SMALL BUSINESS  INDICATORS OF MACROECONOMIC ACTIVITY

 

William C. Dunkelberg

Chief Economist, National Federation of Independent Business

Professor of Economics, Temple University

 

Jonathan A. Scott

Professor of Finance, Temple University

 

William J. Dennis

National Federation of Independent Business

 

September, 2003

 

 

 

The National Federation of Independent Business (NFIB) began quarterly economic surveys of its membership (about 350,000 firms) in 1973.[1]  Since that time, a virtually identical three-page questionnaire has been mailed to a sample of NFIB’s small-business owner members on a regular basis.  A copy of the current questionnaire is included in Appendix 2.  From October of 1973 through 1985, a random sample of the NFIB membership list was selected and survey form was mailed them on the first day of every quarter.  This mailing was followed by a second about 10 days later.  Since January 1986, the same procedure has been followed monthly.  Responses are collected for about 25 days after which duplicate responses are eliminated.  The yield is 1,300 to 1,800 responses in the first month of each quarter and 500 to 700 responses in the following two months.    A monthly report, Small Business Economic Trends,  based on the findings from the survey is available from NFIB in both electronic (nfib.com/research) and printed forms.  The data are also presented in models and used to forecast selected macroeconomic phenomena.  The objective of this paper is to assess how well the NFIB quarterly economic survey data anticipate changes in highly visible measures of macroeconomic activity.  

 

Because small firms make up such a large fraction of the total economy, it is logical to look to indicators of their collective economic health as reliable indicators of the entire economy’s performance.[2]  This argument is reinforced by the notion that the same basic economic forces impact all firms, large or small.   Federal Reserve policy, tax based fiscal policy, shifts in consumer spending, for example, all affect businesses of every size. Therefore, owners of small firms should experience the same economic forces that are experienced by the managers of large firms.[3]  The sectoral composition of “large” and “small” firms does differ,  with manufacturing dominated by larger firms and construction and many services dominated by small firms.  Large firms are heavily involved in international trade while small firms are domestically focused.  So, from time to time, the economic fortunes of large and small firms may collectively diverge.  But, if the same fundamental economic forces are impacting all firms, large and small, these differences should not seriously compromise the usefulness of small business based indicators for economic analysis.

 

The NFIB membership reasonably reflects the small-business population.[4]  However, members tend to be somewhat older and are over-represented in the Midwestern, Plains, and Mountain states.  Nevertheless, indicators based on surveys of non-representative samples of economic agents can provide reliable indicators of economic activity.   As long as sample frame and response biases are stable over time, thereby not disturbing the relationship between changes in the indicators and changes in the economic activity measures of interest, the indicators will be reliable predictors.  Therefore, the practical value of data from surveys are that: (1) they are not subject to revision since they are taken at one point in time; (2) they can be empirically related to changes in macroeconomic indicators that are historically correct (after all revisions are in) and, (3) the sample of units included need not be representative of all units in the population nor do all units in the sample need to respond to the survey.[5]  Although the industry composition of the NFIB membership has shifted toward services as has the makeup of the U.S. business sector, the employment distribution of the membership has not changed since the surveys were started in 1973.

 

In the following sections, the contribution of the NFIB indicators to the prediction of dependent variables of interest is quantified using multiple regression analysis.  For simplicity, standard errors are not presented in the equations below.  All variables are significant at the 95% level unless otherwise indicated.[6]   No specific theory, such as that underlying the consumption function in macroeconomics, lies behind the equations.  Still, many of the NFIB indicators have strong theoretical counterparts such as those described by the generalized stock adjustment model.   The analysis is a quantification of the parameters of empirical relationships which would be observed in the use of the NFIB measures as leading indicators.  If the relationship between the economic variable and the NFIB indicator is reasonably stable, it becomes possible to make quantitative estimates or predictions of the values of the economic variables based on the NFIB survey results. 

 

The quarterly survey data used in this analysis are collected in the first month of each quarter, January, April, July and October.   This timing builds in a small implicit “lead” of at least one to two months into a forecast.  For example, if the January survey is the best predictor of the dependent variable of interest, say GDP growth, this means that the January survey  predicts the value of the dependent variable for January, February and March.  Thus, the NFIB survey data “leads” the GDP growth number by two months.  Furthermore, the value of the dependent variable may not be officially known (first estimate) for several weeks after the end of the quarter.  If NFIB data lead one-quarter, then the January survey anticipates the second quarter (April - June) value of the dependent variable.  So, the NFIB data always lead the variable of interest, sometimes by several quarters. 

 

Leads are denoted by negative subscripts on the variables.  A subscript of “-1” means that the NFIB data lead by 1 quarter (plus the built-in two months discussed above), “-2” by two quarters and so on.    The forecasting literature often refers to such a relationship as a “lag”, meaning that the current value of the dependent variable to be predicted is empirically determined by an earlier value of the NFIB variable.  If the first quarter reading of the NFIB variable determines the second quarter value of the change in GDP, this is called a “lag” in the equation, %DGDP = a + b NFIB-1.  The percentage change in GDP depends on the value of the NFIB variable in the prior quarter.  Thus, changes in GDP in the current period depend on changes in the NFIB variables in earlier periods and the NFIB variables lead changes in GDP.  This lead is represented by a “lag” of the NFIB variable in the equation, meaning that values of the NFIB variables occur before the relevant value of the dependent variable of interest.  

 

 

1. Employment, Unemployment and Labor Market Indicators

 

Because small firms play such a critical role in the job creation process, the NFIB employment measures should have a strong relationship to measures of aggregate employment growth and other labor market indicators.  Two survey measures are used to explain variations in employment, the net percent of owners who report plans to expand total employment at their firms, HIREPLN[7], and the percent of owners who report at least one hard-to-fill job opening, JOBOPEN.,[8]  The larger the percent of owners planning to expand total employment in the months following the survey, the larger the expected employment growth in current and future periods.[9]

 

A high level of unfilled job openings indicates disequilibrium between the desired level of employment at the firm and its actual level.  This disequilibrium takes time to resolve (collecting applications, interviewing candidates, etc.).   When the percent of owners reporting one or more hard-to-fill job openings is high, owners have more difficulty getting employees and employment will grow more slowly.  Job openings exhibit a slightly positive bivariate correlation with employment change (.17), and a correlation with hiring plans of .75.  Thus, after accounting for the effect of hiring plans, the percent of owners with hard to fill job openings will likely be negatively associated with the growth in employment.[10]  When job openings are pervasive, it is hard to hire and consequently employment grows more slowly.[11] 

 

Two commonly used measures of growth in total employment are the quarterly change in total employment (%DEMPT) and the quarterly change in private sector employment (%DEMPP).  NFIB’s hiring plans indicator (HIREPLN) performs as expected, with a higher net percent of firms planning to expand employment associated with stronger growth in employment over the survey period.  The higher the percent of owners with hard to fill openings (JOBOPEN), the harder it is to grow employment.   But these indicators, individually and jointly, do not do a good job predicting the change in the level of employment (Equation 1.1).  The NFIB variables perform better when predicting private sector employment changes as might be expected (Equation 1.2).  Still, neither equation forecasts changes in employment levels well, with or without leads.   Small business owners simply fail to anticipate the very large changes (volatility) in employment.  The predicted change in total employment from Equation 1.1 is plotted against the actual change in Exhibit 1.1.

