growth diagnostics in practice i applications to paraguay and peru ricardo hausmann

Growth Diagnostics in Practice IApplications to Paraguay and Peru

Ricardo Hausmann

Outline

The inadequacy of current practice A Growth Diagnostic framework First step: describe growth Second step: The decision tree Third step: Infer a syndrome and check it out

Traditional growth diagnostic I: Growth accounting Is unable to make any causal inferences Discards a lot of information (e.g. about prices) Decomposes growth into endogenous and

interrelated factors Changes in A and K are not independent

A

dA

k

dk

ss

ss

1

1

Traditional growth diagnostic II:Barro-style growth regressions

Parameter heterogeneity: mean effect is not effect in a given country.

Economics is mainly about prices and quantities But regressions normally have just quantities

There is much more relevant information about a specific country than that which can be used for a large cross-section of countries

Governments are pro-active to market failures But we do not observe the underlying failures, so coefficients on policies

are biased.

Variables are not policy levers Even if we get the perfect instrument, this is still a problem (e.g.

openess = Trade/GDP ? )

Traditional growth diagnostic II:Barro-style growth regressions Growth = a * Policy1 + b * Policy2

+ c * Policy 3 + d * ……… This assumes separability and linearity Assume instead that the relationship is Growth = a * (Policy1*Policy2*(Policy)2) Then the effect of one policy will depend on

the state of the others

The linearization assumption applied to making cakes

Milk

Water

Flour

Yeast

Eggs

Sugar

Ingredients are substitutes

The alternative view about making cakes

Milk

Eggs

Fluor

Yeast

Cocoa

Sugar

The barrel contains as much water as the lowest component allows.

Here the most binding constraint are… The eggs

Now we can move to growth diagnostics...

Low

social conflict

Self discovery

Infrastructure

Hum

an capital

Low

taxes

Financing

What is binding for the

barrel’s capacity ?

Different measures can beComplements or Substitutes

Low

social conflict

Self discovery + R

&D

Infrastructure

Hum

an capital

Low

taxes

Financing

Low social conflict

Self discovery

Infrastructure

Human capital

Low taxes

Financing

Multiplication SummationThe minimum

How do measures produce results ? © Rodrigo Wagner, 2006

Reality lies somewhere in

between. Growth

accelerations suggest it is

closer to complements

What is the impact of relaxing a constraint? If it is non-binding: very little If it binds: it depends on how far away is the

next constraint

Growth policies are analogous to non linear numeric optimizationFactor 1

Factor 2

0%

2%

2%

4%4%

6%

8%

If X is a binding constrain then …1. The (shadow) price of the constraint is high.2. Movements in the constraint should produce

significant movements in growth3. Agents in the economy must be engaging in

efforts to overcome or by-pass the constraint.4. Camels and hippos: Agents less intensive in that

constraint are more likely to grow / survive. • Rajan and Zingales

5. Ex ante risks imply high current profits, as static rents should be compensation for expected dynamic losses. High rents do not trigger entry.

If X is a binding constraint then

6. Fan belt effect: an initial binding constraint may have affected the rest of the economy causing now other constraints to become binding

7. There might be a constraint that is the consequence of a combination of factor (x,y,z)

8. You can learn something from international rankings, but be careful. Keep in mind both price and quantity.

A Bayesian interpretation of the process

How suprising is this signal given my priors? Note that a typical p-value is not the relevant

question We care about the opposite That’s why we should use Bayes rule

Which depend on our priors !)Pr(

)Pr()|Pr()|Pr(

i

XXiiX S

HHSSignalHipothesis

)|Pr( Xi HipothesisSignal

)Pr( XH

How suprising is this signal given my priors? We can condition that the country belong to a given

subgroup

That is why we often condition on the level of development or some other variable that we know is related

If the denominator is large, i.e. if some variable is normal, it will not change priors by much

),Pr(),Pr(),|Pr(

),|Pr(XS

XHXHSXSignalHipothesis

i

XXiiX

Three steps to growth diagnostics

I. Description

II. Nested symptoms: the decision tree approach

III. From syndromes to symptoms

First step:Describe the growth process

Describing the context of the growth problem

Long run pattern The world before 1960

Demographics Note events: accelerations, collapses Growth accounting & GDP composition by sector GDP vs. GNP:

natural resources and foreign capital intensity Exports performance and growth Politics, institutions and growth

150

02

00

02

50

03

00

03

50

0G

DP

pe

r ca

pita

(M

add

iso

n (

19

90

Inte

rnatio

na

l G

eary

-Kh

am

is d

ollars

))

1940 1960 1980 2000year

Paraguay: GDPpc in the long run: barely two decades of growth

200

04

00

06

00

08

00

01

00

00

1940 1960 1980 2000year

Paraguay gdp_pc_maddison Argentina gdp_pc_maddisonBolivia gdp_pc_maddison Brazil gdp_pc_maddisonUruguay gdp_pc_maddison

PIB per capita en Paraguay y sus vecinos, desde 1940

Paraguay: lousy comparative long-run performance

Evolución PIB per capita Real Mercosur (1990=100)

90

100

110

120

130

140

150

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

Brazil

UruguayArgentina

Paraguay

Paraguay: lousy recent performance

Growth in Paraguay and neighboring provinces 1990-2000

Pais_Provincia Crecimiento 90-00 Ranking

Mato Grosso do Sul 2.1% 34Misiones 1.9% 38Formosa 1.0% 52Paraná 0.8% 55Salta 0.5% 57Corrientes 0.3% 60Chaco -0.3% 64Paraguay Total -0.5% 65

Ranks based on 72 Mercosur states and provinces

PIB per capita (dolares constantes 2000)

500

1000

1500

2000

2500

1960

1962

1964

1966

1968

1970

1972

1974

1976

1978

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

Peru: a growth star?


500

1000

1500

2000

2500

1960

1962

1964

1966

1968

1970

1972

1974

1976

1978

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

…or just recovering from a growth collapse?

Peru’s long-run growth: a sustained growth collapse

-6.0%

-4.0%

-2.0%

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

Gap GDP GNI

Rising gap between GDP and GNP

In both countries: demography and human capital would have predicted more growth Falling dependency ratios Higher female labor force participation Hence, rising workers per capita Greater schooling attainment Greater urbanization Global technological progress

Symptoms

Is it lack of finance? Paraguay

1997

19981999

2000

2001

2002

2003

2004

20052006

2007

2530

3540

4550

Lend

ing in

teres

t rate

(%)_

__EI

U

17 18 19 20 21 22Gross fixed investment (% of GDP)___EIU

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

1520

2530

35RL

ENDR

ATE


Is it finance? Peru

1997

1998

1999

2000

2001

2002

2003

2004

20052006

2007

1015

2025

30RL

ENDR

ATE

16 18 20 22 24Gross fixed investment (% of GDP)___EIU

1997

19981999

2000

2001

200220032004

20052006

2007

1520

2530

Lend

ing in

teres

t rate

(%)_

__EI

U

16 18 20 22 24Gross fixed investment (% of GDP)___EIU

Is it human capital?Educacion Promedio al 2000 segun Fecha de Nacimiento

5.96.2

6.7

7.2

7.6

8.0

8.4

8.7

9.3

6.2

6.9

7.9

8.2

9.0

9.5

10.1

10.610.8

4.8

5.6

6.6

7.2

8.5

8.9

9.49.5

9.9

4.3

5.0

5.9

6.7

7.3

7.6

8.1

8.5

9.0

4.0

5.0

6.0

7.0

8.0

9.0

10.0

11.0

1,935 1,940 1,945 1,950 1,955 1,960 1,965 1,970 1,975 1,980 1,985

Paraguay Peru Mexico Colombia

Average schooling of cohort by year of birth

Is it human capital?

Retornos a la Educacion (MINCER)

11.7%

12.4%12.0%

12.7%

10.7%

11.4%

6.0%

8.0%

10.0%

12.0%

14.0%

16.0%

18.0%

1990 1992 1994 1996 1998 2000 2002 2004

Paraguay Mexico Peru Colombia

Mincerian returns to Education, Urban Males

Inferring a syndrome

What are stylized facts of growth collapses?

