Transcript Slide 1

Diagnosis and Challenges of
Infrastructure
Experiences and Lessons from
Latin America
J. Luis Guasch,
World Bank and University of California-San Diego
Cape Town, South Africa, 29-31 May 2006
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
Stocks Lag Behind East Asia
Infrastructure stock index
7
6
5
Latin America
East Asian Tigers
4
3
2
1
0
1980
2000
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.
LAC has Fallen Behind China and
Middle Income Countries
2500
LAC
2000
MIC
China
1500
1000
500
0
Access to
electricity (%)
Roads (km/km2)
Source: World Development Indicators
Mainline per 1000
pers
What Happened?
Public retrenchment never fully offset by private entry
5%
TOTAL
Public
Private
4%
share of GDP
4%
3%
3%
2%
2%
1%
1%
0%
1980
1982
1984
1986
1988
Source: Calderon and Serven (2004)
1990
1992
1994
1996
1998
2000
2002
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
80
70
US$ billion
60
50
40
30
20
10
0
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
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)
Infraestructura and Growth
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?
19802000
1. Growth of of the gap in output
per capita (cambio en el log de PIB relativo
91.9
por trabajador)
2. Share attributed to the increase
in the infrastrcture gap
20.2
(mediana de inforrmacion de país)
[2] / [1] (percent)
21.9
Contributión to the infrastrcuture gap to the gap in output
Relative to East Asia (1980-2000, percent)
Venezuela
Jamaica
Nicaragua
Ecuador
El Salvador
Peru
Honduras
Costa Rica
92%
México
Guatemala
Bolivia
Brazil
Argentina
Colombia
Panama
Dom. Rep.
Infr contribution
Uruguay
Output gap
Chile
0%
20%
40%
60%
80%
100%
120%
140%
Figure 1. Infrastructure Stocks vs. Economic Growth
8%
Growth Rate of GDP per capita
6%
BWA
THA
CHN
-4
SGP
7%
y = 0.0056x + 0.0206
R2 = 0.2547
TWN
KOR
5%
HKG
JPN
CYP IRL
PRT
MUS
ROM
IDN
ESP
TUN
GRC
3%
AUTBEL
PAK
FINITANOR
ISR
BRA
IND
MAR SYRDOM
FRA
EGY
USA
PAN
NLD
CHL
CAN
TUR
TTO
LKA
HUN
AUS
DNK
SWE
GBR
2%
IRN
DEU
MEX
COL
PRYZWE
NPL
DZA
CHE
ECU
PHL
JOR
UGA GNB
CRI
URY
NZL
BGD
KEN GHA GTM
ZAF
1%
ARG
PER SLV
JAM
MRT
PNG
BFA
ETH TZA
HND
BOL
CIV
0%
MLI
RWA GINSLE
SEN
VEN
-3
-2
-1 ZMB
0
1 POL
2
MDG NGA
-1%
NIC
NER
-2%
4% MYS
Infrastructure Stocks (1st. Principal Component)
3
Figure 2. Infrastructure Quality vs. Economic Growth
8%
y = 0.0081x + 0.0226
R = 0.2027
6%
Growth Rate of GDP per capita
SGP
7%
2
BWA
5%
TWN
KOR
HKG
THA
CHN
-2.0
JPNIRL
4% MYS PRT
CYP
MUS
ROM
IDN
ESP
LUX
TUN
GRC
3%
AUT
PAK BRA
FIN
ITA NOR
DOM
ISR
BEL
IND
SYR
FRA
EGY
MAR
USA
NLD
PAN
CHL LKA
TTO
CAN
TUR
HUN
DNK
AUS
SWE
GBR
IRN
DEU
MEX
2%
COL
ZWE
PRY
NPLECU
DZA
CHE
PHL
JOR
UGA
GTM
URY CRI
NZL
GNB
BGD
KEN
GHA
ZAF
ARG
1%
PER
JAM
PNGSLVETH
TZAHNDMRT
BFA
BOL
CIV
SLE
GIN RWA
0%
MLI
SEN
VEN-0.5
POL
-1.5
-1.0
0.0
0.5
1.0
1.5
2.0
ZMB
NGA
MDG
-1%
NIC
NER
-2%
Infrastructure Quality (1st. Principal Component)
2.5
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)
Country
Improvement to levels of LAC Leader
Stocks
Quality
Total
Improvement to levels of EAP Median
Stocks
Quality
Total
Argentina
Bolivia
Brazil
Chile
Colombia
Costa Rica
Ecuador
Mexico
Peru
Uruguay
Venezuela
1.3%
3.8%
1.5%
1.3%
1.9%
...
2.0%
1.4%
3.0%
0.7%
1.1%
0.4%
0.5%
1.4%
0.0%
1.2%
...
1.0%
0.2%
0.6%
0.4%
0.4%
1.7%
4.3%
2.9%
1.3%
3.1%
...
3.0%
1.7%
3.5%
1.1%
1.4%
2.2%
4.8%
2.4%
2.3%
2.9%
1.0%
3.0%
2.4%
4.0%
1.7%
2.0%
0.9%
1.0%
1.9%
0.6%
1.7%
0.5%
1.5%
0.8%
1.1%
0.9%
0.9%
3.2%
5.8%
4.4%
2.8%
4.6%
1.5%
4.5%
3.2%
5.0%
2.6%
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
0.7
0.6
ZWEBRA
KEN
HND
BWA
MEXPAN
COL
ECU
CHL
ZAF
BOL
GTM0.5
MDG
TUR
BFA
ZMB PER
DOM
SLVVEN CRI
PHLMYS
THA
NGA
TTO
PNG
IRN
JAM
URY
MAR
PRY
TUN
TZA
ARG
CIV LKA
HKGSGP
0.4
ETH
JOR
MUS
UGA
EGY
AUS IRL
FRA
PRT
NZL
USA
GHA
GRC
ITA
JPN
CHN
KOR
NOR
BGD IDN
DNK
YSR CAN
SWECHE
IND
DEU
PAK
ISR
TWN
0.3
RWA
NLD
FIN
GBR
AUT
ROMPOLESP
BEL
CYP
HUN
BGR
0.2
Gini Coefficient (0-1)
SEN
y = -0.0303x + 0.403
R2 = 0.2157
0.1
0.0
-4
-3
-2
-1
0
1
Infrastructure Stocks (1st. Principal Component)
2
3
Figure 4. Infrastructure Quality vs. Income Inequality
0.7
Gini Coefficient (0-1)
0.6
BRAKEN
ZWE
HND
SEN
MEX
COLECUBWA
PAN
BOL
GTMCHL
0.5 ZAF
MDG
PER
TUR BFA SLV
ZMB
DOM
PHL
MYSTTO
VEN
THA CRI
BHS
NGA
PNG URY
IRNPRY TUN
JAM
MAR
TZA
ARG
LKA
CIV
HKG SGP
0.4
ETH
JOR
MUS
UGA
EGY
AUS
FRA
PRT GRC
NZL
USA
GHA
JPN
ITA IRL
CHN
KOR
NOR
BGD
IDN
CHE
DNK
YSR
SWE
IND
PAK
DEU
CAN
ISR
TWN
RWA
0.3
NLD
FIN
GBR
AUT
ESP
POL
ROM
BEL
CYP
HUN
BGR
0.2
y = -0.0523x + 0.3887
0.1
2
R = 0.2942
0.0
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
Infrastructure Quality (1st. Principal Component)
1.5
2.0
2.5
Infrastructure and Inequality
• 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.
Infrastructure and Inequality
Changes of Inequality in LAC Countries due to Higher Infrastructure Development
(Changes in the Gini coefficient)
Country
Improvement to levels of LAC Leader
Stocks
Quality
Total
Improvement to levels of EAP Median
Stocks
Quality
Total
Argentina
Bolivia
Brazil
Chile
Colombia
Costa Rica
Ecuador
Mexico
Peru
Uruguay
Venezuela
-0.03
-0.08
-0.03
-0.03
-0.04
...
-0.04
-0.03
-0.06
-0.02
-0.02
-0.01
-0.01
-0.02
0.00
-0.02
...
-0.02
0.00
-0.01
-0.01
-0.01
-0.03
-0.09
-0.06
-0.03
-0.06
...
-0.06
-0.03
-0.07
-0.02
-0.03
-0.05
-0.10
-0.05
-0.05
-0.06
-0.02
-0.06
-0.05
-0.08
-0.04
-0.04
-0.02
-0.02
-0.03
-0.01
-0.03
-0.01
-0.03
-0.01
-0.02
-0.02
-0.02
-0.06
-0.12
-0.09
-0.06
-0.09
-0.03
-0.09
-0.06
-0.10
-0.05
-0.06
Average
-0.04
-0.01
-0.05
-0.06
-0.02
-0.08
Costs of inadequate infrastructure (% of value of goods)
30
OECD
LAC
4
25
3
20
15
2
10
1
5
0
Losses en route
Logistic costs
Inventory levels
Proportion of agric. goods As a proportion of
value of products
not reaching markets
(index, right scale)
Sources: LCSFP reports; LAC Chambers and producers associations reports, 2003..
Impact of a 12 percentage point reduction of
logistic costs (Guasch 2005)
Sector
Agro-indust
Wood and
Furniture
Textiles
Leather and
Shoes
Mining
Increase in
Output
Demand
12%
14%
8%
18%
10%
Increase in
Employment
6%
16%
9%
15%
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)
Mean
Minimum
1st Quartile
Median
3rd Quartile
Maximum
Chile
2.17
0.00
0.36
1.28
2.66
68.92
Venezuela
2.82
0.30
1.87
2.61
3.12
7.21
Peru
4.19
0.10
1.25
2.30
3.90
31.1
Bolivia
4.20
0.11
1.39
2.90
4.49
34.97
Colombia
2.22
0.52
1.45
1.80
2.52
13.59
Ecuador
5.06
0.86
2.55
3.80
5.64
20.61
Mexico
1.58
0.42
1.06
1.36
2.06
3.26
Brazil
2.98
0.8
1.6
2.00
3.1
7.1
Mexico
1.46
0.35
0.82
1.36
2.14
4.91
Brazil
1.98
0.75
1.1
1.60
2.00
5.2
Fianl Goods Inventory Levels: Ratio to U.S. Level by Industry
(average of all available data for 1990s)
Mean
Minimum
1st Quartile
Median
3rd Quartile
Maximum
Chile
1.76
0.01
0.17
0.72
1.38
31.61
Venezuela
1.63
0.10
0.87
1.60
2.1.4
5.29
Source: Guasch and Kogan (2000)
Peru
1.95
0.39
1.17
1.54
2.11
3.87
Bolivia
2.74
0.11
1.13
2.02
3.18
21.31
Colombia
1.38
0.19
1.05
1.28
1.63
5.31
Ecuador
2.57
0.67
1.67
1.98
2.86
7.94
IMPACT OF INFRASTRUCTURE
(2)
ON
PRODUCTIVITY
EXPORTS
FDI
WAGES
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)
Yit  C  A'j CDit  B ' CTRit  D ' ICit  it
(2)
Yit  C  A' TLit  B ' CTRit  D ' ICit   it
(3)
TFPit  C  B ' CTRit  D ' ICit  it , with TFPit  Yit  ' j CDit , and
(4)
TEFFit  C  B ' CTRit  D ' ICit   it , 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 (%)
7
6
5
4
3
2
1
0
Brazil
Ecuador
El Salvador Guatemala
Honduras
Average duration of power outages
Average duration of water outages
Average time to clear customs
Fraction of workers using computers in their job
Source: Escribano and Guasch. (2005).
Indonesia
Nicaragua
Loss in sales due to transport interruptions
CHILE
Productivity Elasticities and Semielasticities with Respect to IC Variables
Infrastructures
Red Tape, Corruption and
Crime
Finance
Quality, Innovation and
Labor Skills
0.3
Other Control Variables
0.26
0.23
0.25
0.18
0.2
0.13
0.15
0.09
0.1
0.04
0.05
0.05
0.03
0.02
0.005
0
-0.01
-0.05
-0.1
-0.05
-0.07
-0.09
-0.12
-0.12
-0.15
-0.17
-0.2
1.1
1.2
1.3
1.4
2.1
2.2
-0.14
2.3
-0.12
2.4
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.
Elasticities are indicated by blue bars, semielasticities by yellow bars.
3.1
4.1
4.2
4.3
4.4
5.1
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.
5.2
5.3
5.4
5.5
5.6
CHILE
Export Linear Probability Coefficients with Respect to IC Variables
Productivity
Infrsts.
