Economics of Small Business

1. Economics of Small Business Drexel University Spring Quarter 2014 Fourth Week

2. Is Small Business the Engine of Job Creation?

3. From Bloomberg Businessweek, Feb, 10, 2014

4. Are Small Businesses The Engine Of Growth? • “ … it is not small businesses per se that are important, but flexible, innovative, risk-taking businesses, which tend to be small.” • “As such, the concept of “small business” as an analytical category seems rather useless.”

5. Source • The quotes are from a paper by Veronique de Rugy, published by the American Enterprise Institute, AEI • There is a political angle. • AEI is opposed to all government intervention in the economy – including policies that support small business.

6. Public Programs • De Rugy begins with an inventory of public programs that support small business. • Save it. We will come back to that! • She puts the overall bill at over 14 billion. • That is probably an underestimate, but a more careful estimate would also look at the benefits – other than growth.

7. Gross versus Net 1 • It could be that SMEs (or some other size category) open many new jobs, but also eliminate many jobs through dismissals and discontinuations of the business. • Then they would create many jobs on a gross basis, but many fewer, perhaps a negative number on a net basis. • Therefore, economists tend to emphasize net job creation. • De Rugy argues that this creates a bias in favor of SMEs.

8. Example • In this example, all net new jobs are created by the one small business, but 80% gross are created by big business.

9. Second Thoughts? • Is this persuasive? • After all, in year 2 the additional 50 people who are employed are employed by the small business! • The proportion of people employed in small business has increased from 20% to 22.5%. • Here is perhaps a better example to get the point:

10. Another Example • The point is, big firms, growing and declining at the same rate, create – and destroy – more jobs.

11. Proportion • In the second example, the proportion of employees who work for the small business grows from 9% to 11%. • If it is true that small businesses lead in net job creation, then we should see an increase in the proportion of employees who are employed by small business. • Do we? De Rugy says no.

13. Denominator • Clearly de Rugy has a point – net job creation statistics can be misleading. • (So can gross job creation statistics, for other reasons.) • The problem seems to be that the denominator is NOT the sum of the numerator numbers but something more complicated.

14. Balancing • “Judging the role of small businesses in job creation based on their large share of net job growth is therefore very misleading because firms of all sizes contribute large numbers of new jobs.” • “ … small employers both create and destroy jobs at much higher rates than large employers. Consequently, net job creation exhibits no strong relationship to employer size.”

15. Part of a Table • As we see, SMEs both create and destroy jobs at a higher rate.

16. Gross versus Net 2 • Both approaches have advantages and disadvantages. • Gross job creation simply ignores the fact that a sector with larger turnover (such as SMEs) will both create and destroy more jobs. • Net, as we have seen, can distort the picture as the numbers do not add up in straightforward ways.

17. Regression Fallacy 1 • Suppose some firms are below their “normal size” (based on economies of scale, etc) in period 1. • Others are above. • Those below their “normal size” will be smaller on the average, than those that are above. • Moving toward the normal size, the first group grow and the last group shrink. • Looking at that, we might conclude – wrongly! -- that smaller firms grow faster on the average.

18. Regression Fallacy • The fallacy is that if companies tend to approach a “natural” size, on the whole, the companies that grow will be the ones below the natural size. • They don’t grow because they are small but because they are recovering. • Here is a numerical example to illustrate the problem.

19. Three Firms 1

20. Three Firms 2 • In year zero, each firm is at its “natural” scale. • In year 1, shocks shift firms 1 and 3 away from the “natural” scale. • In later years, each firm makes up half the difference of “natural” scale and the scale last year.

21. Firm Size and Growth Firm Growth from Year 1 to Year 2

22. Reflections • Thus we see an apparent negative relation between firm size and firm growth. • Nevertheless, they are converging to the same “natural” size. • Moreover, the average firm size remains constant at 100.

23. Reflections • The regression fallacy is particularly important and probably explains why we see a relatively stable proportion of employees employed in small firms at the same time we observe smaller firms opening new jobs at a higher rate on the average.

