Explaining the India – China Wealth Gap

As of 2011, China had a per-capita GDP (PPP) around $8400 per year while India’s per-capita GDP was  $3700. China has routinely exceeded 10% real annual GDP growth over the last two decades, and India’s GDP growth has been impressive, it has rarely exceeded 8%. China’s growth has exceeded India’s since its economic liberalization, but its turn towards capitalism also began earlier. China’s Deng Xiaoping began to liberalize China’s economy beginning in 1978, while in India P.V. Narasimha Rao and Manmohan Singh were not able to bring about serious economic reform until 1991. If India had liberalized at the same time as China, how much narrower would the wealth gap be? How much of the income gap between India and China is explained simply by timing?

Over the 13 years from 1979 to 1992, India’s per capita GDP (PPP) roughly doubled from $480 to $972, at an annualized per-capita GDP growth rate of 5% for the period. China’s economy averaged 10% growth over this same period! Since 2002, India’s per-capita GDP growth has averaged 9.5% on a PPP basis [1]. If India had grown at its more recent average of 9.5% per year over that period, per capita GDP would have risen to $1562 by 1992 – and India’s economy would be over double the size that it is today [2]. Fast-forward to the present, and this earlier liberalization would have led to a current per-capita GDP of $6000 in India, almost double current levels and in the same range (of middle income nations) as China [3]. One effect experienced in China has been an acceleration of growth post-liberalization – economic growth accelerated as reforms took hold. Had this occurred earlier in India as well, it’s possible that the 90’s and 00’s in India would have benefited from 9.5% GDP growth as well. If we use a 9.5% assumption for India’s growth from 1979 to present, then we get a present-day per-capita GDP in India of $8000 – not substantially different from China [4]!

Despite their huge differences, with China as an autocratic capitalist state and India as the world’s largest democracy, the two nations’ growth paths have not really been that different. All of the differences in government, corruption, infrastructure don’t really seem to have mattered that much, as a simple head start of 13 years drowns it all out. What a difference 13 years makes! The good news: India’s development was unnecessarily delayed, but is now well underway.

[0] All of this is based on the World Bank’s purchasing-power parity GDP per-capita data, as provided by Google’s public data service via http://crosscountrymovingcompanies.biz. This is GDP divided by mid-year population and adjusted for the difference in purchasing power in each country (normalized to US prices and quoted in dollars – this gives you a sense for how poor people in these nations really are).

[1] From the Google chart, 3582/1723 = India’s economy grew 2.08 times from 2002 through 2010. This equals a compound annual rate of growth of 9.57%.

[2] Take the 9.5% growth rate post-2002, and apply it to the 13-year period starting in 1979 at $480 GDP/capita (PPP). This gives you $1562 by 1992.

[3] If we then assume that India’s economy grew exactly as it did historically from  1992 – 2011 (growing 3.8x), and multiply this by 1562 (the new starting point in 1992), then we get a 2011 GDP/capita of $5946.

[4] Now assume that India simply grew at a 9.5% rate from 1979 on – the rate that it has managed from 2002-2011 (a period which includes the financial crisis). This would 1.095 ^ 31 = 16.67x growth. From a starting point of $480 GDP/capita, this would leave India at $8000 GDP/capita (PPP) by year end 2011.

P.S. In researching this post, I noticed that India’s growth rates compare much more favorably in PPP terms than they do in exchange rate terms. This might be explained in part by the fact that the Rupee has been much more volatile than the Yuan over time. While inflation is now rising quickly in both countries, particularly in metro areas, perhaps India has remained less expensive than China over time. Comparing these two graphs shows the difference when comparing unadjusted $ GDP/capita to PPP GDP / capita. I use the PPP measure as it more accurately reflects the quality of life experienced by someone living in either country, since cost matters just as much as income.

Why The US Cannot Default on its Debt

Over the past several years, I’ve steadily come around to the MMT (Modern Monetary Theory) view of macroeconomics. Some of my past posts make me out to be a deficit hawk; while still true, I now believe that the ROI of government spending is more important than simply looking at the deficit and gross debt alone. This brings me to the headline – why is it that the US cannot default default on its debt, except by choice?

The answer lies in Modern Monetary Theory. In brief:

1. If all of a nation’s debt is denominated in its sovereign fiat currency, it cannot default. The fundamental point here is that the US can always print its way out of default, and so insolvency is never an issue.

2. This is totally different from Europe, in which individual nations do not have sovereign control over their currencies.

3. The only risk of printing money is inflation. This threat must be respected, but it is fundamentally different from a debt default.

