Some main macroeconomic indicators and measuring methods of them: unemployment and inflation rate | Статья в журнале «Молодой ученый»

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Рубрика: Экономика и управление

Опубликовано в Молодой учёный №9 (113) май-1 2016 г.

Дата публикации: 04.05.2016

Статья просмотрена: 34 раза

Библиографическое описание:

Мирзаев, М. А. Some main macroeconomic indicators and measuring methods of them: unemployment and inflation rate / М. А. Мирзаев, Ш. Б. Мирзаев. — Текст : непосредственный // Молодой ученый. — 2016. — № 9 (113). — С. 657-659. — URL: (дата обращения: 21.04.2021).

The better living condition and high real income rate means the better economic condition. Today we can consider some main macroeconomic indicators influencing to the economy of world countries as the blood of the financial and economic activities. The unemployment rate is the most widely used indicator of the well-being of a labour market and an important measure of the state of an economy in general. While the unemployment rate is in theory straightforward, classifying working age persons as either employed, unemployed, or out of the labour force is difficult in practice. To facilitate comparisons of unemployment rates over time and across countries, the International Labour Organization (ILO) has since 1954 set forth guidelines for categorizing individuals into these labour market states. These have now been adopted, at least in some form, by most developed and a large number of developing countries, which has allowed the ILO to compile a sizeable number of roughly comparable labour market statistical series across countries and over time. [1]

For instance, the requirement of job search is attractive because it requires active demonstration of attachment to the labour force, but it also classifies a large number of non-searchers as out of the labour force. Some economists would argue that availability and willingness to work are sufficient to distinguish workers in the labour force from the non-attached. Moreover, while the requirement of active job search may be meaningful in industrialized countries where the bulk of the population engages in paid employment and where there are clear channels for the exchange of labour market information. This may not be the case in many developing countries where search may be more costly and job search behavior is less meaningful, especially in large rural sectors. The determinants of unemployment duration are of high interest in social and applied economic research alike. A broad range of empirical and theoretical research in Germany is concerned with this topic. The empirical studies are based on different data such as surveys or administrative registers which have both advantages and disadvantages.

We first direct attention to international and national legal definitions of unemployment and their application. We take Germany as an example and show that the definition of unemployment is not stationary but a social category with different characteristics. As reference we present international standardised unemployment rates, mainly based on the definitions of the International Labour Organization (ILO), and the main German regulations for the support of unemployed stated in the Third Volume of the Social Code (SGB III). [2] International and National Concepts to Measure Unemployment in Germany. The international standartised unemployment rates, published by the Statistical Office of theEuropean Communities (EUROSTAT) and the Organization for Economic Co-operation andDevelopment (OECD), are mainly based on the definitions of the International Labour Organization(ILO) and calculated using cross sectional random sample survey data sources, namelythe European Labour Force Survey (LFS). Also, longitudinal data sources can be used to measureunemployment using different concepts, namely the European Community Household Panel (ECHP) or the German Socio Economic Household Panel (GSOEP) [3]. Last but not least,measurement can be based on register data of the German social security system.The national unemployment rate, officially announced in regular intervals by the Federal Employment Service (Bundesagentur für Arbeit (BA)), is based on the number of registeredunemployed persons as part of the labour force. The definition used is codified in the Third Volume of the Social Code (Sozialgesetzbuch III (SGB III)), which replaced the former LabourPromotion Act (Arbeitsförderungsgesetz (AFG)). The Second Volume of theSocial Code (Sozialgesetzbuch II (SGB II)), introduced in December 2004, broadens the definition of unemployment to all individuals capable of working, as well as the indigent, where the first is interpreted individually and the latter in a household context. This also refers to concepts of labour reserve, hidden unemployment, hidden labour force and discouraged workers.

Based on the general information available in German register data, unemployment duration can be measured according to one the following concepts:

Concept 1: Each uninterrupted unemployment period shown by the administrative record.Concept 2: Concept 1, corrected for periods of dependent employment over 15 hours a week. Concept 3: Concept 2, corrected also for periods of dependent employment less than 16 hours a week. Concept 4: Concept 2, with added periods of participating in (any) active labour market policy measures. Concept 5: Concept 4, with added periods of illness, identified by a variable on the reason for leaving and entering the registered status of unemployment. Concept 6: Concept 5, with added period(s) without information on the employment status of individuals, presuming that most people have to search for employment and are willing to work under good conditions, even if they are not registered. (Problem: This concept includes also self-employed, civil servants, etc. — imputation could be a solution.)

