Internet Appendix for Collateral Shocks and Corporate Employment
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1 Internet Appendix for Collateral Shocks and Corporate Employment Nuri Ersahin Rustom M. Irani University of Illinois at Urbana-Champaign March 1, 2018
2 Appendix IA.I: First-stage for IV estimation This table presents estimates of the impact of land supply elasticity on real estate prices. The unit of observation in each regression is an MSA-year pair. The dependent variable is the real estate price index (single-family home, residential) defined at the MSA-year level. The MSA-level land supply elasticity is interacted with the interest rate on a 30-year, fixed-rate conventional home mortgage. Column [1] uses the land supply elasticity directly. Column [2] uses the quartiles of the land supply elasticity. Each regression is estimated using OLS and controls for year and MSA fixed e ects. All variables are defined in Appendix A. Standard errors (in parentheses) are clustered at the MSA level. Dependent variable: RE Price Index Elasticity Mortgage Rate (0.004) [1] [2] Elasticity (First Quartile) Mortgage Rate (0.007) Elasticity (Second Quartile) Mortgage Rate (0.008) Elasticity (Third Quartile) Mortgage Rate (0.007) Year fixed e ects Y Y MSA fixed e ects Y Y N 1,358 1,358 R
3 Appendix IA.II: Real estate ownership and firm-level characteristics Panel A presents estimates of the firm-level determinants of the real estate ownership decision in The unit of observation in each regression is a firm. Column [1] uses RE Owner as the dependent variable, which is an indicator variable equal to one if the firm reports any real estate holdings on its balance sheet in Column [2] uses the market value of real estate assets in 1993 as the dependent variable. Each regression is estimated using OLS and includes for firm characteristics (five quintiles of Return on Assets, Total Assets, Age) and industry and MSA fixed e ects. All variables are defined in Appendix A. Robust standard errors are shown in parentheses. Panel B provides summary statistics for renters and owners for the Compustat universe from 1993 until The unit of observation is a firm-year. Renters (owners) report zero (positive) real estate ownership in a given year. Panel A: Determinants of real estate ownership Dependent variable: RE Owner RE Value [1] [2] Return on Assets (Second Quintile) (0.026) (0.067) Return on Assets (Third Quintile) (0.027) (0.070) Return on Assets (Fourth Quintile) (0.027) (0.069) Return on Assets (Fifth Quintile) (0.026) (0.067) Total Assets (Second Quintile) (0.026) (0.066) Total Assets (Third Quintile) (0.026) (0.068) Total Assets (Fourth Quintile) (0.028) (0.073) Total Assets (Fifth Quintile) (0.031) (0.081) Age (Second Quintile) (0.026) (0.068) Age (Third Quintile) (0.02) (0.066) Age (Fourth Quintile) (0.025) (0.064) Age (Fifth Quintile) (0.027) (0.071) Industry fixed e ects Y Y MSA fixed e ects Y Y N 2,474 2,474 R
4 Panel B: Summary statistics for renters and owners Renters Owners N Mean Std. N Mean Std. [1] [2] [3] [4] [5] [6] RE Value 8, , Return on Assets 8, , Cash Flow 8, , q 8, , Total Assets 8, ,281 2,554 12,163 Age 9, ,
5 Appendix IA.III: Further summary statistics for Compustat-LBD sample This table provides sample summary statistics for the Compustat-LBD matched sample separately for single- and multi-unit firms. The unit of observation is an establishment-year. All variables are defined in Appendix A. Compustat-LBD Single-unit Multi-unit Rounded N Mean Std. Rounded N Mean Std. Rounded N Mean Std. [1] [2] [3] [4] [5] [6] [7] [8] [9] Panel A: Firm-level Employment Expenditures 13, , , Number of Employees 13, , , Number of Employees (Alt.) 13, , , Average Wage 13, , , RE Value 13, , , RE Value (Employment-Weighted) 13, , , RE Value (Employment-Maximum) 13, , , RE Value (HQ Owner) 9, , , RE Owner 13, , , Return on Assets 13, , , Cash Flow 13, , , q 13, , , Total Assets 13,000 1,512 5,911 2, ,000 1,786 6,402 Age 13, , , Panel B: Establishment-level Employment Expenditures 912, , , Number of Employees 912, , , Age 912, , ,
6 Appendix IA.IV: Collateral shocks and firm-level investment This table presents estimates of the firm-level impact of real estate collateral value on corporate investment. The unit of observation in each regression is a firm-year pair. The dependent variable is investment defined as capital expenditure divided by the lagged book value of plants, properties, and equipment (PPE). The main independent variable is the market value of real estate assets scaled by lagged PPE, which is calculated assuming assets are located in the same MSA as firms headquarters (see Section 1.2). Columns [1] to [3] show the results of the OLS estimation. Column [4] instruments for the market value of real estate using the triple-interaction of the local land supply elasticity, the nationwide mortgage interest rate, and the market value of real estate holdings in Each regression controls for firm fixed e ects, as well as initial firm characteristics (five quintiles of Return on Assets, Total Assets, Age, and two-digit SIC industry dummies) interacted with either MSA-level real estate prices or MSA-year fixed e ects. All variables are defined in Appendix A. Standard errors (in parentheses) are clustered at the MSA-year level. ***, **, and * denote 1, 5, and 10 percent statistical significance, respectively. Dependent variable: Investment [1] [2] [3] [4] RE Value 0.064*** 0.051*** 0.059*** 0.060*** (0.005) (0.005) (0.006) (0.007) Cash Flow 0.030*** 0.026*** 0.027*** (0.003) (0.004) (0.004) q 0.068*** 0.064*** 0.066*** (0.003) (0.004) (0.004) Firm fixed e ects Y Y Y Y Year fixed e ects Y Y N N RE price index Y Y N N RE price index init. controls Y Y N N MSA year fixed e ects N N Y Y MSA year fixed e ects init. controls N N Y Y N 25,479 23,153 17,067 13,000 R
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