Ecological Footprint and Human Development Index 2016

PACKAGE              VERSION
Python               3.8.5
requests             2.24.0
json                 2.0.9
re                   2.2.1
numpy                1.19.2
pandas               1.1.3
seaborn              0.11.0
altair               4.1.0

1. Introduction¶

The goal of this data analysis is to study the relationship between the ecological footprint (EF) and the Human Development Index (HDI) of countries worldwide using the latest data available which dates from 2016 (as of October 2020).

The EF measures the surface area needed to provide the resources necessary to meet consumer demand in any given country. This includes the surface area used for food crops, fiber production, timber regeneration, absorption of carbon dioxide emissions from fossil fuel burning, and built infrastructure. Imports and exports are taken into account. Further details can be found here. This indicator is provided by the Global Footprint Network (GFN), a non-profit organization whose mission is to help end ecological overshoot by making ecological limits central to decision-making.

The HDI measures the level of development of a country by combining indices that assess three key dimensions: health, education and standard of living. Health is assessed by life expectancy at birth, education is measured by mean of years of schooling for adults aged 25 years and more and expected years of schooling for children of school entering age. Standard of living is measured by gross national income per capita. Further details can be found here. The HDI is calculated by the United Nations Development Programme using data from various sources.

This notebook presents the entire data analysis process starting with the extraction of the raw data from online sources, followed by data preparation operations and finishing with a few descriptive graphs, the one of key interest being the scatterplot showing the relationship betweenship ecological footprint and the HDI for countries worldwide.

2. Data extraction and preparation¶

2.1 Import data through the GFN API¶

The data concerning both the EF and the HDI can be accessed on the GFN website as an excel file containing all the data, as individual datasets through the Ecological Footprint Explorer open data platform or through the website's API for which an API key must be requested using their API Key Request Form to be able to access the data. The data is provided by the GFN under a Creative Commons Attribution-ShareAlike 4.0 International License.

For this data analysis, the data will be imported through the API as it makes the whole process easier to reproduce and requires fewer files.

Different datasets can be accessed through the API, the types dataset lists all the indicators that are available.

API HTTP response code: 200

For this data analysis, these three variables will be extracted: Earths, Population and Human Development Index. Earths measures how many Earths would be required to provide the surface required to meet consumption needs if everyone on the planet were to consume at the same level as the given country.

The HDI can also be accessed through the API of the United Nations Human Development Report Office where the data may be slightly more up to date as it is recalculated retroactively each year with the integration of new methodologies and updated data.

API HTTP response code: 200

The record column shows that the values of three variables of interest are contained on different lines of the value column. Country names and the ISO 3166-1 alpha-2 country codes are also needed for merging with other datasets later on.

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 601 entries, 0 to 600
Data columns (total 4 columns):
 #   Column     Non-Null Count  Dtype  
---  ------     --------------  -----  
 0   shortName  601 non-null    object 
 1   isoa2      547 non-null    object 
 2   record     601 non-null    object 
 3   value      601 non-null    float64
dtypes: float64(1), object(3)
memory usage: 18.9+ KB

The record column needs to be split into separate columns containing the variables of interest and the columns can be renamed for better readability.

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 188 entries, 0 to 187
Data columns (total 5 columns):
 #   Column      Non-Null Count  Dtype  
---  ------      --------------  -----  
 0   country     188 non-null    object 
 1   code        188 non-null    object 
 2   Earths      184 non-null    float64
 3   HDI         175 non-null    float64
 4   population  188 non-null    float64
dtypes: float64(3), object(2)
memory usage: 7.5+ KB

The dataset contains data on 187 countries, of which 12 are missing a value for the HDI (shown in a later section).

We can now look at how Earths and HDI are distributed.

The list contains mainly fossil fuel producing countries with the exceptions of Luxembourg and Bermuda. As a wealthy country, Luxembourg has a high per capita consumption level and it also experiences a high level of tank tourism. Bermuda also is a wealthy country with a high per capita consumption level and it additionaly relies heavily on fossil fuels for all its energy needs.

To get a better understanding of how much of the world is covered in the dataset, we can compare the countries listed in the dataset to a comprehensive list of countries considered to be independent States.

2.2 Import list of independent States by scraping online HTML table¶

A list of independent States will be used as reference to better understand the coverage of the available data, with the assumption that countries or territories that are not independent are small in size and therefore have less impact on the global consumption of resources.

