Download Official Information Act Response 20160262

Reference: 20160262
23 August 2016
Thank you for your Official Information Act request, received on 26 July 2016. You
requested the following:
“- Communication between the Treasury and the government ministers regarding
concerns over low GDP per capita
- Any information regarding the Treasury outlining the economy may be suffering
because of the low GDP per capita”
On 26 July 2016 this request was clarified to:
“Communication between the Treasury and the government ministers regarding
concerns over low GDP per capita, including reports, aide memoires or emails
sent to Ministers that contain advice regarding “concerns over low GDP per
capita” in say the last two years
Any information regarding the Treasury outlining the economy may be suffering
because of the low GDP per capita, including reports, aide memoires or emails
sent to Ministers that contain advice regarding “concerns over low GDP per
capita” in say the last two years”
Information Being Released
Please find enclosed the following documents:
Item
Date
Document Description
Decision
1.
1 December 2014
Evidence Brief on economic
growth and productivity
Release in full
2.
16 December 2014
Treasury Report: OECD Growth
and Inequality Working Paper
Release in part
I have decided to release the relevant parts of the documents listed above, subject to
information being withheld under one or more of the following sections of the Official
Information Act, as applicable:
•
personal contact details of officials, under section 9(2)(a) – to protect the privacy
of natural persons, including deceased people.
Context
The Evidence Brief on economic growth and productivity is an internal document
undertaken as background to ‘Holding on and letting go’. It was never circulated to
Ministers.
Information Publicly Available
The following information is also covered by your request and is publicly available on
the Treasury website:
Item
Date
Document Description
Website Address
3.
29 September 2014
Taking forward your priorities
4.
20 October 2014
The Treasury Annual Report:
2013/14
5.
12 November 2014
6.
28 September 2015
Holding on and letting go:
Opportunities and challenges for
New Zealand's economic
performance: A perspective from
the Treasury
The Treasury Statement of Intent
July 2015 - June 2019
http://www.treasury.govt.nz/
publications/briefings/2014priorities
http://www.treasury.govt.nz/
publications/abouttreasury/
annualreport/13-14/
http://www.treasury.govt.nz/
publications/briefings/holdin
g-on-letting-go/
7.
14 October 2015
8.
21 October 2015
9.
26 May 2016
Thirty Year New Zealand
Infrastructure Plan 2015
Annual Report of the Treasury for
the Year Ended 30 June 2015
2016 Budget Economic and Fiscal
Update
http://www.treasury.govt.nz/
publications/abouttreasury/
soi
http://www.infrastructure.go
vt.nz/plan/2015/
http://www.treasury.govt.nz/
publications/abouttreasury/
annualreport/14-15/
http://www.treasury.govt.nz/
budget/forecasts/befu2016/
003.htm
Accordingly, I have refused your request for the documents listed in the above table
under section 18(d) of the Official Information Act – the information requested is or will
soon be publicly available.
Information to be Withheld
There are additional documents covered by the request listed in the table below that
we propose to withhold in full, under the following section of the Official Information Act,
as applicable:
•
advice still under consideration, section 9(2)(f)(iv) – to maintain the current
constitutional conventions protecting the confidentiality of advice tendered by
Ministers and officials
2
Item
Date
Document Description
Proposed Action
10.
17 July 2016
Productivity Forum - Cover Paper
Withhold in full
11.
22 July 2016
Growth and Productivity background paper
Withhold in full
In making my decision, I have considered the public interest considerations in section
9(1) of the Official Information Act.
Please note that this letter (with your personal details removed) and enclosed
documents may be published on the Treasury website.
This fully covers the information you requested. You have the right to ask the
Ombudsman to investigate and review my decision.
Yours sincerely
Angela Mellish
Manager, Forecasting
3
TOIA 20160262
Information for release
1.
2.
Evidence Brief on economic growth and productivity
16 December 2014 Treasury Report - OECD Growth and Inequality working paper
1
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EVIDENCE BRIEF ON ECONOMIC GROWTH AND PRODUCTIVITY
Draft as at December 2014
1.
Introduction
There is no single best approach to organising the theory and empirics around economic
growth. The February 2013 Issues Paper identified five focus areas: economic
performance; competitiveness; savings and growth; inclusive growth; and state sector
performance. These five focus areas were identified through:
•
a re-examination of the five policy areas used in the 2011 economic narrative (ie,
macroeconomic environment; business environment; international connections;
human capital and labour supply; state sector);
•
a consideration of alternative perspectives on New Zealand’s economic
performance (eg, size and distance; policy settings; economic structure and export
composition; income inequality and exclusion).
This ‘Evidence Brief’ brings together material on New Zealand’s growth and productivity
performance, including: the relationship between growth, productivity and competitiveness
(Section 2); the choice of comparator countries (Section 2); and New Zealand’s actual and
‘predicted’ GDP per capita (Sections 3 and 4). Section 4 covers the role of structural
policies, size and distance, and R&D. Section 5 splits GDP per capita into its components,
labour inputs and labour productivity. In turn, labour productivity is decomposed using
both growth and levels accounting, with a focus on the role of capital deepening and
multi-factor productivity (Section 6). The brief concludes with a section on industry
productivity, including structural change (within-industry and reallocation). Comparisons
are generally for the OECD, with some more detailed trans-Tasman evidence. Firm-level
productivity is covered elsewhere. .
There is a growing base of detailed information on aspects of the New Zealand economy
(eg, the MBIE sectors report). The ‘value-add’ of this Brief is in looking at New Zealand
specific and international evidence on the proximate drivers of economic performance.
While the Brief supplements material used in previous narrative related presentations, the
wide-range and evolving nature of evidence means the Brief necessarily reflects a pointin-time assessment.
A related brief covers the evidence on competitiveness, trade, and growth. International
competitiveness has a broad definition that is often used inter-changeably with overall
economic performance, and a narrower definition that is more closely associated with the
real exchange rate and its effects on trade.
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Country abbreviations
Grouping
ISO3V10
OECD
member
countries
BRIICS
Other
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AUS
AUT
BEL
CAN
CHE
CHL
CZE
DEU
DNK
ESP
EST
FIN
FRA
GBR
GRC
HUN
IRL
ISL
ISR
ITA
JPN
KOR
LUX
MEX
NLD
NOR
NZL
POL
PRT
SVK
SVN
SWE
TUR
USA
BRA
CHN
IDN
IND
RUS
ZAF
SGP
Country
(Nine suggested benchmark countries are indicated in blue)
text)
Australia
Austria
Belgium
Canada
Switzerland
Chile
Czech Republic
Germany
Denmark
Spain
Estonia
Finland
France
United Kingdom
Greece
Hungary
Ireland
Iceland
Israel
Italy
Japan
South Korea
Luxembourg
Mexico
Netherlands
Norway
New Zealand
Poland
Portugal
Slovakia
Slovenia
Sweden
Turkey
United States
Brazil
China
Indonesia
India
Russia
South Africa
Singapore
2
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2.
2.1
Growth, productivity, and competitiveness
International approaches and the approach of this Brief
Many countries have put growth, productivity, and competitiveness on the policy agenda.
They have created productivity commissions, competitiveness councils, and undertaken
systematic and regular benchmarking analyses (see Table 1). Reports on productivity and
competitiveness are also prepared by think-tanks and non-governmental organizations.
In contrast to growth and productivity, ‘competitiveness’ is a less well-defined concept,
which may partly explain why it is used less in the economic research community than in
the public debate. Competitiveness means different things to different people (see Box 1).
There is disagreement on the appropriate level where it can be applied (eg, firms,
industries, regions, entire countries). There is also debate about the relative importance of
price- versus non-price competitiveness. To simplify, price competitiveness focuses
mainly on cost strategies (eg, unit labour costs and exchange rates), while non-price
competitiveness focuses mainly on value creation (eg, innovation, quality upgrading,
capturing more value in global value chains).
Distinctions are also made between driver based measures of competitiveness which
focus on price and non-price factors, and outcome based measures. At its broadest level,
competitiveness is used interchangeably with concepts of economic performance,
economic growth, productivity, and living standards.
Many of the reports listed in Table 1 adopt the broader view and examine a range of
drivers, including: policies towards rewarding creativity, risk-taking and entrepreneurship;
infrastructure; functioning of markets; innovation; institutions; regulations; business
environment; tax policies; and macroeconomic factors. The reports differ in their choice of
drivers and how they organise these into their respective model of competitiveness. Each
driver is examined through a wide range of cross-country (and country-specific) indicators,
including hard data and information from surveys. For example:
•
The WEF Global Competitiveness Report groups indicators into 12 ‘pillars’, which
range from institutions, infrastructure, and health and primary education to labour
market efficiency, technological readiness, business sophistication, and innovation.
These are aggregated into a Global Competitiveness Index (GCI).
•
Ireland uses a ‘competitiveness pyramid’, with sustainable growth in living standards
placed at the top. Below this are the essential conditions for achieving
competitiveness, including business performance (such as trade, investment, and
business sophistication), productivity, prices and costs and labour supply. These
can be seen as the metrics of current competitiveness. Lastly, there are the policy
inputs covering three pillars of future competitiveness, namely the business
environment, physical infrastructure, and knowledge infrastructure.
•
New Zealand’s cross-agency Economic Development Indicators (EDI) report
includes various measures of wellbeing and prosperity. It examines immediate
drivers of income growth, and then looks at underlying determinants of productivity
growth, ranging from firm and market performance to the business environment.
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Table 1: Selected reports on growth, productivity, and competitiveness
Country
Report
Australia
Australian Trade Commission (2013) Why Australia: Benchmark Report Update.
Denmark
Report of the Productivity Commission (2013).
Finland
McKinsey and Company (2007) Finland’s economy: Achievements, Challenges and Priorities.
Ireland
The National Competitiveness Council, Annual Competitiveness Report 2012.
New
Zealand
Ministry of Economic Development, Treasury, and Statistics New Zealand, Economic
Development Indicators 2011.
Business Growth Agenda (BGA), Six reports on: export markets, innovation, infrastructure,
skilled and safe workplaces, natural resources, and capital.
Productivity Commission, various research reports and specific enquiries.
United
Kingdom
Other
Department for Business Innovation and Skills (2012) Benchmarking UK Competitiveness in
the Global Economy.
World Economic Forum, Global Competitiveness Report.
Institute for Management Development (IMD), IMD World Competitiveness Yearbook.
Box 1: Growth, productivity and competitiveness
The concept of competitiveness has been defined at various levels, ranging from individual firms,
industries, regions, entire countries, and even supra-national regions.
The OECD (1992) has described the competitiveness of a country as being the ...degree to which it can,
under free trade and fair market conditions, produce goods and services which meet the test of
international markets, while simultaneously maintaining and expanding the real incomes of its people over
the long-term”. Krugman (1994, 1996a) argued that competitiveness is a problematic concept when
applied to entire nations, and even “a dangerous obsession” which can lead to the wrong policy
prescriptions. He has also argued that what drives competitiveness at the level of the firm is not
necessarily applicable to a country (Krugman, 1996b), and that ‘competitiveness’ is just ‘a poetic way of
saying productivity’ (Krugman, 1997). However, many analysts are comfortable examining
competitiveness at the country level, as this level of analysis aggregates information on what is happening
at a more detailed (firm) level, and it is the environment in which firms operate under similar conditions
(eg, institutions and business environment).
