A rough guide to New Zealand’sLongitudinal Business Database
Richard Fabling∗
Paper prepared for the plenary session on
Access to enterprise microdata in Asia
Comparative Analysis of Enterprise Data ConferenceTokyo
October 2009
Abstract
Statistics New Zealand’s prototype Longitudinal Business Databaseis a rich resource for understanding the behaviour of New Zealandfirms. In this paper we describe the elements of the database, accessprotocols for researchers, and potential future developments in thelinking and availability of business data in New Zealand.

∗

Reserve Bank of New Zealand (richard.fabling@rbnz.govt.nz); Motu Economic andPublic Policy Research. The views expressed in this paper are those of the author and do
not necessarily reflect those of the Reserve Bank of New Zealand, or of Motu.

11Introduction

The prototype Longitudinal Business Database (LBD), maintained by Statistics NZ, is a rich resource for understanding the behaviour and performanceof New Zealand firms. The primary selling point of the database is wide coverage administrative sources of financial performance, employment and wage
data, coupled with a variety of sample survey and other (notably Customs
merchandise trade) data providing a detailed view of firm practices across a
broad range of topics. In addition, through the integration of the Business
Operations Survey (BOS), researchers have the opportunity to design andintegrate entirely new content that can be used with panels of BOS data andother longitudinal information held on the LBD.
The database is a work-in-progress and is continuing to expand as new research needs are identified, and as new data become available. As such thispaper can only provide a rough guide to the contents of the database. Section
2 outlines the current components of the LBD, while section 3 covers accessand the process for adding data. Finally, section 4 outlines the potential for
future developments on both access and content.

2Components of the LBD

The LBD comprises tax- and survey-based financial data, merchandise andservices trade data, a variety of sample surveys on business practices andoutcomes, and government programme participation lists (Table 1 provides
a summary of the components). The database has expanded organically tomeet the needs of agencies whose researchers use the data, and now includes
a substantial proportion of the business data that Statistics NZ holds anduses to compile official statistics.
To improve accessibility, each constituent dataset has a “fact table” in which
variables are aggregated to a consistent unit of observation and periodicity.
Most financial variables are only observed at the enterprise (or tax reporting)level, not at the individual plant, so the enterprise (hereafter, firm) is thecommon unit of these tables. An annual frequency is imposed on the databy tax-filed financial accounts, working proprietor income declarations, andmost sample surveys. Hence, all sub-annual data (goods and services tax,
trade, and employment) is annualised to each firm’s financial year and thenallocated to the 31st March year-end that has the greatest overlap with the2financial year.1
Aside from the construction of fact tables, Statistics NZ applies a light touch
to the data – the main technologies added specifically for the benefit of LBD
users being editing and imputation of tax-filed financial information, andprobabilistic matching methods for linking Customs clients and government
programme participants to Statistics NZ firm identifiers (Enterprise Numbers).2 Where data is transformed, the database usually includes the raw(“load”) data so that researchers can choose to undo any processing thathas occurred. For example, while Statistics NZ construct a Customs fact
table that includes the aggregate annual merchandise exports of each firm,
the database includes the underlying daily shipment level data.
Subsequent subsections outline the contents of the database making note ofadditional methods for treating data issues that have been developed by the
research community.3

2.1

Longitudinal Business Frame and Linked EmployerEmployee Data

The Longitudinal Business Frame (LBF) is a monthly “unwinding” of Statistics NZ’s Business Frame – this latter being a set of history tables recording
changes in the sampling frame for firms. The LBF contains longitudinal information on – among other things – business name and location, industry,business type, sector, parent-subsidiary relationships, and foreign ownership.
The quality of the LBF’s representation of firm characteristics, and changes
in those characteristics, is a function of the maintenance processes for theBusiness Frame, the ability of respondents to answer survey questions, andthe quality of supplementary sources used. Goods and Services Tax (GST)
data is used to help maintain the accuracy of the Business Frame (particularly
to track the births and deaths of firms and to allocate industry characteristics to small firms) and, consequently, a significance threshold exists at the
mandatory GST filing level – previously NZD30K, then NZD40K and now
1

Tax filing rules mean that most firms actually have a 31st March balance date. Variations
from that rule are generally industry-specific, eg, the public sector operates to an end ofJune financial year.
2
Statistics NZ also anonymise the data in accordance with the Statistics Act 1975.
3
Interested readers can find detailed survey metadata on Statistics NZ’s website
www.stats.govt.nz.

