Introduction

_Arguably, the biggest benefit an individual and the society can gain is through education.

While per capita GDP tells how financially well of are the individuals in a nation, the Human Development Index (HDI) has been used to tell how much “developed” are the people.

United Nations Development Programme (UNDP) calculates the Human Development Index (HDI) based on four variables − life expectancy at birth, expected years of schooling, mean years of schooling, and per capita PPP gross national income. So HDI does cover some aspects of education.

However, in this paper, we are interested to understand the “Education of Nations” using a few more parameters than the ones used in HDI. Therefore, we will deep-dive into some of the parameters including the ones used in HDI, and try to draw general inferences from the same.

Section 1: Deep Dive into Various Parameters of Education

In this section, we will examine various other parameters of education and understand their association with Development (HDI) and Wealth (GDP). But before that let us examine the association between Development and Wealth itself.

As per capita GDP rises from low to medium, the HDI also rises. Once a GDP threshold is reached, the HDI also has reached its peak. There is a little flattening seen around $15k and a further flattening around $30k. At the $15k level, an HDI of 0.75 is achieved. So, $15k might be a desirable minimum level that a country could strive to achieve in Development terms.

Indicator: Mean years of schooling (years)

Mean years of schooling is one of the variables of HDI therefore there is bound to be an association between these. This happens to be the most correlated with HDI.

Most of Europe, North America, and Oceania are the best performers on this indicator, while some in Africa and a few in Asia are the worst performers.

As expected, there is a high degree of association between this indicator and HDI.

We can see this strong association across continents. However, in Europe where this indicator is on the higher side across countries, this association is not as strong.

The GDP per capita does seem to affect mean years of schooling and it tapers after achieving $50k.

In Africa the effect is strongest with a little rise in per cap GDP, mean years of schooling rises fastest. In Americas+Oceania, the effect is almost linear throughout. In Asia, the effect is very strong until GDP per cap of $25k after which the schooling tapers at around 10 years. The effect is not visible in Europe at all where this indicator is on the high side, mostly 10+, across countries.

Expected years of schooling female (years)

On a comparative scale, except for a few countries in Oceania, South America, and Europe, the expected years of schooling for a female is not very high.

Many countries in central parts of Africa and a few in Asia perform very low on this parameter. The rest of the countries are on an average level.

Being one of the variables in HDI, this indicator has a strong association with HDI.

This association is quite strong across the continents.

However, GDP per Capita does not seem to have a very strong effect on this indicator. There is some effect up to the GDP level of 25k achieving over 15 years of schooling and then tapering around this level.

In Africa, the effect is strongest. In Americas+Oceania, the effect is strong u to about $20k. In Asia, the effect is strong up to $25k. In Europe, the effect is far less.

Expected years of schooling male (years)

This indicator is quite like its female counterpart. Almost the same pattern is seen.

The association is like the female indicator. However, the max this indicator reaches for most countries is around 18 years as against 21 years for females.

The association across continents is strong.

Like the female indicator, the GDP per capita is quite flat.

There is some effect in Africa and Americas+Oceania, but much less in Asia and Europe.

Gross enrolment ratio, secondary (% of secondary school-age pop)

This indicator is calculated as all those in secondary schooling divided by the children of secondary age group (which makes this indicator go above 100% too).

Australia and a few European countries are high on this. Most countries are in the middle. While many countries in central parts of Africa and a few in Asia are on the low side.

There is a strong linear relationship of this indicator with HDI.

The relationship can be seen in varying degrees across continents.

GDP per cap does affect this indicator up to a level of $15k, where it becomes almost 100% and tapers off.

All continents show a similar pattern but with differing cut-off for plateauing of the curve. In Africa, the tapering happens at it a little lower level of GDP. In Europe, the effect is far less.

Pupil-teacher ratio, primary school (pupils per teacher)

Most countries have a good pupil-teacher ratio except for countries in Africa and the Indian Sub-continent.

There is a strong negative relationship of this indicator with HDI.

The negative relationship can be seen across all continents.

GDP per cap does affect pupil-teacher ratio up to a level of $15k to $25k after which the curve flattens.

