01 The Great Enrichment &
the Great Divergence
Readings: Broadberry, et al. (2011); Allen, et al. (2011); Nordhaus (1996)
Living standards
• Living standards / standard of living
• The quantity and quality of goods and services, material comforts, and leisure
consumed by an individual (typically, the average individual in a society)
• Material well-being
• NOT happiness, satisfaction, purposefulness, pleasure, joy, etc.
• NOT some normative level
• i.e., NOT a standard to be achieved or some standard set by expectations
Living standards in 1800 CE
Source: Francisco Goya, Summer (1786-87)
Living standards in 1800 CE
Source: Clark (2007), pg. 38-39
Two phenomena:
• The Great Enrichment
•
•
•
•
Dierdre McCloskey’s term
Modern living standards are vastly higher than living standards 200 years ago
Growth rates in living standards are vastly faster than 200 years ago
Even for relatively low-income economies!
• The Great Divergence
• The transition to modern economic growth happened in different places at
different paces
• Large differences in living standards between economies today
Measuring living standards
• Real GDP,
• Market value of all final goods and services in produced in the economy in one
year
• Adjusted for prices
• AKA real output, real income, real aggregate output, real aggregate income,
economic output, etc.
• Real GDP per capita,
• Real GDP divided by population,
=
• AKA average output, average income, income per capita, output per capita,
GDP per capita, etc.
Why use GDP per captia not GDP?
Economy #1
• $3,300 billion of real output ( )
50 x larger economy than #2
• 194,000,000 people ( )
= $17,000
Economy #2
• $65 billion of real output ( )
• 580,000 people ( )
= $112,000
*IMF estimates, 2020, measured in
2020 international USD
Source: https://ourworldindata.org/economic-growth
Sophomoric criticism…
“GDP per capita doesn’t measure X, therefore it’s a poor measure of
living standards.”
• Here X is some desirable attribute.
•
•
•
•
Income equality
Health
Dire poverty
Etc…
• GDP per capita tends to be correlated with X
Correlation coefficient,
1
Stronger negative correlation
No
correlation
0
Stronger positive correlation
Perfect
negative
correlation
+1
Perfect
positive
correlation
Correlation
Causation
Gini Coefficient (2010-2018)
Higher household pretax/transfer market income
1
inequality
GDP per capita and Gini Coefficient (2010-2018)
0.9
0.8
0.7
R² = 0.1617
ZAF
0.6
NAM
SWZ
BRA
BWA
HND AGO
LCA
GNB
COL
PAN
COG
GTM
CRI
BEN
SYC
CMR
NIC
PRY
MEX CHL
ECU
COM
LSO
MWI
PHL
ZWE
RWA
DOM
GHA
TCD
TGO
NGA
PER
UGA
MDG
CPV BOL
COD
TUR
ARG
USA
HTI
MYS
IRN
TZA SEN KENCIV DJI MAR
BGR
LKA
URY
IDN
ISR
MNE
SLV
BDI
CHN
GAB
IND
RUS
BTN
LTU
MUS
FJI
YEM
LAO
THA
GEO
SRB
ROU
ITA
GMB
VNM
LVA
BFA
ETH
GBR
ESP
GRC
ARM
AUS CHELUX
NER LBR SLE
MKD
TJKSDN
PRT JPN
CAN
JOR
GIN
PAK PSE
ALB
MLI
BIH
NPL
IRL
TUN
MNG
MRT
BGD
LBNMDV HRV
KOR
FRADEU ARE
EGY
CYP
MMR
HUN
ESTMLT
POL
AUT
IRQ
SWE
DNK
NLD
KGZ
DZA
KAZ
BEL
FINISL
NOR
MDA UKR
BLR SVK SVN
CZE
CAF
0.5
0.4
0.3
0.2
MOZ
ZMBSTP
0.1
Most high
income
countries have
extensive taxes
& transfers. Gini
coefficients for
consumption are
likely lower.
0
6
=
0.40
7
8
9
10
11
12
ln(GDP per capita, 2018), 2017 USD PPP
Higher GDP
per capita
Caution: Correlation is NOT causation. Limited sample.
Source: Penn World Table [link], United Nations [link], own analysis
Labor sahre (as modeled by ILO)
Higher fraction of
real GDP accruing to
80
labor
GDP per capita and labor share of income, 2017, latest available
R² = 0.0674
70
CHE
LSO
NGA
HND
TTO
NLD
BELISL
CHL
ESP
FRACAN
AUT
BRA
DEU
MDA BRB
NIC
USA
ARG
SVN ITA
EST
HRV BHS
GIN
GBRDNK
AUS
BDI
FINSWE
LUX
CRI
TCD
CAF
GUY
OMN JPN
PRT
BOL
ZAF
HKG
KOR
LVA
COG
ISR
ALB
COL SRB
NOR
PRY
ECU
RUS
CHN
NZL
AZE
SVK CZE
MLI ZMB
DOMBGR GNQ
GRC
BLZ LAO
LCA
MOZ HTI BFA
GEO
TZA
CYP MLT
THA BLR HUN
POL
BRNSGP
GHA INDAGO NAMVCT SUR
LTU
LBN
MDG SLE
URY
BEN
TKM
DZA
ARM
PER
FJIBWA MNE
BTN
MUS
SLV TUN
ROU
ETH
KEN
MKD
MAR
MRT
UKR
YEM
PAK DJICPV
BIH MDV MYS
UZB
STPBGD
KAZ
VNMSWZ MNG
GMB
COD
SYR
TGO
LBRGNB
GTM
UGA
PSE
IDNLKA
KHM
NPL
ARE
IRL
JOR
CMR
TUR
IRN
RWA
MWI
EGY
COM SDN
MEX
ZWE
SEN
SAU
NER
PAN
BHR KWT
TJK CIV
IRQ GAB
PHL
60
50
40
30
JAM
20
QAT
10
0
6
= +0.26
7
8
9
10
ln(GDP per capita, 2018), 2017 USD PPP
11
12
Higher GDP
per capita
Caution: Correlation is NOT causation.
Source: Penn World Table [link], United Nations [link], own analysis
Fraction <$1.90 USD PPP/day, 2008-18
More dire poverty
=
GDP per capita and Poverty ($1.90/day), 2008-2018, latest available
80
COD
MDG
BDI
70
MWI
CAF
MOZ
60
GNB
RWA
50
ZMB
NGA
TGO
MLI BEN
TZA
NER
40
BFA
LBR UGA
SLE
TCD
GIN
HTI
20
YEM
10
SEN KENCOG
ZWE STP
ETH
LSO
30
GMB
7
0.80
Note: Threshold value uses 2011 USD PPP
CIV
R² = 0.6355
SWZ
CMR
LAO
IND
ZAF
COM
HNDDJI
BWA
NPL BGD
NAM
SDNGHA
GTM
PHL
SRB
TJK MRT
LCA
BOL IDN
GEO
BRA
MKD
COL
PAK
ROU
GAB
ECU
EGY
NIC CPV
PER
IRQ
ARM
MMR
VNM
ALB
MEX
MNE
PAN
PRY
BTN
SLV
FJI
CRI
SVK
ITA
BGR
USA
SYC
PSEMARMDA
ARG
LTU
GRC
KGZ
LVA
LKA
ESP
JPN
HUN
DZA
CHN
HRV
CAN
MNG
AUS
DOM
PRT
CHL
EST
IRN
POL
AUT
TUN
GBR
MUS
ISR
KOR
SWE
NLD
NORLUX
BIH
FIN
DNK
URY
TUR
BEL
IRL
JOR
THA
BLR
CYPCZE
FRA
DEU
CHE
UKR LBN
MDV
MYS
KAZ
RUS
SVNMLT
ISL
ARE
0
6
AGO
8
9
10
11
12
ln(GDP per capita, 2018), 2017 USD PPP
Higher GDP
per capita
Caution: Correlation is NOT causation. Limited sample.
