DEMAND-DRIVEN SOLUTIONS TO ILLEGAL ELECTRICITY CONNECTIONS- A PRELIMINARY EVALUATION OF THE “CONVIVER” PROGRAM IN THE URBAN FAVELAS OF
BELO HORIZONTE, BRAZIL
A Thesis submitted to the Graduate School of Arts & Sciences
at Georgetown University in partial fulfillment of the requirements
for the degree of Master of Public Policy
in the Georgetown Public Policy Institute
By
Luisa Maria Mimmi
Washington, DC April 14, 2008
ii
Copyright 2008 by Luisa M. Mimmi All Rights Reserved
iii
DEMAND-DRIVEN SOLUTIONS TO ILLEGAL ELECTRICITY CONNECTIONS- A PRELIMINARY EVALUATION OF THE “CONVIVER” PROGRAM IN THE URBAN FAVELAS OF
BELO HORIZONTE, BRAZIL
Luisa Maria Mimmi
Thesis Advisor: Dr. Sencer Ecer
ABSTRACT In Brazilian urban favelas, the development of informal connections is common, relatively easy to obtain, and allow citizens to acquire electricity eluding or reducing the regular charging of consumption. Genuine affordability problems and limited means of subsistence are the most important but not exhaustive explanation of theft and nonpayment problems afflicting energy distribution companies. With a preliminary evaluation of the project “Conviver: energia para viver melhor”, implemented in Belo Horizonte, this thesis studies the determinants of illegal connections and energy fraud in the context of low-income urban favelas. Using the tecnique of logistic regression, the study illustrates how the probability of engaging in illegal behavior is explained by the following concurring factors: inadequate energy provision and lacking equipment; income and affordability; inefficient and incorrect use of domestic electric appliances. Additionally, based on the Conviver sample of 15,279 households, the thesis provides an evaluation of the effectiveness of different pro-poor schemes (government poverty subsidies and electricity block tariffs) at subsidizing energy costs for poor customers. The analysis proves that social tariffs, even though not exclusively responsible, can be considered as factors that mitigate the occurrence of illegality. The type of social tariffs that work better are the ones that are more strictly connected to consumption, as opposed to those linked to poverty subsidies. This make a strong case for a “tariff engineering” that takes into serious account the electricity needs in term of consumption, and try to segment the customers to realistically match the actual needs (minimum; low-medium; houses with micro-businesses in the dwelling). As the preliminary outcomes of the Conviver program document, the positive effect of pro-poor tariff schemes will be even multiplied if combined with effort for the orientation of low-income customers to energy-saving behaviors.
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ACKNOWLEDGEMENTS This thesis is written and dedicated with thanks to the many people whose
unconditional support made this project possible. First and foremost, I am grateful to the AVSI representatives, Ezio Castelli,
Maria Teresa Gatti and Paola Galafassi, for offering me the opportunity to work with them in Brazil during the summer of 2007. It was this incredibly enriching experience that inspired and motivated this work. I am equally thankful to the CDM staff in Belo Horizonte and in particular to Giorgio Capitanio, Ernane Souza, Bruno Amorim and all the Conviver dedicated staff for illustrating me the program and giving me the opportunity to study it. A very special thanks goes to Flavia Gomes for her kind and patient help in navigating the data.
I am also deeply grateful to my advisor, Dr. Sencer Ecer, who accompanied this work all along the year with his valuable orientation and positive attitude.
Last but not least, I want to thank my friends: Marinella, Jackie and Martina who helped me revising and editing the work, as well as Monica and Adria who have always been there with their friendship and support.
The study was also completed with the support of Ingenio, a financial tool of
Regione Lombardia, aimed to support research an innovation and technological transfer. I was granted the “Ingenio grant for mobility studies” in 2007.
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Table of Contents
Introduction....................................................................................................... 1 Chapter 1. Energy poverty in Brazilian urban favelas .................................. 4
1.a Energy poverty in recent literature..................................................................4 1.b Universal and equal provision of electricity....................................................5 1.c The challenge of affordability: public policy options ......................................9 1.d Demand-driven solutions for cost-effective energy provision to the poor...... 12 1.e The urban slums of Belo Horizonte .............................................................. 16 1.f Energy provision and costs in the urban favelas of Belo Horizonte ............... 18 1.g Policy relevance of the research ................................................................... 21
Chapter 2. The Conviver Program................................................................ 24 2.a Conviver: project rationale and expected outcomes ...................................... 24 2.b Conviver preliminary analysis and organizational details.............................. 26 2.c Conviver Project: research design and selection criteria ................................ 28 2.d Scope of the analysis and sampling strategy ................................................. 33
Chapter 3. Determinants of illegal connection and energy fraud in the Conviver communities ..................................................................................................... 35
3.a Hypotheses about the causes of illegal connection and electricity thefts........ 35 3.b Hypotheses about the impact and targeting of social tariffs in preventing poor households from illegal connection or electricity theft ....................................... 37 3.c Dependent variables (three measures of illegality)........................................ 39 3.d Independent variables................................................................................... 41 3.e Analysis of the dependent variables.............................................................. 46 3.f Expected signs of the independent variables ................................................. 51 3.g Regression models and results discussion..................................................... 58
Conclusions, policy remarks and next steps ................................................... 72 Bibliography .................................................................................................... 78 Appendix 1 Additional regression Models (Chapter 3).................................. 82 Appendix 2 AVSI & CDM .............................................................................. 87
1
Introduction
Although situated within the same urban area as the wealthier neighborhoods,
Brazilian urban low-income settlements (favelas) sometimes lack basic infrastructure,
such as electricity, water, sanitation, gas, and phone landlines. More often, such
infrastructures exist but present inefficiencies, higher costs, or fail to reach all the
households. In such circumstances, the development of informal connections is
common, relatively easy to obtain and allow citizens to acquire the services for free
using clandestine wiring (the so called “gato”) or other methods that elude the regular
charging of consumption.
The general belief that slum residents do not pay for the services provided to
them or are unwilling to pay for regular electric connections is a gross misconception.
Poor people living in the favelas are willing to pay for higher-quality energy services.
In fact, besides the obvious benefit of enjoying safe and legal access to basic services,
such as energy, water, or transportation, being regular customers gives them access to
financial services. In Brazil, the bills represent an important proof of residency that
allows access to credit in commercial establishments.1
The more realistic facet of the problem is that the poor people cannot pay real
cost-based tariffs, or they may be deterred from obtaining service by high connection
costs (relative to their income) or non-availability of service. To overcome
disproportionate access barriers requires in the first place to identify the Willingness-
2
To-Pay (WTP) for the service and the consequent fee levels at which participation in
the electrification programs will drop.2
In the summer 2007, I had the opportunity to work in Brazil and to contribute
to the project “Conviver: energia para viver melhor” (Conviver: energy for a better
life”), implemented in Belo Horizonte (capital of the State of Minas Gerais) to improve
the delivery of electricity in the peri-urban low-income communities.
The main sponsor of the program is the local utility company Companhia
Energética de Minas Gerais (from now on CEMIG), which is interested in reducing
fraud and energy thefts and increasing the collection ratio among its customers. At the
same time, this project was explicitly designed for social purposes, hence a number of
specific measures are taken that directly help poor residents to reach legal, safe, and
sustainable access to electricity.
In order to develop Conviver, CEMIG turned to an Italian-Brazilian NGO
AVSI-CDM (Associazione Volontari per il Servizio Internazionale – Cooperação para
o Desenvolvimento e Morada Humana)3 with extensive experience in the favela’s
environment. AVSI-CDM adapted for this case an innovative approach that it had
previously designed for a similar program in Salvador de Bahia (Brazil),4 implemented
in collaboration with the local utility company COELBA. Both programs, build on the
1 Anjali et al. (2005). 2 See Brook and (2003), ESMAP (2006), Komives et al. (2005), Manzetti and Rufin (2006). 3 Further details about AVSI-CDM in Annex N.3
3
Brazilian law according to which the Energy Authority ANEEL (Agencia Nacional de
Energia Eletrica) mandates local power utilities to invest part of their annual net
operating revenue in energy saving programs and R&D programs. The Conviver
Program is still ongoing, but the initial facts show a very positive response and a strong
level of participation among the assisted families.
This thesis focus on the problem of energy poverty in the context of Brazilian
urban low-income settlements. The first chapter will provide an overview of relevant
literature and empirical analysis in the context of peri-urban slums in Brazil.
In Chapter 2 I will present a detailed description of the Conviver program with
an emphasis on its rationale, expected outcomes, and selection criteria.
Chapter 3 focuses on the analysis of the determinants of illegal behaviors
related to energy. Among the causes of illegality, particular attention is devoted to the
issues of low quality of equipments and services, incorrect use of electrical appliances,
and non affordability of connection.
I also study the effectiveness of existing low-income energy payment schemes
in reducing the probability of illegal connections and use of electricity.
4 The name of the program is Projeto Agente.
4
Chapter 1. Energy poverty in Brazilian urban favelas
1.a Energy poverty in recent literature Economists define poverty as the inability to afford a specific bundle of goods
and services deemed necessary for survival under agreed minimum living standards.
The main aspect addressed in this study is energy poverty, which is defined as: “lack of
choice in terms of reliability, quality, safety and sustainability of energy.”5
A consequence, or perhaps a symptom, of energy poverty in urban slums is the
high incidence of illegal behavior and fraud. Genuine affordability problems and
limited means of subsistence are clearly the first explanation of illegal behavior. Low-
income households often face cash flow problems and lack financial reserves or access
to credit from which to cover large monthly or bi-monthly bills, and it become even
worse when they let un-paid bills pile up. Also, when dealing with basic infrastructure
services, the one-time setup connection charges can represent a major financial barrier
for low-income households even more than regular bills.6
Nonetheless, different authors warn that it is important not to overestimate this
reason. 7 Often, in the perception of Latin American people, basic services, most of all
5 World Energy Association (WEA, 2000). 6 Typical connection charges in Latin America, that frequently take the form of one-time, up-
front capital payments, amount to US$70 for water, US$130 for sanitation, and US$110 for electricity though they vary widely. Moreover, in the case of services such as electricity, sanitation and natural gas, the cost of the in-house upgrades required to make full use of the network service can, in many cases, exceed the cost of the connection itself. Such costs can be prohibitive for poor households. Foster and Yepes (2006).
7 Manzetti and Rufin (2006), page iv, .assess that: “Purchasing power per se is not a good predictor of lack of payment or fraud”.
5
water and sanitation, are regarded as entitlements that government should provide
alongside other tax-financed public goods such as health care or law and order. 8
So, while slum dwellers’ purchasing power plays an important role in the
severe theft and nonpayment problem afflicting distribution companies, a number of
other factors must be taken into account such as cultural attitudes, government
opportunism and lack of law enforcement. For this reasons, an effective energy poverty
policy in the context of the Brazilian urban favelas will require a thorough
consideration of all the involved factors.
In the following sections of this chapter, I will outline the key challenges posed
by the electricity provision in the context of urban informal settlements (namely,
universal access, affordability and subsidy targeting). I will also briefly report some
lessons learned from recent empirical studies based on pro-poor electricity delivery
schemes in Latin America.
1.b Universal and equal provision of electricity Most countries have an explicit policy goal of promoting universal access to
certain infrastructure utilities. Universal access to infrastructures is typically justified
8 Compounding this cultural bias is the perception that government agencies, politicians, and the upper strata of society often get away without paying for utility services, which discredits the imposition of fees on lower income households. Government opportunism has also been relevant in some Latin American Countries. In Dominican Republic for example, after a crisis that required the government to step in and take politically painful measures such as raising rates and cutting subsidies,
6
through a combination of three factors.9 First the positive externalities related to the
consumption of infrastructure services related to health, education and other connected
effects that benefit society as a whole.10 Second, basic infrastructures services tend to
be considered merit goods, that means services that society believes everyone should
have for reasons that go beyond mere economic utility. Finally, political factors or
regional development goals may induce a government to transfer resources to rural or
low-income constituents.
Recent data about Brazil report very positive figures, indicating an average
electricity coverage of 95% of households11. But the same figure also show the gap
between poor households (with 91% of coverage) and non-poor (with 99%). Plus
general sources usually lack information about informal or illegal connections. The
figure below shows recent figures about the discrepancy that still exist in electricity
coverage between poor and non poor population in Latin American countries.
distributors preferred to let the government maintain some subsidies rather than addressing theft and nonpayment in other ways. Manzetti and Rufin, (2006) page iv.
9 Clarke G.R.G. and Wallstein S. J. (2003) Universal Services to Rural and Poor Urban Consumers in Brook and Irwin (2003), pg. 21-75.
10 For reference about the role of infrastructure in alleviating poverty and in relationship with the Millennium Development Goals see: Estache et al. (2002) and Fay et al. (2003) and Brook & Irwin (2003). 11 See Komives et al. (2005) pg. 185. In the source of this data, electricity coverage is here defined as “percentage of households with electricity or nonzero expenditure on electricity”.
