Non-profit Institutions Satellite Account: 2013

Classification of non-profit institutions The United Nations Handbook on Non-Profit Institutions in the System of National Accounts recommends the International Classification of Non-Profit Organizations (ICNPO) as a tool to differentiate between the types of institutions defined as NPIs. This is primarily based on their ‘economic activity’, as is the International Standard Industrial Classification (ISIC) on which NZSCNPO is based. Some purpose criteria are included where activities are similar. By grouping together institutions in this way, we form a basis for meaningful data analysis. The main ICNPO groups are: 01 culture and recreation 02 education 03 health 04 social services 05 environment 06 development and housing 07 law, advocacy and politics 08 philanthropic intermediaries and voluntarism promotion 09 international 10 religion 11 business and professional associations, unions 99 not elsewhere classified. See Appendix 2 in Non-profit Institutions Satellite Account: 2004 for the full New Zealand Standard Classification of Non-profit Organisations (groups and subgroups). As the ICNPO is mainly an activity-based classification, it has no specific categories for population groups such as women or people with disabilities. Many Māori groups fit within the NPI criteria and are included in the NPISA. Categories for institutions targeting a population subgroup are classified on the predominant activity of the institution. For example, if an institution provides medical treatment for a disability then it is coded to the ‘health’ group. If it provides social assistance for people with disabilities, it comes under the ‘social services’ group. Many NPIs have multiple activities, each activity falling under a separate ICNPO group, but the institution can only be classified in one group. In these cases, we use the institution’s ‘primary economic activity’ to assign an appropriate ICNPO category. It is usually measured as the activity with the largest share of: value-added – a measure of the institution’s contribution to gross domestic product gross output, if value-added is not available employment, if neither value-added nor gross output is available. Classifying non-profit institutions in the satellite account Coding NPIs from the Business Register For the Statistics NZ Business Register population we made the initial classification using a concordance with the Australian and New Zealand Standard Industrial Classification 2006 (ANZSIC06). For example, preschool education (ANZSIC P8010) is concorded with early childhood education (NZSCNPO 2 110), hospitals (ANZSIC Q8401) with hospitals and rehabilitation (NZSCNPO 3 100), and residential property operators (ANZSIC L6711) with housing (NZSCNPO 6 200). Note that where an NPI carries out two or more distinct activities, its ANZSIC06 code is that of the majority activity. For unallocated institutions from this first analysis, we applied extensive keyword search lists. Although several hundred keywords (eg ‘tennis’ and ‘church’) were used, the list is still not exhaustive. Finally, we made manual classifications for many institutions that remained, and for units where their activity was known. Coding outside the Business Register For the institutions we added from Inland Revenue’s administrative database, the Companies Office register, and the Charities Services’ register, the overall method was similar. We applied the ANZSIC-NZSCNPO concordance if an Inland Revenue ANZSIC tag was available. For the remainder, we used the keyword analysis. A further refinement was based on the results of analysis to eliminate duplicates from the data, and after analysing lists of donee organisations and organisations receiving government contracts. The NPIs we could not code were those where either their activity was truly different to those we included under any other main group or where insufficient information was available to allow coding. We used keyword coding where no industrial code was available. Where multiple keywords were in the name, we applied a precedence list. For example, ‘The Church of XYZ Tennis Club’ would be classified to sports institutions because the keyword ‘tennis’ has a higher classification ranking than ‘church’. It is difficult to identify the sometimes very specific NPIs in some NZSCNPO subgroups; for example, the ‘income support and maintenance’ subgroup or the ‘employment and training’ subgroup. Institutions may have been coded to the overarching ‘social services’ or ‘development and housing’ groups instead, based on their industry code or keywords. However, the results at the main group level are robust. Another difficulty revolves around precisely what NPIs and activities are included under each subgroup. For example, the ‘fundraising’ subgroup includes large, nationally active NPIs with fundraising as their main activity. However, institutions fundraising to support a specific activity covered under another main group will be coded to ’support and ancillary services’ under their main group. A second example is that rest homes and other aged residential care (except nursing homes providing first and foremost medical services) are under ‘social services’, not under the health subgroup ‘nursing homes’. Distinguishing market and non-market non-profit institutions A ‘market’ producer is an institution that sells its goods or services