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The Need for Speed: Impacts of Internet Connectivity On Firm Productivity

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The Need for Speed:
Impacts of Internet Connectivity on
Firm Productivity
Arthur Grimes1, Cleo Ren2 and Philip Stevens3
Motu Working Paper 09-15
Motu Economic and Public Policy Research
October 2009
1 Motu Economic and Public Policy Research and University of Waikato
2 Motu Economic and Public Policy Research
3 Ministry of Economic Development
Author contact details
Arthur Grimes
Motu Economic and Public Policy Research and University of Waikato
arthur.grimes@motu.org.nz
Cleo Ren
Motu Economic and Public Policy Research
Philip Stevens
Ministry of Economic Development
Philip.Stevens@med.govt.nz
Acknowledgements
We thank Kate Chambers for excellent research assistance, and Richard Fabling,
Steven Stillman, Dave Maré, Brad Ward, Rosemary Spragg, Nick Manning and
NZAE and MED conference participants for helpful comments on an earlier draft.
Statistics New Zealand provided all data used in the study as well as providing
research facilities at its on-site data lab. The Ministry of Economic Development
provided additional research inputs and funded Philip Stevens’ time for the study;
while the Foundation for Research, Science and Technology funded Arthur Grimes’
and Cleo Ren’s inputs through FRST grant MOTU0601 (Infrastructure). We thank
each of these agencies for their support; however, the authors remain solely
responsible for the analysis and views expressed in the paper.
Motu Economic and Public Policy Research
PO Box 24390
Wellington
New Zealand
Email info@motu.org.nz
Telephone +64-4-939-4250
Website www.motu.org.nz
© 2009 Motu Economic and Public Policy Research Trust and the authors. Short extracts, not
exceeding two paragraphs, may be quoted provided clear attribution is given. Motu Working Papers
are research materials circulated by their authors for purposes of information and discussion. They
have not necessarily undergone formal peer review or editorial treatment. ISSN 1176-2667 (Print),
ISSN 1177-9047 (Online).
Abstract
Fast internet access is widely considered to be a productivity-enhancing
factor. Internet access speeds vary regionally within countries and even within cities.
Despite articulate pleas for network upgrades to a
ccelerate internet access, there is
little rigorous research quantifying benefits to individual firms that arise from
upgraded internet connectivity. We use a large New Zealand micro-survey of firms
linked to unit record firm financial data to determine the impact that differing types
of internet access have on firm productivity. Propensity score matching is used to
control for factors, including the firm’s (lagged) productivity, that determine firms’
internet access choices. Having matched firms, we examine the productivity impacts
that arise when a firm adopts different types (speeds) of internet connectivity.
Broadband adoption is found to boost productivity but we find no productivity
differences across broadband type. The results provide the first firm-level estimates
internationally of the degree of productivity gains sourced from upgraded internet
access.
JEL classification
O33
Keywords
Internet, broadband, productivity
Disclaimer
The opinions, findings, recommendations and conclusions expressed in this report
are those of the authors. Statistics NZ, MED and Motu take no responsibility for any
omissions or errors in the information contained here.
Access to the data used in this study was provided by Statistics NZ in accordance
with security and confidentiality provisions of the Statistics Act 1975. Only people
authorised by the Statistics Act 1975 are allowed to see data about a particular,
business or organisation. The results in this paper have been made confidential to
protect individual businesses from identification.
The results are based in part on tax data supplied by Inland Revenue to Statistics NZ
under the Tax Administration Act 1994. This tax data must be used only for
statistical purposes, and no individual information is published or disclosed in any
other form, or provided back to Inland Revenue for administrative or regulatory
purposes. Any person who had access to the unit-record data has certified that they
have been shown, have read and have understood section 81 of the Tax
Administration Act 1994, which relates to privacy and confidentiality. Any discussion
of data limitations or weaknesses is not related to the data's ability to support Inland
Revenue's core operational requirements.
Any table or other material in this report may be reproduced and published without
further licence, provided that it does not purport to be published under government
authority and that acknowledgement is made of this source.
Contents
1 Introduction ................................................................................................................... 6
2 Prior Literature and Hypotheses ................................................................................. 8
2.1 Prior research ........................................................................................................ 8
2.2 New Zealand Context .......................................................................................10
2.3 Hypotheses..........................................................................................................12
3 Data ...............................................................................................................................14
4 Matching Models and Treatment Effects ................................................................27
4.1 Methodology .......................................................................................................27
4.2 Prediction Models ..............................................................................................29
5 Conclusions ..................................................................................................................35
References ..............................................................................................................................39
Appendix ................................................................................................................................41
6
1 Introduction
Fast internet access is considered to be a productivity-enhancing factor
(OECD, 2003a). As new technologies are introduced (e.g. copper-wire-based ADSL1
Despite well-articulated pleas for upgraded internet access, reference to
rigorous research that quantifies benefits actually accruing from network upgrades is
generally absent in supporting materials. A key reason for this conspicuous absence is
that little rigorous research exists that measures the productivity impacts of a shift
from one type of internet access to another. Most research in the field has been
conducted at an aggregated (regional or national) level or has bundled together
various types of information and communications technology (ICT) rather than
separating out the internet access component. Neither type of aggregation enables
reliable conclusions to be drawn about the extent of productivity improvements that
might arise if, say, an ADSL network is upgraded to a fibre network.
in place of dial-up, fibre optic cable in place of ADSL), calls are made to upgrade
telecommunications networks that service firms and households lest the local
community is left on the wrong side of the ‘digital divide’. Many of these calls
originate from think tanks or lobby groups (for example, New Zealand Institute,
2007).
Our study uses a large New Zealand micro-survey of firms to determine
the impact that differing types of internet access have on firm productivity. Our data
allow us to control for a wide range of factors (including the firm’s own lagged
productivity) that may determine a firm’s access choice. The work utilises data from
the 2006 Business Operations Survey (BOS), an official economy-wide sample survey
of firms that includes a wide range of questions on firms’ business practices,
including questions relating to their access to, and use of, the internet. The BOS data
is linked to a wealth of data from a variety of sources in the form of the prototype
Longitudinal Business Database (LBD) (Fabling et al, 2008). We link the BOS data
to data for each firm’s productivity, derived from administrative tax and employment
data. In addition, we are able to control for other firm characteristics recorded within
the LBD.
1 ADSL stands for Asymmetric Digital Subscriber Line.
7
We use propensity score matching (PSM) to control for factors that
determine firms’ internet access choices, and examine the impacts of various types of
internet access ‘treatment’. Specifically, we examine the productivity impact that
arises (ceteris paribus) when a firm: (a) adopts broadband (of any type) relative to no
broadband; (b) adopts ‘fast’ broadband (defined here as a cable connection) relative
to ‘slow’ broadband (all other broadband types); and (c) adopts slow broadband
relative to no broadband. We determine that the productivity benefits that arise from
a switch from no broadband to broadband access are material; we do not find
evidence for productivity differentiation based on the type of broadband connection
(i.e. cable versus other).
Section 2 of the paper outlines prior studies that have linked internet
access and related factors to productivity (or other economic outcomes). The review
is brief simply because few studies have examined the issues in depth. We set the
scene for subsequent analysis by reviewing the New Zealand ICT environment in
relation to internet access, then formulate hypotheses from the review material that
we subject to test in the paper.
Section 3 describes our data sources and presents descriptive statistics on
firms’ internet access. First, we report proportions of firms with particular
characteristics (e.g. employment size) that have broadband access. Second, we report
proportions of firms that use the internet for particular commercial purposes (e.g.
making internet sales) according to whether they have broadband or not. Third, we
report overall broadband and fast broadband access rates by region within the
country, demonstrating considerable geographic diversity in broadband access. This
diversity reflecting different geographic availability of services - can be considered
as a type of random assignment of firms across internet types within the economy.
We use this diversity in our subsequent estimates. Fourth, we reverse the first and
second comparisons; for each internet access type, we report characteristics and
internet uses of firms. The descriptive statistics provide considerable information
regarding firms’ uses of the internet, information that has been largely lacking in
prior studies.
Our PSM model results are presented in section 4. Probit and ordered
probit models are used to predict firms’ internet access choices; the results of these
models are instructive in understanding why certain types of firms choose various
8
internet access types. We match firms and calculate the treatment effects attributable
to internet access type. Treatment effects are calculated using two differing matching
technologies, across differing samples, and comparing differing access choices to test
robustness of results. We pay particular attention to whether treatment effects differ
according to urban or rural firm location given debates about whether to prioritise
fibre upgrades to rural or urban regions (Forman et al, 2009).
Conclusions are presented in section 5 together with an outline of
potential future work. We note that a follow-up survey, with a longitudinal element
to the ICT questions, will be accessible in a forthcoming Statistics New Zealand
survey, and suggest ways that the current analysis can be extended.
2 Prior Literature and Hypotheses
2.1 Prior research
Very little, if any, prior research specifically addresses the impacts of
broadband and, in particular, fast broadband on firms. Some research has been
conducted estimating aggregate economic impacts arising from Information and
Communications Technology (ICT) in general (OECD, 2003a; Clayton, 2005; Hagen
and Zeed, 2005) and broadband in particular (Greenstein and McDevitt, 2009).
Other studies have conducted analysis of broadband deployment at the regional
and/or industry level. For instance, Crandall et al (2007) estimate the benefits of U.S.
broadband penetration on output and employment by sector at the state level over a
three-year period. The study estimates that for every one percentage point increase
in broadband penetration within a state, employment increases by 0.2-0.3 percent per
year for the U.S. private, non-farm economy. The report identifies a positive
relationship between employment and broadband penetration in the manufacturing
and service industries (particularly finance, education, and health care). Based on
these findings, the authors recommend that policies should stimulate broadband
industry competition and encourage investment in broadband infrastructure.
A prior study (Lehr et al, 2006) analyzes broadband penetration at the
industry, community (zipcode), and state level but not at the firm level. The study
includes employment growth, wages, rent, business growth, and industry structure as
dependent variables. The results support the hypothesis that broadband penetration
enhances economic activity. The most significant effects are seen on job growth
9
(which diminishes as penetration rises) and business growth, particularly for larger
businesses and for IT intensive sectors. The study found no significant impact of
broadband penetration on wages, although did find an association with higher
residential property values in broadband-enabled communities. More recent research
(Forman et al, 2009) finds an association between firms’ internet use and wage
growth (at the county level in the United States) for richer counties, but finds little
impact for poorer rural areas.
One reason for such contradictory findings is flagged by Lehr et al who
note that state level data is at too high a level of aggregation to evaluate true
measurable impacts. For example, the authors hypothesize two ways that job growth
could be affected by internet access: (a) greater access could stimulate the economy,
leading to job growth; and (b) job growth could decline as typically labour-intensive
jobs are minimized because broadband facilitates capital-labour substitution. At the
state level, the study finds that broadband penetration has a positive (but
insignificant) impact on employment growth. However, when they add controls for
urbanization and coefficients for growth in employment during the late 1990s, the
direction of impact changes (albeit still insignificant). The aggregation dilemma is
further emphasized when the authors contrast state level results with community
level (zipcode) results; the community level data show a statistically significant
positive impact of broadband availability on employment growth.
Australian research is summarized by Collins et al (2007) who cite two
studies conducted by the Allen Consulting Group using firm level survey data. The
survey, which focused on business perceptions as opposed to firm financial statistics,
asked firms whether the internet has increased their knowledge of the market,
increased sales, customers, or business revenue, and increased efficiencies in sale and
distribution. Across all factors, broadband users were more likely to report higher
impacts than their dial-up counterparts. However the differences were not large
(67% to 61%, 49% to 40%, and 57% to 46%, respectively) and the results are
difficult to interpret since the studies did not control for the two groups’ other
characteristics.
Finnish work (Marilanta and Rouvinen, 2006) has evaluated the use of
“readily accessible technology” (laptops, data processing and storage devices
networked with wireless capability) at the firm level. The models use a wide variety of
10
technology-related input (independent) variables. The authors assign levels of
technology, called ‘ICT bundles’ based on three primary areas: 1) processing and
storage capabilities, 2) portability, and 3) connectivity. For example, a firm will be
considered to be in ICT bundle group ‘c’ if they have desktop computers and a LAN
connection, while another firm would be in ICT bundle ‘f’ if they have laptops and
WLAN connectivity. The authors control for the firms’ workforce composition (i.e.,
education, age, gender). Their results suggest that processing and storage capabilities
will increase a worker’s productivity by 9%, portability increases productivity by
nearly 32%, and wireline and wireless connectivity boosts productivity by 14% and
6%, respectively.
2.2 New Zealand Context
According to OECD (2008), New Zealand falls around the middle of
OECD countries for a range of broadband-related statistics. The country ranks 20th
(of 30 countries) for broadband subscribers per capita, 14th for household
broadband access, 13th for DSL coverage (at 93%), 5th for average advertised
download speeds, and 15th least expensive for average broadband access costs (in
PPP terms). Ford et al (2008) find that New Zealand’s per capita broadband
subscription rate is poor relative to other OECD countries once other factors (e.g.
incomes and education) are accounted for. The low population density of the
country is one reason they cite for New Zealand’s low subscription rate.
