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The effect of home owning on business development micro-level evidence bingbing wang-slides

This paper evaluates the effect of home-owning on business. There are both positive and negative spillovers from home-owning. The positive spillover is that owners are more stable and they build better amenities, which attracts business. The negative one arises through the NIMBY effect. Both impacts vary with the distance between residence and business, neighborhood income levels, and business types. The aim of this paper is to firstly identify the distance at which the positive effect exceeds the negative one or vice versa. Secondly, I want to find whether the net impact differs for higher or lower residential income groups. Finally, I investigate the impacts for different industries. I employ a K-means clustering method to study the spatial effect with distance by clustering the business first and then drawing donut rings of residents around business clusters. The major endogeneity concern might be the reverse causation. I incorporate multiple identification strategies to cross check the results: fixed effects (FE), first difference (FD) and Instrumental Variable methods (IV). Using American Community Survey (ACS) and Longitudinal Employer-Household Dynamics (LEHD) Work Area Characteristics (WAC) panel data from 2009 to 2014, I conclude that home-owning only decreases the job counts of adjacent distances of within .3 miles and benefits the business in 3-5 miles. Negative impacts are only identified in higher income groups while the lower income groups benefit business development. Service industries like Retail, Art and Professional Services are welcomed in higher income groups while Manufacturing, Real Estate and Car Rental and Leasing are welcomed in lower income groups.

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The effect of home owning on business development micro-level evidence bingbing wang-slides

  1. 1. 1/52 The Effect of Home-owning on Business Development: Micro-level Evidence Bingbing Wang Lusk Center of Real Estate, University of Southern California 01/04/2018 Bingbing Wang The Effect of Home-owning on Business Development: Micro
  2. 2. 2/52 Introduction NIMBYism Protesting Cases Spring Valley residents in Las Vegas vs. the expansion of an existing asphalt mixing plant; Local residents vs. shopping centers in Lake Brandt North Carolina for natural beauty preservation; New York upper east side residents vs. subway entrances. A material impact from home-owning on business? Bingbing Wang The Effect of Home-owning on Business Development: Micro
  3. 3. 3/52 Introduction: background Home-owning impact on unemployment Blanchflower and Oswald (2013); Oswald (1996 and 1997); the aggregate and individual level studies (Goss and Phillips 1997; Green, 2001; Genesove and Mayer, 2001; Chan, 2001; Engelhardt, 2001; Coulson and Fisher, 2001 and 2009; Munch, 2006 and 2007; Mumford, 2013; Valletta, 2013); Mechanism: transaction cost, lock-in effect, job mismatch, crowding out effect on renters and NIMBY effect. Bingbing Wang The Effect of Home-owning on Business Development: Micro
  4. 4. 4/52 Introduction: micro-level effect of home-owning Home-owning negative spillovers on neighboring business: NIMBYism (Oswald, 2013) Home-owning positive spillovers on neighboring business: 1. Stable employees (Coulson and Fisher, 2002); 2. Better amenities (DiPasquale and Glaeser, 1998; Rohe, Zandt and McCarthy, 2002; Dietz and Haurin, 2003); 3. YIMBYism: Yes, in my back yard (Stephens, 2017). Bingbing Wang The Effect of Home-owning on Business Development: Micro
  5. 5. 5/52 Introduction Three factors: distance, income, and industry types Identify and quantify the home-owning spatial impact on neighboring business: At what distance, for which neighborhoods, for what kind of business, the net impact is positive or negative. Bingbing Wang The Effect of Home-owning on Business Development: Micro
