WHEN:	Sometime	in	the	1930s
WHERE:	The	office	of	the	Gosplan,	the	central	planning	authority	of	the	USSR
WHAT:	Interview	for	the	post	of	the	chief	statistician
The	first	candidate	is	asked	by	the	interview	board,	 What	is	two	plus	two,	comrade? 	He	answers:
Five.
The	chairman	of	the	interview	board	smiles	indulgently	and	says:	 Comrade,	we	very	much	appreciate
your	revolutionary	enthusiasm,	but	this	job	needs	someone	who	can	count. 	The	candidate	is	politely
shown	the	door.
The	second	candidate s	answer	is	 Three. 	The	youngest	member	of	the	interview	board	springs	up	and
shouts:	 Arrest	that	man!	We	cannot	tolerate	this	kind	of	counter-revolutionary	propaganda,	under-
reporting	our	achievements! 	The	second	candidate	is	summarily	dragged	out	of	the	room	by	the	guards.
When	asked	the	same	question,	the	third	candidate	answers:	 Of	course	it	is	four. 	The	professorial-
looking	member	of	the	board	gives	him	a	stern	lecture	on	the	limitations	of	bourgeois	science,	fixated	on
formal	logic.	The	candidate	hangs	his	head	in	shame	and	walks	out	of	the	room.
The	fourth	candidate	is	hired.
What	was	his	answer?
How	many	do	you	want	it	to	be?
Output
Gross	Domestic	Product,	or	GDP
Output	figures	are	rarely	 manufactured 	blatantly,	even	in	socialist	countries,	except	in	the	most
extreme	political	situations	–	such	as	the	early	days	of	Stalin s	rule	or	the	Great	Leap	Forward	under	Mao
Zedong	in	China.	Still,	it	would	be	wrong	to	think	that	we	can	measure	economic	output,	or	any	other
number	in	economics	for	that	matter,	in	the	way	we	measure	things	in	natural	sciences,	such	as	physics	or
chemistry.
The	economists 	favoured	measure	for	output	is	Gross	Domestic	Product,	or	GDP.	It	is,	roughly
speaking,	the	total	monetary	value	of	what	has	been	produced	within	a	country	over	a	particular	period	of
time	–	usually	a	year,	but	also	a	quarter	(three	months)	or	even	a	month.
I	said	 roughly ,	because	 what	has	been	produced 	needs	definition.	In	calculating	GDP,	we	measure
output	–	or	product	–	by	value	added.	Value	added	is	the	value	of	a	producer s	output	minus	the
intermediate	inputs	it	has	used.	A	bakery	may	earn	£150,000	a	year	by	selling	bread	and	pastries,	but	if	it
has	paid	£100,000	in	order	to	buy	various	intermediate	inputs	–	raw	materials	(e.g.,	flour,	butter,	eggs,
sugar),	fuel,	electricity	and	so	on	–	it	has	only	added	£50,000	of	value	to	those	inputs.
If	we	didn t	take	away	the	value	of	the	intermediate	inputs	and	simply	added	up	the	final	outputs	of	all
the	producers,	we	would	be	double-,	triple-	and	multiple-counting	some	components,	inflating	the	actual
output.	The	baker	bought	its	flour	from	a	milling	company,	so	if	we	simply	added	up	the	output	of	the
baker	and	the	miller,	the	flour	that	the	baker	bought	would	be	counted	twice.	The	miller	bought	the	wheat
from	a	farmer,	so	if	we	added	the	output	of	the	wheat	farmer	to	those	of	the	baker	and	the	miller,	the
portion	of	the	wheat	output	that	the	farmer	had	sold	to	the	miller	and	then	was	sold	on	to	the	baker	would
be	counted	three	times.	Only	by	counting	the	 added 	value	can	we	measure	the	true	size	of	the	output.*
What	about	the	 Gross 	bit	in	GDP?	It	means	that	we	still	have	not	taken	away	something	that	could
have	been	removed	from	the	picture,	as	when	a	can	of	tuna	specifies	gross	weight	and	net	weight	(that	is,
the	weight	of	the	fish	without	the	oil	or	brine).	In	this	case,	that	something	is	the	used-up	parts	of	capital
goods	–	basically	machines,	so	we	are	talking	the	baker s	ovens,	dough	mixers	and	bread	slicers.	Capital
goods,	or	machines,	are	not	 consumed 	and	incorporated	into	the	output	in	the	same	way	in	which	flour	is
to	bread,	but	they	experience	reduction	in	economic	value	with	use	–	this	is	known	as	depreciation.	If	we
take	away	the	wear	and	tear	of	machines	from	GDP,	we	get	Net	Domestic	Product,	or	NDP.
Net	Domestic	Product,	or	NDP
As	NDP	accounts	for	everything	that	has	gone	into	producing	the	output	–	intermediate	inputs	and
capital-goods	inputs	–	it	provides	a	more	accurate	picture	of	what	the	economy	has	produced	than	GDP
does.	But	we	tend	to	use	GDP	instead	of	NDP	because	there	is	no	one	agreed	way	of	estimating
depreciation	(suffice	it	to	say	here	there	are	several	contending	ways),	which	makes	the	definition	of	N	in
NDP	quite	tricky.
