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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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For example, in order to get the total employment of 1-4 employee establishments in a given industry, county and year, we multiply the number of establishments by 2.5.

5 period of 2002 to 2007. This measure is available for 269 metropolitan statistical areas that we match to a total of 776 counties using the correspondence between MSAs and counties for the year 1999 provided by the Census Bureau. 5 Although employment growth and our other controls are available for a much larger sample of counties, all our regressions focus on the subset of counties for which we have the housing supply elasticity measure.

An important measure for our analysis is the amount of capital needed to start a firm, since these investment requirements might affect how much a given industry depends on the housing collateral channel. In order to construct this variable we use the Survey of Business Owners (SBO) Public Use Microdata Sample (PUMS). The SBO PUMS was created using responses from the 2007 SBO and provides access to survey data at a more detailed level than that of the previously published SBO results. The SBO PUMS is designed to study entrepreneurial activity by surveying a random sample of businesses selected from a list of all firms operating during 2007 with receipts of $1,000 or more provided by the IRS. The survey provides business characteristics such as firm size, employer-paid benefits, minority- and women-ownership, access to capital and firm age. For the purposes of this paper we focus on the “Amount of start-up or acquisition capital” for each firm and we group the answers to this question at the 2-digit NAICS industry level (the finest level available in the data) for firms established in 2007. The classification is virtually identical if we use all years in the data or if we focus on 1-4 employee firms only. The median amount of capital needed to start a business in the data is 215 thousand dollars. We follow Hurst and Lusardi (2004) and split industries above and below the median to measure the differential effect of the collateral channel on business creation for industries in the two groups. The average amount of capital needed by firms below the median is 132 thousand dollars, whereas the average amount needed for industries above the median is 260 thousand dollars.

Our classification of “non-tradable”, “tradable” and “construction” industries at the 4-digict NAICS level is obtained from Table 2 of the Appendix to Mian and Sufi (2011a). 6 Non-tradable codes are mostly included in the 44 and 45 sectors (Retail Trade), as well as under 72 (Accommodation and food services). Construction industries include most codes under the Construction 2-digit NAICS This correspondence is available at http://www.census.gov/population/estimates/metro-city/a99mfips.txt and also 5 http://www.census.gov/population/estimates/metro-city/a99nfips.txt for the New England Metropolitan Component Areas used by Saiz (2010).

6 The current version of the online appendix can be found here: http://faculty.chicagobooth.edu/amir.sufi/data-and

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appendices/unemployment_miansufi_EMTRAR2_APPENDIX .pdf

6 sector (23), as well as some subsectors in manufacturing, retail trade and services that are directly connected to construction (e.g., 3273 – Cement and Concrete Products Manufacturing).

Manufacturing industries include all 31-33 subsectors (Manufacturing), and in some specifications we restrict the sample to manufacturing industries that are also classified as “tradable” in Mian and Sufi (2011a) (i.e. those not in construction or in “other industries”).

We also use data on county-level births and deaths of establishments for each 2-digit NAICS industry between 2002 and 2010 from the Census Statistics of US Businesses (SUSB). Data on births and deaths of establishments is provided under the “Employment Change” section of SUSB and it does not include a breakdown by establishment size at the county and industry level, which is why we cannot use it as our main dataset. However, given that most establishment births are of a very small scale (Haltiwanger, Jarmin and Miranda, 2011), we view the regressions performed on this dataset as an important test of the mechanism in our main results. We compute the cumulative number of births and deaths between 2002 and 2007 for each county and industry as our dependent variable of interest and scale this number by the total number of establishments as of 2002 in the same county-industry cell.

The net creation of sole proprietorships at a county level is obtained from two sources. We use both the yearly local area personal income and employment data from the Bureau of Economic Analysis (BEA), as well as the Census nonemployer statistics. From the BEA we use Non-Farm Proprietorship employment at a county level between 2002 and 2007 to estimate the growth of sole proprietorships in this period. From the Census we obtain the number of establishments for the period of 2002 to 2007 at the 2-digit NAICS level. We use both sources of data in the regressions to ensure the robustness of our results.

Unemployment and unemployment rate at the county level are obtained using the Bureau of Labor Statistics Local Area estimates. Local Area Unemployment Statistics (LAUS) are available for approximately 7,300 areas that range from Census regions and divisions to counties and county equivalent and this data is available between 1976 and 2012. We match the county equivalent data to the CBP data using Federal Information Processing Standard (FIPS) county unique identifiers.





The migrations data is extracted from the IRS county to county migration data series. The migration estimates are based on year-to-year address changes reported on individual income tax returns filed with the IRS. The dataset presents migration patterns by county for the entire United States and is 7 split by inflows – the number of new residents who moved to a county and where they migrated from – and outflows – the number of residents leaving a county and where they went. 7 We also compute net flows as inflows minus outflows and we scale all figures by the number of non-movers in the county. The data is available from 1991 through 2009 filling years.

To better identify the effect of house prices on self-employment we include a set of controls that capture some of the cross-sectional differences across counties. We use county level information from the Census Bureau Summary Files for 2000 on: the number of households in a county; the natural logarithm of county-level population; the percentage of college educated individuals defined as the number of people over 25 with a bachelor degree or higher as a proportion of the total population over 25 years old; the percentage of employed people, defined as the employed population over the total population 16 years old or older; the share of the population in the workforce, defined as the total population in the civilian labor force over 16 year old divided by the total population 16 years old or older; and the percentage of owner occupied houses.