  6. 6. 6/52 Framework Producer function: Max q πe = pqe − TCe s.t. qe = f(K, L, M) TCe = vK + wL + r Incorporating business location: qe,i = f(K, Zi) TCe,i = vK + ZiPZi e: establishment e i: location i Zi: attributes in location i including labor, land resources, neighborhood amenity (population and income), tax policy transportation access, CBD distance, agglomeration PZi :land, labor, friction cost in location i Bingbing Wang The Effect of Home-owning on Business Development: Micro
  7. 7. 7/52 Framework Max z πi = p(f(K, Zi)) − Kv − PZi Zi (1) Max z πi = p(f(Zi)) − PZi Zi (2) Zi(labor stability) = fdistance(Owni) Zi(neighborhood amenity) = fdistance,income(Owni) PZi(friction cost) = fdistance,income,industry(Owni) Max Own πi = Fdistance,income,industry(Owni) (3) Bingbing Wang The Effect of Home-owning on Business Development: Micro
  8. 8. 8/52 Framework: with respect to distance Max z πi = p(f(Zi)) − PZi Zi (2) ∂π ∂D = p ∂f ∂Z ∂Z ∂D − ∂PZ ∂D Z − PZ ∂Z ∂D (4) Preferred and nuisance creating: S1 : ∂f ∂Z > 0 and PZ > 0 Preferred and not nuisance creating: S2 : ∂f ∂Z > 0 and PZ = 0 Not preferred: S3 : ∂f ∂Z < 0 Bingbing Wang The Effect of Home-owning on Business Development: Micro
  9. 9. 9/52 Framework: with respect to distance ∂π ∂D = p ∂f ∂Z ∂Z ∂D − ∂PZ ∂D Z − PZ ∂Z ∂D (4) NIMBY distance Da : If D < Da, ∂Pz ∂D < 0 and PZ > 0 If D >= Da, PZ = 0 Positive spillover distance D0 : If Da < D < D0, ∂Z ∂D = 0 and PZ = 0 If D >= D0, ∂Z ∂D < 0 and PZ = 0 Bingbing Wang The Effect of Home-owning on Business Development: Micro
  10. 10. 10/52 Predictions: distance ∂π ∂D = p ∂f ∂Z ∂Z ∂D − ∂PZ ∂D Z − PZ ∂Z ∂D (4) S1 : If D = 0, PZ = +∞ and π < 0 If 0 < D < Da, ∂π ∂D > 0 If Da <= D <= D0, ∂π ∂D = 0 and π(Da) = π(D0) > 0 If D >= D0, ∂π ∂D < 0 and π > 0 Bingbing Wang The Effect of Home-owning on Business Development: Micro
  11. 11. 11/52 Predictions: distance Distance Net impact D0Da Bingbing Wang The Effect of Home-owning on Business Development: Micro
  12. 12. 12/52 Predictions: income Distance Net impact D0Da 12 I2 I0 I1 Bingbing Wang The Effect of Home-owning on Business Development: Micro
  13. 13. 13/52 Predictions: industry ∂π ∂D = p ∂f ∂Z ∂Z ∂D − ∂PZ ∂D Z − PZ ∂Z ∂D (4) S2 : If 0 <= D <= D0, ∂π ∂D = 0 and π > 0 If D > D0, ∂π ∂D < 0 and π >= 0 S3 : π < 0 Bingbing Wang The Effect of Home-owning on Business Development: Micro
  14. 14. 14/52 Predictions 1: Home-owning increases the job counts of preferred business for low income neighborhoods for all distances. 2: Home-owning reduces the job counts of the preferred but nuisance creating business located at adjacent distances for high income neighborhoods. 3: Home-owning increases the job counts of preferred business located at close but not adjacent distances for high income neighborhoods. 4: Home-owning decreases the job counts of not preferred business at all distances for both income groups. Bingbing Wang The Effect of Home-owning on Business Development: Micro
  15. 15. 15/52 Outline Research Design Methodology Data Results and Conclusion Bingbing Wang The Effect of Home-owning on Business Development: Micro
  16. 16. 16/52 Research design: the optimal one Owner Renter Bingbing Wang The Effect of Home-owning on Business Development: Micro
  17. 17. 17/52 Research design: real position Bingbing Wang The Effect of Home-owning on Business Development: Micro
  18. 18. 18/52 Research design: former research on distance effect Project Bingbing Wang The Effect of Home-owning on Business Development: Micro
  19. 19. 19/52 Research design Bingbing Wang The Effect of Home-owning on Business Development: Micro
  20. 20. 20/52 Methodology: alternative mechanisms Transportation access: FE: cluster Agglomeration: Employee population density and education Local policies (tax policy): CBSA FE Common economic and local factors: Space and time FE Bingbing Wang The Effect of Home-owning on Business Development: Micro
  21. 21. 21/52 Methodology Endogeneity: reverse causation Owners do not choose to be near the business Existing owners move to other places Bingbing Wang The Effect of Home-owning on Business Development: Micro
