Diagnosis and Challenges of Infrastructure

Diagnosis and Challenges of Infrastructure

Experiences and Lessons from Latin America

Cape Town, South Africa, 29-31 May 2006

J. Luis Guasch,

World Bank and University of California-San Diego

Objective: Answer Key Questions

• Diagnosis– How large is the infrastructure gap?– Why did the gap emerge?– What is the cost-investment needed- of closing the

gap?• Impact

– On growth/productivity/competitiveness– On poverty and inequality

• Moving Forward– What needs to be done?– Role of public vs private-PPPs

What are we doing in Latin America (LAC) ?

• Impact Analysis of:– Infrastructure on

Growth/Productivity/Competitiveness/Exports/FDI and Poverty

– Reforms and Private Sector Participation on Sector/Firm Performance

• Forecasting Needs (linked to objectives)• Evaluating the Infrastructure/Investm Gap• Strategies and Policies to close the Gap• Special Attention/Policies for the Poor

A Comprehensive Approach

• Put together all the pieces in an homogenous framework

• To identify common problems and impacts

• Build on existing work but complete the diagnosis• Existing work: on gap, needs, impact, concession

design, impact of PPI• Need for more: fiscal space, political economy of private

participation, cost recovery

• Formulate a regional and country infrastructure strategy

• Within existing economic and political constraints

Key Messages

• LAC needs to spend more on infrastructure

• LAC needs to spend better

• Governments remain at the heart of the infrastructure challenge

• State Owned Enterprises need to improve performance and sector reform are needed

• The private sector can contribute, but lessons from the experience need to be applied

LAC needs to spend more on infrastructure

Latin American Infrastructure Latin American Infrastructure Stocks Lag Behind East AsiaStocks Lag Behind East Asia

Notes: Infrastructure stock index includes paved roads, electricity generating capacity and telephones (main lines and mobile) per worker. The index is calibrated so that East Asian Tigers had a value of 1 in 1980.Source: Calderon and Serven 2004.

0

1

2

3

4

5

6

7

1980 2000

Latin America

East Asian Tigers

Infrastructure stock index

LAC has Fallen Behind China and Middle Income Countries

0

500

1000

1500

2000

2500

Access toelectricity (%)

Roads (km/km2) Mainline per 1000pers

LAC

MIC

China

Source: World Development Indicators

What Happened?

0%

1%

1%

2%

2%

3%

3%

4%

4%

5%

1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002

shar

e o

f G

DP

TOTAL

Public

Private

Source: Calderon and Serven (2004)

Public retrenchment never fully offset by private entry

How Much is Needed Depends on the Goal (Fay and Yepes 2004)

• Universal coverage of water and sanitation? • ~ 0.25% of GDP over 10 years

• To maintain and rehabilitate existing assets?• ~1% of GDP for adequate maintenance• Impossible to estimate rehabilitation needs

• For business as usual?• ~ 2% of GDP to satisfy consumer and firm demand based on

modest growth assumptions

• To grow and take off?• ~4% to 6% of GDP to catch up with Korea and keep up with

China

Who Pays?Users and Taxpayer

• Users • Cost recovery higher in LAC than in other developing regions

– but still low• Implications:

– Improve payment culture – government must support enforcement of payment requirements

– Protect those who really cannot pay (less than 10% of households in most countries) – well targeted subsidies

• Taxpayers: • Where cost recovery is limited• Where there are externalities (social, economic)

The Costs are very High

• Lost competitiveness :• 58% of firms in LAC rate infrastructure a major problem vs 18%

in East Asia• High logistics cost: 25c of every dollar of product exported (vs.

9c in OECD)

• Foregone growth: • Infrastructure gap explains a third of the income gap with East

Asia

• Hampering fight against poverty• Directly: 75 million without potable water, 116 million without

adequate sanitation, 56 million without electricity • Through inequality: raising stock and quality of LAC

infrastructure to Korea level would decrease Gini by 5 to 20 points

Issues About PPI

• PPI’s impact mostly positive: • Coverage, quality and efficiency have increased

significantly• No evidence it increased inequality• The record on unemployment is mixed, sector vs

firm,

• But, issues of transparency, fairness and capture of rents, and better contract design need to be addressed

Infrastructure Deals with Private Participation have Declined

0

10

20

30

40

50

60

70

80

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

US

$ b

illio

n

Shows total value of projects with PPI. Source: World Bank PPI database

LAC Needs to Spend Better

- To increase value of investments-To capture a larger share of the benefits

Better Expenditure Allocation

• New investments must focus on strategic goals

• Tackling bottlenecks but not at the expense of the poor

• More needs to be spent on maintenance• High rate of return (Rioja 2003; project

evaluations)

• Decentralization and participatory planning can help

Better Subsidy Targeting

• Central to expenditure efficiency goal: • Large amount of resources: electricity subsidies 1% of

GDP in Mexico• Poorly targeted: 95% Guatemalan households, 85% in

Honduras benefit from social tariff in electricity

• Critical for feasibility of cost recovery tariffs

• But difficult• Substantial improvements in consumption subsidies are

technically and politically difficult• Connection subsidies may be better as unconnected

poor will benefit

Better PPP Framework

• Renegotiations (over 50%) are costly• Regulatory risk bids up the cost of capital 2-6%

• Contingent liabilities can be ruinous• Mexico toll roads: 1- 1.7% GDP; Colombia: 4% of GDP• Governments have often taken on more risk than necessary

• Technical progress can mitigate institutional weaknesses:

• Technical – improving regulatory and contract/concession design much: has been learnt, questioning price caps, clear and transparent award criteria, clear mechanisms for contract renegotiations

• Institutional – remains difficult: independent regulator, capacity for regulatory enforcement

Governments Remain at the Heart of the Infrastructure

Challenge

Responsible for Sector Reform and Regulation

• True with or without PPI:• PPI failures often due to governments offloading their

responsibilities • Government support critical to payment culture and

enforcement

• Includes the management of the political economy of reform

• Reforms complex, can provoke backlash• Must prevent the gains of one group being perceived as the

losses of another (redistribution traps)

Responsible for Social Goals

• True with or without PPI• Design and funding are public responsibilities• But private sector can be tapped: output based aid;

small scale providers

• Critical for poverty goals

• Central to success of reforms

Responsible for Financing and Financing Framework

• Direct financing still needed • At the peak, PPI about 1.7% of GDP concentrated in a few

sectors and a few countries

• Lack of fiscal space a challenge

• Financing frameworks can help• Local currency long term finance or creative financing structure

to minimize FX risk• Prudent framework for sub-national borrowing• Wholesaling partial risk guarantees

The private sector can contribute, particularly when the lessons from the past are

incorporated

Winning Over Public Opinion

• Understanding and addressing public discontent with PPI

• Required steps: • More transparent transactions• Better contract and regulatory design• Fewer renegotiations• Governments shouldering their responsibilities

– On painful reforms– On appropriate safety nets for losers and poor – On perception management

Attracting Back the Private Sector

• PPI is risky business• Private operators have not made excess profits• Concessions generally profitable in the long run• Many never profitable (30%)

• PPI risk-return ratio can be improved• Decrease regulatory risk and improve PPI

framework• Develop risk mitigation mechanisms• Does not mean governments must take on undue

amount of risk

IMPACT OF INFRASTRUCTURE (1)

ON GROWTH AND ON

POVERTY AND INEQUALITY

Source: Calderon and Serven (2004)

The growth costs of the deficiency in infrastructure in LAC.

The contribution of infrastructure deficiencies in the output gap vis a vis East Asia countries?

1980-2000

1. Growth of of the gap in output per capita (cambio en el log de PIB relativo por trabajador)

91.9

2. Share attributed to the increase in the infrastrcture gap (mediana de inforrmacion de país)

20.2

[2] / [1] (percent) 21.9

Infraestructura and Growth

Contributión to the infrastrcuture gap to the gap in output Relative to East Asia (1980-2000, percent)

0% 20% 40% 60% 80% 100% 120% 140%

Chile

Uruguay

Dom. Rep.

Panama

Colombia

Argentina

Brazil

Bolivia

Guatemala

México

Costa Rica

Honduras

Peru

El Salvador

Ecuador

Nicaragua

Jamaica

Venezuela

Infr contribution

Output gap

92%

Figure 1. Infrastructure Stocks vs. Economic Growth

ARG

AUS

AUTBEL

BFABGD

BOL

BRA

BWA

CAN

CHE

CHL

CHN

CIV

COLCRI

CYP

DEUDNK

DOM

DZA ECU

EGY

ESP

ETH

FINFRAGBR

GHA

GIN

GNB

GRC

GTM

HKG

HND

HUN

IDN

IND

IRL

IRN

ISRITA

JAM

JOR

JPN

KEN

KOR

LKAMAR

MDG

MEX

MLI

MRT

MUSMYS

NERNGA

NIC

NLDNOR

NPLNZL

PAKPAN

PERPHL

PNG

POL

PRT

PRY

ROM

RWA SEN

SGP

SLE

SLV

SWESYR

THA

TTO

TUN

TUR

TWN

TZA

UGA URY

USA

VEN

ZAF

ZMB

ZWE

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

Infrastructure Stocks (1st. Principal Component)

Gro

wth

Ra

te o

f G

DP

pe

r c

ap

ita

Figure 2. Infrastructure Quality vs. Economic Growth

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

MYSMUS

MRT

MLI

MEX

MDG

MAR

LUX

LKA

KOR

KEN

JPN

JOR

JAM

ITA ISR

IRN

IRL

IND

IDN

HUN

HND

HKG

GTM

GRC

GNB

GIN

GHA

GBRFRA

FIN

ETH

ESP

EGY

ECU DZA

DOM

DNKDEU

CYP

CRI

COL

CIV

CHN

CHL

CHE

CAN

BWA

BRA

BOL

BGD

BFA

BELAUT

AUS

ARG

ZWE

y = 0.0081x + 0.0226

R2 = 0.2027

-2%

-1%

0%

1%

2%

3%

4%

5%

6%

7%

8%

-2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5

Infrastructure Quality (1st. Principal Component)

Gro

wth

Ra

te o

f G

DP

pe

r c

ap

ita

Infrastructure and Growth

• Economic Implications of Calderon and Serven 2004 estimates

• :

i. 1 s.d. Infrastructure Stocks and Quality leads to higher growth by 3.6 pp. (2.9 pp attributed to higher quantity and 0.7 pp to higher quality).

ii. Raising infrastructure development of Peru (25th percentile) to Chile (75th percentile in LAC), we increase growth by 2.2 pp. (1.7 pp due to larger stocks and 0.5 pp to better quality).

Growth Payoff from Infrastructure Development

• Growth gains by LAC countries relative to leader (CRI) range from 1.1 to 4.8 pp.

• Growth gains of LAC leader relative to EAP median (KOR) is 1.5 pp.

Infrastructure and Growth

Growth Improvement in LAC Countries due to Higher Infrastructure Development(in percentages)

Improvement to levels of LAC Leader Improvement to levels of EAP Median

Country Stocks Quality Total Stocks Quality Total

Argentina 1.3% 0.4% 1.7% 2.2% 0.9% 3.2%

Bolivia 3.8% 0.5% 4.3% 4.8% 1.0% 5.8%

Brazil 1.5% 1.4% 2.9% 2.4% 1.9% 4.4%

Chile 1.3% 0.0% 1.3% 2.3% 0.6% 2.8%

Colombia 1.9% 1.2% 3.1% 2.9% 1.7% 4.6%

Costa Rica ... ... ... 1.0% 0.5% 1.5%

Ecuador 2.0% 1.0% 3.0% 3.0% 1.5% 4.5%

Mexico 1.4% 0.2% 1.7% 2.4% 0.8% 3.2%

Peru 3.0% 0.6% 3.5% 4.0% 1.1% 5.0%

Uruguay 0.7% 0.4% 1.1% 1.7% 0.9% 2.6%

Venezuela 1.1% 0.4% 1.4% 2.0% 0.9% 2.9%

Average 2.0% 0.6% 2.6% 2.9% 1.1% 4.0%

Infrastructure and Inequality

• Infrastructure development can have a positive impact on income and welfare of the poor above its impact on average income (Lopez, 2003).

• Infrastructure helps under-developed areas to get connected to core economic activities and access to additional productive opportunities.

• Infrastructure has a disproportionate impact on the human capital of the poor (education and health), and hence on their job opportunities and income prospects.

• Distributive impact of private participation in infrastructure involves micor and macro linkages.

