Yearly Archives: 2022

Statistics for Economics Class 11 Notes Chapter 8 Index Numbers

Index Number
An index number is a statistical device for measuring changes in the magnitude of a group of related variables. It represents the general trend of diverging ratios from which it is calculated.
According to Croxton and Cowden, “Index numbers are devices for measuring difference in the magnitude of a group of related variables.”

Methods of Constructing Index Numbers

Construction of Simple Index Numbers
There are two methods of constructing simple index numbers.
(i) Simple Aggregative Method In this method, we use the following formula
P01=ΣP1ΣP0×100
Here, P₀₁ = Price index of current year
ΣP₁ = Sum of prices of the commodities in the current year
ΣP₀ = Sum of prices of the commodities in the base year
(ii) Simple Average of Price Relatives Method
According to this method, we first find out price relatives from each commodity and then take simple average of all the prices relatives.
Price relatives, P₀₁ =  Current year price (P1) Base year price (P0)×100
We can find out price index number of the current year by using the following formula
P01=∑[P1P0×100]N

Construction of Weighted Index Numbers
(i) Weighted Average of Price Relative Method
According to this method, weighted sum of the price relatives is divided by the sum total of the weight. In this method, goods are given weight according to their quantity, thus
P01=ΣRWΣW
Here, P₀₁ = Index number for the current year in relation to the base year
W = weight
R = price relative
(ii) Weighted Aggregative Method Under this method, different goods are accorded weight according to the quantity bought therefore, suggested different techniques of weighting some of well known methods are as under

Fisher’s Method is considered as ‘Ideal’ because

• It is based on variable weights.
• It takes into consideration the price and quantities of both the base year and current year.
• It is based on Geometric Mean (GM) which is regarded as the best mean for calculating index number.
• Fisher’s index number satisfies both the Time Reversal Test and Factor Reversal Test.

Consumer Price Index or Cost of Living Index Number
The consumer price index is the index number which measures the averages change in prices paid by the specific class of consumers for goods and services consumed by them in the current year in comparison with base year.

Construction of Consumer Price Index

• Selection of the consumer class
• Information about the family budget
• Choice of base year
• Information about prices
• Weightage – There are two ways of according weights
  • Quantity weight
  • Expenditure weight

The following formula is used to find consumer’s price index
Consumer Price Index (CPI) = ΣWRΣW

Wholesale Price Index (WPI)
The Wholesale Price Index (WPI) measures the relative changes in the prices of commodities traded in the wholesale markets. In India, the wholesale price index numbers are constructed on weekly basis.

Industrial Production Index
The index number of industrial production measures changes in the level of industrial production comprising many industries. It includes the production of the public and the private sector. It is a weighted average of quantity relatives. The formula for the index is
P01=Σq1×WΣW×100

Construction of Index Number of Industrial Production

• Classification of industries
• Statistics or data related to industrial production
• Weightage

Agricultural Production Index
Index number of agricultural production is weighted average of quantity relatives.

Sensex
Sensex is the index showing changes in the Indian stock market. It is a short form of a Bombay Stock Exchange sensitive index. It is constructed with 1978-79 as the reference year or the base year. It consists of 30 stocks of leading companies in the country.

Purpose of Constructing Index Number

• Purpose of constructing index number of prices is to know the relative change or percentage in the price level over time. A rising general price level over time is a pointer towards inflation, while a falling general price level over time is a pointer towards deflation.
• Purpose of constructing index number of quantity is to know relative change or percentage change in the quantum or volume of output of different goods and services. A rising index of quantity suggests a rising level of economic activity and vice-versa.

Statistics for Economics Class 11 Notes Chapter 7 Correlation

Correlation
It is a statistical method or a statistical technique that measures quantitative relationship between different variables, like between price and demand.
According to Croxton and Cowden, “When the relationship is of a quantitative nature, the appropriate statistical tool for discovering and measuring the relationship and expressing it in a brief formula is known as correlation.”

Types of Correlation
Correlation is commonly classified into negative and positive correlation.

• Positive Correlation When two variables move in the same direction, such a relation is called positive correlation, e.g., Relationship between price and supply
• Negative Correlation When two variables changes in different directions, it is called negative correlation. Relationship between price and demand.

Degree of Correlation
Degree of correlation refers to the coefficient of correlation

(ii) Absence of Correlation
(iii) Limited Degree of correlation
The degree of correlation between 0 and 1 may be rated as

• High (0.75 and 1)
• Moderate (0.25 and 0.75)
• Low (0 and 0.25)

Methods of Estimating Correlation
(i) Scatter Diagram Scattered diagram offers a graphic expression of the direction and degree of correlation.

