and destruc-tive entrepreneurship (Baumol, 1990). In this context, institutions matter either by creat-ing opportunities directly, or by creating an environment in which local demandconditions, macroeconomic stability, competition, and rule of law are conducive to entre-preneurial action, where it influences the discovery and exploitation of opportunities. Assuch, institutional influence appears in multiple conceptual models, including Porter’sdiamond, Shane’s individual–opportunity nexus, and the GEM conceptual model (Porter,1990; Reynolds et al., 2005; Shane & Eckhardt, 2003; Sobel, 2006), but these models lackthe specificity to inform questions of the appropriateness of a given policy. Policies, forexample, to curb greenhouse gas emission may spur development in new, less pollutingtechnologies, but may also create a disincentive to invest in basic infrastructure andproductive capacity as the future value of those assets becomes uncertain.

Another basic weakness in current theory highlighted by Minniti (2004) is the abilityto capture the impact of incentives and social norms on the perceptions, identification, andexploitation of opportunities. To date, entrepreneurship theorists have not ventured intothe debate over health care and health insurance beyond the discussion of its impact on theindividual entrepreneur found in the job lock literature. This literature has focused on theavailability of insurance to the would-be entrepreneurs, and as such has concluded withpossible caveats for spousal insurance and female entrepreneurs (Blanchflower & Oswald,1998; Brunetti, Nayeri, Dobkin, & Brady, 2000; Buchmueller & Valletta, 1996; Gruber &Poterba, 1994; Wellington, 2001) that by and large, health insurance has no impact on theentrepreneurs’ decision to exploit (Holtz-Eakin & Rosen, 2004). The literature has notexplored the impact of health insurance or health care on the viability of the enterprise orthe ability to attract talent except for a 2003 National Federation of Independent Business(NFIB) survey, which found that benefits packages provided by very small firms weresomewhat more generous than those at slightly larger firms. Managers used healthinsurance to aid in recruiting (67%), reducing employee turnover (48%), and responding


to employee demand (41%) for insurance coverage (Morrisey, 2003). This same studyfound that 41% of employers with 10 or fewer employees offered health insurance, while78% of those with 20–249 employees offered health insurance. These results suggest thatthe competitive impact may differ for small versus slightly larger employers.

In the language of Shane’s individual–opportunity nexus (Shane & Eckhardt, 2003),macroenvironment variables, such as public policy, affect the discovery, exploitation, andexecution of opportunities by affecting the costs of the assemblage of resources, the designof the organization, and the business strategy. Due to its impact on startup costs, mandatoryinsurance coverage may be very negative for small and entrepreneurial firms; however, itmay be beneficial to slightly larger, fast-growing or gazelle firms. To the extent this decisionremains discretionary, entrepreneurs may choose to allocate resources with optimumflexibility between wages and benefits in order to maximize their comparative advantagerelative to other competitors in the labor market and achieve some state of technicalefficiency (Leibenstein, 1978).An insurance mandate, however, removes that flexibility andconsequently removes health insurance benefits from the tool kit for attracting talent. Thismay have detrimental effects not only on employers, but also on employees. Thus, thepolicy may fall more heavily on low-wage positions and employers who tended to employminimum wage workers, but also on very small firms by consuming additional financialresources, putting downward pressure on entrepreneurial activity, and suppressing new firmformation activity (Evans & Leighton, 1989; Kihlstrom & Laffont, 1979).

Given a long observed equilibrium of firm formation activity between communitiesalong the border of the two adjacent states, if this policy has no effect, then one wouldexpect no observable change in the proportion of new organizations started in Massachu-setts versus New Hampshire before and after the policy’s implementation ceteris paribus.The hypothesis of this study is that one of the effects of the policy is to diminish neworganization formation activity in Massachusetts relative to New Hampshire. This resultsfrom an increase in x-efficiency raising the minimum cost of production in a limitedfinance environment and thereby limiting the maximum number of potential firms. Whilethis diminishment should affect all organizations, it would be less profound for publicorganizations and not-for-profit organizations, which have nonmarket revenue streams(e.g., tax receipt transfers, grants).

MethodologyAt the time of the analysis, traditional U.S. government data sources were unavailable

for the period and location in question. Firm-level data for the analysis is drawn froma publically available subscription service, the InfoUSA business directories of newand current businesses for a group of 64 towns located in close proximity to theMassachusetts–New Hampshire border, and within the Boston–Cambridge–Quincy, NewEngland City and Town Area (NECTA)-defined metropolitan statistical area. Thesecommunities can be further organized in the Nashua, Lowell–Billerica–Chelmsford,Haverhill–North Andover–Amesbury, Lawrence–Methuen–Salem, and parts of theBoston–Cambridge–Quincy NECTA divisions, whose contributions to the study regionare summarized in Figure 1 and Table 2.