Then	how	about	D	in	GDP?	 Domestic 	here	means	being	within	the	boundary	of	a	country.	Not	all
producers	in	a	country	are	its	own	citizens	or	companies	registered	in	it.	Seen	from	the	other	side,	not	all
producers	produce	in	their	home	countries;	companies	run	factories	abroad,	and	people	get	jobs	in	foreign
countries.	The	number	that	measures	all	the	output	produced	by	your	nationals	(including	companies),
rather	than	the	output	produced	within	your	border,	is	called	Gross	National	Product,	or	GNP.
Gross	National	Product,	or	GNP
In	the	US	or	Norway,	GDP	and	GNP	are	more	or	less	identical.	In	Canada,	Brazil	and	India,	with	many
foreign	firms	inside	their	borders	and	few	domestic	firms	producing	abroad,	GDP	could	be	more	than	10
per	cent	bigger	than	GNP.	For	Sweden	and	Switzerland,	which	have	more	of	their	national	firms	operating
abroad	than	foreign	firms	operating	within	their	borders,	GNP	is	bigger	than	GDP,	around	2.5	and	5	per
cent	respectively	as	of	2010.
GDP	is	more	frequently	used	than	GNP,	since,	in	the	short	run,	it	is	the	more	accurate	indicator	of	the
level	of	productive	activities	within	a	country.	But	GNP	is	a	better	measure	of	an	economy s	long-term
strength.
A	country	may	have	a	higher	GDP	(GNP)	than	another,	but	that	may	be	because	it	has	a	larger
population	than	the	other.	So,	we	really	need	to	look	at	GDP	or	GNP	figures	per	capita	(per	head,	or	per
person,	if	you	like)	if	we	want	to	know	how	productive	the	economy	is	–	it	is	actually	somewhat	more
complicated	than	that,	but	we	can	leave	this	aside;	if	you	are	interested,	read	the	footnote.*
Limitations	of	GDP	and	GNP	measures
A	critical	limitation	of	GDP	and	GNP	measures	is	that	they	value	outputs	at	market	prices.	Since	a	lot
of	economic	activities	occur	outside	the	market,	the	values	of	their	outputs	need	to	be	somehow	calculated
–	 imputed 	is	the	technical	word.	For	example,	a	lot	of	farmers	in	developing	countries	engage	in
subsistence	farming	in	which	they	consume	most	of	the	food	they	produce.	So	we	need	to	estimate	that
quantity	and	impute	market	values	to	what	those	farmers	produced	but	did	not	sell	in	the	market	(and
consumed	themselves).	Or,	when	people	live	in	houses	they	own,	we	impute	the	value	of	the	 dwelling
services 	involved,	as	if	the	house-owners	are	paying	the	rents	at	market	rates	to	themselves.	Unlike
outputs	exchanged	through	markets,	the	imputation	of	market	values	to	non-marketed	outputs	involves
guesswork,	imparting	inaccuracy	to	the	numbers.
Worse,	there	is	a	particular	class	of	non-marketed	output	whose	value	isn t	even	imputed.	Household
work	–	including	cooking,	cleaning,	care	work	for	children	and	elderly	relatives	and	so	on	–	is	simply	not
counted	as	part	of	GDP	or	GNP.	The	classic	 joke 	among	economists	is	that	you	reduce	your	national
output	if	you	marry	your	housekeeper.	The	standard	excuse	is	that	it	is	difficult	to	impute	values	to
household	work,	but	it	is	a	very	weak	defence.	After	all,	we	impute	values	to	all	sorts	of	other	non-
marketed	economic	activities,	including	living	in	one s	own	house.	As	the	vast	bulk	of	household	work	is
done	by	women,	women s	work	is	grossly	under-valued	as	a	result	of	this	practice.	Many	estimates	put
the	value	of	household	work	to	be	equivalent	to	around	30	per	cent	of	GDP.
REAL-LIFE	NUMBERS
Why	do	you	need	to	know	 real-life	numbers ?
Despite	the	common	impression	that	it	is	a	 numbers 	subject,	economics	as	it	is	taught	today	is	rather
short	on	numbers.	It	is	common	that	someone	with	an	economics	degree	does	not	know	some	 obvious
economic	numbers,	such	as	the	GDP	or	the	average	working	hours	of	her	own	country.
There	is	no	way	anyone	can	remember	more	than	a	handful	of	those	numbers.	Indeed,	in	this	internet
age,	you	don t	have	to	remember	any	of	them,	because	you	can	easily	look	them	up.	But	I	believe	it	is
important	that	my	readers	familiarize	themselves	with	some	of	these	 real-life	numbers ,	even	just	to
know	what	numbers	to	look	up.	More	importantly,	they	need	to	develop	a	sense	of	what	our	economic
world	looks	like	in	reality:	when	we	talk	about	China s	GDP,	are	we	talking	hundreds	of	billions	or	tens
of	trillions	of	US	dollars?	Are	we	talking	15	per	cent	or	30	per	cent	when	we	say	that	South	Africa	has
one	of	the	highest	unemployment	rates	in	the	world?	When	we	say	that	a	high	proportion	of	people	in
India	live	in	poverty,	do	we	mean	20	per	cent	or	40	per	cent?	Thus,	in	this	and	all	subsequent	chapters,	I
provide	a	selection	of	the	most	important	real-life	economic	numbers.