Porter and Rivkin (2012) make the link between firms and countries explicit in their definition of US
competiveness “...as the extent to which firms operating in the US are able to compete successfully in the
global economy while supporting high and rising living standards for Americans”. In a similar vein, Ireland’s
National Competitiveness Council refers to competitiveness as “...the ability of firms to compete in
markets. By extension, Ireland’s national competitiveness refers to the ability of the enterprise base in
Ireland to compete in international markets”.
Regarding the scope of competitiveness, definitions range from the narrow (price and cost factors) to
the broad (which also includes so-called “non-price factors”).
The narrow definition (price or cost competitiveness) considers that a firm’s competitiveness
depends mainly on the prices of its goods and services relative to its competitors. This is why the focus
is on cost drivers (eg, factor markets, infrastructure, energy). In this view, high costs are ‘bad’, and low
costs are ‘good’ for competitiveness. For example, price is the ultimate sales criterion for so-called
homogeneous goods (eg, a certain type of steel that is identical no matter where it is produced in the
world). As a result, the appropriate firm strategy is to minimize costs. At the aggregate level, the real
effective exchange rate (REER) is often used as a proxy of international competitiveness of a country,
which subsumes at the national level all the prices and costs that exist within industries and firms.
A broader definition of competitiveness examines, in addition to prices and costs, non-price
factors, where products are differentiated by quality. When a given product exists in different qualities,
a firm has to decide where on the quality spectrum it wants to be positioned. This spectrum ranges
from the low end (low quality and low price) to the up-market (high quality and high price due to high
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quality inputs such as human capital and R&D). A firms’ positioning on the quality spectrum depends
not only on its own capabilities (in terms of technology, management and marketing), but also on
industry-specific and country-specific factors, such as the business environment. As a result, high
prices do not necessarily reflect insufficient price competitiveness; they may on the contrary reflect
strong non-price competitiveness. For example, German companies – and by extension Germany as a
country – are often referred to as having strong non-price competitiveness. The reputation of the
‘Made in Germany’ label means for many customers ‘high price, but also high quality’, and they are
willing to pay a price premium for quality in terms of functionality, design, reliability, etc.
Finally, a distinction can be made between input and output measures of competitiveness. For
example, the UK Department for Business Innovation and Skills (2012) explicitly distinguish between
driver based measures that focus on underlying factors, and outcome based measures such as
productivity performance and export performance. The OECD (2012) gauge international competitiveness
using both REER measures and export performance.
There is no single best approach to organising the proximate and ultimate drivers of
economic growth. The Treasury Productivity Research Papers set out New Zealand’s
productivity performance and highlighted issues across: investment; enterprise;
innovation; skills; international connections natural resources.1 The broad approach taken
in the Treasury Productivity Research Papers is also reflected in the 2011 economic
narrative, where the analysis and policy priorities were grouped under five areas:
macroeconomic environment; business environment; international connections; human
capital and labour supply; state sector. This Brief builds on previous work on the drivers
of economic growth and productivity undertaken by the Treasury in 2004 (Analysis of
Performance and Policy) and 2008-2010 (Productivity Research Papers). More recently,
New Zealand’s productivity performance was the subject of a July 2013 symposium
organised by the Productivity Hub, a partnership of public sector agencies with interests in
productivity analysis and policy.2
2.2
Comparator countries
This Brief makes extensive use of comparisons with specific OECD countries and/or
some form of OECD average, either all 34 current member countries or particular subsets. There is however no simple way to select benchmark comparator countries, as there
are many possible criteria. Among a large set of countries – all 34 OECD member
countries, the BRIICS (Brazil, Russian Federation, India, Indonesia, China, and South
Africa), and Singapore – the following can be observed:
•
An absolute indicator like population would suggest other small countries, such as
Ireland, Norway, Singapore, the Slovak Republic, Finland, and Denmark (Table 2,
Panel A).
1
The earlier 2004 paper, New Zealand’s economic growth: An analysis of performance and policy, is
available at http://www.treasury.govt.nz/publications/research-policy/tp/economicgrowth. The
productivity papers are available at: http://www.treasury.govt.nz/publications/research-policy/tprp/.
2
The Productivity Hub partners include the Treasury, the New Zealand Productivity Commission, the
Reserve Bank of New Zealand, Statistics New Zealand, and the Ministries of Business Innovation and
Employment, Foreign Affairs and Trade, Primary Industries, Transport and Health. The productivity
symposium papers can be accessed at: http://www.productivity.govt.nz/event/unpicking-newzealand%E2%80%99s-productivity-paradox-symposium
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•
Some indicators need to be scaled by GDP or population to ensure comparability
across countries. GDP per capita is such an indicator and suggests that the
countries that are most similar to New Zealand are Italy, Israel, Korea, and Spain (.
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•
Table 2, Panel B).
•
Combining these two indices suggests the countries most similar to New Zealand in
terms of population and per capita GDP are: Ireland, Finland, Denmark, the Slovak
Republic, Israel, Norway, Slovenia and Singapore (.
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•
Table 2, Panel C).
•
Distance from world markets would suggest benchmark countries such as Chile,
Australia, Brazil, South Africa, Mexico, Indonesia, Singapore and the United States (
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Table 3, Panel A).
•
Population density is relevant from an economic geography perspective. Norway
and Finland have almost the same population density as New Zealand, followed by
Sweden,
Chile,
and
Brazil
(
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Table 3, Panel B).
•
In terms of the similarity of the structure of goods exports, Denmark is most similar
to New Zealand, followed by Greece, Estonia, Spain, Canada, and the Netherlands
(
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Table 3, Panel C). Although even in the case of Denmark there is a large gap in the
similarity index.
A useful set of nine comparator countries for New Zealand would comprise: Australia,
Denmark, Finland, Ireland, Israel, Sweden, Singapore, United Kingdom, and the United
States. This set of nine is based on the following:
•
Denmark, Finland, and Ireland score highly in several indicators of the indicators in
Table 2.3
•
The addition of Israel and Singapore would encompass New Zealand’s five partner
countries in the “Small advanced economies initiative”.
•
Australia, the United Kingdom and the United States score lower on many of the
indicators. However, they are selected for other reasons, mainly because they are
natural reference countries given historic, economic and cultural reasons.
Nonetheless, because we do not apply this set of comparator countries on a consistent
basis across the various indicators, this Brief does not constitute a detailed
competitiveness or benchmarking report as per some of the examples in Table 1 above.
3
See Box (1999) and Frame (2000) for detailed (albeit earlier) examinations of Ireland and Finland
respectively.
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Table 2: Examples of countries that are similar to New Zealand in terms of
population and per capita GDP
A. Population
B. GDP per capita in 2012 US$
(PPPs)
Compared to New Zealand,
countries are larger (red) or smaller (green).
Compared to New Zealand,
countries are richer (red) or poorer (green).
Countries
New Zealand
Ireland
Norway
Singapore
Slovak Republic
Finland
Denmark
Israel
Switzerland
Austria
Sweden
Slovenia
Hungary
Czech Republic
Portugal
Belgium
Greece
Estonia
Netherlands
Chile
Australia
Canada
Luxembourg
Poland
Spain
Korea
South Africa
Italy
Iceland
United Kingdom
France
Turkey
Germany
Mexico
Japan
Russia
Brazil
Indonesia
United States
India
China
Indicator
Similarity
index*
4,433,100
4,588,798
5,018,869
5,312,400
5,410,267
5,414,293
5,590,478
7,907,900
7,997,152
8,462,446
9,516,617
2,058,152
9,943,755
10,514,810
10,526,703
11,142,157
11,280,167
1,339,396
16,767,705
17,464,814
22,683,600
34,880,491
531,441
38,542,737
46,217,961
50,004,000
51,189,307
60,917,978
320,137
63,227,526
65,696,689
73,997,128
81,889,839
120,847,477
127,561,489
143,533,000
198,656,019
246,864,191
313,914,040
1,236,686,732
1,350,695,000
100
97
88
83
82
82
79
56
55
52
47
46
45
42
42
40
39
30
26
25
20
13
12
12
10
9
9
7
7
7
7
6
5
4
3
3
2
2
1
0.4
0.3
Countries
Indicator
New Zealand
Italy
Israel
Korea
Spain
Slovenia
France
Czech Republic
Japan
Finland
United Kingdom
Ireland
Denmark
Greece
Germany
Belgium
Slovak Republic
Netherlands
Canada
Sweden
Austria
Portugal
Iceland
Switzerland
Australia
Poland
United States
Estonia
Hungary
Russia
Norway
Singapore
Chile
Mexico
Turkey
Luxembourg
China
Brazil
South Africa
Indonesia
India
32,022
31,760
32,407
32,954
30,923
28,962
35,813
27,476
37,449
38,940
39,269
39,391
39,764
25,480
40,511
41,684
23,806
43,841
43,996
45,126
45,599
22,349
46,137
48,386
48,738
20,956
49,428
20,493
19,225
18,323
59,691
60,206
16,132
14,943
13,380
84,310
10,371
10,292
9,655
5,408
4,431
Similarity
index*
100
99
99
97
97
90
89
86
86
82
82
81
81
80
79
77
74
73
73
71
70
70
69
66
66
65
65
64
60
57
54
53
50
47
42
38
32
32
30
17
14
C. Population and
GDP per capita
Countries are larger and richer
(red) or smaller and poorer
(green) (yellow otherwise).
Composite
Countries
Similarity
index**
New Zealand
100
Ireland
89
Finland
82
Denmark
80
Slovak Republic
78
Israel
77
Norway
71
Slovenia
68
Singapore
68
Czech Republic
64
Austria
61
Switzerland
61
Greece
59
Sweden
59
Belgium
58
Portugal
56
Italy
53
Spain
53
Korea
53
Hungary
52
Netherlands
50
France
48
Estonia
47
Japan
44
United Kingdom
44
Canada
43
Australia
43
Germany
42
Poland
38
Iceland
38
Chile
38
United States
33
Russia
30
Mexico
25
Luxembourg
25
Turkey
24
South Africa
19
Brazil
17
China
16
Indonesia
9
India
7
* The Similarity Index divides the smaller value of the country and New Zealand by the larger value of the two,
multiplied by 100. It shows how large (in percent) the smaller country is in terms of the larger one.
** The Composite Similarity Index is the weighted average of the similarity indices of population (50%) and
GDP per capita (50%).
Countries in blue are selected as benchmark countries.
Source: Population: World Bank, World Development Indicators; GDP per capita: Conference Board, Total
Economy Database.
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Table 3: Examples of countries that are similar to New Zealand in terms of distance,
population density and export structure
A. Trade-weighted distance
from world markets* (km)
B. Population density
(Population divided by area)
Compared to New Zealand,
countries are closer to world markets
(green).