Source: Penn World Table [link], United Nations [link], own analysis
Fraction <=$3.20 USD PPP/day, 2017
More dire poverty
GDP per capita and Poverty ($3.20/day)
80
TZA
70
STP
ZWE
60
R² = 0.6274
50
LSO
40
DJI
HND
30
20
MMR KGZ
10
GEO
BTN COL
GAB
PER BRA
MKD
SRB
ECU
ALB
ARM
ROUPAN
PRY
ARG
CRIBGR
IRN DOM
MUS
ITA CAN
GRC
LVA
LTU
TUR
HUN
HRV
ESP
MDA
PRT
CHL
POL
EST
AUT
RUS
KAZ
NLD
UKR THA URY
MLT
BEL
DNK
CYP
CZE
FIN SWE
BLR
SVN
FRA
BOL
SLV
0
7
=
EGY
IDN
7.5
0.79
Note: Threshold value uses 2011 USD PPP
8
8.5
9
9.5
10
10.5
11
NOR LUX
CHE
11.5
12
ln(GDP per capita, 2018), 2017 USD PPP
Higher GDP
per capita
Caution: Correlation is NOT causation. Limited sample.
Source: Penn World Table [link], The World Bank [link], own analysis
Fraction <=$5.50 USD PPP/day, 2017
More dire poverty
GDP per capita and Poverty ($5.50/day)
100
TZA
90
STP
ZWE
80
LSO
70
60
KGZ
IDN
MMR
HND
50
GEO
ARM
BTN
ALB GAB
SLV
COL
BOL
PER
ECU
BRA
MKD
SRB
DOM
MDA PRY
IRN
MUSROUPAN
CRI ARG TUR
THA BGR KAZ
GRC
UKR
LTU ESP
RUS
HRV
LVA
ITA
HUN
URYCHL
PRT
POL
EST
BLR
AUT
SWE
CZE
NLD
BEL
MLT
DNK
CYP
FRA
FINCAN
SVN
40
R² = 0.7602
30
20
10
0
7
=
EGY
DJI
7.5
0.87
Note: Threshold value uses 2011 USD PPP
8
8.5
9
9.5
10
10.5
11
NOR LUX
CHE
11.5
12
ln(GDP per capita, 2018), 2017 USD PPP
Higher GDP
per capita
Caution: Correlation is NOT causation. Limited sample.
Source: Penn World Table [link], The World Bank [link], own analysis
GDP per capita and life expectancy at birth, 2018
Longer life expectancy
Life expectancy at birth (years)
90
85
HKG
JPN
SGP
ESP
ITA
AUS CHE
ISL
ISR
KOR
SWE
FRA
MLT
CAN
GRCPRT
NZL
NLD NORLUX
IRL
FIN
BEL
AUT
SVN GBRDEU
CYP
DNK
QAT
CRI CHL
CZE
BRB
USA
LBNMDV HRVPAN
EST
POL
DMA ALB
URY
ARE
OMN
TUR
SVK
BIH
BHR
COLATG
THAMNE
LKA
ECU
DZA
CHN
HUN
TUN
ARG
MAR
PER
IRN
LCA
MYS
ROU
SRBMEX
BRA
MKD
LTU
BRN
VNM
HND
SAU KWT
ARM
MUS LVA
BGR
BLR
KNA
BLZPSE
JAM JORPRY
NIC
GTM
DOM
BHS
GEO
TTO
SYC
KAZ
AZE
CPVSLV
GRD
RUS
BGD
UKR
SYRPHL
MDAVCT
EGY
UZB
SUR
BTN
IDN
BOL
TJK
NPL
IRQ
STP
GUY
MNG
KHM
IND
BWA
RWA
TKM
SEN
LAO
FJI
PAK
MDG
DJI
KEN
GAB
YEM ETH
SDN
TZA
MRT
COG
COM
GHA
MWI LBR
HTI UGA
ZMB
NAM ZAF
NER
GMB
BEN
BDI COD
BFA
GIN ZWE
TGO
AGO
MOZ
SWZ
CMR
GNQ
GNB MLI
CIV
80
75
70
R² = 0.652
65
60
55
TCD SLE
CAF
LSO
NGA
50
45
40
6
= +0.81
7
8
9
10
ln(GDP per capita, 2018), 2017 USD PPP
11
12
Higher GDP
per capita
Caution: Correlation is NOT causation.
Source: Penn World Table [link], United Nations [link], own analysis
GDP per capita and Gender Inequality Index
More gender inequality
Gender Inequality Index, 2019
1
0.9
0.8
YEM
0.6
0.5
VEN
0.4
0.3
0.2
0.1
0
4
=
0.85
R² = 0.7225
TCD
0.7
5
6
CAF
MLI
NER LBR
CIV
HTI SLE
MRT
COD
GMB BEN
BFA
TGO
COG
SWZ IRQ
MWI
CMR
TZA
LSO
SDN
ZMB
PAK
GHA AGO
BGD
UGA
SENSTP
ZWE
GAB
MOZ
KEN
ETH
BDI
SYR
GTM IDN
MMR IND
KHM
GUY
LAO
IRNBWA
DOM
MAR
NPL
JOR
EGY
PRY
NAM
SUR
PHL
DZA
NIC
COL
HND
BTN
BOL
BLZ
LBN
BRA
PAN
ZAF
RWA
LKA
LCA
CPV
JAM
PER
KGZ SLV ECU FJITHA
MDV
MUS
BHS
ARG
TTO
AZEMEX
MNG GEO
TJK
TUR
OMN
VNM TUN
UZB CRI
URYROU
BRN
MYS
BRB
SAUKWT
CHL
ARM
UKR
HUN
RUS
BHRUSA
BGR KAZ
MDA
SVK
QAT
ALB
LVA MLT
CHN
BIH
MKD
CZE
SRB BLR
LTU
NZL
GBR
GRC
POL ISR
MNEHRV
AUS
IRL
CYP
ESTJPN
DEU
PRT
ESP
ITACAN
AUTARESGP
LUX
KOR
SVN
ISL
FRA
FIN
NOR
NLD CHE
BEL
SWE
DNK
7
8
9
10
ln(GDP per capita, 2018), 2017 USD PPP
11
12
Higher GDP
per capita
Caution: Correlation is NOT causation.