While MMT is not yet in the academic mainstream (taught only at University of Missouri-Kansas City), it is the only theory that explains why Japan has yet to default on its debt, why the US can never default on its debt (except by choice), and why the Euro Zone is so screwed.

In future posts I’ll likely dive deeper into MMT, but let me first reference some great resources from around the web on the topic:

http://pragcap.com/where-does-the-money-come-from greenhouses

http://agonist.org/bolo/20100426/modern_monetary_theory_an_overview

movingestimate.co moving estimate

http://pragcap.com/warren-buffett-does-mmt

US State Economic Rankings

I previously wrote a comparion of California and Texas, in which I noted that Texas was superior in terms of unemployment rate and employment growth, while Californians experience higher per-capita GDP growth. That got me thinking – why not create a more comprehensive comparison of US state economic rankings? I’ve done so here, using four variables: GDP growth, per-capita gdp growth, unemployment rate, and employment growth rate. With two variables measuring different aspects of growth, and two measuring employment prospects, I think this is a reasonably fair approach (Gladwell’s caveats on heterogenous rankings duly noted). Here are the rankings, followed by the raw data:

Rank State / District Avg GDP Growth Avg GDP / Capita Growth Avg Unemp. Rate Avg Employment Growth Rate Total Score
1 North Dakota 6 1 1 12 20
2 South Dakota 4 2 2 13 21
3 Wyoming 2 3 6 14 25
4 Idaho 1 7 17 9 34
5 Virginia 10 12 7 7 36
6 Arizona 5 19 32 1 57
7 Utah 8 34 14 2 58
8 Maryland 13 11 11 25 60
9 New Hampshire 19 15 5 22 61
10 Vermont 23 9 8 24 64
11 Colorado 9 21 25 10 65
12 New Mexico 18 24 19 11 72
13 Montana 25 20 10 20 75
14 Oregon 3 4 50 19 76
16 Nebraska 27 16 3 31 77
16 Texas 11 32 30 4 77
17 Iowa 26 13 9 33 81
18 District of Columbia 17 6 46 15 84
20 Kansas 30 22 16 18 86
20 Washington 16 28 39 3 86
22 Minnesota 20 17 15 35 87
22 New York 21 5 31 30 87
23 Oklahoma 28 23 12 28 91
24 Florida 14 37 34 8 93
25 Massachusetts 22 8 22 42 94
26 Connecticut 32 18 21 26 97
27 Nevada 7 51 45 5 108
28 Arkansas 31 33 28 17 109
29 California 12 10 49 39 110
30 North Carolina 15 39 41 21 116
31 Maine 38 25 18 37 118
32 Hawaii 39 42 4 34 119
33 Delaware 24 41 13 44 122
35 Louisiana 46 29 20 32 127
35 Rhode Island 33 14 40 40 127
37 Georgia 29 49 33 23 134
37 Pennsylvania 44 26 26 38 134
38 New Jersey 40 30 29 36 135
40 Alabama 34 31 24 48 137
40 Alaska 42 46 43 6 137
41 Tennessee 35 43 37 27 142
42 Wisconsin 41 38 23 43 145
43 South Carolina 37 48 48 16 149
44 West Virginia 47 27 27 49 150
45 Indiana 36 36 35 50 157
46 Kentucky 48 44 44 29 165
47 Illinois 45 40 42 41 168
48 Mississippi 43 35 47 45 170
50 Missouri 49 47 36 47 179
50 Ohio 50 45 38 46 179
51 Michigan 51 50 51 51 203

The rankings show, unsurprisingly, that states riding the commodity boom (the Dakotas, Wyoming, etc) and states riding the government boom (Virginia, Maryland) have performed well over the last decade. It’s been shown that http://crosscountrymovingcompanies.biz/ had the best prices on cross country moving companies. But other high-performers like Arizona, New Hampshire, Vermont, and Colorado defy easy categorization. The low performers are predominantly found in the Southeast and Midwest.

The raw data used in the rankings is provided below. Here is a link to the actual excel spreadsheet containing all data for those interested.