This geography was selected for four reasons. First, in order to control for macro-levelvariations and features of the consumer and labor market variables common to theeconomic literature (Armington & Acs, 2002), the analysis was constrained to a singleMSA or labor market area. This approach was suggested by Lucas (1988) and Acs andArmington (2006), and follows from the high level of integration of such markets in citiesand their broader economic areas, which are more homogenous, open economies with

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extremely fluid capital, labor, and knowledge. Second, in keeping with this logic, theentire Boston–Cambridge–Quincy NECTA MSA is a massive economic geography pro-ducing 9,000+ new establishments on a base of 80,000+ establishments in an ordinarycalendar year. An analysis that covered such a large database would have been unwieldybut also, owing to the overwhelming size of the Massachusetts contribution to the data set,would have mathematically swamped any contribution from New Hampshire, renderingthe analysis useless. By contrast, the smaller subset of this geography represented by thegeographic specification in this analysis contains a sample of sufficient size to permitanalysis without a loss of resolving power. This smaller geographic subset produces onaverage 2,000–3,000 new establishments per year based on data from the DynamicEstablishment Data 1989–2002, Office of Advocacy, U.S. Small Business Administration.Third, the smaller geography is approximately 60 by 20 miles, and thus well within the

Figure 1

Study Region Map

0 10 20 30 40 50 60 75 Mile0 s

Legend

State Line

Boston-Cambridge-Quincy NECTA (Parts)

Lowell-Billerica-Chelmsford NECTA

Lawrence-Meuthen-Salem NECTA

Haverhill-North Andover-Amesbury NECTA

Nashua NECTA

Hollis Nashua

Amherst

Wilton

Hudson

Mason

Milford

Merrimack

Greenfield

Lyndeborough

Brookline

Litchfield

Mont Vernon

Greenville

Derry

Chester

Raymond

Windham

Londonderry

TOWNSEND

PEPPERELL

Epping

Exeter

Kingston

FremontBrentwood

SandownDanville

Newton

AtkinsonPlaistow

Hampstead

Kensington

Seabrook

Hampton Falls

Newfields

East Kingston

South Hampton

HAVERHILL

NORTH ANDOVER

SALISBURYAMESBURY

WEST NEWBURY

GEORGETOWN

MERRIMAC

GROVELAND

Salem

METHUEN

LAWRENCE

Pelham

DRACUT

WESTFORD

BILLERICA

LOWELL

TEWKSBURYCHELMSFORD

DUNSTABLETYNGSBOROUGH

GROTON

ANDOVER

BOXFORD

NEWBURY

ROWLEY

NEWBURYPORT


natural spillover distance of 75 miles from the major universities of the region (Acs &Armington, 2006). This creates an environment where the relative information asymmetryfor new technology between employers is essentially equal and the probability of knowl-edge spillovers from the various public institutions is essentially constant. Finally, at thetime of this analysis, only a handful of states had intervened in the health care markets inso dramatic a fashion, and none aside from Massachusetts have an urbanized border.Therefore, this analysis presented a unique opportunity to use the physical environmentand the timing of a policy’s implementation to assess the impact of the policy.

Data for the Test of Proportions. The final geographic specification yielded a base dataset of 50,630 organizations started from 19841 through 2007. From this data set, thedescriptive statistics were drawn, and the z-test conducted. These organizations weregeo-coded in order to affix their location, assign location attributes, and associate themwith features of their local community, such as amenity mix. As a directory, the data arenot a sample, but the complete universe of firms within the specified geography; however,attribute data such as size or owner/manager gender were not available for every organi-zation listed in the directory. This causes results for firm-specific characteristics to besubject to selection bias. Regional level data was drawn from American Fact Finder andboundary files from the U.S. Census Bureau.

Data Used in the Random Effects Modeling Process. Since these data are drawn from adynamic source, which contains only active firms; since these data do not include firmdeath data, which has been demonstrated to be important in explaining firm formationrates; and since firm death data were unavailable from any source at the time of theanalysis, the analysis is subject to both survivor bias and selection bias (Acs & Audretsch,2003; Aldrich, 1990; Phillips & Kirchoff, 1989).

1. It was 1984 when the database was established, and therefore, this particular year is not accurate in termsof start year.

Table 2

Study Area Population and Organization Prevalence

Geography(NECTA division)

2006estimate†

Area(mi2)

Density(ppl/mi2) Organizations‡

Boston–Cambridge–Quincy, MA (parts) 82,319 142.02 580 4,358Haverhill–North Andover–Amesbury, MA–NH 241,503 372.19 649 11,410Lawrence-Methuen-Salem, MA–NH 144,501 56.64 2,551 6,428Lowell–Billerica–Chelmsford, MA–NH 285,915 199.51 1,433 10,495Nashua, NH–MA 307,280 572.25 537 17,939Total 1,061,518 1,342.63 791 50,630Massachusetts (contribution) 641,803 539.56 1,189 24,943New Hampshire (contribution) 419,715 803.07 523 25,687

† Source: U.S. Census Bureau, Current Population Survey and boundary files.‡ Source: InfoUSA data set, organizations established between 2000 and 2008.MA, Massachusetts; NH, New Hampshire.

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In an effort to produce a data set suitable for random effects modeling, which has theeffect of eliminating time-invariant elements in the estimation process, and to reduceselection bias resulting from the practice of researchers trimming their data sets ofincomplete observations, the aforementioned data set was balanced by replicating obser-vations such that an observation of each firm exists for each year in the study period(2000–2007), and by adding a term to control for entry and exit from the environment viaa dummy variable (exist). This produced what in effect are repeated observations of thesame agent over time (Johnston & Dinardo, 1997), and while not true panel data, a dataset most similar in character to panel data. This subset of the original data set represented25,152 establishments and 201,216 observations for analysis. A second source of selectionbias is the failure to include individuals who otherwise would have chosen to enterentrepreneurship in the absence of the policy (Iyign & Owen, 1998; Kihlstrom & Laffont,1979; Minniti, 2004). This source of bias is present in these findings, and may beaccounted for in part in the observed geographical arbitrage.