Similarity
Countries
Indicator
index**
New Zealand
14,328
100
Chile
13,321
93
Australia
13,027
91
Brazil
12,052
84
South Africa
10,585
74
Mexico
10,427
73
Indonesia
9,461
66
Singapore
8,893
62
United States
8,740
61
Canada
8,176
57
Japan
7,973
56
China
7,440
52
Korea
7,274
51
India
6,553
46
Portugal
6,180
43
Spain
5,971
42
Israel
5,863
41
Iceland
5,841
41
Italy
5,574
39
Germany
5,564
39
Greece
5,561
39
Ireland
5,518
39
France
5,464
38
Turkey
5,459
38
United Kingdom
5,450
38
Switzerland
5,329
37
Netherlands
5,322
37
Belgium
5,321
37
Russia
5,305
37
Slovenia
5,231
37
Norway
5,214
36
Hungary
5,194
36
Austria
5,194
36
Luxembourg
5,192
36
Slovak Republic
5,169
36
Sweden
5,161
36
Czech Republic
5,152
36
Finland
5,143
36
Poland
5,130
36
Denmark
5,121
36
Estonia
5,116
36
Compared to New Zealand,
countries have a higher (red) or
lower (green) population density.
Similarity
Countries
Indicator
index**
New Zealand
17
100
Norway
16
97
Finland
18
94
Sweden
23
73
Chile
23
72
Brazil
23
72
Estonia
32
53
Russia
9
52
United States
34
49
South Africa
42
40
Mexico
61
27
Ireland
66
25
Canada
4
23
Greece
88
19
Iceland
3
19
Spain
93
18
Turkey
95
18
Australia
3
17
Slovenia
102
16
Austria
102
16
Hungary
110
15
Slovak Republic
112
15
Portugal
115
14
France
119
14
Poland
127
13
Denmark
131
13
Indonesia
135
12
Czech Republic
136
12
China
144
12
Switzerland
198
8
Luxembourg
200
8
Italy
206
8
Germany
235
7
United Kingdom
259
6
Japan
351
5
Israel
359
5
Belgium
365
5
India
411
4
Netherlands
495
3
Korea
513
3
Singapore
7,405
0
C. Export similarity
index***
(%)
Countries who export similar
products will be closer to New
Zealand.
Countries
New Zealand
Denmark
Greece
Estonia
Spain
Canada
Netherlands
Portugal
Brazil
France
Poland
Austria
Italy
Australia
Belgium
Russia
Luxembourg
Sweden
Norway
United States
South Africa
Finland
Indonesia
Turkey
United Kingdom
Chile
Slovenia
Germany
India
Hungary
Ireland
Czech Republic
Mexico
Slovak Republic
China
Switzerland
Singapore
Israel
Korea
Japan
Iceland
Index***
100
42
40
39
38
37
37
36
36
35
34
34
33
33
32
32
32
32
31
31
31
31
30
30
30
29
29
28
28
28
27
26
26
25
25
23
23
22
21
20
19
* Trade-weighted distance from world markets. For each listed country, the bilateral distance to a partner
country is weighted by the partner country’s share in world imports. These trade-weighted bilateral distances
are then summed for each listed country.
** The Similarity Index divides the smaller value of the country and New Zealand by the larger value of the
two, multiplied by 100. It shows how large (in percent) the smaller country is in terms of the larger one.
*** The Export Similarity Index compares the structure of goods exports in 2012 at the 2-digit level, excluding
product groups: 27 (mineral fuels, oils, distillation products, etc); 96 (miscellaneous manufactures); 97 (works
of art, collectors pieces and antiques); and 99 (commodities not elsewhere classified). It is the sum, across all
product groups, of the minimum export shares for the listed country and New Zealand. The index ranges from
0 (completely dissimilar: if one country exports, the other does not) to 100 (completely similar: each export
product has exactly the same relative importance in both countries). The index is sensitive to the level of
aggregation, and would be lower at the 4-digit levels (covering some 1,200 product groups) and the 6-digit
levels (more than 5,000 product items).
Sources: Distance: CEPII distance database; Population density: World Bank, World Development Indicators;
Export similarity: International Trade Centre, Trade Map.
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3.
GDP per capita
Although it is a commonly used in international comparisons, there is no single best
measure of ‘GDP per capita’ (see Box 2). The appropriateness of any measure depends
on the type of comparison being considered (eg, spatial or temporal) and the notion of
‘output’ or ‘income’. For example, higher income per capita can be achieved through
producing more output per capita, typically measured by GDP per capita, or from
improvements in the terms of trade. A third influence on income is the net effect of
international investment income. The two latter effects can play a stronger role for open
economies.
National statistical agencies typically produce estimates of real income that incorporate
adjustments for the terms of trade and net investment income. In New Zealand, the
relevant series is Real Gross National Disposable Income (RGNDI) and is derived as:
Constant price GDP (production based)
+
constant price terms of trade effect (trading gain/loss) defined as current price
exports deflated by an imports implicit price index, less constant price exports
=
Real Gross Domestic Income (RGDI)
+
real value of total net investment income
=
Real Gross National Income (RGNI)
+
real value of total net transfers
=
Real Gross National Disposable Income (RGNDI)
where an implicit price index for imports is used to derive the real values of net investment
income and net transfers.
Figure 1 decomposes growth in New Zealand’s Real Gross National Income (RGNI) into
its component parts: (net) investment income, the terms of trade effect, labour
productivity, and average hours worked per capita.4 (Given their relatively small size, net
transfers are ignored.)
4
Conway and Meehan (2013) provide a similar analysis (see their Figure 2).
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Figure 1: Sources of New Zealand’s Real Gross National Income per capita growth
(3-year moving average, 1995-2014)
Note: Log point contributions and 1995/96 prices. Labour utilisation is hours worked per capita. Labour
productivity is on an hours worked basis and for the total economy.
Source: Statistics New Zealand.
Figure 1 shows that:
•
Labour utilisation made a strong contribution to growth in RGDP and RGNI per
capita in the mid-1990s. Labour utilisation made a negative contribution during the
recent recession.
•
The terms of trade have made positive contributions to New Zealand’s RGNI per
capita since 2002. Before that, RGDP and RGNI per capita grew at similar rates
with labour productivity being a dominant driver.
•
The terms of trade improvements from 2002 have not been large enough to offset
the fall in labour productivity growth (total economy basis) and in average hours
worked per capita, and so RGNI growth has fallen from the rates observed in the
mid-2000s.
Grimes (2006) concludes that the terms of trade have been an important driver of New
Zealand GDP growth through time:
Consistent with the international evidence, we find that terms of trade developments
explain a considerable portion of New Zealand’s growth performance across a range of
economic regimes. We can explain approximately half the variance in annual GDP growth
over 45 years by two variables: the level of the terms of trade and the volatility of real
import prices. (Export price volatility does not show a statistically significant effect on GDP
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growth.) The relationship is robust across four economic regimes: (i) the pre-oil shock
period (1960-1975); the period following the first oil shock and prior to the major economic
reforms (1976-1984); the period during which major reforms were implemented (19851994); and the “post-reform” period (1995-2004) (Grimes, 2006, p.iv).
The posited transmission channels are described as follows:
A high terms of trade increases returns to producers and so raises investment and thence
economic growth. High variability in the terms of trade causes reallocation of both inputs
(production processes) and outputs, with a loss in output while reallocation takes place.
Former investments may no longer be profitable to continue operating and may have to
be scrapped so reducing the effective capital stock. Ex ante uncertainty associated with
high relative price volatility of both inputs and outputs may reduce investment where
hedge markets are incomplete (Grimes, 2006, p.12).
Grimes (2009) sets out a model that links changes in the terms of trade to capital intensity
and productivity (see sub-section 6.2 below).
Box 2: Alternative measures of GDP per capita
International comparisons of GDP per capita used in this Evidence Brief draw primarily on OECD
measures. The OECD GDP per capita measures are published in two forms: current PPPs and constant
PPPs. The OECD recommends measures based on constant PPPs for the analysis of relative growth
performance between countries and over time, and measures based on current (benchmark) PPPs for the
latest snapshot comparisons (see www.oecd.org/std/prices-ppp/1961296.pdf). Feenstra, Inklaar and
Timmer (2013a) make the same distinction, noting that ‘current-price real GDP” refers to calculations
across countries that are not comparable over time.
The empirical literature on economic growth also makes extensive use of the cross-country Penn World
Tables (PWT). Feenstra, Heston, Timmer and Deng (2004) comment that:
...it has been shown that there is a fundamental difference between real GDP measured from the output
side or from the expenditure side in international comparisons. The difference between the two
concepts is in the treatment of the terms of trade. Real GDP from the expenditure side represents the
ability to purchase goods and services while real GDP from the output-side measures the production
possibilities. It is the latter concept of real GDP which is of interest to many studies of growth and
convergence in the world economy. However, the available data from the OECD or the Penn World Tables
is based on a mix of cross-country expenditure side measures of real GDP for benchmark years with
national growth rates of real GDP based on output-side measures (p.25).
As a result, the measurement of real GDP in earlier versions of the PWT was closer to a measure of
‘command-basis GDP’ or ‘real income’ (Feenstra, Inklaar and Timmer, 2013a).
The latest version of the PWT (Version 8: http://www.rug.nl/reserach/ggdc/data/penn-world-table)
introduces two specific measures that distinguish between output and expenditure:
RGDP(O) is an output based measure to compare relative productive capacity across countries and
over time.
RGDP(E) is an expenditure based measure to compare relative living standards across countries
and over time.
The construction, interpretation, and implications of RGDP(O), RGDP(E) and other measures are
discussed in Feenstra, Inklaar and Timmer (2013a,b). (Note that PWT measures do not adjust for net
investment income.) Feenstra, Inklaar and Timmer (2013b) recommend that:
RGP(O) and RGDP(E) should only be used as a measure of the relative level across countries.
RGDP(NA) should be used for growth rates (as it retains domestic National Accounts growth rates).
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Observations in PWT that are directly based on PPP benchmark data (from the International
Comparison Program, ICP) or interpolations between PPP benchmarks are more reliable than
observations based on extrapolations from benchmark years. The first PPP benchmark year for New
Zealand (and Australia) is 1985.
There is a greater margin of uncertainty when comparing countries with very different spending
patterns.
Revisions to National Accounts data can have a substantial impact on the level of GDP and GDP
growth rates and are typically the dominant reason for changing data between PWT versions.
The OECD (2010) also discusses the implications of terms of trade effects and the measurement of real
income levels:
...given the conventions used in constructing OECD GDP PPPs, the current Going for Growth practice of
comparing real GDP per capita levels across countries using PPPs comes in fact close – although it is not
fully equivalent – to comparing real GDIs per capita, and as such it already largely incorporates terms-oftrade effects (pp.82-83).
As a result, the ‘Standard’ GDP PPP used by the OECD comes closer in practice to a measure of GDI at
PPP than to an output measure at PPP.