3

NZD50K – below which coverage is limited.4 Large economic units are surveyed by Statistics NZ either annually or triennially to maintain the accuracyof the data held.
Because the Business Frame tracks legal entities, firm id continuity can be
broken by events that do not imply the exit of a firm. For example, if thepartners in a firm decide to reestablish their existing business as a limited
liability company that firm may be issued a new firm id despite continuity in location, economic activity and ownership. Fortunately, Statistics NZ
puts effort into repairing plant-level ids, making use of continuity of locationand employees as measured in the Linked Employer-Employee Data (LEED).
Fabling (2007b) quantifies the scope of the issue at the firm level and discusses ways of repairing firm ids using plant-level ids (Permanent Business
Numbers), while Fabling and Sanderson (2009a) and Fabling and Grimes(forthcoming) provide methods for constructing “permanent” firm ids.
At the time of writing, the primary source of employment data (LEED) isavailable for eight (March) financial years 2000-2008 (table 1 includes the
temporal span of each data source). LEED is constructed by Statistics NZ
from Inland Revenue Department (IRD) tax data, notably Pay-As-You-Earnreturns for employees. It is generally assumed by researchers that missing
employment data implies zero employees on the grounds that personal income
tax non-compliance is likely to be negligible in the population of firms thatmeet the mandatory GST filing threshold.
To protect the confidentiality of individuals, LEED variables have been aggregated to the firm-level before being included in the LBD. Variables available
in this manner include counts of employers (on an annual firm-level basis)and employees (on a monthly plant-level basis) with matching firm-level dataon income. Summary characteristics of individuals also include gender andage-band breakdowns, tenure distributions of employees, and summary measures of the dispersion of wages within the firm. Accessions and separations
are summarised at the firm level, as are counts of contractors employed (withcorresponding remuneration). This data source is also the basis of an example of a researcher-supplied output that has been added to the LBD – being
estimated average worker and firm fixed effects created within LEED (byMaré and Hyslop 2008).
4

Consistent with the principle of “keeping everything”, tax id-only and Customs id-onlyrecords are held on the database where there is filed data that cannot be linked to firmids. If firms have not been allocated an id by Statistics NZ, they will not have an industry
classification.

4

2.2

GST and the Business Activity Indicator

GST data include information on sales and purchases and is collected ona monthly, bi-monthly or six-monthly basis by IRD, depending on the sizeof the firm. Statistics NZ manipulate this raw data to create the Business
Activity Indicator (BAI) dataset (also included in the LBD). The primarymanipulations applied to generate the BAI data are to temporally apportion
the GST data down to a monthly frequency, apportion returns across GST
group members, and apply limited imputation in cases where a single returnappears to be missing.5
GST in New Zealand has very broad coverage and non-compliance with GST
reporting (conditional on filing above the threshold at some point in time)
appears very low so that these data are present for almost all employing
firms. Certain financial transactions are GST exempt and exported goods
are zero-rated. Some researchers have used this latter fact as an indicatorof exporting, since the zero-rated component of GST sales is recorded in thedata.62.3

Filed tax returns (IR10 and IR4) and the Annual
Enterprise Survey

IR10s are essentially a set of company accounts composed of a profit and loss
statement and a balance sheet. Included is information on sales (and otherincome) and purchases, as well as a detailed breakdown of expenditure including depreciation, research and development costs, and salaries and wages.
Balance sheet items include fixed assets (broken down into vehicles; plant andmachinery; furniture and fittings; land and buildings; and other), liabilities(current and term), and shareholders’ funds.
The Annual Enterprise Survey (AES) is Statistics NZ’s primary data source
for the production of National Accounts, and as such is the benchmark for
estimating value-added. The survey is full coverage for large firms with astratified sample survey for smaller firms, and has industry-specific questions
in order to accurately measure aggregate gross domestic product. IR10s and5

GST/BAI data are GST-inclusive whereas most other financial data, including survey
responses, are collected on a GST-exclusive basis.
6
The database contains more detailed export data for goods and services (subsection 2.4).
Some income sources unrelated to exports are also zero-rated for GST purposes implying
a trade-off between the comprehensive nature of a GST-based indicator of exporting andits likely accuracy.