AFGAGOANT

ARE

ARG

ATG AUSAUT

BDI

BELBENBFABGDBGRBHR

BHS

BLZ

BMU

BOL

BRA

BRB

BTNBWA

CAF

CAN

CHE

CHLCHN

CIV

CMRCOG

COL

COM

CRICYP DEU

DMA

DNKDOM

DZA

ECUEGYESPFINFJIFRA

GAB

GBR

GHA

GMB

GNB

GRC

GRD

GTM

GUYHKG

HNDHTI

HUN

IDN

INDIRL

IRN

IRQ

ISL

ISR

ITA

JAM

JOR

JPN

KEN

KIR

KNA

KOR

KWT

LBR

LBY

LCA

LKA LSOMAR

MDG

MEXMLI

MLTMMR

MOZMRT MUS

MWI

MYS

NAM

NCL

NER

NGANIC

NLD

NOR

NPL

NZL

OMN

PAKPAN

PER

PHL

PNG

PRT

PRY

PYF

ROM

RWA

SAU

SDN

SEN

SGP

SLB

SLE

SLVSUR

SWESWZ

SYCSYR

TCD

TGO

THATTOTUNTUR

URY

USAVCT

VEN

VUT

WSM

ZAF

ZAR

ZMB

ZWE

196

01

970

198

01

990

200

0

1960 1970 1980 1990 2000LOCMAXTIMEX

MAXPCTIME LOCMAXTIMEX

Most growth collapses coincide with export collapses

Date of export collapse

Dat

e of

gro

wth

col

laps

e

AFGANT

ARE

BDI

BHS

BOL

BRB

CAF

CIV

CMR

COG

COM

DMA

DOM

DZA

GAB

GHA

GMB

GNB

GRD

GTM

HND

HTI

IDN

IRN

IRQ

ISR

JAM

KEN

KIR

KNA

KWT

LBR

LCA

MDG

MLTMWI

NAM

NCL

NER

NGA

NIC

NPL OMN

PER

PNGPRY

ROM

RWASAU

SEN

SLB

SLE

SLV

SUR

SYR

TGO

URY

VEN

VUTZAF

ZAR

ZMB

ZWE

0.2

.4.6

.81

.2 .4 .6 .8 1GAPXPC

GAPPCGDP GAPXPC

Collapses in exports were typically larger than those in output

Fall of exports

Fal

l of

outp

ut

In Perú, exports seem to explain the growth collapse and the recovery

1960

1961

19621963

19641965

19661967

19681969

197019711972

1973

197419751976

1977

1978

19791980

1981

1982

1983

19841985

1986

1987

1988

1989

19901991

1992

1993

1994

19951996

1997

199819992000

2001

20022003

2004

3.5

53

.63

.65

3.7

3.7

5L

YP

CL

CU

K

5.2 5.4 5.6 5.8 6 6.2LXPCKUS

Caida de Exportaciones 79-93: Causantes

trad-agric12.2%

minería metálica43.1%

petróleo23.4%

maquinaria y equipos2.1%

nt-pesca2.1%

nt-agric0.2%

textiles5.1%

químicos1.9%

mineria no metalica1.8% metalurgia y joyeria

0.5%

trad-pesca2.6%

otro5.0%

- Exp 79 (US$ reales pc): 522- Exp 93 (US$ reales pc): 176- Caida de exportaciones pc: $346- 81% de la perdida de exps proviene de productos tradicionales

Recuperación de Exportaciones 93-05: Causantes

minería metálica63.6%

petróleo10.6%

otro0.8%

trad-pesca3.0%

trad-agric1.7%

metalurgia y joyeria1.6%

mineria no metalica0.7%químicos

3.6%

textiles6.4%

nt-agric6.1%

nt-pesca0.8%

maquinaria y equipos1.0%

- Exp 93 (US$ reales pc): 176- Exp 05 (US$ reales pc): 547- Aumento de exps pc: $346- 78% de la recuperación se debe a productos tradicionales

Interpretation

Peru’s growth collapse / recovery was caused by a collapse / recovery in exports

Exacerbated by a history of expropriation in the exportable sector that led to declines in market share Agriculture, fishing, mining, oil

Recovery associated with reestablishment of private property rights in old sectors under very generous terms Vicious circle?

Interpretation of Peru

Very little structural transformation, in spite of large changes in relative prices (terms of trade and real exchange rate)

Export sector is land and (foreign) capital intensive Mining expansion has created no net jobs GDP > GNI “No chorrea”

In a highly urbanized society No labor demand for tradables in cities

What about Paraguay?

Very short periods of growth in history 1965-1980

Associated with finding products that can be exported profitably Cotton, electricity, soybeans

Very peripheral position in the product space …but in spite of this, unexpectedly low structural

transformation Significant country dummy in probit regressions of

discoveries Very little movement to nearby products

Paraguay: interpretation

Little transformation in spite of Mercosur Privileged access to a much larger protected market Should have been a pretty effective “industrial policy”

Paraguay is the only country in Mercosur where the EXPY within Mercosur is lower than EXPY outside Mercosur

Of the top 15 Paraguayan exports, Argentina is a net exporter of all 15 and Brazil is a net exporter of 13. But Brazilian imports of the other 2 are a small fraction of Argentine exports of those goods

Hence, no discovery of regional complementarities


Currently the country is growing Is it sustainable?

3,000.00

3,100.00

3,200.00

3,300.00

3,400.00

3,500.00

3,600.00

3,700.00

3,800.00

3,900.00

4,000.00

4,100.00

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

The country has very bad institutional indicators

AGO

ALB ARGARM

ATG

AUSAUT

AZE

BDI

BEL

BENBFA

BGD

BGR

BIH

BLR

BLZ

BOL

BRA

BWA

CAF

CANCHE

CHL

CHN

CIV

CMRCOG

COL

COM

CPV

CRI

CYPCZE

DEU

DJI

DMA

DNK

DOMDZAECU

EGY

ERI

ESPEST

ETH

FIN

FJI

FRA

GAB

GBR

GEO

GHA

GIN

GMB

GNB

GRC

GRD

GTM

GUY

HKG

HND

HRV

HTI

HUN

IDN

IND

IRL

IRN

ISL

ISRITA

JAM

JOR

JPN

KAZKEN KGZKHM

KNA KOR

LAO

LBN

LCA

LKALSO

LTU

LUX

LVA

MAR

MDA

MDG MEXMKD

MLI

MLT

MNG

MOZ MRT

MUS

MWI

MYS

NAM

NER

NGA

NIC

NLDNOR

NPL

NZL

PAK

PAN

PERPHLPNG

POL

PRT

PRY

ROM

RUSRWA

SAU

SDN

SEN

SGP

SLBSLE

SLV

SVK

SVN

SWE

SWZ

SYCSYR

TCDTGO

THA

TJK

TKM

TON

TTOTUNTUR

TZA

UGA UKR

URY

USA

UZB

VCT

VEN

VNM

VUT

YEM

ZAF

ZAR

ZMB

PRY

-2-1

01

2e

st. R

ule

of L

aw

2.5 3 3.5 4 4.5 5LYPPPK

est. Rule of Law est. Rule of Law

Composicion superficie plantada agricola2006

Algodón6%

Arroz c/ riego1%

Caña de Azúcar (Uso Indust.)2%

Girasol1%

Maíz11%

Mandioca8%

Maní1%

Poroto2%

Soja57%

Tabaco2%

Trigo9%

Total: 3,884,000

Composicion superficie plantada agricola1990

Algodón25%

Girasol0%

Maíz14%

Mandioca10%Maní

2%

Poroto3%

Soja33%

Tabaco0%

Trigo9%

Caña de Azúcar (Uso Indust.)3%

Arroz c/ riego1%

Total: 1,692,000

But people have been willing to expand the agricultural frontier

Area: 1,692,000 Area: 3.884,000

Soybeans: from 558,000 to 2.2 million


Soybeans (camel) is very un-intensive in infrastructure No need to process 24 private ports on the Parana river

But also un-intensive in labor In the US, 6 man-hours per hectare per year “No chorrea”

The country has been able to control foot and mouth disease …which is an indication of social capital

Has made an industry out of limited law enforcement Sacoleiros in Ciudad del Este

Are bad institutions binding or are there enough opportunities around them?

There is plenty of land for a sugarcane / ethanol expansion

CommodityHarvest

(H)

Very Suitable

(VS)Suitable

(S)

Moderately Suitable

(MS) VS+SVS+S+M

SH/

(VS+S)H/

(VS+S+MS)

Sugar cane 75 610 3994 4870 4604 9474 1.6% 0.8%

Soybeans 2200 6 536 3915 542 4457 405.9% 49.4%

Maize 410 178 3243 2952 3421 6373 12.0% 6.4%

Wheat 300 0 912 5311 912 6223 32.9% 4.8%

Sugar is very different from soybeans Needs to be processed locally Requires much more labor input

About a factor of 25 times including processing Would require much more infrastructure Tolerates smaller units of production

More Jeffersonian Mechanization improves working conditions

Paraguay: implications

Maybe the country is paying the price for its weak institutional basis

But there are plenty of investment opportunities that are already perceived as profitable Soybeans, meat

And a huge potential expansion in biofuels …which may create the incentives to “get

their act together” Stepping stone

Recommendation for Paraguay Just do it Focus on the public requirements of existing

opportunities Infrastructure, internationalization of ethanol

market, trade agreements, etc. This may generate a significant supply

response …and may facilitate other jumps

Stepping stone

Conclusion

Apparently similar diagnostics lead to very different emphases

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

15

20

25

30

35

RLE

ND

RA

TE


Paraguay: falling dependency ratios. More potential workers per capita

.7.8

.91

1.1

Ag

e de

pend

ency

ra

tio (

depe

nden

ts to

wor

king

-ag

e po

pula

tion

)- W

DI (

2005

)

1960 1970 1980 1990 2000year

Paraguay: increasing female labor force participation

26

27

28

29

30

31

Lab

or fo

rce,

fem

ale

(%

of t

ota

l la

bor

forc

e)-

WD

I (2

005)

1960 1970 1980 1990 2000year

Paraguay: increased urbanization

35

40

45

50

55

60

Urb

an

popu

latio

n (

% o

f to

tal)-

WD

I (20

05)

1960 1970 1980 1990 2000year

Agricultural productivity has risen a lot, especially since 1970

100

01

500

200

02

500

Ag

ricu

lture

va

lue

adde

d pe

r w

orke

r (c

ons

tan

t 20

00 U

S$)

- W

DI (

200

5)

1960 1970 1980 1990 2000year

Educacion Promedio al 2003 segun Fecha de Nacimiento

5.949

6.180

6.701

7.152

7.604

8.049

8.353

8.749

9.303

5

5.5

6

6.5

7

7.5

8

8.5

9

9.5

10

1,935 1,940 1,945 1,950 1,955 1,960 1,965 1,970 1,975 1,980 1,985

Educacion al 2003

Paraguay: rising levels of school attainment

Average years of schooling by date of birth, household survey 2003

So, the problem seems to be in urban areas

-2.5%

-2.0%

-1.5%

-1.0%

-0.5%

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

Agriculture Non-Agriculture Composition Total

1965-1980

1988-2002

Difference

Evolución PIB per capita Real Mercosur (1990=100)

90

100

110

120

130

140

150

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

Brazil

UruguayArgentina

Paraguay

Paraguay had some lackluster growth in the early 1990s

2000=100

60

70

80

90

100

110

120

130

199

0

199

1

199

2

199

3

199

4

199

5

199

6

199

7

199

8

199

9

200

0

200

1

200

2

200

3

200

4

200

5

Water & Electricity Construction Commerce Other Services GDP

…but the growth was mainly in (KF-financed?) construction


500

1000

1500

2000

2500

1960

1962

1964

1966

1968

1970

1972

1974

1976

1978

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

Peru: a growth star?


