Abstract
There remains considerable debate in the theoretical and empirical literature about the differences in the cyclical dynamics of firms by firm size. This paper contributes to the debate in two ways. First, the key distinction between firm size and firm age is introduced. The evidence presented in this paper shows that young businesses (that are typically small) exhibit very different cyclical dynamics than small/older businesses. The second contribution is to present evidence and explore explanations for the finding that young/small businesses were hit especially hard in the Great Recession. The collapse in housing prices accounts for a significant part of the large decline of young/small businesses in the Great Recession.
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Notes
See evidence for the Kauffman Firm Survey, the Survey of Small Business Finance, and the Statistics of Business Owners.
Sharpe (1994) uses Compustat data from 1959 through 1985. Gertler and Gilchrist (1994) use the Quarterly Financial Report for Manufacturing Corporations, from 1958:4 through 1991:1. Chari, Christiano, and Kehoe (2007) extend the analysis in Gertler and Gilchrist (1994) to cover 1952:1 through 2000:3. Davis, Haltiwanger, Jarmin, and Miranda (2007) show the COMPUSTAT data are not representative of the economy as a whole.
Note that Moscarini and Postel-Vinay measure the net difference as the difference between large and small firms. In what follows, we use large/mature firms as the base so all of our differentials are for a group minus the large/mature firms. So in our analysis when we find a positive correlation, for example, between the net differential between old/small and large/older businesses with the unemployment rate, this is the same finding from that in Moscarini and Postel-Vinay. However, as will become clear we find the opposite pattern in our state-level analysis in response to state-specific cyclical shocks.
These authors use the U.S. Census Bureau Country Business Patterns data. These data provide geographic information about employment by establishment, not firm, size.
The BDS is built up from establishment-level data so we know the detailed geographic location of economic activity. The firm characteristics are based on the national firm but the state-level activity is for all establishments in that state in the given firm size and firm age group. The BDS is a public use database and can be downloaded from http://www.census.gov/ces/dataproducts/bds/index.html.
For a detailed description of differences between this and other sizing methodologies, see Haltiwanger, Jarmin, and Miranda (2013). We include some analysis below and in the online appendix using firm size groups defined by initial size. Our results are robust to using this alternative.
If the age composition of establishments in the firm change due to M&A this does not change firm age.
This measure of net growth is bounded between (−2,2) and is symmetric around zero. Its desirable properties are discussed extensively in Davis, Haltiwanger, and Schuh (1996).
Note that the level of aggregation “s” that we consider, it is not critical we use the DHS net growth rate at the cell level (for example, the log difference of E st and Est−1 yields very similar growth rates as the DHS net growth rate at this level of aggregation—this is not surprising since the DHS net growth rate is a second-order approximation to the log first difference). The advantage of the DHS net growth rate approach is the establishment entry and exit are readily integrated into the net growth rate measures.
The measurement appendix includes discussion and formulas that show how net and gross job flow rates are calculated for size and age groups.
Real GDP at the quarterly level is available at the national level so we construct annual averages using the retimed data. At the state level, real GDP can be constructed on an annual basis, but not for the properly retimed year. We use state GDP for robustness purposes, but note that it is off by quarter. We therefore also use real personal income at the state level which we can construct for the retimed year. Additional details are in the appendix.
As described in Decker and others (2013), this is associated with a secular decline in the firm entry rate over this period of time. See that paper for more analysis and references to the literature on the secular decline in job flows observed over our sample period.
We also find that the employment shares by firm age and firm size classes are relatively stable at the state-year level which is the focus of much of our analysis.
These first two points echo the findings in Haltiwanger, Jarmin, and Miranda (2013).
In unreported results, we have found that the job creation and job destruction patterns reflect consistent movements in the underlying components of job creation from continuers, job creation from entry, job destruction from continuers, and job destruction from exit. That is, all margins contribute to the patterns.
The online appendix can be found at http://www.palgrave.com/imfer/.
The results in Haltiwanger, Jarmin, and Miranda (2013) and Foster, Haltiwanger, and Syverson (2012) show that the rich dynamics of young businesses extends through the first 10 years following entry. In our analysis, we restrict our attention to very young businesses in order to be able to track young businesses dynamics back to 1981. If we use the definition of young businesses as being 10 years or less then we would have to restrict our analysis to commence in 1987. But it is clear that young businesses so defined contribute very substantially to cyclical dynamics of employment.
