Lecture 6: Entreprenuership Part 1: Some Data Sources For Entrepreneurship Data for Today’s Class • Distribution of Firms by Size and Age (aggregates) Statistics of U.S. Businesses (SUSB) http://www.sba.gov/advo/research/data.html Measures number of employees, number of firms, births, deaths, and growth (all in total and by firm size) by detailed industries. Allows one to compute the distribution of firm size over time by industry. Large literature trying to explain the size distribution of firms. Data for Today’s Class • Micro Data on Small Firms Survey of Small Business Finances (SSBF) http://www.federalreserve.gov/ssbf/ Conducted by Board of Governors 1987, 1993, 1998, and 2003 (repeated cross sections) Measures detailed descriptive and financial data on firms with less than 500 employees (non-agricultural) Data for Today’s Class • Micro Data on Small Firms Kauffman Firm Survey (KFS) http://www.kauffman.org/research-and-policy/kauffman-firm-survey.aspx Conducted by Kauffman Foundation 2004 – 2007 (Longitudinal Survey) Measures detailed descriptive and financial data on small businesses Starts with new businesses in 2004. Follows survivors through 2007. Data for Today’s Class • Micro Data on Small Firms PSID (longitudinal)/CPS (some longitudinal/mostly repeated cross sections) Collects data on the self employed. Collects data on business owners. Most micro data empirical work on “entrepreneurs” use household level data where entrepreneur is equated with the self employed or with small business owners. Cool Data that is Underutilized • Longitudinal Business Database From U.S. Census (available for use only at Census Research Data Centers (there is one at the Chicago Fed). Tracks the dynamics of all firms in the U.S. (across all industries and all sizes) since 1976 (including births and deaths). Based on tax records. Part 2: Standard Model: Liquidity Constraints and Small Business Formation Why Do People Start Businesses? • Small Business Skills (Innovators) (Schumpter (1934), Evans and Jovanovic (1989)) • Risk Preferences (Kihlstorm and Laffont (1979), Jovanovic (1979)) • “Jack of All Trades” (have better management skills) (Lazear (2005)) Two major questions in the literature: Why can’t innovation take place in the existing firms? Can the new firms get financing? Evans and Jovanovic (1989) Choice: Become a worker: Earn wage: (wζ) Become an “entrepreneur”: Earn income: ( y k ) where: θ is entrepreneurial ability (know when making choice) k is capital necessary to start a business ζ is returns to scale on capital: (0,1) Note: Assume innovations to w and y are uncorrelated. Assume that ability (θ) is uncorrelated with market wage. Assume risk neutrality. Evans and Jovanovic (1989) Entrepreneurial Income: y r(z k ) where: z is initial wealth Constraint: 0 k z (where 1) Firms can at most borrow λ times their initial wealth to fund their capital project. Note: Borrowing rate = lending rate = r (same for everyone). Optimal Capital Stock max [ k r ( z k )] k [0, z ] F .O.C. : k 1 r 0 1/ (1 ) k r Implication, entrepreneur is unconstrained when: ( z) 1 r Probability of Entrepreneurship Increasing in Wealth Finish Solving The Model Entrepreneurial Income as a function of constrained/unconstrained k. Compare Entrepreneurial Earnings to Wages max[ k r ( z k )] w rz Unconstrained: w(1 ) 1 r 1 ( z ) r Constrained: 1 r max ( z ) 1 , w( z ) r ( z ) Evans and Jovanovic Conclusions • Richer households are less bound by liquidity constraints and as a result are more likely to enter entrepreneurship. • Should see a positive relationship between initial wealth and entry into small business ownership. Part 3: Testing for the Importance of Liquidity Constraints Old School Tests of Liquidity Constraints for Entrepreneurs Basically, the majority of empirical papers regress business ownership (the propensity to become a business owner, the propensity to survive as a business owner) on household wealth. Prob (Start Business (t, t+1)) = α0 + α1 ln(Wealth(t)) + γ X + ε Early research concluded that if wealth is significant in predicting business entry, liquidity constraints are binding. (i.e., α1 > 0) Approach taken: Evans and Jovanovic (1989, JPE) Evans and Leighton (1989, AER) Fairlie (1999, Journal of Labor Economics) Quadrini (1999, Review of Income and Wealth) Limitations of Approach Is the level of wealth exogenous from other