why not adopt better institutions?

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Why Not Adopt Better Institutions?Brian Kelleher Richter a & Jeffrey F. Timmons aa Economics, and Public Policy, Richard Ivey Business School,University of Western Ontario , 1151 Richmond Street North,London , ON , N6A 3K7 , Canadab IE Business School, IE University, Calle Álvarez de Baena 4,1 ,Madrid , 28006 , SpainPublished online: 31 May 2012.

To cite this article: Brian Kelleher Richter & Jeffrey F. Timmons (2012) Why Not Adopt BetterInstitutions?, Oxford Development Studies, 40:2, 272-281, DOI: 10.1080/13600818.2012.677819

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Why Not Adopt Better Institutions?

BRIAN KELLEHER RICHTER & JEFFREY F. TIMMONS

ABSTRACT How much growth do (economic and legal) institutions cause? To quantify this effect,we adapted the baseline regression in Acemoglu, Johnson and Robinson’s (2002, Quarterly Journalof Economics, 117(4), pp. 1231–1294) seminal work on the causal relationship between the qualityof institutions and differences in modern-day income levels was adapted. We found that improvinginstitutional quality by one standard deviation increased a country’s average annual growth rate byonly 0.4% from 1820 to 1995.

JEL Classification: O4, N1, E0

1. Introduction

Long run is a misleading guide to current affairs. In the long run we are all dead.

John Maynard Keynes (1923)

Social scientists have long debated whether and by how much institutions affect

development outcomes, notably economic growth (North, 1990). In seminal work,

Acemoglu, Johnson and Robinson (hereafter AJR) (2001, 2002, 2005) used an instrumental

variable approach to identify the causal effect of institutions. Even though AJR’s approach

has been subject to considerable criticism—some have taken issue with their instrument

(Albouy, forthcoming), others their sample (McArthur & Sachs, 2001) and others their

measures of institutions (Glaeser et al., 2004)—their findings are generally seen by

economists as providing the most compelling empirical evidence that “institutions are the

fundamental cause of differences in economic development” (Acemogly et al., 2005, p. 385)

across countries. Rather than challenging AJR on statistical grounds, we seek to understand

howmuchgrowth institutions cause, assuming their econometric framework is a validway to

generate causal inference.

In this paper, we transform the income levels regression in AJR’s paper into growth rate

regressions. This exercise allows us to assess howmuch growth institutions cause, a question

ISSN 1360-0818 print/ISSN 1469-9966 online/12/020272-10

q 2012 Oxford Department of International Development

http://dx.doi.org/10.1080/13600818.2012.677819

This paper has benefited from conversations with Romain Wacziarg and Krislert Samphantharak along with

comments from David Meyer and participants in a seminar at UCLA Anderson. Richter also acknowledges the

support of the Center for International Business Education and Research (CIBER) at UCLA Anderson. Timmons

thanks the UCLA International Institute for financial support. Any errors in this draft, however, are our own.

Brian Kelleher Richter (corresponding author), Assistant Professor of Business, Economics, and Public Policy,

Richard Ivey Business School, University of Western Ontario, 1151 Richmond Street North, London, ON N6A

3K7, Canada. Email: [email protected]. Jeffrey F. Timmons, Assistant Professor of Strategy, IE Business

School, IE University, Calle Alvarez de Baena 4,1, Madrid 28006, Spain. Email: [email protected]

Oxford Development Studies,Vol. 40, No. 2, 272–281, June 2012

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http://dx.doi.org/10.1080/13600818.2012.677819

that has largely beenoverlooked, despite its importance.We focus on the long run because this

is when institutions should matter, according to most of the literature, including AJR, who

specifically assert that “early institutions persisted to the present” (2000, p. 20), allowing them

to use measures of institutions from recent periods as proxies for the entire time horizon.1

Using AJR’s narrative and econometric framework as a guide, we estimate that if a

country could have improved its institutional quality by one standard deviation, it would

have added 0.4% to its average annual growth rate between 1820 and 1995. This number

may provide an insight into the question of why not every country adopts better

institutions: from the perspective of the average person, the annualized pay-off from

improving institutional quality appears relatively modest.