In Africa that has the lowest level of GDP, the curve doesn’t flatten and the effect is the strongest. In Americas+Oceania and Asia, there is a flattening after $15k. In Europe, the effect is much weaker with the curve relatively flatter than in other continents.

Summary of Section 1

We can see that the additional indicators other than those used in the Human Development Index also show similar patterns. So the education aspect is largely covered in HDI.

GDP per capita between US$15,000 to US$25,000 at 2017 prices gives an inflection point for many indicators up to which GDP affects the indicator. This must be used by countries as a target to achieve.

Although Africa comes out with worse cases, it is not all of Africa and some Asian countries too show up in worse case scenarios. Therefore, the continents do not completely give the health picture. We need to cluster countries into different sets.

Section 2: Country Clusters

We intend to do country clusters at two levels. First, we form main clusters, and then within each, we will form sub-clusters. Since we desire to form a two-level hierarchy, we will use a hierarchical clustering algorithm. For linkage, we will use the “ward” method as it minimizes within-cluster variance which will help in forming more homogenous groups of countries.

Based on the indicators (scale standardized to give equal importance to each indicator), we have the following four clusters of countries. Central Africa and a few for Asia make the lowest cluster c3 (in red). Parts of the Indian Subcontinent, parts of South-East Asia, and some African and South American countries make the next cluster c2 (in yellow). Northern parts of LatAm, many from Asia, a few from Europe, a few from Africa make the next cluster c1 (in light blue). Northern America, Southern parts of South America, most of Europe, and Oceania make the highest cluster c0 (in green).

Countries of Cluster c0

There are 54 of 169 countries in this cluster. Out of these 34 of 38 European, 10 of 41 Americas+Oceania, and 10 of 42 Asian countries are in this cluster. No African country is in this cluster.

This splits into five sub-clusters.

Countries of Cluster c1

There are 31 of 169 countries in this cluster. Out of these 23 of 41 Americas+Oceania, 19 of 42 Asian, 6 of 48 African, and 4 of 38 European countries are in this cluster.

This splits into four sub-clusters.

Countries of Cluster c2

There are 32 of 169 countries in this cluster. Out of these 15 of 48 African 10 OF 42 Asian, and 7 of 41 Americas+Oceania are in this cluster. There are no European countries in this cluster.

This splits into four sub-clusters.

Countries of Cluster c3

There are 31 of 169 countries in this cluster. Out of these 27 of 48 African, 3 of 42 Asian, and 1 of 41 Americas+Oceania are in this cluster. There are no European countries in this cluster.

This splits into three sub-clusters.

Summary of Section 2

The following inferences we draw from the clustering analysis:

• Except for Africa which has many countries in the lowest cluster, continents do not define the level of education of countries.
• The four clusters represent the most educated (c0), less educated but not critical (c1), less educated a little critical (c2), and the least educated most critical (c3) nations.
• European countries never go into the lowest two clusters, and African countries never go into the highest cluster.

With these clusters, we can now analyze countries in these homogenous groups.

Section 3: Analysis of Indicator Patterns and Trends by Clusters

Having formed these clusters, we would now like to see how countries fare and compare within and across clusters. Since we have 169 countries, we will not look at all of these but take a subset. We have decided to take countries with a sizable population (in this case over 50 million) to be representative of their clusters. Thus, we have 32 countries to analyze in the section.

We will first look at patterns and trends with HDI and GDP and then further with each of the five indicators analyzed in section 1.

Indicator: Human Development Index

The pattern of HDI is very straight forward. As we go down the clusters the HDI steadily decreases.

Over the years, the clusters have witnessed an almost parallel progression. This means the gap between clusters on HDI has not been narrowing.

Cluster 1, and cluster 3 countries have been progressing in a narrow band of HDI, while there is a much larger band in cluster 0 and cluster 2.

Indicator: GDP per capita (2017 PPP $)

There is indeed heavy discrimination on GDP per capita. The highest cluster countries have a much higher level than the other three. However, the progressive decrease is still there.

Over the years the GDP gap appears to have increased. The highest cluster (c0) countries have had better growth, than the other three clusters. The bottom cluster (c3) has been on the verge of being stagnant.