 

   [1.1]  %DEMPT = 1.57 + .178 HIREPLN - .08 JOBOPEN    R2 = 13%

   [1.2]  %DEMPP = 1.59 + .273 HIREPLN - .11 JOBOPEN    R2 = 20%

 

a. Unemployment

 

Although the change in employment is not well anticipated by the NFIB labor market measures, the national unemployment rate (UNE) as reported monthly by the Bureau of Labor Statistics (BLS) is accurately predicted by the same two variables. Apparently these variables capture shifts in the balance between movements into and out of the labor force (changes in the labor force participation rate) and the creation and destruction of jobs.  The unemployment rate is the ratio of the number of people who want a job (and searched in the prior 4 weeks) divided by the sum of those who want a job and those who have a job (the labor force).   This gives the unemployment a variance that is not perfectly related to changes in employment.  The best fit is obtained with NFIB variables leading by one-quarter.    Strengthening hiring plans produce an increase in employment but the unemployment rate will depend additionally on net flows into or out of the labor force.  Often, the unemployment rate and the growth of employment move in the same, not opposite, directions.  Rising reports of hard-to-fill job openings indicate a reduced unemployment rate [Equation 1.3].    The plot of the predicted unemployment rate based on Equation 1.3 against the actual unemployment rate is shown in Exhibit 1.2.

           

            [1.3]  UNE = 10.22 - .04 HIREPLN-1  -  .17 JOBOPEN-1      R2 = 72%

 

The percentage change in total (or private, non-farm) employment has a much higher variance than the unemployment rate.  This volatility explains at least in part the difference in predictive power of the independent variables.  Larger firms contribute substantially more to the volatility in total employment over the business cycle than do small firms.[12]  Thus, it is unlikely that the employment decisions of small-business owners would anticipate percentage changes in total employment.   However, owners do sense “tightness” in their local labor markets and, collectively, do a very solid job of anticipating changes in the unemployment rate over the business cycle. 

 

 

 

 

            The Help Wanted Index (HWI) should be highly correlated with the percent of owners reporting hard to fill job openings and the strength of hiring plans.   The NFIB variables do carry the expected sign, but explain only 32  percent of the variation in the HWI.  If larger firms with more formal personnel departments are more likely to use help wanted ads and have more cyclical employment, the correlation would be expected to be weaker.  Small employers often use employee networks (hiring friends, family members etc.) to locate, screen and qualify employees, not want ads and search agencies.[13]  

 

[1.4] HWI = 54.92 + .47 HIREPLN + .94 JOBOPEN     R2 = 32%

 

 

            b. Labor Compensation

 

The major cost incurred by small business is labor cost, although changes in labor costs are less volatile than changes in energy or other business costs.  Overall, the path of labor costs drives the price level because firms that cannot cover labor costs will fail.  Since April 1982, the NFIB survey has asked a series of questions about past and planned labor cost changes in addition to the indicators of the demand for labor. 

 

WAGEUP[14] is the net percent of owners reporting that they raised labor compensation in the prior three-month period; PLNWAGE[15] is the net percent of owners planning to increase compensation during the next three-month period.  Both are seasonally adjusted.  HIRED[16] is the net percent of owners who reported increasing employment in the previous 3 months and HIREPLN is the net percent of owners who planned to increase the total number of people working at the firm.  Again, both are seasonally adjusted.  JOBOPEN is the percent of owners reporting at least one hard-to-fill job opening.   JOBOPEN, HIREPLN and HIRED are measures of the strength of the demand for labor and tightness in the labor market.  Both should be positively related to measures of labor cost (wages and benefits). 

 

WAGEUP and PLNWAGE are direct measures of actual and anticipated changes in labor compensation,  or planned changes to wages and benefits.  These variables should have positive relationship to macro measures of labor compensation, but they should occur prior to ( lead) actual events because implementation of compensation changes takes time (especially benefit changes). 

 

The level of the Bureau of Labor Statistic’s Employment Cost Index (ECI) is well anticipated by the NFIB data.  Hiring plans and job openings explain 59 percent of the variation in the ECI with a 2-quarter lead (Equation 1.5).  Similarly, reports of past and planned changes in employee compensation (WAGEUP and PLNWAGE) explain 58 percent of the variation in the ECI, but with a three quarter lead (Equation 1.6).  Although the fit of the equations is fairly good, the ability of the NFIB variables to predict the ECI outside of the sample period is compromised by the fact that the ECI is a trend variable and continually rises, sometimes faster, sometimes more slowly, while the NFIB variables are percentages and move within a limited range.  Equation 1.7 uses the percentage change in the ECI as the dependent variable [Exhibit 1.3].  The explanatory power of a difference or percentage change equation is always lower since the advantage of the presence of a time trend is eliminated.  Equation 1.7 does not capture the amplitude of the fluctuations in the ECI but does a fair job of anticipating its directional changes (with a 3 quarter lead).

 

            [1.5] ECI = 50.0 + .82 HIREPLN-2  +  2.64 JOBOPEN-2    R2 = 59%

            [1.6] ECI = 88.39 + 4.72 WAGEUP-3 – 4.69 PLNWAGE-3           R2 = 58%

            [1.7] %DECI = 1.78  -.04 WAGEUP-3 – .17 PLNWAGE-3            R2 = 24%

                                   

                                    (not significant at the 95% level)        (estimated 1984-2002)

 

Overall, the NFIB labor market indicators are highly correlated with two of three important macro labor market variables.  Movements in the ECI can be reasonably well anticipated with the NFIB survey measures while the unemployment rate is very well anticipated by them.    The lead  time in the “best fit” relationships make the survey measures particularly useful indicators of future economic developments as measured by popular government labor market statistics.