Against this factual background,2 there are several studies that discuss
potential benefits of broadband for aggregate GDP in New Zealand, but no studies
have been conducted at a micro-level. IDC Market Research (2006)3
2 Castalia (2008) provides further factual information on New Zealand broadband provision as at
2008.
forecast
additional nominal GDP that could be produced by additional broadband
penetration. The forecasting model is based on the Gompertz curve (where growth is
slow at the start and end of a period), and projects that New Zealand will reach
broadband penetration of 50 subscribers per 100 of population by 2023 (compared
with 16.5 per 100 in 2007; OECD, 2008). If this rate were accelerated and the level
of 50 subscribers per 100 was reached within 10 years, the study predicts nominal
3 The broadband diffusion analysis for this report was conducted by the Economist Intelligence Unit
(EIU).
11
annual GDP would increase by NZ$314 million by 2010, NZ$2,740 million by 2020,
and NZ$7,215 million by 2030; these projected increases compare with nominal
GDP of $131,500 million in 2007. However these figures must be treated as
speculative; they are not based on New Zealand-specific research or on firm-level
research linking broadband penetration to productivity gains.
One think-tank estimated that national economic benefits of moving from
existing broadband to high-speed broadband would be in the range of $2.7 - 4.4
billion per year (New Zealand Institute, 2007). These estimates were based on
international sources plus forecast global growth rates for industries and for the New
Zealand economy. Examples of input variables included: growth in the digital media
sector, ‘telepresence’ (the cost-benefit of domestic and international telecommuting),
increase in speed efficiency, growth in the data storage and manipulation sector,
increased internet access, growth in the online education sector, and potential for
innovation and business retention in the “weightless” economy. There were no
citations to firm-level analyses of productivity benefits arising from a move to fibre.
A follow-up report (New Zealand Institute, 2008) argued that recent fibre
developments were well behind its aspirations, and recommended regulatory and
investment intervention to hasten fibre provision and uptake. Castalia (2008), by
contrast, questioned the extent of demand by New Zealand subscribers for high-
speed broadband, and provided evidence that existing services, coupled with planned
broadband roll-outs and improved compression techniques, will cater for most uses
that New Zealand subscribers are willing to pay for over the foreseeable future.
The review of prior research indicates there is little research that directly
addresses the impact of broadband (fast or otherwise) on firm productivity. Some
studies raise the issue of what is an appropriate definition of ‘broadband’. Because of
the changing nature of broadband, it is difficult to pinpoint a definitive definition;
today’s broadband will be regarded as tomorrow’s narrowband. While acknowledging
that broadband is a moving target, the majority of studies adopt the OECD’s (2002)
definition that broadband has the capacity to provide transmission speeds of at least
256 Kbps. “High-speed’ or ‘fast’ broadband is generally regarded as internet access
facilitated through fibre-optic cable or through other mechanisms that allow much
faster speeds; e.g. 10Mbps (Castalia, 2008).
12
2.3 Hypotheses
We treat internet access as a “productivity shifter” within the firm
production function. Specifically, we assume that firm i in industry j has production
function:
Yij = AijFj[Lij, Kij] (1)
where Yij is the firm’s value added; L ij (Kij) is labour (capital) employed by firm i, F j
is a linearly homogeneous production function specific to industry j, and A ij is a firm-
specific productivity variable. Given the linear homogeneity of Fj, the logarithm of
average labour productivity is given by:
ln(Yij/Lij) = ln(Aij) + ln(Fj[1, Kij/Lij]) (2)
All firms in industry j face the same Fj and the same factor prices so, at the optimum,
the last term in (2) can be replaced by an industry-specific constant, ln(Cj), thus:
ln(Pij) ln(Yij/Lij) ln(Cj) = ln(Aij) (3)
where ln(Pij) is (log of) firm i’s labour productivity relative to the industry average;
this is the dependent variable in our empirical applications.
We hypothesise that Aij is potentially a function both of inherent
characteristics of the firm (A*ij) and of the firm’s speed of internet access. The type
of internet access is split into broadband which, in turn, may be split into fast
broadband (cable) and slow broadband (i.e. all other broadband types) and no
broadband (including both dial-up and no internet access). We hypothesise that,
ceteris paribus, firms with fast broadband will be more productive than firms with
slow broadband which in turn will be more productive than firms without broadband
access.
The ceteris paribus assumption is important since broadband access may
be correlated with variables that influence A*ij and some of these variables may also
influence choice of internet access. We address this issue by estimating an internet
access discrete choice equation, then matching each ‘treated’ firm with a set of
‘control’ firms where the treated firm and control set have similar likelihoods of
choosing the treatment. The treatment versus control options are variously modelled
13
as: (i) having broadband (treatment) versus no broadband (control); (ii) having fast
broadband (treatment) versus slow broadband (control); and (iii) having slow
broadband (treatment) versus no broadband (control). In order to determine (i), we
estimate a probit equation that splits firms into the two categories (broadband versus
none). For categories (ii) and (iii), we estimate an ordered probit equation that divides
firms into the three categories of fast, slow and no broadband. We use the same
explanatory variables across the two equations.
Given prior studies, we hypothesise that a firm’s internet access choice is
determined by a range of factors (our alternative hypotheses, relative to the null of
no effect, are outlined in parentheses): firm size (positive, reflecting resource
availability within the firm, possibly with a non-linear effect); firm age (negative,
reflecting older management); industry structure facing the firm (perfectly
competitive firms may gain less from broadband adoption than other firms); the
quality of ICT infrastructure in the firm’s locality (positive); the knowledge of the
firm’s management regarding ICT issues (positive); application of ‘modern’ general
management approaches within the firm (positive, reflecting openness to new ideas
that may boost productivity); knowledge intensity of the firm’s sector and whether
the firm conducts R&D (both positive, reflecting greater need for information flows
for high knowledge intensity sectors); being foreign-owned (positive, reflecting a
need for communication with the parent and receipt of parental experience with
improved connectivity); and the firm itself having a foreign subsidiary (positive,
reflecting a need for communication with the subsidiary).
We examine data relating to each of these variables, together with labour
productivity data, in the next section. Productivity data are used as the treatment
outcome variable. It is possible that the firm’s inherent productivity (A*ij) is one
determinant of the firm’s internet access choice. We cater for this possibility by also
including the firm’s five-year lagged productivity as an explanatory variable in the
broadband prediction equations. Thus lagged productivity is one of the variables on
which we match firms. This mitigates the potential problem that the observed
productivity treatment effect may be due to inherently more productive firms
adopting faster internet access.
14
In section 3, we provide descriptive data for uses of the internet by firms
with differing internet connectivity. These latter variables are not used, however,
when we estimate firms’ probability of being treated since patterns of internet usage
are likely to be endogenous to the firm’s internet choice.
3 Data
Our data are obtained from two sources. First, we access unit record
responses to Statistics New Zealand’s Business Operations Survey 2006 (BOS06).
This survey comprises three modules, A: Business Operations; B: Information and
Communications Technology; C: Employment Practices. Each module includes
detailed questions in relation to the individual respondent firm. The survey was
posted to over 7,000 firms by Statistics New Zealand (Statistics NZ), the country’s
official statistical agency. Firms were selected from all firms within the country that
had at least six employees4
Our second data source is Statistics New Zealand’s prototype longitudinal
business database (LBD) that links firm data derived from Statistics NZ’s firm-based
surveys and from various administrative sources to Statistics NZ’s Longitudinal
Business Frame, LBF (Seyb, 2003). The LBF contains descriptive information on
each firm (e.g. sector, age, foreign-ownership status). The administrative data sources
include firm tax data sourced from the Inland Revenue Department. The latter data
enable us to formulate a measure of each firm’s labour productivity defined as firm
value added relative to the firm’s employee count. We calculate labour productivity
using random sampling within strata defined by sector
and firm size. Each sampled firm is assigned a weight so that the weighted sample is
representative of all firms with at least six employees in the economy. Unless
otherwise specified, all descriptive statistics and estimation uses weighted data. Under
the Statistics Act 1975, it is a compulsory requirement for respondents to complete
the survey; in practice, this resulted in an 81.7% response rate (with 6,051 usable
responses). The rigorous sampling from the universe of firms with at least six
employees, coupled with the very high response rate, makes this an ideal source of
information on firms’ internet use and other characteristics.
4 Using a rolling mean employee (RME) count; see Statistics New Zealand (2006) for further details.
15
for a panel of years for each firm and for each four-digit sector.5
The productivity variable used to measure the treatment effect, lnLP2yr, is
the average of the firm’s 2005 and 2006 (log) labour productivity (relative to the
four-digit sector); we use the two year average to reduce noise in the data that may be
due to timing effects in reporting value added components across years.
In order to abstract
from labour productivity differences attributable to differing sector capital intensities
and other sector-specific characteristics, we express each firm’s labour productivity as
a ratio of the four-digit sector average. The sector average is calculated for all firms
within the four-digit sector across the entire population of New Zealand firms. The
resulting (log) labour productivity data therefore accord with ln(Pij) as defined in
section 2.
6
5 In a very few cases we aggregate to the three-digit sector where numbers of firms for the four-digit
sector calculation falls below 30 firms.
Figure 1
presents a kernel density graph of lnLP2yr for the firms in the BOS06 sample
(excluding each of the top and bottom 1% of firms for confidentiality reasons),
together with a normal density function. The productivity density is approximately
normal albeit with greater density both at the mean and (fractionally) at each tail.
Firm productivity (relative to the sector average) from 2001 is used as an explanatory
variable in the prediction equations.
6 We have also calculated the treatment effects using (unsmoothed) 2006 data and find similar results,
albeit with slightly higher standard errors.
16
Figure 1: Log labour productivity (2 year)
We access a number of variables from BOS06. Our key variable relates to
firms’ internet access. Firms are asked a number of questions regarding their internet
access, which we use to create a single index of internet access. Firms are asked
whether they can access the internet or not; if so, they are asked whether they have
broadband access7
Table 1 summarises the internet access modes recorded by all firms in the
BOS06 sample. Data are presented for the numbers of sampled firms,
, dial-up access only, or “don’t know”. If a firm has broadband
access, it is asked the nature of that access from the following list: (i) DSL (including
ADSL); (ii) cable; (iii) cellular; (iv) wireless; (v) satellite; (vi) don’t know. Of these
categories, we define cable as “fast broadband” and all others as “slow broadband”.
Within the cable category, fibre optic cable is included with other forms of cable (such
as hybrid fibre coax). The match between ‘cable’ and ‘fast’ broadband is therefore
imperfect, and this is one reason we compare ‘broadband versus none’ as well as ‘fast
broadband versus slow broadband versus none’ in our empirical work.
8
7 Firms are asked if they have broadband access only, or have both broadband and dial-up access; we
combine the two categories to a single broadband access category.
plus unweighted
8 All count data throughout the paper are randomly rounded to base 3 (a Statistics New Zealand
confidentiality requirement); hence totals do not always add exactly.
17
and weighted percentages in each category. Using weighted data, 91% of firms have
internet access, 76% have broadband access, while 7% have a cable connection.
Table 1: Internet access
Internet Access Mode
No. of
Firms*
% of
Firms
Weighted
% of Firms
No Internet Access
282
4.66
9.16
Internet access:
- dial-up only
390
6.45
11.08
- broadband (total)
5,145
85.03
76.40
- don't know/DNA**
231
3.82
3.35
Broadband type:
- cable (fast)
786
12.99
7.46
- other (slow)
4,359
72.04
68.94
Slow broadband:
- DSL
2,694
44.52
49.67
- cellular
54
0.89
0.86
- wireless
171
2.83
2.85
- satellite
30
0.50
0.41
- unknown
1,407
23.25
15.15
Total
6,051
* All counts randomly rounded to base 3 for confidentiality reasons.
** DNA = did not answer.
Table 2 examines the propensity for firms with certain characteristics to have
broadband (of any type) and to have fast broadband. In each case, we test whether the
propensity for firms with that characteristic to have (fast) broadband is significantly
different from the overall propensity to have (fast) broadband. These tests are all
conducted without controls, so are simply descriptive. The prediction models in section
4 provide multivariate tests of significance.
