The location of initial public offering headquarters:An empirical examination

Michael Cichello & Douglas J. Lamdin

# Springer Science+Business Media New York 2014

Abstract We study the headquarters location of U.S. firms with an initial publicoffering (IPO) over the 2001–2011 period. Specifically, we examine IPO intensity,defined as IPOs in a state scaled by state population. We find that IPO intensity ispositively related to various measures of education. We also find that IPO intensity ispositively related to an economic climate (freedom) index, degree of urbanization, andwhether a state contains a financial center. Some economists see IPOs as a driver ofeconomic growth. Thus, our results suggest factors that government officials mayconsider to increase the number of IPOs headquartered in their states.

Keywords Initial public offering . Firm location . Financial center . Economic freedomindex

JEL Classification R38 . H32 . G24 . R58

1 Introduction

The growth of new firms and industries has been a focus of economic analysis for years.Some believe that the determination of new firm location is somewhat by chance. Forinstance, Baumol et al. (2007, pp. 109–110) state, “Locations hosting vibrant activity andinnovation develop largely by serendipity, but once one or two firms in a location in aparticular industry (or industries) become successful, they attract labor and entrepreneurs,and other services and suppliers, who build thicker and thicker networks, which in turn helpspawn other new firms.”UnderArmour, the athletic wear company, had its IPO in 2005, andis headquartered in Baltimore, MD. It was founded by Kevin Plank, a graduate of the

J Econ FinanDOI 10.1007/s12197-014-9283-5

M. CichelloMcDonough School of Business, Georgetown University, 37th and O Streets, NW, Washington, DC20057, USAe-mail: msc235@georgetown.edu

D. J. Lamdin (*)Department of Economics, University of Maryland Baltimore County, 1000 Hilltop Circle, Baltimore,MD 21250, USAe-mail: lamdin@umbc.edu

University of Maryland. Chipotle Mexican Grill, which had its IPO in 2006, isheadquartered in Denver, CO. Its founder, Steve Ells, graduated from the University ofColorado. Facebook, the social network firm, had its IPO in 2012. It is headquartered inMenlo Park, CA, not where it began in Massachusetts where Mark Zuckerberg startedFacebook while a Harvard student. Are these corporate headquarters location decisionsmainly serendipitous, with little explanation to be found, or can we explain why some statesattract more IPO headquarters than others? Our goal is to shed some light on this issue.

Despite an appearance of serendipity, policymakers at the federal, state and locallevel have made efforts to encourage entrepreneurs to form and grow their businesses.For example, federal legislation in the form of the Jumpstart Our Business Startups Act(JOBS Act) was signed into law in April 2012 with the intention to increase smallbusiness funding. However, unless there is a good understanding of the factors thatmatter for business growth, policies designed to enhance it are not likely to succeed. Weattempt to increase this understanding.

From a geographic perspective, differences exist in both institutions and populationcharacteristics at the state level. Do these differences matter for the spatial distributionof IPOs across states? More specifically, we examine whether education, economicclimate, urbanization, and the location of a financial center in a state influences state-level IPO intensity. Our results show that a higher level of education attainmentcorresponds to higher IPO intensity. We also find a positive relationship between IPOintensity and the economic climate measure, degree of urbanization, and having afinancial center.

There is a large IPO-related research literature. There is another large literature thatfocuses on new business formation (but not IPOs in particular). Our focus appears to beunique viewed from the perspective of either of these literatures. We are unaware ofstudies that have studied geographic differences in IPO headquarters location. Wojcik(2009, p. 193) comments: “Although Initial Public Offerings (IPOs) belong to the mostpublicised events on financial markets and one of the most researched areas in financeand economics, little research has been conducted within these disciplines on thegeography of IPOs within countries, as if it is assumed that geography of regions andurban centres did not matter for public equity markets.” He continues (p. 194) “there isno systematic literature on why propensity to go public could depend on corporatelocation within a country.” Although not a large focus of interest in their work Kenneyet al. (2012)) comment that their data show “there are marked differences betweenstates in terms of their ability to create new firms that grow sufficiently to be eligible foran IPO.” It is these “marked differences” that we try to explain, and the gap in theexisting literature we seek to begin to fill.

As mentioned earlier, policymakers have made efforts to encourage and enhancenew business formation and growth. Without a research base that informs policydevelopment, these policies are less likely to achieve their goals. Our results may shedfurther light on what factors appear to be conducive to a particular form of businessgrowth, the IPO. These results can be useful to economic development policy.

It is not surprising that state and local governments seek to retain existing corporateheadquarters, and be the location for new ones. The contribution to the local economyof, for example, the Home Depot headquarters (IPO in 1981) to the Atlanta, GA area,or the Microsoft headquarters (IPO in 1986) to the Redmond, WA area, or the Appleheadquarters (IPO in 1980) to the Cupertino, CA area is undeniable. Of course, not all

J Econ Finan

of Home Depot’s 331,000 full-time employees, or Microsoft’s 94,000 full-time em-ployees, or Apple’s 63,300 full-time employees reside in the immediate area of theheadquarters. 1 The corporate headquarters do, however, tend to have highly-paidemployees. Headquarters activities also tend to require highly-paid providers of busi-ness services (Katz 2002). Card et al. (2010) also document the financial importance ofcorporate headquarters to local charities. One aspect of the Facebook IPO that gener-ated much attention was the expected large income tax revenue windfall that Californiawould receive when Facebook shareholders residing there realized large capital gainsafter the IPO.

We see at least two reasons for the state-level focus. One is that at a national level,variation in IPOs over time within a nation is unlikely to uncover differences in factorsthat influence IPO activity because factors measured at this level change slowly overtime. The time-series variation in IPO activity at the national level is strongly influ-enced by the state of the stock market. IPOs activity rises and falls with bull and bearmarkets. This makes detecting of other factors difficult to identify. States, however,have the potential to exhibit the type of cross-sectional differences in characteristics thatcan help explain why IPO intensity differs. A second reason for a state-level focus isthat public policies designed to influence new business location are often a state-levelpolicy matter.2 States differences in policies, as well as institutions and demographiccharacteristics can be large. Thus, these differences can be used to potentially identifyfactors that influence IPO intensity.