Most	of	world	output	is	produced	by	a	small	number	of	countries
The	world	GDP	in	2010,	according	to	the	World	Bank	data,	was	around	$63.4	trillion.	The	five	largest
economies	by	GDP	were	the	US	(22.7	per	cent	of	the	world	economy),	China	(9.4	per	cent),	Japan	(8.7
per	cent),	Germany	(5.2	per	cent)	and	France	(4.0	per	cent).*	Thus	these	five	economies	accounted	for
half	of	world	output.
In	2010,	the	 high-income	countries 	in	the	World	Bank	classification	(countries	with	above	$12,276
per	capita	income)	had	collective	GDP	of	$44.9	trillion.†	They	accounted	for	70.8	per	cent	of	the	world
economy.	The	rest	of	the	world,	or	the	developing	world,	collectively	had	a	GDP	of	$18.5	trillion,	or
29.2	per	cent	of	world	GDP.	But	two-thirds	(66.6	per	cent)	of	this	$18.5	trillion	was	accounted	for	by	the
five	largest	developing	economies,	China,	Brazil,	India,	Russia	and	Mexico.*	The	rest	of	the	developing
world,	with	a	collective	GDP	of	$6.3	trillion,	accounted	for	just	under	10	per	cent	of	the	world	economy.
Most	developing	economies	produce	tiny	–	I	mean	tiny	–	fractions	of	what	the	richest	countries	produce
The	typical	GDP	of	very	poor	small	developing	countries	(5–10	million	people),	such	as	the	Central
African	Republic	or	Liberia,	is	in	the	region	of	one	or	two	billion	dollars,	or	$0.001	trillion	to	$0.002
trillion.	These	are	not	even	0.01	per	cent	of	the	US	GDP,	which	was	$14.4	trillion	as	of	2010.
The	thirty-five	low-income	countries	according	to	the	World	Bank	classification	(countries	with	less
than	$1,005	per	capita	GDP	in	2010)	collectively	had	a	GDP	of	$0.42	trillion.	This	is	0.66	per	cent	of	the
world	economy	or	2.9	per	cent	of	the	US	economy.
Even	the	larger	middle-income	developing	countries	(30–50	million	people),	such	as	Colombia	or
South	Africa,	may	have	GDP	of	$300–400	billion.	These	are	only	as	large	as	the	GDP	of	a	mid-sized	US
state,	such	as	Washington	or	Minnesota.
In	terms	of	GDP	per	capita	figures,	we	have	a	huge	range.	Since	these	figures	are	similar	–	actually
identical	in	theory,	although	not	necessarily	so	in	practice	–	to	income	per	capita	figures	that	we	discuss
shortly,	suffice	it	to	say	here	that	we	are	talking	about	differentials	over	500	times.
Income
Gross	Domestic	Income,	or	GDI
GDP	may	be	seen	as	a	sum	of	incomes,	rather	than	outputs,	as	everyone	who	is	involved	in	the
production	activity	is	paid	for	his/her	contribution	(whether	the	amounts	paid	are	 fair 	is	another	matter).
Going	back	to	the	baker s	example,	having	paid	for	flour,	eggs	and	other	intermediate	inputs,	the	bakery
will	divide	up	its	value-added	between	wages	for	its	workers,	profits	for	its	shareholders,	interest
payments	for	the	loan	it	may	have	contracted	and	the	indirect	taxes	that	are	automatically	included	in	the
revenue	that	it	generates	(that	is,	value	added	tax	(VAT)	or	sales	tax).
The	sum	of	these	incomes	is	known	as	Gross	Domestic	Income,	or	GDI.	In	theory,	GDI	should	be
identical	to	GDP,	as	it	is	simply	a	different	way	of	adding	up	the	same	thing.	But	in	practice	it	is	slightly
different,	as	some	of	the	data	used	in	compiling	the	two	of	them	may	be	collected	through	different
channels.
Gross	National	Income,	or	GNI,	and	per	capita	GNI
Like	GNP	is	to	GDP,	Gross	National	Income,	or	GNI,	is	to	GDI.	GNI	is	the	result	of	adding	up	the
incomes	of	a	country s	citizens,	rather	than	the	incomes	of	those	who	are	producing	within	its	border,
which	gives	us	GDI.	The	World	Bank	publishes	GDP	and	GNI,	rather	than	GNP	and	GDI.	This	is
presumably	on	the	reasoning	that	income,	as	a	measure	of	earnings,	is	better	measured	according	to	the
nationality	of	those	who	claim	it,	while	product,	as	a	measure	of	outputs,	is	better	measured	according	to
where	the	production	activities	are	happening.
Per	capita	income,	usually	measured	by	GNI	(or	its	product	equivalent,	GNP)	per	capita,	is	considered
by	many	people	to	be	the	single	best	measure	of	a	country s	living	standard.	But	saying	that	it	is	the	best
does	not	mean	that	it	is	good	enough.