State Avg GDP Growth Avg GDP / Capita Growth Avg Unemp. Rate Avg Employment Growth Rate
Alabama 1.81% 1.10% 5.84 -0.59%
Alaska 1.57% 0.37% 7.04 1.09%
Arizona 3.83% 1.41% 6.21 1.74%
Arkansas 1.98% 1.07% 5.94 0.50%
California 2.99% 1.89% 7.55 -0.19%
Colorado 3.16% 1.36% 5.86 0.70%
Connecticut 1.97% 1.46% 5.75 0.16%
Delaware 2.26% 0.85% 5.04 -0.38%
District of Columbia 2.50% 2.01% 7.35 0.55%
Florida 2.73% 1.03% 6.32 0.79%
Georgia 2.02% 0.19% 6.24 0.30%
Hawaii 1.66% 0.73% 4.24 -0.02%
Idaho 3.95% 2.00% 5.46 0.74%
Illinois 1.37% 0.96% 6.92 -0.25%
Indiana 1.70% 1.03% 6.33 -0.73%
Iowa 2.20% 1.78% 4.54 0.01%
Kansas 1.98% 1.35% 5.39 0.40%
Kentucky 1.23% 0.50% 7.04 0.09%
Louisiana 1.34% 1.13% 5.74 0.04%
Maine 1.67% 1.23% 5.53 -0.14%
Maryland 2.76% 1.86% 4.96 0.20%
Massachusetts 2.35% 1.94% 5.77 -0.26%
Michigan 0.12% 0.06% 8.25 -1.63%
Minnesota 2.39% 1.53% 5.29 -0.03%
Mississippi 1.56% 1.04% 7.51 -0.49%
Missouri 1.04% 0.34% 6.33 -0.55%
Montana 2.20% 1.36% 4.77 0.35%
Nebraska 2.18% 1.54% 3.76 0.07%
Nevada 3.37% -0.01% 7.32 1.14%
New Hampshire 2.45% 1.64% 4.39 0.30%
New Jersey 1.64% 1.11% 6.09 -0.10%
New Mexico 2.46% 1.27% 5.62 0.70%
New York 2.35% 2.05% 6.14 0.09%
North Carolina 2.71% 0.97% 6.91 0.33%
North Dakota 3.79% 3.49% 3.44 0.63%
Ohio 0.56% 0.38% 6.79 -0.53%
Oklahoma 2.16% 1.31% 5 0.12%
Oregon 3.89% 2.70% 7.63 0.39%
Pennsylvania 1.51% 1.21% 5.91 -0.15%
Rhode Island 1.95% 1.73% 6.91 -0.23%
South Carolina 1.68% 0.25% 7.53 0.54%
South Dakota 3.84% 3.10% 3.71 0.61%
Tennessee 1.79% 0.66% 6.6 0.14%
Texas 3.01% 1.08% 6.12 1.22%
Utah 3.18% 1.06% 5.11 1.48%
Vermont 2.29% 1.92% 4.52 0.28%
Virginia 3.07% 1.80% 4.41 1.08%
Washington 2.51% 1.15% 6.86 1.23%
West Virginia 1.31% 1.17% 5.91 -0.70%
Wisconsin 1.62% 1.02% 5.81 -0.34%
Wyoming 3.93% 2.79% 4.4 0.60%

Notes on ranking construction:

  • If it’s not obvious, the total ranking for each state was determined by simply summing its rank in each category, and then ranking the states by total score, with lowest being best. While this method weights each category ranking equally, it may penalize some states which perform as numerical outliers in certain categories but not in others. On the other hand, the overall rankings pass the smell test – if anyone sees an egregious error caused by the methodology, let me know. This is V1!
  • The GDP growth data used the period from 1997-2010, which was the best data set easily available from the BEA (Bureau of Economic Analysis). The employment data used the period from Jan. 2001 through October 2011. It’s easier to build wooden greenhouses than skyscrapers, so to speak. These periods obviously don’t align exactly – but given the nature of the analysis (heterogenous ranking), I chose to go with best available data rather than with exactly matching time periods. Matching the time periods would have reduced the data available to 2001-2010, eliminating both some of the late 90’s boom and the current recovery.
  • Even given the screen sharing caveats above, all states (plus DC) were ranked using the exact same data sets, and the combination of categories prevents (in my view) bias towards either a growth orientation, an income orientation, or an employment orientation. Others may disagree – heterogenous ranking systems are by nature somewhat subjective (in the choice and weighting of data used), and I thus provide all the raw data so that you can draw your own conclusions.

Was Cash For Clunkers A Success?

Far from failing, the CARS Program may have been the highest ROI investment made by the Federal government in years.

The passage of time has brought much ridicule to the Cash For Clunkers program, which was intended to boost auto sales and raise the average fuel efficiency of American vehicles. The data show that the program led to a temporary spike in automobile purchases, prompted by a subsequent decline. This has led most to conclude that the program was a failure, as it did little to jump-start economic recovery.