  22. 22. 22/52 Methodology: IV Instrument: 1) the Federal Housing Administration (FHA) loan limit of the county where the donut ring resides divided by the median house price of the donut ring; 2) the ratio of families with children under 18. log(JobCountj,t) =α + βOwnj,i,t + Controlsj,i,t + j,t j: cluster j i: donut ring i (.3, .3-1, 1-2, 2-3, 3-5, 5-10) Crosscheck with OLS, FE and FD Lagged home-ownership rates with OLS, FE, FD and IV Bingbing Wang The Effect of Home-owning on Business Development: Micro
  23. 23. 23/52 Data Job counts and employee controls (education) Longitudinal Employer-Household Dynamics (LEHD) Origin-Destination Employment Statistics (LODES) Work Area Characteristics (WAC): 2009-2014 Ownership rates and residential controls (population, education, income) American Community Survey (ACS): 2009-2014 Latitude, longitude and CBSA codes 2010 Decennial Census geocodes Bingbing Wang The Effect of Home-owning on Business Development: Micro
  24. 24. 24/52 Data: K-means clustering method arg min k i=1 X∈Si ||X − µi||2 Wiki: https://en.wikipedia.org/wiki/K-means_clustering Bingbing Wang The Effect of Home-owning on Business Development: Micro
  25. 25. 25/52 Kmeans: USC Bingbing Wang The Effect of Home-owning on Business Development: Micro
  26. 26. 26/52 Kmeans: Orange County Bingbing Wang The Effect of Home-owning on Business Development: Micro
  27. 27. 27/52 Job clusters 217,778 block groups in 2011/50,138 are job block groups 10,000 job clusters in the U.S. 1) The number of block groups for each cluster varies from 1 to 38 and averages at 5.0138 and the median is 4. 2) The job cluster average land area is 31.79 square miles with an average radius of 2.008 miles. Bingbing Wang The Effect of Home-owning on Business Development: Micro
  28. 28. 28/52 Cluster and donut rings Bingbing Wang The Effect of Home-owning on Business Development: Micro
  29. 29. 29/52 Data: summary statistics Residential features: home-ownership rates Blkgp .3 .3-1 1-2 2-3 3-5 5-10 Own .6536 .6323 .6529 .6666 .6735 .6845 .6916 (.26) (.20) (.18) (.17) (.16) (.15) (.13) Obs 1,069,231 54,643 51,790 53,991 53,993 57,168 58,896 Table: Resident characteristics descriptive statistics. Standard errors of the means are in the parenthesis. The unit is mile. The data is from ACS from 2009 to 2014. Bingbing Wang The Effect of Home-owning on Business Development: Micro
  30. 30. 30/52 Data: summary statistics Residential features: home-ownership rate change Blkgp .3 .3-1 1-2 2-3 3-5 5-10 ∆ Own -.0059 -.0066 -.0067 -.0063 -.0058 -.0054 -.0048 (.07) (.05) (.05) (.04) (.04) (.03) (.02) Obs 1,069,231 54,643 51,790 53,991 53,993 57,168 58,896 Table: Resident characteristics descriptive statistics. Standard errors of the means are in the parenthesis. The unit is mile. The data is from ACS from 2009 to 2014. Bingbing Wang The Effect of Home-owning on Business Development: Micro
  31. 31. 31/52 Data: summary statistics Job features: the job count Blkgp Ratio Cluster Ratio Pooled 661 8859 Downtown 2685 .0089 17255 .2550 Suburbs 616 .9110 7592 .7450 High 604 .3980 11394 .5940 Low 698 .6020 6656 .4060 Downtown high 3435 .0225 20353 .1582 Downtown low 2442 .0654 13814 .0976 Suburban high 575 .3753 9826 .4359 Suburban low 643 .5368 5721 .3083 Obs 1070442 59772 Table: Work characteristics descriptive statistics. The data is from LEHD LODES WAC from 2009 to 2014.Downtown is defined as the block groups adjacent to the CBD, the total population of which are no more than 5% of the CBSA population. The high income group is categorized as the block groups with higher median income than the average median income of the block groups. Bingbing Wang The Effect of Home-owning on Business Development: Micro
  32. 32. 32/52 Data: summary statistics Job features: job count change Blkgp Growth Cluster Growth US Pooled -35 -.0530 569 .0642 .0162 Downtown -253 -.0942 991 .0574 Suburbs -30 -.0487 505 .0665 High -28 -.0464 754 .0662 Low -40 -.0573 398 .0598 Downtown high -244 -.0710 1178 .0579 Downtown low -243 - .0995 773 .0560 Suburban high -26 -.0452 681 .0693 Suburban low -34 -.0529 348 .0608 Obs 1070442 59772 Table: Work characteristics descriptive statistics. The data is from LEHD LODES WAC from 2009 to 2014 and the Bureau of Labor Statistics. Bingbing Wang The Effect of Home-owning on Business Development: Micro