 

2. Inflation

 

Along with “full employment,” inflation is the other major concern of economic policy.  Two survey questions address this economic measure:  reported changes in average selling prices over the past 3 months (PASTP)[17] and reported plans for raising selling prices in the next 3 months (PLANP).[18]  The variable PASTP is the percent of owners who report raising average selling prices less the percent who report lowering prices (the net percent, seasonally adjusted).  PASTP should impact current Consumer Price Index (CPI) changes, since price changes implemented in the three months prior to the survey will impact price measures in the current period.  PLANP, the percent of owners planning to increase average selling prices less the percent planning to reduce average selling prices, should lead the CPI measures.  Plans from the prior quarter should show up as changes in prices in the current period. 

 

The NFIB survey also asks for the actual magnitude of past and planned price changes in categorical classifications.[19]  During periods of rapid price changes, movements in the tails of the distribution of reported price changes should have an important impact on changes in the average price level. The information in the tails of the distribution, that is to say, the incidence of extremely high or extremely low reports of actual and planned selling price changes adds substantial predictive content.  The findings also suggest that the process of inflation is fairly gradual.  The R2 statistics on the equations with longer lead structures (not shown) do not deteriorate substantially as the leads are lengthened.  Thus, plans to raise prices expressed several quarters earlier or reports of actual changes in average prices in recent past quarters appear to take some time to feed into the CPI.

 

 PASTP>5 is the percent of firms reporting price hikes of 5 percent or more in the past three months, and PLNP>5 is the percent of firms planning to raise prices by 5 percent or more in the next 3 to 6 months (not seasonally adjusted).  %DCPI is the annualized percentage change in the headline Consumer Price Index.  As shown in Equation 2.1, the NFIB price measures anticipate most of the quarterly variation in the CPI.  Exhibit 2.1 plots the predicted quarterly CPI inflation rate against the actual percentage change in the CPI.

           

         [2.1]%DCPI = -.20 + .07 PASTP + .08 PASTP>5 +.23 PLNP>5-1 R2 = 78% 

 

Converting these coefficients into Beta Coefficients (denoted b),[20] the most important variable in the model is PASTP (b=.41), followed by PLAN>5 (b=.34), and finally, PASTP>5 (b=.21).  Reported past changes are major determinants of the percentage change in the CPI for the current period.  But plans to change prices, leading one quarter, also exert a heavy influence on the current period inflation measure.

 

Substituting the core CPI, %DCORECPI, for the %DCPI, to eliminate the volatile energy and food components of the CPI, produces a similar relationship (Equation 2.2).  The NFIB variables were not able to capture the decline in the CPI in the late 1980s due to energy price declines, but did capture the tumble in the CPI in 2002.  Excluding energy and food, the NFIB measures perform well in the 1980s, but still predict a major deceleration in the inflation rate in 2002 that does not appear in the Core CPI (see Exhibits 2.2 and 2.3).           

 

 [2.2] %DCORECPI = -1.15 + .09 PAST + .32*PAST>5 + .06*PLAN>5-1   R2=79%

 

 

Two other important measures of inflation, based on the GDP deflator (%DGDPDEFLATOR) and the Personal Consumption Expenditures deflator (%DPCEDEFLATOR), are also well anticipated by NFIB owner reports of past and planned future price changes.  Equations 2.3 and 2.4 illustrate how well the NFIB survey measures anticipate changes in these popular inflation measures.

 

            [2.3]  %DPCEDEFLATOR =  -1.71 + .07 PAST  + .22 PAST>5  

                                                +.12 PLAN5-1                                                                R2 = 80% 

 

            [2.4]  %DGDPDEFLATOR = -1.74 + .10 PAST  +.24 PAST>5  

                                                +.08 PLAN>5-1                                                           R2 = 81% 

 

When the time period is shortened to exclude the volatile 1970s, the variables reflecting the tails of the distribution become insignificant and PASTP and lagged PLANP explain the same fraction of the variance in the CPI.  Shortening the time period to exclude the 1970s (Equation 2.5) reduces the R2 to 75% and including only the 1990s (Equation 2.6) reduces the R2 to 41 percent.   There is much less predictable variance in the price measures in the 1990s and into 2000 compared to prior decades, while the “noise” remains.  The coefficients in the three regressions covering the various sample periods are consistently similar, suggesting that if inflationary forces were to reappear, the model based on NFIB survey responses would quickly pick it up.

 

          [2.5]  %DCPI = -.70 + .125 PASTP +.152 PLANP-1  [1980-2002]  R2 = 75%

          [2.6]  %DCPI = -.14 + .095 PASTP +.126 PLANP-1  [1990-2002]  R2 = 41%

 

Overall, small business owner reports of past and planned price changes do a very good job of anticipating inflation.   Reports of actual price changes in prior months and plans to raise prices in the current quarter expressed in the previous quarter both make substantial contributions to the anticipation of changes in the various price indices.

           

3. Business Inventories

 

Changes in non-farm business inventories are notoriously difficult to predict.  These changes are the direct result of owner decisions to actively increase or decrease inventories, and of consumer (customer) decisions to buy more or buy less in a given period of time.  Mismatches between these two sets of decisions can produce wide swings in business inventories.  The basic model for examining inventory investment in macroeconomics is the stock adjustment model:  the desired stock of inventories depends on expected sales in the future period, the cost of holding inventories, and the ratio of inventory to sales that is desireable for that particular type of business.  Comparing the desired stock to the stock on hand produces a gap that, if positive, must be closed by additional inventory accumulation and, if negative,  must be closed by reducing inventories. 

 

The net percent of owners characterizing their current stocks as “too low” (INVSAT) is a direct proxy for the gap between desired and actual stocks. [21]  The percent of owners planning to intentionally add to inventory stocks (INVPLN) is driven directly by the pervasiveness among small businesses of a gap between desired and actual inventory stocks. [22]

 

Equation 3.1 relates the actual change in business inventories (DINV) as reported in the National Income and Product Accounts to the NFIB survey measures of inventory satisfaction  (INVSAT), the net percent of owners reporting that current holdings are too low, and inventory plans (INVPLN), the net percent of owners planning to intentionally increase inventory holdings.  The best model incorporates a lead of one quarter for the NFIB measures.  A plot of the actual and predicted values from Equation 3.1 is shown in Exhibit 3.1).

