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«Manuel Adelino, Duke University Antoinette Schoar, MIT and NBER Felipe Severino, MIT December, 2012 Abstract This paper explores the role of the ...»

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The breakdown at the industry level allows us to address an important alternative hypothesis to the mechanism we identify, namely that higher house prices caused increased demand which then prompted the growth in new businesses. This type of demand story (as opposed to the collateral channel) comes in two versions: On the one hand one could argue that rising house prices lead to an increase in demand since households feel richer or have access to home equity. This channel is proposed in Mian and Sufi (2011a) to explain the drop in employment during the Great Recession of 2007-2009. A second version of the demand hypothesis is that increasing house prices may benefit certain industries more than others and that these industries happen to be made up of smaller establishments on average (i.e., a “composition” effect).

We address these alternative demand hypotheses in a few different ways. First, by holding constant industry fixed effects we identify how employment in the smallest establishments reacts differently from that of large establishments within each 4-digit NAICS industry. This addresses the composition effect described above. Second, as we have argued before, a pure local demand story should affect establishments of all sizes similarly while the credit collateral channel is relevant mainly for small business. There is, however, still the possibility that smaller firms are more sensitive to local demand shocks than large firms. In order to see if this effect could explain our results we exclude the most obvious candidate industries that might directly benefit from local demand shocks due to higher house prices, namely those linked to construction and firms in the non-tradable sector as classified in Mian and Sufi (2011a) and we also repeat our tests only for manufacturing firms, those that should be least affected by local demand shocks.

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3.1. House Prices and Employment at Small Establishments Out central hypothesis is that the availability of more valuable collateral (in our case through increased real estate prices) in the period before the financial crisis has an effect on the creation of small firms or on self-employment, since it provided individuals with easier access to startup capital.

As a result we should see a sharper increase in self-employment and employment in small businesses in areas that had steeper house price appreciation. We also expect this effect to be concentrated in firms in the smaller size categories, since large firms cannot finance themselves using home equity.

This hypothesis is tested in Table 2, where we run two-stage least squares regressions of the growth in employment between 2002 and 2007 on 5 different establishment size categories, and their interaction with house price growth in the same period. The instrument for house price growth, as we discuss above, is the Saiz (2010) measure of housing supply elasticity. In the first column of Table 2 we show the first stage regression of house price growth on the Saiz measure of housing supply elasticity to confirm the validity of the instrument. The coefficient of -0.09 means that a one standard deviation increase in elasticity of housing supply is associated with an 11.7 percentage point lower growth in prices (for an average house price growth of 33.9 percent). The F statistic on this regression is 14.5 (above the conventional threshold of 10 for evaluating weak instruments). This reflects that metropolitan statistical areas with higher elasticity of supply experienced significantly lower house price growth between 2002 and 2007, in line with previous literature. In column (2) we run a regression of employment change between 2002 and 2007 on the change in house prices during the same time period. In this regression we do not instrument the change in house prices in order to show the raw correlation between house prices and employment. The effect is positive and economically large. A one standard deviation increase in house prices is associated with an increase in total employment of 3.95 percent over this period, for an average growth in employment of 10.6 percent. In the simple weighted least squares regression we see no distinction between the effect of house prices on small and large establishments. This result highlights the need for an instrument for our dependent variable of interest given the numerous factors that are likely to drive both employment creation and house prices (income growth, investment opportunities, etc.).

12 In column (3) of Table 2 we repeat the same regression but instrument the change in house prices with the Saiz measure for the elasticity of housing supply. We see that there is a positive but not significant causal relationship between county level employment change and house price growth on average, in contrast to the results in the previous column. However, when we look at the differential effect of instrumented house price changes, the increase in house prices has a significant and large positive effect on the small establishments but no significant effect on employment growth for big establishments (more than 50 employees). In fact, the coefficient on the interaction term between house price growth and the 1-4 employee size category shows that a 1 percentage point increase in house prices translates into a 0.19 percentage point increase in employment at these establishments relative to the largest ones. This translates into an increase in employment of 5.3 percentage points for a one standard deviation change in house prices, for an average change in employment at the smallest establishments of 9.4 percent (magnitudes are shown in the appendix Table A3).

Furthermore, the effect of collateral is monotonically decreasing with the size of the firm. For firms with more than 10 employees the effect is indistinguishable from that of the very largest firms. This is consistent with the collateral channel of house price appreciation being an important mechanism for small firm creation, since the amount of collateral that is provided by real estate appreciation is not be enough to start a larger firm. Also, these results suggest that the causal impact of house prices on employment growth during 2002 to 2007 did not work through increased demand, since in that case firms of all sizes (including the very large) should have been affected.

One concern with the above specification could be that the house prices change in areas with low Saiz housing elasticity induces a local demand shock that especially affects certain industries. If those industries are also, on average, disproportionately made up of smaller establishments, the result above might reflect a composition effect, rather than the collateral channel as we suggest. While it would need a number of factors to line up in a very specific way, we cannot rule it out on face value with the specifications in Table 2. In order to eliminate the alternative hypothesis about industry composition, we now use our more disaggregated data, which provides data at the county, 4-digit NAICS and establishment size level. This allows us to hold industry fixed effects constant and test whether, conditional on an industry, the growth of small establishments is significantly stronger than that of large establishments in counties where house prices grew more. Intuitively, this specification asks whether within an industry the fraction of employment generated by small firms grows more quickly than that of large firms. This way we can confirm that the results are not a consequence of 13 changing industry composition. The results for this specification are shown in column 4 of Table 2.

