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Productivity in New Zealand 1988 to 2002.

1. Introduction

Productivity growth is inextricably linked to economic growth and increases in welfare. As some recent research suggests that most of the difference in cross-country per capita GDP growth is due to differences in multifactor productivity growth rather than input accumulation (Easterly and Levine, 2002), understanding the evolution and determinants of New Zealand's productivity is important.

The aims of this paper are to provide aggregate and industry productivity series for the market sector of the New Zealand economy, to give an initial analysis of these series, and to compare the productivity performance of Australia and New Zealand. Furthermore, these series are intended as a basis for ongoing monitoring of New Zealand's productivity performance and for use in further analyses investigating the evolution, sources and determinants of New Zealand's productivity growth.

There are a number of approaches to measuring productivity. This paper produces annual aggregate and industry productivity series for the market sector of the New Zealand economy for the period 1988 to 2002, using index number techniques and industry data sourced from Statistics New Zealand (SNZ). Throughout, this paper draws on the techniques from Diewert and Lawrence (1999), a major study that examined New Zealand's aggregate and industry productivity performance using index number techniques.

The remainder of this paper is structured as follows. Section 2 discusses the index number methodology and the choice of an index number formula. Data sources and construction are discussed in Section 3. Section 4 reports aggregate and industry productivity series and compares these with Diewert and Lawrence (1999) productivity series. Section 4 also discusses some of the limitations of the industry data used in productivity calculations. A comparison of Australia and New Zealand's productivity performance is provided in Section 5. Section 6 summarises key conclusions and suggests several avenues for further work.

2. Index number methodology

This paper uses the index number approach to measure aggregate and industry productivity. (1) Construction of aggregate and industry productivity series using index number techniques is common internationally, especially amongst national statistical agencies. For example, the Australian Bureau of Statistics (ABS) publishes productivity series for the Australian economy using the index number approach. What follows is a brief introduction to productivity measurement using the index number methodology. A more detailed review of the index number approach to productivity measurement is available in Diewert and Nakumara (forthcoming) and McLellan (2003).

In general, a productivity index is defined as the ratio of an output index to an input index, that is:

(1) [A.sup.t] = [Q.sup.t]/[I.sup.t]; t = 0, ..., T

where [A.sup.t] is a productivity index, [Q.sup.t] is an output index and It is an input index. Each index represents accumulated growth from period 0 to period t.

When [I.sup.t] is comprised of a single type of input, say labour or capital, [A.sup.t] is a partial productivity index. The two most common partial productivity measures are labour productivity and capital productivity. Labour and capital productivity indexes measure changes in the ability of the labour and capital inputs, respectively, to produce output over time. Caution should be exercised when using partial productivity measures as changes in the mix of inputs can influence these measures. For example, substitution of physical capital for labour, owing to a relative change in the price of labour to physical capital, may raise labour productivity. In this case, "... labour productivity statistics do not always represent true changes in the underlying productivity of labour ..." (Dixon, 1990, p. 6).

When [I.sup.t] is a composite index of two or more inputs, [A.sup.t] is a multifactor productivity index. Most often [I.sup.t] is formed using labour and capital inputs, although researchers have also included other inputs, such as land, in addition to labour and physical capital inputs. This paper presents a suite of productivity measures, including labour and capital productivity. However, owing to limitations with the partial productivity measures, more emphasis is given to multifactor productivity series throughout this paper.

Calculating productivity at the aggregate and industry level requires the construction of both output and input indices. Because outputs and inputs are heterogenous it is not possible to simply add all the outputs to get an output index and, likewise, to add all the inputs to get an input index. Both outputs and inputs need to be weighted to form aggregate and sub-aggregate output and input indices. Output prices and input costs are often used as respective weights to form output and input indices.

When constructing productivity indices, it is not immediately apparent which weighting procedure should be used to form output and input series and on what basis this weighting procedure should be chosen. There is a multiplicity of index number formulae available to users when constructing output and input indices. Some of the better known indexes include the Laspeyres, Paasche, Fisher and Tornqvist.

Suppose information on the price and quantity of I outputs is available for period t=0, ..., T. Denoting the price and quantity vectors in period t as [p.sup.t][equivalent to] ([p.sup.t.sub.l] and [q.sup.t][equivalent to] ([q.sup.t.sub.l], ...,[q.sup.t.sub.l]), the Laspeyres ([L.sup.t.]), Paasche ([P.sup.t]t), Fisher ([F.sup.t]) and Tornqvist ([T.sup.t]) quantity indexes are defined as follows:

(2) [L.sup.t] = [[summation of].sub.i][P.sup.0.sub.i][q.sup.t.sub.i]/[[summation of].sub.i][P.sup.0.sub.i][q.sup.0.sub.i]

(3) [P.sup.t] = [[summation of].sub.i][P.sup.t.sub.i][q.sup.t.sub.i]/ [[summation of].sub.i][P.sup.t.sub.i][q.sup.0.sub.i]

(4) [F.sup.t] = [([L.sup.t][P.sup.t]).sup.1/2]

(5) [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII].

for t = 0, ..., T and i = 1, ..., I, and where [w.sup.t.sub.i] = [p.sup.t.sub.i][q.sup.t.sub.i]/[[summation of].sub.i][p.sup.t.sub.i][q.sup.t.sub.i].

There are two main approaches to choosing an index number formula: the economic approach and the axiomatic approach. The economic approach bases the choice of an index number formula on an assumed underlying aggregator function (ie, production, cost, revenue or profit function). This approach assumes competitive optimising behaviour and embodies production technology. For example, firms are assumed to. maximise profit for a given production technology. The axiomatic (or test) approach bases the choice of an index number formula on properties the index should exhibit, with these properties being embodied in axioms. One of the appealing features of this approach is that it does not make any assumptions about competitive optimising behaviour. A strong case can be made in favour of using the Tornqvist and Fisher index formulae as both possess properties that are desired under the economic and axiomatic approaches.

In addition to deciding on an index formula, a decision needs to be made whether to construct direct or chained productivity indices. A direct quantity index compares quantities in period t relative to some fixed base period (which is why a direct index is also known as fixed-weight index or a fixed-base index). Information on price movements and therefore weighting changes in the intervening period is ignored. In contrast, a chained quantity index compares quantities between two periods taking into account information on weighting changes in the intervening period. Put another way, a chained index uses price information that is more representative of that faced by economic agents in each period than does a direct index.

