INANCIAL VIDENCE FROM TRANSACTION-LEVEL DATApeople.stern.nyu.edu/aweber/PDF/AW_JMP.pdfStern School...

FINANCIAL MANAGEMENT SKILLS AND ENTREPRENEURIAL SUCCESS:EVIDENCE FROM TRANSACTION-LEVEL DATA

Andreas Weber∗

JOB MARKET PAPER

Abstract

This paper shows that financial management skills are important for entrepreneurialsuccess. To measure founders’ financial management skills independent of their star-tups, I link high-frequency financial data from U.S. entrepreneurs’ pre-startup per-sonal financial accounts to their startup’s financial business accounts. I find thatentrepreneurs who are better at managing their personal finances before starting abusiness are also more successful in steering their startup through its infancy and inscaling their business. The economic magnitudes are meaningful: At the intensivemargin, better financial management skills increase revenues by 7.6%, an effect equalto increasing a founder’s available pre-startup liquidity by around two thirds. Atthe extensive margin, annual exit probabilities are 1.1 percentage points higher forless financially skilled entrepreneurs, relative to a baseline probability of 8.8%. I alsoshow that financial management skills are especially valuable in industries wherecash flow management is particularly challenging. To determine which industriesrequire stronger cash flow management skills, I develop a new set of industry charac-teristics based on high-frequency cash flow profiles of 1.5 million startups. Finally, Idocument that founders with better financial management skills are more successfulin taking advantage of local investment opportunity shocks to grow.

Keywords: Entrepreneurship, Financial Management, Financial Literacy,Liquidity Management, Managerial Capital

∗Stern School of Business, New York University: [email protected] version: November 2018. I am deeply indebted to my advisors Holger Mueller and Johannes Stroebelfor their encouragement and guidance. I also benefited from conversations with Mohsan Bilal, PeterGanong, Theresa Kuchler, Pascal Noel, and Siddharth Vij. Seminar participants at NYU Stern providedinsightful comments.

1 Introduction

Entrepreneurs create a significant share of new employment in the economy, are highlysuccessful inventors, and play a key role in determining how an economy responds to ag-gregate shocks. Given entrepreneurship’s important role for the economy, a long researchtradition has tried to understand what factors enable entrepreneurial success, amongthem a growing line of inquiry focusing on managerial capital as a distinct form of humancapital (Graham et al., 2018; Haltiwanger, Jarmin and Miranda, 2012; Adelino, Ma andRobinson, 2017; Bernstein et al., 2018; Azoulay et al., 2018; Levine and Rubinstein, 2016;Bruhn, Karlan and Schoar, 2010).1

In this paper, I rely on a large sample of founders in the U.S. to document that avery specific form of managerial capital – the founder’s financial management skills – iscrucial for their entrepreneurial success. My particular focus will be on the importanceof effective short-term cash flow management skills for entrepreneurial survival, growth,and the ability to take advantage of unexpected investment opportunities.

I find that founders who are better at managing their personal finances before startinga business are also more successful entrepreneurs. At the extensive margin, startupsof founders with better financial management skills are more likely to survive; at theintensive margin, startups of entrepreneurs with better financial management skills aremore likely to grow into larger businesses.

Based on new industry cash flow characteristics that are elicited from the cash flowprofiles of 1.5 million startups, I also show that cash flow management skills are espe-cially valuable in industries where cash flow management is particularly challenging.These include industries with high return-adjusted revenue volatility, a high downsidecorrelation between revenue and operating cost, and a higher prevalence of negativelyskewed and fat-tailed revenue profiles.

Finally, I show that founders with stronger financial management skills are betterequipped to take advantage of local investment opportunity shocks to grow their busi-nesses.2 In fact, my results suggest that the entire reaction of a regional economy to

1While some entrepreneurs just create self-employment opportunities for themselves, others alsoprovide employment opportunities for others. See Schoar (2010) and Hurst and Pugsley (2011) on thequalitative difference between growth-oriented vs. subsistence-oriented entrepreneurs.

2Put differently, financial management skills are an important ingredient for Kirznerian entrepreneur-ship. While Schumpeterian entrepreneurs’ distinctive characteristic is their involvement in the processof creative destruction through disruptive innovation (Schumpeter, 1942), Kirznerian entrepreneurs’distinguishing feature is their ability to take advantage of investment opportunities as they presentthemselves (Kirzner, 1973, 1985); see Bernstein et al. (2018) for a discussion.

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a local investment opportunity shock may be driven by founders with better financialmanagement skills.

To study the role of financial management skills for entrepreneurial outcomes, I col-laborate with a large financial institution in the U.S. to develop a new data set that al-lows me to link the personal financial accounts of founders before they decide to starta business with the financial accounts of their businesses post-startup. The data setcontains transaction-level information from personal and business deposit and creditcard accounts for around 40 million potential founders and more than two million smallbusinesses; around one million of these businesses were started over the sample period Iobserve. Crucially, for a subset of these startups, I can link personal and business accountsfor the same individual. The data also includes a rich set of financial and non-financialfounder demographics and business characteristics.

The richness of the data is uniquely suited to address my research question: It allowsme to study the financial behavior of founders independent from their businesses and atfrequencies relevant to short-term cash flow management. It also allows me to directlyconnect the founders’ observed financial behavior to economically meaningful outcomesof their businesses.

Since cash flow management challenges tend to be short-term, I have to study afounder’s financial management skills at a high frequency.3 To elicit a potential founder’shigh-frequency financial management skills, I observe how efficiently they manage theirpersonal short-term finances before they start a business. For example, I classify in-dividuals that borrow very short-term against their checking accounts, while cheapersources of finance would have been available, as having lower financial managementskills. Growing evidence from the household finance literature suggests that even suchrelatively small sub-optimalities are predictive of financial management skills in widersettings, including with respect to consumption smoothing (Parker, 2017; Gelman, 2018),debt accumulation and paydown (Lusardi and Tufano, 2015; Kuchler and Pagel, 2018), in-vestment decisions (Calvet, Campbell and Sodini, 2007), and refinancing decisions (Keys,Pope and Pope, 2016); see Jorring (2018), whom my measurement approach follows.

To study entrepreneurial outcomes that are economically meaningful, I aggregate thetransaction-level data from financial business accounts into measures of entrepreneurialentry and exit, revenue growth, profitability, and employment. When I study how fi-nancial management skills affect these outcomes, I find large economic effects: At the

3For example, Barrot and Nanda (2018) show that accelerating payments to small governmentcontractors by just two weeks has substantial effects for employment growth.

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intensive margin, better financial management skills increase revenues by 7.6%, an effectequal to increasing a founder’s available pre-startup liquidity by around two thirds, ormore than one third of a standard deviation. At the extensive margin, annual exit proba-bilities are 1.1 percentage points higher for less financially skilled entrepreneurs, relativeto a baseline probability of 8.8%. These results suggest that financial management skillsare a key form of intangible capital that enables entrepreneurial success in my sample ofU.S. founders.

These magnitudes, while large, are not implausible. In particular, they are consistentwith evidence from a pair of 2015 small business credit surveys administered by theFederal Reserve, in which employer and non-employer businesses alike have cited cashflow management as a key business challenge, ahead of government regulation, taxes,and access to credit (Federal Reserve Bank, 2015a,b).

The granularity of my data allows me to address a variety of important potentialchallenges to my interpretation of these results. First, once a business is established, thefinances of owners and their ventures often become co-mingled. Therefore, one cannot besure anymore whether observed venture outcomes are the result of concurrently observedfinancial behavior of the founder or vice versa post-startup. This introduces a seriousreverse causality threat into any such research setting. The nature of my data allows meto circumvent this problem by observing the financial behavior of future founders beforethey open a business.

Second, I need to rule out that my results are driven by confounding factors thatare correlated with both financial skills and entrepreneurial success at the same time. Alarge literature has surfaced potential candidates for such factors, including a founder’swealth, education, professional experience, and life cycle situation (Evans and Jovanovic,1989; Holtz-Eakin, Joulfaian and Rosen, 1994; Blanchflower and Oswald, 1998; Nanda,1998; Hurst and Lusardi, 2004; Andersen and Nielsen, 2012; Levine and Rubinstein, 2016;Azoulay et al., 2018). The richness of my data set as it pertains to financial and non-financial demographics of the founders allows me to tightly control for such confoundingeffects.

In addition, founder characteristics can not only affect business outcomes conditionalon entry into entrepreneurship, but also the decision to enter industries of varying growthtrajectories. For example, Levine and Rubinstein (2016) find that well-educated and smartindividuals, who showed a tendency to engage in illicit activities when young, are morelikely to enter entrepreneurship and to be successful with their ventures conditional onentry. Similarly, differences in risk preferences and growth ambitions might lead to entry

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into different industries (see, e.g., Kihlstrom and Laffont, 1979; Moskowitz and Vissing-Jorgensen, 2002; Schoar, 2010; Hurst and Pugsley, 2011). If financially less skilled individ-uals systematically enter industries with higher exit rates and lower growth prospects,this might reveal unobserved complementarities or bad judgment, but nothing about theimportance of financial management skills for venture outcomes. Detailed informationon the industry a founder enters into allows me to control for such industry fixed effects.

Moreover, recent research by Bernstein et al. (2018) has provided evidence that theabove selection effect is not always static – founder characteristics also affect who entersa market in response to positive local investment opportunity shocks. If founders withbetter financial management skills systematically enter booming local markets and trans-form these positive initialization shocks into long-term growth, my results may not reflectthe importance of financial management skills for venture outcomes. Knowing the timeand location of market entry at a granular level allows me to control for any differencesin local economic conditions between founders at the time of market entry with cohort-location fixed effects.

