Secondary Data

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Data which is collected from already existing sources. This is very easy way of obtaining data but not that much reliable compare to primary data. Secondary data is useful to understand the basic problem. This could be either in published or unpublished form. There are many sources available for obtaining secondary data.

Sources of Secondary Data:

  • Published reports of newspapers, RBI and periodicals

  • Publication from trade associations, Official publications

  • Financial data reported in annual reports

  • Data available in online portals of corresponding companies.

  • Publication of international bodies such as UNO, World Bank etc.

  • Internal reports of the government departments

  • Records maintained by the institutions

  • Research reports prepared by students in the universities.

  • Books and Magazines

  • Internet

Importance of Data in Economics

Data is having huge applications. Without data we can achieve nothing. Every important decision is taken by analyzing lots of data to get accurate solution. This is done with the help of more complicated soft-wares. Here are some the most important applications of data in economics.

  • In economic planning: Every country prepares their economic plan to estimate the upcoming expenditures. This economic planning is done with the help of huge past data. Based on analysis of past data government will predict the future.

  • To determine national income: National Income is determined to know the state of economy. National income can be determined by using certain methods which require quantitative information on various things which are contributing to national income.

  • Basis of government policies: Government implements policies based on analysis of available data. Ex: Based on population data, it was found that male and female population was in 938:1000 ratio, this is because of infanticide of girl child. On the basis of this data now the government is making policy to save the girl child.

Not only these, every solution in real life linked with data.

Presentation of Data

Data collected from above sources is not self-explanatory. The collected data is in raw form. Collected data should be presented in a proper manner to do further analysis to get accurate solution. Presentation of data includes Editing, Classification, Tabulation and Graphical representation.


Editing is the process of reviewing collected survey data. The main aim of editing is to remove any spelling mistakes, written mistakes.

Classification: Classification is a process of arranging data into classes or groups according to their resemblances. Main aim of the classification is to give a definite form and a coherent structure to the data collected. That will facilitate their use in most systematic and effective manner.

Classification is done based on variables and attributes.

Image of bases of veriable

Image of Bases of Veriable

Image of bases of veriable

Variable: When data is capable of being classified in the magnitude of time or size it is called as variable. Ex: Height, weight, length, distance are example of variables.

  • Discrete variable: Discrete variable usually has a specific value or measurement and it cannot be broken into factors. A variable can have different values. How frequently a value occurs is its frequency.

  • Continuous variable: It has continuity in its scale and measurement, such as scale of height, weight, length, distance etc.

Attributes: When data cannot be classified in the magnitude of time or size it is known as an attribute. Ex: Beauty, bravery, intelligence, laziness etc. Classification and study of attributes is difficult compared to variables.

Statistical Series:

Individual series: In this kind of series items are shown individually with their corresponding value. If those values are arranged either in descending or ascending order then it will become an array.

Discrete Series: This type of series is designed to show variables with definite break with their respective frequencies. Theoretically this kind of series is prepared only for a discrete variable, however, in practice continuous and discrete variables are used interchangeably.

Continuous Series: Also called interval series. Available data is classified into different equal groups. And the values in between the interval will be noted. In case of inclusive series frequency corresponding to the upper limit of group is included in the same group, while it is included in subsequent group in case of exclusive series.


Tabulation: It is the process of placing classified data into tabular form is called Tabulation. It consists both rows (horizontal) and columns (vertical). Table may be simple or complex depending on number of variables. A statistical table may be a simple one or it may be a complex one, depending upon number of variables incorporated into it.

Tabulation may be one way or two ways or manifold.

  • Simple Tabulation or One-way Tabulation: Data are tabulated using single characteristic.

    Ex: Population classification basing on age.

  • Two-way Tabulation: Tabulation is done basing on two characteristics.

    Ex: Population table basing on age and gender.

  • Complex Tabulation: Tabulation basing on more than two characteristics.

    Ex: Population table basing on age, gender, religion etc.

Diagrammatic and Graphic Presentation of Data in Economics

In everyday life we see many diagrams relating to stock markets, company performances etc. By seeing them we are able to understand the situation easily.

Diagrammatic presentation is a geometrical version of the data. Single diagram can depict more information in a simple manner. They can represent complex data in easy way.

Diagrams and graphs may be in 1Dimensional, 2Dimensional or 3-Dimensional. Diagrams include Bar Graphs, Circular Graphs, Linear Graphs etc.

One-dimensional diagrams are also called bar diagram which are most commonly used in practice.