Karl Pearson’s Coefficient of Correlation
This is also known as product moment correlation and simple correlation coefficient.
Karl Pearson has given a quantitative method of calculating correlation Karl Pearson’s coefficient correlation is generally written as V.
Formula According to Karl Pearson’s method, the coefficient of correlation is measured as
r=ΣxyNσxσy
Where,
r = Coefficient of correlation;
x = x – x¯¯¯
y= y – y¯¯¯
σ_x = Standard deviation of x series
σ_y = Standard deviation of y series
N= Number of observations
If there is no need to calculate standard deviation of x and y directly using the following formula
r=ΣxyΣx2×Σy2√
Here, x(x – x¯¯¯), y = (y – y¯¯¯)

Short-cut Method
This method is used when mean value is not in whole number but in fractions. In this method, deviation is calculated by taking the assumed mean both the series.
Coefficient of correlation is calculated using the following formula

Here, d_x = deviation of x series from the assumed mean = (x – A)
d_y = deviation of y series from the assumed mean = (y – A)
Σ dxdy – sum of the multiple of dx and dy
Σ dx² = sum of square of dx
Σ dy² = sum of square of dy
Σdx= sum of deviation of x-series
Σdy = sum of deviation of y-series
N = Total number of items

Step Deviation Method
Coefficient of correlation is calculated using the following formula

Spearman’s Rank Correlation Coefficient
In 1904, ‘Charles Edwards Spearman’ developed a formula to calculate coefficient correlation of qualitative variables. It is popularly known as Spearman’s rank. Difference formula or method.

Coefficient of Rank Correlation when Ranks are Equal formula

Here, m = number of items of equal ranks.

Importance or Significance of Correlation

• The study of correlation shows the direction and degree of relationship between the variables.
• Correlation coefficient some times suggests cause and effect relationship.
• Correlation analysis facilitates business decisions because the trend path of one variable may suggest the expected changes in the other.
• Correlation analysis also helps policy formulation.

Statistics for Economics Class 11 Notes Chapter 6 Measures of Dispersion

Dispersion
“It is the measure of the variation of the item”. According to Spiegel, ‘The degree to which numerical data tend to spread about an average value is called the variation or dispersion of the data”.
Different methods of measuring dispersion are

• Range
• Quartile deviation
• Mean deviation
• Standard deviation

Range Range is the difference between the highest value and the lowest value in a series.
R = H – L or L – S
H or L = Highest or Largest value of series
L or S = Lowest or Smallest value of series

Coefficient of range = H−LH+L or L−SL+S
Calculation of Range and Coefficient of Range
(i) Individual Series and Discrete Series
Range = H – L or L – S
Coefficient of Range = H−LH+L or L−SL+S

(ii) Frequency Distribution Series

• Mid values of the class interval are found, difference between the highest and lowest values would be the range.
• According to this method, we find the difference between lower limit of the first class interval and upper limit of the last class interval in the series would be the range.

(iii) Inter Quartile Range
Difference between third quartile ( Q₃) and first quartile of a series, is called Inter quartile range.
IQR = Q₃ – Q₁

Quartile Deviation
Quartile deviation is half of inter quartile range.
QD = Q3−Q12
It is also called semi-inter quartile range.
(i) Coefficient of Quartile Deviation (Coefficient of QD)
Coefficient of QD = Q3−Q1Q3+Q1
(ii) Calculation of Quartile Deviation
(a) Individual Series and Discrete Series First find out Q₁ and Q₃ from the following equations

(b) Frequency Distribution

Mean Deviation
“Mean deviation is the arithmetic average of deviation of all the values taken from a statistical average of series. In taking deviation of values, algebraic signs + and – are not taken into consideration, that is negative deviations are also treated as positive deviations”.
(i) Formulas for Mean Deviation
(a) If deviations are taken from median, the following formula is used

(b) If deviation are taken from arithmetic mean of the series

(ii) Coefficient of Mean Deviation

• Coefficient of mean deviation from Mean = MDX¯¯¯X¯¯¯¯¯
• Coefficient of MD from Median = MDMM
• Coefficient of MD from Mode = MDZZ

(iii) Calculation of Mean Deviation or Coefficient of Mean Deviation
(a) Individual Series
Estimating MD through Median, MD = Σ|dM|N
Estimating MD through Mean, MD = Σ|dX¯¯¯¯¯|N
Estimating Coefficient of MD through Median Coefficient of MD = MDMM
Estimating Coefficient of MD through Mean Coefficient of MD = MDX¯¯¯X¯¯¯¯¯

(b) Discrete Series
Estimating MD through median, MD_M = Σf|dm|N
Estimating MD through mean, MDX¯¯¯¯¯ = Σf|dX¯¯¯¯¯|N
Estimating Coefficient of MD through Median Coefficient of MD = MDMN
Estimating Coefficient of MD through Median Coefficient of MD = MDX¯¯¯X¯¯¯¯¯

(c) Frequency Distribution Series
Mean deviation from Median, MD_M = Σf|dM|Σf
Coefficient of MD = MDMM
Mean deviation from Mean, MDX¯¯¯¯¯ = Σf|dX¯¯¯¯¯|Σf
Coefficient of MD = MDX¯¯¯X¯¯¯¯¯

Standard Deviation
Standard deviation is the square root of the arithmetic mean of the squares of deviations of the items from their mean values.