Figure 3. Infrastructure Stocks vs. Income Inequality

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

Infrastructure Stocks (1st. Principal Component)

Gin

i Co

eff

icie

nt

(0-1

)

Figure 4. Infrastructure Quality vs. Income Inequality

ARG

AUS

AUT BEL

BFA

BGD

BGR

BHSBOL

BRA

BWA

CANCHE

CHL

CHN

CIV

COL

CRI

CYP

DEUDNK

DOMECU

EGY

ESP

ETH

FIN

FRA

GBR

GHA GRC

GTM

HKG

HND

HUN

IDNIND

IRL

IRN

ISR

ITA

JAM

JORJPN

KEN

KOR

LKAMAR

MDG

MEX

MUS

MYSNGA

NLD

NORNZL

PAK

PANPER

PHLPNG

POL

PRT

PRY

ROMRWA

SEN

SGP

SLV

SWE

THA TTO

TUN

TUR

TWN

TZAUGA

URY

USA

VEN

YSR

ZAFZMB

ZWE

y = -0.0523x + 0.3887

R2 = 0.2942

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

-2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5

Infrastructure Quality (1st. Principal Component)

Gin

i Co

eff

icie

nt

(0-1

)


• Infrastructure development affects income shares:(a) It reduces the ratio of income shares of top to bottom

quintiles.

(b) It increases the share of the middle income quintile.

Redistributive Benefits of Infrastructure Development• Reduction of Gini coefficient by LAC countries relative

to leader in infrastructure development (CRI) range from 0.02 (URY) to 0.10 (NIC).

• Reduction of Gini coefficient by LAC leader relative to EAP median (KOR) is 0.03. For countries like GTM, HND, NIC, PER the reduction is greater than 0.1.


Changes of Inequality in LAC Countries due to Higher Infrastructure Development(Changes in the Gini coefficient)

Improvement to levels of LAC Leader Improvement to levels of EAP Median

Country Stocks Quality Total Stocks Quality Total

Argentina -0.03 -0.01 -0.03 -0.05 -0.02 -0.06

Bolivia -0.08 -0.01 -0.09 -0.10 -0.02 -0.12

Brazil -0.03 -0.02 -0.06 -0.05 -0.03 -0.09

Chile -0.03 0.00 -0.03 -0.05 -0.01 -0.06

Colombia -0.04 -0.02 -0.06 -0.06 -0.03 -0.09

Costa Rica ... ... ... -0.02 -0.01 -0.03

Ecuador -0.04 -0.02 -0.06 -0.06 -0.03 -0.09

Mexico -0.03 0.00 -0.03 -0.05 -0.01 -0.06

Peru -0.06 -0.01 -0.07 -0.08 -0.02 -0.10

Uruguay -0.02 -0.01 -0.02 -0.04 -0.02 -0.05

Venezuela -0.02 -0.01 -0.03 -0.04 -0.02 -0.06

Average -0.04 -0.01 -0.05 -0.06 -0.02 -0.08

Sources: LCSFP reports; LAC Chambers and producers associations reports, 2003..

0

5

10

15

20

25

30

Costs of inadequate infrastructure (% of value of goods)

Losses en routeProportion of agric. goods not reaching markets

Logistic costsAs a proportion of value of products

OECD LAC

1

2

3

4

Inventory levels (index, right scale)


0

5

10

15

20

25

30




OECD LAC

1

2

3

4



0

5

10

15

20

25

30




OECD LAC

1

2

3

4


Impact of a 12 percentage point reduction of logistic costs (Guasch 2005)

Sector Increase in Output

Demand

Increase in Employment

Agro-indust 12% 6%

Wood and Furniture

14% 16%

Textiles 8% 9%

Leather and Shoes

18% 15%

Mining 10% 2.5%

Inventory Levels in Latin America - Ratios over Inventory Levels in the U.S.

Raw Materials Inventories Ratios: Ratio to U.S. Level by Industry (average of all available data for 1990s)

Chile Venezuela Peru Bolivia Colombia Ecuador Mexico Brazil Mean 2.17 2.82 4.19 4.20 2.22 5.06 1.58 2.98

Minimum 0.00 0.30 0.10 0.11 0.52 0.86 0.42 0.8

1st Quartile 0.36 1.87 1.25 1.39 1.45 2.55 1.06 1.6

Median 1.28 2.61 2.30 2.90 1.80 3.80 1.36 2.00

3rd Quartile 2.66 3.12 3.90 4.49 2.52 5.64 2.06 3.1

Maximum 68.92 7.21 31.1 34.97 13.59 20.61 3.26 7.1

Fianl Goods Inventory Levels: Ratio to U.S. Level by Industry (average of all available data for 1990s)

Chile Venezuela Peru Bolivia Colombia Ecuador Mexico Brazil Mean 1.76 1.63 1.95 2.74 1.38 2.57 1.46 1.98

Minimum 0.01 0.10 0.39 0.11 0.19 0.67 0.35 0.75

1st Quartile 0.17 0.87 1.17 1.13 1.05 1.67 0.82 1.1

Median 0.72 1.60 1.54 2.02 1.28 1.98 1.36 1.60

3rd Quartile 1.38 2.1.4 2.11 3.18 1.63 2.86 2.14 2.00

Maximum 31.61 5.29 3.87 21.31 5.31 7.94 4.91 5.2

Source: Guasch and Kogan (2000)

IMPACT OF INFRASTRUCTURE (2)

ON

PRODUCTIVITYEXPORTS

FDIWAGES

EMPLOYMENT

(FROM ICA SURVEYS)SOURCE: Escribano and Guasch (2005,2006)

Methodology, Data and Estimation

(Escribano and Guasch 2005)

Investment climate indicators are used to explain the relative competitiveness (productivity or technical efficiency) of firms in the Investment Climate Survey. The significance and the robustness of their explanatory power across different specifications were assessed by comparing the results obtained from the following pooled OLS regressions:

(1) ititititjit ICDCTRBCDACY '''

(2) ititititit ICDCTRBTLACY '''

(3) itititit ICDCTRBCTFP '' , with itjitit CDYTFP ' , and

(4) itititit ICDCTRBCTEFF '' , with TEFFit being the technical efficiency

residual in a stochastic production frontier model, where:

Yit is a log measure of output of firm i in period t (t=2000 - 2002), CD is a standard Cobb-Douglas specification (linear combination of log-inputs), TL is a standard translog specification (second-order polynomial of log-inputs), CTR is a set (vector) of control variables (not easily affected by managerial or public policy decisions), IC is a vector of “investment climate” variables (that can be potentially affected by such decisions), j is an industry index, and is a vector of input coefficients equal to input shares in total cost (under the constant returns to scale assumption).

Methodology, Data and Estimation (con’t)

Regressions (1) and (2) are standard production function regressions with vectors CTR and IC added as explanatory factors for TFP. An alternative, two-step, approach would be to estimate TFP as the residual of a standard production function (where output depends only on inputs, i.e. labor, capital and, depending on the definition of the output, materials) – and then regress that residual on vectors CTR and IC. However, this approach is likely to suffer from an omitted variable bias, as the estimated input coefficients may be affected by CTR and IC (see Escribano and Guasch (2005) for a detailed discussion). Therefore, the residual from the standard production function is modeled as a linear combination of CTR and IC variables – and this combination is directly included into the TFP regressions.

Regression (3) has a non-parametrically estimated (basically, a calculated) TFP measure on the left-hand side. The reason here is, again, that the input choices may be correlated with the residual - and the non-parametric TFP estimation is another way to avoid that. Finally, (4) has a technical efficiency measure, TEFF, on the left-hand side. For technical reasons, estimation of this measure was done separately (i.e., at the first stage).

Productivity Gains from a 20% Improvement in Selected Investment Climate Variables (%)

0

1

2

3

4

5

6

7

Brazil Ecuador El Salvador Guatemala Honduras Indonesia Nicaragua

Average duration of power outages Average duration of water outages Loss in sales due to transport interruptions

Average time to clear customs Fraction of workers using computers in their job

Source: Escribano and Guasch. (2005).

Productivity Elasticities and Semielasticities with Respect to IC Variables

-0.07

-0.12

-0.05

-0.12-0.14

-0.12

0.02

-0.01

0.04 0.03

0.09

0.13

0.05

0.23

0.18

-0.09

-0.17

0.26

0.005

-0.2

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

0.2

0.25

0.3

1.1 1.2 1.3 1.4 2.1 2.2 2.3 2.4 3.1 4.1 4.2 4.3 4.4 5.1 5.2 5.3 5.4 5.5 5.6

Infrastructures FinanceRed Tape, Corruption and Crime

Quality, Innovation and Labor Skills

Other Control Variables

1.1 Days to clear customs for exports.1.2 Power outages.1.3 Shipment losses.1.4 Internet page.2.1 Security.2.2 Number of inspections.2.3 Cost of entry.2.4 Absenteeism.3.1 Financing line program.

4.1 R + D.4.2 Internal training.4.3 University staff.4.4 Experience of the manager.5.1 Incorporated company.5.2 Foreign direct investment.5.3 Exporter.5.4 Capacity utilization.5.5 Rent land.5.6 Trade union.

Elasticities are indicated by blue bars, semielasticities by yellow bars.

CHILE

CHILE

Export Linear Probability Coefficients with Respect to IC Variables

0.03

0.15

-0.23

0.06

0.13

0.06 0.07 0.060.09 0.1000.12

0.24

0.031-0.001

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

1.1 2.1 3.1 4.1 4.2 4.3 5.1 5.2 5.3 5.4 5.5 6.1 6.2 6.3

Infrsts.Finance and Corporate

Governance

Red Tape, Corr.

& Crime

Q uality, Innovation and Labor Skills O ther Control Variables

1.1 Productivity.2.1 E-mail.3.1 Illegal payments for protection .4.1 Trade Association.4.2 Derivatives.4.3 External auditory.

5.1 Quality certification.5.2 R + D.5.3 New technology purchased.5.4 Internal training.5.5 Experience of the manager.6.1 Incorporated company.6.2 Age.6.3 Rent buildings.

Productivity

CHILE

Foreign Direct Investment Linear Probability Coefficients with Respect to IC Variables

0.03

-0.07

0.05

0.15

0.070.06 0.05

0.18

0.09 0.09

0.002

-0.1

-0.05

0

0.05

0.1

0.15

0.2

1.1 2.1 3.1 3.2 3.3 3.4 4.1 4.2 4.3 4.4 5.1

Infrastructures Finance and Corporate Governance Quality, Innovation and Labor Skills Other Control Variables

1.1 Productivity.2.1 Days to clear customs for imports.3.1 Trade Association.3.2 Credit.3.3 Derivatives.3.4 External auditory.

4.1 Quality certification.4.2 R + D new product.4.3 Internal training.4.4 University staff.5.1 Rent buildings.

Productivity

CHILE

Wage Per Employee Elasticities and Semielasticities with Respect to IC Variables

0.48

-0.15

0.30

0.04

-0.21

-0.04

0.01

-0.03

0.01

-0.14

0.76

0.10 0.13

0.002

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

1.1 2.1 2.2 3.1 3.2 3.3 4.1 4.2 4.3 5.1 5.2 6.1 6.2 6.3

Infrastructures Finance and Corporate Governance

Red Tape, Corruption and Crime

Quality, Innovation and Labor Skills

Other Control Variables

1.1 Productivity.2.1 Average duration of power outages.2.2 Internet page.3.1 Security.3.2 Cost of entry.3.3 Absenteeism.

4.1 Trade association.4.2 Credit line.4.3 External auditory.5.1 University staff.5.2 Experience of the manager.6.1 Foreign direct investment.6.2 Age.6.3 Trade union.


Productivity

Figure 6

CHILE: Employment Elasticities and Semielasticities With Respect to IC Variables

-0.09-0.27

-0.08

0.13

-0.30

-0.77

-0.28

0.01 0.010.12 0.18

0.040.24

0.070.24

0.14 0.13

-1.89

-1.00

-0.0030.180 0.31

-2.5

-2

-1.5

-1

-0.5

0

0.5

1.1 2.1 3.1 3.2 4.1 4.2 4.3 4.4 5.1 5.2 6.1 6.2 6.3 6.4 6.5 6.6 7.1 7.2 7.3 7.4 7.5 7.6

Infrstrc. Finance and Corp. Gov.

Red Tape, Corruption and Crime

Q uality, Innovation and Labor Skills

O ther Control Variables

1.1 Productivity.2.1 Real wage per employee.3.1 Power outages.3.2 Internet page.4.1 Security.4.2 Number of inspections.4.3 Cost of entry.4.4 Absenteeism.5.1 Trade association.5.2 External auditory.

6.1 Quality certification.6.2 New product.6.3 Internal training.6.4 External training.6.5 University staff.6.6 Experience of the manager.7.1 Incorporated company.7.2 Age.7.3 Exporter.7.4 Trade union.7.5 Small.7.6 Medium.


Prdvty. Real Wage

IMPACT OF SECTOR REFORMS AND PRIVATE

SECTOR PARTICIPATION (3) ON

SECTOR PERFORMANCE:PRODUCTIVITY

COVERAGEQUALITY OF SERVICES

PRICES

SOURCE: Guasch (2004) and Andres, Foster and Guasch (2006)

WHAT HAS BEEN THE OUTCOME OF PRIVATE SECTOR PARTICIPATION?