The list of Independent States in the World published by the United States Department of State (USDS) will be used as reference. It includes GENC country codes which are based on the ISO 3166 country code standard and will be used to merge with the other dataset. This data is in the public domain and may be copied and distributed without permission.

The USDS allows scraping of its website but for some reason the pandas function pd.read_html(url) does not work on this page so the BeautifulSoup package is used here instead.

API HTTP response code: 200

There are 195 independent States according to the USDS.

This sample of the table shows that country names contain extra characters and notes which can be removed for better readability.

<class 'pandas.core.frame.DataFrame'>
Int64Index: 207 entries, 0 to 206
Data columns (total 3 columns):
 #   Column        Non-Null Count  Dtype 
---  ------        --------------  ----- 
 0   country_USDS  195 non-null    object
 1   code          207 non-null    object
 2   country       188 non-null    object
dtypes: object(3)
memory usage: 6.5+ KB

The following cell displays the independent States not included in the EF dataset.

                       country_USDS code country
3                           Andorra   AD     NaN
74                         Holy See   VA     NaN
77                          Iceland   IS     NaN
90                         Kiribati   KI     NaN
93                           Kosovo   XK     NaN
102                   Liechtenstein   LI     NaN
108                        Maldives   MV     NaN
111                Marshall Islands   MH     NaN
115  Micronesia,Federated States of   FM     NaN
117                          Monaco   MC     NaN
123                           Nauru   NR     NaN
134                           Palau   PW     NaN
146           Saint Kitts and Nevis   KN     NaN
150                      San Marino   SM     NaN
155                      Seychelles   SC     NaN
163                     South Sudan   SS     NaN
181                          Tuvalu   TV     NaN
187                         Uruguay   UY     NaN
189                         Vanuatu   VU     NaN

Number of independent countries not included in EF dataset: 19

It is also interesting to see which are the 10 out of the 187 countries listed in the EF dataset that are not independent.

    country_USDS code                 country
195          NaN   AW                   Aruba
196          NaN   BM                 Bermuda
197          NaN   VG  British Virgin Islands
198          NaN   KY          Cayman Islands
199          NaN   CK            Cook Islands
200          NaN   GF           French Guiana
201          NaN   PF        French Polynesia
202          NaN   GP              Guadeloupe
203          NaN   MQ              Martinique
204          NaN   MS              Montserrat
205          NaN   PS               Palestine
206          NaN   RE                 Reunion

Number of dependent territories included in EF dataset: 12

10 dependent territories are included in the EF dataset with a computed ecological footprint. As they are not independent states they have not been included in the HDI, along with North Korea and Somalia, as shown in the table below.

Now that we better understand the coverage of the dataset, it would be interesting to include information about the region to which each country belongs as it would provide a better overview of the geographical distribution of the scores of both indicators.

2.3 Import list of countries with regions from GitHub repository¶

This information can be obtained from different places. A list built by GitHub user lukes last updated on 19 March 2019 will be imported here. This list has been obtained by merging two sources, the Wikipedia ISO 3166-1 article table containing the alpha and numeric country codes, and the United Nations Statistics Division table containing regional, and sub-regional names and codes. The information on regions may also be obtained by downloading a CSV or Excel file from the United Nations Statistics Division page.

<class 'pandas.core.frame.DataFrame'>
Int64Index: 188 entries, 0 to 187
Data columns (total 6 columns):
 #   Column      Non-Null Count  Dtype  
---  ------      --------------  -----  
 0   country     188 non-null    object 
 1   code        188 non-null    object 
 2   Earths      184 non-null    float64
 3   HDI         175 non-null    float64
 4   population  188 non-null    float64
 5   continent   188 non-null    object 
dtypes: float64(3), object(3)
memory usage: 10.3+ KB

The merging appears to be successful as all countries and territories have an attributed continent.

3. Data analysis¶

Now that the dataset is ready for further analysis, it is time to look at the distribution of the country scores for the EF and HDI grouped by continent and to visualize the relationship between these two variables.

3.1 Ecological Footprint by continent¶

The dashed line represents the one planet limit. The boxplots are drawn with whiskers that reach out to farthest data point within the interval contained by the 5th and 95th percentiles. Countries from both Europe and Oceania are all consuming in excess of Earth's resources, as well as nearly all of North and South American countries with a few exceptions:

Many Asian and African countries appear to be within the planet's limits. These can be identified in the scatterplot further below.