7
Figure 1 Electricity coverage: percentage of households in selected Latin American countries (Country data from different years between 1999 and 2005)
0
20
40
60
80
100
120
Argenti
na
Bolivia
, Urban
Brazil
Chile
Colombia
, Urba
n
El Salv
ador
Guatemala
Hondura
s
Mexico
-ENIG
H
Nicaragu
a Peru
Urugua
y
Venez
uela, R
.B. d
e
Poor (%) NonPoor (%)
Source: Komives et al. Appendix B.1, pg. 185. Authors report data from different sources. Note: Poor=Poorest 40%
During the 1990s Brazilian infrastructure services, just like in most of Latin
American countries, have undergone a huge privatization process which in many cases
made the gap even worst. In fact, although public monopolies also failed to ensure
access for rural and low-income urban consumers, serving disadvantaged areas is not
economically attractive for private profit-maximizing utility suppliers. Bringing
electricity to low-income communities involves inherent difficulties which may require
additional costs to have infrastructure and distribution systems installed. Poor urban
areas or peripheries may suffer levels of violence, abandonment, or unsecurity that
promote social exclusion. Often, inhabitants of slums do not have formal title to the
8
land occupied or, when land tenure is formalized, they will more likely be tenants, not
owners. Lack of freehold title can present various problems for service providers that
may be legally required to contract with the actual owner of the property, or need the
title to the property as security against non-payment. Finally, in many slums, liability
for utility bills may also be uncertain. Poor communities often have more than one
family living in a single house or unstable residency. This can pose a problem in
billing and collection.12
In all these cases, private operators may not make the investment of new,
extended, or more efficient infrastructures, having little hope of recovering the cost
through additional tariffs. As a result, the infrastructure is not put in place to provide
the service, unless public entities have adequate methods to incentive or finance private
ones.
In this framework, social issues of access and affordability of a service justify
the adoption of public policies in order to take into account the needs of the
disadvantaged customers. Different solutions have been implemented with a different
mix of public and private action.
12 Ehrnhardt in Brooke and Irwin (2003), pp. 179-208.
9
1.c The challenge of affordability: public policy options Despite the improvements in increasing coverage, a lot of work is ahead to
reach universal access as well as equal standards of quality service reliability.
Moreover, as the high incidence of fraud and unfulfilment attest, a lot of work needs to
be done to ensure effective affordability. What policy is better suited to target the most
needy people? Are energy subsidies the solution?
Cross-subsidies are the typical approach when a single firm provides service. to
help the poor overcoming the access barriers caused by high access costs or non-
availability of service.13 In a context of competition and liberalization or where service
networks are nonexistent, subsidies can also be in the form of incentives to businesses
to develop such service networks. 14 While these arrangements may sound simple in
theory, in practice they have not always worked well. The empirical literature reports
mixed results for the application of energy subsidies.15
Subsidies are usually assessed by cost-effectiveness, sector efficiency, and
efficacy.16 Cost-effectiveness means that the subsidy achieves social goals at the
lowest program cost while providing incentives to businesses to serve poor and rural
populations. Sector efficiency means that the subsidy is structured in such a way that it
13 “Cross-subsidies imply that some users are charged prices above cost to subsidize other users who are charged prices below cost”. Source: Clarke G.R.G. and Wallstein S. J. (2003) Universal Services to Rural and Poor Urban Consumers in Brook and Irwin (2003), pg. 28.
14 For a useful recent review of the debate and survey of the empirical evidence, see Ravallion (2003).
10
encourages provision of service at least cost. Efficacy means that the subsidy reaches
the intended target, the poor minimizing errors of inclusion and exclusion.17 Cross-
subsidies have often been poorly targeted and have typically failed to reach poor
consumers.
An alternative to subsidies would be market solutions, but they are usually
considered unsuitable to improve the conditions of poor household unless a creative
combination of options is introduced. Previous case studies in Brazil,18 attest that
attempts to charge prices for energy at the actual cost of supply have resulted in an
increase in illegal connections. In this sense, utility services that are provided on credit
and that entail periodic bills covering relatively long service periods are ill-suited to the
financial condition of the poor. More creative marketing approaches might be part of
the answer. The cellular telephony sector, for example, has been very innovative in
designing service packages with different payment schemes that cater to a wide variety
of customer needs.19 Unfortunately, utilities tend to be very conservative in their
approach to commercial policy; they rarely attempt to differentiate their products in
commercial terms. Clearly, dealing with vulnerable low-income communities may
15 For reference about the effectiveness of subsidies on the poor, see for example: Komives et al. (2005) or Foster V. and Tré J-P. (2003) Measuring the Impact of Energy Interventions on the Poor – An Illustration from Guatemala, in Brook and Irwin (2003), pages 125-179.
16 See: Barnes and Halpern (2005) 17 Urdinola and Wodon (2007) provide a framework to analyze the determinants of the
targeting performance of social programs and transfers, with an application to electricity subsidies in three African countries.
11
have higher risks for profit-making providers. Therefore, regulatory programs need to
be implemented with sustainable tariffs instead of a pure market approach. If
accompanied by well-designed incentives, new forms of pro-poor pricing and payment
options can be explored.
Once accessibility has been ensured, utilities need to maintain their slum
consumers as “reliable” consumers. Accurate measures should identify the billable
price and amount of subsidy (when necessary) according to the actual willingness-to-
pay (WTP) for the service and the fee levels at which participation in the electrification
programs will drop. Foster and Yepes (2006) have published a very interesting work
that discusses the issue of reaching cost recovery tariffs in areas where low income
people face the problem of affordability. According to the authors, with the exception
of the poorest countries like Bolivia or Nicaragua, in Latin American countries the
segment of population with serious affordability problems appears to be relatively
small (especially compared to regions like India or Africa).20 Therefore, at least in
principle, there appears to be a promising case for using targeted incentives to
18 ESMAP (2006). 19 de Souza and Silva (2007). 20 Their conclusion is that in Latin America only about 20% of households would have to pay
more than 5% of their income if tariffs were set at cost recovery level (whereas in India or Africa around 70% of households would have to pay so). So they suggest that even in cases where tariffs might have to double to reach cost recovery levels, the overall impact on poverty levels in Latin America would be negligible.
12
reconcile the cost recovery objective with social protection concerns, while letting the
market mechanism adjust the prices where possible.
1.d Demand-driven solutions for cost-effective energy provision to the poor Recent work has stressed in particular the approach that see the world's billions
of poor people as an immense untapped buying power, that can be filled with
customized goods and services while at the same time providing a profit opportunity
for suppliers in the fastest-growing new market.21
Different utilities in Latin America have employed different strategies to match
low-income customers’ income levels and cash flows, but the successful examples
placed great emphasis in appreciably improving the quality, availability and
affordability of service within a relatively short period. 22 The outcomes of the 4 case-
studies reported by Manzetti and Rufin (2006) provide consistent evidence of the
importance of customer relations effort. Strategies like publicity campaign about social
initiatives, hotlines to report malfunctions and complaints, dropped previous fines and
legal actions have proved to be effective ways to persuade nonpaying customers or
those who stole water/electricity to regularize their situation. In general, once service
improved in a noticeable way, the nonpayment culture started to slowly change.
21 For a general ideas referring to the “bottom of the pyramid” refer to: Hammond et al. (2007) or Prahalad (2006).
13
Low-income customers (just as wealthy ones) will be more likely to pay when
they are provided with good quality service, but companies must first prove themselves
to customers to overcome hostility and sense of disparity in treatment. The cases
mentioned by Manzetti & Rufin (2006) and the two cases implemented by AVSI-CDM
(Conviver and Projeto Agente) provide some creative examples of poor customers
oriented services:
o Different pay rates and installment procedures adjusted to clients’ ability to pay
like daily or weekly payments (like in the case of AAA23 in Colombia and
Interagua in Ecuador)
o Simplified billing and payment procedures that are easier to understand. (Union
Fenosa in Colombia made mobile units available to allow customers to pay their
bills in their own neighborhoods).
o Gifts and discounts to customers who paid their bills on time, while fraud and theft
charges were dropped if clients start to pay their bills (AAA in Colombia).
o Improved customer relationships through the appointment of staff specifically
dedicated to serve customers in the disadvantaged areas (like in the Projeto Agente
and Conviver Program).
22 Among works reporting relevant case studies see: Manzetti and Rufin (2006), Rojas and Lallement (2007), ESMAP (2006).
23 Alcantarillado Y Aseo de Barranquilla S.A. (AAA)
14
o Anti-fraud actions like using meters that are better protected and more difficult to
tamper with (adopted by Conviver as well as by Interagua in Ecuador).
Clearly, adapting the billing systems and payment options to the poor
consumers’ constraints represents a major challenge and implies additional costs but,
in the long run, it seems more advantageous for providers to grant more flexible
payment options, rather than let consumers recede into illegality. In addition, some
costs can be shared. In some cases, the government can fill in with targeted subsidies to
the private utilities. In other cases, the process can be facilitated by developing a
network of payment points in collaboration with banks, supermarkets, post offices, or
other local retailers. Costs can also be reduced in the long run by investments in new
technologies, such as remote meter reading technology.
The literature about transaction cost theory can help us understand the necessity
of well-designed incentives and regulations for utilities services in the presence of
poverty and fraud. Transaction cost theory24 postulates that the easiest way to enforce
contracts is to rely on voluntary compliance rather than sanctions. In fact, sanctions
require additional costs related to monitoring and punishment. Furthermore, in
developing countries, like Brazil, law interpretation and enforcement can be
systematically ignored or poorly administered. So, precisely because of poor
enforcement it is preferable to emphasize incentives leading to voluntary cooperation.
15
Involvement of the community and efforts toward social inclusion
In the absence of strong institutions that can enforce formal rules, trust is
essential to encourage voluntary compliance. Within this approach, several case studies
have documented the crucial importance of grassroots intermediary institutions in
playing a mediating role between the needs of the community and the financial and
technical concerns of the utility companies in Latin America.
The literature dedicated to practical experiences similar to the Conviver Project
shows the crucial importance of the involvement of the local communities and their
interaction in the design and implementation phase of the project.25 This involvement
is usually possible through the important intermediation of organizations such as
Community Based Organizations (CBOs), as well as NGOs, that are already present
and active in the urban slums with many different programs dealing with development,
education and health. The key asset of CBOs and NGOs is that they have is the deep
understanding of the reality of the favela and an established network of relationships
with dwellers.26 It is also often stressed the importance of sustaining such involvement
over time.
24 Initially formulated by Coase (1937) and fully developed by Williamson (1985) 25 See among others: Un-habitat, Cities Alliance (2006) and Rojas and Lallement (2007).
16
1.e The urban slums of Belo Horizonte In Brazil, poverty has traditionally been more acute in rural areas, especially in
the Northeast. However, although rural poverty has declined in recent years, urban
poverty has increased, particularly in big cities in the Southeast. The growing weight of
marginal groups in the outskirts of large cities represents a huge challenge, in terms of
both providing social services and public services and controlling violence and
insecurity.
Belo Horizonte, the state capital of Minas Gerais in Southeast Brazil, has a
population of some 2.2 million inhabitants living in a territorial area of 330 km2.27 The
metropolitan region comprises some 5 million people within 34 municipalities. In
terms of the size of its population and economy, Belo Horizonte is the third largest
metropolitan region in the country, after São Paulo and Rio de Janeiro.
Like many cities of Brazil’s industrial belt, Belo Horizonte (BH) witnessed
rapid population growth between 1940 and 1970, declining in later years. The 1990s
saw a profound economic restructuring in the city’s economic base, with a decline in
industry and manufacturing, but growth in the service sectors. As a result, the economy
has had less capacity to absorb a workforce with traditional skill profiles, which
explains the large pockets of poverty and informality throughout the city.
26 Rojas and Lallement (2007), pp. xviii describe their role saying they help “build trust and confidence relationships, mobilize the community to understand their rights and obligations, link utilities to slum dwellers and raise government awareness.”
17
Approximately 400,000 inhabitants (20%) of the population live in areas called vilas
and favelas (precarious settlements, also called slums or shantytowns), in which social
vulnerability is rife.28
To address these problems, the municipal government of Belo Horizonte has
designated these poor, unplanned areas as Zones of Special Social Interest (ZEIS),
conferring on them special status for purposes of planning and intervention. 29 Under
an umbrella program designed 1998,30 general guidelines for intervention have been
established. Based on extensive field surveys of living conditions in these areas, the
plan has identified the primary issues and types of intervention required. Priority areas
include: physical improvements in housing and basic infrastructure, reduction of
vulnerability, land titling, social inclusion and social development, environmental
improvement, and employment and income generation.31
Such issues and priorities indicated by the municipality’s program have been
considered and integrated in the design of the Conviver Project.
27 Brazilian Statistics Bureau – IBGE, 2000. 28 Gambrill, 2005. 29 The greater Belo Horizonte currently contains some 180 areas considered to be slums and
shantytowns, in addition to 22 low-income housing complexes. These areas are called ZEIS (Special Social Interest Zones).
30 The municipal government’s ‘Strategic Plan for Guiding Interventions in Zones of Special Social Interest’, drawn up in 1998, for prioritizing actions and investments in ZEIS.