 

            [3.1] DINV= 19.41 +2.41 INVSAT-1 + 4.01 INVPLN-1    R2 = 32% 

 

 

 

The NFIB inventory model tracks changes in business inventory fairly well, except for the period 1997-2002 where it consistently underestimates the buildup of inventories and then misses the dramatic reduction in 2002.  This poor performance in 1997-2002 may be the result of inventory changes confined to sectors of the economy that are dominated by large firms, such as manufacturing and telecommunications.  There was certainly no gain to be expected from inventory building in anticipation of rising prices as the economy has been experiencing “disinflation” for two decades.  Re-estimating the equation through 1997 provides virtually identical coefficients, but much higher explanatory power (Equation 3.2):[23]

 

[3.2] DINV = 20.5 +2.35 INVSAT-1 + 3.05 INVPLN-1    R2 = 42%

 

Economic growth faltered substantially in the second half of 2000, signaling an end to the frenetic growth that typified the last half of the decade, creating large excess inventory holdings.  Cash flow also came under pressure and the gap between S&P reported operating profits and NIPA profit measures diverged.[24]  This helped trigger reductions in inventories and, as of March 2003, NFIB owners more frequently reported reductions than additions to inventory holdings for 24 straight months (there were 41 consecutive months of net reductions in the 1990-91 recession).  This was preceded by 4 months beginning in December, 2000 when the net percent of firms adding to inventory was either 1 percent or 0.  Unable to raise prices, the carrying costs of inventory (the nominal interest rate minus the inflation rate) became positive and, in some industries, substantial.  This triggered a record decumulation in inventory no longer needed to support economic growth which slowed dramatically in the second half of 2000.  The predictive power of the model was substantially degraded by events during this boom and bust period that included the largest period of inventory liquidation in modern economic history.

 

Beginning in the fourth quarter of 1982, NFIB began asking about actual changes in inventories in the preceding 3-month period.  ACTUAL is the seasonally adjusted net percent of firms reporting an increase in inventory holdings over the past 3 months.[25]  There is no lead for this variable as it reports actual behavior in the months preceding the survey.  Adding this variable to the inventory satisfaction variable and the inventory plan variable yields an improved fit (Equation 3.3):

 

[3.3]  DINV = 19.0 +3.94 INVSAT-1 + 3.84 INVPLN-1 + 4.24 ACTUAL  

 R2 = 50%                               [1982:4 – 2002:4]

 

Considering the volatility of inventory investment in the NIPA accounts, this equation explains well the actual dollar amount of inventory investment.  Since NFIB members cover all sectors of the economy (NAICS), better measures of inventory behavior for predictive purposes might be obtained by selecting firms to include in the creation of the survey indicators that are from inventory intensive sectors (e.g. manufacturing, construction, wholesale trades etc.).  However, sample size considerations prevent the creation of industry based indicators.

 

4. Capital Expenditures

 

Although individual small firms rarely make massive capital outlays, the accumulation of small outlays by six million small-business employers can have a substantial impact on aggregate capital spending in the U.S.  The median outlay for NFIB members is $20,000 (in the prior six months), typically reported by 55 percent to 70 percent of the owners from quarter to quarter.  One percent of the owners typically report outlays in excess of $500,000 (in the prior six months).

 

The relationship between gross private domestic investment (NIPA) and the NFIB indicators (CXPLAN),[26] the percent of owners planning capital outlays in the next 3 to 6 months, and the incidence of past capital spending (CXPAST)[27] is not particularly strong (Equation 4.1).  Incorporating leads did not improve the R2.  The results fit the predictions that would follow from a typical capital stock adjustment model:  higher levels of past spending (CXPAST) are associated with lower levels of spending in the current period since the prior expenditures brought the actual stock closer to that desired by owners.  The higher the percent of owners reporting that they planned outlays in future months (CXPLAN), the higher the actual level of expenditures in the months following the survey, an indication that the desired stock of capital must have risen relative to current capacity.[28] 

 

            Narrowing the definition of investment spending does improve the fit, with the major gain coming from the shift from the gross private domestic investment measure to a measure of private fixed investment or equipment only (Equations 4.2, 4.3 and 4.4).   All equations are estimated beginning with 1979, the first year that past expenditure data are available.

 

                                                 Constant   CXPLAN            CXPAST        R2

[4.1]  CAPX%                              4.18          2.16                -1.22            12%

[4.2]  PRIVFIXED%                -10.17           1.74                -  .73            31%

[4.3]  NONRESPRIVFIX%      -27.20          1.50                -  .28            32%

[4.4]  NONRESEQUIP%         -16.61          1.80                -  .62            28%

 

            Profitability is an important determinant of capital spending in most macroeconomic models.  Adding the net percent of owners reporting that earnings in the prior quarter were higher than in the quarter before (EARN)[29], adds some explanatory power, and leaves the other coefficients fundamentally unchanged in most cases.  Again, narrowing the definition to fixed investment measures, with or without residential structures (Equation 4.5 vs 4.6), improves the explanatory power of the equation as does a focus on equipment (Equation 4.8).  The predictions from Equation 4.6 are shown in Exhibit 4.1.

 

                                                 Constant   CXPLAN            CXPAST   EARN      R2

[4.5]  CAPX%                            24.02          1.40                -1.01         .47         15%

[4.6]  PRIVFIXED%                -  4.21           1.51                -  .67         .14         32%

[4.7]  NONRESPRIVFIX%      -20.09          1.21                -  .20         .18         33%

[4.8]  NONRESEQUIP%         -  7.39          1.45                -  .52         .21         31%

 

            Restricting the estimation period to 1990-2002 provides much the same result, indicating that the relationships are quite stable over time.  Although there was more variability in capital spending in the 1970s than in later periods, the stability of the equations suggest that the NFIB contribution to anticipating changes in capital spending is fairly stable.

 

1990-2002                              Constant   CXPLAN      CXPAST   EARN            R2

[4.9]  CAPX%                           20.30           1.56             -1.06            .28         11%

[4.10]  PRIVFIXED%              -17.12           1.36             -  .40            .01         29%

[4.11]  NONRESPRIVFIX%  -15.25            1.37             -  .36            .24         39%

[4.12]  NONRESEQUIP%       -11.78          1.14             -  .28            .18         22%

 

 

5. Capital Market Indicators

 

            Although most small firms are financed primarily with the entrepreneur’s own savings when started, the cost and availability of capital is critical to the survival of small firms once the business is operating.  NFIB asks about the ease of obtaining the most recent loan relative to prior attempts (CREDHARD).[30]    CREDHARD is the net percent of owners who said that it was “harder” to get the loan on the last attempt.  Aggregated over all business owners, this is a proxy for how tight monetary policy is in the months prior to the survey.  The demand for credit varies substantially over the period of analysis.  To compensate, CREDHARD normalized by the percent of firms reporting that they borrow on a regular basis [BOR], which varies from a high of 53 percent in 1979 to a low of 29 percent in 1999.