18
Table 2: Who has broadband (BB)? (Full sample)
Firm characteristic
No. of
firms*
Weighted
% with
BB*
Weighted
% with
fast BB*
% with
BB cf
Total
(p-val)
% with
fast BB
cf Total
(p-val)
Employees:
- [6, 20]
2,253
73.16
5.93
(0.0000)
(0.0000)
- (20, 50]
1,605
84.61
9.53
(0.0000)
(0.0410)
- (50, ∞)
2,193
92.02
18.23
(0.0000)
(0.0000)
Age (yrs):
- [0, 5]
1,281
72.89
7.35
(0.1488)
(0.9291)
- (5, 10]
1,551
80.85
7.29
(0.0297)
(0.8754)
- (10, ∞)
3,216
75.88
7.63
(0.7189)
(0.8151)
Foreign owned
912
93.36
19.55
(0.0000)
(0.0000)
Have foreign subsidiary
348
91.73
19.60
(0.0001)
(0.0011)
Competition:
- monopoly
258
76.15
7.89
(0.9534)
(0.8637)
- oligopoly
1,002
77.33
6.01
(0.7633)
(0.1522)
- Chamb. comp
3,216
80.52
7.90
(0.0023)
(0.5049)
- perfect comp
1,173
73.76
9.34
(0.3260)
(0.2689)
Conduct R&D
609
87.47
15.33
(0.0057)
(0.0051)
Knowledge Intensity (KI):
- below median
2,931
72.29
6.37
(0.0109)
(0.1748)
- above median
3,120
79.30
8.23
(0.0109)
(0.1748)
Management (MGMT):
- below median
2,874
71.02
5.80
(0.0000)
(0.0013)
- above median
3,177
84.19
9.86
(0.0000)
(0.0013)
Quality of ICT in area:
- bad
621
64.48
3.32
(0.0019)
(0.0002)
- mid
1,533
77.21
5.77
(0.7267)
(0.0427)
- good
3,588
81.41
9.32
(0.0000)
(0.0005)
- don't know
207
38.87
4.05
(0.0000)
(0.1480)
Firm has a webpage
4,140
90.20
11.48
(0.0000)
(0.0000)
Purchased G&S on internet
3,798
91.33
10.35
(0.0000)
(0.0000)
Entered new export market
384
91.80
14.13
(0.0000)
(0.0224)
% of sales made over internet:**
- 0
4,317
70.66
5.50
(0.0000)
(0.0004)
- (0, 25]
1,524
93.15
11.68
(0.0000)
(0.0082)
- (25, 100]
207
82.63
20.75
(0.5204)
(0.0850)
% internet sales international:
- 0
1,005
92.18
8.49
(0.4132)
(0.0180)
- (0, 25]
549
91.51
22.15
(0.8411)
(0.0394)
- (25, 100]
141
81.71
16.13
(0.3517)
(0.6910)
% sales from tourism:
- 0
5,316
76.36
6.95
(0.9505)
(0.3175)
- (0, 25]
504
75.64
4.21
(0.8764)
(0.0043)
- (25, 100]
231
78.34
19.62
(0.7781)
(0.0685)
Total
6,051
76.40
7.46
* All counts randomly rounded to base 3 for confidentiality reasons.
** DK/DNA allocated to zero sales.
19
Small firms (those with 20 or fewer employees) are less likely than other
firms to have general broadband (i.e. any kind of broadband) or fast broadband.
Firm age has no significant association with fast broadband, and only mid-aged firms
differ from the total in general broadband propensity (with slightly higher
penetration). Foreign ownership (in both directions) is associated significantly both
with greater general broadband and fast broadband penetration. By contrast, the
firm’s industrial market structure has no significant relationship with fast broadband
adoption, and only one significant relationship with general broadband.9
We calculate a direct measure of industry knowledge intensity. The
question structure and stratified sampling of the BOS enables us to compile a
knowledge intensity measure across sectors that is more graduated than a simple
distinction between knowledge-intensive and other sectors used in other applications.
BOS06 surveys the firm’s total staff level and its composition according to four
groupings: (i) managers and professionals; (ii) technicians and associate professionals;
(iii) tradespersons and related workers (including apprentices); and (iv) all other
occupations. We calculate the ratio of [(i)+(ii)]/total staff for firms within each
three-digit industry (denoted KI).
As expected,
the nature of a firm’s activities has a strong association with connectivity. Firms that
conduct research and development (R&D) have much higher general and fast
broadband penetration, and the degree of knowledge intensity of the firm’s industry
has a positive association with connectivity.
1011
9 To assess market structure for each firm we use the firm’s response to the question: “How would
you describe this business’s competition?” (BOS06 question A47). Choices, other than “don’t know”,
and our shortened descriptor (in brackets) are: “captive market / no effective competition”
(monopoly); “no more than one or two competitors” (oligopoly); “many competitors, several
dominant” (Chamberlinian monopolistic competition [Chamb]); “many competitors, none dominant”
(perfect competition).
Our prior is that connectivity is important for at
least the first two categories of employee, so we use KI as our measure of knowledge
intensity; each firm’s KI is given by the value for its three-digit industry. Table 3
presents KI for each industry. This measure may have applicability for studies
beyond the scope of this study.
10 In a few cases, we split three-digit industries into finer distinctions where OECD information
indicates a split between knowledge-intensive (KI) and other categories within the three-digit
classification.
11 We also produced another measure of knowledge intensity, calculated as the ratio of (i)/total staff
for firms within each three-digit industry. The correlation between the two measures is very high as is
their ranking of knowledge intensity across industries.
20
Table 3: Knowledge intensity by industry
Industry - 3 digit*
KI**
Business Services – “KI
0.7478
Services to Finance and Insurance
0.4839
Health Services
0.4416
Electricity, Gas & Water Supply
0.4215
Finance & Insurance
0.4155
Education
0.4093
Business Services
0.3919
Rail Transport
0.3586
Machinery & Motor Vehicle Wholesaling
0.3308
Motion Picture, Radio and TV Services
0.3112
Property Services
0.3112
Manufacturing – “KI
0.2697
Mining
0.2697
Communication Services
0.2615
Printing, Publishing and Recorded Media
0.2331
Personal & Household Good Wholesaling
0.2243
Air & Space Transport, water transport and storage
0.2238
Basic Material Wholesaling
0.2229
Agriculture
0.2096
Commercial Fishing
0.2054
Machinery and Equipment Manufacturing
0.2031
Service to Transport
0.1829
Sport and Recreation
0.1723
Personal & Household Good Retailing
0.1721
Petrol, Coal, Chemical & Assoc Prod Mfg
0.1706
Forestry and Logging
0.1698
Motor Vehicle Retailing and Services
0.1674
General Construction
0.1673
Metal Product Manufacturing
0.1658
Non-Metallic Mineral Product Manufacture
0.1577
Other Manufacturing
0.1570
Food, Beverage and Tobacco Manufacturing
0.1522
Community Services
0.1510
Construction Trade Services
0.1477
Textile, Clothing, Footwear, Leather Mfg
0.1461
Accommodation, Cafes & Restaurants
0.1291
Wood & Paper Product Manufacturing
0.1220
Services to Agric, Hunting and Trapping
0.1132
Food Retailing
0.0995
Road Transport
0.0915
* Where an industry has a “KI” suffix, the industry includes a finer gradation than the 3-digit level,
corresponding to OECD (2003c) definitions of knowledge-intensive sub-sectors.
** KI = proportion of total staff who are “managers, professionals, technicians or associate
professionals”.
21
One of our hypotheses is that the firm’s general management capability is
related to its likelihood of adopting better ICT systems, including faster internet
access. We use the rich question structure of BOS06 to formulate a proxy for each
firm’s general management capability. Module C contains ten questions on
employment practices, each of which can be considered a component of a suite of
‘high performance work systems’ (Fabling and Grimes, forthcoming). Table 4 sets
out the ten questions. We calculate the ten principal components for these questions
and associated eigenvalues. The first eigenvalue (4.53) is more than four times the
size of the second eigenvalue (1.09), summarising almost one half of the information
in the responses to the ten questions. Given its dominance, we adopt the first
principal component (MGMT) as our measure of each firm’s general management
capability.
Table 4: General management capability variable (MGMT)
Questions forming basis of MGMT principal component*
Does this business have any of the following practices in place on a formal basis for any non-
managerial employees?
- employee feedback programmes (e.g. satisfaction surveys);
- flexible job design (e.g. job rotation);
- information sharing (e.g. joint management/staff meetings, information
on performance or changes);
- problem-solving teams (e.g. teams limited to specific areas such as quality
or work flow);
- employees engaged in regular decision making;
- employee participation in health and safety;
- performance reviews;
- childcare (allowances or facilities);
- 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.
Principal Component**
Eigenvalue
Component 1 (denoted MGMT)
4.53075
Component 2
1.09004
Component 3
0.89271
Component 4
0.86988
Component 5
0.66726
Component 6
0.56015
Component 7
0.42061
Component 8
0.37622
Component 9
0.33234
Component 10
0.26005
* Each question can either be answered Yes or No.
**Principal components are calculated using tetrachoric correlations.
22
Table 2 indicates that firms with above median values for MGMT have a
significantly greater likelihood of having general and fast broadband than do below
median firms. The questions on which the management variable is based have no
direct relationship with ICT use in general (or internet connectivity in particular);
hence any significant impact of MGMT on broadband choice is unlikely to be driven
by reverse causality.
BOS06 questions firms on the quality of a range of infrastructure in their
local area. One question specifically asks the firm to rate whether “information and
communications technology (e.g. broadband availability, mobile phone coverage)” is
“bad”, “neither bad nor good” (which we denote “mid”), “good” or “don’t know”.
Table 2 shows that the “bad/mid/good” responses to this question are related
strongly to firms’ adoption of both general and fast broadband with the expected
relationships. One strength of the BOS design is that many questions give the option
of a “don’t know” response. Firms that indicate they don’t know about the quality of
ICT infrastructure in their area are far less likely to adopt general broadband even
than those faced with bad ICT conditions. This response is indicative of a lack of
ICT capability within the firm.
One reason that a firm may not have broadband, and especially fast
broadband, is that the service may not be available in the firm’s locality. A
complication that arises in interpreting answers with respect to broadband adoption,
and similarly for responses to the quality of local ICT infrastructure, occurs with
multi-plant firms. In these cases, the nature of internet access could differ across
plant locations for the same firm. In these situations, the BOS06 ICT infrastructure
quality question directs the firm to respond in relation to the location where the
largest share of the business’s activities occur.
In our econometric work, we address the issue of multi-plant firms in two
ways. For the full sample results, we adhere to the BOS06 guideline, and consider
that the firm has answered relevant questions with respect to its largest operation.
This approach may introduce noise in cases where internet access differs across
plants. Our alternative approach reduces the sample size and considers just single
plant firms. This results in a cut in the sample size for the probit equations
determining broadband adoption from 5,598 (full sample) to 4,110 (single plant
firms).
23
Table 5: Geographic distribution of broadband
TLA Group
Full sample
weighted
% with
broadband
Single plant
weighted
% with
broadband
Far North District+Kaipara District
75.84
75.91
Whangarei District
82.54
81.10
Rodney District
73.09
71.00
North Shore City
79.91
78.89
Waitakere City
72.88
72.92
Auckland City
89.79
88.32
Manukau City
80.45
81.73
Papakura District+Franklin District
75.38
72.88
Thames-Coromandel District+Hauraki
District+Waikato District +Matamata-Piako
District+Waipa District+Otorohanga District +South
Waikato+Waitomo District
60.67
56.44
Hamilton City
84.54
85.87
Taupo District+Western Bay of Plenty
District+Whakatane District+Kawerau
District+Opotiki District
65.72
61.16
Tauranga City
61.85
51.78
Rotorua District
71.88
73.09
Gisborne District+Wairoa District
58.39
54.93
Hastings District+Napier City
67.73
66.43
Central Hawke's Bay District+Tararua
District+Masterton District+Carterton District+South
Wairarapa District
71.65
67.39
New Plymouth District
67.95
76.04
Stratford District+South Taranaki District+Ruapehu
District +Wanganui District+Rangitikei
District+Manawatu District
61.51
69.21
Palmerston North City
71.01
66.55
Horowhenua District+Kapiti Coast District+Porirua
City
69.70
62.75
Upper Hutt City+Lower Hutt City
76.68
69.99
Wellington City
89.61
91.69
Tasman District+Nelson City
71.27
70.74
Marlborough District
73.03
70.76
Kaikoura District+Buller District+Grey
District+Westland District+Hurunui
District+Waimakariri District
79.84
78.38
Christchurch City
83.67
84.83
Selwyn District+Ashburton District+Timaru
District+Mackenzie District+Waimate
District+Waitaki District
69.73
74.31
Central Otago District+Queenstown-Lakes District
83.96
81.46
Dunedin City
68.85
68.39
Clutha District+Southland District+Gore District
47.47
44.09
Invercargill City
86.99
88.44
Total
76.40
75.35
24
The association of location with internet access type can be seen from
Table 5 that divides New Zealand into 31 groups of Territorial Local Authorities
(TLAs); each geographical grouping includes at least 40 firms (as sampled in BOS06)
and at least 9 firms with (and without broadband). In some cases, a group comprises
a single TLA; in other cases, TLAs have been grouped together according to
contiguity and similar rural/urban characteristics. For each geographical grouping,
the table presents the (weighted) percentage of firms that had general broadband in
2006. The percentages are presented both for single plant firms and for the full
sample. Across the two samples, proportions of general broadband adoption vary
between 44% and 92% by area.
