Statistics in Economics: Introduction, Positives, Limitations and Examples

The compilation of these Statistics for Economics Notes makes students exam preparation simpler and organised.

Statistics in Economics

A lot of economic experts have their fair share of opposition and support for the policies put forward by our finance minister. Of course, these future-defining policies aren’t a shot in the dark. So how are these formulated? The answer is statistics, and specifically statistics in economics.

What is Statistics in Economics?

Generally, the subject matter of statistics deals with the quantification of data. It revolves around concrete figures to represent qualitative information. Simply, it is a collection of data. But that’s not all. As economics students, we need to learn about the techniques of dealing with a collection of data, tabulation, classification, and presentation of data.

Further, we need to learn about the reduction and condensation of data. Lastly, we also need to gain insights into the techniques for analysis and interpretation of data.

Statistics in a Plural Sense

We are concerned with statistics in economics in its plural sense. That is to say, statistics are numerical statements or quantitative data in scenarios placed in relation to each other. In simpler words, a way to identify a plural statistical statement is that there should be an aggregate for an entity that is placed in comparison with another entity. For example, an average Indian is expected to live for 65 years compared to a mere 57 in Bangladesh.

Features of Statistics in its Plural Sense
It is numerically expressed: Statistics in economics deal with numbers and is quantitative. Qualitative adjectives like rich, poor, tall, etc. have no attached significance in the statistical universe.

Reasonably accurate: A statistical conclusion should be reasonably accurate which depends on the purpose of an investigation, its nature, size, and available resources.

Can involve estimation: If the field of study is large, for example, the number of people attending a rally, then a fair bit of estimation can do the trick. However for small fields of study, take, for example, the number of students in each field of study in a college, exact number calculation is easy and essential.

Systematic collection of data: The collection of data should be done systematically, which means, accumulating just raw data without any information about its origin, purpose, etc is not valid in the statistical universe.

Relative: Statistics in economics in its plural sense has the feature of comparability. This means that the same kind of data from different sources can be compared.

Multiple factors: Statistics is affected by a large number of factors and not just a single factor. For example rise in the price of a commodity is not because of a change in one factor but it is an effect of a large number of factors.

Aggregation: Statistics is a game of averages or aggregates. A number expressed for a single entity is in no way related to statistics. For example, the height of a single student is not statistical data but the average height of students in a class is.

Statistics in its Singular Sense

Whenever we employ statistical methods for the collection, classification, presentation, analysis, and interpretation of quantitative data, we term statistics as a singular noun. Further, this involves grasping the various stages of the statistical study. Each stage has its respective tools to affect a particular job. These are:

Stage 1 – Collection of data: We first need to collect statistical data to commence the journey for statistical study. Census and sampling techniques are generally used for this stage.

Stage 2 – Organisation of data: Of course data in a raw or chaotic format is hard to interpret. This is the reason the second stage deals with the organization of the collected data. The organization of data is done with the help of arrays of data and tally bars.

Stage 3 – Presentation of data: After organization, this data needs to be nicely presented. The presentation of data is widely achieved with the help of tables, graphs, and diagrams.

Stage 4 – Analysis of data: Before moving on to the final stage, we need to first find percentages, averages and so on to draw inferences about the data. Percentages, averages, correlation, and regression coefficients form the toolbox for the analysis of data.

Stage 5 – Interpretation of data: Finally, we need to interpret the data and hence conclude or form opinions about the data. This is done with the help of the magnitude of percentages, averages, and degree of relationship between different economic variables.

Positives of Statistical study

It’s impossible now to imagine economics without its statistical section. It has emerged as a major player and pushed economics to new heights by making it more of concrete science. The various positives about statistics in economics are:

1. Quantitative Expression
As has been mentioned time and again, statistics adds a touch of reliability and concreteness to economics by quantitatively expressing data. Evidently, our first step towards solving an economic problem is to gain an idea about its magnitude using statistical data.

2. Deduction of Economic Theories
A statistical agreement is a very significant step towards establishing a general statement about economic entities. Therefore, statistics in economics helps in establishing theoretical concepts and models by providing evidence.

3. Identification of Patterns and forecasting Economic Events
Armed with statistical tools, economists can easily study data for a particular purpose and identify patterns in the data. What it does is puts them in a great position to predict future trends. Moreover, such knowledge can be used for future planning.

4. Formulation of Policies
The policies introduced can make or break the progress of a nation. Such important decisions are made after a rigorous study of the nation’s statistical data. In fact, this is done with the help of statistical tools.

5. Economic Equilibrium
Economic equilibrium is the point of operation of both producers and consumers. This is because, at this point, both of them are satisfied with the events in the market. Remember that market equilibrium is deduced with the help of statistics in economics.

6. Inter-sectoral and Inter-temporal Comparisons
Comparisons for a department of inquiry in terms of time or sectors facilitate the purpose of comparison. This means it allows for a wider sense of comparison and also helps in checking progress. Further intersectoral comparisons mean comparison across different sectors. Whereas, inter-temporal comparisons mean comparison across different time periods.

Statistical Limitations

Similar to everything, statistics in economics are also not free of limitations. These are as follows:

1. Only Quantitative Study
The biggest advantage of statistics is also one limitation of the same. Although it is good at studying quantitative data, it fails at analyzing qualitative entities like honesty, wisdom, health, etc.

2. Study of Aggregates
Another shortcoming of statistics is that it deals only with aggregates and cannot handle data about a single entity.

3. Homogeneous Data
One essential requirement for statistics is that data should be uniform and homogeneous. As statistics involve comparison, heterogeneous data cannot be compared.

4. Specific Usage
Statistics can be used specifically by people who possess knowledge about statistical methods. Interestingly, it makes no sense to those who have no knowledge of statistical methods.

5. Prone to Misuse and Questions Common Sense
Statistical data can be manipulated easily by those who have good knowledge about this subject to propagate false statements. Further, statistics can question common sense. For example, suppose a class of 30 students has an average shoe size 8. That doesn’t mean that the school authorities should buy shoes of size 8 for all the 30 students.

6. Reference is Required for Final Analysis
Blindly trusting statistical results does no good. Instead, we need o study the conditions under which conclusions are drawn. For example, assume that a cloth manufacturer earns a profit of 3000, 2000, and 1000 in a span of three months and a paper manufacturer earns a profit of 1000, 2000, 3000.

Both would have averages of 2000, which can lead to a conclusion that both businesses are equally rewarding. However, it can be clearly seen that this is not the case because profits from the cloth manufacturing business are on a drop.

Example:

Question:
What is the first stage of statistical study in a singular sense?