 

            Monetary policy is implemented through bond market transactions by the Fed in New York.  Thus, the first banks to “feel” a change in policy should be the money center banks.  The Federal Reserve surveys senior loan officers in about 50 money center banks and ascertains the percent of these loan officers that report “tightening” or “loosening” the lending standards for “small” firms, presumably in response to changes in market conditions created by the Fed or permitted by Fed policy.  These changes in credit market conditions should be transmitted to the remaining 8,000 plus commercial banks, ultimately being reflected in NFIB owner reports of “easier” or “harder” credit conditions.  The percent of money center bank senior loan officers who report tightening lending conditions for small businesses (net of those reporting easier terms) is used as a predictor to explain variations in the NFIB data on “harder to get the last loan.”  With little theoretical guidance other than “long and variable lags” in monetary policy, lags of varying lengths are tested looking for the highest R2.  The R2 peaks in both regressions with the lenders’ assessments demonstrating a 17 quarter lag (over 4 years!).  The coefficients are more stable, but largest when the R2 peaks (Exhibit 5.1).

 

            [5.1]     CREDHARD/BOR = 9.40 + .18 FED-17                 R2 = 34%

            [5.2]     CREDHARD =         3.32 + .07 FED-17                   R2 = 35%

 

           

 

 

           

Plots of the predicted percent of owners reported credit “harder” to get are shown in Exhibit 5.2 and Exhibit 5.3 (the period over which forecasts are available due to the availability of Fed data and the 17 quarter lag).   In simple terms, this relationship indicates that changes in credit availability reported by senior loan officers of money center banks takes 17 quarters to have its maximum impact on credit availability for small business owners.  Since policy transmission effects are not one quarter events, a more complex distributed lag model that incorporates many past quarterly observations on loan officer reports may produce higher explanatory power, but would not alter the conclusion that small business borrowers detect tighter or easier borrowing conditions long after money center banks report implementing such policy changes in response to market conditions.  Unfortunately, Fed Loan Officer survey data are not available during the volatile 1770s and early 1980s (see Exhibit 5.2).  However, even in the “tame” credit markets of the 1990s, the relationship between the Fed survey and CREDHARD with a 17 quarter lag is fairly clear (Exhibit 5.3).  If this is any commentary on the span between the time money center banks sense a change in market conditions and when this filters out to the smaller and rural banks in the system, lags are long indeed, and variable, reaching the most sophisticated banks first and the small banks last.  Rising rates may not make borrowing “harder”, and, for more infrequent borrowers with longer term loans, some considerable time many pass before they notice a change in credit market conditions.

 

 

 

 

6. Real GDP Growth

 

 

 

The most widely reported indicator derived from the NFIB survey is the Index of Small Business Optimism (INDEX).  The 10 questions included in the INDEX have been part of the questionnaire since October 1974.  They include: 

 

·        Good Time for Business Expansion (GTEX)[31]

·        Outlook For The Economy: Better Or Worse (EXBUS)[32]

·        Net Earnings Trends: Higher Or Lower (EARN)

·        Expected Real Sales Volume:  Higher or Lower (EXSALE)[33]

·        Plans To Increase/Decrease Employment (HIREPLN)

·        Job Openings Not Able To Fill (JOBOPEN)

·        Current Inventory Satisfaction: Too High Or Low (INVSAT)

·        Planned Inventory Change: Increase or Decrease (INVPLN)

·        Expected Change In Credit Market Conditions: Easier or Harder (EXCRED)[34]

·        Planned Capital Expenditures (CXPLAN)[35]

 

Most of the questions used to construct the INDEX are symmetric, such as whether the owner expects the economy to be “better” or “worse” in the next 6 months or plans to “increase” or “decrease” the total number of people working for the firm.  For these questions, a balance variable (or diffusion index) is formed. The percent of unfavorable responses (“worse;” “reduce”) is subtracted from the favorable responses (“better;” “increase”) to provide a net percent variable.   For the three other questions, only the percent of owners offering an affirmative answer is included, e.g. the percent planning capital spending or reporting that the current period is a good time for capital expansion.  Some variables have strong seasonal patterns such as hiring plans, though others have little or none, such as capital spending plans or expected credit conditions.  All ten variables are seasonally adjusted.  The INDEX is computed as the sum of the ten seasonally adjusted components plus 100 to keep the INDEX from becoming negative.  Finally, the INDEX is based to its average value in 1986 (1986=100), the middle of the 1980s expansion.

 

Perhaps the most important indicator of an economy’s overall economic health is growth in the Gross Domestic Product (GDP).   Two GDP measures are used, the percentage change in real GDP quarter-to-quarter (annualized), %DGDP, and the quarter over quarter change in real GDP (from 1st quarter to 1st quarter, etc.), %DGDPQQ.

 

The quarterly percentage change in GDP is quite volatile (Exhibit 6.1) and the INDEX does not do particularly well in explaining the actual magnitude of the change (Equation 6.1).  Equation 6.1’s predicted values are plotted against the actual quarterly change in real GDP in Exhibit 6.2.  Note in Exhibit 6.2 how poorly the NFIB indicator forecasts the wild ride of the late 1970s and early 1980s.  Even the extremes of the more docile1990s are not well anticipated, although the trend in changes is reasonably well anticipated.

 

 

 The INDEX does a better job of predicting %DGDPQQ because %DGDPQQ smoothes the volatility of GDP growth, and the less volatile measure more accurately reflects the path of real economic activity (Equation 6.2).  However, it is more difficult to interpret year over year equations in a quarterly forecasting context. The best prediction for growth over any 4-quarter period is obtained using the survey results from the middle of the period.[36]  Thus, calendar year growth is best predicted using the July survey (the “third quarter” survey in the NFIB sequence).

 

 

            [6.1]  %DGDP = -47.75 + .51 INDEX                            R2 = 37%

            [6.2]  %DGDPQQ = -36.11 + .40 INDEX -1                  R2 = 51%

 

Final domestic sales (%DFSALES) as a dependent variable produces a better fitting model than GDP. The R2 in Equation 6.3 is substantially higher than in the GDP equation, 6.1.  The coefficients on INDEX in the GDP and the Final Sales equations are very close, even though the R2 is higher in the Final Sales equations.

 

[6.3]  %DFSALES = -45.27 + .49 * INDEX            R2 = 43%

            [6.4]  %DFSALESQQ = -35.81+ .39 * INDEX-1    R2 = 54%

 

            Using all 10 INDEX components raises the R2 (to 44%, 38% adjusted R2) compared to Equation 6.1, but all coefficients are statistically insignificant and often carry theoretically incorrect signs.  This is a result of the collinearity that exists among the components (see Appendix 1).  Two of the variables, inventory investment plans, INVPLN and expected changes in the real sales volume, EXSALES, perform as well as the INDEX in predicting changes in GDP.