Source: Penn World Table [link], United Nations [link], own analysis
GDP per capita and child labor, 2010-2019, latest available
% of children age 5-17 engaged in child
labor
More children working in
80
dire conditions
=
0.79
70
60
R² = 0.6211
50
ETH
40
NER
BDI CAF
COD
30
20
MWI
10
BFA
TCD
HTIGNB
CMR
NGA
COM
ZWE
SLEGIN
TZA
BEN
ZMB
SEN
TGO
CIV
NPL
GHA
RWA
STPSDN
UGA GMB
COG
LBR
MRT
MLILSO
PAK
KHM
LAO
AGO
VNM
PSE SLV
SWZ
BLZ
0
6
6.5
7
7.5
8
8.5
JAM
9
PRY
MNG
PER
GUY
GAB
MNE
MKD
SRB
DOM
CHL
BRA
EGY
MEX
IRQ
DZA
SUR
URY
ARM
COL
ZAF
BTN
LCA
ALB
UKR
PAN
TUN
JOR
BRB GEO CRI BLR TKM
TTO
9.5
ln(GDP per capita, 2018), 2017 USD PPP
“Definition: Percentage of children ages 5–11 who, during the reference week,
engaged in at least one hour of economic activity and/or involved in unpaid
household services for more than 21 hours; children ages 12–14 who, during the
reference week, engaged in at least 14 hours of economic activity and/or involved
in unpaid household services for more than 21 hours; children ages 15–17 who,
during the reference week, engaged in at least 43 hours of economic activity; or
children ages 5–17 who, during the reference week, engaged in hazardous
working conditions or any worst forms of child labor other than hazardous.”
10
10.5
11
Higher GDP
per capita
Caution: Correlation is NOT causation.
Source: Penn World Table [link], United Nations [link], own analysis
% under 5 Two SD below median height
for age (according to WHO)
More childhood
malnutrition
=
0.72
GDP per capita and childhood malnutrition, 2010-2019, latest available
80
70
60
BDI
50
NER
GTM
YEM
COD MOZMDG
MWI TCD RWA
SDNPAK
AGO
CAF
ETH
NGA
NPL
IND
LSOZMB
BTN
DJI
LAO
TZA BEN
COM KHM
BGD
IDN
GIN
PHL
LBR UGA
SLE
CMR
BWA
SYR
GNB MLI
ZAF
KEN
GNQ
SWZ
BFA
ECU
TGO ZWE
VNM
MRT
NAM EGY
HND
HTI
CIV
COG
MYS
BRN
GMB SEN
PAN
AZEMDV
TJK
LKAGAB
NIC
STP GHA
BOL
BLZ MAR
SLV
COL
IRQ
PER
DZA
ALB
GUY
GEO
UZB
URY TKMOMN
THAMNE
ARM
MNG
TTO
BIH
SUR MEX
TUN
CHN
KAZ
ARG
SYC
JOR
BRB
PSE JAM
JPN
DOM
BRA
BGR
IRN
KWT
MDA PRY SRB
TUR
CRI
MKD
USA
POL
KOR DEU
LCA
AUS
CHL
40
30
20
10
0
6
7
R² = 0.5142
8
9
10
ln(GDP per capita, 2018), 2017 USD PPP
11
12
Higher GDP
per capita
Caution: Correlation is NOT causation.
Source: Penn World Table [link], United Nations [link], own analysis
More murders per capita
GDP per capita and homicide rate, 2013-2018, latest available
80
Homicides per 100,000
70
60
SLV
50
HND
BLZ
NGA
VCTZAF
30
DMA
20
GTM
CAF
10
UGA
YEM TZA
HTI
RWA
SLE
BFA
GNB
BDI
0
6
=
0.33
R² = 0.1064
JAM
LSO
40
7
BRA
COL
LCA
MEX
TTOBHS
GUY
URY SYC
CRI
IRQ
DOM
BRB GRD
PAN
RUS
PER
NIC CPVPHL
PRY
UKR
BOL MNG
ECU
ZMB KEN
SUR
ARG
KAZ
USA
LTU
LVA
CHL
MDA
PAK IND
MUS
THAMNE
TUR
IRN
LBN
HUN
LKA
BLR
NPL
FJI
ALB
GEO
AZE
MYS
ESTMLT
CAN
BEL
ARM
FIN
ISR
MAR JOR
CMR BGDGHA PSE
DZA
ROU
SAU
BGR
CYP
GBR
SRB
MKD
FRA
BTN
BIH
SWE
UZB
SVK
BEN
DNK
AUT
SYR
ISL
IRL QAT
GRC
DEU
AUS
PRT
MDV
POL
NZL
HKG
CHE
ESP
ITA
KOR
NLDARE
HRV
CZE
SVN
NOR
CHN
BHR
BRN
IDN
SEN
OMN
JPN
LUX
SGP
SWZ
8
9
10
ln(GDP per capita, 2018), 2017 USD PPP
11
12
Higher GDP
per capita
Caution: Correlation is NOT causation.
Source: Penn World Table [link], United Nations [link], own analysis
GDP per capita and Political Rights, 2018
Political Rights, 2018
More political
rights
50
45
40
NZL
AUS
NLD NOR
SWE
JPN
CAN
DNK
URY
GBR
FIN
PRT
SVN BELDEU
CHE IRL
LTU
BHS
LUX
BRBLCA CRI
CYP
EST ESP
FRA
MUS
AUT
DMA
CHL
CZE TWN
ISL
GHA CPV
GRD
HRV
MNG
BLZ
KNASVKPOL MLT
ISR
ITA
VCT
ROU
PAN
LVA
IND
GRC
STP
SLV
SUR
JAM
BEN
ATG BGR
ARGTTO
KOR
USA
ZAF
GUY
PERBRA
SEN
NAM
TUN
IDN
COL
ALB BWA
SLE
SRB
SYC
BOL BTN
HUN
PHL
PRY
LBR
LSO
MWI
MDA UKRGEO
MEX
DOM
MNE
NGA
NPL
ECU
MDG
LKA
COM
FJI
BFA
GTM
TZA ZMB
BIHMKD
MOZ
NER
HND
PAK
KEN
BGD
CIV
SGP
MYS
TGO
MLI
HTI
GIN
IRQ
TUR
GNB
ARM
HKG
MAR
MDV
MMR
KWT
NIC KGZ
JOR
LBN
UGA
AGO
DZA
EGY
ZWE KHM
CMR MRT
RWA
QAT
DJI
IRN
OMN
THA BLR
BRN
RUS
ARE
KAZ
BDICAF
TCD ETH
SDN
COD
GAB
AZE
VNM
BHR
COG
TJK
LAOSWZ
YEM
UZB
GNQ
SYR
TKM
SAU
CHN
35
30
R² = 0.157
25
20
15
10
5
0
6
= +0.40
7
8
9
10
ln(GDP per capita, 2018), 2017 USD PPP
11
12
Higher GDP
per capita
Caution: Correlation is NOT causation.
Source: Penn World Table [link], Freedom House [link], own analysis
GDP per capita and Civil Rights, 2018
More civil
liberties
60
FINSWE
NLD NORLUX
URY
ISL
BRB
PRT NZL CAN
AUS
DNK
CHL
CHE IRL
AUT
TWN
DMA
ESTMLT
ESP
CYPCZE
JPN
BELDEU
VCT
GBR
SVN
USA
CPV
LCA CRI KNA
LTU
BHS ITA
MUSSVK
FRA
LVA
GRD
KOR
GRC
BLZ
ATG
ARG
HRV
BEN STP
MNG
ROU
POL
PAN
NAM ZAFBRA
BGR TTO
GHA
SRB
HKG
SUR
BWA
HUN ISR
SEN
IND JAM
GUY DOMMNE SYC
PER
TUN
ALB
BOL
GEO
SLE
MWI
MKD
BFA LSO
PRY
UKR
COL
MEX
SLV
ECU
FJI
LBR
PHL
MDAIDN
BIH
ZMB
GTM
SGP
CIV
MOZ MDG
NIC
LBN
COM
LKA
TZA
NPL
ARM
TGO
KEN
NER
MLI
BTN
MYS
UGA
HND
BGDNGA
THA
DZA
HTIGNB GIN
PAK KGZ MAR JOR
KWT
BRN
MRT
MDV
ZWE KHM
COG
DJI
GAB
MMR
VNM
KAZOMN
QAT
EGY
AGO
SWZ IRQ
RWA
BLR TUR
RUS
TCD
CHN
BDI COD
CMR
YEM
ARE
IRN
LAO
TJK
BHR
AZE
ETH
SAU
UZB
GNQ
CAF
TKM
SDN
SYR
Civil Rights, 2018
50
R² = 0.2202
40
30
20
10
0
6
= +0.47
7
8
9
10
ln(GDP per capita, 2018), 2017 USD PPP
11
12
Higher GDP
per capita
Caution: Correlation is NOT causation.
