Tuesday, September 23, 2014

Per Capita CO2 Emissions (Before- & After-Trade) Rankings of G20 Countries 1993-2013

China has been the world's number one carbon emitter since 2006. However, the country complains that developed countries' consumption of made-in-China goods is to blame for a big part of its CO2 emissions. It is true that China's export-driven manufacturing has led to dramatic increases in CO2 emissions. So a recent study (Le Quéré et al., 2014) analyzed how big the difference between before- and after-trade emissions in each country is. In those consumer countries such as the United States and the developed countries in Europe, the after-trade (consumption-based) CO2 emissions per capita should be larger than the before-trade (production-based) CO2 emissions per capita.
Because I have compared energy intensities of G20 countries, I decided to compare the G20 members by their COemissions per capita both in before- and after-trade terms. It was a simple task. I have divided each country's emissions (Le Quéré et al., 2014) by their population (UN DESA, 2013). The following figures show the results.
Let's take a look at the Table below. In 2012, French people emitted 56% more CO2 by consumption-based (after-trade) emissions (8.32 tCO2/person/year) than that by production-based (before-trade) emissions (5.34 tCO2/person/year). China's per capita emissions decrease significantly using the same comparison. In 2012, Chinese people emitted 5.84 tonnes of CO2 per person by after-trade emissions, which is 16% less than the before-trade emissions (6.97 tCO2/person/year). (See also Figure 1 & Figure 2).
How about comparing those G20 countries by rankings? Before the trade-adjustment (Figure 3), China ranked 17th among G20 countries by the production-based per capita CO2 emissions in 1993. In 2013, the country ranked 6th.
After adjusting the emissions by international trade (Figure 4), it is a different story. China was No. 17 in 1993 by the after-trade emissions per capita. In 2012, it stepped up only three ranks (No. 14). By comparison, South Korea leaped up 6 ranks. It was No. 11 in 1993 before its rank rose to No. 5 in 2012. Russia beat China in terms of rank ascension. The country rose by 4 ranks from No. 12 to No. 8.

Table. Difference between Before-Trade Emissions and After-Trade Emissions
([Difference] = [B-A]/A, where
A: CO2 emissions per person before trade-adjustment,
B: CO2 emissions per person after trade-adjustment)

Korea, South22.3%17.3%10.6%22.3%15.9%14.7%14.2%
Saudi Arabia-7.4%-5.5%-30.9%-27.0%-13.0%-16.5%-17.0%
South Africa-28.8%-28.1%-26.6%-27.9%-25.7%-26.6%-25.1%
United Kingdom13.1%13.1%20.7%28.5%30.7%37.0%35.2%
United States-1.1%-0.9%4.9%8.1%6.8%7.6%8.7%

Figure 1

Figure 2

Figure 3

Figure 4


Le Quéré, C., et al. (2014). Global Carbon Budget 2014. Earth System Science Data Discussions, (In Review). [Full-text at http://dx.doi.org/10.5194/essdd-7-521-2014; Data at http://cdiac.ornl.gov/GCP/]

United Nations Department of Economic and Social Affairs (UN DESA). (2013). World Population Prospects: The 2012 Revision. [Data at http://esa.un.org/wpp/]

Friday, September 12, 2014

What Made South Korea the Saddest Country in the OECD? (Part 3)

This is a third installment of my interest in how South Korea became the highest suicide rate country in the OECD (See the First and Second posts on this topic.). The timing of writing this post coincides with a publication of the latest global suicide rate report from the WHO (World Health Organization), the worst 20 countries by sex is listed below. So, South Korea is now the saddest country in the OECD and the third-saddest country in the world. (By the way, North Korea's suicide rates are higher than those of South Korea. Now I fear that Koreans are innately sad, then.... I leave this question to be answered later.)

