Final Report

N~w Business Starts and Economic Activity:

An Empirical Investigation

Robert Smi ley

and

Richard Highfield

Johnson Graduate School of Management

Cornell University . , o

June 1"986

Prepared under contract #SBA-g241-OA-85 for the Office of Advocacy of

the U.S. Small Business Administration. Robert Frank, Robert Hutchens

and Bruce Phillips provided helpful comments on an earlier draft. We

would llke to thank Margaret Forster for excellent research assistance.

The conclusions are the author's and do not represent off icial positions

of the Small Business Administration.

JUN I?

• L 4

p

C o n d u c t e d u n d e r SBA c o n t r a c t . S t a t e m e n t s a n d c o n c l u s i o n s h e r e i n a r e t h e c o n t r a c t o r ' s a n d n o t v i e w s o f t h e U . S . G o v e r n m e n t o r S m a l l B u s i n e s s A d m i n i s t r a t i o n .

|

I. Introduction

What factors influence the rate of the new business starts? This

question has be~n studied in some detail for other economies such as

Sweden (Hause and DuRietz, 1984), and for sectors of the U.S. economy

such as food (MacDonald, 1986), but never for the U.S. economy as a

whole and never for a long period of time. This study presents the

analysis of factors influencing the rate of new business starts in a

large number of industries, and over a long period of time for the U.S.

economy. A number of d i f f i cu l t methodological issues are confronted,

and several improvements in empirical estimation techniques are

presented.

The importance of understanding the determinants of new business

act iv i ty should be obvious. Although there are some disputes about the

exact numbers, recent empirical work indicates that new small business

starts account for a large proportion, of new Job creation (U.S. Small

Business Administration 1983, Chapter 3 and Birch, lgB1). Furthermore,

small business accounts for a disproportionately large amount of

technical innovation (Scherer, Ig80, pp. 407-438). Finally, i f economic

policy makers should want to influence the rate of new business starts,

they must f i r s t understand the interrelationships between policy

variables and th is ra te .

In Section I I we present an analysis of the aggregate rate of new

business starts (the new incorporations series) for the period 1948-

lg84. Section I l l presents an analysis of the determinants of new

business starts across four-digit industries over the time period 1976-

1981. A short conclusion section follows.

O

I I . Time Series Analysis of New Flrm Startups

One of the po ten t i a l l y confounding problems In cross-sectional

Industry studle~ of economic phenomena such as entry Is the existence of

economy-wide factors that can af fect behavior In a l l Industr ies. The

preferred method of examining such effects would be analysis of a pooled

cross-sect ional- t ime-ser ies data set covering a su f f i c ien t number of

data periods to allow modelling and test ing of both the macro and

Indust ry-speci f ic factors determining entry. In the present case our

Indust ry-speci f ic data are avai lable for only three two-year periods,

making Inference regarding macro effects a pract ica l Imposs ib i l i t y . As

an a l te rnat ive we wt l l present a basic analysts of economy wtde affects

In th ls section, using aggregate macro data, and w i l l turn to the

examination of Indust ry-speci f ic ef fects In the fo l lowing section.

To examine the macro effects we have gathered time series data on

several macro variables which mlght reasonably be related to the rate of

entry of new f i rms. In a l l the analysis that fol lows the var iable to be o,

explained ts the number of new Incorporations na t iona l ly over the period

1948 to 1984. In pa r t i cu la r we w i l l constder the determinants of the

quar ter ly growth rate (seasonally adjusted at annual rates) of the

number of new Incorporations 1. The growth rate Is used In preference to

the levels fo r two reasons. The f i r s t ts that the number of new

Incorporations displays a strong trend upward that ts quite s imi la r to

that of most real a c t i v i t y measures, and t t ts the deviations from th is

trend that are of pa r t i cu la r Interest . The second reason Is a s ta t l s -

1Thls data series Is compiled monthly by Dun and Bradstreet from State government data. We are uslng the quarterly totals. The growth rates are calculated as DLINC+ - (ln (INCt) - In (INCt_l)) * 400 where INC t Is the quarterly total. ~

3

t ical one. The growth rate is approximately statlonary 2 over the period

studied, and most of the techniques below rely on the assumption of

stationarity.

We should note that the new incorporation series is not a perfect

measure of new business act ivi ty. In addition to new business starts,

the number of new incorporations in a quarter wi l l also capture firms

that changed their form of ownership, e.g. from partnership to corporate

status. We wi l l have more to say about this issue later in the paper.

As potential explanatory variables we w111 consider the quarterly

growth rates of real GNP (DLRGNP), the growth rate in real expenditures

on new plant and equipment 3 (DLRPEX), the change in the unemployment

rate (DURATE), the rate of inf lat ion 4 (DLIPD) and the real interest rate

(RRATE) 5. All of these data series are approximately stationary, a fact

that wi l l be important below.

What type of business climate leads to a greater increase in the

rate of formation of new firms? At least two different macroeconomic

scenarios can be constructed, each of which could conceivably lead to an

2The concept of weakstatlonarlty {or covarlance statlonarity) is all that Is required here. A time series is weakly statlonarlty if it has constant mean, constant variance and if the covarlance between two points In the series depends only on the amount of time separating them. These properties allow consistent estimation of the tlme series mean, variance, autocorrelatlons and cross correlations with other stationary time series.

)From the Bureau of Economic Analysis quarterly P&E survey.

4Growth rate of the implicit GNP deflator

5Calculated as the quarterly average gO-day Treasury Blll rate less inflation (DLIPD).

4

increase in new business activity. In a "naive forecasting" scenario,

individuals look at present rates of change of macroeconomlc variables

and forecast that these rates will continue to change at the same rate.

Further, indlvl(luals would prefer to start new businesses when the

economy Is robust. In this case, high rates of growth of new

incorporations would be accompanied by (or would lag by several

quarters, since the process of incorporation requires some time) hlgh

rates of growth of GNP, low real interest rates, hlgh inflation rates,

high real expenditures on new plant and equipment, and decreases In the

unemployment rate.