500

1000

1500

2000

2500

1960

1962

1964

1966

1968

1970

1972

1974

1976

1978

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

…or just recovering from a growth collapse?

-6.0%

-4.0%

-2.0%

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

Gap GDP GNI

Rising gap between GDP and GNP

1960

1961

19621963

19641965

19661967

19681969

197019711972

1973

197419751976

1977

1978

19791980

1981

1982

1983

19841985

1986

1987

1988

1989

19901991

1992

1993

1994

19951996

1997

199819992000

2001

20022003

2004

3.5

53

.63

.65

3.7

3.7

5L

YP

CL

CU

K

5.2 5.4 5.6 5.8 6 6.2LXPCKUS

Peru Log of GDP pc vs. Log of Real Exports pc

Caida de Exportaciones 79-93: Causantes

trad-agric12.2%

trad-mineria43.1%

petroleo23.4%

maquinaria y Equipos2.1%

nt-pesca2.1%

nt-agric0.2%

textiles5.1%

químicos1.9%

mineria no metalica1.8% metalurgia y joyeria

0.5%

trad-pesca2.6%

otro5.0%

- Exp 79 (US$ reales pc): 522- Exp 93 (US$ reales pc): 176- Caida de exportaciones pc: $346- 81% de la perdida de exps proviene de productos tradicionales

Composition of the export collapse 1979-1993

Recuperación de Exportaciones 93-05: Causantes

trad-mineria63.6%

petroleo10.6%

otro0.8%

trad-pesca3.0%

trad-agric1.7%

metalurgia y joyeria1.6%

mineria no metalica0.7%químicos

3.6%textiles6.4%

nt-agric6.1%

nt-pesca0.8%

maquinaria y Equipos1.0%

- Exp 93 (US$ reales pc): 176- Exp 05 (US$ reales pc): 547- Aumento de exps pc: $346- 78% de la recuperación se debe a productos tradicionales

Composition of the export recovery 1993-2005

II. The decision tree approach

The decision tree approach

Start from very broad classes of constraints Sequentially refine them as you go

Low FinanceLow returns

Appropriability Coordination

Lack ofcomplementary

factors

Savings Intermediation

Low investment

Ex ante

Ex post

Human Capital

Infrastructure

What symptoms should we see if the constraint X is relatively important ?

CoordinationMarket fail.

Ex ante

Hu

man

Cap

ital

Infr

astr

uct

ure

&

pu

bli

c g

oo

ds

(geo

gra

ph

y ?

)

Ex

ante

ris

ks

Tax

Lo

w p

rop

erty

ri

gh

ts,

crim

e &

co

rru

pti

on

Lo

w R

&D

Lo

w S

elf

dis

cove

ry

Low infrastructure wrt comparable

countries

High static markups & low

entry; in industries with entry costs

Expropriation

Low sophystication (EXPY) and few new industries

Inward migration high skills

Political risk, social risk

Social unrestGrowth

responds to new indus

High deposit interest rate

High spreadHigh returns to

educationTax policy risk

High taxes: Top marginal tax rate,

corporate tax, VAT

Open conflict

If it's high risk, then low profits

Procyclical mincerian returns

Growth elastic to infrastruct.

ChangeLabor market risks

Restrictive labor regulations

Corruption (illegal tax rate)

(Kaufman)

High operating expens /assets

Low tertiary for level of

developmentCongestion

History of expropriation

Inflation taxHigh protection

costs (ICA)

High correlation of growth with

TOTMonopoly

powers: high (P/E) ratio of

banks

Returns decrease as education grows

Port quality. High losses in transport

(ICA)

High expectation of loosing future

profits

High returns to coordination

activities

Low growth and investment

Lack of complementary factors

Cost of doing business

Low lending interest rate

Binding Finance Binding social returnsL

ow

ag

gre

gat

eS

avin

gs

Bad

fin

ance

Negative relation between growth

and current account.

Few products "nearby" to

move (openforest low)

Investment elastic to interest rate

Access to external finance (EMBI, Default risk, CAD, Unsustainable

debt)

Lack of investment response to interest rate change

Ex postGovernment failure

Growth Diagnostics : What signals are likely if X is binding?

Short loan duration, credit rationingShocks to

infrastructure (hurricane, war)

Monopoly power, high markups.

Regulated entry

Low appropriability

Large negative cash flow from banks Normal cash flow from banks (dC/C - i)High lending interest rate

Is it returns or finance?

( - r)




factors


Low investment

Ex ante

Ex post

Human Capital

Infrastructure

Is it low finance ?

High cost of domestic lending Difficult access to foreign finance

Soverign risk, CAD, Foreign debt. Movements in cost of finance significantly affect

investment Investment vs lending rate

Net Cash flows: dlog(Credit) – lending rate vs time Camels and Hippos: Rajan and Zingales


Ex ante

Hu

man

Cap

ital

Infr

astr

uct

ure

&

pu

bli

c g

oo

ds

(geo

gra

ph

y ?

)

Ex

ante

ris

ks

Tax

Lo

w p

rop

erty

ri

gh

ts,

crim

e &

co

rru

pti

on

Lo

w R

&D

Lo

w S

elf

dis

cove

ry


countries



Expropriation




Social unrestGrowth






corporate tax, VAT

Open conflict







(Kaufman)






costs (ICA)


TOTMonopoly


banks



(ICA)


profits


activities






ow

ag

gre

gat

eS

avin

gs

Bad

fin

ance






Limited access to external finance (EMBI, Default risk, CAD,

Unsustainable debt)







Regulated entry

Low appropriability

Negative cash flow from banks (dC/C- Normal cash flow from banks (dC/C - i)High lending interest rate

Brazil: real lending rate 2006

Albania

AlgeriaAngola

Argentina

Armenia

AustraliaAustria

Azerbaijan

BahrainBangladesh

Belarus

Belgium

Belize

Bolivia

Bosnia and Hercegovina

Botswana

Brazil

Bulgaria

Burundi

CambodiaCameroon

CanadaCape VerdeChad

ChileChina

ColombiaCongo (Brazzaville) Costa RicaCote d'Ivoire Croatia

CyprusCzech RepublicDenmark

Dominican Republic

EcuadorEgyptEl Salvador

Equatorial Guinea

EstoniaFinland

France

GabonGeorgia

GermanyGhana GreeceGuatemala

Guinea

GuyanaHaitiHonduras

Hong KongHungary

Iceland

IndiaIndonesia

IranIreland

IsraelItaly

Jamaica

JapanJordan

KazakhstanKenya

Kuwait

Kyrgyz Republic

Laos

Latvia

LebanonLesothoLibyaLithuania Luxembourg

MacedoniaMadagascarMalawi

Malaysia Malta

Mauritius

MexicoMoldova

Morocco

Mozambique

Myanmar

NamibiaNetherlands

New Zealand

Nicaragua

Nigeria

NorwayOmanPakistan

PanamaPapua New Guinea

Paraguay

Peru

Philippines Poland

PortugalQatar

Romania

Russia

Saudi Arabia

Senegal

SingaporeSlovakiaSlovenia

South AfricaSouth Korea

SpainSri Lanka

SudanSwaziland

SwedenSwitzerlandSyria

Taiwan

Tajikistan

Tanzania

Thailand

The Gambia

Trinidad & Tobago

Turkey

Turkmenistan

Uganda

Ukraine

United Arab Emirates

United KingdomUnited StatesUruguay

VenezuelaVietnam

Yemen

Yugoslavia

Zambia

Mexico

Brazil

01

02

03

04

05

0L

RA

TR

EA

L

-1 0 1 2 3 4LAGDPPC

LRATREAL LRATREALLRATREAL

Sovereign risk in Brazil pre-2006 was high

Algeria

Argentina

Australia

Azerbaijan

Brazil

Bulgaria

Chile

China

Colombia

Czech Republic

Ecuador

Egypt

Greece

Hong Kong

HungaryIndia

Indonesia

IranIsrael

Italy

Kazakhstan

Malaysia

Mexico

New Zealand

Nigeria

Pakistan

Peru

Philippines

Poland

Portugal

RomaniaRussia

Saudi Arabia

Singapore

SlovakiaSouth Africa

South KoreaSpain

Sri Lanka

Taiwan

Thailand

Turkey

UkraineVenezuela

VietnamMexico

Brazil

20

40

60

80

So

vere

ign:

Ove

rall

risk

sco

re (

100=

high

)__

_EIU

6 7 8 9 10 11LYPCP

Sovereign: Overall risk score (100=high)___EIUSovereign: Overall risk score (100=high)___EIUSovereign: Overall risk score (100=high)___EIU

Sovereign Risk: Brazil and Mexico

2040

6080

100

Sov

ere

ign

: De

bt (

100

=hi

gh)_

__E

IU

1996 1998 2000 2002 2004 2006year

Brazil Mexico

Source: Economist Intelligence Unit.

