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The need for speed: impacts of internet connectivity on firm productivity

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Abstract

Broadband access is widely considered to be a productivity-enhancing factor, but there are few firm-level estimates of its benefits. We use a large micro-survey of firms linked to longitudinal firm financial data to determine the impact that broadband access has on firm productivity. Propensity score matching is used to control for factors, including the firm’s own lagged productivity, that determine a firm’s internet access choice. Instrumental variables estimates are employed as a robustness check. Results indicate that broadband adoption boosts firm productivity by 7–10%; effects are consistent across urban versus rural locations and across high versus low knowledge intensive sectors.

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Notes

  1. ADSL (Asymmetric Digital Subscriber Line) normally provides data transmission speeds of at least 256 Kbps, consistent with the OECD (2002) definition of broadband.

  2. Fibre, or ‘high-speed’ or ‘fast’ broadband, facilitates data transmission speeds of at least 10 Mbps (Castalia 2008).

  3. For New Zealand examples, see New Zealand Institute (2007, 2008).

  4. See IDC Market Research (2006), conducted in collaboration with the Economist Intelligence Unit, New Zealand Institute (2007, 2008) and Castalia (2008).

  5. Extensive discussions of PSM models are provided in Dehejia and Wahba (2002), Becker and Ichino (2002) and Caliendo and Kopeining (2008). Becker and Ichino (2002) outline the Stata programmes that form the basis of our application.

  6. The number of strata is determined by the requirements that we cannot reject the average propensity scores for treated and control firms being equal within each stratum and that the balancing hypothesis holds for each variable. We begin with five strata in each case, and increase the number of strata until these requirements are met.

  7. Nearest neighbour matching would assign each control firm to, on average, four treated firms. This would create considerable noise if a few control firms had large idiosyncratic productivity outcomes.

  8. We use the default kernel and bandwidth from Becker and Ichino (2002). Both our kernel and strata matching methods are restricted to areas of common support.

  9. The two-step procedure embodied in (4) and (5) requires that the residuals from (5) are uncorrelated with the fitted values and with the covariates from (4); OLS estimation of (5) guarantees this property but probit estimation of (5) does not do so (Kelejian 1971; Angrist and Pischke 2009).

  10. In addition, Angrist and Hahn (2004) note that propensity score matching may yield more efficient estimates than a regression approach when dealing with finite samples.

  11. In each case we state (in parentheses) our alternative hypothesis relative to the null of no effect, plus reference to relevant prior studies that have established the importance of the factor for ICT adoption.

  12. Altrostic and Nguyen (2005) note that “strong cross-section effects often become muted after controlling for prior conditions.” We match on firms’ five-year lagged productivity, and include that variable as an explanatory variable in the IV approach, to control for unobserved characteristics.

  13. Using a rolling mean employee (RME) count; see Statistics New Zealand (2006).

  14. The four-digit level distinguishes, for instance, between “Pump and compressor manufacturing” versus “household appliance manufacturing”. In a few cases we aggregate to the three-digit sector where numbers of firms for the four-digit sector calculation falls below 30 firms.

  15. We have also calculated the treatment effects using single year (2006) data and find similar results, but with slightly higher standard errors.

  16. Firms are asked if they have broadband access only, or have both broadband and dial-up access; we combine the two categories into a single broadband access category.

  17. Another possible explanation is that fibre access did not in fact increase firm productivity relative to ADSL access for most firms in our dataset in 2006. We cannot distinguish between these explanations.

  18. All count data throughout the paper are randomly rounded to base 3 (a Statistics NZ confidentiality requirement). For instance, a number that is reported as 36 may in fact lie in the range [34, 38]; hence totals do not always add exactly.

  19. In a few cases, we split three-digit industries into finer distinctions where OECD information indicates a split between knowledge-intensive and other categories within the three-digit classification.

  20. The employment relations question, with ten sub-questions is: Does this business have any of the following practices in place on a formal basis for any non-managerial employees?: employee feedback programmes; flexible job design; information sharing; problem-solving teams; employees engaged in regular decision making; employee participation in health and safety; performance reviews; childcare; being able to buy extra annual leave or take leave without pay; using personal sick leave, unpaid leave or compassionate care leave to care for other people who are sick.

  21. The least dense authority within this group had 214 people/km2, over twice the density of the next densest authority (102 people/km2). Dunedin City (New Zealand’s sixth largest city, with a major university) has lower density still, owing to inclusion of a large rural hinterland within its boundaries. Most Dunedin firms are located in the city proper, so we include Dunedin in the Urban group.

  22. Forman and Goldfarb (2006) note that the effect of size on adoption is not well understood; the quadratic term allows a non-linear relationship given this lack of theoretical guidance.

