Luis Servén The World Bank ECLAC January 2005 Latin America’s infrastructure gap: a macroeconomic perspective

Luis ServénThe World Bank

ECLACJanuary 2005

Latin America’s infrastructure gap: a macroeconomic perspective

1. The changing policy framework

2. The infrastructure gap

3. The output cost

4. The lessons

Plan

• Until the 1970s, the public sector dominated infrastructure provision in both industrial and developing countries.

• Since the 1980s (earlier in Chile and the UK) Latin America led the worldwide drive towards opening up of infrastructure to private initiative – in various forms and extents.

• The drive was propitiated by a hardening of fiscal discipline in response to financial instability and macroeconomic crises

• In most countries, the fiscal retrenchment led to a sharp contraction of public infrastructure investment (similarly to the post-Maastritch fiscal adjustment in the EU)

The changing policy framework

Latin America: Public Investment in Infrastructure(weighted average of 7 countries, percent of GDP)

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

1980

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

%

Total Roads plus Rails Power Water Telecommunications

Note: 7 Latin America countries, ARG, BOL, BRA, CHL, COL, MEX, PER.


Latin America’s fiscal adjustment: Contribution of consumption and investment


Public infrastructure investment

(1)

Public consumption

(2)

Primary surplus

(3)

Public infrastructure investment

-(1)/(3)

Public consumption

-(2)/(3)

Argentina -2.87 8.22 (a) 6.23 0.46 -1.32

Bolivia -2.48 2.38 6.15 (b) 0.40 -0.39Brazil -2.57 9.97 4.12 0.62 -2.42Chile -1.38 -2.51 0.73 1.89 3.45Colombia -0.59 11.30 3.50 0.17 -3.23Mexico -2.20 1.31 5.24 0.42 -0.25Peru -1.43 0.53 0.68 2.10 -0.78

Source: Calderón and Servén (2004b).

Contributions to fiscal adjustment

Changes between 1980-84 and 1999-2001 (percent of GDP)

Latin America: Private Investment in Infrastructure(weighted average of 7 countries, percent of GDP)

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1980

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

%





Latin America: Total investment in Infrastructure

(weighted average of 7 countries, percent of GDP)


Latin America: Total investment in Infrastructure

(6 major countries, percent of GDP)

Latin America: Investment in Infrastructure (public + private) (weighted average of 7 countries, percent of GDP)

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

1980

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

%




The private sector response

• Private initiative surged in the 1990s -- but with diversity across industries (and countries)

• Strong response in telecommunications, much less in transport.

• Evidence of public-private complementarity, not only substitution: countries maintaining higher public investment attracted more private investment (Chile, Bolivia, Colombia)

• The rise in private investment was not enough for asset accumulation to keep up with other world regions

• The investment fall contributed to widen Latin America’s infrastructure gap – in terms of quantity and quality -- widened over the 1980s and 1990s


Capacity change

Investment

Brazil: the power sector


The infrastructure gap

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

1980 1985 1990 1995 2001

Power Generation Capacity(megawatts per 1,000 workers, Medians by Region)

LAC (19) EAP7 (7) MIDDLE (64) IND (21)


0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1980 1985 1990 1995 2001

Road plus Railway Length(km per area, Medians by Region )



0

500

1,000

1,500

2,000

2,500

3,000

1980 1985 1990 1995 2001

Total Telephone Lines(lines per 1,000 workers, Medians by Region)



0

1

2

3

4

5

6


Figure 2.17. Overall Infrastructure QualityMedians by region and income level, 2000

Question: The quality of the infrastructure is among the best in the world (1=strongly disagree; 7 =strongly agree).Source: World Competitiveness Report.

Perceived infrastructure quality

(Medians by region, 2000)

Why do we care about infrastructure ?

• The availability and quality of infrastructure services is key for productivity and profitability

• Robust association between infrastructure availability and aggregate output / growth within and across countries

• Partly driven by reverse causality (growth encourages demand for infrastructure services)

• But there is broad agreement that infrastructure development has a strong causal effect of on economic development.

• Evidence that infrastructure development helps reduce income inequality – makes it easier for the poor to access economic opportunities, jobs, health and education.