An alternatlve scenario, with very different results, might be

called an "opportunistic" scenario. In thls case, entrepreneurs begin

new business ventures when they sense an opportunity or vacuum In

current economic activity. For example, a decrease in expenditures on

new plant and equipment might Indlcate ~n opportunity or niche Into

which a new firm, with a newly constr.ucted plant, might move. They are

also more likely to start new buslness ventures I f the unemployment rate

Is growing because a higher unemployment rate might indicate a lower

opportunity cost of their own salary forgone. Further, a higher

unemployment rate means that the cost of attracting qualified workers

might also be lower. In thls scenario we would expect low growth rates

of GNP, low growth rates of real expenditures on new plant and

equipment, hlgh real Interest rates, and increasing unemployment rates

to lead to increases In the rate of formation of new firms.

As wlll be seen, and as would also be expected, the search for

"causal" determlnants of entry in the economy at large Is a dlfflcult

task. The methodology employed orlglnates In the 11terature as the

analysis of stat ist ical time series and forecasting methods. The

concept is that true determinants of entry should, i f discovered,

increase our ab i l i ty to forecast the growth of entry. This notion of

causal determination is by no means generally acceptable on

philosophical grounds. I t is really a notion of econometric exogeneity

based on the simple but restrictive idea that the future cannot cause

the past, and i t is theoretically applicable only to stochastic

variables 6. Nevertheless even severe cr i t ics of this notion of

"causality" might agree that i ts implementation can lead to the

generation of important empirical results which must then be squared

with economic theory / . I t is in this spi r i t that we proceed.

As a f i r s t step of our analysis we have tabulated the sample cross

correlations between our new incorporation growth rate time series

DLINC, and our macro data series.

stationary data series is:

(I) pyx(k) = c°v(Yt+k'Xt) f ~

(VAR(Xt)VAR(Yt)) I/2

i .e. pyx(k) Is the correlation between Y and X

time. 8

The cross correlation between two

k perlods earl ier in

6See Granger (1969)

7For a comprehensive discussion of causality in econometrics and economic theory see Zellner (1978). The notion of causality in the sense of Granger (1969) has been implemented extensively in macroeconomic studies on the role of money. See, for example~Sims (1972, 1977) and Pierce (1977), and Feige and Pearce (1976).

81n general pyx(k) # pyx(-k). "Causality" in the sense of Granger (1969) is closely tied to this notion of cross correlation. In

particular, i f X "causes" Y but Y does not "cause" X we would expect to

find pyx(k)=O for k~-I (Y is uncorrelated with future X's) but we would

also expect pyx(k)#O for some k~O (Y correlated with past X's). In the

following Y wil l stand for our variable to be explained (DLINC) and X wil l represent one of our macro varlables.

Sample estimates of (1) were calculated according to Box and Jenkins

(1976) and are tabulated in Table 1 using the data for 1948:3 through

1977:4, (The remaining data are held back for some forecasting tests

discussed belowJ. The picture painted is an interesting one. The

growth rate in new incorporations is negatively correlated with measures

of real activity in the prior year (DLRGNP) and DLRPEX) and positively

correlated with these measures in the succeeding year. A bold reading

of this finding is that firms may enter to f i l l the vacuum created by

slower than average growth, with the result being that real activity

increases in later periods. The same picture is apparent in the cross

correlations with the change in the unemployment rate. The growth rate

of incorporations is positively correlated with growing unemployment in

the preceeding year and falling unemployment in the succeeding year.

Finally, DLINC appears to be positively related to the real interest

rate in the preceding year. Necessity may be the mother of invention.

This interpretation, however, is really too bold. The fact that

growth of incorporations is correlated with real variables at both leads

and lags suggests that these variables may in fact be Jointly

determined, and perhaps both "caused" by other factors not investigated

here. The cross correlations with the inflation (DLIPD) rate are more

111ustrative of a finding consistent with the notion of causality

discussed above. The only cross correlations which are greater than two

standard deviations from zero (an approximate 5% test) occur when

inflation leads incorporation growth. Indeed the growth of

incorporations seems to follow a slowlng rate of inflatlon quite quickly

(I quarter) but does not appear to significantly lead Inflation. This

amounts to evidence (albeit weak) that inflation is exogenous with

respect to incorporations. Knowledge of the inflation rate may help

I

%

t

Macro

Var iables

Lagging

DLINC

Macro

Var iables

Leading

DLINC

1The

Sample Cross Cor re la t ions Period

X--DLRGNP

__k SE_ k/!LL

-12 .10 ÷. 04

-11 .10 ÷.04

-10 .10 - . Og

-9 .10 - .22*

-8 .10 - . 3 0 .

-7 .10 - . 20

-6 .Og - .18

-5 .Og - .02

-4 .Og - .05

-3 .09 ÷.13

-2 .Og ÷.23,

-1 .09 ÷.34*

0 .Og ÷.25.

1 .09 .00

2 .Og - .31*

3 .09 - . 3 2 ,

4 .09 - . 3 0 ,

5 .09 - . 07

6 .Og .03

7 .10 .04

8 .10 - . 03

g .10 - . 03

10 .10 - .10

11 .10 ÷.04

12 .10 ÷.06

large sample standard e r ro r of p(k)

ind icates tha t ; ( k ) is greater

Table 1

of DL.INC wi th various Macro 1946:3 to 1977:4

Var iables

X-.-OURATE X=DI_IPD X=DI.RPEX

- .08 .05 - .05

- .01 .05 - . 14

.15 .03 - . 24,

.27, - .08 - .19

.23, - .09 - .09

.18 - .01 - . 03

.07 .05 .09

.01 .04 .22.