Indicator: Mean years of schooling (years)

Mean years of schooling is a good discriminator of clusters, however, there are some overlaps.

Mean years of schooling indicator has been rising across clusters.

Almost all countries across clusters have been improving on this indicator.

Indicator: Expected years of schooling female (years)

Expected years of schooling for female discriminates the clusters well with minimal overlaps.

All clusters have been improving on this.

Most countries in cluster 0 had been historically high on this and a few that were not have dramatically improved. In cluster 3 some countries appear to be plateauing although these are still far lower than the other cluster countries.

Indicator: Expected years of schooling male (years)

Expected years of schooling for male discriminates the clusters well with little more overlaps than in females.

All clusters have been improving on this.

Like the female indicator, most countries in cluster 0 had been historically high on this, and a few that were not have dramatically improved. In cluster 3 some countries appear to be plateauing although these are still far lower than the other cluster countries.

Indicator: Gross enrolment ratio, secondary (% of secondary school-age pop)

Gross enrolment ratio, secondary discriminates the clusters well with a little overlap.

All clusters have shown varying degrees of improvement on this indicator.

Most cluster 0 countries were historically high on this so remained flat, but those that were not showed the most rapid improvement. Most cluster 1 and cluster 2 countries have been improving rapidly. Although there is an improvement in cluster 3 countries, the improvement is much slower causing the gap between cluster 3 and others to widen.

Indicator: Pupil-teacher ratio, primary school (pupils per teacher)

The pupil-teacher ratio discriminates clusters well but with some overlaps.

There has been an improvement in this indicator in all clusters except the worst cluster of c3.

Cluster 0 countries have improved and formed a narrow low band. Cluster 1 countries have improved a little slowly. Cluster 2 countries have improved a little more rapidly than cluster 1 countries. Cluster 3 countries have generally not improved, some have even become worse.

Summary of Section 3

The pattern and trend analysis has shown that all indicators discriminate the clusters quite well. We can also see that in some cases cluster 3 countries have not shown much improvement which should be a cause of worry.

Final Takeouts

In summary, we make the following points:

Per capita GDP on purchasing power parity between $15,000 to $25,000 at 2017 prices could be used by countries as a target.

Cluster 3 countries need special attention as these are far away from the above GDP target and have shown slow or lower improvement over the years.

The indicators that have not seen much improvement especially in cluster 3 countries should be targeted for improvement in countries that are weak on those indicators.