The paper is organized as follows. In Section II, we review the literature. Section IIIprovides a description of the data and empirical model. Section IV provides results ofour analysis. Finally, Section V concludes the paper.

2 Literature review

This work is obviously related to the IPO research literature. It is also related to theliterature that focuses on new business formation and economic development. Theformer is the domain of financial economists and appears predominately in financejournals. The latter is primarily the focus of economists and others who specialize inregional economics and economic development. Our work is more in the spirit of thelatter. Thus, we focus on surveying what appears to be the most relevant backgroundfor what we examine here—the literature on the location of business formation andeconomic development. The IPO literature focuses on issues such as the persistentunderpricing of IPOs, the long-run under-performance of IPO stocks, “excessive”commissions charged by underwriters, and the drop in number of IPOs in the U.S.over the last decade.3 Geographic variation in IPOs largely does not appear to havebeen addressed in the finance literature. Perhaps in the future the two parallel literatureswill cross paths to the benefit of both.

1 Employment data are from Thomson Reuters as of August 2012.2 See Hart (2008), for a discussion of the growth of state-level entrepreneurial economic developmentstrategies.3 See Ritter and Welch (2002), Ritter (2003), Ljungqvist (2007), Yong (2007), and Ritter (2011) for a reviewof this literature.

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Many scholars have studied regional, state, or metropolitan differences regardingmeasures of new business development. We build upon and extend these studies.Examples include Lee et al. (2004), who examined new firm formation at the metro-politan and labor market area levels. They found that new firm growth is relatedpositively to a “creativity index,” which measures the proportion of residents in anarea who are, for example, authors, designers, and musicians; human capital (percent-age of adults with a bachelor’s degree); income growth; and population growth. Thenew firm growth measure in their analysis uses the Longitudinal Establishment andEnterprise Microdata (LEEM), created by the U.S. Bureau of the Census. We willreturn to this later, but point out that 580,803 new firms were created in 1997–1998 inthe LEEM data. This is much larger than the 757 U.S. IPOs during the 1997–1998period.4 Aggregated to the state level, the LEEM data in Lee, Florida, and Acs (2004)show that per capita new firms was highest in Colorado, Wyoming, Nevada, Montana,Idaho, and Utah. Well down the list are states usually associated with new firm growth,such as California (23rd) and Massachusetts (38th). At the metropolitan statistical arealevel, the two leaders with the largest number of new firms per capita are Naples, FL,and Wilmington, NC. San Francisco is 23rd, and Boston does not appear as among thetop 50 metropolitan areas. Bartik (1989) examined small business formation at the statelevel, rather than the metropolitan level; Glaeser and Kerr (2009) examinedmanufacturing firm formation at the metropolitan area level; and Kreft and Sobel(2005) examined sole proprietorships as well as patents at the state level. We concurwith Glaeser and Kerr when they state (2009, p. 626): “Despite the extensive effortdevoted to characterizing entrepreneurship, there is little consensus about the mostappropriate metric.”

Compared to the various new firm formation measures in previous studies, we breakfrom this approach. We use a much more narrow measure—the IPO. The moreencompassing measures of new firm formation used in the studies mentioned aboveare certainly of interest. However, we view IPOs not simply as an alternative measure.If the desired focus is on existing successful businesses with a high likelihood ofsurvival and future growth, IPOs may be a preferred measure than more encompassingmeasures used in the existing literature. Reaching the IPO stage is a far higher hurdlethan establishment of a new firm.

A focus on IPOs rather than other metrics is also consistent with Shane (2008). Herefers to the Acs and Armington (2006) study showing per capita new businessformation (also using the LEEM data) is higher in Laramie, WYand Bozeman, MN,for example, as compared to San Francisco and San Jose, the endpoints of California’sSiliconValley. Out of 394metropolitan areas, San Francisco ranks 121st and San Jose165th. Laramie andBozeman rank above SanFrancisco andSan Jose becauseworkersin the latter twocities havemore jobopportunities at large andgrowingcompanies thanworkers in Laramie and Bozeman. Lack of large and growing companies creates thesituation where new business formation (likely small and with little prospect forgrowth) is higher on a per capita basis in Laramie and Bozeman. Thus, “new busi-nesses” can be a misleading measure of “innovation and entrepreneurship,” as gener-ally perceived. Shane also comments that (2008, p. 164): “larger businesses that are

4 Data are from Table 1 of Jay Ritter’s website http://bear.warrington.ufl.edu/ritter/IPOs2011Statistics70512.pdf.

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more heavily capitalized, organized as corporations, started on a full-time basis by ateam of entrepreneurs who have a written business plan will, on average, be moresuccessful than other new businesses.” Our use of IPOs will focus on the type ofbusinesses Shane refers to, rather than more encompassing “new business”measures.

3 Data and empirical models

Our primary variable of interest is the number of IPOs with their corporate headquarters ina state that were brought to themarket during the 2001–2011 period. This is divided by thestate population in millions in 2000 to calculate what we label IPO Intensity.5 Our sourcefor the IPO data is the website www.renaissancecapital.com. Renaissance Capital usesSecurities and Exchange Commission filings to tabulate all IPOs by the state in which theIPO is headquartered for each year beginning in 1999. The period used consciously didnot include the 1999–2000 IPOs even though the data were available beginning in 1999.Pre-2001 data are distorted by the tech boom and bust which included many IPOs thatwould not be representative of a more normal period of business formation and accessingof capital markets. For example, in 1999–2000, California had 286 IPOs. A similarnumber of 290 occurred in the 11 years that followed. Likewise, Massachusetts had 67in 1999–2000, and 75 in the next 11 years. Taking a longer and broader perspective,Kenney et al. (2012) comment that the U.S. IPO market from 1980–2000 had an averageof about 300 IPOs per year, while in the 2001–2010 period IPOs fell to a little less thanone-third of the pre-2001 annual level. Restricting ourselves to the 2001–2011 period, weexamine 1,227 IPOs.