  23. We have also estimated the equations dropping all firms that had zero employment in 2001; results are robust to this change and so are not reported separately.

  24. I.e. six separate samples, each with two matching techniques.

  25. I.e. from a 7.1% to a 10.2% productivity improvement since exp(0.069) = 1.071 and exp(0.097) = 1.102.

  26. The coefficient on five-year-lagged productivity in each case is 0.44 (significant at 1%) indicating a strongly persistent firm-specific productivity effect relative to other firms in their industry.

References

  • Altrostic B, Nguyen S (2005) IT productivity in U.S. manufacturing: do computer networks matter? Econ Inq 43(3):493–506

    Article  Google Scholar 

  • Angrist J, Hahn J (2004) When to control for covariates? Panel asymptotics for estimates of treatment effects. Rev Econ Stat 86:58–72

    Article  Google Scholar 

  • Angrist J, Pischke J-S (2009) Mostly harmless econometrics: an empiricist’s companion. Princeton University Press, Princeton

    Google Scholar 

  • Astebro T (2002) Noncapital investment costs and the adoption of CAD and CNC in U.S. metalworking industries. RAND J Econ 33:672–688

    Article  Google Scholar 

  • Becker S, Ichino A (2002) Estimation of average treatment effects based on propensity scores. Stata J 2(4):358–377

    Google Scholar 

  • Bharadwaj A (2000) A resource-based perspective on IT capability and firm performance: an empirical investigation. MIS Q 24:169–196

    Article  Google Scholar 

  • Black S, Lynch L (2001) How to compete: the impact of workplace practices and information technology on productivity. Rev Econ Stat 83:434–445

    Article  Google Scholar 

  • Bresnahan T, Brynjolfsson E, Hitt L (2002) Information technology, workplace organization, and the demand for skilled labor: firm-level evidence. Quart J Econ 177:339–376

    Google Scholar 

  • Brynjolfsson E, Hitt L (2003) Computing productivity: firm-level evidence. Rev Econ Stat 85:793–808

    Article  Google Scholar 

  • Caliendo M, Kopeining S (2008) Some practical guidance for the implementation of propensity score matching. J Econ Surv 22(1):31–72

    Article  Google Scholar 

  • Castalia (2008) Getting the most from high speed broadband in New Zealand: investing in productivity growth. Report to Telecom, TelstraClear, & Vodafone. Wellington

  • Clayton T (2005) IT investment, ICT use, and UK firm productivity. Office for National Statistics, London

  • Collins P, Day D, Williams C (2007) The economic effects of broadband: an Australian perspective. Research Statistics and Technology Branch, Department of Communications, Information Technology and the Arts

  • Crandall R, Lehr W, Litan R (2007) The effects of broadband deployment on output and employment: a cross-sectional analysis of U.S. data. Issues in Economic Policy No. 6, The Brookings Institute

  • David P (1969) A contribution to the theory of diffusion. Stanford Center for Research in Economic Growth, Memorandum No. 71, Stanford University

  • Debruyne M, Reibstein D (2005) Competitor see, competitor do: incumbent entry in new market niches. Market Sci 24:55–66

    Article  Google Scholar 

  • Dehejia R, Wahba S (2002) Propensity score-matching methods for nonexperimental causal studies. Rev Econ Stat 84(1):151–161

    Article  Google Scholar 

  • Fabling R, Grimes A (2010) HR practices and New Zealand firm performance: what matters and who does it? Int J Hum Resour Manage 21(4):488–508

    Article  Google Scholar 

  • Fabling R, Grimes A, Stevens P (2008) A comparison of qualitative and quantitative firm performance measures. Occasional Paper 08/04. Ministry of Economic Development, Wellington

    Google Scholar 

  • Ford G, Koutsky T, Spiwak L (2008) The broadband efficiency index: what really drives broadband adoption across the OECD? Phoenix Center Policy Paper Number 3

  • Forman C (2005) The corporate digital divide: determinants of internet adoption. Manage Sci 51:641–654

    Article  Google Scholar 

  • Forman C, Goldfarb A (2006) Information and communication technology diffusion to businesses. In: Hendershott T (ed) Economics and information systems, handbooks in information systems, vol 1. Amsterdam, Elsevier, pp 1–52

    Google Scholar 

  • Forman C, Goldfarb A, Greenstein S (2005) How did location affect adoption of the commercial internet? Global village vs. urban leadership. J Urban Econ 58:389–420

    Article  Google Scholar 

  • Forman C, Goldfarb A, Greenstein S (2009) The internet and local wages: convergence or divergence? NBER working paper series, vol w14750

  • Gaspar J, Glaeser E (1998) Information technology and the future of cities. J Urban Econ 43:136–156