– EVALUATION:

• PRODUCTIVITY• COVERAGE• QUALITY OF SERVICES• PRICES• RENEGOTIATION

Based on the analysis of more than 1,300 concessions in the infrastructure sector awarded since the 1980s to date, in Latin America and Caribbean (Guasch 2004). The data set has seven blocks describing: (i) country characteristics; (ii) type of project or transaction; (iii) award and bidding details; (iv) regulatory environment; (v) concession details; (vi) renegotiation details; and (vii) risk bearing details

Data Set

Telecoms

Electricity

Airports

Water & Sanitation

Roads Rail

Ports

Framework

• Comprehensive analysis of indicators: output, labor, efficiency, labor productivity, efficiency, quality, coverage and prices.

• Use data with a longer span (data starts 5 years previous to the change in ownership and continues 5 years after the privatization). This allowed us to identify the short-run or transitional effects but also, those of long run results.

• Explores alternative explanations for the change in the outcomes: increase in competition.

Significant Changes in Private Participation in Electricity Distribution…

1990: only 3% of the HHs 2003: +60% of the HHs

… also in private participation in Fixed Telecommunication …

1990: only 3% of the HHs 2003: +86% of the HHs

… and in Water Distribution

1990: only ~0% of the HHs 2003: +11% of the HHs

General Diagnosis pre Privatization

• Low labor productivity, poor service quality and high system losses.

• Distorted prices in levels and structure that did not cover the economic costs of the service.

• Weak regulatory framework without independency of decisions, and governmental budget dependency.

• Critical need of big investments in the sector and lack of incentives to attract private capitals.

• Debt crisis and worsening financial performance in these decades.

FINANCING/ OPERATIONAL PARTICIPATION OF THE PRIVATE

SECTORVERY POSITIVE

OPERATIONAL EFFICIENCY FAIRLY POSITIVECOVERAGE ADEQUATE

ALIGNMENT OF COSTS AND TARIFFS

PROBLEMATIC

SUSTAINABILITY DEFFICIENT

OVERALL CONCLUSIONMIXED, PARTIALLY

POSITIVE

RESULTOBJECTIVE

OUTCOME

Empirical Results: Changes in Trends…

Transition Post-transición Transición Post-transición Transición Post-transiciónNúmer of users (*)

Output (*)

Númer of workers

Productivity of labor (*)

Technical losses in distribution of servicesQuality

Coverage (*)

Prices

? ?

Fuente: Andres, Foster y Guasch (2004).Nota: (*) Estas variables fueron reportadas tras considerar los efectos fijos de la firma y otros fenómenos contemporáneos en la economía.

Electricity Distribution Telecomunications- fix Water

Númer of workers –Sector

Description: Electricity Distribution

• Three sectors: Generation, Transmission and Distribution. They were vertically integrated geographic monopolies.

• The general process: Vertical separation of competitive segments (e.g. Generation) from regulated segments (e.g. Transmission and Distribution) and privatization.

• Competition arises in the generation stage.

• In transmission and distribution firms: Competition for the monopolistic market.

• Meanwhile, all countries created a regulatory board in order to set quality standards, regulate tariffs and monitor compliance of the privatized firms.

Description: Fixed Telecomm• During the 80s and 90s, governments owned the fixed

telecommunication company which operated in a monopolistic market.

• After the experience of Chile in 1980s, most countries privatized their telecomm companies (exceptions: Colombia among other countries).

• The new owner had several requirements to commit on the expansion of the network as well as some quality standards.

• They were granted a period of monopolistic market (~5 years).

• After that, competition with new firms.

Introduction: Empirical Approach

FIRST APPROACH:

• Comparison of the statistics of the indicators and test the significance of the changes.

• Used by Megginson et al (1994) & La Porta and Lopez-de-Silanes (1999)

SECOND APPROACH:

• Fixed Effects models with a non-spherical errors correction.

• Used by Frydman et al (1999), Ros (1999) & Ros and Banerjee (2000)

Empirical Analysis: First Approach

Mean and Median Analysis (Megginson’s approach):

• Test the significance of the statistics before vs after for the change in growth in Outputs, Inputs, Labor productivity, Efficiency, Quality, Coverage and Prices.

• Pros: Provides good intuition of what’s going on.

• Cons: Does not allow to control for other factors such as initial conditions and firm specific time trend.

Table 3a: Electricity Distribution[In levels]

Variable stats

Preprivat Transition Postprivat (2)-(1) (3)-(2) (3)-(1) (2)-(1) (3)-(2) (3)-(1)(1) (2) (3) (4) (5) (6) (7) (8) (9)

OutputsResidential mean 85.83 102.26 120.48 17.32 17.11 35.16 -16.209*** -17.493*** -16.809***Connections p50 85.94 102.00 119.59 17.11 16.55 34.33 -7.843*** -7.306*** -7.459***

sd 9.20 2.53 10.04 9.68 8.76 16.94N 82 116 74 82 74 71

MWH sold mean 82.29 102.67 119.22 20.82 15.60 36.74 -13.119*** -11.882*** -7.554***per year p50 82.59 101.20 117.13 19.88 15.17 34.60 -7.399*** -6.945*** -6.128***

sd 14.11 6.44 21.12 14.28 17.77 25.69N 81 116 74 81 74 69

InputsNumber of mean 162.71 100.65 86.59 -61.37 -14.27 -78.19 8.949*** 8.678*** 5.432***Employees p50 147.46 100.00 86.17 -48.38 -14.76 -63.63 6.252*** 5.903*** 5.057***

sd 54.42 6.76 23.63 52.22 20.18 63.71N 58 116 59 58 59 50

EfficiencyConnections mean 60.24 103.33 147.42 45.38 40.83 88.62 -14.738*** -13.344*** -9.334***per employee p50 59.90 100.00 135.26 44.65 32.10 88.86 -6.543*** -6.093*** -6.438***

sd 18.65 9.86 42.10 23.25 33.31 46.49N 57 116 58 57 58 49

GWH per mean 58.56 103.97 145.09 47.50 37.64 86.27 -17.097*** -11.362*** -6.901***employee p50 59.68 100.00 129.76 46.04 26.76 71.15 -6.567*** -6.093*** -6.182***

sd 18.58 11.98 53.86 20.98 41.54 53.15N 57 116 58 57 58 49

Distributional mean 112.19 98.73 87.78 -12.92 -9.75 -25.14 3.658*** 4.657*** 3.515***losses p50 104.37 100.00 85.34 -6.13 -11.06 -19.93 3.268*** 4.272*** 3.341***

sd 26.96 7.33 26.03 27.14 21.12 37.79N 59 116 58 59 58 49

* significant at 10%; ** significant at 5%; *** significant at 1%

Mean Diff in LevelsT-stat (Z-stat) for difference

in means (medians) in Levels

Table 3a: Electricity Distribution[In levels]

[Cont.]Variable stats


QualityDuration of mean 134.49 100.34 72.42 -30.61 -25.32 -41.34 3.250*** 2.687*** 3.782***Interruptions p50 123.37 100.00 65.42 -24.11 -30.41 -34.37 3.477*** 3.143*** 4.019***per year sd 67.57 20.00 42.58 57.28 41.80 75.35per consumer N 37 116 39 37 39 24Frequency of mean 132.59 98.63 82.71 -34.90 -13.65 -31.66 4.256*** 1.300 1.078Interruptions p50 119.54 100.00 67.96 -21.20 -29.20 -32.86 3.809*** 3.571*** 4.326***per year sd 57.83 13.77 93.00 49.88 79.05 119.29per consumer N 37 116 39 37 39 24

CoverageResidential mean 94.93 101.17 110.66 6.93 8.67 16.46 -6.886*** -8.162*** -8.333***Connections p50 95.35 100.00 108.92 5.60 7.62 14.16 -6.016*** -6.110*** -6.323***per 100 HHs sd 7.91 2.22 10.09 8.42 8.26 15.09

N 70 116 63 70 63 56Prices

Avg Tariff per mean 106.24 98.48 94.87 -9.49 -2.88 -9.91 3.305*** 2.808*** 1.313*residential GWH p50 97.85 100.00 95.61 -0.09 -1.38 -16.37 2.437** 2.690*** 1.702*(in dollars) sd 23.68 7.52 24.63 23.85 18.73 26.18

N 69 116 73 69 73 55Avg Tariff per mean 91.77 100.81 109.61 9.21 8.46 17.90 -5.164*** -5.143*** -5.067***residential GWH p50 88.27 100.00 107.07 15.25 4.64 24.26 -4.774*** -4.181*** -4.643***(in real local sd 12.83 4.97 18.59 14.81 14.27 25.81currency) N 69 116 73 69 73 55


Mean Diff in LevelsT-stat (Z-stat) for difference

in means (medians) in Levels

Table 3b: Electricity Distribution[In growth rates]

Variable stats


OutputsResidential mean 4.3% 5.5% 3.4% 1.3% -2.8% -0.8% -1.787** 3.590*** 1.976**Connections p50 4.4% 4.7% 3.2% 0.4% -1.7% -1.0% -1.456 5.116*** 2.366**

sd 2.6% 5.5% 2.0%N 79 84 60 79 60 56

MWH sold mean 6.7% 6.7% 3.2% -0.5% -5.0% -3.2% 0.616 3.085*** 3.362***per year p50 6.6% 5.9% 2.8% -0.7% -2.9% -2.7% 0.708 4.096*** 3.159***

sd 4.5% 8.7% 4.7%N 74 85 57 74 57 51

InputsNumber of mean -6.6% -9.9% -2.1% -3.2% 9.7% 2.1% 2.056* -5.398*** -1.519*Employees p50 -6.1% -9.0% -1.8% -3.8% 8.7% 4.0% 2.306** -4.505*** -1.776*

sd 8.1% 10.0% 4.8%N 53 69 44 53 44 32

EfficiencyConnections mean 13.4% 18.4% 5.5% 4.2% -16.4% -4.2% -1.813** 5.691*** 2.183**per employee p50 11.1% 14.0% 5.6% 4.5% -10.6% -3.5% 2.333** 4.975*** 2.300**

sd 12.6% 16.8% 5.1%N 53 66 43 53 43 32

GWH per mean 15.1% 20.3% 5.5% 3.7% -19.9% -6.7% 1.426* 6.539*** 2.826***employee p50 12.8% 15.0% 4.0% 3.0% -16.4% -6.3% -1.624 5.084*** 3.011***

sd 13.5% 16.9% 7.6%N 53 66 43 53 43 32

Distributional mean 0.6% -5.5% -1.3% -4.7% 6.4% -2.0% 3.301*** -3.474*** 0.960losses p50 0.1% -4.9% -0.1% -4.5% 6.5% -1.5% 3.317*** -2.944*** 0.786

sd 7.8% 10.2% 9.6%N 57 73 46 57 46 36


Avg. Annual Growth Annual Diff in growthT-stat (Z-stat) for difference

in means (medians) in growth

Table 3b: Electricity Distribution[in growth rates]

[Cont.]

Variable stats


QualityDuration of mean 4.1% -9.8% -3.8% -11.2% 3.4% -10.5% 1.788* 4.476*** 5.122***Interruptions p50 -5.2% -12.9% -3.2% -7.0% 8.5% -5.1% 2.132** -0.749 0.711per year sd 31.6% 25.7% 24.8%per consumer N 32 51 26 32 26 11Frequency of mean 2.7% -10.6% -11.4% -11.1% -2.9% -17.8% 1.653* 0.378 3.093***Interruptions p50 -5.0% -10.8% -6.6% -2.8% -2.4% -14.4% 1.664* -0.165 2.490**per year sd 29.0% 20.3% 20.5%per consumer N 32 51 26 32 26 11

CoverageResidential mean 2.0% 2.2% 1.9% 0.4% -1.0% -0.6% -0.903 1.702** 0.780Connections p50 1.5% 1.9% 1.3% 0.4% -0.9% -0.3% -1.408 3.186*** 0.619per 100 HHs sd 3.9% 3.0% 3.6%

N 65 76 50 65 50 42Prices

Avg Tariff per mean 9.3% -3.3% 2.0% -15.2% 4.3% -11.4% 6.251*** -'1.821** 3.172***residential GWH p50 9.7% -6.3% 0.1% -15.1% 1.3% -13.1% 5.329*** -1.442 2.785***(in dollars) sd 16.0% 9.0% 14.1%

N 59 86 57 59 57 35Avg Tariff per mean 10.2% 2.0% 0.6% -7.8% 0.2% -12.3% 4.744*** -0.172 4.899***residential GWH p50 5.9% 2.3% 1.8% -5.3% 0.9% -9.7% 4.454*** -0.734 4.063***(in real local sd 12.6% 7.3% 7.9%currency) N 59 86 56 59 56 35




Empirical Analysis: 2nd ApproachFixed Effects Models:

Yijt = Outcome of interest for firm i, in country j for the year t.