African countries are very frugal in their consumption, very few exceed the 2 Earths mark:

Asian countries on the other hand show a very wide distribution in resource consumption. Their EF ranges from 0.3 Earth equivalents up to 8.8 with a median of 1.3:

count    47.00
mean      2.12
std       1.78
min       0.31
25%       0.83
50%       1.30
75%       2.91
max       8.91
Name: Earths, dtype: float64

3.2 Human Development Index by continent¶

The HDI scores are widely spread within each continent and a few countries stand out as high-performers while some others lag behind the rest. These outliers are the following:

3.3 Ecological Footprint vs Human Development Index¶

The horizontal dashed line marks the one planet limit. The vertical dashed line marks the median HDI. Nearly all countries consuming resources within Earth's total biocapacity score low on the HDI. The only exception is Sri Lanka which has an HDI score of 0.77 while maintaining consumption at a sustainable level. It is followed by Jamaica with an HDI score of 0.73 just below the median HDI. All other countries in the the top half of HDI scores are consuming resources at an unsustainable level.

The duty of policy-makers and other development stakeholders is to shift countries to the bottom-right quadrant. This means that those in high-consumption countries must put in place policies to decrease resource use, while those in low-consumption countries must find alternative ways to increase health, education and standard of living, as they cannot follow the development path of high-consumption countries.

In facing these challenges, it can be interesting to look more closely at which countries are the most efficient in resource consumption relative to their HDI score. Knowing which countries are most resource-efficient per HDI point may bring to light successful policies that may serve as examples for others. The development efficiency indicator is computed in the next section to identify those countries.

3.4 Development efficiency¶

count    172.00
mean       0.52
std        0.29
min        0.10
25%        0.33
50%        0.45
75%        0.65
max        2.04
Name: development_efficiency, dtype: float64

Among the top 10 countries in terms of development efficiency, none are even close to the median HDI, only Timor-Leste and Tajikistan make it above the 0.6 mark. To get a list of potential standard-setters, we can further refine the list by setting a minimum HDI.

As observed in the scatterplot, only Sri Lanka and Jamaica are within the one planet limit, with a near-median HDI. Uruguay stands out as the only country reaching the 0.8 HDI mark. Albania makes it in the list as the only European country and, along with the last three countries in the list, it is considered as an economy in transition by the United Nations country classification in the World Economic Situation and Prospects report from 2019. The other 6 countries are considered to be developing economies.

Each of these countries would have to be studied further to better understand how they score so high on the HDI in such a resource-efficient manner. Some of them may have policies in place that could serve as examples of good practice for other countries facing similar challenges.

Finally, we can have a look at what countries are the least resource efficient relative to the HDI:

These are mainly fossil fuel producing countries with the exception of Luxembourg. These countries would have to be investigated further to better understand why they make it into this list.

4. Conclusion¶

This analysis has shed some light on the situation of countries regarding their ecological footprint and their level of development. Sri Lanka stands out as being the only country with a sustainable level resource consumption while achieving an above-median score on the HDI. None of the countries among what are considered as developed economies according to the United Nations are even close to the one planet limit, most are well above the 2 Earths mark, with the exception of Romania.

This goes to show that all countries still have a lot of work to do in order to achieve a sustainable level of development on both social and environmental issues. As the UN Sustainable Development Goals are becoming the new benchmark for assessing the level of development of countries, one could question the use of the classification 'developed', 'in transition', and 'developing', as the scores on the ecological footprint clearly show that the so-called developed countries still have a way to go in order to achieve a sustainable level of consumption while maintaining a high standard of living.

	id	version	code	record	note	enabled	name
0	3	None	BCpc	BiocapPerCap	Biocapacity divided by population in global he...	1	Biocapacity per person
1	4	None	BCtot	BiocapTotGHA	Total biocapacity in global hectares (gha)	1	Biocapacity
2	5	None	EFCpc	EFConsPerCap	Ecological Footprint of consumption in global ...	1	Ecological Footprint per person
3	6	None	EFCtot	EFConsTotGHA	Total Ecological Footprint of consumption in g...	1	Ecological Footprint
4	13	None	earth	BiocapTotGHA	EFConsPerCap of Country / EFConsPerCap of World	1	Earths
5	14	None	pop	Population	Population	1	Population
6	15	None	hdi	HDI	Human Development Index; Source: Trends in the...	1	Human Development Index
7	16	None	gdp	GDP	GDP per capita (constant 2010 US$); Source: Wo...	1	Gross Domestic Product