To explore the importance of financial management skills for entrepreneurial successacross industries with varying degrees of cash flow management complexities, I developa new set of industry characteristics based on high-frequency cash flow profiles of 1.5million startups in my sample: (1) As a simple starting point, I estimate an industry’sgrowth-adjusted volatility. (2) To hone in on the core of cash flow management – matchinginflows and outflows to keep a business operating and to enable growth – I estimate the(downside) correlation between revenue and operating cost growth. Finally, I estimatethe likelihood with which an entrepreneur faces (3) relatively low (negatively skewed),as well as (4) extreme (fat-tailed) revenue growth shocks. The resulting characterizationof industries varies richly: When we look at the five industries with the most challengingcash flow profiles along each dimension, we find two finance and two mining industriesbased on growth-adjusted volatilities, mainly manufacturing industries based on down-side correlation and skewness, and primarily non-tradables industries such as restaurantsbased on fat-tailed revenue growth shocks.

Despite this variety, when I return to the sample of startups that I can link to theirfounders, I can confirm that cash flow management skills are indeed particularly valu-able in industries with more challenging cash flow profiles – across all four dimensions.Compared to averages over all industries, I estimate that the effect of a lack of financialmanagement skills is up to 90% higher at the extensive margin (exits) and up to 60%higher at the intensive margin (revenue) in industries with more complex cash flow pro-

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files.

In the final part of the paper, I turn to the question of whether financially skilledentrepreneurs are more successful in responding to time-varying local growth opportu-nities. I start by estimating local investment opportunity shocks based on the cash flowdynamics of close to two million separate young and more-established small businessesin my data. When I return to the sample of startups that I can link to their founders, Ifind that entrepreneurs with stronger financial management skills are more successful intaking advantage of local growth opportunities. A lack of financial management skills onthe other hand fully offsets the positive growth effect of the shock.

My results also have intriguing policy implications: Given the opacity and lack of col-lateral of startups and small businesses, a long line of academic research has documentedthe challenges involved in funding them (Evans and Jovanovic, 1989; Petersen and Rajan,1994; Berger and Udell, 1995; Hurst and Lusardi, 2004; Bianchi and Bobba, 2013). To theextent that financial management skills allow founders to use existing resources moreefficiently, providing entrepreneurs and small business owners with the skills and toolsto manage their cash flows more effectively may spur economic growth and increase theresponsiveness of the economy to positive shocks, even in highly developed economiessuch as the U.S.4

My paper contributes to several strands of literature: First, there is a well-establishedline of research that studies the nature and characteristics of entrepreneurs. Some ofthe most recent contributions to this literature include Levine and Rubinstein (2016),who document that smart but illicit individuals are more likely to be (successful) en-trepreneurs. Azoulay et al. (2018) show that on average, successful high-growth en-trepreneurs tend to be middle-aged, while others have focused on the risk attitudes andgrowth ambitions of entrepreneurs (Kihlstrom and Laffont, 1979; Moskowitz and Vissing-Jorgensen, 2002; Schoar, 2010; Hurst and Pugsley, 2011). I complement this literature byfocusing on a very specific skill that is of fundamental importance for entrepreneurialsuccess given the cash flow complexities of startups: financial management skills. I alsoshow that this very particular skill has explanatory power for entrepreneurial outcomesabove and beyond general education and life-cycle considerations.

Second, my paper speaks to a literature that studies the importance of managerialcapital as an input factor distinct from capital and labor, which has primarily been fo-

4A separate literature studies how such financial literacy interventions impact outcomes for microentrepreneurs in developing countries; see, e.g. Bruhn, Karlan and Schoar (2010) for a brief overview andNaudé (2009) for background.

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cused on managerial capital as a missing ingredient for economic growth in developingcountries (Naudé, 2009; Bruhn, Karlan and Schoar, 2010; Desai, 2011; Bloom et al., 2013;Bruhn and Zia, 2013; Gennaioli et al., 2013; McKenzie, 2014; Drexler, Fischer and Schoar,2014; Uwonda and Okello, 2015; Bruhn, Karlan and Schoar, 2017; Anderson, Chandy andZia, 2018; Bloom et al., 2018). I show that the lack of a specific aspect of managerial capital– financial management skills – also matters for the growth and survival of startups inhighly developed countries such as the U.S.

Third, my paper is related to a burgeoning household finance literature that studiesfinancial decision making. Several authors in the literature have provided evidence thatsmall suboptimalities such as the one I study in my setting are predictive of financialmanagement skills in a wider sense and discuss related welfare implications (see, e.g.,Calvet, Campbell and Sodini, 2007; Driscoll, 2009; Lusardi and Tufano, 2015; Campbell,2016; Keys, Pope and Pope, 2016; Parker, 2017; Gelman, 2018; Jorring, 2018; Kuchler andPagel, 2018). I document that these findings extend to the startup sector, where a lack offinancial skills of the founder leads to higher exit rates at the extensive margin and lowerrevenue growth at the intensive margin.

Fourth, my paper also contributes to a literature that emphasizes the role of startupsand young businesses for how an economy responds to and transmits aggregate shocks.From a microeconomic perspective, recent research has documented that new firms areresponsible for most job creation in response to changes in local investment conditionsemanating from income-driven demand shocks (Adelino, Ma and Robinson, 2017) as wellas localized commodity market shocks (Decker, McCollum and Upton Jr, 2017). An evenmore nascent strand of literature has started to combine the above two research agendasby documenting the kinds of entrepreneurs that are behind the dynamic response ofstartups to exogenous fluctuations in the economy. For example, Bernstein et al. (2018)find that firm creation in response to commodity price shocks is driven by young yetskilled and experienced individuals. I add to this literature by documenting that financialmanagement skills play an important role in enabling entrepreneurs to respond to sucheconomic shocks.

The remainder of this paper proceeds as follows: Section 2 describes the various datasources used in the analysis. Section 3 describes the empirical strategy. Section 4 presentsresults on the importance of financial management skills for entrepreneurial success ingeneral, and in response to economic fluctuations in particular. Section 5 concludes.

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2 Linked Financial Data on Founders and Their Ventures

Studying financial management skills of founders as they relate to their entrepreneurialsuccess is challenging for multiple reasons: First, once a business is established, thefinances of owners and their ventures often become co-mingled. Therefore, one cannot besure anymore whether observed venture outcomes are the result of concurrently observedfinancial behavior of the founder or vice versa post-startup. Second, since cash flowmanagement challenges tend to be short-term, one has to study a founder’s financialmanagement skills at a high frequency. Finally, one needs to be able to connect theobserved financial behavior of the founder to economically meaningful outcomes of theirbusinesses.

In this section, I describe the transaction-level data set that I have developed in col-laboration with a large financial institution in the U.S. It allows me to link the personalfinancial accounts of founders before they decide to start a business with the financialaccounts of their businesses post-startup, and is therefore uniquely suited to answer myresearch question.

2.1 Data Overview5

Personal Financial Accounts of Potential Founders The sample of potential foundersconsists of de-identified transaction- and account-level financial records drawn from theuniverse of private households with a checking account at the financial institution be-tween October 2012 and July 2018, which includes more than forty million individuals.Following the household finance literature that has worked with transaction-level infor-mation provided by financial institutions and financial aggregators for some time now,I restrict the sample to individuals for which the financial institution is likely to be theprimary financial services provider and which reside in one of the more than twentystates in which the financial institution has a branch footprint over this period of time.6

Specifically, I only include an individual in a given twelve-month period in my sampleif I can observe at least five monthly checking account outflows for that period.7 Alarge fraction of these transactions have been pre-categorized into various consumptionand income categories by the financial institution. I also require individuals to have at

5Following disclosure standards, all statistics from financial institution’s data (including medians)reflect cells with multiple observations.

6See Baker (2018); Ganong and Noel (2018a,b) for the representativeness of such data sets.7Outflows include cash withdrawals, electronic payments, paper checks, or debit card transactions.

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least one open credit card account with the financial institution over the same period oftime, as well as sufficient information to calculate daily liquidity positions for demanddeposits and credit card accounts as described in Section 2.2. This is important since I aminterested in studying how efficiently potential founders substitute across their varioussources of liquidity at high frequencies in order to infer their financial management skills.I further restrict the sample to those potential founders for which I can observe a fullset of financial and non-financial demographics. Finally, I restrict my sample to the coreworking-age population between 18 and 65. Since I want to study founders before theyopen a business, I also have to restrict myself to the years between 2013 and 2015. Thefinal sample consists of more than two and a half million individuals.

Business Financial Accounts of Startups The sample of startups consists of de-identifiedtransaction- and account-level financial records drawn from the universe of minimallyactive small businesses with a business checking account at the financial institution be-tween October 2012 and July 2018. It includes more than two million entities. As with thehousehold side, the sample is restricted to businesses for which the financial institution islikely the primary financial services provider. In particular, based on conversations withthe data provider, I require a business to achieve at least five months with five hundreddollars of outflows and ten transactions per month to be included in the sample.8 Since anon-trivial fraction of startups take a few months to ramp up their operations, I includean account opening by a new business as a startup in my sample if the above five-monthactivity threshold is cumulatively met by the 18th month of its life.9 In sum, more than90% of startups that ever accumulate more than five such core activity months over therelevant sample period enter my startup sample. I consider each founder who startsa business in the same month of the same year to be a member of the same founder-cohort. Finally, I restrict my sample to valid NAICS code ranges and drop startups fromthe primary and government sector, as well as nonprofit organizations, in order to focuson profit-seeking ventures. I also drop accounting firms that often manage the finances ofothers in a fiduciary role.10 The final sample consists of more than 750 thousand startups.