Parallel to before, we find that impact of house price changes (instrumented with the Saiz measure) is stronger for establishments with 1-4 employees when compared to the bigger firm categories. We again find that the effect is monotonically decreasing and not statistically significant beyond firms with 10 employees.

In order to confirm that the effect we estimated runs through the collateral channel, we test whether our estimated effect is stronger in industries that have lower start-up capital needs. We expect this to be the case given that the median total amount of home debt at its peak in 2006 for all US households was approximately 117 thousand dollars (Mian and Sufi, 2011b) and that only a fraction of this amount would be available for use in starting a business. Also, Adelino, Schoar and Severino (2012) show that the average value of a single family home during this period is approximately 309 thousand dollars and that most families obtain an 80 percent LTV loan. Even accounting for the fact that most entrepreneurs are over 35 years old, and that almost half are over 45, and so we expect them to have built home equity relative to the initial 80 percent LTV, it is not plausible to finance a very large amount of capital using home equity as collateral.

We split our sample of industries at the median amount of capital needed to start a firm to explore this source of variation. As we describe in Section 2, we obtain this information from the Census Survey of Business Owner Public Microdata Survey by selecting the sample of new firms in each industry and averaging the amount of capital needed to start those firms.

We show the results split by the amount of start-up capital needed in each industry in columns 5 through 8 of Table 2. The results show that the effect of collateral on employment growth in small establishments is stronger for industries where the amount of capital needed to start a firm is lower (the average amount of start-up capital for industries below the median is approximately 132 thousand dollars). In fact, for this subset of industries the effect is statistically significantly different from that of the largest group even for establishments up to 49 employees, i.e. the causal effect of house prices extends to establishments other than the very smallest. When we include industry fixed effects only the coefficient on the smallest establishments is statistically different from zero. For the group of industries that require more start-up capital the effect of house prices on employment is smaller and only statistically significant for the very smallest group both with and without fixed effects. These results confirm that job creation at small businesses in response to house prices 14 changes is strongest in industries with low startup capital needs that can reasonably be financed through loans on home equity.

In addition, we also document that our results are not driven by certain industries, in particular not by construction or non-tradable industries. One might be concerned that the increase in house prices led to an increase in demand for construction services or for local services (e.g. local retail or restaurants) and thus new firms got started in these industries because that (e.g. more remodeling and new housing construction, more dry-cleaners, etc.). This would be a consequence of increased demand rather than an effect through the collateral channel. We re-run our main specifications excluding all industries linked to either construction or the non-tradable industries as classified by Mian and Sufi (2011a) and the direction and magnitude of the effects are virtually unchanged. We also run our regressions only for the manufacturing sector given that these are the industries that should be least affected by local demand. We report these results in Table 3. This confirms that a simple demand side story is not driving our results and thus confirms the importance of the collateral channel for the creation of smaller establishments in the period between 2002 and 2007.

3.2. Births and Deaths of Establishments

Our measure of growth of establishments by size category does not allow us to directly observe the creation and destruction of establishments, as all we can measure is the change in the number of establishments in each category as of March of each year. In a separate set of regressions shown in Table 4 we use the Statistics of US Businesses from the Census to look at births and deaths of establishments at the 2-digit NAICS industry level. The disadvantage of this dataset is that it does not include the breakdown of establishments by their employment size, but it does help us to check that our result holds when we consider births of all establishments. Given that an overwhelming percentage of new businesses are very small businesses (Haltiwanger, Jarmin and Miranda, 2011;

Robb and Robinson, 2012), this robustness test directly speaks to the validity of our main results.

We find that births of establishments are very strongly affected by increasing house prices instrumented with the elasticity of housing supply. The result holds when we consider the net creation of establishments (i.e. births minus deaths) and the coefficient is unchanged when we include 2 digit NAICS fixed effects (which is the finest industry category available in this dataset at a county level). A one standard deviation increase in house prices is associated with a nine percentage point increase in the number of births of establishments (between 2002 and 2007) as a percentage of 15 the number of establishments as of 2002 (or about ten percent of the average cumulative number of births of establishments as a percentage of total 2002 establishments). The effect is stronger for industries with below median capital needs, although that difference disappears when we include NAICS fixed effects.

3.3. Sole Proprietorships

We now expand our analysis to include the creation of businesses without employees, also called sole proprietorships or nonemployer businesses. Table 5 shows the effect of house price growth on net creation of proprietorships relative to all the establishment categories that we have in the previous tables using the Saiz measure to instrument for exogenous movements in house price changes. The first column in this table uses employment data on sole proprietorships from the Bureau of Economic Analysis, while the last three columns rely on data on nonemployer establishments from the Census (which includes information on the 2 digit NAICS sector in which the establishment operates). The coefficient on house price growth in Column (1) interacted with the sole proprietorship category is significantly different from that on the largest establishments and close in magnitude to that on the 1-4 employee category. In Column (2) we use data from the Census and find a smaller coefficient on the sole proprietorships and we cannot distinguish that coefficient from the others in the regression.

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