2. A Brief Reprise of Acemoglu, Johnson and Robinson

Although AJR’s theory and results are well known, we summarize them briefly before

proceeding to ourmodificationof their analysis. They argue that present-day income levels are

a function of postcolonial institutions. Specifically, they claim colonial powers established

institutions conducive to long-run growth when the cost of settlement was low, allowing

colonizers to transplant their people and rules en masse. When the costs of settlement were

high, by contrast, they set up rules that facilitated predation and resource extraction—in effect

institutions that were not conducive to long-run growth. This hypothesis justifiedAJR’s use of

settlermortality rates as an instrumental variable for present-day institutions.With a sample of

64 former colonies, they show the instrumented measure of institutions has a strong effect on

contemporaneous income levels across countries.

The specific equation AJR (2002) estimated to establish the causal role of institutions in

explaining levels of income today takes the form:

logðIncomeiÞ ¼ aþ bDi þ X0igþ 1i;

where Incomei represents per capita income today, Di is an instrumented measure of

institutions and Xi is a vector of other covariates.

AJR’s framework establishes causality, but it does not readily allow one to infer the amount

of growth caused by institutions, arguably a more relevant question to contemporary

policymakers. Moreover, such regressions are easily misinterpreted because time is embedded

into the equation. To take one example: Rodrik & Subramanian (2003, p. 32) wrote “ . . . if

Boliviawere somehow to acquire institutions of the quality ofKorea, its GDPwould be close to

$18,000, rather than its current level of $2,700”.This interpretation conflates income levelswith

growth rates—a common mistake. Endowing Bolivia with first-rate institutions today would

not instantly transform it from poor to rich. Instead, it would merely accelerate the growth rate.

3. Data

3.1 Constructing Long-run Average Growth Rates

The first piece of data needed for our regressions is a long-run average growth rate. An

average annual growth rate (in percentage terms) can be calculated using the formula:

g ¼ 100*Incomet

Incomet2h

� �1h

2100:

Why Not Adopt Better Institutions? 273

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Assuming we have the basic inputs—per capita income levels today (Incomet), per capita

income levels at the beginning of the horizon period (Incomet2h), and the length of

a horizon over which growth compounds (h)—this equation can be used to compute long-

run average annual growth rates for any country. One barrier to using this measure is data

availability, as widely accepted historic levels of per capita income (Incomet2h) are scarce.

The most comprehensive source, Maddison (2003), lists only 27 countries in 1500, of

which only nine are in AJR’s sample.

We used Maddison’s data to calculate long-run average annual growth rates over the

periods 1500–1995 and 1820–1995 for all countries with estimates of initial income

levels. We chose these periods because they fit best with AJR’s (2002) time horizons.

They claim many modern-day institutions were established after the first European

colonies (approximately 1500), but did not trigger growth until the Industrial Revolution

(approximately 1820). To make the results comparable to AJR, we used 1995 as an

end date.

We also calculated a separate series of growth rates for all countries in AJR’s sample by

filling in missing data with the best available estimates. If Maddison did not provide

a specific income figure in 1500 we used $400 because it is the minimum and modal value.

For 1820, Maddison provides more country-level income estimates as well as regional

estimates for all countries; when the former weremissing, we used the latter. Consequently,

we may marginally over- or under-estimate growth rates for a few countries.2

All of the long-run growth rate data we constructed are available in Table A1 in the

Appendix. That table also includes the income levels we used from Maddison in

constructing those growth rates. Moreover, Table A1 includes all other data used in our

analysis.

3.2 Measuring Institutions

There is no consensus on how best to measure economic institutions. AJR (2002) used two

relatively broad and widely used metrics: Political Risk Services’ expropriation risks,

which proxies for the general security of property; and Polity IVs constraints on the

executive, which incorporates the independence of legislative and judicial branches of

government in its construction. Woodruff (2006) notes that an advantage of AJR’s (2002)

measures are that they incorporate both formal and informal institutional features, as they

factor in not only what the institutions represent on paper, but also how they work in

practice. Nevertheless, even AJR (2000) accept that their choice of variables for

institutions is not immune to criticism, as they could have focused instead on any number

of measures representing slightly different concepts. In an early working paper version of

their now famous work, they wrote there are “a variety of institutional guarantees,

including constraints on government expropriation, independent judiciary, property rights

enforcement, equal access to education, and respect for civil liberties, that are important to

encourage investment and growth” (Acemoglu et al., 2000, p. 3). They went on to test

some of these other measures before settling parsimoniously on expropriation risks as

a final measure because it relates to all of these other concepts and because it provides

strong statistical inferences.3

In this analysis, AJR’s (2002) measures were used for several reasons. First, we wanted

to remain consistent with AJR’s entire econometric framework in asking how much

institutions matter. Second, AJR’s measures are the benchmark in the literature, as they are