8As for households, outflows include cash withdrawals, electronic payments, paper checks, or debitcard transactions.

9If a business passes the relevant activity threshold before its 18th month and exits before its 18th month,it is included in the sample as well. Note that this approach will also ensure that I exclude low-activity“lifestyle businesses” from my sample.

10The primary sector is NAICS 11, the government sector is NAICS 92, nonprofit organizations areNAICS 813/4 (“Religious Institutions” and “Households”), NAICS 6241/2 (“Social Assistance”), andNAICS 7121 (“Museums”); accounting firms are NAICS 5412. Most NAICS codes in my sample are basedon the 2012 revision of NAICS codes. For consistency, I map NAICS codes based on older and newer

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Linking Personal and Business Financial Accounts Even though personal and busi-ness checking accounts are treated as separate entities, the financial institution establishesa virtual link between the owners and their businesses. This allows me to study the fi-nancial behavior of founders relative to the financial success of their ventures. In 2013 forexample, the match rate between the unrestricted sample of private households describedabove and startups is around 80%.

Since I need at least twelve months before a venture is established to study the founder’sfinancial behavior and at least 18 months from the startup period to determine whethera new venture enters the sample, I have to restrict myself to startups founded betweenJanuary 2014 and December 2016. This leaves me with 29,656 founder-venture pairs thatsatisfy all founder-level as well as all startup-level sampling restrictions. They rangeacross a wide variety of industries and geographies as summarized in Table I. The topthree industries by 2-digit NAICS code are “Professional Services” (16.2%), “Real Estateand Leasing” (12.2%), and “Retail” (10.5%). The top three states in which founders arelocated are New York (20.0%), California (19.3%), and Texas (15.1%).

2.2 Founder Variable Construction

All founder variables are calculated for the year before business opening. In what follows, Icompare the relevant founder characteristics to the overall population of personal accountholders that satisfy my sampling critera:

Demographics The sample includes information on the age of the account holder aswell as education levels.11 As summarized in Panel A of Table II, founders tend to beslightly younger than the overall population – the median age of a founder is 39 years,while the median age in the population is 43 years. Founders also tend to be slightlybetter educated than the overall population – the median probability that a founder hasat least four years of college education is 40% compared to 35% in the overall population.52% of founders vs. 44% of the population in my sample are male, while 24% of foundersvs. 36% of the population in my sample are female.12

revisions into the 2012 version. If a mapping is not unique, I retain the old NAICS code; see Patnaik (2017)on accounting firms.

11Education levels are measured at the Census Tract level.12For the remainder, gender is unkown.

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Table I: Industry & Geography of Startups

Obs Frequency

(A) INDUSTRY

Professional Services 6,031 16.2%Real Estate & Leasing 4,537 12.2%Retail 3,888 10.5%Construction 3,323 8.9%Other Services 3,295 8.9%Health Care & Social 2,502 6.7%Accomodation & Food Services 2,467 6.6%Transportation & Warehousing 2,447 6.6%Admin Support & Waste Mgm 1,713 4.6%Arts, Enterntainment, Recreation 1,562 4.2%Wholesale 1,284 3.5%Manufacturing 1,237 3.3%Information 980 2.6%Educational Services 798 2.1%Finance & Insurance 774 2.1%Management 143 0.4%Mining 127 0.3%Utilities 31 0.1%

(B) GEOGRAPHY

New York 7,410 20.0%California 7,180 19.3%Texas 5,624 15.1%Illinois 3,029 8.2%Florida 2,186 5.9%New Jersey 1,591 4.3%Michigan 1,453 3.9%Arizona 1,460 3.9%Ohio 1,375 3.7%Colorado 1,022 2.8%Indiana 981 2.6%Lousiana 782 2.1%Washington 557 1.5%Georgia 406 1.1%Connecticut 319 0.9%Wisconsin 326 0.9%Oregon 290 0.8%Kentucky 276 0.7%Utah 247 0.7%Oklahoma 251 0.7%Nevada 259 0.7%West Virginia 62 0.2%Idaho 53 0.1%

Note: This table reports summary statistics on theindustry composition of startups for 2-digit NAICScodes and the geographic location of startups by statefor the sample of founders as described in Section 2.1.

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Financial Demographics The data set also includes rich information on the liquidityposition of founders split by liquid assets and available credit card borrowing capacity.Cash balances are available at a monthly frequency. I use these monthly cash balances incombination with transaction-level flows in and out of accounts to calculate daily cashbalances. Available credit card borrowing capacity at a daily frequency is somewhatharder to infer, since the data includes information on rolling credit card balances only.For individuals with rolling balances, I use the rolling balance as a starting point atthe cycle-end date. For individuals that never carry a rolling balance, I infer cycle-endbalances based on monthly payments. In particular, if I observe a single payment percycle and no rolling balance, I assume that the payment matched the balance at the end ofthe previous cycle. Finally, for individuals that open a new credit card account during mysample period, I know that the initial balance was zero. As with cash balances, I then usedaily transactions to extrapolate daily credit card balances from these starting points. Theactual borrowing capacity is then the difference between the card’s credit limit and thedaily balance. The accuracy with which I can calculate daily liquidity positions as a resultis crucial for inferring financial management skills at a high frequency. As summarized inPanel B of Table II, the median founder has access to comparable amounts of liquidity asthe overall population (ten to eleven thousand dollars), roughly half in terms of cash andhalf in terms of credit card borrowing capacity. The bottom panel of Table II compares thedistribution of founders across wider financial asset, credit, and income quintiles relativeto the overall population. Note that the pre-startup income distribution of founders skewsto the right compared to the overall population.

Financial Management Skills To infer financial management skills of future founders,especially as they pertain to high-frequency cash flow management, I study how foundersmanage their personal finances before they start a business. In particular, I study howefficiently individuals substitute across cash and credit by looking at instances where veryshort-term borrowing facilities were used, while cheaper alternative sources of liquiditywould have been available. Even though I study relatively minor suboptimal decisions,the household finance literature has provided evidence that these are predictive for fi-nancial management skills in a wider sense, in particular with respect to consumptionsmoothing (Parker, 2017; Gelman, 2018), debt accumulation and paydown (Lusardi andTufano, 2015; Kuchler and Pagel, 2018), investment decisions (Calvet, Campbell and So-dini, 2007), and refinancing decisions (Keys, Pope and Pope, 2016); see Jorring (2018),whom my measurement approach follows. In particular, I check whether individualsborrow very short-term against their checking accounts or skip minimum payments on

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Table II: Founder Demographics

(A) BASIC DEMOGRAPHICS

Obs Mean SD P25 P50 P75

Population

Age (Years) 5,182,949 43.3 12.3 32.0 43.0 54.0Edu: ≥ High School (Share) 5,182,949 88% 11% 84% 91% 95%Edu: ≥ 4y College (Share) 5,182,949 38% 20% 21% 35% 52%

Gendera 5,182,949 Female: 36% Male: 44%

Founders

Age (Years) 29,656 40.5 10.8 32.0 39.0 49.0Edu: ≥ High School (Share) 29,656 89% 10% 85% 92% 96%Edu: ≥ 4y College (Share) 29,656 42% 20% 26% 40% 57%Owners (# per Firm) 29,656 1.4 0.8 1.0 1.0 2.0

Gendera 29,656 Female: 24% Male: 52%

(B) FINANCIAL DEMOGRAPHICS

Obs Mean SD P25 P50 P75

Population

Liquid Assets (1,000$) 5,182,949 10.6 21.8 1.3 3.5 9.7Available Credit (1,000$) 5,182,949 8.2 9.9 0.9 4.4 11.9Total Liquidity (1,000$) 5,182,949 18.9 26.6 3.4 10.2 23.5

Founders

Liquid Assets (1,000$) 29,656 15.5 36.7 1.6 4.3 12.1Available Credit (1,000$) 29,656 8.2 10.1 1.0 4.3 11.6Total Liquidity (1,000$) 29,656 23.8 41.1 4.0 11.0 25.7

Obs Q1 Q2 Q3 Q4 Q5

Founders Relative to Population Quintiles

Asset Quintile (Share) 29,656 17% 19% 20% 20% 23%Credit Quintile (Share) 29,656 25% 23% 21% 19% 13%Income Quintile (Share) 29,656 11% 16% 19% 23% 32%

Note: This table reports summary statistics for the sample of founders and the populationsample as described in Section 2.2. Population statistics are for yearly observations between2013 and 2015. Founder statistics are for yearly observations between 2013 and 2015 in the pre-startup year. Panel A reports basic demographics. Panel B reports financial demographics. Alldollar values are winsorized at one percent.a

aFor gender, the omited category is “Unkown”.

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credit cards. I assume that sufficient alternative sources of liquidity would have beenavailable on a given day when available cash and credit card capacity cover the averageobservable monthly consumption of an individual over a rolling twelve month window,plus the relevant financial transaction (i.e., a minimum payment or the purchase thatwas financed by borrowing short-term against a checking account). To construct a proxyfor financial management skills, I simply count the number of such inefficient financialdecisions per year for each individual.

As summarized in Figure I, around 15% of the population and 17% of future foundersdisplay one instance of inefficient financial management over the course of a year, whileanother 15% of the overall population and 19% of future founders display more than oneinstance of such behavior.