Source: Penn World Table [link], Freedom House [link], own analysis
GDP per capita: Correlated with good things
Positively correlated with:
Negatively correlated with:
•
•
•
•
• Inequality as measured by Gini coefficient
(moderate)
• % <=$1.90/day (strong)
• % <=$3.20/day (strong)
• % <=$5.50/day (very strong)
• Gender inequality index (very strong)
• % child labor (very strong)
• Childhood malnutrition (strong)
• Homicide rate (weak)
Life expectancy at birth (very strong)
Labor share (weak)
Political rights (weak/moderate)
Civil rights (weak/moderate)
GDP per capita: Correlated with good things
Positively correlated with:
•
•
•
•
Negatively correlated with:
Life expectancy
birth
(veryisstrong)
Inequalitymeasure
as measured
by Gini coefficient
GDP at
per
capita
an excellent •catch-all
for
human
(moderate)
Labor share
(weak)
population well-being, as it is •correlated
with(strong)
most
%
<=$1.90/day
Political rights (weak/moderate)
everything one thinks of when• thinking
about(strong)
human well%
<=$3.20/day
Civil rights (weak/moderate)
being.
• % <=$5.50/day (very strong)
• It’s not perfect, but it does•an
outstanding
Gender
inequalityjob
index (very strong)
• What’s the superior alternative?
• % child labor (very strong)
• Childhood malnutrition (strong)
• Homicide rate (weak)
GDP per capita and UN Human Development Index (HDI), 2018
Higher HDI
Human Development Index (HDI)
1
NOR IRL
DEU
ISL
HKG
SWE
AUS
NLD CHE
DNK
FIN
BEL
GBR
CAN
NZL
AUTUSA SGP
JPN
ISR
KOR
LUX
SVN
ESP
FRA
MLT
ITA
ARE
ESTCZE
CYP
GRC
POL
LTU
LVA
PRT
SVK
SAU
BHR
HUN
CHL
HRV
QAT
ARG
BRN
MNE
BLRMYS
ROU
RUS
KAZ
TUR
URY
BGR
OMN
BHS
PAN
BRB
CRI
KWT
GEO
SRB
MUS
TTO
ALB
SYC
IRN
LKA
BIH
MEXKNA
UKR
GRD
THA
ATG
ARM
PER
MKD
COL
BRA
ECU
LCA
AZE
DOM
LBN
DZACHN
MDA
FJI
TUN
DMA
VCT
MNG
SUR
JAM
MDV
BWA
JOR
PRY
UZB
BLZPSE
BOL IDN
PHL
TKM
ZAF
EGY
VNM
GUY GAB
MAR
IRQ
SLV
CPV
TJK
NIC IND
GTM
NAMBTN
HND
STPBGDGHA
LAOSWZ
KEN
NPL
ZMB KHM
AGO
GNQ
COG
ZWE
SYR
CMR MRT
COM
PAK
BEN
RWA
CIV
NGA
MDG UGA
TZA
LSO
DJI
SEN SDN
HTI TGO
GMB
COD
ETH
MWI LBR
GNB GIN
YEM
MOZ
SLE
BFAMLI
BDI
CAF NER TCD
0.9
0.8
0.7
0.6
0.5
0.4
R² = 0.8488
0.3
0.2
All HDI is measuring is GDP per captia!!!
0.1
0
= +0.92
6
7
8
9
10
ln(GDP per capita, 2018), 2017 USD PPP
11
12
Higher GDP
per capita
“Definition: A composite index measuring average • HDI is about 1/3 GDP per capita
achievement in three basic dimensions of human • Life expectancy & educational attainment
development—a long and healthy life, knowledge
highly correlated w/ GDP per capita Caution: Correlation is NOT causation.
and a decent standard of living.”
Source: Penn World Table [link], United Nations [link], own analysis
Levels vs. rates of change
• Levels
• Economic output (GDP, )
• Living standards (GDP per capita, )
• Rates of change
• Economic growth rate per annum (how GDP changes each year, %)
=
• Growth rate of living standards p.a. (how GDP per capita changes each year, %)
=
y
Measuring living standards over time
• Economic growth
• “sustained increase in the total output of goods and services produced by a
given society” (Cameron & Neal, 2003, p. 8)
• Sustained increase in real GDP
• Long-run phenomenon
• Do not confuse with “economic expansion” (a short-run, business cycle
phenomenon)
• Growth in living standards
• Sustained increase in real GDP per capita over time
• Since we’re looking at changes over time, we need to account for price
changes over time…
• Unless otherwise specified, I’ll always talk in real terms (i.e., inflation-adjusted)
Measuring living standards over time
• Economic development
“economic growth accompanied by a substantial structural or organizational
change in the economy, such as a shift from a local subsistence economy to
markets and trade or the growth of manufacturing and service outputs relative
to agriculture”
(Cameron & Neal, 2003, p. 9)
• Typically,
•
•
•
•
Growing GDP per capita
Growing urbanization
Growing internal & external trade
Shift from agriculture to manufacturing & services
United Kingdom sectoral shares of GDP, 1381 CE - 1841 CE
Sectoral share of GDP at current prices
50.00%
40.00%
30.00%
20.00%
Agriculture
Industry
10.00%
0.00%
1350
Structural
change
economic
development
Services
1450
1550
1650
1750
1850
Year
Note: England only before 1700.
Source: Broadberry, et al. (2011), p. 35
Two basic approaches estimating historical
living standards over time
Sector price Sector real output
1) Wage-based approach
• Most people were workers, so use
real wages
• Archival data on wages &
consumer prices
• Some assumptions
2) Output-based approach
• Archival data on outputs and prices
of each of sub-sectors
• Some assumptions
• Sum them all up
• Divide by population
=
•
•
•
Agriculture
• Grain
• Beef
• Etc.
Industry
• Metals
• Textiles
• Etc.
Service
• Commerce
• Household
• Etc.