To provide a better sense of the dependent variable we analyze, Table 1 lists (i) thenumber of IPOs by state, and (ii) this number scaled by state population. During 2001–2011, the largest three states in terms of the raw number of IPOs were California (288),New York (131), and Texas (127). The highest three population states as of 2000 wereCalifornia, Texas, and New York. Once IPOs are scaled by state population in 2000 toarrive at our IPO Intensity measure, the three highest IPO Intensity states are Massa-chusetts (11.8 IPOs per million people), Connecticut (9.1), and California (8.5). Theaverage state had 3.1 IPOs per million people from 2001–2011. Three states had zeroIPOs during the period under examination–Alaska, New Mexico, and Vermont. Ar-kansas, Hawaii, Maine, Mississippi, North Dakota, and West Virginia each had onlyone IPO.

Our empirical strategy is similar to that used in Kreft and Sobel (2005), and Garrettand Rhine (2011). In these studies the dependent variable, sole proprietorship growth inKreft and Sobel, and employment growth in Garrett and Rhine, is regressed onindependent variables measured to temporally precede the measurement of the depen-dent variable. We believe that this approach is a good way to mitigate possibleendogeneity issues. Also, although the dependent variable data are potentially a panelof 50 states over 11 years, we deem panel estimation as inappropriate for two reasons.First, there is relatively minor variation in the independent variables over the 11-yearperiod. Second, there is a large clustering of dependent variable observations equal to 0when examined on a year-by-year basis. In fact, 268 of what would be 550 dependent

5 The population data are from the U.S. Census Bureau website.

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variable observations are equal to 0. Thus, we estimate a cross-section on 50 stateobservations.

The variables selected to explain the dependent variable were chosen, in part, basedon the literature on new business formation. Those variables expected to influence newbusiness formation would be similar, though not necessarily identical, to those expectedto influence IPO formation. We will get to the specifics shortly, but in a general sensethe models include state-level measures of education of the population, economicclimate, the degree of urbanization, and access to capital markets. 6 With 50

Table 1 IPOs per State

State Number IPOs(2001–2011)

IPOsper million

State Number IPOs(2001–2011)

IPOsper million

Alabama 7 1.6 Montana 2 2.2

Alaska 0 0.0 Nebraska 3 1.8

Arizona 11 2.1 Nevada 13 6.5

Arkansas 1 0.4 New Hampshire 1 0.8

California 288 8.5 New Jersey 48 5.7

Colorado 34 7.9 New Mexico 0 0.0

Connecticut 31 9.1 New York 131 6.9

Delaware 3 3.8 North Carolina 21 2.6

Florida 49 3.1 North Dakota 1 1.6

Georgia 23 2.8 Ohio 14 1.2

Hawaii 1 0.8 Oklahoma 19 5.5

Idaho 3 2.3 Oregon 2 0.6

Illinois 51 4.1 Pennsylvania 44 3.6

Indiana 16 2.6 Rhode Island 2 1.9

Iowa 5 1.7 South Carolina 3 0.7

Kansas 3 1.1 South Dakota 2 2.6

Kentucky 5 1.2 Tennessee 22 3.9

Louisiana 5 1.1 Texas 127 6.1

Maine 1 0.8 Utah 8 3.6

Maryland 27 5.1 Vermont 0 0.0

Massachusetts 75 11.8 Virginia 42 5.9

Michigan 14 1.4 Washington 22 3.7

Minnesota 22 4.5 West Virginia 1 0.6

Mississippi 1 0.4 Wisconsin 10 1.9

Missouri 11 2.0 Wyoming 2 4.1

This table reports the number of IPOs in each state over the period 2001–2011. Number IPOs represents thecumulative number of IPOs headquartered in a given state over the eleven year sample period. The totalsample includes 1,227 IPOs. IPOs per million represents the number of IPOs over the eleven year period forevery one million people in a given state. Data is from Renaissance Capital (www.renaissancecapital.com).

6 The academic perspective is echoed in a financial executive trade journal article by Cheney (2011). He statesthat companies look for havens from irrelevant regulation and complicated taxation, and a skilled workforce.

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observations, parsimony in choice of independent variables for inclusion in theregression model is warranted, without glaring omissions. Descriptive statistics ofregression variables used are summarized in Table 2.

We examined three education variable measures: (i) the percent of the populationage 25 or older with a bachelor’s degree only (Bachelor); (ii) the percent of thepopulation aged 25 or older with a graduate or professional degree beyond thebachelor’s degree (Advanced); and (iii) the percent of the population aged 25 or olderwith a minimum of a bachelor’s degree (Min Bach), which is the sum of measures (i)and (ii).7 The average state had 15.39 % for Bachelor, 8.38 % for Advanced, and theaverage for Min Bach was 23.78 %. There is large variation in these values acrossstates. Bachelor had a minimum of 8.90 % (West Virginia) and a maximum of 21.60 %(Colorado). Advanced had a minimum of 5.50 % (North Dakota), and a maximum of13.7 % (Massachusetts). Min Bach had a minimum of 14.80 % (West Virginia), and amaximum of 33.20 % (Massachusetts). It is expected that higher educational levelscontribute to higher IPO intensity. We see two reasons for this, which are not mutuallyexclusive. First, a higher-skilled population is more likely to contain the entrepreneurswho start firms that progress to the IPO stage. Entrepreneurs need not have formaleducation as evidenced by a college degree, with Steve Jobs of Apple and Bill Gates ofMicrosoft being two well-known examples. Jobs and Gates, however, are the exceptionrather than the rule. Baumol et al. (2009) show that the vast majority of noted inventors,entrepreneurs, and investor-entrepreneurs in the U.S. born in the twentieth century havecollege degrees. While Jobs and Gates were not degree holders, their firms depend on askilled labor force with such training. Therefore, a second reason for the potentialimportance of formal education is that firms, both prior to, and after their IPO, generallyrequire access to college-educated employees. Ancillary business services used by thefirm (e.g., accountants, lawyers) also require college-educated employees. The resultsin the Doms et al. (2010) study of new firms lends support to the view that theeducation level of the entrepreneur as well as the workforce in the area of the firmboth matter for the creation and success of new businesses.