Figure I: Financial Management Skills

14.7

16.8

14.6

19.2

0

5

10

15

20

25

Population Founders

Pe

rce

nt

S(Low) 1 S(Low) 2+

Note: This figure displays summary statistics on financial man-agement skills for the sample of founders and the populationsample as described in Section 2.2. Population statistics are foryearly observations between 2013 and 2015. Founder statisticsare for yearly observations between 2013 and 2015 in the pre-startup year. “S(Low) 1” is the share of single-instance non-optimizers. “S(Low) 2+” is the share of serial non-optimizers.

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2.3 Business Variable Construction

Next, I turn to the construction of venture outcome variables. All business variables areaggregated up into meaningful economic concepts from their respective business depositaccounts as follows:

Operating Cash Flows The financial institution has tagged all transactions with in-formation on their channel (e.g. “Check” or “Wire”), and labeled a large fraction oftransactions along various other dimensions. I use this information to construct measuresof revenue and operating cost. Combined, the transaction attributes I use yield morethan 50,000 unique transaction characterizations. I start with all inflows and outflowsand remove all transactions labeled as transfers or financial transactions such as loandisbursements. I also remove all unlabeled transactions in channels that are predomi-nantly used for transfers and financial transactions.13 The residual cash flows are myestimates for revenues and operating expenses.14 As summarized in Table III, the medianstartup achieves a revenue of 85 thousand dollars by year three, or 107 thousand dollarsconditional on surviving up to year three. Interestingly, most startups arrive at whatseems to be their equilibrium scale within the first two years. After that, growth at theintensive margin moves into the tails above the 90th percentile.

Operating Surplus & Margins Operating surplus is calculated as the difference be-tween revenue and operating cost. Margins are calculated as operating surplus dividedby revenue. As summarized in Table III, most businesses just break even (presumablybecause the operating cost includes the founder’s salary). Similarly, median margins arelow, around 4% on average. In the tails, operating surplus reaches half a million dollarsand operating margins exceed 90%.

Employment Employment is inferred based on payments to payroll processors out ofbusiness checking accounts. Such payments are identified based on the tagging of redactedtransactions and counterparties that appear to be involved with payroll processing. I cal-culate monthly employment in terms of minimum-wage full-time equivalents at the statelevel assuming a forty hour work week.15 Yearly employment is calculated as average

13I treat payments to credit cards as indirectly observed operating expenses.14I was able to verify the accuracy of my cash flow assumptions against a coarse truth set of self-reported

revenue numbers for a small sample of business loan applicants in unreported results.15Information on yearly state-level minimum wages are available from the Department of Labor under

https://www.dol.gov/whd/state/stateMinWageHis.htm.

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https://www.dol.gov/whd/state/stateMinWageHis.htm

Table III: Startup Performance

Obs Mean SD Q10 Q25 Q50 Q75 Q90 Q95 Q99

(A) UNCONDITIONAL

Revenue (1,000$)Year 2 27,784 302 662 5 31 97 262 688 1,241 4,652Year 3 20,858 318 728 0 14 85 266 741 1,437 5,563Year 4 10207 314 741 0 0 69 249 752 1,466 5,871

Op. Surplus (1,000$)Year 2 27,784 15 104 -28 -3 2 27 83 152 516Year 3 20,858 17 104 -23 -2 1 25 85 161 516Year 4 10,207 15 104 -20 -1 0 21 80 153 518

MarginYear 2 27,784 4% 39% -39% -4% 3% 22% 51% 68% 91%Year 3 20,858 4% 38% -32% -2% 1% 19% 49% 67 % 94%Year 4 10,207 4% 36 % -24% -1% 0% 14% 46% 65% 95%

Employment (#)Year 2 27,784 2 12 0 0 0 0 1 6 31Year 3 20,858 2 14 0 0 0 0 1 7 35Year 4 10,207 2 14 0 0 0 0 1 7 37

(B) CONDITIONAL ON SURVIVAL

Revenue (1,000$)Year 2 27,112 310 668 9 35 101 270 705 1,269 4,771Year 3 18,659 356 761 4 32 107 306 830 1,572 6,050Year 4 8,264 388 806 3 32 114 336 943 1780 7,048

Op. Surplus (1,000$)Year 2 27,112 16 106 -29 -4 3 28 85 155 528Year 3 18,659 19 109 -28 -3 3 30 94 174 547Year 4 8,264 18 116 -27 -3 3 31 99 186 583

MarginYear 2 27,112 4% 40% -41% -5% 3% 23% 52 68% 92%Year 3 18,659 4% 40% -40% -4% 3 22% 52% 69% 95%Year 4 8,264 4% 40% -37% -3% 3 21% 52% 70% 97%

Employment (#)Year 2 27,112 2 12 0 0 0 0 1 6 31Year 3 18,659 2 15 0 0 0 0 2 8 38Year 4 8,264 2 15 0 0 0 0 2 10 43

Note: This table reports summary statistics on startup performance for the sample as described inSection 2.3. Panel A reports unconditional statistics, i.e. values are set to zero after exit. Panel B reportsconditional statistics, i.e. observations are dropped after exit. Margins are winsorized at +/- 100%. Alldollar values are winsorized at one percent.

15

monthly employment. As summarized in Table III, most startups are nonemployers, atleast as measured by payroll processor activity.16 Only 10% of startups end up employingat least one observable FTE for a whole year. In the upper tail, startups employ more than40 FTEs by year four.

Startup & Exit For all businesses that enter the sample as described in Section 2.1, I takethe date of the first business account opening as the startup date. Symmetrically, I take thedate of the last business account closing as the exit date of a venture.17 As summarizedin Figure II, the yearly exit probability conditional on surviving up to a given year rangesbetween 8.4% and 9.2%. Exit probabilites are relatively stable between a business in itssecond and third year given the relatively high hurdle imposed for entry. Startups thatnever grow beyond initial ambitions and never accumulate five months of core activitysimply do not enter the sample in the first place.18 For the startup cohorts that can befollowed for up to four years, the cumulative probability of surviving up until year fouris slightly below 75% as shown in Figure II.

Industry At least one account of each startup in my sample has a NAICS code attachedto it. For some businesses that operate multiple accounts across multiple operations, morethan one NAICS code may be attached to it. In such cases, I follow the U.S. CensusBureau and assign a business’s NAICS code based on its primary activity in a givenperiod. Since many startups do not post revenues initially, I use operating outflows todetermine business activity.19

16The numbers reported in Table III should therefore be considered as lower bounds; they miss at leastall payroll paid for directly by check.

17There is little evidence of lingering “zombie businesses” in the data; once business activities slowdown substantially, businesses tend to exit relatively quickly. There is also little evidence of businessestransferring out of the financial institution as opposed to closing shop.

18A cross-check against a substantially smaller but differently sampled data set within the financialinstitution suggests that up to 50% of account openings might not pass the core activity requirement.

19The Census Bureau uses the same methodology to assign NAICS codes, but has to resortto information about revenue, value of shipments, or employment to determine business activity,because data on cost is not readily available to the Census Bureau for most businesses. Fordetails, see https://www.census.gov/eos/www/naics/faqs/faqs.html#q4 and https://www.census.gov/eos/www/naics/faqs/faqs.html#q10.

16

https://www.census.gov/eos/www/naics/faqs/faqs.html#q4



Figure II: Unconditional Survival Probabilities

11.2

19.3

26.1

8.4 9.2 8.7

0

5

10

15

20

25

30

2 3 4

Pe

rce

nt

Years

Cumulative Exit Probability (%) Yearly Exit Probability (%)

Note: This figure shows yearly exit probabilities for the fullsample and cumulate exit probabilities for startups for whichup to four years of data are available.

3 Empirical Strategy

Assessing financial management skills as a distinct characteristic relevant to entrepreneurialsuccess faces a number of challenges:

First, once a business is established, the finances of owners and their ventures oftenbecome co-mingled. Therefore, one cannot be sure anymore whether observed ventureoutcomes are the result of concurrently observed financial behavior of the founder orvice versa post-startup. This introduces a serious reverse causality threat into any suchresearch setting. I address this concern by measuring financial management skills before apotential founder starts a new venture. Figure III summarizes this research design.

Second, we need to rule out that the results we obtain are driven by confoundingfactors that are correlated with both, financial management skills and entrepreneurialsuccess at the same time. A large literature has surfaced potential candidates for such fac-tors, including a founder’s wealth (Andersen and Nielsen, 2012; Hurst and Lusardi, 2004;Holtz-Eakin, Joulfaian and Rosen, 1994; Evans and Jovanovic, 1989), life cycle situation(Azoulay et al., 2018), or education and professional experience (Levine and Rubinstein,2016). The richness of my data set as it pertains to financial and non-financial demograph-ics of the founders allows me to tightly control for many such confounding factors.

17

Figure III: Measurement: Founder Skills and Startup Performance

Pre-startup Period Post-startup Period

Observe Financial Management Skills of Potential Founders Observe Startup Performance

Startup Period

Note: This figure shows the temporal flow of the research design. Founder characteristics areestimated prior to the formation of a new venture.

Table IV provides a first look at simple differences in average financial managementskills across various financial and non-financial demographic characteristics in my sam-ple of founders. Financial management skills are measured by a binary indicator thattakes the value one for founders with at least one incidence where personal finances werenot optimally managed in the pre-startup period as described in Section 2.2.

As we can see from Panel A, financial skills are positively correlated with financialwealth. Fortunately, my data set provides information on various aspects of a founder’sfinancial situation pre-startup, which allows me to control for it in great detail. Panel Bdocuments that financial management skills are negatively correlated with age, while theliterature has emphasized being young to middle-aged as entrepreneurial success factors(see, e.g, Azoulay et al., 2018; Bernstein et al., 2018). Panel D documents that educationis uncorrelated with financial management skills in my sample. Finally, to the extent thatincome pre-startup proxies for professional experience, it is only very weakly correlatedwith financial management skills, as can be seen in Panel A.

