Competitiveness and Growth

in Latin America: The Chilean Case

Matías BraunUniversidad Adolfo Ibáñez & IM Trust

Ignacio BrionesUniversidad Adolfo Ibáñez

Christian JohnsonUniversidad Adolfo Ibáñez

September 2007

Non-Binding Constraints

We dismiss the following as being material constraints • Property rights• Macroeconomic unstability• Tax scheme• Infrastructure• Public institutions

Selected scores at the WEF-Global Competitiveness Report 2006-2007

Global Competitiveness

index

Tax Efficiency (a)

Property rights

Macroeconomic stability

Public Institutions

Corruption Infrastructure

Chile 4,9 4,3 5,5 5,6 5,7 6,4 5,11st Quartile Raw per capita GDP Growth 4,2 3,2 4,4 4,8 (*) 4,2 4,6 3,31st Quartile pc GDP Growth controlling for Initial pc GDP 4,3 3,3 4,5 4,8 (*) 4,4 4,8 (*) 3,51st Quartile Raw per worker GDP Growth controlling for Initial pw GDP 4,2 3,2 4,5 4,8 4,4 4,8 3,4High Growth Countries (union of the three above) 4,2 3,2 4,5 4,8 4,3 4,7 3,4Latin America & Caribbean 4,0 (**) 2,9 (*) 4,2 (*) 4,4 (**) 4,3 (**) 5,2 (**) 3,3Rich OECD Countries 5,0 2,8 6,1 4,6 5,8 6,2 5,4All Countries 4,1 3,0 4,4 4,6 (*) 4,3 4,7 3,4

Source: WEF-Global Competitiveness Report 2006-2007. (a) Data from WEF-GCR 2005-2006.* 10%, **5%, *1%. Significance levels of the test of whether Chile is different than the one of each group of countries

Selected binding “suspects”

1. Poor quality of Education

2. Bad Income Distribution

3. Micro Constraints on Growth: a) labor, b) energy constraints

4. External sector and self-discovery

5. Financial Constraints

Methodology• We consider a factor to be a considerable constraint if three conditions

apply:1. If, compared to a relevant group of countries, a measure of the relevant

issue is shown to be significantly smaller in Chile. 2. If there is an economic and statistically significant association between the

measure of the particular concept and the rate of growth and/or the level of income.

3. If we can show either shadow prices or behavior that is consistent with the hypothesis that there is a significant constraint steaming from supply rather than demand factors.

• For each candidate, we compute and rank dXX

YY

per capita GDP

Growth 1995-

2004

pc GDP Growth

controlling for Inicial pc GDP

1995-2004

per worker

GDP Growth

1995-2004

per worker GDP Growth controlling for Inicial pw GDP

1995-2005

per capita GDP

2000 USD 1995-

2004 Chile 3.3% 1.2% 2.5% 0.9% 4,2951st Quartile Raw per capita GDP Growth 5.7% 3.3% 5.3% 3.3% 3,3201st Quartile pc GDP Growth controlling for Initial pc GDP 5.7% 3.3% 5.2% 3.3% 3,8191st Quartile Raw per worker GDP Growth controlling for Initial pw GDP 5.6% 3.3% 5.3% 3.4% 5,092High Growth Countries (union of the three above) 5.3% 3.0% 5.0% 3.0% 4,629Latin America & Caribbean 1.2% -0.9% 0.3% -1.4% 3,831Rich OECD Countries 2.4% 0.7% 2.3% 1.0% 21,631All Countries 2.4% 0.1% 1.9% 0.0% 5,352

* The sample consists of up to 166 countries. Data are from World Development Indicators.

Benchmark Groups

Our own implementation of GDM

y=f(X)

dy=δy/δX·dX1) dX lower than benchmark (expected, desired, rich or

fast-growing countries)

2) δy/δX relatively large and in the right direction3) δy/δX X=>y and not the other way around

(behavior or prices consistent with lack of supply instead of lack of demand)

• For X to be binding we need all 1, 2, and 3 to be true.