at competitive market (or ‘economically significant’) prices. Most NPIs are classified as ‘non-market’ because they provide their services for free or below market prices. However, a significant number of NPIs are market producers, and are included in the NPI population as long as they meet the structural/operational definition. Examples of NPIs classified as market producers include: most business associations gaming trusts most NPI hospitals racing clubs. These units are distinctive within the NPI population. To generate a positive operating surplus, these NPIs need to sell their output at market prices. At the same time, they do meet the legal requirements of being not for profit because the surplus is generally not retained within the institution, but is distributed to another NPI or for a charitable purpose. Business associations are classified as market producers where they are financed by dues and subscriptions rather than by government. The rationale is that business associations are NPIs that support market producers. Gaming trusts need to produce large operating surpluses in order to make community grants. Because of this, they are implicitly assumed to be charging economically significant prices, even though the market in which they operate is heavily regulated. Similar comments apply to racing clubs, although for them any operating surplus goes back to industry participants (through measures such as increased stakes). Most hospitals are classified as market producers because they charge market, or close to market, prices. They are classed as non-market where they clearly charge below-market rates (eg charity hospitals where staff are volunteers). Many NPIs rely on government funding that is contested. NPIs that receive funding from government, on the basis of winning contested contracts, are not necessarily market producers. If they have a significant volunteer labour component, they may charge out their services at below-market rates. From these examples it is apparent that it is not immediately obvious whether or not an NPI should be classified as market. If it is unclear whether economically significant prices are being charged by the NPI but it is making a positive operating surplus over time it usually implies it is operating on a market basis. By applying these principles we can establish an acceptable set of market and non-market classifications for the NPI population. Making international comparisons A market and non-market split also allows for better comparisons with the NPI satellite accounts of other countries. Canada has both a ‘core’ and a ‘non-core’ set of NPI satellite accounts. The non-core set includes hospitals, universities, and colleges. The non-core estimates are provided separately because hospitals are very significant within the Canadian NPI account. The Australian NPI estimates treat hospitality clubs and business and professional associations as market producers. Data sources and methods Counting the number of non-profit institutions Although many NPIs are found on the Business Register, we need to include other information sources to get a clear picture on the number of NPIs. Business Register population The primary source of information for counting NPIs is Statistics NZ’s Business Register, which identifies enterprises. For an enterprise to be on the Business Register it must meet any one of certain criteria; for counting NPIs, the most relevant criteria are: annual goods and services tax (GST) expenses or sales of more than $60,000 an employment count greater than zero IR10 income (rent received, interest and dividends, and total income) greater than $40,000. The Business Register classifications used to identify NPIs included: business type institutional sector (NZISC) industrial activity (ANZSIC 06). At the highest level, the NZISC recognises five distinct sectors: non-financial producer enterprises financial enterprises general government NPIs serving households households. All NPIs serving households were in scope for the count in this report by default. Incorporated societies in other sectors (eg racing clubs, business associations, and industry training organisations) were included by definition unless under government control. We assessed unincorporated associations in other sectors by industry to determine whether they were NPIs. We included charitable companies but excluded trading or family trusts, which made up the majority of trusts. Non-Business Register population Because the Business Register only includes NPIs that pass one of the size thresholds listed above, we needed other sources for the thousands of institutions that do not meet these criteria. The three main information sources we used for these smaller institutions were administrative databases maintained by Inland Revenue, the Companies Office, and the database maintained by Charities Services. Institutions potentially in scope from the Inland Revenue and Companies Office databases were those classified as qualifying trusts, incorporated societies, and unincorporated associations. The Charities Services register provided a list of registered charities, which we added to these. Reconciling the populations We integrated and reconciled the NPIs from the three (overlapping) sources. We took a hierarchical approach to remove institutions duplicated in the overall list. For example, we first removed NPIs in the Business