- .07 .15 .29,

- . 21 , .16 .31,

- . 44 , .09 .34,

- .43* .07 .11

- . t 6 - . 14 - . 13

.25* - .22* - . 3 1 ,

.45, - .09 .40,

.47* - . 10 - . 44,

.21, .04 - .10

- .06 .15 .01

- .06 .20 .03

- .07 .22, .11

- .04 .23* .04

- .01 .19 .03

- . 07 .07 .11

- .08 .03 .20

- .06 .05 .11

is 1 / ~ here n=118.

than

X=RRATE

- . 08

- . 0 6

.(X)

16

16

10

04

07

- og

- . 1 3

- . 0 7

- . 0 8

.11

.19,

.02

.09

- .02

- .11

- .14

- .14

- .16

- .12

.01

- .02

--.04

two standard dev ia t ions from zero.

" 8

increase the accuracy of forecasts of the incorporation growth rate, but

the reverse is not expected to be true.

Before accepting the picture above tooreadily it is important to

examine some of~the potential pitfalls. Perhaps the most important

danger for this analysis is to realize that significant cross

correlatlon between DLINC and future values of the macro variables can

occur even if these macro variables are indeed causal determinants of

DLINC. A method of examining this possibi l i ty is suggested by Box and

Jenkins (1976). The procedure is to derive univariate time series

models that capture the systematic movement in the macro variables. The

residuals from these models wi l l appear, therefore, to be white noise.

I f systematic movement in a macro variable induces systematic movement

in DLINC, we can abstract from this behavlor by f i l te r ing DLINC by the

model for the macro variable. This procedure is termed "prewhitening"

by Box and Jenkins. Cross correlations between the residuals from the

macro variable model and the f l l tere~ DLINC series wi l l then display the

manner in which the macro variable leads DLINC. I f , on the other hand,

the variables are Jointly determined, we should s t i l l see significant

cross correlations between the f i l tered DLINC series and future

residuals from the macro variable model. This procedure has been

carried out and the results are tabulated in Table 2. In each case an

Autoregressive-Moving Average (ARMA) model was f i t to the macro variable

in question in order to model the systematic variation and to f i l t e r

9

Table 2

Sample Cross Cor re la t ions of Prewhitened DLTNC with Residuals from ARMA models f o r Macro Var iables

Period 1948:3 to 1977:4

Macro

Variables

Lagging

DLINC

Macro

Variables

Leading

DITNC

X=res(DLRGNP) 2 X=DURATEres. 3 X=DLRPEXresid. 4

__..k SE.C, .___.p(k)

-12 .10 - .05 - .02 .03

-11 .10 .10 -.OS .02

-10 .10 - .04 .05 .20

-9 .10 - . 1 0 .17 - . 0 5

-8 .10 - . 2 2 - . 0 1 - . 0 2

-7 .10 .01 .06 - .02

-6 .09 - . 0 3 - . 1 0 - . 0 2

-5 .09 .12 .01 .18

-4 .09 - .15 .06 .09

-3 .09 - .02 .17 .10

-2 .09 - .05 - .11 .25,

-1 .09 - . 20 , - .17 - .04

0 .09 - .13 - .09 - .10

1 .09 .07 .21, - .15

2 .09 - .28 .10 - . 23 ,

3 .09 - .10 .24, - . 33 ,

4 .09 - . 2 0 .05 .10

5 .09 .05 - . 2 4 * .11

6 .10 - . 0 7 .12 - . 0 4

7 .10 .01 .02 .14

8 .10 - . 1 1 .01 - . 0 2

9 .10 .01 .08 - . 0 4

10 .10 - .14 - .09 .03

11 .10 .13 .06 ~ . 1 6

12 .10 .04 - .05 - .03 1See Footnotes to Table 1

t4ning model fo r DLRCJ¢°:

rrewn=~ening model fo r DURATE:

fo r DLRPEX:

fo r DLZPD:

4prewhitening model

5prewhibening model

(1-. 40223+ 215584) DLRGNP=2. 9402÷e t

(1-1.4268B+. 6733B 2) DURATE=e t

(1-. 4424B) DLRPEX=e t

(1-. 7925B) DLZPD=. 7086. (1-. 25898) e t

X=DLIPD 5

.03

.04

.10

- .13

- .08

.03

.06

- .08

14

12

O0

12

- 15

- 27*

O4

04

- 16

09

.09

.08

.11

.10

- .07

- .08

- .03

10

DLINC. g'lO In each case we see that the qualitative aspects of our

remarks above still remain. It seems as If indicators of real activity

both lead and follow the growth rate of incorporations, leading to a

preliminary con~luslon of Joint determination.

As was mentioned at the outset of thls section, one test of the

notion that the macro variables discussed above play a role In

determining entry Is the predictive test. Indeed Ansley (1977) has

remarked that cross correlation analysis of the type discussed above Is

better viewed as tests of model identification {In the sense that a tlme

series analyst identifies the best forecasting model from within a class

of possible models) and that post-san~)le forecasting tests are what Is

required. If the rate of growth of real GNP Is an important determinant

of entry, for example, then the inclusion of real GNP growth In a

forecasting model should allow us to predict DLINC wlth greater accuracy

than a model without It. In a sense we are asking whether the macro

variables have any marginal predictive value. To investigate thls

*,

9Suppose ~(B)X t = e(B)e t represents a standa~ APJ~Jk (p,q) for a

covarlance stationary serles Xt, ~(B) = I - ~I B - ... - ~pB P,

e(B) = I - e, B - ... eqB q, B Is the backshlft (or lag) operator such

that BX t = Xt_ I and e t represents a zero mean, constant variance nolse

process. Let a t be the DLINC t serles after prewhltenlng, a t Is

calculated as a t = ~(B)e-I(B) DLINCt, t = l , . . , T . Thus systematic

variat ion In DLINC t d l rec t l y induced by systematic variat ion In X t Is

removed. What remains Includes systematic variat ion In DLINC t induced

be even non-systematic variat ion In X t , Of course, I f the only channel

by which X t af fects or "causes" DLINC t ts through I ts systematic

variat ion, then we are, to use a phrase attr ibuted to Hilton Friedman, "throwing the baby out with the bath."

lOpotenttal problems with such "prewhltenlng" f i l t e r s are discussed tn Zellner (1978). Indeed even I f Granger's notion of causality of causality Is accepted, tests of this notion can be affected by the nature of thts prewhltentng f i l t e r .