Coefficient of Standard Deviation
This is a relative measure of the dispersion of series.
Coefficient of standard deviation (Coefficient of σ) = σX¯¯¯¯¯
(i) Calculation of Standard Deviation
(a) Direct Method

Here, σ = Standard Deviation;
ΣX² = Sum total of the squares of deviation,
X¯¯¯¯ = Mean Value,
X−X¯¯¯¯ = Deviation from mean value;
N = number of items
(b) Short-cut Method

(c) Step Deviation Method

(ii) Calculation of Coefficient of Variation
(a) Individual series = σX × 100
(b) Discrete series = σX × 100
(c) Frequency distribution series = σX × 100

Lorenz Curve
It is a curve that shows deviation of actual distribution from the showing equal distribution.
(i) Construction of the Lorenz Curve

• Calculate class mid-points
• Calculate cumulative frequencies as in column 6
• Express the grand total of column 3 and 6 as 100 and convert the cumulative totals in these columns in to percentage.
• Now, on the graph paper, take the cumulative percentage of the variable on Y-axis and cumulative percentages of X-axis.
• Draw a line joining co-ordinate (0, 0) with (100,100) this is called the line of equal distribution.
• Plot the cumulative percentages of the variable with cumulative percentages of frequency.

Statistics for Economics Class 11 Notes Chapter 5 Measures of Central Tendency

Central Tendency
A central tendency refers to a central value or a representative value of a statistical series.
According to Clark, “An average is a figure that represents the whole group”.

Types of Statistical Averages
Averages are broadly classified into two categories

• Mathematical Averages
• Positional Averages

Arithmetic Mean
Arithmetic Mean is the number which is obtained by adding the values of all the items of a series and dividing the total by the number of items.
Arithmetic Mean is generally written as X. It may be expressed in the form of following formula
X¯¯¯¯=x1+x2+x3+……xNN or ΣX¯¯¯¯¯N

Types of Arithmetic Mean

• Simple Arithmetic Mean
• Weighted Arithmetic Mean

Methods of Calculating Simple Arithmetic Mean
(i) Individual Series In the case of individual series, Arithmetic Mean may be calculated by two methods

• Direct Method According to this method, we find the Arithmetic mean from the following formula
  X¯¯¯¯=ΣXN or X¯¯¯¯= Total value of the item  Number of items 
• Short-cut Method By short cut method, we find the Arithmetic Mean from the following formula
  X¯¯¯¯=A+ΣdN
  Here, X¯¯¯¯ = Arithmetic Mean, A = Assumed average of Ed = Net sum of the deviations of the different values from the assumed average; and N = Number of items in the series,

(ii) Discrete Series There are three methods of calculating mean of the discrete series

• Direct Method Direct method of estimating mean of the discrete frequency series uses the formula
  X¯¯¯¯=ΣfXΣf
• Short-cut Method Short cut method of estimating mean of the discrete frequency series uses the following formula
  X¯¯¯¯=A+ΣfdΣf
• Step-deviation Method This method is a variant of short-cut method. It is adopted when deviations from the assumed mean have some common factor
  X¯¯¯¯=A+ΣfdΣf×c

(iii) Frequency Distribution
There are three methods of calculating mean in frequency distribution
(a) Direct Method Direct method of estimating mean of the discrete frequency series uses the formula
X¯¯¯¯=ΣfmΣf
m = mid-value, mid-value = L1+L22
L₁ = lower limit of the class
L₂ = upper limit of the class
(b) Short-cut Method Short cut method of estimating mean of the frequency distribution uses the formula
X¯¯¯¯=A+ΣfdΣf
(c) Step Deviation Method According to this method, we find the Arithmetic Mean by the following formula
X¯¯¯¯=A+Σfd′Σf×c
(d) Weighted Arithmetic Mean It is the mean of weighted items of the series. Different items are accorded different weights depending on their relative importance. The weighted sum of the items is divided by the sum of the weights.

Calculation of Weighted Mean
According to this way, we find weighted mean from the following information
X¯¯¯¯W=ΣWXΣW
(i) Merits

• Simplicity
• Certainty
• Based on all items
• Algebraic treatment
• Stability
• Basis of comparison
• Accuracy test

(ii) Demerits

• Effect of extreme value
• Mean value may not figure in the series at all
• Laughable conclusions
• Unsuitability
• Misleading conclusions

Median
“The Median is that value of the variable which divides the group into two equal parts, one part comprising all values greater than the Median value and the other part comprising all the values smaller than the Median value”.
(i) Calculation of Median
(a) Individual Series Calculation of Median in individual series involves the following formula
M = Size of (N+12)th item
When N of the series is an even number, Median is estimated using the following formula