When relative prices change, relative quantities also tend to change For example, if the price of a particular good rises relative to all other goods in the economy due to a demand increase, then price taking producers will tend to produce more of this good relative to other goods. Using a direct quantity index to measure quantity changes in the face of relative price changes will introduce substitution bias into the quantity index. Biases arise because changes in producer behaviour in response to relative price changes are not taken into account when using direct indexes. Moreover, the substitution bias usually becomes cumulatively larger with the passage of time, as historical fixed-weights become increasingly unrepresentative. Chaining direct indexes usually reduces substitution bias.

The chained quantity index is formed by linking direct quantity indexes. Generally, a chained index is constructed as follows:

(6) [C.sup.0,t] = 1 x [D.sup.0,1] x [D.sup.1,2] x ... x [D.sup.t-1,t]; t = 0, ..., T

where [C.sup.0,t] denotes the chained index between time 0 and time t and [D.sup.t-1,t] the direct index between time t - 1 and time t. Chaining can be applied to any of the index number formulae outlined in equations (2) to (5). One of the consequences of chaining is that it usually reduces the index number spread (the range between the Laspeyres and Paasche indices). A situation where it can be inappropriate to adopt chaining is when price and quantities exhibit large fluctuations. In this situation, the chained index and counterpart direct index will diverge and the index number spread will be accentuated.

This paper uses the Fisher index to construct market sector and industry productivity series to provide methodological continuity with Diewert and Lawrence (1999), who also used the Fisher index when constructing productivity series for New Zealand. Furthermore, chaining is also employed throughout. The paper also reports productivity series constructed using alternative indexes to test the sensitivity of New Zealand productivity series to different index number formulae.

3. Data

To form aggregate and industry productivity series for the market sector of the New Zealand economy, data are needed on the values and volumes of output, labour and capital. Appendix A in Black et al (2003) documents the data sources and describes how data have been transformed for use in constructing productivity series. What follows is a brief discussion of the data used in producing productivity series for the New Zealand economy.

Data on the values and volumes of output, labour and capital were sourced from Statistics New Zealand. Annual industry nominal and volume GDP were drawn from the industry income and production GDP accounts. Annual industry hours worked data were obtained from Statistics New Zealand's Household Labour Force Survey (HLFS), and industry capital stock data were sourced from Statistics New Zealand's productive capital stock series. Industry labour and capital cost series were constructed using industry compensation of employees and operating surplus data taken from Statistics New Zealand's income GDP accounts. Industry compensation of employees data were also adjusted to account for sole proprietors' labour income being classified as operating surplus in the System of National Accounts.

The market sector of the New Zealand economy was around 85% of GDP in the year to March 2002. Industries excluded from the market sector are: Central government administration and defence; Local government services; and Ownership of owner occupied dwellings. The level of industry disaggregation for which market sector productivity series can be constructed is determined by the industry hours worked data. (3) Compared with the production and income GDP accounts and the capital stock data, ANZSIC industry hours worked data are constructed at a more aggregate level. Therefore, even though data on production and income GDP (compensation of employees and operating surplus) and the capital stock are available for 31 industries (ie, at the two digit industry level), hours worked data are only available for nine industries (ie, at the one digit industry level). This made it necessary to form one digit industry level aggregate industry output and capital series using data on production and income GDP and the capital stock at the two digit level in order to approximate the ANZSIC industry hours worked level of industry disaggregation as closely as possible.

4. New Zealand's productivity performance

4.1 Aggregate productivity

Figure 1 presents partial and multifactor productivity estimates for the market sector of the New Zealand economy. The output index used in forming each of the productivity series is a chained Fisher output index that has been constructed using industry volume GDP and implicit prices as elemental series. The input index used in forming the multifactor productivity series is a chained Fisher input index that has been constructed using industry hours worked, capital stock, and labour and capital cost data as elemental series. The corresponding chained Fisher input index for the labour productivity series was constructed using industry hours worked and labour cost data. The chained Fisher input index for the capital productivity series was formed using industry capital stock and capital cost data.

[FIGURE 1 OMITTED]

The productivity series shown in Figure 1 represent accumulated growth from 1988. The percentage difference in productivity between two years is found by taking the ratio of the index value at the last year to the index value at the first year and then subtracting 1. (4)

Changes in input utilisation arising from business cycle fluctuations are reflected in productivity estimates. Although during slack periods some labour is usually shed, workers that are retained or do not have their hours reduced are often underutilised.

Underutilisation of the capital stock tends to be relatively greater, because the capital stock cannot be shed as easily as labour. For this reason, annual productivity growth is usually positively correlated with annual output growth (see Figure 2).

[FIGURE 2 OMITTED]

One way of accounting for changes in input utilisation arising from business cycle fluctuations is to calculate average productivity growth between two consecutive cyclical peaks or troughs in the level of activity. Providing input utilisation rates are the same at consecutive cyclical peaks or troughs, this is a valid method to account for changes in input utilisation and gives a measure of trend productivity growth over the classical cycle. Alternatively, trend productivity growth can be measured over the growth cycle. This is done by measuring average growth rates between three consecutive points at which the economy is deemed to be on trend. On-trend points can be identified in one of two ways. First, a variety of survey based measures of input utilisation can be used to judge when the economy is on-trend. Second, statistical filters can be used to measure the economy's trend level of output and on-trend points identified where the trend level of output and actual output are equal.

The period covered by these productivity series is relatively short and consequently there are few business cycles over which to compare trend productivity growth. The annual output series for the market sector suggests that peaks in the level of economic activity occur in 1989 and 1997 and troughs in the level of economic activity occur in 1990 and 1998. McLellan (2001) also argued that cyclical peaks and troughs in the level of economic activity in New Zealand occurred in these years based on the Haugh (2001) aggregate production GDP series. In addition, Downing, Janssen, McLellan and Szeto (2002), who used the Quarterly Survey of Business Opinion (QSBO) to identify growth cycles for the New Zealand economy, suggested a growth cycle between 1993 and 1999.