31 Gambrill, 2005.
18
1.f Energy provision and costs in the urban favelas of Belo
Horizonte
In the second half of the 20th century, the Brazilian energy distribution
networks were quickly expanded and subsidies were used to offer low-income social
groups access to liquefied petroleum gas (LPG) and electricity. In the 1990’s, however,
the Brazilian energy policy changed radically. Under the new Constitution (1988)
energy markets were liberalized, forcing Brazilian energy companies to review their
pricing policies at the very time when energy costs were going up. Cross-subsidies that
in the past had charged rates compatible with the income of the poor were reduced or
eliminated. Programs for expanding distribution networks to connect low-income
consumers were decelerated or closed down. The reduced use of cross-subsidies and
the inevitable increase in tariffs for vulnerable social groups magnified the energy
poverty problem.32
A baseline survey conducted for the Conviver program reported that when
asked if the energy bill was suited to the family income, 42.16% of the interviewees
answered “no”. Data also show that the monthly expenditure on electricity as a
percentage of the average income in the sample is 7.3% which is very high particularly
in relation to an average income that is quite small in absolute terms. Typically this
32 World Energy Council, 2006.
19
figure in industrialized countries like the U.S. is 3-5%.33 Figure 2 clearly indicates that
in Brazil, as well in many other countries in the region, expenditures on electricity as a
percentage of monthly income are still disproportionately high for poor and
disadvantaged areas like urban slums.
Figure 2 Monthly expenditure on electricity as a % of household income in selected Latin American countries (Country data from different years between 1999 and 2005)
0
1
2
3
4
5
6
7
8
9
10
Brazil Urb.
Colombia
, Urba
n
Guatemala
Peru
Urugua
y
Mexico-E
NIGH
Bolivia,
Urban
Nicarag
ua
Argen
tina
Total (%) Poor (%) NonPoor (%)
Note: Poor=Poorest 40% Source: Komives et al. (2006) Appendix B.1, pg. 185. Authors report data from different sources. * The numbers for Brazil are taken from the study: Instituto de Economía -Universidade Federal do Rio de Janeiro. September 2005. Energy Poverty -Cajú Shantytown Case Study, page 31.
33 Calculations based on data from the Energy Information Administration (EIA), Official Statistics from the US Government, and results of the 2004 census published by The Wall Street Journal
20
In addition to this general framework, the State of Minas Gerais (where Belo
Horizonte is located) experiences higher prices than the average Brazilian ones because
of the very high sales tax Imposto Sobre Circulação De Mecadorias E Serviços
(ICMS). This tax is particularly important for this thesis because it is regulated by each
single state and in Minas Gerais (where the Conviver communities are located) reaches
30%, compared with an average 25% or even 18% in Rio de Janeiro.
When referring to informal urban settlements (like most of the Conviver
communities), the issue of legal tenure for housing is also a major barrier to expanding
utility services into low-income urban areas. Municipalities are often reluctant to
provide utility services to households located on land that they do not own—or on
public land that is planned for another use—for fear that the services imply an
acceptance of the unauthorized settlements. Securing legal tenure by providing land
titles for residents can seem obvious in theory, but in practice it is a very slow and
often controversial solution. For example, a pretty common phenomenon is that, once
legalized properties becomes more valuable, the poorest occupants sell their land and
move to another settlement without city services. So, often regularization transfers the
problem of un-served poor households to another location.
A thorough analysis of the Brazilian energy market and reforms goes beyond
the scope of this work. Nonetheless, the synthetic overview offered in this section
on August 31, 2005.
21
provides further evidence in support of the approach that has been applied by the
Conviver Program.
In the actual framework of energy distribution in Belo Horizonte, the rate of
access is relatively high but utilities prices still raise relevant problems in terms of
affordability. The favela population’s response to the increased prices after the
liberalization wave was to use clandestine connections (the gato) as a way to keep their
electricity bill affordable. This practice only worsened the problem and deepened
unsafety and social exclusion. In a market where the utilities are no longer entirely
public, and follow profit maximizing strategies with little availability of subsidies, it is
crucial to design flexible policies that make the most of public and private synergies
and find ways to incentivize utilities to serve untitled areas. The Conviver Project
provides an example of an approach that emphasizes targeting of social schemes and
accompanies an effort of legalizing the access to electricity with a tangible increase in
the service quality for the most disadvantaged customers.
1.g Policy relevance of the research The goal of this project is to address some problems in the provision of
electricity to poor people living in the urban slums, while providing a preliminary
evaluation of the project “Conviver: energia para viver melhor”, implemented in Belo
Horizonte.
22
This research contributes to the ongoing debate about the delivery of basic
services (energy, water, telecommunication and transportation) to the urban poor. It is
not easy to find rigorous, empirical studies about the favelas, as they are generally
illegal or informal settlements so, by definition there is a huge lack of information and
data about these communities. The value added by the empirical analysis of the
Conviver Project will consist of the following:
1) The generation of a panel data set (baseline and follow-up surveys) with
detailed socioeconomic data on poor households in peri-urban slums of Belo
Horizonte, plus valuable information about their access to and consumption of
electricity.
2) An evaluation of how such household-level demographics best predict
the access, patterns of electricity consumption and willingness to pay for safe, legal
electricity. The analysis focuses on identifying the most important determinants of
illegal connections to electricity in order to orient future policy actions and pro-poor
delivery schemes.
3) An initial impact evaluation of the proposed flexible payment /repayment
options offered by the local utility CEMIG via the intermediation of the project
Conviver. In particular, for the sub sample that have actually being addressed by some
of the project proactive actions (education to energy saving behavior, orientation to
social tariffs, renegotiation of debt, distribution of energy saving free lamps) the study
23
will evaluate the impacts on energy consumption, effective access to social tariffs; and
household’s general well-being.
4) An evaluation of the targeting of the program among the poor households
in the urban favelas of the Belo Horizonte Metropolitan area, in terms of poverty
outreach and sustainability. The aim is to highlight what kind of household have been
reached so far, and to provide suggestions to the project managers on how to target the
project actions even more effectively.
5) The interest of an impact evaluation of Conviver project is also to validate
the proposed model of interaction between a private company (CEMIG), and an NGO
(AVSI-CDM), which, encouraged by a government-originated incentive, have come
together to promote social inclusion for the urban poor.
24
Chapter 2. The Conviver Program
2.a Conviver: project rationale and expected outcomes According to the preliminary studies conducted by CEMIG and AVSI-CDM, 34
the Conviver project was designed to address problems in the provision of electricity to
the poor people living in the metropolitan surroundings of Belo Horizonte. The main
problems to address were violent acts against CEMIG technicians, illegal access, high
rates of nonpaying customers, lack or precarious condition of infrastructures and
residential equipments, unequal provision of utilities services, and insufficient
maintenance.
The main expected outcomes of the project were identified as follows:
1. Reduction of violent acts against CEMIG technicians
2. Conversion of illegal connection to legal ones
3. Regularization of nonpaying or indebted customers
4. Improvement of the network coverage
5. Improvement of the quality standard and maintenance of equipment
6. Improvement of the social and organizational capital of the target neighborhoods.
The utility CEMIG is the main sponsor of the program. The non-profit AVSI-
CDM has been working with CEMIG as an intermediary between CEMIG and the
34 AVSI-CDM. 2003.
25
community and has designed the actual strategy of the intervention. The program has a
strong focus on community participation and direct, frequent contact with the
population that the project intends to serve. As a matter of fact, the first step of the
Conviver project was hiring some young agents (students or young unemployed people
living in the very targeted communities) who have been trained to visit the families and
evaluate each specific case.35 The Conviver agents are in fact the ones who
implemented the program’s different activities:
• Collection of detailed socio-economic data through face-to-face interviews with
every single household in the communities assisted by the program:
• Orientation about special social tariffs for low-income people and how to qualify
for them;
• Environmental education and orientation about energy saving behaviors and
efficient use of electrical domestic appliances;
• Personalized debt renegotiation for those who have outstanding debt and cannot
comply with the conditions of the standard repayment plan (personalized
conditions negotiated with CEMIG for each case);
• Distribution of free efficient equipment such as lamps, fridges, chuveiros(electrical
apparatus for water heating) for those who qualify.
35 As of the first quarter of 2008, almost 50 Conviver Agents are visiting about 50 neighborhoods.
26
2.b Conviver preliminary analysis and organizational details Consistent with the main goals of the program and the specific requests of the
sponsor CEMIG, the communities to be treated have been chosen through a complex
and rigorous preliminary diagnostic phase.
The main source of contacts with the communities were the local municipal
government agencies.36 In the first preliminary meetings, representatives of the
municipal agencies were involved in the choice, mobilization and interaction with the
communities. Afterwards, AVSI-CDM took the lead leveraging on its field experience
gained through previous urban development projects conducted in the area.
The initial facts about the population and number of residences of the areas of
the project were collected from the demographic Census of the Instituto Brasileiro De
Geografia e Estatística. (IBGE), of 2001. These facts were brought up to date in the
information of the Municipal city Hall of Belo Horizonte.
The external sources used by AVSI-CDM to build the comprehensive picture of
chosen communities are:
CEMIG:
• Interview with employees from the utility;
• Archive of power network from the utility;
• Clients dataset from the utility, and
• Bills readings from the utility.
27
Community Representatives:
• Household sample survey;
• Household qualitative survey;
• Action plan map discussed with the community representatives.
Other sources:
• Brazilian Statistics Bureau – IBGE;
• Municipal agency for urban slums, URBEL.
After the preliminary phases of diagnostic study and meetings with the
involved stakeholders, the activity of the Conviver agents was launched in the second
half of October 2006. All along the process, meetings were held with the communities
in order to continue to involve the benefited population in fine-tuning and subsequent
adjustment of the goals and scope of interventions.
The project is expected to be concluded in 5 years and aims to reach 252,000
households. During the first step of the program, October 2006- December 2007,
CEMIG invested almost R$(Brazilian reasis) 21,5 millions. In that timeframe, about 52
thousand families were visited in 11 neighborhoods (aglomerados).37 75 thousand
36 Namely, URBEL (Companhia Urbanizadora de Belo Horizonte) and Rede Pólos. 37 Aglomerado da Serra (região centro-sul), Aglomerado Santa Lúcia (região centro-sul),
Ventosa (região oeste), Cabana Pai Tomaz (região oeste), Morro das Pedras (região oeste), Landi (Ribeirão das Neves), Apolônia (Venda Nova), Vila Cemig (Barreiro), Vila Vista Alegre (região oeste), Alto Vera Cruz (região leste) e Conjunto Felicidade (região norte).
28
efficient lamps, 300 efficient refrigerators, and 1000 electric heating recuperators
(chuveiros). were delivered to the most needy families.
The initial facts show a very positive response to the program and a strong level
of participation. It is noteworthy that the Conviver Agents have been able to establish
strong trust relationships with visited families.
2.c Conviver Project: research design and selection criteria The population of interest for this study is households in the low income
communities of the Metropolitan area of Belo Horizonte. The population that is
accessible to this study consists of all the households that were reached by the
Conviver Agents from October 2006 (the very beginning of the program) to October
2007 (the end of the first of the Conviver’s planned 5 years).
The choice of the communities to be addressed has been done according to the
goals of the program and in agreement between the utility CEMIG (main sponsor and
promoter) and the NGO AVSI-CDM (main operational actor and social mediator).
The following tables show the timeline of the first round of visits that allowed
to build the baseline survey (Table1), and of the households that have received second
visits all along the first year of the project (Table2). These tables show the progression
with which the project has been implemented, starting from the community Jardim
29
Felicidade which is used as the pilot area for every subsequent introduction of new
actions.
Table 1 Monthly count of first round of visits (baseline survey) by community / zone (successful visits for baseline survey)
Monthly count by
Community
JARDIM FELICIDADE
SANTA LÚCIA
SERRA VENTOSA VISTA ALEGRE
CABANA PAI
TOMAZ
TOT
06-Oct 351 273 331 - - - 95506-Nov 708 735 1,408 - - - 2,85106-Dec 648 475 1,062 - - - 2,18507-Jan 822 915 1,720 - - - 3,45707-Feb 239 339 537 - - - 1,11507-Mar 361 267 223 - - - 85107-Apr 92 47 174 1 3 - 31707-May 2 79 694 2 - - 77707-Jun 54 115 274 1 1 2 44707-Jul 24 40 124 - - 262 45007-Aug 24 25 54 126 377 728 1,33407-Sep 11 11 28 245 332 706 1,33307-Oct
1 - - 4 5 32 42
TOT 3,337 3,321 6,629 379 718 1,730 16,114Source: Author’s analysis from the Conviver database
Table 2 Monthly count of second visits or return visits) by community
Monthly count by Community
JARDIM FELICIDADE Totale
SANTA LÚCIA Totale
SERRA Totale
VENTOSA Totale
VISTA ALEGRE Totale
CABANA PAI TOMAZ Totale
TOT Totale
Jun-07 45 50 68 0 0 0 163Jul-07 403 395 597 0 0 0 1,395Aug-07 333 399 854 0 0 0 1,586Sep-07 362 369 560 0 0 0 1,291Oct-07 38 22 38 0 0 0 98TOT 1181 1235 2117 0 0 0 4,533
Source: Author’s analysis from the Conviver database
30
The definition of the communities to be involved has been conducted following
a consistent process including:
1- Preliminary collection of information – CDM
2- Visits in locu for recognition of the area and informal contacts with residents– CDM
3- Designation of a proper team of Conviver agents – CDM
4- Presentation, discussion and approval obtained from CEMIG
The communities have been selected upon verification of the following criteria:
• Being low income communities located in the Belo Horizonte Metropolitan
Area. Starting, whenever possible from the areas defined as ZEIS, 38 the general
criterion for intervention has been legal-lawful, social, and physical-environmental
vulnerability. Certain areas have been excluded due to the advancement of the
occupations in adjacent areas that presented a challenge to the project feasibility.