Red Tape,
Corr.
& Crime
Finance and Corporate
Governance
Q uality, Innovation and Labor Skills
O ther Control Variables
0.3
0.24
0.2
0.15
0.13
0.12
0.1
0.06
0.03
0.06
0.07
0.09
0.100
0.06
0.031
-0.001
0
-0.1
-0.2
-0.23
-0.3
1.1
1.1
2.1
3.1
4.1
4.2
4.3
2.1
3.1
Productivity.
E-mail.
Illegal payments for protection .
T rade Association.
Derivatives.
External auditory.
4.1
4.2
4.3
5.1
5.2
5.1
5.2
5.3
5.4
5.5
6.1
6.2
6.3
5.3
5.4
Quality certification.
R + D.
New technology purchased.
Internal training.
Experience of the manager.
Incorporated company.
Age.
Rent buildings.
5.5
6.1
6.2
6.3
CHILE
Foreign Direct Investment Linear Probability Coefficients with Respect to IC Variables
Productivity
Finance and Corporate Governance
Infrastructures
Quality, Innovation and Labor Skills
0.2
Other Control
Variables
0.18
0.15
0.15
0.09
0.09
0.1
0.07
0.05
0.03
0.05
0.06
0.05
0.002
0
-0.05
-0.07
-0.1
1.1
1.1
2.1
3.1
3.2
3.3
3.4
2.1
3.1
Productivity.
Days to clear customs for imports.
T rade Association.
Credit.
Derivatives.
External auditory.
3.2
3.3
3.4
4.1
4.1
4.2
4.3
4.4
5.1
4.2
Quality certification.
R + D new product.
Internal training.
University staff.
Rent buildings.
4.3
4.4
5.1
CHILE
Wage Per Employee Elasticities and Semielasticities with Respect to IC Variables
Productivity
Infrastructures
Red Tape, Corruption and
Crime
Finance and Corporate
Governance
Quality, Innovation
and Labor Skills
Other Control Variables
1
0.76
0.8
0.6
0.48
0.4
0.30
0.2
0.13
0.10
0.04
0.01
0.002
0.01
6.2
6.3
0
-0.04
-0.2
-0.15
-0.03
-0.14
-0.21
-0.4
1.1
1.1
2.1
2.2
3.1
3.2
3.3
2.1
2.2
3.1
3.2
3.3
Productivity.
Average duration of power outages.
Internet page.
Security.
Cost of entry.
Absenteeism.
Elasticities are indicated by blue bars, semielasticities by yellow bars.
4.1
4.2
4.3
4.1
4.2
4.3
5.1
5.2
6.1
6.2
6.3
5.1
5.2
T rade association.
Credit line.
External auditory.
University staff.
Experience of the manager.
Foreign direct investment.
Age.
T rade union.
6.1
Figure 6
CHILE: Employment Elasticities and Semielasticities With Respect to IC Variables
Prdvty.
Real
Wage
Infrstrc.
Red Tape, Corruption
and Crime
0.5
0.12
0.18
0.13
O ther Control Variables
Q uality, Innovation and Labor
Skills
Finance and
Corp. Gov.
0.24
0.24
0.07
0.04
0.14
0.180
-0.003
0.13
0.31
0.01
0.01
0
-0.08
-0.09
-0.27
-0.5
-0.28
-0.30
-0.77
-1
-1.00
-1.5
-2
-1.89
-2.5
1.1
1.1
2.1
3.1
3.2
4.1
4.2
4.3
4.4
5.1
5.2
2.1
3.1
3.2
4.1
4.2
4.3
4.4
5.1
Productivity.
Real wage per employee.
Power outages.
Internet page.
Security.
Number of inspections.
Cost of entry.
Absenteeism.
T rade association.
External auditory.
Elasticities are indicated by blue bars, semielasticities by yellow bars.
5.2
6.1
6.2
6.3
6.1
6.2
6.3
6.4
6.5
6.6
7.1
7.2
7.3
7.4
7.5
7.6
6.4
6.5
6.6
7.1
Quality certification.
New product.
Internal training.
External training.
University staff.
Experience of the manager.
Incorporated company.
Age.
Exporter.
T rade union.
Small.
Medium.
7.2
7.3
7.4
7.5
7.6
IMPACT OF SECTOR
REFORMS AND PRIVATE
SECTOR PARTICIPATION (3)
ON
SECTOR PERFORMANCE:
PRODUCTIVITY
COVERAGE
QUALITY 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
Data Set
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
Telecoms
Roads
Rail
Water & Sanitation
Airports
Ports
Electricity
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.
OUTCOME
OBJECTIVE
FINANCING/ OPERATIONAL
PARTICIPATION OF THE PRIVATE
SECTOR
OPERATIONAL EFFICIENCY
COVERAGE
ALIGNMENT OF COSTS AND
TARIFFS
SUSTAINABILITY
OVERALL CONCLUSION
RESULT
VERY POSITIVE
FAIRLY POSITIVE
ADEQUATE
PROBLEMATIC
DEFFICIENT
MIXED, PARTIALLY
POSITIVE
Empirical Results: Changes in Trends…
Númer of users (*)
Electricity Distribution
Transition
Post-transición
Telecomunications- fix
Transición
Post-transición
Water
Transición
Post-transición
Output (*)
Númer of workers
Númer of workers –
Sector
Productivity of labor (*)
Technical losses in
distribution of services
Quality
Coverage (*)
Prices
?
Nota: (*) Estas variables fueron reportadas tras considerar los efectos fijos de la firma y otros fenómenos contemporáneos en la economía.
Fuente: Andres, Foster y Guasch (2004).
?
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 Lopezde-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
Mean
Preprivat
(1)
Outputs
Residential
Connections
MWH sold
per year
Inputs
Number of
Employees
Efficiency
Connections
per employee
GWH per
employee
Distributional
losses
Diff in Levels
Transition Postprivat
(2)
(3)
(2)-(1)
(4)
(3)-(2)
(5)
(3)-(1)
(6)
mean
p50
sd
N
mean
p50
sd
N
85.83
85.94
9.20
82
82.29
82.59
14.11
81
102.26
102.00
2.53
116
102.67
101.20
6.44
116
120.48
119.59
10.04
74
119.22
117.13
21.12
74
17.32
17.11
9.68
82
20.82
19.88
14.28
81
17.11
16.55
8.76
74
15.60
15.17
17.77
74
35.16
34.33
16.94
71
36.74
34.60
25.69
69
mean
p50
sd
N
162.71
147.46
54.42
58
100.65
100.00
6.76
116
86.59
86.17
23.63
59
-61.37
-48.38
52.22
58
-14.27
-14.76
20.18
59
-78.19
-63.63
63.71
50
mean
p50
sd
N
mean
p50
sd
N
mean
p50
sd
N
60.24
59.90
18.65
57
58.56
59.68
18.58
57
112.19
104.37
26.96
59
103.33
100.00
9.86
116
103.97
100.00
11.98
116
98.73
100.00
7.33
116
147.42
135.26
42.10
58
145.09
129.76
53.86
58
87.78
85.34
26.03
58
45.38
44.65
23.25
57
47.50
46.04
20.98
57
-12.92
-6.13
27.14
59
40.83
32.10
33.31
58
37.64
26.76
41.54
58
-9.75
-11.06
21.12
58
88.62
88.86
46.49
49
86.27
71.15
53.15
49
-25.14
-19.93
37.79
49
* significant at 10%; ** significant at 5%; *** significant at 1%
T-stat (Z-stat) for difference
in means (medians) in Levels
(2)-(1)
(3)-(2)
(3)-(1)
(7)
(8)
(9)
-16.209*** -17.493*** -16.809***
-7.843*** -7.306*** -7.459***
-13.119*** -11.882***
-7.399*** -6.945***
8.949***
6.252***
8.678***
5.903***
-7.554***
-6.128***
5.432***
5.057***
-14.738*** -13.344***
-6.543*** -6.093***
-9.334***
-6.438***
-17.097*** -11.362***
-6.567*** -6.093***
-6.901***
-6.182***
3.658***
3.268***
4.657***
4.272***
3.515***
3.341***
Table 3a: Electricity Distribution
[In levels]
[Cont.]
Variable
stats
Mean
Preprivat
(1)
Quality
Duration of
Interruptions
per year
per consumer
Frequency of
Interruptions
per year
per consumer
Coverage
Residential
Connections
per 100 HHs
Diff in Levels
Transition Postprivat
(2)
(3)
(2)-(1)
(4)
(3)-(2)
(5)
(3)-(1)
(6)
T-stat (Z-stat) for difference
in means (medians) in Levels
(2)-(1)
(3)-(2)
(3)-(1)
(7)
(8)
(9)
mean
p50
sd
N
mean
p50
sd
N
134.49
123.37
67.57
37
132.59
119.54
57.83
37
100.34
100.00
20.00
116
98.63
100.00
13.77
116
72.42
65.42
42.58
39
82.71
67.96
93.00
39
-30.61
-24.11
57.28
37
-34.90
-21.20
49.88
37
-25.32
-30.41
41.80
39
-13.65
-29.20
79.05
39
-41.34
-34.37
75.35
24
-31.66
-32.86
119.29
24
3.250***
3.477***
2.687***
3.143***
3.782***
4.019***
4.256***
3.809***
1.300
3.571***
1.078
4.326***
mean
p50
sd
N
94.93
95.35
7.91
70
101.17
100.00
2.22
116
110.66
108.92
10.09
63
6.93
5.60
8.42
70
8.67
7.62
8.26
63
16.46
14.16
15.09
56
-6.886***
-6.016***
-8.162***
-6.110***
-8.333***
-6.323***
Prices
Avg Tariff per
mean
residential GWH p50
(in dollars)
sd
N
Avg Tariff per
mean
residential GWH p50
(in real local
sd
currency)
N
106.24
97.85
23.68
69
91.77
88.27
12.83
69
98.48
100.00
7.52
116
100.81
100.00
4.97
116
94.87
95.61
24.63
73
109.61
107.07
18.59
73
-9.49
-0.09
23.85
69
9.21
15.25
14.81
69
-2.88
-1.38
18.73
73
8.46
4.64
14.27
73
-9.91
-16.37
26.18
55
17.90
24.26
25.81
55
3.305***
2.437**
2.808***
2.690***
1.313*
1.702*
-5.164***
-4.774***
-5.143***
-4.181***
-5.067***
-4.643***
* significant at 10%; ** significant at 5%; *** significant at 1%
Table 3b: Electricity Distribution
[In growth rates]
Variable
stats
Avg. Annual Growth
Preprivat
(1)
Outputs
Residential
Connections
MWH sold
per year
Inputs
Number of
Employees
Efficiency
Connections
per employee
GWH per
employee
Distributional
losses
Transition Postprivat
(2)
(3)
Annual Diff in growth
T-stat (Z-stat) for difference
in means (medians) in growth
(2)-(1)
(3)-(2)
(3)-(1)
(7)
(8)
(9)
(2)-(1)
(4)
(3)-(2)
(5)
(3)-(1)
(6)
1.3%
0.4%
-2.8%
-1.7%
-0.8%
-1.0%
-1.787**
-1.456
3.590***
5.116***
1.976**
2.366**
79
-0.5%
-0.7%
60
-5.0%
-2.9%
56
-3.2%
-2.7%
0.616
0.708
3.085***
4.096***
3.362***
3.159***
74
57
51
2.056*
2.306**
-5.398***
-4.505***
-1.519*
-1.776*
mean
p50
sd
N
mean
p50
sd
N
4.3%
4.4%
2.6%
79
6.7%
6.6%
4.5%
74
5.5%
4.7%
5.5%
84
6.7%
5.9%
8.7%
85
3.4%
3.2%
2.0%
60
3.2%
2.8%
4.7%
57
mean
p50
sd
N
-6.6%
-6.1%
8.1%
53
-9.9%
-9.0%
10.0%
69
-2.1%
-1.8%
4.8%
44
-3.2%
-3.8%
9.7%
8.7%
2.1%
4.0%
53
44
32
mean
p50
sd
N
mean
p50
sd
N
mean
p50
sd
N
13.4%
11.1%
12.6%
53
15.1%
12.8%
13.5%
53
0.6%
0.1%
7.8%
57
18.4%
14.0%
16.8%
66
20.3%
15.0%
16.9%
66
-5.5%
-4.9%
10.2%
73
5.5%
5.6%
5.1%
43
5.5%
4.0%
7.6%
43
-1.3%
-0.1%
9.6%
46
4.2%
4.5%
-16.4%
-10.6%
-4.2%
-3.5%
-1.813**
2.333**
5.691***
4.975***
2.183**
2.300**
53
3.7%
3.0%
43
-19.9%
-16.4%
32
-6.7%
-6.3%
1.426*
-1.624
6.539***
5.084***
2.826***
3.011***
53
-4.7%
-4.5%
43
6.4%
6.5%
32
-2.0%
-1.5%
3.301***
3.317***
-3.474***
-2.944***
0.960
0.786
57
46
36
* significant at 10%; ** significant at 5%; *** significant at 1%
Table 3b: Electricity Distribution
[in growth rates]
[Cont.]