Table 1 panel I reports trend productivity growth over the classical cycles identified by McLellan (2001) and the growth cycles identified by Downing et al (2002). The geometric average growth rate has been used to calculate trend productivity growth.

Between 1989 and 1997 trend growth in multifactor productivity was 0.82% per annum. Trend capital productivity growth was almost identical to trend multifactor productivity growth, and labour productivity growth was somewhat weaker at 0.73% per annum. Trend multifactor and capital productivity growth measured from the trough in 1990 to the trough in 1998 were slightly higher than trend growth measured from the peak in 1989 to the peak in 1997. Trend labour productivity growth was essentially the same whether measured from peak to peak or from trough to trough.

The trend growth estimates for multifactor productivity, labour productivity and capital productivity measured over the growth cycle identified by Downing et al (2002) are 1.19% per annum, 0.91% per annum, and 1.29% per annum, respectively. These trend productivity growth estimates are higher than those obtained for the classical cycle, whether measured from consecutive cyclical peaks or consecutive cyclical troughs.

Figure 1 indicates there may have been a change in New Zealand's multifactor and capital productivity growth around 1993, with these series showing an upward trend after 1993 in contrast to the period before 1993. This is also evident from Table 1, panel II, which shows an increase in average multifactor and capital productivity growth in the period 1993 to 2002, compared to the period 1988 to 1993. However, it is difficult to conclude that there has been a structural improvement in New Zealand's multifactor productivity growth given the short time period covered by the data. Formal time-series tests for structural breaks in New Zealand's productivity will require productivity data that cover a longer period. (5)

Nonetheless, some recent research using longer time series has suggested the New Zealand economy experienced a structural break in the early 1990s. For example, Razzak (2002) argued that trend labour productivity growth during the 1990s was different than in the previous two decades. Similarly, Buckle, Haugh and Thompson (2002) found evidence of a significant change in New Zealand's GDP growth characteristics dating back to 1993.

4.2 Sensitivity analysis." alternative index number formulae

Section 2 argued there were good justifications on both economic and axiomatic grounds for using the Fisher index to calculate productivity series. However, to give an idea of the sensitivity of New Zealand productivity series to alternative index number formulae, this section presents multifactor productivity series using alternative index number formulae. Alternative multifactor productivity series are also shown in Figure 3. Average growth rates for the alternative productivity series for the period 1988 to 2002 and the sub-periods 1988 to 1993 and 1993 to 2002 are presented in Table 2. (6)

[FIGURE 3 OMITTED]

Figure 3 shows that the chained Fisher and Tornqvist indices are almost identical. This is consistent with other studies, which have found little difference between Fisher and Tornqvist indices. In addition, the chained Laspeyres and Paasche multifactor productivity series are also very similar to the Fisher and Tornqvist multifactor productivity series. Overall, New Zealand market sector multifactor productivity series appear to be relatively insensitive to the choice of index number formulae when productivity series are chained.

Chaining appears to be quite important when measuring market sector productivity in New Zealand. Figure 3 shows a marked increase in the index number spread when comparing unchained Laspeyres and unchained Paasche multifactor productivity series. The unchained Paasche series is similar to the chained Paasche series up to 1993, but diverges thereafter. The unchained Laspeyres series is similar to the Fisher and Tornqvist series until 1999, whereafter it diverges. The range of estimates for average multifactor productivity growth for the period 1988 to 2002 is 0.74% per annum, using the unchained Paasche series, to 1.00% per annum using the unchained Laspeyres series. For the sub-period 1993 to 2002, the range for average multifactor productivity growth is 1.12% per annum, using the unchained Paasche series, to 1.50% per annum, measured using the unchained Laspeyres series.

4.3 Comparison with Diewert and Lawrence productivity estimates

The productivity series constructed by Diewert and Lawrence (1999) used two databases. The first database was one that the authors had previously used for analysing the impact of tax changes in the New Zealand economy. The authors' 'preferred' multifactor productivity series for the market sector of the New Zealand economy for the period 1972 to 1998 was constructed using this database (see Diewert and Lawrence, 1999, Table 1, 'Diewert-Lawrence preferred' series). The second database was provided by the Reserve Bank of New Zealand, the New Zealand Treasury, and the Department of Labour, and was designated Official Database by Diewert and Lawrence. The 'Preferred Base Case' multifactor productivity series for the market sector of the New Zealand economy for the period 1978 to 1998 (see Diewert and Lawrence, 1999, Table 1, 'Preferred Base Case' series) was formed using the Official Database. (7) Further details on the 'Official' Database are provided in Keegan (1998) and in Appendix C The 'Official' Database (Diewert and Lawrence, 1999).

The most comparable Diewert and Lawrence multifactor productivity series to the market sector multifactor productivity series presented in Figure 1 is found in Table 4.3 of Diewert and Lawrence (1999, p. 45, 'Philpott' Lives series). The comparable Diewert and Lawrence labour and capital productivity series to the market sector labour and capital productivity series presented in Figure 1 are found in Table 4.12 of Diewert and Lawrence (1999, p. 66, 'Labour' and 'Net capital' series). These series were constructed using the Official Database. The chained Fisher output index was formed using production GDP data for 20 industries comprising the market sector of the New Zealand economy. Chained Fisher input indices were formed using information on industry hours worked, and industry net capital stocks for plant and equipment and building and construction, weighted by user costs with industry specific depreciation rates and asset lives based on Philpott (1992).

Figure 4 compares the Diewert and Lawrence productivity series constructed using the 'Official' Database with the productivity series presented in Figure 1. Because the Diewert and Lawrence series have a base of 1 in 1978, it was necessary to rebase these series to unity in 1988 to aid comparison with the productivity series shown in Figure 1. Table 3 reports average growth for the Diewert and Lawrence productivity series for the period 1988 to 1998 and for the sub-periods 1988 to 1993 and 1993 to 1998.

[FIGURE 4 OMITTED]

In general the Diewert and Lawrence productivity series are quite similar to the market sector productivity series presented in Figure 1. Table 3 shows that over the period 1988 to 1998 average growth in the Diewert and Lawrence multifactor productivity series was 0.10% per annum higher than average growth in the multifactor productivity series shown in Figure 1. In contrast, average labour productivity growth was 0.21% per annum higher in the Diewert and Lawrence labour productivity series over the period 1988 to 1998. Average growth in the Diewert and Lawrence capital productivity series over the period 1988 to 1998 displays higher growth than the capital productivity series shown in Figure 1. Moreover, the two series appear to be diverging from the mid-1990s.