(b) Discrete Series Calculation of Median in case of discrete series or frequency array involves the following formula
M = Size of (N+12)th item
(c) Frequency Distribution Series
The following formula is applied to determine the Median Value

Quartiles
If a statistical series is divided in to four equal parts, the end value of each part is called a Quartile.
(i) Calculation of Quartiles Quartile values (Q₁ and Q₃) are estimated differently for different sets of series,
(a) Individual and Discrete Series

(b) Frequency Distribution Series In frequency distribution series, the class interval of Q₁ and Q₃ are first identified as under

Percentiles
Percentiles divide the series into 100 equal parts, and is generally expressed as P.
Percentiles are estimated for different types of series as under
(i) Individual and Discrete Series

(ii) Frequency Distribution Series

Mode
The value of the variable which occurs most frequently in a distribution is called the mode.
According to Croxton and Cowden, “ The mode may be regarded as the most typical of a series of value”.
(i) Calculation of Mode

• Individual Series There are two ways of calculating Mode in individual series
  • By inspection
  • By converting individual series into discrete series
• Discrete Series There are two methods for calculation of mode indiscrete frequency series
  • Inspection Method
  • Grouping Method
• Frequency Distribution Series The exact value of Mode can be calculated with the following formula
  Z=L1+f1−f02f1−f0−f2xi

Relative Position of Arithmetic Mean, Median and Mode Suppose we express,
Arithmetic Mean = M_e
Median = M_i
Mode = M_o
The relative magnitude of the three are M_e > M_i > M_o or M_e < M_i < M_o The Median is always between the Arithmetic Mean and the Mode.

Statistics for Economics Class 11 Notes Chapter 3 Organisation of Data

Organisation of Data
Organisation of data refers to the arrangement of figures in such a form that comparison of the mass of similar data may be facilitated and further analysis may be possible.

Classification
Classification is the process of arranging things in groups or classes according to their resemblances and affinities and gives expression to the unity of attributes that may exist amongst a diversity of individuals.

Objectives of Classification

• Simplification and Briefness
• Utility
• Distinctiveness
• Comparability
• Scientific arrangement
• Attractive and effective

Characteristic of a Good Classification

• Comprehensiveness
• Clarity
• Homogeneity
• Suitability
• Stability
• Elastic

Basis of Classification

• Geographical Classification This classification of data is based on the geographical or locational differences of the data.
• Chronological Classification When data are classified on the basis of time, it is known as chronological classification.
• Qualitative Classification This classification is according to qualities or attributes of the data.
  This classification may be of two types
  • Simple classification
  • Manifold classification
• Quantitative or Numerical Classification Data are classified in to classes or groups on the basis of their numerical values. Quantitative classification is also called classification by variables.
• Concept of Variable A characteristic or a phenomenon which is capable of being measured and changes its value overtime is called a variable.
  The variable may be either discrete or continuous
  • Discrete Variable These are those variables that increase in jumps or in compete numbers.
  • Continuous Variable Variable that assume a range of values or increase not in jumps but continuously or in fractions are called continuous variables.
• Raw Data A mass of data in its crude form is called raw data.

Types of Statistical Series Statistical series are of two types

• Individual Series These are those series in which the items are listed singly. These series may be presented in two ways
  • According to serial numbers
  • Ascending or descending order of data
• Frequency Series Frequency series may be of two types
  • Discrete Series or Frequency Array It is that series in which data are presented in way that exact measurement of items are clearly shown. In this series there are no class intervals and a particular item in the series.
  •  Frequency Distribution It is that series in which items cannot be exactly measured. The items assume a range of values and are placed within the limits is called class interval.

Frequency distribution is also known as continuous series or series with class-intervals, or series of grouped data.

Types of Frequency Distribution

• Exclusive Series It is that series in which every class-interval excludes items corresponding to its upper limit.
• Inclusive Series An inclusive series is that series which includes all items upto its upper limit.
• Open End Series An open end series is that series in
  which lower limit of the first class-interval and the upper limit of last class- interval is missing like as below – 5, 20 and above
• Cumulative Frequency Series It is that series in which the frequencies are continuously added corresponding to each class-interval in the series.
  There are two ways of converting this series into cumulative frequency series
  • Cumulative frequencies may be expressed on the basis of upper class limits of the class-intervals.
  • Cumulative frequencies may b expressed on the basis of lower class limits of the class-intervals.
• Mid Values Frequency Series Mid value frequency series are those series in which we have only mid values of the class intervals and the corresponding frequencies.
• Univariate Distribution The frequency distribution of a single variable is called a univariate distribution.
• Bivariate Distribution A bivariate distribution is the frequency distribution of two variables.

Statistics for Economics Class 11 Notes Chapter 2 Collection of Data

Sources of Data There are two sources of data

• Primary Source of Data It implies collection of data from its source of origin.
• Secondary Source of Data It implies collection of data from some agency or institution which already happens to have collected the data through statistical survey.