11

question, some simple forecasting models have been devlsed for DLINC.

The f t r s t , whlch may be considered a benchmark, Is a unlvarlate APJqA

model for DLINC. See footnote 9 for a detailed explanation of ARMA

models. Such ~model wl]1 set a level of predlcttve accuracy that can

be expected from forecasting DLINC on the basls of i ts own past. Of

course, since an A~A model is purely a s ta t i s t i ca l model, tn which

changes In entry rates are modeled as based soley on the i r own past

values, the fom of the model chosen may depend Ind i rect ly on al l

factors detemtntng DLINC 11. To the extent that the factors we have

chosen to focus on here are par t icu lar ly important In detemintng entry,

we would expect to tmprove upon the perfomance of the ARMA mode].

The evtdence from a simple forecasting competition Is presented tn

Table 3. Several simple forecasting models were employed to make

l terat lve out-of-sample predictions 12 for DLINC for the twenty-eight

quarters 1978:1 through 1984:4. As the table suggests, the forecasts

from the ARMA model for DLINC are both hard to beat and not very good.

The mean absolute forecast error of the ARMA model Is 12.05 for twenty-

e|ght one-step-ahead forecasts during the period, which is s l i gh t l y

higher than the average absolute deviation of the DLINC series i t s e l f

from i ts mean (11.83) and the root mean squared error of the twenty-

eight forecasts Is 14.78, only s l igh t l y lower than the average squared

deviation of DLINC from i t s mean (15.50). We are, however, interested

In the a b i l i t y to improve upon thts forecast through the use of models

11Thts fact often goes unrecognized as an explanation of the fact that ARI~ forecasts are often competitive with or even superior to forecasts from complex econometric models.

1Zt.e. each model was estimated ustng data from the pertod used tn the tables described above, 1948:3 to 1977:4. Future DLINC was then predicted, the model updated to Include data for 1978:1, another prediction was made, the model again updated and so on.

, 12

Table 3 Summary of Forecasting Results

One-step-ahead Out of Sample Forecasts of DLINC for the Period 1978:1 through 1984:4

Forecast i n 9 Mode I Mean Absolute Forecast Error

Root Mean Squared Forecast Error

1) UnivariateARMAModel 12.05 14.78

2) Transfer function Model Znput variable: DLI:PD

11.85 14.64

3) BivariateARMA Model DLTNC and DLRCNP

11.37 14.67

iat, e ARMA Model and OURATE

11.03 14.48

6) Bivar iate ARMA Model DLINC and DLRPEX

12.41 16.17

8) Vector Autoregreemion involving DITNC, DLZPD, DRGNP, I~JRATEand DLRPEX

11.29 14.g3

13

incorporating our macro variables as explanatory variables. Forecasts

from models which include these variables are evaluated In the remaining

rows of Table 3. The models attempted are a univariate transfer

function model #nvolving DLIPD as an exogenous explanatory variable for

DLINC, three separate blvariate ARMA models relating DLINC to DLRGNP,

DURATE and DLRPEX respectively with no exogeneity assumptions imposed,

and a vector autoregression involving all five variables and no

exogeneity assumptions.

The transfer function model involving DLIPD was identif ied and

estimated following the suggestions of Box and Jenkins (1976). Much

l lke a regresslon model, i t is assumed that the explanatory variable is

exogenous (for DLIPD this assumption Is supported by the cross

correlation analysis above) but has added f l e x i b i l i t y allowing

identif ication of ARMA models for non-whlte-noise residuals and

specification of very f lexible lag strUctures for the exogenous

variable. The linear regression model can be viewed as a special case

of a t ransfer function model. Forecasts from the t ransfer function

model are obtained by f i r s t forecasting DLIPD using a unlvar late ARMA

model and then employing these forecasts In the t ransfer function to

obtain predictions for DLINC.

The blvarlate ARMA models for the three real variables and DLINC

were formulated according to the methods advocated by Tiao and Box

(1981). Such models are the natural extension of univariate ARMA models

and, in this case, embody no exogeneity assumptions. Both variables are

modeled as functions of current and past values of both variables and/or

current and past values of their error terms. Thus both varlables are

forecast simultaneously. Multivariate regression, blvariate

autoregresslon, transfer functions and Independent univariate ARMA

models are al l special cases of this rlch class of models.

14

The f ina l model considered Is a vector autoregresslve model, whlch,

as jus t discussed, ls a speclal case of a mul t ivar ia te ARHA mode] and

can also be regarded as an unrestr icted reduced form model employing no

exogenelty rest l - lc t lons. Since models of th is sort can Involve very

large numbers of parameters to estimate 13 they can only be estimated

with any degree of precision tn practtce by employing Bayestan

estimation techniques. Lltterman (1984) has used such models with a

high degree of success to forecast many macroeconomlc variables. A

f u l l y mul t i var ia te Bayesian vector autoregresston technique by Highfte ld

(1984) Is employed here. As wtth mul t ivar ia te ARHAmodels, a l l

variables are forecast simultaneously.

As seen, none of the models achieves dramatic improvements over the

univartate model for DLINC. The best model, the Blvartate APJ~ model

with DURATE achieves about 9% reduction tn mean absolute forecast error ,

but a somewhat smaller reduction tn root mean squared error , Indicat ing

the presence of some large forecasting errors. The second best model,

the Blvar late APJ4A model with DLRGNP achieves a 6.4¢ reduction tn mean

absolute forecast error , and also reduces the root mean squared er ror by

a smaller amount. The coef f ic ients In both these models Imply the same

re la t ionship as was Indicated by the cross corre lat ion resul ts .