Founder characteristics can not only affect business outcomes conditional on entryinto entrepreneurship, but also the decision to enter industries of varying growth tra-jectories. For example, Levine and Rubinstein (2016) find that individuals who are gen-erally well-educated and smart, while showing a tendency to engage in illicit activitieswhen young, are more likely to enter entrepreneurship as well as to be successful withtheir ventures conditional on entry. Similarly, differences in risk preferences or growthambitions might lead to entry into different industries (see, e.g., Kihlstrom and Laffont,

18

Table IV: Difference in Means of Financial Skills by Demographics

(A) FINANCIAL DEMOGRAPHIC QUINTILES

Q1 Q2 Q3 Q4 Q5 p-val.

Liquidity 0.353 0.404 0.375 0.349 0.309 0Assets 0.355 0.380 0.372 0.360 0.325 0Credit 0.439 0.404 0.363 0.289 0.215 0Income 0.348 0.356 0.359 0.367 0.353 0.070

(B) AGE

18-25 26-35 36-45 46-55 56-65 p-val.

Age Bins 0.365 0.367 0.356 0.353 0.338 0.001

(C) GENDER

Female Male Unknown p-val.

Gender 0.362 0.359 0.348 0.047

(D) CONTINUOUS CONTROLS

SHIGH SLOW ∆ Means p-val.

Edu: ≥ 4y College 0.422 0.420 0.002 0.349Owners per Firm 1.454 1.448 0.006 0.426

log(Logins) 3.247 3.176 0.070 0

Note: This table shows differences in means of financial skills by founder demographics.Panel A provides SLOW means for quintiles of financials, where SLOW is an indicatorfor founders with at least one incidence where personal finances were not optimallymanaged in the pre-startup period as described in Section 2.2. Panel B provides SLOW

means by age groups. Panel C provides SLOW means by gender. Panel D provideseducation, owner-per-firm and log(Logins) means for SLOW and SHIGH. SHIGH includes allother founders not included in SLOW, and log(Logins) is the natural logarithm of depositaccount logins.

19

1979; Moskowitz and Vissing-Jorgensen, 2002; Schoar, 2010; Hurst and Pugsley, 2011). Iffinancially less skilled individuals systematically enter industries with higher exit ratesand lower growth prospects, this might reveal unobserved complementarities or badjudgment, but nothing about the importance of financial management skills for ventureoutcomes. Detailed information on the industry a founder enters into allows me to controlfor such industry fixed effects.

Moreover, recent research by Bernstein et al. (2018) has provided evidence that theabove selection effect is not always static – founder characteristics also affect who entersa market in response to positive local investment opportunity shocks. If founders withbetter financial management skills systematically enter booming local markets and trans-form these positive initialization shocks into long-term growth, my results may not reflectthe importance of financial management skills for venture outcomes. Knowing the timeand location of market entry at a granular level allows me to control for any differencesin local economic conditions at the time of market entry between founders with cohort-location fixed effects.

In Section 4.2, I turn to heterogeneous effects. First, I develop a new set of industrycharacteristics based on high-frequency cash flow profiles of 1.5 million startups in mysample:20 an industry’s growth-adjusted volatility, the (downside) correlation betweenrevenue and operating cost growth, and the likelihood with which an entrepreneur facesrelatively low (negatively skewed), as well as extreme (fat-tailed) revenue growth shocks.Next, I return to the sample of startups that I can link to their founders to study theimportance of financial management skills in industries with varying degrees of cashflow management complexities along these characteristics.

In the final part of the paper, Section 4.3, I turn to the question of whether financiallyskilled entrepreneurs are more successful in responding to time-varying local growthopportunities. I start by estimating local investment opportunity shocks based on the cashflow dynamics of close to two million young and more-established small businesses inmy data. Next, I return to the sample of startups that I can link to their founders again tostudy if and how entrepreneurs of varying financial sophistication respond differentiallyto such a shock.

20This sample is larger than the sample described in Section 2.1 since it also includes startups up to fiveyears of age founded before my sample begins.

20

4 Financial Management Skills and Venture Outcomes

4.1 Baseline Specifications

In this section, I evaluate whether founders with better financial management skills achievebetter entrepreneurial outcomes. I start with a binary indicator for financial managementskills as measured in Section 2.2 to establish baseline effects, before moving to a moregranular characterization. The section concludes with a survival analysis.

4.1.1 Binary Financial Management Skills

To assess whether founders with better financial management skills are more successfulentrepreneurs, I run regressions of the following form:

Yicjnτ = αcj + αcτ + αn + SLOWi + Xi,pre + εicnjτ, (1)

where αcj is a founder-cohort times county fixed effect, αcτ is a founder-cohort timesage fixed effect, αn is an industry fixed effect, and SLOW

i is an indicator for founders whodisplayed lower financial management skills with respect to their personal finances be-fore becoming an entrepreneur.21 Xi,pre is a vector of rich founder-level controls measuredpre-startup as discussed in Section 3.

The dependent variable Yicjnτ is either (i) a business exit indicator, (ii) (log) revenue,or (iii) an indicator for achieving above-median operating surplus or above-median oper-ating margins relative to all startups within the same 4-digit NAICS code that have beenestablished within +/- 6 months of each others. To account for any correlation of errorterms across time and space within a county, standard errors are clustered at the countylevel. The sample is restricted to founder cohorts that can be followed for three years inthe post-startup period. Given the relatively strict activity-based sample-entry threshold,the startup period itself is also excluded from the analysis.

Column 1 of Table V provides baseline results for various venture outcomes. Condi-tional on county fixed effects and founder-cohort times firm-age fixed effects, founderswho are less efficient at managing their pre-startup personal finances are 1.3 percentagepoints more likely to exit in any given year, and have 10.6% lower revenues conditionalon operating. They are also 3.0 percentage points less likely to post an operating surplus

21See Section 2.2 for details on how SLOW is measured.

21

above their relative benchmark group, and 2.8 percentage points less likely to achieveoperating margins that are above their relative benchmark group as defined above. Allresults are highly statistically significant.

In Columns 2 to 7, I sequentially add several controls and fixed effects to addressvarious identification concerns as discussed in Section 3. In Column 2, I add deciles offounder financials as summarized in Table IV to address the potential concern that lessfinancially skilled founders are less successful because they also lack financial resourcesto fund their business. Note that it is important to control non-linearly for a founder’sfinancial resources as evidenced by previous research on the relationship between wealthand entrepreneurial outcomes (see, e.g., Hurst and Lusardi, 2004). Unsurprisingly, lowerfinancial management skills are negatively correlated with financial wealth accumulationpre-startup, which is positively correlated with venture outcomes. The observed effectsfor financial management skills shrink by around one third for exit probabilities andrevenue, and by around one sixth for profitability relative to benchmark groups. Giventhis marked change in the coefficient of interest, I tighten the specification by addingfiner non-linear measures of the founders’ financial endowments pre-startup in Column3. Reassuringly, this leaves results unchanged.

Note that the county fixed effects in the above specifications already absorb any time-invariant heterogeneity across counties, which includes pre-existing differences in localindustry or lending structures (see, e.g., Glaeser and Kerr, 2009; Guzman and Stern, 2015).It also includes any other pre-existing differences in local economic conditions, includingthose arising from differential exposures to the Great Recession with potentially long-lasting structural effects.22 Also note that the founder-cohort times firm-age fixed effectsalready control for firm-life-cycle-specific time trends in these specifications.

In Column 4, I include industry fixed effects at the 4-digit NAICS level to addressthe potential concern that financially less skilled founders select into industries withhigher exit rates. This adds predictive power as measured by a marked (relative) increasein the adjusted R2, but leaves the coefficient of interest on financial management skillsunaffected.

To address the potential concern that financially more skilled founders are more likelyto start a business when local conditions are more favorable, I tighten the specificationwith founder-cohort times county fixed effects in Column 5. The results suggest the op-posite – controlling with founder-cohort times county fixed effects increases marginal exitprobabilities by 0.2 percentage points, increases the (negative) effect on revenues by 0.6

22See, e.g. Yagan (2017) for such long-lasting structural effects on employment.