274 B. K. Richter & J. F. Timmons

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the most widely used (Woodruff, 2006). Third, AJR’s measures have a far stronger causal

relationship with present income levels than many alternatives, including formal measures

of judicial independence (Acemoglu & Johnson, 2005; Acemoglu et al., 2011). In

summary, AJR picked the measures of institutions that should have the highest impact on

growth. Consequently, our estimates should be thought of as approaching the upper bound,

relative to any other measure of institutions.

4. Estimating the Impact of Institutions on Long-run Growth

We quantify the effect of institutions on long-run average growth rates using the same

regression framework that establishes causality, changing only the dependent variable,

from the log of the level of modern-day (1995) income to a long-run average growth rate.

Our estimate takes the form:

gi ¼ aþ bDi þ 1i;

where b should approximate the impact of a one-unit change in the institutions variable

over the time horizons suggested by AJR.4 Figure 1 provides scatter plots of AJR’s

original second-stage results and ours; our work mirrors theirs.

Table 1 shows AJR’s preferred instrumented measure of institutional quality (average

expropriation risk from 1985 to 1995) against long-run average annual growth rates for the

two time horizons.5 We do not show the first stages as they are identical to those in AJR’s

original paper.

The results, unsurprisingly, show that institutions play a causal role in determining long-

run average annual growth rates across countries.6 The coefficients estimated in

regressions with and without filled initial income values are hardly distinguishable,

suggesting that our method of filling in missing values does not bias any findings.7

6

7

8

9

10

11

4 5 6 7 8 9

AGO

ARG

AUS

BFABGD

BHS

BOL

BRA

CAN

CHL

CIV CMRCOG

COLCRI

DOMDZAECU

EGY

ETH

GAB

GHAGIN

GMB

GTM

GUY

HKG

HND

HTIIDNIND

JAM

KEN

LKA

MAR

MDG

MEX

MLI

MLT

MYS

NERNGA

NIC

NZL

PAK

PAN

PERPRY

SDNSEN

SGP

SLE

SLV

TGO

TTO

TUN

TZA

UGA

URY

USA

VEN

VNM

ZAF

ZAR

Institutions (Expropriation Risk), Instrumented Value

Log(

1995

Per

Cap

ita In

com

e)

Acemoglu Johnson and Robinson (2002) Regression

–0.5

0.0

0.5

1.0

1.5

2.0

2.5

4 5 6 7 8 9

AGO

ARG

AUS

BFA

BGD

BHS

BOL

BRA

CAN

CHL

CIVCMR

COG

COLCRI

DOM

DZAECUEGY

ETH

GAB

GHA

GIN

GMB

GTM GUY

HKG

HND

HTI

IDN

IND

JAM

KEN

LKAMAR

MDG

MEX

MLI

MLT

MYS

NER

NGANIC

NZL

PAK

PAN

PERPRY

SDN

SEN

SGP

SLE

SLV

TGO

TTO

TUN

TZA

UGA

URY

USA

VEN

VNM

ZAF

ZAR

Institutions (Expropriation Risk), Instrumented Value

Ave

rage

Gro

wth

Rat

e, 1

820-

1995

, Fill

ed

Our Regression changing the Dependent Variable

Figure 1. Comparing Acemoglu, Johnson and Robinson’s regression with ours.