Given the improvements in the National Accounts data since Diewert and Lawrence (1999) undertook their study, especially the introduction of productive capital stocks estimates, the productivity series reported shown in Figure 1 are likely to give a more accurate picture of New Zealand's productivity performance. However, this comparison highlights the impact that data upgrades and revisions can have on measured productivity.

4.4 Industry productivity

The market sector productivity series provide an understanding of aggregate trends in New Zealand's productivity growth. However, aggregate trends can mask differences in industry productivity trends. Buckle, Haugh and Thomson (2001) found considerable changes in industry growth rates and the proportion of GDP that these industries comprised. In particular, the primary and services sectors showed increasing growth rates. Industry based multifactor productivity series should be approached with caution due to considerable measurement problems at this level of disaggregation. While many of the problems discussed here have implications for results at the aggregate level, these are likely to have a more significant impact on industry level results. For example, the most basic problem--a lower rate of surveying of the population at an industry level--is overcome through aggregation. In contrast, the lower survey rate at the industry level is likely to result in greater volatility in the industry series.

The key measurement issues raised by Diewert and Lawrence (1999) included double deflation in GDP, omitted variables, misallocation of demand, and measurement of financial sector output. Problems with capital stock data and consistency of definition of industry between labour data series were also raised. These last two issues were discussed in section 3 of this paper.

Double deflation is the ideal method of calculating industry based GDP. This requires price and quantity information on gross outputs and intermediate consumption purchases between industries for every period. In practice, sufficient information for each industry to calculate GDP on a double deflation basis is not readily available, and is therefore only used in a few industries. A comprehensive source of information for double deflation is input-output tables, but these are generally only produced on a 5-year basis, with significant lag times between collection and analysis. The alternative method of calculating GDP is the single indicator method. This method assumes that the structure between industries is constant, although the weighting assigned to each industry is updated annually.

The calculation of multifactor productivity would ideally take into account a number of currently omitted variables such as land use, natural resource depletion (eg, fishery and forestry stocks) and inventories. (8) Measures of these items are not currently available in New Zealand. Changes in resource use and prices would lead to changes in multifactor productivity growth. At the industry level, differences in usage of land, natural resources and other unaccounted inputs between sectors may create misleading results. For example, land, a key input into agricultural production, has not been valued, while the key inputs in other industries may have been more comprehensively considered (eg, capital services in the manufacturing industry).

Misallocation of demand can arise when intermediate industry consumption is not correctly identified, or allocated to the wrong industry. (9) For example, consider the situation when a firm provides an employee with a company vehicle. The cost of the vehicle is an expense to the firm, reducing GDP. By contrast if the firm paid the worker more in order to purchase their own vehicle, this would increase final demand and GDP. Changes in policies leading to altered incentives for providing fringe benefits, or leading to more self-employed people, may lead to changes in the way consumption is split between intermediate and final demand.

By its nature, output from the service sector can be extremely difficult to measure. This is particularly so in the financial services industry because output depends on net interest earnings. These must be split between intermediate or final consumption and between the different industries of the economy. Value added in other service sector industries, such as education, can also be hard to ascertain because of the difficulty in identifying a measurable output. In some cases employment indicators may be used along with an assumed constant employment to output ratio. In other words, for some of the sub-industries within the service sector, constant labour productivity may be assumed (Statistics New Zealand 1996). Difficulties in measuring services sector output has led to the exclusion of parts of this sector from the Australian Bureau of Statistics (ABS) multifactor productivity series (see Section 5 for more detail on which service sector industries are excluded from the ABS productivity measures).

Overall, measurement issues at the industry level suggest industry multifactor productivity series should be treated with a degree of caution. This is particularly the case for service sector industries where measurement problems are more pronounced.

Industry multifactor productivity estimates were constructed by dividing the chained Laspeyres volume GDP index by a Fisher input index. The chained Fisher input index was formed by combining industry hours worked and industry capital stock data, using industry labour cost and industry capital cost data as weights. Industry multifactor productivity series are presented in Appendix B of Black et al (2003).

Over the 1988 to 2002 period, the productivity growth of two industries stands out: the Transport, storage and communications industry (average growth of 6.0% per annum) and Primary industry (average growth of 1.38% per annum) (see Table 4 and Figures 5 and 7). The Personal and community services industry also appears to have had comparatively strong average productivity growth (1.24% per annum, see Table 4 and Figure 6).

[FIGURES 5-7 OMITTED]

At the aggregate level, New Zealand's productivity growth appears to have accelerated post 1993. Of the nine industries presented here, estimates for six industries show a stronger productivity performance in the period 1993 to 2002 than for the period 1988 to 1993 period. These industries are the Primary, Retail and wholesale trade, Business and property services, Mining and quarrying, Construction and Personal and community services. For all these industries, the increase in average productivity growth between the 1988 and 1993 and 1993 and 2002 was in excess of 0.6% per annum.

The three industries that did not experience an acceleration in productivity growth post 1993 are the Electricity, gas and water, Transport and communications and the Manufacturing industries. Multifactor productivity in the Transport and communication industry was increasing relatively strongly in the period 1988 to 1993 (6.75% per annum), and the rate of productivity growth slowed slightly in the period 1993 to 2002 (5.52% per annum). The Electricity, gas and water industry, which experienced a modest increase in productivity between 1988 and 1993 (1.11% per annum), appears to have declined sharply since the mid 1990s (see Figure 6), resulting in a decline in productivity over the period 1993 to 2002. The manufacturing industry, having experienced some positive productivity growth in the period prior to 1993, experienced a slight decline post 1993. Estimates suggest that manufacturing industry productivity in 2002 was very similar to productivity in 1988.