GDP per capita remains an important indicator of economic performance, especially over
longer time periods where terms of trade effects are likely to impart level differences in
contrast to the ongoing gains from sustained increases in productivity. The evolution of
New Zealand’s GDP per capita relative to the OECD (see Figure 2) has been welldocumented and Carroll (2013) provides a recent overview.
Figure 2: GDP per capita: New Zealand and selected countries relative to the OECD
average (1970-2011) [Update]
Source: OECD.
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Earlier work by Mawson (2002) examines alternative data sources for GDP per capita.
Mawson concludes that each data source confirms New Zealand’s falling rank over time,
but different data sources do influence the timing of falls and consequently may support
different explanations as to the major events contributing to such falls.
4.
GDP per capita relative to predicted performance
In addition to the GDP per capita gap relative to OECD countries highlighted in Figure 2,
there is also some evidence that New Zealand also has gap relative to the performance
predicted by policy settings and structural factors.
4.1
OECD simulation framework
Barnes, Bouis, Briard, Dougherty and Eris (2011) use a simulation framework to assess
the impact of a wide range of structural policy reforms on GDP per capita. The simulation
framework uses estimates from a number of underlying empirical studies (mostly) carried
out by the OECD. Given policy settings in areas such as taxation, labour markets and
product markets, New Zealand’s per capita GDP is predicted to be about 20% higher than
the OECD average, while it is actually 20% lower. No other country in the sample has
such a strong predicted under-performance (see Figure 3).5
Figure 3: New Zealand’s GDP per capita ‘gap’ relative to the OECD: Predicted
versus actual
Relationship between predicted and actual economic performance
80
Above the line: over-performers / perform better than predicted
Luxembourg
Actual GDP per capita gap (%)
60
40
USA
20
Switzerland
Norway
Ireland
Belgium
0
France
Austria
Sweden
Finland
Germany
Japan
Netherlands Canada
Australia
Denmark
United Kingdom
Spain
Greece
-20
Israel
Italy
Korea
Portugal
New Zealand
Below the line: under- performers / perform worse than predicted
-40
-40
Barnes et al (2011)
-30
-20
-10
0
Predicted GDP per capita gap (%)
10
20
Source: Barnes, Bouis, Briard, Dougherty and Eris (2011). Table 1.
5
Note that the ‘OECD average’ in many of the OECD growth studies discussed here comprises a smaller
set of advanced OECD countries than the full set of 34 OECD member countries typically used in GDP per
capita comparisons.
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However the authors acknowledge that “...the framework performs poorly for some
countries including Italy, Luxembourg or New Zealand, although specific factors may
account for low explanatory power in each of these cases (eg, ... geographic distance to
main international markets...) (pp.16-17)”.
In explaining and reviewing the OECD simulation framework, the NZIER (2012) note that
Barnes et al (2011, pp.5-6) are explicit about the weaknesses in the simulation approach:
•
The simplicity and tractability of the approach comes at the expense of not being
able to analyse the impact of reforms in a consistent theoretical framework that
takes account of the complex interrelationships between policies as well as spillover effects.
•
The effects of structural policies are point estimates from the preferred specification
in the underlying studies, and so are subject to both model and parameter
uncertainty.
•
Possible double-counting, by including the effect of a policy on GDP through a
particular channel more than once.
•
Because some explanatory variables were not included in all equation estimates
featured in the underlying studies, there is a risk of omitted variable bias.
•
Some variables are potentially determined simultaneously, despite equations being
estimated independently, so there is a possibility of endogeneity in the estimates.
•
Absence of policy interaction effects (eg, the extent to which product market reform
may mitigate or increase GDP impacts from FDI reform).
Barnes et al (2011) conclude that: “...the results of the model should be treated as only
illustrative. They do not provide precise estimates of the impact of proposed policies on
GDP per capita, and cannot be used as a mechanical tool to identify policy priorities
(p.6)”.
The NZIER (2012) list additional weaknesses in the structural approach as:
•
New Zealand‘s relatively pro-growth policy settings. If New Zealand‘s position is
exceptional this reduces the number of observations available to measure or
describe what might happen if policy is improved. Nonetheless, the NZIER judge
that New Zealand policy is becoming increasingly ‘less exceptional; over time.
•
Impacts are potentially limited only by the size of the shock employed (typically a
shift to the OECD average).
•
There is very little connection between actual policy and the shock admitted by the
model.
•
The underlying policy measures (ie, benchmarks) are often controversial because
they may not adequately describe countries policy settings and in that case the
results may not be reliable. This last point is generally more of a problem for New
Zealand than elsewhere, as New Zealand is unique in a lot of ways which can
confound benchmark analysis (eg, relative purity of the tax system, absence of
payroll-based social insurance schemes for funding unemployment benefits).
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•
4.2
4.2.1
The simulation models are not very flexible in the kinds and number of questions
they can answer. They lack sufficient detail.
OECD econometric approaches
Previous work on distance, natural resources, transport costs and agglomeration
In terms of variables omitted from the structural approach, earlier OECD analysis by
Boulhol, de Serres and Molnar (2008a,b) considers the role of distance to markets using
an econometric approach based on Mankiw, Romer and Weil (1992). Mankiw, Romer and
Weil (MRW) use an augmented Solow growth model (Solow, 1956, 1957), which adds
human capital as a factor of production alongside capital and labour. The augmented
model has a steady-state where output per capita is determined by the investment rate in
physical capital, human capital, the level of technology, and population growth. Sustained
growth in output per capita is driven solely by technology, which is assumed to grow at an
exogenous and constant rate.
In the reference model of Boulhol et al (2008b), the level of GDP per working age person
in country i and year t is regressed on the rate of (gross) investment, the average number
of years of schooling of the population aged 25-64, and the population growth rate (n).6
The latter term is augmented by a constant factor which reflects the trend growth rate of
technology and the rate of capital depreciation (g + d). These last three terms reflect the
‘break-even’ investment needed to ‘compensate’ capital accumulation for: population
growth, depreciation, and required capital for new effective workers created by
technological change (ie, technological change is labour-augmenting). The model is
estimated, in level and error correction specifications for a panel of 21 OECD countries
over 35 years (1979-2004).
Boulhol et al (2008b) assess the effects of distance by introducing three variables to the
reference model: the sum of bilateral distances; market potential; and the weighted sum of
market access and supplier access. Each of the three measures of has a statistically
significant effect on GDP per capita (see their Table 4).
Boulhol et al (2008b, p.10) prefer ‘market access and supplier access’ because it is a
measure: “…proposed in the new economic geography literature, which has revived the
concept of proximity to markets and formalised the role of economic geography in
determining income. Using the methodology proposed by Redding and Venables (2004)
and described in [Box 1], measures of market and supplier access have been derived
from bilateral trade equations estimated over the period 1970 and 2005 for the 32
countries/areas covering 98.5% of world trade flows in goods (see Boulhol and de Serres,
2008, for details)”.
Over the model estimation period, country differences in access to markets are persistent.
Figure 4 below is from Boulhol and de Serres (2008, p.13) and shows market access
estimates in 1970 and 2005. The main changes relate to the market access gain of China
and Korea. In addition, Spain and Portugal record improvements in market access due to
better integration into the European Union. Canada and Mexico benefit from both NAFTA
6
Note that in empirical applications the investment rate is used as a proxy for the saving rate that
features in the (closed-economy) Solow model.
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and the large US market.7 Despite the rising economic importance of Asia, the impact for
other OECD countries’ access to markets is muted for two reasons. First, the share of
Asia minus Japan in world GDP has gained less than 4 points since 1970. Second, even
Australia and New Zealand, often seen as significant beneficiaries of strong growth in
Asia, are far from the centres of growth in this area. For example, the geodesic capital-tocapital distance between Australia, on the one hand, and China and Korea, on the other
hand, is 9,000 and 8,400 km respectively. For Germany, these distances are 7,400 and
8,100 km respectively. Moreover, over a time period ending in 2005, Australia and New
Zealand had not yet benefited from trade integration as in North America and Europe.
Figure 4: Market access (2005 versus 1970)
Source: Boulhol and de Serres (2008), Figure 3.
Compared to the average country in the sample of 21 OECD countries, New Zealand’s
distance to markets (based on the market and supplier access specification) lowers ts
GDP per capita by 10% on average over the period 2000-2004 (Figure 5 below).
Conversely, the benefit from a favourable location could account for as much as 6-7% of
GDP in the case of Belgium and the Netherlands.
7
This paper was subsequently published as Boulhol and de Serres (2010).
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Figure 5: Estimated effect of market and supplier access on GDP per capita
Source: Boulhol, de Serres and Molnar (2008a) Table 8, and OECD (2008) Table 6.1.
In an earlier version of this work, Boulhol et al (2008a) also include a variable for
endowments in natural resources, as measured by the ratio of net exports of primary
products (excluding agriculture) to GDP. They comment that:
This variable is only an imperfect indicator of resource endowments but is nonetheless
found to have a positive effect on GDP per capita, suggesting that OECD countries have,
on average, escaped the natural resource curse or severe forms of Dutch disease. Not
surprisingly, the main beneficiaries of rich resource endowments are Norway and, to a
lesser extent, Australia and Canada. Higher net exports of primary products as a share of
GDP relative to the OECD average in the early 2000s could have contributed to raise
GDP per capita by 8% in Norway and by 2% in Australia and Canada (pp.6-7).
Boulhol et al (2008b) also add indicators of transport costs to the reference model in
order to assess their impact on both trade and GDP per capita. Although changes in
transport modes and costs are implicitly captured in the measures of market and supplier
access, they include transport cost indicators to assess their direct impact. The transport
cost indicators are based on estimates of freight charges for air, maritime and road
transportation of merchandise goods. The overall indicator of transport costs over the
period 2000-2004 (see their Figure 4) indicates that costs are highest for Australia and
New Zealand, being over 2.5 times that observed in North America.
The impact of transport costs on GDP per capita is assessed by adding a trade intensity
variable to the reference model. This trade intensity variable is derived as the residual
from a regression of raw trade intensity (ie, X+M/GDP) on population size. Prima facie the
results indicate that greater trade intensity generates higher GDP per capita, but the
results cannot be treated as conclusive given uncertainty regarding the direction of
causality. To address this, Boulhol et al (2008b) use overall transport costs, international
telecommunications costs, and the sum of distance, as instruments. They find that
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transport costs contribute to reduce the exposure to international trade and this in turn
appears to have a significant effect on GDP per capita. High transport costs relative to the
OECD average reduce GDP per capita by between 1% and 4.5% for Australia and New
Zealand.
Lastly, Boulhol et al (2008b) examine the interactive effects of three structural variables
(PMR; business R&D as a share of GDP; and human capital) with variables reflecting:
distance (market and supplier access), urban concentration (share of population in cities
greater than one million), and population density (ratio of population to surface area). The
latter two variables are used to proxy agglomeration influences. The conclusions are:
•
The impact of business R&D intensity on GDP per capita is influenced (positively)
by the degree of urban concentration, but not by distance.
•
The impact of human capital on GDP per capita is strengthened by urban
concentration but the opposite effect occurs with population density.