One	obvious	problem	is	that	GNI	per	capita	only	measures	the	average	income.	But	the	average	may
conceal	a	much	greater	variation	among	different	individuals	and	groups	in	one	country	than	in	another.	To
give	a	simple	numerical	example,	Countries	A	and	B	may	both	have	$5,000	per	capita	income	and	ten
people	(therefore	GNI	of	$50,000	each),	but	A	may	consist	of	one	person	with	$45,500	income	and	nine
people	with	$500	each,	while	B	may	consist	of	one	person	with	$9,500	income	and	nine	people	with
$4,500	each.	In	this	case,	$5,000	per	capita	income	will	be	a	relatively	accurate	description	of	the
standard	of	living	in	Country	B	but	will	be	completely	misleading	for	Country	A.	To	use	a	more	technical
term,	you	would	say	that	the	average	income	is	a	more	accurate	indicator	of	the	living	standard	for	a
country	with	a	more	equal	distribution	of	income.	(More	on	this	in	Chapter	9.)
Adjusting	for	different	price	levels:	purchasing	power	parity
One	important	adjustment	that	is	often	made	to	the	GNI	(or	GDP)	figures	is	that	for	different	price
levels	in	different	countries.	The	market	exchange	rate	between	the	Danish	krone	and	the	Mexican	peso
may	be	around	one	krone	to	2.2	pesos,	but	with	2.2	pesos	you	can	buy	more	goods	and	services	in	Mexico
than	you	can	with	one	krone	in	Denmark	(I	will	explain	shortly	why).	So	the	official	exchange	rate
between	the	Danish	krone	and	the	Mexican	peso	under-estimates	the	actual	living	standards	in	Mexico.
The	problem	is	that	market	exchange	rates	are	largely	determined	by	the	supply	and	demand	for
internationally	traded	goods	and	services,	such	as	the	Galaxy	phones	or	international	banking	services,
while	what	a	sum	of	money	can	buy	in	a	particular	country	is	determined	by	the	prices	of	all	goods	and
services,	including	those	that	are	not	internationally	traded,	such	as	eating	out	or	taking	a	taxi.1
To	deal	with	this	problem,	economists	have	come	up	with	the	idea	of	an	 international	dollar .	Based
on	the	notion	of	purchasing	power	parity	(PPP)	–	that	is,	measuring	the	value	of	a	currency	according	to
how	much	of	a	common	set	of	goods	and	services	(known	as	the	 consumption	basket )	it	can	buy	in
different	countries	–	this	fictitious	currency	allows	us	to	convert	incomes	of	different	countries	into	a
common	measure	of	living	standards.
The	result	of	the	conversion	is	that	PPP	incomes	of	countries	with	expensive	service-sector	workers
(the	rich	countries,	excluding	a	few	with	a	lot	of	cheap	immigrant	labour,	such	as	the	US	and	Singapore)
are	significantly	lower	than	their	market-exchange-rate	incomes,	while	those	of	countries	with	cheap
service	workers	(the	poor	countries)	tend	to	become	much	higher	than	their	market-exchange-rate
incomes.*
Sticking	to	the	Denmark–Mexico	comparison	above,	Danish	PPP	per	capita	income	in	2010	is	around
30	per	cent	lower	than	its	market-exchange-rate	income	($40,140	vs.	$58,980),	while	the	Mexican	PPP
per	capita	income	is	around	60	per	cent	higher	than	its	market-exchange-rate	income	($15,010	vs.
$9,330).	So	the	income	gap	of	over	six	times	($58,980	vs.	$9,330)	is	reduced	to	the	living	standard	gap
of	under	three	times	($40,140	vs.	$15,010)	after	the	PPP	adjustments.
PPP	adjustment	is	very	sensitive	to	the	methodology	and	the	data	used,	not	least	because	it	relies	on	the
rather	heroic	assumption	that	all	countries	consume	the	same	basket	of	goods	and	services.	And	we	are
not	talking	about	minor	differences.	By	changing	its	method	of	estimating	PPP	incomes	in	2007,	the	World
Bank	reduced	China s	PPP	income	per	capita	by	44	per	cent	(from	$7,740	to	$5,370)	and	increased
Singapore s	by	53	per	cent	(from	$31,710	to	$48,520)	overnight.
Income	figures	do	not	fully	represent	living	standards,	even	with	PPP	adjustments
Even	with	PPP	adjustments,	income	figures,	such	as	GNP	per	capita	and	GNI	per	capita,	do	not	fully
represent	living	standards.	There	are	a	number	of	reasons	for	this.
One	obvious	but	important	point	is	that	we	don t	live	by	monetary	income	alone.	We	want	political
freedom,	vibrant	community	life,	self-fulfilment	and	many	other	things	that	money	cannot	buy.	The
increase	in	monetary	income	does	not	guarantee	increases	in	these	things	and	may	even	undermine	them.
For	example,	if	higher	income	is	gained	at	the	cost	of	working	longer	and	with	greater	intensity,	we	may
have	less	time	and	energy	for	community	life	or	self-fulfilment.
Another	is	that,	as	pointed	out	above,	income	figures	do	not	reflect	household	work	(including	care
work),	which	to	a	substantial	part	of	the	humanity	–	children,	the	elderly	and	the	sick	–	are	the	most
important	things.
Even	regarding	things	that	can	be	bought	with	money,	we	often	make	poor	decisions	as	consumers
(recall	Chapter	5).	Influenced	by	advertising	or	in	our	desire	to	 keep	up	with	the	Joneses 	(or	the	Zhangs,
the	Patels,	the	Castros,	or	whoever,	depending	on	where	you	live),	most	of	us	have	bought	things	that	we
never	knew	we	needed.	Beyond	providing	the	fleeting	joy	of	purchase	itself,	these	goods	add	little	to	our
well-being.