Variable
stats
Preprivat
(1)
Quality
Duration of
Interruptions
per year
per consumer
Frequency of
Interruptions
per year
per consumer
Coverage
Residential
Connections
per 100 HHs
(2)-(1)
(4)
(3)-(2)
(5)
(3)-(1)
(6)
T-stat (Z-stat) for difference
in means (medians) in growth
(2)-(1)
(3)-(2)
(3)-(1)
(7)
(8)
(9)
Avg. Annual Growth
Transition Postprivat
(2)
(3)
Annual Diff in growth
mean
p50
sd
N
mean
p50
sd
N
4.1%
-5.2%
31.6%
32
2.7%
-5.0%
29.0%
32
-9.8%
-12.9%
25.7%
51
-10.6%
-10.8%
20.3%
51
-3.8%
-3.2%
24.8%
26
-11.4%
-6.6%
20.5%
26
-11.2%
-7.0%
3.4%
8.5%
-10.5%
-5.1%
1.788*
2.132**
4.476***
-0.749
5.122***
0.711
32
-11.1%
-2.8%
26
-2.9%
-2.4%
11
-17.8%
-14.4%
1.653*
1.664*
0.378
-0.165
3.093***
2.490**
32
26
11
mean
p50
sd
N
2.0%
1.5%
3.9%
65
2.2%
1.9%
3.0%
76
1.9%
1.3%
3.6%
50
0.4%
0.4%
-1.0%
-0.9%
-0.6%
-0.3%
-0.903
-1.408
1.702**
3.186***
0.780
0.619
65
50
42
Prices
Avg Tariff per
mean
residential GWH p50
(in dollars)
sd
N
Avg Tariff per
mean
residential GWH p50
(in real local
sd
currency)
N
9.3%
9.7%
16.0%
59
10.2%
5.9%
12.6%
59
-3.3%
-6.3%
9.0%
86
2.0%
2.3%
7.3%
86
2.0%
0.1%
14.1%
57
0.6%
1.8%
7.9%
56
-15.2%
-15.1%
4.3%
1.3%
-11.4%
-13.1%
6.251***
5.329***
-'1.821**
-1.442
3.172***
2.785***
59
-7.8%
-5.3%
57
0.2%
0.9%
35
-12.3%
-9.7%
4.744***
4.454***
-0.172
-0.734
4.899***
4.063***
59
56
35
* significant at 10%; ** significant at 5%; *** significant at 1%
Empirical Analysis: 2nd Approach
Fixed Effects Models:
lnyijt   T DUMMY _ TRANijt   P DUMMY _ POSTijt  ij Dij  ijt
ij
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.
lnyijt   T DUMMY _ TRANijt   P DUMMY _ POSTijt  ij Dij   ij t ij ijt
ij
tij
= Firm specific time trend.
ij
Table 6: Electricity Distribution
(1)
(2)
(3)
Number of Energy Sold Number of
Connect's
per year
Employees
(4)
Connect's
per
employee
(5)
Energy per
employee
(6)
Distribut
Losses
(7)
Duration of
interrupt's
(8)
Frequency
of
interrupt's
(9)
(10)
(11)
Coverage Avg price per Avg price per
MWH
MWH (in real
(in dollars)
local
currency)
Model 1: Log levels without firm-specific time trend
Transition
0.150***
0.201***
-0.307***
(-2<=t<=1) (0.005)
(0.007)
(0.016)
Post Transition 0.326***
0.370***
-0.500***
(t>=2)
(0.006)
(0.008)
(0.018)
Observations
823
808
586
Log Likelihood 1082.0
839.9
217.8
0.442***
(0.019)
0.810***
(0.021)
575
180.7
0.474***
(0.021)
0.819***
(0.023)
570
149.4
-0.031**
(0.013)
-0.172***
(0.014)
614
407.9
-0.144***
(0.028)
-0.488***
(0.031)
376
29.0
-0.107***
(0.025)
-0.415***
(0.028)
377
83.0
0.053***
(0.004)
0.130***
(0.004)
698
1185.8
-0.013
(0.018)
-0.041**
(0.019)
687
315.2
0.105***
(0.008)
0.177***
(0.009)
685
677.4
Model 2: Log levels with firm-specific time trend
Transition
-0.002
0.040***
-0.054***
(-2<=t<=1) (0.002)
(0.005)
(0.013)
Post Transition 0.007**
0.026***
-0.007
(t>=2)
(0.003)
(0.009)
(0.022)
Observations
823
808
586
Log Likelihood 2214.9
1415.6
723.3
0.049***
(0.012)
0.013
(0.022)
575
623.4
0.086***
(0.017)
0.006
(0.030)
570
541.9
0.021
(0.013)
-0.018
(0.023)
614
736.9
0.068**
(0.033)
-0.047
(0.055)
376
138.8
0.076***
(0.029)
-0.043
(0.047)
377
230.7
-0.007***
(0.002)
0.002
(0.003)
698
1898.1
0.078***
(0.012)
0.114***
(0.018)
687
659.0
0.034***
(0.008)
0.041***
(0.012)
685
1046.0
0.075***
(0.016)
0.024
(0.028)
554
401.5
-0.016
(0.016)
0.026
(0.027)
592
486.6
-0.065*
(0.035)
-0.032
(0.059)
339
16.6
-0.031
(0.037)
-0.029
(0.061)
341
56.1
-0.002
(0.002)
-0.003
(0.002)
669
1667.1
-0.094***
(0.014)
0.019
(0.021)
633
379.3
-0.044***
(0.010)
0.022
(0.015)
631
750.2
Model 3: Growth
Transition
0.005***
0.021***
-0.057***
0.073***
(-2<=t<=1) (0.002)
(0.006)
(0.012)
(0.012)
Post Transition 0.006*
0.016
-0.021
0.053**
(t>=2)
(0.003)
(0.010)
(0.020)
(0.021)
Observations
803
783
566
557
Log Likelihood 1999.6
1265.7
575.0
486.7
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)
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
Sector
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
Competitive
bidding
245
95
Direct
adjudication
(bilateral
negotiation)
15
Total
Transport
231
Water
and
sanitation
125
Total
696
Share of total
(percent)
78
(46%
renegotiated)
143
37
4
199
22
(8%
renegotiated)
260
Source: Guasch (2004)
238
268
129
895
100
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
Government
Operator
and Operator
All sectors
13%
26%
61%
Water and
10%
24%
66%
16%
27%
57%
Sanitation
Transport
Source: Author’s calculations
Who Initiated the Renegotiation Conditioned on
Regulatory Regime?
(% of Total Requests)
Both
Government
Government
and
Operator
Operator
All sectors
Price Caps
11%
6%
83%
Rate of Return
39%
34%
26%
Hybrid Regime
30%
26%
44%
Source: Author’s calculations
Common Outcomes of the Renegotiation Process
Delays on Investment Obligations Targets
Acceleration of Investment Obligations
Tariff Increases
Tariff Decreases
Increase in the number of cost components
with automatic pass-through to tariff increases
Extension of Concession Period
Reduction of Investment Obligations
Adjustment of canon-annual fee paid by
operator to government
Favorable to operator
Unfavorable to operator
Changes in the Asset-Capital Base
Favorable to Operator
Unfavorable to Operator
Source: Guasch (2004)
Percentage of renegotiated
concession contracts with
that outcome
69%
18%
62%
19%
59%
38%
62%
31%
17%
46%
22%
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
ANNEX
Brazil
Average 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
Topic**
1
1
1
1
1
1
1
1
1
1
1
1
1
1
Relevant Explanatory Variables
Macroeconomic instability (inflation, exchange rate):
degree of obstacle*
Exporters who just started (vs. non-exporter)
Exported for 1 year (vs. non-exporter)
Exported for 2 year (vs. non-exporter)
Exported for 3+ years (vs. non-exporter)
Main competitor-imports (vs. not)
% inputs that are imported
Delays of imports in customs, average days
Received municipal investment incentives (vs. not)
Received state investment incentives (vs. not)
Received federal investment incentives (vs. not)
Received tariff exemptions (vs. not)
Monopoly (0 competitors, vs. > 2)
Monopoly (1-2 competitors, vs. > 2)
All
-4.4
15.8
13.1
11.3
9.7
7.2
-0.4
-2.2
2.4
-11.9
-13.3
Dependent Variable and Sample
Output
Value-Added
Large Small All Large Small
-22.3
17.3
17.7
2.8
9.1
13.2
-0.2
-18.1
10.5
-2.9
6.3
-17.7
15.7
5.3
6.7
7.4
3.9
-0.7
5.8
-1.4
41.1
-15.2
-31.5
34.2
31.7
29.2
26.8
2.8
0.2
-0.7
3.0
-25.4
-17.2
-59.7
21.0
24.3
14.1
8.3
-0.3
4.5
-5.4
20.0
-15.4
-23.5
35.9
25.3
25.7
33.4
0.3
-1.3
20.8
-15.4
26.3
Brazil
Average 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 (con’t)
Topic**
2
2
2
2
2
2
2
2
2
2
Relevant Explanatory Variables
Firm was a bidder on a government contract
(vs. not)
At least 10% of sales to the government (vs.
not)
Number times asked for bribe: infrastructure
Number times asked for bribe: inspections
Number of inspection visits
Managerial time spent on regulation, %
Perception index: public service
Legal case open (vs. not)
Perception index: quality of Judiciary*
Access to land: degree of obstacle*
Dependent Variable and Sample
Output
Value-Added
All Large Small All Large Small
-1.1
2.4
-3.2
-11.8
-7.5
-6.7
3.9
15.9
9.3
-0.1
-0.3
1.8
-5.9
8.7
8.1
-30.9
-3.4
-0.1
-0.5
-8.1
27.8
-14.6
-0.3
1.7
13.6
-3.7
Brazil
Average 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 (con’t)
Topic**
Relevant Explanatory Variables
3
Have or share electricity generator (vs. not)
3
Power interruptions: index*
3
Communications interruptions: index*
3
Transport interruptions: index*
3
Wait time for a phone connection, days
3
Wait time for a power connection, days
3
Perception index: infrastructure as obstacle*
Dependent Variable and Sample
Output
Value-Added
All Large Small All Large Small
6.1
13.9 21.0
33.3
-6.3
-9.9
-5.3 -12.9 -14.9 -11.8
-1.6
-3.7
-6.2
-17.0
-8.3 -27.8
-3.2 -12.8 -31.4
0.0
-0.1
0.0
-0.1
-0.1
0.1
0.0
-0.1
0.1
-0.1
-7.9 -10.0 -10.1 -14.3
-6.4 -19.5
Brazil
Average 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 (con’t)
Topic**
Relevant Explanatory Variables
% staff using computers
4
4
4
4
4
4
New technology acquired with help from
consultants (vs. simple purchase of new
machinery, etc.)
New technology acquired by hiring key
personnel (vs. simple purchase of new
machinery, etc.)
Quality certification (vs. no certification)
Agreed a new joint venture with a foreign
partner (vs. not)
Obtained a new licensing agreement (vs.
not)
Dependent Variable and Sample
Output
Value-Added
All Large Small All Large Small
0.4
0.1
0.4
0.9
0.5
1.0
11.7
-8.7
19.6
8.0
2.2
1.4
11.6
1.6
2.5
8.8
21.8
23.6
9.2
15.3
13.2
38.2
17.4
17.5
9.6
Brazil
Average 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 (con’t)
Topic**
5
5
5
5
5
5
5
5
Relevant Explanatory Variables
Manager has at least some college education
(vs. without)
Manager experience, years
Labor force with incomplete high school, %
External training offered (vs. not)
% informal workers among full-time
employees
% informal workers among part-time
employees
Optimal employment is lower than existing
(vs. optimal)
Optimal employment is higher than existing
(vs. optimal)
All
9.8
0.3
0.0
3.0
Dependent Variable and Sample
Output
Value-Added
Large Small All Large Small
9.8
0.1
21.2
0.7
-0.2
11.5
-0.1
-0.3
-0.3
0.2
0.2
3.4
-0.1
0.1
9.9
0.3
0.0
0.0
-4.6
-4.6
20.8
0.2
-0.3
3.4
21.8
0.8
-0.2
11.5
-3.5
-7.0
-10.7
Brazil
Average 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 (con’t)
Topic**
Relevant Explanatory Variables
6
Family business (vs. not)
Source of pressure to reduce costs: shareholders
6
(vs. competitors)
Source of pressure to reduce costs: creditors (vs.