Given the significance of the distance effects in the OECD work, Crawford (2010)
reviewed the analysis and concluded that:
The work at the OECD (Boulhol et al., 2008; Boulhol and de Serres, 2008) builds on a
well established model, uses a range of different measures of distance ... and tests
robustness of results against a range of specifications. Notwithstanding difficulties in
growth econometrics ..., their finding lends support to the wide range of evidence set out
in McCann (2009) that distance from markets affects productivity.
The evidence suggests that distance explains only a part of New Zealand’s relatively
weak productivity performance among OECD countries. It also suggests that the effects of
distance change only a little over time (for New Zealand they were a little stronger at the
end of the period than at the beginning). Policies to address the effects of relatively poor
market access are likely to have only small (but not necessarily negligible) effects on GDP
per capita (pp.1-2).
4.2.2
Recent work on R&D intensity, distance, agglomeration and labour composition
More recently, de Serres, Yashiro and Bouhol (2014) have re-examined New Zealand’s
per capita GDP gap relative to the OECD average, again using the MRW approach, with
estimation for a panel of 20 OECD countries over the period 1981 to 2010.8 In the base
case, GDP per capita is regressed on the rate of total economy non-residential gross
investment, the average number of years of schooling of the population aged 25-64, and
the population growth rate (again, augmented by g + d).
They find that most of the deviation of GDP per capita against the average of 20 OECD
countries is not due to the initial variables of interest (physical capital and human capital),
but to country fixed effects (see Figure 6 below). New Zealand is slightly above the 20
country OECD average for the non-residential gross investment rate and human capital
stock. De Serres et al then introduce and assess additional explanatory variables.
8
USA, Switzerland, Ireland, Netherlands, Canada, Norway, Austria, Australia, Denmark, UK, Sweden,
Belgium, Germany, Finland, France, Japan, Italy, Spain, Portugal, New Zealand.
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Although they discuss the measurement, transmission channels, and potential policy
implications of these additional explanatory variables, here we focus on the key empirical
results.
Figure 6 should be read as follows: on average during the period 2000-2010, New
Zealand’s actual GDP per capita was 26% below the average of 20 OECD countries (the
red dot). This 26% is comprised of:
•
Contributions from a country fixed effect (-25% points, the blue bar and cited
number), physical capital (+0.9% points) and human capital (+1.3% points). These
three contributions sum to a predicted -22.8% point gap (the blue diamond).
•
A residual of -3.2% points.
Figure 6: Contribution of factors to the average gap in GDP per capita (2000-2010)
Note: The bold numbers correspond to contribution from country fixed effects. Figures for Norway correspond
to the mainland.
Source: De Serres, Yashiro and Boulhol (2014).
De Serres et al (2014) suggest that the sum of fixed effects and residuals for each country
over time can be loosely interpreted as that countries multifactor productivity (MFP) level
relative to the average of the 20 OECD countries. For New Zealand, the MFP gap was
already at around 20% in 1980 (the start of the sample) before trending down to almost
30% in the late 1990s, where it broadly stabilised (see their Figure 5, Panel B).
Note that in Figure 6 above, Australia has small country fixed effect (-3% points) which is
offset by an investment rate and human capital stock that are slightly above the OECD
averages. Australia’s actual and predicted GDP per capita are approximately equal at
about 5% above the OECD average. So in these estimates, Australia does not represent
much of a ‘puzzle’. Australia’s MFP gap (in Figure 5, Panel B) is always below 10% in the
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period 1980- 2010, and closed by 2010. ‘Correcting’ for R&D intensity shifts Australia’s
relative productivity up by around 5%. Distance has a similar effect to that in New
Zealand – around 15% points.
De Serres et al (2014) argue that an important factor missing from the MRW approach is
investment in knowledge-based capital (KBC or intangible assets) and they outline
various approaches to measuring this and its growth accounting contribution. An R&D
intensity variable (total R&D expenditure-to-GDP) is added to the base case regression,
yielding a statistically significant but modest positive long-run effect on GDP per capita. As
an alternative, they run a cross-section regression of the fixed effects from the base case
regression against R&D intensity. The deviation from the OECD average in R&D intensity
could account for up to 11% points of New Zealand’s 27% point MFP gap. However, the
relative stability of the R&D gap cannot explain the deterioration in MFP since the start of
the sample period.
With regard to size and distance factors, de Serres et al (2014) suggest that the smaller a
country is then the more intensively it needs to trade on foreign markets to benefit from
the advantages of specialisation. Using the sum of exports plus imports relative to GDP
as a measure of trade intensity, New Zealand’s ratio is below that suggested by its size
(see Figure 7). Trade-related policies (eg, tariffs) and language differences are seen as
unlikely to be driving this deviation, thereby pointing to remoteness as a factor.
Figure 7: Country size and trade intensity
Source: De Serres, Yashiro and Boulhol (2014).
De Serres et al (2014) follow Boulhol et al (2008b) and also consider a measure of
‘market access’ (ie, weighted sum of market and supplier access). The distance variables
are added to the ‘base case plus R&D intensity’ specification. Both the measure of trade
intensity (residuals from a regression of the raw trade intensity variable on population
size) and the measure of market access have a significant impact on GDP per capita.
At 15% points, slightly more than half of New Zealand’s 27% point MFP gap against the
20 OECD countries can be accounted for by reduced market access. While they discuss
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New Zealand’s participation in global value chains (GVCs) and the potential implications
for productivity, they do not explore this relationship empirically.
As in Boulhoul et al (2008b), the paper includes a model specification that interacts
human capital with a measure of agglomeration (ie, urban concentration, measured via
share of population in cities greater than one million). As before, the effect of human
capital on GDP per capita is magnified by the measure of urban concentration.
Finally, the authors cite a previous OECD estimate that around 3% of the New Zealand
productivity gap vis-à-vis OECD countries during the mid-2000s can be attributed to
differences in the labour force composition and the higher share of low-skilled workers
(Boulhol and Turner, 2009).
Summing the (upside) estimated effects of: R&D intensity (11% points), distance (15%
points) and labour composition (3% points), effectively ‘accounts’ for New Zealand’s MFP
gap.
Overall, Barnes et al (2011) predict New Zealand should have above OECD average
performance because while their simulation framework does not include distance it does
include a range of other policy variables. In contrast, using an econometric framework,
Boulhol et al (2008b) and de Serres et al (2014) include distance (and other variables) but
no policy variables. They effectively attempt to explain why New Zealand’s GDP per
capita performance is below the OECD average.
The differences across the two frameworks highlights the limitations noted above
regarding the application of multiple approaches. For example, it is tempting to conclude
that de Serres et al explain the gap to the OECD average while Barnes et al explain why
New Zealand should be above it. But these gaps and variables are not tested
simultaneously. In addition, New Zealand’s distance from markets and R&D intensity will
likely be inter-related and de Serres et al do not allow for this. Finally, Holding on and
letting go features three, overlapping explanations of New Zealand’s economic
performance (policy, distance, saving/real exchange rate/exports). Only the first two are
reflected in the OECD empirical work.
5.
Decomposing GDP per capita
Figure 8 sets out the relationship between GDP per capita and the key elements of
‘growth accounting’ – the decomposition of the growth rate of output (or output per unit of
labour) into its proximate sources, growth in inputs and growth in MFP. MFP can be
equated with technological change if certain conditions are met (eg, firms seek to
maximise profits, markets are competitive, and the coverage of inputs is complete).
However, measured MFP will, in addition to technological change, include a range of
effects including: model misspecification, errors in the measurement of the variables,
deviations from constant returns to scale, and unobserved changes in capacity utilisation.
The term MFP is often used interchangeably with total factor productivity (TFP). Although
the ‘M’ and the ‘T’ distinguish these measures from partial productivity indicators such as
labour and capital productivity, we follow Hulten (2009) in using MFP because TFP
presumes that all (or total) inputs are counted.
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Figure 8: Decomposing GDP per capita from a growth accounting perspective
Source: Statistics New Zealand.
Figure 9 traces the evolution of GDP per capita gap and its components, labour
productivity (GDP per hour worked) and labour utilisation (hours worked per capita),
against an average of 20 OECD countries from 1970 to 2010. Figure 10 takes a snapshot
view for 2012, decomposing GDP per capita levels into labour productivity and labour
utilisation for a wider set of OECD countries, relative to the United States.
In the following sections we examine New Zealand’s labour productivity from two
perspectives:
•
The aggregate level, typically ‘economy-wide’, ‘total economy’ or some ‘measured’
or ‘market’ sector aggregate. We decompose labour productivity into contributions
from physical capital intensity and MFP using both growth accounting and levels
accounting.
•
At the industry level, evidence suggests substantial differences in labour productivity
and changes in the relative importance of low- and high-productivity industries).
Finally, while there is evidence of substantial variation in labour productivity across firms
within the same industry, we leave that evidence for a separate survey.
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Figure 9: Decomposition of GDP per capita: New Zealand compared to OECD
average (20 countries, 1970-2010)
Source: Conway and Meehan (2013).
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Figure 10: Sources of GDP per capita differences in 2012 (relative to United States)
Source: OECD.
6.
Aggregate productivity
Figure 9 above suggests that New Zealand’s labour productivity has continued to drift
downward relative to other OECD countries since the early 1990s. However, there is
debate as to what extent this reflects the substitution of labour for capital (IMF, 2002;
Parham and Roberts, 2004; Hall and Scobie, 2005; Treasury, 2008b) and/or the pulling
into the labour force of workers with lower productivity (Maré and Hyslop, 2006; Szeto and
McLoughlin, 2008; Boulhol and Turner, 2009). The extent and causes of the apparent
productivity slow down are also subject to uncertainty given sensitivity to business cycle
dating and global trends in productivity. In particular, it is well recognized that labour
productivity varies across the business cycle, as output tends to be more volatile than
labour input. The ‘stickiness’ of labour inputs (so-called labour hoarding) means that
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measured labour productivity tends to fall during recessions (less output with the same
inputs) and rise during upturns (more output with the same inputs).9
6.1
Decomposition of aggregate labour productivity growth
Error! Not a valid bookmark self-reference.1 decomposes New Zealand’s measured
sector labour productivity growth since 1979 and Conway and Meehan (2013) summarise
this performance as:
Strong labour productivity growth in the late 1980s and early 1990s reflected reduced
labour input given recessionary conditions in New Zealand (and globally) and economic
restructuring most likely related to the commencement of economic reform. From the mid1990s, the labour market began to recover and the pace of capital deepening slowed.
However, this was offset to some extent by reasonably robust MFP growth. Over the
2000s, MFP growth has slowed, dragging down New Zealand’s rate of labour productivity
growth (p.12).
Figure 11: Decomposition of New Zealand measured sector labour productivity
growth (1979-2013)
Note: Former measured sector from 1979-1995, Measured sector from 1996 (see Table 6 below for industry
classification details).
Source: Statistics New Zealand.
9
Productivity typically falls in recessions as output falls faster than the ability or willingness of firms to
reduce labour and capital inputs, and existing capacity is not fully used. As hiring and firing is costly, this
makes sense when the reduction in demand is expected to be temporary.