In 2006, fast broadband (cable) was primarily available in major cities,
particularly Wellington/Hutt Valley, and Christchurch. Table 6 presents the same
information as Table 5, but pertaining only to fast broadband for major city areas
(plus all other areas grouped together).12
Table 6: Geographic distribution of fast broadband
The geographic location of a firm in relation
to general and fast broadband provision can be considered a random element in
deciding whether the firm receives (fast) broadband treatment or not.
TLA Group
Full sample
weighted
% with
broadband
Single plant
weighted
% with
broadband
Auckland City
11.19
9.37
Upper Hutt City+Lower Hutt City
32.83
35.00
Wellington City
19.60
17.27
Christchurch City
16.50
13.86
All other areas
4.01
3.26
Total
7.46
6.23
Table 2 indicates a strong association between firms that use the internet
for business purposes and the prevalence of general and fast broadband adoption.
For example, 92% of firms that make internet sales have a broadband connection
whereas only 71% of firms that make no internet sales have broadband. Over 90% of
firms that have a webpage, purchase goods and services over the internet, and have
recently entered a new export market have broadband. One surprising statistic is the
only moderate adoption of general broadband by firms involved in tourism, possibly
25
reflecting many tourist operators being located in rural areas that have relatively low
broadband availability.
The differences in broadband adoption according to firm characteristics
(Table 2) has the corollary that firms with differing internet access types, on average,
have different characteristics from firms with other access types. Table 7 summarises
characteristics of firms from the full sample according to internet access type in
relation to key variables used in our predictive models of section 4. The variables
correspond closely to the variables in the top portion of Table 2. One exception is
market competition where the variables “monopoly”, “oligopoly” and
“Chamberlinian monopolistic competition” are reduced to a single variable,
“imperfect competition”, based on tests in which we could not reject the hypothesis
of identical coefficients for the three competition variables. We also omit the “mid”
category for ICT quality since this is the omitted category for that question in the
prediction equations.
For each variable, the table presents the p-value for two F-tests: (a) a test
of the difference between means for firms with fast broadband versus slow
broadband; and (b) a test of the difference between means for firms with slow
broadband versus firms with no broadband. We find a clear gradation between firms
with fast, slow and no broadband respectively for firm size (firms with broadband
tend to be larger), quality of local ICT infrastructure, having foreign ownership
and/or a foreign subsidiary, having an R&D operation, sector knowledge intensity
and general management quality. In addition, there is a clear distinction between
having broadband (fast or slow) versus none for firms that don’t know about the
quality of local ICT infrastructure and for firms in imperfectly relative to perfectly
competitive markets. Firm age appears unrelated to broadband adoption. Table 8,
which summarises characteristics for firms in the single plant sample, shows very
similar patterns.
12 A majority of firms that report having fast broadband within ‘all other areas’ are situated in Kapiti
Coast District which had a cable service in 2006.
26
Table 7: Characteristics of firms with fast, slow & no broadband (full sample)
Variable*
Mean
F-test of
difference in
means; fast v
slow
[p-value]
F-test of
difference in
means;
slow v none
[p-value]
Fast Slow None
SIZE (Employees)
78.6
29.0
11.5
0.0000
0.0000
AGE (Years)
14.6296
13.2355
13.7569
0.3134
0.6636
ICT-GOOD
0.7051
0.5906
0.4327
0.0032
0.0001
ICT-BAD
0.0495
0.0988
0.1860
0.0071
0.0018
ICT-DK
0.0248
0.0231
0.1065
0.9111
0.0004
IMP-COMP
0.7049
0.7381
0.6141
0.5108
0.0015
FOWN
0.1774
0.0725
0.0135
0.0000
0.0000
FSUB
0.0735
0.0293
0.0046
0.0028
0.0000
R&D
0.1331
0.0678
0.0341
0.0146
0.0300
KI
0.3997
0.2827
0.1896
0.0000
0.0000
MGMT
0.8702
0.7907
0.5586
0.0249
0.0000
lnLP2yr
0.0035
-0.0365
-0.1666
0.5713
0.0013
* Variables in Tables 7, 8 & 9 are defined in the Appendix (with sources)
Table 8: Characteristics of firms with fast, slow & no broadband (single plant)
Variable
Mean
F-test of
difference in
means; fast v
slow
[p-value]
F-test of
difference in
means;
slow v none
[p-value]
Fast Slow None
SIZE (Employees)
22.7
16.6
10.8
0.0029
0.0000
AGE (Years)
11.8615
12.62939
13.5962
0.5492
0.4621
ICT-GOOD
0.7059
0.5975
0.4328
0.0266
0.0002
ICT-BAD
0.0474
0.0918
0.1732
0.0596
0.0040
ICT-DK
0.0289
0.0247
0.0911
0.8493
0.0012
IMP-COMP
0.7168
0.7311
0.6175
0.8193
0.0061
FOWN
0.1307
0.0633
0.0147
0.0094
0.0000
FSUB
0.0610
0.0265
0.0037
0.0564
0.0001
R&D
0.1616
0.0653
0.0189
0.0095
0.0000
KI
0.4094
0.2822
0.1873
0.0003
0.0000
MGMT
0.8490
0.7823
0.5319
0.1653
0.0000
lnLP2yr
-0.0055
-0.0530
-0.1807
0.5968
0.0061
Also included in Tables 7 and 8 are data for the mean log labour
productivity measure (lnLP2yr). Both tables indicate a significant “raw” productivity
differential of approximately 13% for firms that have slow broadband relative to
those that have none. There is a 4-5% productivity differential, on average, between
firms with fast and slow broadband, but this difference is not significantly different
from zero in either sample. In each case, these are raw productivity differences, i.e.
prior to incorporation of any controls. We control for firm characteristics that may
influence internet access type in the next section.
27
4 Matching Models and Treatment Effects
4.1 Methodology
We control for confounding influences on productivity by estimating
probit and ordered probit models to predict each firm’s connectivity type. In our
basic approach, firms are separated into broadband (treatment) and no broadband
(control) groups and their propensity to have broadband is estimated using a probit
model. In this approach, firms with all types of broadband connection are included
in the treatment group. In our extended approach, we estimate an ordered probit
model with three types of firm: those with fast broadband (cable), those with slow
broadband (all other broadband types) and those with no broadband. This approach
technically enables us to differentiate between three broadband dichotomies: fast
(treatment) versus slow (control); slow (treatment) versus none (control); and fast
(treatment) versus none (control). In reporting our results, we exclude the last of
these comparisons since these results reflect the combined effects of the first two
dichotomies.
Tables 5 and 6 showed considerable diversity of broadband uptake across
regions, with a discernable urban/rural split (i.e. according to population density). We
account for this factor by ranking all TLAs according to their population density (in
2001) and forming a variable (HIDEN) for those authorities that have a density of at
least 200 people per km2. Palmerston North is the least dense of this group with 214
people/km2, over twice the density of the next densest TLA, Invercargill (102
people/km2). One anomaly is that Dunedin City (New Zealand’s sixth largest city,
with a major university) is excluded from the high density group owing to inclusion
of a large rural hinterland within its boundaries. Most Dunedin firms are located in
the city proper, and so we include Dunedin in the HIDEN group.13
13 The HIDEN group includes the following TLAs: North Shore City, Hamilton City, Christchurch
City, Wellington City, Tauranga District, Manukau City, Napier City, Waitakere City, Auckland City,
We deal with the
two groups in two separate ways. First, we include HIDEN as a separate variable in
the prediction equations. This allows a level shift in probabilities between firms
located in high and low density areas. Second, we estimate separate equations for
firms in the high and low density authorities, and subsequently calculate separate
treatment effects for urban and rural areas. Results from this latter approach are
28
particularly relevant to policy debates about whether broadband (fibre) roll-outs
should be focused primarily on urban or rural areas.
We are most confident about the connectivity data for single plant firms,
for reasons already discussed. We therefore estimate the ordered probit models solely
for the single plant sample. Furthermore, since cable is rarely available in low density
areas, we do not estimate the ordered probit equation for the rural sub-set of firms.
The probit models (broadband versus none) are presented for both the full sample
and the single plant sample (all areas, and urban and rural sub-samples). We use each
of the eight prediction equations to calculate propensity scores where the propensity
score is the conditional probability of a firm within the relevant sample receiving the
relevant treatment, given the covariates included in the prediction model
(Rosenbaum and Rubin, 1983). The scores are used to match treated firms with
suitable controls (using two different matching methods) thereby enabling us to
determine the average treatment effect for the treated firms (ATT).
Extensive discussions of PSM models are provided in Caliendo and
Kopeinig (2008), Dehejia and Wahba (2002), and Becker and Ichino (2002).14 A key
element required to implement the PSM approach is that firms with similar
propensity scores must look the same statistically whether or not they have been
treated. In order to test this ‘balancing hypothesis’, we split each sample into five or
more strata according to their propensity score.15
For our basic model (broadband versus none), the number of treated
firms is over four times the number of control firms. We take account of this
Within each stratum, we perform a
t-test of the means of the covariates for treated and control firms. If all differences
are insignificant, the balancing hypothesis is met. This property holds for each
variable across all equations at the 0.1% significance level. For the four equations
with the most homogeneous samples (single plant, high and low density; probit and
ordered probit), the balancing hypothesis holds at (a minimum of) the 1% level, as is
also the case for the full plant high density sample.
Papakura District, Kawerau District, Porirua City, Lower Hutt City, Palmerston North City, Dunedin
City. The variable HIDEN=1 for firms in these TLAs and 0 otherwise.
14 Becker and Ichino (2002) outline the Stata programmes that form the basis of our application.
15 The number of strata is determined by the requirement that we cannot reject the average propensity
scores for treated and control firms being equal within each stratum; we begin with five strata in each
case, and reduce the stratum width (i.e. increase the number of strata) until this requirement is met.
29
characteristic by choosing matching methods that use multiple control firms for each
treated firm rather than using nearest neighbour matching. The latter would have to
assign each control firm to at least three or four different treated firms which could
create considerable noise in cases where a few control firms had large idiosyncratic
productivity determinants. Instead, we choose strata matching and kernel matching
approaches. Kernel matching utilises the full set of controls, with weights assigned
according to the ‘closeness’ of control firms to the treated firm. Weights are inversely
proportional to the distance between the propensity scores of the treated and control
firms.16 Compared with nearest neighbour matching,17
4.2 Prediction Models
the declining weight kernel
specification (adopted here) trades off greater bias for lower variance. The greater
bias arises because we do not choose the closest potential match for each treated
firm, but the spreading of weights across numerous controls reduces the chance of a
match to a large idiosyncratic outlier, so reducing variance. Strata matching adopts
the strata used to test the balancing hypothesis for each model and computes the
within stratum average treatment effect as the average of the outcome for the treated
firms minus the average for the untreated firms in that stratum. The overall ATT is
calculated as the weighted average treatment effect across the strata where the
weights depend on the fraction of treated firms across the strata. Standard errors are
calculated analogously.
We begin by estimating the probit (and ordered probit) models for
whether firms have broadband or not (respectively have fast, slow, no broadband).
The probit models are estimated both for the full sample and single plant sample; the
ordered probit models are estimated only for single plant firms.
SIZE (number of employees) is entered as a quadratic to account for
potential non-linear effects. One complication is that SIZE may be endogenous with
respect to broadband adoption, since if broadband affects firm performance, firm
size may change as a result. In order to account for this possibility, we enter SIZE as
the employment level of the firm lagged five-years (which is enabled by the
16 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.
17 Nearest neighbour matching is a special case of kernel matching where a weight of one is placed on
the control firm that is closest to the treated firm, with zero weights on all other firms.
30
longitudinal element of the LBD). Some firms surveyed in 2006 did not exist five
years prior and therefore have no lagged employment level. We deal with this
situation by recording these firms’ (lagged) SIZE as zero and including a dummy
term in the prediction equation (SIZEMISS) where SIZEMISS=1 if the firm had
zero employment (i.e. did not exist in an economically significant form) in 2001 (and
0 otherwise).18
Other variables included in the prediction equations are a constant plus:
firm age in years (AGE), good local ICT infrastructure (ICT-GOOD), bad local ICT
infrastructure (ICT-BAD), don’t know about local ICT infrastructure (ICT-DK), a
dummy if the firm is in an imperfectly competitive sector (IMP-COMP), a dummy if
the firm is foreign-owned (FOWN), a dummy if the firm has a foreign subsidiary
(FSUB), a dummy if the firm conducts research and development (R&D), the
management quality variable (MGMT), the relevant sector’s knowledge intensity
(KI), and a dummy if the firm is located in a high density area (HIDEN) where this
latter variable is included only for the all-density samples. (High density and low
density sub-samples are chosen on the basis of whether HIDEN =1 or =0 for the
firm.)