5

AES have been used together to construct consistent measures of labour andmulti-factor productivity (Fabling and Maré forthcoming).7 Maré (2008)
provides a clever set of techniques allowing firm-level labour productivity to
be used with plant-level location and employment data to examine agglomeration benefits.
IR4s are declarations of taxable income for companies and, as such, include
variables on overseas income, interest and dividends, and income from “business or rental activities.” They also contain a binary foreign-ownership indicator.8

2.4

Trade data (Customs and ITSS)

Customs merchandise trade data contains daily shipment-level information
over two decades (1988-2008) covering (10-digit Harmonised System) goods,
countries of origin and destination, values, volumes, weights, currency of
trade, port of entry/exit and mode of transportation. The quality of thematch deteriorates (at least in terms of the share of aggregate trade linked)
prior to 1996, and some currency variables are only available on a comprehensive basis subsequent to the introduction of mandatory electronic filing(April 2004). The dataset is linked to the LBF initially using tax ids, thenprobabilistic matching on names and addresses, and finally manually to link
any remaining unmatched large-value Customs clients. As with LEED, researchers often assume that the Customs data represents a complete pictureof firm-level merchandise trade since the threshold for filing is NZD1,000.
The structure of New Zealand enterprise groups (ie, parent-subsidiary relationships) presents an obstacle to the use of the data, since a large proportionof exported goods pass from manufacturing firms to traders within the group
before crossing the border. Fabling and Sanderson (2009a) present a methodology for reallocating trade back to the producing manufacturer.
The International Trade in Services and Royalties Survey (ITSS) provides
data on services trade (both exports and imports) by country and service
type. The survey runs on a quarterly basis and is an approximate census
with a cut-off designed to allow an accurate trade aggregate to be input into
7

The advantage of using both sources together being that AES has limited coverage ofsmall firms, and IR10s are less likely to be present for firms that are full coverage in AES.
8
A foreign ownership indicator (percentage of offshore ownership) is also held on the LBF.
The advantage of the IR4 indicator is its superior coverage for small companies and potentially more frequent updating for larger firms.

6

Balance of Payments (BOP) statistics. Two censuses of firms that are likely
service exporters (1999 and 2005) appear in the LBD, along with a “birth”
survey aimed at ensuring coverage is maintained.9 Like the Customs data, thedataset covers a relatively long time period (1996-2008), and is likely to have
similar group structure allocation issues, since the target survey respondentis the group-top firm. The integration of this data is still underway at thetime of writing, so no research has yet been produced.

2.5

Business Operations Survey and its predecessors(BPS and INN)

The Business Operations Survey (BOS) in an omnibus collection of survey
data related to firm behaviour and performance. The population for thesurvey is all private-for-profit firms with at least a rolling mean employmentof six (roughly 35,000 firms). From that population between six to seventhousand useable responses are collected (depending on the year), with therealised response rate always being at least eighty percent.10
The survey has a modular design (three parts) with Module A collecting
annual financial and employment data, and qualitative information on firm
performance.11 Module B alternates between collecting innovation statistics(in odd years, using the revised Oslo Manual classification of four types:
product, process, marketing and organisational design) and information andcommunication technology use (in even years, following OECD guidelines).
Finally, Module C is open to competitive bidding between government agencies. Topics addressed so far are general business practices (2005 and 2009);
employment practices (2006); international engagement (2007); and strategy
and skills (2008). In 2010, the primary topic for the module will be pricesetting and wage-bargaining sponsored by the Reserve Bank of New Zealand,
University of Tasmania, and Victoria University of Wellington.12
9

The ITSS census population is determined by a set of indicators on the Business Frame.
These, in turn, are maintained by the Annual Frame Update Survey (firms are asked toreport activities over NZD10K) and by monitoring media reports. The history tables for
the BOP population indicator set are available in the database.
10
Response is mandatory under the provisions of the Statistics Act for all the surveys in the
LBD.
11
The quantitative data collected has largely been dropped on the grounds that most of
this data is now available from other LBD sources. Outside of the research sphere, theassociated reduction in BOS respondent load is probably the greatest achievement of thedata integration.
12
Currently the cost charged by Statistics NZ for the development, testing and administra-

