### Download Statistics Notes with respect to the economics

**Statistics in
Economics**

Scarcity
is the root of all economic problems. Economics is often discussed in three
parts: consumption, production and distribution.

We
want to know how the consumer decides, given his income and many alternative
goods to choose from, what to buy when he knows the prices. This is the study
of Consumption. We also want to know how the producer, similarly, chooses what
and how to produce for the market. This is the study of Production. Finally, we
want to know how the national income or the total income arising from what has
been produced in the country (called the Gross Domestic Product or GDP) is distributed
through wages (and salaries), profits and interest (We will leave aside here
income from international trade and investment). This is the study of
Distribution.

Economics
is the study of how people and society choose to employ scarce resources that
could have alternative uses in order to produce various commodities that satisfy
their wants and to distribute them for consumption among various persons and groups
in society.”

Statistics
deals with the collection, analysis, interpretation and presentation of
numerical data.

**Collection of Data**

Primary
and Secondary Data Sources.

The
questionnaire should start from general questions and proceed to more specific
ones. For example: (i) Is the electricity supply in your locality regular? (ii)
Is increase in electricity charges justified?

**Pilot Survey**

Once
the questionnaire is ready, it is advisable to conduct a try-out with a small
group which is known as Pilot Survey or Pre-testing of the questionnaire.

**CENSUS AND SAMPLE
SURVEYS**

A
survey, which includes every element of the population, is known as Census or
the Method of Complete Enumeration.

**SAMPLING AND
NON-SAMPLING ERRORS**

Sampling
error refers to the difference between the sample estimate and the
corresponding population parameter (actual value of the characteristic of the
population for example, average income, etc). Thus, the difference between the
actual value of a parameter of the population and its estimate (from the
sample) is the sampling error. It is possible to reduce the magnitude of
sampling error by taking a larger sample.

Non-sampling
errors are more serious than sampling errors because a sampling error can be minimized
by taking a larger sample. It is difficult to minimize non-sampling error, even
by taking a large sample. Even a Census can contain non-sampling errors. Some
of the non-sampling errors are: Sampling Bias, Non-Response Errors, Errors in
Data Acquisition.

**CENSUS OF INDIA AND
NSSO**

The
Census is being regularly conducted every ten years since 1881. The first Census
after Independence was conducted in 1951. The NSS was established by the Government
of India to conduct nationwide surveys on socio-economic issues. Census of
India and National Sample Survey are two important agencies at the national
level, which collect, process and tabulate data on many important economic and
social issues.

**CLASSIFICATION OF
DATA**

The
data collected from primary and secondary sources are raw or unclassified. Once
the data are collected, the next step is to classify them for further
statistical analysis. Classification brings order in the data. Likewise the raw
data is classified in various ways depending on the purpose. They can be
grouped according to time. Such a classification is known as a Chronological Classification.
In such a classification, data are classified either in ascending or in
descending order with reference to time such as years, quarters, months, weeks,
etc. In Spatial Classification the data are classified with reference to geographical
locations such as countries, states, cities, districts, etc. Sometimes you come
across characteristics that cannot be expressed quantitatively. Such characteristics
are called Qualities or Attributes. For example, nationality, literacy,
religion, gender, marital status, etc. They cannot be measured. Yet these
attributes can be classified on the basis of either the presence or the absence
of a qualitative characteristic. Such a classification of data on attributes is
called a Qualitative Classification.

Characteristics,
like height, weight, age, income, marks of students, etc., are quantitative in
nature. When the collected data of such characteristics are grouped into
classes, it becomes a Quantitative Classification.

**Continuous and
Discrete. Variables**

A
continuous variable can take any numerical value. It may take integral values
(1, 2, 3, 4, ...), fractional values (1/2, 2/3, 3/4, ...), and values that are not
exact fractions ( 2 =1.414, 3 =1.732, … , 7 =2.645). For example, the height of
a student, as he/she grows say from 90 cm to 150 cm, would take all the values
in between them. It can take values that are whole numbers like 90cm, 100cm,
108cm, 150cm. It can also take fractional values like 90.85 cm, 102.34 cm,
149.99cm etc. that are not whole numbers.