factors that cause entrepreneurial entry? High ability earn more (accumulate more for retirement) and may be better at innovating. Risk preferences can cause high wealth and taste for entrepreneurship People planning for self employment accumulate assets for their retirement (do not have pensions). Try to find an “instrument”. Inheritances as an Instrument • Instrument for wealth - look at liquidity windfalls which are uncorrelated with the decision to become an entrepreneur. • Many use inheritances as instrument. • Find inheritances are strongly correlated with entrepreneurial entry. Receiving an inheritance in year t predicts entrepreneurial entry between t and t+k. • Holz-Eakin, Joulfaian, and Rosen (JPE, 1994) • Blanchflower and Oswald (1998, Journal of Labor Economics). 20 Up Though 2003: Conventional Wisdom • Liquidity constraints are an important deterrent to small business formation. • Liquidity constraints to small business formation is an important explanation of the dispersion in wealth (rich people keep accumulating wealth to relax their liquidity constraint for their small business). - Cagetti and DeNardi (2006, JPE). • Welfare costs of liquidity constraints to entrepreneurship is large - Buera (2009, Annals of Finance) Both papers use as the basis of their models, the relationship between wealth and starting a business using household micro data. 21 A Re-Evaluation of The Facts Liquidity Constraints, Household Wealth and Entrepreneurship? Erik Hurst University of Chicago and NBER Annamaria Lusardi Dartmouth College and NBER 22 Goal • Are people interpreting the data correctly? This paper • I think that the relationship between wealth and small business start-up using micro data (or firm level data) is not what people think. Paper with Ben (coming later) • In the micro data, do small business match our conceptual models of “entrepreneurs”? • If not, what can explain the propensity to become small business owners in the data? 23 Some Facts About Small Business Owners • How much money do small business owners need to start their business? • 1987 NSSBF: Median amount of capital to start a business is $22,700 25% start with less than $5,000 • 1982 Characteristics of Business Owners (Meyer 1990) report even smaller figures: – 63% of non minority males and 78% of black business owners started with less than $8,700 (1996 dollars) • Inc Magazine 500 fastest growing companies in the U.S. (Bhidé 2000) – 26% started with less than $5,000 in upfront capital – Median was not much higher. 24 Starting Capital Value 1st Quartile Median 3rd Quartile % of Firms Construction $2,860 $9,500 $30,100 10.9% Services $3,450 $19,400 $62,719 30.3% Mining $1,730 $37,800 $394,375 1.2% Transportation, Communication and Public Utilities $15,120 $47,300 $143,300 3.0% Finance, Insurance and Real Estate $7,900 $36,500 $173,260 4.8% Manufacturing $16,165 $47,300 $151,200 7.9% Wholesale Trade $11,010 $41,400 $145,860 8.5% Retail Trade $21,880 $55,200 $118,150 33.3% Industry Low Starting Capital Industries High Starting Capital Industries 25 What We Do in this Paper • Formally Test The Importance of Liquidity Constraints and Business Ownership – – – Examine the relationship between own wealth and business entry Examine the relationship between parental wealth and business entry Look at the wealth/business entry relationship by types of business – Instruments for wealth changes • • – Inheritances Capital gains on housing. Look at survival probabilities 26 Data Source • • Panel Study of Income Dynamics (PSID) Can follow households in and out of business ownership. Business ownership is asked in every year. Business wealth (and all other wealth) asked every five years starting in 1984. • Main sample of analysis focuses: Stacked panel: Transition into business ownership between 1989 and 1990 and Transition into business ownership between 1994 and 1995 Focus on: Non business owners Households aged 22 to 60 Sample size: 7,645 observations (almost 5,000 distinct households). For some analysis, we will only use the 1989-1990 panel (occupation and industry codes are not available beyond 1993). 3,645 observations. 