Some of these differences in productivity performance across industry and across time periods are likely to reflect the consequences of industry reforms. Sub-industries within the Transport and communication industry, such as telecommunications and postal services, along with the Primary industry, were some of the first to undergo reform in the mid 1980. The benefits of these reforms, and the benefits of later reform in other industries, may be reflected in these results. However, results for the manufacturing industry, which has experienced considerable reform, show no multifactor productivity growth. Manufacturing industry productivity results also show surprisingly little volatility. These differences in productivity performance are consistent with Buckle, Haugh and Thomson (2001) who found a decline in volatility for manufacturing GDP growth, but also that manufacturing sector GDP growth was slower than for the Primary or Services sectors.

This section earlier noted the possible impact of omitted variables in biasing multifactor productivity estimates for individual industries. Land, one of the key inputs to the primary industry, is omitted from calculations. The extent to which changes in land use patterns and land prices have impacted upon results for the Primary industry are unknown. The Primary industry also includes fisheries. The introduction of fisheries quotas will have increased firm costs and therefore decreased value added in this industry. However this is only a partial consideration of resource value, as the resource input is not included in the input index.

Because measuring service sector output is difficult, multifactor productivity growth for the three service industries should be viewed with caution. Retail and wholesale trade, which spans both the goods and services sectors, has been the third strongest performing industry in terms of productivity growth since 1993. This industry appears to have experienced a fall in multifactor productivity during the early 1990s, which coincided with a decline in New Zealand's domestic absorption rate.

These results show considerable variation in the productivity growth between industries. While some sectors have shown notable growth (Transport and communications, Primary), others have stagnated. When looking at these results, it must be remembered that output of some industries, particularly those in the service sector can be difficult to measure. These measurement problems are recognised by the ABS, and consequently their productivity measures exclude some parts of the service sector.

4.5 Comparison with Diewert and Lawrence industry multifactor productivity estimates

Diewert and Lawrence's Official Database included series for 20 industries in the market sector of the New Zealand economy, for the period 1978 to 1998. Industry analysis in this paper has been at a considerably more aggregated level, looking at nine industries, due to the data limitations discussed in section 4.4.

Like the multifactor productivity results, the Diewert and Lawrence official database also suggests that Transport, storage and communications and Primary have been the industries with fastest growing productivity. In addition, the magnitudes of the growth rates appear roughly comparable.

Over all industries, the Diewert and Lawrence results appear to be more positive over the 1987 to 1998 period. Contrasting this, multifactor productivity results appear more positive in the shorter period from 1993. More positive results in the shorter period are a pattern across the three service industries. However, the multifactor productivity growth in Business and Property services industry is more positive across the entire 1987-1998 period. These results may reflect ongoing measurement improvements in what are hard to measure sectors, particularly the methodological change, discussed above, in the business and property services industry.

Multifactor productivity results suggest that the Electricity, gas and water industry experienced poor growth in productivity. Diewert and Lawrence also reported on this industry and their dataset produces a considerably higher estimate of productivity growth. Manufacturing multifactor productivity also performed poorly and this is similar to the results for manufacturing industries reported by Diewert and Lawrence (1999).

5. Australia and New Zealand productivity

This section compares productivity growth in the New Zealand and Australian economies for the period 1988 to 2002. The Australian and New Zealand economies are often compared on the basis of similarities in history, institutions and economic policies (see for example Brook, 1998; Matheson, 2002). Diewert and Lawrence (1999) also compared Australian and New Zealand productivity using index number techniques.

The ABS produces multifactor productivity, labour productivity and capital productivity series for the market sector of the Australian economy. What follows is a brief discussion of the methodology and the data used to construct these productivity series. Further information on the methodology and data used to construct the ABS productivity series can be found in the Australian System of National Accounts: Concepts, Sources and Methods (ABS, 2000). These productivity series have been used to evaluate the productivity performance of the Australian economy (see for example Parham, 1999; Quiggin, 2001)

The market sector of the Australian economy is defined by the ABS to include the following industries: Agriculture, forestry and fishing; Mining; Manufacturing; Electricity, gas and water; Construction; Wholesale trade, Retail trade; Accommodation, cafes and restaurants; Transport and storage; Communication services; Finance and insurance; and Cultural and recreational services. The hard to measure industries excluded from the market sector are: Property and business services, Government administration and defence; Education, health and community services; personal and other services; and ownership of dwellings. The ABS market sector comprised approximately 64% of total volume GDP in 2002.

Labour productivity is formed by taking the ratio of market sector gross value added to hours worked. Capital productivity is formed by taking the ratio of market sector gross value added to a measure of capital services. Multifactor productivity is formed by taking the ratio of market sector gross value added to a composite input series of hours worked and capital services. The composite input series is formed using the Tornqvist index formula. An estimate of market sector capital services is formed using detailed capital stock data for each asset type at the industry level. User cost series, calculated using an industry specific internal rate of return, are used to weight the capital stocks for each asset type.

To maximise scope for comparison with the ABS productivity series it was necessary to construct measures of multifactor productivity, labour productivity and capital productivity using the Tornqvist index and excluding the business and property services and personal and community services industries, to more closely align the New Zealand industry coverage with the ABS definition of the market sector. The resulting 'ABS equivalent' New Zealand market sector was 58% of total volume GDP in 2002.

While the New Zealand 'ABS equivalent' productivity series comes relatively close to the industry coverage and specification of the ABS productivity series, some differences remain. First, it was not possible to construct comparable industry data that separate out the finance and insurance industry from the business and property services industry or the cultural and recreational services industry form the personal and community services industry. Second, the ABS uses market prices whereas data used in this paper are formed using producer prices. Third, the ABS capital stock data include inventories, land and livestock whereas the Statistics New Zealand productive capital stock estimates exclude these asset types. Finally, the ABS aggregate theft elemental industry asset type capital stock data using user cost series constructed with an industry specific internal rate of return. Statistics New Zealand forms their industry capital stock data by summing the constant price elemental series on different asset types at the industry level. (10)

Before examining the productivity performance of Australia and New Zealand, it is useful to consider the evolution of output and capital in the two countries. Figure 8 shows these series for the New Zealand market sector (as defined in Appendix Table 1 of Black et al (2003)), the 'ABS' equivalent market sector, and the Australian market sector. The ABS market series have been rebased to unity in 1988.