Table A.1 of the online appendix presents simple descriptive regressions that show that all groups’ net growth is procyclical, with young/small businesses being especially procyclical.
Moscarini and Postel-Vinay (2012) also note that their result is only robust to considering cyclical indicators based on deviations from trend and not robust to using cyclical indicators of expansions or contractions. We find that when the latter indicators are used, young/small and young/medium businesses are more cyclically responsive than older/large businesses. Moscarini and Postel-Vinay (2012) use initial firm size to classify firms in their analysis. In Table A.2 of the online appendix, we show the results of Table 1 are robust to this alternative so this is not driving differences. Moreover, in Table A.5 of the online appendix we show that the state by year patterns emphasized in our subsequent analysis are robust to using initial firm size to classify firms. We also show in Figure A.2.7 that the impulse responses to state-specific cyclical and housing price shocks are robust to using initial size to classify firms.
One way to emphasize that there is an inherent difference between considering firm size and firm age effects is simply to consider correlations where one focuses on only firm age effects and those where one only focuses on firm size effects. We find that if we use only firm age and consider two age groups where young is <5 and mature is 5+ that the correlation between the change in unemployment rate and the net differential between young and mature is −0.65 (and significant). In contrast, if we only consider firm size with two size groups where small/medium is <500 and large is 500+ (and to be similar to Moscarini and Postel-Vinay use initial size classification), then the correlation between the change in the unemployment rate and the net differential between small/medium and large is −0.26 and not significant. Turning to the indicator used by Moscarini and Postel-Vinay we find that the latter correlation is 0.36 and significant. The latter differs some from the correlation emphasized by Moscarini and Postel-Vinay (recall they have the opposite sign convention and so this is equivalent to a −0.36 correlation with their sign convention). We find that this is associated, at least in part, with the specific time-series sample. That is, if we use the 1981–2009 sample (closer to what Moscarini and Postel-Vinay use), we obtain a correlation between the HP filtered unemployment rate and the net differential between small and large of 0.54 which is very similar to their highlighted correlation. Simply adding/subtracting one year alters this correlation nontrivially.
Even using the HP-filtered unemployment rate, the young-small differential with old-large is smaller in magnitude than the old-small differential with old-large. The implication is that young-small are more cyclically sensitive than old-small even with the HP-filtered unemployment rate.
We also show in Table A.5 that the results in Table 2 are robust to using initial size.
Like the results in Table 2, we also find that older/small businesses are less cyclically sensitive than young/small businesses as the coefficients are substantially smaller in magnitude for the older/small businesses. But we find that even older/small businesses respond more to the state-specific component of this indicator than large/older businesses (although the estimate for the old/small differential is only significantly different from zero at the 10 percent level).
We note that Moscarini and Postel-Vinay (2012) also consider state-level variation. Unlike our analysis, they did not control for state and year effects. We show in Table A4 of the online appendix that the results in Table 2 using the change in the unemployment rate are robust to not controlling for year effects for young/small and young/medium net differentials. However, in Table A4, we find that estimated effect for the old/small differential with old/large turns positive and significant when controlling only for state fixed effects. Moreover, in unreported results, we find that when we don’t control for year effects but do control for state effects and use the HP filtered unemployment rate that we obtain the Moscarini and Postel-Vinay result for old/small net differentials with large/old businesses but don’t find their result for small/young net differentials. Thus, our findings suggest that their results are being driven by old/small businesses relative to old/large and by aggregate variation in their measure and not by state-specific variation in their measure. We also note that in all of these alternative specifications, we always find that young/small businesses are more sensitive to housing price shocks. We find this for the descriptive regressions as well as the panel VAR analysis regardless of the cyclical indicator we use.
We thank Inessa Love for her STATA code (pvar.ado) to implement a panel VAR procedure in STATA. We have modified the code for our application (code available upon request). Consistent with Love and Zicchino (2006) (building on the insights of Arellano and Bover, 1995) we use the Helmert transformation to control for state fixed effects. This forward differencing procedure overcomes the problem that fixed effects and lagged dependent variables are inherently correlated.
The results using the net employment growth rate are in Figures A.2.1–A.2.3, for the HP filtered unemployment rate in Figures A.2.4–A.2.5, for real GDP growth in A.2.14–A.2.16 and for real Personal Income in A.2.17–A.2.19.
Their approach to identification is to instrument the local leverage ratio with the housing supply elasticity from Saiz (2010). Our approach is to use the panel VAR with the Cholesky decomposition to identify a housing price shock that is orthogonal to general business conditions in the state.