6
competitors)
6
Has bank loan (vs. not)
6
Did not need bank loan (vs. applied and failed)
Needed bank loan, but did not apply (vs. applied
6
and failed)
6
Number banks engaged with
6
Hired external auditing (vs. not)
6
Does project assessment (NPV, etc. - vs. not)
7
Loss due to theft, % sales
Dependent Variable and Sample
Output
Value-Added
All Large Small All Large Small
-2.1
-15.0 -25.5
-5.5
8.2
9.5
4.2
12.0
7.2
30.2
12.2
16.3
3.5
3.2
0.9
1.4
0.5
-1.2
8.0
-1.8
-0.9
-1.0
29.0
10.4
29.3
15.9
3.5
2.2
-3.3
50.7
30.0
39.4
13.3
25.6
3.9
3.3
20.5
-5.1
15.7
3.9
11.7
-3.5
-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.
Percentage of private investment
Composition of Private Flows to Infrastructure by Sector for Selected
Countries ( %)
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
r
s
a si a
d
il
a
u
lic
ia
o
a
le
a
el ado x ic Per raz
n e ays i mb pub Chi
i n il an mal
i
e
t
u
p
n
n
z
o
l
a
B
e
l v Me
ge Th
at ndo hi li p Ma Col
Re
n e l Sa
r
u
e
I
n
A
P
V
G
E
ic a
in
m
Do
Telecom
Source:
Energy
PPI database.
Transport
Water and sew erage
BRAZIL: Productivity Effects of Counterfactual Improvements in Investment
Climate Indexes (from the 25th to 75th Percentiles)
Investment Climate Index
Infrastructure
Exporter Premium at 25th Percentile
Exporter Premium at 75th Percentile
Change in Exporter Premium (from 25th to
75th percentile)
Change in Productivity (25th to 75thperc.)
Governance
Exporter Premium at 25th Percentile
Exporter Premium at 75th Percentile
Change in Exporter Premium (from 25th to
75th percentile)
Change in Productivity (25th to 75th perc.)
Access to Finance
Exporter Premium at 25th Percentile
Exporter Premium at 75th Percentile
Change in Exporter Premium (from 25th to
75th percentile)
Change in Productivity (25th to 75th perc.)
Skills and Technology
Exporter Premium at 25th Percentile
Exporter Premium at 75th Percentile
Change in Exporter Premium (from 25th to
75th percentile)
Change in Productivity (25th to 75th perc.)
Total Change in Exporter Premium (25th to
75th percentile of all investment climate ind.)
Total Change in Productivity (25th to 75th
percentile of all investment climate indexes)
Three or Average
Year of One Year Two Years More
Exporters Average Difference
Entry into After First After First
Years
Non(exporters
Exports
Exports
Exports After First
exporters minus nonExports
exporters)
44.52%
61.05%
16.53%
34.04%
63.67%
29.63%
24.35%
74.45%
50.10%
50.51%
49.21%
-1.30%
94.96%
35.17%
46.84%
11.67%
28.55%
48.01%
19.47%
24.25%
53.09%
28.84%
24.17%
51.13%
26.95%
86.92%
22.63%
41.02%
18.39%
31.16%
26.16%
-5.00%
20.68%
36.08%
15.40%
28.50%
28.50%
0.00%
28.79%
37.62%
45.40%
7.78%
5.44%
-5.13%
10.57%
15.63%
5.86%
9.78%
14.16%
2.22%
11.95%
26.07%
27.70%
-1.63%
61.31%
30.64%
30.66%
36.16%
43.57%
7.41%
39.30%
40.87%
1.57%
43.45%
37.32%
-6.13%
10.62%
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
Sources of Productivity
Increases
After Five Years (*)
After One Year
Incumbents Entrants Total Incumbents Entrants
Total
After Ten Years(*)
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 Nonexporters
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
Mean
Preprivat
(1)
Outputs
Residential Water mean
Connections
p50
sd
N
Residential Sewer mean
Connections
p50
sd
N
Cubic Meter of
mean
produced water
p50
sd
N
Inputs
Number of
mean
Employees
p50
sd
N
Efficiency
Water Connectionsmean
per employee
p50
sd
N
Distributional
mean
losses
p50
sd
N
Diff in Levels
Transition Postprivat
(2)
(3)
(2)-(1)
(4)
(3)-(2)
(5)
(3)-(1)
(6)
T-stat (Z-stat) for difference
in means (medians) in levels
(2)-(1)
(3)-(2)
(3)-(1)
(7)
(8)
(9)
85.85
87.37
6.32
23
84.88
85.48
11.21
20
99.98
100.99
8.89
16
103.15
102.61
3.72
49
102.75
101.89
5.02
49
103.62
100.00
22.20
49
119.74
117.09
13.17
34
122.59
119.62
15.08
32
97.27
99.04
14.80
31
16.20
15.18
7.07
23
18.83
18.62
10.62
20
2.21
1.95
11.88
16
16.31
13.88
10.85
34
19.43
17.46
12.28
32
-2.91
-0.72
11.45
31
29.43
28.10
10.35
18
32.90
29.38
13.09
15
-1.33
3.15
16.60
14
-10.988***
-4.197***
-8.762***
-5.086***
-12.059***
-3.724***
-7.932***
-3.883***
-8.950***
-4.937***
-9.735***
-3.408***
-0.745
-0.879
1.416*
1.078
0.299
-0.973
141.43
125.11
49.22
17
103.97
100.00
14.22
49
92.35
97.04
23.85
27
-37.20
-21.34
38.72
17
-12.18
-8.36
17.26
27
-57.36
-52.01
46.62
15
3.961***
3.527***
3.668***
3.339***
4.766***
3.237***
70.50
68.46
18.93
17
107.22
106.01
16.43
16
103.34
100.00
12.65
49
100.02
100.00
7.42
49
144.11
125.05
59.84
28
82.08
81.64
21.22
23
36.53
36.39
15.09
17
-8.70
-8.33
13.51
16
38.73
20.71
48.79
28
-18.26
-16.63
23.33
23
83.86
69.30
62.73
15
-23.18
-20.12
27.88
14
-9.979***
-3.621***
-4.201***
-4.532***
-5.177***
-3.408***
2.577**
2.327**
3.755***
3.254***
3.110***
2.605***
* significant at 10%; ** significant at 5%; *** significant at 1%
Table 4a: Water and Sewerage
[In levels]
[Cont.]
Variable
stats
Mean
Preprivat
(1)
Quality
Continuity
(hs per day)
mean
p50
sd
N
% of the samples mean
that passed the
p50
potability test
sd
N
Coverage
Residential Water mean
Connections
p50
per 100 HHs
sd
N
Residential Sewer mean
Connections
p50
per 100 HHs
sd
N
Diff in Levels
Transition Postprivat
(2)
(3)
(2)-(1)
(4)
(3)-(2)
(5)
(3)-(1)
(6)
T-stat (Z-stat) for difference
in means (medians) in levels
(2)-(1)
(3)-(2)
(3)-(1)
(7)
(8)
(9)
78.34
97.11
37.52
9
88.35
99.50
27.92
8
101.01
100.00
4.68
49
100.30
100.00
1.53
49
116.79
104.35
24.68
15
103.89
100.51
6.87
14
21.81
2.48
36.74
9
11.55
0.58
26.14
8
14.94
2.17
21.06
15
2.58
0.46
4.62
14
21.66
4.05
46.07
8
4.94
1.08
7.20
6
-1.781*
-2.192**
-2.748***
-2.774***
-1.330
-1.971**
-1.250
-1.630
-2.088**
-2.603***
-1.682*
-1.941*
94.25
95.13
5.70
22
91.47
91.72
8.76
17
101.84
100.00
3.96
49
101.77
100.00
6.88
49
111.12
106.88
14.11
29
110.03
106.87
11.55
20
6.52
4.86
6.80
22
10.23
8.02
9.29
17
8.71
5.26
10.71
29
8.67
5.76
9.74
20
10.37
8.76
10.10
19
13.59
8.98
9.29
13
-4.498***
-4.107***
-4.379***
-4.584***
-4.478***
-3.823***
-4.539***
-3.479***
-3.981***
-3.920***
-5.277***
-3.180***
* significant at 10%; ** significant at 5%; *** significant at 1%
Table 4a: Water and Sewerage
[In levels]
[Cont.]
Variable
stats
Mean
Preprivat
(1)
Transition Postprivat
(2)
(3)
Prices
Avg price per cub. mean
93.62
101.39
106.70
meter of water
p50
87.95
100.00
98.60
(in dollars)
sd
43.54
9.53
37.16
N
10
49
13
Avg price per cub. mean
84.00
103.53
130.09
meter of water
p50
82.76
100.00
121.21
(in real local
sd
23.18
11.71
32.81
currency)
N
10
49
13
Avg price per cub. mean
114.61
100.53
107.79
meter of sewer
p50
79.43
100.00
107.68
(in dollars)
sd
89.74
6.94
32.73
N
3
49
4
Avg price per cub. mean
93.06
101.80
152.44
meter of sewer
p50
74.75
100.00
135.93
(in real local
sd
45.93
10.88
51.26
currency)
N
3
49
4
* significant at 10%; ** significant at 5%; *** significant at 1%
Diff in Levels
(2)-(1)
(4)
(3)-(2)
(5)
(3)-(1)
(6)
10.43
11.81
51.89
10
25.70
22.22
32.80
10
-19.43
16.46
89.77
3
13.26
30.91
44.86
3
1.46
3.27
30.57
13
17.68
19.65
21.96
13
0.03
-12.60
35.56
4
32.25
33.12
21.42
4
40.24
32.70
50.34
8
57.87
44.80
39.44
8
44.29
44.29
75.05
2
53.34
53.34
2.02
2
T-stat (Z-stat) for difference
in means (medians) in levels
(2)-(1)
(3)-(2)
(3)-(1)
(7)
(8)
(9)
-0.635
-1.274
-0.173
-0.314
-2.261**
-2.240**
-2.478**
-1.988**
-2.903***
-0.411**
-4.150***
-2.521**
0.375
0.000
0.001
0.365
-0.835
-0.447
-0.512
-0.535
-3.012**
-1.826*
-37.266***
-1.342
Table 4b: Water and Sewerage
[In growth rates]
Variable
stats
Avg. Annual Growth
Preprivat
(1)
Outputs
Residential Water mean
Connections
p50
sd
N
Residential Sewer mean
Connections
p50
sd
N
Cubic Meter of
mean
produced water
p50
sd
N
Inputs
Number of
mean
Employees
p50
sd
N
Efficiency
Water Connectionsmean
per employee
p50
sd
N
Distributional
mean
losses
p50
sd
N
Transition Postprivat
(2)
(3)
Annual Diff in growth
(2)-(1)
(4)
(3)-(2)
(5)
(3)-(1)
(6)
T-stat (Z-stat) for difference
in means (medians) in growth
(2)-(1)
(3)-(2)
(3)-(1)
(7)
(8)
(9)
4.4%
4.1%
3.0%
17
3.8%
4.3%
5.9%
15
2.1%
1.6%
4.6%
12
6.5%
5.2%
4.4%
43
6.7%
5.5%
6.8%
40
7.5%
1.0%
38.6%
38
4.7%
3.8%
4.6%
24
7.4%
3.6%
10.7%
23
0.5%
0.9%
5.0%
21
0.9%
-0.1%
3.5%
17
3.1%
2.1%
9.8%
15
-0.9%
0.0%
4.1%
12
-1.9%
-1.8%
5.6%
24
1.5%
-1.4%
12.3%
23
-1.8%
0.0%
7.3%
21
1.5%
1.2%
3.2%
6
0.0%
0.1%
3.2%
5
1.6%
1.5%
5.0%
5
-1.095
-0.923
1.649*
2.229**
-1.113
-0.943
-1.222
-0.966
-0.569
0.693
0.009
-0.135
0.741
0.000
1.117
0.817
-0.718
-0.674
-0.4%
0.1%
4.2%
12
-10.0%
-8.3%
10.2%
32
-1.5%
-1.0%
7.2%
18
-9.6%
-9.8%
9.7%
12
7.5%
7.8%
9.2%
18
-1.0%
-1.4%
7.4%
5
3.425***
2.432***
-3.460***
-2.765***
0.309
0.135
5.5%
4.9%
5.4%
13
-3.1%
-2.6%
3.8%
11
17.5%
15.8%
13.5%
32
-0.6%
-2.0%
21.5%
26
7.3%
4.5%
10.1%
19
-5.5%
-5.1%
9.1%
17
11.6%
9.9%
13.7%
13
0.5%
-0.1%
5.3%
11
-9.6%
-7.8%
14.3%
19
0.5%
0.3%
6.2%
17
1.2%
0.1%
8.3%
6
0.6%
0.8%
4.0%
6
-3.068***
2.551**
2.939***
2.656
-0.348
0.105
-0.297
-0.267
-0.310
-0.450
-0.363
-0.843
* significant at 10%; ** significant at 5%; *** significant at 1%
Table 4b: Water and Sewerage
[in growth rates]
[Cont.]