	id	version	year	countryCode	countryName	shortName	isoa2	record	cropLand	grazingLand	forestLand	fishingGround	builtupLand	carbon	value	score
0	221204	None	2016	1	Armenia	Armenia	AM	Earths	0.33	0.14	0.15	4.62e-03	0.03	0.51	1.17e+00	3A
1	121257	None	2016	1	Armenia	Armenia	AM	HDI	NaN	NaN	NaN	NaN	NaN	NaN	7.51e-01	None
2	125727	None	2016	1	Armenia	Armenia	AM	Population	NaN	NaN	NaN	NaN	NaN	NaN	2.92e+06	None
3	221261	None	2016	2	Afghanistan	Afghanistan	AF	Earths	0.18	0.11	0.04	1.41e-04	0.02	0.09	4.39e-01	3A
4	121285	None	2016	2	Afghanistan	Afghanistan	AF	HDI	NaN	NaN	NaN	NaN	NaN	NaN	4.91e-01	None
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
596	231913	None	2016	2005	Oceania	º Oceania	None	Earths	0.34	0.10	0.57	8.78e-02	0.03	2.82	3.94e+00
597	216927	None	2016	2005	Oceania	º Oceania	None	HDI	NaN	NaN	NaN	NaN	NaN	NaN	8.38e-01	None
598	231970	None	2016	5001	World	º World	None	Earths	0.32	0.09	0.17	5.58e-02	0.04	1.03	1.70e+00	3A
599	125701	None	2016	5001	World	º World	None	HDI	NaN	NaN	NaN	NaN	NaN	NaN	7.27e-01	None
600	135252	None	2016	5001	World	º World	None	Population	NaN	NaN	NaN	NaN	NaN	NaN	7.47e+09	None

	shortName	isoa2	record	value
0	Armenia	AM	Earths	1.17e+00
1	Armenia	AM	HDI	7.51e-01
2	Armenia	AM	Population	2.92e+06
3	Afghanistan	AF	Earths	4.39e-01
4	Afghanistan	AF	HDI	4.91e-01
...	...	...	...	...
596	º Oceania	None	Earths	3.94e+00
597	º Oceania	None	HDI	8.38e-01
598	º World	None	Earths	1.70e+00
599	º World	None	HDI	7.27e-01
600	º World	None	Population	7.47e+09

	country	code	Earths	HDI	population
0	Afghanistan	AF	0.44	0.49	3.47e+07
1	Albania	AL	1.23	0.79	2.93e+06
2	Algeria	DZ	1.48	0.75	4.06e+07
3	Angola	AO	0.57	0.57	2.88e+07
4	Antigua and Barbuda	AG	2.69	0.77	1.01e+05
...	...	...	...	...	...
183	Venezuela	VE	1.55	0.75	3.16e+07
184	Viet Nam	VN	1.37	0.69	9.46e+07
185	Yemen	YE	0.40	0.48	2.76e+07
186	Zambia	ZM	0.60	0.58	1.66e+07
187	Zimbabwe	ZW	0.65	0.55	1.62e+07

	country	code	Earths	HDI	population
139	Qatar	QA	8.91	0.85	2.57e+06
102	Luxembourg	LU	7.99	0.90	5.76e+05
179	United Arab Emirates	AE	5.52	0.86	9.27e+06
12	Bahrain	BH	5.47	0.84	1.43e+06
93	Kuwait	KW	5.29	0.81	4.05e+06
181	United States	US	5.11	0.92	3.22e+08
33	Canada	CA	5.10	0.92	3.63e+07
173	Trinidad and Tobago	TT	4.90	0.80	1.36e+06
114	Mongolia	MN	4.77	0.73	3.03e+06
19	Bermuda	BM	4.72	NaN	6.17e+04

	country	code	Earths	HDI	population
170	Timor-Leste	TL	0.31	0.63	1.27e+06
58	Eritrea	ER	0.32	0.43	4.95e+06
185	Yemen	YE	0.40	0.48	2.76e+07
78	Haiti	HT	0.41	0.50	1.08e+07
29	Burundi	BI	0.41	0.43	1.05e+07
143	Rwanda	RW	0.43	0.52	1.19e+07
0	Afghanistan	AF	0.44	0.49	3.47e+07
131	Palestine	PS	0.47	0.69	4.79e+06
118	Mozambique	MZ	0.48	0.44	2.88e+07
42	Congo DR	CD	0.48	0.45	7.87e+07