Even	if	we	are	totally	rational	as	consumers,	the	existence	of	positional	goods	makes	income	an
unreliable	gauge	of	true	living	standard	(or	happiness,	satisfaction	or	what	you	will).2	Positional	goods
are	goods	whose	values	derive	from	the	fact	that	only	a	small	proportion	of	potential	consumers	can	have
them.*	Even	if	our	personal	income	rises,	we	may	still	be	unable	to	acquire	things	like	houses	in	prime
locations,	Rembrandt s	paintings	or	elite	education	that	gives	access	to	top	jobs,	if	others	have	also
become	richer	and	are	able	to	stump	up	even	more	money	than	we	can.	This	problem	is	more	severe	in
richer	economies,	as	the	finer	things	in	life	tend	to	be	positional	goods,	while	essential	goods	are	usually
not.
These	limitations	don t	mean	that	income	is	unimportant	in	measuring	living	standards.	Especially	in
the	poorer	countries,	a	higher	income	is	largely	a	positive	thing.	In	those	countries,	even	a	slightly	higher
income	can	make	all	the	difference	between	eating	properly	and	starvation,	between	working	in	a
dangerous,	back-breaking	job	and	having	just	a	hard	job,	and	between	having	your	child	die	at	the	age	of
one	and	seeing	it	grow	up.	In	the	richer	societies,	the	positive	impacts	of	a	higher	income	on	living
standards	are	less	certain.	But	even	there,	higher	incomes	will	help	people	have	higher	standards	of
living,	if	they	are	used	well.	For	example,	a	higher	income	will	allow	a	country	to	reduce	working	hours
and	thus	enable	people	to	have	more	time	with	family	and	friends	or	get	more	adult	education,	while
maintaining	previous	levels	of	material	consumption.
REAL-LIFE	NUMBERS
What	are	the	income	figures	like	in	the	real	world?	Here	we	will	look	at	income	per	capita	figures,	given
that	we	have	already	talked	a	lot	about	overall	output	figures,	such	as	GDP	and	GNP,	that	are	identical	to
overall	income	figures	in	theory	and	are	very	similar	to	them	in	practice.
Countries	that	we	typically	know	as	the	richest	countries	have	over	$40,000	per	capita	income
According	to	the	World	Bank,	in	2010,	the	country	with	the	highest	income	(GNI)	per	capita	in	the
world	was	Monaco	($197,460),	followed	by	Liechtenstein	($136,540).	However,	both	these	are	tax
havens	with	tiny	populations	(33,000	and	36,000	respectively).	So,	if	we	exclude	countries	with	a
population	of	less	than	half	a	million,	Norway,	with	a	per	capita	income	of	$85,380,	is	the	richest	country
(that	is,	it	has	the	highest	per	capita	GNI).
A	selection	of	the	richest	countries	is	listed	in	Table	6.1.	They	are	mostly	in	Western	Europe	and
Western	offshoots.	A	few	Asian	countries	belong	to	this	group,	with	Japan	and	Singapore	firmly	in	the
upper	league.	South	Korea,	together	with	a	couple	of	Eastern	European	countries,	are	there	too	–	only
just.
The	average	person	in	the	poorest	four	countries	doesn t	even	earn	$1	a	day
At	the	other	extreme,	Burundi,	with	$160	per	capita	income,	was	the	poorest	country	in	the	world	in
2010.	In	several	of	the	poorest	countries,	the	average	person	did	not	even	earn	$1	of	income	per	day
($365	per	year).
Countries	with	less	than	$1,000	per	capita	income	are	officially	classified	as	 low-income 	countries
in	the	World	Bank	classification	(the	World	Bank	cut-off	line	is	$1,005),	or	as	least-developed	countries
(LDCs)	by	various	international	treaties	and	organizations.
Table	6.2	lists	a	selection	of	LDCs.	It	shows	that	most	of	them	are	in	Africa,	with	a	few	in	Asia	(Nepal,
Bangladesh,	Cambodia,	Tajikistan,	Kyrgyz	Republic)	and	only	one	in	Latin	America	(Haiti).
INCOME	RANGE COUNTRIES	(FROM	THE	RICHEST	TO	THE	POOREST	IN	EACH	GROUP)
$50,001	and	above Norway	($85,380),	Switzerland	($70,350),	Denmark	($58,980)
$45,001	–	$50,000 Sweden	($49,930),	the	Netherlands	($49,720),	Finland	($47,170),	the	USA	($47,140),	Belgium	($45,420)
$40,001	–	$45,000 Australia	($43,740),	Germany	($43,330),	France	($42,390),	Japan	($42,150),	Canada	($41,950),	Singapore	($40,920)
$30,001	–	$40,000 The	UK	($38,540),	Italy	($35,090),	Spain	($31,650)
$20,001	–	$30,000 New	Zealand	($29,050),	Israel	($27,340),	Greece	($27,240)
$15,001	–	$20,000 South	Korea	($19,890),	Czech	Republic	($17,870),	Slovakia	($16,220)
Table	6.1
Incomes	of	the	richest	countries	(GNI	per	capita,	2010)
Source:	World	Bank,	World	Development	Report,	2012.