[FIGURE 8 OMITTED]

Over the entire period 1988 to 2002, average output growth in the Australian market sector has been stronger than in the 'ABS equivalent' New Zealand market sector. However, most of the difference arises in the period 1988 to 1993. Average output growth in both countries between 1993 and 2002 was almost identical at 3.80% for New Zealand and 3.95% for Australia. During this period output growth was stronger in New Zealand up until 1998, but slowed compared to Australian in 1998 and 1999. Higher average output growth in Australia between 1988 and 1993 was sourced from higher growth in hours worked and the capital stock. The higher rate of capital accumulation is one of the striking differences between New Zealand and Australia from Figure 8 (a point which is discussed in more detail later in this section).

Figure 9 compares 'ABS equivalent' New Zealand market sector productivity, the ABS market sector productivity and New Zealand market sector productivity reported shown in Figure 1. Again, the ABS productivity series have been rebased to unity in 1988. Table 5 reports average growth rates in the 'ABS equivalent' New Zealand market sector productivity series and the ABS market sector productivity series.

[FIGURE 9 OMITTED]

Excluding the business and property services and the personal and community services industries from the New Zealand market sector makes a considerable difference to multifactor and labour productivity. In both cases the 'ABS equivalent' New Zealand multifactor and labour productivity series lie above the corresponding New Zealand market sector series. The capital productivity series are very similar. The change in industry coverage highlights the impact the difficult-to-measure sectors can have on measured productivity growth.

Average multifactor productivity growth for the period 1988 to 2002 increases from 0.88% per annum using the New Zealand market sector series to 1.12% per annum using the 'ABS equivalent' series. Average labour productivity growth increases from 1.33% per annum to 1.91% per annum. These differences in average growth are partly due to the alternative paths taken by the 'ABS equivalent' and New Zealand market sector series up to the early 1990s. Thereafter, the two productivity series follow similar growth paths (albeit with a level difference). This is also confirmed when looking at average growth for the sub-periods 1988 to 1993 and 1993 to 2002. Differences in average multifactor and labour productivity growth are more marked between the 'ABS equivalent' and New Zealand market sector series in the period 1988 to 1993, than in the period 1993 to 2002 (see Table 5).

For the period 1988 to 2002, average multifactor productivity growth in Australia and New Zealand (ABS equivalent) was almost identical at 1.13% and 1.12% per annum, respectively. During this period, the one time in which the Australian and New Zealand multifactor productivity series diverge is in 1999. Buckle, et al (2002) argued that the 1997 and 1998 summer droughts had a substantial adverse impact on New Zealand's GDP during this period. Climatic shocks are likely to be captured within multifactor productivity and the stagnation and then decline in multifactor productivity during 1998 and 1999 is consistent with the idea that adverse climate shocks had a negative impact on New Zealand's GDP during this time.

Within a growth accounting framework, labour productivity growth can be decomposed into multifactor productivity growth and growth in the capital-labour ratio. This provides a useful organising framework for analysing the proximate sources of labour productivity growth, although it is not a model of economic growth and therefore does not capture the interaction between factor accumulation and multifactor productivity or the ultimate influences on input accumulation and multifactor productivity growth.

While average multifactor productivity growth in New Zealand and Australia has been similar over the period 1988 to 2002, the evolution of the capital-labour ratios in the two countries has been different. Between 1988 and 1993 average growth in the capital labour ratio was higher in New Zealand than in Australia (see Figure 10), and labour productivity growth was also higher in New Zealand. In contrast, labour productivity growth was higher in Australia than in New Zealand over the period 1993 to 2002. During this period Australia experienced higher growth in the capital-labour ratio compared to New Zealand. In summary, New Zealand's lower labour productivity growth after 1993 was associated with a lower rate of capital accumulation per unit of labour. (11)

[FIGURE 10 OMITTED]

On the other hand, New Zealand's slower rate of capital accumulation compared to Australia since 1994 is reflected in New Zealand's stronger capital productivity growth. As New Zealand has sourced more of its output growth from growth in the labour input since 1994, capital productivity has increased in New Zealand. In contrast, capital productivity has declined in Australia. Relatively stronger growth in the labour input in New Zealand is likely to have been welfare enhancing by the extent to which growth in hours worked has been sourced from increases in labour force participation, unemployed workers finding employment, and underemployed workers working more hours.

There are several potential explanations for the difference in the evolution of capital-labour ratios between Australia and New Zealand.

The higher rate of capital accumulation per unit of labour input in Australia compared to New Zealand after 1993 may reflect differences in the industrial structure between the two economies. For example, the Australian economy has a larger mining and quarrying industry compared to New Zealand which, given the high degree of capital intensity in the mining and quarrying industry, may be a factor behind Australia's higher rate of capital accumulation.

The IMF (2002) has suggested a high concentration of household wealth in housing assets and the small size of New Zealand's domestic market as being additional reasons for New Zealand's lower rate of capital accumulation compared to Australia. A high concentration of wealth in housing assets means there is less domestic savings to finance domestic investment. (12) New Zealand's small domestic market makes it difficult to achieve internal economies of scale and hence potentially reduces the opportunities for profitable investment.

A further explanation is the impact of changes in factor market regulation on firms' incentive to source output growth from employing more labour versus investing more in physical capital. For example, the impact of welfare and labour market reform in New Zealand in the early 1990s may have resulted in firms employing more labour rather than investing more in physical capital in meeting output growth.

The relative price of labour to capital is a measure of the cost to firms of investing in more capital versus employing more labour. When the relative price of labour to capital increases, firms are likely to invest more in physical capital and employ less labour. Conversely, when the relative price of labour to capital decreases, firms will employ more labour and invest less in physical capital.

Figure 11 shows the 'ABS' equivalent' New Zealand capital-labour ratio and the relative price of labour to capital. The price of labour (or implicit hourly wage) is calculated by dividing sole proprietors adjusted compensation of employees by the Tornqvist labour input index. Likewise, the price of capital (or implicit cost of capital) is calculated by dividing the sole proprietors adjusted operating surplus by the Tornqvist capital input series.