In case of parallel full-time or part-time employment and unemployment information, the researcher has to decide if the information is assessed as employment or unemployment. While there are some regulations that allow registration as unemployed parallel to dependent employment, this, for example, could be interpreted as underemployment. In a second step the researcher has to make a decision about the unemployment-status of periods of training measures, illness or out of the labour force. Here we face similar questions as when analysing employment. In general (short) illness is not shown by register data on employment spells and therefore it is not counted as an interruption. While some “training measures” in Germany are used to check for the readiness to engage in work, others are used to train unemployed persons to write a letter of application or give them practical advice in direct connection to a subsequent job. Further vocational training can range from short modules of several weeks to long term measures lasting two years or more, providing a recognized vocational qualification. For instance, we find that the marginally attached females are more likely to live in households in which there are elderly persons relative to the non-employed job seekers. Regardless, overall our results for females suggest that the ‘marginally attached’ females should perhaps best be classified as a separate, fourth labour market state group. We discuss several theoretical and legal concepts of unemployment. As a consequence, given the theoretical notions of unemployment we focus on the question how the labour market state unemployment and the duration of unemployment can be defined in real world data. In our empirical work we use the Sample of the Integrated Employment Biographies (IEBS), which is the German merged administrative individual data. In addition to two well-known benchmarks, we develop more than 60 different implementations of unemployment in this data. A short descriptive analysis shows considerable differences in the number of unemployment spells and in the length of unemployment periods, which provides evidence for the importance of our work.

Inflation is a simple concept, but price rises are surprisingly hard to measure. First, statisticians must work out what stuff people buy, and in what proportions (the “basket” of goods). Then they must track the prices of those goods over time. Finally they must decide how to account for new products, changing tastes and the fact that if the price of, say, apples rise, some of people will buy another fruit instead rather than pay more. Big data could make all of this easier. At the moment, calculating America’s consumer-price index (CPI) involves sending people into shops to note down prices. The basket is based on a survey of consumers which is updated only every three years or so. This looks increasingly cumbersome in a world where every online purchase is logged, somewhere, in a database. In theory online baskets and prices, at least, could be tracked digitally. Adobe, a technology firm, is trying to do just that. The firm collects anonym sales data from websites that use its software. The amount of data available is vast: according to the firm, it includes three quarters of online spending at America’s top 500 retailers. It is using this ocean of information to compile a “digital price index” (DPI) to rival official measures of inflation. [3]

Two economists, Pete Klenow of Stanford University and Austan Goolsbee of the University of Chicago, are helping the firm to crunch the numbers. The DPI has several advantages over the conventional approach. It tracks 1.4m goods, compared with the CPI’s 80,000. It is based on actual purchases rather than advertised prices, increasing its accuracy. And the volume of data allows Messrs Klenowand Goolsbee to use fancier statistical methods to account for people changing what they buy as prices move. The new index completely misses changes in offline prices and spending on things like petrol and rent. It will not replace the CPI any time soon. It does suggest, however, that official statistics may themselves be missing big price movements, especially for consumer technology. The researchers found that the price of computers fell by 13.1 % in the year to January, almost double the 7.1 % fall recorded in the CPI. Televisions fell more in price than the CPI reports, too. The speed of innovation in technology might account for the difference. The researchers found that fully 80 %oftechnology spending is on new products, which the more nimble DPI can incorporate quickly. If this is a widespread phenomenon, and inflation is lower than officially recorded, that has implications for central bankers, borrowers, savers and anyone who strikes long term contracts. It also means that GDP might be understated, says Mr Klenow. If overall spending is recorded accurately but inflation is exaggerated, output must be higher than thought. Official statisticians are improving their methods. The CPI includes some prices that are collected automatically by “scraping” websites (something Britain’s statisticians are also experimenting with). But if their take-up of big data is sluggish, official statistics could eventually face disruptive private-sector competition.


  1. David Byrne and Eric Strobl. CREDIT Research Papers. No. 01/09
  2. T.Kruppe. E. Müller and others. FDZ Methodenreport. On the Definition of Unemployment and its Implementation in Register Data-The Case of Germany.
  3. The Economist magazine. March 19th–25th 2016.
Основные термины (генерируются автоматически): CPI, ILO, DPI, III, SGB, AFG, CREDIT, ECHP, EUROSTAT, FDZ.

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