Survivor bias results from the natural culling process over time, as more fit organi-zations survive the entrepreneurial ecology (Geroski, 1995; Shane, 1996, 2003). Theeffects of survivor bias in this case may be mitigated by considering shorter time periods(Acs, Arenius, Hay, & Minniti, 2004; Levie & Autio, 2007), or considering only thechange from an equilibrium state. Given that all the MSAs and towns in the analysis arepart of the same economic geography and experience the same consumer, labor, andmacroenvironments, it is reasonable to posit that the relationships between their rates ofentrepreneurship would be stable.

AnalysisThe analysis proceeded through three steps: frontier identification, a test of propor-

tions (z-test), and finally a random effect model to confirm the significance of the shiftwhile controlling for industry, organization size, and other relevant variables. Under moretraditional analytical regimes, the use of a Poisson distribution with individual dataaggregated to some meaningful geography is used. This approach sacrifices data in theservice of technique and assumes the ability to predict rates of entrepreneurship ex ante,which may in fact not be possible (Minniti, 2004).

The Policy FrontierThe policy frontier was set at the boundary between 2005 and 2006, which corre-

sponds with the legislative approval of April 12, 2006. This choice is consistent with abounded rationality approach, because prior to legislative approval, the legislation couldhave been defeated or substantially altered, and thus, earlier dates would have obligedentrepreneurs to assume greater levels of uncertainty than their ventures would haveotherwise demanded. Given extensive media coverage and repeal of an earlier measureunder economic pressure in the 1980s, it is likely that nascent entrepreneurs would haveonly considered including the possible costs from the law once the uncertainty about itwas essentially settled.

Alternately, later dates would have failed to account for announcement effects(Bomfim, 2000) and would have resulted in a greater probability of false negatives. Whilethe final details are not firm until implementation, sufficient uncertainty about the policyhas been removed from the situation upon legislative approval. Since the legislationpassed 71 days into 2006, and since month and day data was not available, it was not


possible to exclude organizations started in 2006 before the April 12th passage of thelegislation. If we assume that the rate of formation is largely constant over the course ofa calendar year or that the first part of the calendar year is not spectacular with regard toentrepreneurial activity, then the impact of this inclusion would be to cause a slightunderestimation of the impact of the policy. The alternative of including 2006 in the beforeeffects would however result in a greater risk of a false negative, i.e., concluding there wasno significant event when one was present.

Chow tests were also performed for dummy variables representing policy frontiersat 2001, 2002, 2003, 2004, 2005, and 2006 in the econometric model. The years 2003(c2 = 95.47), 2004 (c2 = 37.06), and 2005 (c2 = 67.01) were the only frontier variables thatwere statistically significant. For the 2003 frontier, 2006 and 2007 data represent 68% ofthe data set, and for the 2004 frontier, 2006 and 2007 data represent 78% of the data. Thus,the inclusion of 2006 and 2007 in these frontiers could overwhelm the results. The 2007results were not significantly different from those of 2006 alone. The latter comprises 33%of the reference for the 2006 frontier, and therefore averaging may cause this frontiervariable to be insignificant.

Given that the region of analysis is a physically small geographical space whosegrowth patterns, laws, zoning regulations, tax systems, etc. have been stable for a longperiod of time, one might reasonably expect that the relative proportion of new firmsformed in one state versus the other to have reached a stable equilibrium. The 2005–2006policy frontier may also be observed visually by plotting the proportion of firms by stateand year (see Figure 2). The plot reveals a largely stable pattern with some noise up to2005. Excursions across the 50% boundary appear to be transient (i.e., noise), and do notappear to coincide with a sustained shift in overall formation. Subsequent to 2005, thereappears to be a substantial and sustained shift in the preferred location of new firms in theregion. This comports with the results of the Chow tests. The plot suggests that a tipping

Figure 2

Proportion of New Organizations in Massachusetts

30

40

50

60

70

Pe

rcen

t

1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007Year

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point (Minniti, 2004), which coincides with the April 2006 passage of the Massachusettshealth reform law, has been reached, and while one might consider the initial shift to bea transient, anticipation effect, the plot suggests a more persistent movement.

The Random Effects ModelThe purpose of the random effects model was to confirm the results of the z-test while

controlling for other potential factors. In developing this model, the principle questionunder investigation was “what impact does the policy have on the likelihood of a nascententrepreneur in this region choosing to locate their new enterprises in Massachusettsversus New Hampshire?” This particular formulation of the question suggests the use ofa logistic regression, where the dependant variable is the location decision in Massachu-setts (Y = 1) versus New Hampshire (Y = 0), and the independent variables organized intoelements representing the firm and characteristics of the local environment.

Human Capital and Institutional Fitness. Research indicates that institutional efficiencyis important in economic growth and entrepreneurship (Bartik, 1985, 1991, 1992;Baumol, 1990; Gwartney & Lawson, 2003; Sobel, 2006, 2007; Wasylenko, 1997). Thisstudy opts for a novel measure of institutional performance in the use of secondaryeducation performance as a proxy for both institutional efficiency and human capitalquality. Human capital is typically proxied using educational attainment of advanceddegrees (Acs & Armington, 2006); however, in a broader context, this measure ignores thequality of the general workforce. Secondary educational performance, however, reflectslocal institutional quality and the quality of the entry-level labor force in a much largercross-section of industries, as well as identifying concentrations of adults in the 28–44 agegroup, which has also been shown to be important in entrepreneurial activity (Shane,2003). Thus, institutional performance and human capital are proxied as the percentageof high school sophomores who scored in the advanced category on standardized tests ofmath and science in the previous academic year (lagHSMat). The 1-year lag on schoolperformance reflects the availability of this information, which is released when thetesting year is largely completed. In New England, these tests are also standardized acrossthe states of the region.