Increases In the unemployment rate and decreases In the tea) growth rate

of GNP lead Increases In the rate of growth of business Incorporations.

Although the evidence ts by no means overwhelming, the business c11mate

apparently most hospitable to new f i rm Incorporation has not been a

robust one: sluggish economtc growth seems more ] l k e l y to spur creation

13The model here employs eight lags of each of the ftve variables In each of the f lve equations, for a to ta l of 200 slope coef f ic ien ts .

15

of new firms (or lntenslons to go Into business). The magnitude of the

forecasting Improvements Indicates, however, that most of the growth In

the new Incorporations series remains unexplained. When one considers

the number of ~n t i t les tncluded In the new Incorporations series in

addit ion to new business star ts, such as firms changing the i r legal

status, perhaps I t is not surl~rlslng that we have been unable to explain

14 more using slmple macroeconomlc variables.

14For a somewhat humorous explanation of the d i f f i c u l t i e s encountered when attempting to determine the number of actual new business star ts occurring over time, see Flnegan, 1986.

16

I l l . Entry Determinants at the Micro Level-Cross Sectional

Relationships

The factor~ affecting the rate of entry by new firms into different

industries are discussed in this section. The section is organized as

follows: The measure of entry rates is described f i r s t ; second is a

discussion of signals to entry or factors which should attract entry;

Deterrents or barriers to entry are examined third; and the empirical

results are presented last.

Measures of the Rate of Entry by New Firms

The U.S. Small Business Administration has been compiling entry

s ta t i s t i cs by 4 d i g i t industr ies only since 1976.15 The data, which

or ig inate from Dun and Bradstreet, are avai lable in two year increments

- 1976-77, 1978-7g, and 1980-81. Our entry measures are taken for these

three time periods.

The entry rate (EntrYl j ) is s tmplythe number of new firms formed in

industry i wi th in the time period J, divided by the number of firms In

existence in the industry at the beginning of the period. This variable

15For a descript ion of the Small Business Data Base Fi les, see The State of Small Business: A Report of the President: March 1984, Appe-~-dix C, (Government Pr int ing Off ice, Washington D.C.)

17

Is then transformed in two ways, and results are presented for each

formulation of the dependent variable. First 16

(2) Loglt EntrYlj = In(EntrYij/1-EntrYlj)

Next the relative entry rate for period J is formed:

EntrYlj - Entryj where Entryj is the average entry rate

(across all Industrles-i) for period j .

Thls later formulation should control for any economy-wlde factors

(In the separate time periods) affecting entry rates.

Entry Signals

Entry by new firms could be signalled or attracted by several

factors, all of course reflecting expected future profits. The most

simple forecast of the prof i t rate for an industry wil l be Its prof i t

rate (income after taxes/equity capital) In the preceding year-PROFIT. 17

Thls would be a reasonable forecastlngmethod I f profits follow a random

walk, and thus the best predictor for the next period profits was

16If Entry Is construed to represent a probablllty of entry, It should be bounded by 0 and I. OLS would be Inapproprlate If applled dlrectly to the dependent varlable Entry slnce estlmated values could lle outslde thls Interval. To alleviate thls problem, we apply a loglstlc functlon of the form:

Probab111ty of EntrYlj - PIJ " I/(l + e pXIJ)

then

Loglt EntrYlj = ln(PIj/l-Pij ) - PXlj

whlch can be estlmated by OLS.

17The source for a l l independent variables described In thts sectlon ts the Compustat tape with the exception of minimum e f f i c i en t scale, the concentration rat ion and the tndustry growth rate.

18

present period profits. This explanatory variable, utilized in nearly

all other studies, would be appropriate i f potential entrepreneurs

observed profits in any particular year and utilized this information in

making decisions about whether or not to enter a particular industry. A

different method of forecasting future profit rates will be presented

later in this section.

Emerging industries that are early in their evolution will attract

new entrants who hope to position themselves as dominant players In the

mature stages of industry evolution. Emerging industries should have

hlgh values of GROWTH, the annual growth rate In sales or value of

18 shipments from 1972-77.

A high rate of technical progress In an industry will also indicate

a dynamic and evolving situation, and thus might attract new entrants

seeking to discover and develop new products and processes. A high

ratio of expenditure on research and development to sales, lagged one

year, (RDEXP) would thus indicate an ~merglng industry and signal new

entrants.

Entry Deterrents or Barriers

Rlsk averse potential entrants w111 be deterred by high investment

risk. We measure RISK as the average variance for firms in the industry

in PROFIT over the five years prior to the period In question. This is

a reasonable risk measure for individuals who are not well diversified,

which would seem an appropriate description of an individual starting up

19 a new business.

18Source-Census of Manufacturers, Reta i lers , Wholesalers, etc. - 1972, 1977.

19I f an ind iv idua l is pe r fec t l y well d i ve r s i f i ed , we would want to use the indust ry beta,

19

The l i terature on barriers to entry is a rich and well documented

one. 20 But wl l l these traditional barriers deter entry by new firms,

which are l i ke ly to be smaller than new entrants that are already

established in ~nother industry, and are entering for diversif ication

purposes21? Potential entry barriers which should not require

elaboration include the proportion of industry sales required to operate

a plant of minimum eff icient scale (MESMKT), the amount of capital

required to bulld a plant of that size (CAPREQ), the ratio of

advertlsing to sales expenditures In the prior year (ADS), and the four

firm concentration ratio - C0N7722. Of these variables, only ADS varies

over the three tlme periods.

We also attempt to account for the possibl l i ty that some incumbent

firms may be attempting to preempt the market through capacity expansion

(Spence Ig77). CAPEXP measures capital expenditures lagged one year

dlvlded by net plant. A high value for CAPEXP could thus reflect

preemption attempts, which I f anticipated would deter entry.

t

Estimation Techniques and Results

The basic results reported in thls section are regressions in which

we have pooled observatlons from the three tlme periods in our sample.