Types of Data There are two types of data

• Primary Data Data collected by the investigator for his own purpose for the first time, from beginning to end are called primary data.
• Secondary Data These data have already been collected by somebody else, these are available in the form of published or unpublished report.

Principal Differences between Primary and Secondary Data

• Primary data are original and secondary data are already in existence and therefore, are not original.
• Primary data do not need any adjustment, secondary data need to be adjustment to suit the objective of study in hand.
• Primary data are expensive and secondary data are less expensive.

Statistical Methods of Data Collection
(i) Direct Personal Investigation
It is the method by which data are personally collected by the investigator from the information. Merits and demerits of this method are follows.
(a) Merits

• Originality
• Reliability
• Uniformity
• Accuracy
• Related information
• Elastic

(b) Demerits

• Difficult to cover wide areas
• Costly
• Personal bias
• Limited coverage

(ii) Indirect Oral Investigation
It is the method by which information is obtained not from the persons regarding whom the information is needed. It is collected orally from other persons who are expected to possess the necessary information. Merits and demerits of this method are given below
(a) Merits

• Wide coverage
• Expert opinion
• Simple
• Less expensive
• Free from bias

(b) Demerits

• Less accurate
• Doubtful conclusions
• Biased

(iii) Information from Local Sources or Correspondents
Under this method, the investigator appoints local persons or correspondents at different places. Merits and demerits of this method are given below
(a) Merits

• Economical
• Wide coverage
• Continuity
• Suitable for special purpose

(b) Demerits

• Loss of originality
• Lack of uniformity
• Personal bias
• Less accurate
• Delay in collection

(iv) Information Through Questionnaries and Schedules
There are two ways of collecting information on the basis of questionnaire
(a) Mailing Method Under this method questionnaires are mailed to the informants. The method is most suited when

• The area of the study is very wide.
• The informants are educated.

(b) Enumerator’s Methods Under this Method enumerator himself fills the schedules after seeking information from the informants. This method is mostly used when

• field of investigation is large.
• the investigation need specialised and skilled investigation.
• the investigators are well versed in the local language and cultural norms of the informants.

(c) Collection of Secondary Data There are two main sources of secondary data

• Published sources
• Unpublished sources

(d) Published Sources Some of the published source of secondary data are

• Government publication
• Semi-government publication
• Reports of committees and commissions
• Publications of trade associations
• Publication of research institutions
• Journals and papers
• Publication of research scholars
• International publication

(e) Unpublished Sources These data are collected by the government organisations and others, generally for their self use or office record.

• In order to assess the reliability, suitability and adequacy of the data, the following points must be kept in mind
• Ability of the collecting organisation
• Objective and scope
• Method of collection
• Time and condition of organisation
• Definition of the unit
• Accuracy

(v) Census ‘Method
Census method is that method in which data are collected covering every item of the universe or population relating to the problem under investigation. Merits and demerits of this method are given follows
(a) Merits

• Reliable and accurate
• Less biased
• Extensive information
• Study of diverse characteristic
• Study of complex investigation
• Indirect investigation

(b) Demerits

• Costly
• Large manpower
• Not suitable for large investigation

(vi) Sample Method
It is that method in which data is collected about the sample on a group of items taken from the populations for examination and conclusions are drawn on their basis. Merits and demerits of this method are given below
(a) Merits

• Economical
• Time saving
• Identification of error
• Large investigation
• Administrative convenience
• More scientific

(b) Demerits

• Partial
• Wrong conclusions
• Difficulty in selecting representative sample
• Difficulty in framing a sample
• Specialised knowledge

Methods of Sampling
(i) Random Sampling Random sampling is that method of sampling in which each and every item of the universe has equal chance of being selected in the sample.
Random sampling may be done in any of the following ways

• Lottery method
• Tables of random number

(ii) Purposive or Deliberate Sampling It is that method in which the investigator himself makes the choice of the samples items which in his opinion are the best representative of the universe.
(iii) Stratified or Mixed Sampling According to this method of sampling population is divided into different strata having different characteristics and some of the items are selected from each strata, so the entire population gets represented.
(iv) Systematic Sampling According to this methods, units of the population are numerically, geographically and alphabetically arranged. Every nth item of the numbered is selected as a sample item.
(v) Quota Sampling In this method, the population is divided into different groups or classes according to different characteristics of the population.
(vi) Convenience Sampling In this method, sampling is done by the investigator in such a manner that suits his convenience.

Reliability of Sampling Data
It depends mainly on the following factors

• Size of the sample
• Method of sampling
• Bias of correspondents and enumerators
• Training of enumerators

Important agencies at the national level which collect process and tabulate the statistical data. NSSO (National Sample Survey Organisation), RGI (Registrar General of India), DGCIS (Directorate General of Commercial Intelligence and Statistics) and Labour Bureaus.