DUMM_TRANijt = Dummy with value 1 for the years since the announcement and after 1 year following the change in ownership (-2<=Sijt<=+1).

DUMM_POSTijt = Dummy with value 1 for the years following the transition (Sijt>=+2).

Dij = Firm Fixed Effect.vijt = Error term.

tij = Firm specific time trend.

ij

ijtijijijtP

ijtT

ijt DPOSTDUMMYTRANDUMMYy __ln

ij

ijtij

ijijijijijtP

ijtT

ijt tDPOSTDUMMYTRANDUMMYy __ln

Table 6: Electricity Distribution

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11)Number of Connect's

Energy Soldper year

Number of Employees

Connect'sper

employee

Energy peremployee

DistributLosses

Duration ofinterrupt's

Frequency of

interrupt's

Coverage Avg price perMWH

(in dollars)

Avg price perMWH (in real

local currency)

Model 1: Log levels without firm-specific time trendTransition 0.150*** 0.201*** -0.307*** 0.442*** 0.474*** -0.031** -0.144*** -0.107*** 0.053*** -0.013 0.105*** (-2<=t<=1) (0.005) (0.007) (0.016) (0.019) (0.021) (0.013) (0.028) (0.025) (0.004) (0.018) (0.008)Post Transition 0.326*** 0.370*** -0.500*** 0.810*** 0.819*** -0.172*** -0.488*** -0.415*** 0.130*** -0.041** 0.177*** (t>=2) (0.006) (0.008) (0.018) (0.021) (0.023) (0.014) (0.031) (0.028) (0.004) (0.019) (0.009)Observations 823 808 586 575 570 614 376 377 698 687 685Log Likelihood 1082.0 839.9 217.8 180.7 149.4 407.9 29.0 83.0 1185.8 315.2 677.4

Model 2: Log levels with firm-specific time trendTransition -0.002 0.040*** -0.054*** 0.049*** 0.086*** 0.021 0.068** 0.076*** -0.007*** 0.078*** 0.034*** (-2<=t<=1) (0.002) (0.005) (0.013) (0.012) (0.017) (0.013) (0.033) (0.029) (0.002) (0.012) (0.008)Post Transition 0.007** 0.026*** -0.007 0.013 0.006 -0.018 -0.047 -0.043 0.002 0.114*** 0.041*** (t>=2) (0.003) (0.009) (0.022) (0.022) (0.030) (0.023) (0.055) (0.047) (0.003) (0.018) (0.012)Observations 823 808 586 575 570 614 376 377 698 687 685Log Likelihood 2214.9 1415.6 723.3 623.4 541.9 736.9 138.8 230.7 1898.1 659.0 1046.0

Model 3: GrowthTransition 0.005*** 0.021*** -0.057*** 0.073*** 0.075*** -0.016 -0.065* -0.031 -0.002 -0.094*** -0.044*** (-2<=t<=1) (0.002) (0.006) (0.012) (0.012) (0.016) (0.016) (0.035) (0.037) (0.002) (0.014) (0.010)Post Transition 0.006* 0.016 -0.021 0.053** 0.024 0.026 -0.032 -0.029 -0.003 0.019 0.022 (t>=2) (0.003) (0.010) (0.020) (0.021) (0.028) (0.027) (0.059) (0.061) (0.002) (0.021) (0.015)Observations 803 783 566 557 554 592 339 341 669 633 631Log Likelihood 1999.6 1265.7 575.0 486.7 401.5 486.6 16.6 56.1 1667.1 379.3 750.2

Standard errors in parentheses* significant at 10%; ** significant at 5%; *** significant at 1%

Conclusions

• Changes in ownership generated significant improvements in labor productivity, efficiency, and product/service quality.

• We also observed that for telecommunications there were significant improvements in output and coverage.

• Competition (for Telecom) affects mainly reducing the level of my price indicators, not the other variables.

• Most of the improvements happens during the transition period (-1,+1years). In the post-transition period the improvements are modest.

• Results are remarkably heterogeneous across firms.

RENEGOTIATION THE NORM RATHER THAN THE

EXCEPTION

• Very high incidence 52% of concessions have been renegotiated

• Very quickly, on average 2.1 years after the award

• CORRELATION BETWEEN RENEGOTIATION AND PROFITABILITY

• CORRELATION BETWEEN AGGRESIVE BIDDING AND RENEGOTIATION

• AGGRESSIVE BIDDING-LOW PROFITABILITY-RENEGOTIATION

• AGGRESSIVE BIDDING: PQ-OC-T-D<rK • FINANCIAL EQUILIBRIUM ISSUE

CORRELATION BETWEEN RENEGOTIATION INCIDENCE AND PROFITABILITY: Average Profitability by Sector of

Privatized and Concessioned Firms and the Cost of Equity in Latin American and Caribbean Countries, 1990-2002(percent)

Sector

IRR (adjusted) a

Initial Cost of Equityb

Telecommunications 21.0 14

Water and Sanitation 11.0 15.5

Energy 14.5 14

Transport 11.5 13.5

a. The IRR has been adjusted to incorporate management fees. b. Cost of equity is evaluated at the time of the transaction.

Contract Award Processes for Concessions in Latin America and the Caribbean by Sector, mid-1980s–

2000 Award process

Telecom

Energy

Transport

Water and

sanitation

Total

Share of total (percent)

Competitive bidding

245 95 231 125 696 78

(46% renegotiated)

Direct adjudication (bilateral negotiation)

15 143 37 4 199 22

(8% renegotiated)

Total 260 238 268 129 895 100

Source: Guasch (2004)

Distribution of Concessions by Type of Regulation

Price Caps 56%

Rate of Return 20%

Hybrid* 24%

*Hybrid regimes are defined when, under a price cap regulatory regime, a large number of costs components are allowed automatic pass through into tariff adjustments

Source: Author’ s calculations

Distribution of Concessions by Existence of Investment Obligations in Contract

Investment Obligations in Contract 73%

No Investment Obligations in Contract but Performance Indicators

21%

Hybrid 6%

Source: Author’s calculations

Typology of Renegotiation

Initiated by Government

Opportunistic (politically)

Change in priorities

Initiated by Operator

Opportunistic (rent seeking)

Shock related

Ambiguous

Who initiated the Renegotiation?(% of total requests)

Both

Government

and Operator

Government

Operator

All sectors 13% 26% 61%

Water and

Sanitation

10% 24% 66%

Transport 16% 27% 57%


Who Initiated the Renegotiation Conditioned on Regulatory Regime?

(% of Total Requests)

Both Government

and Operator

Government

Operator

All sectors

Price Caps 11% 6% 83%

Rate of Return 39% 34% 26%

Hybrid Regime 30% 26% 44%


Common Outcomes of the Renegotiation Process

Percentage of renegotiated concession contracts with

that outcome Delays on Investment Obligations Targets 69% Acceleration of Investment Obligations 18% Tariff Increases 62% Tariff Decreases 19% Increase in the number of cost components with automatic pass-through to tariff increases

59%

Extension of Concession Period 38% Reduction of Investment Obligations 62% Adjustment of canon-annual fee paid by operator to government Favorable to operator Unfavorable to operator

31% 17%

Changes in the Asset-Capital Base Favorable to Operator Unfavorable to Operator

46% 22%

Source: Guasch (2004)

ISSUES ON RENEGOTIATION

• FINANCIAL EQUILIBRIUM• SANCTITY OF THE BID: R= PQ-0C-T-D<rKi• REGULATORY ACCOUNTING• INFORMATIONAL ASSYMETRIES• CONTINGENT EVENTS TO TRIGGER

RENEGOTIATION• CREDIBLE COMMITMENT TO DEMANDS OF

OPPORTUNISTIC RENEGOTIATION EVEN IF IT IMPLIES ABANDONMENT OF CONCESSION BY OPERATOR

Conclusion

• Positive Effects

• Better than counterfactual

• Benefits could have been larger with better concession and regulatory design

• Overall: approach and model correct, faulty implementation

Dependent Variable and Sample Output Value-Added

Topic** Relevant Explanatory Variables All Large Small All Large Small

1

Macroeconomic instability (inflation, exchange rate): degree of obstacle* -4.4 -22.3 -31.5 -59.7 -23.5

1 Exporters who just started (vs. non-exporter) 15.8 17.3 15.7 34.2 21.0 35.9 1 Exported for 1 year (vs. non-exporter) 13.1 17.7 5.3 31.7 24.3 25.3 1 Exported for 2 year (vs. non-exporter) 11.3 2.8 6.7 29.2 14.1 25.7 1 Exported for 3+ years (vs. non-exporter) 9.7 9.1 7.4 26.8 8.3 33.4 1 Main competitor-imports (vs. not) 7.2 13.2 3.9 2.8 1 % inputs that are imported 0.2 0.3 1 Delays of imports in customs, average days -0.4 -0.2 -0.7 -0.7 -0.3 -1.3 1 Received municipal investment incentives (vs. not) -2.2 -18.1 5.8 20.8 1 Received state investment incentives (vs. not) 2.4 10.5 -1.4 4.5 -15.4 1 Received federal investment incentives (vs. not) -2.9 -5.4 1 Received tariff exemptions (vs. not) 6.3 3.0 20.0 1 Monopoly (0 competitors, vs. > 2) -11.9 -17.7 41.1 -25.4 -15.4 26.3 1 Monopoly (1-2 competitors, vs. > 2) -13.3 -15.2 -17.2

BrazilAverage Coefficients (% Changes in TFP corresponding to a Unit Increase in

the Explanatory Variable, ceteris paribus) for Selected Variables, by Groups of TFP Models and Firm’s Size



2 Firm was a bidder on a government contract (vs. not) -1.1 2.4 -6.7

2 At least 10% of sales to the government (vs. not) -3.2 3.9 9.3

2 Number times asked for bribe: infrastructure -11.8 15.9 -30.9 2 Number times asked for bribe: inspections -7.5 -3.4 2 Number of inspection visits -0.1 -0.1 -0.3 2 Managerial time spent on regulation, % -0.3 -0.5 2 Perception index: public service -8.1 2 Legal case open (vs. not) 1.8 8.7 27.8 1.7 2 Perception index: quality of Judiciary* -5.9 8.1 -14.6 13.6 2 Access to land: degree of obstacle* -3.7


the Explanatory Variable, ceteris paribus) for Selected Variables, by Groups of TFP Models and Firm’s Size (con’t)




Topic** Relevant Explanatory Variables All Large Small All Large Small 3 Have or share electricity generator (vs. not) 6.1 13.9 21.0 33.3 3 Power interruptions: index* -6.3 -9.9 -5.3 -12.9 -14.9 -11.8 3 Communications interruptions: index* -1.6 -3.7 -6.2 -17.0 3 Transport interruptions: index* -8.3 -27.8 -3.2 -12.8 -31.4 3 Wait time for a phone connection, days 0.0 -0.1 0.0 -0.1 -0.1 3 Wait time for a power connection, days 0.1 0.0 -0.1 0.1 -0.1 3 Perception index: infrastructure as obstacle* -7.9 -10.0 -10.1 -14.3 -6.4 -19.5




Topic** Relevant Explanatory Variables All Large Small All Large Small 4 % staff using computers 0.4 0.1 0.4 0.9 0.5 1.0

4

New technology acquired with help from consultants (vs. simple purchase of new machinery, etc.) 11.7 -8.7 19.6 21.8 38.2

4

New technology acquired by hiring key personnel (vs. simple purchase of new machinery, etc.) 1.4 23.6

4 Quality certification (vs. no certification) 8.0 2.2 11.6 9.2 17.4

4 Agreed a new joint venture with a foreign partner (vs. not) 1.6 2.5 15.3 17.5

4 Obtained a new licensing agreement (vs. not) 8.8 13.2 9.6



5 Manager has at least some college education (vs. without) 9.8 9.8 9.9 21.2 20.8 21.8

5 Manager experience, years 0.3 0.1 0.3 0.7 0.2 0.8 5 Labor force with incomplete high school, % 0.0 0.0 -0.2 -0.3 -0.2 5 External training offered (vs. not) 3.0 3.4 11.5 3.4 11.5

5 % informal workers among full-time employees -0.1 -0.1 -0.3 -0.3

5 % informal workers among part-time employees 0.1 0.0 0.2 0.2

5 Optimal employment is lower than existing (vs. optimal) -4.6 -3.5

5 Optimal employment is higher than existing (vs. optimal) -4.6 -7.0 -10.7






Topic** Relevant Explanatory Variables All Large Small All Large Small 6 Family business (vs. not) -2.1 -15.0 -25.5 -5.5

6 Source of pressure to reduce costs: shareholders (vs. competitors) 8.2 7.2 30.2 50.7

6 Source of pressure to reduce costs: creditors (vs. competitors) 9.5 12.2 29.0 39.4

6 Has bank loan (vs. not) 4.2 10.4 13.3 6 Did not need bank loan (vs. applied and failed) 12.0 16.3 3.5 29.3 30.0 25.6

6 Needed bank loan, but did not apply (vs. applied and failed) 3.2 15.9 15.7

6 Number banks engaged with 0.9 1.4 0.5 3.5 3.9 3.9 6 Hired external auditing (vs. not) 2.2 3.3 11.7 6 Does project assessment (NPV, etc. - vs. not) 8.0 -0.9 20.5 -3.5 7 Loss due to theft, % sales -1.2 -1.8 -1.0 -3.3 -5.1 -3.0

Notes: Empty cells correspond to zero coefficients. *Index, 0-4 scale. **Topics: 1- Macroeconomic instability, Globalization and Competition; 2- Governance, Red Tape, and Corruption; 3- Transport and Infrastructure; 4- Investment, Technology and Innovation; 5- Labor Market and Human Capital Development; 6- Finance and Corporate Governance; and, 7- Crime.