Variable
stats
Avg. Annual Growth
Preprivat
(1)
Quality
Continuity
(hs per day)
mean
p50
sd
N
% of the samples mean
that passed the
p50
potability test
sd
N
Coverage
Residential Water mean
Connections
p50
per 100 HHs
sd
N
Residential Sewer mean
Connections
p50
per 100 HHs
sd
N
Transition Postprivat
(2)
(3)
Annual Diff in growth
(2)-(1)
(4)
(3)-(2)
(5)
(3)-(1)
(6)
T-stat (Z-stat) for difference
in means (medians) in growth
(2)-(1)
(3)-(2)
(3)-(1)
(7)
(8)
(9)
0.0%
0.0%
0.0%
3
0.8%
0.6%
1.0%
4
7.2%
0.0%
16.0%
18
5.2%
0.2%
16.4%
18
4.6%
0.9%
8.7%
11
0.4%
0.0%
0.7%
9
22.4%
0.0%
38.7%
3
18.6%
2.2%
34.6%
4
-0.1%
0.0%
6.0%
11
-0.5%
0.0%
1.2%
9
0.0%
0.0%
.
1
-1.0%
-1.0%
1.4%
2
-1.000
-1.000
0.057
0.075
-
-1.074
-0.928
1.273
1.315
1.000
1.000
1.0%
0.3%
1.7%
16
1.6%
1.4%
17.9%
14
4.1%
2.8%
5.0%
34
8.0%
2.9%
17.9%
25
3.3%
1.6%
4.4%
19
2.8%
0.6%
6.1%
14
1.1%
0.2%
2.1%
16
2.9%
0.1%
6.0%
14
-1.3%
-1.3%
6.1%
19
-0.9%
-1.6%
6.2%
14
0.4%
0.1%
1.7%
5
-1.6%
-0.9%
1.3%
5
-2.050**
-1.448
0.914
1.690*
-0.570
-0.944
-1.815
-1.036
0.529
1.601
2.735**
2.023**
* significant at 10%; ** significant at 5%; *** significant at 1%
Table 4b: Water and Sewerage
[in growth rates]
[Cont.]
Variable
stats
Avg. Annual Growth
Preprivat
(1)
Transition Postprivat
(2)
(3)
Prices
Avg price per cub. mean
12.2%
1.9%
-3.4%
meter of water
p50
10.9%
-2.2%
-1.1%
(in dollars)
sd
10.4%
22.2%
20.0%
N
8
17
9
Avg price per cub. mean
10.1%
9.4%
4.5%
meter of water
p50
10.1%
5.4%
2.6%
(in real local
sd
6.7%
18.4%
10.0%
currency)
N
8
17
9
Avg price per cub. mean
-0.6%
-5.1%
-7.9%
meter of sewer
p50
-0.6%
-8.7%
-7.9%
(in dollars)
sd
17.1%
16.1%
11.6%
N
2
5
3
Avg price per cub. mean
-1.1%
7.0%
9.7%
meter of sewer
p50
-1.1%
1.4%
9.8%
(in real local
sd
13.9%
13.5%
16.0%
currency)
N
2
5
3
* significant at 10%; ** significant at 5%; *** significant at 1%
Annual Diff in growth
(2)-(1)
(4)
(3)-(2)
(5)
(3)-(1)
(6)
-12.1%
-13.8%
13.8%
8
-6.0%
-4.3%
8.1%
8
2.3%
2.3%
10.8%
2
5.0%
5.0%
1.8%
2
-7.2%
-3.3%
26.0%
9
-8.9%
-6.5%
25.1%
9
-6.4%
-10.8%
13.9%
3
-4.3%
-18.4%
24.7%
3
-3.9%
-2.1%
10.1%
3
-0.8%
-2.5%
4.0%
3
-7.7%
-7.7%
.
1
-15.1%
-15.1%
.
1
T-stat (Z-stat) for difference
in means (medians) in growth
(2)-(1)
(3)-(2)
(3)-(1)
(7)
(8)
(9)
2.493**
1.820*
0.835
0.889
0.666
0.535
2.078**
1.540
1.060
1.007
0.346
0.000
-0.298
-0.447
0.799
1.069
-
3.881*
-1.342
0.302
0.000
-
Table 5a: Fixed Telecomm
[In levels]
Variable
stats
Mean
Preprivat
(1)
Outputs
Total number
of lines
Total number
of minutes
Inputs
Number of
employee
Efficiency
Total number
of lines per
employee
Total number
of minutes per
employee
Percentage of
Incomplete Calls
Diff in Levels
Transition Postprivat
(2)
(3)
(2)-(1)
(4)
(3)-(2)
(5)
(3)-(1)
(6)
T-stat (Z-stat) for difference
in means (medians) in levels
(2)-(1)
(3)-(2)
(3)-(1)
(7)
(8)
(9)
mean
p50
sd
N
mean
p50
sd
N
78.98
76.93
12.55
16
107.32
97.39
41.60
6
115.39
112.16
13.76
16
103.05
100.00
5.04
16
181.31
178.47
48.91
15
146.89
146.89
8.32
2
36.41
33.90
14.53
16
0.82
9.05
40.84
6
65.70
67.92
37.74
15
41.13
41.13
3.00
2
102.77
93.40
46.14
15
69.57
69.57
24.76
2
-10.022***
-3.516***
-8.627***
-3.408***
-6.742***
-3.408***
-0.049
0.105
-3.973*
-1.342
-19.420**
-1.342
mean
p50
sd
N
117.88
111.71
30.44
15
100.72
100.28
7.88
16
82.02
81.31
29.61
14
-17.12
-22.64
29.96
15
-18.37
-20.05
25.70
14
-37.18
-50.94
52.09
14
2.213**
1.761*
2.671***
2.166**
2.675***
2.291**
mean
p50
sd
N
mean
p50
sd
N
mean
p50
sd
N
72.98
70.13
24.63
15
79.81
76.03
22.83
6
580.77
111.56
1133.58
6
119.54
110.66
26.54
16
105.38
100.00
12.63
16
141.09
100.00
167.34
16
262.84
217.38
126.18
14
238.94
238.94
135.73
2
101.20
74.51
74.92
7
47.86
38.93
37.28
15
34.53
44.60
29.38
6
-368.95
-17.23
860.92
6
140.97
102.05
106.41
14
123.54
123.54
117.59
2
-93.78
-27.47
180.06
7
191.73
154.59
136.35
14
172.50
172.50
118.47
2
-472.93
-37.37
1055.53
6
-4.972***
-3.237***
-5.262***
-3.233***
-4.957***
-3.233***
-2.879**
-1.782*
-2.059
-1.342
-1.486
-1.342
1.050
1.782*
1.098
2.201**
1.378
2.366**
* significant at 10%; ** significant at 5%; *** significant at 1%
Table 5a: Fixed Telecomm
[In levels]
[Cont.]
Variable
stats
Mean
Preprivat
(1)
Diff in Levels
(2)-(1)
(4)
(3)-(2)
(5)
(3)-(1)
(6)
199.92
136.01
161.58
14
51.75
41.82
42.33
13
81.00
29.26
129.55
14
138.97
78.72
169.03
13
-4.407***
-3.180***
-2.964***
-3.180***
-2.339**
-3.129***
mean
83.65
113.47
167.28
p50
80.18
109.18
169.15
sd
12.73
13.75
45.46
N
16
16
15
* significant at 10%; ** significant at 5%; *** significant at 1%
29.82
28.25
15.75
16
53.25
56.28
34.23
15
84.53
68.99
42.48
15
-7.573***
-3.516***
-7.708***
-3.408***
-6.025***
-3.351***
Quality
Percentage of
Digitalized
Network
Coverage
Number of Lines
per 100 HHs
mean
p50
sd
N
68.64
70.82
22.80
13
Transition Postprivat
(2)
(3)
T-stat (Z-stat) for difference
in means (medians) in levels
(2)-(1)
(3)-(2)
(3)-(1)
(7)
(8)
(9)
116.56
107.27
31.58
16
Table 5a: Fixed Telecomm
[In levels]
[Cont.]
Variable
stats
Mean
Preprivat
(1)
Prices
Avg Price for a
3-minute call
(in dollars)
Avg monthly charge
for residential
Service (in dollars)
Avg Charge for the
installation of a
residential line
(in dollars)
Avg Price for a
3-minute call
(in real local
currency)
Avg monthly charge
for residential
Service (in real
local currency)
Avg Charge for the
installation of a
residential line (in
real local currency)
mean
p50
sd
N
mean
p50
sd
N
mean
p50
sd
N
mean
p50
sd
N
mean
p50
sd
N
mean
p50
sd
N
144.83
57.48
219.85
10
55.46
41.00
36.35
10
634.94
95.78
887.73
10
84.40
64.40
50.71
8
60.42
49.78
35.69
10
842.23
108.37
1045.40
8
Diff in Levels
Transition Postprivat
(2)
(3)
100.45
99.98
15.00
16
101.25
100.00
19.28
16
123.11
101.06
40.50
16
100.65
100.00
7.71
16
100.26
100.00
12.69
16
122.99
100.00
41.81
16
99.89
91.72
63.61
12
143.43
120.51
124.99
13
100.51
77.29
108.31
10
97.58
87.14
44.03
10
135.11
115.76
77.55
11
132.07
58.62
152.59
8
* significant at 10%; ** significant at 5%; *** significant at 1%
(2)-(1)
(4)
(3)-(2)
(5)
(3)-(1)
(6)
-46.64
34.44
205.46
10
39.02
53.32
41.36
10
-502.46
11.18
875.99
10
12.63
30.96
50.24
8
36.59
49.77
41.60
10
-699.77
-6.06
1033.62
8
-1.03
-11.25
61.29
12
41.60
15.16
115.87
13
-25.83
-39.79
72.80
10
-3.46
-14.01
43.72
10
34.54
16.83
69.27
11
1.25
-31.83
126.57
8
-58.79
1.74
248.59
9
105.49
43.43
151.92
9
-256.72
8.92
808.89
6
16.28
25.78
76.87
8
88.96
79.48
97.05
9
-252.68
1.91
894.37
6
T-stat (Z-stat) for difference
in means (medians) in levels
(2)-(1)
(3)-(2)
(3)-(1)
(7)
(8)
(9)
0.718
-0.866
0.710
-0.178
0.058
1.255
-2.983***
-2.293**
-2.083**
-2.073**
-1.295
-0.804
1.814*
0.051
0.777
-0.314
1.122
1.376
-0.711
-0.980
-0.599
-1.120
0.250
1.478
-2.782**
-2.191**
-2.750**
-2.310**
-1.654*
-1.334
1.915**
0.700
0.692
-0.105
-0.028
0.420
Table 5b: Fixed Telecomm
[In growth rates]
Variable
stats
Avg. Annual Growth
Preprivat
(1)
Outputs
Total number
of lines
Total number
of minutes
Inputs
Number of
employee
Efficiency
Total number
of lines per
employee
Total number
of minutes per
employee
Percentage of
Incomplete Calls
Transition Postprivat
(2)
(3)
Annual Diff in growth
(2)-(1)
(4)
(3)-(2)
(5)
(3)-(1)
(6)
T-stat (Z-stat) for difference
in means (medians) in growth
(2)-(1)
(3)-(2)
(3)-(1)
(7)
(8)
(9)
mean
p50
sd
N
mean
p50
sd
N
6.9%
7.2%
6.2%
16
4.1%
4.6%
1.9%
5
12.7%
11.7%
6.3%
16
2.1%
1.7%
15.3%
6
7.2%
6.6%
8.2%
14
3.8%
3.8%
.
