Elizabeth Lynch, Women's World Banking - Transforming Institutions by Investing in Human Talent
Institutions and the Allocation of Talent
description
Transcript of Institutions and the Allocation of Talent
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Institutions and the Allocation of Talent
Higher School of Economics, Moscow
Timur Natkhov, Leonid Polishchuk
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Motivation: Selection of Subject Areas by Russian University Applicants
Health
Agriculture
Science
Education
Humanities & Social Sciencies
Law & Public Administration
Engineering
Economics & Management
3%
4%
7%
10%
10%
13%
17%
30%
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Most Able Applicants Choose Law
32.5 37.5 42.5 47.5 52.5 57.5 62.5 67.5 72.5 77.5 82.5 87.5 92.5 97.5
Law
32.5 37.5 42.5 47.5 52.5 57.5 62.5 67.5 72.5 77.5 82.5 87.5 92.5 97.5
Aviation and space technologies
Unified State Test Results
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Institutions affect returns to human capital in various economic activities and hence occupational choice
Strong institutions reward productive economic activities and create incentives for value-creating Schumpeterian entrepreneurship
Weak institutions reward rent-seeking which draws talents and entrepreneurial energy away from wealth creation towards redistribution
Economic development
Allocation of talent Institutions
Hypotheses
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Antecedents: William Baumol (1990)
Institutions affect allocation of talent between productive and unproductive activities«Entrepreneurs are always with us and always play some substantial role. How they act at a given time and place depends heavily on the rules of the game – the reward structure in the economy – that happen to prevail»
Ancient Rome Medieval China Dark Ages in Europe Later Middle Ages
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Economic growth is driven by energy and innovations produced by a relatively small group of most talented individuals
Hence the choice by such individuals between production and rent-seeking is critically important for economic development
Excessive enrollment of best and brightest in law at the expense of sciences and engineering adversely related to growth rates
Antecedents: K.Murphy, A.Shleifer, R.Vishny (1991)
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Empirical strategy
• (Un)productive activities – share of young talents in country i pursuing education which equips for (un) productive activities
• Institutional quality – quality of institutions in country i• X – control variables • e – random error
Coefficient reflects the impact of institutions on the allocation of talent
Regression model
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Data UNESCO database on graduates by 28 fields of study in
100 countries1. Law2. Science3. Engineering
Governance Matters Indicators by the World Bank1. Rule of Law2. Control for Corruption
Control variables
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Institutions and students’ choices
Sample average
Rule of Law
above median
Rule of Law
below median
Law, % 6.2 4.2 8.3
Sciences, % 8.3 9.7 6.8
Difference between shares of law and science students, % -2.1 -5.5 1.4
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Quality of institutions and enrollment in law schools Share of Law graduates Rule of Law -0.530*** -0.589*** -0.578*** -0.581*** -0.563*** -0.466***
Log GDP per capita 0.218 0.218 0.207 0.232 0.0571 0.157
School Tertiary, % -0.335 -0.422 -0.433 -0.366 -0.0516 -0.581
Services, % GDP 0.777 0.837 0.776 1.500 1.466
Log Oil reserves 0.00545 -2.57e-05 0.0626** 0.0542
Ethnic Fractionalization 0.285 0.327 0.286
Log Populaion -0.219*** -0.357***
Gini coefficient 0.925