=
N
Adjusting for price changes
• In history, no government-collected official CPI data
• Step #1: calculate nominal prices for a basket of good
•
•
•
•
Come up with a yearly consumer basket of goods
Archival research/extrapolate/guess how much of each good is purchased ( )
Archival research/extrapolate/guess the price of each good each year ( )
Price of basket each year ( ) for goods:
=
+
+
+
• Allen (2001) provides a typical example basket for Europe
• Widely used/cited in economic history
Add up the products
×
About 17 oz. of
beer per day
410.074
Price of the yearly
consumer basket in
Strasbourg in 1745—1754
in grams of silver
Note: The table reports rounded prices, but a non-rounded sum. I’ve added the
correct sum (in red) from the rounded figures show. The non-rounded calculations
are available here: [link]
Source: Allen (2001), p. 421, own calculation
Adjusting for price changes
• Step #2: construct a price index
• Put each year’s basket price in
terms of some base year
× 100
• Here, base year is 1500
• Example:
288.7821
147.7
195.5182
Consumer basket prices in
London (grams of silver)
1300
1400
288.7821
292.3010
Price Index
(1500=100)
147.7
149.5
1500
1600
1700
195.5182
457.5061
542.4459
100.0
234.0
277.4
1800
1,377.7546
704.7
Year ( )
Source: Robert Allen’s Research Website (accessed 7/28/21) [link], own calculations
Adjusting for price changes
• Step #3: For each year, divide
nominal value by its price
index number
Nominal
× 100
Price Index
• Here, agricultural wages in
Southern England ( ) to
estimate living standards
Daily wage of Southern England
agricultural laborers (grams of silver)
Year ( )
Price Index Nominal
(1500=100) (grams of
silver)
1300
147.7
1.9844
Real
(1500
London
prices)
1.34
1400
1500
149.5
100.0
3.8033
2.8768
2.54
2.88
1600
1700
234.0
277.4
4.1521
5.5679
1.77
2.01
1800
704.7
7.8879
1.12
Source: Robert Allen’s Research Website (accessed 7/28/21) [link], own calculations
Output-based approach
Modern-day (2018)
equivalents
GDP per capita, 2011 USD PPP
United Kingdom GDP per capita, 1252 CE - 2018 CE
$40,000
Belgium ($39,800)
$35,000
New Zealand ($35,300)
$30,000
Czech Rep. ($30,700)
$25,000
Russia ($24,700)
$20,000
Romania ($20,100)
$15,000
Costa Rica ($14,700)
$10,000
Ukraine ($9,800)
Honduras ($5,000)
$5,000
$0
1250
Malawi ($1,100)
1350
1450
1550
1650
1750
1850
1950
Year
Note: England only before 1700.
Sources: Broadberry, et al. (2015), Maddison Project Database 2020 [link]
English Gini coefficient over time
Gini coefficient
interpretation:
0 = perfect equality
1 = perfect inequality
Year
1688
1759
1798
1846
1867
Post-war period
Today
Gini Estimate
0.54
0.53
0.60
0.58
0.48
<0.30
0.40
Modern-day Brazil
Pre-tax/transfer
market income only
Substantial
taxes/transfers from
1945 onward, mean
these are likely
overestimates from a
consumption
perspective
Source: Allen (2019), p. 110-111
(England)
From the 1650s to the 1850s, the annual earnings of
English agricultural workers (i.e., the lowest on the
occupational hierarchy) rose in tandem with GDP
per capita.
Source: Modified from Humphries & Weisdorf (2017), p. 12; own edits
Using the log of GDP per capita, ln
Modern-day (2018)
equivalents
United Kingdom GDP per capita, 1252 CE - 2018 CE
ln(GDP per capita, 2011 USD PPP)
11.5
11.0
Switzerland (11.0)
10.5
South Korea (10.5)
10.0
Chile (10.0)
9.5
Brazil (9.5)
9.0
Philippines (9.0)
8.5
Nicaragua (8.5)
8.0
Tanzania (8.0)
7.5
Ethiopia (7.5)
7.0
Malawi (7.0)
6.5
1250
Burundi (6.5)
1350
1450
1550
1650
1750
1850
1950
Year
Note: England only before 1700.
Sources: Broadberry, et al. (2015), Maddison Project
Database 2020 [link], own calculations
Calculating average yearly growth rate
• Continuous compounding formula (PERT)
=
• is the future value
• is the present (initial) value
• is Euler’s number 2.71828
• is the continuous growth rate of value
• is the number of years in the future
Calculating average yearly growth rate
• Example: Real GDP per capita in the United States is about $63,500.
Predict what real GDP per capita in the United States will be in 20
years, continuously compounded at a 1.5% annual rate.
$63,500
.
×
$85,716.03
• What if real GDP per capita only grows at a 1.0% annual rate?
$63,500
.
×
$77,559.08
Notice: Small changes in growth rates compound greatly over time. Here, a 0.5pp slower grow
.
.
rate reduced GDP per capita 20 years from now by
× 100
9.5%
.
Calculating average yearly growth rate
=
• Re-arranging,
ln
=
• For two years periods,
= ln
+
ln
ln
ln
ln
and
=
Calculating average yearly growth rate
=
ln
• For growth rate of living standards,
ln
=
Year
ln
over two years
ln
Year
=
• Convention: multiply by 100% for percentage terms
ln
rise
=
= slope
run
Calculating average yearly growth rate
• Example: Real GDP per capita in the United Kingdom in 1252 CE was
$1,285. By 1649 CE, it was $1,508. Both figures are in 2011 USD PPP.
Find the annual growth rate over this period.
ln 1508
1649
7.318
1649
ln 1285
× 100
1252
7.185
× 100
1252
0.033%
*I rounded the logs to the nearest thousandth for demonstration purposes. The answer is 0.04% if you don’t round.
Sources: Broadberry, et al. (2015), Maddison Project Database 2020 [link], own calculations
United Kingdom GDP per capita, 1252 CE - 2018 CE
ln(GDP per capita, 2011 USD PPP)
11.5
Slope of line between two points
on a log-GDP per capita graph is
the average growth rate of GDP
per capita over that period
11.0
10.5
10.0
9.5
9.0
8.5
8.0
1252-1649 7.318
Average
1649
growth rate:
0.03% p.a.
7.185
1252
0.00033
7.5
7.0
6.5
1250
1350
1450
1550
1650
1750
1850
1950
Year
Note: England only before 1700. Growth rates
calculated as average yearly growth rate.
Sources: Broadberry, et al. (2015), Maddison Project
Database 2020 [link], own calculations
United Kingdom GDP per capita, 1252 CE - 2018 CE
ln(GDP per capita, 2011 USD PPP)
11.5
11.0
10.547 9.311
2018 1950
10.5
10.0
9.5
9.0
8.5
8.0
1252-1649
Average
growth rate:
0.03% p.a.
8.394
1849
7.277
1650
0.018
9.314
1949
0.0056
8.374
1850
0.0095
7.5
7.0
6.5
1250
1350
1450
1550
1650
1750
1850
1950
Year
Note: England only before 1700.
Sources: Broadberry, et al. (2015), Maddison Project
Database 2020 [link], own calculations
United Kingdom GDP per capita, 1252 CE - 2018 CE
ln(GDP per capita, 2011 USD PPP)
11.5
1950-2018
Average
growth rate:
1.8% p.a.
11.0
10.5
10.0
9.5
9.0
8.5
8.0
1650-1849
Average
growth rate:
0.56% p.a.
1252-1649
Average
growth rate:
0.03% p.a.
1850-1949
Average
growth rate:
0.95% p.a.
7.5
7.0
6.5
1250
1350
1450
1550
1650
1750
1850
1950
Year
Note: England only before 1700.
Sources: Broadberry, et al. (2015), Maddison Project
Database 2020 [link], own calculations
The Great Enrichment
GDP per capita, select European economies, 1250 CE - 2018 CE
United Kingdom
Netherlands
France
Spain
Italy
11.5
ln(GDP per capita, 2011 USD PPP)
11
10.5
10
9.5
9
8.5
8
7.5
7
6.5
1250 1300 1350 1400 1450 1500 1550 1600 1650 1700 1750 1800 1850 1900 1950 2000
Note: United Kingdom is England only
Year
before 1700. Italy is Northern Italy
only before 1871. Imagine error bars
around each estimate.