The state economic policy environment measure used is developed and calculatedby Karabegovic et al. (2002) in the publication Economic Freedom of North America.They label this measure the economic freedom index (Freedom). We provide a generaloverview of the index. Details of the index construction are provided in EconomicFreedom of North America. The index is designed to capture the economic climate in astate on three dimensions: size of government (which has two components), taxation(which has four components), and labor market flexibility (which has three compo-nents). The size of government component of the index considers government expen-diture as a percent of state gross domestic product (GDP) and transfers and subsidies asa percent of state GDP. The taxation portion of the index considers total governmentrevenue as a percent of state GDP, the top marginal income tax rate and theincome level at which the top marginal rate is applied, indirect taxes as a percentof state GDP, and sales taxes as a percent of state GDP. Finally, the labor marketfreedom component of the index considers the ratio of the annual income at theminimum wage divided by per capita GDP (i.e., state productivity), and

7 Data are from the National Center for Education Statistics publication, The Digest of Education Statistics:2010, Table 11.

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government employment as a percent of total employment. Each of the indexes iscalculated as the average of its components. Likewise, the overall index, which we focuson, is calculated as the average of the three component indexes. The overall index iscalculated such that a higher number implies more economic freedom (smaller govern-ment spending, less taxation, a less restrictive labor market). Kreft and Sobel (2005) andGarrett and Rhine (2011) use this measure in their studies.8 The index has been shown tobe positively and significantly related to sole proprietor growth by Kreft and Sobel(2005), and to employment growth by Garrett and Rhine (2011).

We use the 2000 index values from the Appendix Table 2 in Karabegovic et al. (2002).To provide a sense of the index values, the average was 7.28. The highest three index stateswere Delaware (8.4), Tennessee (8.3), and Colorado (8.2). The lowest three were WestVirginia (5.8), Alaska (6.1), andMontana (6.2).With the index designed to measure overall“business friendliness,” we expect it to have a positive relationship to IPO intensity.

Interestingly, Kenney et al. (2012)) note what seems to be a paradoxical inverserelationship between state IPO activity and perceived business friendliness. Theydo not refer to any particular measure of business climate or friendliness inmaking this conjecture. In particular, they note that California and Massachusettsare major IPO headquarters states, and are not perceived to be as business friendlyas Utah, Texas, and Florida, the specific states that they mention. The freedomindex values for California and Massachusetts in 2000 were 6.9 and 7.9. In Utah,Texas, and Florida these were 6.9, 7.8, and 7.6. Although California is the lowestamong these five states, and below the average of the 50 states of 7.28, it is thesame as Utah. Massachusetts actually has the highest value of these five states.Thus, the perception of business friendliness, at least in this circumstance, may notcomport with measured business friendliness that the economic freedom index isdesigned to objectively gauge.

8 This freedom index measure has also been used by others, such as Campbell et al. (2012), Apergis et al.(2014), and Heller and Stephenson (2014).

Table 2 Sample Summary Statistics

Variable Mean Median Min Max Standard Deviation

IPO Intensity 3.08 2.27 0.00 11.81 2.64

Bachelor (%) 15.39 15.55 8.90 21.60 2.61

Advanced (%) 8.38 8.10 5.50 13.70 2.04

Min Bach (%) 23.78 23.35 14.80 33.20 4.28

Freedom 7.28 7.30 5.80 8.40 0.62

Urban (%) 71.69 71.50 38.20 94.40 14.90

FinCent 0.10 0.00 0.00 1.00 0.30

This table reports summary statistics for the sample of 50 U.S. states. IPO Intensity is the number of IPOsheadquartered in a given state per one million residents. Bachelor is the percentage of residents who hold onlya bachelor’s degree. Advanced is the percentage of residents who hold an advanced degree (e.g., masters,Ph. D., law, medical). Min Bach is the percentage of residents with at least a bachelor’s degree (or the sum ofBachelor and Advanced). Freedom is the Freedom Index measuring the economic climate in a given state.Urban represents the percentage of a state’s population designated as living in an urban environment. FinCentis an indicator variable equal to one if the state contains a financial center.

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Glaeser (2009), among others, emphasizes the importance of an urbanized area tofoster economic activity. The reasons for this include access to various inputs (e.g.,suppliers), and a large customer base. The percent of a state’s population living in anurbanized area is the measure used here (Urban).9 The average state had 71.7 % of itspopulation located in urban areas, with a minimum of 38.2% and amaximum of 94.4%.The largest urbanized populations are in California and New Jersey (94.4), and Hawaiiand Nevada (91.5). The states with the lowest urbanized population are Vermont (38.2),Maine (40.2), and West Virginia (46.1). We note that population density (populationdivided by land area) is not appropriate to use. It is a poor measure of urbanization incases where states have large geographic areas that are sparsely populated, but most oftheir population is concentrated in urban areas. For example, Nevada and Arizona havelow population densities, but highly urbanized populations.

Wojcik (2009) and others have pointed out that geographic proximity of firms toinvestors matters.10 He labels the importance of proximity of access financial capital“financial centre bias.” Loughran (2006) finds that firms with headquarters in rural areasare less likely to have secondary equity offerings, which is consistent with this view.Access to a large pool of investors would seem related to having a large urbanpopulation, however, the financial capital access measure is sufficiently different fromurbanization per se that a separate variable seems warranted. We examine this hypoth-esis primarily through a dummy variable for states with a financial center city (FinCent).We follow El Ghoul et al. (2013) in their definition of U.S. financial centers. Theprimary U.S. financial centers are New York, Chicago, Los Angeles, San Francisco,Boston, and Philadelphia. 11 Thus, we treat the five states of New York, Illinois,California, Massachusetts, and Pennsylvania as financial center states. Again, afterEl Ghoul et al. (2013), we examine the impact of broader measures of U.S. financialcenters, which include Minneapolis and Baltimore in the “top eight” financial centers,and Atlanta and Milwaukee in the “top 10” centers. Thus, Minnesota, Maryland,Georgia, and Wisconsin are treated as financial center states in some specifications.