  33. 33. 33/52 Pooled sample result: cluster with distance .3 OLS FE FD OLSL FEL FDL Own -.0319 -.1291 -.1187 -.0281 -.0592 -.0570 Std (.0097) (.0521) (.0536) (.0097) (.0284) (.0270) P .0011 .0132 .0267 .0036 .0375 .0353 Obs 46498 44370 35483 45303 43253 34366 Table: The dependent variable is the log of job counts. Controls include lagged job counts, residential population, employee education, employee population density, residential median income, educational levels, ownership rates of other rings, distance to CBD, cbsa and year fixed effects. The interpretation of the coefficient (.1291) for within .3 miles for Column (2) is that if ownership rate increase by 1%, then job counts increase by 12.91%. So if ownership rate increases from 65% to 66%, then job counts might increase from 1000 to 1012.91. Standard errors are clustered at the cluster level and are in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Bingbing Wang The Effect of Home-owning on Business Development: Micro
  34. 34. 34/52 Pooled sample result: cluster with distance .3-1 OLS FE FD OLSL FEL FDL Own -.0253 .0775 .0820 -.0327 -.0170 -.0039 Std (.0113) (.0552) (.0657) (.0117) (.0230) (.0258) P .0255 .1714 .2117 .0053 .4603 .8656 Obs 44013 44012 33749 42971 42970 34156 Table: The dependent variable is the log of job counts. Controls include lagged job counts, residential population, employee education, employee population density, residential median income, educational levels, ownership rates of other rings, distance to CBD, cbsa and year fixed effects. Standard errors are clustered at the cluster level and are in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Bingbing Wang The Effect of Home-owning on Business Development: Micro
  35. 35. 35/52 Pooled sample result: cluster with distance 1-2 OLS FE FD OLSL FEL FDL Own -.0256 -.0266 -.0588 -.0253 .0102 .0190 Std (.0128) (.0641) (.0690) (.0132) (.0304) (.0302) P .0454 .6780 .3941 .0547 .7373 .5282 Obs 45666 45665 34887 44835 44834 35690 Table: The dependent variable is the log of job counts. Controls include lagged job counts, residential population, employee education, employee population density, residential median income, educational levels, ownership rates of other rings, distance to CBD, cbsa and year fixed effects. Standard errors are clustered at the cluster level and are in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Bingbing Wang The Effect of Home-owning on Business Development: Micro
  36. 36. 36/52 Pooled sample result: cluster with distance 2-3 OLS FE FD OLSL FEL FDL Own .0166 -.1722 -.2062 -.0086 .0001 .0586 Std (.0148) (.0674) (.0759) (.0146) (.0288) (.0295) P .2640 .0106 .0066 .5563 .9984 .0475 Obs 45614 45613 34890 44841 44842 34179 Table: The dependent variable is the log of job counts. Controls include lagged job counts, residential population, employee education, employee population density, residential median income, educational levels, ownership rates of other rings, distance to CBD, cbsa and year fixed effects. Standard errors are clustered at the cluster level and are in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Bingbing Wang The Effect of Home-owning on Business Development: Micro
  37. 37. 37/52 Pooled sample result: cluster with distance 3-5 OLS FE FD OLSL FEL FDL Own -.0053 -.2247 -.1891 .0035 .1409 .1107 Std (.0172) (.1002) (.0981) (.0174) (.0450) (.0451) P .7588 .0249 .0540 .8387 .0018 .0142 Obs 48103 48102 36651 47505 45285 36106 Table: The dependent variable is the log of job counts. Controls include lagged job counts, residential population, employee education, employee population density, residential median income, educational levels, ownership rates of other rings, distance to CBD, cbsa and year fixed effects. Standard errors are clustered at the cluster level and are in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Bingbing Wang The Effect of Home-owning on Business Development: Micro