18 We 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.
31
Table 9: Prediction Modelsa
FULL PLANT SAMPLE
SINGLE PLANT SAMPLE
Probit
Probit
Ordered Probit
Density
All
High
Low
All
High
Low
All
High
SIZE (Lagged)
0.0034***
0.0032***
0.0050*
0.0056**
0.0047*
0.0088
0.0059***
0.0056***
[3.3959]
[3.2251]
[1.9182]
[2.5455]
[1.8515]
[1.6086]
[3.6009]
[2.9574]
SIZE2 (Lagged)
-0.0000***
-0.0000***
-0.0000*
-0.0000**
-0.0000*
0
-0.0000***
-0.0000**
[3.2603]
[3.0083]
[1.8913]
[2.2773]
[1.6542]
[1.0804]
[2.7571]
[2.2956]
SIZEMISS
0.0516
-0.1131
0.2687
0.0933
-0.0516
0.351
0.0605
-0.0799
[0.2975]
[0.5908]
[0.9459]
[0.5575]
[0.2477]
[1.3180]
[0.3787]
[0.4260]
PROD (Lagged)
-0.1079
-0.0951
-0.068
-0.0663
-0.057
-0.0596
-0.0374
-0.0231
[1.5509]
[1.1012]
[0.7353]
[0.9288]
[0.5760]
[0.6467]
[0.6805]
[0.3132]
PRODMISS
-0.2176
-0.1267
-0.2845
-0.2083
-0.1454
-0.3737
-0.0583
0.0177
[1.3587]
[0.7605]
[1.0236]
[1.3718]
[0.7824]
[1.4484]
[0.4228]
[0.1118]
AGE
-0.0059**
-0.0088***
-0.0023
-0.0064*
-0.0108***
-0.0027
-0.0044
-0.0068*
[1.9896]
[2.6790]
[0.4531]
[1.7717]
[2.5957]
[0.4567]
[1.3716]
[1.6995]
ICT-GOOD
0.0735
0.1519
0.0183
0.1591
0.2545*
0.011
0.1714*
0.2116*
[0.6978]
[1.1742]
[0.1150]
[1.4289]
[1.7972]
[0.0666]
[1.8697]
[1.8708]
ICT-BAD
-0.3782***
-0.2372
-0.4145**
-0.3057**
-0.253
-0.3904**
-0.2424*
-0.1383
[2.5829]
[0.9809]
[2.3029]
[2.0143]
[0.9043]
[2.0991]
[1.7277]
[0.5089]
ICT-DK
-0.8069***
-0.4464*
-1.5352***
-0.6737***
-0.3512
-1.6455***
-0.5878**
-0.2545
[4.0111]
[1.7433]
[6.1061]
[3.2520]
[1.2843]
[5.6941]
[2.4990]
[0.8687]
IMP-COMP
0.2313**
0.0163
0.4698***
0.2298**
0.0532
0.4815***
0.1634
0.0942
[2.4319]
[0.1360]
[3.3120]
[2.3232]
[0.4026]
[3.3320]
[1.6167]
[0.7831]
FOWN
0.6348***
0.5676***
0.7108**
0.5600***
0.6010***
0.4958*
0.3717***
0.3544***
[4.5549]
[4.3780]
[2.2805]
[3.5865]
[3.4844]
[1.7118]
[3.9117]
[3.2715]
FSUB
0.5616**
0.4517
0.6104*
0.3672
0.37
0.224
0.227
0.2166
[2.1158]
[1.5338]
[1.8269]
[1.3651]
[1.1276]
[0.4619]
[1.4176]
[1.2029]
R&D
0.1772
0.4365***
-0.2009
0.3888***
0.4698***
0.3484
0.3810***
0.4118***
[0.9242]
[2.8012]
[0.5787]
[2.6941]
[2.5936]
[1.4175]
[3.2042]
[2.9118]
MGMT
0.5838***
0.4573***
0.7113***
0.6704***
0.5374***
0.8132***
0.5529***
0.4353***
[4.7983]
[2.9580]
[3.8677]
[5.3073]
[3.2264]
[4.3162]
[4.6959]
[2.8442]
KI
1.0562***
0.9646***
1.1832***
1.0538***
0.9433***
1.2635***
0.9258***
0.9032***
[5.2424]
[4.2089]
[3.3508]
[4.9720]
[3.7842]
[3.3655]
[5.6497]
[5.1003]
HIDEN
0.2045**
0.2086**
0.3029***
[2.2355]
[2.1330]
[3.2232]
Obs.
5,598
4,284
1,632
4,110
2,823
1,287
4,110
2,820
Pseudo-R2; (F-stat)
0.1288
0.0932
0.1841
0.1291
0.0977
0.1844
(9.5314***)
(5.3932***)
Blocks (FvS;SvN)b
19
11
30
11
6
7
7; 10
6; 5
a Constant included in all equations, but not reported. Robust z-statistics in parentheses; ***significant at 1%, **significant at 5%, *significant at 10%.
b All equations balance at the 1% level (or higher) other than columns 1, 3 and 4 (balance at 0.1%).
32
Table 9 presents the results for each of the eight probit and ordered probit
prediction equations. Several consistent findings emerge. Larger firms have greater
uptake of faster internet modes, although the marginal effect in almost all cases
diminishes with size. Strong results are obtained for the importance of the quality of
management (positive, always significant at 1%), knowledge intensity of the sector
(positive, always significant at 1%), foreign ownership (positive, significant at 5% in
all but one case), and, for the all-density samples, the urban/rural dummy variable
(positive, always significant at 5%). Other generally consistent results across
equations include impacts of firm age (negative), firm R&D (positive), competitive
market structure (perfectly competitive firms have less uptake of broadband), lack of
ICT knowledge within the firm (negative), good and bad local ICT infrastructure
(positive and negative respectively), and having an overseas subsidiary (positive).
Lagged firm productivity is negative in each equation, but is never
significant. This is important for our study since it indicates that any estimated
positive treatment effects cannot be attributed to reverse causality, i.e. to inherently
high productivity firms adopting (fast) broadband. (If the consistent negative
coefficient were taken to indicate a true negative relationship between a firm’s
productivity and broadband adoption, the implication could be that formerly less
productive firms subsequently adopt faster internet access in an effort to raise their
productivity.)
The significance of HIDEN in the all-density equations indicates that
there is at least a level shift in propensity to adopt broadband between urban and
rural areas. A comparison of the high-density versus low-density equations suggests
that some variables are more important in urban and rural areas respectively. For
instance, bad local ICT infrastructure has a greater negative effect on broadband
uptake in rural than urban areas, likely reflecting greater diversity of infrastructure
standards in low-density authorities than in high-density (larger urban) areas. Lack of
ICT knowledge also has much greater effect in rural than urban areas, consistent
with greater knowledge spillovers between firms in urban areas. The conduct of
R&D within the firm is significant in high-density areas, but not in low-density areas
reflecting a general lack of R&D facilities outside major cities. Market structure is
33
more important in rural than urban areas, possibly reflecting differing degrees of firm
competition between the two types of area. These urban/rural differences make it
important to interpret the ATT results for the separate area types as well as for the
all-density samples.
Table 10 presents estimated average treatment effects (ATTs) for firm
productivity based on the prediction equations in Table 9. We concentrate initially on
the estimates of the treatment effects of moving from no broadband to broadband
(of any type). All twelve estimates19 are positive and significant (at the 5% level or
less) with the ATT point estimates ranging from 0.073 (7.6% productivity
improvement) to 0.122 (13.0% productivity improvement). The low-density samples
indicate slightly higher productivity gains than do the high-density samples, but there
is not a statistically significant difference between them.20
19 I.e. six separate samples - all/single plants by all/high/low density - each with two matching
techniques.
20 To test robustness, we have calculated the ATTs excluding the top and bottom 1% of firms by
productivity in case these data are spurious. The magnitude of results is similar for the all density and
high-density samples, but the low-density ATT is now slightly lower than the high-density ATT, albeit
again not statistically significantly different. Even if there were a difference in urban versus rural
effects, this study is not a cost-benefit analysis since costs of infrastructure provision in different areas
are not included here.
34
Table 10: ATT estimates, Relative Labour Productivity (lnLP2yr)
Based on prediction equations in Table 9
N.
Treated*
N.
Control*
ATT Std. Err.** t***
Full Sample; all-density; broadband versus none
Stratified
4,632
795
0.087
0.032
2.698
Kernel
4,632
786
0.114
0.033
3.450
Full Sample; high-density; broadband versus none
Stratified
3,474
426
0.073
0.043
1.713
Kernel
3,477
426
0.098
0.034
2.851
Full Sample; low-density; broadband versus none
Stratified
1,203
378
0.113
0.045
2.543
Kernel
1,206
378
0.119
0.058
2.049
Single Plants; all-density; broadband versus none
Stratified
3,069
675
0.100
0.041
2.442
Kernel
3,072
675
0.122
0.029
4.216
Single Plants; high-density; broadband versus none
Stratified
2,061
273
0.089
0.048
1.837
Kernel
2,061
273
0.112
0.043
2.600
Single Plants; low-density; broadband versus none
Stratified
852
324
0.114
0.055
2.076
Kernel
852
327
0.114
0.056
2.026
Single Plants; all-density; slow versus no broadband
Stratified
2,688
681
0.091
0.033
2.802
Kernel
2,688
681
0.120
0.030
4.008
Single Plants; high-density; slow versus no broadband
Stratified
1,863
351
0.099
0.046
2.165
Kernel
1,863
348
0.099
0.041
2.437
Single Plants; all-density; fast versus slow broadband
Stratified
369
2,685
-0.024
0.051
-0.475
Kernel
369
2,688
0.002
0.045
0.041
Single Plants; high-density; fast versus slow broadband
Stratified
342
1,863
-0.034
0.037
-0.921
Kernel
345
1,863
-0.018
0.056
-0.314
* All counts have been randomly rounded to base 3 for confidentiality reasons.
** Kernel standard errors are calculated using a bootstrap with 60 replications.
*** Critical values for the t-statistic (one-tailed test) at 1%, 5% and 10% are: 2.326, 1.645 and 1.282
respectively; a one-tailed test is appropriate since our alternative hypothesis is that the treatment raises
productivity of the treated firm.
For the single plant (all-density and high-density) samples, the effects of
shifting from no broadband to slow broadband are estimated to be of similar
magnitudes (and similar significance) to the impact of moving to broadband in
general. This is to be expected given that the majority of broadband connections in
the sample are included in our definition of slow broadband. The average of the two
all-density estimates indicates a productivity gain of 11.1% arising from adoption of
slow broadband relative to no broadband. This compares with a raw productivity
35
difference (before adding any controls) of 13.6% between single plants with slow
broadband relative to none (from Table 8). Thus approximately a fifth of the
differing productivity measures between the two sub-sets of firms is accountable by
observable factors (including lagged firm productivity), with four-fifths attributable
to the differing broadband status.
A quite different result emerges for the shift from slow to fast broadband.
Our ATT estimates indicate no significant impacts arising from such a shift. In other
words, our estimates imply no average firm productivity improvement as a result of a
shift from say an ADSL connection to a cable connection. (We discuss why this may
be the case in the Conclusions.) For this comparison, our controls play a major role
in explaining productivity differences between the two sub-samples of firm. Table 8
indicates a raw average productivity difference between firms with fast relative to
slow broadband of 4.9%. The approximately zero estimated ATT (after controlling
for firm characteristics) suggests that this difference is attributable to inherently more
productive firms adopting cable rather than a productivity-enhancing effect arising
from cable adoption.
5 Conclusions
Much has been written in the popular press of the benefits of the internet
revolution, and of the even greater benefits that could be brought about by linking
firms (and households) to the internet through fibre optic cable. Yet, despite huge
investment budgets associated with broadband (especially fibre) roll-outs, there has
been little rigorous supporting evidence indicating that such connectivity brings
material productivity benefits. Our study is the first, internationally, to estimate the
productivity impacts of connectivity upgrades using firm level data after controlling
for firms’ connectivity choices based on their characteristics. The study utilises a
representative, economy-wide sample survey, the Business Operations Survey 2006
(BOS06), of firms with at least six employees undertaken by Statistics New Zealand,
commanding an over 80% response rate.
Amongst the sample of 6,051 firms, 13% have a fast (cable) broadband
connection (7% when weighted), 72% (69%) have slow broadband; a majority of the
remaining firms have a dial-up connection. Internet connection type is affected by
the location of firms, with urban firms more likely to have broadband than rural
36
firms; firms in the Wellington/Hutt and Christchurch areas are most likely to have a
cable connection. Both our raw data comparisons between firms with and without
(fast and general) broadband and our predictive probit models of connectivity type
accordingly find that firms’ urban/rural location and their assessment of local ICT
quality helps to predict their internet access type.