Before proceeding to the regression analyses, Table 3 shows the bivariate correlationcoefficients between all of the regression variables. The first column of the table showsthat all of the variables have a correlation coefficient that is positive and significantlycorrelated with IPO intensity. Some of the independent variables we use are significantlycorrelated (e.g., FinCent and Urban), while others are not (e.g., Freedom and FinCent).With these correlations noted, we present results from our regression analyses.

4 Regression results

4.1 Baseline results

Table 4 shows the results of 5 model specifications to explain IPO Intensity. Models (1)– (3) include as explanatory variables the economic freedom index, the percent of

9 Data are from The Statistical Abstract of the United States, 2011, Table 29.10 See also Zook (2002), and Cumming and Dai (2010) for evidence that venture capital firms and the firmsthey fund tend to be located near each other.11 Wojcik (2009) has the same list of cities except for Philadelphia.

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population living in an urban area, and the financial center dummy variable. Thespecific measure of education is not clearly apparent. Therefore, models (1) – (3) eachuse the three different measures described earlier. Model (1) uses the percent of thepopulation who have only a bachelor’s degree (Bachelor); model (2) uses the percent ofthe population who have an advanced degree beyond a bachelor’s degree (Advanced);and model (3) employs the percent of the population who have at minimum abachelor’s degree (Min Bach). Each of the education measures is positively andstatistically significantly related to IPO Intensity, so we cannot distinguish a clearlypreferred measure. Table 3 shows that the three measures are highly correlated witheach other (the three bivariate correlations range from 0.69 to 0.94), so this is notsurprising. Regardless of the education measure, the economic freedom index, urban-ization, and financial center variables are all of the expected positive sign and statis-tically significant at the 5 % level or better. Finally, the models exhibit a large amountof explanatory power, with adjusted R-squared values ranging from 0.51 to 0.53.

Model (4) uses the minimum bachelor’s degree education measure as in model (3),but explores the expansion of the definition of financial center states. Model (4)includes a separate dummy variable (FinCent2) for Maryland and Minnesota. Thisvariable is not statistically significant. Model (5) examines the even broader measure ofsecondary financial centers and includes Georgia, Wisconsin, Maryland and Minnesotaas a separate dummy variable. This variable is not statistically significant. Thus, theprimary measure of the major financial centers matters, while the secondary ones donot. In a sense, this confirms a reasonable expectation that primary financial centers areinfluential, and secondary ones are not.

4.2 Freedom Index Disaggregated

In unreported results, we also separated out the economic freedom index into its threecomponents–government size, taxation, and labor. The three individual components are

Table 3 Correlation Coefficients for State Level Variables

IPO Intensity Bachelor Advanced Min Bach Freedom Urban

Bachelor 0.45***

Advanced 0.57*** 0.69***

Min Bach 0.55*** 0.94*** 0.90***

Freedom 0.36** 0.06 0.13 0.10

Urban 0.60*** 0.41*** 0.52*** 0.49*** 0.25*

FinCent 0.50*** 0.15 0.36** 0.26* 0.06 0.36**

This table reports correlation coefficients for U.S. state level variables. IPO Intensity is the number of IPOsheadquartered in a given state per one million residents. Bachelor is the percentage of residents who hold onlya bachelor’s degree. Advanced is the percentage of residents who hold an advanced degree (e.g., masters,Ph. D., law, medical). Min Bach is the percentage of residents with at least a bachelor’s degree (or the sum ofBachelor and Advanced). Freedom is the Freedom Index measuring the economic climate in a given state.Urban represents the percentage of a state’s population designated as living in an urban environment. FinCentis an indicator variable equal to one if the state contains a financial center. Significance for a two-tailed test atthe 1 %, 5 %, and 10 % levels are denoted by ***, **, and *, respectively.

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Tab

le4

Modelsof

IPO

Location,

2001–2011

.1

23

45

Bachelor(%

)0.26**

(2.35)

Advanced(%

)0.38**

(2.50)

Min

Bach(%

)0.19***(2.70)

0.18**

(2.43)

0.19**

(2.62)

Freedom

1.02**

(2.34)

0.97**

(2.24)

1.01**

(2.35)

1.02**

(2.35)

1.01**

(2.32)

Urban

(%)

0.06**

(2.63)

0.05**

(2.38)

0.05**

(2.31)

0.05**

(2.29)

0.05**

(2.28)

FinC

ent

2.89***(3.11)

2.38**

(2.51)

2.63***(2.87)

2.71***(2.90)

2.64***(2.81)

FinC

ent2

0.74

(0.54)

FinC

ent3

0.03

(0.03)

Constant

–12.61***(–3.73)

–11.10***(–3.50)

–12.52***(–3.84)

–12.38***(–3.76)

–12.51***(–3.77)

N50

5050

5050

Adj

R-Squared

0.51

0.52

0.53

0.52

0.52

ThistablereportsOLSregression

results

forh

eadquarterlocationof

IPOsintheU.S.T

hedependentvariable,IPOIntensity,isthenumbero

fIPO

sheadquarteredinagivenstatepero

nemillionresidents.Bacheloristhepercentage

ofresidentswho

holdonlyabachelor’sdegree.A

dvancedisthepercentage

ofresidentswho

holdan

advanced

degree

(e.g.,masters,Ph.D.,

law,m

edical).MinBachisthepercentage

ofresidentswith

atleasta

bachelor’sdegree

(orthe

sumof

Bachelorand

Advanced).F

reedom

istheFreedomIndexmeasuring

theeconom

icclim

ateinagivenstate.Com

ponentsof

theaggregated

indexaregovernmentsize,taxatio

n,andlabor.Urban

representsthepercentage

ofastate’spopulatio

ndesignated

asliv

inginan

urbanenvironm

ent.FinC

entisan

indicatorv

ariableequaltooneifthestatecontains

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includes

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ois,Massachusetts,N

ewYorkandPennsylvania).

