A blog post on GDP that explores the use of ChatGPT to write a definitions article and my attempts to make it more interesting. Was it worth it?

Background

Large Language Models (LLMs) are very useful in learning. Last week I wrote a post on “Learning about the GDP Indicator”, in which I used a Q&A approach to self-direct and personalise my learning on the Gross Domestic Product indicator.

In today’s blog, I thought I would try a different angle.

At ClarusFT we wrote a lot of blogs to explain concepts and I recall that some of the most popular evergreen ones had titles starting with “Mechanics and Definitions of …”.

So instead of a Q&A, why not ask ChatGPT to directly write an article for me to read and learn from.

My Prompt

So I decided to try the following in ChatGPT 4o:

I would like a blog post titled “Mechanics and Definitions of Gross Domestic Product”. This should be of up to 800 words in length and include a display of visual charts with data for some major economies for a recent 5-year period with the source referenced. The blog post should used a similar style and layout, as much as possible of the following ClarusFT blogs, https://www.clarusft.com/mechanics-and-definitions-of-spreadovers-swap-spreads/ and https://www.clarusft.com/mechanics-of-cross-currency-swaps/

And below is what I got.

Mechanics and Definitions of Gross Domestic Product

Gross Domestic Product (GDP) serves as a cornerstone metric in modern economics, representing the total monetary value of all goods and services produced within a country’s borders over a specified period. Understanding the mechanics and nuances of GDP is essential for interpreting the health and trajectory of economies. This blog explores GDP’s definitions, its underlying mechanics, and recent trends across major global economies.

Definitions of GDP

GDP can be measured in three distinct ways, each providing a unique perspective:

1. Production Approach (or Output Method): This approach calculates GDP as the total value of output produced in an economy minus the value of intermediate goods used in production. It highlights contributions from different sectors, such as agriculture, manufacturing, and services.
2. Income Approach: GDP is derived by summing all incomes earned within the economy, including wages, rents, interest, and profits. This approach emphasises how economic output translates into earnings for households and businesses.
3. Expenditure Approach: Perhaps the most commonly referenced method, this calculates GDP by summing up all expenditures within an economy:GDP=C+I+G+(X−M)GDP = C + I + G + (X – M)Where:
   • C = Consumption
   • I = Investment
   • G = Government Spending
   • X = Exports
   • M = Imports

Each method should theoretically produce the same GDP figure, though discrepancies may arise due to data collection limitations.

Nominal vs Real GDP

It is also important to distinguish between nominal GDP and real GDP:

• Nominal GDP: Measured using current prices without accounting for inflation. It reflects both changes in output and price levels.
• Real GDP: Adjusted for inflation, providing a clearer view of actual economic growth by isolating changes in output.

The Mechanics of GDP Growth

GDP growth is influenced by several factors, including:

• Labour Force Growth: An expanding workforce contributes to higher production capacity.
• Productivity Improvements: Innovations, technological advancements, and efficient resource use drive growth.
• Capital Investments: Investments in infrastructure, machinery, and technology bolster production.
• Government Policies: Fiscal and monetary policies play a pivotal role in stimulating or restraining growth.

Comparative GDP Performance: Major Economies (2018-2022)

To illustrate recent GDP trends, we examine data from the world’s largest economies: the United States, China, Germany, and India. The chart below shows real GDP growth rates over the past five years.

Chart: Real GDP Growth Rates (2018-2022)

The line chart above visualises annual real GDP growth for the United States, China, Germany, and India. The x-axis represents years (2018 to 2022), and the y-axis represents GDP growth percentages. Each country’s trend line is clearly marked, revealing important insights into their economic performance.

Observations:

• United States: The US experienced consistent growth pre-pandemic, followed by a sharp contraction in 2020 and a robust recovery in 2021, fuelled by fiscal stimulus and strong consumer demand.
• China: Growth rates slowed slightly over the period, reflecting structural adjustments and challenges from the COVID-19 pandemic, though it maintained relatively high growth.
• Germany: As Europe’s largest economy, Germany saw modest growth, with a significant dip in 2020 due to reduced exports and supply chain disruptions.
• India: India’s growth trajectory was marked by rapid expansion pre-pandemic, a sharp contraction in 2020, and a swift rebound driven by strong domestic demand.