Other strong predictors of connectivity choice are: firm size (larger firms
choose faster connectivity), the firm’s general management capability, being foreign
owned, knowledge intensity within the firm’s sector and (in urban areas only) R&D
activity by the firm (all positively associated with connectivity). Sector market
structure, firm age, ownership of a foreign subsidiary and firm-specific ICT
knowledge also appear relevant.
Broadband-enabled firms are more likely to use the internet in their
commercial transactions. In particular, they are more likely to have a webpage, to
purchase goods and services over the internet, to enter new export markets and to
make sales over the internet than firms without broadband. Perhaps curiously,
tourism firms are no more likely to have broadband than other firms, possibly
reflecting the geographic isolation of many firms in this industry.
In formulating variables to model connectivity choice, we derive two
variables that may be of use in other studies. Our firm management capability
variable (MGMT) is derived as the first principal component from ten questions
relating to the firm’s practices with regard to human resource matters. Despite its
lack of direct ICT content, this variable proves to be a strong predictor of a firm’s
connectivity choices in keeping with our hypothesis that firms with high
management capabilities will also tend to have high capability in other areas including
ICT.
Our knowledge intensity variable (KI) is also a strong predictor of firms’
connectivity choices. This variable is derived at the 3-digit (or finer) industry level
from answers from all firms in the BOS06 survey. The industry stratification of the
survey enables us to be confident that these figures are broadly representative for
each 3-digit sector. We take the ratio of “managers, professionals, technicians and
associate professionals” to total staff for firms across each sector and use this ratio to
proxy the knowledge intensity of the sector. The resulting cardinal variable orders
37
sectors in an intuitively appealing sequence that accords with other studies (e.g.
OECD, 2007). A key benefit of our measure is that firms are not assigned to be in
either a “knowledge-intensive” or “other” industry based on an arbitrary cut-off
between the two groups; rather each sector has a value of between zero and one for
its degree of knowledge intensity. It is likely that this variable could be used in other
studies both in New Zealand and in similar economies internationally.
After estimating our prediction models for connectivity choices, we match
each “treated” firm with a set of like “control” firms (both in terms of their
estimated propensity to have broadband and on their observable characteristics).
Two types of matching (kernel and stratified) are adopted to check robustness of
results. We then estimate the average treatment effect for treated firms (ATT) across
a range of samples. We focus on ATTs relating to shifts from: (i) broadband versus
no broadband, (ii) slow versus no broadband, and (iii) fast versus slow broadband.
We find a (levels) productivity effect of broadband relative to no broadband of
approximately 10% across all firms. The estimates indicate a marginally stronger
impact on firm productivity for firms in rural (low population density) relative to
urban (high density) areas but the differences are not significantly different. Our
estimates show that all of these productivity gains can be attributed to adoption of
slow relative to no broadband, with no discernable additional effect arising from a
shift from slow to fast broadband.
The finding that a move to fast broadband (cable) from any other form of
broadband has no estimated effect should be interpreted with care. At least four
explanations (other than an actual nil effect) could account for this result. First, our
split between fast and slow broadband based on the distinction between cable and
other broadband types may be a poor representation of differing internet speeds. In
particular, ‘cable’ in New Zealand may include technologies with anything from
average download speeds of 8Mbps to speeds of up to 1Gbps; by contrast, the
average ADSL download speed is 5Mbps (Castalia, 2008). A distinction within the
cable category, rather than between cable and other, may be more meaningful (but is
unavailable within our dataset). Second, not all survey respondents may be aware of
the technical nature of their firm’s broadband connectivity type, introducing noise
into the data. Third, the cable/other distinction may be meaningful but firms may
have only recently adopted cable and are yet to achieve the full productivity benefits
38
from doing so. Fourth, the productivity benefits of moving to fast broadband may
currently only be relevant to a small proportion of firms, and so the full future
benefits may not be apparent in the existing data. If this were the case, our average
firm effect would not be appropriate as an estimate of future benefits of fibre
upgrades across the full economy.
Future work could expand on our rural/urban distinction to test whether
the benefits of a shift to (fast) broadband are greater for certain types of firm than
others. This may include an examination of whether firms with greater propensity to
adopt broadband (i.e. firms with a higher propensity score) also, on average, gain
more from doing so. Alternatively, firms in certain sectors may gain more or less
from broadband adoption reflecting factors that are separate from those captured by
our knowledge intensity and other explanatory variables.21
21 Preliminary work found no evidence that the average treatment effect varies according to the
likelihood of treatment (i.e. as the propensity score rises). However, we leave it to future work to test
whether the ATT varies across other definitions of firm type (e.g. sector, R&D intensity, etc).
Current and future
upgrades from ADSL to fibre across areas should produce a growing longitudinal
sample of firms that gain faster internet access by virtue of an event exogenous to the
firm (i.e. a spatially-specific upgrade to fibre). Until the data for such longitudinal
analysis become available, we conclude that firms with faster connectivity make
greater use of the internet in their commercial transactions. Furthermore, on the
basis of our propensity score matching, we conclude that a shift to broadband
connectivity (from dial-up) appears to raise firm productivity.
39
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41
Appendix Definitions of variables in Tables 7, 8, 9*
SIZE
Number of employees (Average of twelve monthly PAYE employee counts in the year. These monthly employee counts are taken as at 15
th
of the month. )
plus working proprietors (Linked Employer Employee Database definition).
Source: Statistics New Zealand (SNZ) prototype Longitudinal Business Database (LBD).
SIZEMISS
Dummy =1 if no 2001 employment data available (=0 otherwise).
Source: LBD.
PROD
ln(labour productivity) = value added/employment (where employment given by SIZE), where value added is sales less purchases from the ‘Business Activity
Indicator’ database (GST tax return, GST101). Source: LBD.
PRODMISS
Dummy =1 if no 2001 labour productivity data available (=0 otherwise).
Source: LBD.
AGE
Number of years since establishment of the firm.
Source: LBD.
ICT-GOOD
Dummy =1 if firm answers “good” to question: “When thinking about the city, town or district in which this business operates, how would you rate …
information & communications technology infrastructure (eg broadband availability, mobile phone coverage)?” (=0 otherwise).
Source: 2006 Business Operations Survey (BOS06/LBD).
ICT-BAD
Dummy =1 if firm answers “bad” to question: “When thinking about the city, town or district in which this business operates, how would you rate …
information & communications technology infrastructure (eg broadband availability, mobile phone coverage)?” (=0 otherwise).
Source: BOS06/LBD.
ICT-DK
Dummy =1 if firm answers “don’t know” to question: “When thinking about the city, town or district in which this business operates, how would you rate …
information & communications technology infrastructure (eg broadband availability, mobile phone coverage)?” (=0 otherwise). Source: BOS06/LBD.
IMP-COMP
Dummy =1 if firm answers either “captive market/no effective competition” or “no more than one or two competitors” or “many competitors, several
dominant” to question: “How would you describe this business’s competition?” (=0 otherwise). Source: BOS06/LBD.
FOWN
Dummy =1 if firm answers yes to the question “Did any individual or business located overseas hold any ownership interest or shareholding in this
business?” (=0 otherwise). Source: BOS06/LBD.
FSUB
Dummy =1 if firm answers yes to the question ”As at the end of the financial year, did this business hold any ownership interest or shareholding in an
overseas-located business (including its own branch, subsidiary or sales office)?” (=0 otherwise). Source: BOS06/LBD.
R&D
Dummy =1 if firm answered “yes” to question “did this firm undertake or fund any research and development activities”.
Source: BOS06/LBD.
MGMT
Cardinal measure of firm’s general management capability as derived in Table 4.
Source: BOS06/LBD.
KI
Cardinal measure of knowledge intensity in the firm’s 3-digit sector as derived in Table 3.
Source: BOS06/LBD.
HIDEN
Dummy =1 if firm is located in a high-density area (i.e. within: North Shore City, Hamilton City, Christchurch City, Wellington City, Tauranga District,
Manukau City, Napier City, Waitakere City, Auckland City, Papakura District, Kawerau District, Porirua City, Lower Hutt City, Palmerston North City,
Dunedin City), (=0 otherwise). Source: LBD.
lnLP2yr
Average value of PROD over 2005 and 2006.
Source: LBD.
*(Lagged) in Table 9 indicates that the 2001 value for this variable is included in place of the current value.
Motu Working Paper Series
All papers are available online at www.motu.org.nz or by contacting Motu Economic
and Public Policy Research.
09-14 Coleman, Andrew and Arthur Grimes. 2009. "Fiscal, Distributional and Efficiency Impacts of
Land and Property Taxes”.
09-13 Coleman, Andrew. 2009. "The Long Term Effects of Capital Gains Taxes in New Zealand”.
09-12 Grimes, Arthur and Chris Young. 2009. "Spatial Effects of 'Mill' Closure: Does Distance
Matter?"
09-11 Maré, David C and Steven Stillman. 2009. "The Impact of Immigration on the Labour Market
Outcomes of New Zealanders”.
09-10 Stillman, Steven and David C Maré. 2009. "The Labour Market Adjustment of Immigrants in
New Zealand”.
09-09 Kerr, Suzi and Kelly Lock. 2009. “Nutrient Trading in Lake Rotorua: Cost Sharing and
Allowance Allocation”.
09-08 Coleman, Andrew and Grant M Scobie. 2009. “A Simple Model of Housing Rental and
Ownership with Policy Simulations”.
09-07 Crawford, Ron. 2009. “Variations in Earnings Growth: Evidence from Earnings Transitions in
the NZ Linked Income Survey”.
09-06 Maré, David C. 2009. “Agglomeration Elasticities in New Zealand”.
09-05 Poot, Jacques. 2009. “Trans-Tasman Migration, Transnationalism and Economic
Development in Australasia”.
09-04 Hengyun, Ma; Les Oxley and John Gibson. 2009. “China’s Energy Situation and Its
Implications in the New Millenium”.
09-03 Hengyun, Ma; Les Oxley and John Gibson. 2009. “Testing for Energy Market Integration in
China”.
09-02 Hengyun, Ma; Les Oxley, John Gibson and Bongguen Kim. 2009. “China's Energy Economy:
Technical Change, Factor Demand and Interfactor/Interfuel Substitution”.
09-01 Cumming, Jackie; Steven Stillman and Michelle Poland. 2009. “Who Pays What for Primary
Health Care? Patterns and Determinants of the Fees Paid by Patients in a Mixed Public-
Private Financing Model”.
08-14 Coleman, Andrew. 2008. “Tax, Credit Constraints, and the Big Costs of Small Inflation”.
08-13 Coleman, Andrew. 2008. “Storage Under Backwardation: A Direct Test of the Wright-
Williams Conjecture”.
08-12 Maré, David C. 2008. “Labour Productivity in Auckland Firms”.
08-11 Stillman, Steven; Malathi Velamuri and Andrew Aitken. 2008. “The Long-Run Impact of
New Zealand's Structural Reform on Local Communities”.
08-10 Grimes, Arthur and Andrew Aitken. 2008. “Water, Water Somewhere: The Value of Water in
a Drought-Prone Farming Region”.
08-09 Coleman, Andrew. 2008. “Inflation and the Measurement of Saving and Housing
Affordability”.
08-08 Coleman, Andrew and Özer Karagedikli. 2008. “The Relative Size of New Zealand Exchange
Rate and Interest Rate Responses to News”.
08-07 Grimes, Arthur and Yun Liang. 2008. “Bridge to Somewhere: The Value of Auckland's
Northern Motorway Extensions".
08-06 Stillman, Steven and David C Maré. 2008. “Housing Markets and Migration: Evidence from
New Zealand”.
08-05 Lock, Kelly and Suzi Kerr. 2008. “Nutrient Trading in Lake Rotorua: Choosing the Scope of a
Nutrient Trading System”.
08-04 Kerr, Suzi and Andrew Sweet. 2008. “Inclusion of Agriculture and Forestry in a Domestic
Emissions Trading Scheme: New Zealand’s Experience to Date”.
08-03 Kerr, Suzi and Kit Rutherford. 2008. “Nutrient Trading in Lake Rotorua: Reporting and
Modelling Net Nutrient Inputs”.
08-02 Lock, Kelly and Suzi Kerr. 2008. “Nutrient Trading in Lake Rotorua: Overview of a Prototype
System”.
08-01 Stroombergen, Adolf. 2008. “ESSAM General Equilibrium Model: Estimation of 2005/06
Input-Output Tables”.
07-13 Hall, Viv and John McDermott. 2007. "A quarterly post-World War II real GDP series for
New Zealand".
07-12 Gibson, John; Trinh Le and Steven Stillman. 2007. "What Explains the Wealth Gap Between
Immigrants and the New Zealand Born?"
07-11 Maré, David C.; Melanie Morten and Steven Stillman, 2007. “Settlement Patterns and the
Geographic Mobility of Recent Migrants to New Zealand”.
07-10 Grimes, Arthur; David C. Maré and Melanie Morten, 2007. “Adjustment in Local Labour and
Housing Markets.”