• Also, dy=δy/δX·dX allows us to rank them

1- Lack of resources: human capital

• Quantitative educational indicators

• Qualitative educational indicators

• Behavior: Industry panel regression

CHL

05

1015

tyr2

5+

0 10000 20000 30000 40000GDP pc (2000, ppp)

62 data predicted data62

CHL

010

2030

4050

lh25

+

0 10000 20000 30000 40000GDP pc (2000, ppp)

8 data Fitted values

Average years of Education (pop, 25+), 2000 Percentage of Higher school attained (pop, 25+),2000

•Both attainment and years of school in Chile are in line or over the country’s economic development.

Education: Quantity

•Gross tertiary enrolment is higher than LA and than the fast growing economies•In line in 1990, over the expected value in 2004 •Quantity is NOT a problem

Quantitative educational indicators

Wage premia and % of labor force with tertiary education completed

•At first sight Chile’s wage premia looks high…

•However, it is in line with its quantity of skilled workers (and also level of development and capital per worker)

•Quantity rather than quality?

Quantity. Indirect measures wage premium

Wage premia skilled/unskilled workers in the World

0

2

4

6

8

10

12

14

16

ITA

GR

C

PO

L

HU

N IND

DE

U

CH

N

CA

N

AU

S

OA

N

HK

G

TS

R

JPN

SW

E

GB

R

LKA

BO

L

NLD

CH

E

PH

L

TU

N

PE

R

VE

N

GH

A

DO

M

MY

S

SLV NIC

PA

K

UR

Y

US

A

FR

A

PR

T

AR

G

TH

A

KO

R

CR

1

CY

P

PR

Y

EC

U

CH

L

SG

P

PA

N

CO

L

ME

X

BR

A

GT

M

KE

N

IDN

HN

D

BW

A

Secondary equivalentstertitiary equivalents CHL

05

10

15

Wag

e pr

em

ia te

rtia

ry

0 10 20 30% pop higher edu. compl.

rw_t Fitted values

Quantity. Indirect measures wage premium0

24

68

Pre

mia

(#

tim

es)

1970 1980 1990 2000year

higher over primary higher over secondarysecondary over primary

• Skilled wage premia roughly constant during the 1990s

• Ceateris paribus, supply of high skilled workers has compensated the demand expansion.

• Supply was not a mayor constraint

• Wage premia secondary vs primary are converging. Quality?

Wage premia in Chile 1970-2003

Quality: indirect measures

•Mincer> ex-post => negative quality effect at school level

•Mixed Results:

•No difference from national data

•Mincer > ex-post, Santiago’s data

Table 1.2. Mincer estimates vs effective returns Returns to education 1 (Mincer specification) 1990 1992 1994 1996 1998Primary (8 yrs) 2.9 3.6 4.2 3.2 3.6Secondary (4 yrs) 9.1 9.9 9.1 11.3 9.3Higher (4 yrs) 20.6 22.1 22 21.4 21

Returns to education 2 (Mincer specification) 1990 1992 1994 1996 1998

(Sapelli 2003)(Arellano and Braun 1999)

(Beyer 2000) (Sapelli 2003)

Primary (8 yrs) 5.8 5.2 3.8 7.1Secondary (4 yrs) 13.2 12.3 10.2 13.9Higher (4 yrs) 18.9 20.9 20.5 22.8

Implicit Ex-post (effective) returns (1) 1990 1992 1994 1996 1998Primary (8 yrs)Secondary (4 yrs) 9.5 11.1 11.3 12.1 12.1Higher (4 yrs) 26.2 29.9 26.8 26.3 27.8

Implicit Ex-post (effective) returns (2) 1990 1992 1994 1996 1998Primary (8 yrs)Secondary (4 yrs) 10.3 7.6 9.1 8.9 6.9Higher (4 yrs) 39.4 35.4 36.0 34.6 34.2

* (1) data from Mideplan (2000), (2) from Gallego (2006).