Composition of Private Flows to Infrastructure by Sector for Selected Countries ( %)

0%10%20%30%40%50%60%70%80%90%

100%

Perc

enta

ge o

f private

investm

ent

Telecom Energy Transport Water and sew erage

Source: PPI database.

BRAZIL: Productivity Effects of Counterfactual Improvements in Investment Climate Indexes (from the 25th to 75th Percentiles)

Investment Climate Index

Year of Entry into Exports

One Year After First

Exports

Two Years After First

Exports

Three or More Years

After First Exports

Average Exporters Average

Non-exporters

Difference (exporters minus non-exporters)

Infrastructure

Exporter Premium at 25th Percentile 44.52% 35.17% 22.63% 37.62%


Change in Exporter Premium (from 25th to 75th percentile)

16.53% 11.67% 18.39% 7.78%

Change in Productivity (25th to 75thperc.) 5.44% -5.13% 10.57% Governance Exporter Premium at 25th Percentile 34.04% 28.55% 31.16% 36.16% Exporter Premium at 75th Percentile 63.67% 48.01% 26.16% 43.57%


29.63% 19.47% -5.00% 7.41%

Change in Productivity (25th to 75th perc.) 15.63% 5.86% 9.78% Access to Finance




50.10% 28.84% 15.40% 1.57%

Change in Productivity (25th to 75th perc.) 14.16% 2.22% 11.95% Skills and Technology




-1.30% 26.95% 0.00% -6.13%

Change in Productivity (25th to 75th perc.) 26.07% 27.70% -1.63% Total Change in Exporter Premium (25th to 75th percentile of all investment climate ind.) 94.96% 86.92% 28.79% 10.62% Total Change in Productivity (25th to 75th percentile of all investment climate indexes) 61.31% 30.64% 30.66%

Reported exporter premiums are calculated using coefficients from columns (6) from Table 10.6 by multiplying the interactives of investment climate indexes with EXP0, EXP1, EXP2 and EXP3 by the 25th and 75th percentiles of the indexes, and adding the result to the coefficients on EXP0, EXP1, EXP2 and EXP3. The average change in productivity for non-exporters is calculated by multiplying the coefficients on the free-standing investment climate indexes (in column (6) of Table 10.6) by the counterfactual changes in those indexes. In the case of exporters, the productivity effect obtained for non-exporters is added to the weighted average of the changes in exporters premia calculated by years since first exports.

BRAZIL: Changes in Total Exports Due to Productivity Increases Assuming Counterfactual Improvements From 25th To 75th Percentiles In Investment

Climate Indexes

After One Year After Five Years (*) After Ten Years(*) Sources of Productivity Increases

Incumbents

Entrants

Total

Incumbents

Entrants

Total

Incumbents

Entrants

Total

Infrastructure Index 0.18% -0.03% 0.16% 0.91% -0.40% 0.51% 1.83% -1.47% 0.36% Governance Index 0.38% 0.02% 0.39% 1.90% 0.23% 2.13% 3.79% 0.85% 4.64% Access to Finance Index 0.40% 0.02% 0.42% 2.00% 0.30% 2.29% 3.99% 1.08% 5.08% Skills/Technology Index 0.58% 0.13% 0.71% 2.90% 1.91% 4.81% 5.80% 7.00% 12.80% Total All Indexes 1.54% 0.14% 1.68% 7.71% 2.04% 9.74% 15.41% 7.46% 22.88% Assuming 50% Productivity Increase for Exporters and Non-exporters 1.13% 0.23% 1.36% 5.64% 3.52% 9.16% 11.28% 12.91% 24.20%

Reported increases in exports are calculated as follows. For incumbent exporters, the marginal effect on export shares (conditional on being uncensored) of the sales per worker variable in column (4) of Table 10.3 (2.29%) is multiplied by the counterfactual productivity increases calculated in Table 10.7, and then by the share of incumbent exporters in total exports (98.4%). For entrants into exports, the coefficient on the sales per worker variable in column (6) of Table 10.3 is divided by the average entry rate in to exports in the sample (2.82%), and then multiplied by the counterfactual productivity increases calculated in Table 10.7 and by the share of entrants in total exports (1.6%). (*) The figures represent cumulative increases in exports with respect to the total value of exports in the initial year.

Table 4a: Water and Sewerage[In levels]

Variable stats


OutputsResidential Water mean 85.85 103.15 119.74 16.20 16.31 29.43 -10.988*** -8.762*** -12.059***Connections p50 87.37 102.61 117.09 15.18 13.88 28.10 -4.197*** -5.086*** -3.724***

sd 6.32 3.72 13.17 7.07 10.85 10.35N 23 49 34 23 34 18

Residential Sewer mean 84.88 102.75 122.59 18.83 19.43 32.90 -7.932*** -8.950*** -9.735***Connections p50 85.48 101.89 119.62 18.62 17.46 29.38 -3.883*** -4.937*** -3.408***

sd 11.21 5.02 15.08 10.62 12.28 13.09N 20 49 32 20 32 15

Cubic Meter of mean 99.98 103.62 97.27 2.21 -2.91 -1.33 -0.745 1.416* 0.299produced water p50 100.99 100.00 99.04 1.95 -0.72 3.15 -0.879 1.078 -0.973

sd 8.89 22.20 14.80 11.88 11.45 16.60N 16 49 31 16 31 14

InputsNumber of mean 141.43 103.97 92.35 -37.20 -12.18 -57.36 3.961*** 3.668*** 4.766***Employees p50 125.11 100.00 97.04 -21.34 -8.36 -52.01 3.527*** 3.339*** 3.237***

sd 49.22 14.22 23.85 38.72 17.26 46.62N 17 49 27 17 27 15

EfficiencyWater Connectionsmean 70.50 103.34 144.11 36.53 38.73 83.86 -9.979*** -4.201*** -5.177***per employee p50 68.46 100.00 125.05 36.39 20.71 69.30 -3.621*** -4.532*** -3.408***

sd 18.93 12.65 59.84 15.09 48.79 62.73N 17 49 28 17 28 15

Distributional mean 107.22 100.02 82.08 -8.70 -18.26 -23.18 2.577** 3.755*** 3.110***losses p50 106.01 100.00 81.64 -8.33 -16.63 -20.12 2.327** 3.254*** 2.605***

sd 16.43 7.42 21.22 13.51 23.33 27.88N 16 49 23 16 23 14


Mean Diff in LevelsT-stat (Z-stat) for difference in means (medians) in levels


[Cont.]

Variable stats


QualityContinuity mean 78.34 101.01 116.79 21.81 14.94 21.66 -1.781* -2.748*** -1.330(hs per day) p50 97.11 100.00 104.35 2.48 2.17 4.05 -2.192** -2.774*** -1.971**

sd 37.52 4.68 24.68 36.74 21.06 46.07N 9 49 15 9 15 8

% of the samples mean 88.35 100.30 103.89 11.55 2.58 4.94 -1.250 -2.088** -1.682*that passed the p50 99.50 100.00 100.51 0.58 0.46 1.08 -1.630 -2.603*** -1.941*potability test sd 27.92 1.53 6.87 26.14 4.62 7.20

N 8 49 14 8 14 6Coverage

Residential Water mean 94.25 101.84 111.12 6.52 8.71 10.37 -4.498*** -4.379*** -4.478***Connections p50 95.13 100.00 106.88 4.86 5.26 8.76 -4.107*** -4.584*** -3.823***per 100 HHs sd 5.70 3.96 14.11 6.80 10.71 10.10

N 22 49 29 22 29 19Residential Sewer mean 91.47 101.77 110.03 10.23 8.67 13.59 -4.539*** -3.981*** -5.277***Connections p50 91.72 100.00 106.87 8.02 5.76 8.98 -3.479*** -3.920*** -3.180***per 100 HHs sd 8.76 6.88 11.55 9.29 9.74 9.29

N 17 49 20 17 20 13




[Cont.]

Variable stats


PricesAvg price per cub. mean 93.62 101.39 106.70 10.43 1.46 40.24 -0.635 -0.173 -2.261**meter of water p50 87.95 100.00 98.60 11.81 3.27 32.70 -1.274 -0.314 -2.240**(in dollars) sd 43.54 9.53 37.16 51.89 30.57 50.34

N 10 49 13 10 13 8Avg price per cub. mean 84.00 103.53 130.09 25.70 17.68 57.87 -2.478** -2.903*** -4.150***meter of water p50 82.76 100.00 121.21 22.22 19.65 44.80 -1.988** -0.411** -2.521**(in real local sd 23.18 11.71 32.81 32.80 21.96 39.44currency) N 10 49 13 10 13 8Avg price per cub. mean 114.61 100.53 107.79 -19.43 0.03 44.29 0.375 0.001 -0.835meter of sewer p50 79.43 100.00 107.68 16.46 -12.60 44.29 0.000 0.365 -0.447(in dollars) sd 89.74 6.94 32.73 89.77 35.56 75.05

N 3 49 4 3 4 2Avg price per cub. mean 93.06 101.80 152.44 13.26 32.25 53.34 -0.512 -3.012** -37.266***meter of sewer p50 74.75 100.00 135.93 30.91 33.12 53.34 -0.535 -1.826* -1.342(in real local sd 45.93 10.88 51.26 44.86 21.42 2.02currency) N 3 49 4 3 4 2



Table 4b: Water and Sewerage[In growth rates]

Variable stats


OutputsResidential Water mean 4.4% 6.5% 4.7% 0.9% -1.9% 1.5% -1.095 1.649* -1.113Connections p50 4.1% 5.2% 3.8% -0.1% -1.8% 1.2% -0.923 2.229** -0.943

sd 3.0% 4.4% 4.6% 3.5% 5.6% 3.2%N 17 43 24 17 24 6

Residential Sewer mean 3.8% 6.7% 7.4% 3.1% 1.5% 0.0% -1.222 -0.569 0.009Connections p50 4.3% 5.5% 3.6% 2.1% -1.4% 0.1% -0.966 0.693 -0.135

sd 5.9% 6.8% 10.7% 9.8% 12.3% 3.2%N 15 40 23 15 23 5

Cubic Meter of mean 2.1% 7.5% 0.5% -0.9% -1.8% 1.6% 0.741 1.117 -0.718produced water p50 1.6% 1.0% 0.9% 0.0% 0.0% 1.5% 0.000 0.817 -0.674

sd 4.6% 38.6% 5.0% 4.1% 7.3% 5.0%N 12 38 21 12 21 5

InputsNumber of mean -0.4% -10.0% -1.5% -9.6% 7.5% -1.0% 3.425*** -3.460*** 0.309Employees p50 0.1% -8.3% -1.0% -9.8% 7.8% -1.4% 2.432*** -2.765*** 0.135

sd 4.2% 10.2% 7.2% 9.7% 9.2% 7.4%N 12 32 18 12 18 5

EfficiencyWater Connectionsmean 5.5% 17.5% 7.3% 11.6% -9.6% 1.2% -3.068*** 2.939*** -0.348per employee p50 4.9% 15.8% 4.5% 9.9% -7.8% 0.1% 2.551** 2.656 0.105

sd 5.4% 13.5% 10.1% 13.7% 14.3% 8.3%N 13 32 19 13 19 6

Distributional mean -3.1% -0.6% -5.5% 0.5% 0.5% 0.6% -0.297 -0.310 -0.363losses p50 -2.6% -2.0% -5.1% -0.1% 0.3% 0.8% -0.267 -0.450 -0.843

sd 3.8% 21.5% 9.1% 5.3% 6.2% 4.0%N 11 26 17 11 17 6




Table 4b: Water and Sewerage[in growth rates]

[Cont.]