  38. 38. 38/52 Pooled sample result: cluster with distance 5-10 OLS FE FD OLSL FEL FDL Own .0303 .0966 .0552 .0260 -.0374 -.0083 Std (.0215) (.1855) (.1702) (.0218) (.0709) (.0562) P .1590 .6023 .7457 .2321 .5977 .8824 Obs 49275 49273 37519 48940 48939 39070 Table: The dependent variable is the log of job counts. Controls include lagged job counts, residential population, employee education, employee population density, residential median income, educational levels, ownership rates of other rings, distance to CBD, cbsa and year fixed effects. Standard errors are clustered at the cluster level and are in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Bingbing Wang The Effect of Home-owning on Business Development: Micro
  39. 39. 39/52 IV for .3, 2-3 and 3-5 .3 FHA First Stage Exogenous IV IVlag Own .0745 -.0060 -.1086 -.1297 Std (.0047) (.0040) (.0524) (.0566) P .0000 .1390 .0380 .0220 Obs 35519 35519 37211 37215 Table: The dependent variable is the log of job counts. The instrument is the FHA loan limit divided by the median house price. Standard errors are clustered at the cluster level and are in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Bingbing Wang The Effect of Home-owning on Business Development: Micro
  40. 40. 40/52 IV for .3, 2-3 and 3-5 2-3 FHA First Stage Exogenous Own .0968 -.0096 Std (.0035) (.0044) P .0000 .0301 Obs 34931 34931 3-5 FHA Own -.1596 -.0156 Std (.0499) (.0050) P .0014 .0019 Obs 36712 35519 Table: The dependent variable is the log of job counts. The instrument is the FHA loan limit divided by the median house price. Standard errors are clustered at the cluster level and are in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Bingbing Wang The Effect of Home-owning on Business Development: Micro
  41. 41. 41/52 IV for .3, 2-3 and 3-5 2-3 Child First Stage Exogenous IV Own .2621 .0189 .0495 Std (.0130) (.0131) (.0470) P .0000 .1489 .2918 Obs 34944 34944 34944 3-5 Child First Stage Exogenous IV IVlag Own .2618 .0253 .0832 .1462 Std (.0145) (.0187) (.0660) (.0780) P .0000 .1773 .2078 .0608 Obs 36716 36716 36716 36716 Table: The dependent variable is the log of job counts. The instrument is the ratio of families with children under 18. Standard errors are clustered at the cluster level and are in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Bingbing Wang The Effect of Home-owning on Business Development: Micro
  42. 42. 42/52 Result: pooled sample and different income groups Pooled .3 .3-1 1-2 2-3 3-5 5-10 Own - OwnLag - + Income .3 .3-1 1-2 2-3 3-5 5-10 Own -high OwnLag +low +low Table: The results for different income groups are from the FE specification and are consistent across other specifications. Bingbing Wang The Effect of Home-owning on Business Development: Micro
  43. 43. 43/52 Result: .3-1 from high income groups .3-1 >55 >60 >65 >70 >75 Own -.1191 -.1244 -.1373 -.1045 -.0807 Std (.0737) (.0740) (.0823) (.0926) (.1011) P .1062 .0930 .0952 .2590 .4248 Obs 17358 15578 13474 11165 9327 .3-1 >80 >85 >90 >95 Own -.1792 -.2125 -.5416 -.6165 Std (.1164) (.1430) (.1767) (.2725) P .1239 .1376 .0022 .0242 Obs 7242 5018 3174 1300 Bingbing Wang The Effect of Home-owning on Business Development: Micro
  44. 44. 44/52 Result: .3 of lagged ownership rates from low income groups .3 <5 <10 <15 <20 <25 Own .2060 .1962 .1241 .0547 .0516 Std (.2045) (.0870) (.0725) (.0611) (.0561) P .3143 .0244 .0870 .3707 .3580 Obs 1284 3162 5083 7345 9564 .3 <30 <35 <40 <45 <50 Own .0149 -.0171 -.0182 -.0131 -.0293 Std (.0517) (.0469) (.0416) (.0410) (.0371) P .7736 .7157 .6616 .7485 4292 Obs 11414 13849 16043 17805 21212 Bingbing Wang The Effect of Home-owning on Business Development: Micro
  45. 45. 45/52 Result: 3-5 of lagged ownership rates from low income groups 3-5 <5 <10 <15 <20 <25 Own .2852 .1821 .1576 .3042 .2443 Std (.3543) (.1539) (.1228) (.1134) (.0990) P .4215 .2371 .1996 .0074 .0137 Obs 1244 3023 4860 6975 9048 3-5 <30 <35 <40 <45 <50 Own .2430 .2296 .2086 .1938 .1622 Std (.0992) (.0889) (.0832) (.0757) (.0719) P .0143 .0099 .0122 .0105 .0241 Obs 10832 13153 15210 16974 20224 Bingbing Wang The Effect of Home-owning on Business Development: Micro
