In terms of broad peer groups, this is in the same range as lower SE Asia, Middle East, Central Asia and South America.
10 year income growth vs SE Asian peers in the $10K-15K income group (Philipppines, Vietnam, Indonesia). India exceeds all three between 2014-2024.
2/
But how does it compare against the gold standard - China ? Let's pull up the original chart, but stick it next to China's chart:
Side by side, the sustained high growth China demonstrated is easily visible - several years of double digit per-capita growth, now moderating.
3/
Let's look a little deeper because there are some curious artifacts. First, as seen earlier, India is adding over $1K in per capita GDP (PPP) annually now. How does that compare to China ?
India today is adding purchasing power at the same rate as China in the mid 2010s.
4/
There's a curious aspect to the graphs in post 3 that's more clearly seen in this graph: How does average per capita GDP growth look for India and China at various income ranges ? Take a look:
The 7.5-10K and 10-15K ranges are remarkable - India is outdoing Chinese growth.
5/
In effect, China had sustained double digit growth in almost every range upto $7.5K, then dropped and consolidated over time.
India struggled to grow fast at lower income levels, but it outstripping China by several percentage points past $7.5K per capita GDP PPP.
6/
Aquick explanation is that Indian population growth fell dramatically so that explains it all. Well so lets look at population growth since 2017 in particular, when India hit $7.5K per capita GDP PPP.
In short, this is not the case.
7/
Indian population growth is at Chinese rate in the early to mid 2010s, and dropping.
But China dropped even faster from the mid 2010s. So at the point where India per-capita growth exceeded Chinas (~$7.5K per capita), India was growing its population faster too.
8/
The following chart is a good measure of the years at whicih various multiples of $1000 in GDP PPP per capita were achieved.
$1K - 2K : 13 yrs
$2K - 3K: 7 yrs
$3K - 4K - 5K: 4 yrs each
$5K - 6K - 7K - 8K: 2 yrs each
$9K - 10K - 11K - 12K : one year each.
9/
In effect India did quite a bit poorly vs China at income growth at lower levels but is outstripping it at average income growth beyond the >$7.5K per capita income level.
This is happening despite India registering a slower drop in population growth than China.
10/
There are various ways to look at this.
1. Democratic systems take longer to build consensus around faster inequitable growth vs slower equity growth at lower levels.
But rising incomes enables faster consensus and risk appetite for ifaster nequitable growth .
11/
2. India had too much of a social capital deficit relative to China to grow fast at lower income levels. Poor education, health, sanitation, access to food & shelter, supply of public goods, infrastructure.
All of these had to be built - many within the past 10 years.
12/
3. Governments are themselves getting more efficient at delivering on growth and increasing purchasing power effectively.
Let's look at average GDP PPP per capita growth over every full term government since liberalization (1991-92):
13/
The 2014-19 and 2019-24 govts returned the fastest growth in GDP PPP per capita ever - the latter close to double digits.
These figures include the -2.5% in 2020 due to COVID, (and 3.5% in 2008 due to the GFC). Post COVID, 2020s have seen >10% per capita PPP GDP growth.
15/
The dichotomy of the Incian and Chinese GDP PPP per capita growth up to and post the $7.5K level is the most interesting aspect of this analysis.
India hit $5K only in 2015. $7K in 2019. In 2025 it will be at $12K, having already crossed $11K for 2024 - 2.5x in a decade.
16/
There's no parallel to China's per-capita GDP growth at low income levels. They were registering ~2x Indian growth then.
But curiously, India has been outdoing China in per capita GDP PPP since ~$7.5K.
As described earlier, there are various ways to interpret this.
17/
Probably the most tenable explanation is China had vastly better social capital at low income levels to harness network effect of growth.
India took until the 2020s to match, and is now seeing rapid accretion of purchasing power at a rate outstripping China at this level.
18/
A lot of narratives point to India having per capita GDP on par with China 40 years ago. But that's a mirage.
India had very little capacity to harness capital and productivity then. It took way longer than China to get through the low income phase.
Seen here:
19/
India took 13 years from $1K-2K, China took 5. 7 years from $2K-3K, China again took 5. China especially accelerated in the $3-5K phase.
At $5K-7.5K, the countries were roughly at par and then India accelerates to 10K and now at a year for each $1K increment.
20/
In effect the disparity between the countries is the story of China's growth at lower levels. However, what's been happening for the past half a decade is that India is outstripping Chinese growth rates at its current levels.
21/
This is an unappreciated aspect of Indian growth that deserves greater understanding .
At the very least, wrong lessons must not be derived.
Further, what is today the fastest per capita GDP growth for a major economy at this income level ever seen, must be sustained.
22/22
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The IPL is also far more lucrative in revenue per game, despite the fact that with a mere 74 games, it’s in the bottom 5 of sports leagues by number of games played per season.
Each of those games is a money spinner. Let’s look at how much.
2/
In terms of total revenue, the IPL ranks a reasonable 13th position among global leagues, despite having so few games in a single season.
There are only three countries on the planet with $1 trillion+ exports - China, USA and Germany. Even Japan is around $850-900B in FY 2021.
India jumped ~10 ranking places to provisionally #8 this year. Positions 4-7 are Japan, UK, France and Netherlands - all $700-900B.
India began 1949 the 9th biggest exporter.
It left the top 10 by end of that year.
It left the top 20 in 1957.
It reentered the top 20 in 2010, but didn't rise further until 2017-18 when it was 18th ranked. 2021-22 saw a big jump to 8th position.
Today was a day of symmetry as the vaccination total hit 175 crore (1.75 billion), exactly on day 400 since start of vaccination. This corresponds to an average of almost 4.4 million vaccinations a day, over four hundred days.
The monthly total to date is now almost 88 million. February should easily cross 100 million and will likely finish at around 125 million. This is expected as it is predominantly second dose + boosters. March will be much higher as the 12+ group becomes eligible.
2/
With even the second dose numbers not very high now, the daily average over the past week was just 4 million - multiple days towards the end of week being around 3.98 million, and thus still in the 3.x bucket.
So how do you fix sampling error ? You get a small number of people to agree - keeping pop standard deviation down because sample size is tiny.
Disagreement is a problem - if you get half of them to disagree, your sampling error is 20-45% depending on sample size. Oops.
2/
So let us pretend these experts indeed know what they’re doing. Let us look at the data. This author has the entire VDem dataset, analyzed in detail together with @jai_menon :
Finally it has happened - In the trailing 12 month period (Feb 2021 - Jan 2022), the aggregate UPI transaction volume touched $1.013 trillion, up from $960 billion for the 12 months to Dec 2021.