INCOME	RANGE COUNTRIES	(FROM	THE	POOREST	TO	THE	RICHEST	IN	EACH	GROUP)
$300	and	below Burundi	($160),	Democratic	Republic	of	Congo	($180),	Liberia	($190)
£301	–	$400 Malawi	($330),	Eritrea	($340),	Sierra	Leone	($340),	Niger	($360),	Ethiopia	($380),	Guinea	($380)
$401	–	$500 Mozambique	($440),	Togo	($440),	Central	African	Republic	($460),	Zimbabwe	($460),	Uganda	($490),	Nepal	($490)
$501	–	$600 Tanzania	($530),	Rwanda	($540),	Burkina	Faso	($550),	Mali	($600)
$601	–	$800 Bangladesh	($640),	Haiti	($650),	Benin	($750),	Cambodia	($760),	Tajikistan	($780)
$801	–	$1,000 Kyrgyz	Republic	($880)
Table	6.2
Incomes	of	the	poorest	countries	(GNI	per	capita,	2010)
Source:	World	Bank,	World	Development	Report,	2012.
INCOME	RANGE COUNTRIES	(FROM	THE	RICHEST	TO	THE	POOREST	IN	EACH	GROUP)
$8,001	–	$10,000 Chile	($9,940),	Russia	($9,910),	Turkey	($9,500),	Brazil	($9,390),	Mexico	($9,330),	Argentina	($8,450)
$6,001	–	$8,000 Malaysia	($7,900),	Costa	Rica	($6,580),	Bulgaria	($6,240),	South	Africa	($6,100)
$4,001	–	$6,000 Colombia	($5,510),	Ecuador	($4,510),	Algeria	($4,460),	China	($4,260),	Thailand	($4,210),	Tunisia	($4,070)
$3,001	–	$4,000* Angola	($3,960),	El	Salvador	($3,360)
$2,001	–	$3,000 Indonesia	($2,580),	Egypt	($2,340),	Sri	Lanka	($2,290),	the	Philippines	($2,050)
$1,001	–	$2,000 Bolivia	($1,790),	India	($1,340),	Ghana	($1,240),	Vietnam	($1,100),	Pakistan	($1,050)
$1,000	and	below* Least	Developed	Countries	(LDCs)
Table	6.3
Incomes	of	selected	developing	countries	(GNI	per	capita,	2010)
Source:	World	Bank,	World	Development	Report,	2012.
Thus,	the	per	capita	income	of	the	richest	(Norway)	is	a	staggering	534	times	greater	than	that	of	the
poorest	(Burundi)	as	of	2010.	Even	if	we	take	the	less	extreme	cases	of	the	US	(no.	7	from	the	top	with
$47,140)	versus	Ethiopia	(no.	8	from	the	bottom,	with	$380),	the	income	differential	is	still	124	times.
There	are	poor	countries	and	there	are	poor	countries:	gaps	between	developing	countries
In	between	these	extremes	lie	the	vast	majority	of	countries	that	are	called	middle-income	countries	in
the	World	Bank	classification.	People,	including	myself,	often	call	them	developing	countries	or	simply
poor	countries,	but	there	is	poor	and	there	is	poor.
Table	6.3	provides	per	capita	incomes	of	a	selection	of	developing	countries,	to	give	the	reader	some
idea	of	who	belongs	where	and	also	the	gaps	that	exist	between	developing	countries	themselves.
At	the	top	of	the	developing	country	grouping	are	countries	like	Brazil	and	Mexico,	with	$8,001–
$10,000	per	capita	incomes.	These	countries	have	per	capita	incomes	that	are	fifty	to	sixty	times	higher
than	those	of	the	poorest	countries	that	we	discussed	in	Table	6.2,	when	their	own	differentials	with	the
richest	countries	are	no	more	than	ten	times.
Countries	that	we	typically	think	of	when	we	hear	the	words	 developing	countries 	–	such	as
Indonesia,	Egypt,	Sri	Lanka,	the	Philippines,	India	and	Ghana	–	are	mostly	found	in	the	$1,001–$3,000
range	of	per	capita	income.	Even	these	countries	have	per	capita	incomes	that	are	five	to	ten	times	those
of	the	poorest	countries.
PPP	adjustments	show	that	gaps	in	living	standards	are	not	as	severe	as	gaps	in	productivity
To	more	precisely	learn	about	different	countries 	living	standards	instead	of	their	productivity,	we
need	to	convert	their	incomes	(outputs)	into	PPP	terms.	This	adjustment	results	in	significant	changes	in
the	rankings	of	countries.
In	PPP	terms,	Luxembourg,	at	$63,850,	becomes	the	richest	country	in	the	world,	followed	by	Norway,
Singapore,	Kuwait,	Switzerland	and	the	US.*	With	PPP	adjustments,	per	capita	incomes	of	poor	countries
rise	in	relative	terms,	as	non-traded	services	(and	some	goods)	are	cheaper	in	these	countries.	In	PPP
terms,	the	Democratic	Republic	of	Congo	(DRC)	($310),	Liberia	($330)	and	Burundi	($390)	are	the	three
poorest	countries	in	the	world.†
With	these	PPP	adjustments,	the	income	differences	between	the	rich	and	the	poor	countries	are
diminished,	compared	with	the	ones	calculated	in	terms	of	market	exchange	rate	incomes.	The	difference
between	the	highest	and	the	lowest	GNI	per	capita	is	diminished	from	534	times	(Norway	vs.	Burundi)	to
only 	206	times	(Luxembourg	vs.	the	DRC).