27 Initial Methodology • Run three different types of regressions Prob (Start Business (t, t+1)) = α0 + α1 Wealth(t) + γ X + ε Prob (Start Business (t, t+1)) = α0 + α1 Wealth(t) + α2 Wealth(t)2 + α3 Wealth(t)3 + α4 Wealth(t)4 + α5 lnWealth(t)5 + γ X + ε Prob (Start Business (t, t+1)) = α0 + α1 Dummy_Wealth_80-95 + α2 Dummy_Wealth_95+ γ X + ε • X includes controls for age, education, income, family structure, prior employment status, and prior business ownership. • Wealth is defined as the sum of savings and checking accounts, bonds, stocks, IRAs, housing equity, other real estate, and vehicles, minus all debts. 28 0-80th Percentile 80th - 95th Percentile 95th-98th Percentile Predicted Probability 7.0% 6.0% 5.0% 4.0% 3.0% 2.0% -2 0 20 60 0 10 0 14 0 18 0 22 0 26 0 30 0 34 0 38 0 42 0 46 0 50 Wealth Level (in $1,000) 29 30 Importance of Parental Wealth Variables Include a full set of income and demographic controls? Yes Household’s Own Non-Business Net Worth in 1989 8.78 E-8 (6.98 E-8) Dummy: Husband/Wife Father a Business Owner? 0.049 (0.023) Dummy: Parental Wealth 20th - 40th percentile 0.024 (0.020) Dummy: Parental Wealth 40th - 60th percentile 0.002 (0.018) Dummy: Parental Wealth 60th - 80th percentile 0.021 (0.019) Dummy: Parental Wealth 80th - 90th percentile 0.032 (0.021) Dummy: Parental Wealth 90th - 97th percentile 0.025 (0.024) Dummy: Parental Wealth > 97th percentile 0.072 (0.039) 31 Wealth and Business Start Up by Industry • Wealth should be more important for starting a business with high starting capital requirements. • You need to be rich to start a car factory. However, wealth should not matter much to start a house-cleaning business. We explore heterogeneity in starting businesses of differing starting capital amounts. Perhaps the heterogeneity is masking evidence that liquidity constraints exist. Create Two Categories: 1. Low Starting Capital (Construction and Services) 2. High Starting Capital (FIRE, Manufacturing, Transportation, Wholesale and Retail Trade, Communications) Note: PSID has two additional industries: Farming and Professionals We will look at professionals separately 32 4.0% 0-90th Percentile 90th - 98th Percentile Predicted Probability 3.0% 2.0% High Starting Capital Industries 1.0% Professional Industries Low Starting Capital Industries 0.0% 0 40 80 0 12 0 16 0 20 0 24 0 28 0 32 0 36 0 40 0 44 0 48 Wealth Level (in $1,000) 33 What about Inheritances as an Instrument? • Fact is replicated in our data set. 1. Many business are transferred at the time of death (5% of NSSBF sample) 2. More importantly, inheritances are not randomly distributed in the population. Is the case closed? No…… Why? Those who get inheritances are just different (on average) from those who do not. A counterfactual…… Test of the latter proposition Do future inheritances (received after the business is started) predict current business entry? 34 35 A New Instrument We use an alternative measure of liquidity: Regional variation in housing prices. Much evidence that households do borrow against home equity to sustain consumption or finance investment projects. – Brady, Canner and Maki (2000) – 20% of those who removed equity during the late 1990s when refinancing used it to fund business investment. – Hurst and Stafford (2002) – find household who lost their jobs in the early 1990s used home equity to prop up consumption. We predict that households who receive increases in home equity – all else equal – should have access to more liquidity. Are they more likely to start a business? We find NO effect of housing capital gains on business entry! 36 Some Additional Facts about New Business Owners 37 Conclusions For Policy Crowds… • Our findings do NOT promote cutting funding to the Small Business Administration (SBA). Part of the reason why liquidity constraints may not be binding is because of SBA policies. • Existing evidence on the existence of liquidity constraints for small businesses not very conclusive. • Why is it the effect is so large for the really rich? Outstanding Questions: • Are the business owners in typical household or business survey important for economic growth? • Are there existing households who would start a profitable business if they had wealth that just are not showing up in the data? • What drives business ownership decisions for median household? 38 Part 5: The Non-Pecuniary Benefits of “Entrepreneurship” Some Interesting Facts… • Does the data on small business owners match the concept of entrepreneurship in our model? 