As our independent varlables are al l cross sectional In nature, there is

the possibi l i ty that omitted macroeconomic factors (such as those

20See Bain (1956), and Demsetz (1982) for d i f ferent points of view.

21See Scherer (1980) for a discussion of barrters to large vs. small scale entrants.

22MESMKT Is the ratio of the average sales per establishment for establishments In the median size class, to sales for the industry - source Census of MFG, etc. - 1977. CAPREQ is MESMKT multiplied by total assets for the industry - source - Census of Mfg., etc., 1977. ADS Is the average ratio of advertising expenditures to sales for firms in the industry, lagged I year - source - Compustat Tape. CON// - four firm concentration ratio - source - Bureau of the Census.

20

discussed in Section I I) can bias the results. To the extent that these

economy-wide factors affect entry in a similar way in all industries, we

can control for these macro effects in either of two ways. The f i r s t is

to include dumfiy variables for two of the three time periods in our

sample. I f there are common macro effects, we would expect the

intercept in our cross sectional regressions to vary across time

periods. The time dummies allow for this possibi l i ty. This method is

used in the f i r s t regressions reported in each of the following tables.

The second method is to devise a dependent variable which abstracts

from common macro effects. This is the purpose of the (EntrYij -

Entryj) variable described above. (Regressions using this formulation

are reported on the right hand side of each of the following tables.)

Using this formulation no time dummies are expected to be necessary.

Indeed in al l the regressions reported in the following tables, the

dummy variables had coefficients signif icantly different from zero in

the equations involving Logit EntrYij as the dependent variable, but not

in the equations involving (EntrYlj - Entryj).

The results for the data set that includes measures for al l

independent varlables are presented in Table 4. Data are avallable for

one or more time periods for th i r ty nine industries (4 d ig i t ) , nearly

al l of which are manufacturing. 23 The second and third columns show the

results for the logi t dependent variable, and the fourth and f i f t h

columns present the relative entry rate formulation of the dependent

variable. The F statlstics for the equations indicate we can reject the

null hypothesis of no explanatory power at the 0.1% level.

2~See Table 5 for the Industr ies used In Tables 4, 6 and 7. These industries were selected from those for which start data was provided by Dun and Bradstreet, soley on the basis of data avai labi l i ty (for the independent variables).

21

Table 4 - 102 Observations

Varl able

Loglt EntrYlj

Coefficlent T Statist ic

EntrYij - Entryj

Coefficient T Statist ic

Constant -3.18 -10.54 -0.04 -2.08

PROFIT -1.46 -I.04 -0.11 -1.21 (Profit rate)

ADS -1.11 -0.36 -0.13 -0.63 (adv. + sales)

CAPEXP 1.09 1.46 0.05 1.13 (capital expendlture)

RDEXP 10.02 2.52** 0.36 1.43 (R+D exp + sales)

RISK -0.15 -1.05 -0.01 -1.43 (variance in profits)

GROWTH 5.13 4.84*** 0.36 5.37** (growth In sales)

CON77 -0.00 - 0 . 4 4 -0.00 -0.16 (1977 conc. rate)

MESMKT 1.19 0 . 3 1 0.31 1.27 (MES + MKT sales)

CAPREQ 0.00 0.33 -0.00 -0.19 (capital required for MES)

DPER2 -0.45 - 3 . 3 8 * * * (Period 2 Dumny)

DPER3 -0.63 - 4 . 2 4 * * * (Period 3 Dununy)

II 2 ," .377, F(12, go) = 6.5499 I~ 2 = .310, F(12, 92) = 6.036

Statistical significance of coefficients is indicated at I% I

22

Table 5

Industries Used In Analysis

Industries Used in Table 4

SIC Code Industry Description

2041 2046 2065 2082 2086 2121 2711 2761 2834 2841 2844 2911 3079 3221 3241 3443 3444 3452 3494 3531 3533 3622 3651 3662 3674 3693 3711 3714 3721 3811 3823 3825 3841 3842 3861 3931 5012 5065 8911

Flour and other grain mill products Wet corn milling Confectionary products Malt beverages Bottled and canned soft drinks Cigars Newspapers Manifold business forms Pharmaceutical preparations Soap and other detergents Toilet preparations Petroleum refining Miscellaneous plastic products Glass containers Cement, hydraulic Fabricated plate work, boiler shops Sheet metal work Bolts, nuts, rivets, and washers Valves and pipe f i t t ings Construction machinery Ollfleld machinery Industrial controls • .. Radio and TV receiving sets Radio and TV communication equipment Semiconductors and related devices X-ray apparatus and tubes Motor vehicle and car bodies Motor vehicle parts and accessories Aircraft Engineering and scientific instruments Process control instruments Instruments to measure electricity Surgical and medical instruments Surgical appliances and supplies Photographic equipment and supplies Musical instruments Automobiles and other motor vehicles Electronic parts and electronic communication equipment Engineering and Architectural Services

- con t i nued on next page -

23

Table 5 (continued)

Industries Used in Analysis

Additional Industries Used in Tables 6 and 7

SIC Code ~ Industry Description

1021 1211 1311 1381 1382 2111 3442 3661 5211 5411 5712 7011 7372 7374 7391 7392 7393 7395

Copper ores Bituminous coal and 11gnlte Crude petroleum and natural gas Dri l l ing oil and gas wells Oil and gas exploration services Cigarettes Metal doors, sash, and trim Telephone and telegraph apparatus Lumber and other building materials - retai l Grocery stores - retail Furniture stores Hotels, rooming houses, camps, and other lodging places Computer programing and other software services Data processing and computer fac l l l t ies management Research and development laboratories Consulting services Protective services Photofinishing laboratories