1
5.8%
3.8%
9.1%
16
-6.7%
-4.1%
12.9%
5
-6.5%
-12.0%
12.8%
14
3.2%
3.2%
.
1
0.4%
-2.1%
10.7%
14
-0.8%
-0.8%
.
1
-2.546**
-2.223**
1.917**
1.852*
-0.152
-0.157
1.158
1.219
-
-
mean
p50
sd
N
-0.5%
-0.8%
6.9%
15
-3.1%
-4.5%
9.8%
15
-6.9%
-7.7%
9.0%
14
-2.6%
-1.5%
11.1%
15
-3.4%
-1.3%
10.0%
14
-6.5%
-3.9%
8.4%
14
0.916
0.909
1.258
0.785
2.861***
2.291**
mean
p50
sd
N
mean
p50
sd
N
mean
p50
sd
N
7.8%
6.6%
11.6%
15
5.2%
9.5%
9.6%
5
-1.5%
-1.5%
1.0%
6
17.6%
21.3%
15.3%
15
13.2%
16.3%
11.7%
6
-16.4%
-7.8%
23.4%
8
16.0%
15.7%
11.5%
14
28.6%
28.6%
.
1
-14.3%
-9.3%
14.7%
7
9.8%
10.9%
15.5%
15
5.5%
4.4%
4.1%
5
-13.9%
-5.1%
26.4%
6
-3.1%
-9.9%
18.9%
14
11.9%
11.9%
.
1
-0.2%
0.0%
14.0%
7
8.0%
9.4%
16.7%
14
19.1%
19.1%
.
1
-13.7%
-8.8%
15.6%
6
-2.452**
-2.101**
0.610
0.659
-1.791**
-1.726*
-3.000**
-2.023**
-
-
1.293
1.363
0.046
0.000
2.145**
2.201**
* significant at 10%; ** significant at 5%; *** significant at 1%
Table 5b: Fixed Telecomm
[in growth rates]
[Cont.]
Variable
stats
Avg. Annual Growth
Preprivat
(1)
Quality
Percentage of
Digitalized
Network
Coverage
Number of Lines
per 100 HHs
Transition Postprivat
(2)
(3)
Annual Diff in growth
(2)-(1)
(4)
(3)-(2)
(5)
(3)-(1)
(6)
T-stat (Z-stat) for difference
in means (medians) in growth
(2)-(1)
(3)-(2)
(3)-(1)
(7)
(8)
(9)
mean
p50
sd
N
51.5%
22.1%
116.3%
13
17.1%
14.2%
15.9%
14
4.9%
0.9%
6.8%
13
-33.1%
-4.4%
110.1%
13
-13.5%
-12.0%
13.5%
13
-50.1%
-11.9%
121.1%
12
1.085
1.293
3.602***
2.734***
1.434*
2.824***
mean
p50
sd
N
4.9%
4.4%
5.9%
16
11.0%
9.4%
6.2%
16
6.0%
4.9%
7.8%
14
6.1%
4.5%
8.1%
16
-5.9%
-8.0%
10.8%
14
1.2%
-0.1%
10.0%
14
-3.001***
-2.637***
2.040**
1.852*
-0.438
-0.471
* significant at 10%; ** significant at 5%; *** significant at 1%
Table 5a: Fixed Telecomm
[In levels]
[Cont.]
Variable
stats
Avg. Annual Growth
Preprivat
(1)
Prices
Avg Price for a
3-minute call
(in dollars)
Avg monthly charge
for residential
Service (in dollars)
Avg Charge for the
installation of a
residential line
(in dollars)
Avg Price for a
3-minute call
(in real local
currency)
Avg monthly charge
for residential
Service (in real
local currency)
Avg Charge for the
installation of a
residential line (in
real local currency)
mean
p50
sd
N
mean
p50
sd
N
mean
p50
sd
N
mean
p50
sd
N
mean
p50
sd
N
mean
p50
sd
N
46.7%
40.9%
69.0%
8
42.8%
15.7%
54.6%
9
-1.9%
-1.8%
25.8%
9
35.7%
44.3%
55.4%
7
35.6%
-0.9%
50.1%
9
-8.6%
-26.3%
32.3%
7
Transition Postprivat
(2)
(3)
-3.1%
-1.3%
16.8%
13
13.9%
6.0%
31.0%
14
-14.7%
-2.3%
38.7%
14
-2.5%
4.3%
19.1%
10
16.5%
15.6%
32.1%
12
-16.1%
-20.0%
46.4%
10
-5.7%
-0.4%
12.4%
10
5.2%
0.0%
28.1%
11
-13.7%
-29.3%
33.7%
9
-0.6%
0.6%
4.9%
9
7.1%
3.2%
13.1%
10
-11.6%
-30.5%
40.4%
7
* significant at 10%; ** significant at 5%; *** significant at 1%
Annual Diff in growth
(2)-(1)
(4)
(3)-(2)
(5)
(3)-(1)
(6)
-44.4%
-41.4%
63.5%
8
-21.9%
-33.1%
60.4%
9
-9.6%
-5.2%
36.5%
9
-30.5%
-32.1%
47.6%
7
-12.7%
-32.9%
52.9%
9
-4.7%
-35.0%
43.5%
7
-2.3%
-7.9%
25.1%
10
-10.5%
-3.3%
41.9%
11
-5.7%
-2.6%
44.6%
9
2.7%
-5.2%
21.1%
9
-9.4%
-1.9%
30.9%
10
-6.7%
-2.0%
48.0%
7
-60.8%
-52.5%
83.3%
6
-45.8%
-28.4%
67.9%
7
-32.6%
-18.2%
40.1%
4
-36.7%
-21.2%
58.0%
6
-29.4%
0.6%
54.6%
7
-19.1%
1.4%
48.4%
4
T-stat (Z-stat) for difference
in means (medians) in growth
(2)-(1)
(3)-(2)
(3)-(1)
(7)
(8)
(9)
1.981**
1.820*
0.295
0.459
1.788*
1.572
1.088
1.007
0.830
0.978
1.785*
1.272
0.785
1.008
0.381
0.533
1.626
1.826*
1.696*
1.352
-0.389
0.178
1.549*
1.153
0.721
0.770
0.959
0.866
1.426
0.676
0.289
0.000
0.370
0.845
0.789
-0.365
Table 7: Water and Sewerage
(1)
(2)
(3)
(4)
(5)
(6)
Number of Number of
Cubic
Number of
Water
Cubic
Water
Sewerage Meters per Employees Connec. Meters per
Connect's Connect's
year
per
Employee
Employee
(7)
Distrib.
Losses
(8)
Continuity
of the
Service
(9)
Potability
(10)
(11)
(12)
Water
Sewerage Avg price
Coverage Coverage per M3 of
water (in
dollars)
(13)
(14)
Avg price Avg price
per M3 of per M3 for
water (in sewerage
R.L.C.) (in dollars)
(15)
Avg price
per M3 for
sewerage
(in R.L.C.)
Model 1: Log levels without firm-specific time trend
Transition
0.141***
0.174***
0.040***
-0.180***
(-2<=t<=1)
(0.010)
(0.016)
(0.009)
(0.030)
Post Transition
0.280***
0.347***
0.056***
-0.374***
(t>=2)
(0.025)
(0.064)
(0.020)
(0.123)
Firm Fixed Effects Yes
Yes
Yes
Yes
Firm-specific trend No
No
No
No
Observations
259
239
195
201
Log Likelihood
361.4
251.5
288.2
62.3
0.268***
(0.034)
0.622***
(0.149)
Yes
No
199
41.3
0.216***
(0.039)
0.434***
(0.199)
Yes
No
160
39.0
-0.039**
(0.017)
-0.193***
(0.023)
Yes
No
179
149.4
0.038
(0.064)
0.112*
(0.064)
Yes
No
97
96.8
0.053***
(0.009)
0.118***
(0.068)
Yes
No
198
187.2
0.055
(0.041)
0.152***
(0.052)
Yes
No
112
475.6
0.146***
(0.026)
0.360***
(0.027)
Yes
No
112
406.9
-0.014
(0.142)
-0.110
(0.169)
Yes
No
37
22.2
0.104
(0.083)
0.325***
(0.086)
Yes
No
37
70.0
-0.014
(0.142)
-0.096
(0.110)
Yes
No
37
-11.6
0.104
(0.083)
0.222***
(0.077)
Yes
No
37
7.2
Model 2: Log levels with firm-specific time trend
Transition
0.006
-0.006
-0.007
(-2<=t<=1)
(0.004)
(0.009)
(0.010)
Post Transition
0.004
-0.011
-0.020
(t>=2)
(0.031)
(0.031)
(0.052)
Firm Fixed Effects Yes
Yes
Yes
Firm-specific trend Yes
Yes
Yes
Observations
259
239
195
Log Likelihood
699.1
481.4
394.9
-0.076***
(0.023)
-0.103***
(0.135)
Yes
Yes
199
185.5
-0.071**
(0.036)
-0.114**
(0.213)
Yes
Yes
160
128.7
-0.014
(0.012)
-0.014
(0.045)
Yes
Yes
179
293.8
0.000
(0.006)
0.000
(0.006)
Yes
Yes
96
276.7
-0.005
(0.006)
-0.014
(0.013)
Yes
Yes
198
257.5
0.003
(0.050)
-0.044
(0.050)
Yes
Yes
112
895.3
-0.048
(0.034)
-0.072
(0.035)
Yes
Yes
112
536.9
0.026
(0.093)
0.038
(0.170)
Yes
Yes
37
88.7
0.017
(0.082)
0.062
(0.121)
Yes
Yes
37
120.9
0.026
(0.093)
0.013
(0.088)
Yes
Yes
37
21.3
0.017
(0.082)
0.045
(0.078)
Yes
Yes
37
26.5
0.047***
(0.018)
0.010
(0.183)
Yes
No
178
125.8
0.072**
(0.035)
0.014
(0.267)
Yes
No
140
72.3
-0.000
(0.012)
-0.013
(0.028)
Yes
No
160
207.1
0.002
(0.020)
0.001
(0.020)
Yes
No
81
156.9
0.003
(0.004)
-0.005
(0.074)
Yes
No
180
193.6
-0.203***
(0.034)
-0.221***
(0.035)
Yes
No
101
686.0
-0.099***
(0.027)
-0.110***
(0.032)
Yes
No
101
384.6
-0.054
(0.080)
-0.059
(0.131)
Yes
No
31
62.3
0.007
(0.059)
0.013
(0.074)
Yes
No
31
80.0
-0.054
(0.080)
-0.005
(0.076)
Yes
No
31
7.5
0.007
(0.059)
0.006
(0.065)
Yes
No
31
13.1
0.083***
(0.026)
0.152***
(0.033)
Yes
Yes
201
188.0
Model 3: Growth
Transition
0.001
0.006
-0.008
-0.048***
(-2<=t<=1)
(0.004)
(0.006)
(0.009)
(0.018)
Post Transition
-0.009**
-0.005
-0.033*** 0.000
(t>=2)
(0.011)
(0.077)
(0.054)
(0.031)
Firm Fixed Effects Yes
Yes
Yes
Yes
Firm-specific trend No
No
No
No
Observations
235
216
172
176
Log Likelihood
539.7
392.4
258.5
128.7
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)
Number of
Connections
(2)
Minutes
per year
(3)
(4)
(5)
(6)
(7)
Number of Connections Minutes per
% of
Percentage
Employees
per
employee Uncomplete
of
employee
d Calls
Digitalized
Network
Model 1: Log levels without firm-specific time trend
Transition
0.253***
0.079**
-0.097***
0.301***
0.278***
-0.133
0.310***
(-2<=t<=1)
(0.030)
(0.035)
(0.033)
(0.054)
(0.059)
(0.083)
(0.053)
Post Transition
0.747***
0.398***
-0.361***
1.027***
0.935***
-0.486***
0.768***
(t>=2)
(0.031)
(0.039)
(0.035)
(0.058)
(0.094)
(0.084)
(0.056)
Firm Fixed Effect Yes
Yes
Yes
Yes
Yes
Yes
Yes
Firm-specific trend No
No
No
No
No
No
No
Observations
168
71
161
162
69
70
131
Log Likelihood
67.9
28.7
30.6
-39.6
-0.4
-4.5
-13.3
0.168***
(0.025)
0.590***
(0.028)
Yes
No
165
86.5
Model 2: Log levels with firm-specific time trend
Transition
-0.050**
0.002
0.031
(-2<=t<=1)
(0.024)
(0.038)
(0.026)
Post Transition
0.064
0.135**
-0.038
(t>=2)
(0.042)
(0.062)
(0.046)
Firm Fixed Effect Yes
Yes
Yes
Firm-specific trend Yes
Yes
Yes
Observations
168
71
161
Log Likelihood
186.1
66.7
163.1
-0.101***
(0.038)
0.083
(0.069)
Yes
Yes
162
105.3
Model 3: Growth
Transition
0.027**
0.069***
-0.041***
0.070***
(-2<=t<=1)
(0.011)
(0.012)
(0.015)
(0.021)
Post Transition
0.024**
0.122***
-0.066***
0.104***
(t>=2)
(0.011)
(0.033)
(0.017)
(0.022)
Firm Fixed Effect Yes
Yes
Yes
Yes
Firm-specific trend No
No
No
No
Observations
165
60
158
158
Log Likelihood
0.26
0.13
0.40
0.46
Standard errors in parentheses
* significant at 10%; ** significant at 5%; *** significant at 1%
Note: "R.L.C." means "Real Local Currency"
(8)
Coverage
(lines per
100 inhabit.)