Quality: direct measures

Prueba TIMSS 2003 Prueba Pisa 2000

CHL400

450

500

550

600

Tim

ss 2

003

0 10000 20000 30000 40000GDP pc (2000,ppp)

timss_all03 Fitted values

CHL

300

350

400

450

500

550

TP

isa

2000

0 10000 20000 30000 40000GDP pc (2000,ppp)

pisa_all Fitted values

•Chile’s results in TIMSS and PISA test should be 15% higher (=1 standard deviation)

•In principle, economic effect could be large: 1% in in per capita GDP growth per year. (Hanushek and Kimko, 2000)

•However, quantity x quality= ok. Combined economic effect 0.4% in per capita GDP growth per year.

•Quality could be binding

CHL

50

100

150

200

250

300

Pis

a200

0xls

25

0 10000 20000 30000 40000GDP pc (2000,ppp)

pisa_ls Fitted values

Quality. Further tests: Industry panel approach

• 2 specifications:• A- Dependent variable is value added growth at

isic3 level, over interaction of human capital intensity x availability + other control variables

• B- Dependent variable is the share of isic3 value added (to GDP), over interaction of human capital intensity x availability + other control variables

• Idea is to test behavior: given Chile´s endowment of human capital, do its HK intensive industries are growing slowly than in other countries?

Quality. Further tests: Industry panel approach

• 1985 - 2002 Chile was not different from other countries (value added growth an share)

• 85-95 binding= 0.4% GDP growth.

• 95-02 the opposite• All in all, industrial

behavior not consistent with the quality binding hypothesis. Latent restriction?

Panel A: Industry Growth Variable 1985-2002 1985-95 1995-2002

Ln(Adult Total Years of Schooling) x Industry Human Capital Intensity

0.0963273 *** 0.0722401 *** 0.0914181 **

(0.0253915) (0.0195681) (0.0350952)Ln(Natural ressources per capita) x Industry Natural ressources Intensity

0.0000546 -0.0022444 0.0260225 **

(0.00682) (0.0059767) (0.0120633)Ln(physical capital per worker) x Industry Physical Capital Intensity

-0.0488542 -0.0077716 0.0945287

(0.0919342) (0.0809609) (0.1318218)Initial Share of Indutry of Total Manufacturing Value Added

-0.0656481 -0.2296529 *** -0.0686246

(0.0478216) (0.0474045) (0.1110064)

Dummy_Chile Ln(Adult Total Years of Schooling) x Industry Human Capital Intensity

0.0035107 -0.0219832 *** 0.0178446 *

(0.0052558) (0.0046378) (0.0103143)

R-squared 0.3666 0.4032 0.2558Obs. 1392 1351 1128*** 1% ; ** 5% ; * 10%

Panel B: Industry Composition Variable 1985-2002 1985-95 1995-2002

Ln(Adult Total Years of Schooling) x Industry Human Capital Intensity

0.5414796 * 0.381451 1.22883 **

(0.2832166) (0.2777096) (0.5044525)Ln(Natural ressources per capita) x Industry Natural ressources Intensity

0.1938891 * 0.159585 0.2528286 **

(0.1028954 ) (0.1006135) (0.0971905)Ln(physical capital per worker) x Industry Physical Capital Intensity

0.1991707 0.0735766 -1.777133

(1.015046) (1.039054) (1.361254)Dummy_Chile Ln(Adult Total Years of Schooling) x Industry Human Capital Intensity

-0.0620676 -0.0664267 -0.2497667 ***

(0.092631) (0.0988869) (0.0969371)

R-squared 0.4899 0.487 0.424Obs. 1564 1479 1181*** 1% ; ** 5% ; * 10%

2. Inequality

• High inequality in Chile• Empirical (weak) link (income inequality)= 0.5-

0.8 % GDP gth.• Asset inequality: 0.6 % GDP gth.