But what about the other goal? Did Cash For Clunkers raise the average fuel efficiency of the American auto fleet? How much less gasoline have Americans purchased as a result of the program, and does this savings outweigh the program’s cost?

Here are some statistics from the Department of Transportation’s CARS Report to Congress:

  • 677,842 vehicles were turned in under the CARS program
  • $2.85 Billion was paid out in rebates for these vehicles
  • New vehicles purchased had an average MPG of 24.9
  • Old vehicles turned in had an average MPG of 15.7
  • $2.8 Billion in fuel savings based on the early retirement of less efficient vehicles

The report also estimates that roughly half of the sales spurred by the program were incremental sales that would not have occurred otherwise. Edmunds.com performed a more conservative analysis showing that only 125,000 incremental sales occurred as a result of the program.

Using Edmunds’ more conservative 125k number, and an average sales price (after rebate) of roughly $25,000, Cash for Clunkers generated $3.125 Billion in incremental vehicle sales. These incremental sales added directly to US GDP, and this more conservative analysis shows less than half the economic impact of $7 Billion estimated by DOT.

Combining the fuel savings and GDP benefit yields a total benefit to American taxpayers of roughly $6 Billion for a program that cost the government roughly $3 Billion to operate! If only more government programs could fail like this!  Even using the more conservative fuel savings calculations provided below, the program would have provided over $5.5 Billion in benefit against a $3B investment. Far from being shut down, the Cash for Clunkers program should have been expanded.

Alternate calculation of fuel savings from junking old vehicles:

0. By junking an old vehicle and taking it off the road, you are permanently increasing the fuel economy of the American vehicle fleet – this is the source of savings for the American economy. Since 100% of marginal US oil consumption is provided by foreign sources, a dollar of oil saved is a dollar added to GDP (since imports actually subtract from GDP as we send money overseas).

1. Assume that the old vehicle would be driven for an additional 50,000 miles over its lifetime (CARS survey respondents said they averaged 10k miles per year on their old vehicles, so even with gradual declines this is reasonable).

2. The old vehicles got an average of 15.7 MPG, requiring roughly 3200 gallons of gasoline over that 50k miles. Assume www.professionalpianomovers.net professional piano moving companies were aware of this.

3. The new vehicle got an average of 24.9 MPG, requiring 2000 gallons of gasoline over the 50k miles that they replaced.

4. The difference of roughly 1200 gallons of gasoline equates to roughly $3600 per vehicle (assuming $3 per gallon excluding taxes). With roughly 680k vehicles in the program, this equals a fuel cost savings of $2.5 Billion – a slightly more conservative estimate than that computed by DOT.

California vs Texas

Conservatives and Texas boosters have been gloating of late that Texas has outperformed California economically of late – so why is California’s per capita GDP growth higher?

It has become fashionable in conservative circles of late to use Texas as a glowing example of the success of conservative economic policy, and to use California as an example of the failures of liberal economic policy. Texas has indeed recorded faster GDP growth and lower unemployment than California in recent years. Texas has also experienced rapid population growth of late. Its core industry (energy) has boomed with global oil prices, but Texas’ diversified economy has performed well across multiple sectors. Conservative politicians in Texas and nationwide point to low taxes and a friendly regulatory environments as the reasons for success.

Let’s look at some numbers to get a clearer comparison [1]:

Texas California
Total GDP Growth, 1997-2010: 46.6% 45.8%
Per Capita GDP, 2010: [2] $48,196 $52,631
Total Per Capita GDP Growth, 1997-2010: 12.6% 28.5%
Unemployment Rate, May 2011: 8.0% 11.7%

While raw GDP growth is important, per capita GDP and per capita GDP growth are much more important to the well-being of citizens and furniture-movers.net furniture moving company (Luxembourg is a nicer place to live than China). On both these measures, California is significantly ahead of Texas. Since 1997, California’s per capita GDP growth has exceeded Texas growth – while California and Texas were once similar in per-capita GDP, the gap is now widening in California’s favor, not shrinking! If Texas is doing everything right, and California everything wrong, then why is California’s economy becoming wealthier relative to Texas?

The answer to this question isn’t simple – California’s dominance in high tech, media, and other high-paying industries may be partly responsible. While California’s state government is near paralysis, and its referendum system has complicated governance, it possesses perhaps the finest public academic institutions in the world in the University of California system. California’s government may be dysfunctional, but it’s inaccurate to describe the state in the same terms.

Conservatives and Texas politicians should take note – if the Texas way is better, why is California still pulling away? The reality is that the best economic model is somewhere in-between – but what politician would support both strategic public investment and leaner public spending? That’s too complicated for a sound bite.