22

Table V: Financial Management Skills and Venture Outcomes

(1) (2) (3) (4) (5) (6) (7)

(A) EXIT

SLOW 0.013∗∗∗ 0.009∗∗∗ 0.009∗∗∗ 0.009∗∗∗ 0.011∗∗∗ 0.011∗∗∗ 0.011∗∗∗

(0.003) (0.003) (0.003) (0.003) (0.004) (0.003) (0.004)

Mean Dep. Var 0.088 0.088 0.088 0.088 0.088 0.088 0.088Observations 36,733 36,733 36,733 36,733 36,733 36,733 36,733Adjusted R2 0.009 0.017 0.016 0.025 0.057 0.057 0.057

(B) LOG(REVENUE)

SLOW −0.106∗∗∗ −0.070∗∗ −0.074∗∗∗ −0.072∗∗∗ −0.078∗∗∗ −0.074∗∗∗ −0.076∗∗∗

(0.029) (0.028) (0.028) (0.027) (0.030) (0.030) (0.030)


(C) PROFITS ABOVE MEDIAN

SLOW −0.030∗∗∗ −0.025∗∗∗ −0.026∗∗∗ −0.025∗∗∗ −0.023∗∗∗ −0.022∗∗∗ −0.022∗∗∗

(0.008) (0.007) (0.007) (0.007) (0.009) (0.009) (0.008)


(D) PROFIT MARGINS ABOVE MEDIAN

SLOW −0.028∗∗∗ −0.024∗∗∗ −0.024∗∗∗ −0.024∗∗∗ −0.022∗∗∗ −0.022∗∗∗ −0.021∗∗∗

(0.008) (0.007) (0.007) (0.007) (0.008) (0.008) (0.008)


County X X X XCohort x County X X XCohort x Firm Age X X X X X X XFounder-Fin XFounder-Fin+ X X X X XIndustry X X X XAge & Gender X XEdu & Attntn X

Note: This table shows the results from Regression 1. All specifications include at least county andfounder-cohort times firm-age fixed effects. Other fixed effects and controls are included as indicated inthe bottom panel of the table. The unit of observation is a founder-year. The period of observation is yearlyrelative to the venture’s startup month. SLOW is an indicator for founders with at least one incidence wherepersonal finances were not optimally managed in the pre-startup period as described in Section 2.2. Thedependent variables in Panels A to D are a business exit indicator, log revenue, and indicators for earningan operating surplus or for achieving operating margins above the medians of the relevant benchmarkgroup of a business as defined in Section 4. Industry fixed effects are at the 4-digit NAICS level. Founderfinancials include deciles of liquidity, assets, credit, and income measured relative to the overall populationof personal (non-business) account holders in the year before a venture was started. Founder financials+includes finer measures of founder financial endowments. Education (Edu) is the probability of holding atleast a four-year college degree. Attention (Attntn) is measured as the number of average monthly depositaccount logins. Standard errors are clustered at the county level. Revenues are winsorized at one percent.Significance levels : ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01. 23

percentage points, and reduces the probability for achieving above-median profitabilityby another 0.2 percentage points. This could suggest that the marginal entrepreneur whoenters in response to a long-lasting positive local growth shock has slightly worse finan-cial management skills than the average founder.23 As with industry fixed effects, theaddition of founder-cohort times county fixed effects increases predictability as measuredby the relative increase in R2s markedly.

To address the potential concern that financial skills proxy for general life experienceor any life cycle considerations of the founder, I control flexibly for age with various agebins as summarized in Table IV as well as the founder’s gender in Column 6.24 This doesnot change the results in any meaningful way. Note that to the extent that experience isreflected in pre-startup wages, the income deciles added as part of the founder financialsspecifications also control for experience.

In Column 7, I add the probability of having a four-year college degree to addressthe potential concern that my measure of financial skills simply captures general intelli-gence or educational attainment. Column 7 also controls for the (log) of monthly depositaccount logins pre-startup to address the potential concern that my measure of financialskills captures some form of rational inattention. As before, this does not change theresults in any meaningful way.

The effects I find are economically meaningful: In the tightest specification, betterfinancial management skills increase revenues by 7.6% at the intensive margin, an ef-fect equal to increasing a founder’s available pre-startup liquidity by around two thirds,or more than one third of a standard deviation. Exit probabilities are 1.1 percentagepoints higher for less financially skilled entrepreneurs, relative to a baseline probabilityof 8.8%. Achieving above-benchmark operating profits and margins is 2.2 and 2.1 per-centage points less likely respectively for less financially skilled founders. These resultssuggest that financial management skills are a key form of intangible capital that enablesentrepreneurial success in my sample of U.S. founders.

So far, I have lumped all founders with at least one instance of inefficient financialmanagement pre-startup together into a monolithic group. Maybe categorizing an indi-vidual with a single instance of a simple suboptimal behavior in a given year is a bit too

23Note that this would not be at odds with the results in Bernstein et al. (2018), who find thatyoung, yet experienced and skilled individuals are particularly responsive to local investment opportunityshocks that emanate from commodity price movements. In particular, age is negatively correlated withfinancial management skills, and education and experience are not or only weakly correlated with financialmanagement skills in my sample.

24The gender variable includes “Female”, “Male”, and “Unkown”.

24

extreme. To address this issue, I split the group of financially less skilled founders intomore granular subgroups in the next section.

4.1.2 Beyond Binary Financial Management Skills

Moving beyond a binary view, I employ a more granular characterization of financialmanagement skills next. Specifically, I distinguish between single-instance and serial non-optimizers with two separate indicator variables and re-run the specifications from TableV. The results are displayed in Table VI, where SLOW

i (1) is an indicator for founders whodisplayed exactly one instance of lower financial management skills with respect to theirpersonal finances before becoming an entrepreneur, and SLOW

i (2+) is an indicator forfounders who displayed more than one such instance. The general pattern across spec-ifications and outcomes closely matches the results from the specifications with a singlebinary financial-managment-skills indicator. However, all statistically significant effectsare concentrated in serial non-optimizers now. Moreover all magnitudes are larger ineconomically meaningful ways, by more than a third across specifications and outcomes.In the tightest specification, exit rates are 1.5 percentage points higher, revenues are 10.3%lower, and the probability of achieving an above-median operating surplus and operatingmargins is reduced by 3 percentage points relative to financially skilled entrepreneurs.These results confirm that the degree of financial management skills (or lack thereof) alsomatters for entrepreneurial success.25

4.1.3 Survival Analysis

In Table VII, I investigate how financial management skills affect cumulative survivalprobabilities for the cohort of founders that I can follow for up to four years, the longesthorizon in my sample. Panel A shows results for a binary financial skill indicator asdiscussed in Section 4.1.1, while Panel B shows results for two separate indicators, onefor single-instance and one for serial non-optimizers as discussed in Section 4.1.2. Theoutcome variable is an indicator for having survived up to firm age two, three, andfour respectively. Odd-numbered columns include the baseline specification and even-numbered columns include all controls as discussed in Section 4.1.1 (except firm age). Asbefore, controlling for the financial wealth of the founder is responsible for attenuating

25They also make it less likely that the effects we observe are driven by alternative explanations relatedto some form of rational inattention, for example. If that was the case, we would arguably expect relativelystronger effects for individuals with a positive but lower incidence of suboptimally managed personalfinances in the pre-startup period.

25

Table VI: Varying Financial Management Skills and Venture Outcomes

(1) (2) (3) (4) (5) (6) (7)

(A) EXIT

SLOW 1 0.008∗ 0.005 0.005 0.005 0.006 0.006 0.006(0.004) (0.004) (0.005) (0.005) (0.006) (0.006) (0.006)

SLOW 2+ 0.018∗∗∗ 0.013∗∗∗ 0.013∗∗∗ 0.015∗∗∗ 0.015∗∗∗ 0.013∗∗∗ 0.015∗∗∗

(0.004) (0.003) (0.003) (0.004) (0.004) (0.004) (0.004)


(B) LOG(REVENUE)

SLOW 1 −0.054 −0.026 −0.031 −0.037 −0.046 −0.046 −0.046(0.033) (0.035) (0.034) (0.034) (0.040) (0.039) (0.039)

SLOW 2+ −0.152∗∗∗ −0.110∗∗∗ −0.113∗∗∗ −0.104∗∗∗ −0.106∗∗∗ −0.101∗∗∗ −0.103∗∗∗

(0.034) (0.032) (0.031) (0.029) (0.032) (0.032) (0.033)


(C) PROFITS ABOVE MEDIAN

SLOW 1 −0.019∗∗ −0.017∗ −0.017∗ −0.016∗ −0.013 −0.013 −0.013(0.009) (0.009) (0.009) (0.009) (0.010) (0.010) (0.010)

SLOW 2+ −0.039∗∗∗ −0.033∗∗∗ −0.034∗∗∗ −0.034∗∗∗ −0.031∗∗∗ −0.031∗∗∗ −0.030∗∗∗

(0.009) (0.008) (0.008) (0.009) (0.010) (0.010) (0.010)


(D) PROFIT MARGINS ABOVE MEDIAN

SLOW 1 −0.017∗ −0.015 −0.014 −0.014 −0.012 −0.012 −0.012(0.010) (0.010) (0.010) (0.009) (0.011) (0.011) (0.011)

SLOW 2+ −0.037∗∗∗ −0.033∗∗∗ −0.033∗∗∗ −0.033∗∗∗ −0.032∗∗∗ −0.031∗∗∗ −0.030∗∗∗

(0.008) (0.008) (0.008) (0.008) (0.009) (0.009) (0.009)


County X X X XCohort x County X X XCohort x Firm Age X X X X X X XFounder-Fin XFounder-Fin+ X X X X XIndustry X X X XAge & Gender X XEdu & Attntn X

Note: This table shows the results from Regression 1 with SLOW split into two separate binary indicatorsfor founders with exactly one (1) or at least two incidences (2+) where personal finances were not optimallymanaged in the pre-startup period as described in Section 2.2. All other attributes correspond to the setupin Table V. Standard errors are clustered at the county level. Revenues are winsorized at one percent.Significance levels : ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01. 26

the coefficient of interest as we move from odd- to even-numbered columns.26 By movingto the right in Panel A of Table VII, we can see that the difference in survival probabilitiesbetween financially skilled and less-skilled entrepreneurs increases as time passes by. Asdisplayed in Column 6, the cumulative survival probability is reduced by 2.1 percentagepoints by year four. Consistent with findings from Section 4.1.2, effects are concentratedin serial non-optimizers and substantially larger when compared to the binary setupdisplayed in Panel A when we move to a more granular characterization of financial man-agement skills in Panel B. Column 6 indicates that the difference in cumulative survivalprobabilities between serial non-optimizers and optimizers (the reference group) appearsto level off at around 2.6 percentage points in year four.