Trade to GDP ratio -0.526**
Constant -1.707 -2.123 -2.072 -2.379 1.947 3.726 Observations 95 95 95 95 95 81R-squared 0.165 0.171 0.171 0.175 0.230 0.322
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Lawlessness increases the appeal of legal profession
BLR
LBN
RUS
PAN
ALB
GEO
ARG
GTM
MKD
HND
UKR
SLV
ECU
VEN
MEX
HKG
BRA
BGRKEN
HRVIRQ
KHM
TUR
ROMAZE
CYP
LVA
LAO
SVK
IRNGRC
ITA
KOR
LUX
DZA
ARM
BEL
IDN
GUY
URY
PHL
NAM
LTU
BRN
BOL
EST
POLBWA
KGZCZE
CRI
SVN
SAU
BGD
PRTIRL
COMFRA
JOR
ESPHUN
BDI
MAR
ETH
CHE
THA
USAMYS
SWETZA
NLD
UGA
GBRLSO
FIN
ERI
AUT
MWI
ISL
GHA
AUSNZL
DNK
NER
CHL
CANNOR
-2-1
01
2S
hare
of L
aw G
radu
ates
, res
idua
ls
-1.5 -1 -.5 0 .5 1 1.5Rule of Law index, residuals
coef = -.466, (robust) se = .119, t = -3.92
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Quality of institutions and enrollment in sciences Share of Science graduates Rule of Law 0.257**
*0.205* 0.258** 0.258** 0.252*
*0.262*
*Log GDP per capita 0.194 0.194 0.134 0.137 0.191 0.250School Tertiary, % -1.261** -1.339** -1.395** -1.386** -1.482*** -1.179**Services, % GDP 0.700 1.020 1.012 0.789 0.235Log Oil reserves 0.0289 0.0281 0.00887 -0.0119Ethnic Fractionalization 0.0378 0.0249 0.493Log Populaion 0.0674 0.114**Gini coefficient -0.0210Trade to GDP ratio 0.289Constant -0.240*** -1.487 -1.861 -1.594 -1.635 -2.964* Observations 95 95 95 95 95 81R-squared 0.199 0.208 0.223 0.223 0.233 0.339
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Rule of Law increases the appeal of sciences
BLR PAN
GEO
ALB
RUS
ARG
HNDCOL
GTM
MKD
UKR
ECU
HKG
SWZ
AGO
VENKEN
BGR
HRV
BRA
MEXCYP
KHM
LVA
ROMTUR
GRC
CMR
LAO
SVK
IRQ
KOR
AZE
ITA
BELARM
URY
GUY
IRN
EST
NAM
LTU
DZA
CRI
PHL
BWAKGZ
SVN
POL
BGD
PRT
CZE
BOLIDN
COM
JOR
IRL
BRN
FRA
BDI
HUN
ESP
CHE
ETHMLT
SWE
MOZ
TZA
USA
THA
MRT
NLDLSO
MYSERI
FIN
GBR
UGA
MDG
MWIISL
AUTGHA
DNK
NZL
AUS
NERCHL
NOR
CAN
-1.5
-1-.5
0.5
11.
5S
hare
of S
cien
ce G
radu
ates
, res
idua
ls
-1.5 -1 -.5 0 .5 1 1.5Rule of Law index, residuals
coef = .326, (robust) se = .092, t = 3.55
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Law vs. Sciences and quality of institutions Difference between Shares of Law and Science
Rule of Law -0.552***Government Effectiveness -0.387**Control for Corruption -0.383***Private Property Protection -0.294**
Log GDP per capita -0.116 -0.188 -0.228 -0.339*School Tertiary, % 1.152** 1.153** 1.140** 1.013Services, % GDP 0.298 0.0628 0.218 0.101Log Oil reserves 0.0318 0.0440 0.0520* 0.0589**Ethnic Fractionalization 0.182 0.236 0.144 0.0169Log Populaion -0.190*** -0.179** -0.208*** -0.219*** Observations 95 95 95 83R-squared 0.310 0.246 0.266 0.301
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Net attractiveness of law and the quality of institutions
BLR
PAN
RUS
GEO
ALB
ARG
GTM
HND
SLV
UKR
MKDECU
HKG
AGO
VEN
KEN
BGR
BRA
MEX
HRVKHM
CYP
ROM
LVA
TUR
CMR
GRC
LAO
SVK
IRQKOR
ITA
AZE
BEL
ARM
IRN
URY
GUY
DZA
EST
NAMLTU
PHL
KGZ
CRI
POL
SVNIDN
BWABGD
BOL
CZE
PRT
JOR
BRNIRL
FRA
COM
BDI
ESP
HUN
ETH
CHE
MOZ
SWE
USA
TZA
THA
NLD
MYS
LSO
FIN
ERI
UGA
GBR
MDG
MWI
AUT
ISL
GHANZL
DNKAUS
NERCHL
CAN
NOR
-2-1
01
2D
iffer
ence
bet
wee
n La
w a
nd S
cien
ce g
radu
ates
, res
idua
ls
-1.5 -1 -.5 0 .5 1 1.5Rule of Law index, residuals
coef = -.537, (robust) se = .110, t = -4.9
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Robustness checks Difference between Shares of Law and Science Without OECD
and High Income Countries
Without Low Income
Countries
Full Sample with dummy for Asia
Full Sample with dummy for
Africa
Rule of Law -0.697*** -0.488*** -0.636*** -0.631***Log GDP per capita 0.0930 -0.368 0.00175 -0.0149School Tertiary, % 0.307 1.210** 0.995* 1.354**Services, % GDP 0.680 0.804 -0.613 0.397Log Oil reserves 0.0577 0.0360 0.0153 0.0301Ethnic Fractionalization 0.435 0.00826 -0.0105 0.115Log Populaion -0.288*** -0.198** -0.133* -0.187***Asia -0.777***Africa 0.421
