Gender Wage Differentials and the Spatial Concentration of High-Technology Industries

  • Elsie Echeverri-CarrollEmail author
  • Sofía G. Ayala
Part of the Advances in Spatial Science book series (ADVSPATIAL)


Moretti (2004) finds that the distribution of human capital across cities in the United States became more unequal during the 1990s. He believes that one reason for the increased concentration of human capital in some metropolitan areas was the high-tech boom of that decade, since it benefited a handful of already highly skilled cities. This trend reflects the decisions of skilled workers and the skill-intensive industries that employed them to colocate in the same cities or regions (high-tech clusters). Zucker et al. (1998), for instance, find that the entry decisions of new biotechnology firms in cities depends on the stock of human capital in outstanding scientists there, as measured by the number of relevant academic publications. Colocation benefits workers (who enjoy the productivity-enhancing effects associated with local learning processes) as well as high-tech firms (which profit from highly productive and creative workers who enhance the firms’ innovation processes). The primary cooperative linkages in high-technology clusters are those related to knowledge exchange. As Fingleton et al. (2004) note, sharing knowledge is the key to the generation and maintenance of innovation flows that are particularly relevant in these clusters. A strong evidence of the learning networks-innovation relationship comes from studies showing that patents (a proxy for innovations) are more likely to emerge from the same states or metropolitan areas as the cited patents than one would expect based in the preexisting concentration of related research activity (Jaffe et al. 1993).


Instrumental Variable Female Worker Wage Premium Location Quotient Wage Elasticity 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This study is based on work supported by the National Science Foundation under Grant No. 0318174 and by the Bureau of Business Research, The University of Texas at Austin. Opinions, findings, and conclusions or recommendations are those of the authors and do not necessarily reflect the view of any of these organizations. The authors thank two anonymous reviewers and Robert A. Peterson for constructive comments.