4.2 Heterogenous Effects – Financial Management Skills in Industries

With Challenging Cash Flow Dynamics

In this section, I turn to heterogeneous effects. First, I develop a new set of industrycharacteristics based on high-frequency cash flow profiles of 1.5 million startups. Then, Ireturn to the sample of startups that I can link to their founders to study the importanceof financial management skills across industries with varying degrees of cash flow man-agement complexities along these characteristics. If financial management skills matterfor entrepreneurial success, we would expect them to matter more in industries with morechallenging cash flow dynamics.

The sample I rely on for this exercise includes all for-profit startups up to five years ofage that are actively operating for a full firm-year, and located in states in which thefinancial institution has a branch footprint. The final sample includes more than 1.5million young firms and more than 3.5 million firm-year observations.27

As a simple starting point, I estimate an industry’s growth-adjusted volatility. Tohone in on the core of cash flow management – matching inflows and outflows to keepa business operating and to enable growth – I estimate the (downside) correlation be-tween revenue and operating cost growth. Finally, I estimate the likelihood with whichan entrepreneur faces relatively low (negatively skewed), as well as extreme (fat-tailed)revenue growth shocks.

26Reassuringly again, the coefficients remain stable when moving from various founder wealth decilesto a more granular characterization of founders’ financial endowments as described in Section 4.1.1. Thesemore granular controls are included in the even-numbered specifications of Table VII.

27This sample is larger than the sample described in Section 2.1 since it also includes startups up to fiveyears of age founded before my sample begins.

27

Table VII: Survival Rates and Financial Management Skills

Survive ≥ 2 Survive ≥ 3 Survive ≥ 4

(1) (2) (3) (4) (5) (6)

(A) BINARY FINANCIAL SKILL INDICATOR

SLOW −0.014∗ −0.011 −0.027∗∗∗ −0.015∗∗ −0.030∗∗∗ −0.021∗∗

(0.007) (0.009) (0.007) (0.009) (0.008) (0.010)

Mean Dep. Var 0.888 0.888 0.807 0.807 0.739 0.739Observations 7,996 7,996 7,994 7,994 7,992 7,992Adjusted R2 0.014 0.037 0.010 0.046 0.009 0.054

(B) TERNARY FINANCIAL SKILL INDICATOR

SLOW 1 −0.007 −0.007 −0.015 −0.012 −0.019 −0.015(0.008) (0.011) (0.010) (0.011) (0.012) (0.016)

SLOW 2+ −0.020∗∗ −0.016 −0.038∗∗∗ −0.027∗∗ −0.039∗∗∗ −0.026∗

(0.010) (0.011) (0.011) (0.011) (0.013) (0.014)

Mean Dep. Var 0.888 0.888 0.807 0.807 0.739 0.739Observations 7,996 7,996 7,994 7,994 7,992 7,992Adjusted R2 0.014 0.037 0.010 0.038 0.009 0.054

County X X XCohort X X XCohort x County X X XFounder-Fin+ X X XIndustry X X XAge & Gender X X XEdu & Attntn X X X

Note: This table shows the results of a survival analysis. All specifications includeat least county and founder-cohort fixed effects. Other fixed effects and controls areincluded as indicated in the bottom panel of the table. See the note on Table V fordetails on the control variables. The unit of observation is a founder-year. The periodof observation is yearly relative to the venture’s startup month. SLOW is an indicator forfounders with at least one, exactly one (1), or at least two incidences (2+) where personalfinances were not optimally managed in the pre-startup period as described in Section2.2. The dependent variable is an indicator for a startup having survived up until agetwo (Columns 1 & 2), age three (Columns 3 & 4), or age four (Columns 5 & 6). Fordetails on the control variables see Table V. Standard errors are clustered at the countylevel. Revenues are winsorized at one percent. Significance levels : ∗p<0.1; ∗∗p<0.05;∗∗∗p<0.01.

28

For each firm-year in the sample, I calculate four weekly cash-flow characteristics:The revenue growth coefficient of variation (a growth-adjusted measure of volatility), thedownside correlation between revenue and operating expense growth, revenue growthskewness, and revenue growth kurtosis.

The motivation for each of these statistics is as follows: (1) The coefficient of varia-tion (COV) is calculated as a variable’s standard deviation divided by its mean; whilevolatility (as measured by standard deviations) in and of itself might simply represent ahigher risk-return tradeoff, the coefficient of variation provides a return-adjusted volatil-ity measure. (2) Downside correlation is defined as the correlation between revenue andoperating expense growth when revenue growth is negative. It hones in on the core ofcash flow management – matching inflows and outflows to keep a business operating andto provide growth funds as needed. When downside correlation is high, operations haveto be cut back when revenues recede.28 (3) Entrepreneurs that face negatively skewedrevenue growth need to plan for more negative (or low) growth shocks relative to their“business as usual”. (4) Finally, a high kurtosis implies that an entrepreneur has to dealwith extreme growth outcomes more frequently in general.29

To elicit industry-level cash flow characteristics, I aggregate these measures up to 4-digit NAICS industries by taking the median across observations. Figure IV displays thefive highest and lowest 4-digit NAICS industries for each metric. Industries with morechallenging cash flow profiles along each metric are listed on the left. The picture variesrichly, and the cash flow profiles certainly do not appear to simply re-capture informationalready embedded in existing industry classifications. The highest COV-industries tendto be in finance and mining. The highest downside correlation industries tend to bein manufacturing. The most negatively-skewed industries are also in manufacturing,and the industries with the highest kurtosis tend to be in the non-tradables sector. Theindustries with the lowest COV tend to be in the non-tradables sector. Industries with thelowest downside correlation appear to be distributed across sectors. The most positively-skewed industries tend to be in the non-tradables sector, and the industries with thelowest kurtosis appear to be distributed across sectors again.

Returning to the sample of startups that I can link to their founders, we can nowask the question whether financial management skills are particlarly valuable in indus-tries with less predictable and more complex cash flow dynamics. Specifically, I re-run

28I interpret a high downside correlation over high frequencies as indicative of cash constraints. Overlower frequencies, a low downside correlation might be indicative of sticky operating costs.

29When calculating these measures, I use symmetric growth rates to allow for periods with zerorevenues.

29

Specification 1 including all control variables and fixed effects in industries above andbelow the median with respect to the above four industry-level cash-flow characteristics.As shown in Table VIII, financial management skills are indeed particularly valuable inindustries with high COVs, high downside correlations, negative skewness, and highkurtosis. At the extensive margin, excess exit probabilities of less financially skilledfounders are 0.7 percentage points higher in industries with below-median skewnessand 1.3 percentage points higher in industries with above-median kurtosis. Downsidecorrelation or the coefficient of variation on the other hand do not seem to affect exitprobabilities differentially. At the intensive margin, financial management skills matteracross the board. Revenues of financially less skilled entrepreneurs are 10.6% lower inhigh COV industries, 8.9% lower in high downside correlation industries, 12.4% lower innegatively skewed industries, and 11.0% lower in high kurtosis industries. Compared toaverages across all industries, I estimate that the effect of a lack of financial managementskills is up to 90% higher at the extensive margin (exits) and up to 60% higher at theintensive margin (revenue) in industries with more complex cash flow profiles. Thus, thedifference in magnitudes when we move from industries with low to industries with highcash flow complexities is larger than the difference in magnitudes when we zoom in fromall non-optimizers to serial non-optimizers only.

4.3 Local Investment Opportunities and Financial Management Skills

In this section, I turn to the question of whether financially skilled entrepreneurs aremore successful in responding to time-varying local growth opportunities. I start byestimating local investment opportunity shocks based on the cash flow dynamics of closeto two million young and more-established small businesses in my data. Next, I returnto the sample of startups that I can link to their founders again to study if and howentrepreneurs of varying financial sophistication respond differently to such a shock.

The responsive quality of entrepreneurs to local investment opportunities has firstbeen discussed by Kirzner (1973, 1985). While Schumpeterian entrepreneurs’ distinctivecharacteristic is their involvement in the process of creative destruction through disrup-tive innovation (Schumpeter, 1942), Kirznerian entrepreneurs’ distinguishing feature istheir ability to take advantage of local growth opportunities as they present themselves.30

Why would financial sophistication matter in this context? Intuitively, liquid capitalis often a requirement for the ability to react to new investment opportunities. It stands to

30See also Bernstein et al. (2018) for a discussion.