Variable stats


QualityContinuity mean 0.0% 7.2% 4.6% 22.4% -0.1% 0.0% -1.000 0.057 -(hs per day) p50 0.0% 0.0% 0.9% 0.0% 0.0% 0.0% -1.000 0.075 -

sd 0.0% 16.0% 8.7% 38.7% 6.0% .N 3 18 11 3 11 1

% of the samples mean 0.8% 5.2% 0.4% 18.6% -0.5% -1.0% -1.074 1.273 1.000that passed the p50 0.6% 0.2% 0.0% 2.2% 0.0% -1.0% -0.928 1.315 1.000potability test sd 1.0% 16.4% 0.7% 34.6% 1.2% 1.4%

N 4 18 9 4 9 2Coverage

Residential Water mean 1.0% 4.1% 3.3% 1.1% -1.3% 0.4% -2.050** 0.914 -0.570Connections p50 0.3% 2.8% 1.6% 0.2% -1.3% 0.1% -1.448 1.690* -0.944per 100 HHs sd 1.7% 5.0% 4.4% 2.1% 6.1% 1.7%

N 16 34 19 16 19 5Residential Sewer mean 1.6% 8.0% 2.8% 2.9% -0.9% -1.6% -1.815 0.529 2.735**Connections p50 1.4% 2.9% 0.6% 0.1% -1.6% -0.9% -1.036 1.601 2.023**per 100 HHs sd 17.9% 17.9% 6.1% 6.0% 6.2% 1.3%

N 14 25 14 14 14 5




Table 4b: Water and Sewerage[in growth rates]

[Cont.]

Variable stats


PricesAvg price per cub. mean 12.2% 1.9% -3.4% -12.1% -7.2% -3.9% 2.493** 0.835 0.666meter of water p50 10.9% -2.2% -1.1% -13.8% -3.3% -2.1% 1.820* 0.889 0.535(in dollars) sd 10.4% 22.2% 20.0% 13.8% 26.0% 10.1%

N 8 17 9 8 9 3Avg price per cub. mean 10.1% 9.4% 4.5% -6.0% -8.9% -0.8% 2.078** 1.060 0.346meter of water p50 10.1% 5.4% 2.6% -4.3% -6.5% -2.5% 1.540 1.007 0.000(in real local sd 6.7% 18.4% 10.0% 8.1% 25.1% 4.0%currency) N 8 17 9 8 9 3Avg price per cub. mean -0.6% -5.1% -7.9% 2.3% -6.4% -7.7% -0.298 0.799 -meter of sewer p50 -0.6% -8.7% -7.9% 2.3% -10.8% -7.7% -0.447 1.069 -(in dollars) sd 17.1% 16.1% 11.6% 10.8% 13.9% .

N 2 5 3 2 3 1Avg price per cub. mean -1.1% 7.0% 9.7% 5.0% -4.3% -15.1% 3.881* 0.302 -meter of sewer p50 -1.1% 1.4% 9.8% 5.0% -18.4% -15.1% -1.342 0.000 -(in real local sd 13.9% 13.5% 16.0% 1.8% 24.7% .currency) N 2 5 3 2 3 1




Table 5a: Fixed Telecomm[In levels]

Variable stats


OutputsTotal number mean 78.98 115.39 181.31 36.41 65.70 102.77 -10.022*** -8.627*** -6.742***of lines p50 76.93 112.16 178.47 33.90 67.92 93.40 -3.516*** -3.408*** -3.408***

sd 12.55 13.76 48.91 14.53 37.74 46.14N 16 16 15 16 15 15

Total number mean 107.32 103.05 146.89 0.82 41.13 69.57 -0.049 -3.973* -19.420**of minutes p50 97.39 100.00 146.89 9.05 41.13 69.57 0.105 -1.342 -1.342

sd 41.60 5.04 8.32 40.84 3.00 24.76N 6 16 2 6 2 2

InputsNumber of mean 117.88 100.72 82.02 -17.12 -18.37 -37.18 2.213** 2.671*** 2.675***employee p50 111.71 100.28 81.31 -22.64 -20.05 -50.94 1.761* 2.166** 2.291**

sd 30.44 7.88 29.61 29.96 25.70 52.09N 15 16 14 15 14 14

EfficiencyTotal number mean 72.98 119.54 262.84 47.86 140.97 191.73 -4.972*** -5.262*** -4.957***of lines per p50 70.13 110.66 217.38 38.93 102.05 154.59 -3.237*** -3.233*** -3.233***employee sd 24.63 26.54 126.18 37.28 106.41 136.35

N 15 16 14 15 14 14Total number mean 79.81 105.38 238.94 34.53 123.54 172.50 -2.879** -2.059 -1.486of minutes per p50 76.03 100.00 238.94 44.60 123.54 172.50 -1.782* -1.342 -1.342employee sd 22.83 12.63 135.73 29.38 117.59 118.47

N 6 16 2 6 2 2Percentage of mean 580.77 141.09 101.20 -368.95 -93.78 -472.93 1.050 1.098 1.378Incomplete Calls p50 111.56 100.00 74.51 -17.23 -27.47 -37.37 1.782* 2.201** 2.366**

sd 1133.58 167.34 74.92 860.92 180.06 1055.53N 6 16 7 6 7 6


T-stat (Z-stat) for difference in means (medians) in levels

Mean Diff in Levels


[Cont.]

Variable stats


QualityPercentage of mean 68.64 116.56 199.92 51.75 81.00 138.97 -4.407*** -2.964*** -2.339**Digitalized p50 70.82 107.27 136.01 41.82 29.26 78.72 -3.180*** -3.180*** -3.129***Network sd 22.80 31.58 161.58 42.33 129.55 169.03

N 13 16 14 13 14 13Coverage

Number of Lines mean 83.65 113.47 167.28 29.82 53.25 84.53 -7.573*** -7.708*** -6.025***per 100 HHs p50 80.18 109.18 169.15 28.25 56.28 68.99 -3.516*** -3.408*** -3.351***

sd 12.73 13.75 45.46 15.75 34.23 42.48N 16 16 15 16 15 15



Mean Diff in Levels




PricesAvg Price for a mean 144.83 100.45 99.89 -46.64 -1.03 -58.79 0.718 0.710 0.0583-minute call p50 57.48 99.98 91.72 34.44 -11.25 1.74 -0.866 -0.178 1.255(in dollars) sd 219.85 15.00 63.61 205.46 61.29 248.59

N 10 16 12 10 12 9Avg monthly charge mean 55.46 101.25 143.43 39.02 41.60 105.49 -2.983*** -2.083** -1.295for residential p50 41.00 100.00 120.51 53.32 15.16 43.43 -2.293** -2.073** -0.804Service (in dollars) sd 36.35 19.28 124.99 41.36 115.87 151.92

N 10 16 13 10 13 9Avg Charge for the mean 634.94 123.11 100.51 -502.46 -25.83 -256.72 1.814* 0.777 1.122installation of a p50 95.78 101.06 77.29 11.18 -39.79 8.92 0.051 -0.314 1.376residential line sd 887.73 40.50 108.31 875.99 72.80 808.89(in dollars) N 10 16 10 10 10 6Avg Price for a mean 84.40 100.65 97.58 12.63 -3.46 16.28 -0.711 -0.599 0.2503-minute call p50 64.40 100.00 87.14 30.96 -14.01 25.78 -0.980 -1.120 1.478(in real local sd 50.71 7.71 44.03 50.24 43.72 76.87currency) N 8 16 10 8 10 8Avg monthly charge mean 60.42 100.26 135.11 36.59 34.54 88.96 -2.782** -2.750** -1.654*for residential p50 49.78 100.00 115.76 49.77 16.83 79.48 -2.191** -2.310** -1.334Service (in real sd 35.69 12.69 77.55 41.60 69.27 97.05local currency) N 10 16 11 10 11 9Avg Charge for the mean 842.23 122.99 132.07 -699.77 1.25 -252.68 1.915** 0.692 -0.028installation of a p50 108.37 100.00 58.62 -6.06 -31.83 1.91 0.700 -0.105 0.420residential line (in sd 1045.40 41.81 152.59 1033.62 126.57 894.37real local currency) N 8 16 8 8 8 6



Mean Diff in Levels

Table 5b: Fixed Telecomm[In growth rates]

Variable stats


OutputsTotal number mean 6.9% 12.7% 7.2% 5.8% -6.5% 0.4% -2.546** 1.917** -0.152of lines p50 7.2% 11.7% 6.6% 3.8% -12.0% -2.1% -2.223** 1.852* -0.157

sd 6.2% 6.3% 8.2% 9.1% 12.8% 10.7%N 16 16 14 16 14 14

Total number mean 4.1% 2.1% 3.8% -6.7% 3.2% -0.8% 1.158 - -of minutes p50 4.6% 1.7% 3.8% -4.1% 3.2% -0.8% 1.219 - -

sd 1.9% 15.3% . 12.9% . .N 5 6 1 5 1 1

InputsNumber of mean -0.5% -3.1% -6.9% -2.6% -3.4% -6.5% 0.916 1.258 2.861***employee p50 -0.8% -4.5% -7.7% -1.5% -1.3% -3.9% 0.909 0.785 2.291**

sd 6.9% 9.8% 9.0% 11.1% 10.0% 8.4%N 15 15 14 15 14 14

EfficiencyTotal number mean 7.8% 17.6% 16.0% 9.8% -3.1% 8.0% -2.452** 0.610 -1.791**of lines per p50 6.6% 21.3% 15.7% 10.9% -9.9% 9.4% -2.101** 0.659 -1.726*employee sd 11.6% 15.3% 11.5% 15.5% 18.9% 16.7%

N 15 15 14 15 14 14Total number mean 5.2% 13.2% 28.6% 5.5% 11.9% 19.1% -3.000** - -of minutes per p50 9.5% 16.3% 28.6% 4.4% 11.9% 19.1% -2.023** - -employee sd 9.6% 11.7% . 4.1% . .

N 5 6 1 5 1 1Percentage of mean -1.5% -16.4% -14.3% -13.9% -0.2% -13.7% 1.293 0.046 2.145**Incomplete Calls p50 -1.5% -7.8% -9.3% -5.1% 0.0% -8.8% 1.363 0.000 2.201**

sd 1.0% 23.4% 14.7% 26.4% 14.0% 15.6%N 6 8 7 6 7 6




Table 5b: Fixed Telecomm[in growth rates]

[Cont.]

Variable stats


QualityPercentage of mean 51.5% 17.1% 4.9% -33.1% -13.5% -50.1% 1.085 3.602*** 1.434*Digitalized p50 22.1% 14.2% 0.9% -4.4% -12.0% -11.9% 1.293 2.734*** 2.824***Network sd 116.3% 15.9% 6.8% 110.1% 13.5% 121.1%

N 13 14 13 13 13 12Coverage

Number of Lines mean 4.9% 11.0% 6.0% 6.1% -5.9% 1.2% -3.001*** 2.040** -0.438per 100 HHs p50 4.4% 9.4% 4.9% 4.5% -8.0% -0.1% -2.637*** 1.852* -0.471

sd 5.9% 6.2% 7.8% 8.1% 10.8% 10.0%N 16 16 14 16 14 14







PricesAvg Price for a mean 46.7% -3.1% -5.7% -44.4% -2.3% -60.8% 1.981** 0.295 1.788*3-minute call p50 40.9% -1.3% -0.4% -41.4% -7.9% -52.5% 1.820* 0.459 1.572(in dollars) sd 69.0% 16.8% 12.4% 63.5% 25.1% 83.3%

N 8 13 10 8 10 6Avg monthly charge mean 42.8% 13.9% 5.2% -21.9% -10.5% -45.8% 1.088 0.830 1.785*for residential p50 15.7% 6.0% 0.0% -33.1% -3.3% -28.4% 1.007 0.978 1.272Service (in dollars) sd 54.6% 31.0% 28.1% 60.4% 41.9% 67.9%

N 9 14 11 9 11 7Avg Charge for the mean -1.9% -14.7% -13.7% -9.6% -5.7% -32.6% 0.785 0.381 1.626installation of a p50 -1.8% -2.3% -29.3% -5.2% -2.6% -18.2% 1.008 0.533 1.826*residential line sd 25.8% 38.7% 33.7% 36.5% 44.6% 40.1%(in dollars) N 9 14 9 9 9 4Avg Price for a mean 35.7% -2.5% -0.6% -30.5% 2.7% -36.7% 1.696* -0.389 1.549*3-minute call p50 44.3% 4.3% 0.6% -32.1% -5.2% -21.2% 1.352 0.178 1.153(in real local sd 55.4% 19.1% 4.9% 47.6% 21.1% 58.0%currency) N 7 10 9 7 9 6Avg monthly charge mean 35.6% 16.5% 7.1% -12.7% -9.4% -29.4% 0.721 0.959 1.426for residential p50 -0.9% 15.6% 3.2% -32.9% -1.9% 0.6% 0.770 0.866 0.676Service (in real sd 50.1% 32.1% 13.1% 52.9% 30.9% 54.6%local currency) N 9 12 10 9 10 7Avg Charge for the mean -8.6% -16.1% -11.6% -4.7% -6.7% -19.1% 0.289 0.370 0.789installation of a p50 -26.3% -20.0% -30.5% -35.0% -2.0% 1.4% 0.000 0.845 -0.365residential line (in sd 32.3% 46.4% 40.4% 43.5% 48.0% 48.4%real local currency) N 7 10 7 7 7 4




Table 7: Water and Sewerage

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15)Number of

Water Connect's

Number of Sewerage Connect's

Cubic Meters per

year

Number of Employees

Water Connec.

per Employee

Cubic Meters per Employee

Distrib. Losses

Continuity of the

Service

Potability Water Coverage

Sewerage Coverage

Avg price per M3 of water (in dollars)

Avg price per M3 of water (in R.L.C.)