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Comparing New Zealand’s labour productivity growth and especially the contributions of
physical capital and MFP, with other countries is more challenging (see Box 3). In terms
of labour productivity growth, New Zealand had one of the lowest economy-wide labour
productivity growth rates between 1985 and 2010 among OECD countries, with this
reflecting relatively low MFP growth (Figure 12).
Figure 12: Decomposition of economy-wide labour productivity growth: Selected
OECD countries (1985-2010)
Note: 2010 or latest available year.
Source: Conway and Meehan (2013).
Guillemette (2009) cites similar evidence and concludes that capital intensity and MFP
interact with each other in complex ways: “MFP growth is mainly driven by the expansion
of the world’s technological frontier and New Zealand’s degree of access to it. But better
technology raises the productivity of capital and thus the returns to capital investments,
which should increase capital intensity. In addition, lots of new technologies result from
innovation and research and are embodied in capital goods, such as new equipment, or
intermediate goods. Therefore, the impediments to capital deepening and to MFP growth
in New Zealand probably overlap to a large degree” (p.9).
Box 3: Productivity growth in Australia and New Zealand
The statistical agencies of Australia and New Zealand publish official productivity growth statistics that are
comparable between the two countries but differ from those published by the OECD, especially in terms of
industry coverage and construction of the capital stock. Differences between official productivity statistics
and those prepared by the OECD are outlined in Warmke and Janssen (2012). The table below sets out
the productivity growth statistics published by SNZ and the ABS for the period 1996-2013. These data
are for measured sector equivalents and :
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Confirm New Zealand’s lower labour productivity growth rate. This appears to have come from lower
growth in capital per unit of labour than Australia. Australia had particularly strong capital growth during
the mining boom.
In contrast, New Zealand and Australia had similar rates of MFP growth. However, lags in returns to
the large scale Australian investment in mining might translate into higher Australian multi-factor
productivity in the future.
Australia’s MFP16 and New Zealand’s measured sector
(Annual average growth rates, 1996-2013)
Source: Statistics New Zealand.
6.2
Decomposition of aggregate labour productivity levels
It is possible, although more challenging, to compare and decompose levels of
productivity across countries. The main challenge lies in finding appropriate conversion
factors to express output and capital in a common currency. Readily available market
exchange rates are volatile and subject to ‘misalignment’, and those obtained from
purchasing power parities (PPPs) are typically based on expenditures rather than industry
output.
New Zealand’s capital intensity, especially relative to Australia has been examined in a
number of papers using a range of methodologies, especially with regard to capital
measurement. The key results (for the closest common year) are summarised in Table 4.
This table also includes estimates from Mason and Osborne (2007), who compare New
Zealand to the United Kingdom. The most recent comparison between Australia and New
Zealand (Mason, 2013) is summarised in Box 4. Schreyer (2005) carries out a detailed
levels accounting exercise for a small group of OECD countries, including New Zealand
(Table 5), albeit for 2002.
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Table 4: Levels accounting for New Zealand, Australia and UK (all 2002 except IMF
which is 1999)
New Zealand – Australia (=1.00)
Study
Method
Y/L
K/L
Human
capital
MFP
IMF
(2002)
Market sector. Productive capital stock with
5 assets (including land). Specific
expenditure-based PPPs for output and
capital (adjusted for margins and indirect
taxes).
0.73
0.83
1.01
0.86
Hall &
Scobie
(2005)
Total economy. Net capital stock, excludes
land but includes housing. Conversion via
GDP(E) PPP.
0.77
0.72
-
0.83
Mason
(2013)
Market sector. Net capital stock: Structures
(non-residential and other) and land
improvement; Plant, machinery &
equipment; Transport equipment,
Intangibles (excluding R&D). Specific PPPs
for industry output and investment goods by
asset types.
0.63
0.70
0.98
0.76
0.77
0.69
1.07
0.87
New Zealand – United Kingdom (=1.00)
Mason &
Osborne
(2007)
Market sector. Net capital stock: Structures
(non-residential buildings and other);
vehicles; computers; other plant and
machinery; intangibles (principally
software). Specific PPPs for industry output
and for investment goods by asset types.
Note: See individual studies for details on methodology (eg, labour input, income shares, and human capital).
IMF (2002) source the stock of land used for productive purposes from Diewert and Lawrence (1999).
Table 5: Levels accounting for selected OECD economies in 2002 (USA = 100)
Y/L
Y/K
K/L
MFP
New Zealand
61.1
125.5
48.6
72.8
Australia
79.7
102.6
77.7
85.0
United Kingdom
85.9
137.3
62.6
97.2
Canada
81.7
110.0
74.3
88.0
France
108.3
93.7
115.6
103.5
Japan
70.9
53.2
133.3
65.5
Note: As per the OECD Productivity Database, the estimates are for total economy. Capital is on a productive
capital stock basis and excludes residential assets and land. PPPs are for specific investment goods.
Source: Schreyer (2005) Tables 2 and 3.
Schreyer stresses the uncertainty in making levels comparisons and rankings. He does
however generate upper and lower bounds for the point estimates (see his Tables 4 to 7).
Figure 13 below plots the plus/minus 5% bounds at a 99% confidence level for the MFP
point estimates in Table 5. These bounds are generated on the assumption that GDP,
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PPPs, hours worked, and capital services are all subject to measurement error. The
bounds indicate that precise rankings may be difficult when countries are clustered,
although this is less the case for New Zealand.
Figure 13: Upper and lower bounds for MFP in 2002 (US = 100)
Source: Schreyer (2005) Figure 2.
Table 4 indicates that the treatment of ‘Land’ varies across productivity studies. While
earlier studies (eg, Diewert and Lawrence, 1999; IMF, 2002) included land, subsequent
studies such as Schreyer (2005) and Mason (2013) exclude land. Official productivity
growth statistics for Australia and New Zealand (as discussed in Box 3) include the flow of
capital services from land assets. In contrast, the OECD excludes land from its
productivity growth statistics.
Scobie and Hall (2005) note that land is often excluded because the quantity of land in
use is seen to remain relatively constant over time, and hence can be treated as a fixed,
unchanging factor. Diewert and Lawrence (1999) argue that even though the quantity of
land may remain constant, the price of land is generally strongly increasing over time.
When constructing a price weighted quantity index of input growth for the economy, the
fixed quantity of land for many economies will receive a higher price weighting over time,
leading to a lower growth of aggregate input and hence leading to a higher measure of
productivity growth. For New Zealand, Diewert and Lawrence find that excluding land has
a negligible impact on their TFP estimates. This is because land has a relatively small (ex
post) user cost and consequently has a small weight in forming the overall total inputs
index. The main reason for land’s relatively small user cost is that it does not include a
depreciation component. It is assumed that maintenance activities are captured
elsewhere and so the quality of land is constant. Furthermore, increases in the observed
price of land have led to significant capital gains which have largely offset (and in some
years exceeded) the interest cost associated with holding land. This has led to land
having a negative (ex post) user cost in some years. Where this occurs land effectively
becomes an output instead of an input to the production process.
In the context of capital intensity levels comparisons, Hall and Scobie (2005) conclude
that changes in the coverage and method of computing estimates of the capital stock alter
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their conclusions. Whether New Zealand is really capital shallow relative to Australia may
well hinge on whether land and inventories are adequately measured and incorporated in
a more comprehensive concept of capital.
Box 4: Productivity levels in Australia and New Zealand
Mason (2013) compares levels of productivity between New Zealand and Australia for industries in the
market sector using industry-specific PPP conversion factors (see Box 6 below for the industry results).
The market sector covers the 16 industries in the SNZ measured sector, with ‘Manufacturing’ split into nine
2-digit industries, yielding a total of 24 industries overall. To improve comparability with Australia, the SNZ
‘Rental, hiring and real estate services’ industry is adjusted to remove the value of private rental dwellings
from output (there is no corresponding labour input and residential buildings are excluded from Mason’s
capital stock – see Table 4 in main text).
For the market sector as a whole, Mason finds that New Zealand’s average labour productivity level was
63% of the Australian level in 2010, down from 68% in the late 1990s. The gap in labour productivity is
mainly due to differences in MFP and capital intensity, while skill differences make a small contribution.
Consistent with the official productivity growth statistics presented above, the contribution of capital
intensity increased over time, while MFP has become less important.
Relative average labour productivity and MFP levels on total market sectors (Australia = 100)
While MFP can be seen as an indicator of the efficiency with which capital and labour inputs are utilised,
Mason cautions that the estimate of MFP will also include: the effects of unmeasured capital inputs such
as land and investments in R&D. MFP will also reflect a range of unmeasured influences such as intercountry differences in production scale, the scope for agglomeration effects, as well as measurement
errors.
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Estimated contributions of relative capital-intensity, skills and MFP
to the average labour productivity gap
Source: Mason (2013).
The studies comparing labour productivity, capital intensity and MFP in Australia and New
Zealand have also considered the role of relative factor costs, as noted previously (see
IMF, 2002; Parham and Roberts, 2004; Hall and Scobie, 2005; Treasury 2008b).
6.3
Capital and MFP
The levels accounting decompositions discussed above have generated considerable
debate about the relative roles of capital and MFP. Importantly, Treasury (2008b) recaps
the point that a relatively low level of labour productivity can be associated with both low
relative capital intensity and low relative MFP. Box 5 sets out a stylised example.
Box 5: Growth accounting and levels accounting
(Adapted from Hulten and Isaksson, 2007)
Suppose that there two economies, A and B, that both start with the same capital-labour ratio, k70.
However, A and B have different levels of output per worker, because they start with different levels of
productive efficiency, that is, economy A is on the higher of the two production functions at point ‘e’, and
economy B on the lower one at point ‘a’.
Suppose that, from this starting point, both economies only grow by capital deepening, which proceeds at
the same rate. They then move along their respective production functions at the same rate, but neither
experiences any growth in (multifactor) productivity (ie, neither function shifts). As a result, the growth rate
in output per worker is due entirely to capital deepening, but all the difference in the level of output per
worker is due to the different level of productive efficiency. Moreover, economy B may become richer over
time, but will never narrow the gap with economy A.
This simple example illustrates the insufficiency of studying comparative growth rates in isolation from the
corresponding levels. Studying comparative levels at a given point in time is also insufficient, since it
cannot indicate the growth dynamics and future prospects of the two economies. The empirical growth
literature provides two ways to implement the intuition of this example, one based on growth rates (growth
accounting) and the other on levels (development accounting).
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Overall, the dichotomy between capital accumulation and MFP is not so clearly drawn in
reality as it is in growth and levels accounting exercises: “First, R&D expenditures are a
form of capital formation, yet they are also the source of much technical change. Second,
there are mutual feedback effects in which an increase in the MFP residual causes the
amount of capital to increase, and the increase in capital leads to spillovers that increase
MFP. And, third, improvements in technology are often embodied in the design of new
capital goods” (Hulten, 2009, p.52).