Register population from all other population subsets – because the Business Register provides the most information. Second, the Companies Office and Charities Services registers of incorporated societies and charitable trusts took precedence over Inland Revenue’s administrative databases. Beyond this, other methods we employed included name matching and ‘fuzzy’ matching. Fuzzy matching is a search function that enables names of institutions to be grouped according to whether they have a high probability match, a medium probability match, or some other possibility. We need these matches because an institution’s name may be on two or more lists but with different formatting, spelling, or completeness. Limitations of the data The number of NPIs identified may still be undercounted due to being unable to identify institutions of a more ‘informal’ nature. For example, groups with large memberships but which are organised on a relatively informal basis (eg local walking, gardening, or tree planting groups; groups that are organised online). In contrast, the population may be overstated through failing to remove all duplicates from the various registers and by including (due to a lack of information) institutions that do not meet the full definition of an NPI. Overstatement could also result from the Inland Revenue and Companies Office registers being maintained for non-statistical purposes, which means registered NPIs may not be ‘ceased’ at the same time as they are on the Business Register. The administrative databases that Inland Revenue maintains do not easily allow charitable trusts to be distinguished from trading or family trusts – to identify charitable trusts, our analysis relied on the Charities Services register. Inland Revenue’s administrative databases list many thousands of institutions as unincorporated associations. For a large number of these, no associated tax data exists (eg GST sales or purchases). We verified that a small number of NPIs reported GST as part of a group return. Therefore, although the individual NPI was recorded with zero GST, it was still active. However, we cannot know if many other small NPIs are actively operating or if they are still on the administrative databases maintained by Inland Revenue because they have an Inland Revenue number. It is therefore possible that the number of unincorporated associations is overstated. Counting the number of employees The count of salary and wage earners is sourced from taxation data on a monthly basis. The employee count comes primarily from administrative databases maintained by Inland Revenue and from the Charities Services databases. Annual Enterprise Survey The Annual Enterprise Survey (AES) provides financial information by industry and sector groups. This includes measures of financial performance and financial position. Output variables include income, expenditure, profit, purchases of fixed assets, and equity. AES data is also the basis of national accounting variables such as value-added, gross output, and gross fixed capital formation. Population The target population for AES is all economically significant businesses operating within New Zealand. The population for this survey is selected from the Business Register. In total, we estimate AES covers approximately 90 percent of New Zealand’s gross domestic product (GDP). We exclude some industries; the ANZSIC06 industry exclusions are: residential property operators not elsewhere classified (L671100) foreign government representation (O752200) religious institutions (S954000) private household employing staff (S960). Design of the Annual Enterprise Survey The current AES design was introduced in the 2009 financial year. AES was designed to be the principal collection vehicle for data used in compiling New Zealand’s national accounts. The data collected feeds into calculating the economy’s GDP, through the current-price annual industry accounts, which are compiled within an input-output framework. AES collects financial data for most industries operating in New Zealand’s economy. The survey is designed at approximately the four-digit ANZSIC level (it has 107 industries). Sample design AES is a stratified sample. Each industry contains one to four strata, defined by size of turnover (sourced from GST information) and rolling mean employment. Each industry has a full-coverage stratum made up of large units with significant economic activity within their industry group. This includes non-profit units sampled from units collected by the Charities Services survey of charitable trusts. Most industries also have a tax stratum, where IR10 information is used for self-employed individuals and partnerships up to a level of $10 million turnover. The remaining strata contain a sample of medium-sized units. Religious institutions We exclude religious institutions from AES. Since AES is the primary data source for compiling the NPISA, we needed an alternative method of estimating the contribution of religious institutions. The estimate we used was based on income and expenditure data from the Charities Services data collection, supplemented by annual financial accounts information for larger institutions. This is a much more comprehensive and consistent collection than we used for the 2004 NPISA, which had a sample of annual accounts supplemented by reports collected from previous studies, plus reports available on registers