Income Gini index (2000)

Land Gini Index (Frankema)

Chile 57,1 84,11st Quartile Raw per capita GDP Growth 35,7 ** 49,8 **1st Quartile pc GDP Growth controlling for Initial pc GDP 36,0 ** 55,3 **1st Quartile Raw per worker GDP Growth controlling for Initial pw GDP 35,4 ** 53,5 **High Growth Countries (union of the three above) 36,1 ** 54,7 **Latin America & Caribbean 51,5 77,8Rich OECD Countries 32,1 * 62,7 *All Countries 40,4 59,5 *

Sources: Frankema (2007), WDI (2005) and Deininger and Squire (1996)* 10%, **5%, *1%. Significance levels of the test of whether Chile is different than the one of each group of countries

2. Inequality

• High growth coupled with high but stable inequality. Binding? Not clear.

• Channels:– Political making

process? No– Social unstability? No– Capital market? No

• Conclusion: NOT binding

CHILE: Gini (income) index

30

35

40

45

50

55

60

1958-63 1964-69 1970-73 1974-81 1982-86 1987-90 1991-98 1999-01 2002-06

CHL

20

40

60

80

Gin

i

-.05 0 .05 .1 .15 .2GDP pc growth

gini predicted gini

3- Micro Distortions on Growth

• A) Labor Market Regulations– Policy-induced price distortions and regulations in some

sectors – World Economic Forum Competitiveness Reports indicate

that by far labor regulation is a problem

• B) Energy– Experiences such as the oil shock of the recent years, the

shortage of gas from Argentina during the last few years, and the low levels energy investment in the economy were circumstances that might be constraining Chilean’s future growth prospects.

A) Labor Market Rigidities

• Labor market would be binding constraint to Chilean growth?– Evidence supporting the view that labor regulation and

labor market rigidity (Job security Indexes) does not affect• Employment growth• Investment • Industrial production growth • Substitution of Labor and Capital

– And what about Investment Climate Survey?• Labor regulations does not affect employment growth• Labor regulations does not affect production

B) Energy Constraints

Dependent Variable: Per worker GDP Growth controlling for initial pw GDP 1995-2005---------------------------------------------------------------------------------------------------------------------------------------------------------------Constant -0.0057419 ** -0.015554 *** -0.0074231 ** -0.0079032 *** -0.003021

0.0027338 0.0022397 0.003316 0.0030425 0.002877

Energy Growth 0.2711198 ** 0.1361062 0.2747035 ** 0.2818019 * 0.2823193 ***0.1067166 0.0943757 0.1069168 0.1074592 0.1080365

grupogthun~n 0.0412029 ***0.0045801

pcgdp2000us 3.00E-071.99E-07

Rich OECD 0.0139734 ***0.0033288

Latin American Country -0.0181801 ***0.0041249

---------------------------------------------------------------------------------------------------------------------------------------------------------------# Obs 166 166 166 166 166R2 0.0914 0.4298 0.0974 0.1145 0.1362F Test 6.45 40.63 4.75 11.77 10.28P-value 0.0120 0.0000 0.0099 0.0000 0.0001Root MSE 0.03034 0.02410 0.03033 0.03004 0.02967---------------------------------------------------------------------------------------------------------------------------------------------------------------*10%, **5%, ***1%. Robust standard errors below the coeff.Cross Section Sample of 166 countries, average of indicators for 1995-2005.

Robust Elasticity

Dependent Variable: Gross Output (Log) Group All Firms

All SampleFirms with HighUse of Energy

Firms with LowUse of Energy

ln(electc) 0.24597 *** 0.51799 *** 0.34754 ***0.00342 0.00576 0.00479

Fixed Assets 5.42E-10 *** 1.60E-10 *** 7.78E-09 ***7.94E-11 6.80E-11 4.51E-10

Investments 1.31E-09 9.18E-10 9.96E-09 ***9.92E-10 9.18E-10 2.39E-09

Gross Output Growth 18.90312 15.23884 22.43303Electricity Consumption Growth 29.44707 47.71685 11.84724# Obs 53126 26999 26127# Groups 8834 6153 6314R2 Overall 0.6225 0.8478 0.7876F Test 1754.72 2710.33 1925.5P-value 0.0000 0.0000 0.0000*10%, **5%, ***1%. Robust standard errors below the coeff.Panel Data (ENIA): 1990-2000, 6153 firms Industrial SectorConstant estimated but not reported in tableHigh Use of energy defined as firms with electc/Gross Output higher than the median: 0.0002069 (dummy=1)