7

The innovation module and general business practices modules have predecessor surveys also included in the LBD – the Business Practices Survey (BPS2001) and the Innovation Survey (INN 2003). While there have been changes
over time in the way innovation data is collected (reflecting in part changing
international standards), there is sufficient overlap in question design and alarge enough panel to allow analysis of innovation behaviour between 2001
and 2005 (Fabling 2007a; Maré et al forthcoming).
The BOS sampling strategy includes a top-up element that surveys any respondent to the 2005 survey who would not otherwise be sampled on an
ongoing basis – that is, Statistics NZ are running a panel (without replacement) from 2005 onwards. This panel can be linked back usefully to similar
qualitative data from 2001. Beyond the explicit panel design, Statistics NZ’s
method for random sampling means that there is also substantial repeat
sampling of firms across any two years of BOS.13

2.6

Other sample surveys (MEUS, BFS and R&D
Survey)The Manufacturing Energy Use Survey (MEUS) 2006 is a precursor to the
New Zealand Energy Use Survey (NZEUS). MEUS contains energy use broken down by type (eg, oil, electricity, etc). NZEUS has rolling industry
coverage, and will survey the whole economy over a five year period. In
2009, agriculture, forestry, fishing and mining will be surveyed.
The Business Finance Survey (BFS) 2004 was a one-off survey of small businesses, including a mix of quantitative questions about balance sheet structure and qualitative information about the availability of finance. Qualitative
questions on the success of firms seeking debt and equity financing have been
included in the annual BOS Module A. The 2009 Business Operations Survey
includes an expanded set of questions on finance issues, and these additionalquestions are also largely based on questions originally asked in the BFS.14
tion of a completely new module is NZD200K (excluding GST of 12.5 percent).
Specifically, Statistics NZ allocate a permanent random number (between zero and one)
to a firm when it appears on the Business Frame. Random sampling is then conductedeach year by starting at 0.35 on that number line and picking up firms to the right until
the appropriate sample fraction is reached. In the absence of entry/exit or redesign ofthe stratification the same firms would be drawn each year. BOS has been subject to one
major redesign (in 2007) to account for revisions to the industry classification system. The
survey was dual sampled under the old and new system in that year.
14
These additional finance questions are also scheduled to appear in the 2010 BOS.
13

8

The Research and Development (R&D) Survey collects information on (current and capital) expenditure and employment in R&D as well as summary
data on funding and the purpose of the research. The survey is conducted
every even year and excludes industries that are deemed to be non-R&D
performing (eg, wholesale and retail trade). The BOS and IR10s also collecta measure of expenditure on R&D.15
Due to the way Statistics NZ samples these business surveys, there is a highprobability that firms that meet the population criteria for BOS and R&D
will be surveyed for both. This is true for any of the sample surveys discussed
above, with the exception of the Annual Enterprise Survey which is sampled
in a way designed to minimise the overlap with other surveys for smallerfirms in the sample.16

2.7

Government assistance programmes

Lists of firms that have received assistance from government agencies, together with information on the nature of the assistance, have also been probabilistically matched (on contact details) to the dataset to enable evaluationof these schemes (eg, MED 2009). Agencies supplying data are the Foundation for Research, Science and Technology; New Zealand Trade and Enterprise; the Ministry of Tourism; Te Puni Kōkiri; and the Ministry of Social
Development. The data includes the period in which the firm received assistance, the specific programme accessed and, where appropriate, an estimateof the value of that aid.

3

Protocols

As can be seen from the breadth of agencies contributing unit-record programme data, the New Zealand legislative environment presents few obstacles
to supplying data to Statistics New Zealand, provided there is an end goal of
an official statistic. This section covers the legal basis for accessing the LBD,
the practical process of applying for research access and for adding new data
sources to the database.
15

Changes to the BOS design in 2007 mean that the survey now also collects (in odd years)
a broader measure of total product development costs that itemises design and marketing
expenditure.
16
This is achieved by AES starting its draw of firms on the Business Frame from the top
end of the number line and working left.

9

3.1

Legal framework

Access to the LBD (and other data) is granted at the discretion of the Government Statistician in accordance with the Statistics Act 1975. Section 37of the Act conveys the authority to grant access (under controlled conditions)to employees of government departments for research purposes:
Section 37C Disclosure of individual schedules to other
Government Departments for bona fide research or statistical purposes(1) . . . the Statistician may disclose individual schedules to any
officer of another Government Department solely for bona fide research or statistical purposes pursuant to the functions and dutiesof that Government Department. . . .