Market Demand and Knowledge Congestion Effects. Research has shown that the sizeof local market demand is important in the survivability and initiation of entrepreneurialactivity (Porter, 1990; Shane, 2003), and that knowledge spillovers are crucial in thetransmission of tacit knowledge and facilitated by higher population density or congestion(Florida, 2005; Jacobs, 1969). Therefore, market demand and spillover congestion aresimultaneously proxied by the population density (density) of the specific community inwhich the organization chose to locate.

Previous Entrepreneurship. Research suggests that entrepreneurs who are boundedlyrational take signals about the suitability of a community from other entrepreneurs in theirsocial network (Minniti, 2004), and thus, the rate of entrepreneurship is important inpredicting new firm formation activity. Earlier research (Shane, 1996) utilizes the rateof entrepreneurship subjected to a 1-year lag to account for information asymmetry,utility maximizing behavior, and bounded rationality in tight social networks (Gifford,1998; Minniti, 2004, 2005; Murphy, Shleiter, & Vishny, 1991). In this study, the rate ofentrepreneurship is computed using the number of new firms as a percentage of all firmsin the NECTA for the previous year (lag_entryrate).


Amenities. Amenities appear to play an important role in location decisions for individu-als, the vitality of communities, and in general rates of entrepreneurship (Aldrich, 1990;Aldrich & Zimmer, 1986; Florida, 2002). Therefore, amenities were controlled for in thisanalysis by the fact that each of the primary model terms was aggregated to the level ofthe specific jurisdiction, New England Cities and Town Areas, and since towns tend topresent amenity mixes to potential residents who sort themselves accordingly (Clark,Lloyd, Wong, & Jain, 2004; Gottlieb, 1994; Zelenev, 2004). Since amenities are not easilyadjusted in the short run, this has the effect of incorporating amenity mix into the analysis.

Size. Once the base model was developed, firm size was incorporated into the modelusing both the actual number of employees and dummy variables corresponding to theactual specific size groupings. Size groups were based on the cut point of 11 employeesin the legislation and a somewhat arbitrary upper limit of 20 employees intended to reducethe influence of potential corporate venturing or multisight organizations on the resultsand the long run size of the firm (Acs, Audretsch, Braunerhjelm, & Carlsson, 2004; Bates,1990; Iyign & Owen, 1998; Otani, 1996).

Industrial Sector. Dummy variables were also used to account for sector specialization inrecognition that each individual sector would have its own capital/labor ratio and featuresspecific to the dominant design of the business model. This approach is consistent with theprevalence in the literature of isolating the analysis, often a Cobb–Douglas formulation, toa smaller group of entities drawn from a specific group of standard industrial classificationcodes (Acs & Audretsch, 1987; Audretsch, 1995; Audretsch & Keilbach, 2004).

Thus, the base LOGIT model utilized as its dependent variable the location inMassachusetts, and its independent variables consisting of the percentage of high schoolsophomores who scored advanced on standardized tests of math and science in theprevious academic year (lagHSMat), the population density of the community (density),and the proportion of new organizations in the community in the previous year (lag_entryrate), and incorporated variables for sector and size. Since no technique currentlyexists to measure the goodness of fit of this type of regression, a measure was developedthat computed the percentage of all correct predictions, with bias assessed by examiningthe propensity to incorrectly predict one location over another.

LimitationsIn addition to the potential for selection bias and survivor bias already discussed, there

remains a potential for endogeneity resulting from the policy and location being simul-taneously determined by some omitted variable or variables. The policy results from thedecision of the state to use an insurance surcharge to gather revenue for a free-care systemversus some form of direct taxation. Endogeneity in this context refers to the geographyand not the firm or entrepreneur; therefore, nonserial entrepreneurs or New Hampshireresidents have not contributed directly to the legislation. Since endogeneity is to someextent present in all public policy because policy agents are endogenous, policy is pathdependent, and policy agents serve multiple roles in the political system. Furthermore, inthis context, since the signaling of entrepreneurs already in the market must be presentin the model to account for the dense social networks that are inherently endogenous,some endogeneity exists (Minniti, 2005). After extensive analysis, there were no adequatevariables used as an instrument that were correlated enough with the Massachusetts lawbut uncorrelated with the residuals. Under such circumstances, such endogeneity mightresult in a biased estimation (Johnston & Dinardo, 1997).

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An important assumption in this study is that there are limited financial resourcesavailable to all enterprises in a given region at a given period in time, and that the overalllevel of available financing is relatively constant, increasing or decreasing proportionallyor otherwise stable over time. This assumption is likely true in the relatively short periodsof time covered by the study and for the vast majority of new small enterprises whichdo not draw funding from commercial banks, venture capital, or private equity markets.However, in a larger context, some jurisdictions are capable of attracting financing fromoutside their political jurisdiction, and this ability might mitigate the effects of such apolicy locally while exacerbating it in less attractive jurisdictions.

One additional limitation is that the economy in question is one of the most vibrant inthe United States, and much of the country’s population lives in less dynamic environments.Thus, the observed effects may represent a best-case result of this and similar policies, andtherefore, underreport the true impact of such policies on physically larger, more ruralstates, where arbitrage is impossible, or states with urbanized international borders wherenew firm formation may be shifted more aggressively into lower labor cost environments.