Avg price per M3 for sewerage (in dollars)

Avg price per M3 for sewerage (in R.L.C.)

Model 1: Log levels without firm-specific time trendTransition 0.141*** 0.174*** 0.040*** -0.180*** 0.268*** 0.216*** -0.039** 0.038 0.053*** 0.055 0.146*** -0.014 0.104 -0.014 0.104 (-2<=t<=1) (0.010) (0.016) (0.009) (0.030) (0.034) (0.039) (0.017) (0.064) (0.009) (0.041) (0.026) (0.142) (0.083) (0.142) (0.083)Post Transition 0.280*** 0.347*** 0.056*** -0.374*** 0.622*** 0.434*** -0.193*** 0.112* 0.118*** 0.152*** 0.360*** -0.110 0.325*** -0.096 0.222*** (t>=2) (0.025) (0.064) (0.020) (0.123) (0.149) (0.199) (0.023) (0.064) (0.068) (0.052) (0.027) (0.169) (0.086) (0.110) (0.077)Firm Fixed Effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes YesFirm-specific trend No No No No No No No No No No No No No No NoObservations 259 239 195 201 199 160 179 97 198 112 112 37 37 37 37Log Likelihood 361.4 251.5 288.2 62.3 41.3 39.0 149.4 96.8 187.2 475.6 406.9 22.2 70.0 -11.6 7.2

Model 2: Log levels with firm-specific time trendTransition 0.006 -0.006 -0.007 0.083*** -0.076*** -0.071** -0.014 0.000 -0.005 0.003 -0.048 0.026 0.017 0.026 0.017 (-2<=t<=1) (0.004) (0.009) (0.010) (0.026) (0.023) (0.036) (0.012) (0.006) (0.006) (0.050) (0.034) (0.093) (0.082) (0.093) (0.082)Post Transition 0.004 -0.011 -0.020 0.152*** -0.103*** -0.114** -0.014 0.000 -0.014 -0.044 -0.072 0.038 0.062 0.013 0.045 (t>=2) (0.031) (0.031) (0.052) (0.033) (0.135) (0.213) (0.045) (0.006) (0.013) (0.050) (0.035) (0.170) (0.121) (0.088) (0.078)Firm Fixed Effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes YesFirm-specific trend Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes YesObservations 259 239 195 201 199 160 179 96 198 112 112 37 37 37 37Log Likelihood 699.1 481.4 394.9 188.0 185.5 128.7 293.8 276.7 257.5 895.3 536.9 88.7 120.9 21.3 26.5

Model 3: GrowthTransition 0.001 0.006 -0.008 -0.048*** 0.047*** 0.072** -0.000 0.002 0.003 -0.203*** -0.099*** -0.054 0.007 -0.054 0.007 (-2<=t<=1) (0.004) (0.006) (0.009) (0.018) (0.018) (0.035) (0.012) (0.020) (0.004) (0.034) (0.027) (0.080) (0.059) (0.080) (0.059)Post Transition -0.009** -0.005 -0.033*** 0.000 0.010 0.014 -0.013 0.001 -0.005 -0.221*** -0.110*** -0.059 0.013 -0.005 0.006 (t>=2) (0.011) (0.077) (0.054) (0.031) (0.183) (0.267) (0.028) (0.020) (0.074) (0.035) (0.032) (0.131) (0.074) (0.076) (0.065)Firm Fixed Effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes YesFirm-specific trend No No No No No No No No No No No No No No NoObservations 235 216 172 176 178 140 160 81 180 101 101 31 31 31 31Log Likelihood 539.7 392.4 258.5 128.7 125.8 72.3 207.1 156.9 193.6 686.0 384.6 62.3 80.0 7.5 13.1

Standard errors in parentheses* significant at 10%; ** significant at 5%; *** significant at 1%Note: R.L.C. means "Real Local Currency"

Table 7: Fixed Telecomm(1) (2) (3) (4) (5) (6) (7) (8)

Number of Connections

Minutesper year

Number of Employees

Connectionsper

employee

Minutes peremployee

% of Uncomplete

d Calls

Percentage of

Digitalized Network

Coverage(lines per

100 inhabit.)

Model 1: Log levels without firm-specific time trendTransition 0.253*** 0.079** -0.097*** 0.301*** 0.278*** -0.133 0.310*** 0.168*** (-2<=t<=1) (0.030) (0.035) (0.033) (0.054) (0.059) (0.083) (0.053) (0.025)Post Transition 0.747*** 0.398*** -0.361*** 1.027*** 0.935*** -0.486*** 0.768*** 0.590*** (t>=2) (0.031) (0.039) (0.035) (0.058) (0.094) (0.084) (0.056) (0.028)Firm Fixed Effect Yes Yes Yes Yes Yes Yes Yes YesFirm-specific trend No No No No No No No NoObservations 168 71 161 162 69 70 131 165Log Likelihood 67.9 28.7 30.6 -39.6 -0.4 -4.5 -13.3 86.5

Model 2: Log levels with firm-specific time trendTransition -0.050** 0.002 0.031 -0.101*** -0.010 0.142*** 0.048** -0.065*** (-2<=t<=1) (0.024) (0.038) (0.026) (0.038) (0.044) (0.042) (0.024) (0.019)Post Transition 0.064 0.135** -0.038 0.083 0.163** 0.148** 0.072* 0.027 (t>=2) (0.042) (0.062) (0.046) (0.069) (0.082) (0.075) (0.044) (0.035)Firm Fixed Effect Yes Yes Yes Yes Yes Yes Yes YesFirm-specific trend Yes Yes Yes Yes Yes Yes Yes YesObservations 168 71 161 162 69 70 131 165Log Likelihood 186.1 66.7 163.1 105.3 107.1 58.5 124.6 206.1

Model 3: GrowthTransition 0.027** 0.069*** -0.041*** 0.070*** 0.085** -0.062 -0.008 0.037*** (-2<=t<=1) (0.011) (0.012) (0.015) (0.021) (0.042) (0.041) (0.026) (0.010)Post Transition 0.024** 0.122*** -0.066*** 0.104*** 0.168*** -0.096** -0.065** 0.039*** (t>=2) (0.011) (0.033) (0.017) (0.022) (0.062) (0.042) (0.027) (0.011)Firm Fixed Effect Yes Yes Yes Yes Yes Yes Yes YesFirm-specific trend No No No No No No No NoObservations 165 60 158 158 59 64 122 162Log Likelihood 0.26 0.13 0.40 0.46 0.12 0.84 0.09 0.30

Standard errors in parentheses* significant at 10%; ** significant at 5%; *** significant at 1%Note: "R.L.C." means "Real Local Currency"

Table 7: Fixed Telecomm (Cont.)(9) (10) (11) (12) (13) (14)

Av Price for a 3-min call (in dollars)

Monthly charges

(in dollars)

Price for an installation(in dollars)

Av Price for a 3-min call(in R.L.C.)

Monthly charges

(in R.L.C.)

Price for an installation(in R.L.C.)

Model 1: Log levels without firm-specific time trendTransition 0.384*** 0.565*** 0.095 0.371*** 0.486*** -0.178 (-2<=t<=1) (0.080) (0.118) (0.114) (0.081) (0.113) (0.171)Post Transition 0.370*** 0.774*** -0.215 0.282*** 0.683*** -0.464** (t>=2) (0.083) (0.122) (0.137) (0.084) (0.118) (0.186)Firm Fixed Effect Yes Yes Yes Yes Yes YesFirm-specific trend No No No No No NoObservations 104 114 107 91 110 87Log Likelihood -30.9 -65.9 -82.2 -17.2 -63.5 -88.2

Model 2: Log levels with firm-specific time trendTransition 0.523*** 0.281*** 0.300*** 0.358*** 0.067 0.118 (-2<=t<=1) (0.104) (0.100) (0.063) (0.082) (0.092) (0.154)Post Transition 0.573*** 0.214 0.522*** 0.191 -0.032 0.362* (t>=2) (0.160) (0.145) (0.115) (0.142) (0.145) (0.207)Firm Fixed Effect Yes Yes Yes Yes Yes YesFirm-specific trend Yes Yes Yes Yes Yes YesObservations 104 114 107 91 110 87Log Likelihood -1.1 -13.2 4.1 24.9 16.5 -6.0

Model 3: GrowthTransition -0.052 -0.101 -0.003 -0.056 -0.047 -0.140 (-2<=t<=1) (0.077) (0.097) (0.048) (0.065) (0.067) (0.107)Post Transition -0.033 -0.136 -0.023 -0.081 -0.046 -0.104 (t>=2) (0.078) (0.103) (0.063) (0.067) (0.076) (0.113)Firm Fixed Effect Yes Yes Yes Yes Yes YesFirm-specific trend No No No No No NoObservations 93 105 98 82 102 79Log Likelihood 0.16 0.23 0.39 0.20 0.24 0.33

Standard errors in parentheses* significant at 10%; ** significant at 5%; *** significant at 1%Note: "R.L.C." means "Real Local Currency"

Alternative Explanations• Increase in Competition (for the case of Fixed

Telecomm)

– Two different proxies: dummy for liberalization and number of cellular connections.

– None of them affect my main results.

– Prices are particularly affected by competition.

• Endogeneity of ownership dummies:

– Macro variables as instruments

– Results are higher. Hence, FGLS results are a lower bound of the total effect.

– But instruments have to be improved.

Table 13: Fixed Telecomm[Competition]

(1) (2) (3) (4) (5) (6) (7) (8)Number of

ConnectionsMinutesper year

Number of Employees

Connectionsper employee

Minutes peremployee

% of Uncompleted

Calls

Percentage of Digitalized Network

Coverage(lines per 100

inhabit.)

Model 1: Log levels without firm-specific time trendTransition 0.232*** 0.064* -0.046 0.272*** 0.232*** -0.140* 0.307*** 0.166*** (-2<=t<=1) (0.027) (0.036) (0.030) (0.049) (0.050) (0.081) (0.057) (0.025)Post Transition 0.664*** 0.343*** -0.197*** 0.873*** 0.664*** -0.475*** 0.753*** 0.531*** (t>=2) (0.032) (0.056) (0.035) (0.056) (0.091) (0.098) (0.072) (0.029)Liberalization 0.275*** 0.065 -0.361*** 0.673*** 0.487*** -0.027 0.023 0.230***

(0.037) (0.046) (0.047) (0.083) (0.082) (0.088) (0.069) (0.035)Firm Fixed Effect Yes Yes Yes Yes Yes Yes Yes YesFirm-specific trend No No No No No No No NoObservations 168 71 161 162 69 70 131 165Log Likelihood 83.1 29.7 48.1 -19.0 12.2 -4.9 -13.8 98.3

Model 2: Log levels with firm-specific time trendTransition -0.050** 0.001 0.026 -0.089** -0.006 0.133*** 0.044* -0.066*** (-2<=t<=1) (0.024) (0.043) (0.026) (0.038) (0.049) (0.043) (0.025) (0.020)Post Transition 0.066 0.128** -0.040 0.103 0.158* 0.143* 0.068 0.025 (t>=2) (0.043) (0.064) (0.046) (0.068) (0.085) (0.074) (0.045) (0.035)Liberalization 0.002 0.037 -0.046 0.117** 0.108 -0.041 -0.016 -0.007

(0.032) (0.063) (0.042) (0.049) (0.090) (0.053) (0.028) (0.025)Firm Fixed Effect Yes Yes Yes Yes Yes Yes Yes YesFirm-specific trend Yes Yes Yes Yes Yes Yes Yes YesObservations 168 71 161 162 69 70 131 165Log Likelihood 185.6 66.8 163.5 107.1 107.0 58.9 124.4 206.1

Model 3: GrowthTransition -0.050** 0.001 0.026 -0.089** -0.006 0.133*** 0.044* -0.066*** (-2<=t<=1) (0.024) (0.043) (0.026) (0.038) (0.049) (0.043) (0.025) (0.020)Post Transition 0.066 0.128** -0.040 0.103 0.158* 0.143* 0.068 0.025 (t>=2) (0.043) (0.064) (0.046) (0.068) (0.085) (0.074) (0.045) (0.035)Liberalization 0.002 0.037 -0.046 0.117** 0.108 -0.041 -0.016 -0.007

(0.032) (0.063) (0.042) (0.049) (0.090) (0.053) (0.028) (0.025)Firm Fixed Effect Yes Yes Yes Yes Yes Yes Yes YesFirm-specific trend No No No No No No No NoObservations 165 60 158 158 59 64 122 162Log Likelihood 203.5 74.2 144.8 110.9 69.8 40.2 90.7 211.8


Table 13: Fixed Telecomm Competition (Cont.)(9) (10) (11) (12) (13) (14)

Av Price for a 3-min call (in

dollars)

Monthly charges

(in dollars)



Monthly charges

(in R.L.C.)