held by the Companies Office. Estimating the contribution of non-economically significant units The non-economically significant units consist of some NPIs on the Business Register and all NPIs from other administrative databases or registers. We estimated these two groups independently, then added them to produce an estimate for the financial contribution of non-economically significant units. NPIs on the Business Register were represented by units in AES, supplemented by data from sources such as government departments, crown entities, and funding agencies. For all other NPIs we based the estimates on data collected by Charities Services, which surveys most registered charities. Calculating and valuing hours of formal unpaid work Based on the Activity Classification for Time Use Surveys (ACTUS), we identified formal volunteer labour within the ‘committed activity’ from the Time Use Survey 2009/10. We extracted information about all committed time activity that was worked for an organisation. We analysed these activities according to the 12 NZSCNPO codes used in the Time Use Survey. These were: 01 Culture, sport, and recreation 02 Education and research 03 Health 04 Social services 05 Environment 06 Development and housing 07 Law, advocacy and politics 08 Grant making, fundraising, and voluntarism promotion 09 International 10 Religion 11 Business and professional association, unions support, and services 99 Not elsewhere classified (residual category). The number of hours volunteered for these organisation groups was calculated and totalled, which gave us the total number of hours volunteered for the period 1 September 2009 to 31 August 2010. It also provided the average hours volunteered for each New Zealander over the age of 12 years. We extrapolated from the Time Use Survey to find the total hours for the year to March 2013 (the reference period and coinciding with the census). We multiplied the average number of hours volunteered for each person in the population aged over 12 years, by the over-12-years population (at 31 March 2013). Doing this assumes the number of hours volunteered was constant, and that the number of volunteers grew at the same rate as the population between 2010 and 2013. We did it this way because no adequate time-use data was available for 2013 – the process had a negligible effect on the overall estimate. We also assumed that the types of activities remained the same as in 2009/10. Key assumptions for volunteer labour Because the Time Use Survey year did not coincide with the NPISA reference year, and because it did not place a monetary value on time spent volunteering, we made the following assumptions: The average hours worked per volunteer did not change between the 2009/10 Time Use Survey and March 2013. The types of activities (and therefore their proportional representation in each organisation group) have not changed since the 2009/10 Time Use Survey. The monetary value of one hour of work for any activity in the paid sector equals the implicit monetary value of one hour of work for the same activity in the unpaid sector. The 2009/10 Time Use Survey provided for ancillary activities (ie doing two or more activities at once). We assume these activities have the same productivity as primary activities, since adjusting for any productivity reduction due to multi-tasking (and therefore a reduced wage rate) would be arbitrary. Differences between the census and the Time Use Survey The Census of Population and Dwellings is a self-administered questionnaire that asks a range of questions. The census has advantages: it collects data from the whole population of New Zealand, the data can be broken down to regional level, and it is timely for these NPISA statistics. Although the census collects data about unpaid work, this is not its primary aim. The census provides a lower-end estimate for the number of people involved in informal and formal unpaid work outside the home. The 2009/10 Time Use Survey collected data about formal unpaid work in two modes: through a personal questionnaire, which collected demographic and activity data for the four weeks before completing the questionnaire; and a 48-hour diary, which recorded all activities for a 48-hour period. A Time Use Survey is the most finely-tuned survey instrument available for getting a good estimate of volunteer labour, both for the number of volunteers and the hours they work. However, its limitation is that the data is now some years out of date. Both the census and the Time Use Survey collect data about the number of volunteers in New Zealand. However, their estimates differ quite significantly for several reasons. The census is a self-administered survey and the Time Use Survey is interviewer administered. This affects the response rates; when self-administered, no interviewer is present to probe for further answers or to explain a misunderstood question. Interviewer-administered surveys have fewer misunderstood questions and fewer inappropriate responses. An advantage of the Time Use Survey is using diaries that can pick up respondents’ unpaid activities that may be overlooked or misunderstood in the survey questions. This may increase response rates. While the census is a key part of a wider integrated population and social statistics system, it cannot provide the depth of information of a targeted social survey such as the Time Use Survey. en-NZ