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ent2

isan

indicatorvariableequaltooneforthestates

ofMarylandandMinnesota.F

inCent3

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ofMaryland,Minnesota,G

eorgia

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-statisticsarein

parentheses.Significance

foratwo-tailedtestatthe1%,5

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***,

**,and

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ely

J Econ Finan

significantly correlated with each other. The three bivariate correlations range from0.43 to 0.59. Each individual component has a higher correlation with the overall index,with a range of 0.76 to 0.86. This of course reflects that the sub-index is one-third of theoverall index. Using model (3) but replacing the overall index with each separate indexshows that all three components have positive signs, but they all are statisticallyinsignificant. Given the small sample size, it is likely that the collinearity amongstthe three sub-indexes does not allow us to discern the effects of each sub-index. Kreftand Sobel (2005) find this phenomenon as well. They similarly find statistical signif-icance for the overall economic freedom index, but not for each of the three sub-indexes entered simultaneously in their regressions explaining sole proprietorshipgrowth rates.

We also reestimated model (3), substituting each sub-index one at a time for theaggregate index. Thus for model (3), we regressed IPO Intensity on Min Bach, Urban,FinCent, and GovtSize, the government size sub-index. We then ran the same regres-sion, replacing GovtSize with the Taxation sub-index. We do this for the Labor sub-index as well. All sub-indexes coefficients are positive. The GovtSize sub-index wassignificant at the 5 % level. The Taxation sub-index was significant at the 10 % level.Finally, the Labor sub-index was nearly significant only at the 10 % level (p=0.106).These results mimic somewhat those of Garrett and Rhine (2011) who perform asimilar exercise. They also find the overall economic freedom index exhibits signifi-cance, and some, but not all of the sub-indexes entered individually are significant intheir regressions explaining employment growth rates.

While the overall index clearly exhibits statistical significance, the disaggregatedresults are mixed. Collinearity amongst the three sub-indexes does not allow us todiscern any significant effects of any sub-index when jointly considered. Individually,there is some evidence of statistical significance of the sub-indexes, but the differencesare not large. In sum, a strong conclusion about which individual index component“matters most,” or “matters least” is not warranted. Evidence suggests looking at theoverall index.

4.3 Further Assessment of the Models

We examined two extensions of the Table 4 results. The first extension examines theeconomic significance of our results. We use model (3) to illustrate. In Table 5 we showthe impact on IPO Intensity of increasing each continuous independent variable by onestandard deviation, holding all the other variables at their mean. For this analysis, weset the financial center variable equal to zero. We then examine the absolute andpercentage increases in IPO Intensity that result.

For example, holding the Minimum Bachelor Degree, Freedom Index and Urbanvariables at their means and setting the dummy variable, Financial Center, to zero, weexpect 2.94 IPOs per million of state population, denoted as BaseIPO in Table 5. Whenwe increase only the Minimum Bachelor Degree variable by one standard deviation,IPOs per million population increases to 3.75. This is an economically significantincrease of 27.7 % in IPO Intensity. Likewise, for a one standard deviation increase inthe Freedom Index and Urban variables, IPO Intensity increases by 21.3 % and 25.4 %,respectively. Finally, holding the continuous independent variables at their means andgoing from a non-Financial Center state to a Financial Center state increases IPO

J Econ Finan

intensity by 89.6 %. In light of these percentage increases, all the independent variablesare economically significant influences on IPO Intensity, in addition to their statisticalsignificance. In particular, the financial center dummy has a particularly powerfulinfluence, perhaps to the point of being implausibly large. We hasten to add, however,that the increase in per capita IPOs of 2.63 per million (5.57 minus 2.94) attributable tohaving a financial center in a state occurs over the eleven-year sample period. Having0.24 (2.63/11) more IPOs per capita per year is not an implausibly large value.

A second extension is to examine the residuals and influential observations. Inanalyzing the residuals, we do not see any particular patterns that would lead us tobelieve we have an obvious missing variable or misspecification of the model.12 Toexamine influential observations, we find the Cook’s distance statistic (Cook 1977) formodels (1) – (3). We then rank the Cook’s distance statistic and rerun the regression,eliminating the three most influential observations, corresponding to those at the 95thpercentile and greater of the distribution.13 We do so one at a time and collectivelyeliminating all three. This yields twelve additional regression runs (or four for eachmodel). Out of these twelve runs, all variables remain statistically significant at the10 % level or better, with most variables significant at the 5 % level or better.

4.4 An alternative dependent variable

We also considered an alternative definition of the dependent variable. One mightbelieve that the total dollar volume raised by the IPOs is an appropriate measure of IPOactivity rather than the number of IPOs. This alternative measure, IPO Dollar Intensity,

Table 5 Economic Significance of Independent Variables from Model (3) Table 4 Results

Variable Base IPO Plus SD Pct Incr

Min Bach (%) 2.94 3.75 27.7 %

Freedom 2.94 3.56 21.3 %

Urban (%) 2.94 3.68 25.4 %

FinCent (%) 2.94 5.57 89.6 %

This table reports the change in the IPO Intensity measure in absolute amount and in percent from its valuewhen all variables are held at their mean and the specified variable is increased by one standard deviation fromthe mean. Min Bach is the percentage of residents with at least a bachelor’s degree (or the sum of Bachelor andAdvanced). Freedom is the Freedom Index measuring the economic climate in a given state. Components ofthe aggregated index are government size, taxation, and labor. Urban represents the percentage of a state’spopulation designated as living in an urban environment. FinCent is an indicator variable equal to one if thestate contains a financial center. Base IPO is the number of IPOs per million population using mean inputs forthe independent variables. Plus SD is the number of IPOs per million population, increasing the indicatedindependent variable by one standard deviation from Base IPO. PctIncr is the percentage increase in thenumber of IPOs per million population for one standard deviation increase in the independent variable. TheFinancial Center dummy variable is set equal to zero in all calculations.