    Article  Google Scholar 

  • Goldfarb A (2006) The (teaching) role of universities in the diffusion of the internet. Int J Ind Organ 24:203–225

    Article  Google Scholar 

  • Greenstein S, McDevitt R (2009) The broadband bonus: accounting for broadband internet’s impact on U.S. GDP, NBER working paper. National Bureau of Economic Research, Cambridge

  • Grimes A, Ren C, Stevens P (2009) The need for speed: impacts of internet connectivity on firm productivity. Working Paper 09/15, Motu Economic and Public Policy Research, Wellington

  • Hagen H-O, Zeed J (2005) Does ICT use matter for firm productivity? Yearbook on productivity 2005, Statistics Sweden

  • Hubbard T (2000) The demand for monitoring technologies: the case of trucking. Quart J Econ 116:533–560

    Google Scholar 

  • IDC Market Research (2006) The New Zealand ICT sector profile—the economic impact. The HiGrowth Project, Version 1.7

  • Kelejian H (1971) Two stage least squares and econometric systems linear in parameters but non-linear in the endogenous variables. J Am Stat Assoc 66:373–374

    Article  Google Scholar 

  • Kraemer K, Gibbs J, Dedrick J (2005) Impacts of globalization on e-commerce use and firm performance: a cross-country investigation. Inf Soc 21:1–18

    Article  Google Scholar 

  • Lehr W, Osorio C, Gillett S, Sirbu M (2006) Measuring broadband’s economic impact. Presented at the 33rd research conference on communication, information, and internet policy (TPRC), Arlington, Virginia, September 23–25, 2005. Revised January 17, 2006

  • Maliranta M, Rouvinen P (2006) Informational mobility and productivity: finnish evidence. Econ Innov New Technol 15(6):605–616

    Google Scholar 

  • Milgrom P, Roberts J (1990) The economics of modern manufacturing: technology, strategy, and organization. Am Econ Rev 80:511–528

    Google Scholar 

  • Mundlak Y (1961) Empirical production function free of management bias. J Farm Econ 43:44–56

    Article  Google Scholar 

  • New Zealand Institute (2007) Defining a broadband aspiration: how much does broadband matter and what does New Zealand need?

  • New Zealand Institute (2008) Assessing New Zealand’s current broadband path: the need for change

  • OECD Directorate for Science, Technology, and Industry (2002) Broadband infrastructure deployment: the role of government assistance

  • OECD Directorate for Science, Technology, and Industry (2008) OECD broadband Portal. Accessed February 25, 2008

  • OECD, Information and Communications Technologies (2003) ICT and economic growth: evidence from OECD countries. Industries, and Firms

  • Rosenbaum P, Rubin D (1983) The central role of the propensity score in observational studies for causal effects. Biometrika 70(1):41–55

    Article  Google Scholar 

  • Rosenbaum P, Rubin D (1985) The bias due to incomplete matching. Biometrics 41:106–116

    Article  Google Scholar 

  • Seyb A (2003) The longitudinal business frame. Statistics New Zealand, Christchurch

    Google Scholar 

  • Statistics New Zealand (2006) Business operation survey 2006. Technical Notes, Wellington

  • Stevens P (2009) Competition in New Zealand: an analysis using micro data’. Paper presented to the New Zealand Association of Economists conference, Wellington

  • Van Reenen J, Bloom N (2007) Measuring and explaining management practices across firms and countries. Q J Econ 122:1351–1408

    Article  Google Scholar 

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Acknowledgments

We thank Kate Chambers for excellent research assistance, and Richard Fabling, Steven Stillman, Dave Maré, Brad Ward, Rosemary Spragg, Nick Manning, seminar participants and two referees of this journal for helpful comments on an earlier draft. We also thank: Statistics New Zealand for providing all data used in the study and for providing research facilities at its on-site datalab; and the Foundation for Research, Science and Technology (FRST grant MOTU0601, Infrastructure) and the Ministry of Economic Development for funding assistance.

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Correspondence to Arthur Grimes.

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Access to the data used in this study was provided by Statistics New Zealand in accordance with security and confidentiality provisions of the Statistics Act 1975 and the Tax Administration Act 1994. The results in this paper have been confidentialised to protect individual businesses from identification. See Grimes et al. (2009) for the full disclaimer. The authors remain solely responsible for the analysis and views expressed in the paper.

Appendix

Appendix

See Table 6.

Table 6 Definitions of variables

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Grimes, A., Ren, C. & Stevens, P. The need for speed: impacts of internet connectivity on firm productivity. J Prod Anal 37, 187–201 (2012). https://doi.org/10.1007/s11123-011-0237-z

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