Because the LBD includes filed tax returns, the privacy and confidentiality
provisions of the Tax Administration Act 1994 also apply to researchers using
the data, and the Inland Revenue Department is consulted regarding who
accesses the database. Researchers sign declarations to the effect that they
have read and understood the relevant provisions of both acts.
For non-departmental researchers, Statistics NZ enables access to the data
through secondments – ie, by making the researchers employees of Statistics
NZ. To justify a direct secondment, Statistics NZ must be satisfied that thework carried out has benefits to the Official Statistics System.

3.2

Access

The starting point for accessing the LBD is a Datalab application outlining
the data required, the research question, the reasons why there is no alternative to using micro data, and the benefits to the Official Statistics System. If
approved (either as a government employee or as a secondee) researchers access an anonymised version of the LBD at one of Statistics NZ’s three offices(Wellington, Auckland or Christchurch).
Results of research are subject to confidentiality provisions designed to “protect individual businesses from identification.”17 The methods used to confidentialise outputs are blunt tools, since they must be effective across an unknowable range of situations. Researchers apply these confidentiality rules
to their own results and then must demonstrate to Statistics NZ checkers
that the rules have been correctly applied. The process can place substantial
17

Code written by researchers within Statistics NZ’s offices must also be submitted forchecking if it is to be taken outside the secure environment.

10

costs on researchers both in terms of preparing the results for checking and,particularly with new projects or new checkers, in explaining the analysis,
outputs and application of the rules.18 The turn-around time for checking
outputs is a maximum of three working days, but is often less in practice.
After results have been written up into a paper, Statistics NZ require thecompleted work to be returned to them for a final check that the researcher
has not inadvertently breached any of the rules through, eg, their descriptionof their method or interpretation of their results. The paper checking process
can take up to ten working days. Papers must display a disclaimer explaining
how the principles of the legislation have been met and distancing Statistics
NZ from the analysis. Further, the LBD must be described as a “prototype”
to remind readers that the database is a work-in-progress.
With the support of domestic government agencies and Statistics NZ it has
been possible for overseas-based researchers to work with the data though,as indicated above, unit record data is accessible only within New Zealand.19
Statistics NZ charges for access to the data on a cost recovery basis which
may include a fixed fee for any data preparation or linking, and a per-day fee
for use of the secure research facilities (the Datalab) and for confidentiality
checking.

3.3

Adding data to the LBD

The primary way to add new firm-level data is via a Datalab application explaining the need for research requiring such data to be linked to the LBD.20
Examples of data that have been added through this process include the gov18

Statistics NZ may relax the rules where researchers can clearly demonstrate that thestandard rule is not necessary to satisfy confidentiality requirements, though it can takesome effort to make the case for an exemption and such an exemption is given on an
output-by-output basis.
19
There are currently no “general use” business Confidentialised Unit-Record Files (CURFs)
in New Zealand. While there are some off-site anonymised and confidentialised datasets,
notably unlinked BOS survey data, these are held under tight security for the sole useof the funding government departments. Unconfidentialised unit record data only leaves
Statistics NZ in limited circumstances: for BOS, when explicit consent for a follow-up
study has been given by the respondent; and where the survey has been run jointly withanother agency (for example, the R&D Survey is jointly run with the Ministry of Research,
Science and Technology).
20
This section covers the integration of additional micro data. There are no general issues
with researchers bringing in macro data sources (eg, deflators or trade partner country
characteristics) and linking that data directly themselves.

11

ernment assistance programme data and the trade in services (ITSS) data.
Where data integrated in this manner has not been subject to an official firstrelease, then Statistics NZ must publish an official statistic prior to allowing
research use.
The other mechanism for adding new data to the LBD is through commissioned BOS content (subsection 2.5 above), since the BOS dataset is currently being updated annually within the database.21 The point of differencewith the standard process is the ability to design content in the knowledge
that additional longitudinal data can be accessed via the LBD (particularly
quantitative performance data and other BOS surveys).
Once an agency has successfully applied for additional data to be added to
the LBD it becomes accessible to all research users.22 That is, researchers
all have access to the same dynamic database.23

4

Possible developments

There is currently an annual update process, funded by a consortium of government agencies,24 extending the temporal coverage of the LBD. Hopefullyin the future, Statistics NZ will gain permanent funding to maintain the
LBD. The database can only become more useful as data from the recent
economic recession and future recovery is added.
In addition to annual updates, there is still plenty of scope to expand thenumber of components in the database. There is untapped potential in tax
sources particularly from adding sole proprietor and partnership equivalentsof the IR4 income tax declaration (the IR3 for sole proprietors and the IR7 forpartnerships) and the IR10 profit and loss statement (the IR3B). Statistics
NZ hold a number of unlinked business datasets that could form the basis ofinteresting research projects. While not an exhaustive list, these data include
21