Unlike
a continuous variable, a discrete variable can take only certain values. Its
value changes only by finite “jumps”. It “jumps” from one value to another but
does not take any intermediate value between them. For example, a variable like
the “number of students in a class”, for different classes, would assume values
that are only whole numbers.

**FREQUENCY
DISTRIBUTION**

It
shows how different values of a variable (here, the marks in mathematics scored
by a student) are distributed in different classes along with their
corresponding class frequencies. In this case we have ten classes of marks:
0–10, 10–20, … , 90–100. The term Class Frequency means the number of values in
a particular class.

**BIVARIATE FREQUENCY
DISTRIBUTION**

Very
often when we take a sample from a population we collect more than one type of
information from each element of the sample. For example, suppose we have taken
sample of 20 companies from the list of companies based in a city. Suppose that
we collect information on sales and expenditure on advertisements from each company.
In this case, we have bivariate sample data. Such bivariate data can be
summarised using a Bivariate Frequency Distribution.

**Presentation of
Data**

There
are generally three forms of presentation of data:

Textual
or Descriptive presentation, Tabular presentation, Diagrammatic presentation.

Classification
used in tabulation is of four kinds:

•
Qualitative

•
Quantitative

•
Temporal and

•
Spatial

A
good table should essentially have the following: Table Number, Title: It finds
place at the head of the table, Column Headings, Row Headings, Body of the
Table, Unit of Measurement, Source: Source is generally written at the bottom
of the table.

Diagrams
may be less accurate but are much more effective than tables in presenting the
data.

There
are various kinds of diagrams in common use. Amongst them the important ones
are the following:

(i)
Geometric diagram: Bar diagram and pie diagram come in the category of
geometric diagram.

(ii)
Frequency diagram: Data in the form of grouped frequency distributions are
generally represented by frequency diagrams like histogram, frequency polygon,
frequency curve and ogive. A histogram looks similar to a bar diagram. But
there are more differences than similarities. In histogram no space is left
between two rectangles, but in a bar diagram some space must be left between
consecutive bars The width in a histogram is as important as its height. We can
have a bar diagram both for discrete and continuous variables, but histogram is
drawn only for a continuous variable.

(iii)
Arithmetic line graph

Correlation:
Correlation is commonly classified into negative and positive correlation. The
correlation is said to be positive when the variables move together in the same
direction. When the income rises, consumption also rises. When income falls, consumption
also falls. Sale of ice cream and temperature move in the same direction. The
correlation is negative when they move in opposite directions. When the price
of apples falls its demand increases. When the prices rise its demand
decreases. Three important tools used to study correlation are scatter
diagrams, Karl Pearson’s coefficient of correlation and Spearman’s rank
correlation.

**Index Numbers**

The
value of money does not remain constant over time. It rises or falls and is
inversely related to the changes in the price level. A rise in the price level
means a fall in the value of money and a fall in the price level means a rise
in the value of money. Thus, changes in the value of money are reflected by the
changes in the general level of prices over a period of time. Changes in the
general level of prices can be measured by a statistical device known as ‘index
number.’

Price
index number indicates the average of changes in the prices of representative
commodities at one time in comparison with that at some other time taken as the
base period.

Steps
or Problems in the Construction of Price Index Numbers:

1.
Selection of Base Year:

The
first step or the problem in preparing the index numbers is the selection of
the base year. The base year is defined as that year with reference to which
the price changes in other years are compared and expressed as percentages. The
base year should be a normal year.

In
other words, it should be free from abnormal conditions like wars, famines,
floods, political instability, etc. Base year can be selected in two ways- (a)
through fixed base method in which the base year remains fixed; and (b) through
chain base method in which the base year goes on changing, e.g., for 1980 the
base year will be 1979, for 1979 it will be 1978, and so on.