Results

Test of ProportionsThe first empirical test is a one-tailed test of proportions or z-test. This test determines

whether the proportion of an event occurring in two samples in equivalent. The long-runaverage distribution between Massachusetts and New Hampshire in the study area is54.5% in Massachusetts and 45.5% in New Hampshire. The proportion of new firmslocated in Massachusetts was tested before and after the policy frontier. Prior to thepassage of the legislation, the equilibrium oscillated about 55.9% in Massachusetts and44.1% in New Hampshire, but subsequent to 2005, this relationship shifted, droppingto 38.2% for Massachusetts, and rising to 61.8% for New Hampshire. This 17.6% shiftwas found to be statistically significant (z = 35.9960, p = .000), and therefore supports theexperimental hypothesis that the Massachusetts health reform law diminishes entrepre-neurial activity. Using data from the 1989 to 2002 Small Business Administration (SBA)dynamic establishment data, the study region produces between 2,000 and 3,000 neworganizations per year. If Massachusetts contributed 55.9% before the shift, this wouldcorrespond to a loss of between 197 and 295 new organizations per year. The mean firmsize in the data set for organizations started after 2005 is six employees; therefore, this lossmight also be expressed as 1,182–1,770 full-time equivalents (FTEs) or 47,280–70,800man hours to the local economy per year.

Random Effect ModelsAs shown in Table 3, the random effects model (regression 1) was estimated using the

three general variables mentioned earlier and dummy variables to account for differentindustrial sectors (Acs & Audretsch, 1987, 1989; Audretsch & Acs, 1994; Audretschet al., 2002; Dinc & Haynes, 1999) and a variable for the policy frontier (After2005).Variants of the model examined the impact of organization size in regression 2 continuousand regression 3 discrete forms. Regression 4 considers the main model without the policyfrontier for the entire data set, and regression 5 considers only organizations formedbefore the policy frontier. Changing significance in coefficients has been highlighted. Thedependant variable in all cases is discrete, where a value of 1 corresponds to a location inMassachusetts.


Table 3

Random Effects LOGIT Model (Y = MA, 1; All Organizations)

1 2 3 4 5

MA MA MA MA MA

Exist 0.226 0.235 0.226 0.392 0.252(2.59)** (2.58)** (2.59)** (5.52)** (2.65)**

Density 16,003.043 14,917.295 16,001.726 10,811.506 13,820.653(49.75)** (48.45)** (49.74)** (60.29)** (47.15)**

Lag_entryrate -46.474 -32.176 -46.461 -97.124 6.621(17.51)** (10.12)** (17.51)** (44.11)** (2.04)*

LagHSMath 1.869 1.792 1.869 1.071 1.699(52.93)** (51.94)** (52.91)** (81.66)** (48.76)**

After2005 -16.151 -14.789 -16.158(34.50)** (30.95)** (34.49)**

SIC_16 2.895 2.846 2.886 1.439 2.949(2.37)* (2.34)* (2.36)* (1.62) (2.45)*

SIC_57 -2.237 -2.166 -2.237 -1.433 -2.323(5.20)** (4.77)** (5.21)** (4.87)** (4.74)**

SIC_58 1.581 1.473 1.667 1.453 1.294(4.02)** (3.67)** (3.89)** (4.63)** (3.04)**

SIC_59 -1.447 -1.285 -1.447 -1.260 -0.991(4.14)** (3.53)** (4.14)** (4.89)** (2.59)**

SIC_62 -3.048 -3.841 -3.055 -3.097 -3.691(2.86)** (3.25)** (2.87)** (4.07)** (2.65)**

SIC_64 -2.290 -1.938 -2.297 -2.654 -1.820(2.65)** (2.05)* (2.66)** (4.58)** (1.46)

SIC_65 -1.315 -1.097 -1.317 -1.052 -1.491(3.52)** (2.64)** (3.52)** (4.10)** (3.20)**

SIC_73 -1.162 -1.129 -1.167 -0.936 -1.026(3.93)** (3.69)** (3.94)** (4.20)** (3.23)**

SIC_86 -1.869 -2.182 -1.876 -0.880 -2.362(3.31)** (3.63)** (3.32)** (1.91) (3.62)**

SIC_87 -1.125 -1.004 -1.119 -0.919 -1.220(3.15)** (2.61)** (3.14)** (3.35)** (3.02)**

SIC_99 -1.740 -2.153 -1.747 -2.104 -1.959(6.59)** (7.19)** (6.61)** (10.73)** (6.09)**

No. of employees -0.004(6.35)**

11–19 employees -0.178(0.53)

Constant -31.097 -30.533 -31.088 -15.789 -31.701(50.79)** (49.95)** (50.74)** (56.95)** (49.77)**

Observations 201,216 147,400 201,216 201,216 91,008No. of infousaid 25,152 18,425 25,152 25,152 11,376Pr 0, 0 113,342 75,267 113,377 114,610 91,087Pr 0, 1 2,456 2,482 2,464 3,448 2,562Pr 1, 0 13,818 51,893 13,783 12,550 36,073Pr 1, 1 108,808 108,782 108,800 107,816 108,702% Correct 93.17% 77.19% 93.19% 93.29% 83.80%% Correct MA 97.79% 97.77% 97.79% 96.90% 97.70%% Correct NH 89.13% 59.19% 89.16% 90.13% 71.63%

Absolute value of z statistics in parentheses. * Significant at 5%; ** significant at 1%.MA, Massachusetts; SIC, standard industrial classification; NH, New Hampshire.