Sources: Broadberry, et al. (2015); Van Zanden & van Leeuwen (2012); Smits, et al. (2000);
Scheidel & Friesen; Ridolfi (2016); Álvarez-Nogal & de la Escosura (2013); Malanima (2010);
Baffigi (2011); Maddison Project Database 2020 [link]; own calculations
GDP per capita, select Asian economies, 1250 CE - 2018 CE
China
India
Japan
Singapore
11.5
ln(GDP per capita, 2011 USD PPP)
11
10.5
10
9.5
9
8.5
8
7.5
7
6.5
1250 1300 1350 1400 1450 1500 1550 1600 1650 1700 1750 1800 1850 1900 1950 2000
Year
Note: Imagine error bars
around each estimate.
Sources: Broadberry, et al. (2018); Xu, et al. (2016); Wu (2014); Broadberry, et al. (2015); Bassino, et
al. (2018); Fukao, et al. (2015); Maddison Project Database 2020 [link]; own calculations
GDP per capita, select American economies, 1250 CE - 2018 CE
USA
Mexico
Argentina
Peru
Brazil
11.5
ln(GDP per capita, 2011 USD PPP)
11
10.5
10
9.5
9
8.5
8
7.5
7
6.5
1250 1300 1350 1400 1450 1500 1550 1600 1650 1700 1750 1800 1850 1900 1950 2000
Year
Note: Imagine error bars Sources: McCusker, Sutch (2006), de la Escosura (2009), Abad & van Zanden (2016), Barro & Ursua (2008),
around each estimate.
Bertola & Ocampo (2012), Seminario (2015), Maddison Project Database 2020 [link]; own calculations
GDP per capita, select African economies, 1250 CE - 2018 CE
Egypt
South Africa
Angola
Kenya
Botswana
11.5
ln(GDP per capita, 2011 USD PPP)
11
10.5
10
9.5
9
8.5
8
7.5
7
6.5
1250 1300 1350 1400 1450 1500 1550 1600 1650 1700 1750 1800 1850 1900 1950 2000
Year
Note: Imagine error bars
around each estimate.
Sources: Pamuk & Shatzmiller (2011), Pamuk (2006), Fourie & Van Zanden
(2013), Maddison Project Database 2020 [link]; own calculations
Increase in real GDP per capita from 1800 to 2018
1800
2018
How much
better off?
11×
11×
United Kingdom
Netherlands
$3,343
$4,184
$38,058
$47,474
United States
China
Japan
$2,545
$926
$1,317
$55,335
$13,102
$38,674
22×
14×
29×
Mexico
South Africa
$1,305
$1,529
$16,494
$12,166
13×
8×
Note: All in 2011 USD PPP. Sources: Broadberry, et al. (2015); Van Zanden and van Leeuwen (2012); Sutch (2006); Prados de la
Escosura (2009); Broadberry, et al. (2018); Xu, et al. (2016); Bassino, et al. (2018); Arroyo & van Zanden (2016); Fourie & Van
Zanden (2013); Maddison Project Database 2020 [link]; own analysis
But this underestimates improvement in living
standards since 1800 CE
1) Improvements in life expectancy & health
• Quantity & quality of life contribute to living standards
• Life expectancy at birth ( ) is the average number of years somebody born at
a certain time is expected to live
2) Improvements in leisure consumption
• Leisure ( ) is time spent not working in the labor market
3) Technological change’s effect on consumption good quality and new
consumption goods
Jean Nocret’s The Family of Louis XIV, 1670
Elisabeth Marguerite
dead at 49
Marguerite Louise
Francois Madeleine
dead at 76
dead at 15
Anne Marie
dead at 65
(bladder infection)
Henrietta of England
Average age at
King Louis XIV
dead at 26
death of these
dead at 76
Anne of Austria
people: 36 years if
Philippe I
(gangrene)
dead at 64
dead at 60
Queen Maria Theresa you include Louis
François, 38 years
(stroke) Marie Louise
dead at 44
Henrietta Maria
if you don’t
(infection)
dead at 26
dead at 59
Louis
Philippe Charles
(infection)
(opioid overdose)
dead at 49
dead at 3
(smallpox)
Not pictured:
(chest infection)
Louis’s last son
Marie Therese
Ann & Marie
Louis François,
dead at 5
dead
at
1
mo.
dead at 5 mo.
Sources: Charles
(tuberculosis)
Kenny & Wikipedia
Source: Fogel (2004), p. 2
Anne, Queen of Great Britain (1702—1707)
17 pregnancies
• 7 miscarriages
• 5 still births
• 5 live births
• Mary, died at 20 months (smallpox)
• Anne Sophia, died at 11 mo.
• William, died at 11 years old
(either scarlet fever or smallpox)
• Mary, died after 2 hours
• George, died after a few minutes
Anne herself died at 49 years old (stroke).
Sources: Phillip Magness &
Wikipedia
Source: Galor (2012)
Crude, back-of-the envelope calculation
= discount rate
Example calculation for UK
• Real GDP per capita in 1800:
$3,343
• Real GDP per capita in 2018:
$38,058
• That’s the 11 × increase
Assuming = 0.03 &
in 1800: 36
•
= 0.011
• Discounted present value of
lifetime average income
$88,599
•
• This is an underestimate
• From 1800—2018 real income
per capita grew ca. 1.1% per
annum
in 2018: 81
+45 years!
• Discounted present value of
lifetime average income
$1,609,437
That’s an
18 × increase
Here, ignoring life expectancy gains missed out
on 40% of the gains in EPV lifetime income
Source: Numbers from Fogel (2004), Broadberry, et al. (2015), Maddison
Project Database (2020), United Nations, own analysis
Late 20th century older veterans
were less likely to have chronic
diseases that older veterans in
the early 20th century
Source: Fogel (2004), p. 31
5’11”
5’7¾”
5’6”
Average stature is far taller than in the past
Source: Clark (2007), pg. 60
Improvements in life expectancy & health
• For the USA from 1900 to 1995, the welfare gains from life-expectancy
& health comparable to the gains from consumption (Nordhaus 2003)
• This suggests that consumption alone underestimates improvement by approx.
50%
• From 1960-2000 across the globe, the welfare gains from life
expectancy & health comparable to the gains in income (Becker, et al.
2005)
• For the poorest half of the globe, 40% of total welfare gains stemmed from life
expectancy gains (Becker, et al. 2005)
Improvements in life expectancy & health
• Average incomes have risen relative to history, but so too has quantity
and quality of life
•
only focusing on average incomes ignores the lifetime utility gains from
longer life expectancy & health
• improvements in average income underestimate improvements over
historical living standards (well-being, utility) by a wide margin
• People today are much better off relative to the past than implied only
by per capita income b/c of improvements in life expectancy & health
Leisure time
• Leisure also enters the utility
function
( , )
• Leisure ( ) is time spent not
working in the labor market
• “True leisure”
• Household labor
• Chores
• Childcare
Yes, chores
make you
better off!