• Worldscope• Data ENIA

– Energy is consistently a relevant factor in the productive process

B) Energy• BUT: Is Energy really a Problem? No

– WB Inv. Climate Survey– Not binding for

• Sales• Employment• production

(1) (2) (3)

Dependent VariableEmployment

GrowthSales Growth

Market Value of Production

GrowthElectricity (c218b) 0.1169947 0.6314801 0.4900666

0.2281866 0.4994278 0.9084524

Firm Size (Total Employment) 0.9429716 *** 2.562169 *** 0.72949750.242563 0.5292068 0.9612507

Cost of Financing (c218L) -0.1865434 -0.0703586 0.66084040.2144453 0.4710471 0.8981119

Macroeconomic Instability (c218n) -0.4875233 ** -0.3124173 -0.68290680.2336334 0.5115749 1.006361

Legal System/Conflict Resolution (c218r) -0.4247077 * -0.7668764 -0.5533730.2391423 0.526087 0.9455888

# Obs 11759 10388 4985R2 0.0025 0.0026 0.0003F Test 5.96 5.51 0.35P-value 0.0000 0.0000 0.8853*10%, **5%, ***1%. Robust standard errors below the coeff.Constant not Reported. Similar resukts with HP Index instead of Djankov et al.Codes from WB Inv. Climate Survey: Employment: c262a1y-c262a2y; Sales: c274a1y-c274a2y;Market Value of Production: c274c1y-c274c2y

B) Chile: Is Power Supply a Problem? No

• Based on official projections, the Chilean economy will require additional energy in an amount close to 400-450 Mega Watts– Planned infrastructure will satisfy demand (see annex.)

• Many actions taken by the Energy Minister to decrease our energy dependence in short and long run:– Efficient use of energy from households, firms and

government agencies (Programa Pais Eficiencia Energetica: PPEE)– Promoting a tripartite agreement between Chile,

Argentina and Brazil to make energy swaps in case of shortage

– To control the use of hydro resources, to avoid leakages, etc.

B) Energy: Conclusion

• Energy supply is important for

• …However from World Bank’s Investment Climate Survey– energy has not been an obstacle for the

operation and growth of businesses

• And supply planned power infrastructure is in line

• Energy will not be a binding constraint for growth in Chile

4-External sector and self-discovery

• Despite its outstanding growth rate and its relevant size, the export sector is still very concentrated in a few primary products.

• This lack of diversification could be hampering Haussmann’s Self-Discovery process and thus constraining Chile’s future growth.

4- External sector and self-discovery

• Chile shows a level of open forest that is lower than expected from the growth equation

ECU

NIC

TTO

NICECUGUY

TTO

GUYSLV

TTO

GUYGUYGUYGUYECU

TTO

ECUJAM

TTO

TTO

JAM

SLV

SLVNICGTMNIC

JAMCHLJAMECUJAM

SLVPAN

GTMJAMGTM

NICCHLCHLPAN

NIC

PANPAN

ECU

GTM

COLSLVCOL

GTM

COL

SLV

PANCHL

CHL

COL

BRA

GTM

ARG

PAN

CHL

ARG

ARG

COL COL

ARG

ARG

BRABRABRA

ARG

BRABRA

-20

00

02

000

400

06

000

800

0e

( g

dpp

cpp

p |

X )

-500000 0 500000 1000000 1500000e( open_forest | X )

coef = .00330884, (robust) se = .00066759, t = 4.96

4-External sector and self-discovery

• Chile’s current export basket is very peripherical– export sophistication is low– specialized in activities with few new

applications– however, output and export growth has been

high• The ability of the economy to transit to a new

development stage is not for granted• Chile’s nearby efficient frontier favors:

– agricultural-based manufactures, shipbuilding, industrial chemicals, non-electric machinery, and forestry-based manufactures

4-External sector and self-discovery

• It is Chile’s ability to move to these sectors where future growth probably lies

– education quality, and energy, and financial constraints, can play an important role

• Given the low density of its open forest, Chile will likely require the other determinants of growth to be much more enhanced than in other countries without this condition

5. Financial Constraints

Chile’s Financial System is Large

• And is not perceived as a constraint for growth

Bank Credit to

the Private

Sector to GDP

1995-2004

Stock Market

Capitalization to

GDP 1995-

2004

Private Bond Market

Capitalization to GDP 1995-

2004 Chile 55.4% 85.2% 19.0%1st Quartile Raw per capita GDP Growth 25.3% 24.6% 11.5%1st Quartile pc GDP Growth controlling for Initial pc GDP 27.7% 30.5% 11.1%1st Quartile Raw per worker GDP Growth controlling for Initial pw GDP 33.0% 43.9% 14.9%High Growth Countries (union of the three above) 31.8% 42.6% 15.3%Latin America & Caribbean 27.8% 19.3% 6.9%Rich OECD Countries 89.5% 72.8% 37.7%All Countries 36.3% 39.9% 22.6%

* The sample consists of up to 166 countries. Data are from World Development Indicators and Beck et al (2006).

Ranking of Contraints for Doing Business: Chilean Firms

All Firms

Financing 4Infrastructure 7

Political Instability 1Inflation 6

Exchange Rate 2Street Crime 3Organized Crime 9Taxes and Regulations 5Corruption 8

Ranking of Financial Contraints for Doing Business: Chilean vs. Other Countries' Firms

All Firms

Chile 41st Quartile Raw per capita GDP Growth 2

1st Quartile pc GDP Growth controlling for Initial pc GDP 21st Quartile Raw per worker GDP Growth controlling for Initial pw GDP2

High Growth Countries (union of the three above) 2Latin America & Caribbean 5Rich OECD Countries 2All Countries 3

Financial Development has a large effect of Growth

• Across countries [King and Levine (1993)

• Across countries and time [Levine, Loayza, and Beck (1998)]

• Across industries [Rajan and Zingales (1998)]

• Across firms [Demirguc-Kunt and Maksimovic (1998)]

COG

TZA

AGO

CMR

CAF

KHM

UGA

LAO

DZA

GEO

VEN

GHA

ROM

ARMALB

BEN

LSO

BTN

RWA

BWA

LTU

GTM

TUR

MLI

SEN

COL

BGR

IRN

NIC

LVA

IDNETH

PAK

PER

DOM

BGDNPL

SLV

BRA

MRT

LKA

POL

EST

IND

HND

PHLOMN

SVK

HUNWSM

NAM

TON

SVNCZE

VNM

BOL

HRV

EGYUSA

TUN

MUS

FINGRC

CHL

CAN

JORZAF

NOR

BEL

ITATHAFRA

SWE

KOR

ISR

AUS

MYS

AUTESP

JPN

SGPIRL

NZL

DEU

CHN

DNK

GBR

PRTNLD

HKG

-.6

-.4

-.2

0.2

e( p

cgdp

gth5

| X

)

-.5 0 .5 1e( privatec5 | X )

coef = .09184159, se = .04229492, t = 2.17

A one standard deviation increase in financial development is associated with around 1% faster growth per year.

There is behavior consistent with financing being a constraint in Chile

• Firms are more sensitive to internal cash and less to investment opportunities than elsewhere, particularly the young, small and intangible.

• More tangible industries grow disproportionaly faster in Chile

• Young and small firms were more severely affected during the post-Asian Crisis growth deceleration period.

• However, large, listed firms appear not to be particularly constrained in Chile.