  1. Acemoglu D, Angrist J (2000) How large are human-capital externalities? Evidence from compulsory schooling laws. NBER Macroecon Annu 15:9–59Google Scholar
  2. Arthur WB (1996) Increasing returns and the new world of business. Harv Bus Rev 74:99–109Google Scholar
  3. Baum CF (2006) An introduction to modern econometrics using Stata. Stata Press, TexasGoogle Scholar
  4. Baum CF, Schaffer ME, Stillman S (2007) Enhanced routines for instrumental variables/GMM estimation and testing, CERT Discussion Paper 0706, Centre for Economic Reform and Transformation, Heriot-Watt UniversityGoogle Scholar
  5. Baum CF, Schaffer ME, Stillman S (2007) IVREG2: Stata module for extended instrumental variables/ 2SLS, GMM and AC/HAC, LIML and k-class regression. Available online:, 27 Feb. 2009
  6. Baum CF, Schaffer ME, Stillman S (2003) Instrumental variables and GMM: estimation and testing. Stata J 3:1–31Google Scholar
  7. Belke AH, Fehn R, Foster N (2003) Does venture capital investment spur employment growth? CESifo Working Paper Series No. 930. National Bureau of Economic Research, Cambridge, MAGoogle Scholar
  8. Black D, Henderson V (1999) A theory of urban growth. J Polit Econ 107:252–284CrossRefGoogle Scholar
  9. Blau F, Kahn L (1994) Rising wage inequality and the U.S. gender gap. Am Econ Rev 84:23–28Google Scholar
  10. Blinder AS (1973) Wage discrimination: reduced form and structural estimates. J Hum Resour 8:436–455CrossRefGoogle Scholar
  11. Bound J, Jaeger DA, Baker R (1995) Problems with instrumental variables estimation when the correlation between instruments and the endogenous explanatory variable is weak. J Am Stat Assoc 90:443–450Google Scholar
  12. Chapple K, Markusen A, Schrock G, Yamamoto D, Yu P (2004) Gauging metropolitan “high-tech and i-tech” activity. Econ Dev Q 18:10–29CrossRefGoogle Scholar
  13. Chmelarova V, Hill RC (2004) Finite sample properties of the Hausman test. Southern Economic Association Meeting, New Orleans, LA. Available online:, 10 Aug. 2008
  14. Ciccone A, Peri G (2006) Identifying human capital externalities: theory with applications. Rev Econ Stud 73:381–412CrossRefGoogle Scholar
  15. Cutillo A, Di Pietro G (2006) The effects of overeducation on wages in Italy: a bivariate selectivity approach. Int J Manpow 27:143–168CrossRefGoogle Scholar
  16. Echeverri-Carroll EL, Ayala SG (2004) Economic growth and linkages with Silicon Valley: the cases of Austin and Boston. Texas Business Review (December), Bureau of Business Research, Red McCombs School of Business, The University of Texas at AustinGoogle Scholar
  17. Echeverri-Carroll EL, Ayala SG (2006) High-technology agglomeration and gender inequalities. Paper presented at the American Economic Association Meetings, Boston, MAGoogle Scholar
  18. Echeverri-Carroll EL, Ayala SG (2009) Wage differentials and the spatial concentration of high-technology industries. Pap Reg Sci 88:623–641CrossRefGoogle Scholar
  19. Edin P-A, Zetterberg J (1992) Interindustry wage differentials: evidence from Sweden and comparison with the United States. Am Econ Rev 82:1341–1349Google Scholar
  20. Ellison G, Glaeser EL (1997) Geographic concentration in U.S. manufacturing industries: a dartboard approach. J Polit Econ 105:889–927CrossRefGoogle Scholar
  21. Faggian A, McCann P, Sheppard S (2007) Some evidence that women are more mobile than men: gender differences in U.K. graduate migration behavior. J Reg Sci 47:517–539CrossRefGoogle Scholar
  22. Fingleton B, Igliori DC, Moore B (2006) Cluster dynamics: new evidence and projections for computing services in Great Britain. J Reg Sci 45:283–311CrossRefGoogle Scholar
  23. Fingleton B, Igliori DC, Moore B (2004) Employment growth of small high-technology firms and the role of horizontal clustering: evidence from computing services and R&D in Great Britain, 1991–2000. Urban Stud 41:773–799CrossRefGoogle Scholar
  24. Fingleton B, Igliori DC, Moore B, Odedra R (2007) Employment growth and clusters dynamics of creative industries in Great Britain. In: Polenske KR (ed) The economic geography of innovation. Cambridge University Press, Cambridge, pp 60–86CrossRefGoogle Scholar
  25. Fields J, Wolff EN (1995) Interindustry wage differentials and the gender wage gap. Ind Labor Relat Rev 49:105–120CrossRefGoogle Scholar
  26. Florida R, Smith Jr. DF (1993) Venture capital formation, investment and regional industrialization. Ann Assoc Am Geogr 83:434–451CrossRefGoogle Scholar
  27. Gannon B, Plasman R, Rycx F, Tojerow I (2007) Inter-industry wage differentials and the gender wage gap: evidence from European countries. Econ Soc Rev 38:135–155Google Scholar
  28. Garofalo G, Fogarty MS (1979) Urban income distribution and the urban hierarchy-equality hypothesis. Rev Econ Stat 61:381–388CrossRefGoogle Scholar
  29. Glaeser EL, Maré DC (2001) Cities and skills. J Labor Econ 19:316–342CrossRefGoogle Scholar
  30. Glaeser EL, Kallal HD, Scheinkman JA, Shleifer A (1992) Growth in cities. J Polit Econ 105:889–927Google Scholar
  31. Gorman M, Sahlman WA (1989) What do venture capitalists do? J Bus Venturing 4:231–248CrossRefGoogle Scholar
  32. Grossman GM, Helpman E (1991) Innovation and growth in the global economy. MIT, Cambridge, MAGoogle Scholar
  33. Hadlock P, Hecker D, Gannon J (1991) High-technology employment: another view. Mon Labor Rev 114:26–30Google Scholar
  34. Hecker DE (2005) High-technology employment: a NAICS-based update. Mon Labor Rev 128:57–72Google Scholar