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Table

1.Institutionsregressionwithlong-runaveragegrowth

ratesas

thedependentvariable

(1)

(2)

(3)

(4)

(5)

(6)

Dependentvariable

Averagegrowth

rate,

1500–1995

Averagegrowth

rate,

1500–1995;n/a

obser-

vationsfor1500income

filled

usingminim

um

knownvalue($400)

Averagegrowth

rate,

1820–1995

Averagegrowth

rate,1820–

1995;n/a

observationsfor

1820incomefilled

using

regional

mean

Inst

itu

tio

ns(e

xp.

risk)

0.176**

0.209**

0.200*

0.424**

0.495**

0.541**

(0.043)

(0.022)

(0.028)

(0.086)

(0.073)

(0.109)

Co

nve

rgen

ceh

ypo

thes

isco

ntr

ols

log(1500

Inco

me,

Fil

led)

20.003*

(0.001)

log(1820

Inco

me,

Fil

led)

20.0004

(0.0006)

Constant

20.926**

20.988**

0.301

21.999**

22.332**

22.387**

(0.262)

(0.142)

(0.473)

(0.676)

(0.479)

(0.539)

Noofobsaervations

964

64

19

64

64

No

tes:

Theseregressionsreplicatetheresultsin

tableVIII,column2,panelCofAcemoglu

eta

l.(2002),butchangethedependentvariablefrom

thelogofincome

levelsin1995tolong-runaveragegrowth

rates.Thefirst-stageregressionisnotshownbecause

itisthesameas

intheoriginalpaper.T

heinstitutionsvariable

inthisregressionisaverageexpropriationrisk

from

1985to

1995;itsoriginalsourceisPoliticalRiskServices.Long-runaveragegrowth

ratesarecalculated

asdescribed

inthetextbased

onMaddison(2003).Allother

datacomefrom

Albouy(forthcoming)andwereprovided

tohim

byAcemoglu

eta

l.*Signficant

atthe5%

level;**significantat

the1%

level. **significantat

the1%

level.


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5. Interpretation and Implications

To facilitate interpretation of the coefficient for institutions from Table 1, Table 2 presents

three scenarios: improving institutions by one standard deviation, from the median to the

best, and in the extreme case from the worst to the best. Under all of the scenarios, the

impact of institutions on long-run average annual growth rates is larger over the 1820–

1995 horizon than over the 1500–1995 horizon, consistent with AJR’s argument that

institutions mattered more following the Industrial Revolution.8

Whether institutions’ impact is large or small depends on the benchmark used to

evaluate their effect. If it were “free money on the sidewalk”, every country would have

wanted to pick up the 1.95% gain in average annual growth rates by moving from worst to

best; however, only one country had the worst institutions. Most countries could have

made only modest gains in their institutional quality: a one standard deviation

improvement would have yielded a 0.42% increase in its growth rate. This number implies

that improved institutional quality would have had a hardly perceptible impact on the

common person—insufficient to double their income by the end of their lifetime. The

bottom row of each scenario in Table 2 shows the number of years it would take improved

Table 2. Impact of institutions on long-run growth rates

Dependent variablein regression

Average growth rate,1500–1995;n/a observations for 1500

income filled using minimumknown value ($400)

Average growth rate,1820–1995; n/a observationsfor 1820 income filled using

regional mean

Regression coefficient (fromTable 1)

0.209 0.495

Impact of improving institutions by one standard deviation on growthOne standard deviation ofunderlying data

0.85 0.85

Predicted impact 0.177% 0.418%Years needed for institutionaleffects to double incomes

174 117

Impact of improving institutions from the median to the best on growthValue of best institutions 8.73 8.73Value of median institutions 6.36 6.36Predicted impact 0.495% 1.173%Years needed for institutionaleffects to double incomes

107 63

Impact of improving institutions from theworst to best on growthValue of best institutions 8.73 8.73Value of worst institutions 4.79 4.79Predicted impact 0.823% 1.950%Years needed for institutionaleffects to double incomes

79 43

Notes: “Predicted impact” is calculated by multiplying the regression coefficient by the improvement ininstitutional quality in the scenario. “Years needed for institutional effects to double incomes” iscalculated assuming that a country would grow at a 1% rate regardless of the improvement ininstitutional quality by solving for n in the equation: 2 ¼ (1.01 þ impact of institutions)n 2 (1.01)n.The mean average annual growth rate for 1820–1995 was 0.9% and the mean for 1500–1995was 0.4%.