Gordon (2014) makes similar points about capital deepening and changes in capital
quality, quoting Domar (1961): “without technological change, capital accumulation would
amount to piling wooden plows on top of wooden plows”.
The labour productivity accounting used above is based on the following:
= .
(1)
where Y, L and K represent output, labour and physical capital inputs respectively, A
represents (residual) MFP and α is the capital share. An alternative form of labour
productivity accounting is given by:10
10
This formulations treats ‘A’ as Hicks technology and is used by Klenow and Rodriguez-Clare (1997),
Hsieh and Klenow (2010) and Madsen (2010). Hall and Jones (1999) start directly with ‘A’ as Harrod
technology. All three studies include a measure of human capital, which for simplicity is excluded in the
formulation used here. Madsen (2010) uses the alternative decomposition in growth accounting and
growth regressions. For G17 economies over a long time period, he finds a dominant role for MFP.
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=
/(
)
/(
)
.
(2)
Hall and Jones (1999) use this alternative formulation because:
•
Along a balanced growth path, the capital-output ratio is proportional to the
investment rate, so this form of decomposition has a natural interpretation.
•
In the case of an exogenous increase in productivity and a constant investment rate,
the capital-labour ratio will rise over time. Given that MFP is calculated as a
residual, some of the increase in output (per unit of labour) that is fundamentally
due to productivity is attributed to capital accumulation in a framework based on the
capital-labour ratio. In the alternative decomposition of (2), the rise in capital will be
associated with the rise in output so that there is less movement in K/Y and more of
residual MFP will reflect the exogenous productivity shock.
Similarly, Hsieh and Klenow (2010) also note that physical capital per worker will
endogenously increase in response to increases in effective labour or MFP. Because
investments in physical capital are final goods, unlike human capital or MFP, any increase
in output will tend to bring forth higher physical capital. The larger exponent on MFP (ie,
1/(1- α) instead of 1) reflects the impact of MFP on output directly and indirectly through
capital per worker. More broadly, the papers by Hall and Jones (1999) and Hsieh and
Klenow (2010) give greater prominence to the dispersion and persistence of cross-country
differences in MFP levels as explanations of differences in GDP per capita levels. This
emphasis is a counter to the so-called neo-classical growth revival associated with MRW,
which gives relatively more prominence to physical and human capital (see Klenow and
Rodriguez-Clare, 1997).
Figure 14 below plots labour productivity and (labour augmenting) MFP for the set of
OECD countries used in de Serres et al (2014), with the exception of Norway. The year
2005 is selected because it is a PWT base year, and predates the GFC and Canterbury
earthquakes for New Zealand.
Grimes (2009) develops a two-sector general equilibrium to explore a range of factors that
might influence capital intensity in a small open economy. One sector in the model
produces internationally tradable goods (with some domestic consumption) and one
sector produces non-traded goods for domestic consumption only. The terms of trade are
defined as the ratio of the price of tradable goods to the price of imported capital goods.
Treasury (2008b) notes that the results of the Grimes model are similar to the basic
model, but offers additional insights, including the following:
•
A currency premium (or any impediment) that raises the domestic economy’s cost of
capital above the international level will lower capital intensity and labour
productivity.
•
A fall in the terms of trade lowers capital intensity and labour productivity (similar to
a fall in MFP in the basic model).
•
A rise in the MFP of the traded sector boosts capital intensity and labour
productivity; whereas non-traded sector MFP does not matter for capital intensity
and has a lesser effect on labour productivity. Grimes notes the policy implication in
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terms of government interventions - achieving the same percentage increase in
traded-sector MFP is preferable to interventions that generate the same percentage
boost in non-traded MFP.
Figure 14: Productivity levels (2005, USA = 1.0)
Note: Total economy basis. MFP is in ‘labour augmenting’ form and calculated via equation (2) above.
Labour productivity is on an hours basis and calculated as rgdpo/(empl.avh). The capital-output ratio is
rkna/rgdpo. Capital is on a net basis and includes: Structures (residential and non-residential);
Transport equipment; Computers; Communication equipment; Software; Other machinery and assets.
Capital shares are (1-labsh) and are country specific.
Source: Author estimates using PWT8.0.
In summary, being somewhat agnostic about the respective roles of capital and MFP, and
recognising the interconnections, is important in order to avoid over/under emphasis in
policies influencing the two factors, as well as recognising the measurement and
identification challenges. Nonetheless, while the various studies use alternative
decompositions, use different input measures (especially for capital), and are for different
points in time, it seems reasonable to conclude that a substantial portion of New
Zealand’s labour productivity levels gap is associated with a gap in MFP.
7.
Industry productivity
The industry perspective is potentially important because industries differ in terms of
labour productivity growth rates and levels, and the relative importance of low- and highproductivity industries is changing over time.
In this brief, ‘industry’ refers to a group of firms that have the same main activity as
classified within ANZSIC06. Industry and sector groupings, together with the split across
former and current SNZ measured-sectors are set out in Table 6. The other common
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sector grouping of industries (not shown in Table 6) is that of ‘tradables’ and ‘nontradables’ (see the evidence brief on competitiveness, trade, and growth).
Table 6: Statistics New Zealand industry and sector classifications
Productivity analysis on an industry basis is more challenging than for aggregates
because:
•
Industry coverage in the New Zealand productivity statistics varies over time, with a
wider set of 16 industries available from 1996 and a narrower set (the so-called
former measured sector) from 1978.
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•
7.1
Unlike for the total economy, New Zealand is, like Australia, not included in the
OECD industry productivity database, PDBi (see Arnaud, Dupont, Koh and
Schreyer, 2011).
Variation across industries in terms of labour productivity growth and levels
Conway and Meehan (2013) provide an overview of productivity trends and growth
accounting decompositions in New Zealand sectors and industries (see their Section 4).
They comment that growth accounting can only ever identify areas of relative over- and
under-performance at the industry level and industry-specific studies are required to gain
a deeper understanding of the ultimate causes of industry performance (p.12).
For the 16 industries in the current measured sector, Figure 15 decomposes labour
productivity growth into industry contributions in terms of capital deepening and MFP
growth.
Figure 15: Industry contributions to New Zealand measured sector growth in capital
deepening, MFP and labour productivity (1996-2011)
Source: Conway Meehan (2013). See their Figure 9 for details.
Over the period 1996-2011, overall measured sector labour productivity growth averaged
1.4% per year. In terms of absolute contributions, the ‘Manufacturing’ industry made the
largest contribution to overall labour productivity growth, partly reflecting its size. Although
smaller in size, labour productivity growth in ‘Information, media and telecommunications’
and ‘Finance and insurance’ meant these industries also made comparatively large
contributions to New Zealand’s aggregate labour productivity growth. In contrast,
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‘Administrative and support services’ and ‘Professional, scientific and technical services’
made the largest negative contributions (see Conway and Meehan, 2013).
Labour productivity levels vary across New Zealand industries. Figure 16 shows that
(nominal) GDP per hour paid in 2010 ranged from around $20 in ‘Accommodation and
food services’ to $333 in ‘Mining’. These differences in value-added per hour do not
automatically translate to differences in wages because ‘wages’ are only part of value
added, the other being returns to capital.
Figure 16: Labour productivity levels in New Zealand industries in 2010 (current
dollars)
Source: Conway and Meehan (2013).
Cross-industry differences in labour productivity levels are to be expected because
industries have different capital-to-labour ratios. Figure 17 plots industry labour income
shares against (nominal) labour productivity in 2010. Relatively capital intensive industries
(ie, with low labour income shares) such as ‘Electricity, gas, water and waste services’
and ‘Mining’ have relatively high labour productivity in terms of (nominal) value-added per
hour paid. Krugman (1994) notes that (US) industries with high (nominal) value-added
per worker are in sectors with very high capital to labour ratios. This reflects that capital
intensive industries must earn a normal return on capital, so they must charge prices that
are a larger mark-up over labour costs than labour intensive industries, which means they
have high (nominal) value-added per worker.
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Figure 17: New Zealand industry labour productivity levels and labour income
share in 2010
Source: Conway and Meehan (2013).
Given that aggregate productivity is the outcome of differences across industries and their
relative importance, and that within a country labour productivity differs across industries,
international comparisons of industry productivity levels can be useful. However, these
types of comparison are even more challenging in terms of data requirements than crosscountry comparisons of industry growth rates and are therefore less common (see Box 6).
Mason (2013) identifies some specific similarities and differences with regard to other
Australia-New Zealand comparisons:
•
The finding of Australia’s higher labour productivity in ‘Agriculture, forestry and
fishing’, most branches of ‘Manufacturing’ (except ‘Food and beverages’),
‘Construction’, ‘Wholesale trade’, and ‘Financial services’ is consistent with IMF
(2002) estimates for 1999 (based on expenditure PPPs). New Zealand’s higher
labour productivity in ‘Electricity, gas, water and waste services’ is also consistent
with IMF (2002). However, the IMF found near parity in ‘Retail trade’ and had
Australia ahead on ‘Arts and recreation’.
•
The results are broadly consistent with those of NZIER (2011) for ‘Manufacturing’,
‘Construction’, ‘Wholesale’ and ‘Retail’, and ‘Financial services’. However, the
NZIER study finds a very substantial labour productivity lead for New Zealand
agriculture. Mason suggests that this could reflect the choice of currency conversion
(the NZIER used market exchange rates) and differences in the composition of
output. For example, sheep farming accounts for 20% of total value added in New
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Zealand ‘Agriculture, forestry and fishing’, compared to 8% in Australia. Forestry
represents 12% of this industry in New Zealand and 4% in Australia.
Box 6: Industry productivity levels in Australia and New Zealand
As discussed in Box 4, Mason (2013) estimates that by the late 2000s, New Zealand’s market sector
labour productivity was just over 60% of the Australian level. This aggregate comparison masks important
differences across industries. The market sector in Mason’s analysis covers the 16 industries in the SNZ
measured sector, with ‘Manufacturing’ split into nine 2-digit industries, yielding a total of 24 industries
overall.
Source: Mason (2013) Table 17. Estimates are for 2009.
In terms of the 15 industries in which Australia has higher labour productivity:
Capital intensity plays the dominant role in six of them: Metal products; Agriculture, forestry and
fishing; Chemicals and related; Wood and paper products; Accommodation and food services; and
Clothing.
MFP plays the dominant role in eight industries: Printing; Financial services; Construction; Mining;
Non-metallic minerals; Transport equipment and machinery; Transport, postal and warehousing; and
Retail trade.
Capital intensity and MFP contribute around equal parts to Australia’s lead in Wholesale trade.
In terms of the 7 industries in which New Zealand has higher labour productivity:
Capital intensity plays the dominant role in: Furniture and other manufacturing; and Other services.
MFP dominates in: Rental, hiring and real estate services; Electricity, gas, water and waste services;
Professional and technical services; Food and beverages; and Arts and recreation.
Two industries, ‘Information, media and telecoms’ and ‘Administrative and support’ are judged to have
similar labour productivity levels. In the case of the former, the result is sensitive to the PPP factor. Using
the OECD (expenditure) PPP puts Australia’s labour productivity in this industry ahead of New Zealand by
27% points.