Time
endowment
Leisure
time
=
+
Market labor time
• Over the past 150 years or so, in
high-income countries, hours
worked per year by employed
persons have likely diminished
substantially
• Implies more
Daily Time Use, Employed Adult Male
1800 CE London
Daily Time Use, Employed Adult Male
2019 CE United States
Other, 4
Sleep, 9
Work, 11
Other, 8.3
Personal care,
including sleep, 9
Working and
work-related,
6.7
Source: Voth (1998), American Time Use Survey [link]
Daily Time Use, Employed Adult Male
1800 CE London
Other, 4
Sleep, 9
Daily Time Use, Employed Adult Male
2019 CE United States
Other (minus
leisure &
sports), 4.02
Other, 8.3
Personal
Personal care,
care,
including
sleep,
including
sleep, 99
Leisure &
sports, 4.28
Work, 11
Workingand
Working
and workwork-related,
related,
6.7 6.7
Work hours per day have fallen
since 1800
Source: Voth (1998), American Time Use Survey [link]
Work hours per week
have fallen since 1900
The great decline in work hours in the USA
occurred before Federal private sector collective
bargaining rights & overtime rules
Source: Sundstrom (2006)
Annual hours of work (full-time employed workers)
Select countries, 1870 CE -- 2000 CE
Annual work hours
3200
Annual hours of work have
fallen from 1870
2700
2200
1700
1200
1870
1890
1910
1930
1950
1970
1990
Year
Australia
France
Germany
UK
USA
Source: Huberman and Minns (2007), Table 3
Income elasticity:
=
%
<0
inferior good
>0
normal good
<1
income inelastic
> 1 income elastic
Why have work hours fallen? Incomes are
higher and people “buy” more leisure (income
effect is greater than substitution effect)
Leisure is an income
elastic normal good
People disproportionately
“spent” increased incomes
on leisure (not working)
Source: Fogel (2004), p. 89
Increased
“spending”
on
leisure
Total real
Real wage
consumption
=
=
=
=
=
Labor time
=
+
+
• Can think of
as real income
• Can “spend” it on consumption
or leisure (foregoing
consumption)
• What is the “price” of leisure?
• Two effects as
income rises
• Income effect
• Substitution effect
and real
Our previous
comparisons were
modern vs. 1800s
People worked many
more days per year in
the 1800s than in the
1300s
Supposing an 11 hour/day schedule, agricultural workers in
1400s CE England worked around 1,375 hours per year
compared to 1,650 hours per year in England in 2000
Source: Humphries & Weisdorf (2019), p. 2880
Leisure time
• Thus, GDP per capita underestimates the improvement in well-being
and living standards because it doesn’t account for increased leisure
time
Real GDP per capita only
measures this
• People consume more stuff and more leisure time over longer,
healthier lives
Technology, quality improvement, and new
goods
• Inflation adjustments don’t take quality improvements in consumption
goods due to technological change into account
• Or quality improvement in leisure, for that matter…
• Inflation adjustments don’t take new consumer goods from
technological change into account
• Therefore, they overestimate inflation and underestimate the change
in real GDP per capita
• i.e., traditional price indexes overestimate inflation over the long-run
•
things have improved versus history far, far more than the numbers
suggest
Hedonic adjustment
• When you buy consumption goods, you’re buying service characteristics
• Consumption goods are the input
• Service characteristics are the output
• For example, when you buy a lighting, some service characteristics
•
•
•
•
•
•
•
Illumination (measured in lumens)
Wavelength
Reliability
Convenience
Nordhaus: Let’s just focus on lumens
Safety
Durability
Etc…
Source: Nordhaus (1996), p. 45
“True” price of light
• Lumens measure flow of light from
source
• Wax candle: 13 lumens
• 100-Watt filament bulb: 1,200 lumens
• 18-Watt compact florescent bulb:
1,290 lumens
• Lighting efficiency: lumen hours per
BTU
Lumens per hour
BTU (1,000s)
• When you buy a source of light, it’s
quality-adjusted “true” price is
=
Efficiency
• Traditional price indexes ignore
lighting efficiency and just measure
prices of light sources.
Source: Nordhaus (1996), p. 31
Source
Open wood fire
Approx. date
Dawn of
humankind
Neolithic lamp with animal or 38,000 BCE to
vegetable fat
9,000 BCE
Babylonian lamp with sesame 1750 BCE
oil
Tallow candle
1800 CE
Sperm oil lamp
1855 CE
Kerosene lamp
1875-85 CE
Edison bulb
1883 CE
Filament bulb
1990
1st gen. compact florescent
1992
Lumen-hours per 1,000 BTU
0.69
4.4
17.5
22.2
23.0
46.6
762.0
4,152.0
20,011.1
Source: Nordhaus (1996), p. 36
“True” price of light
• Modern 100-Watt incandescent
bulb burning for 3 hours each
night for a year
• 1.5 million lumen-hours per year
• Price: $1.86 (1992 $)
• 8 minutes of work at 1992 CE
average wage rate
• 1800 CE, to get 1.5 million
lumen-hours per year
• 17,000 tallow candles
• Price: $604 (1992 $)
• Around 1,000 hours of work at
average 1800 CE wage rate
Source: Nordhaus (1996), p. 50-53, own calculations
Date
Hours worked True Price of
per 1000
Lighting
lumen-hours
CPI
“Light 1”
“Light 2”
Various traditional price indexes
Price per 1000 lumen hours
(efficiency/quality-adjusted)
500,000 BCE
10,000 BCE
1750 BCE
58
50
41.5
1800
1850
1900
5.4
3.0
0.2
100
92.5
10.0
100
62.3
67.0
100
59.8
55.0
100
59.8
55.0
1950
1992
0.002
0.0001
0.3
0.03
190.7
1066.3
85.2
503.9
65.8
281.1
Implies lighting prices
3,333x lower in 1992
versus 1800
Implies lighting prices 5x
higher 1992 versus 1800
Note: True price is for technological frontier. Price indexes use 1800 as base year, i.e., 1800=100 Source: Nordhaus (1996), p. 46-47
Price indexes miss big technological changes
• These big technological changes
• Change the quality/efficiency of consumer goods
• i.e., 1 BTU in 1800 buys a very different number of lumen-hours than in 1992 (or 2021)
• Add new consumer options
• Traditional price indexes adjust for neither
• The bias is particularly bad in historical studies, where our indexes are prices
consumer baskets held constant over time (Nordhaus 1996, p. 56)
• Bias from not accounting for technological changes
True Price ± Bias =
Price
Effect of price index bias
• Even by 1800, already
substantial improvements
in the quality and
composition of the
consumer basket vs. 1300
•
•
•
•
•
•
Tea, sugar, tobacco, coffee
Oranges, potatoes
Coal instead of wood
Earthenware instead of bark
Cotton & silk instead of wool
Gin instead of mead
Daily wage of Southern England
agricultural laborers (grams of silver)
Year ( )
Price Index Nominal
(1500=100) (grams of
silver)
Real
(1500
London
prices)
1300
1400
147.7
149.5
1.9844
3.8033
1.34
2.54
1500
1600
100.0
234.0
2.8768
4.1521
2.88
1.77
1700
1800
277.4
704.7
5.5679
7.8879
2.01
1.12
Effect of price index bias
• Since the price index does not
take these into account, it is an
overestimate
Daily wage of Southern England
agricultural laborers (grams of silver)
Year ( )
Price Index Nominal
(1500=100) (grams of
silver)
Real
(1500
London
prices)
• Since we divide nominal wage by
the price index to get real, the
real wage must be an…
underestimate
1300
1400
147.7
149.5
1.9844
3.8033
1.34
2.54
1500
1600
100.0
234.0
2.8768
4.1521
2.88
1.77
• How much? Difficult to say…
1700
1800
277.4
704.7
5.5679
7.8879
2.01
1.12
Overestimate
Underestimate
410.074
Note: The table reports rounded prices, but a non-rounded sum. I’ve added the
correct sum (in red) from the rounded figures show. The non-rounded calculations
are available here: [link]
Source: Allen (2001), p. 421, own calculation
Source: Nordhaus (1996), p. 57—58
Source: Nordhaus (1996), p. 57—58
Historic price level adjustments are tricky
• Price indexes don’t take quality improvements into account
• Price indexes don’t take new goods into account
• Thus, they likely overestimate inflation and underestimate the increase
in real GDP per capita and living standards
• Speculation from Nordhaus (1996, p. 63)
• Conventional estimates of median real wages increase 13x from 1800-1992
• If there is low bias in the price index due to quality, increase is 40x
• If there is high bias in the price index due to quality, increase is 190x
That’s no error: the median wage earner would be consuming
Up to 190 times more real value in 1992 than in 1800.