 

                                                        Bivariate             Full Equation

                        Component              Coeff.        R2     Coefficient

                        HIREPLN                   .23         .14         -.09

                        JOBOP                    .06         .01           .05

                        CXPLAN                  .22         .09          -.01

                        INVPLN                   .51         .36           .29

                        INVSAT                   .72         .22           .10

                        GTEX                      .29         .26           .13

                        EXBUSCOND         .04         .05           .00

                        EXCREDIT              .28         .15           .02

                        EARN                      .17         .17          -.07

                        EXSALES                 .20          .39           .10

 

7. Other Indicators

 

A number of popular survey indicators used by analysts to anticipate changes in economic activity include the University of Michigan Consumer Confidence Index (MICH) and the Conference Board Consumer Confidence Index (CONF).  Business owners are, of course, consumers and as such, their views should be reflected in both indices (Equations 7.1 and 7.2). 

Although the NFIB Index is related to these indicators in a way that would be expected, the relationships are not very strong. 

           

[7.1] MICH = -8.42 + 1.02 INDEX                                        R2 = 13%

            [7.2] CONF = -228.59 + 3.27 INDEX                                  R2 = 32%

 

The variance in the two consumer confidence indices is substantial relative to the INDEX (see Exhibit  7.1].  Thus, it is not surprising that the NFIB INDEX explains relatively little of the variance in these two measures.

 

 

The Index of Leading Economic Indicators (LEI) is also a popular and widely used forecasting measures.  The LEI leads changes in economic activity (GDP), but its components are, with few exceptions (e.g. the University of Michigan Consumer Confidence Index), subject to revisions that are often substantial.  Thus, one cannot be sure of the existence of a signal unless the change in the LEI is substantial and persistent.  Its relationship to the INDEX is not strong, either on a concurrent basis or with NFIB data leading.  (Equation 7.3). 

 

            [7.3] LEI = -3.12 + .96 INDEX                                  R2 = 12%       

 

The LEI and the consumer sentiment measures receive considerable attention in the press and their release often moves financial markets.  One would expect, then, to observe a substantial relationship between changes in GDP and these measures.  However, the R2 is only 2 percent between the LEI and %DGDP.   The University of Michigan Index (MICH) and the Conference Board Index (CONF) performances are somewhat better with R2 statistics of 19 percent and 12 percent respectively and neither performs better with one or two quarter leads.  None of the three perform as well as the NFIB INDEX with its R2 of 37 percent.

 

           [7.4] %DGDP = -7.01 + .11 MICH                                        R2 = 19 %

            [7.5] %DGDP = -2.06 + .05 CONF                                       R2 = 12%

           [7.6] %DGDP = -1.49 + .05 LEI                                           R2 =   2%

            [7.7] %DGDP = -47.75 + .51 INDEX                                 R2 = 37%

 

A priori, it is not clear whether the level of these indices should be used to anticipate changes in GDP or changes in these indices.  Converting the predictors to percentage changes, the R2 for the LEI rises to 25 percent, while MICH falls to 9 percent; CONF remains about the same, 13 percent, and INDEX falls to 4 percent.  Percentage changes perform more poorly for all except the LEI, but the INDEX level produces a better predictive performance than all of the other three indicators.

  

9. Summary

 

Small business produces half of the private sector GDP and accounts for an even larger fraction of the private sector labor force and new jobs created.  As a consequence, the collective actions of small firm owners have a major impact on the U.S. economy.  The economic indicators pioneered by NFIB are shown to have strong empirical relationships to important economic measures such as the growth in GDP, the inflation rate, the unemployment rate, inventory investment and the Employment Cost Index.

 

For the most part, the best models anticipate economic activity by 1 or 2 quarters, making the NFIB measures useful indicators of future change.  Because the NFIB survey measures are never revised, their relationship to NIPA and other BLS and BEA data that are subject to revision can provide a sound guide to the direction of the economy though they may differ from the preliminary figures released by government agencies.  The NFIB indicators are good predictors of changes in final BLS/BEA numbers and thus may be better indicators of changes in these measures than the preliminary releases of their values.

 

Though survey data cannot be effectively used to produce long-term forecasts that require knowing future values of the survey variables, the NFIB measures contain useful information not captured by variables traditionally employed in forecasting models.  The NFIB data provide helpful data for identifying the near-term path of the economy and insight into which forecast scenario might be developing.  Because most of the forecasting relationships use NFIB measures to forecast subsequent events, their usefulness as predictors of near-term future economic activity is enhanced.

 


 

Bibliography

 

Bram, Jason and Sydney Ludvigson, “Does Consumer Confidence Forecast Household Expenditure? A Sentiment Index Horse Race”  Federal Reserve Bank of New York Research Paper # 9708, printed in FRBNY Economic Policy Review, June, 1998

 

Campbell, John Y., and Gregory Mankiw, “Consumption, Income, and Interest Rates:  Reinterpreting the Time Series Evidence” in O. Blanchard and S, Fischer, eds., NBER Macroeconomics Annual, Cambridge, MIT Press, 1989

 

Carroll, Christopher D., Jeffrey C. Fuhrer, and David W. Wilcox, “Does Consumer Sentiment Forecast Household Spending? If So, Why?”, American Economic Review, 84, no. 5, pp1397-1408, 1994.

 

Cashell, Brian W., “Measures of Consumer Confidence:  Are They Useful?”, Congressional Research Service, Library of Congress, June, 2003.

 

Dennis, William J. Jr. and William C. Dunkelberg, “Small Business Economic Trends: A Quarter Century Longitudinal Data Base of Small Business Economic Activity”, J.A. Katz ed., Data Bases for the Study of Entrepreneurship, New York, JAI Press, 2000.

 

Dunkelberg, William C. and Jonathan A. Scott, “Report on the Representativeness of the National Federation of Independent Business Sample of Small Firms in the United States, office of Advocacy, U.S. Small Business Administration, contract # SBA2A-0084-01, mimeo, 1983.

 

Howrey, Phillip, “The Predictive Power of the Index of Consumer Sentiment”, Brookings Papers on Economic Activity, #1, 2001.

 

Mishkin, Frederic S., “Consumer Sentiment and Spending on Durable Goods”,

Brookings Papers on Economic Activity, no. 1, pp217-32, 1978

 

Otoo, Maria Ward, “Consumer Sentiment and the Stock Market”, Board of Governors, Federal Reserve System, November, 1999.

 

Popkin Joel and Company,Small Business During the Business Cycle.  Office of Advocacy, U.S. Small Business Administration, contract # SBAHQ-01-R-0011, 2003.

 

Popkin Joel and Company , “Labor Shortages, Needs and Related Issues in Small and Large Businesses:  Part A: Labor Shortages in Small Firms.”  Office of Advocacy, U.S. Small Business Administration contract # SBAHQ-98-C-00017, 1999.