	country	code	Earths	HDI	population
128	Norway	NO	3.42	0.95	5.25e+06
165	Switzerland	CH	2.88	0.94	8.40e+06
69	Germany	DE	2.98	0.94	8.19e+07
85	Ireland	IE	3.16	0.94	4.73e+06
8	Australia	AU	4.02	0.94	2.41e+07
164	Sweden	SE	3.84	0.93	9.84e+06
150	Singapore	SG	3.96	0.93	5.62e+06
122	Netherlands	NL	3.12	0.93	1.70e+07
50	Denmark	DK	4.33	0.93	5.71e+06
62	Finland	FI	3.85	0.92	5.50e+06

	country	code	Earths	HDI	population
125	Niger	NE	1.00	0.37	2.07e+07
35	Central African Republic	CF	0.73	0.37	4.59e+06
36	Chad	TD	1.10	0.40	1.45e+07
28	Burkina Faso	BF	0.71	0.42	1.86e+07
107	Mali	ML	0.92	0.42	1.80e+07
149	Sierra Leone	SL	0.62	0.42	7.40e+06
29	Burundi	BI	0.41	0.43	1.05e+07
58	Eritrea	ER	0.32	0.43	4.95e+06
118	Mozambique	MZ	0.48	0.44	2.88e+07
42	Congo DR	CD	0.48	0.45	7.87e+07

	Short-form name	Long-form name	GENC 2A Code (see Note 2)	GENC 3A Code (see Note 2)	Capital
0	Afghanistan *+	Islamic Republic of Afghanistan	AF	AFG	Kabul
1	Albania *+	Republic of Albania	AL	ALB	Tirana
2	Algeria *+	People’s Democratic Republic of Algeria	DZ	DZA	Algiers
3	Andorra *+	Principality of Andorra	AD	AND	Andorra la Vella
4	Angola *+	Republic of Angola	AO	AGO	Luanda
...	...	...	...	...	...
190	Venezuela *+	Bolivarian Republic of Venezuela	VE	VEN	Caracas
191	Vietnam *+	Socialist Republic of Vietnam	VN	VNM	Hanoi
192	Yemen *+	Republic of Yemen	YE	YEM	Sanaa
193	Zambia *+	Republic of Zambia	ZM	ZMB	Lusaka
194	Zimbabwe *+	Republic of Zimbabwe	ZW	ZWE	Harare

	country_USDS	code
30	Cambodia *+	KH
31	Cameroon *+	CM
32	Canada *+	CA
33	Central\nAfrican Republic *+	CF
34	Chad *+	TD
35	Chile *+	CL
36	China *+ (see note 3)	CN
37	Colombia *+	CO
38	Comoros *+	KM
39	Congo (Brazzaville) *+\n(see note 4)	CG
40	Congo (Kinshasa) *+\n(see note 4)	CD
41	Costa Rica *+	CR
42	Côte d’Ivoire *+	CI
43	Croatia *+	HR
44	Cuba *+	CU
45	Cyprus *+	CY
46	Czechia *+	CZ
47	Denmark *+	DK
48	Djibouti *+	DJ
49	Dominica *+	DM
50	Dominican Republic *+	DO
51	Ecuador *+	EC
52	Egypt *+	EG
53	El Salvador *+	SV
54	Equatorial Guinea *+	GQ
55	Eritrea *+	ER
56	Estonia *+	EE
57	\n \nEswatini *+	SZ
58	Ethiopia *+	ET
59	Fiji *+	FJ

	country_USDS	code
30	Cambodia	KH
31	Cameroon	CM
32	Canada	CA
33	CentralAfrican Republic	CF
34	Chad	TD
35	Chile	CL
36	China	CN
37	Colombia	CO
38	Comoros	KM
39	Congo (Brazzaville)	CG
40	Congo (Kinshasa)	CD
41	Costa Rica	CR
42	Côte d’Ivoire	CI
43	Croatia	HR
44	Cuba	CU
45	Cyprus	CY
46	Czechia	CZ
47	Denmark	DK
48	Djibouti	DJ
49	Dominica	DM
50	Dominican Republic	DO
51	Ecuador	EC
52	Egypt	EG
53	El Salvador	SV
54	Equatorial Guinea	GQ
55	Eritrea	ER
56	Estonia	EE
57	Eswatini	SZ
58	Ethiopia	ET
59	Fiji	FJ