Technically, the process for the creation of new BOS data is accompanied by a Datalab
application enabling access.
22
Subject to the constraint that the work using the data fits with the research question
outlined in the researcher’s own Datalab application.
23
Archived versions of the data are also accessible to enable replication and journal revisions.
A number of the datasets are subject to historical revisions because of, eg, late filed tax
returns, tax id-only firms being subsequently included on the Business Frame (hitting themateriality threshold), changes to link tables or firm-level balance dates.
24
The Department of Labour; Ministry of Economic Development; Ministry of Foreign Affairs and Trade; Ministry of Research, Science and Technology; Reserve Bank of New
Zealand; and the Treasury.

12the Quarterly Employment Survey (providing a measure of hours paid); input
and output producer price data; Quarterly International Investment Survey(including foreign affiliates information); and a number of industry-specificsurveys (eg, Retail and Wholesale Trade Surveys, Accommodation Survey,
Agricultural Production Survey,25 and other production survey datasets).
Finally, there is the potential to add further firm-level measures of worker
characteristics, via an enhanced LEED. Options here could include recent
developments in the addition of education data to LEED or, potentially,
integration of LEED with five-yearly Census information.
Currently, the integration of any of these data will require government agencies to satisfy the Datalab process outlined above. Alternatively, a more
systematic process for determining what the LBD should contain could be
developed ending, say, with the database evolving into a central repository
for all Statistics NZ-held business data.
Aside from adding to the database, it seems there are opportunities toincrease the returns on the current investment by improving accessibility.
Those improvements could be made on at least three fronts. Firstly, theStatistics Act could be amended to remove the bias in favour of employeesof government departments. It is hard to estimate the potential impact ofthat restriction. Roughly half the researchers currently accessing the data are
not primarily employed by government departments and have their access enabled by direct secondment to Statistics New Zealand or another government
department.26 As a rule of thumb, securing access this way is more costly
and may, therefore, discourage access. Secondly, all researchers would benefit
from a more streamlined Datalab application process – currently perceived
as unnecessarily bureaucratic by many who have been through it. Improvements could usefully focus on committing to a time-limited process, shiftingemphasis away from trying to assess confidentiality risk at the proposal stage
given that sound checking rules apply to outputs, and engaging the applicant more in the assessment process.27 Thirdly, confidentiality rules could
be relaxed to allow more of an assessment of risk from first principles – ie,by asking the question, is there a real risk here?28 Such an approach would
25

This has a potential partner in the IR3F which is a tax declaration of farming income
expenditure, also including detailed information on stocks and farming inputs.
26
The Reserve Bank of New Zealand has statutory independence and so is not listed as adepartment in the relevant legislation.
27
More effort on this last point may help overcome a key problem of asymmetric information– the researcher often doesn’t know what data Statistics NZ has that may be relevant totheir research. Negotiating variations to Datalab agreements to add additional variables
can be as time-consuming as the original application process.
28
For example, why should the sample size from a randomly sampled survey need to be con-13

place more emphasis on the ability of checkers to exercise judgement.
Finally, the LBD is a recent development and there are only a small number of research economists with access, so that very few papers using thedatabase have appeared at international conferences.29 The new cuttingedge for firm-level research using Statistics NZ-held data is projects using
linked LEED-LBD data. Two approved Datalab applications currently exist
looking at the contribution of (LEED-based measures of) worker skills to productivity growth (Hyslop and Maré), and nominal wage rigidity (Fabling).30
Hopefully, as the profile of the LBD improves, more researchers will find use
for it – either directly or working collaboratively with New Zealand-based
researchers.

fidentialised? Why should researchers not be able to release the maximum and minimum
value of the residual produced by estimating a production function? These requirements
arise because there are cases where counts and mins/maxs do present confidentiality risks.
29
There are approximately ten researchers actively using the data. Two papers were presented at CAED last year in Budapest (Fabling and Grimes forthcoming; Fabling et al
forthcoming) and two papers are appearing this year in Tokyo (Fabling and Grimes 2008;
Fabling and Sanderson 2009b).
30
The latter project also involves the linking (outside of the LBD) of unit record Labour
Cost Index data, which is survey information on wage changes for specific jobs withinfirms.