24

The hypothesis tests are consistent with the finding that high

industry growth strongly attracts entry by new firms. This finding is

similar to the results of Hause and DuRietz (1984) for Swedish

manufacturing industries. Industries characterized by high R&D

expenditures to sales ratios also experience higher rates of entry, at

least in the logit formulation. Rather than acting as a barrier to

entry, research intensity in an industry seems to attract entry by new

firms. Hlgh profit rates in the year immediately prior to the time

period of observation do not appear to signal new entry. The dummies

for period 2 (1978-79) and 3 (1980-81) indicate that more entry by new

firms was recorded In 1976-71 In response to economy-wlde effects not

explicit ly delineated in thls model. None of the coefficients on the

deterrents or barriers to entry are stat ist ical ly significant. Small

scale entry by new firms does not appear to be deterred by those factors

which pose a barrier to entry for exlsling firms of larger scale. 24

Although we hesitate to put too much emphasis on a lack of significant

results, I t ls at least Interesting that those barriers to entry that

have t rad i t iona l ly resulted in higher prof i ts (because they apparently

allowed firms within an industry to earn high returns without signal l ing

entry by large scale r iva ls) , seem to be ineffective against entry by

new firms.

The sample size can be substantial ly enlarged by eliminating

independent variables with missing va!ues. In Table 6, the variables

ADS, C0N77 and CAPREQ have been dropped. The findings are quite similar

to those tn TABLE 1, even though 18 Industries (65 observations) have

ZCThese observations are consistent with most of the findings of MacDonald (1986) for food industries.

25

Table 6 - 167 Observations

Variable

Logit EntrYij

Coef f i c i en t T S t a t i s t i c

EntrYlj - ~yj

Coefficient T Statistic

Constant -2.91 -20.92 -0.02 -2.51

-0.23 -0.42 -0.02 -0.44 PROFIT (Profit rate)

CAPEXP 0.14 1.18 (capi ta l expenditure)

RDEXP 3.34 2.05** (R+D exp + sales)

RISK 0.06 (variance in profits)

0.01 0.71

0.16 1.44

0.65 0.01 1.43

GROWTH 4.48 6.69*** (growth In sales)

MESMKT -0.56 -0.34 (MES + MKT sales) • .

DPER2 -0.52 -4.88*** (Period 2 Dumny) .

DPER3 -0.47 -4 .31"** (Period 3 OunBy)

0.30 6 . 2 9 * * *

-0.15 -1.28

I~ 2 = .32, F(9, 158) - 10.79 I~ 2 = .23, F(7, 160) = 9 .08

Statistical slgnlflcance of coefficients Is indicated at I% - ***, 5% -

26

been added, mostly In agriculture and services. We note that PROFIT is

s t i l l not significant In Table 6; indeed i t has the wrong sign.

Simple lagged profit rates to not appear to be good predictors (or

signals) of fut~re entry. 25 What is not clear is whether present profit

rates contain no information for enterpreneurs considering future entry,

or whether a simple one period lag fails to capture the subtlety with

whlch entrepreneurs use this information.

Entry Is presumed to be induced by expected future profits. One

explanation for our finding Is that lagged profit rates per se are

simply poor estimates of this profl t expectation. To examine thls

matter further we have formulated a simple forecasting model for profits

which we have applied to each industry. The model Is a Bayeslan

Implementatlon of an autoregressive model in which profits in any period

are related In a llnear way to profits In the two preceding periods

(3) PROFITIt = C + e I PROFITIt.I + e2"PROFITIt.2 + e t I = industry subscript

where e t Is a zero mean, normally dlstrlbuted error term. Forecasts

from thls model were generated Iteratlvely 26 beginning In 1974, using

25MacDonald (1986) also ftnds lagged p ro f l t s to have the wrong slgn and to not be s ign i f i can t .

26By " l t e ra t l ve l y " we mean that no future data were used in the generation of the p r o f i t forecasts. They are true forecasts, not f i t t e d values from the model,

27

annual data, and the forecasts for the years 1976, 1978 and 1980 (called

FPROFBIII) were used as independent variables In the regressions in v

27 FPROFBI, which is essentially a Table 7 in place of PROFITIj.

predicted profl¢ rate for the industry using Baysian techniques, Is

positively and significantly related to both measures of entry. The

Baysian autoregresslve forecasting technique clearly dominates a simple

lagged prof i t rate forecast. Prospective entrepreneurs do ut i l ize the

information impounded In prof i t rates, but they do not ut i l ize I t In

such a slmple fashion as economists have presumed. Entrepreneurs do use

forecasts of p ro f i t rates In the l r decisions to s tar t new f irms, but

the i r imp l i c i t forecasting method (described in Equation 3 above) Is

more complicated than has been or ig ina l ly assumed by economists.

• °

270ur p ro f i t series for each industry beglns tn 1971, leaving very few observations wtth whtch to esttmate the model. A Bayesian method of estimation was employed to overcome this problem. Given the data through 1973 we assume a normal pr ior d is t r ibut ion on Prof i ts tn 1974. The mean of thts d is t r ibut ion being zero I ts standard deviation being 0.5 ( I .e . a p ro f i t or loss rate of 50¢ of equity capital ls one standard deviation from the mean). Using this very spread out d is t r ibut ion as our star t ing point we Infer a pr ior d is t r ibut ion for the parameters of equatton (3).

