GDP Part 1: A Plain-English Introduction

When a recent IMF report sparked debate about India’s gross domestic product (GDP), the discussion spilled into my undergraduate WhatsApp group. The group members are sharp, accomplished engineers, but many are unfamiliar with issues related to GDP; I realized I know more about the topic than some of them do.

It’s all about the background; in my MBA group, I know a lot less than most of the members do.

This middle-of-the-road perspective allows me to explain GDP in plain, accessible language. If GDP headlines leave you puzzled, you’re in good company—even my wife, a chemist, finds them baffling. This post is for you.

This three-part weekly post first explains what GDP is, how it is estimated, and why it is inherently imperfect. Part 2 outlines India’s challenges in measuring GDP, and Part 3 focuses on contentious issues in measuring GDP during the Modi era.

What is GDP?

GDP is the monetary value of all final goods and services produced within a country over a specific period, usually a quarter or a year.

Let’s break that down. The loaf of bread I bought this morning for $7 counts toward GDP (I once worked for a leading baked-goods company, so bread will be my go-to example in all three parts).

Key rule: No double-counting. The wheat sold by the farmer to the mill, and the flour sold by the mill to the bakery, are not counted separately, because their value is already embedded in the final $7 price of the loaf.

Now imagine capturing every such transaction across the economy; that’s almost impossible. Accordingly, GDP is never exact; it’s always an estimate.

There are three estimation approaches:

Expenditure Approach
Add up how much households, businesses, the government, and foreigners spend on goods and services produced in the country, like the $7 spent on bread.

Income Approach
Add up the income earned from producing goods and services—wages, profits, rents, and taxes, such as the $7 the bakery earns from my bread purchase.

Production Approach
Add up the value created at each stage of production. Using the bread example:

• Farmer: Sells wheat for $1. The full $1 counts as value added.

• Miller: Sells flour for $3 but paid $1 for wheat. Value added: $2.

• Bakery: Sells the loaf for $7 but paid $3 for flour. Value added: $4.

The total value added across all stages is $7.

Data Collection Challenge

As you can imagine, tabulating expenditure, income, or value added of economic entities nationwide is complicated.

For example, consider household spending measured in the Expenditure Approach. How can the GDP statistical agency know that I spent $7 on bread? There’s only one way: it has to ask me.

Statistical agencies survey a sample of households to estimate total spending across the population.  Because these surveys are expensive, logistically complex, and time-consuming, they are conducted only every five to seven years and have several limitations; in the intervening years, older data is extrapolated using assumptions.

GDP measurement works well for the formal economy, where production, sales, and wages are documented. In the informal sectors, small vendors and the like, data are scarce, forcing reliance on estimates. Estimates, and more estimates!

Which of the Three GDP Estimates is Used?

Ideally, all three measurement methods should yield similar results. In practice, they don’t because each approach has different data gaps.

In developed countries, the discrepancy among the three estimates is usually slight, under 1%; in many developing countries, with lower-quality data, it can be significant, sometimes up to 10%.

Most countries, including India, rely primarily on the Production Approach for official GDP figures because it generally produces the most reliable estimates.

GDP estimates using the Expenditure and Income approaches are published for analysis and consistency checks.

GDP Growth Rate: How is it Computed

Beyond GDP’s absolute size, we’re interested in how it grows or shrinks. To do this meaningfully, we must distinguish between nominal and real GDP.

Suppose a country’s GDP was $100 million in 2024 and $110 million in 2025. These figures represent nominal GDP, which values economic output in each year using that year’s prevailing prices. Nominal GDP is typically what we refer to when discussing the overall size of an economy.

In our example, the 10% year-to-year increase in nominal GDP can arise from higher production of goods and services (real economic activity) and higher prices in 2025 than in 2024. What we care about more, however, is how much actual economic output increased. To determine that, we need to remove the effect of price changes from the 10% headline number.

We do this by recalculating the value of goods and services (economic output) produced and consumed in 2025 using 2024 prices. The resulting price-adjusted measure is called real GDP.