[FIGURE 11 OMITTED]

Figure 11 shows that over the period 1988 to 1992 the relative price of labour to capital increased. During this period New Zealand's capital-labour ratio increased (ie, the New Zealand economy experienced capital "deepening"). Between 1992 and 1996 the relative price of labour to capital fell by 22%. This occurred shortly after the introduction of the Employment Contracts Act (1991) and welfare reform. Maloney and Savage (1996) have suggested that the ECA was at least in part responsible for lower real wage growth in New Zealand relative to Australia after 1991. Firms appear to have employed labour rather than capital during this period resulting in a decline in the capital-labour ratio between 1992 and 1996 (ie, New Zealand experienced capital "shallowing"). A similar phenomenon occurred in Australia between 1988 and 1993 when Australia experienced strong growth in the labour input (although the capital ratio did not decline as it did in New Zealand). Parham (1999) has pointed out that strong labour growth was associated with a decline in real wages.

Lower productivity (quality) workers finding employment in the 1990s is a further possible reason for relatively lower labour productivity growth. Maloney and Savage (1996, p.107), in comparing labour market outcomes between Australia and New Zealand, questioned whether
 "... recent labour market reforms slowed the gains in productivity
 made under earlier product market reforms? Or is this poor
 productivity performance only a temporary by-product of accelerated
 job growth in New Zealand, as lower skilled, less productive
 workers find employment during this rapid expansion? More time will
 have to elapse before we can make any assessments over the long
 term effects of changes in the industrial relations system on labour
 productivity."


Because no quality adjustment has been made to the labour input, changes in labour quality will be reflected in multifactor productivity. It is not apparent from Figure 9 that there was any deceleration in multifactor productivity growth following the introduction of the Employment Contracts Act (1991) and welfare reform. However, this does not mean that lower productivity workers gaining employment had no impact on multifactor productivity growth because other factors, such as a general up-skilling of the existing workforce, may have had offsetting impacts. Hence, the affect of changes in labour market regulation and welfare reform on capital accumulation and labour productivity warrants further investigation.

6. Conclusions

This paper has provided new market sector and industry productivity series for the New Zealand economy for the period 1988 to 2002. These series were constructed using an industry database containing official Statistics New Zealand data, including upgraded National Accounts and productive capital stock data that were first released in 2000. Throughout, productivity series have been constructed using index number techniques, building on the substantial work of Diewert and Lawrence (1999) in Measuring New Zealand's Productivity.

Measured over alternative business cycles, average multifactor productivity growth for the market sector of the New Zealand economy ranged from around 0.8% to 1.2% per annum. Average labour productivity growth varied in a much tighter range between approximately 0.7% and 0.9% per annum. There appears to have been a noticeable improvement in market sector multifactor productivity after 1993. Average multifactor productivity growth increased from 0.09% per annum in the period 1988 to 1993 to 1.32% per annum in the period 1993 to 2002. This result is consistent with both a structural improvement in New Zealand productivity growth and earlier research showing improvements in New Zealand GDP growth dating from the early 1990s (Razzak, 2002; Buckle, Haugh and Thomson, 2002). However, owing to the short period covered by the productivity data it was not possible to use formal tests for structural breaks in New Zealand's productivity after 1993.

The comparable Diewert and Lawrence (1999) productivity series to those reported in this paper were constructed using the 'Official' Database. In general the New Zealand market sector productivity series were similar to the Diewert and Lawrence (1999) productivity series, although the two capital productivity series appeared to diverge from the mid-1990s.

Multifactor productivity growth has been strongest in the Transport and communications industry, followed by the Primary industry. Diewert and Lawrence (1999) also reported strong multifactor productivity growth in these industries. Excluding hard to measure industries from the market sector, to form 'ABS equivalent' productivity series for New Zealand, resulted in significantly higher average multifactor and labour productivity growth in New Zealand.

In the period up to 1993, labour productivity growth was higher in New Zealand than in Australia, however between 1993 and 2002 labour productivity growth was lower in New Zealand. Because multifactor productivity growth has been similar in both countries, the difference in the evolution of capital-labour ratios in the two countries accounts for the difference in labour productivity growth. The impact of changes in labour market regulation and welfare reform on New Zealand's capital accumulation and labour productivity growth warrants more detailed investigation.

This paper has provided a basis for further work on New Zealand's productivity. One strand of work is to improve the coverage and quality of the industry database used in this paper. Existing data could be backdated further and additional inputs (such as human capital, land and inventories) could be collected or constructed. This could also include the construction of industry capital stock series that have been weighted using user cost of capital series to aggregate asset type capital stock data at the industry level. Sensitivity analysis of the productivity series to alternative data (for example, the QES hours paid data) could also be undertaken. Another strand of work is to use the industry productivity series to examine the industry sources of aggregate productivity growth. This will provide policy makers with a basis for evaluating why some industries have contributed more to aggregate productivity growth than have others. Another area that warrants investigation is the relative importance of technological change versus economies of scale in generating industry productivity growth, using a technique proposed by Diewert and Fox (2003). Insights from this work are important as policy settings are likely to differ depending on whether scale economies or technological change is the main driver of productivity growth.
Table 1: Trend annual productivity growth

Time period Multifactor Labour Capital
 productivity productivity productivity

I. Business Cycles
Classical cycles
Peak to peak: 1989 to
1997 0.82% 0.73% 0.81%
Trough to trough:
1990 to 1998 0.90% 0.72% 0.95%
Growth cycles
1993 to 1999 1.19% 0.91% 1.29%

II. Growth pre- and
post-1993
1988 to 1993 0.09% 1.41% -0.68%
1993 to 2002 1.32% 1.29% 1.32%
1988 to 2002 0.88% 1.33% 0.60%

Table 2: Alternative index formulae multifactor productivity series:
average growth rates

Time Chained Chained Chained Chained
period Fisher Tornqvist Laspeyres Paasche

1988 to
1993 0.09% 0.09% 0.13% 0.05%
1993 to
2002 1.32% 1.32% 1.30% 1.34%
1988 to
2002 0.88% 0.88% 0.88% 0.88%