Happiness
Not	everything	that	counts	can	be	measured,	not	everything	that	can	be	measured	counts:	can	–	and	should	–	happiness	be
measured?
Recognizing	the	limitations	of	using	monetary	income	to	measure	living	standards,	some	economists
have	resorted	to	directly	asking	people	how	happy	they	are.	These	 happiness 	studies	allow	us	to	get
around	a	lot	of	problems	involved	in	measuring	living	standards:	what	needs	to	be	included	in	the
measurement;	how	we	assign	values	to	difficult-to-measure	elements	that	affect	our	living	standards	(even
though	this	has	not	stopped	people	from	coming	up	with	things	like	 political	freedom	index );	and	what
weight	to	give	to	each	element.	The	best-known	of	this	type	of	study	are	the	Gallup	Happiness	Survey	and
the	World	Values	Survey.
Many	people	question	whether	happiness	can	be,	and	indeed	should	be,	measured	at	all.	The	fact	that
happiness	may	be	conceptually	a	better	measure	than	income	does	not	mean	that	we	should	try	to	measure
it.	Richard	Layard,	the	British	economist	who	is	a	leading	scholar	trying	to	measure	happiness,	defends
such	attempts	by	saying,	 If	you	think	something	matters	you	should	try	to	measure	it	[italics	added]. 3	But
other	people	disagree	–	including	Albert	Einstein,	who	once	famously	said,	 Not	everything	that	counts
can	be	measured.	Not	everything	that	can	be	measured	counts.
We	can	try	to	quantify	happiness,	say,	by	asking	people	to	rate	their	happiness	on	a	scale	of	ten,	and
come	up	with	numbers	like	6.3	or	7.8	for	the	average	happiness	of	Countries	A	and	B.	But	such	numbers
are	not	even	half	as	objective	as	$160	or	$85,380	per	capita	incomes	–	and	we ve	discussed	why	even
the	income	numbers	are	not	totally	objective.
Adaptive	preference	and	false	consciousness:	why	we	cannot	totally	rely	on	people s	judgements	on	their	own	happiness
More	importantly,	it	is	debatable	whether	we	can	trust	people s	judgement	on	their	own	happiness.
There	are	all	kinds	of	adaptive	preferences,	in	which	people	reinterpret	their	situations	to	make	them
more	bearable.	 Sour	grapes ,	namely,	deciding	that	what	you	could	not	get	is	actually	not	as	good	as	you
had	thought,	is	a	classic	example.
Many	people	who	are	oppressed,	exploited	or	discriminated	against	say	–	and	they	would	not	be	lying
–	that	they	are	happy.	Many	of	them	even	oppose	changes	that	will	improve	their	lot:	many	European
women	opposed	the	introduction	of	female	suffrage	in	the	early	twentieth	century.	Some	of	them	may	even
play	an	active	part	in	perpetuating	injustice	and	brutality	–	like	those	slaves	who	took	a	lead	in	the
oppression	of	other	slaves,	such	as	Stephen,	the	character	played	by	Samuel	L.	Jackson	in	the	movie
Django	Unchained.
These	people	think	they	are	happy	because	they	have	come	to	accept	–	 internalize 	is	the	fancy	word
here	–	the	values	of	the	oppressors/discriminators.	Marxists	call	these	cases	of	false	consciousness.
The	Matrix	and	the	limits	of	happiness	studies
The	problem	that	false	consciousness	poses	for	happiness	studies	has	been	most	brilliantly	illustrated
by	the	Wachowski	siblings 	mind-blowing	1999	movie	The	Matrix.	In	the	movie,	we	have	those,	like
Morpheus,	who	think	that	a	happy	life	under	false	consciousness	is	unacceptable.	Others,	like	Cypher,
would	rather	live	in	false	consciousness	than	lead	a	dangerous	and	hard	life	of	resistance	in	reality.	And
who	are	we	to	say	that	Cypher s	choice	is	necessarily	the	wrong	one?	What	right	does	Morpheus	have	to
rescue 	people	only	to	make	them	feel	miserable?
The	issue	of	false	consciousness	is	a	genuinely	difficult	problem	that	has	no	definite	solution.	We
should	not	approve	of	an	unequal	and	brutal	society	because	surveys	show	that	people	are	happy.	But	who
has	the	right	to	tell	those	oppressed	women	or	starving	landless	peasants	that	they	shouldn t	be	happy,	if
they	think	they	are?	Does	anyone	have	the	right	to	make	those	people	feel	miserable	by	telling	them	the
truth ?	There	are	no	easy	answers	to	these	questions,	but	they	definitely	tell	us	that	we	cannot	rely	on
subjective 	happiness	surveys	to	decide	how	well	people	are	doing.
Happiness	studies	with	more	objective	measures
Given	these	limitations	of	subjective	happiness	measures,	most	happiness	studies	now	combine	more
objective	measures	(e.g.,	income	level,	life	expectancy)	with	some	element	of	subjective	assessment.