14

References
Fabling, R (2007a), “How innovative are New Zealand firms? Quantifying and relating organisational and marketing innovation to traditional science and technology indicators,” in Science, Technology
and Innovation Indicators in a Changing World: Responding to Policy Needs, OECD: Paris.
Fabling, R (2007b), “What chance a longitudinal firm?” Statistics New
Zealand mimeo.
Fabling, R and A Grimes (2008), “Over the hedge? Exporters’ optimal and selective hedging choices,” Reserve Bank of New Zealand,
Discussion Papers, DP2008/14.
Fabling, R and A Grimes (forthcoming), “The “suite” smell of success:Complementary personnel practices and firm performance,” Reserve
Bank of New Zealand, Discussion Papers.
Fabling, R, A Grimes, and L Sanderson (forthcoming), “Export market choices of New Zealand firms,” Reserve Bank of New Zealand,
Discussion Papers.
Fabling, R and D Maré (forthcoming), “Production function estimation
using New Zealand’s Longitudinal Business Database,” Motu Economic and Public Policy Research, Working Paper.
Fabling, R and L Sanderson (2009a), “Entrepreneurship and aggregate
merchandise trade growth in New Zealand,” Reserve Bank of New
Zealand, Discussion Papers, DP2009/09.
Fabling, R and L Sanderson (2009b), “Exporting and performance: The
impact of destination characteristics on learning effects,” paper presented at the 9th Comparative Analysis of Enterprise Data Conference, Tokyo, Japan.
Maré, D (2008), “Labour productivity in Auckland firms,” Motu Economic and Public Policy Research, Working Paper, 08-12.
Maré, D, R Fabling, and S Stillman (forthcoming), “Immigration andinnovation,” Motu Economic and Public Policy Research, Working
Paper.
Maré, D and D Hyslop (2008), “Cyclical earnings variation and the composition of employment,” Statistics New Zealand, LEED Research
Paper.
MED (2009), “Evaluation of the Growth Services Range: Statisticalanalysis using firm-based performance data,” Ministry of Economic
Development.

15

16

Linked Employer-Employee Data
Goods and Services Tax(GST 101 form)
Business Activity Indicator
Tax-filed financial accounts (IR10)
Company tax return (IR4)Government assistance programmes
Overseas Merchandise Trade Data

LEED
GST

Business Operations Survey
Business Practices Survey
Innovation Survey
Research and Development Survey

Business Finance Survey

Manufacturing Energy Use Survey

BOS
BPS
INN
R&D

BFS

MEUS

Financial year

Financial year

Financial year
Financial year
Financial year
Financial year

Financial year

QuarterlyMonthlyOne-, two- or
six-monthly
Monthly
Financial year
Financial year
Various
Daily

Frequency
Monthly2006

2005-2007
2001
2003
1996-2006(biennially)
2004

1999-2007

96Q2-09Q1

Apr92-Mar08
1999-2007
1999-2007
1994-2008
Jan88-May08

Apr99-Sept08
Apr92-Mar08

Availability
Apr99-Sept08

SNZ

SNZ
SNZ

SNZ

SNZ

SNZ

IRD
IRD
IRD
Various
Customs

IRD
IRD

SourceSNZ

Tax idTax idTax idMatching
Tax id;matching

Tax idTax idLinking

Mandatory filing threshold ($1K);
match quality drops pre-1996
Approximate census satisfying
BOP materiality threshold
Stratified random sample; IR10s
used for small firms
Stratified random sample; 6+RME
Stratified random sample; 6+FTE
Stratified random sample; 10+FTE
Stratified random sample;
excludes “non-R&D” industries
Stratified random sample;< 100FTE
Stratified random sample;
manufacturing; 10+RME

Registered companies only

Mandatory filing threshold

Mandatory filing threshold

Key population restrictions
Non-employing firms below
GST filing threshold

Availability of financial year data relates to “notional” March 31st balance dates (eg, the 1999 year is the financial year ending
31st March 1999 or the financial year that has the greatest overlap with that period if the firm’s balance date is not March 31st).
FTE=Full-Time Equivalents; RME=Rolling Mean Employment.

AES

International Trade in Services
and Royalties Survey
Annual Enterprise Survey

ITSS

BAI
IR10
IR4
GAP
Customs

DatasetLongitudinal Business Frame

Acronym
LBF

Table 1: Datasets integrated into the Longitudinal Business Database