Time Unchaine Unchained
period d Paasche
 Laspeyres

1988 to
1993 0.12% 0.06%
1993 to
2002 1.50% 1.12%
1988 to
2002 1.00% 0.74%

Table 3: Diewert and Lawrence average productivity growth

 Time period Multifactor Labour Capital
 productivity productivity productivity

 1988 to 1993 0.34% (0.09%) 1.61% (1.41%) -0.70% (-0.68%)
 1993 to 1998 1.65% (1.69%) 0.57% (1.16%) 2.46% (1.93%)
 1988 to 1998 0.99% (0.89%) 1.08% (1.29%) 0.87% (0.62%)

Source: Diewart and Lawrence (1999)
Note: New Zealand market sector estimates are in parentheses

Table 4. Average multifactor productivity growth by industry

Time Primary Mining and Construction Manufacturing Electri-
 period Quarrying city
 gas and
 water

1988 to -0.52% -1.91% -4.59% 0.29% 1.11%
1993
1993 to 2.45% 0.72% 0.25% -0.16% -0.93%
2002
1988 to 1.38% -0.23% -1.51% 0.00% -0.21%
2002

Time Transport Business Personal and Retail and
 period and and community wholesale
 communi property services trade
 cations services

1988 to 6.75% -2.54% 0.82% -0.38%
1993
1993 to 5.52% 0.74% 1.48% 1.40%
2002
1988 to 5.96% -0.44% 1.24% 0.76%
2002

Figure 5--Multifactor productivity : Primary, Mining and quarrying,
Construction and Manufacturing industries

Table 5. Australia and New Zealand productivity comparison

 Multifactor Labour productivity
 productivity

Time Australia New Australia New
perio Zealand Zealand
d 'ABS 'ABS
 equivalent equivalent
 ' '

1988
to 0.83% 0.54% 2.14% 2.53%
1993
1993
to 1.29% 1.44% 2.43% 1.56%
2002
1988
to 1.13% 1.12% 2.32% 1.91%
2002

 Capital productivity

Time Australia New Zealand
perio 'ABS
d equivalent'

1988
to -1.13% -1.31%
1993
1993
to -0.34% 1.33%
2002
1988
to -0.62% 0.38%
2002

Source: Calculated from ABS (2002) data


(1) Alternative approaches to measuring productivity include the growth accounting, econometric, and other non-parametric methods (eg, a distance function based approach). A survey of alternative approaches to productivity measurement is provided in Mawson, Carlaw and McLellan (2003).

(2) Multifactor productivity and total factor productivity are often used interchangeably. Strictly speaking, total factor productivity is measured by dividing an output index by a composite input index that is formed using all inputs in the production process. Rarely is this the case, hence the preference for the terminology multifactor productivity.

(3) The hours worked data do not include a market/non-market split so the Personal and community services sector includes some non-market areas.

(4) For example, the percentage difference in multifactor productivity between 1988 and 2002 is equal to 13.09% ((1.1309/1)-1=0.1309); and the percentage difference in labour productivity between 1993 and 2002 is equal to 12.57% (1.2036/1.0725-1=0.1222) (see Appendix B of Black et al (2003) for multifactor productivity series).

(5) Mawson (2002), in the context of measuring economic growth in New Zealand, pointed out that average growth rates can be quite sensitive to the time period chosen. This is also the case for New Zealand productivity series. For example, if average New Zealand market sector multifactor productivity growth is measured from 1994 to 2002 rather than 1993 to 2002, average growth is 0.35% per annum lower. A longer New Zealand productivity time series would allow comparison of productivity growth between different business cycles.

(6) Productivity series used in Figure 3 and underlying the average growth rates reported in Table 2 are available in Appendix b of Black et al (2003).

(7) Diewert and Lawrence (1999) refer to both of these series as total factor productivity rather than multifactor productivity.

(8) The impact of omitted variables is captured by multifactor productivity.

(9) The misallocation of demand can also be a problem for aggregate MFP measurement (see, for example, Triplett (1997) and Diewert and Fox (1999, p. 262))

(10) Hall (1968) showed that the rental price (user cost of capital) is the price that should be used to aggregate different types of capital goods. When user cost of capital data are used to aggregate asset type capital stocks, greater weight is given to assets that depreciate relatively faster, compared to the approach of directly aggregating capital stocks. This may mean that growth in the New Zealand market sector and the New Zealand 'ABS equivalent' market sector capital stock series is lower than if industry capital stocks had been constructed using user cost of capital data to aggregate industry asset type capital stocks. This may occur because certain asset types such as plant and machinery and transport equipment, which usually have higher depreciation rates compared to building and structures, have been growing faster than other asset types (such as building and structures) at the economy-wide level. Norsworthy and Harper (1981) found that US capital stock growth was around 0.2% per annum higher when the economy-wide capital stock was constructed using user cost of capital data to aggregate asset type capital stocks, compared to the approach of directly aggregating asset type capital stocks.

(11) Footnote 10 noted there may be a downward bias in New Zealand 'ABS equivalent' market sector capital stock growth. However, if this bias is present it is unlikely to explain all of the substantial difference in growth in the capital-labour ratios between Australia and New Zealand after 1993.

(12) However, Claus, Haugh, Scobie and Tornquist (2001) have argued that New Zealand's domestic investment does not appear to have been constrained by the level of domestic savings as New Zealand has used foreign savings to meet investment demand.

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Melleny Black, Melody Guy and Nathan McLellan *

* Melleny Black: The Treasury, PO Box 3724, Wellington, New Zealand. Email: melleny.black@treasury.govt.nz. Melody Guy: The Treasury, PO Box 3724, Wellington, New Zealand. Email: melody.guy@treasury.govt.nz. Nathan McLellan: The Treasury, PO Box 3724, Wellington, New Zealand. Email: nathan.mclellan@treasury.govt.nz. The views expressed in this paper are those of the authors and do not necessarily reflect the views of the New Zealand Treasury. The authors would like to thank an anonymous referee, Nargis Bharucha, Mark Blackrnore, Bob Buckle, Ken Carlaw, Iris Claus, John Creedy, Paul Dalziel, Kevin Fox, John Janssen, Struan Little, Denis Lawrence, Peter Mawson, Grant Scobie, Kam Szeto and Bruce White and participants at seminars held at Statistics New Zealand, the New Zealand Treasury and the Reserve Bank of New Zealand. We would also like to thank Ken Fletcher, John Morris and Nick Treadgold from Statistics New Zealand for invaluable discussions and the provision of data.
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