2  Background and data

This section provides a brief review of recent New Zealand literature applying firm unit record data to understand firm dynamics and productivity. We then describe the various data sources, and how they are combined and used in this study. Variable construction, procedures for dealing with missing data and weighting are discussed. The section concludes by outlining some of the potential problems with the data.

In New Zealand, several firm unit record datasets have been utilised to understand aspects of firm dynamics. Using Statistics New Zealand’s Business Practices Survey (BPS), Fabling and Grimes (2003, 2004) examine the relationship between various firm practices, including innovation, and firm performance. Unit record data from the NZ Institute of Economic Research’s Quarterly Survey of Business Opinion (QSBO) has been applied by Buckle and Carlson (2000, 1998) to evaluate the microeconomic foundations of business cycles, including the properties of firm pricing and output decisions. Maré and Timmins (2005) and Maré (2005) have applied Statistics New Zealand’s Business Demography (BD) and Goods and Services Tax (GST) data to examine patterns of concentration, specialisation and agglomeration of firms and the association with firm productivity. BD and GST data has also been used by Mills and Timmins (2004) to evaluate the distribution of New Zealand firm size and turnover and by Dixon, Maré and Timmins (2005) to examine changes in the size distribution of firms and to assess some reasons for these changes.

These various sources of New Zealand firm unit record data are quite different, each with their own strengths and weaknesses. The BPS provides data on a wide range of variables but gives a snapshot of a firm only, has a relatively small sample size and is restricted to firms with 6 or more full-time employees. Data from the QSBO dates back to the 1960s and covers a wide range of variables. However, these data are primarily categorical. BD and GST data capture almost all firms in New Zealand over at least the last decade, but compared to the QSBO and BPS, contain significantly fewer variables.

Despite the modest number of variables contained within the BD and GST database, Law and McLellan (2005) have shown that it offers considerable potential to measure and enhance our understanding of firm productivity dynamics. This potential will be enhanced further when this database is combined with the Annual Enterprise Survey (AES). However, to date little is known about the properties of the BD and GST database or its reliability when used to form measures of labour productivity. Some potential difficulties arise for example from the fact that sales and purchases of capital goods are included in the variables that measure firms’ sales and purchases. The typically infrequent and lumpy nature of capital goods transactions means that measures of value-added for individual firms could be quite volatile from period to period and could even include large negative values for some firms, with corresponding negative measures of labour productivity.

2.1  Description of source data

We derive a real value-added measure of firm-level labour productivity using firm-level sales, purchases and employment data supplemented by industry-level producer price indexes and hours worked data. Producer prices and hours worked data are not available at the firm level.

The primary firm-level data for sales, purchases and numbers employed come from two sources and cover the period 1994 to 2003:

1. Statistics New Zealand’s Business Demography Statistics Database (BD)
2. Inland Revenue Department’s Goods and Services Tax Database (GST)

The BD contains demographic and employment (both employees and working proprietors) information on enterprises (firms) from the New Zealand Business Frame that are deemed to be economically significant.[1] [2] These data are collected for mid February of each year as part of Statistics New Zealand’s (SNZ) Annual Business Frame Update survey (ABFU).[3] From 1994 onwards an enterprise was deemed to be economically significant and therefore covered by the BD if it satisfied any one of the following criteria:

• The enterprise had annual GST expenses or sales greater than $30,000;
• The enterprise had more than two full time equivalent paid persons employed;
• The enterprise was in a GST exempt industry (except residential property leasing and rental);
• The enterprise was part of a group of enterprises;
• The enterprise was a new GST registration that was compulsory, special or forced (which normally means the enterprise was expected to have GST sales or expenses that exceed $30,000); or
• The enterprise was registered for GST and was involved in agriculture or forestry.

The BD industry coverage is not the same from year to year. Additional industries were included in the ABFU in some years while in other years industries were dropped. To maintain constant industry coverage over the period 1994 to 2003 it was necessary to drop enterprises in industries that were not included in the BD in every year between 1994 and 2003. This means that agriculture and livestock production, residential property leasing and rental, commercial property and leasing, child care services, residential and non-residential services, business professional and labour organisations, religious organisations, social and community groups, and sporting and recreational services industries have been excluded from the database of firms described in this study.

The Inland Revenue Department’s GST data contain monthly GST sales and purchases information for all enterprises registered for GST. Enterprises can file GST returns at different frequencies over the year. Enterprises that are members of a group may file on each other’s behalf and the responsibility for filing within a group can vary over time. We make no attempt to apportion GST returns across firms within a group.