The output cost

Infrastructure Stocks and Economic Growth (1960-2000)

ZWE

ZMB

ZAF

VEN

USA

URYUGA

TZA

TWN

TUR

TUN

TTO

THA

SYRSWE

SLV

SLE

SGP

SENRWA

ROM

PRY

PRT

POL

PNG

PHLPER

PANPAK

NZLNPL

NORNLD

NICNGA

NER

MYS MUS

MRT

MLI

MEX

MDG

MARLKA

KOR

KEN

JPN

JOR

JAM

ITAISR

IRN

IRL

IND

IDN

HUN

HND

HKG

GTM

GRC

GNB

GIN

GHA

GBRFRA

FIN

ETH

ESP

EGY

ECUDZA

DOM

DNKDEU

CYP

CRICOL

CIV

CHN

CHL

CHE

CAN

BWA

BRA

BOL

BGDBFA

BELAUT

AUS

ARG

y = 0.0056x + 0.0206

R2 = 0.2547

-2%

-1%

0%

1%

2%

3%

4%

5%

6%

7%

8%

-4 -3 -2 -1 0 1 2 3

Index of Infrastructure Stocks (1st. Principal Component)

Gro

wth

Ra

te o

f G

DP

pe

r c

ap

ita

Source: Calderón and Servén (2004b)

Infrastructure accumulation and growth (1960-2000 country averages, percent)

y = 0.505x + 0.0006R2 = 0.3253

-3%-2%

-1%0%1%2%

3%4%5%

6%7%

-2% 0% 2% 4% 6% 8% 10%

Growth in infrastructure stocks per worker

Gro

wth

in

GD

P p

er

wo

rke

r

Rest lac eap7

Source: Calderón, Easterly and Servén (2003)

Infrastructure Stocks and Income Inequality (1960-2000)

ZWE

ZMBZAF

YSR

VEN

USA

URY

UGATZA

TWN

TUR

TUN

TTOTHA

SWE

SLV

SGP

SEN

RWAROM

PRY

PRT

POL

PNGPHL

PERPAN

PAK

NZLNOR

NLD

NGA MYS

MUS

MEX

MDG

MARLKA

KOR

KEN

JPNJOR

JAM

ITA

ISR

IRN

IRL

INDIDN

HUN

HND

HKG

GTM

GRCGHA

GBR

FRA

FIN

ETH

ESP

EGY

ECUDOM

DNKDEU

CYP

CRI

COL

CIV

CHN

CHL

CHECAN

BWA

BRA

BOL

BGR

BGD

BFA

BELAUT

AUS

ARG

y = -0.0303x + 0.403

R2 = 0.2157

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

-4 -3 -2 -1 0 1 2 3

Index of Infrastructure Stocks (1st. Principal Component)

Gin

i Co

eff

icie

nt

(0-1

)

Source: Calderón and Servén (2004b)

• What is the contribution of infrastructure services to aggregate output and/or its growth rate ?

Three main empirical approaches in the literature:

1. Empirical growth models

2. Augmented production (or cost) function

3. VARs

Caveats:

-- technical problems often severe (identification / reverse causality, spurious regressions…)

-- all else equal: the costs of “getting there” are not explored – large tax rises or cuts in other expenditures that may have an output cost…

The output cost

The long-run growth approach:

• Adding infrastructure into a standard growth regression

• Infrastructure usually proxied by telecommunications indicators (e.g., Easterly 2001, Loayza et al 2003)

Calderón and Servén 2004b: panel of 100+ countries, 40 years

Consider both infrastructure quantity and quality

Synthetic infrastructure indicator: first principal component of {power, roads, telecom} – accounts for 80% of their variance.

Endogeneity: identification via GMM-IV with (a) internal instruments; (b) demographic variables

Growth contribution of infrastructure quantity and quality is statistically and economically significant.

The output cost

The output cost

Source: Calderón and Servén 2004b

Additional growth in LAC countries due to increased infrastructure development

The augmented production function approach:

• Unlike VARs and growth regressions, it is a structural approach

Y = F (K, H, Z); K = physical capital; H = human capital (often omitted) ; Z = infrastructure capital (power, phone lines, roads)

• Productive services assumed proportional to asset stocks

• In actual data, Z often is already included in K: The coefficient on Z captures the return differential on Z over K

• In addition to usual reverse causality problem, spurious correlation problem when using time series: nonstationarity of Y, K, Z leads to huge infrastructure coefficient estimates (Aschauer 1990)

The output cost

The augmented production function approach

Calderón and Servén 2005: panel time-series estimation for 90 countries, 40 years.

• Spurious regression problem does not arise here (due to large N)

• Only one long-run relation found – resolves identification problem

• Pooled and country-specific estimates – permit assessing heterogeneity across countries / regions

• Synthetic index and disaggregated infrastructure assets

• Results broadly similar to Calderón, Easterly and Servén 2003 – in spite of very different approach (GMM-IV to deal with identification; first-differencing to deal with nonstationarity)

The output cost

Estimated (log) infrastructure coefficients

(DOLS estimates, 1960-2001, synthetic index)

The output cost

Source: Calderón and Servén 2005

Coefficient S.E.