40 Industry Table 1: Data on Employer Firms from Statistics of U.S. Businesses (SUSB): 1989 – 1997 Pooled Percent of Percent of Within Share Firms with Firms with Within Industry Births of 1-9 1-99 Industry Percent Firms with Percent Percent Employees Employees Percent Employees 1-9 Firms of Employees Out of All Out of All Firms In Firms Employees Out of all Out of All Firms With Firms With With 1-9 with 1-9 Out of All Firms Employees 1-9 Emps. 1-99 Emps Employees Employees Births All Firms Fraction Of Firms More than 10 Years Old in Industry With 1-9 Employees - - - - 0.606 0.120 0.735 Retail Trade 0.180 0.139 0.153 0.155 0.473 0.154 0.101 0.780 General Services 0.162 0.114 0.193 0.179 0.746 0.295 0.137 0.851 Health Care Services 0.100 0.126 0.109 0.106 0.589 0.110 0.051 0.806 FIRE 0.096 0.073 0.091 0.083 0.473 0.144 0.063 0.860 Construction 0.091 0.051 0.117 0.108 0.727 0.236 0.104 0.784 Wholesale Trade 0.081 0.066 0.070 0.079 0.526 0.133 0.049 0.638 Accomm./Food Service 0.077 0.088 0.056 0.071 0.472 0.074 0.048 0.581 Professional Services 0.069 0.058 0.085 0.078 0.675 0.182 0.063 0.827 Manufacturing 0.063 0.193 0.046 0.060 0.370 0.033 0.033 0.598 Transportation/Warehouse 0.042 0.059 0.035 0.036 0.403 0.080 0.033 0.695 Entertainment Services 0.019 0.018 0.019 0.020 0.608 0.121 0.017 0.755 Agriculture/Mining 0.018 0.013 0.022 0.021 0.566 0.137 0.018 0.799 41 Small Business Employment Share vs. Probability of Growth .35 By 2 Digit Industry .2 .25 .3 30 26 16 34 36 33 3235 14 2220 25 2848 23 24 3738 29 83 82 3958 84 45 52 44 79 61 31 70 46 12 50 27 73 54 42 55 41 8 80 21 51 78 49 53 87 56 62 60 10 63 13 7 17 15 47 57 59 75 86 76 72 89 9 81 64 65 .15 67 0 .1 .2 .3 Share of Industry Employment in Small Firms Fitted values .4 grow_1_9_timeavg 42 Industry Table 2: Statistics from the 2004-2008 Panel of New Firms from the Kaufman Firm Survey I II III IV Percent w/ Percent w/ Percent Percent Percent Percent ΔEmps. ΔEmps. Percent With Percent of with 0-4 with 0-9 with 20+ >5 > 10 With TradeFirms Employees Employees Employees Over 4 yrs Over 4 yrs Patent mark All Percent With Patent or Trade-mark 81.7% 91.6% 3.5% 10.8% 3.6% 2.7% 8.9% 10.2% Profess. Services 19.4 86.0 96.4 1.1 8.2 1.7 3.5 14.6 15.5 General Services 18.3 86.8 92.8 3.1 9.8 4.3 1.9 7.3 8.2 Retail Trade 13.9 84.1 94.0 1.7 8.2 0.8 0.8 7.5 7.2 Construction 11.7 77.1 88.5 6.5 16.9 8.0 0.4 1.7 2.0 FIRE 10.5 88.9 95.5 2.9 5.4 1.0 0.5 4.1 4.1 Manufacturing 6.2 79.0 88.8 6.1 12.3 6.1 16.0 8.5 19.2 Wholesale Trade 4.9 75.0 87.8 4.6 16.9 3.7 6.8 8.8 13.5 Health Services 3.2 71.2 88.6 4.9 14.6 5.9 0 7.7 7.7 Transportation 3.0 80.1 90.4 2.3 14.6 3.8 0 1.6 1.6 Information 3.0 74.9 89.4 1.5 10.9 1.4 1.2 34.8 35.4 Arts/Ent. Services 2.7 81.8 97.6 0 0 0 0 20.5 20.5 Restaurant/Hotel 2.0 28.0 40.3 20.4 33.7 15.1 0 10.0 10.0 Ag/Mining 1.6 70.4 89.8 10.2 15.7 6.9 10.3 4.5 14.8 43 Industry Table 3: Data on the Self Employed from the Current Population Survey (CPS): 1992-2002 Fraction of Fraction of Continuing Self Average Year over Continuing Self Employed Firms Percent of Self Year Wage Change Employed Firms That Increased Employed in Each For Entrants Into That Increased Workers in the Firm Industry Self Employment Workers in the Firm By Five Employees Net Change in Employees by Continuing Self Employed Firms By Industry Retail Trade 0.105 -0.063 0.055 0.015 -0.005 General Services 0.175 -0.128 0.029 0.002 0.004 Professional Services 0.051 -0.022 0.049 0.004 -0.002 Health Care Services 0.069 -0.074 0.050 0.006 -0.074 FIRE 0.235 -0.030 0.040 0.000 -0.003 Construction 0.053 -0.109 0.106 0.013 0.028 Accomm./Food Service 0.033 -0.006 0.097 0.007 -0.013 Wholesale Trade 0.079 -0.029 0.044 0.002 -0.021 Manufacturing 0.060 -0.048 0.084 0.003 0.002 Transportation/Warehouse 0.057 -0.023 0.049 0.005 -0.012 Entertainment Services 0.026 -0.029 0.043 0.005 0.010 Agriculture/Mining 0.047 -0.307 0.033 0.000 0.009 44 Two Additional Results of • Moskowitz and Vissing Jorgensen (AER 2002) “Private Equity Puzzle”….Measured risk adjusted return to public equity is much higher than the measured risk adjusted return to private equity. • Hamilton (JPE 2000) Wages of individuals fall sharply (~30% at median) when household transition into small business ownership from wage workers. Potential explanation: There are non-pecuniary benefits to small business formation. Consistent with micro data that most small firms never grow, never innovation and are concentrated in a few industries. 45