12 Interestingly, Oklahoma and Connecticut are the two most under-predicted states for IPO intensity. Aconnection between these two states is not readily apparent.13 The most influential observations are Massachusetts, Illinois, and West Virginia in Model (1); Massachu-setts, Connecticut, and Illinois in Model (2); and Massachusetts, Illinois, and Nevada in Model (3).

J Econ Finan

is defined as the total dollar value raised by the IPOs with a headquarters in a statedivided by state population in millions. As before, we sum the values over 2001–2011to generate 50 state-level observations. The summed amounts are the dollar values ofall the IPOs in the state measured in year 2000 dollars, using the GDP deflator.14 Thelargest three states in terms of raw (year 2000) dollar value of summed IPOs areCalifornia ($49.1 billion), New York ($38.0 billion), and Texas ($30.0 billion). Thelargest three states in terms of dollar value of IPOs per million in state population areConnecticut ($2,976), New York ($2,003), and Colorado ($1,624).

Before presenting the results based on the alternative measure of IPO intensity somepoints warrant comment. One is that this dollar-based measure would not providedifferent information from the count-based measure if all IPOs are the same size indollars raised. Of course, all IPOs will not be the same size. What is important to note,however, is that any difference in results with this alternative measure is due tovariation across states in the average dollar size of IPOs. The correlation coefficientbetween the between IPO Dollar Intensity and IPO Intensity is 0.78. This implies thatthe two measures are not the same, but are far from unrelated. To be more concreteabout the extent of the similarity in the two variables, the “top ten” IPO Dollar Intensityand “top ten” IPO Intensity states have an overlap of eight states. The “bottom ten” IPODollar Intensity and “bottom ten” IPO Intensity states have an overlap of seven states.Our a priori belief is that the number of IPOs is a preferred measure as it is less subjectto a few large and possibly idiosyncratic IPOs having a larger impact on the results.15

We will discuss other reasons why we prefer the number of IPOs shortly.Table 6 contains descriptive statistics of IPO Dollar Intensity and its bivariate

correlation with the independent variables used earlier. For comparison purposes,correlation coefficients for IPO Intensity and the independent variables are also pre-sented. The dollar amount of IPOs headquartered in a state per million population has amean (median) value of $672 ($484), with a minimum of $0 and a maximum of$2,976. Interestingly, the IPO Dollar Intensity variable has a lower correlation witheach of the independent variables than the IPO Intensity variable, although all of thecorrelations remain statistically significant.

The regression results in Table 7 are analogous to models (1) - (5) in Table 4. Thealternative IPO dollar-based measure, IPO Dollar Intensity, replaces the IPO count-based measure, IPO Intensity. The independent variables remain the same as those inthe models displayed in Table 4. In all the regressions, the coefficients for theindependent variables remain positive, except for the FinCent2 and FinCent3 variables,which are negative, but insignificant. However, the t-ratios diminish for all estimatedpositive coefficients. The economic freedom variable, Freedom, is statistically signif-icant only in Model (5), while the financial center variable, FinCent, and the percentageof population in urban areas variable, Urban, are only statistically significant in Model(1). The education variables, Bachelor, Advanced, and Min Bach, are statisticallysignificant at the 10 % level in Models (2) – (5). Finally, the adjusted R-squared rangefrom 0.28 to 0.31, well below those in Table 4.

14 Results are similar using a CPI deflator.15 The three largest IPOs in our sample were in fact not “ordinary” IPOs, but mature businesses. These wereVisa in 2008 (raised $17.9 billion), General Motors in 2010 (raised $15.8 billion), and Kraft, a spinoff fromPhilip Morris in 2001 (raised $8.7 billion).

J Econ Finan

The results with the IPO Dollar Intensity measure are weaker than those with theIPO Intensity measure. Thus, some comment about the relative importance of each iswarranted. We view the count-based IPO results as deserving more attention for twoprimary reasons beyond the outlier observations issue raised earlier. One is mainlyeconomic, the other mainly econometric. Suppose that a state has 10 IPOs that eachraise $100 million (total raised $1 billion). Another has only one IPO that raises $1billion. The IPO Intensity measure deems the former as a greater incubator of IPOs thanthe latter. The IPO Dollar Intensity would view the two states as equivalent. We wouldside with the IPO Intensity measure as the more appropriate because 10 firms haveexpanded to the stage at which an IPO occurred, not only one. We refer to this result asthe volume effect. Geographic serendipity is more likely to account for one, but not 10IPOs. In our hypothetical example, the “one large IPO” seems more likely to be of the“geographic serendipity” variety a regression model will not explain well. Most or all10 IPOs in the other hypothetical state are unlikely to be of “geographic serendipity”variety. Rather, some of these can be explained by underlying economic factors in aneconometric model, which is our goal here.

We can see some merit to considering IPO size as relevant. Again a hypothetical isuseful. Suppose again that a state has 10 IPOs, of $100 million each. Another state alsohas 10 IPOs, of $1 bilion each. The IPO Intensity measure would view the two states asequivalent IPO incubators. The IPO Dollar Intensity would view the second state as oneof greater IPO activity. If our goal was to measure factors that influence the size ofaggregate IPOs, obviously IPO Dollar Intensity is the appropriate measure.Kooli and Meknassi (2007) examine the survival rate of IPOs. They show that

Table 6 Descriptive Statistics and Correlation Coefficients for IPO Dollar Intensity Measure

Mean Median Min Max Standard Deviation

IPO Dollar Intensity 671.8 484.2 0 2,975.6 625.2

IPO Intensity IPO Dollar Intensity

IPO Dollar Intensity 0.78***

Bachelor 0.45*** 0.34**

Advanced 0.57*** 0.47***

Min Bach 0.55*** 0.43***

Freedom 0.36** 0.30**

Urban 0.60*** 0.48***

Fin Cent 0.50*** 0.37**

This table reports summary statistics and correlation coefficients for the sample of 50 U.S. states. IPO DollarIntensity is the dollar value of IPOs (in 2000 constant dollars) headquartered in a given state per one millionresidents. IPO Intensity is the number of IPOs headquartered in a given state per one million residents. Thistable reports correlation coefficients for U.S. state level variables. IPO Intensity is the number of IPOsheadquartered in a given state per one million residents. Bachelor is the percentage of residents who holdonly a bachelor’s degree. Advanced is the percentage of residents who hold an advanced degree (e.g., masters,Ph. D., law, medical). Min Bach is the percentage of residents with at least a bachelor’s degree (or the sum ofBachelor and Advanced). Freedom is the Freedom Index measuring the economic climate in a given state.Urban represents the percentage of a state’s population designated as living in an urban environment. FinCentis an indicator variable equal to one if the state contains a financial center. Significance for a two-tailed test atthe 1 %, 5 %, and 10 % levels are denoted by ***, **, and *, respectively.