2.2. Summary Statistics

Panel A of Table 1 provides descriptive statistics for our data set: The first row shows total employment in 2002 for all counties in our sample, as well as the employment growth between 2002 and 2007 estimated from the CBP data. Our data includes a total of 775 counties with non-missing total employment data. Employment in all counties in our data grew by an average of 10.6 percent during the sample period, with the unemployment rate dropping by 0.9 percentage points. We also split the sample into counties above and below the median of the housing supply elasticity measure.

We see that counties with low supply elasticity are larger but have similar unemployment rates as those with high supply elasticity. The growth in total employment is somewhat lower in counties with high supply elasticity, and we discuss this fact in more detail when we discuss the regressions involving total employment and unemployment at the county level. As expected, counties with low elasticity of housing supply experienced much stronger growth in house prices than did counties with high elasticity of supply, and similarly experienced a much larger increase in average debt-toincome ratio (consistent with Mian and Sufi, 2011a).

The data used to produce migration data products come from individual income tax returns filed prior to late 7 September of each calendar year and represent between 95 and 98 percent of total annual filings.

8 Panel B of Table 1 shows how employment is distributed across the different employment-size categories. The biggest firm category, 50 employees or more, accounts for 51.7% of the total employment in 2002, whereas the smallest category, 1-4 employees, accounts for 8.9% of the total employed population. Growth in employment is stronger among larger companies in the 2002-2007 period, and especially so among the industries that we classify as having low start-up capital needs.

2.3. Empirical Model

This paper aims to test whether increases in real estate prices affect the growth in employment by facilitating the creation of small businesses (collateral channel). To differentiate the collateral channel from a pure (expansionary) demand shock, we look at the differential effect of house prices on the net creation of establishments in different size categories. 8 Our identification relies on the idea that improved availability of collateral in the form of higher house prices can positively affect the creation of small businesses, while it is likely to have no effect on the creation of larger establishments since these firms cannot be started with capital that can be extracted from a house.

We measure the availability of collateral to small business entrepreneurs by the growth in house prices in the area where the establishment is located. However, it is challenging to establish a causal link from the availability of collateral to the creation of small businesses, since there are many omitted variables that could simultaneously affect both the value of real estate collateral and the demand faced by small businesses, for example changes in household income in the area or improvements in investment opportunities. In order to overcome this difficulty, we instrument for the changes in house prices during the period of interest for our study (2002-2007) using the elasticity of housing supply at the metropolitan statistical area, which was developed by Saiz (2010).

Our identification relies on the assumption that the elasticity of housing supply only impacts employment creation at establishments of different sizes through its effect on house prices. The exclusion restriction will be violated if housing supply elasticity is correlated with employment or business creation for reasons other than house price growth. Similar approaches have been used As we discuss in the data section, our data does not include changes in employment within establishments (i.e. along 8 the intensive margin), so our measure of changes in employment relies on multiplying the number of establishments in each size category by the midpoint of the number of employees in each bin. It is thus equivalent to interpret our results in terms of number of employees or number of establishments.

9 extensively in the recent literature – see, for example, Mian and Sufi (2011a, 2011b), Charles, Hurst and Notowidigdo (2012); Robb and Robinson (2012).

We rely on two basic regression specifications for our analysis. The first specification aggregates data up to the level at which our instrument varies, i.e. at the county-year-establishment size– level. Each individual observation is the change between 2002 and 2007 of employees in a given county, year and establishment size. Therefore we add up the number of employees in all industries in each establishment category and take the growth in total number of employees as the dependent variable.

We then run two-stage least squares regressions of the type:

–  –  –

We index counties by j and establishment size categories by i. Δ02−07 , is the change Δ02−07 is the growth in house prices at the county level for the same time period where, as we in employment for establishment size category i in county j between 2002 and 2007. Similarly,

–  –  –

Saiz (2010). 1 is a set of dummy variables for each of the four included establishment categories discuss above, we instrument for the growth in house prices using the housing supply elasticity of establishment size dummies and the growth in house prices, and 3 is the coefficient of interest in (we omit the largest category of more than 50 employees). We then also include the product of the our regressions. In particular, the test we are interested in is whether the coefficient for the smallest house prices had a stronger impact on the creation of small establishments. is a set of county establishments is larger (and positive) than those of the larger categories, which would confirm that level controls that include the size of the county, the percentage of the population with a bachelor degree or higher, the percentage of the population that is employed, the percentage of the population in the labor force, and the percentage of owner occupied houses. Standard errors in this specification are heteroskedasticity robust and clustered at the MSA level (given that the variation in the instrument we use is at this level as well) and all regressions are weighted by the number of households in a county as of 2000 as in Mian and Sufi (2011a).

The second specification disaggregates observations to the county, year, establishment size and 4digit NAICS level, yielding a much larger number of observations than the specification above (as each county now appears multiple times for each industry). When using this disaggregated data we can include industry fixed effects in the regression, which allows us to control even further for 10 common shocks (namely nationwide demand shocks) to each 4-digit industry. The coefficients in this case represent the differential impact that house prices have on establishments of different sizes

within each industry. The specification becomes:

–  –  –

Where z indexes the industries and 1 is a set of indicator variables for each industry.



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