Sovereign risk: debt EIU

Movements in the binding constraint should have large effects

r

finance

Finance Demand(investment)

Finance Supply 1

Finance Supply 2

Brazil: interest rates and investment 2000-2006

20002001

2002

2003

20042005

2006

50

55

60

65

70

Len

ding

inte

rest

rat

e (%

)___

EIU

18 19 20 21Gross fixed investment (% of GDP)___EIU

Mexico: interest rates and investment 2000-2006

2000

2001

2002

20032004

2005

2006

51

01

52

0L

endi

ng in

tere

st r

ate

(%)_

__E

IU

19 19.5 20 20.5 21 21.5Gross fixed investment (% of GDP)___EIU

The decline in investment happened in the context of falling interest rates

-1.5

-1-.

50

Ne

tCre

ditG

row

th

1995 2000 2005 2010year

Brazil Mexico

Source: Economist Intelligence Unit.Using BP03 (Bank loans US dollars)as Cand LRAT(Lending interest rate) as i

Net cash flows [dC/C - i ]

Negative cash flows in Brazil have been very large

Systematizing the search for Camels & Hippos

Ricardo Hausmann & Bailey Klinger

Camels & Hippos

You can infer binding constraints by the revealed structure of production:

If a country surprisingly under-specialized in products intensive in roads, then road infrastructure may be the binding constraint

This identification strategy can be systematized using exogenous measures of industry dependence on a given factor

Camels & Hippos: Example

Take the Rajan & Zingales (1998)’s measure of an industry’s dependence on external finance.

Tobacco (-.45) and apparel (0.03) are the camels: they do not require much external finance, as investors can finance operations more easily through cashflows.

Textiles (.4) and plastic products (1.14) are the hippos: they are intensive in external finance

Camels & Hippos: Example

Rajan & Zingales provide this measure of an industry’s dependence on external finance for almost 40 industries

Using UNIDO INDSTAT, we can measure the % of total value added of country C that is in industry I

Camels & Hippos Example

Regress this % of total value added on: Industry dummies Industry dummies interacted with GDPpc (to

capture differing patterns of production across different levels of development)

The industry’s intensity in external finance The industry’s intensity in external finance

interacted with a dummy variable for the country of interest


This interaction term tells you if the country’s production is concentrated in industries intensive in external finance or not

Controlling for the fact that most industries dependent on external finance are found in countries with higher GDP per capita

Camels & Hippos Estimated coefficient on country/external

dependence on finance interaction

-0.015 -0.01 -0.005 0 0.005 0.01 0.015

Russia

Pakistan

Egypt

El Salvador

Colombia

Relatively more hippos: firm access to external finance less likely to be a binding constraintRelatively more

camels: firm access to finance more likely to be a binding constraint


This type of analysis could be repeated for any constraint, as long as you can get data on an industry’s intensity in that good

But, is it insufficient aggregate savings or poor financial intermediation?

No FinanceNo returns



factors


No investment

Ex ante

Ex post

Human Capital

Infrastructure

Is aggregate savings the binding constraint ? How to distinguish it from bad financial intermediation? Is the interest rate paid by the government

through the capital markets high? Independent of the banking system, is the marginal

cost of savings high? Are deposit rates high ?

Why do banks pay high rates? Note: high deposit rates makes banks riskier

and financial intermediation more limited Adverse selection

Are real deposit rates high?

Albania

Algeria

Angola

Argentina

Armenia

AustraliaAustria

Azerbaijan

Bahrain

Bangladesh

BelarusBelgium

Belize

Benin

Bolivia


Botswana

Brazil

Bulgaria

Burkina Faso

Cambodia

CameroonCanada

Cape Verde

Chad

ChileChina

Colombia

Congo (Brazzaville) Costa Rica

Cote d'Ivoire

Croatia

Cyprus

Czech Republic

Denmark

Dominican Republic

Ecuador

Egypt

El Salvador

Equatorial GuineaEstonia

Ethiopia

FinlandFranceGabon

Georgia

Germany

Ghana

Greece

GuatemalaGuyana

Haiti

Honduras

Hong Kong

Hungary

Iceland

India

Indonesia

IranIreland

Israel

ItalyJamaica

Japan

Jordan

Kazakhstan

Kenya

Kuwait

Kyrgyz RepublicLaos

Latvia

Lebanon

Lesotho LibyaLithuania

Luxembourg

Macedonia

Madagascar

Malawi

MalaysiaMaltaMauritius

MexicoMoldova Morocco

Mozambique

Myanmar

Namibia

NetherlandsNew Zealand

Nicaragua

NigeriaNorway

OmanPakistan

Panama

Papua New Guinea

Paraguay

Peru

Philippines

Poland

Portugal

Qatar

Romania

Russia

Rwanda

Saudi Arabia

Senegal

Seychelles

SingaporeSlovakia

Slovenia


Spain

Sri Lanka

Sudan

Swaziland

SwedenSwitzerland

Syria

Taiwan

Tajikistan

TanzaniaThailand

The Gambia

Togo

Trinidad & Tobago

Tunisia

Turkey

Turkmenistan

Uganda

Ukraine


United Kingdom

United States

Uruguay

Venezuela

Vietnam

Yugoslavia

Zambia

-10

-50

51

0R

AT

2R

EA

L

7 8 9 10 11LYPCP

Real deposit rates and deposit cashflow as % of GDP (2004-2006)

Algeria

Argentina

AustraliaAustriaAzerbaijan

Belgium

Brazil

Bulgaria

Canada

ChileChina

Colombia

Czech Republic

DenmarkEcuador

Egypt

FinlandFranceGermanyGreece

Hong Kong

Hungary

India

Indonesia IranIreland

Israel

ItalyJapan

Kazakhstan

Malaysia

Mexico

Netherlands

New Zealand

Nigeria

Norway

Pakistan

Peru

Philippines

PolandPortugal

Romania

Russia

Saudi Arabia

SingaporeSlovakia


Spain

Sri Lanka

SwedenSwitzerlandTaiwan

Thailand

Turkey

Ukraine

United KingdomUnited States

Venezuela

Vietnam

-10

-50

51

01

5R

AT

2R

EA

LS

M3

-10 0 10 20 30DCASHFLOWGDPSM3

Insufficient deposit flow

More than enough

Real deposit rates: Brazil and Mexico

-50

51

01

52

0R

AT

2R

EA

L

1998 2000 2002 2004 2006 2008year

RAT2REAL RAT2REAL

Brazil

Mexico


Ex ante

Hu

man

Cap

ital

Infr

astr

uct

ure

&

pu

bli

c g

oo

ds

(geo

gra

ph

y ?

)

Ex

ante

ris

ks

Tax

Lo

w p

rop

erty

ri

gh

ts,

crim

e &

co

rru

pti

on

Lo

w R

&D

Lo

w S

elf

dis

cove

ry


countries



Expropriation




Social unrestGrowth






corporate tax, VAT

Open conflict







(Kaufman)






costs (ICA)


TOTMonopoly


banks



(ICA)


profits


activities






ow

ag

gre

gat

eS

avin

gs

Bad

fin

ance







debt)







Regulated entry

Low appropriability

Low net cash flow from banks High net cash flow from banks (dC/C - i)High lending interest rate

What constrains aggregate savings? Crowding out

High interest rates Limited access to foreign finance Bank lending to government as % of total bank

assets Unsustainable and/or high public debt. High taxes plus redistribution to agents with los

savings propensity

Public debt interest payments as % of GDP

05

1015

Deb

t int

eres

t pay

men

ts (

% o

f GD

P)_

__E

IU

1998 2000 2002 2004 2006 2008year

MexicoBrazil

Algeria

Argentina

Australia

AustriaAzerbaijan

Belgium

BulgariaCanada

Chile

China

Colombia

Czech Republic

Denmark

Ecuador

Egypt

Finland

FranceGermany

Greece

Hong Kong

Hungary

India

Indonesia

Iran

Ireland

Israel

Italy

Japan

KazakhstanMalaysia NetherlandsNew Zealand

Nigeria

Norway

Pakistan

Peru

PhilippinesPoland

PortugalRomania

Russia

Saudi Arabia

Singapore

Slovakia

South Africa

South KoreaSpain

Sri Lanka

Sweden

SwitzerlandTaiwanThailand

Turkey

Ukraine

United Kingdom

United States

Venezuela

Vietnam

Brazil

Mexico

020

4060

80Le

ndin

g to

pub

lic s

ect

or/to

tal l

end

ing_

__E

IU

7 8 9 10 11log( real GDP per capita)

correlation r = -0.3784, linear coefficient = -6.8918

The Brazilian syndrome

Extremely high tax rates No public savings, almost no investment Limited after-tax revenue by potential savers Imbalance between limited savings and

ample investment opportunities High real interest rate equilibrium …which worsens the fiscal imbalance




factors


No investment

Ex ante

Ex post

Human Capital

Infrastructure

What is the problem with banks ? High interest margin and bank spreads

Corrected for C/D and/or reserve requirements Low deposit mobilization

Deposit/GDP Low credit mobilization

Low Credit/Deposits High direct cost

Operating expenses / Assets High risk

Risk indexes High profits but low P/E ratios

Monopoly power High profits and high P/E ratios

Credit rationing

Deposit mobilization (Deposits/GDP)