• Having an existing electricity distribution network to the door or been qualified
for the implementation of the power network. Communities have been avoided when
they presented dwellings in areas not subject to regular supply of energy, of geological
risk, or areas with any other situation that renders useless the supply of electric
energy.39
38 ZEIS: Zone Of Special Social Interest defined by the regulatory plan and law regulating distribution, occupation, and use of soil of the town of BH.
39 I.e. those areas that present dwellings under lines of transmission can not be involved in the program, except for situations in that will have concrete possibilities of removal of the families in specific projects promoted by Cemig or government.
31
• Having territorial information available: maps, estimate of population, etc.
• Not having pending trials of removal due illegal settlements, except for some
specific situations, as for Aglomerado da Serra;
To guarantee the feasibility of the program, according to the method of action
developed, priority as been given to:
1. Bigger agglomerates (more of 1,200 families) and communities more organized
and with delegate interlocutors defined (associations of neighborhood, institutions,
leaderships);
2. Communities identified in the project presented for the Electricity Authority
ANEEL;
3. Strategic Communities for the utility CEMIG and/or for the NGO AVSI-CDM
(meaning places where the entity is already present with other actions or projects).
Once the communities had been selected, a rigorous procedure has been
designed in order to plan the visits of the Conviver Agents to the households. Each
community is assigned to a small group of agents directed by a supervisor. Each agent
follows a rigorous and efficient path to organize her visits so that no household is
disregarded. If the family is absent or cannot attend the interview. The agent is
instructed to go back and /or reschedule a visit in a proper time. In this way we can be
sure that families are not disregarded due to timing issues. On the other hand, we do
32
have a bias because some of the families might refuse to respond to the interview, so
they are voluntarily excluding themselves from the program.
In the initial planning of baseline survey one of the variable was designed to
keep track of the different possible outcomes of each requested visit. The possible
outcomes registered was: (1) Successful visit; (2)Closed house; (3)Not found the
household responsible; (4) Agent not received (5) Abandoned house; (6) Demolished
house; (7) House in construction (8) Other; (9) Missing.
The distribution of household responses to the baseline surveys conducted
during the first year of the projects, is shown in Table 3.
Table 3 Visit classification by household response (From 10/11/2006 to 10/03/2007)
Visit classification Household response to the visit # of visits %
1 Successful 15,185 91.2% 2 Closed House 527 3.2%
3 Head of the house not available 672 4.0% 4 Agent Interview refused 98 0.6% 5 Abandoned House 75 0.5% 6 Demolished House 20 0.1% 7 House in construction 3 0.0% 8 Other 54 0.3% 9 Not reported 9 0.1% TOTAL 16,643 100.00%
Source: Author’s analysis from the Conviver database
33
The category that can probably concern more in terms of potential selection
bias is the number (4) “Agent not received”, which account for 0.6% as it might
represent a subgroup that had some relevant reason to stay out of the program.
2.d Scope of the analysis and sampling strategy
The empirical analysis conducted in the next chapter aims at analyzing the
critical determinants of illegal behaviors in the access to electricity with a particular
focus on the issue of affordability and on the pro-poor schemes already in place to
subsidize low income families energy costs.
The sample used for the analysis consists of all the 15,279 households that have
actually been reached by the Conviver Agents from October 2006 (the very beginning
of the program) to October 2007 for the initial baseline survey.
Table 4 Distribution of the household in the actual sample by response to the 1st visit
Household response to the visit
value Freq. Percent
Successful visit 1 15,276 99.98 Close house 2 2 0.01 Not found the house responsible
3 1 0.01
Total 15,279 100 Source: own analysis from the Conviver database
Table 4 shows that, with respect to the variable “Household response to the
visit”, the vast majority (99.98%) of the used sample is made of those households
where the interview was realized successfully, so that we were actually able to collect
34
information. As regards the way agents contact the households for the visits, a rigorous
procedure have been implemented in order to guarantee the most accurate and efficient
outreach in each and every community. Besides the technical training about the
specific activities to perform in their visits to families, Conviver Agents have been
specifically trained and instructed about efficient planning and performance of their
field work. Following the directions of their close supervisor, they follow a rigorous
methodology to organize the visits in the blocks they are in charge for. They
consistently report about failed visits and keep track of every attempts. Whenever
possible they propose to reschedule when the family is not available and strive to build
a solid relationship with the households they are assigned to based on genuine trust and
a proactive approach that favors the solution of every specific issue.
Most of the data used in the analysis exposed in Chapter 3 come from the first
round of visits (or survey baseline) conducted by the Conviver Agents. Such data have
been integrated with some information (about households’ energy expenses and
consumption) that the utility CEMIG have provided to the AVSI-CDM staff from
whom I was given the data.
35
Chapter 3. Determinants of illegal connection and energy fraud in the Conviver communities
Based on the Conviver baseline survey, this chapter studies the determinants of
illegal connections and energy fraud in the context of low-income urban favelas and
attempts an evaluation of the effectiveness of different social tariffs aimed at
subsidizing energy costs for poor customers.
3.a Hypotheses about the causes of illegal connection and electricity thefts
The main hypotheses that will be tested in are all aimed at understanding about
the causes of illegal connection and electricity thefts. As anticipated in the discussion,
of recent literature and empirical case studies, such causes can be classified into three
core groups:
1. Lack of adequate energy provision and equipment.
For the reasons discussed in the literature review, poor and disadvantaged areas (like
urban favelas) often suffer form poor coverage of infrastructures or lack of quality and
maintenance of the equipment and services. This happens because the informality of
the neighborhoods and difficult billing make these areas not attractive for private
companies that tend to disregard them. But this works also as a vicious circle, as the
low quality and reliability of the service often becomes a further incentive for
illegality. For this reason one of the core goals of the Conviver Project is to extend the
36
electric network, improve the level of equipment maintenance and reach standard
levels of provision reliability.
2. Income and affordability. As anticipated in the previous sections, in my
evaluation of the Conviver project, I intend to verify the hypothesis that clandestine
connections (the gato) are mostly explicable as the urban slum dwellers’ response to
non-affordable prices of electricity. By analyzing my sample, controlling for other
characteristics and circumstances, I intend to determine what level of income (and
tariff as a % of income) best predict the probability to opt for illegal behaviors in the
electricity connections.
3. Lack of information and incorrect use of electric appliances Another
important finding of the preliminary studies conducted for the Conviver Program is
that often in the urban slum households electrical appliances are not used in a correct
and efficient way. Sometimes people lack awareness about the best ways to save
energy or even have erroneous ideas about how to manage it. For example, many turn
off the fridge during the night hoping to reduce consumption and don’t know that they
are actually raising it. For this reason the baseline survey focused some questions on
understanding these kind of behaviors and much of Conviver effort has been devoted to
education and correct information about efficient use of energy based home appliances.
37
3.b Hypotheses about the impact and targeting of social tariffs in preventing poor households from illegal connection or electricity theft In strict connection to the hypothesis of affordability, I was also interested in
understanding how effective social tariffs are in preventing low income families from
illegal connection and electricity thefts.
According to the law 10.438/2002,40 all the consumers classified as
“residential” and “mono-phase” are entitled to a special low-income tariff (tarifa de
baixa-renda) if their average monthly consumption is below 80 kWh/month (referred
to as Brasil 1).41
A similar “social tariff” (referred to as Brasil 2) applies to those customer
whose average monthly consumption is between 80 and 220 kWh/month if they can
prove that they are registered for some government subsidy.42 In this specific case, the
social tariff is granted to families conditional on their proving that they are government
subsidy recipients. Under the government of President Luiz Inacio Lula da Silva, these
subsidy programs have grown to provide financial support to families and ensure
40 In 2002, a new legal document for the energy sector was launched (The Law Nº 10.438, approved on the 26th of April 2002 altered by Law 10.762/2003). Among many different interventions, this new sector law created the Program to Encourage Alternative Sources – PROINFA. It also set basic rules for strengthening universal access to electricity, including a disposition to create extraordinary tariffs to promote universal public provision of electricity in rural and urban disadvantaged areas. It also established the Energy Development Account – CDE, which is primarily to promote universal access - 2003: R$1.075 billion (US$ 370 million). A new definition for Low Income Consumer –up to 80 kWh/month, plus a second group under special condition to be defined by ANEEL (up to 220 kWh/month).
41 Conselho de Consumidores de Cemig (2005) pg. 21. 42 Conselho de Consumidores de Cemig (2005) pg. 23
38
access to basic social rights, such as: health, food, education and social assistance.
Within our sample of households, 26. % (4,010) receive some kind of subsidy. The
most popular one, hold by 2,749 households (18% of the sample) is “Bolsa Familia”
(Family aid) that offers R$50 per month to families with a monthly per capita income
of up to R$100. Families with children under 15 receive an additional R$15 per child
(for a maximum of 3 children). It is too early to estimate the impact of this program,
but demand for it far outweighs the supply of benefits, a situation that produce perverse
outcomes such as bribes and disincentive to work come as results. Subsidies offer a
safety net, but most experts suggest that policies to combat poverty and exclusion must
aim at eliminating their causes and create the right conditions for social inclusion.
Government aid should be coordinated and backed by economic policies, and be able
to generate employment and revenues in the community.
A third type of social tariff (indicated in the dataset as NoTax) is applied in
connection to low consumption levels. In fact, for those residential costumers whose
average monthly consumption is between 80 and 90 kWh/month (so slightly over
Brasil1 condition) the commercial tax ICMS (Imposto Sobre Circulação De
Mecadorias E Serviços) is not charged. This tariff is particular interesting in this case
because of the high aliquote of ICMS tax in Minas Gerais.
Some of the households in the program are entitled to the three different types
of special low-income tariff (social tariffs), either based on income level, subsidy
39
entitlement or level of consumption. Therefore, I analyzed the effectiveness of each
special tariffs
Verifying this particular hypothesis has been challenging, due to the difficulty
of isolating the effect of social tariffs from other socio-economic characteristics. Also,
there is a problem of endogeneity regarding this variable with respect to the dependent
variables of illegality. Another issue I had to take into account is the crucial aspect of
awareness. Particularly with the tariff Brasil 2, many families do not exploit the
opportunity of cheaper energy tariffs simply because they don’t know they are entitled
to them. For this reason, the Conviver project put a strong emphasize on informing
families about the opportunities of reduced payment plans and encouraging them to
take advantage of these oppoertunities.
Despite all these challenges, the analysis of the incidence of illegal behavior in
relation to the presence of direct and indirect subsidies provided some valuable insight
about effectiveness of different policies addressing energy poverty and poverty in
general.
3.c Dependent variables (three measures of illegality) The dependent variables studied in this section are two dummy variables that
indicate respectively a stronger and a more relaxed definition of “illegal access and/or
illegal use of electricity”. The two are both built as a combination of original variables.
40
“clandestino” indicates that the household verifies one OR more of the following
conditions:
• LC: illegal connection (legal=1) • NPE: not possess circuit breaker panel (legal=2) • EFR: electric energy is supplied by another domicile (legal=3) • The domicile supplies energy to another premise (enerprov=1) • Doesn’t have integer sealing wax in circuit breaker panel (sealwax=0)
“clandestini2” indicates that the household verify one OR more of the same
conditions as above, but excluding the last condition (sealwax=0).
In addition, a third dummy variable “enerprov” indicates that the household
supplies energy to a neighbor through illegal connection.
Table 4 Dependent variables Idea Description Variable Type of
variable # obs Missing Notes
Description of legality access (based on circuit breaker panel)
Original variable legal Categorical 15,265
14
with errors
Description of legality access (based on circuit breaker panel)
Corrected legal (1,2,3,4,missing)
legal2 categorical
15254 25
Defined as: 1 (3.93%), 2 (1.56%), 3
(10.22%), 4 (84.13%), missing
(0.16%) Integer Sealing wax in circuit breaker panel
Original variable sealwax Dummy 15279 0 Only 15.78% yes
Gives energy to other premises?
Original variable enerprov Dummy 15279 0 8.21% yes
Stronger definition of illegal behavior
defined as: Or (legal=1,2,3; sealwax=0;enerprov=1)
clandestino
Dummy 15279 0
85.80% perform some kind of
illegal behavior
Less rigid definition of illegal behavior
defined as: Or (legal=1,2,3; enerprov=1) clandestin
i2
Dummy 15255 24
23.08% perform some kind of
illegal behavior
41
3.d Independent variables The above dependent variables have been studied as resulting from a group of
explanatory variables that allows testing hypothesis of interest. Such explanatory
variables can be summarized in the following groups:
LACK OF ADEQUATE PROVISION AND EQUIPMENT (as potential cause
of illegal connection and electricity thefts):
• Reliability of provision (how often the connection breaks) • State of conservation of Equipment (external circuit breaker & meter)
Table 5 Independent variables about equipment Idea Description Variable
name Type of variable
# obs Missing Notes
Reliability of provision (Circuit breaker panel
turns off often?)
Original variable desarma Dummy 14,987 292 6.68% of cases
turns off often(=1)
Is the circuit breaker panel well
conserved?
Original variable sitpadrao dummy 14,778 501 80.99%
Is the electrical meter well conserved?