921September, 2010

This model correctly predicts location 92% of the time. It is important to note that thebase terms in the model do not gain or lose statistical significance in regressions 4 and 5,though specific sectors appear to be affected differently. This supports the veracity of theunderlying model. The model reveals that the shift is statistically significant when con-trolling for firm size in the continuous form and for industrial sector using sector-specificdummy variables. The -16.151 coefficient of the policy frontier implies that after thelegislation was passed, there was a 16.151-unit decrease in the log of the odds ratio, ora 16.2% decrease in the odds ratio for locating the new firm in Massachusetts. Thiscorresponds to an odds ratio of 9.68 ¥ 10-08, which might be compared with the impact ofthe lagged entry rate, whose odds ratio is 6.55 ¥ 10-21. Thus, the policy has a 13-foldgreater impact on new firm formation than the previous year’s level of entrepreneurialactivity.

Organization size was only significant when expressed in its continuous form, anddummy variables for sector were also significant. This conforms to the observation in theliterature that entry is a function of human capital as measured in the long-run size ofthe firm, and affirms the importance of sector specialization and jurisdiction (Acs &Audretsch, 1989; Audretsch & Acs, 1994; Audretsch & Feldman, 1996; Audretsch,Klomp, Santarelli, & Thurik, 2004; Bates, 1990; Highfield & Smiley, 1987; Iyign &Owen, 1998; Otani, 1996). While these results also support the experimental hypothesis,it remains unclear whether this effect is largely suppression, displacement, or both.

Impact on Private OrganizationsFurther refinements were made by examining the impact on private entities alone,

and eliminating organizations with zero employees and more than 50 employees (seeTable 4). Organizations of 50 employees were selected to reduce the affects of corporateventuring without erroneously discarding truly entrepreneurial ventures. Zero employeefirms were removed because of their potential for being non-entrepreneurial small busi-nesses. This removed approximately 20.3% of the organizations (see Table 1).

Regression 1 shows the analysis after the removal of government entities, regression2 after the removal of zero employee firms, regression 3 after the removal of firms with50+ employees. Regressions 4 and 5 are regression 3 with dummy variables correspond-ing to firms with <11 employees and 11–19 employees, and regression 6 is regression 3with a continuous term for the actual firm size.

These results continue to provide supporting evidence for the experimental hypothesisthat the policy diminishes entrepreneurial activity in Massachusetts. Coefficients for thevariables are comparable, the policy frontier variable continues to be negative and sig-nificant (p < .01), and the relationship with firm size appears unchanged.

Discussion

Implications for Public PolicyAs this analysis has demonstrated, the insurance mandate deployed in Massachusetts

to improve funding in the insurance market and subsidize the free care pool appears tohave adversely affected new firm formation activity. The extent to which the observedshift is comprised of both suppression and displacement of entrepreneurial activity isunclear and cannot be resolved by the present analysis. Further research is required.

If the observed shift is a transient effect, then one must still consider the possibilitythat the policy may push into part-time employment or unemployment individuals whowere heretofore employed, and thereby cause an amount of harm to those it was intended


Table 4

Reanalysis with Only Private Entities

1 2 3 4 5 6

MA MA MA MA MA MA

Exist 0.226 0.226 0.236 0.237 0.237 0.238(2.57)* (2.57)* (2.57)* (2.58)** (2.57)* (2.59)**

Density 15,975.471 15,972.690 14,819.955 14,817.584 14,816.630 14,838.942(49.41)** (49.41)** (47.62)** (47.59)** (47.61)** (47.49)**

Lag_entryrate -46.899 -46.931 -32.559 -32.529 -32.466 -32.777(17.53)** (17.53)** (10.13)** (10.12)** (10.09)** (10.18)**

LagHSMath 1.864 1.864 1.778 1.777 1.778 1.776(52.60)** (52.61)** (51.25)** (51.23)** (51.23)** (51.20)**

After2005 -16.064 -16.076 -14.638 -14.641 -14.648 -14.632(34.23)** (34.24)** (30.49)** (30.50)** (30.49)** (30.48)**

SIC_16 2.879 2.872 2.783 2.745 2.768 2.917(2.36)* (2.35)* (2.29)* (2.26)* (2.28)* (2.40)*

SIC_57 -2.237 -2.245 -2.181 -2.174 -2.181 -2.185(5.20)** (5.22)** (4.81)** (4.81)** (4.82)** (4.82)**

SIC_58 1.577 1.570 1.375 1.641 1.537 1.764(4.01)** (3.99)** (3.38)** (3.69)** (3.43)** (4.14)**

SIC_59 -1.452 -1.459 -1.274 -1.270 -1.273 -1.309(4.15)** (4.17)** (3.48)** (3.46)** (3.48)** (3.55)**

SIC_62 -3.049 -3.053 -3.831 -3.870 -3.844 -3.960(2.87)** (2.87)** (3.26)** (3.29)** (3.27)** (3.36)**

SIC_64 -2.296 -2.301 -1.918 -1.956 -1.931 -2.038(2.66)** (2.67)** (2.02)* (2.06)* (2.03)* (2.13)*

SIC_65 -1.317 -1.323 -1.115 -1.121 -1.118 -1.131(3.52)** (3.54)** (2.69)** (2.70)** (2.70)** (2.71)**

SIC_73 -1.166 -1.173 -1.184 -1.207 -1.193 -1.258(3.93)** (3.96)** (3.83)** (3.90)** (3.86)** (4.04)**

SIC_86 -1.870 -1.877 -2.182 -2.209 -2.195 -2.214(3.31)** (3.32)** (3.63)** (3.66)** (3.64)** (3.67)**

SIC_87 -1.126 -1.133 -1.021 -1.024 -1.014 -1.036(3.15)** (3.17)** (2.64)** (2.67)** (2.64)** (2.71)**

SIC_99 -1.740 -1.746 -2.164 -2.199 -2.179 -2.249(6.58)** (6.60)** (7.21)** (7.29)** (7.24)** (7.45)**

<11 employees 0.442(1.55)