Statistics for Economics Class 11 Notes Chapter 1 Introduction

• Economics by Alfred Marshall, “The study of man in the ordinary business of life”.
• Consumer “A consumer is one who consumes goods and services for the satisfaction of his wants”.
• Consumption “Consumption is the process of using up utility value of goods and services for the direct satisfaction of our wants”.
• Producer “A producer is one who produces/or sells goods and services for the generation of income”.
• Production “Production is the process of converting raw material into useful thing”.
• Saving It is the part of income which is not consumed. It is an art of abstinence from consumption.
• Investment It is expenditure by the producers on the purchase of such assets which help to generate income.
• Economic Activity It is an activity which is related to the use of scarce means. Means are always scarce in relation to our wants.
• Economic Problem It is the problem of choice arising on account of the facts that resources are scarce and these have alternative uses.

Components of Economics There are three components of economics

• Consumption
• Production
• Distribution

Statistics – A Plural Sense Statistics refers to information in terms of numbers or numerical data, such as population statistics, employment statistics etc.
Accqrding to Bowley, “Statistics are numerical statements of facts in any department of enquiry placed in relation to each other.”

Features of Statistics in the Plural Sense

• Aggregate of facts
• Numerically expressed
• Affected by multiplicity of causes
• Reasonable accuracy
• Placed in relation to each other
• Predetermined purpose
• Estimated

Statistics – A Singular Sense It refers to techniques or methods relating to collection, classification, presentation analysis and interpretation of quantitative data.

According to Seligman, “Statistics is the science which deals with the methods of collecting, classifying, presenting, comparing and interpreting numerical data collected to throw some light on any sphere of enquiry”.

Importance of Statistics in Economics:

• Quantitative expression of economic problem
• Inter-sectoral and inter-temporal comparisons
• Working out cause and effect relationship
• Construction of economic theories or economic models
• Economic forecasting
• Formulation of policies

Limitations of Statistics:

• Study of numerical facts only
• Study of aggregates only
• Results are true only on an average
• Without reference, results may prove to be wrong
• Can be used only by the experts
• Prone to misuse

Comparative Development Experience of India with its Neighbours Class 11 Notes Chapter 10 Indian Economic Development

Introduction
With the unfolding of the globalisation process, developing countries are keen to understand the developmental processes pursued by their neighbours as they face competition from developed nations as also amongst themselves.

Foreign Direct
Investment It is much larger compared to India and Pakistan, and a much stronger driver of growth. SEZs (Special Economic Zones) policy of China is of central significance inducing FDI. SEZs are offering robust infrastructural facilities for FDI.

Demographic Profile
Both for India and China, large size of population is a hindrance in the process of growth, as it requires a huge amount of ‘maintenance investment’.

Human Development Some important parameters of human development are as these

• Life expectancy-higher the better.
• Adult literacy rate-higher the better.
• Infant mortality rate-lower the better.
• Maternal mortality rate-lower the better.
• Percentage of population having access to improved water sources-higher the better.
• Percentage of undernourished population-lower the better.

With a view to accelerating the pace of growth, different countries are forming regional and global economic grouping based on common agreements of bilateral relations. e.g., SAARC, EU, ASEAN, G-8, G-20.

Common Success Story of India and Pakistan

• A substantial rise in GDP per capita.
• Self-sufficiency in food production.
• Dualistic nature of the economy is gradually declining.
• Considerable reduction in the incidence of poverty.

Common Failures of India and Pakistan

• Relatively slow pace of GDP growth, compared with China.
• Poor perfomance in HDI ranking.
• Dismal Fiscal management.
• Political survival of a dominating issue rather than good governance.

Sex Ratio Sex ratio is found to be low in all three countries pointing to social backwardness, where people hold high preference for a son in the family.

This chapter will not be examined. Open Text Based Assessment (OTBA) will be based on this chapter.
Nations are also eager to know and understand about the developmental process pursued by their neighbouring nations. It allows them to comprehend their strengths and weaknesses. In the process of globalisation, it is essential for every nation to compete with developed countries.

In this chapter, we are comparing the developmental strategies pursued by India with its neighbouring economies-Pakistan and China. This will help in understanding where do we stand today in comparison to others.

Development Strategies of India, China and Pakistan
India, China, Pakistan have many similarities in their development strategies which are as follows

• India, Pakistan and China have started towards their developmental path at the same time. India and Pakistan became independent nations in 1947. While Peoples Republic of China was established in 1949.
• All the three countries had started planning their development strategies in similar ways. India announced its Five Year Plan in 1951-56, while ’ Pakistan announced its first Five Year Plan in 1956, which is called Medium Term plan. China announced its First Five Year Plan in 1953.
• India and Pakistan adopted similar strategies such as creating a large public sector and raising public expenditure on social development.
• Till the 1980s, all the three countries had similar growth rates and per capita incomes.
• Economic reforms took place in all the three countries. Reforms started in India in 1991, in China in 1978 and in Pakistan in 1988.

Development Strategies of India
Some of the prominent strategies of India are discussed below
1. Sound Trade System India was a country which had the history of closed trade. Because of this historical background; there is a critical challenge for India in order to make a new policy which can support the new open trade system. This new reform in economies of India has been introduced and accelerates the economic growth of India.