One	good	–	and	quite	comprehensive	–	example	in	this	category	is	the	Better	Life	Index,	launched	in
2011	by	the	OECD.	This	index	looks	at	people s	subjective	judgements	on	life	satisfaction,	together	with
ten	other	more	(although	not	completely)	objective	indicators,	ranging	from	income	and	jobs	to	community
life	and	work–life	balance	(and	each	of	these	indicators	has	more	than	one	constituent	element).
Even	while	a	happiness	index	that	includes	more	elements	is	conceptually	more	defensible,	its
numerical	outcome	is	more	difficult	to	defend.	As	we	try	to	incorporate	more	and	more	dimensions	of	our
life	into	the	happiness	index,	we	are	made	to	include	more	and	more	dimensions	that	are	very	difficult,	if
not	impossible,	to	quantify.	Civic	engagement	and	the	quality	of	community	life	in	the	OECD	index	are
such	examples.	Moreover,	as	the	number	of	elements	grow	in	the	index,	it	becomes	more	difficult	to
assign	a	weight	to	each	element.	It	is	interesting	to	note	that,	in	open	recognition	of	this	difficulty,	the
OECD	Better	Life	Index	website	lets	you	make	up	your	own	index	by	varying	the	weights	between
different	elements	according	to	your	own	judgements.
REAL-LIFE	NUMBERS
Happiness	index	numbers,	whether	they	are	completely	subjective	or	combined	with	more	objective
indicators,	are	not	really	meaningful	in	themselves.	You	simply	cannot	compare	different	types	of
happiness	indexes	with	each	other.	The	only	thing	that	you	can	reasonably	do	with	them	is	track	changes	in
happiness	levels	for	individual	countries	according	to	one	index	or,	less	reliably,	rank	countries
according	to	one	index.
Different	happiness	indexes	include	very	different	elements.	As	a	result,	the	same	country	can	rank	very
differently	depending	on	the	index.	But	some	countries	–	the	Scandinavian	countries	(especially
Denmark),	Australia	and	Costa	Rica	–	tend	to	rank	highly	in	more	indexes	than	other	countries	do.	Some
countries	–	such	as	Mexico	and	the	Philippines	–	tend	to	do	better	in	indexes	with	greater	weight	given	to
subjective	factors,	suggesting	higher	degrees	of	 false	consciousness 	among	their	people.
Concluding	Remarks:	Why	Numbers	in	Economics	Can	Never	Be	Objective
Defining	and	measuring	concepts	in	economics	cannot	be	objective	in	the	way	such	exercises	in	physics
or	chemistry	can	be.	Even	such	an	exercise	regarding	what	are	seemingly	the	most	straightforward	of
economic	concepts,	such	as	output	and	income,	is	fraught	with	difficulties.	A	lot	of	value	judgements	are
involved	–	for	example,	the	decision	not	to	include	household	work	in	output	statistics.	There	are	many
technical	problems	–	especially	in	relation	to	the	imputation	of	value	to	non-marketed	activities	and	to	the
PPP	adjustments.	In	the	case	of	the	poorer	countries,	there	are	also	issues	with	data	quality	–	collecting
and	processing	the	raw	data	require	financial	and	human	resources	that	these	countries	do	not	have.
Even	if	we	do	not	dispute	the	numbers	themselves,	it	is	difficult	to	say	that	output/income	figures
correctly	represent	living	standards,	especially	in	richer	countries,	in	which	most	people	can	meet	their
basic	needs	for	food,	water,	clothing,	shelter,	basic	health	care	and	basic	education.	It	is	also	necessary
to	make	allowances	for	differences	in	purchasing	power,	working	hours,	non-monetary	aspects	of	the
standard	of	living,	irrational	consumer	choices	(whether	due	to	manipulation	or	herd	behaviour)	and
positional	goods.
Happiness	studies	try	to	obviate	these	needs,	but	they	have	their	own,	even	more	serious,	problems	–
the	inherent	immeasurability	of	happiness	and	the	problem	of	adaptive	preferences	(especially	of	the	false
consciousness	variety).
All	of	this	does	not	mean	that	we	should	not	use	numbers	in	economics.	Without	having	some
knowledge	of	key	numbers	–	like	output	levels,	growth	rates,	unemployment	rates	and	measures	of
inequality	–	an	informed	understanding	of	the	real-world	economy	is	impossible.	But	we	need	to	use	them
in	full	awareness	of	what	each	number	does	and	doesn t	tell	us.
Further	Reading
J.	ALDRED
The	Skeptical	Economist:	Revealing	the	Ethics	Inside	Economics	(London:	Earthscan,	2009).
F.	HIRSCH
Social	Limits	to	Growth	(London:	Routledge	and	Kegan	Paul,	1978).
M.	JERVEN
Poor	Numbers:	How	We	Are	Misled	by	African	Development	Statistics	and	What	to	Do	about	It	(Ithaca:
Cornell	University	Press,	2013).
R.	LAYARD
Happiness:	Lessons	from	a	New	Science	(London:	Allen	Lane,	2005).
A.	MADDISON
The	World	Economy:	A	Millennial	Perspective	(Paris:	OECD,	2001).
D.	NAYYAR
Catch	Up:	Developing	Countries	in	the	World	Economy	(Oxford:	Oxford	University	Press,	2013).
J.	STIGLITZ	ET	AL.
Mis-measuring	Our	Lives:	Why	GDP	Doesn t	Add	Up	(New	York:	The	New	Press,	2010).