Algeria

Argentina

Australia

Austria

Azerbaijan

Brazil

Bulgaria

Canada

Chile

China

Colombia

Czech RepublicDenmark

Ecuador

Egypt

Finland

France

Germany

Greece

Hungary

India

Indonesia

IranItaly

Japan

Kazakhstan

Malaysia

MexicoNigeria

Norway

Pakistan

Peru

PhilippinesPoland

Portugal

Romania

Russia

Saudi Arabia

Slovakia

South Africa

South Korea

Spain

Sri Lanka

Sweden

Thailand

Turkey

Ukraine

United Kingdom

United States

Venezuela

Vietnam

05

01

00D

EP

GD

P

7 8 9 10 11LYPCP

Algeria

Argentina

AustraliaAzerbaijan

Brazil

Bulgaria

Canada

Chile

China

Colombia

Czech Republic

Denmark

Ecuador

Egypt

Finland FranceGreece

Hungary

India

Indonesia IranItaly

Kazakhstan

Mexico

Nigeria

Norway

Pakistan

Peru

Philippines

Poland

Romania

Russia

Saudi Arabia

Slovakia


Sri Lanka

Sweden

Thailand

Turkey

Ukraine


Venezuela

Vietnam

-50

51

01

5R

AT

2R

EA

LS

M3

0 .2 .4 .6 .8 1DEPGDPSM3

Deposit mobilization vs. real interest rate, 2005

Real deposit rates vs. deposit cash flow (2004-2006)

Algeria

Argentina

AustraliaAustria

Azerbaijan

Belgium

Brazil

Bulgaria

Canada

ChileChina

Colombia

Czech Republic

DenmarkEcuador

Egypt


Hong Kong

Hungary

India

Indonesia IranIreland

Israel

ItalyJapan

Kazakhstan

Malaysia

Mexico

Netherlands

New Zealand

Nigeria

Norway

Pakistan

Peru

Philippines

PolandPortugal

Romania

Russia

Saudi Arabia

SingaporeSlovakia


Spain

Sri Lanka

SwedenSwitzerland

Taiwan

Thailand

Turkey

Ukraine


Venezuela

Vietnam

-10

-50

51

01

5R

AT

2R

EA

LS

M3

0 10 20 30 40DCASHFLOWSM3

Lending to private sector vs. deposits, (% of GDP)

AlgeriaArgentina

Australia

Austria

AzerbaijanBrazil

Bulgaria

Canada

Chile

China

Colombia

Czech Republic

Denmark

EcuadorEgypt

Finland

France

GreeceHungary

India

Indonesia

Iran

Italy

Kazakhstan

MexicoNigeria

Norway

PakistanPeruPhilippinesPoland

Portugal

RomaniaRussia

Saudi ArabiaSlovakia

South Africa

South Korea

Spain

Sri Lanka

Sweden

Thailand

TurkeyUkraine

United Kingdom

United States

Venezuela

Vietnam

05

01

001

50

0 20 40 60 80 100DEPGDP

PRLOANGDP DEPGDP

Algeria

Argentina

Australia

Austria

Azerbaijan

Belgium

Bulgaria

Canada

Chile

China

Colombia

Czech Republic

Denmark

Ecuador

Egypt Finland

France

Germany

Greece

Hong Kong

HungaryIndia

Indonesia

IranIreland

IsraelItaly

Japan

Kazakhstan

Malaysia

Netherlands

New Zealand

Nigeria

Norway

Pakistan

Peru

PhilippinesPoland

Portugal

RomaniaRussia

Saudi Arabia

Singapore

SlovakiaSouth Africa

South KoreaSpain

Sri Lanka


Thailand

TurkeyUkraine

United Kingdom

United States

Venezuela

Vietnam

Brazil

Mexico

02

46

8N

et i

nte

rest

ma

rgin

(ne

t int

ere

st in

c/a

sset

s)_

__E

IU



Is the banking system inefficient: Net interest margin (2005)

Bank spreads

AlbaniaAlgeria

Angola

Argentina

Armenia

Australia

Austria

Azerbaijan

Bahrain

Bangladesh

Belarus

BelgiumBelize

Bolivia


Botswana

Brazil

Bulgaria

Cambodia

Cameroon

Canada

Cape Verde

Chad

ChileChina

Colombia

Congo (Brazzaville)Costa Rica

Cote d'Ivoire

Croatia

CyprusCzech Republic

Denmark

Dominican Republic

Ecuador

Egypt

El Salvador

Equatorial Guinea

EstoniaEthiopia

Finland

France

Gabon

Georgia GermanyGhanaGreece

Guatemala

Guinea

Guyana

Haiti

Honduras

Hong Kong

Hungary

Iceland

IndiaIndonesiaIran

Ireland

Israel

Italy

Jamaica

Japan

JordanKazakhstan

Kenya

Kuwait

Kyrgyz Republic

Laos

LatviaLebanon

Lesotho

Libya

Lithuania Luxembourg

Macedonia

Madagascar

Malawi

MalaysiaMalta

Mauritius

MexicoMoldova

MoroccoMozambique

Myanmar Namibia

Netherlands

New ZealandNicaraguaNigeria

NorwayOmanPakistan

Panama

Papua New Guinea

Paraguay

Peru

PhilippinesPoland

Portugal

Qatar

Romania

RussiaSaudi Arabia

Senegal

Seychelles

SingaporeSlovakiaSlovenia

South Africa

South KoreaSpain

Sri Lanka

Sudan

Swaziland

SwedenSwitzerlandSyria

Taiwan

Tajikistan

Tanzania

Thailand

The Gambia

Trinidad & Tobago

TurkeyTurkmenistan

Uganda

Ukraine



Uruguay

VenezuelaVietnam

Yemen

YugoslaviaZambia

01

02

03

0S

PR

EA

D

7 8 9 10 11LYPCP

ArgentinaAustralia

Austria

Belgium

Bulgaria

Canada

Chile

China

Colombia

Czech Republic

Denmark

Ecuador

EgyptFinlandFranceGermany

Greece

Hong Kong

Hungary

India

Indonesia

Iran

Ireland

IsraelItaly

Japan

Kazakhstan

Malaysia NetherlandsNew Zealand

Nigeria

Norway

Pakistan

Peru

Philippines

Poland

Portugal

Romania

Russia

Saudi Arabia

Singapore

Slovakia

South Africa

Spain

Sri Lanka


Thailand

Turkey

Ukraine

United Kingdom

United States

Venezuela

Vietnam

BrazilMexico

02

46

8O

per

atin

g e

xpe

nse

s/as

sets

___

EIU



Is the banking system inefficient (2003)

How risky are banks?

Algeria

Argentina

Australia

AzerbaijanBrazil

BulgariaChile

China

Colombia

Czech Republic

Ecuador

Egypt

Greece

Hong Kong

Hungary

India

Indonesia Iran

IsraelItaly

Kazakhstan

Malaysia

Mexico

New Zealand

Nigeria Pakistan

PeruPhilippines

PolandPortugal

Romania

Russia

Saudi Arabia

Singapore

Slovakia

South Africa

South KoreaSpain

Sri Lanka

Taiwan

Thailand

Turkey

Ukraine

Venezuela

Vietnam

20

40

60

80

Ba

nkin

g: O

vera

ll ris

k sc

ore

(1

00=

hig

h)_

__E

IU

7 8 9 10 11LYPCP

How about credit rationing?

Using Investment Climate Assessments in Morocco

“Out”: 53%

46.6

%53

.4%

“In”

“Out”

59%

41%Asked for

loan

Never asked for

loan

17%

83%Accepted

Rejected

39%

61%Don’t need

Can’t

69%

31%

Collateral issues

Profitability

83%

39%

61%

69%

31%

83%

39%

61%

69%

31%

Morocco: Are firms constrained by finance(Source: Najy Benhassine. Data from Investement Climate Assessment)


Ex ante

Hu

man

Cap

ital

Infr

astr

uct

ure

&

pu

bli

c g

oo

ds

(geo

gra

ph

y ?

)

Ex

ante

ris

ks

Tax

Lo

w p

rop

erty

ri

gh

ts,

crim

e &

co

rru

pti

on

Lo

w R

&D

Lo

w S

elf

dis

cove

ry


countries



Expropriation




Social unrestGrowth






corporate tax, VAT

Open conflict







(Kaufman)






costs (ICA)


TOTMonopoly


banks


(ICA)


profits


activities

Government failure




Regulated entry

Low appropriability

Low net cash flow from banks Normal net cash flow from banks (dC/C - i)High lending interest rate



debt)

Small investment response to interest rate change

Ex postLack of complementary factors


Low to normal lending interest rate

Binding Finance Binding social returns

Lo

w a

gg

reg

ate

Sav

ing

s

Bad

fin

ance





Is it retuns or finance?

( - r)




factors


Low investment

Ex ante

Ex post

Human Capital

Infrastructure

Low returns ?

Investment should be low Low Either low CAD, or low external debt Good access to external finance. Investment low in spite of access to finance Small investment response to interest rate

movements

KK

El Salvador: lending rates and investment rates

51

01

52

0

1980 1990 2000 2010year

Gross fixed investment (% of GDP)___EIU Lending interest rate (%)___EIU

Mexico: interest rates and investment 2000-2006

2000

2001

2002

20032004

2005

2006

51

01

52

0L

endi

ng in

tere

st r

ate

(%)_

__E

IU

19 19.5 20 20.5 21 21.5Gross fixed investment (% of GDP)___EIU

The decline in investment happened in the context of falling interest rates


Ex ante

Hu

man

Cap

ital

Infr

astr

uct

ure

&

pu

bli

c g

oo

ds

(geo

gra

ph

y ?

)

Ex

ante

ris

ks

Tax

Lo

w p

rop

erty

ri

gh

ts,

crim

e &

co

rru

pti

on

Lo

w R

&D

Lo

w S

elf

dis

cove

ry


countries



Expropriation




Social unrestGrowth






corporate tax, VAT

Open conflict







(Kaufman)






costs (ICA)


TOTMonopoly


banks



(ICA)


profits


activities




Low to normal lending interest rate


ow

ag

gre

gat

eS

avin

gs

Bad

fin

ance







debt)

Low investment response to interest rate change






Regulated entry

Low appropriability

Low net cash flow from banks Normal net cash flow from banks (dC/C - i)High lending interest rate




factors


No investment

Ex ante

Ex post

Human Capital

Infrastructure


Ex ante

Hu

man

Cap

ital

Infr

astr

uct

ure

&

pu

bli

c g

oo

ds

(geo

gra

ph

y ?