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Mason (2013) also decomposes the market sector labour productivity gap between Australia and New
Zealand into differences in the labour productivity of each industry (the within-industry effect) and the size
or labour share of each industry (the between-industry effect).
In 2009, about 30% of the Australian-New Zealand gap in labour productivity can be attributed to
differences in industrial structure as reflected in employment shares. This 30% contribution is largely
accounted for by: Mining (17% points); Electricity, gas, water and waste services (9% points); and
Financial and insurance services (5% points). (Note that the 30% reflects the net contributions across
industries, there are some industries where New Zealand has a lower level of productivity but employs
relatively less labour.)
Shift-share analysis for 1997 (the earliest year for comparable data) indicates that the Australia-New
Zealand difference has increased since the late 1990s. In 1997, New Zealand’s market sector labour
productivity was 68% of Australia’s, with about 18% attributable to differences in industry structure (again,
predominately due to Mining).
Mason’s last point on output composition reflects a broader issue in that his analysis is,
apart from ‘Manufacturing’, still at the 1-digit industry. So it is possible that we are dealing
with ‘apples and oranges’. For example, Table 7 sets out the 2- to 4-digit classifications
within ‘Electricity, gas, water and waste services’. Whereas ‘hydro’ accounts for around
60% of New Zealand’s electricity generation, Australia’s share is one-tenth of this. Around
90% of Australia’s electricity generation is from coal and gas, compared to around 23% in
New Zealand. To the extent these different generating methods have different production
functions and compositions of intermediate consumption then levels comparisons are less
appropriate.
Table 7: ANZIC 2006 classifications for Electricity, Gas, Water and Waste Services
Note: The two trailing zeroes are included for possible future sub-division.
Source: Statistics New Zealand.
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7.2
Effects of structural change at the industry level on aggregate labour
productivity
Aggregate labour productivity growth reflects not only the rate of productivity growth in
each industry but also the change in industrial composition. These changes can be
examined using ‘shift-share’ analysis and Sharpe (2013) defines the three components of
this as:
•
Within-industry/sector effect: Captures the contribution of labour productivity
growth in a particular industry (or sector) to aggregate labour productivity growth.
•
Reallocation-level effect: Aggregate labour productivity can increase even when
industry labour productivity remains constant, as long as labour moves from
industries with below-average labour productivity levels towards industries with
above-average levels. The reallocation-level effect captures these shifts, and is
positive when there is an increase in the labour share of industries with aboveaverage labour productivity levels.
•
Reallocation-growth effect: Captures the propensity of labour to move towards
industries where labour productivity is stagnant or declining. This effect is positive if
the labour share of industries with above-average labour productivity growth
increases.
The reallocation-level effect and reallocation-growth effect are also referred to as ‘staticshift’ and ‘dynamic-shift’ effects respectively. The two reallocation effects are often
combined into a single term, denoted as either ‘reallocation’, ‘between-industry’ or
‘structural change’.
Meehan (2014) is the most recent and comprehensive shift-share analysis of New
Zealand’s labour productivity and she cites several caveats with regard to shift-share
analysis:
•
Compared to ‘developing’ countries, the role of structural change is less clear in
‘developed’ countries where employment has moved from agriculture and
manufacturing industries to services. So although she considers structural change
and productivity growth in developing countries (Box 1), Meehan’s main focus is on
New Zealand and other OECD countries.
•
Related to the previous point, rising employment in ‘low’ productivity, labourintensive service industries may reflect a combination of so-called ‘Baumol’ effects
(Baumol, 1967) and rising demand for these services as incomes rise. However, the
lack of consideration of demand-side factors is a limitation of shift-share analysis,
with its focus on supply-side concepts (p.11).
•
It uses data from two points in time and is therefore sensitive to the choice of startand end-period.
•
Shift-share analysis assumes that all labour inputs in an industry are equally
productive (ie, marginal and average labour productivity are equal) and that a focus
on industry shares of employment does not take into account changes in utilisation
(ie, unemployment). Both limitations may be especially relevant during periods of
economic reform.
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•
It provides a first step in understanding the role of resource (re)allocation in
productivity developments. In of itself it does not provide information on drivers.
Firm-level analysis offers the opportunity to explore reallocation in more detail,
especially since a high level of industry aggregation can mask structural change
between sub-industries.
Starting with New Zealand, Figure 18 provides a shift share decomposition (into three
components) for 11 industries across productivity cycles, as well as the entire 1978-2011
period. These 11 industries are essentially the former measured sector (with ‘Arts and
recreation’ excluded’) and amount to almost 60% of (nominal) GDP. This compares to the
16 industries in the full measured sector (ie, as in Figure 15) which comprise around
three-quarters of nominal GDP. This reduced coverage means that the analysis is not
picking up shifts into those service industries that have been excluded. The gain from
reduced coverage is a longer time series and arguably more robust productivity
measures.
Figure 18: Labour productivity shift share decomposition, New Zealand (1978-2011)
Note: 2008-2011 is an incomplete cycle.
Source: Meehan (2014).
Between 1978 and 2011, aggregate labour productivity for the 11 industries averaged
2.73% per year, with 3.02% points coming from the within-industry effect, 0.04% points
from the structural change level effect, and minus 0.34% points from the structural change
growth effect. Using almost the same productivity cycles over the period 1978 to 2010,
Rajanayagam and Warmke (2012) also find a dominant role for the within-industry effect.
Examining the shift-share effects over 1978-2011 for the 11 industries, Meehan finds that:
•
All industries, except ‘Accommodation and food services’ experienced positive
labour productivity growth and so made a positive contribution to aggregate withinindustry labour productivity growth. Although ‘Manufacturing’ had slightly slower
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labour productivity growth than the aggregate, it made the largest contribution to
within-industry growth due to its large employment share.
•
The small, positive structural change level effect reflects decreases in the
employment shares for agriculture and manufacturing and increases in employment
shares for service industries, resulting in largely offsetting industry contributions.
•
The negative structural change growth effect was largely due to the movement of
employment towards service industries with low labour productivity growth.
‘Wholesale trade’; ‘Retail trade’ and ‘Accommodation and food services’ all
accounted for an increasing share of employment and had below-average labour
productivity growth. ‘Construction’ also had an increasing share of employment and
relatively low productivity growth.
•
The negative structural change effect seen in New Zealand during the 1990s may
partly reflect the limitations of shift-share analysis discussed above. The negative
structural change effect during this period was, in large part, attributable to
industries undergoing significant reforms, particularly the electricity, gas, water &
waste industry.
The cross-OECD shift share analysis undertaken by Meehan (2014) is for 20 OECD
economies over the period 1990-2005. Importantly, it covers the total economy with a split
into 14 industries.11 Figure 19 below plots the shift share decomposition of overall labour
productivity growth into within-industry effects and structural change effects. Given data
limitations, Meehan cautions that small differences between countries should be treated
with caution.
Consistent with Figure 12, New Zealand’s labour productivity growth rate was below the
OECD average (1.8% per year versus 2.4%). Within-industry productivity accounted for
2.2% points of New Zealand’s overall labour productivity growth rate, versus 2.5% points
for the other OECD countries. As with all the countries considered (and the New Zealand
specific evidence above), the majority of overall labour productivity in New Zealand came
from the within-industry effect. The biggest contributor to New Zealand’s lower than
average within-industry effect was ‘Manufacturing, which averaged 2% per year labour
productivity growth compared to an OECD average of 5.3%.
Like most countries, New Zealand experienced shifts of employment towards industries
with below-average levels of labour productivity. Although New Zealand’s structural
change effect was a smaller absolute share of overall labour productivity growth than in
other economies, New Zealand was one of nine countries that experienced a negative
structural change effect. Of those nine countries, New Zealand’s structural effect of minus
0.4% points was the largest as a percentage of overall labour productivity growth.
11
Agriculture; Mining; Manufacturing; Electricity, gas and water; Construction; Wholesale and retail trade;
Hotels and restaurants; Transport, storage and communications; Finance; Real estate, rental and
business services; Public administration; Education; Health; Other services.
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Figure 19: Labour productivity growth shift share decomposition, selected OECD
countries (1990-2005)
Source: Meehan (2014).
New Zealand’s comparatively large negative structural effect reflects the below-average
performance of its within-industry labour productivity growth and a larger employment shift
towards low productivity industries.
As a small open economy, Meehan argues that New Zealand could, in principle, increase
output in high-productivity industries through exporting. However, the absence of an
export-led employment shift leads her to suggest impediments in the form of a
consumption-led boom that shifted domestic resources into supplying non-tradable output,
high fixed costs of exporting for New Zealand firms, and an over-valued and volatile real
exchange rate. These issues are covered in the evidence brief on competitiveness, trade,
and growth.
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IN-CONFIDENCE
Treasury Report:
Date:
OECD Growth and Inequality Working Paper
16 December 2014
Report No:
T2014/2234
File Number:
TY-6
Action Sought
Minister of Finance
Action Sought
Deadline
For Information
None
(Hon Bill English)
Contact for Telephone Discussion (if required)
Name
Position
Telephone
Kristie Carter
Principal Advisor, ERA
04 890 7287 (wk)
Nick Carroll
Manager, Social Inclusion
04 917 6282 (wk)
Actions for the Minister’s Office Staff (if required)
Return the signed report to Treasury.
Enclosure:
No
Pages 2-3 redacted as Out of scope of
request
Treasury:3086932v1
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IN-CONFIDENCE
Out of scope of request
Trends and explanations for New Zealand’s GDP growth rate
9.
New Zealand incomes, as measured by gross domestic product (GDP) per capita,
began to slip from among the highest in the world around the mid-1950s (Easton,
1997). New Zealand’s ranking fell quite dramatically in the latter half of the 1970s and
in the 1980s (Figure 2). Economic performance has improved since the early 1990s
and New Zealand’s per capita GDP growth has subsequently broadly kept pace with
other advanced economies. However, this improvement has been insufficient to close
the gap that had opened up with other developed economies.
Figure 2. Gross Domestic Product (GDP) per capita in New Zealand compared the OECD
average.
10.
The OECD and others have undertaken a number of cross-country studies of economic
performance. The cross-country economic growth literature has generally thrown up a
small number of consistent variables as being important: the initial level of country
income, human capital variables and the rate of investment.
11.
There remains considerable debate about the drivers of New Zealand economic
performance.
1
Living Standards Background Note: ‘Increasing Equity’
http://www.treasury.govt.nz/abouttreasury/higherlivingstandards
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12.
A 2014 OECD authored report for the Productivity Commission largely ‘explained’ New
Zealand’s economic performance relative to a set of top OECD countries in terms of
‘distance’ and ‘R&D intensity’.2
13.
While other OECD studies have looked at structural policies (tax, regulations) and
found New Zealand performs well below predictions given generally favourable
policies, those studies have not included distance. Moreover, the role of transmission
channels (say from saving to exchange rates to exports and trade) are not covered.
Out of scope of request
T2014/2234 : OECD Growth and Inequality Working Paper
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