28

Table 7 - 167 Observations

Varlable

Loglt EntrYlj

Coefficient T Stat ist ic

EntrYlj - Entryj

Coefflclent T Statlstlc

Constant -2.94

FPROFB1 2.54 (Baysfan Pro f i t Forecast)

CAPEXP 0.12 (capltal expenditure)

RDEXP 3.87 (R+D exp + sales)

RISK -0.05 (variance in p ro f i t s )

GROWTH 4.30 (growth in sales)

MESMKT 0.24 (MES + MKT sales)

DPER2 -0.53 (Period 2 Dunmy)

DPER3 -0.50 (Period 3 Dummy)

-23.64 -0.03 -3.55

3.11"** 0.26 4 .48** *

1.08 0.00 0.54

2.77*** 0.21 2.15"*

-0.53 -0.00 -0.19

6.74***

0.15

-5 .19"**

-4.84"**

0.29 6.42***

-0.07 -0 .64

n 2 - .36, F(9, 158) = 12.63 R 2 = .31, F(7, 160) = 13.53

S ta t i s t i ca l s igni f icance of coef f ic ients Is indicated at 1% - * * * , 5% -

IV. Summary and Conclusions

29

This paper contributes to the Industrlal organization literature on

entry through e~abllng us to better understand what economic conditions

are more hospitable to the creation of new finns, and through

application of new methods to better Understand these complex

relationships. We have identified two types of factors that influence

the rate of creation of new finns; macroeconomlc and mlcroeconomlc

factors. Although the evidence is somewhat weak, the macroeconomlc

climate that appears to be most conducive to the formation of small

businesses Is what might loosely be called sluggish. Lower rates of

growth of GNP and higher unemployment rates were followed by increases

In the rate of new incorporations. These new incorporations In turn

tend to lead periods of more robust economic activity. The cross-

sectional or mlcroeconomlc factors which affect rates of entry Into

different industries include higher g~wth rates In sales, higher

research and development intensity, and higher profit rates. We do not

flnd any of the traditional barriers to entry to be related to changes

In the rate of new finn creation.

Putting the tlme series and cross-sectlonal results together, we

have a consistent and interesting picture of entry through the creation

of new finns. Individuals decide to form new finns when economic

conditions are relatively poor -- but they decide to enter industries

which are dynamic and robust, as measured by technical progresslvlty and

profitability. Perhaps Schumpeter (Ig50) was right.

We have utilized some tlme series techniques which appear to be new

to the industrial organization literature. After observing the patterns

of leads and lags of new incorporation activity as they are correlated

30

wlth leads and lags of important macroeconomlc variables, we Investlgate

whether knowledge of these macroeconomlc variables wlll allow us to

improve on forecasts of the new Incorporatlons growth rate. In the

cross-sectlonal study, we conclude that the lack of relationship In

other studies between the profit rate and subsequent new business

creation Is the result of lack of sophistication In modellng

entrepreneurs expectations about future profit rates. The Bayesian

autoregresslve model we used to forecast future profit rates seems to

indicate quite clearly that present profit rates do influence future

entry.

-~ . ' 31

References

Ansley, C.F. "Report on the NBER-NSF Seminar on Time Series," Graduate School of Business, University of Chicago, MimeD, 1977.

Baln, Joe S., Barriers to New Competition, Harvard University Press, 1956.

Birch, David L., "Who Creates Jobs?" The Public Interest, 1981.

Box, G.E.P. and G.M. Jenkins, Tlme Series Analysis - Forecasting and Control, Holden-Day, 1976.

Demsetz, Harold, "Barriers to Entry" American Economic Revlew, March 1982, pp. 47-57.

Felge, E.L. and O.K. Pearce, "Economlcally Rational Expectations: Are Innovations in the Rate of Inflation Independent of Innovations In Measures of Monetary and Fiscal Policy?', Journal of Polltical Economy, 84, June 1976, 449-552.

Flnnegan, Jay "The Entrepreneurlal Numbers Game', INC, May 1986, p. 31.

Lnger, C.W.J. "Investigating Causal Relations by Econometric Models and Cross-spectral Methods," Econometrica, 37, July, 196g, 424-438.

Hause, John C. and Gunnar DuReltz, "Entry, Industry Growth and the Microdynamics of Industry Supply', Journal of Political Economy, Vol. 92, No. 4, August, 1984.

H1ghfleld, R.A. "Forecasting wlth Bayesian State Space Models," Technical Report, H.G.B. Alexander Research Foundation, Graduate School of Business, University of Chtcago, 1984.

Lltterman, Robert B., "Speclfylng Vector Autoregresslons for Macroeconomic Forecasting', Research Department Staff Report 92, Federal Reserve Bank of Minneapolis.

MacDonald, James M., "Entry and Exit on the Competltlve Fringe" Southern Economic Journal, Vo1. 52, No. 3, January 1986.

Pierce, D.A. "Relationships - and the Lack Thereof -- Between Economic Time Series, wlth Special Reference to Money and Interest Rates," Journal of the American Statistical Association, 7Z, March 1917, 11-21.

Scherer, Frederic M., Industrial Market Structure and Economic Performance, Znd ed., Rand McNally, 1980.

:humpeter, Joseph, A., Capltallsm~ Socialism and Democracy, 3rd ed., Harper and Row, 1950.

Standard and Poors, "Annual Industrial Compustat Tape', 1984.

ib

• ¢

,D I

32

Sims, C•A., "Exogeneity and Causal Ordering in Macroeconomic Models," in C.A. Sims (editor) New Methods in Business Cycle Research: Proceedings from a Conference, Minneapolis, Minnesota: Federal Reserve Bank of Minneapolis, 23-43, 1977.

i

~;. Sims, C.A., "Money, Income and Causality, "American Economic

Review, LXII, September 1972, 540-552. o . .

Tiao, G.C. and G.E.P. Box, "Modeling Multiple Time Series with Applications", Journal of the American Statistical Association, 76, 802-816, 1981.

U.S• Bureau of the Census, Census of Manufacturers - 1977, Census of Retail Trade - 1977, Census of Wholesale Trade -1977, Census of Mineral Industries - 1977, Census of Construction Industries - 1977, Census of Service Industries - 1977. °

U.S. Small Business Administration, The Start of Small Business, March 1983, (Government Printing Office, Washington, D.C.). °

J .

Zellner, Arnold, "Causality and Econometrics", in K. Bruhnef:and A.H. Meltzer (editors)" Three Aspects of Policy and Poli~pakin~, Carnegie-Rochester Conference Series• Vol. 10, North-Holland, 9-54. ~.