Table. Suicide rates by sex in 2012 (OECD countries in boldface)
Suicide rate: Suicide mortality per 100,000 (age-standardized)
RankBoth Sexes (rate)RankFemale (rate)RankMale (rate)
1Guyana (44.2)1Korea, North (35.1)1Guyana (70.8)
2Korea, North (38.5)2Guyana (22.1)2Lithuania (51.0)
3Korea, South (28.9)3Mozambique (21.1)3Sri Lanka (46.4)
4Sri Lanka (28.8)4Nepal (20.0)4Korea, North (45.4)
5Lithuania (28.2)5Tanzania (18.3)5Suriname (44.5)
6Suriname (27.8)6Korea, South (18.0)6Korea, South (41.7)
7Mozambique (27.4)7India (16.4)7Kazakhstan (40.6)
8Nepal (24.9)8Sri Lanka (12.8)8Russia (35.1)
8Tanzania (24.9)8South Sudan (12.8)9Mozambique (34.2)
10Kazakhstan (23.8)10Burundi (12.5)10Burundi (34.1)
11Burundi (23.1)11Uganda (12.3)11Belarus (32.7)
12India (21.1)12Suriname (11.9)12Turkmenistan (32.5)
13South Sudan (19.8)13Sudan (11.5)13Hungary (32.4)
14Turkmenistan (19.6)14Bhutan (11.2)14Tanzania (31.6)
15Russia (19.5)15Zambia (10.8)15Latvia (30.7)
15Uganda (19.5)16Comoros (10.3)16Poland (30.5)
17Hungary (19.1)16Myanmar (10.3)17Ukraine (30.3)
18Japan (18.5)18Japan (10.1)18Nepal (30.1)
19Belarus (18.3)19Zimbabwe (9.7)19Zimbabwe (27.2)
20Zimbabwe (18.1)20Pakistan (9.6)20South Sudan (27.1)
Source: World Health Organization (2014)

In my first post, I found a significant relationship between suicide rates and income inequality. Here, I present the latest data on the income inequality.
The authors (Kim & Kim) of the paper I am citing used a method that's very similar to what was used by Thomas Piketty in his enormously successful book, "Capital in the Twenty-First Century." Realizing South Korea's household income survey is not reflecting actual income inequality due to sampling biases, Kim and Kim used global income tax data instead of the survey. The results reveals deeper income inequality.
To show historical changes in income inequality, I have drawn three figures covering the period from 1995 to 2012. By the way, Kim and Kim's database dates back all the way to 1933. If you are more interested, please visit the database website linked at the end of this post.
Here are the figures. I want to stress two points with them.
First, the shares of top income groups have been growing steadily (Figure 1). In 1995, the share of top 10% households was 29.2%. In 2012, it soared to 44.9%. In addition, the average growth rates of income shares were greater in higher income groups. For example, the top 0.01% households' income share have risen at 5.44% annually, while the CAGR of top 0.1%, 1%, 10% households' income shares were 4.80%, 3.44%, 2.56%, respectively.

Figure 1

Second, the top income groups' income has increased while the bottom 90% households' real income has actually decreased. For this explanation, I have drawn two figures containing the same data. Figure 2 was plotted with a logarithmic scale on Y-axis. Figure 3 is using a natural scale on Y-axis.
Top 0.01% families' average annual income was 917,193 dollars in 1995. In 2012, they earned $2,639,751 per family. It means 6.42% annual growth rate. Top 0.1%, 1%, 10% families have shown positive CAGRs, although the rates are descending with their income levels (5.70%, 4.35%, 3.45%, each).
However, the bottom 90% households could not catch up. Their income has shrunk by 0.60% annually. In 1995, the entire family in the group earned $10,840. In 2012, their income fell below $10k line. The average income of the bottom 90% households was $9784.

Figure 2

Figure 3


Kim, N. N., & Kim, J. (2014). Top Incomes in Korea, 1933-2010: Evidence from Income Tax Statistics. WTID Working Paper, 2014/2. [Full-text at http://j.mp/Korea_Income_Inequality; Data from http://j.mp/Income_DB (updated by the authors after writing paper.)]

World Health Organization. (2014). Preventing Suicide: A Global Imperative. Geneva, Switzerland: World Health Organization. [Full-text at http://j.mp/WHO_Suicide_Report]