Original variable with
mismeasurement recoded as
missing values
Sitmeter2 Dummy 14484 795 83% is well
conserved (=1)
Is either the circuit breaker panel OR the
electrical meter damaged/precarious?
precario=1 if (sitpadrao==0
OR sitmeter2==0)
precario dummy 14536 80.23% is (either
equip) well conserved (=1) 16.59
% precarious
(=0)
42
LACK OF INFORMATION AND INCORRECT USE OF ELECTRIC
APPLIANCES (as potential cause of illegal connection and electricity thefts)
• Electricity pattern of consumption (the family use energy for a
business in the dwelling)
• Energy-saving behaviors (proxied by the variable “ironw” = 1 when
the family collect laundry for weekly iron)
Table N.6 Independent variables about energy consumption patterns
Idea Description Variable Name
Type of variable
# obs Missing Notes
Is energy used for
Business in dwelling?
Original variable
givebus Dummy 15,279 0 Only 4.19% use for
business (=1)
Energy saving
indicator (Does the
family collect laundry for
weekly ironing?)
Original variable
ironw Dummy 15,279 0 66.02% collect rope once a week
(=1)
INCOME AND AFFORDABILITY (as potential cause of illegal connection
and electricity thefts).
• Family economic situation defined by income or proxies for income
such as number of rooms, number of people in the house etc
• Female head of the family
43
Table 7 Independent variables about family income
Idea Variable name
Type of
variable
# obs Mean Std. dev.
Min Max Missing Notes
House characteristics: # people
in the house
numpeop (Original variable)l
I/R 15,260 3.7 2 0 18 19
House characteristic
s: # rooms
numroom (Original variable)l
I/R 15,218 4.5 2 0 91 61 Some outliers (too big probably
measurement error) but not
many Income
estimator: # of rest rooms / # of people in the house
roompercap I/R 15124 1.488702
1.23029
0 61 155
Tot family Income
(Brazilian Reais)
tot_ (Original variable)l
I/R 15,205 524.4 469 0 13,000 74
Tot family Income
(Brazilian Reais)
totinc (same as
tot_ with 0 values
recoded as missing)
I/R 12,481.00
639 442 10 13000 2,798.00 Excluded 0 values
Income brackets
according to IBGE
definitions (Brazilian
Reais)
hhinc (Tot_ by income
ranges [0-7])
categ 15205 3.376061
1.830378
0 7 74 Defined as: 0 if tot_ "<=0"
1 ">0 & <=88" 2 ">88 &<=175"
3 ">175& <=350"
4 ">350 & <=700" 5 ">700 & <=1050"
6 ">1050& <=1750"
7 ">1750"
female head of the
household
headfemale dummy
15241 00.44 0.49 0 1 38 43% yes
44
Hypotheses about the impact and targeting of Social Tariffs in preventing poor
households from illegal connection or electricity theft
• Government subsidies • Type of tariff (absolute & relative to income) • Magnitude of energy consumption
Table 8a Independent variables about government subsidies
Idea Variable name
Type of variable
# obs Mean Std. Dev.
Min Max Missing Notes
bolsagas Government subsidy:
BolsaAuxilioGas (Original variable)l
Dummy 15,279 0 0 0 1 0 1.05% yes (=1)
bolsaalim Government subsidy:
BolsaAlimentacao (Original variable)l
Dummy 15,279 0 0 0 1 0 0.22% yes (=1)
bolsaesc Government subsidy:
BolsaEscola (Original variable)l
Dummy 15,279 0.1 0 0 1 0 8.33% yes (=1)
bolsafam Government subsidy:
BolsaFamilia (Original variable)l
Dummy 15,279 0.2 0 0 1 0 17.99% yes (=1)
cidad Government subsidy:
CartaoCidadao (Original variable)l
Dummy 15,279 0 0 0 1 0 1.56% yes (=1)
subsidy2 Does the family get any
Government subsidy at all?
(any of above variables =1)
Dummy 15279 0.26 0.44 0 1 26.25% yes (=1) (444
households receive more
than 1 subsidies)
45
Table 8b Independent variables about type of tariff (Social tariffs depending on level of consumption, income and existence of government subsidies)
Idea Variable name Type of
variable # obs Mean Std.
Dev.Min Max Missing Notes
Type of tariff (as declared by the
interviewee)
Tartyp (Original variable)l
Nominal 2,747 2 1 1 4 12,532 Defined as: 1= Brasil1; 2= Brasil2;
3= NoTax; 4= Normal tariff
Type of tariff (the agent
deducts from looking at the
most recent bill)
tarcalc (Original variable)l
nominal 8,991 2 1 1 4 6,288 Defined as: 1= Brasil1; 2= Brasil2;
3= NoTax; 4= Normal tariff
Social Tarif (The agent realizes a social tariff has been applied in the last invoice)
tarsoc (Original variable)l
dummy 15,279 0 0 0 1 0 Don’t trust this very much (too many missing in the 0
value)
Mediaconsv (Avg cons in
KWH/m) I/R 8,506 75 59 0 1,546 6,773
CEMIG data
relative to (11/2005 -
12/2006) / used as proxy of
previous 12m at the time of first
visit
mediavalv (Avg bill in Braz
Reais)
I/R 8,506 38 51 0 2,046 6,773
Comprehensive variable created to estimate the
type of tariff
totaltar (Collapsed 3 var: tartype, tarcalc
(baseline survey), syntarv
(CEMIG)
categ 11,907 2 1 1 4 3,372 tartype & tarcalc have priority on
CEMIG estimates.
Dummy version of totaltar
dummy1, dummy2, dummy3, dummy4
dummy
Comprehensive var created to estimate the type of tariff
tariffa (only based on
tartype & tarcalc)
categ 11,668 2 1 1 4 3,611 This was to double check, because
totaltar gave unexpected results
on y. Dummy version
of tariffa tariffa1, tariffa2, tariffa3, tariffa4
dummy
46
3.e Analysis of the dependent variables One of the original sources of information used to build my dependent
variables is the categorical variable “legal2”, that classifies each household according
to the type of connection to electricity. This information, registered by the Conviver
agents, during the visits conducted for the baseline survey give a fundamental
classification in terms of regular access to electricity.
Figure N.3
Frequency distribution of "legal2" (100%=15,279 obs.)
Illegal connection,
3.9%
No circuit breaker panel,
1.6%
Energy supplied by another domicile.,
10.2%
Legal, 84.1%
n.d. , 0.2%
The following table shows how the same variable “legal2” is characterized
according to the level of monthly family income.
47
Table N.9 Frequency distribution of “legal2” (in percent) by household income brackets
Illegal connec
tion
No circuit breaker panel
Energy supplied by
other domicile
Legal N.R.
<= 1/2 of Min salary (175R$) 22.5 18.1 26.9 19.3 40
From half to min salary (350R$) 27.8 31.1 24.5 17.5 12
From Min Salary to 2X Min salary (700R$) 33.6 39.1 34.9 36.6 16
Over 2xMin Salary 16.2 11.8 13.8 26.6 32
TOT 100 100 100 100 100
(TOT # of observations) 601 238 1,561 12,854 25* As for “minimum salary” level, it has been used the amount of 350 R$ (Brazilian Reais) following the IBGE estimates updated in September 2006.43
In order to identify the irregular or illegal situations, the survey also used two
other questions (a sort of double check) providing the variables “enerprov” and
sealwax”. These additional original variables that are distributed as shown in the pie
charts below and identify those houses where energy is given to other premises
(enerprov) or the sealing wax in the electric meter is not integer (sealwax).
43 IBGE (2007)
48
Figure 3 Frequency distribution of the variables “enerprov” and sealwax”
A first dependent variable used in the regressions is “clandestino”, defined as a
composition of the original variables presented above (legal2, enerprov and sealwax).
“Clandestino” is designed to capture ALL possible illegal behavior as a dummy
variable that equals 1 if the following condition is verified: legal2=1,2,3 OR sealwax=0
OR enerprov=1. Especially due to the high incidence of manipulation to the sealing
wax, the variable “clandestino” indicates an extremely high incidence of illegal
behaviors related to electricity (86% of the sample).
Even though the variable “clandestino” was the most accurate to capture all
possible illegal behavior or electricity thefts, another variable was also studied
“clandestini2”. This second variable is less rigid as it excludes from the construct of
“illegality” the manipulation or the electric meter (sealwax=0). The reason for this
choice is that, due to inefficiency and lack of complete reliability of the equipment in
our communities, households might touch the sealing wax of the electric meter for
Is the Sealing wax in circuit breaker panel Integer?
(100%= 15,279 obs)
Yes, 15.78%
No (installment illegally modif ied), 84.22%
Does it give energy to other premises?(100%= 15,279 obs)
Yes, 8.21%
No, 91.79%
49
reasons other than illegal manipulating, for instance in the attempt to repair the
connection when it fails.
Figure 4 Frequency distribution of the variables “clandestino” and “clandestini2”
As shown in the two comparable pie charts, the more relaxed definition of the
variable “clandestini2” indicates that only 23,08% of households perform illegal acts
as opposed to the much more concerning 85.8% captured by “clandestino”.
The following table shows the distribution of both indicators (“clandestino” &
“clandestini2”) by household income bracket.
Is there any illegal use of electricity? (more rigid variable "clandestino)
100%=15,279 obs.
Yes, 85.8%
No, 14.2%
Is there any illegal access/use of electricity (less rigid variable
"clandestini2")?(100%= 15,279 obs)
No, 76.77%
Yes, 23.08%
50
Table N.10 Frequency distribution of illegality (in percent), by household income brackets Income brackets (Brazilian Reais)
Illegality (clandestino)
Illegality (clandestini2)
No Yes No Yes n.a. <= 1/2 of min salary (<= 175R$) 5.4 22.7 19.5 22.6 41.7
From 1/2 of min salary to min salary (350R$) 4.7 21.2 17.1 24.6 12.5
From min salary (350R$) to 2X Min salary (700R$) 50.8 33.9 36.7 35.2 12.5
Over 2x min salary 39.1 22.3 26.8 17.6 33.3
TOT 100 100 100 100 100 (TOT # of observations) 2170 13109 11,729 3,526 24
It is consistent with the hypothesis about affordability that the majority of
households with irregularity have the lowest levels of monthly income (up to half the
minimum salary). Yet, it is noticeable how according to both “clandestino” and
“clandestini2”, the highest incidence of illegal behavior (respectively 33.9% and
35.2%) is found in those households in which the income is above the minimum salary
(R$350) and below twice the minimum salary (R$700). This might seem
counterintuitive, but actually reflects the socio-economic texture of these communities.
In fact those households that have an income that exceed the minimum salary are the
ones that are more likely to have a business in the dwelling (often informal) and
connected to the main meter of the house. There is a positive correlation between the
51
variables for income “totinc” and business in dwelling “givebus”, but very low at
3.46%.. It is important to notice that although only 4% of our sample (640 households)
explicitly declare to provide energy to a business in the dwelling, the survey shows that
30% of the households have an income source defined as “other than job” that earns
them an average of R$329 per month.44 My interpretation of this is that our sample
reflects the typical reality of Brazil, where millions of people work in the informal
economy as micro entrepreneurs or in all sort of ways that only the astonishing
Brazilian creativity could imagine.
Another possible explanation of the important incidence of illegal behavior
within the “relatively” better of households is that those households that enjoy a bigger
income are typically the ones that have a working connection and resell or redirect
informally electricity to their neighbors. As a matter of fact, 60% of the households
that provide energy to other premises (enerprov=1) have a monthly income that
exceeds the minimum salary (R$350).
3.f Expected signs of the independent variables As explained in detail in the introduction section, the following signs are
expected for each of the independent variables in a logistic regression against the
dependent variables indicating different types of illegal behaviors.
44 The variables of interest are “otherinc” (dummy for existence of a source of income “other than
52
Table 11: Expected Signs of Independent Variables
Variable Name Explanators Expected sign Unexpected Outcomes
desarma Non reliability of provision
positive
precario Precarious equipments (either external or internal)
positive
givebus Energy used for Business in dwelling
positive
ironw Energy-saving behavior (collect laundry for weekly iron)
negative
totinc Family Income negative * numroom Income estimated as: #
of rooms negative
roompercap Income estimated as: (# of rooms / # of residents)
negative
enerexp Energy bill as % of family disposable income
negative
dummy1 Social tariff based on Consumption (Brasil1)
negative *
dummy2 Social tariff based on Subsidies (Brasil2)
negative *
dummy3 Social tariff based on Consumption (No Tax)
negative *
subsidy2 Any government subsidy negative (since I am
controlling for income)
headfemale Female head of the house negative *
Contrary to expectations, the preliminary descriptive statistic analysis showed
that illegal behaviors had a high incidence among households with social tariffs (not
controlling for other factors).
job” and “other_” indicating the amount of such income share.