Data Sources

Data for this analysis is sourced from the World Bank and International Monetary Fund (IMF). These institutions provide reliable and standardised metrics, ensuring comparability across countries.

Limitations of GDP

While GDP is a critical economic indicator, it has notable limitations:

1. Non-Market Activities: GDP excludes informal economic activities and unpaid work, which are significant in many developing countries.
2. Environmental Impact: It does not account for environmental degradation or resource depletion.
3. Income Inequality: GDP growth figures provide no insight into income distribution or disparities within an economy.
4. Quality of Life: GDP measures economic output but does not capture well-being or happiness.

Conclusion

Gross Domestic Product remains an indispensable metric for gauging economic performance. Understanding its definitions, mechanics, and trends enables policymakers, businesses, and individuals to make informed decisions. However, to gain a holistic understanding of an economy, GDP should be complemented with other indicators, such as income distribution, environmental metrics, and quality-of-life measures.

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This blog aimed to distil the mechanics and definitions of GDP while showcasing recent trends in major economies. For further insights, we encourage readers to explore the underlying data sources and related economic indicators.

What Do I Think?

Well, it is decent, but a tad boring.

A few observations:

• A bit verbose in the parts, with some repetition in the opening and final two paragraphs.
• I would have expected GDP per capita to be introduced and defined, as this is GDP adjusted by population size and useful in comparing countries.
• The chart shows real GDP Growth Rates, which while interesting would not have been my first choice, I would have preferred GDP per capita.
• The chart has annual data points, but the x-axis contains grid lines for half-year too – am I splitting hairs
• Observations are useful, but repetitive for each country, the same observation for all for Covid would have been more concise, followed by any specific divergence
• I can’t say the language style reminds me of the referenced Clarus Blogs, though the structure is consistent

In addition, I cannot say that I learnt anything new from the article, given my prior knowledge of GDP, but that won’t be true for everyone.

It is a decent 700 word introductory article on GDP and hews close to the title of Mechanics and Definitions.

What do you think?

Interesting?

Thinking about what would make it more interesting, I thought why not ask the LLM.

As you have 100 words to spare, can you add a paragraph to make the article more interesting to different from generic articles on GDP, perhaps a fun fact or controversial opinion or cool graph?

And it came up with the following:

A Surprising Fact About GDP

Did you know that GDP as a concept was first formalised in the 1930s during the Great Depression? Economist Simon Kuznets, who pioneered the framework, cautioned against using GDP as a sole measure of a nation’s welfare. Despite its widespread adoption, Kuznets’ warning remains relevant today. For instance, Bhutan measures progress using “Gross National Happiness,” emphasising well-being over economic output. This highlights a growing global interest in alternative metrics to complement GDP.

I like that, definitely a nice addition to the blog.

Thoughts

ChatGPT can write well, given good prompts.

But, by now, we all know that.

The real question is what content value is it creating.

Is it making it easy for anyone to write “generic articles”.

And filling the Internet with synthetic data, that adds no value.

Just more similar stuff to read.

I hope the training of AI models, can skips using such datasets.

I suppose LLMs can juxtapose different topics that might be beyond a human’s knowledge.

So I tried the following:

Can you relate GDP which is an economic measure to a concept in biology?

And it added the following to the article.

Relating GDP to Biology

GDP can be likened to a biological concept such as an organism’s metabolism. Just as metabolism reflects the sum of all chemical reactions that sustain life, GDP reflects the sum of all economic activities that sustain a nation’s economy. A high metabolic rate, while indicative of activity, can also signify stress or inefficiency—paralleling how rapid GDP growth may sometimes come at the cost of environmental or social well-being. Similarly, a well-balanced metabolic system, like a sustainable economy, ensures long-term health and resilience.

Interesting, I like that.

Is this an example of creating value or novelty?

I need to keep prompting.

When should I stop, the never ending prompting.

Only time will tell.