	country	code	Earths	HDI	population
7	Aruba	AW	4.11	NaN	1.05e+05
19	Bermuda	BM	4.72	NaN	6.17e+04
25	British Virgin Islands	VG	2.07	NaN	3.07e+04
34	Cayman Islands	KY	3.65	NaN	6.08e+04
43	Cook Islands	CK	NaN	NaN	1.74e+04
64	French Guiana	GF	1.50	NaN	2.76e+05
65	French Polynesia	PF	2.47	NaN	2.80e+05
73	Guadeloupe	GP	2.56	NaN	4.50e+05
109	Martinique	MQ	2.80	NaN	3.85e+05
116	Montserrat	MS	2.82	NaN	5.15e+03
127	North Korea	KP	0.54	NaN	2.54e+07
140	Reunion	RE	2.98	NaN	8.70e+05
154	Somalia	SO	0.60	NaN	1.43e+07

	name	alpha-2	alpha-3	country-code	iso_3166-2	region	sub-region	intermediate-region	region-code	sub-region-code	intermediate-region-code
0	Afghanistan	AF	AFG	4	ISO 3166-2:AF	Asia	Southern Asia		142	034
1	Åland Islands	AX	ALA	248	ISO 3166-2:AX	Europe	Northern Europe		150	154
2	Albania	AL	ALB	8	ISO 3166-2:AL	Europe	Southern Europe		150	039
3	Algeria	DZ	DZA	12	ISO 3166-2:DZ	Africa	Northern Africa		002	015
4	American Samoa	AS	ASM	16	ISO 3166-2:AS	Oceania	Polynesia		009	061
...	...	...	...	...	...	...	...	...	...	...	...
244	Wallis and Futuna	WF	WLF	876	ISO 3166-2:WF	Oceania	Polynesia		009	061
245	Western Sahara	EH	ESH	732	ISO 3166-2:EH	Africa	Northern Africa		002	015
246	Yemen	YE	YEM	887	ISO 3166-2:YE	Asia	Western Asia		142	145
247	Zambia	ZM	ZMB	894	ISO 3166-2:ZM	Africa	Sub-Saharan Africa	Eastern Africa	002	202	014
248	Zimbabwe	ZW	ZWE	716	ISO 3166-2:ZW	Africa	Sub-Saharan Africa	Eastern Africa	002	202	014

	country	code	Earths	HDI	population	continent
100	Libya	LY	2.24	0.69	6.29e+06	Africa
111	Mauritius	MU	2.18	0.79	1.26e+06	Africa
140	Reunion	RE	2.98	NaN	8.70e+05	Africa

		country	code	Earths	HDI	population	continent
continent
Africa	35	Central African Republic	CF	0.73	0.37	4.59e+06	Africa
	36	Chad	TD	1.10	0.40	1.45e+07	Africa
	125	Niger	NE	1.00	0.37	2.07e+07	Africa
Americas	78	Haiti	HT	0.41	0.50	1.08e+07	Americas
Americas	79	Honduras	HN	0.96	0.62	9.11e+06	Americas
Asia	0	Afghanistan	AF	0.44	0.49	3.47e+07	Asia
	166	Syria	SY	0.72	0.54	1.84e+07	Asia
	185	Yemen	YE	0.40	0.48	2.76e+07	Asia
Europe	113	Moldova	MD	1.07	0.70	4.06e+06	Europe
Europe	178	Ukraine	UA	1.74	0.75	4.44e+07	Europe
Oceania	133	Papua New Guinea	PG	1.13	0.54	8.08e+06	Oceania

	country	code	Earths	HDI	population	continent	development_efficiency
136	Philippines	PH	0.82	0.70	1.03e+08	Asia	0.86
158	Sri Lanka	LK	0.95	0.77	2.08e+07	Asia	0.82
88	Jamaica	JM	1.01	0.72	2.88e+06	Americas	0.72
54	Ecuador	EC	1.06	0.76	1.64e+07	Americas	0.71
53	Dominican Republic	DO	1.05	0.74	1.06e+07	Americas	0.70
113	Moldova	MD	1.07	0.70	4.06e+06	Europe	0.66
47	Cuba	CU	1.18	0.77	1.15e+07	Americas	0.65
6	Armenia	AM	1.17	0.75	2.92e+06	Asia	0.64
1	Albania	AL	1.23	0.79	2.93e+06	Europe	0.64
10	Azerbaijan	AZ	1.23	0.75	9.73e+06	Asia	0.61

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