53
Table 12: Cross-tab between tariff type and clandestino
Type of tariff (totaltar)
clandestino Brasil1 Brasil2 Notax Normal Total
Freq
988 201 210 735 2,134
% row
46.30 9.42 9.84 34.44 100.00
legal
% col 18.24 6.98 24.65 26.65 17.92
Freq
4,429 2,679 642 2,023 9,773
% row
45.32 27.41 6.57 20.70 100.00
illegal
% col 81.76 93.02 75.35 73.35 82.08
Freq
5,417 2,880 852 2,758 11,907
% row
45.49 24.19 7.16 23.16 100.00
Total
% col 100.00 100.00 100.00 100.0
0
100.00
According to the frequency distribution of “clandestino” by type of tariff,
among illegal behaving households, 45. % have Brasil1 , 27. % have Brasil2 and 21%
have the normal tariff. The good news is that the lowest incidence of illegal behavior is
in the Notax type of social tariff, pending my regression results.
Looking at the frequency distribution of the second variable “clandestini2” with
respect to the type of tariff, I found gives pretty much the same outcomes: even though
the total sub sample of household with irregularities is smaller, the distribution across
type of tariff is pretty consistent.
54
Table 13: Cross-tab between tariff type and clandestini2
Type of tariff (totaltar) clandestini2 Brasil1 Brasil2 Notax Normal Total
Freq
4,843 2,530 767 2,399 10,539
% row
45.95 24.01 7.28 22.76 100
Legal (=0)
% col 89.42 87.91 90.45 87.08 88.59 Freq
573 348 81 356 1,358
% row
42.19 25.63 5.96 26.22 100
Illegal (=1)
% col 10.58 12.09 9.55 12.92 11.41 Freq
5,416 2,878 848 2,755 11,897
% row
45.52 24.19 7.13 23.16 100
Total
% col 100 100 100 100 100 Pearson chi2(3) = 14.1384 Pr = 0.003
Once again, 26.22% of illegal household (according to “clandestini2”) are in
the Normal tariff group (which coincides pretty much with the better off subset of
households). Since these data are persistent also in the distribution of clandestini2, we
can exclude that this is only referred to manipulation of sealing wax (only taken into
account in clandestino).
The fact that the sub-groups of households with social tariffs Brasil1 and
Brasil2 show persistent high incidence of illegality was not as expected, and it will be
further analyzed in the regression models. Based on the preliminary statistics (as table
14 suggest), in the case of Brasil1 and Brasil2 an explanation could be that those who
have such a little consumption level are typically the poorest ones, so the most
vulnerable and likely to perform irregularities due to severe affordability problems.
55
Table 14: Income by tariff type
Summary of tot_
totaltar Mean Std. Dev. Freq.
Brasil 1 529.93477 472.08594 5381
Brasil 2 484.51505 360.59166 2879
Notax 3 590.00219 403.90258 849
Normal Tariff 684.13134 593.53641 2741
Total 558.87035 480.9727 11850
Yet, two facts tell that this cannot be the only explanation: first those who are
better off (with normal tariff) also have illegal connections. Second, among the
household with the Notax type of social tariff (848) only 9.55% have clandestini2=1.
And these are not households with particularly high income: although the average is
higher than in Brasil1 and Brasil2, R$590 per month is certainly not a “high income”
and is significantly lower than the average of the group charged with normal tariff
(R$648).
Looking at the following table (Table 15) provides another potential
explanation for the puzzling incidence of illegality across income/tariff levels. Table
15 reports the average income and consumption levels broken into the 2 sub samples of
household with “No Illegal” connection and “With Illegal” connection.
56
Table 15: Income & consumption by tariff type and by “clandestini2”
LEGAL Monthly Income Consumption (at 1st visit time) Mean (R$) Std. Dev. # families Mean
(KWh/m) Std. Dev.
# families
Brasil 1 530 472 5381 47 33 1031Brasil 2 485 361 2879 88 65 712Notax 3 590 404 849 88 32 156Normal Tariff 684 594 2741 145 163 466
Total 559 481 11850 82 92 2365 ILLEGAL Monthly Income Consumption (at 1st visit time)
Mean (R$) Std. Dev. # families Mean (KWh/m)
Std. Dev.
# families
Brasil 1 513 360 543 50 44 131Brasil 2 487 366 332 103 94 108Notax 3 556 437 76 91 67 29Normal Tariff 571 471 337 265 906 86
Total 524 399 1288 122 457 354
It is quite evident that, when we moving from “legal” to “illegal”, the average
income basically doesn’t change across the sub samples. Conversely, the consumption
level is much higher for the tariff groups Brasil2 and normal tariff. This means that the
increased demand of energy is a strong incentive to turn to illegal behavior, regardless
of the income level. In my opinion, this make a strong case for a “tariff engineering”
that take into serious account the electricity needs in term of consumption, and with
price schemes that consider all the possible factors that contribute to raise electric
power consumption. In other words, the household consumption may growth for many
different reasons. One can be inefficiency (including defective wiring) either due to the
57
supplier that fails to fix equipment, or for incorrect use of domestic electrical
appliances. Consumption might also be growing due to a business activities that
requires energy provided by the house. In this case, it seems shortsighted (also from
the supplier standpoint) to charge the dweller with a non-sustainable price, because it is
also in the interest of the utility to promote income generating activities.
All these hypothesis need further confirmation by the regressions, but it is
possible to draw some preliminary observations. Socially motivated ceilings on
residential tariffs are successful in reducing the incentive to illegal access or use of
electricity. Among all the different kind of social tariffs in place, Notax seems to work
particularly well. A closer look to the patterns of consumption in the households with
different type of tariffs, clearly suggest that a thorough analysis of the customer needs
and consumption pattern is key to design effective price schemes. In this regard, the
Conviver program moved from a rigorous and extensive round of baseline surveys that
collected a lot of relevant information. This thesis itself is intended as a contribution to
understand in a structured way the collected data. Getting to a more proper and better
targeted tariff policy, will greatly gain from Conviver’s thorough and realistic
understanding of the assisted families’ structure, activities, number and type of
domestic appliances and patterns of consumption.
58
3.g Regression models and results discussion The technique used in this research is a logistic regression used for prediction
of the probability of occurrence of illegal access and use of electricity. As previously
illustrated, the dependent variable(s) have been set up as 0-1 dummy. The model’s
purpose is to study the causal relationship between the dependent variable(s) (the
probability to choose illegal behaviors in the electricity connections and the proposed
explanatory variables investigated by the household-level baseline survey.
A particular attention is also devoted to identify the role of subsidies and social
tariffs in reducing the incidence of illegal behaviors. In the course of the analysis this
aspect has proved to be the more challenging because the signs of the explanatory
variables where not as expected.
The model has been run with different specifications to explain these
unexpected outcomes and to overcome all possible distortions due to multicollinearity
or misspecification of the parameters.
Multicollinearity
Multicollinearity is a ubiquitous problem in regression analysis. It is something
that I have routinely examined in each model I run to avoid making erroneous
inferences with my hypothesis tests. Multicollinearity has been particularly challenging
with respect to the variables dealing with income, social tariffs and subsidies.
59
Table N. 16 Correlation matrix
desarma
precario
givebus
ironw
totinc
roompe~p
dummy1
dummy2
dummy3
subsidy2
headfe~e
mediac~v
desarma 1.00 precario 0.08 1.00 givebus 0.04 0.05 1.00 ironw -0.03 -0.02 0.00 1.00 totinc 0.01 -0.01 0.02 0.05 1.00 roompercap -0.06 -0.01 0.02 -0.01 -0.05 1.00 dummy1 -0.05 0.00 -0.04 0.01 -0.02 0.23 1.00 dummy2 0.05 0.04 0.02 -0.05 -0.12 -0.28 -0.58 1.00 dummy3 -0.02 -0.01 -0.01 0.04 0.01 0.06 -0.23 -0.17 1.00 subsidy2 0.05 0.04 0.02 -0.06 -0.12 -0.28 -0.58 1.00 -0.17 1.00 headfemale 0.04 0.03 -0.01 -0.07 -0.10 0.06 -0.01 0.04 -0.03 0.04 1.00 mediaconsv 0.04 -0.04 0.06 0.01 0.17 -0.08 -0.46 0.02 0.03 0.02 -0.01 1.00
Whenever possible variables have been collapsed. This was the case for the
“sitmeter2” “sitpadrao” that indicate precarious conditions in the external circuit
breaker panel and in the internal electrical meter and were highly correlated (ρ=0.8).
These variables have been collapsed as the variable “precario” which is a
comprehensive indicator for inadequate equipment.
Also, different specification of the models have been run in order to avoid or at
least reduce correlation among independent variables.
The proposed logistic regression models have been studied looking at the
marginal effects.45
45 The marginal effect is not constant because it depends on the value of Z, which in turn depends on the values of the explanatory variables. A common procedure is to evaluate it for the sample means of the explanatory variables.
60
Starting from the logistic function: P=(y=1| X) =G (β0 + βX) with
βixZ = and z
z
eeZZG+
=Λ=1
)()(
this first model studies how the dependent variable “clandestino” can be explained by
some socio-economic characteristics of the sample.
Z= Probability to have illegal behavior (clandestino) = β0 + β1desarma +
β2precario + β3givebus + β4ironw + β5roompercap + β6dummy1 + (β7dummy2)
+(β8dummy3) + β9subsidy2 + β10headfemale + ε
To estimate the effect of income, I initially adopted “totinc” defined as “family
disposable monthly income”.46 This explanatory variable, that is crucial for our
purposes, was not significant and presented an unexpected positive sign. An issue with
this variable was that the original variable47 (“tot_”) presented some measurement
errors. In fact it had a lot of 0 values (2,724 out of 15,279 tot obs.). I assumed most of
these were missing values, so I recoded “tot_” as “totinc”. At this point there were too
many missing values (with 2,724 + 74 = 2798) that were undermining the reliability of
jZ
Z
jj
i
eeZf
xyP ββ 2)1(
)()1(+
==∂
=∂ .
46 See Appendix 1, Model 1 47 The original variable was “tot_”, then recoded as “totinc” where the 0 values as considered
missing.
61
this information. For these reason the number of rooms (“numroom”) was adopted as a
proxy for family income.48
In order to get a more accurate coefficient, the number or rooms in the house
was divided by the number of people living in the house (“numpeopl”) obtaining a new
variable “roompercap”. In fact, the number of rooms by itself may be misleading in the
context. During field visits, I realized that often the houses in the favelas have many
different (tiny) rooms, but the premise is shared by various families, typically made of
relatives. So “roompercap” resulted more reliable to estimate income.
48 See Annex 1, Model 2
62
Table 17: Results of logit model for probability of having an illegal access or use of electricity (clandestino=1) - Marginal effects are reported Model 3 Model 3.a Model 3.b Model 3.c Model 3.d Model 3.e Model 3f
desarma 0.0303** 0.0313** 0.0190* 0.0315** 0.0313** 0.0292** Lack of Reliability of provision std dev 0.0139 0.014 0.0112 0.01450 0.0145 0.0146
precario 0.107*** 0.101*** 0.178*** 0.105*** 0.106*** 0.107*** Precarious equipments std dev 0.0155 0.0156 0.0109 0.01620 0.0162 0.0162
givebus 0.0435*** 0.0416** 0.0192 0.0399** 0.0393** 0.0350** Business in dwelling std dev 0.0166 0.0169 0.0135 0.01750 0.0175 0.0175
ironw -0.0959*** -0.0979*** -0.0806*** -0.106*** -0.106*** -0.107*** Collect laundry for weekly iron std dev 0.0078 0.00784 0.00626 0.00810 0.0081 0.0081
roompercap -0.0235*** -0.0204*** -0.0195*** -0.0245*** -0.0244*** -0.0233***#of rooms / #of residents std dev 0.00344 0.00335 0.00265 0.00356 0.00357 0.00352
dummy1 0.0689*** 0.0684*** 0.0379*** 0.0344*** Tariff B1
std dev 0.00746 0.00757 0.00818 0.00914
dummy2 0.369*** 0.189*** Tariff B2
std dev 0.0241 0.0108
dummy3 0.0208* 0.0211* -0.0149 -0.0365***Tariff NoTax
std dev 0.0122 0.0124 0.0138 0.0123
subsidy2 -0.197*** 0.0507*** 0.0880*** 0.0849*** 0.0661*** Gov. subsidy
std dev 0.0206 0.00694 0.0107 0.0115 0.00916
headfemale 0.0204*** 0.0188*** 0.0176*** 0.0219*** 0.0218*** 0.0221*** female head
std dev 0.00664 0.00673 0.0053 0.00695 0.00696 0.00696
_cons 0.231*** 0.221*** 0.240*** 0.271*** 0.274*** 0.295*** constant
std dev 0.0101 0.0102 0.00784 0.0109 0.0115 0.00952 Obs. 11612.00 11612.00 14358.00 11612.00 11612.00 11612.00 Log Likel. -4999.29 -5073.25 -5573.75 -5195.00 -5194.33 -5203.43 Pseudo R2 0.08 0.07 0.07 0.04 0.04 0.04
Statistically significant at: *** p<0.01, ** p<0.05, * p<0.1
63
In the initial model, “dummy1”, “dummy2”, “dummy3” stand for the 3
different type of social tariff (Brasil1, Brasil2 & Notax), while “dummy4” is the
baseline representing the normal tariff. With the exception of the version (3f),49 this
initial model suggested that being entitled to a social tariff is apparently associated
with a higher probability of engaging in any illegal behavior. Since the sign of the
marginal effects was not as expected, I decided to run a slightly different model to
check the robustness of this result.