-0.010
(0.044)
0.163**
(0.082)
Yes
Yes
69
107.1
0.142***
(0.042)
0.148**
(0.075)
Yes
Yes
70
58.5
0.048**
(0.024)
0.072*
(0.044)
Yes
Yes
131
124.6
-0.065***
(0.019)
0.027
(0.035)
Yes
Yes
165
206.1
0.085**
(0.042)
0.168***
(0.062)
Yes
No
59
0.12
-0.062
(0.041)
-0.096**
(0.042)
Yes
No
64
0.84
-0.008
(0.026)
-0.065**
(0.027)
Yes
No
122
0.09
0.037***
(0.010)
0.039***
(0.011)
Yes
No
162
0.30
Table 7: Fixed Telecomm (Cont.)
(9)
(10)
(11)
(12)
(13)
Av Price for Monthly
Price for an Av Price for Monthly
a 3-min call
charges
installation a 3-min call
charges
(in dollars) (in dollars) (in dollars) (in R.L.C.) (in R.L.C.)
(14)
Price for an
installation
(in R.L.C.)
Model 1: Log levels without firm-specific time trend
Transition
0.384***
0.565***
0.095
(-2<=t<=1)
(0.080)
(0.118)
(0.114)
Post Transition
0.370***
0.774***
-0.215
(t>=2)
(0.083)
(0.122)
(0.137)
Firm Fixed Effect Yes
Yes
Yes
Firm-specific trend No
No
No
Observations
104
114
107
Log Likelihood
-30.9
-65.9
-82.2
0.371***
(0.081)
0.282***
(0.084)
Yes
No
91
-17.2
0.486***
(0.113)
0.683***
(0.118)
Yes
No
110
-63.5
-0.178
(0.171)
-0.464**
(0.186)
Yes
No
87
-88.2
Model 2: Log levels with firm-specific time trend
Transition
0.523***
0.281***
0.300***
(-2<=t<=1)
(0.104)
(0.100)
(0.063)
Post Transition
0.573***
0.214
0.522***
(t>=2)
(0.160)
(0.145)
(0.115)
Firm Fixed Effect Yes
Yes
Yes
Firm-specific trend Yes
Yes
Yes
Observations
104
114
107
Log Likelihood
-1.1
-13.2
4.1
0.358***
(0.082)
0.191
(0.142)
Yes
Yes
91
24.9
0.067
(0.092)
-0.032
(0.145)
Yes
Yes
110
16.5
0.118
(0.154)
0.362*
(0.207)
Yes
Yes
87
-6.0
-0.047
(0.067)
-0.046
(0.076)
Yes
No
102
0.24
-0.140
(0.107)
-0.104
(0.113)
Yes
No
79
0.33
Model 3: Growth
Transition
-0.052
-0.101
-0.003
-0.056
(-2<=t<=1)
(0.077)
(0.097)
(0.048)
(0.065)
Post Transition
-0.033
-0.136
-0.023
-0.081
(t>=2)
(0.078)
(0.103)
(0.063)
(0.067)
Firm Fixed Effect Yes
Yes
Yes
Yes
Firm-specific trend No
No
No
No
Observations
93
105
98
82
Log Likelihood
0.16
0.23
0.39
0.20
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)
Number of
Connections
(2)
Minutes
per year
(3)
Number of
Employees
(4)
Connections
per employee
(5)
Minutes per
employee
(6)
(7)
(8)
% of
Percentage of
Coverage
Uncompleted
Digitalized
(lines per 100
Calls
Network
inhabit.)
Model 1: Log levels without firm-specific time trend
Transition
0.232***
0.064*
-0.046
(-2<=t<=1)
(0.027)
(0.036)
(0.030)
Post Transition
0.664***
0.343***
-0.197***
(t>=2)
(0.032)
(0.056)
(0.035)
Liberalization
0.275***
0.065
-0.361***
(0.037)
(0.046)
(0.047)
Firm Fixed Effect Yes
Yes
Yes
Firm-specific trend No
No
No
Observations
168
71
161
Log Likelihood
83.1
29.7
48.1
0.272***
(0.049)
0.873***
(0.056)
0.673***
(0.083)
Yes
No
162
-19.0
0.232***
(0.050)
0.664***
(0.091)
0.487***
(0.082)
Yes
No
69
12.2
-0.140*
(0.081)
-0.475***
(0.098)
-0.027
(0.088)
Yes
No
70
-4.9
0.307***
(0.057)
0.753***
(0.072)
0.023
(0.069)
Yes
No
131
-13.8
0.166***
(0.025)
0.531***
(0.029)
0.230***
(0.035)
Yes
No
165
98.3
Model 2: Log levels with firm-specific time trend
Transition
-0.050**
0.001
0.026
(-2<=t<=1)
(0.024)
(0.043)
(0.026)
Post Transition
0.066
0.128**
-0.040
(t>=2)
(0.043)
(0.064)
(0.046)
Liberalization
0.002
0.037
-0.046
(0.032)
(0.063)
(0.042)
Firm Fixed Effect Yes
Yes
Yes
Firm-specific trend Yes
Yes
Yes
Observations
168
71
161
Log Likelihood
185.6
66.8
163.5
-0.089**
(0.038)
0.103
(0.068)
0.117**
(0.049)
Yes
Yes
162
107.1
-0.006
(0.049)
0.158*
(0.085)
0.108
(0.090)
Yes
Yes
69
107.0
0.133***
(0.043)
0.143*
(0.074)
-0.041
(0.053)
Yes
Yes
70
58.9
0.044*
(0.025)
0.068
(0.045)
-0.016
(0.028)
Yes
Yes
131
124.4
-0.066***
(0.020)
0.025
(0.035)
-0.007
(0.025)
Yes
Yes
165
206.1
-0.089**
(0.038)
0.103
(0.068)
0.117**
(0.049)
Yes
No
158
110.9
-0.006
(0.049)
0.158*
(0.085)
0.108
(0.090)
Yes
No
59
69.8
0.133***
(0.043)
0.143*
(0.074)
-0.041
(0.053)
Yes
No
64
40.2
0.044*
(0.025)
0.068
(0.045)
-0.016
(0.028)
Yes
No
122
90.7
-0.066***
(0.020)
0.025
(0.035)
-0.007
(0.025)
Yes
No
162
211.8
Model 3: Growth
Transition
(-2<=t<=1)
Post Transition
(t>=2)
Liberalization
-0.050**
0.001
0.026
(0.024)
(0.043)
(0.026)
0.066
0.128**
-0.040
(0.043)
(0.064)
(0.046)
0.002
0.037
-0.046
(0.032)
(0.063)
(0.042)
Firm Fixed Effect Yes
Yes
Yes
Firm-specific trend No
No
No
Observations
165
60
158
Log Likelihood
203.5
74.2
144.8
Standard errors in parentheses
* significant at 10%; ** significant at 5%; *** significant at 1%
Table 13: Fixed Telecomm Competition
(Cont.)
(9)
Av Price for a
3-min call (in
dollars)
(10)
Monthly
charges
(in dollars)
(11)
Price for an
installation
(in dollars)
(12)
Av Price for a
3-min call
(in R.L.C.)
(13)
Monthly
charges
(in R.L.C.)
(14)
Price for an
installation
(in R.L.C.)
Model 1: Log levels without firm-specific time trend
Transition
0.422***
0.558***
0.033
(-2<=t<=1)
(0.088)
(0.131)
(0.073)
Post Transition
0.433***
0.778***
-0.118
(t>=2)
(0.101)
(0.140)
(0.097)
Liberalization
-0.097
0.001
-0.491***
(0.088)
(0.144)
(0.171)
Firm Fixed Effect Yes
Yes
Yes
Firm-specific trend No
No
No
Observations
104
114
107
Log Likelihood
-30.3
-67.4
-80.5
0.359***
(0.085)
0.197*
(0.104)
0.150*
(0.091)
Yes
No
91
-16.0
0.398***
(0.112)
0.500***
(0.124)
0.443***
(0.155)
Yes
No
110
-58.9
-0.107
(0.191)
-0.238
(0.221)
-0.529**
(0.221)
Yes
No
87
-90.0
Model 2: Log levels with firm-specific time trend
Transition
0.441***
0.192
0.245***
(-2<=t<=1)
(0.109)
(0.136)
(0.083)
Post Transition
0.430***
0.081
0.442***
(t>=2)
(0.166)
(0.187)
(0.133)
Liberalization
-0.356***
-0.410***
-0.030
(0.116)
(0.147)
(0.092)
Firm Fixed Effect Yes
Yes
Yes
Firm-specific trend Yes
Yes
Yes
Observations
104
114
107
Log Likelihood
1.9
-14.0
3.7
0.296***
(0.082)
0.103
(0.139)
-0.240***
(0.090)
Yes
Yes
91
27.9
-0.007
(0.087)
-0.142
(0.139)
-0.500***
(0.136)
Yes
Yes
110
22.5
0.130
(0.165)
0.376*
(0.219)
0.035
(0.169)
Yes
Yes
87
-5.9
0.296***
(0.082)
0.103
(0.139)
-0.240***
(0.090)
Yes
No
82
16.3
-0.007
(0.087)
-0.142
(0.139)
-0.500***
(0.136)
Yes
No
102
-4.1
0.130
(0.165)
0.376*
(0.219)
0.035
(0.169)
Yes
No
79
-29.9
Model 3: Growth
Transition
(-2<=t<=1)
Post Transition
(t>=2)
Liberalization
0.441***
0.192
0.245***
(0.109)
(0.136)
(0.083)
0.430***
0.081
0.442***
(0.166)
(0.187)
(0.133)
-0.356***
-0.410***
-0.030
(0.116)
(0.147)
(0.092)
Firm Fixed Effect Yes
Yes
Yes
Firm-specific trend No
No
No
Observations
93
105
98
Log Likelihood
0.5
-25.9
-20.0
Standard errors in parentheses
* significant at 10%; ** significant at 5%; *** significant at 1%
Table 14: Fixed Telecomm
[Competition – mobile]
(1)
Number of
Connections
(3)
(4)
(5)
(6)
(7)
(8)
Number of Connections Minutes per
% of
Percentage Coverage
Employees
per
employee Uncomplete
of
(lines per
employee
d Calls
Digitalized 100 inhabit.)