  46. 46. 46/52 Summary statistics: industry NAICS Max Max Location Total 467929 - NAICS sector 11 (Agriculture, Forestry, Fishing and Hunting) 11322 North CA NAICS sector 21 (Mining, Quarrying, and Oil and Gas Extraction) 17320 Houston downtown NAICS sector 22 (Utilities) 11712 LA downtown NAICS sector 23 (Construction) 12023 Vegas NAICS sector 31-33 (Manufacturing) 33260 Seattle (Boeing) NAICS sector 42 (Wholesale Trade) 26227 NYC Manhattan NAICS sector 44-45 (Retail Trade) 18221 New York NAICS sector 48-49 (Transportation and Warehousing) 52392 NYC (JFK airport) NAICS sector 51 (Information) 47206 Seattle (Microsoft headquarters) NAICS sector 52 (Finance and Insurance) 75889 NYC Manhattan Bingbing Wang The Effect of Home-owning on Business Development: Micro
  47. 47. 47/52 Summary statistics: industry NAICS Max Max Location Total 467929 - NAICS sector 53 (Real Estate and Rental and Leasing) 15293 NYC Manhattan NAICS sector 54 (Professional, Scientific, and Technical Services) 73292 Chicago NAICS sector 55 (Management of Companies and Enterprises) 13630 Arkansas (Walmart home office) NAICS sector 56 (Administrative and Support and Waste Management and Remediation Services) 26435 Chicago NAICS sector 61 (Educational Services) 173587 NYC Manhattan (NYU) NAICS sector 62 (Health Care and Social Assistance) 54194 Houston (Texas Medical Center) NAICS sector 71 (Arts, Entertainment, and Recreation) 41671 Orlando Disney NAICS sector 72 (Accommodation and Food Services) 91530 Vegas NAICS sector 81 (Other Services [except Public Administration]) 23551 Seattle downtown NAICS sector 92 (Public Administration) 90563 NYC Lower Manhattan Bingbing Wang The Effect of Home-owning on Business Development: Micro
  48. 48. 48/52 Summary statistics: industry Agricultural: North CA Oil: Houston Utility: LA Construction: Vegas Manufacturing: Seattle Wholesale Trade: NYC Retail Trade: NYC Transportation: JFK Information: Seattle Finance & Insurance: NYC Real Estate: NYC Professional service: Chicago Bingbing Wang The Effect of Home-owning on Business Development: Micro
  49. 49. 49/52 Summary statistics: industry Management of companies: Arkansas Admin and support: Chicago Educational service: NYC Health care: Houston Arts and entertain: Orlando Accommodation and food: Vegas Other service: Seattle Public admin: NYC Bingbing Wang The Effect of Home-owning on Business Development: Micro
  50. 50. 50/52 Result: industry Positive Lower Income Higher Income Manufacturing (+:1-2) Retail Trade (-: .3/+: 3-5) Real Estate and Car Rental and Leasing (+:1-2) Arts, Entertainment (+:.3) Information (+:.3;+: .3-1) Public Administration (+: .3; -: 5-10) Accommodation and Food Services (+:.3-1;-: 3-5) Professional, Scientific, and Technical Services (-: .3/+: 3-5) Administrative and Support and Waste (+: .3-1; -: 2-3) Negative Lower Income Higher Income Management of Companies (3-5) Manufacturing (.3-1) Wholesale Trade (.3) Real Estate and Rental and Leasing (.3; 2-3; 5-10) Other Services [except Public Administration] (.3-1; 5-10) Wholesale Trade (.3) Retail Trade (2-3) Management of Companies (.3; .3-1; 2-3) Arts, Entertainment (3-5) Finance and Insurance (.3; 1-2) Public Administration (.3; 1-2: 3-10) Educational Services (.3; 5-10) Bingbing Wang The Effect of Home-owning on Business Development: Micro
  51. 51. 51/52 Conclusion Comfortable distance: 3-5 miles Friction distance: adjacent distance of .3 or .3-1 miles from high income groups The combination of neighborhoods and industries High income with service industries like Art and Retail Low income with job generating industries like Manufacturing and Real Estate and Car Rental and Leasing Bingbing Wang The Effect of Home-owning on Business Development: Micro
  52. 52. 52/52 Q and A Thank you! bingbinw@usc.edu; bingbingwang123@gmail.com Bingbing Wang The Effect of Home-owning on Business Development: Micro

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