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institutional quality to double incomes under a conservative scenario: these values range

from 43 to 174 years.9

In other words, if AJR’s narrative were true, differences in income levels today that

stem from institutional divergence many years ago represent the power of compounding

small differences in growth rates over long time horizons as much as the potency of the

institutions themselves. From a political economy perspective, it is possible that heads of

state with the weakest institutions might have preferred the private benefits of

expropriation (or lack of constraints) over the (small) risk of regime change prompted by

citizens’ unlikely collective outrage over the nearly imperceptible consequences of living

with second-rate institutions (Olson, 1996). Put succinctly, better institutions cause more

growth, but perhaps not enough to compel countries to adopt them.

Notes

1 Jones & Olken (2008) also found, for example, that institutional change is not correlated with

extraordinary up or down movements in growth over shorter intervals.2 Errors in initial income levels are insignificant over long time horizons (calculations available).3 When we attempted to extend the AJR framework and use the AJR instrument on two alternative

measures of institutions, namely “independence of courts” as a formal institution and “trust” as an

informal institution, we found that the first-stage regression failed to confirm that the instrument was

viable when applied to measures of institutions other than AJR’s.4 As a specification check, we also estimated regressions including the initial income levels as a control

for convergence. The results appear in Table 1, columns 3 and 6, respectively.5 We obtain very similar results using AJR’s alternative institutions measure: constraints on the executive

in 1990 (results available).6 These findings (and AJR’s) are subject to the McArthur & Sachs’s (2001) caveat: simple cross-country

regressions capture only some elements of the growth process observable across countries, and none of

the elements within countries, including context-specific conditioning factors.7 Any bias appears to make the coefficients larger, rather than smaller.8 While the greater impact of institutions following the Industrial Revolution is a mechanical result of

rescaling the dependent variable, it also suggests that institutional quality may matter more in absolute

terms in periods of higher growth.9 These values were calculated under the assumption that baseline growth rates were 1% annually by

solving for n in the equation 2 ¼ (1.01 þ impact of institutions)n 2 (1.01)n. These estimates are

conservative because the mean average annual growth rate for 1820–1995 was 0.9% and the mean for

1500–1995 was 0.4%. With a baseline growth rate of 0%, doubling times range from 57 to 623 years.

References

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An Empirical Investigation, NBER Working Paper 7771.

Acemoglu, D., Johnson, S. & Robinson, J. A. (2001) The colonial origins of comparative development: an

empirical investigation, American Economic Review, XCI, pp. 1369–1401.

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making of the modern world income distribution, Quarterly Journal of Economics, 117(4), pp. 1231–1294.

Acemoglu, D., Johnson, S. & Robinson, J. A. (2005) Institutions as a fundamental cause of long-run growth, in:

P. Aghion & S. N. Durlauf (Eds) Handbook of Economic Growth, Vol. 1A (Amsterdam: Elsevier BV),

chapter 6.

Acemoglu, D., Johnson, S. & Robinson, J. A. (2011) Hither Thou Shalt Come, But No Further: Reply to

“The Colonial Origins of Comparative Development: An Empirical Investigation: Comment”, NBER

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Economic Growth, 9(3), pp. 271–303.

Jones, B. & Olken, B. (2008) The anatomy of start–stop growth, The Review of Economics and Statistics, 90(3),

pp. 582–587.

Keynes, J. M. (1923) A Tract on Monetary Reform (London: Macmillan).

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MA), Working Paper No. 8114.

North, D. C. (1990) Institutions, Institutional Change and Economic Performance (Cambridge: Cambridge

University Press).

Olson, M. (1996) Big bills left on the sidewalk: why some nations are rich, and others poor, Journal of Economic

Perspectives, 10(2), pp. 3–24.

Rodrik, D. & Subramanian, A. (2003) The primacy of institutions (and what this does and does not mean),

Finance & Development, 40(2), pp. 31–34.

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Economics of Corruption (Northampton, MA: Edward Elgar), chapter 3.

Appendix 1. Data

The data we used in our analysis are provided here, so that our work can be replicated. Of

particular note, Table A1 shows the values we constructed for long-run average growth

rates and the income levels we used from Maddison (2003). All of the other data come

from the sources stated above and are readily downloadable.


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http://www-personal.umich.edu/~albouy/AJRreinvestigation/ajrcomment.dta



Table

A1.Datausedin

analysis

Observation

Long-runGrowth

Rates,

based

onMaddison

(DependentVariables)

AJR

’sMeasuresof

Institutions(K

eyIndependent

Variable)

Instrumentsfor

1stStageRegressions

Convergence

Hypothesis

ControlVariables

Other

Data

forGrowth

Construction

Countr

yA

bbr.