07-09 Grimes, Arthur and Yun Liang, “Spatial Determinants of Land Prices in Auckland: Does the
Metropolitan Urban Limit Have an Effect?
07-08 Kerr, Suzi; Kit Rutherford and Kelly Lock, “Nutrient Trading in Lake Rotorua: Goals and
Trading Caps”.
07-07 Hendy, Joanna; Suzi Kerr and Troy Baisden, “The Land Use in Rural New Zealand Model
Version 1 (LURNZ v1): Model Description”.
07-06 Lock, Kelly and Suzi Kerr, “Nutrient Trading in Lake Rotorua: Where Are We Now?”
07-05 Stillman, Steven and David C Maré, “The Impact of Immigration on the Geographic Mobility
of New Zealanders”.
07-04 Grimes, Arthur and Yun Liang, “An Auckland Land Value Annual Database”.
07-03 Kerr, Suzi; Glen Lauder and David Fairman, “Towards Design for a Nutrient Trading
Programme to Improve Water Quality in Lake Rotorua”.
07-02 Lock, Kelly and Stefan Leslie, “New Zealand’s Quota Management System: A History of the
First 20 Years”.
07-01 Grimes, Arthur and Andrew Aitken. 2007. “House Prices and Rents: Socio-Economic Impacts
and Prospects”.
06-09 Maani, Sholeh A.; Rhema Vaithianathan and Barbara Wolf. 2006. “Inequality and Health: Is
House Crowding the Link?
06-08 Maré, David C and Jason Timmins. 2006. “Geographic Concentration and Firm Productivity”.
06-07 Grimes, Arthur; David C Maré and Melanie Morten. 2006. “Defining Areas Linking
Geographic Data in New Zealand”.
06-06 Maré, David C and Yun Liang. 2006. “Labour Market Outcomes for Young Graduates”.
06-05 Hendy, Joanna and Suzi Kerr. 2006. “Land-Use Intensity Module: Land Use in Rural
New Zealand Version 1”.
06-04 Hendy, Joanna; Suzi Kerr and Troy Baisden. 2006. “Greenhouse Gas Emissions Charges and
Credits on Agricultural Land: What Can a Model Tell Us?”
06-03 Hall, Viv B; John McDermott and James Tremewan. 2006. “The Ups and Downs of
New Zealand House Prices”.
06-02 McKenzie, David; John Gibson and Steven Stillman. 2006. “How Important is Selection?
Experimental vs Non-Experimental Measures of the Income Gains from Migration”.
06-01 Grimes, Arthur and Andrew Aitken. 2006. “Housing Supply and Price Adjustment”.
05-14 Timmins, Jason. 2005. “Is Infrastructure Productive? Evaluating the Effects of Specific
Infrastructure Projects on Firm Productivity within New Zealand”.
05-13 Coleman, Andrew; Sylvia Dixon and David C Maré, “Māori Economic Development
Glimpses from Statistical Sources”.
05-12 Maré, David C 2005. “Concentration, Specialisation and Agglomeration of Firms in
New Zealand”.
05-11 Holmes, Mark J and Arthur Grimes. 2005. “Is There Long-Run Convergence of Regional
House Prices in the UK?”
05-10 Hendy, Joanna and Suzi Kerr. 2005. “Greenhouse Gas Emission Factor Module: Land Use in
Rural New ZealandClimate Version 1”.
05-09 Poland, Michelle and David C Maré. 2005. “Defining Geographic Communities”.
05-08 Kerr, Suzi; Joanna Hendy, Emma Brunton and Isabelle Sin. 2005. “The Likely Regional
Impacts of an Agricultural Emissions Policy in New Zealand: Preliminary Analysis”.
05-07 Stillman, Steven. 2005. “Examining Changes in the Value of Rural Land in New Zealand
between 1989 and 2003”.
05-06 Dixon, Sylvia and David C Ma. 2005. “Changes in the Māori Income Distribution: Evidence
from the Population Census”.
05-05 Sin, Isabelle and Steven Stillman. 2005. “The Geographical Mobility of Māori in
New Zealand”.
05-04 Grimes, Arthur. 2005. “Regional and Industry Cycles in Australasia: Implications for a
Common Currency”.
05-03 Grimes, Arthur. 2005. “Intra and Inter-Regional Industry Shocks: A New Metric with an
Application to Australasian Currency Union”.
05-02 Grimes, Arthur; Robert Sourell and Andrew Aitken. 2005. “Regional Variation in Rental
Costs for Larger Households”.
05-01 Maré, David C. 2005. “Indirect Effects of Active Labour Market Policies”.
04-12 Dixon, Sylvia and David C Maré. 2004. “Understanding Changes in Māori Incomes and
Income Inequality 1997–2003”.
04-11 Grimes, Arthur. 2004. “New Zealand: A Typical Australasian Economy?”
04-10 Hall, Viv and John McDermott. 2004. Regional Business Cycles in New Zealand: Do They
Exist? What Might Drive Them?”
04-09 Grimes, Arthur; Suzi Kerr and Andrew Aitken. 2004. “Bi-Directional Impacts of Economic,
Social and Environmental Changes and the New Zealand Housing Market”.
04-08 Grimes, Arthur and Andrew Aitken. 2004. “What’s the Beef with House Prices? Economic
Shocks and Local Housing Markets”.
04-07 McMillan, John. 2004. “Quantifying Creative Destruction: Entrepreneurship and Productivity
in New Zealand”.
04-06 Maré, David C and Isabelle Sin. 2004. “Māori Incomes: Investigating Differences Between
Iwi”.
04-05 Kerr, Suzi; Emma Brunton and Ralph Chapman. 2004. Policy to Encourage Carbon
Sequestration in Plantation Forests”.
04-04 Maré, David C. 2004. “What do Endogenous Growth Models Contribute?”
04-03 Kerr, Suzi; Joanna Hendy, Shuguang Liu and Alexander S. P. Pfaff. 2004. “Uncertainty and
Carbon Policy Integrity”.
04-02 Grimes, Arthur; Andrew Aitken and Suzi Kerr. 2004. “House Price Efficiency: Expectations,
Sales, Symmetry”.
04-01 Kerr, Suzi; Andrew Aitken and Arthur Grimes. 2004. “Land Taxes and Revenue Needs as
Communities Grow and Decline: Evidence from New Zealand”.
03-19 Maré, David C. 2003. “Ideas for Growth?”
03-18 Fabling, Richard and Arthur Grimes. 2003. “Insolvency and Economic Development:
Regional Variation and Adjustment”.
03-17 Kerr, Suzi; Susana Cardenas and Joanna Hendy. 2003. “Migration and the Environment in the
Galapagos: An Analysis of Economic and Policy Incentives Driving Migration, Potential
Impacts from Migration Control, and Potential Policies to Reduce Migration Pressure”.
03-16 Hyslop, Dean R and David C Maré. 2003. “Understanding New Zealand’s Changing Income
Distribution 198398: A Semiparametric Analysis”.
03-15 Kerr, Suzi. 2003. Indigenous Forests and Forest Sink Policy in New Zealand”.
03-14 Hall, Viv and Angela Huang. 2003. Would Adopting the US Dollar Have Led to Improved
Inflation, Output and Trade Balances for New Zealand in the 1990s?”
03-13 Ballantyne, Suzie; Simon Chapple, David C Maré and Jason Timmins. 2003. “Movement into
and out of Child Poverty in New Zealand: Results from the Linked Income Supplement”.
03-12 Kerr, Suzi. 2003. Efficient Contracts for Carbon Credits from Reforestation Projects”.
03-11 Lattimore, Ralph. 2003. “Long Run Trends in New Zealand Industry Assistance”.
03-10 Grimes, Arthur. 2003. “Economic Growth and the Size & Structure of Government:
Implications for New Zealand”.
03-09 Grimes, Arthur; Suzi Kerr and Andrew Aitken. 2003. “Housing and Economic Adjustment”.
03-07 Maré, David C and Jason Timmins, “Moving to Jobs”.
03-06 Kerr, Suzi; Shuguang Liu, Alexander S. P. Pfaff and R. Flint Hughes. 2003. “Carbon
Dynamics and Land-Use Choices: Building a Regional-Scale Multidisciplinary Model”.
03-05 Kerr, Suzi. 2003. “Motu, Excellence in Economic Research and the Challenges of ‘Human
Dimensions’ Research”.
03-04 Kerr, Suzi and Catherine Leining. 2003. “Joint Implementation in Climate Change Policy”.
03-03 Gibson, John. 2003. “Do Lower Expected Wage Benefits Explain Ethnic Gaps in Job-Related
Training? Evidence from New Zealand”.
03-02 Kerr, Suzi; Richard G Newell and James N Sanchirico. 2003. “Evaluating the New Zealand
Individual Transferable Quota Market for Fisheries Management”.
03-01 Kerr, Suzi. 2003. Allocating Risks in a Domestic Greenhouse Gas Trading System”.
... Theoretically, broadband networks can reduce the five economic costs associated with digital economic activities: search, replication, transportation, tracking, and verification [5]. Broadband networks may accelerate information exchange and processing [6], reduce information asymmetries in labor markets [7], promote product innovation, entrepreneurial activity, job matching, and market competition [8], increase firm productivity [9,10], and promote economic growth [11]. Researchers have further focused on the impact of broadband on innovation [12,13] and mainly proposed two impact mechanisms, that is, broadband reduces the cost of information [5,14,15] and facilitates cooperation [16][17][18]. ...
... Several studies have examined the impact of broadband penetration on important economic variables such as economic growth, employment, and innovation. For instance, some studies have found that broadband adoption can increase employment [19,20], reduce unemployment in rural areas [21], increase business productivity [9], and promote economic growth [6]. However, others have revealed a limited effect of broadband on these economic variables [22,23]. ...
... Several studies, based on different samples of OECD countries over time, have found that broadband infrastructure promotes economic growth [8,32]. At the enterprise level, a study on a large-scale micro survey in New Zealand found that adopting broadband increased an enterprise's productivity by 7-10% [9]. ...
Article
Full-text available
Previous studies have associated broadband with innovation, but most of them have focused on broadband access or broadband penetration rate rather than broadband speed. However, with the increase in the global broadband penetration rate, network speed is becoming a more important aspect of broadband development. Using a fixed effect model based on panel data from 274 cities in China from 2016 to 2018, we explore the impact of broadband speed on innovation and further validate the relationship by instrumental variable estimation and a series of robustness tests. The main findings are as follows: First, broadband speed significantly promotes patenting, and this effect is greater than the increase in broadband penetration rate. Second, the impact of broadband speed on promoting innovation is more substantial in cities with higher Internet penetration, GDP per capita, science and technology investment, and foreign direct investment. Third, broadband speed could promote innovation by facilitating patenting cooperation among firms. Fourth, we find no evidence of a diminishing marginal effect of broadband speed in this setting. This study has significant practical implications for emerging countries to encourage innovation by increasing broadband speed.
... A small number of studies have constructed instruments based on the infrastructure of ICT (Abramovsky and Griffith, 2006;Grimes et al., 2012;Bertschek et al., 2013;Akerman et al. 2015;Haller and Lyons, 2015;Fabling and Grimes, 2016;DeStefano et al., 2018;Canzian et al., 2019). These studies often show a strong relationship between ICT and firm scale (such as employment), but the link to productivity is much more mixed (Bertschek et al., 2015). ...
... In both cases these effects are short-run, occurring within a three-year window. Evidence of other firm level impacts in a variety of country contexts can be found in Van Gaasbeck (2008), Grimes et al. (2012), Kolko (2012), Bertschek et al. (2013) and Haller and Lyons (2015). 10 To our knowledge, only one broadband delivery program explicitly targeting rural areas has been evaluated to date (the US Rural Broadband Loan Program), providing mixed results with positive effects on local employment, wages and firm entry (Kandilov and Renkow, 2010;Younjun and Orazem, 2017). ...
Article
Full-text available
Differences in access to high-speed broadband between urban and rural locations are known as the ‘digital divide’. Governments around the world have committed to spending considerable amounts of money to alleviate disparities in broadband infrastructure. However, to date, there is limited causal evidence for broadband and firm performance with even less of an understanding on whether the effects are distinct between firms located in urban versus rural localities. In this article, we exploit geographical discontinuities in broadband availability across the UK to capture the causal effects on firms on both sides of this divide. We find for both urban and rural firms that broadband causes an increase in their size, but not labor productivity. In addition, we find evidence that these size effects are strongest for urban firms, but for both urban and rural firms, the effects are concentrated in knowledge-intensive industries.
... Digital connectivity such as broadband is important as Qiang and Rossotto (2009) pointed out that broadband penetration helped in increasing growth in both developing and developed countries. Grimes et al. (2012) stated that broadband access enhances the productivity of firms by around 10%. Hence, the competitiveness of firms increases at both domestic and international levels (Bernard et al., 2007). ...
