Download Analysis of Industry Life Cycles for New Zealand Businesses

Analysis of Business Statistics for New Zealand
Industries
Dr. Michael Slyuzberg1, Graeme Buckley, Bhaskaran Nair
New Zealand Inland Revenue Department, Wellington, New Zealand
Introduction
The project “Economic fluctuations, business demographics and tax revenue collected
from New Zealand businesses” is exploratory research funded by the government through
its Official Statistics Research programme leading by Statistics New Zealand. The project
investigates how New Zealand businesses’ life cycles are affected by economic
fluctuations, and what impact changing business demographics and businesses’ life
cycles have on tax revenue collection.
The analysis is based on microdata data available via the Prototype Longitudinal
Business Database (LBD) combining administrative (mostly tax) data and statistical data.
This is the first attempt to use LBD for supporting evidence-based tax policy advice to
the government and enhancing tax forecast capability.
Objectives
The key objectives of the project are to:
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gain a better understanding of the dynamics of business performance and the
determinants of tax revenue collected from New Zealand businesses;
explore the usefulness of LBD for supporting evidence-based tax policy advice to
the government;
improve quality of business entry and exit data by in-depth analysis of abnormal
(“false”) birth / death events.
To achieve the above goals, the project attempts to find answers on the following
research questions:
1. What are comparative characteristics of businesses in different phases of their life
cycles (such as emerging (entering) businesses, mature businesses, contracting
(exiting) businesses)?
2. Is it possible to identify “false” birth / death of a business based on anomalies in
its business life cycle structure?
3. Do the economic fluctuations affect businesses’ life cycles?
4. Do relationships between economic fluctuations and business life cycles differ by
industry?
5. How do economic fluctuations affect business income?
6. Are the income losses of businesses affected by economic fluctuations and
business life cycles?
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Corresponding author ([email protected] )
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This abstract is focussing on research questions 1 to 4. The last phase of the project
(research questions 5 and 6) will be finalised by the time of presentation.
Data Extraction
The analysis required the construction of a panel (time series) data set of individual
businesses from the LBD augmenting administrative and survey data. The core variables
included:
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Enterprise number;
1996 Australia New Zealand Standard Industry Classification (ANZSIC96) code;
Business start date;
Cessation date;
Effective GST date (equivalent to GST return period);
Net GST value.
Post extraction process included conversion of birth, cessation and effective GST dates
into tax year quarters, aggregating net GST values to the appropriate quarter, and forming
entry / exit cohorts based on GST filing activities.
The observation period covers tax years between 2000 and 2007 (the entire time period
available from LBD). Availability of LBD data for a relatively short time period is
considered as a significant limitation for the analysis. The total size of the extracted
database exceeds one Million records.
Industries Selection
Ten industries have been selected for the analysis, using the first four digits of the
ANZSIC96 code as the selector: Sheep Farming, Grape Growing / Vineyard Operation,
Agricultural Services, Wooden Furniture Manufacture, Building Construction, Machine
Tools Importing, Accommodation, Cafes / Restaurants, Courier Services and Business
Management Services.
These industries were chosen because they cover the Primary, Secondary and Tertiary
components of the New Zealand economy and contain representative numbers of
businesses for the three types of cohorts (emerging (entering) businesses, stable/mature
businesses, ceasing (exiting) businesses).
Business Life Cycles
The business life cycle approach provides a convenient model to understand businesses’
development trends and outcomes.
In analysing business life cycles, the fundamental problem is identifying which stage a
business is at a particular time. In trying to do it, one of the most crucial elements is
determining at what point the new businesses transition from initial growth spurt into
maturity.
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In particular, it is necessary to establish the ‘normal’ length of the entry phase for each
industry and to distinguish the real new businesses from “false” birth (businesses with
continuity from previous enterprises).
The characteristic that best distinguishes three distinct phases (growth-maturitycessation) is the growth rate. For the purpose of the project this rate was measured in the
net GST values as in New Zealand it is clearly reflecting the business turnover for the
specified time period. Average net GST values for each quarter were plotted against time.
The graphs depicted a general characteristic of a steep initial rise transforming into a
period of slower growth.