Ave

rage

Gro

wth

Ra

ge,

1500

–19950

Ave

rage

Gro

wth

Rage,

1500

–1995;

n/a

obse

rvati

ons

for

1500

Inco

me

fill

edu

sin

gM

inim

um

know

Valu

e

Ave

rage

Rage,

1820

–1995

Ave

rage

Gro

wth

Rage,

1820

–1995;

n/a

obse

rvati

ons

for

1820

Inco

me

fill

edusi

ng

Reg

ional

Mea

n

Exp

ropri

ati

on

Ris

k(P

rim

ary

)

Const

rain

tson

the

Exe

cuti

ve(A

lter

nati

ve)

log

of

Set

tler

Mort

ali

tyR

ate

s

Popula

tion

Den

sity

in1

50

0

Inco

me

leve

lsin

15

00

;n/a

obse

rvati

ons

for

1500

Inco

me

fill

edusi

ng

Min

imum

know

Valu

e

Inco

me

leve

lsin

1820;

n/a

obse

rvati

ons

for

1820

Inco

me

fill

edusi

ng

Reg

ional

Mea

n

Inco

me

leve

lsin

1995

Algeria

DZA

0.381

1.040

1.040

6.50

24.36

7.00

400.00

430.27

2632.31

Angola

AGO

0.094

0.238

5.36

35.63

1.50

400.00

419.76

635.81

Argentina

ARG

0.607

1.391

6.39

64.23

0.11

400.00

713.20

8004.66

Australia

AUS

0.779

0.779

2.067

2.067

9.32

72.15

0.03

400.00

517.96

18601.61

Baham

as,The

BHS

0.522

1.215

7.50

4.44

1.46

400.00

635.79

5260.67

Bangladesh

BGD

0.122

0.133

5.14

24.27

23.70

400.00

581.02

732.53

Bolivia

BOL

0.363

0.762

5.64

74.26

0.83

400.00

635.79

2400.16

Brazil

BRA

0.523

0.523

1.209

1.209

7.91

74.26

0.12

400.00

646.11

5295.79

Burkina

Faso

BFA

0.128

0.335

4.45

15.63

4.23

400.00

419.76

754.02

Cam

eroon

CMR

0.183

0.490

6.45

25.63

1.50

400.00

419.76

987.62

Canada

CAN

0.786

0.786

1.764

1.764

9.73

72.78

0.02

400.00

904.41

19293.26

Chile

CHL

0.622

1.434

7.82

74.23

0.80

400.00

713.20

8612.47

Colombia

COL

0.528

1.165

7.32

64.26

0.96

400.00

713.20

5417.92

Congo,Dem

.Rep.

ZAR

20.059

20.193

3.50

15.48

1.50

400.00

419.76

299.13

Congo,Rep.

COG

0.343

0.945

4.68

25.48

1.50

400.00

419.76

2177.37

CostaRica

CRI

0.521

1.213

7.05

74.36

1.54

400.00

635.79

5242.35

Cote

d’Ivoire

CIV

0.232

0.630

7.00

26.50

4.23

400.00

419.76

1258.82

Dominican

Republic

DOM

0.387

0.830

6.18

64.87

1.46

400.00

635.79

2702.79

Ecuador

ECU

0.473

1.074

6.55

74.26

2.17

400.00

635.79

4126.00

Egypt,Arab

Rep.