)

Ex

ante

ris

ks

Tax

Lo

w p

rop

erty

ri

gh

ts,

crim

e &

co

rru

pti

on

Lo

w R

&D

Lo

w S

elf

dis

cove

ry


countries



Expropriation




Social unrestGrowth






corporate tax, VAT

Open conflict







(Kaufman)






costs (ICA)


TOTMonopoly


banks



(ICA)

High expectation of losing future profits


activities





Lo

w a

gg

reg

ate

Sav

ing

s

Bad

fin

ance







debt)






Regulated entry

Low appropriability


Ex ante risk of low appropriability

High margins (E) and low P/E ratio for existing activities

High risk indicators: Macroeconomic risk Expropriation risk, Labor market risk, social unrest, Political risk Legal and regulatory risk Tax policy risk

Why high markups for those who exist ?From a dynamic program prospective a firms

stays iff:

Value of being active Current profits Continuation value

)][max( 11

closedactiveactive

tttt VVEV

Are mark-ups high?

We use UNIDO data at the 2 digit level We calculate markup rates

Mark-up = (Value added – Wages)/Output We regress mark-up rates on GDP per

capita, sector dummies and country dummies We report the country dummies A high number implies that mark-ups are

higher than average

Country dummies in mark-up rates

ARG

AUS

AUT

BEN

BGD

BHS

BOL

BRA

BWA

CAN

CHL

CIVCMR

COL

CYP

DNK

DZA

ECU

EGY

ESP

ETH

FIN

FJIFRA

GAB

GBR

GHA

GRC

GTM

HKGHND

IDN

IND

IRN

ISL

ITA

JOR

JPN

KOR

KWT

LCA

LKA

MAC

MAR

MDG

MEX

MKDMLT

MNG

MUSMYS

NAM

NGANIC

NLDNOR

NPL

NZL

PAK PAN

PER

PHLPRI

PRT

RUS

SEN

SLV

SWE

SWZ

THA

TTO

TUN

TUR

TZA

UGA

URYUSA

VENZMB

ZWE

-.1

0.1

.2.3

coef

ficie

nt

2.5 3 3.5 4 4.5KKKK

Log of GDPpc 1990-2004

Ex-ante appropriability risk

Algeria

Argentina

AustraliaAustria

Azerbaijan

Belgium

Bulgaria

CanadaChile

China

Colombia

Czech Republic Denmark

Ecuador

Egypt

FinlandFranceGermanyGreeceHong KongHungary

India

Indonesia

Iran

IrelandIsraelItalyJapan

Kazakhstan

Malaysia


Nigeria

Norway

PakistanPeruPhilippines

Poland Portugal

Romania

RussiaSaudi Arabia

SingaporeSlovakia

South Africa

South KoreaSpain

Sri Lanka

SwedenSwitzerlandTaiwanThailand

Turkey

Ukraine


Venezuela

Vietnam BrazilMexico

12

34

5E

xpro

pria

tion

risk

(5=

low

)__

_EIU


correlation r = 0.7109, linear coefficient = 0.7864

Is it expropriation risk ? (2005)

Ex-ante appropriability risk: Labor

Algeria

Argentina

Azerbaijan

Bulgaria

Chile

China

Colombia

Czech Republic

Ecuador

Egypt

Germany

Greece

Hong Kong

Hungary

India

Indonesia

Iran

Israel

Japan

KazakhstanMalaysia

Nigeria

Pakistan

Peru

Philippines

Poland

Romania

Russia

Saudi Arabia

Singapore

Slovakia

South Africa

South Korea

Sri Lanka

Taiwan

Thailand

TurkeyUkraine

United States

Venezuela

Vietnam

BrazilMexico

2030

4050

6070

Labo

ur m

arke

t ris

k (1

00=

hig

h)_

__E

IU



Risk in the labor market ? (2005)

Ex-ante appropriability risk: Tax

Algeria

Argentina

Azerbaijan

Bulgaria

ChileChina

Colombia

Czech Republic

Ecuador

Egypt Germany

Greece

Hong Kong

Hungary

India

Indonesia

Iran

Israel

Japan

Kazakhstan

Malaysia

Nigeria

Pakistan

Peru

Philippines

Poland

Romania

RussiaSaudi Arabia

Singapore

Slovakia

South AfricaSouth KoreaSri Lanka

Taiwan

Thailand

TurkeyUkraine

United States

Venezuela

Vietnam

Brazil

Mexico

020

4060

80T

ax

pol

icy

risk

(10

0=hi

gh)

___

EIU



Tax policy risk? (2005)


Ex ante

Hu

man

Cap

ital

Infr

astr

uct

ure

&

pu

bli

c g

oo

ds

(geo

gra

ph

y ?

)

Ex

ante

ris

ks

Tax

Lo

w p

rop

erty

ri

gh

ts,

crim

e &

co

rru

pti

on

Lo

w R

&D

Lo

w S

elf

dis

cove

ry


countries



Expropriation




Social unrestGrowth






corporate tax, VAT

Open conflict







(Kaufman)






costs (ICA)


TOTMonopoly


banks



(ICA)


profits


activities






ow

ag

gre

gat

eS

avin

gs

Bad

fin

ance







debt)







Regulated entry

Low appropriability


Actual (ex post) low appropriability Low after tax earnings

High tax rates (Corporate , Top MIT, VAT…) Restrictiveness of labor laws. High unit labor costs Actual Conflict (War…) Protection costs (ICA ) Cost of doing business

Ex-post appropriability: Labor

Algeria Argentina

Australia

AustriaAzerbaijan BelgiumBulgaria

Canada

Chile

China

Colombia

Czech Republic

Denmark

Ecuador

Egypt

Finland

France

Germany

Greece

Hong Kong

Hungary

India

Indonesia Iran

IrelandIsrael

Italy

JapanKazakhstanMalaysia


Nigeria

Norway

Pakistan

Peru

Philippines

Poland

Portugal

RomaniaRussia

Saudi Arabia

SingaporeSlovakia

South Africa

South Korea

SpainSri Lanka

Sweden

Switzerland

Taiwan

Thailand

TurkeyUkraine


Venezuela

Vietnam BrazilMexico

12

34

5R

est

rict

iven

ess

of la

bou

r la

ws

(5=

low

)___

EIU


correlation r = 0.3690, linear coefficient = 0.3680

Restrictive labor laws (2005)

Argentina

Australia Austria

Belgium

Brazil

Bulgaria

Canada

ChileChina

Colombia

Czech Republic

Denmark

Ecuador

Finland

FranceGermany

GreeceHong Kong

Hungary

Indonesia

Ireland

Israel

Italy

Japan

Kazakhstan

Malaysia

Mexico


Norway

Peru

Philippines

Poland

Portugal

RomaniaRussia

Singapore

Slovakia

South Korea

Spain

Sweden

Switzerland

Taiwan

ThailandUkraine

United Kingdom

United States

Venezuela

05

01

001

502

00O

vera

ll u

nit l

abo

ur c

osts

leve

l (in

dex,

US

=10

0)_

__E

IU

0 20000 40000 60000 80000 100000Overall productivity of labour (GDP at PPP, per worker, US$)_PPP__EIU

Are labor costs too high given productivity? 2006

Appropriability: rule of law

AGO

ALBARG

ARM

ATG

AUSAUT

AZE

BDI

BEL

BENBFA

BGD

BGR

BIH

BLR

BLZ

BOL

BRA

BWA

CAF

CAN

CHE

CHL

CHN

CIV

CMRCOG

COL

COM

CPV

CRI

CYPCZE

DEU

DJI

DMA

DNK

DOMDZAECU

EGY

ERI

ESPEST

ETH

FIN

FJI

FRA

GAB

GBR

GEO

GHA

GIN

GMB

GNB

GRC

GRD

GTM

GUY

HKG

HND

HRV

HTI

HUN

IDN

IND

IRL

IRN

ISL

ISRITA

JAM

JOR

JPN

KAZKEN KGZKHM

KNA KOR

LAO

LBN

LCA

LKALSO

LTU

LUX

LVA

MAR

MDA

MDG MEXMKD

MLI

MLT

MNG

MOZ MRT

MUS

MWI

MYS

NAM

NER

NGA

NIC

NLDNOR

NPL

NZL

PAK

PAN

PERPHLPNG

POL

PRT

PRY

ROM

RUSRWA

SAU

SDN

SEN

SGP

SLBSLE

SLV

SVK

SVN

SWE

SWZ

SYC

SYR

TCDTGO

THA

TJK

TKM

TON

TTOTUNTUR

TZA

UGA UKR

URY

USA

UZB

VCT

VEN

VNM

VUT

YEM

ZAF

ZAR

ZMB

PRY

-2-1

01

2 R

ule

of

La

w

2.5 3 3.5 4 4.5 5

Log(GDP pc)

… Ex post appropriability: corruption(“ilegal tax”)