Now that the economic output in 2024 and 2025 is valued at the same price level, any difference in the GDP of 2025 and the GDP in 2024 reflects a change in real economic output.

In practice, separating price changes from real activity for the entire economy is not straightforward. This adjustment is carried out using a tool called the GDP deflator. Let’s see how it is applied by extending our bread example.

GDP Deflator: How is it Used

In 2024 (the reference/base year), I bought one loaf of bread for $6.

Nominal GDP = $6

In 2025, I bought two loaves for $7 each.

Nominal GDP = $14 (2 loaves × $7)

Nominal GDP grew by 133 percent (from $6 to $14). Here’s how the deflator removes the effect of higher bread prices to calculate real GDP.

Price ratio:
The price of bread in 2025 is about 1.17 times the price in 2024 (7 ÷ 6 = 1.17)

Deflating nominal GDP:
To scale 2025 output (14) in 2024 prices, we will lower it by 1.17 times by dividing nominal GDP by the price ratio:
14 ÷ 1.17 ≈ $12

Real GDP in 2025 is $12, a 100% increase from $6, but lower than the nominal GDP growth rate of 133%.

From Bread to the Real Economy 

In our bread example, computing real GDP was easy: we knew exactly how much bread was consumed and its price in both years.

In the real world, though, we can’t possibly track prices for millions of goods and services every year. So, the statistical agency collects detailed production and price data for just one reference year.

In the years that follow, statisticians don’t remeasure every price. Instead, they use price indexes as shortcuts to estimate price changes since the reference year.

The main indexes are:

• CPI (Consumer Price Index): Measures prices that households pay for goods and services.

• PPI or WPI (Producer/Wholesale Price Index): Measures prices at earlier stages, such as factories or wholesale markets.

The GDP deflator is like a patchwork quilt: statisticians apply different indexes to various sectors, and then combine the parts. Accordingly, the calculated GDP growth depends on which price proxies are used.

The Base Year Problem

The year in which statisticians collect detailed price and consumption data is often also chosen as the base year for calculating real GDP. In our bread example, we treated 2024 as the base year and used 2024 prices to calculate real GDP in 2025.

Ideally, we could update the base each year; for instance, we would use 2025 prices to compute 2026 GDP. But collecting detailed price data for millions of goods annually would be prohibitively costly and time-consuming.

Instead, statisticians freeze prices at the chosen base year and use them to calculate real GDP for several years. In our example, 2024 prices would be used to measure GDP in 2025, 2026, and beyond, until the base year is officially updated. This approach creates a series of GDP data (the “2024 series”) where all real GDP is measured in 2024 prices.

Most countries update their GDP base year only every five to ten years. But consumption patterns evolve significantly between updates. We buy different goods, use new technologies, and pay very different prices.

For example, a laptop we buy today costs far less than it did ten years ago. If we calculate real GDP using old prices, that laptop is counted as much more expensive than it actually is today, giving a slightly misleading picture of how large the economy really is.

When the base year is eventually updated, GDP numbers are revised, sometimes significantly, because the new data more accurately reflect current prices, such as in our laptop example, and consumption patterns. These revisions are not reflective of manipulation; they are a regular part of updating estimates as better data replace older assumptions.

Because GDP is inexact, built on assumptions, surveys, and proxies, debates about it arise when changes are made.

Setting the Stage for Part 2

With this foundation, we’re ready to tackle the fuss about India’s GDP in Parts 2 and 3. Here’s a quick cheat sheet for the key concepts:

Cheat Sheet

Old Base Year:
→ GDP levels may be off
→ Revisions are inevitable; Past GDP gets revised—up or down; Some years look better, some worse

Deflator Limitations:
→ Real GDP growth can be overstated or understated

WPI vs PPI:
→ Manufacturing growth may appear higher or lower depending on which index is used

Informal Sector:
→ Heavy reliance on estimates
→ Large uncertainty
→ Revisions likely

Infrequent Surveys:
→ Dependence on proxies
→ Later corrections expected

Methodology Change:
→ GDP levels may jump
→ Trends usually hold
→ Short-term debate, not fraud