 

Appendix 1: Principle Components Analysis

 

The notion of “optimism” is a conceptual construct that cannot be directly measured.  Consequently, subjects are asked a number of questions that relate to dimensions of what might be part of “optimism”.  These measures can then be combined in some way to identify a construct that more closely approximates “optimism.”

 

As the correlation matrix below shows, several single components have a high correlation with GDP growth (REALSAL is correlated .62) and with other INDEX components (EARN and GTEX are correlated .81).  Using all 10 of the INDEX components to predict the percentage change in GDP does produce a higher R2 (44 percent vs. 37 percent for the INDEX alone), but only one of the components is significant.  This may be acceptable for an overall forecast of GDP growth, but no partial analysis would be reliable (e.g. using the change in HIREPLN, ceteris paribus, to assess the possible impact of the change on GDP).

 

                                                                  TABLE 1

 

HIRE

OPEN

EXCRED

EBCOND

REALSAL

NEARN

INVSAT

INVPLN

GTEX

CAPX

SALES

 

HIRE

1

 

 

 

 

 

 

 

 

 

 

 

OPEN

0.775467

1

 

 

 

 

 

 

 

 

 

 

EXCRED

0.420581

0.078659

1

 

 

 

 

 

 

 

 

 

EBCOND

-0.2418

-0.629

0.400516

1

 

 

 

 

 

 

 

 

REALSAL

0.425853

0.008994

0.471566

0.424375

1

 

 

 

 

 

 

 

NEARN

0.632472

0.397864

0.403976

-0.11266

0.60178

1

 

 

 

 

 

 

INVSAT

0.413706

0.065554

0.324099

0.09731

0.629813

0.586218

1

 

 

 

 

 

INVPLN

0.683859

0.277986

0.520037

0.19057

0.780956

0.679639

0.703643

1

 

 

 

 

GTEX

0.709636

0.436378

0.589673

0.016338

0.693577

0.809932

0.512649

0.673083

1

 

 

 

CAPX

0.833069

0.610202

0.33429

-0.31626

0.340427

0.606001

0.385588

0.60225

0.590023

1

 

 

SALES

0.318249

0.348849

-0.03113

-0.27245

0.513351

0.675439

0.384106

0.375165

0.547934

0.321874

1

 

%GDP

0.372992

0.102144

0.386221

0.228409

0.623369

0.420866

0.466718

0.602545

0.516234

0.296841

0.346755

 

 

 

Principle components, and related techniques such as factor analysis and cluster analysis, can be employed when a number of measurements are taken that are related to some underlying conceptual construct such as “optimism.”  The results of the analysis are presented in Tables 2 and 3.  The information content of the ten components of the INDEX can be reasonably represented by linear combinations of the ten components combined into four new indices.  These four independent constructs account for 88 percent of the variation contained in the ten question series. 

 

Good Time to Expand dominates the first component with strong contributions from Hiring Plans, Inventory Plans and Expected Real Sales gains.  A supporting role is played by reports of improved earnings.  The second component is dominated by Expected Business Conditions.  This INDEX component is highest when the percent of firms with hard-to-fill job openings is lowest (at or near the trough in the business cycle).  The third component is dominated by Inventory Satisfaction and, to a lesser degree, by Planned Capital Outlays.  However, the latter never loads more than .6 in any component.  Component four represents the Credit Outlook.  This variable appears to be independent of the other nine Index variables, although it is not a significant factor.[37]  Using the four strongest components in place of the INDEX to explain changes in real GDP produces a somewhat higher R2 (just the use of 4 instead of 1 predictor will do this), but on an adjusted R2 basis, there is little gain and the constructs are more difficult to use and interpret in a forecasting context.

 

 

 

 

TABLE 2

 

 

 

 

PRINCIPAL COMPONENTS ANALYSIS: SMALL BUSINESS OPTIMISM INDEX

 

 

PERCENT OF VARIANCE EXPLAINED

 

 

        EIGENVALUE

COMPONENT

CUMULATIVE

 

 

1

4.9

49.0

 

49.0

 

 

 

2

2.1

21.0

 

70.0

 

 

 

3

1.2

12.0

 

82.0

 

 

 

4

0.6

5.6

 

87.6

 

 

 

5

0.4

4.1

 

91.7

 

 

 

6

0.4

3.7

 

95.4

 

 

 

7

0.2

1.8

 

97.2

 

 

 

8

0.2

1.5

 

98.7

 

 

 

9

0.1

0.8

 

99.5

 

 

 

10

0

0.5

 

100

 

 

 

 

 

 

 

 

 

 

TABLE 3

 

 

 

 

PRINCIPLE COMPONENTS, SMALL BUSINESS OPTIMISM INDEX

 

 

 

 

 

 

 

 

COMPONENT

 

1

2

3

4

5

  Hiring Plans

 

0.85

-0.4

0.14

0

0.24

  Job Openings

 

0.45

-0.79

-0.13

0.25

0.15

  Credit Outlook

 

0.64

0.35

0.34

0.54

0

  Expected Business Conditions

0

0.91

0.25

0

0.15

  Expected Real Sales

0.77

0.46

0

-0.23

0

  Net Earnings Change

0.86

0

-0.14

-0.12

-0.39

  Inventory Satisfaction

0.52

0.23

-0.75

0

0

  Inventory Plans

 

0.88

0.18

-0.2

0

0.25

  Good Time to Expand

0.91

0

0

0

-0.28

  Planned Capital Outlays

0.61

-0.26

0.6

-0.34

0

 

 

 



[1] A description of the origin and content of NFIB’s economic survey can be found in:  William J. Dennis, Jr., and William C. Dunkelberg, “Small Business Economic Trends:  A Quarter Century Longitudinal Data Base of Small Business Economic Activity,” Databases for the Study of Entrepreneurship, (ed.) Jerome A. Katz, JAI, New York, 2000.

 

[2] Although estimates vary, the “small business sector” of the economy is estimated to produce 50 percent of the private Gross Domestic Product (GDP) and employ about 60 percent of the private sector labor force.  Small business is also credited with producing the bulk of net new jobs created in the U.S. economy.   See, www.sba.gov/advo/stats for the latest statistics on the contributions of small business to the American economy.

 

[3] It is argued that indeed the owners of small firms with flatter organizations might sense changes in economic conditions more quickly than their counterparts in large firms, making indicators of the economic health of small firms relatively more responsive to changes in economic conditions.

 

[4] Dunkelberg and Scott, “Report on the Representativeness of the National Federation of Independent Business Sample of Small Firms in the United States, office of Advocacy, U.S. Small Business Administration, contract # SBA2A-0084-01, mimeo, 1983.