My initial doubt was that, since the variables had been partially created with
“surrogate data”, some misspecification could be happening. In fact to obtain the
variables “dummy1/2/3/4” for the entire sample, I had integrated the data available
from the baseline survey as well as those from CEMIG.50 Actually, the parameter
could be uncertain because in some cases it could describe the type of tariff household
are theoretically entitled to, but not necessarily the tariff they are actually charged. So,
as a test, I run the same logistic regression using another set of dummies for the type of
tariff (tariffa1, tariffa2, tariffa3).51 This time the regressors were relative only to the
households in which the agent conducting the baseline survey had explicitly reported
49 What seems to be happening in Model 3.f is that the previous models were over-specified with all the dummies about tariff. Although not biased, dummy3 resulted inefficient (as including irrelevant variables raises the s.e. of our estimates. Thus it is interesting that in 3.f dummy3 becomes significant and negative.
50 As previously shown in section 3.c, the variables dummy1/2/3/4 were created as a synthetic combination of the original variables “tartype”, “tarcalc” and “subsidy2” plus additional information I had form CEMIG describing consumption (one of the preliminary conditions to have special social tariffs.
51 See Annex 1 Model 4
64
the type of tariff. Anyway, also the “actual tariffs” confirmed the same results, and
showed three statistically significant coefficients with positive signs.
The evidence that being entitled to social tariffs might increase the probability
to incur in illegal access to electricity is something that raises some concerns about the
effectiveness of these measures to alleviate energy poverty for low income customers.
Therefore, in order to further check these puzzling outcomes, I run a similar model to
predict (with the same explanatory variables) another variable indicating illegal access.
As already discussed,52 “clandestini2” is less conservative as it detects only the
more severe cases of illegal performance. In this new model, the first set of variables
(studying lack of reliability, equipments, and energy-saving behavior) show consistent
significant effects and signs with the previous model.
Table N.18: Results of logit model for probability of having an illegal access or use of electricity (clandestini2=1) - Marginal effects from logit regression are reported
52 See section 3.c for details about the dependent variables
65
Model 5 Y=clandestini2 Model 5.a Model 5.b Model 5.c Model 5.d Model 5.e Model 5.f
Non Reliable provision
desarma 0.121*** 0.121*** 0.143*** 0.122*** 0.121*** 0.122***
Std dev. 0.0080 0.00798 0.0129 0.00797 0.00797 0.00797Precarious equipments precario 0.0933*** 0.0941*** 0.377*** 0.0939*** 0.0939*** 0.0934*** Std dev. 0.0081 0.00808 0.0083 0.00810 0.0081 0.0081Business in dwelling givebus 0.0257** 0.0258** -0.0158 0.0268** 0.0261** 0.0274** Std dev. 0.0114 0.0114 0.0192 0.01140 0.0114 0.0114Weekly iron ironw -0.0215*** -0.0213*** -0.0459*** -0.0209*** -0.0206*** -0.0206*** Std dev. 0.0058 0.00582 0.00678 0.00582 0.00582 0.00582#of rooms / #of resident roompercap -0.00454 -0.00522* -0.0191*** -0.00477 -0.00458 -0.00503 Std dev. 0.00309 0.00315 0.00425 0.00311 0.00309 0.0031Tariff B1 dummy1 -0.0188*** -0.0189*** -0.00891 -0.0132* Std dev. 0.00704 0.00705 0.00658 0.00706 Tariff B2 dummy2 -0.0504*** -0.0171** Std dev. 0.0162 0.00788 Tariff: NoTax dummy3 -0.0273** -0.0272** -0.0215* -0.0134 Std dev. 0.0123 0.0123 0.0123 0.0115subsidy subsidy2 0.0360** 0.000567 -0.00159 -0.0054 0.00199 Std dev. 0.0152 0.00779 0.00729 0.00765 0.00661female head headfemale 0.0234*** 0.0238*** 0.0235*** 0.0235*** 0.0234*** 0.0232*** Std dev. 0.00559 0.00559 0.00667 0.00561 0.0056 0.00561 Constant -0.192*** -0.190*** -0.248*** -0.202*** -0.197*** -0.205*** Std dev. 0.00845 0.00848 0.00939 0.00814 0.00844 0.00775 Obs. 11603.00 11603.00 14347.00 11603.00 11603.00 11603.00 Log Likelihood -3882.32 -3885.31 -5513.72 -3886.59 -3885.03 -3886.87 Pseudo R2 0.05 0.05 0.24 0.05 0.05 0.05
Statistically significant at: *** p<0.01, ** p<0.05, * p<0.1.
66
Referring to our initial hypothesis, we can convey that both models prove that
lack of adequate provision is actually among the factors that increase the probability of
illegal behaviors. As shown by the variable “desarma”, having a not reliable provision
of electricity has a marginal effect of 0.0305 on the more general probability of
illegality and of 0.122 on the most serious cases of irregularity.
Similarly, precarious equipment (“precario”) has a marginal effect of 0.1067
and 0.0936 that significantly increase the probability of irregularity.
Also providing energy to a business in the dwellings has consistent significant
positive impact on irregularities. The coefficients are almost always significant and
show a marginal effect of about 0.035 in the model with broader definition of illegality
(Table 17) and of about 0.026 in the stricter definition of illegality (Table 18). As we
already observed in the preliminary descriptive statistics, illegality is not explained
exclusively by genuine poverty, and it has important incidence also in the household
that are relatively “better off”. So, holding the considerations proposed in the
discussion of sources of income and illegality (sections 3.e and e.f), this regression
model confirms the positive correlation between running a business in the house and
engaging in illegal behaviors. It is fair to say that illegality can be explained, among
other factors, by an increase in energy demand that automatically pushes the
household’s family consumption beyond the subsidized levels.
67
On the other hand, an interesting message comes from the variable “ironw”.
This variable indicates when families collect laundry for ironing once a week as
opposed to ironing every day or on a random schedule. This apparently trivial
information is relevant because a very important part of the Conviver program is the
effort that each agent devotes in explaining people how to save energy by using
domestic appliances in a more efficient way. It is important to keep in mind that the
correct use of simple appliances (such as lamps, refrigerators or “chuveiros”, electrical
apparatus for water heating) is very important in poor households where consumption
is much below the average in wealthy industrialized countries. The question about
ironing schedule provides evidence, explicitly documented in the survey, that allow us
to appreciate the effectiveness of energy-efficiency orientation. So, the significant
negative coefficient of “ironw” (respectively -0.0961 and -0.0219 in the 2 models)
substantiates the idea that Conviver is heading to the right direction when it focuses on
energy saving education.
It is also very interesting to notice how the explanatory variables about income
and subsidies have changed in the model with “clandestini2”. Despite they still show
some mixed outcomes and they are not always significant, the dummies about social
tariffs are no longer positive, confirming my initial hypothesis about their contribution
in reducing illegality.
68
In most of specifications of Model 5 “roompercap” is negative, but not
statistically significant. This is probably related to the correlation with the dummies
about tariffs that causes a bigger standard error, which means that the estimated
coefficient is less likely to be significant. Actually, when pulling out the dummies, like
in Model 5.c, “roompercap” is negative and significant at 99% confidence level.
Similarly, the sign of “subsidy2” in Model 5.a is positive, but this combination
probably suffers from the high multicollinearity between subsidy2 and dummy2.
Actually, even though it remains non significant, in Model 5.c and 5.d (after
eliminating dummy2) the sign becomes negative.
Despite some contradictions and loss of significance, the various specifications
of Model 5 show that when considering a more realistic definition of illegal behavior
(“clandestini2”) social tariffs, even though not exclusively responsible, can be
considered as factors that mitigate the occurrence of illegality. It is also fair to say that
(as in Model 5.e) the type of social tariffs that work better are the ones that are more
strictly connected to consumption as opposed to Brasil 2 which refers to general
poverty subsidies.
A further analysis was conducted on a third different dependent variable:
“enerprov”. Since this particular type of illegal conduct deals with those households
that give energy to other promises who do not possess a regular connection. This
procedure typically happens in those cases were the income is relatively higher, so the
69
idea was to see if the explanatory variables had a different performance with respect to
income , social tariffs and subsidies. The Model n. 653 was defined as follows:
Probability to illegally provide/re-sell energy (enerprov) = β0
+ β1desarma
+ β2precario + β
3givebus + β
4ironw + β
5roompercap + β
6dummy1 + (β
7dummy2)
+(β8dummy3) + β
9subsidy2 + β
10headfemale + ε
Table N.19: Results of logit model for probability of illegal use of electricity (enerprov=1) - Marginal effects from logit regression are reported
Model 6 Model 6.a Model 6.b Model 6.c Model 6.d Model 6.e Model 6.f desarma 0.115*** 0.115*** 0.113*** 0.114*** 0.114*** 0.116*** Non Reliability of
provision 0.00653 0.00653 0.00564 -0.00652 -0.00652 -0.00652 precario 0.0292*** 0.0295*** -0.0114* 0.0293*** 0.0293*** 0.0286*** Precarious
equipments 0.008 0.00798 0.00593 -0.00799 -0.00799 -0.00801 givebus 0.0291*** 0.0291*** 0.0305*** 0.0301*** 0.0301*** 0.0316*** Business in
dwelling 0.0097 0.00969 0.00884 -0.00967 -0.00967 -0.00968 ironw -0.0231*** -0.0230*** -0.0221*** -0.0225*** -0.0225*** -0.0221*** Collect laundry for
weekly iron 0.00513 0.00513 0.00446 -0.00513 -0.00513 -0.00513 roompercap -0.0000143 -0.0002 0.00032 -0.000278 -0.000278 -0.000678 #of rooms / #of
residents 0.00203 0.00207 0.00167 -0.0021 -0.0021 -0.00216 dummy1 -0.0231*** -0.0232*** -0.0161*** -0.0161*** Tariff: B1 0.00621 0.00622 -0.00579 -0.00579 dummy2 -0.0318** -0.0182*** Tariff: B2 0.0147 0.00692 dummy3 -0.0233** -0.0233** -0.00803 Tariff: NoTax 0.0107 0.0107 -0.0101 subsidy2 0.0146 0.00132 -0.00899 -0.00899 -0.00143 gov subsidy 0.0139 0.00503 -0.00644 -0.00644 -0.00588 headfemale 0.0143*** 0.0144*** 0.0121*** 0.0191*** 0.0191*** 0.0189*** female head of the
house 0.00383 0.00383 0.00323 -0.00498 -0.00498 -0.00499 Constant -0.167*** -0.167*** -0.174*** -0.176*** -0.176*** -0.185*** constant 0.00707 0.00708 0.00545 -0.00691 -0.00691 -0.00653
Obs. 11612 11612 14358 11612 11612 11612 Log
Likelihood -3337.1842 -
3337.7398 -
3957.0277 -3339.3966 -3337.5782 -3343.0908
Pseudo R2 0.0519 0.0517 0.0477 0.0513 0.0518 0.0502
53 See Annex 1 Model 6.
70
The logit regression of “enerprov” reports coefficients in the explanatory
variables that are overall pretty similar to the previous ones in Model 3 an Model 5
(except fro the tariff dummies in Model 3).
The variable “roompercap” (our proxy for income) is negative but not
significant. Subsidy2 is not significant in any of the possible combination used. The
variables of social tariffs are negative, so, consistent with Model 5 in Table 18, we can
argue that social tariffs based on consumption have an impact in reducing illicit
distribution of energy to neighbors. The effect of the tariff Brasil2 is a little more
difficult to appreciate because, despite the significant negative coefficient, this result is
contradicted by the positive and insignificant coefficient on “subsidy2” (for the tariff
Brasil2, household must be recipients of government subsidies). This result is actually
pretty reasonable and confirms the necessity of an intervention that is as closer as
possible to the actual consumption needs of the customer (as opposed to general
subsidies not directly linket to the service demand).
My very last attempt to get an additional confirmation about the effect of social
tariffs on illegality has been to use a variable reflecting the average consumption of the
households instead of the dummies for social tariffs Brasil1 & Notax. Actually the
average consumption of the previous 12 months is the preliminary condition for these
two social tariffs. The usual variable “subsidy2” was supposed to work as a proxy for
the social tariff Brasil2, since being entitled to any subsidy is the preliminary condition
71
for social tariff Brasil2. With this new explanatory variable I run the logit regression
for the 3 usual dependent variables. clandestino / clandestini2/ enerprov.54
Model 7 Z= Probability to have illegal behavior (clandestino / clandestini2/
enerprov) =
= β0
+ β1desarma + β
2precario + β
3givebus + β
4ironw + β
5roompercap +
β6mediaconsv2 + β
7subsidy2 + β
8headfemale + ε
None of the versions of model 7 was particularly satisfactory. In fact, with
“clandestino” there was an error due to autocorrelation. As for the versions studying
“clandestini2” and “enerprov”, the coefficient of income and subsidies were not
significant. The coefficient explaining the marginal effect of average consumption
“mediaconsv2” was very little in magnitude but positive. In a way also this final
attempts corroborate the general message that those subsidies that work are the ones
that deal with people whose consumption is very little, and that are designed in a way
that link the intervention very directly to consumption.