Model 1: Log levels without firm-specific time trendTransition 0.422*** 0.558*** 0.033 0.359*** 0.398*** -0.107 (-2<=t<=1) (0.088) (0.131) (0.073) (0.085) (0.112) (0.191)Post Transition 0.433*** 0.778*** -0.118 0.197* 0.500*** -0.238 (t>=2) (0.101) (0.140) (0.097) (0.104) (0.124) (0.221)Liberalization -0.097 0.001 -0.491*** 0.150* 0.443*** -0.529**

(0.088) (0.144) (0.171) (0.091) (0.155) (0.221)Firm Fixed Effect Yes Yes Yes Yes Yes YesFirm-specific trend No No No No No NoObservations 104 114 107 91 110 87Log Likelihood -30.3 -67.4 -80.5 -16.0 -58.9 -90.0

Model 2: Log levels with firm-specific time trendTransition 0.441*** 0.192 0.245*** 0.296*** -0.007 0.130 (-2<=t<=1) (0.109) (0.136) (0.083) (0.082) (0.087) (0.165)Post Transition 0.430*** 0.081 0.442*** 0.103 -0.142 0.376* (t>=2) (0.166) (0.187) (0.133) (0.139) (0.139) (0.219)Liberalization -0.356*** -0.410*** -0.030 -0.240*** -0.500*** 0.035

(0.116) (0.147) (0.092) (0.090) (0.136) (0.169)Firm Fixed Effect Yes Yes Yes Yes Yes YesFirm-specific trend Yes Yes Yes Yes Yes YesObservations 104 114 107 91 110 87Log Likelihood 1.9 -14.0 3.7 27.9 22.5 -5.9

Model 3: GrowthTransition 0.441*** 0.192 0.245*** 0.296*** -0.007 0.130 (-2<=t<=1) (0.109) (0.136) (0.083) (0.082) (0.087) (0.165)Post Transition 0.430*** 0.081 0.442*** 0.103 -0.142 0.376* (t>=2) (0.166) (0.187) (0.133) (0.139) (0.139) (0.219)Liberalization -0.356*** -0.410*** -0.030 -0.240*** -0.500*** 0.035

(0.116) (0.147) (0.092) (0.090) (0.136) (0.169)Firm Fixed Effect Yes Yes Yes Yes Yes YesFirm-specific trend No No No No No NoObservations 93 105 98 82 102 79Log Likelihood 0.5 -25.9 -20.0 16.3 -4.1 -29.9


Table 14: Fixed Telecomm[Competition – mobile]

(1) (2) (3) (4) (5) (6) (7) (8)Number of

ConnectionsMinutesper year

Number of Employees

Connectionsper

employee

Minutes peremployee

% of Uncomplete

d Calls

Percentage of

Digitalized Network

Coverage(lines per

100 inhabit.)

Model 1: Log levels without firm-specific time trendTransition 0.247*** 0.047 -0.059** 0.291*** 0.178*** -0.143* 0.313*** 0.171*** (-2<=t<=1) (0.027) (0.037) (0.027) (0.043) (0.050) (0.077) (0.053) (0.022)Post Transition 0.660*** 0.268*** -0.147*** 0.791*** 0.448*** -0.479*** 0.755*** 0.513*** (t>=2) (0.030) (0.065) (0.034) (0.051) (0.100) (0.111) (0.066) (0.027)Mobile Subs [x 1M] 0.013*** 0.005** -0.025*** 0.037*** 0.030*** -0.000 0.001 0.014***

(0.002) (0.002) (0.001) (0.002) (0.004) (0.004) (0.003) (0.002)Firm Fixed Effect Yes Yes Yes Yes Yes Yes Yes YesFirm-specific trend No No No No No No No NoObservations 168 71 161 162 69 70 131 165Log Likelihood 88.3 31.8 65.6 3.5 22.1 -4.4 -13.5 108.0

Model 2: Log levels with firm-specific time trendTransition -0.064*** 0.019 0.008 -0.070* 0.029 0.111** 0.017 -0.068*** (-2<=t<=1) (0.025) (0.051) (0.025) (0.039) (0.063) (0.045) (0.022) (0.021)Post Transition 0.056 0.131** -0.036 0.106 0.090 0.133* 0.059 0.031 (t>=2) (0.043) (0.062) (0.044) (0.068) (0.081) (0.069) (0.038) (0.036)Mobile Subs [x 1M] -0.006* 0.010** -0.017*** 0.010** 0.032*** -0.004 -0.021*** -0.003


Model 3: GrowthTransition 0.023** 0.068*** -0.043*** 0.068*** 0.075* -0.062 0.006 0.035*** (-2<=t<=1) (0.011) (0.014) (0.015) (0.021) (0.040) (0.042) (0.025) (0.011)Post Transition 0.035*** 0.136** -0.060*** 0.107*** 0.070 -0.095* -0.024 0.039*** (t>=2) (0.012) (0.055) (0.017) (0.023) (0.076) (0.055) (0.029) (0.012)Mobile Subs [x 1M] -0.002** -0.001 -0.002 -0.001 0.006* -0.000 -0.005*** -0.001



Table 14: Fixed Telecomm [Competition - Mobile (Cont.)]

(9) (10) (11) (12) (13) (14)Av Price for a 3-min call (in dollars)

Monthly charges

(in dollars)



Monthly charges

(in R.L.C.)


Model 1: Log levels without firm-specific time trendTransition 0.432*** 0.506*** -0.030 0.311*** 0.365*** -0.165 (-2<=t<=1) (0.079) (0.120) (0.021) (0.075) (0.102) (0.106)Post Transition 0.470*** 0.695*** 0.002 0.090 0.368*** -0.135 (t>=2) (0.087) (0.125) (0.032) (0.092) (0.111) (0.126)Mobile Subs [x 1M] -0.015*** 0.013 -0.151*** 0.017*** 0.042*** -0.132***


Model 2: Log levels with firm-specific time trendTransition 0.166*** -0.056 0.327*** 0.201*** -0.043 0.349** (-2<=t<=1) (0.063) (0.105) (0.073) (0.047) (0.044) (0.161)Post Transition 0.459*** -0.002 0.522*** 0.284*** -0.048 0.573*** (t>=2) (0.092) (0.127) (0.125) (0.070) (0.072) (0.202)Mobile Subs [x 1M] -0.117*** -0.148*** 0.039* -0.063*** -0.105*** 0.076***

(0.007) (0.015) (0.024) (0.005) (0.011) (0.025)Firm Fixed Effect Yes Yes Yes Yes Yes YesFirm-specific trend Yes Yes Yes Yes Yes YesObservations 104 114 107 91 110 87Log Likelihood 43.2 9.8 4.5 63.3 46.1 -2.5

Model 3: GrowthTransition -0.005 -0.076 -0.031 -0.023 -0.043 -0.175* (-2<=t<=1) (0.063) (0.090) (0.054) (0.059) (0.059) (0.093)Post Transition 0.111* -0.025 -0.094 0.028 0.028 -0.214** (t>=2) (0.067) (0.100) (0.071) (0.065) (0.070) (0.105)Mobile Subs [x 1M] -0.026*** -0.032*** 0.018* -0.014*** -0.025*** 0.028***



Table 15: Fixed Telecomm[Competition - mobile v2](1) (2) (3) (4) (5) (6) (7) (8)

Number of Connections

Minutesper year

Number of Employees

Connectionsper

employee

Minutes peremployee

% of Uncomplete

d Calls

Percentage of

Digitalized Network

Coverage(lines per

100 inhabit.)

Model 1: Log levels without firm-specific time trendTransition 0.243*** 0.044 -0.049* 0.288*** 0.176*** -0.136* 0.312*** 0.169*** (-2<=t<=1) (0.026) (0.038) (0.027) (0.041) (0.051) (0.077) (0.054) (0.022)Post Transition 0.638*** 0.257*** -0.119*** 0.738*** 0.429*** -0.452*** 0.750*** 0.496*** (t>=2) (0.030) (0.069) (0.035) (0.050) (0.105) (0.112) (0.067) (0.027)Mobile Subs [x 1M] 0.005*** 0.002** -0.008*** 0.013*** 0.010*** -0.001 0.001 0.004***

(0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001)Firm Fixed Effect Yes Yes Yes Yes Yes Yes Yes YesFirm-specific trend No No No No No No No NoObservations 168 71 161 162 69 70 131 165Log Likelihood 92.4 31.7 66.2 11.7 20.7 -4.0 -13.4 111.0

Model 2: Log levels with firm-specific time trendTransition -0.042* 0.038 0.008 -0.035 0.032 0.120*** 0.015 -0.060*** (-2<=t<=1) (0.025) (0.050) (0.025) (0.038) (0.066) (0.045) (0.023) (0.020)Post Transition 0.067 0.135** -0.023 0.109* 0.065 0.140** 0.077* 0.030 (t>=2) (0.042) (0.058) (0.044) (0.064) (0.080) (0.071) (0.040) (0.036)Mobile Subs [x 1M] 0.001 0.005*** -0.006*** 0.007*** 0.011*** -0.001 -0.007*** 0.000


Model 3: GrowthTransition 0.024** 0.067*** -0.043*** 0.069*** 0.073* -0.062 0.007 0.035*** (-2<=t<=1) (0.011) (0.014) (0.015) (0.021) (0.040) (0.042) (0.025) (0.011)Post Transition 0.035*** 0.127** -0.059*** 0.106*** 0.062 -0.094* -0.017 0.039*** (t>=2) (0.012) (0.058) (0.017) (0.024) (0.078) (0.055) (0.030) (0.012)Mobile Subs [x 1M] -0.001* -0.000 -0.001 -0.000 0.002* -0.000 -0.001*** -0.000



Table 15: Fixed Telecomm [Competition - Mobile v2 (Cont.)]

(9) (10) (11) (12) (13) (14)Av Price for a 3-min call (in dollars)

Monthly charges

(in dollars)



Monthly charges

(in R.L.C.)


Model 1: Log levels without firm-specific time trendTransition 0.427*** 0.511*** 0.013 0.322*** 0.377*** -0.171 (-2<=t<=1) (0.078) (0.120) (0.028) (0.076) (0.102) (0.114)Post Transition 0.473*** 0.689*** 0.101** 0.097 0.374*** -0.084 (t>=2) (0.088) (0.128) (0.044) (0.094) (0.113) (0.135)Mobile Subs [x 1M] -0.004*** 0.004 -0.046*** 0.005*** 0.012*** -0.042***


Model 2: Log levels with firm-specific time trendTransition 0.144** -0.073 0.317*** 0.194*** -0.078 0.321* (-2<=t<=1) (0.065) (0.110) (0.070) (0.048) (0.051) (0.167)Post Transition 0.400*** -0.105 0.527*** 0.276*** -0.088 0.589*** (t>=2) (0.099) (0.141) (0.123) (0.070) (0.083) (0.213)Mobile Subs [x 1M] -0.033*** -0.039*** 0.008 -0.018*** -0.030*** 0.017**

(0.002) (0.004) (0.007) (0.002) (0.003) (0.007)Firm Fixed Effect Yes Yes Yes Yes Yes YesFirm-specific trend Yes Yes Yes Yes Yes YesObservations 104 114 107 91 110 87Log Likelihood 37.6 6.7 4.1 63.0 43.4 -4.0

Model 3: GrowthTransition -0.009 -0.074 -0.030 -0.024 -0.042 -0.165* (-2<=t<=1) (0.062) (0.091) (0.053) (0.059) (0.060) (0.093)Post Transition 0.112* -0.008 -0.091 0.028 0.035 -0.215** (t>=2) (0.067) (0.101) (0.071) (0.066) (0.072) (0.107)Mobile Subs [x 1M] -0.008*** -0.010*** 0.005 -0.004*** -0.007*** 0.008**



Diagnosis and Challenges of Infrastructure

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