0.661*** 0.559*** 0.609*** 0.529*** 0.636*** 0.518*** 0.465*** 0.473***0.07 0.034 0.046 0.02 0.079 0.0381 0.0847 0.037

-0.0091 0.060** -0.024 0.020 -0.051 0.070* 0.038 0.0330.071 0.026 0.041 0.020 0.083 0.035 0.079 0.028

-0.020 -0.027 -0.009 -0.018 -0.042*** -0.039** -0.017 -0.0150.02 0.0188 0.013 0.0127 0.016 0.016 0.018 0.0188

-0.036* -0.027* -0.039 0.0020.021 0.014 0.027 0.024

0.023 0.014 0.040 -0.0010.022 0.012 0.027 0.0253

-0.287*** 0.065** -0.720*** -0.0600.0358 0.025 0.047 0.037

-0.035 -0.037* 0.885*** 0.162***0.027 0.02 0.038 0.029

# Obs 2091 2102 4539 4560 1536 1547 2128 2135R2 0.325 0.320 0.290 0.289 0.320 0.327 0.176 0.177

* 10%, **5%, ***1%. Constant and Country Fixed Effects included but not reported. Errors clustered at the country level. The sample consists of firm-level observations in 58 countries.

IntangLocal Young

Past Sales Growth X General Financing Constraint

Expected Increase on Sales X CHILE

Past Sales Growth X CHILE

Small

Expected Increase on Sales

Past Sales Growth

Average Importance of non-financial constraints

Expected Increase on Sales X General Financing Constraint

Internal Cash/ Inv. Opp. Sensitivity

Investment Employment

-0.260*** -0.327*** -0.287*** -0.192*** -0.263*** -0.356*** -0.300*** 0.087 -0.073***0.015 0.018 0.015 0.015 0.015 0.0189 0.0222 0.058 0.004

0.036** 0.039** 0.035** -0.009 0.0110.015 0.016 0.015 0.083 0.008

0.268*** 0.270*** 0.269*** 0.025 0.097***0.021 0.021 0.019 0.093 0.014

-0.111***0.0188

0.064**0.021

0.043* 0.044* 0.044* -0.001 0.030***0.020 0.021 0.023 0.088 0.005

0.051** 0.048** 0.045** 0.051 -0.0160.019 0.0198 0.0167 0.107 0.01

-0.0150.0128

0.000.205

# Obs 43767 43767 43767 43767 43767 43767 43768 20400 43768R2 0.229 0.229 0.236 0.232 0.229 0.237 0.247 0.166 0.266Firm FE Yes Yes Yes Yes Yes Yes Yes Yes YesYear FE Yes Yes Yes Yes Yes Yes Yes Yes YesIndustry X Year FE No No No No No No Yes Yes Yes

* 10%, **5%, ***1%. Constant included but not reported. Robust errors clustered at the year level. The sample corresponds to 7346 different firms observed during the 1990-200 period.

Firm Value Added Growth

Foreign

Post98 X Tangible

Post98 X Foreign

Post98 X Old

Post98 X Large

Post98

Old

Large

Tangible

Small, Young Firms and growth decelaration

6. Conclusion

Bad Local Finance and Poor Quality of Human Capital make the Self-

Discovery constraint much more critical

ΔX δGrowth / δX

Behavior /

Shadow

Prices

Overall Ranking

Low Economic ReturnLow Social ReturnsLow Human Capital Quantity None Large No No - Quality Large Large Mixed Maybe 3High Income Inequality Large Medium No No -Energy Constraints Future-Small Large No Maybe 4Low AppropriabilityLabor Market Distortions Medium Small No No -Self-Discovery Large Large ? Maybe 1High Costs of FinanceBad Local Finance Maybe Large Yes Maybe 2

ΔX: difference between Chiles’s and benchmark indicator. δGrowth/δX effect of indicator on growth. Behavior/Shadow Prices consistent with the hypothesis that there is a significant constraint steaming from supply rather than demand factors. See methodological issues section.

Constraints on Growth: Summary

6. Conclusions • Not binding

– Quantity of Education– General Institutions– Finance for large, old, and collateral-rich

firms

• Binding– Self-Discovery– Finance for small, young, and collateral-poor

firms – Quality of Education– Energy (future)