constant 3.129 5.729** 6.363** 1.945
Observations 61 81 95 95R-squared 0.29 0.36 0.41 0.32
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Institutions and settlers mortality (Acemoglu, Johnson, Robinson 2001)
AUS
NZL
HKG
USA
ZAF
CAN
MLT
MYS
SGP
ETHGUY
PAK
IND
TUNEGY
ARG
CHL
LKA
BOL
BRA
COL
ECU
GTM
MEX
PER
URY
BGD
DZA
CRI
SLVHND
MARPRYVEN
BHSTTO
SDN
DOM
HTI
JAM
VNM
KEN
NIC
PANSEN
IDN
ZAR
COG
AGO
BFA
CMR
GAB
TZA
UGA
NER
GIN
SLE
MDG
CIV
GHA
TGO
GMB
NGA
MLI
-4-2
02
4P
rivat
e P
rope
rty P
rote
ctio
n
-2 0 2 4Log Settler Mortality
coef = -.607 se = .126, t = -4.8
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2SLS for former colonies with settlers mortality as instrument Difference between Shares of Law and
Science
Government Effectiveness -1.526**
Private Property Protection -0.931***
Government Effectiveness Private Property Protection
Settler Mortality
-0.246*** -0.403***
Full set of controls
Observations 35 35R-squared 0.70 0.77First Stage F-statistics 9.3 13.3
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The case of transition economies
Share of Law Graduates
Share of Science Graduates
Difference
Rule of Law -0.735** 0.571*** -0.912***
Log GDP per capita -0.286 0.320** -0.432**
School Tertiary, % 0.399 -1.477*** 1.421***
Services, % GDP 3.992* -1.845** 3.946***
Constant 4.181 -4.827* 6.363**
Observations 20 20 20R-squared 0.601 0.735 0.833
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Successful reforms increase relative attractiveness of sciences over law
BLR
MKD
ALB
GEORUS
UKR
ARM
BGRSVK
LVA
SVN
HRV
ROM
POL
EST
LTUCZE
AZEHUN
KGZ
-1-.5
0.5
1D
iffer
ence
bet
wee
n La
w a
nd S
cien
ce g
radu
ates
, res
idua
ls
-1 -.5 0 .5 1Rule of Law index, residuals
coef = -.912, (robust) se = .148, t = -6.16
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Enrollment trends: The tale of two neighbors
2001 2002 2003 2004 2005 2006 2007 20080%1%2%3%4%5%6%7%8%9%
Poland
2001 2002 2003 2004 2005 2006 2007 2008
Ukraine
sciencelaw
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Conclusions
Inefficient state, lack of the rule of law and poor protection of property rights increase the relative attractiveness law and public administration as areas of study for university students
Strong institutions increase the relative attractiveness of sciences and engineering as career choices for young people
Allocation of talent is a transmission mechanism between institutions and growth
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Institutions, Human Capital, and the Allocation Talent
According to the model, more talented individuals are more sensitive to the quality of institutions
With weak institutions, the share of law students among more gifted young people should be higher, then for the whole cohort
With strong institutions, the share of science students among more gifted young people should be higher, then for the whole cohort
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Consistency check: legal origins
Legal origins have strong impact on contemporary institutions:
Property rights protection (La Porta, Shleifer, et al., 1997)
Contract enforcement (La Porta, Shleifer, et al., 1998) Quality of governance (La Porta, Shleifer, et al., 1999) Control of corruption (La Porta, Shleifer, et al., 1999)
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2SLS model
(𝑈𝑛 )𝑝𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑣𝑒 𝐴𝑐𝑡𝑖𝑣𝑖𝑡𝑖𝑒𝑠𝑖=𝛽0+𝛽1 𝐼𝑛𝑠𝑡𝑖𝑡𝑢𝑡𝑖𝑜𝑛𝑎𝑙𝑄𝑢𝑎𝑙𝑖𝑡𝑦 𝑖+𝛽2𝑋 𝑖+𝜀𝑖
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Estimation results Difference between Shares of Law and Science
Rule of Law -1.544***Government Effectiveness -1.781**Control for Corruption -1.750**Private Property Protection -0.740***
Rule of Law Government Effectiveness
Control for Corruption
