Statistics for Economics — Introduction — Practice

Question 1

What is the difference between 'statistics' in the plural sense and 'statistics' in the singular sense? Give one example of each.

Accepted Answer

Plural sense: Statistics refers to numerical facts or data. Example: India's literacy rate is 78%; the inflation rate is 5%. These numbers are 'statistics.' Singular sense: Statistics refers to the science or methods of collecting, organising, presenting, analysing, and interpreting data. Example: Using the arithmetic mean and standard deviation to analyse income distribution is 'statistics' in the singular sense.

Question 2

Explain any four limitations of statistics with examples relevant to Indian economy.

Accepted Answer

1. Studies only aggregates, not individuals: Statistics deals with groups, not specific people. 'Average per capita income of India is ₹2 lakh' tells us nothing about any specific person's income — a street vendor and a software engineer are both averaged together. 2. Quantitative data only: Statistics works with numbers. Qualities like the 'happiness' of rural households or the 'dignity' of employment cannot be directly measured unless quantified. 3. Can be misused: Cherry-picked data creates false narratives. Showing GDP growth without mentioning rising inequality gives a misleading picture of economic progress. 4. Results are valid only on average: Statistical findings are probabilistic. A government scheme may reduce poverty on average but still miss the most vulnerable households. 5. Requires homogeneous data: Comparing literacy rates of India and a Nordic country without accounting for historical context yields misleading conclusions.

Question 3

Explain with four points why statistics is essential for economic policy-making in India. Also discuss two ways in which statistical data can mislead policy-makers.

Accepted Answer

Why statistics is essential: 1. Understanding the economy: Without data, we cannot answer: Has poverty declined? What is the unemployment rate? Is GDP growing? For example, NSO's GDP estimates tell the government whether the economy grew at 6% or 8%, enabling fiscal decisions. 2. Policy formulation: RBI uses CPI inflation data to decide interest rates (target: 4% ± 2%). MGNREGA wages are revised using the Consumer Price Index for Agricultural Labourers — statistical data directly drives policy. 3. Testing economic theories: Does education raise income? Does higher saving lead to higher investment? Statistical analysis of PLFS and NFHS surveys tests these relationships and guides where to spend public money. 4. Comparison across time and regions: Poverty ratio declined from ~55% (1973-74) to ~22% (2011-12) — this comparison shows whether past policies worked and informs future ones. How statistics can mislead: 1. Averages hide inequality: India's per capita income may show an upward trend, but median income may remain low because extreme wealth of a few raises the average. Policy based on average income will miss the bottom 50%. 2. Correlation misread as causation: If both ice cream sales and industrial output rise in summer, a naive reading of data might suggest ice cream drives industrial growth — obviously absurd. Policy-makers must verify logical mechanisms before acting on correlations.

Practice — Statistics for Economics — Introduction

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