AGO

ALB

ARGARM

ATG

AUSAUT

AZEBDI

BEL

BENBFA

BGD

BGR

BIH

BLR

BLZ

BOL

BRA

BWA

CAF

CANCHE

CHL

CHN

CIVCMR

COG

COL

COM

CPV

CRI CYP

CZE

DEU

DJI

DMA

DNK

DOMDZAECU

EGY

ERI

ESP

EST

ETH

FIN

FJI

FRA

GAB

GBR

GEO

GHA

GINGMBGNB

GRCGRD

GTM

GUY

HKG

HND

HRV

HTI

HUN

IDN

IND

IRL

IRN

ISL

ISRITA

JAM

JOR

JPN

KAZKEN KGZKHM

KNAKOR

LAO

LBN

LCA

LKALSO

LTU

LUX

LVAMAR

MDA

MDGMEX

MKDMLI

MLT

MNGMOZ

MRT

MUS

MWI

MYSNAM

NERNGA

NIC

NLDNOR

NPL

NZL

PAK

PAN

PERPHL

PNG

POL

PRT

PRY

ROM

RUS

RWA

SAU

SDN

SEN

SGP

SLB

SLE

SLV

SVK

SVN

SWE

SWZ

SYC

SYR

TCDTGO

THA

TJKTKM

TON

TTOTUN

TUR

TZAUGA

UKR

URY

USA

UZB

VCT

VENVNMVUT

YEM

ZAF

ZAR

ZMBPRY

-2-1

01

23

Con

trol

of C

orru

ptio

n

2.5 3 3.5 4 4.5 5LYPPPK

Log(GDP pc)

Who was the camel? GDP in Telecoms vs. Total in Paraguay

80

120

160

200

240

280

1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004* 2005* 2006**

GDP per capita

GDP in telecoms, per capita

Paraguay: Who was the camel? Agriculture vs. Total

80

90

100

110

120

130

140

1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004*

Agriculture GDP pc

Total GDP pc




factors


No investment

Ex ante

Ex post

Human Capital

Infrastructure


Ex ante

Hu

man

Cap

ital

Infr

astr

uct

ure

&

pu

bli

c g

oo

ds

(geo

gra

ph

y ?

)

Ex

ante

ris

ks

Tax

Lo

w p

rop

erty

ri

gh

ts,

crim

e &

co

rru

pti

on

Lo

w R

&D

Lo

w S

elf

dis

cove

ry


countries



Expropriation

Low sophistication

(EXPY) and few new industries



Social unrestGrowth






corporate tax, VAT

Open conflict







(Kaufman)






costs (ICA)


TOTMonopoly


banks



(ICA)


profits


activities






ow

ag

gre

gat

eS

avin

gs

Bad

fin

ance







debt)







Regulated entry

Low appropriability


Coordination failures ?

Obstacles to productive transformation Low EXPY Low Openforest (value and size) Country dummy of prob of transition to new

products Low investments in R&D…

Paraguay: Low EXPY…

ALB

ARG

ARM

AUS

AUT

AZE

BDI

BEN

BFA

BGD

BGRBHR

BHS

BLRBLZ

BOL

BRA

BRB

CAF

CAN

CHL

CHN

CIV

CMR

COL

CRI

CYPCZE

DEUDNK

DOMDZA

ECU

EGY

ESP

EST

ETH

FIN

FJIGAB

GBR

GEO

GHA

GINGMB

GRC

GTMGUY

HKG

HND

HRV

HTI

HUN

IDN

IND

IRL

IRN

ISLISR

ITA

JAMJOR

JPN

KAZ

KEN

KGZ

KNA

KOR

LBN

LKA

LTULVA

MACMAR

MDA

MDG

MEX

MLI

MLT

MNGMOZ

MUS

MWI

MYS

NCL

NER

NGA

NIC

NLD

NOR

NPL

NZL

OMN

PAK

PAN

PER

PHL

PNG

POL PRT

PRY

ROMRUS

RWA

SAU

SDN

SEN

SGP

SLE

SLV

SVK SVNSWE

SYR

TGO

THA

TJK

TKM

TTOTUR

TZA

UGA

UKRURY

USA

VENWSM

ZAF

ZMB

ZWE

PRY

7.5

88.

59

9.5

10

Log(

EX

PY

)

2.5 3 3.5 4 4.5

Log(GDP per Capita PPP)

Conditional on EXPY, unsurprising growth performance

e(

gro

wth

gdp

| X

,lexp

y19

92 )

+ b

*lex

py1

992

lexpy1992

Residuals Linear prediction

8.10487 9.83871

.31443

.429625

MDG

PRY

BGD

JAM

ECU

BOL LCA

LKA

COL

HTI

PER

KEN

IDN

BLZ

CHL

DZASAU

OMNTUR

TTO

IND

GRC

ROM

THA

CYP

CHN

HRV

PRT

MYS

BRA

HUN

AUS

MEX

ESP

KOR

NZL

SGP

NLD

CANUSADNKSWE

DEU

IRL

FINISL

CHE

Hausmann, Hwang y Rodrik (2006)

Slow progress in EXPY

EXPY, Comparative

0

2000

4000

6000

8000

10000

12000

14000

16000

1975

1977

1979

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

Argentina

Brazil

Colombia

Mexico

Peru

Paraguay

El Salvador

Uruguay

Low open forest

ALB

ARG

ARM

AUS

AUT

BDI

BENBFA

BGD

BGR

BIH

BLR

BOL

BRA

CAF

CAN

CHL

CHN

CIV

CMR

COL

CRI

CZEDEU

DNK

DOM

DZA

ECU

EGY

ESP

ETH

FIN

GBR

GEO

GHA

GIN

GRC

GTM

HKG

HND

HRV

HTI

HUN

IDN

INDIRL

IRN

ISR

ITA

JAMJOR

JPN

KAZ

KENKGZ

KOR

LBNLKA

LTU

LVAMARMDA

MDG

MEX

MLI

MNGMOZ

MWI

MYS

NERNGA

NIC

NLD

NOR

NPL

NZL

OMN

PAK PANPER

PHL

PNG

POL

PRT

PRY

ROM

RUS

RWA

SAUSDNSEN

SGP

SLE

SLV

SVKSWE

SYR

TGO

THA

TJKTKM

TUR

TZA

UGA

UKRURY

USA

VEN

ZAF

ZMB

ZWE

PRY

11

12

13

14

15

lno

pe

n_

fore

st1b

2.5 3 3.5 4 4.5LYPPPK

lnopen_forest1b lnopen_forest1b

Open Forest, Comparative

0

200000400000

600000

800000

10000001200000

1400000

16000001800000

2000000

1975

1978

1981

1984

1987

1990

1993

1996

1999

2002

Argentina

Brazil

Colombia

Mexico

Peru

Paraguay

El Salvador

Uruguay

Slow progress in open forest

Few new products since 1989

Product Year Millions of US$

Sesame 1991 28.7

Rape 1993 6.7

Other wheat 2001 35.3

Cigarettes 1992 9.8

Used agricultural machinery

1992 11,8

Ferrous scrap 1993 8.2

Non-ferrous scrap 1992 9.8

Plastic containers 1992 16.2

Soybeancycle

However,…..

Paraguay has a negative and significant country dummy in the H&K probit regressions Controlling for the position in the product space,

structural transformation has been surprisingly low This means that there are relatively nearby

products (to the sectors that are currently occupied) that haven’t been exploited




factors


No investment

Ex ante

Ex post

Human Capital

Infrastructure


Ex ante

Hu

man

Cap

ital

Infr

astr

uct

ure

&

pu

bli

c g

oo

ds

(geo

gra

ph

y ?

)

Ex

ante

ris

ks

Tax

Lo

w p

rop

erty

ri

gh

ts,

crim

e &

co

rru

pti

on

Lo

w R

&D

Lo

w S

elf

dis

cove

ry


countries



Expropriation




Social unrestGrowth






corporate tax, VAT

Open conflict







(Kaufman)






costs (ICA)


TOTMonopoly


banks


(ICA)


profits


activities

Government failure





Regulated entry

Low appropriability




debt)


Ex post




Low returns to capital, low lending interest rate


ow

ag

gre

gat

eS

avin

gs

Bad

fin

ance





Low social returns:Lack of complementary factors Marginal returns to private capital must be

low Inadequate human capital

Returns to human capital must be high Returns are highly pro-cyclical

Inadequate Infrastructure Low infrastructure quality / rating but no growth Indications of congestion, concern

Educacion Promedio al 2000 segun Fecha de Nacimiento

5.96.2

6.7

7.2

7.6

8.0

8.4

8.7

9.3

6.2

6.9

7.9

8.2

9.0

9.5

10.1

10.610.8

4.8

5.6

6.6

7.2

8.5

8.9

9.49.5

9.9

4.3

5.0

5.9

6.7

7.3

7.6

8.1

8.5

9.0

4.0

5.0

6.0

7.0

8.0

9.0

10.0

11.0

1,935 1,940 1,945 1,950 1,955 1,960 1,965 1,970 1,975 1,980 1,985

Paraguay Peru Mexico Colombia

In many countries, schooling quantity is increasing

Average schooling of cohort by year of birth

PIB real per capitamiles de pesos de 1993

13.0

13.5

14.0

14.5

15.0

15.5

16.0

16.5

17.0

199

0

199

1

199

2

199

3

199

4

199

5

199

6

199

7

199

8

199

9

200

0

200

1

200

2

200

3

200

4

200

5

Mexico’s GDP per capita

Retornos a la Educacion (MINCER)

11.7%

12.4%12.0%

12.7%

10.7%

11.4%

6.0%

8.0%

10.0%

12.0%

14.0%

16.0%

18.0%

1990 1992 1994 1996 1998 2000 2002 2004

Paraguay Mexico Peru Colombia

In Mexico, returns are high and cyclical

Mincerian returns, urban males

Average real wages did not decline in Average real wages did not decline in the 2001-2003 recessionthe 2001-2003 recession

Log of Average Wages

7.79

7.87

7.43

7.51

7.74 7.73

7.88

7.3

7.4

7.5

7.6

7.7

7.8

7.9

8.0

1992 1994 1996 1998 2000 2002 2004

Implication

If Mexico’s growth had been maintained post-2000, what would have happened to wage premia?

Is it infrastructure?