2. Reduction in Poverty India has adopted several poverty alleviation programmes to reduce poverty in India. -This would help in increasing per capita income, rise in nutrition level of poors and there is a subsequent fall in percentage of absolute poor in some states.

3. Rural Development Under this strategy, India adopted various measures for the development of areas that are lagging behind in the overall development of village economy.

4. Employment Generation Several economic reforms were initiated to generate employment in the country and their aim is to provide gainful self-employment and skilled wage employment opportunities.

Development Strategies of China
After the establishment of People’s Republic of China under one party rule, all the critical sectors of the economy, enterprises and lands owned and operated by individuals were brought under government control.
Certain development strategies of China are discussed below

• Great Leap Forward (GLF) This campaign initiated in 1958 aimed at industrialising the country on a massive scale. People were encouraged to set up industries in their backyards. In rural areas, communes were started. Under the commune system, people collectively cultivated lands.
• Great Proletarian Cultural Revolution (1966-76) In 1965, Mao Tse Tung started a cultural revolution on a large scale. In this revolution, students and professionals were sent to work and learn from the country side. Unlike GLF, the cultural revolution did not have an explicit economic rationale.
• 1978 Reforms Since 1978, China began to introduce many reforms in phases. The reforms were initiated in agriculture, foreign trade and investment sector. In agriculture, lands were divided into small plots which were allocated to individual households. They were allowed to keep all income from the land after paying taxes.
  In later phase, reforms were initiated in industrial sector. All enterprises which were owned and operated by local collectives in particular, were allowed to produce goods.

At this stage, enterprises owned by government (known as State Board Enterprises – SOEs), in India we call them public sector enterprises were made to face competition. In reform, prices were fixed in two ways, i.e., farmers and industrial units were required to buy and sell fixed quantities of inputs and outputs on the basis of prices fixed by the government and the rest were purchased and sold at market prices.

Over the years, as production increased, the proportion of goods or inputs transacted in the market also increased. The goal of Chinese economic reforms was to generate sufficient surplus to finance the modernisation of the mainland Chinese economy. In order to attract foreign investors, Special Economic Zones (SEZs) were set up.

Development Strategies of Pakistan
The development strategies of Pakistan are summarised below

• Mixed Economy Pakistan follows a mixed economy system where both public and private sectors co-existed.
• Import Substitution Pakistan adopted a regulatory policy framework in the late 1950s and 1960s for import industrialisation. The -policy combined tariff protection for manufacturing of consumer goods together with direct import controls on competing imports.
• Green Revolution This was introduced to increase the productivity and self sufficiency in food. This increased the output of foodgrains. This had changed the agrarian structure dramatically. In 1970’s nationalisation of capital goods took place. Pakistan shifted its policy orientation in 1970’s and 1980’s when private sector got encouragement.

During this period, Pakistan received financial support from Western. This helped the country in stimulating economic growth. Government also offered incentives to private sector. This had a created climate for new investments. And in 1988 certain reforms were also initiated in the country.

Success and Failure of Strategies
The development strategies brought structural reforms in China, India and Pakistan. Follow the description of their success and failure one by one.
Success of Structural Reforms in China
The success of structural reforms in China are

• There was existence of infrastructure in the areas of education and health and land reforms.
• There was decentralised planning and existence of small enterprise.
• Through the commune system, there was more equitable distribution of foodgrains.
• There was extension of basic health services in rural areas.

Failures of Structural Reforms in China
The failures of structural reforms in China are

• There was slow pace of growth and lack of modernisation in the Chinese economy under the Maoist rule.
• Maoist vision of economic development based on decentralisation, self sufficiency and shunning of foreign technology had failed.
• Despite of extensive land reforms, collectivisation, the great leap forward and other initiatives, the per capita gain output in 1978 was the same as it was in the mid-1950s.

China has an Edge Over India
The Chinese reform process began more comprehensively during the 80s, when India was in the mid-stream of slow growth process.

Rural poverty in China declined by 85% during the period 1978 to 1989. In India, it declined only by 50% during this period, Global exposure of the economy has been far more wider in China than in India. China’s export-driven manufacturing has recorded on exponential growth, while India continues to be only a marginal player in the international markets.

Common Success of Structural Reforms in India and Pakistan
The common success of structural reforms in India and Pakistan are

• Both India and Pakistan have succeeded in more than doubling their per capita incomes inspite of high growth rate of population.
• The incidence of poverty has also been reduced significantly. However, the level of poverty is lower in Pakistan.
• Both the countries have achieved self-sufficiency in the production of food.
• Both the countries have succeeded in developing their service and industry sectors at a fast rate.
• The use of modern technology is improving in both the countries.

Common Failures of Structural Reforms in India and Pakistan
The common failures of structural reforms in India and Pakistan are

• Growth rate of GDP and its sectoral constituents have fallen in 1990’s.
• Poverty and unemployment are still areas of major concerns in both the countries.