Article
Full-text available
Digital trade is increasing rapidly throughout the world whereas digital platforms and Coronavirus have further enhanced the importance of the digital economy and digital trade. Countries are focusing on promoting digital trade and integration through various measures including free trade agreements and bilateral negotiations. This study examined digital trade as defined by WTO E-commerce work and USITC. The study included the items that come under the definition of digital trade and examined the digital trade volume of Pakistan from 2010-2020 through three-step methodology. This includes the identification of digital trade items based on Harmonized System at a six-digit level, examining trade volume for digital goods, and identification of top ten export and import items along with top ten markets for digital trade. Favorable government policies and measures have helped Pakistan in promoting digital trade flows. However, there is a need to develop information and communication technology infrastructure in Pakistan to flourish trading activities. Furthermore, Pakistan has to reduce the fiscal and trade barriers such as rules and regulations for foreign investment in digital space, data and information costs, and ensure online security and data protection to promote digital trade integration.
... The positive impact of ICT on economic growth has been seen through the increase in business outputs, namely due to the reduction of transaction costs of companies with the use of digital technologies (Roller & Waverman, 2001) and/or the modernization of production techniques in the productive units (Pohjola, 2002), in greater productivity (Van Zon & Muysken, 2005), in the indirect growth caused in the non-ICT sectors, in the improvement of the market processes, the creation of employment opportunities, the creation of knowledge, and the reduction of price fluctuations, among others (Bahrini & Qaffas, 2019;Grimes et al., 2012;Haftu, 2019;Jorgenson & Stiroh, 1999;Lee et al., 2012;Pradhan et al., 2018;Vu, 2011). On the other hand, there is a consensus by international organizations, namely the United Nations and the European Commission, that in countries where ICT is used in different sectors ranging from industry, commerce, health, education, and transport, in the public and private sector's supply of goods and services, a better quality of life for the population and economic development is promoted. ...
Article
Full-text available
Purpose: This longitudinal study analyzes the impact of the digital economy on economic growth in Organisation for Economic Co-operation and Development (OECD)’s countries, divided into groups by their level of development (transition and innovation countries). Originality/value: Empirical studies on information and communications technology (ICT)’s impact on countries’ economic growth have increased over the last few years. However, there are still no studies that analyze this problem for the group of OECD countries, let alone divide these countries according to their level of economic development to assess the differences between more developed countries and less developed. The study of this gap in the literature allows us to infer important implications for policymakers to implement the digital agenda more efficiently. Design/methodology/approach: We used a panel data sample from the World Bank covering 36 OECD countries from 2000 to 2019. A statistical analysis of the variables was carried out separately for all OECD countries and the transition and innovation countries groups. We then performed a correlation analysis between the variables, and three models were estimated using the generalized moments panel method (GMM) using a fixed cross-section. Findings: The impact of the digital economy as measured by the technology proxy – internet, mobile phone, and fixed-broadband – on the economic growth of OECD countries depends on their level of development and the measures of the technologies that capture the digital economy. ICTs positively influence the development of the economies of OECD countries and can be used as instruments by policymakers. These agents must implement policies that strengthen the physical and technological infrastructures of the ICTs, the digital empowerment of human capital, and more significant social equity in accessing the ICTs.
... It has been shown that an Internet-related reduction in fixed costs is likely to enhance export growth (Alarcón-del-Amo et al., 2018;Freund & Weinhold, 2004) and business performances increasing the quality and efficiency of production factors (Bertschek et al., 2013;Grimes et al., 2012;Haller & Lyons, 2015;Hagsten, 2016;Majumdar et al., 2010). Likewise, other studies attest a significant effect of indicators of "hard" infrastructures (physical capital and the diffusion of information and communication technology) on the level of exports (de Matteis et al., 2019;Portugal-Perez & Wilson, 2012). ...
Article
Full-text available
This study investigates the role of digital capacities in the internationalisation processes of Italian provinces using a panel dataset built upon the territorial statistical database of ISTAT (Italian National Institute of Statistics) for the period 2014–2017. The purpose is to explore the link between three internationalisation indicators (export value, export intensity and export in most dynamics sectors) and two ICT drivers (use of e-business digital technologies, such as cloud computing, and use of social media) expected to enable firms to share information along the supply chain and to ease firms’ communication and fixed cost investment. To purge the analysis from unobserved determinants of export performance and ICT adoption reverse causation problems, the identification relies on an instrumental variables approach that addresses the endogeneity of our two variables of interest related to ICT. The results show a significant relationship between the ICT capacities related to cloud adoption and our export indicators confirming the role of e-business digital technologies in explaining the export performance of Italian provinces. The use of social media also appears weakly able to impact on the export performances indicators. These results are robust to our endogeneity checks.
... Looking at both developed and developing countries and considering different types of ICT, such as landlines, cell phones and internet access, Stanley et al. (2018) confirm previous results of a positive contribution of ICT on growth, except for the internet. Firm level studies of the productivity effects of different ICT applications, such as broadband, enterprise software, and e-commerce (Rincon et al., 2005;Sánchez et al., 2006;Engelstätter, 2009;Czernich et al., 2011;Grimes et al., 2012) broadly provide evidence of a positive but heterogeneous impact on firm performance. These results confirm the review of the literature by Draca et al. (2007), which mainly focuses on US-based studies. ...
Article
Full-text available
We develop a unifying framework to investigate the effects of firms' internet presence on productivity and market structure. Using information on website adoption as an indicator of online trading, we treat the decision of entering an e-commerce market equivalent to the decision of entering a foreign market. Our theoretical framework draws from a dynamic model of international trade, which accounts for firms' heterogeneity in productivity levels and in the returns to productivity enhancing investments. We test the predictions of our model using UK and Spanish company account data, over the 1995–2010 period merged with information of companies' online status. The period analysed is associated with the early stage of internet diffusion and our sample countries represent fast (the UK) and slow (Spain) diffusion. Our results show that website adoption is associated with higher productivity growth and with a reduction in market concentration in both countries. The increase in competition operates via a negative selection mechanism, whereby productivity growth is inversely related to the pre-entry productivity levels. We also find that productivity gains decline over time.
Article
We study the impact of ultra-fast broadband (UFB) infrastructures on the total factor productivity (TFP) and labor productivity of firms. We use unique balanced panel data for the 2013-2019 period on incorporated firms in Italy. Using the geographical location of the firms, we merge firm-level data with municipality-level information on the diffusion of UFB, which started in 2015. We derive consistent firm-level TFP estimates by adopting a version of the Ackerberg et al.’s (2015) method, which also accounts for firm fixed effects. We then assess the impact of UFB on productivity and deal with the endogeneity of UFB by exploiting the physical distance between each municipality and the closest optical packet backbone node. Our results show an overall positive impact of UFB on productivity. Services companies benefit the most from advanced broadband technologies, as do firms located in the North-West and South of Italy. We further decompose the impact of full-fiber networks from mixed copper-fiber connections and find that the former significantly contribute to enhancing firm productivity. Finally, by exploiting Labor Force Survey data, we provide suggestive evidence that productivity increases from UFB might be related to structural changes at the workforce level.
Article
As Internet adoption and diffusion continues worldwide, little is known about its effects on the restructuring of national urban hierarchies globally. We create a city population panel data with uniform definitions within each of the 133 countries from 2000 to 2018 to study the effect of the Internet on national urban hierarchies and to examine the channels through which the effects take place. Results show that the Internet led to the equalization of urban hierarchies in the early stage and then the polarization. Initially, the Internet helped reduce communication costs, partially replaced transportation, and weakened the agglomeration of enterprises and individuals in large cities, thereby flattening national urban hierarchies. Later, the Internet mainly enhanced the production efficiency and accelerated the shift from manufacturing to services in large cities, thus increasing the large cities' attractiveness and ultimately polarizing the urban hierarchy. Our finding demonstrates that national urban hierarchies are not static and their evolution reflects not only the trade-off between economies of scale and congestion, but increasingly by the Internet—a powerful communication tool. If the trend persists, larger cities will grow at a faster pace than smaller cities and potentially increase regional inequality in countries that have extensively adopted the Internet.
Article
Full-text available
Bien qu'il soit admis qu'Internet a des effets importants sur la façon dont les régions se développent économiquement, les études restent partagées son impact réel. À l'aube d'un élargissement des politiques de connectivité, quels impacts la mise en place de l'Internet haute vitesse dans les territoires du Québec at -elle eu sur la création et la fermeture d'établissements entre 2005 et 2019 ? En nous basant sur un modèle de doubles différences avec appariement par score de propension, nous isolons l'effet de l'implantation de l'Internet haute vitesse (Câble/DSL et Fibre) sur la création et les fermetures d'établissements. Les estimations sont réalisées pour tous les secteurs industriels confondus, ainsi que pour sept secteurs spécifiques. Les résultats montrent que l'accès aux technologies haut débit comme le Câble et la DSL a significativement augmenté la création et réduit le nombre de fermetures d'établissements dans l'ensemble des industries. L'accès à la Fibre, en revanche, a significativement réduit la création d'établissements et augmenté les fermetures, de façon globale pour l'ensemble des industries, avec des effets variés selon les secteurs. Ces résultats confirment que l'accès au haut débit est un enjeu clé pour le développement économique territorial, mais mettent en évidence une hétérogénéité des effets selon le type d'activité, le territoire et la technologie implantée. Mots-clés : Internet, développement économique, création d'établissements, fermeture d'établissements, méthode d'appariement et de doubles différences. JEL classification : O18, O51, P25, R11 Reproduced with permission of the copyright holder. Further reproduction prohibited.
Article
Full-text available
Bien qu'il soit admis qu'Internet a des effets importants sur la façon dont les régions se développent économiquement, les études restent partagées son impact réel. À l'aube d'un élargissement des politiques de connectivité, quels impacts la mise en place de l'Internet haute vitesse dans les territoires du Québec at -elle eu sur la création et la fermeture d'établissements entre 2005 et 2019 ? En nous basant sur un modèle de doubles différences avec appariement par score de propension, nous isolons l'effet de l'implantation de l'Internet haute vitesse (Câble/DSL et Fibre) sur la création et les fermetures d'établissements. Les estimations sont réalisées pour tous les secteurs industriels confondus, ainsi que pour sept secteurs spécifiques. Les résultats montrent que l'accès aux technologies haut débit comme le Câble et la DSL a significativement augmenté la création et réduit le nombre de fermetures d'établissements dans l'ensemble des industries. L'accès à la Fibre, en revanche, a significativement réduit la création d'établissements et augmenté les fermetures, de façon globale pour l'ensemble des industries, avec des effets variés selon les secteurs. Ces résultats confirment que l'accès au haut débit est un enjeu clé pour le développement économique territorial, mais mettent en évidence une hétérogénéité des effets selon le type d'activité, le territoire et la technologie implantée.
Article
Full-text available
It is demonstrated that a variant of the two-stage least squares technique can be used to estimate the parameters of a nonlinear model. To do this, the reduced form equations of such models are derived and discussed; then certain problems particular to the estimation of nonlinear models are considered.
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Full-text available
The propensity score is the conditional probability of assignment to a particular treatment given a vector of observed covariates. Both large and small sample theory show that adjustment for the scalar propensity score is sufficient to remove bias due to all observed covariates. Applications include: (i) matched sampling on the univariate propensity score, which is a generalization of discriminant matching, (ii) multivariate adjustment by subclassification on the propensity score where the same subclasses are used to estimate treatment effects for all outcome variables and in all subpopulations, and (iii) visual representation of multivariate covariance adjustment by a two- dimensional plot.
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In this Paper, we assess the performance and efficiency of OECD countries with respect to broadband Internet subscription. Using the econometric technique of Stochastic Frontier Analysis, we estimate scores indicating the efficiency with which a country converts its economic and demographic endowments into broadband subscriptions. With very few exceptions, we find that broadband subscription in OECD countries is consistent with those endowments -- about two thirds of OECD countries have an efficiency rate of 95% or better. Significantly, the United States has an efficiency index of 96.7%, which is slightly higher than Japan (96.3%) and Korea (95.8%). Consistent with earlier research, we find that economic and demographic endowments explain nearly all of the variation in broadband subscriptions (91%). This finding suggests that public policy's role for broadband adoption may be more effective if it is targeted at improving or mitigating the adverse effects of those underlying demographic and economic conditions, such as computer ownership and education programs. Finally, because countries have different demographic and economic conditions, the most effective mix of policies will vary from country-to-country. As such, our findings indicate that blindly following the policies of countries "ranked" higher in the OECD raw rankings is not likely to result in optimal success.
Article
ABSTRACT Recently, the relative demand for skilled labor has increased dramatically. We investigate one of the causes, skill-biased technical change. Advances in information technology (IT) are amongthe most powerful forces bearing on the economy. Employers who use IT often make complementary,innovationsin their organizations and in the services they offer. Our hypothesis is that these co-inventions by IT users change the mix of skills that employers demand. Specifically, we test the hypothesis that it is a cluster of