Private Property Protection
English Legal Origin
0.430*** 0.373*** 0.379*** 1.024***
Full set of controls
Observations 95 95 95 83R-squared 0.732 0.773 0.679 0.610First Stage F-statistics 33.97 42.23 26.34 16.77
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Direct and indirect impact of legal origins
Legal origins are NOT valid instruments: they directly affect the legal profession
Common law system is more lawyers-intensive than the civil law one due to differences in administration of justice (adversarial vs. inquisitorial approaches)
However the indirect effect of legal origins (trough the quality of institutions) on the popularity of legal profession prevails over the direct one
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Human capital, institutions, and allocation of talent
Data: quality of education indexes PISA and TIMMS for 60 countries
Rule of Law below the median
Rule of Law above the median
High Human Capital Index 7.33% 4.24%
Low Human Capital Index 2.88% 3.20%
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Strong human capital and weak institutions
TZA
MWI
KEN
UGAHND
VEN
PHL
BRAMEX
IDN
ARG
LBN
IRN
MKD
ARM
ROM
RUS-.0
50
.05
Sha
re o
f Law
Gra
duat
es
-.2 -.1 0 .1 .2Human Capital Quality Index (Altinok, Murseli 2007)
coef = .246, se = .055, t = 4.5
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Institutions and economic growth
2 3 4 5 6 7 8 9 105
6
7
8
9
10
11
R² = 0.611322109192881
Property rights protection index
Log
GD
P pe
r ca
pita
Institutions are pivotal for development
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Allocation of talent as a missing link between institutions and growth
Average GDP per capita growth in 2000-2009
Rule of Law in 1998 0.759*** 0.673** 1.672** 0.601
Share of Law graduates -0.136 -1.418***
Share of Science graduates 0.124 0.019***
Full set of controls
Observations 94 94 20 20R-squared 0.17 0.18 0.62 0.78
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Single-country analysis – quality of institutions and preference to law in Russian regions
MAG
KAM
KIR
SAK
KOS
IVA
ADY
TVE
OSE
KBRALT
KCRNGR
YEV
TYV
PRIOMS
VLGKAL
KLU
ZABCHE
DAG
ING
TUL
KYA
SMO
VLA
MUR
CU
YARPER
LIPRYA
BEL
KR
MO
VGGMEPSKAST
NIZ
KGNSVE
SAM
ORL
KGD
BA
BUR
KO
ARK
KK
TAM
KHA
SAR
TOM
UD
PNZ
KRS
ULY
BRY
VOR
NVS
AMU
TA
KEM
IRK
STA
ORE
RAL
SAHROS
-40
-20
020
40Ра
зниц
а до
лей
юри
стов
и и
нжен
еров
-2 -1 0 1 2Индкекс качества инвестиционного климата
coef = -7.10, se = 1.75, t = -4.05
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Dream Employers for Russian youths
Gazprombank
Russian Railroads, JSC
LukOil
Sberbank
Rosneft (Oil company)
Police
President administration
Gazprom
0% 5% 10% 15% 20% 25%
7%
7%
9%
10%
10%
11%
12%
22%
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Dream Employers for European Youths
Ernst & Young
PricewaterhouseCoopers
Coca-Cola Company
Boston Consulting Group
McKinsey & Co
L'Oreal
Apple
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Lawyers crowd out scientists and engineers
41.77 48.87 53.04 56.51 59.83 63.15 66.53 70.17 74.64 83.050%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Engineering
Law & Public administra-tion
Social sciences & Humanities
Economics & Man-agement
Health
Agriculture
Science
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A model
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Selection of activity
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Equilibrium
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Impact of institutions on the allocation of effort
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Higher talents are more sensitive to institutions
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Strong institutions: А > 0
Weak institutions: А < 0
Rent-seeking
Production
w
θ
w
θ
Production
Rent-seeking