Areas Where Pakistan has an Edge Over India
Starting from almost the same level as India, Pakistan has achieved better results with regards to

• Migration of workforce from agriculture to industry,
• Migration of people from rural to urban areas.
• Access to improved water sources.
• Reduction in below poverty line population.

Areas where India has an Edge Over Pakistan
There is little doubt that, in the area of skilled manpower and research and development institutions. India is better placed than Pakistan. Indian scientists excel in the areas of defence technology, space research, electronics and avionics, genetics, telecommunications, etc. The number of Ph.Ds produced by India in science and engineering every year (about 5000) is higher than the entire stock of Ph.Ds in Pakistan. Issues of health facilities in general and infant mortality in particular are better addressed in India.

Comparative Study
With Respect to Demographic Indicators, GDP and HDI .
I. Demographic Indicators
We shall compare some demographic indicators of India, China and Pakistan

• The population of Pakistan is very small and accounts for roughly about one-tenth of China or India. Though China is the largest nation and geographically occupies the largest area among the three nations, its density is the lowest.
• One child norm was introduced in China in late 1970’s to check the problem of population growth. This measure led to decline in the sex ratio. Although sex ratio is biased against females in all three countries, in recent times, all three countries are trying to adopt various measures to improve the situation.
  After few decades there will be more elderly people in proportion to young people due to one child norm.
• The fertility rate is low in China and very high in Pakistan.
• Urbanisation is high in both Pakistan and China.

II. Gross Domestic Product and Sectors
According to the latest data available, we find
(i) China has the second largest GDP (PPP) of US$ 10.1 trillion whereas, India’s GDP (PPP) is US $ 4.2 trillion and Pakistan’s GDP (PPP) is 0.47 trillion US$; roughly about 10% of India’s GDP.
(ii) In 1980’s, Pakistan was ahead of India, China was having double digit growth and India was at the bottom.

Source Key indicators for Asia and Pacific 2011, Asian Development Bank, Phillipines
(iii) In 2000-10 there is a marginal decline in India and Chinas growth rates whereas Pakistan met with drastic decline in 4.7%. The reform processes introduced in 1988 in Pakistan and political instability are reasons behind this trend.
(iv) China and Pakistan have more proportion of urban people than India.
(v) In China, due to topographic and climatic conditions, the area suitable for cultivation is relatively small-only about 10% of its total land area. The total cultivable area in China accounts for 40% of the cultivable area in India.
(vi) Until the 1980s, more than 80% of the people in China were dependent on farming as their sole source of livelihood.
(vii) The government encouraged people to leave their fields and pursue other activities such as handicrafts, commerce and transport.
(viii) In 2008, with 40% of its workforce engaged in agriculture, its contribution to GDP in China is 10%.

(ix) In both India and Pakistan, the contribution of agriculture to GDP was at 19 and 21% respectively. But the proportion of workforce that works in this sector is more in India. In Pakistan, about 45% of people work in agriculture whereas in India it is 56%.
(x) The sectoral share of output and employment also shows that in all the three economies, the industry and service sectors have less proportion to workforce but contribute more in terms of output.
(xi) In China, manufacturing contributes the highest to GDP at 47% whereas in India and Pakistan, it is the service sector which contributes the highest. In both these countries, service sector accounts for more than 50% of GDP. In the normal course of development, countries first shift their employment and output from agriculture to manufacturing and then to services. This is what, is happening in China.
The proportion of workforce engaged in manufacturing in India and Pakistan were low at 49 and 20% respectively.

(xii) The contribution of industries to GDP is also just equal to or marginally higher than the output from agriculture.
In India and Pakistan, the shift is taking place directly to the service sector.
(xiii) Thus, in both India and Pakistan, the service sector is emerging as a major player of development. It contributes more to GDP and, at the same time, emerges as a prospective employer.
(xiv) In the 1980s India, China and Pakistan employed 17, 12 and 27% of its workforce in the service sector respectively. In 2008-10 it has reached the level of 25, 33 and 35% respectively.

III. Human Development Indicator
India, China and Pakistan have performed in some of the selected indicators of human development.
Some Selected Indicators of Human Development, 2009-10

Source Human Development Report 2011 and World Development Indicators (www.worldbank.org)
From the data we would be able to conclude

• China is moving ahead of both India and Pakistan in terms of indicators of human development.
• Pakistan is ahead of India in reducing proportion of people below the poverty line and also its performance in education, sanitation and access to water is better than that of India.
• In China, for one lakh births, only 38 women die whereas, in India 230 women die and in Pakistan 260 women die.
• India is in the worst scenario as compared to the other two countries with respect to access to improved sanitation and clean water.

Human Development Index (HPI)
HDI includes quantitative aspects of per capital, GDP and the quality aspects of performance in. health and education. It is an average of life expectancy index, education index and GDP index.