Turnover indices of wholesale enterprises
Data on turnover indices of wholesale enterprises are used for analysis of wholesale sector development.
Concepts and definitions
Turnover index (wholesale)
Indicator which shows turnover changes during a reference period, in comparison with base period. It is expressed as per cent.
Turnover index in current and constant prices shows the turnover changes in relevant period, when the turnover is at prices of the relevant period.
Turnover index against previous period (month or quarter) characterizes the turnover changes within the corresponding period. This indicator is essentially influenced by the factors of seasonal and calendar character. As very characteristic example rapid increase of enterprise turnover in wholesale of seeds during spring and in purchase of seeds – during autumn can be mentioned.
Turnover index over the corresponding period of previous year shows turnover changes in the framework of 4 quarters. For example, changes in turnover of 2nd quarter of 2015 over 2nd quarter of 2014. This indicator is used for the calculation of Gross Domestic Product in macroeconomic analysis.
Turnover index over average monthly (quarterly) turnover of 2010 shows changes of turnover of relevant period (monthly/quarterly) compared to average (monthly/quarterly) turnover of 2010.
Turnover [trade and services]
Turnover is income from sale of goods and provision of services, from which trade discounts are subtracted, as well as value added tax (VAT) and other taxes, which are directly related with sale.
Index is a numeral indicator, which indicates consecutive changes of some social or economic phenomenon as percent.
It is comparison indicator of one phenomenon in two stages.
Data collection and statistical processing
Survey method and data source
Data are obtained conducting sample survey. Information is collected from:
- CSB mounthly statistical report 4-turnover "Report on turnover";
- For data imputation Value Added Tax declaration data submitted to State Revenue Service are used.
Enterprises and farms which are economically active during the reference year and main economic activity of which is wholesale trade according to Division 46 of NACE Rev. 2.
Enterprises for the survey are selected using the simple stratified random sampling.
Enterprises are stratified by main kind of economic activity according to Statistical Classification of Economic Activities in the European Community (NACE Rev. 2) and turnover. The higher the annual turnover, the higher the possibility for this enterprise to be included in the sample.
The sample is generated as sample independent from other enterprise survey samples.
Turnover indices are calculated as non-adjusted, seasonally adjusted and calendar adjusted data.
Calendar and seasonal adjustment
A time series is a sequence of observations collected at regular time intervals, for example, a monthly time series. It characterises indicator changes or development thereof. Seasonality and calendar effects are present in a large number of economic time series.
Seasonality or seasonal fluctuations of time series mean those movements, which recur with similar intensity in the same season each year. For example, each year Christmas shopping time can be observed in time series reflecting retail sales statistics. Change of seasons, social habits and influence of institutional factors are among the main causes of seasonality.
The calendar effects cover influence of calendar on time series. It is impact left by differing number of working days (or Mondays, Tuesdays and other days of the week) in months on changes of indicator. For example, number of working days differing among the months may affect goods produced time series.
When the time series are influenced by seasonality or calendar effects, it may be difficult to get clear understanding on indicator changes over the time. Seasonal adjustment is made to eliminate seasonal fluctuations and calendar effects in time series.
As a result seasonally adjusted time series, from which seasonality and calendar effects have been removed, are produced. It means that seasonally adjusted time series provide an estimate for what is “new” in the series, for example turning points in trends, business cycle or irregular component. Moreover seasonal adjustment results in calendar adjusted time series, in which calendar effects or varying number of working days in months has been eliminated. Specifics of seasonally adjusted statistics allows improving data comparability over time:
- Seasonally adjusted time series do not contain seasonal fluctuations and calendar effects, thus it is possible to compare, for example, data on the current month with the previous month's data.
- Calendar adjusted time series are not influenced by calendar effects and are used to compare, for example, statistics on current month with the data on corresponding month of the previous year.
The seasonal adjustment is made taking into account seasonal adjustment guidelines developed by the European Statistical System.
Data may be revised in the next publishing period. All reference year quarters may be revised, in case of essential changes data of previous years’ months and quarters are also changed. Main reasons for revision can be update of provisional data, update of the kind of activity of enterprises or methodological changes.
Data are collected and published using Statistical Classification of Economic Activities (NACE Rev. 2).