The best fit polynomial regression was used to model quarterly GST growth for each
industry. The end of the growth phase was defined as the quarter in which the difference
between two successive quarters is less than the average difference for all subsequent
differences.
The same approach has been applied to the cessation phase. It is assumed that the
cessation period for an enterprise should mirror the growth phase but working the other
way, i.e. there is a negative difference between successive quarters with the terminal
phase deemed to have started when this successive quarters’ difference become greater
than the average difference of the mature phase.
Key Findings
The current results relate to research questions 1 - 4 listed above.
Growth Phase (Entries)
For most industries polynomial models provided reasonably good level of fit (R2 > 0.7).
Known disadvantages of polynomial regression were not considered critical for the
purpose of modelling as the models were used for distinguishing between phases of
business life cycle rather than for predictions or explanations of the differences.
Average length of growth phase varies between 7-9 quarters (for grape growing, dairy
farming, agricultural services, wooden furniture manufacturers, couriers, business
management services) to 3-5 years (house construction, accommodation). For a few
industries (machine tool wholesaling, cafes & restaurants) the results were unstable, but
most industries showed reasonably steady length of the start-up phase over time.
Average annual growth for GDP turnover is reasonably stable within industries but
significantly vary between them with business management services having the lowest
average growth rate (221.9%) and dairy farming the highest (418.2%). In line with
expectations, negative correlation was found between average length of the growth phase
and average annual growth rate (r = -0.44).
The proportion of “false” birth (businesses which demonstrate not a good fit to the
average growth curve for the industry) varied significantly for different industries, from
moderate (not more than 15-18%) for diary farming and agricultural services and up to
one third and more for wooden furniture manufacturers and house construction firms.
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Within specific industries we were able to identify years with the proportion of outliers
significantly higher than the industry mean.
Cessation Phase (Exits)
At this point we were unable to obtain robust results for the length and the GST decline
rate for the cessation phase. The major reason for this is a high proportion of businesses
which failed within very brief (two quarters or less) period of time, following a “sudden
death” scenario rather than just being allowed to contract to nothing.
Another problem is that the LBD does not contain data on reasons for termination, and
there is no indication as to the cessation cause. A business may be still growing when the
owner decides to retire or emigrate, or is bought out by some other enterprise.
Therefore only the volume of ceased businesses and the proportion of “sudden death”
were analysed in relation to the macroeconomic dynamics.
Business Life Cycles and Economic Fluctuations
For each selected industry changes in GST turnover, business entry (new businesses,
false birth) and exit (overall terminations, sudden death) dynamics, average turnover
increase rate and average length of the growth phase have been analysed in relation to
changes in the external economic factors, such as GDP growth, capacity utilisation,
headline inflation, 90-days interest rates, export volumes, terms of trade and employment
level.
The key findings include the following:
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Robust positive correlation exists for most industries between net GST values and
employment level (r >0.85). The only two exceptions are wooden furniture
manufacturing and machine tool wholesaling – industries relying on relatively
limited amount of professional skills bearers.
All ten industries demonstrate moderate to strong negative correlation between
net GST values and GDP growth. Introducing a time lag between the above
variables does not significantly affect the relationship.
Most industries demonstrate robust across the time period and moderate to high
positive correlation between net GST values and 90-days interest rate. Machine
tool wholesaling is the only exception.
Amount of new entrants strongly and positively correlates with capacity
utilisation, employment growth, labour participation rates and export volumes. No
significant correlation with inflation rates has been found.
Amount of ceased businesses negatively correlates with GDP growth and
employment growth, and positively with 90-days interest rate. This is true for all
industries. No significant correlation is found with both inflation and export
volume.
Similar but weaker relations are found between the volume of “sudden death” and
the three macroeconomic measures stated above.
For seven out of 10 industries the proportion of “false” birth strongly and
negatively correlates with labour participation rate and employment growth.
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No significant correlation with macroeconomic measures was found for average
length of growth phase and average annual growth rate.
Conclusion
The project has a strong novelty component being the first attempt to explore LBD for
policy development and analytical purposes in tax administration. The first results
obtained up to date prove the effectiveness and high potential of using combined
administrative data and statistical data from Statistics New Zealand’s Business Frame for
supporting informed policy decision.
The presentation will cover the methodology and key findings of the project in more
details.
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