Appendix: Countries by Continents, Regions, Clusters, and Sub-Clusters

country	ISO2	ISO3	Continent	region	cluster	sub_cluster
Afghanistan	AF	AFG	Asia	Indian Subcontinent	c3	sc32
Albania	AL	ALB	Europe	Southern Europe	c1	sc11
Algeria	DZ	DZA	Africa	North Africa	c1	sc11
Angola	AO	AGO	Africa	Central Africa	c3	sc32
Antigua and Barbuda	AG	ATG	Americas+Oceania	NA - North America (Rest)	c1	sc13
Argentina	AR	ARG	Americas+Oceania	SA - Andean States	c0	sc03
Armenia	AM	ARM	Asia	South Caucasus	c1	sc13
Australia	AU	AUS	Americas+Oceania	Oceania	c0	sc01
Austria	AT	AUT	Europe	Western Europe	c0	sc04
Azerbaijan	AZ	AZE	Asia	South Caucasus	c1	sc13
Bahamas	BS	BHS	Americas+Oceania	NA - North America (Rest)	c1	sc10
Bahrain	BH	BHR	Asia	Arabian Peninsula	c0	sc00
Bangladesh	BD	BGD	Asia	Indian Subcontinent	c2	sc21
Barbados	BB	BRB	Americas+Oceania	NA - North America (Rest)	c1	sc11
Belarus	BY	BLR	Europe	Eastern Europe	c0	sc02
Belgium	BE	BEL	Europe	Western Europe	c0	sc01
Belize	BZ	BLZ	Americas+Oceania	NA - Central America	c1	sc10
Benin	BJ	BEN	Africa	West Africa	c3	sc32
Bhutan	BT	BTN	Asia	Indian Subcontinent	c2	sc23
Bolivia	BO	BOL	Americas+Oceania	SA - Andean States	c1	sc11
Botswana	BW	BWA	Africa	Southern Africa	c1	sc10
Brazil	BR	BRA	Americas+Oceania	SA - South America (Rest)	c1	sc11
Brunei	BN	BRN	Asia	South-East Asia	c1	sc11
Bulgaria	BG	BGR	Europe	Eastern Europe	c0	sc02
Burkina Faso	BF	BFA	Africa	West Africa	c3	sc32
Burundi	BI	BDI	Africa	East Africa	c3	sc32
Cambodia	KH	KHM	Asia	South-East Asia	c3	sc32
Cameroon	CM	CMR	Africa	Central Africa	c3	sc32
Canada	CA	CAN	Americas+Oceania	NA - North America (Rest)	c0	sc04
Cape Verde	CV	CPV	Africa	West Africa	c2	sc23
Central African Republic	CF	CAF	Africa	Central Africa	c3	sc30
Chad	TD	TCD	Africa	Central Africa	c3	sc30
Chile	CL	CHL	Americas+Oceania	SA - Andean States	c0	sc00
China	CN	CHN	Asia	East Asia	c1	sc11
Colombia	CO	COL	Americas+Oceania	SA - Andean States	c1	sc11
Comoros	KM	COM	Africa	East Africa	c2	sc20
Congo DRC	CD	COD	Africa	Central Africa	c3	sc32
Costa Rica	CR	CRI	Americas+Oceania	NA - Central America	c0	sc00
Cote d'Ivoire	CI	CIV	Africa	West Africa	c3	sc32
Croatia	HR	HRV	Europe	Southern Europe	c0	sc02
Cyprus	CY	CYP	Europe	Eastern Europe	c0	sc02
Czechia	CZ	CZE	Europe	Western Europe	c0	sc04
Denmark	DK	DNK	Europe	Northern Europe	c0	sc03
Djibouti	DJ	DJI	Africa	East Africa	c3	sc31
Dominican Republic	DO	DOM	Americas+Oceania	NA - North America (Rest)	c1	sc11
Ecuador	EC	ECU	Americas+Oceania	SA - Andean States	c1	sc11
Egypt	EG	EGY	Africa	North Africa	c2	sc23
El Salvador	SV	SLV	Americas+Oceania	NA - Central America	c2	sc21
Equatorial Guinea	GQ	GNQ	Africa	Central Africa	c2	sc20
Estonia	EE	EST	Europe	Western Europe	c0	sc04
Eswatini	SZ	SWZ	Africa	Southern Africa	c2	sc21
Ethiopia	ET	ETH	Africa	East Africa	c3	sc30
Fiji	FJ	FJI	Americas+Oceania	Oceania	c1	sc12
Finland	FI	FIN	Europe	Northern Europe	c0	sc01
France	FR	FRA	Europe	Western Europe	c0	sc02
Gabon	GA	GAB	Africa	Central Africa	c2	sc22