As is typical in the identification of idiosyncratic shocks, it is difficult to distinguish between a true idiosyncratic shock and an idiosyncratic response to a common shock.
We also note that examination of the impulse response functions with respect to these net differential shocks shows only modest dynamic impact on the change in unemployment and housing price growth.
All figures include 95 percent confidence bands.
We show in Online Appendix Figure A.2.12 that we obtain our main results if we focus only on firm age (ignoring firm size) so that we focus on the net differential between young and mature. In Appendix Figure A.2.13, we show that if we instead had focused on firm size only (ignoring firm age) we would obtain substantially mitigated effects of both the local cyclical shock and the local housing price shock. These results are a way of emphasizing that the critical factor for obtaining our results is to distinguish across firms by firm age and not firm size. A simple way of thinking about this and consistent with the results throughout the paper is that young firms are small and medium size (essentially no young/large firms) whereas small firms are both young and mature. The results throughout the paper show that old/small firms behave quite differently than young/small and young/medium firms.
An additional calculation of interest for the upper bound estimates is the overall fraction of the decline in the net differential for young/small that can be accounted for housing prices in the 2007–09 period. Weighting the states by employment, the average overall contribution is 60 percent. Note that this calculation is not applicable for the baseline estimates with year effects since by construction with year effects the average overall effect in any given year from state-specific variation in housing prices is zero. That is, in the baseline specification we are focusing on identifying and accounting for state-specific variation in the net differentials. We focus on the latter in Table 4 and in the accompanying discussion.
It would be of interest to highlight the difference in the role of housing prices in the 2007–09 recession relative to the 1981–83 recession. Our sample period is from 1981–2010 and we exploit variation from the 1981–83 period in our estimation but with a panel VAR with two years of lags our first period of predicted values is in 1983. Note that we start our sample in 1981 given that the LBD starts in 1976 and our focus on identifying the contribution of young businesses. Given left censoring in firm age, we can consistently measure the contribution of young firms less than five years old and five years or more from 1981.
Analogous to the concerns expressed for the analysis of job creation and job destruction, one concern in comparing results across specifications that differ by sector is that the identified state cyclical shocks and housing price shocks and their respective dynamics vary across specifications. In practice, each of these sectoral specifications yields very similar state-specific cyclical shocks and state-specific housing price growth shocks.
In unreported results, we have explored the net responses of all groups rather than the net differential responses to cyclical and housing price shocks. We find that all firm size/age groups in all sectors experience a decline in net employment growth in response to an increase in the state-specific unemployment rate. Consistent with our findings, we find that the magnitude of the response is largest for the young/small firms. The point is that the net differential responses are associated with all firm size and age groups experiencing a decline in local cyclical downturns but young/small experiencing the larger and that this pattern holds for all sectors. In response to housing price shocks, similar remarks apply but with the largest magnitude being for the young/small in the Construction, Retail Trade, FIRE and Service sectors.
Adelino, Schoar, and Severino (2013) similarly find smaller effects in these sectors and suggest this is consistent with a financial transmission channel for home equity.
Pushing on this point further, our housing price shocks are orthogonal to the local cyclical shock. If the latter captures changes in local aggregate demand, then the variation in housing prices we exploit is orthogonal to local demand effects. We note that, in this regard, our results are robust to using a variety of indicators of local cyclical conditions including real GDP growth and real Personal Income growth.
This measure of net growth is bounded between (−2,2) and is symmetric around zero. Its desirable properties are discussed extensively in Davis, Haltiwanger, and Schuh (1996).
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Additional information
Supplementary Information accompanies the paper on the IMF Economic Review website (www.palgrave-journals.com/imfer)
*Teresa Fort is an Assistant Professor at the Tuck School at Dartmouth College. John Haltiwanger i4s a Distinguished Unirsity Professor at the Universi5y of Maryland. Ron Jar5in is Assistant Director for Res6arch and Methodology at the U.S. Census Bureau. Javier Miranda is a Principal Economist at the U.S. Census Bureau. The authors thank participants at seminars at CES, Harvard, and INSEAD, FRB, Bank of Spain, attendees at the IMF ARC Conference and the NBER Entrepreneurship Workshop, Pierre-Olivier Gourinchas, Roberto Fattal Jaef, Ayhan Kose, Giuseppe Moscarini, Johannes Schmeider, Robert Strom, and two anonymous referees for helpful comments, and the Kauffman Foundation for financial support. Any opinions and conclusions expressed herein are those of the authors and do not necessarily represent the views of the U.S. Census Bureau. All results have been reviewed to ensure that no confidential information is disclosed. The authors thank Ryan Decker for his assistance in developing the STATA code used in this paper as well as Inessa Love for the original version of PVAR.