Hedonic Adjustment
Example
1800
2020
•
•
=
• Suppose people only buy one
good: “consumption baskets”
•
•
=
• Suppose people only buy one
good: “consumption baskets”
Price per basket doubled
•
=
=5
,
•
,
=
= 50
Real average real income increased 10 ×
if we just look at consumption baskets
1800
2020
•
•
•
•
=5
•2
“True price” of
consumption
Price per basket doubled
Real average income
increased 10 ×
= 50
“Quality” increased 6 ×
• 12
Price of pleasure declined by 66%
•
=
•
Nominal
•
=
Nominal
= 10
•
,
.
.
People are 60 × better off after considering
quality changes in the basket!
= 600
Increase in real GDP per capita from 1800 to 2018
1800
2018
How much
better off?
11×
11×
United Kingdom
Netherlands
$3,343
$4,184
$38,058
$47,474
United States
China
Japan
$2,545
$926
$1,317
$55,335
$13,102
$38,674
22×
14×
29×
Mexico
South Africa
$1,305
$1,529
$16,494
$12,166
13×
8×
or 200
40××
…or, if lifeexpectancy
gains are 50%
of the welfare
gains
400 ×
Note: All in 2011 USD PPP. Sources: Broadberry, et al. (2015); Van Zanden and van Leeuwen (2012); Sutch (2006); Prados de la
Escosura (2009); Broadberry, et al. (2018); Xu, et al. (2016); Bassino, et al. (2018); Arroyo & van Zanden (2016); Fourie & Van
Zanden (2013); Maddison Project Database 2020 [link]; own analysis
The Great Divergence
Source: https://ourworldindata.org/economic-growth
The “Little Divergence”
• The “Little Divergence”
• Within Europe
• Early modern era (post-Black
Death)
• Chiefly, Northwestern Europe
diverges
• England
• Low Countries (Holland & Flanders)
• Evidence:
• Output-based approach (GDP per
capita)
• Wage-based approach
• Urbanization rates (proxies
structural change)
• Timing:
• Black Death (1348 CE)
• Atlantic Trade Boom ( 1500 CE)
• BEFORE First Industrial Revolution
( 1800 CE)
Around 1300: Centers of European Urban,
Manufacturing, and Commercial Activity
By 1800: Centers of European Urban,
Manufacturing, and Commercial Activity
GDP per capita, select European economies, 1250 CE - 2018 CE
United Kingdom
Netherlands
France
Spain
Italy
11.5
ln(GDP per capita, 2011 USD PPP)
11
10.5
10
9.5
9
8.5
8
7.5
7
6.5
1250 1300 1350 1400 1450 1500 1550 1600 1650 1700 1750 1800 1850 1900 1950 2000
Note: United Kingdom is
Year
England only before 1700. Italy
is Northern Italy only before
1871.
Sources: Broadberry, et al. (2015); Van Zanden & van Leeuwen (2012); Smits, et al. (2000);
Scheidel & Friesen; Ridolfi (2016); Álvarez-Nogal & de la Escosura (2013); Malanima (2010);
Baffigi (2011); Maddison Project Database 2020 [link]; own calculations
Source: Pamuk (2007), p. 297
Source: Pamuk (2007), p. 297
+20.9 pp
+11.7 pp
1.5 pp
+2.5 pp
+1.9 pp
The “Great Divergence”
• The “Great Divergence”
• NW Europe precocious outlier
• Rest of Europe vs. Rest of World
• Mostly 19th c./20th c. phenomenon
• Evidence:
• Output-based approach (GDP per
capita)
• Wage-based approach
• Timing
• NW Black Death (1348 CE)
• NW Europe: Atlantic Trade Boom
( 1500 CE)
• Rest of Europe: Industrialization
(1800 CE)
GDP per capita, select economies, 1250 CE - 2018 CE
United Kingdom
Netherlands
France
China
India
Japan
11.5
ln(GDP per capita, 2011 USD PPP)
11
10.5
10
9.5
9
8.5
8
7.5
A “Little Divergence”
in East Asia, too?
7
6.5
1250 1300 1350 1400 1450 1500 1550 1600 1650 1700 1750 1800 1850 1900 1950 2000
Year
Sources: Broadberry, et al. (2015); Van Zanden & van Leeuwen (2012); Smits, et al. (2000); Scheidel &
Friesen; Ridolfi (2016); Broadberry, et al. (2018); Xu, et al. (2016); Wu (2014); Broadberry, et al. (2015);
Note: United Kingdom is
Bassino, et al. (2018); Fukao, et al. (2015); Maddison Project Database 2020 [link]; own calculations
England only before 1700.
Welfare Ratio interpretation:
= 1 adult male yearly real
labor market earnings
enough to purchase
subsistence basket for a
family of 2 adults, 2 children
Source: Allen, et al. (2011), p. 27
Source: Allen, et al. (2011), p. 28
1700
1800
United Kingdom: 13.3% United Kingdom: 22.1%
Netherlands: 32.8%
Netherlands: 28.8%
Source: Xu, van Leeuwen, van Zanden (2015)
Source: Frankema & van Waijenburg (2012), p. 913
Source: Frankema & van Waijenburg (2012), p. 913
Stylized facts (Great Enrichment)
1) For most of human history, living standards were very low compared
to modern living standards
2) For most of human history, growth rates were very low compared to
modern growth rates
3) High living standards are a relatively new phenomenon
4) Modern growth rates are a relatively new phenomenon
Stylized facts (Great Divergence)
5) Modern living standards are uneven across countries
6) The onset of modern growth was uneven across countries
a) Northwestern Europe (UK/Netherlands) seem to be the first economies to
experience the transition to modern growth (“Little Divergence”)
b) Followed by Western Europe (early-to-mid 1800s), rest of Europe (“Great
Divergence”)
c) The transition to modern growth for the rest of the world later (mid-to-late
1900s)
Sophomoric explanations…
• “Folk” theories explaining the Great Enrichment & Great Divergence
• We’re rich because we’re “good”. They’re poor because they’re “bad”.
• We’re rich because we’re “bad”. They’re poor because they’re “good”.
• Unscientific, value-laden terms
• Economics can provide a guide
Economics & history
Economics & history
Theoretical economic models
• Simplifications of reality
• The goal is NOT to represent reality
• The goal is to understand reality
• The economic method
• Use theory to build an economic model
• Use the model to make predictions
• Test against empirical evidence