Pooled 0.091 0.013

Country-specific: mean by group

All (89 countries) 0.130 0.019

Industrial (21) 0.080 0.027

Developing (68) 0.145 0.024

Country-specific estimates[Synthetic Infrastructure Index, GLS--PIC (1,1)]

0

5

10

15

20

25

30

35

40

<-0.20

-0.10 0.00 0.10 0.20 0.30

The output cost


• The estimated return on infrastructure assets is significantly higher than that on other physical capital in the vast majority of countries.

• Infrastructure has significantly lower returns than other capital only in 3 out of 89 countries [none in LAC]

• Across LAC countries, some heterogeneity too:

The differential return on overall infrastructure is significantly higher than average in Peru, Mexico, Colombia…

Differences also across assets – e.g., the differential return on power generation capacity is significantly lower than average in Paraguay, but higher in Brazil

The output cost

Estimated (log) infrastructure coefficients(DOLS estimates, 1960-2001)

The output cost

Electricity Generation

CapacityRoads

Main Telephone

Lines

Pooled DOLS 0.074 ** 0.060 ** 0.046 **0.018 0.022 0.015

Country-specific: means by Group

All (89 countries) 0.115 ** 0.104 ** 0.052 **0.022 0.042 0.026

Industrial (21) 0.120 ** 0.135 * -0.0160.030 0.070 0.038

Developing (68) 0.113 ** 0.094 * 0.073 **0.027 0.051 0.032


The output cost

Source: Based on Calderón and Servén 2005

The cost of the widening infrastructure gap: EAP vs LAC

Avg. 1991-00 vs. Avg. 1996-00 vs.Avg. 1981-90 Avg. 1981-85

1. Change in relative infrastructure endowments (%)

Main Phone Lines 27.6 41.1Electricity Generating Capacity 37.9 58.0Roads 30.3 50.3

2. Change in Relative GDP per worker (%) 31.6 41.9

3. Contribution of the infrastructure gap 9.3 14.5

4. Relative contribution (as % of [2]) 29.2 34.7

(1) Fiscal adjustment, as commonly measured and enforced, tends to have an anti-investment bias

• One (not the only) major factor is the use of inappropriate fiscal rules targeting liquidity, the cash deficit and gross public debt – rather than solvency and net worth, which are key to fiscal sustainability.

• Infrastructure projects have a negative short-run liquidity effect -- it takes time to build the assets and get the returns.

• The focus on fiscal liquidity discourages such projects – even if they are consistent with good public economics; i.e., they enhance solvency.

The lessons

(2) Infrastructure investment cuts represent an inefficient fiscal adjustment strategy

• The direct effect of the spending cut is to raise liquidity and public sector net worth

• But there is an opposing indirect effect: less infrastructure means less output and lower fiscal revenues tomorrow

• The indirect effect offsets partly the direct effect – and can even make fiscal adjustment self-defeating.

The lessons

Summary

• Latin America’s infrastructure gap widened in the 1980s and early 1990s, at a substantial cost in terms of output and productivity.

• A major factor in the process was the investment slowdown – caused by a public investment decline not offset (except in telecom) by private sector participation.

• The public investment compression reflected a biased and inefficient fiscal adjustment, encouraged by rules targeting liquidity and debt rather than solvency and net worth.

• Ensuring adequate room for productive spending requires fiscal rules that reconcile solvency and growth.

End


Fiscal discipline has led to a public investment fall not only in developing countries – also in the EU

• The fiscal targets imposed in the Maastritch Treaty contributed to a decline in public investment across Europe:

• Out of 9 countries exceeding the Maastritch deficit limit in 1992, 8 met it in 1997. Public investment had fallen in all 8 !

• Infrastructure investment fell along with the total

Fiscal adjustment and public investment(average of 9 EU countries, percent of GDP)

0.6

0.7

0.8

0.9

1.0

1.1

1.2

1.3

1.4

19

87

19

88

19

89

19

90

19

91

19

92

19

93

19

94

19

95

19

96

19

97

19

98

19

99

20

00

% G

DP

-6

-5

-4

-3

-2

-1

0

1

2

3

% G

DP

Transport Investment, mean (left scale)

Primary Deficit, mean (right scale)

Sources: World Development Indicators - World Bank; and provisional data from ECMT.Notes: (a) Total = Roads + Rails + Airports. (b) 9 EU countries: Austria, Finland, France, Netherlands, Norway, Portugal, Spain, Sweden and United Kingdom.


Documents

Luis Servén The World Bank ECLAC January 2005 Latin America’s infrastructure gap: a macroeconomic perspective