Electronic supplementary material
Measurement and Data Appendix
Measurement and Data Appendix
Measurement of Net and Gross Job Flows
Our measures are taken from the Business Dynamic Statistics (BDS). The net job creation and gross job flow measures are based on the methodology of Davis, Haltiwanger, and Schuh (1996). In the BDS, establishments are classified by their parent firm’s size and firm age. This is based on the parent firm for the establishment. Firm size is available using both current average size (the average size of the parent firm in the prior and current year) and initial size (the size of the parent firm in the prior year). Firm age is based on the age of the oldest establishment when a new firm is started and then ages naturally thereafter. It is based on the parent firm in the current year. As noted in the text we collapse the available firm size and firm age categories into broad firm size and firm age categories. For any given cell “s” defined by a firm size and firm age category is equal to:
where E st is employment for cell “s” in period t, X st =0.5 × (E st +Est−1).Footnote 39. In measuring and defining Est−1, it is critical to emphasize that this is the employment in period t−1 of the establishments that are in cell “s” in period t. That is, this is based on the same set of establishments in period t−1 and t (and this is not subject to the “size distribution fallacy” discussed in Davis, Haltiwanger, and Schuh (1996) wherein misleading inferences can be generated by considering cell-based totals of establishments classified by firm size (or firm age) across years as establishments can change firm size and firm age classifications). Another way of making this point is to note that the growth rate for the cell can be equivalently generated by:
The net growth rate for the cell can be decomposed into the contribution of job creation and destruction as follows. Define job creation and job destruction for the cell as:
By construction, net employment growth for the cell can be decomposed into:
Note that the cells for young firms include establishments of new firms (firm age=0). All such establishments have DHS net growth rates at the establishment level equal to 2. For the young firm cell, when there is a decrease in the share of young firm employment accounted for by new firms, the cell-based growth rate will decline. But the net growth rate for the young firm cells will also reflect the job creation of firms older than firm age=0 as well as the job destruction of firms older than firm age=0.
Cyclical Variable Construction
Unemployment rate: The national unemployment rate is based on quarterly data from the Bureau of Labor Statistic’s (BLS) Current Population Survey for 1979–2010. The state-level unemployment data are also quarterly and come from the BLS regional and state-level data releases available on FRED. We construct yearly data for the regression analysis by averaging the unadjusted, quarterly data over the retimed year. We calculate the yearly change as: δU t =U t −Ut−1, where t represents the re-timed year. We also HP filter the unemployment as an alternative measure that captures deviations from the long-term trend.
Real GDP and Real Personal Income: Quarterly Real GDP at the national level is readily available from the BEA (Real GDP is nominal GDP deflated by the GDP implicit price deflator). We take time averages for the retimed year and compute log first differences. At the state level, nominal GDP is available on an annual basis but not for the retimed year. As the re-timed year is only off by a quarter, we use this in our analysis with appropriate caution. We deflate the state-level nominal GDP with the national implicit price deflator and then compute growth rates with log first differences. At the state level, a related alternative measure is available quarterly—personal income. The latter is income from all sources available to households. We deflate the latter on a quarterly basis with the national implicit price deflator, take averages for the retimed year, and then compute growth rates with log first differences.
Housing Prices: The housing price measure is based on the FHFA House Price Index. The HPI is a weighted, repeat-sales index. It measures the average price changes in repeat sales or refinancings on the same properties. The information for the HPI is obtained from repeat mortgage transactions on single-family properties whose mortgages have been purchased or securitized by Fannie Mae or Freddie Mac since January 1975.
We use unadjusted HPI data that are quarterly, by state. We divide the HPI by the BLS Urban Consumer Price Index for all items so that the data are in real terms. We then average the quarterly index over the retimed year and calculate the log first difference in home prices.
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Fort, T., Haltiwanger, J., Jarmin, R. et al. How Firms Respond to Business Cycles: The Role of Firm Age and Firm Size. IMF Econ Rev 61, 520–559 (2013). https://doi.org/10.1057/imfer.2013.15
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DOI: https://doi.org/10.1057/imfer.2013.15