30

Table VIII: Industry Splits By Cash Flow Dynamics

Rev. COV Downside Corr. Rev. Skewness Rev. Kurtosis

Med− Med+ Med− Med+ Med− Med+ Med− Med+

(A) EXIT

SLOW 0.011∗∗ 0.011∗ 0.011 0.011∗∗ 0.016∗∗∗ 0.009 0.008∗∗ 0.021∗∗∗

(0.005) (0.006) (0.008) (0.004) (0.006) (0.005) (0.004) (0.008)

Mean Dep. Var 0.082 0.095 0.108 0.078 0.081 0.093 0.074 0.115Observations 20,699 16,034 12,441 24,292 16,748 19,985 24,416 12,317Adjusted R2 0.077 0.071 0.076 0.066 0.075 0.078 0.062 0.082

(B) LOG(REVENUE)

SLOW −0.055 −0.106∗∗ −0.056 −0.089∗∗ −0.124∗∗∗ −0.043 −0.071∗ −0.110∗∗

(0.044) (0.050) (0.045) (0.040) (0.056) (0.035) (0.038) (0.051)

Mean Dep. Var 4.476 4.534 4.622 4.44 4.495 4.506 4.439 4.625Observations 20,699 16,034 12,441 24,292 16,748 19,985 24,416 12,317Adjusted R2 0.194 0.254 0.263 0.186 0.217 0.215 0.193 0.247

Cohort x County X X X X X X X XCohort x Firm Age X X X X X X X XFounder-Fin+ X X X X X X X XIndustry X X X X X X X XAge & Gender X X X X X X X XEdu & Attntn X X X X X X X X

Note: This table shows the results from Specification 1 for various industry splits along industry cashflow characteristics as described in Section 4.2. All specifications include the full set of fixed effects andcontrols as discussed in the note on Table V. The unit of observation is a founder-year. The period ofobservation is yearly relative to the venture’s startup month. SLOW is an indicator for founders with at leastone incidence where personal finances were not optimally managed in the pre-startup period as describedin Section 2.2. The dependent variable in Panel A is a business exit indicator. The dependent variable inPanel B is log revenue. The first two columns split the sample by industry medians of the coefficient ofvariation of revenue growth, a return-adjusted measure of volatility. Columns 3 and 4 split the sampleby industry medians of downside correlation, which is defined as the correlation between revenue andoperating cost growth when revenue growth is negative. Columns 5 and 6 split the sample by industrymedians of revenue growth skewness. Columns 7 and 8 split the sample by industry medians of revenuegrowth kurtosis. Standard errors are clustered at the county level. Revenues are winsorized at one percent.Significance levels : ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01.

31

reason that founders who are better at managing their finances are also better at finding“spare” cash to take advantage of such favorable business environments. In relatedresearch, Adelino, Ma and Robinson (2017) and Bernstein et al. (2018) have shown thatyoung, experienced, and well-educated potential founders are highly responsive to localinvestment opportunity shocks, and that ventures founded in response to such shockscontribute significantly to local employment growth.

To investigate this question formally, I create a new measure that reflects local invest-ment opportunities. I start from close to two million young as well as more establishedsmall businesses that are actively operating in non-financial for-profit sectors for at least afull year in my data. For each business in this sample, I compute firm-level yearly revenuegrowth rates relative to each startup cohort and aggregate those up to the county level bytaking medians across observations.31 To estimate cohort-location-specific investment op-portunity shocks relevant to the fast-evolving early-stage life cycle of a startup, I estimatethe following cohort-specific model:

grcjτ = αc

j + αcτ + uc

jτ,

where c denotes a startup cohort, αj is a county fixed effect, and ατ is a firm-year fixedeffect. For each cohort, I define a cash flow shock as the deviation from county-levelaverages and aggregate variations over time: uc

jτ. To ease interpretation, I normalize theshock and denote its normalized version by Z c

jτ. I interpret these cash flow shocks asreflective of investment opportunity shocks to my sample of founders.

I am mostly agnostic with respect to the source of the cash flow shock. For example,it could emanate from a local consumer demand shock.32 It could also be a productivityshock to an industry clustered in a particular region that translates into increased indus-trial demand.33. Alternatively, it could also derive from a leverage-driven local boom.34.

To formally investigate whether individuals with better financial management skillsare more responsive to such local investment opportunity shocks, I estimate the following

31I use symmetric growth rates to allow for periods without revenues.32See, e.g., Mian and Sufi (2014), Adelino, Ma and Robinson (2017), Giroud and Mueller (2017), Stroebel

and Vavra (2017), or Bernstein et al. (2018).33A similar idea is essentially behind Bartik-type shocks that interact local industry weights with

national industry trends to determine regionally varying exposures to national shocks based on pre-existingindustry structures (see, e.g., Bartik, 1991; Blanchard et al., 1992).

34See Giroud and Mueller (2018).

32

model:

ln Yicjnτ = αcj + αcτ + αn + SLOWi + βZcjτ + βZcjτ × SLOW

i + Xi,pre + εicnjτ, (2)

where αcj is a county-cohort fixed effect, αcτ is a cohort-year fixed effect, αn is an in-dustry fixed effect, SLOW

i is an indicator for founders who displayed lower financial man-agement skills with respect to their personal finances before becoming an entrepreneur,and Z c

jτ is the local investment opportunity shock. Xi,pre is a rich vector of founder-levelcontrols determined pre-startup. To account for any correlation of error terms across timeand space within a county, standard errors are clustered at the county level.

The results are presented in Table IX. Focusing on the tightest specification in Column7, a one standard deviation increase in the local investment opportunity shock is associ-ated with an increase in revenues of around 2.4% for financially skilled entrepreneurs.However, the effect is entirely reversed for founders with lower financial managementskills. In fact, less financially skilled founders seem to be competed away at the marginby their better-skilled peers as they capitalize on the positive investment opportunity.Such an equilibrium response would be in line with the considerable employment churnAdelino, Ma and Robinson (2017) observe in response to investment opportunities as theyderive from local income shocks. As can be seen by moving from left to right in Table IX,the result is robust across specifications that include varying combinations of founder-controls and fixed effects. Thus, while financially skilled founders are able to pivot theirstartup in response to a local investment opportunity shock in order to profit from it,relatively less skilled founders are not. These results suggest that financial sophisticationis an important enabler for Kirznerian entrepreneurship.

33

Table IX: Financial Management Skills & Local Investment Opportunities

LOG(REVENUE)

(1) (2) (3) (4) (5) (6) (7)

Treatment 0.012 0.025∗ 0.025∗ 0.025∗∗ 0.024∗∗ 0.024∗∗ 0.024∗∗

(0.011) (0.013) (0.013) (0.013) (0.012) (0.012) (0.012)

Treatment x SLOW −0.044∗∗ −0.039∗∗ −0.041∗∗ −0.038∗ −0.039∗∗ −0.039∗∗

(0.021) (0.020) (0.020) (0.020) (0.019) (0.019)

SLOW −0.080∗∗ −0.043 −0.047 −0.059∗ −0.054∗ −0.058∗

(0.037) (0.038) (0.037) (0.032) (0.032) (0.032)


County x Firm Age X X X X X X XCohort x Firm Age X X X X X X XFounder-Fin XFounder-Fin+ X X X XIndustry X X XAge & Gender X XEdu & Attntn X

Note: This table shows the results from Regression 2. Treatment is a standardized county-levelinvestment opportunity shock as described in Section 4.3. All specifications include founder-cohorttimes county and founder-cohort times firm-age fixed effects. Other fixed effects and controls areincluded as indicated in the bottom panel of the table. See the note on Table V for details on thecontrol variables. The unit of observation is a founder-year. The period of observation is yearlyrelative to the venture’s startup month. SLOW is an indicator for founders with at least one incidencewhere personal finances were not optimally managed in the pre-startup period as described inSection 2.2. The dependent variable is log revenue. Standard errors are clustered at the countylevel. Revenues are winsorized at one percent. Significance levels : ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01.

34

Figure IV: Cash Flow Profiles by Industry

2121 CoalMining 8123 Drycleaning&Laundry2372 LandSubdivision 4451 GroceryStores5232 Sec.&Comm.Exchanges 4471 GasolineStations5239 Oth.Fin.Investment 7225 Restaurants2122 MetalOreMining 4453 Beer,Wine&LiquorStores

2121 CoalMining 2122 MetalOreMining3336 Engine,Turbine&PowerManufact. 3117 SeafoodProductPrep.&Pack.6112 JuniorColleges 4862 NaturalGasPipelines4861 OilPipelines 3365 RailroadRoll.StockManufact.3131 Fiber,Yarn&ThreadMills 4879 SightseeingTransport

4862 NaturalGasPipelines 7224 DrinkingPlaces3326 Spring&WireProductManufact. 8123 Drycleaning&Laundry3212 WoodProductManufact. 4451 GroceryStores3113 Sugar&Confect.Manufact. 7225 Restaurants3312 SteelProductManufact. 4471 GasolineStations

7225 Restaurants 4521 DepartmentStores4471 GasolineStations 5251 Insurance&BenefitFunds4451 GroceryStores 3221 Pulp&PaperMills7224 DrinkingPlaces 3365 RailroadRoll.StockManufact.3115 DairyProductManufact. 4879 SightseeingTransport

LowestHighest

Negative Positive

CoefficientofVariation

DownsideCorrelation

Skewness

Kurtosis

Highest Lowest

Highest Lowest

Note: This figure shows the five top and bottom 4-digit NAICS industries for fourdifferent cash flow characterstics inferred from more than 1.5 million startups. Thecoefficient of variation is the standard deviation of revenue growth divided byits mean. Downside correlation is the correlation between revenue growth andoperating cost growth when revenue growth is negative. Skewness and Kurtosisare revenue growth moments. Industry-level rankings are based on medians acrossstartups within that industry.

35

5 Conclusion

In this paper, I have examined the role of financial management skills for entrepreneurialsuccess in the U.S. I find that entrepreneurs who are better at managing their personal fi-nances before starting a business are also more successful in steering their startup throughits infancy and in scaling their business.

I also show that financial management skills are especially valuable in industrieswhere cash flow management is particularly challenging as inferred from on a new setof industry characteristics derived from high-frequency cash flow profiles of 1.5 millionstartups. Finally, I document that founders with better financial management skills aremore successful in taking advantage of local investment opportunity shocks to grow.

These results also have intriguing policy implications – to the extent that financialmanagement skills allow founders to use existing resources more efficiently, providingentrepreneurs and small business owners with the skills and tools to manage their cashflows more effectively may spur economic growth and increase the responsiveness of theeconomy to positive shocks, even in highly developed economies such as the U.S.

36

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