11–19 employees -0.316(0.89)

No. of employees -0.050(3.42)**

Constant -30.972 -30.967 -30.263 -30.654 -30.251 -29.988(50.41)** (50.39)** (49.02)** (45.77)** (48.96)** (48.16)**

Observations 199,296 199,056 143,056 143,056 143,056 143,056No. of infousaid 24,912 24,882 17,882 17,882 17,882 17,882Pr 0, 0 112,392 112,288 72,876 72,014 72,894 72,559Pr 0, 1 2,416 2,416 2,404 2,412 2,406 2,431Pr 1, 0 1,374 13,720 7,932 7,894 7,914 8,249Pr 1, 1 107,432 107,280 90,396 90,388 90,394 90,369% Correct 98.31% 93.15% 94.05% 94.03% 94.06% 93.85%% Correct MA 98.74% 88.66% 91.93% 91.97% 91.95% 91.64%% Correct NH 97.90% 97.89% 96.81% 96.76% 96.80% 96.76%

Absolute value of z statistics in parentheses. * Significant at 5%; ** significant at 1%.MA, Massachusetts; SIC, standard industrial classification; NH, New Hampshire.

923September, 2010

to benefit (Damberg, 1996; Gruber, 1994). As employers adjust to the new fiscal realitiesof the policy, their need to shift resources away from wage compensation toward insurancecompensation may adversely affect the employment market. In businesses, which employlarge amounts of minimum-wage workers, this may prompt an increase in part-time andtemporary employment and a greater degree of unemployment for those at the bottomof the wage-skill hierarchy (Klerman & Goldman, 1994). As a result, the natural conse-quence of the policy might be to create additional unemployment for workers who will beunable to afford even subsidized individual insurance and therefore will seek treatment inthe free care system. The resulting strain on the state’s free care system may be greaterthan before. Demand for the state’s subsidized insurance program has caused a budgetcrisis (Economist, 2007b; Globe, 2008), and health insurance rates in Massachusettscontinue to accelerate faster than the national average (HFMA, 2007). Given the path-dependant, self-reinforcing nature of rates of entrepreneurship (Minniti, 2004), should theduration of the shift be prolonged, it could permanently alter the competitive climate.Potentially fleeing the adverse effects of the policy may be impractical for organizationswhose business model is based on foot traffic, such as retail, and hospitality sectors, whichare especially vulnerable to the adverse effects (Damberg).

If the shift is a more sustained realignment, then other parts of Massachusetts whichdo not have a large economic engine like Boston may suffer more serious suppression anddisplacement effects with consequently higher rates of low wage unemployment and agreater burden on local free care pools and other social services. It may also drive lowerwage service workers and companies out of Massachusetts, reducing the tax base andcreating price inflation in services.

What might Massachusetts’ experiment tell us about national approaches to universalcoverage? Is it possible, for example, that insurers who now have a legal rent to extractfrom the population no longer have an incentive to provide a lower cost product? It isinstructive to note that auto insurance mandates exist in every state, and despite heavyregulation, have not resulted in either lower premiums or universal coverage. If this is thecase for health insurance, there is no reason to believe that overall health-care expendi-tures or insurance premiums will decrease. Moreover, the presence of the state’s subsi-dized product may poison the private market for health insurance, leading to an eventualtakeover of all health financing by the state.

Our current insurance system is based largely on group coverage (Helms, 2001). Ithas created tremendous distortions and fragmentation in the insurance markets, includ-ing large segments without insurance coverage due to self-employment, transient unem-ployment, and predatory treatment by insurance companies. What this study suggests isthat state-level approaches will create arbitrage opportunities for entrepreneurs. Theseopportunities encourage unproductive entrepreneurship, and the insurance mandate hasa significant potential to have regressive consequences (Baumol, 1990; Damberg, 1996).The magnitude of this arbitrage effect at present is impossible to assess, but havingdifferent state-level policies limits labor mobility in the country for individuals andentrepreneurs and adversely affects the competitiveness of small firms. Such approachesare particularly problematic for small states and major metropolitan areas astride politi-cal boundaries.

If the effect of the policy is not jurisdictional arbitrage but outright venture formationsuppression, then policy makers choosing universal or near universal coverage mustalso be prepared to accept a slowing in the knowledge transfer environment and itsconcomitant decreases in entrepreneurship and economic growth (Audretsch et al., 2002).Will providing universal coverage improve industrial competitiveness? At least onecross-country study finds that a richer social welfare systems actually suppresses


entrepreneurship (Ilmakunnas & Kanniainen, 2001). While state-level policies mayhamper labor mobility nationally, they enable individual states to adopt more flexiblearrangements. To the extent that entrepreneurs are footloose, less generous states maypoach entrepreneurs from more generous regions and thereby increase internal migration.State-level efforts may also exacerbate well-documented regional differences in thequality of health care and the resulting impacts on health status (Eberstadt & Satel, 2004;Ohsfeldt & Schneider, 2006).