In order to derive a real value-added measure of labour productivity per hour worked, the BD and GST data are then combined with the Producers’ Price Indexes (PPI) and the Household Labour Force Survey (HLFS) compiled by Statistics New Zealand.

The PPI provide information on producer output and input prices at the two digit industry level. The output price indexes measure changes in the prices received by producers and input price indexes measure changes in the cost of inputs to production (excluding labour and capital costs). These two-digit industry level price indexes are used as deflators for firm-level sales and purchases. If the price of a firm’s output is actually higher than the average industry price, then measured real gross output will be overstated. Similarly, if the price of a firm’s inputs are actually higher than the average industry price, then measured real inputs purchased will be overstated.

A proxy for firm real value-added is then constructed by subtracting input price deflated purchases from output price deflated sales. Measures of value-added should ideally take account of changes in inventories of finished goods, unfinished goods and raw materials. However, data on firm level inventories are not available from the BD/GST data and therefore a measure of value-added based on firm sales and purchases is likely to be more volatile than one based on firm output. This is because inventories tend to act as a buffer and vary inversely with changes in demand (see Buckle and Meads, 1991). Just-in-time inventory methods and the growth in the relative size of the services sector may mean that this issue is becoming less significant over time.

All firm real value-added estimates are expressed in terms of per hour worked. The hours worked per firm are derived by combining employment data from the BD database with information on hours worked from Statistics New Zealand’s Household labour Force Survey (HLFS).

The BD contains enterprise data on the number of full time working proprietors, part time working proprietors, full time employees, and part time employees for mid February of each year. These data could be used to construct a ‘head count’ measure of firm labour input by adding the number of full time working proprietors, part time working proprietors, full time employees, and part time employees. However, the total hours worked or paid for within an enterprise is the preferred measure of that enterprises labour input. This information is however not available from the BD.

The HLFS does however provide information on the average hours worked by full time and part time working proprietors and employees for three digit industries. The HLFS is a private household based survey and has greater industry coverage than the alternative Quarterly Employment Survey (QES),[4] which is a firm based survey that contains information on the average hours paid to full time and part time workers within an industry.[5] The HLFS classifies a person as a full time working proprietor or employee if they work 30 hours or more per week. A person is classified as a part time working proprietor or employee if they work less than 30 hours per week.

The four types of firm employment data from the BD are therefore combined with the three digit industry level average hours worked data from the HLFS to account for differences in the hours worked by different types of workers. Each worker type was assigned the average hours worked by the corresponding type of worker at the three digit industry level. This alternative measure of enterprise labour input assumes there is no variation in the average hours worked by different types of workers within an industry at the three digit level (although there will still be variation in labour inputs within a three digit industry because enterprises have different numbers and types of workers).

These firm data are combined with the aid of unique random firm identification numbers and industry and time classifications. The aim is for every firm in every year of its existence to have sales, purchases and employment information in order to generate annual firm level measures of labour productivity. In practice however for a number of reasons firm records are often incomplete as will be discussed in the next section.

Because BD data on numbers of working proprietors and employees are recorded for mid February in each year, monthly GST sales and purchases data have been collapsed to an annual frequency for the year ending August. When forming annual GST sales and purchases for entering and exiting firms that had monthly sales and purchases data for less than a full year (which suggests these firms were operating for only part of the year), the aggregated monthly sales and purchases were annualised to ensure entering, exiting and continuing firms are analysed on a comparable basis. GST sales and purchases were annualised by multiplying the total recorded for each over any given year, centred around February, by 12/(12-n) where n is the number of months for which the firm had no GST information in that year.[6]

In principle these data sources provide scope to derive two proxies for firm output (real gross output and real value-added) and two measures of firm labour input (total number of workers and a proxy for the total number of hours worked). This means that it is possible to construct four different measures of labour productivity: gross output per worker; gross output per hour; value-added per worker; and value-added per hour. The analysis in this paper concentrates on our measure of firm-level real value-added per hour worked and the components that make up that variable, predominantly sales per hour worked and purchases per hour worked. The use of real value-added is consistent with Statistics New Zealand’s (2006) estimates of aggregate labour productivity for New Zealand. Their measure of labour productivity is however based on estimates of hours paid whereas we use an estimate of hours worked. Industry coverage also differs somewhat.[7]