Network
Model 1: Log levels without firm-specific time trend
Transition
(-2<=t<=1)
Post Transition
(t>=2)
Mobile Subs [x 1M]
0.247***
(0.027)
0.660***
(0.030)
0.013***
(0.002)
Firm Fixed Effect Yes
Firm-specific trend No
Observations
168
Log Likelihood
88.3
(2)
Minutes
per year
0.047
(0.037)
0.268***
(0.065)
0.005**
(0.002)
Yes
No
71
31.8
-0.059**
(0.027)
-0.147***
(0.034)
-0.025***
(0.001)
Yes
No
161
65.6
0.291***
(0.043)
0.791***
(0.051)
0.037***
(0.002)
Yes
No
162
3.5
Model 2: Log levels with firm-specific time trend
Transition
-0.064***
0.019
0.008
-0.070*
(-2<=t<=1)
(0.025)
(0.051)
(0.025)
(0.039)
Post Transition
0.056
0.131**
-0.036
0.106
(t>=2)
(0.043)
(0.062)
(0.044)
(0.068)
Mobile Subs [x 1M] -0.006*
0.010**
-0.017***
0.010**
(0.003)
(0.005)
(0.003)
(0.005)
Firm Fixed Effect Yes
Yes
Yes
Yes
Firm-specific trend Yes
Yes
Yes
Yes
Observations
168
71
161
162
Log Likelihood
186.7
68.6
171.0
107.5
Model 3: Growth
Transition
0.023**
0.068***
-0.043***
0.068***
(-2<=t<=1)
(0.011)
(0.014)
(0.015)
(0.021)
Post Transition
0.035***
0.136**
-0.060***
0.107***
(t>=2)
(0.012)
(0.055)
(0.017)
(0.023)
Mobile Subs [x 1M] -0.002**
-0.001
-0.002
-0.001
(0.001)
(0.002)
(0.002)
(0.002)
Firm Fixed Effect Yes
Yes
Yes
Yes
Firm-specific trend No
No
No
No
Observations
165
60
158
158
Log Likelihood
201.6
73.4
146.0
108.4
Standard errors in parentheses
* significant at 10%; ** significant at 5%; *** significant at 1%
0.178***
(0.050)
0.448***
(0.100)
0.030***
(0.004)
Yes
No
69
22.1
-0.143*
(0.077)
-0.479***
(0.111)
-0.000
(0.004)
Yes
No
70
-4.4
0.313***
(0.053)
0.755***
(0.066)
0.001
(0.003)
Yes
No
131
-13.5
0.171***
(0.022)
0.513***
(0.027)
0.014***
(0.002)
Yes
No
165
108.0
0.029
(0.063)
0.090
(0.081)
0.032***
(0.006)
Yes
Yes
69
114.4
0.111**
(0.045)
0.133*
(0.069)
-0.004
(0.005)
Yes
Yes
70
60.1
0.017
(0.022)
0.059
(0.038)
-0.021***
(0.003)
Yes
Yes
131
141.5
-0.068***
(0.021)
0.031
(0.036)
-0.003
(0.003)
Yes
Yes
165
205.1
0.075*
(0.040)
0.070
(0.076)
0.006*
(0.003)
Yes
No
59
63.2
-0.062
(0.042)
-0.095*
(0.055)
-0.000
(0.002)
Yes
No
64
39.7
0.006
(0.025)
-0.024
(0.029)
-0.005***
(0.001)
Yes
No
122
96.6
0.035***
(0.011)
0.039***
(0.012)
-0.001
(0.001)
Yes
No
162
211.4
Table 14: Fixed Telecomm
[Competition - Mobile (Cont.)]
(9)
Av Price for
a 3-min call
(in dollars)
(10)
Monthly
charges
(in dollars)
(11)
(12)
Price for an Av Price for
installation a 3-min call
(in dollars) (in R.L.C.)
Model 1: Log levels without firm-specific time trend
Transition
0.432***
0.506***
-0.030
(-2<=t<=1)
(0.079)
(0.120)
(0.021)
Post Transition
0.470***
0.695***
0.002
(t>=2)
(0.087)
(0.125)
(0.032)
Mobile Subs [x 1M] -0.015***
0.013
-0.151***
(0.006)
(0.010)
(0.017)
Firm Fixed Effect Yes
Yes
Yes
Firm-specific trend No
No
No
Observations
104
114
107
Log Likelihood
-27.0
-64.2
-41.2
0.311***
(0.075)
0.090
(0.092)
0.017***
(0.004)
Yes
No
91
-11.4
Model 2: Log levels with firm-specific time trend
Transition
0.166***
-0.056
0.327***
0.201***
(-2<=t<=1)
(0.063)
(0.105)
(0.073)
(0.047)
Post Transition
0.459***
-0.002
0.522***
0.284***
(t>=2)
(0.092)
(0.127)
(0.125)
(0.070)
Mobile Subs [x 1M] -0.117***
-0.148***
0.039*
-0.063***
(0.007)
(0.015)
(0.024)
(0.005)
Firm Fixed Effect Yes
Yes
Yes
Yes
Firm-specific trend Yes
Yes
Yes
Yes
Observations
104
114
107
91
Log Likelihood
43.2
9.8
4.5
63.3
Model 3: Growth
Transition
-0.005
-0.076
-0.031
-0.023
(-2<=t<=1)
(0.063)
(0.090)
(0.054)
(0.059)
Post Transition
0.111*
-0.025
-0.094
0.028
(t>=2)
(0.067)
(0.100)
(0.071)
(0.065)
Mobile Subs [x 1M] -0.026***
-0.032***
0.018*
-0.014***
(0.004)
(0.008)
(0.011)
(0.004)
Firm Fixed Effect Yes
Yes
Yes
Yes
Firm-specific trend No
No
No
No
Observations
93
105
98
82
Log Likelihood
10.0
-26.5
-17.1
23.5
Standard errors in parentheses
* significant at 10%; ** significant at 5%; *** significant at 1%
(13)
Monthly
charges
(in R.L.C.)
(14)
Price for an
installation
(in R.L.C.)
0.365***
(0.102)
0.368***
(0.111)
0.042***
(0.009)
Yes
No
110
-52.2
-0.165
(0.106)
-0.135
(0.126)
-0.132***
(0.017)
Yes
No
87
-69.0
-0.043
(0.044)
-0.048
(0.072)
-0.105***
(0.011)
Yes
Yes
110
46.1
0.349**
(0.161)
0.573***
(0.202)
0.076***
(0.025)
Yes
Yes
87
-2.5
-0.043
(0.059)
0.028
(0.070)
-0.025***
(0.007)
Yes
No
102
-7.6
-0.175*
(0.093)
-0.214**
(0.105)
0.028***
(0.011)
Yes
No
79
-27.0
Table 15: Fixed Telecomm
[Competition - mobile v2]
(1)
Number of
Connections
(3)
(4)
(5)
(6)
(7)
(8)
Number of Connections Minutes per
% of
Percentage Coverage
Employees
per
employee Uncomplete
of
(lines per
employee
d Calls
Digitalized 100 inhabit.)
Network
Model 1: Log levels without firm-specific time trend
Transition
(-2<=t<=1)
Post Transition
(t>=2)
Mobile Subs [x 1M]
0.243***
(0.026)
0.638***
(0.030)
0.005***
(0.001)
Firm Fixed Effect Yes
Firm-specific trend No
Observations
168
Log Likelihood
92.4
(2)
Minutes
per year
0.044
(0.038)
0.257***
(0.069)
0.002**
(0.001)
Yes
No
71
31.7
-0.049*
(0.027)
-0.119***
(0.035)
-0.008***
(0.001)
Yes
No
161
66.2
0.288***
(0.041)
0.738***
(0.050)
0.013***
(0.001)
Yes
No
162
11.7
Model 2: Log levels with firm-specific time trend
Transition
-0.042*
0.038
0.008
-0.035
(-2<=t<=1)
(0.025)
(0.050)
(0.025)
(0.038)
Post Transition
0.067
0.135**
-0.023
0.109*
(t>=2)
(0.042)
(0.058)
(0.044)
(0.064)
Mobile Subs [x 1M] 0.001
0.005***
-0.006***
0.007***
(0.001)
(0.002)
(0.001)
(0.002)
Firm Fixed Effect Yes
Yes
Yes
Yes
Firm-specific trend Yes
Yes
Yes
Yes
Observations
168
71
161
162
Log Likelihood
186.6
70.6
169.4
114.2
Model 3: Growth
Transition
0.024**
0.067***
-0.043***
0.069***
(-2<=t<=1)
(0.011)
(0.014)
(0.015)
(0.021)
Post Transition
0.035***
0.127**
-0.059***
0.106***
(t>=2)
(0.012)
(0.058)
(0.017)
(0.024)
Mobile Subs [x 1M] -0.001*
-0.000
-0.001
-0.000
(0.000)
(0.001)
(0.001)
(0.001)
Firm Fixed Effect Yes
Yes
Yes
Yes
Firm-specific trend No
No
No
No
Observations
165
60
158
158
Log Likelihood
201.5
78.8
146.2
108.5
Standard errors in parentheses
* significant at 10%; ** significant at 5%; *** significant at 1%
0.176***
(0.051)
0.429***
(0.105)
0.010***
(0.001)
Yes
No
69
20.7
-0.136*
(0.077)
-0.452***
(0.112)
-0.001
(0.001)
Yes
No
70
-4.0
0.312***
(0.054)
0.750***
(0.067)
0.001
(0.001)
Yes
No
131
-13.4
0.169***
(0.022)
0.496***
(0.027)
0.004***
(0.001)
Yes
No
165
111.0
0.032
(0.066)
0.065
(0.080)
0.011***
(0.002)
Yes
Yes
69
119.9
0.120***
(0.045)
0.140**
(0.071)
-0.001
(0.001)
Yes
Yes
70
59.8
0.015
(0.023)
0.077*
(0.040)
-0.007***
(0.001)
Yes
Yes
131
138.3
-0.060***
(0.020)
0.030
(0.036)
0.000
(0.001)
Yes
Yes
165
205.8
0.073*
(0.040)
0.062
(0.078)
0.002*
(0.001)
Yes
No
59
63.3
-0.062
(0.042)
-0.094*
(0.055)
-0.000
(0.001)
Yes
No
64
39.4
0.007
(0.025)
-0.017
(0.030)
-0.001***
(0.000)
Yes
No
122
96.9
0.035***
(0.011)
0.039***
(0.012)
-0.000
(0.000)
Yes
No
162
211.4
Table 15: Fixed Telecomm
[Competition - Mobile v2 (Cont.)]
(9)
Av Price for
a 3-min call
(in dollars)
(10)
Monthly
charges
(in dollars)
(11)
(12)
Price for an Av Price for
installation a 3-min call
(in dollars) (in R.L.C.)
Model 1: Log levels without firm-specific time trend
Transition
0.427***
0.511***
0.013
(-2<=t<=1)
(0.078)
(0.120)
(0.028)
Post Transition
0.473***
0.689***
0.101**
(t>=2)
(0.088)
(0.128)
(0.044)
Mobile Subs [x 1M] -0.004***
0.004
-0.046***
(0.002)
(0.003)
(0.005)
Firm Fixed Effect Yes
Yes
Yes
Firm-specific trend No
No
No
Observations
104
114
107
Log Likelihood
-27.1
-64.3
-43.0
0.322***
(0.076)
0.097
(0.094)
0.005***
(0.001)
Yes
No
91
-11.8
Model 2: Log levels with firm-specific time trend
Transition
0.144**
-0.073
0.317***
0.194***
(-2<=t<=1)
(0.065)
(0.110)
(0.070)
(0.048)
Post Transition
0.400***
-0.105
0.527***
0.276***
(t>=2)
(0.099)
(0.141)
(0.123)
(0.070)
Mobile Subs [x 1M] -0.033***
-0.039***
0.008
-0.018***
(0.002)
(0.004)
(0.007)
(0.002)
Firm Fixed Effect Yes
Yes
Yes
Yes
Firm-specific trend Yes
Yes
Yes
Yes
Observations
104
114
107
91
Log Likelihood
37.6
6.7
4.1
63.0
Model 3: Growth
Transition
-0.009
-0.074
-0.030
-0.024
(-2<=t<=1)
(0.062)
(0.091)
(0.053)
(0.059)
Post Transition
0.112*
-0.008
-0.091
0.028
(t>=2)
(0.067)
(0.101)
(0.071)
(0.066)
Mobile Subs [x 1M] -0.008***
-0.010***
0.005
-0.004***
(0.001)
(0.002)
(0.003)
(0.001)
Firm Fixed Effect Yes
Yes
Yes
Yes
Firm-specific trend No
No
No
No
Observations
93
105
98
82
Log Likelihood
9.5
-26.6
-17.3
23.2
Standard errors in parentheses
* significant at 10%; ** significant at 5%; *** significant at 1%
(13)
Monthly
charges
(in R.L.C.)
(14)
Price for an
installation
(in R.L.C.)
0.377***
(0.102)
0.374***
(0.113)
0.012***
(0.003)
Yes
No
110
-53.5
-0.171
(0.114)
-0.084
(0.135)
-0.042***
(0.005)
Yes
No
87
-67.5
-0.078
(0.051)
-0.088
(0.083)
-0.030***
(0.003)
Yes
Yes
110
43.4
0.321*
(0.167)
0.589***
(0.213)
0.017**
(0.007)
Yes
Yes
87
-4.0
-0.042
(0.060)
0.035
(0.072)
-0.007***
(0.002)
Yes
No
102
-8.4
-0.165*
(0.093)
-0.215**
(0.107)
0.008**
(0.003)
Yes
No
79
-27.7