J Econ Finan

the average gross proceeds of IPOs that survived was larger than that of thosethat were acquired, and those that were acquired tended to be larger than theIPOs that were non-survivors. In fact, Exhibit 2 from Kooli and Meknassiimplies that the survival rate of the smallest 24% of IPOs is 17%, and thesurvival rate of the largest 15% of IPOs is 57%. This lends support to the viewthat 10 “large” IPOs are more important economically than 10 “small” onesbecause larger IPOs tend to survive at a higher rate than smaller ones. We referto this result as the size effect. We view the volume effect (measured with IPOIntensity) of greater importance than the size effect (measured with IPO DollarIntensity) for our objective. We note, however, that the two sets of regressionresults with the two measures are generally complementary, rather thanconflicting.

5 Conclusion

Overall, our findings confirm our expectations. Our state-level IPO intensity measure,IPOs scaled by state population, is positively related to a state’s education level,economic climate (freedom), degree of urbanization, and whether the state has a(major) financial center. Despite the fact that some IPO headquarter locations are surelyidiosyncratic or serendipitous in nature and therefore these types of observations are

Table 7 Models of IPO Location, 2001-2011

1 2 3 4 5

Bachelor (%) 0.04 (1.40)

Advanced (%) 0.08* (1.80)

Min Bach (%) 0.04* (1.74) 0.04* (1.95) 0.04* (2.00)

Freedom 0.20 (1.61) 0.19 (1.57) 0.20 (1.62) 0.20 (1.57) 0.21* (1.69)

Urban (%) .01* (1.83) 0.01 (1.55) 0.01 (1.56) 0.01 (1.57) 0.01 (1.56)

FinCent 0.49* (1.84) 0.39 (1.42) 0.45 (1.67) 0.41 (1.51) 0.39 (1.46)

FinCent2 –0.40 (–1.00)

FinCent3 –0.40 (–1.42)

Constant –2.34** (–2.40) –2.13** (–2.35) –2.37** (–2.50) –2.44** (–2.57) –2.51** (–2.67)

N 50 50 50 50 50

Adj R-Squared 0.28 0.30 0.30 0.30 0.31

This table reports OLS regression results for headquarter location of IPOs in the U.S. The dependent variable,IPO Dollar Intensity, is the dollar value of IPOs headquartered in a given state per one million residents.Bachelor is the percentage of residents who hold only a bachelor’s degree. Advanced is the percentage ofresidents who hold an advanced degree (e.g., masters, Ph. D., law, medical). Min Bach is the percentage ofresidents with at least a bachelor’s degree (or the sum of Bachelor and Advanced). Freedom is the FreedomIndex measuring the economic climate in a given state. Urban represents the percentage of a state’s populationdesignated as living in an urban environment. FinCent is an indicator variable equal to one if the state containsa financial center (which includes California, Illinois, Massachusetts, New York and Pennsylvania). FinCent2is an indicator variable equal to one for the states of Maryland and Minnesota. FinCent3 is an indicatorvariable equal to one for the states of Maryland, Minnesota, Georgia and Wisconsin. T-statistics are inparentheses. Significance for a two-tailed test at the 1 %, 5 %, and 10 % levels are denoted by ***, **, and*, respectively.

J Econ Finan

less likely to be related to identifiable variables, the preferred regression models haveexplanatory power of a bit over 50%. This is a strong result for a cross-sectionalanalysis in which the dependent variable, the location of IPO headquarters, surely has anontrivial idiosyncratic component

From a public policy perspective, government officials who desire to increase thenumber of corporate headquarters in their state in the form of IPOs appear to have someoptions. First, they can focus on developing or attracting a more highly-educatedworkforce. Second, they can alter the economic climate in their states by promotingpolicies that influence that measure. Empirically we are agnostic about stating which ofthe three factors (government size, taxation, and labor market) that enter into the overallindex measure matters most, so we only suggest that they collectively matter.16 Thefinancial center and urbanization variables, although they were significantly related toIPO intensity, seem to be hard to alter from a policy perspective, at least in a time frameof less than decades, if at all. Rather, these variables are inherent advantages that stateseither have or do not have.

Our results extend the existing research on IPO activity and new businessformation in general. We do, however, maintain that broadly defined measures ofnew firm formation, though an input into the IPO pipeline, are significantlydifferent from IPOs. Factors that influence IPO creation thus cannot be viewed asessentially the same as those conducive to new firm formation. A primarydifference exists between most new firms and a firm which reaches the stageof an IPO. The IPO will require an investment bank, portfolio investors, and,perhaps earlier in the firm’s history, a venture capital firm. Consistent with thisidea, our results reveal the importance of proximity to a major financial center.We also note that with the growth in scholarly interest in “entrepreneurship,” ourwork is related to this field. IPOs are part of what can broadly be defined asentrepreneurship research, and as Stuart and Sorenson state (2003, p. 176): “ acentral research objective in entrepreneurship is to understand the social, struc-tural, and economic conditions that promote new venture formation.” Our studyis a first attempt at examining these factors as they relate to IPOs.

Acknowledgments We wish to thank participants at the 2013 Eastern Finance Association conference, andseminar participants at Wake Forest University, for useful comments on an earlier version of this paper.

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