Gambia	GM	GMB	Africa	West Africa	c3	sc32
Georgia	GE	GEO	Asia	South Caucasus	c0	sc02
Germany	DE	DEU	Europe	Western Europe	c0	sc04
Ghana	GH	GHA	Africa	West Africa	c2	sc21
Greece	GR	GRC	Europe	Eastern Europe	c0	sc00
Grenada	GD	GRD	Americas+Oceania	NA - North America (Rest)	c0	sc00
Guatemala	GT	GTM	Americas+Oceania	NA - Central America	c2	sc20
Guinea	GN	GIN	Africa	West Africa	c3	sc30
Guyana	GY	GUY	Americas+Oceania	SA - South America (Rest)	c1	sc10
Honduras	HN	HND	Americas+Oceania	NA - Central America	c2	sc20
Hong Kong	HK	HKG	Asia	East Asia	c0	sc04
Hungary	HU	HUN	Europe	Western Europe	c0	sc02
Iceland	IS	ISL	Europe	Northern Europe	c0	sc03
India	IN	IND	Asia	Indian Subcontinent	c2	sc21
Indonesia	ID	IDN	Asia	South-East Asia	c1	sc11
Iran	IR	IRN	Asia	Fertile Crescent	c1	sc12
Iraq	IQ	IRQ	Asia	Fertile Crescent	c2	sc22
Ireland	IE	IRL	Europe	Western Europe	c0	sc03
Israel	IL	ISR	Asia	Fertile Crescent	c0	sc04
Italy	IT	ITA	Europe	Southern Europe	c0	sc00
Jamaica	JM	JAM	Americas+Oceania	NA - North America (Rest)	c1	sc10
Jordan	JO	JOR	Asia	Fertile Crescent	c1	sc10
Kazakhstan	KZ	KAZ	Asia	Central Asia	c0	sc02
Kenya	KE	KEN	Africa	East Africa	c2	sc21
Kiribati	KI	KIR	Americas+Oceania	Oceania	c1	sc10
Kuwait	KW	KWT	Asia	Arabian Peninsula	c1	sc11
Kyrgyzstan	KG	KGZ	Asia	Central Asia	c1	sc10
Laos	LA	LAO	Asia	South-East Asia	c2	sc20
Latvia	LV	LVA	Europe	Western Europe	c0	sc04
Lesotho	LS	LSO	Africa	Southern Africa	c2	sc21
Liberia	LR	LBR	Africa	West Africa	c2	sc20
Lithuania	LT	LTU	Europe	Western Europe	c0	sc04
Luxembourg	LU	LUX	Europe	Western Europe	c0	sc02
Madagascar	MG	MDG	Africa	East Africa	c3	sc32
Malawi	MW	MWI	Africa	East Africa	c3	sc30
Malaysia	MY	MYS	Asia	South-East Asia	c1	sc13
Maldives	MV	MDV	Asia	Indian Subcontinent	c2	sc22
Mali	ML	MLI	Africa	West Africa	c3	sc31
Malta	MT	MLT	Europe	Western Europe	c0	sc00
Mauritania	MR	MRT	Africa	West Africa	c3	sc31
Mauritius	MU	MUS	Africa	East Africa	c1	sc11
Mexico	MX	MEX	Americas+Oceania	NA - Central America	c1	sc11
Moldova	MD	MDA	Europe	Eastern Europe	c1	sc10
Mongolia	MN	MNG	Asia	East Asia	c1	sc12
Morocco	MA	MAR	Africa	North Africa	c2	sc23
Mozambique	MZ	MOZ	Africa	East Africa	c3	sc30
Myanmar	MM	MMR	Asia	South-East Asia	c2	sc20
Namibia	NA	NAM	Africa	Southern Africa	c2	sc21
Nepal	NP	NPL	Asia	Indian Subcontinent	c2	sc23
Netherlands	NL	NLD	Europe	Western Europe	c0	sc03
New Zealand	NZ	NZL	Americas+Oceania	Oceania	c0	sc03
Nicaragua	NI	NIC	Americas+Oceania	NA - Central America	c2	sc21
Niger	NE	NER	Africa	West Africa	c3	sc31
Nigeria	NG	NGA	Africa	West Africa	c3	sc32
North Macedonia	MK	MKD	Europe	Eastern Europe	c1	sc13
Norway	NO	NOR	Europe	Northern Europe	c0	sc03
Oman	OM	OMN	Asia	Arabian Peninsula	c1	sc11
Pakistan	PK	PAK	Asia	Indian Subcontinent	c3	sc30
Palau	PW	PLW	Americas+Oceania	Oceania	c0	sc02
Palestine	PS	PSE	Asia	Fertile Crescent	c1	sc10
Panama	PA	PAN	Americas+Oceania	NA - Central America	c1	sc10
Papua New Guinea	PG	PNG	Americas+Oceania	Oceania	c3	sc31
