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
ADSL (Asymmetric Digital Subscriber Line) normally provides data transmission speeds of at least 256 Kbps, consistent with the OECD (2002) definition of broadband.
Fibre, or ‘high-speed’ or ‘fast’ broadband, facilitates data transmission speeds of at least 10 Mbps (Castalia 2008).
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.
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.
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.
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.
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.
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.
Using a rolling mean employee (RME) count; see Statistics New Zealand (2006).
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.
We have also calculated the treatment effects using single year (2006) data and find similar results, but with slightly higher standard errors.
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.
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.
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.
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.
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.
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.
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.
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.
I.e. six separate samples, each with two matching techniques.
I.e. from a 7.1% to a 10.2% productivity improvement since exp(0.069) = 1.071 and exp(0.097) = 1.102.
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.
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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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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.
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Appendix
See Table 6.
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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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DOI: https://doi.org/10.1007/s11123-011-0237-z