54 See Appendix N.1 for these additional regression models.
72
Conclusions, policy remarks and next steps
Evidence about the determinants of illegal access and policy
recommendations to address energy poverty in urban slums
As documented in recent literature, clandestine connections (gato in Brazilian
Portuguese) is mostly explicable as the urban slum dwellers’ response to non-
affordable prices of electricity and the data from the baseline survey of the Conviver
program confirm this explanation. Nevertheless, as similar case studies suggest,
disposable income is not the only explanation. Actually the descriptive statistics from
my sample show that a substantial share of households with illegal access/use of
electricity are in the higher income brackets. Thus, the phenomenon is to be explained
with a variety of causes that take into accounts the socio-economic texture of these
communities.
As the diagnostic studies for the Conviver program had pointed out from the
beginning, an urgent issue to deal with is the low standards of equipment, services and
maintenance that peri-urban slums suffer in the provision of basic infrastructures. A
consistent outcome of my analysis is that lack of adequate provision is actually among
the factors that increase the probability of illegal behaviors. This is not merely a
technical issue, but has also repercussions in the psychological and cultural perception
of the urban favelas residents. Previous to the Conviver Program, CEMIG technicians
barely enter in some neighborhoods for emergencies and maintenance for fear of
73
violent acts. But in a sort of vicious circle, equipments and maintenance service were
ever more lacking. Lacking equipment, together with insufficient delivery and pricing
systems that are disproportionately costly, resulted in a sense of exclusion and
abandonment among the customers, and the creation of a further incentive to illegality.
Starting from this reality, CEMIG and AVSI-CDM realized that a key success
factor for the Conviver program had to be a change toward a demand-driven culture of
delivery. This approach explains two aspects that the program emphasizes: first,
supplying services that fit the needs of this special type of customers; and second, a
focus on information and customer relationship to bring the supplier closer to the
people and let them know they are not abandoned.
I believe the idea of hiring young local residents as Conviver agents was
extremely valuable and contributed effectively in the creation of a relationship of trust
between the utility and its low-income customers. It is hard to provide quantitative
evidence of this, but it was very clear to me last summer (when I joined the Conviver
agents in some of their visits) how people were appreciative when they realized that
somebody is willing to talk about their problems and to flexibly seek a solution.
Effectiveness of “social tariffs” in preventing illegal access of use of
electricity
Another interesting result of this study is its contribution to the impact
evaluation of subsidies and specific social tariffs in preventing poor households from
74
illegal connection or electricity theft. The analysis of the 15,279 families in the
baseline survey supports the idea that many experts have previously documented that
subsidies can be useful but not in a generalized way.
Contrary to expectations, the preliminary descriptive statistic analysis showed
that illegal behaviors had a high incidence among households with social tariffs. In
particular, it is very high in those households charged with the social tariff Brasil1
(minimum consumption) and Brasil2 (households with poverty subsidies from the
government). The first regression of a broader concept of illegality confirmed these
data. However, a further regression using a more restricted (and probably realistic)
definition of illegality, limited to the more severe cases, provided some evidence that
the existence of social tariffs (even though not exclusively responsible) can be
considered a factor that can mitigate the occurrence of illegality.
Despite some contradictions and loss of significance, also due to probable
endogeneity of the parameters and multicollinearity, it is fair to propose some thoughts
for future policy development.
In the case of Brasil1, despite the high incidence of illegality shown by the
descriptive statistics on the sample, the regression on the variables “clandestini2” and
“enerprov” showed a negative effect of this type of tariff on the probability of illegal
behavior. Therefore, socially motivated ceilings on residential tariffs are successful in
reducing the incentive to illegal access or use of electricity. The high incidence of
75
illegality could be explained reminding that those who have such a little consumption
(less than 80 KWh/m) are most likely the most vulnerable ones and likely to perform
irregularities due to genuine poverty problems. Therefore, even though this kind of
energy-related financial support might not be always effective with respect to energy, it
is certainly providing some relief to families in severe poverty situations.
On the other hand, the effectiveness of the social tariff Brasil2 is a little more
problematic, because although the coefficient in the regressions were significantly
negative, they were somehow in contradiction with the positive coefficient of
“subsidy2” which is a variable that is highly correlated to Brasil2. Since this tariff is
directly linked to government subsidies for food, school etc., it is extremely important
to question how efficient and how effective those subsidies are at protecting vulnerable
households.
According to my sample, subsidy-targeting is performed with a certain efficacy
as subsidies are actually given to the households with the lowest average monthly
income (R$ 456). Yet, in terms of sector effectiveness (fighting energy poverty at the
lowest possible cost) they are not completely convincing. An additional concern is that,
even if Lula’s financial transfers to poor families work, the demand for them far
outweighs the supply. Sticking on our specific goal of addressing energy related
poverty, it might be the case that, rather than broad national food subsidies that can be
76
ill-distributed or have distortion effects, anti-poverty schemes with a closer connection
with the actual demand and consumption of infrastructure services could work better.
Furthermore, the social tariffs, based either on very low consumption or on
poverty subsides, like Brasil1 and Brasil2, automatically rule out those households who
run a business (formal or informal) in the family dwelling. It seems to me that it would
be in the interest of both supplier and consumer to design a particular tariff that takes
into account this very common situation in the favelas. In fact, if these customers had
proper incentives to be legal, they could be treated as “micro-entrepreneurs” and in the
long run could become a valuable customer segment for the supplier. Actually, a case
study of a similar program confirms that: “The most important cause of payment
difficulty is the lack of stable and sufficient outcome, which could be considered a
direct consequence of the educational and occupational profile of poor households”.55
Also some very creative initiatives have been proposed in which the utilities have
entered the distribution of electric appliances combining incentives to both ownership
and legality. Codensa in Colombia, for example, was very successful in offering loans
to customers to enable them to purchase electric appliances while allowing them to pay
back the loans through their utility bills.
Finally, some positive remarks on social tariffs comes from the Notax tariff that
seemed to work particularly well. This tariff (directly linked to proven low-medium
55 WEC (2006), pg, 7. The case discussed is about energy poverty in the Greater Buenos Aires area in Argentina.
77
consumption) seems to be the best working socially motivated ceilings in terms of
reducing the incentive to illegal access or use of electricity.
To analyze the cost/benefit pattern from the point of view of the energy
supplier was beyond the scope of this study, but this last kind of social tariff seems a
win-win situation for the supplier and for the customers because in order to be entitled
to it, customers need to pay consistently as the cost reduction is based on the previous
12 months consumption if regularly billed. Therefore the customer is incentivized to
keep consumption under control as well as to pay regularly the bills to achieve a
substantial reduction of costs. Again, these findings corroborate the general message
that effective pro-poor tariff schemes should segment the customers to realistically
match the actual consumption needs (minimum; low-medium; house with micro-
business), and should strive to link the intervention very directly to the households
consumption patterns.
Next steps
The Conviver project is still ongoing and, while extending its outreach to new
communities, it is implementing additional activities like efficient lamps distribution
and debt personalized negotiation. Based on the analysis of a pre-post panel data, I
intend to study the impact of the program on those households that have been “treated”
with some of these activities. In particular, I intend to evaluate how effectively the
program is reducing the incidence of energy costs on households.
78
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82
Appendix 1 Additional regression Models (Chapter 3)
Model 1 Results of logit model for probability of having an illegal access or use of electricity (clandestino=1) - Marginal effects from logit regression are reported dlogit2 clandestino desarma precario givebus ironw totinc dummy1 dummy2 dummy3 subsidy2 headfemale,r
clandestino Coef. Coef. Coef. desarma 0.031 ** 0.031 * 0.022 * 0.0160 0.016 0.013 precario 0.1296 *** 0.124 *** 0.214 *** 0.0177 0.018 0.013 givebus 0.0470 ** 0.045 ** 0.026 * 0.0189 0.019 0.016 ironw -0.1086 *** -0.111 *** -0.096 *** 0.0089 0.009 0.007 totinc 0.0000 0.000 ** 0.000 0.0000 0.000 0.000 dummy1 0.0706 *** 0.071 *** 0.0087 0.009 dummy2 0.4039 *** 0.229 *** 0.0272 0.012 dummy3 0.0211 0.023 0.0140 0.014 subsidy2 -0.1906 *** 0.078 *** 0.0231 0.008 headfemale 0.0181 ** 0.018 ** 0.015 ** 0.0078 0.008 0.006 _cons 0.1825 *** 0.174 *** 0.215 *** 0.0125 0.013 0.008 Obs. 9868 Obs. 9868 Obs. 11877
Log Likelihood -4552.97 Log
Likelihood -4608.57 Log Likelihood -5032.91
Pseudo R2 0.0791 Pseudo R2 0.0679 Pseudo R2 0.0669 Standard errors below the coefficients Statistically significant at: *** p<0.01, ** p<0.05, * p<0.1
83
Model 2 Results of logit model for probability of having an illegal access or use of electricity (clandestino=1) - Marginal effects from logit regression are reported dlogit2 clandestino desarma precario givebus ironw numroom dummy1 dummy2 dummy3 subsidy2 headfemale,r
Y=clandestino Model 2.a Model 2.b Model 2.c
desarma 0.035 ** 0.036 *** 0.024 **
precario 0.105 *** 0.099 *** 0.179 ***
givebus 0.041 ** 0.039 ** 0.018
ironw -0.088 *** -0.090 *** -0.074 ***
numroom -0.005 *** -0.005 *** -0.006 ***
dummy1 0.060 *** 0.060 ***
dummy2 0.369 *** 0.199 ***
dummy3 0.016 0.016
subsidy2 -0.184 *** 0.066 ***
headfemale 0.011 0.010 0.010 *
_cons 0.218 *** 0.214 *** 0.231 ***
Obs. 11682 Obs. 11682 Obs. 14440
Log Likel. -5079.5 Log Likel -5143.97 Log Likel -5656.16
Pseudo R2 0.0728 Pseudo
R2 0.061 Pseudo
R2 0.0602
Standard errors below the coefficients Statistically significant at: *** p<0.01, ** p<0.05, * p<0.1
84
Model 4 Results of logit model for probability of having an illegal access or use of electricity (clandestino=1) - Marginal effects from logit regression are reported
Y=clandestino Model 4.a Model 4.b Model 4.c
desarma 0.030 ** 0.030 ** 0.028 *
precario 0.105 *** 0.098 *** 0.104 ***
givebus 0.044 ** 0.042 ** 0.035 **
ironw -0.099 *** -0.101 *** -0.111 ***
roompercap -0.024 *** -0.021 *** -0.023 ***
tariffa1 0.070 *** 0.070 ***
tariffa2 0.374 *** 0.191 ***
tariffa3 0.022 * 0.022 *
subsidy2 -0.199 *** 0.071 ***
headfemale 0.018 *** 0.017 ** 0.020 *
_cons 0.233 *** 0.224 *** 0.296 ***
Obs. 11387 Obs. 11612 Obs. 11387
Log Likel. -4961.31 Log Likel. -5073.25 Log Likel. -5166.87
Pseudo R2 0.08 Pseudo R2 0.0666 Pseudo R2 0.0418Standard errors below the coefficients Statistically significant at: *** p<0.01, ** p<0.05, * p<0.1
85
Model 7 Logit model for probability (enerprov=1), (clandestini2=1) enerprov Marg Coeff Std. Err Significatnt at
desarma 0.1159011 0.0079639 ***
precario 0.0380127 0.0094094 ***
givebus 0.0230334 0.0115716 *
ironw -0.027396 0.0060475 ***
roompercap 0.0007789 0.0025432
mediaconsv2 0.0003384 0.0000493 ***
subsidy2 -0.0020773 0.0067781
headfemale 0.0200607 0.0059968 ***
_cons -0.2118199 0.0089067 ***
Obs. 7866
Log Likelihood -2265.4629
Pseudo R2 0.0679
Statistically significant at: *** p<0.01, ** p<0.05, * p<0.1 clandestini2 Coef. Std. Err Significant at
desarma 0.1243178 0.0088792 ***
precario 0.0516641 0.0101385 ***
givebus 0.0256952 0.01268 *
ironw -0.0255622 0.0066069 ***
roompercap -0.0025487 0.0031308
mediaconsv2 0.0003174 0.0000548 ***
subsidy2 -0.0015767 0.0073637
headfemale 0.022053 0.0065052 ***
_cons -0.2222608 0.0097376 ***
Obs. 7864.00
Log Likelihood -2502.21
Pseudo R2 0.06
86
87
Appendix 2 AVSI & CDM
AVSI (Association of Volunteers in International Service) is an international
not-for-profit, non-governmental organization (NGO) founded in Italy in 1972,
currently active with 111 development projects in 39 countries all around the world.
AVSI’s mission is to support human development in developing countries with special
attention to education and the promotion of the dignity of every human person,
according to Catholic social teaching. The NGO’s presence in Brazil started in 1984,
when AVSI proposed its first project of land title regularization in the favelas of Belo
Horizonte which, in time, converged in the approbation of the pioneering “law for the
favela” (Lei Pró-Favela). Since the 1980s, AVSI have implemented several different
projects in Brazil aiming at social inclusion, education and vocational training, urban
development, and business promotion.
Presently, AVSI links together 24 NGO’s, most of which are local institutions
in non-western countries, into a global network. CDM (Cooperação para o
Desenvolvimento e Morada Humana) is one of these local partners. Funded in 1986,
mostly hiring Brazilian staff, CDM is the operative partner of AVSI in Belo Horizonte,
Rio de Janeiro, and Salvador de Bahia.
More information about AVSI’s mission and activities are available at:
http://www.avsi-usa.org/