24. Some Data on Hiring

25. JOLTS • Since 2000, the Bureau of Labor Statistics has been collecting sample survey data on the number of job openings, hires, quits, layoffs and dismissals, and job turnover. • This is called the JOLTS database (for Job Openings and Labor Turnover Survey) • Recently, they have produced (tentative) data broken out by enterprise employment scale.

26. Job Openings • For comparison with some of our other data, we will look at job openings. • Sampled businesses are asked whether they have jobs open at the time of the survey, and how many. • These data are weighted to give an estimate of the total number of jobs available by firms of that type. • The data are in thousands of jobs.

29. Job Openings Rate • The job openings rate is the job openings level divided by employment in the category plus the job openings level for the category (similar to the unemployment rate). • This allows for the different overall size of employment in the different categories.

32. We See That • There is less difference than the levels suggest, • The smallest size category is the most volatile, • The second-biggest size category, 1000-4999, seems to lead in the overall rate of job opening, • All are similarly affected by macroeconomic fluctuations. • To what extent do these differences reflect differences in turnover?

33. Turnover • BLS measures labor turnover as the total number of separations, that is • Quits • Dismissals • Layoffs • “Other” • The turnover rate is this sum divided by employment in the corresponding category.

35. We See • Once again, category 1-9 is the most volatile, • Otherwise, bigger firms have less turnover, especially the largest categories, • There may be a downward trend, overall. • Macroeconomic fluctuations have less apparent impact. • (This may be because changes in quits tend to offset changes in layoffs and dismissals.)

36. Summary on JOLTS Data • This is just a hasty first-pass overview! • It seems to reinforce what we have seen in some other sources: • “Job creation” is distributed over all size classes of firms. • Small business is more volatile. • The dynamics of firm size distribution is pretty complex.

37. Interim Summary • Analysis of the statistical measures used leads to reasonable doubts about claims that “small businesses create most jobs.” • The regression fallacy could also make it seem that smaller firms create more jobs, when actually it is firms that are just catching up, regardless of their stable size.

38. High Impact Firms • A study sponsored by the SBA concluded that: • “ … a relatively small class of firms was responsible for generating nearly all net new jobs in the U.S. economy from 1994 to 2006.” • “ …high impact companies are enterprises whose sales have at least doubled over a four-year period and which have an employment growth quantifier of two or more over the same period.”

39. Sketch 1 • “ …there were about 350,000 high impact companies in the U.S. economy.” • “This represents about 6.3 percent of all companies in the economy.” • “These companies are younger and more productive than all other firms and are found in relatively equal shares across all industries, even declining and stagnant ones.”

40. Sketch 2

41. Reflections • VSEs – the 1-19 category – are about 89% of all firms, so they are slightly over-represented. • However, the biggest category are about 0.2% of all firms – they are clearly overrepresented.

42. Biases • This study does not correct for the regression fallacy. Thus it will generally underestimate the size of high-impact firms. • (As we will see, though, there is a bias that might be in the opposite direction.) • There are no start-ups in the data set. Therefore it may overestimate the age of high-impact firms.

43. Employment Quantifier • The “employment quantifier” is the product of the absolute and percent increase in employment. • This is supposed to be a compromise, on the grounds that • Absolute values favor bigger firms • Percent value favor smaller firms

44. Boundary Increase in Absolute Employment

45. Boundary Percent Increase in Employment

46. Contrast • In summary we see that to count as a high-impact firm • A 5-employee firm would have to increase its employment by 60% • A 1000-employee firm by 4.5% • But • The 60% of 5 is 3 new jobs • The 4.5% of 1000 is 45 new jobs. • Which way does the bias go?

47. Sales • Since the company must also double its sales, these limits probably affect relatively few companies – and only those that increase their productivity or price at high rates relative to their work force. • By contrast “gazelles” are defined only by increasing sales, but must increase sales steadily at 20% per year or more (I think).

49. Trend? • We do seem to see some downward trend, except in the VSE size category 1-19. • Given that we have just four observations we should treat this with some caution.

50. Net Job Creation • By various computations it is argued that these companies create 100% or more of net new jobs. • This should not be a surprise! • What it means is that the criteria are successful in capturing the high-impact companies as intended! • But what is remarkable is how little they differ from other companies.