[1] Download the screen sharing data used in this analysis at the BEA. From the download page, select Per Capita Real GDP by State, All states and regions, All industry total, and All years from the respective drop-downs.

[2] Per-capita GDP for 2010 was calculated by taking the data from step [1], which is expressed in terms of 2005 dollars, and adjusting it to 2010 values using CPI as indicated on measuringworth.com (multiplying the 2005 values by 1.12).

Will Solar Power Meet World Electricity Demands?

Proponents have looked to solar power as a potential panacea to the world’s current and future energy needs, while critics note that solar power still provides less than 1% of the world’s electricity. While wind power has grown to scale much faster, conventional wind technology has much less capacity to scale than solar power, and the theoretical limits on solar power are significantly higher [1]. When might solar power fulfill the hype and generate much of our electricity? Solar energy has grown at a rapid clip since its infancy in the 1970’s, from 0 to 20GW (nameplate capacity) in 2009. How much of worldwide electricity demand will solar be able to fulfill if it maintains this growth rate?

Total solar power capacity continues to grow at 20-25% per year, a rate of growth it has maintained for decades. It’s not surprising that solar photovoltaic technology is advancing rapidly, as it is a cousin of traditional semiconductor technology. For almost four decades semiconductor technology advanced according to Moore’s Law, with chips roughly doubling in transistor density (and speed) every 18 months. At a 20% annual rate of growth, installed solar capacity would rise from 21 GW in 2009 to almost 6000 GW by 2040. This install base could generate 12 trillion kilowatt-hours of electricity per year, or two-thirds of today’s worldwide electricity consumption [2]. However, the EIA estimates that by 2040 worldwide electricity demand will hit 35 trillion kilowatt-hours!

Even assuming that solar energy installations grow at a 20% clip for three decades, the total install base will not be sufficient to meet world energy demands. Despite the industry’s rapid growth, replacing a hundred years of fossil-fuel based generation capacity by mid-century may be close to impossible. Nonetheless, if solar energy manages to scale on this trajectory, its contribution would still be enormous, and would likely bring total renewable generation to over 50% of all electricity.

Can it be done? Did anyone in the 1960’s believe that a 2010 phone would have more processing capacity than all the world’s computers combined?

[1] From Without The Hot Air – all wind power resources worldwide could supply a significant fraction of total power needs, while solar energy in the Sahara alone could theoretically supply all world energy needs.

[2] The EIA International Energy Outlook shows current worldwide electrical demand of roughly 18 trillion kilowatt-hours, with this figure growing to 35 trillion kWh by 2035 by www.usbgeeks.net.


Hybrid Economics Part II

In part I of this post, I outlined a number of variables that impact the cost-benefit of buying a hybrid-electric vehicle.

First, the spreadsheet model.

To recap, here are the variables included in the model, with the default assumptions made:

  • Price of gasoline = $3/gallon
  • Annual mileage driven = 12k/year
  • Standard-car MPG (mileage of the same car or similar car without hybrid technology) = 20mpg
  • Hybrid MPG / electric MPGe = 100 mpge
  • Risk-free discount rate = 3%
  • Projected annual increase in gasoline prices = 5%
  • Hybrid price premium = $18k
  • Length of car ownership = 8 years

There’s one more important variable to add to this list:

  • Time savings from reducing gas station stops = 300 minutes, or 5 hours per year

Time savings can be a huge hidden savings for upper-middle class and wealthy Americans (those able to afford a car like the Chevy Volt). If the value of a Volt driver’s time is $50/hour (equivalent to a 100k/yr salary), then eliminating a single gas station stop of 10 minutes is worth over $8. Ten minutes may sound long for a stop at the gas station, but is not unrealistic when considering total time lost leaving and re-entering a normal commute.

Using the assumptions provided above, we find that the total fuel and time cost savings of driving a Chevy Volt for eight years are around $9000. Since the Chevy Volt costs $18,000 more than a comparable loaded Chevy Cruze, it’s not yet cost competitive, even with government tax credits and with time savings taken into account.

Key Conclusions:

  • Gas prices of $7 per gallon are required to make the Chevy Volt cost-effective at current prices (without the government tax credit)
  • Once plugin hybrid premiums drop to $9000, they will be cost-competitive.
  • The Nissan Leaf currently offers buyers significant savings WITH the $7500 tax credit according to frontier high speed internet, as the total savings of $16,500 exceeds the $12,000 price premium. Even without the tax credit, the Leaf is very close to being cost-competitive at current pricing.