 

[5]  Early in the history of the surveys, NFIB experimented with industry weighted and employment weighted indices, but the variance of the INDEX in various forms did not significantly change.  An INDEX was also created as the sum of significant changes among the 10 components, but its performance as a predictor was also inferior to the INDEX.

 

[6] Many research papers report varying levels of significance for estimates of coefficients, such as notations indicating 90 percent, 95 percent and 99 percent levels of significance.  In this paper, one level of significance is applied to all estimates.  All coefficients are significant at the 95 percent level, one tail test, since the expected sign is always know, except where noted.  All equations are estimated using data from 1974:1 to 2002:4 unless otherwise indicated.

 

[7] The survey question for HIREPLN data reads: “In the next three months, do you expect to increase or decrease the total number of people working for you?”

 

[8] The survey question for JOBOPEN data reads:  “Do you have any job openings that you are not able to fill right now?”

 

[9]  The net percent of firms planning to increase total employment will be smaller than the number of firms hiring new employees, since many firms will be replacing workers, leaving total employment unchanged or might hire workers, but terminate even more workers, reducing total employment.  In recent surveys, between 45% and 50% of the owners report looking for at least one employee each month.

 

[10]   The tighter the job market, the higher the percent of owners who will report hard to fill job openings.  The link to employment gains is less clear, since hard to fill openings appear to be linked to the availability of skilled workers.   The question asks about  “qualified” workers and most hires are replacement workers, not hires that expand total employment, making results more difficult to interpret.  Even with a lag, higher levels of past job openings are still negatively related to employment growth in the current period when hiring plans are included in the regression equation.

 

[11] The 8% annualized growth rate in the first quarter of 2000 is an anomaly, totally inconsistent with the behavior of employment in adjacent quarters and logically inconsistent given the low unemployment rate and the record percent of business owners reporting unfilled job openings.

 

[12] Small Business During the Business Cycle.  Presented to the Office of Advocacy, U.S. Small Business Administration by Joel Popkin and Company, 2003.

 

[13] Labor Shortages, Needs and Related Issues in Small and Large Businesses:  Part A: Labor Shortages in Small Firms.  Presented to the Office of Advocacy, U.S. Small Business Administration by Joel Popkin and Company, 1999.

 

[14] The survey question for the WAGEUP data reads:  “Over the pat three months, did you change average employee compensation (wages and benefits but NOT Social Security, U.C. taxes, etc.)?

 

[15] The survey question for PLNWAGE data reads:  “Do you plan to change average employee compensation (wages and benefits but NOT Social Security,, U.C. taxes, etc.) during the next three months?”

 

[16] The survey question for HIRED data reads:  “During the last three months, did the total number of employees in your firm increase, decrease, or stay about the same?”

 

[17] The survey question for PASTP data reads: “How are your average selling prices now compared to three months ago?”

 

[18] The survey question for PLANP data reads:  “In the next three months, do you plan to change the average selling prices of your goods and/or services?”

 

[19]   The categories reported are:  (1) less than 1%; (2) 1-1.9%; (3) 2-2.9%; (4) 3-3.9%; (5) 4-4.9%; (6) 5-7.9%; (7) 8-9.9%; (8) 10% or more.

 

[20]   While regression coefficients cannot be compared to determine which is the most important predictor in an equation, standardized regression coefficients can be compared.  Standardized coefficients can be computed as follows?  Beta Coefficient = (Regression Coefficient x Standard Deviation of X) / Standard Deviation of Y.  If all variables in a regression are converted to standard normal variables, then the resulting coefficients are Beta Coefficients.

 

[21] The survey question for INVSAT data reads:  “At the present time, do you feel your inventories are too large, about right, or inadequate?” 

 

[22] The survey question for INVPLN data reads:  “Looking ahead to the next three to six months, do you expect on balance, to add to your inventories, keep them about the same, or decrease them?”

 

[23]   Adding a dummy variable for this period, 0 through 1996, +1 from 1997-2000 and –1 from 2000 through 2002, raises the R2 for the equation to .61.

 

[24]  During the last half of the decade, reported pro forma operating profits for the S&P 500 grew substantially faster than NIPA profit measures.  This divergence supported the unprecedented rise in equity markets.  Although closing, the gap still persists., part of the process of dealing with the need for liquidity, masked by pro forma accounting  when the economy weakened, was a massive liquidation of inventory.

 

[25] The survey question for ACTUAL data reads:  “During the last three months, did you increase or decrease your inventories?”

 

[26]The survey question for CXPLAN data reads: Looking ahead to the next three to six months, do you expect to make any capital expenditures for plant and/or physical equipment?”

 

[27]The survey question for CXPAST data reads:  During the last six months has your firm made any capital expenditures to improve or purchase equipment, buildings, or land?”

 

[28] The capital stock adjustment model specifies that expenditures in a given period are proportional to the gap between the actual stock of capital on hand and the “desired stock”, based on expected sales for example.  Past expenditures raise the actual stock and, other things equal, lower the gap between desired and actual stocks.  Plans to make expenditures, given the stock, reflect a larger gap, likely due to an increase in the desired stock of capital driven by more optimistic expectations for sales and demand.

 

[29]  The  question asked was: “Were your net earnings or “income” (after taxes) from your business during the last calendar quarter higher, lower, or about the same as they were for the quarter before?

 

[30] The questions asked are: “If you borrow regularly (at least once every three months) as part of your business activity, how does the interest rate paid on your most recent loan compare with that paid three months ago?  Are these loans easier or harder to get than they were three months ago? (CREDHARD)  Do you expect to find it easier or harder to obtain your required financing during the next three months?” (EXPCRED)

 

[31] The question asked is:  “Do you think the next three months will be a good time for small business to expand substantially?”

 

[32]  The question asked is: “About the economy in general, do you think that six months from now general business conditions will be better than they are now, about the same or worse?”

 

[33]  The question asked is: “Were your net earnings or “income” (after taxes) from your business during the last calendar quarter higher, lower, or about the same as they were for the quarter before?”

 

[34]  The question asked is: “Do you expect to find it easier or harder to find you required financing during the next three months?”

 

[35]  The surveys began in October, 1973.  This question was added a year later and consequently, the INDEX is available from 1974:4 with this question included.

 

[36] The survey data used are collected in the first month of each quarter.  To predict growth for a calendar year, the best results are obtained using the July survey (referred to as the third quarter survey).  To predict growth from the second quarter to the second quarter of the next year, the October survey provides the forecast etc.

 

[37]  An analysis of the same 10 factors not seasonally adjusted yields basically the same patterns.