Return on total assets for the health care and social assistance industry in New Zealand
Financial years 2013–2019, % of total assets
Business (KAU, or kind-of-activity unit): An enterprise subdivision that is engaged in predominantly one activity and for which a single set of accounting records is available.
Return on total assets: Total current year taxable profit divided by total assets. This ratio tests the efficiency of investment in fixed assets and is a measure of how effectively the business has converted these assets into net income.
Return on total equity: Total current year taxable profit divided by total proprietor or shareholder funds. The return on equity represents the rate of return earned on the owner’s equity and investment.
Current ratio: Total current assets divided by total current liabilities. This ratio gives an indication of a business’s ability to pay its short term liabilities.
Quick ratio: Total current assets minus closing stock divided by total current liabilities. The quick ratio, also known as the acid test, is very similar to the current ratio, but excludes stock. It tests a business’s ability to pay short-term debt from immediately convertible or liquid assets.
Liabilities structure: Total proprietor or shareholder funds divided by (total proprietor or shareholder funds plus total liabilities). The liability structure ratio represents equity solely as a proportion of equity plus liabilities. A low ratio indicates a low level of owner’s equity in the business, and a higher risk to debt holders.
Margin on sales of goods for resale: Sales of goods not further processed less purchases of goods bought for resale, as a percentage of sales of goods not further processed.
Total income = Sales, government funding, grants and subsidies + Interest, dividends and donations + Non-operating income
Total expenditure = Interest and donations + Indirect taxes + Depreciation + Salaries and wages paid + Redundancy and severance + Purchases and other operating expenses + Non-operating expenses
Data refers to the latest financial year available, which depends on each business.
For more information
Limitations of the data
Data for the 2019 financial year is provisional.
The AES data is to be used with caution below industry design level (NZSIOC level 4). The survey is not designed to support analysis below this level.
Some industries are excluded on statistical grounds due to the difficulty of collecting the data from respondents: superannuation funds (K6330), residential property operators (L6711), foreign government representation (O7552), religious services (S9540), private households employing staff and undifferentiated goods and service-producing activities of households for own use (S960).
Non-market government units have not been included in the AES population for all data published since the AES 2015.
Businesses that are not economically significant are not selected into the AES.
Data provided by
Annual Enterprise Survey: CSV table (provisional) 2019
How to find the data
At URL provided, download 'Annual enterprise survey: 2019 financial year (provisional) – CSV' file.
Import & extraction details
File as imported: Annual Enterprise Survey: CSV table (provisional) 2019
From the dataset Annual Enterprise Survey: CSV table (provisional) 2019, this data was extracted:
- Rows: 2-32,446
- Column: 9
- Provided: 27,566 data points
This data forms the table Business - Financial performance by industry 2013–2019.
Dataset originally released on:
June 26, 2020
About this dataset
The Annual Enterprise Survey (AES) is New Zealand’s most comprehensive source of financial statistics covering around 500,000 businesses. It provides annual information on the financial performance and financial position for industry groups operating in New Zealand. The AES was designed as the principal collection vehicle of data used in the compilation of New Zealand's national accounts. Data used in this survey is compiled from a number of sources and measures industry levels for a given year. Incremental improvements in measurement, sample design, classification, and data collection may influence the inter-period movements, particularly over longer time periods.