Based on the 1989–2002 dynamic establishment data from the U.S. Small BusinessAdministration, on average, the U.S. economy produced 588,214 new establishments peryear, and 454,614 (77.3%) of those establishments had fewer than 20 employees. Usingthe 2001–2002 change as a representative year, 82.9% of those new establishments werecreated in MSAs. Since the majority of U.S. MSAs, and especially the largest MSAs, donot lie adjacent to an international border, the dominant effect on the United States’business environment would likely be the suppression of new firm formation. Returningto our 17.6% shift downwards in Massachusetts’ contribution in the study geography, ifthis entire shift is the result of suppression, then extrapolation to the national level wouldamount to an average loss of 103,526 new firms per year. Since this would fall predomi-nately on the most vulnerable firms, and since these firms are likely concentrated in innercities and rural areas, the consequences on poorer households, states with larger ruralpopulations, and small cities would likely be exacerbated. Unlike other economic shocks,which might be initially absorbed by employment losses in larger firms or reductions inthe temporary workforce, this policy would result in fewer small firms being formed andlikely an increase in the temporary workforce and spreading the employment conse-quences across a much broader and more vulnerable segment of the population.

Using the dynamic establishment data and assuming the impact of Massachusetts-style health legislation is entrepreneurial suppression, if all of the affected firms wouldhave employed only five people at 40 hours per week, then this would mean a loss ofroughly 517,628 jobs per year or 20,705,127 compensated man hours. To the extent itcreates a significant shift into temporary employment, then individuals would bear theburden for their health insurance, and thus, suggesting that focusing on creating a moreadequate individual insurance market might be more appropriate direction for publicpolicy.

Direction for Future Research

Numerous avenues for future inquiry exist, including a need to validate and delvedeeper as more standard data sets become available. This analysis should be viewed asexploratory and preliminary. As the future unfolds, there is a need to revalidate thesefindings and assess the policy’s impact on survivability, and the use of more adequatemethods of assessing the impact in terms of employment. Additional research from theNew Hampshire side of the border may also elucidate the impact of displacement versussuppression in the analytical results. Does this evidence suggest that specific industriesbenefited or were adversely affected by the policy, why, and what might be the effect onhigh-growth firms? What mitigation may be in order to maintain a healthier industrialecosystem? Does the policy positively affect labor productivity by reducing employeeuncertainty? Does the policy create footloose small firms, and if so, what implicationsdoes this have for other similar approaches to health financing? Finally, what can ana-lytical approaches like this one, involving cross-border arbitrage, tell us about other publicand social policies?

925September, 2010

Conclusions

This study has examined the impact of a single public health policy on the ecology ofentrepreneurship in one firm formation ecology. The study observes that well-meaningsocial policies may adversely affect the business environment, with a notable potential tobe self-defeating. While public policy aimed at entrepreneurship may consume tremen-dous amounts of resources with no result (Minniti, 2004), policies that were intendedfor other purposes may also impact entrepreneurship with equally disastrous results. Animportant assumption in this analysis was that there are limited financial resourcesavailable to all firms in a given region at a given period in time, and that the overall levelof available financing is relatively constant; therefore, if Massachusetts has mandated thatthe slices of the pie must be larger by increasing start-up costs, then there can only befewer slices. In the long run, this may affect future economic vitality more profoundlythan its short-run impact.

Are the results of this study surprising? Benefit and wage costs are some of the largestrecurring expenses incurred by entrepreneurs, and at start up must be covered by raisingadditional capital. Mandating such benefits makes them no longer a potential competitiveadvantage for firms, making it more difficult to compete in the labor market and imposea burden on new entrants not borne by their predecessors (Bain, 1956; Carree & Thurik,1996; Caves & Porter, 1977). This places direct upward pressure on labor prices, and,more indirectly, on consumer prices. In the near term, the policy may prove to beinflationary, and as a consequence, have implementation difficulties as the economicclimate worsens (Oliver, 2005). What recent experience may suggest is that the days ofgroup coverage are numbered. Efforts at achieving universal coverage may be better spenton individual subsidies, consistent tax treatment of insurance regardless of purchaser, orattempting to construct a working individual insurance market with safeguards for indi-viduals at risk instead of trying to patch an otherwise inadequate system with insurancemandates. Similarly, data on the uninsured need also to consider those individuals who areeffectively self-insured and policy remedies, such as HSA-catastrophic insurance bundles,to enable the amelioration of catastrophic cost events, to enable an effective individualinsurance market.

The present results also argue for a national approach to health financing. Thisapproach could be merely to regulate health insurance at the national level; it could be todrive the insurance market towards an individualized insurance system or for the nationalgovernment to take a greater role in the financing of health care. National insuranceregulation would improve the fluidity of the national labor market, permit economies ofscale in administrative costs, and push the legal environment toward more standardapproaches to malpractice and insurance law. While each option has its own pitfalls andproblems, federal regulation and an individual insurance market create the greatest poten-tial to use the market to improve individual health, drive cost control, and create incentivesfor research into the most medically and economically efficient directions. In the finalanalysis, one must ask oneself whether or not a system with a disincentive to entrepre-neurship is actually good for America.

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Scott Jackson is a Policy Fellow at George Mason University, School of Public Policy, Fairfax, Virginia, anda Program Economist with the U.S. Agency for International Development. The opinions and views expressedare those of the author and not necessarily those of USAID.

The author would like to thank the generous support of George Mason University faculty Drs. Roger Stough,Naoru Koizumi, and Peter J. Boettke during the execution of this research, which is drawn from the author’sdissertation research.

This subject and study have been drawn from my dissertation research, and in part presented at the 47thAnnual Southern Regional Science Association Meeting, Arlington, VA, March 2008, the George MasonUniversity Entrepreneurship conference in that same month, in my doctoral dissertation defense April 16,2008, and further elaborated in my dissertation which is published electronically at http://mars.gmu.edu/dspace/handle/1920/3056. The paper itself, however, is unique.

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Mulling over Massachusetts: Health Insurance Mandates and Entrepreneurs

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