Paraguay	PY	PRY	Americas+Oceania	SA - South America (Rest)	c2	sc21
Peru	PE	PER	Americas+Oceania	SA - Andean States	c1	sc11
Philippines	PH	PHL	Asia	South-East Asia	c1	sc10
Poland	PL	POL	Europe	Western Europe	c0	sc04
Portugal	PT	PRT	Europe	Southern Europe	c0	sc00
Qatar	QA	QAT	Asia	Arabian Peninsula	c1	sc13
Romania	RO	ROU	Europe	Eastern Europe	c1	sc12
Russia	RU	RUS	Europe	Eastern Europe	c0	sc02
Rwanda	RW	RWA	Africa	East Africa	c3	sc30
Saint Kitts and Nevis	KN	KNA	Americas+Oceania	NA - North America (Rest)	c1	sc11
Saint Lucia	LC	LCA	Americas+Oceania	NA - North America (Rest)	c1	sc11
Saint Vincent and the Grenadines	VC	VCT	Americas+Oceania	NA - North America (Rest)	c1	sc11
Samoa	WS	WSM	Americas+Oceania	Oceania	c1	sc10
Sao Tome and Principe	ST	STP	Africa	Central Africa	c2	sc23
Saudi Arabia	SA	SAU	Asia	Arabian Peninsula	c0	sc00
Senegal	SN	SEN	Africa	West Africa	c3	sc31
Serbia	RS	SRB	Europe	Eastern Europe	c0	sc02
Seychelles	SC	SYC	Africa	East Africa	c1	sc13
Sierra Leone	SL	SLE	Africa	West Africa	c2	sc20
Singapore	SG	SGP	Asia	South-East Asia	c0	sc00
Slovakia	SK	SVK	Europe	Western Europe	c0	sc02
Slovenia	SI	SVN	Europe	Southern Europe	c0	sc03
Solomon Islands	SB	SLB	Americas+Oceania	Oceania	c2	sc20
South Africa	ZA	ZAF	Africa	Southern Africa	c1	sc12
South Korea	KR	KOR	Asia	East Asia	c0	sc04
Spain	ES	ESP	Europe	Southern Europe	c0	sc00
Sri Lanka	LK	LKA	Asia	Indian Subcontinent	c1	sc12
Sudan	SD	SDN	Africa	East Africa	c3	sc31
Suriname	SR	SUR	Americas+Oceania	SA - South America (Rest)	c1	sc13
Sweden	SE	SWE	Europe	Northern Europe	c0	sc01
Switzerland	CH	CHE	Europe	Western Europe	c0	sc04
Tajikistan	TJ	TJK	Asia	Central Asia	c1	sc10
Tanzania	TZ	TZA	Africa	East Africa	c3	sc30
Thailand	TH	THA	Asia	South-East Asia	c0	sc00
Timor-Leste	TL	TLS	Asia	South-East Asia	c2	sc23
Togo	TG	TGO	Africa	West Africa	c3	sc32
Tonga	TO	TON	Americas+Oceania	Oceania	c1	sc12
Trinidad and Tobago	TT	TTO	Americas+Oceania	NA - North America (Rest)	c1	sc13
Tunisia	TN	TUN	Africa	North Africa	c1	sc11
Turkey	TR	TUR	Asia	Fertile Crescent	c0	sc00
Uganda	UG	UGA	Africa	East Africa	c3	sc32
Ukraine	UA	UKR	Europe	Eastern Europe	c0	sc02
United Arab Emirates	AE	ARE	Asia	Arabian Peninsula	c1	sc12
United Kingdom	GB	GBR	Europe	Western Europe	c0	sc03
United States	US	USA	Americas+Oceania	NA - North America (Rest)	c0	sc04
Uruguay	UY	URY	Americas+Oceania	SA - South America (Rest)	c0	sc00
Uzbekistan	UZ	UZB	Asia	Central Asia	c1	sc10
Vanuatu	VU	VUT	Americas+Oceania	Oceania	c2	sc21
Vietnam	VN	VNM	Asia	South-East Asia	c2	sc22
Zambia	ZM	ZMB	Africa	East Africa	c3	sc32
Zimbabwe	ZW	ZWE	Africa	East Africa	c2	sc21

Credits

The source of data for this report is:

• UNDP Human Development Report at (http://hdr.undp.org/en/data). UNDP has shared their data under  Creative Commons Attribution 3.0 IGO which allows free use if credit is given (as we have done here). [No changes to the data were made].

This report has been prepared by gralovis insights private limited, Mumbai – 400078, India.