GDP per capita and Infrastructure quality

Algeria

Argentina

AustraliaAustria

Azerbaijan

Belgium

BrazilBulgaria

Canada

Chile

ChinaColombia

Czech Republic

Denmark

EcuadorEgypt


Hong Kong

Hungary

IndiaIndonesia

Iran

Ireland

IsraelItaly Japan

KazakhstanMalaysiaMexico

Netherlands

New Zealand

Nigeria

Norway

Pakistan

Peru

Philippines

Poland

Portugal

Romania

RussiaSaudi Arabia

Singapore

Slovakia

South Africa

South KoreaSpain

Sri Lanka


ThailandTurkeyUkraine

United Kingdom

United States

Venezuela

Vietnam

78

91

01

1L

YP

CP

.5 1 1.5 2 2.5LINRT

Is infrastructure a constraint on GDPpc?

Algeria

Argentina

Australia

Austria

Azerbaijan

Belgium

Brazil

Bulgaria

Canada

Chile

China

Colombia

Czech RepublicDenmark

EcuadorEgypt

FinlandFrance

Germany

Greece

Hong Kong

HungaryIndia

Indonesia

IranIreland

Israel

Italy

Japan

Kazakhstan

Malaysia

MexicoNetherlands

New Zealand

Nigeria

Norway

Pakistan

Peru

Philippines

PolandPortugal

Romania

Russia

Saudi Arabia

Singapore

Slovakia

South Africa

South Korea

Spain

Sri Lanka

Sweden

Switzerland

Taiwan

Thailand

Turkey

Ukraine

United Kingdom

United States

Venezuela

Vietnam

-.5

0.5

Dev

iati

ons

from

pre

dict

ed G

DP

pc

.5 1 1.5 2 2.5Log of EIU Infrastructure rating

Higher income thanexpected given INFRA

Lower income thanexpected, given INFRA

Better measures

Use ICAs Do a Rajan & Zingales type analysis using

either UNIDO or ICA datasets


Ex ante

Hu

man

Cap

ital

Infr

astr

uct

ure

&

pu

bli

c g

oo

ds

(geo

gra

ph

y ?

)

Ex

ante

ris

ks

Tax

Lo

w p

rop

erty

ri

gh

ts,

crim

e &

co

rru

pti

on

Lo

w R

&D

Lo

w S

elf

dis

cove

ry


countries



Expropriation




Social unrestGrowth






corporate tax, VAT

Open conflict







(Kaufman)






costs (ICA)


TOTMonopoly


banks



(ICA)


profits


activities






ow

ag

gre

gat

eS

avin

gs

Bad

fin

ance







debt)







Regulated entry

Low appropriability


Is it low appropriability?

Macro risksWhat are macro risks?

•It is the risk of loosing the ability to capture returns because of a sudden change in relative prices.•Which price?: tradable/non tradable, domestic/foreign assets, interest rates, real wages

•What to look for: •Risk of inflation accelerating?•Risk of a depreciation?•Risk of a sudden increase in interest rates?

•What could trigger it?•Is there a misalignment in the real exchange rate?•Are the fiscal accounts sustainable (what is happening to the public debt)•Is your country vulnerable to negative external shocks?

III. Posit a syndrome

The syndrome to symptoms approach Posit a syndrome Analytically derive the observable symptoms

of that syndrome Empirically check that those symptoms are

present in the data Is there an alternative syndrome that can

account for this? What symptoms would allow a differential diagnostic?

A few syndromes

The over-borrowing state The over-taxing state The under-investing state The under-protecting state Growth collapses Lack of self-discovery Insufficient competition The under-educated country


Ex ante

Hu

man

Cap

ital

Infr

astr

uct

ure

&

pu

bli

c g

oo

ds

(geo

gra

ph

y ?

)

Ex

ante

ris

ks

Tax

Lo

w p

rop

erty

ri

gh

ts,

crim

e &

co

rru

pti

on

Lo

w R

&D

Lo

w S

elf

dis

cove

ry

•The over-borrowing state XX X X

•The over-taxing state X X XX

•The under-investing state XX X X

•The under-protecting state XX X

•Growth collapse X X XX

•The under-educated country XX

Government failureEx post

Syndrome

What constraints are likely in some growth syndromes ?



Lo

w a

gg

reg

ate

Sav

ing

s

Bad

fin

ance

Lack of complementary

factors

Low appropriability

Example: Peru’s growth collapse Is Peru a case of an adverse shock to the

export sector… …in a country with low open forest… …that consequently had a difficult time

recovering? Let’s check the facts

1960

1961

19621963

19641965

19661967

19681969

197019711972

1973

197419751976

1977

1978

19791980

1981

1982

1983

19841985

1986

1987

1988

1989

19901991

1992

1993

1994

19951996

1997

199819992000

2001

20022003

2004

3.5

53

.63

.65

3.7

3.7

5L

YP

CL

CU

K

5.2 5.4 5.6 5.8 6 6.2LXPCKUS

There was a big export collapse Log of Real Exports pc

1979-1983

-40%

1979-1993

81 %

Export sophistication in Peru is low…

…but open forest in 1980 (when the collapse happened) was not terrible

ARG AUS

AUT

BDI

BEN

BFABGD

BGR

BHRBHS

BLZBOL

BRA

BRB

CAF

CAN

CHL

CHN

CIVCMR

COL

CRICYP

DNK

DOM

DZA

ECUEGY

ESP

FIN

FJI

GAB

GBR

GHA

GMB

GRCGTM

GUY

HKG

HND

HTI

HUN

IDN

IND

IRL

IRN

ISL

ISR

ITA

JAM

JOR

JPN

KENKNA

KOR

LKA

MAR

MDG

MEX

MLI

MLT

MOZ

MUSMWI

MYS

NCLNER

NIC

NLD

NOR

NPL

NZL

OMN

PAK

PANPER

PHL

PNG

PRT

PRY

RWA

SDN

SEN

SGP

SLE

SLV

SWE

SYR

TGO

THA

TTO

TUR URY

USA

VEN

ZAF

ZMB

ZWE

PER

11

12

13

14

15

lno

pen_

fore

st1b

2.5 3 3.5 4 4.5LYPPPK

lnopen_forest1b lnopen_forest1b

1960 200019801970

Agrarian Reform

Cotton and sugar collapse

Fishing nationalized

Fall ofPescaPeru

Colapso Pesca(natural 72)

End of mining concessions

…or is it a case of property rights?

Mining exportscollapse

PetroPeru Collapse in oil exports

1990

Conclusion

Part of the collapse in exports was self-induced The country lost market share in the products it exported

At a time when structural transformation was key, property rights became a problem

The recovery has been lead by the same sectors that collapsed

Associated with stronger property rights… …at a substantial cost in terms of GNI

Very low taxation of natural resource sectors …and the property rights cycle may not be broken

Over-borrowing state: symptoms The state generates an environment of high interest

rates There is a large government deficit …that crowds out private investment Exhaustion of external finance Growth is sensitive to the availability of external

savings Extreme example: Brazil

What do we observe?

Limited external creditworthiness Very high interest rates Domestic financial conditions very related to

external conditions Growth very sensitive to external financial

conditions

Why low savings?

Very high tax burden No government savings Very little after-tax income to save with …but a healthy return on investment So the interest rate must be very high to

prevent greater investment Should government lower taxes to improve

the business environment?

Taxes as a share of GDP

ggrevgdpweo

0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50%

Haiti

Guatemala

Costa Rica

Dominican Republic

El Salvador

Peru

Paraguay

Chile

Bahamas, The

Mexico

Trinidad & Tobago

Jamaica

Panama

Belize

Nicaragua

Bolivia

Argentina

Ecuador

Venezuela

Honduras

Colombia

Uruguay

Suriname

Guyana

Barbados

Brazil

Government spending is very high

Log of GDP per capita PPP

Gen

era

l Gove

rnm

en

t E

xpe

nditu

reas

a s

hare

of G

DP

7.40593 9.6058

.111271

.504702

ARG

BLZBOL

BRA

BRB

CHL

COL

CRIDOM

ECU

GTM

GUY

HND

HTI

JAM

MEXNICPAN

PERPRYSLV

TTO

URY

VEN

High cost of finance

Low return to economic activity

Low social returns

Low appropriability

government failures

market failures

poor

geography

low human capital

bad infrastructure

Micro risks

property rights, corruption,

taxes

macro risks: financial,

monetary, fiscal instability

information externalities:

“self

-

discovery”

coordination externalities

Poor intermediation

-

The over-borrowing state

Low domestic savings

Over-borrowing state: Policy Implications Improving the investment climate is not critical

There is an excess of investment in spite of distortions Lowering taxes to improve the investment climate isn’t

worth it Closing the deficit to be able to lower interest rates

is the key Ideally by cutting inefficient spending Second best: distortionary taxes

Over-taxing state: symptoms

The tax rate is too large relative to the social returns to investment

Investment is low Interest rates are low Significant informality Example: Barbados

Fiscal receipts of 40%, very low interest rates, access to international financial markets growth of 1%

High cost of finance

Low return to economic activity

Low social returns

Low appropriability

government failures

market failures

poor

geography

low human capital

bad infrastructure

Micro risks

property rights, corruption,

taxes

macro risks: financial,

monetary, fiscal instability

information externalities:

“self

-

discovery”

coordination externalities

bad international finance

bad local finance

low domestic saving

poor inter

-

mediation

The over-taxing state

Over-taxing state: Policy Implications

Lowering taxes is crucial

…in areas where they are most distortionary

…even at the cost of increasing the deficit

Under-investing state: Symptoms The economy is constrained by a lack of

infrastructure Private returns are limited by access to

infrastructure Low interest rates Congestion Example: India

growth diagnostics in practice i applications to paraguay and peru ricardo hausmann

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