  35. Hecker DE (1999) High-tech employment: a broader view. Mon Labor Rev 122:18–28Google Scholar
  36. Henderson VJ (2007) Understanding knowledge spillovers. Reg Sci Urban Econ 37:497–508CrossRefGoogle Scholar
  37. Imbens GW, Angrist JD (1994) Identification and estimation of local average treatment effects. Econometrica 62:467–75CrossRefGoogle Scholar
  38. Jaffe AB, Trajtenberg M, Henderson R (1993) Geographic localization of knowledge spillovers as evidenced from patent citations. Q J Econ 108:577–598CrossRefGoogle Scholar
  39. Kleibergen F, Paap R (2006) Generalized reduced rank tests using the singular value decomposition. J Econom 127:97–126CrossRefGoogle Scholar
  40. Krueger AB, Summers LH (1988) Efficiency wages and the inter-industry wage structure. Econometrica 56:259–293CrossRefGoogle Scholar
  41. Krugman PR (1993) Geography and trade. MIT, Cambridge, MAGoogle Scholar
  42. Luker W, Lyons D (1997) Employment shifts in high-technology industries, 1988–1996. Mon Labor Rev 120:12–25Google Scholar
  43. Malecki E (1981) Public and private sector interrelationships, technological change, and regional development. Pap Reg Sci Assoc 47:121–137CrossRefGoogle Scholar
  44. Manigart S, DeWaele K, Wright M, Robbie K, Desbri\tilde{v}cres P, Sapienza HJ, Beekman A (2002) Determinants of required return in venture capital investments: a five-country study. J Bus Venturing 17:291–312Google Scholar
  45. Marshall A (1890) Principles of economics: an introductory text. McMillan, LondonGoogle Scholar
  46. McCall L (2001) Complex inequality gender, class, and race in the new economy. Routledge, New YorkGoogle Scholar
  47. McCall L (1998) Spatial routes to gender wage (in)equality: regional restructuring and wage differentials by gender and education. Econ Geogr 74:379–404CrossRefGoogle Scholar
  48. Midelfart-Knarvik K, Overman H, Redding S, Venables A (2000) The location of European industry. Economic Papers No. 142, European Commission, D-G for Economic and Financial Affairs, BrusselsGoogle Scholar
  49. Mincer J (1974) Schooling, experience and earnings. National Bureau of Economic Research, New YorkGoogle Scholar
  50. Moretti E (2004) Human capital externalities in cities. In: Henderson V, Thisse JF (eds) Handbook of urban and regional economics, vol 4. North-Holland (Elsevier), Amsterdam, pp 2243–2292Google Scholar
  51. Newman NS (1998) Net loss: government, technology and the political economy of community in the age of the internet. Ph.D. Dissertation, Department of Sociology, University of California, BerkeleyGoogle Scholar
  52. Oaxaca R (1973) Male-female wage differentials in urban labor markets. Int Econ Rev 14:693–709CrossRefGoogle Scholar
  53. Office of Technology Assessment (1982) Technology, innovation, and regional economic development. U.S. Congress, Washington, DCGoogle Scholar
  54. Ratanawaraha A, Polenske KR (2007) Measuring the geography of innovation: literature review. In: Polenske KR (ed) The economic geography of innovation. Cambridge University Press, pp 30–59Google Scholar
  55. Rauch JE (1993) Productivity gains from geographic concentration of human capital: evidence from the cities. J Urban Econ 34:380–400CrossRefGoogle Scholar
  56. Richie RW, Hecker DE, Burgan JU (1983) High-technology, today and tomorrow: a small slice of the employment pie. Mon Labor Rev 106:50–59Google Scholar
  57. Saxenian A (1994) Regional advantage: culture and competition in Silicon Valley and Route 128. Harvard University Press, Cambridge, MAGoogle Scholar
  58. Schwanhausser M (2007) In Silicon Valley, few women reach top jobs. San Jose Mercury News. Available online:, 13 Aug. 2008
  59. Segal D (1976) Are there returns to scale in city size? Rev Econ Stat 58:339–350CrossRefGoogle Scholar
  60. Shefer D (1973) Localization economies in SMSAs: a production function approach. J Reg Sci 13:55–64CrossRefGoogle Scholar
  61. Staiger D, Stock JH (1997) Instrumental variables regression with weak instruments. Econometrica 65:557–586CrossRefGoogle Scholar
  62. Stock JH, Yogo M (2005) Testing for weak instruments in linear IV regression. In: Andrews DW, Stock JH (eds) Identification and inference for econometric models: essays in honor of Thomas Rothenberg. Cambridge University Press, pp 80–108Google Scholar
  63. Storper M, Venables AJ (2003) Buzz: face-to-face contact and the urban economy. J Econ Geogr 4:351–370Google Scholar
  64. Sveikauskas L (1975) The productivity of cities. Q J Econ 89:393–413CrossRefGoogle Scholar
  65. Teece D (2002) Knowledge and competitiveness as strategic assets. In: Holsapple CW (ed) Handbook on knowledge management: knowledge matters, vol 1. Birkhäuser, Cambridge, MA, pp 129–152Google Scholar
  66. Yu PD (2004) Focus on high-tech: what’s in a name? Gauging high-tech activity. Reg Rev 14:6–9 (Federal Reserve Bank of Boston)Google Scholar
  67. Wooldridge JM (2006) Introductory econometrics: a modern approach, 3rd edn. Thomson South-Western, MassachusettsGoogle Scholar
  68. Yankow JJ (2006) Why do cities pay more? An empirical examination of some competing theories of the urban wage premium. J Urban Econ 60:139–161CrossRefGoogle Scholar
  69. Zucker LG, Darby MR, Brewer MB (1998) Intellectual human capital and the birth of the U.S. biotechnology enterprises. Am Econ Rev 88:290–306Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  1. 1.IC2 Institute, University of Texas at AustinAustinUSA

Personalised recommendations