2.
Selection of Commodities:

The
second problem in the construction of index numbers is the selection of the
commodities. Since all commodities cannot be included, only representative
commodities should be selected keeping in view the purpose and type of the
index number.

3.
Collection of Prices:

After
selecting the commodities, the next problem is regarding the collection of
their prices:

(a)
From where the prices to be collected;

(b)
Whether to choose wholesale prices or retail prices;

(c)
Whether to include taxes in the prices or not etc.

While
collecting prices, the following points are to be noted:

(a)
Prices are to be collected from those places where a particular commodity is
traded in large quantities.

(b)
Published information regarding the prices should also be utilised,

(c)
In selecting individuals and institutions who would supply price quotations, care
should be taken that they are not biased.

(d)
Selection of wholesale or retail prices depends upon the type of index number
to be prepared. Wholesale prices are used in the construction of general price
index and retail prices are used in the construction of cost-of-living index
number.

4.
Selection of Average:

Since
the index numbers are, a specialised average, the fourth problem is to choose a
suitable average.

5.
Selection of Weights:

Generally,
all the commodities included in the construction’ of index numbers are not of
equal importance. Therefore, if the index numbers are to be representative,
proper weights should be assigned to the commodities according to their
relative importance.

For
example, the prices of books will be given more weightage while preparing the
cost-of-living index for teachers than while preparing the cost-of-living index
for the workers. Weights should be unbiased and be rationally and not
arbitrarily selected.

6.
Calculation.

**SOME IMPORTANT
INDEX NUMBERS**

Consumer
price index Consumer price index (CPI), also known as the cost of living index,
measures the average change in retail prices. Consider the statement that the CPI
for industrial workers (2001=100) is 277 in December 2014. What does this
statement mean? It means that if the industrial worker was spending Rs 100 in
2001 for a typical basket of commodities, he needs Rs 277 in December 2014 to
be able to buy an identical basket of commodities. It is not necessary that
he/she buys the basket. Consumer
Price Index Number Government agencies in India prepare a large number of
consumer price index numbers. Some of them are as follows:

•
Consumer Price Index Numbers for Industrial Workers with base 2001=100. Value
of Index in May 2017 was 278.

•
All-India Consumer Price Index Numbers for Agricultural Labourers with base
1986- 87=100. Value of Index in May 2017 was 872.

•
All-India Consumer Price Index Numbers for Rural Labourers with base
1986-87=100. Value of Index in May 2017 was 878.

•
All-India Rural Consumer Index with base 2012 = 100. Value of Index in May 2017
was 133.3

•
All-India Urban Consumer Price Index with base 2012 = 100. Value of Index in
May 2017 was 129.3

All-India
Combined Consumer Price with base 2012 = 100. Value of Index in May 2017 was
131.4 In addition, these indices are available at the state level.

The
Reserve Bank of India is using the All-India Combined Consumer Price Index as
the main measure of how consumer prices are changing.

Therefore,
some details are necessary about this index number. This index is now being
prepared with base 2012 = 100 and many improvements have been made in accordance
with international standards.

Wholesale
Price Index The Wholesale price index number indicates the change in the
general price level. Unlike the CPI, it does not have any reference consumer
category. The Wholesale Price Index is now being prepared with base 2011-12 = 100.
The value of the index for May 2017 was 112.8.

SENSEX

Sensex
is the short form of Bombay Stock Exchange Sensitive Index with 1978–79 as
base. The value of the sensex is with reference to this period.

Consumer
index number (CPI) or cost of living index numbers are helpful in wage
negotiation, formulation of income policy, price policy, rent control, taxation
and general economic policy formulation.

•
The wholesale price index (WPI) is used to eliminate the effect of changes in prices
on aggregates, such as national income, capital formation, etc.

•
The WPI is widely used to measure the rate of inflation. CPI are used in
calculating the purchasing power of money and real wage.

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