EGY

0.336

0.336

0.953

0.953

6.77

34.22

100.46

475.00

474.96

2496.22

ElSalvador

SLV

0.376

0.800

5.00

4.36

1.54

400.00

635.79

2563.56

Ethiopia

ETH

0.064

0.155

5.73

23.26

6.67

400.00

419.76

550.27

Gabon

GAB

0.504

1.404

7.82

25.63

1.50

400.00

419.76

4811.36

Gam

bia,The

GMB

0.145

0.382

8.27

77.29

4.23

400.00

419.76

817.70

Ghana

GHA

0.212

0.574

6.27

16.50

4.23

400.00

419.76

1143.62

Guatem

ala

GTM

0.423

0.933

5.14

44.26

1.54

400.00

635.79

3228.53


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Guinea

GIN

0.053

0.123

6.55

16.18

4.23

400.00

419.76

520.58

Guyana

GUY

0.428

0.947

5.89

43.47

0.21

400.00

635.79

3309.63

Haiti

HTI

0.134

0.115

3.73

14.87

1.32

400.00

635.79

776.88

Honduras

HND

0.319

0.638

5.32

54.36

1.54

400.00

635.79

1935.48

HongKong,

China

HKG

0.801

2.030

2.030

8.14

2.70

0.09

400.00

615.00

20725.57

India

IND

0.208

0.208

0.607

0.607

8.27

73.88

23.70

550.00

533.10

1537.89

Indonesia

IDN

0.430

0.976

0.976

7.59

25.14

4.28

400.00

611.93

3347.78

Jamaica

JAM

0.453

0.964

0.964

7.09

74.87

4.62

400.00

700.75

3753.41

Kenya

KEN

0.191

0.514

6.05

34.98

2.64

400.00

419.76

1028.81

Madagascar

MDG

0.107

0.276

4.45

36.28

1.20

400.00

419.76

679.82

Malaysia

MYS

0.579

1.408

1.408

7.95

72.87

1.22

400.00

602.79

6965.44

Mali

MLI

0.130

0.339

4.00

17.99

1.00

400.00

419.76

759.17

Malta

MLT

0.750

2.107

7.23

2.79

62.50

400.00

419.76

16145.32

Mexico

MEX

0.537

0.537

1.191

1.191

7.50

34.26

2.62

425.07

759.07

6026.96

Morocco

MAR

0.366

0.366

0.998

0.998

7.09

24.36

9.08

400.00

429.90

2445.93

New

Zealand

NZL

0.735

0.735

2.094

2.094

9.73

72.15

0.37

400.00

400.00

15030.61

Nicaragua

NIC

0.246

0.430

5.23

35.10

1.54

400.00

635.79

1348.03

Niger

NER

0.047

0.105

5.00

35.99

1.00

400.00

419.76

504.33

Nigeria

NGA

0.211

0.570

5.55

17.60

4.23

400.00

419.76

1134.46

Pakistan

PAK

0.308

0.658

6.05

33.61

23.70

400.00

581.02

1830.48

Panam

aPAN

0.525

1.223

5.91

75.10

1.54

400.00

635.79

5333.44

Paraguay

PRY

0.429

0.951

6.95

54.36

0.50

400.00

635.79

3330.70

Peru

PER

0.439

0.914

5.77

74.26

1.56

400.00

713.20

3505.07

Senegal

SEN

0.233

0.634

6.00

35.10

4.23

400.00

419.76

1268.12

SierraLeone

SLE

0.126

0.329

5.82

36.18

4.23

400.00

419.76

745.97

Singapore

SGP

0.785

1.987

1.987

9.32

32.87

0.09

400.00

615.00

19224.80

South

Africa

ZAF

0.457

1.277

1.277

6.86

72.74

0.49

400.00

414.84

3824.11

SriLanka

LKA

0.408

1.038

1.038

6.05

54.25

15.47

400.00

492.17

2996.68

Sudan

SDN

0.137

0.360

4.00

14.48

14.03

400.00

419.76

787.21

Tanzania

TZA

0.047

0.106

6.64

34.98

1.98

400.00

419.76

504.92

Togo

TGO

0.102

0.262

6.91

16.50

4.23

400.00

419.76

663.76

Trinidad

and

Tobago

TTO

0.662

1.615

7.45

74.44

1.46

400.00

635.79

10502.70

Tunisia

TUN

0.450

1.237

1.237

6.45

34.14

11.70

400.00

429.71

3692.45

Uganda

UGA

0.104

0.268

4.45

35.63

7.51

400.00

419.76

670.53

United

States

USA

0.835

0.835

1.711

1.711

10.00

72.71

0.09

400.00

1257.19

24483.91

Uruguay

URY

0.590

1.343

7.00

34.26

0.11

400.00

713.20

7365.43

Venezuela,

RB

VEN

0.630

1.456

7.14

34.36

0.44

400.00

713.20

8950.26

Vietnam

VNM

0.253

0.558

0.558

6.41

34.94

6.14

400.00

527.10

1396.61


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why not adopt better institutions?

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