Quality of Life in Urban Neighborhoods in Costa Rica

Quality of Life in Urban Neighborhoods in Costa Rica

Research Proposal:
Quality of Life in Urban Neighborhoods
in Costa Rica
Juan Robalino, Ph.D.*
Environment for Development Initiative CATIE
and Earth Institute, Columbia University
Roger Madrigal, M.Sc.
Environment for Development Initiative CATIE
Luis Hall, Ph.D.
Department of Economics, New York University
Host Institution:
Environment for Development Initiative Costa Rica at CATIE
In response to the call for proposals “Quality of Life in Urban Neighborhoods in
Latin America and the Caribbean” of the Latin American Research Network
of the Inter-American Development Bank
1 Introduction
The purpose of this project is to evaluate and compare the quality of life of cities and
urban neighborhoods in Costa Rica. The project proposes to incorporate a series of new
neighborhood amenities that have not been considered before for the city of San Jose, and
to consider additional cities outside the metropolitan area where a significant percentage
of the population located in urban areas live (MIDEPLAN 2000).
The estimation of quality of life indexes with multidimensional aspects will allow policy
makers, at the national and local level, to identify what urban areas are in disadvantage
and determine what actions will be more effective for improving the quality of life in
those areas. The information generated by this proposed research will be important not
only for policy makers but also for the public in general. Individuals could take more
informed decisions concerning where to live and what to ask from the local and central
governments as a community.
*
Contact information of the project leader: Address: Programa de Ambiente y Desarrollo, CATIE,
Turrialba, Costa Rica; Email: [email protected] Tel: (506)5582522
The urban areas in Costa Rica represent an excellent opportunity to develop a project of
this type. More than one million people live in the Great Metropolitan Area of San Jose.
This study will reflect how neighborhood amenities and public goods will affect the
preferences of neighborhoods within medium size cities in Latin America. Moreover, the
study of medium and small urban areas in Costa Rica can be also used to draw lessons for
a larger set of medium and small urban places in the rest of Latin America. This study
will permit to identify differences in preferences over specific amenities between small
and large urban areas. Elements such as congestion and economies of scale factors within
the city might affect how one values a specific public good. Focusing in only large scale
cities might be misleading when trying to estimate how people value specific amenities in
smaller urban areas in Latin America. By studying different sized cities, we will be able
to shed some light in this issue.
We are proposing to estimate the quality of life index as suggested by Gyourko et al.
1999 using the 2000 census data. We have information about the characteristics of each
house in the census (more than one million observations) and age, education and
employment characteristics of the members of the household (more than 3.8 million
observations). Moreover, we will be able to associate each household with information
about neighborhood amenities using Geographic Information Systems (GIS). This will
allow us to obtain detailed and precise calculations of neighborhood amenity variables as
well as better controlling for unobservable factors. For instance, instead of just
determining if there are fire departments in the neighborhood, we will be able to
determine how far the closest fire department is located. This type of detailed analysis
will also be applied to other environmental variables such as pollution, health variables,
such as clinic and hospitals, education variables, such as distance to other neighboring
schools and so on.
We will follow Gyourko et al. 1999 to extract the equilibrium weights for the factors in
the analysis that compose the quality of life index. The proposal also includes a
description of methods to be used to control for potential endogeneity of some of these
factors and the presence of spatial correlated errors (group effects). However, due to the
size of our sample and the precision and detail of our neighborhood characteristics, we
believe that these potential problems will only marginally affect our results.
We will also implement the suggested survey. We will ask the same questions that we use
to estimate the index when using the census data. However, we will also ask about
neighborhood amenities, public goods and education and crime characteristics of the
neighborhood. The goals of implementing this survey is 1) validating our results by
testing if there are differences between the elements we find to matter with the census
and the elements we find in the survey and 2) we will also be able to determine if the
assumptions required by the theoretical model hold (e.g. are households that are willing
to move restricted by a market imperfection?).
The team of this project is highly capable of performing this type of analysis. We have a
large experience in field work in Costa Rica addressing issues such as valuation of
environmental amenities, analyzing education and studying natural resource
consumption. Additionally, this team has developed and used GIS data for spatial
econometric analysis for Costa Rica. Additionally, the members have developed
theoretical work of neighborhood dynamics in areas such as education and resource
extraction. These research projects conducted on Costa Rica exploited the variation
across neighborhoods while introducing observable economic variables and controlling
for unobservable effects. The third member of the team has extensive experience in field
work in Costa Rica. He has not only applied valuation methods in Costa Rica but in other
countries Latin America. His experience on valuation methods will be of great value for
this project.
The proposal is organized as follows. In section 2, we present the quantitative analysis
that will be developed with the census data, the challenges and the possible strategies to
solve them. In Section 3, we discuss the implementation of the survey and the expected
results. In Section 4, we discuss the dissemination strategies and discuss strategies that
could be maintained to generate the indicator of quality of live in urban neighborhoods in
Costa Rica. In Section 5, we present the host institution and research team.
2 Quantitative Analysis
In this section, we will first describe the available and required data. Then we will
explain the strategy that we will follow to estimate the quality of life index, the
challenges that we will face and the alternatives and strategies we will use to address
those.
2.1 Data
In this section we discuss and present the data available for the analysis and the data we
propose to obtain and generate with the funds.
2.1.1
Urban available information
We describe the variables we currently have in Table 1 according to the source, year, area
covered, spatial reference and level of aggregation. Our analysis will use two main data
sources: The 2000 National Census of Population and Housing and the National
Household Survey (HHS) for Multiple Purposes. Given that the Census was in 2000, for
this section of the analysis, we are going to use the HHS from 2000 for consistency. We
have a census track map (See Figure 1) that gives us the location of the observations in
the census and the household survey.
From the census as well as from the HHS, we have information about each individual
within the household for variables such as education, age and country of origin, civil
status, employment and economic activity. We have income information in the HHS.
At the household level, we have information about housing infrastructure from the census
as well as from the HHS. We have variables such as type of house, zone (urban, urban
periphery, rural and sparse rural), ownership, rent (if the house is rented), materials of the
house (floors, walls, ceilings), number of rooms, bedrooms, and restrooms among others
shown in Table 1. Also, information about the basic services is available such as
electricity, water and sewage.
Additionally, the HHS contains information about participation in different types of
organizations such as cooperatives, unions, and gremial and community associations.
While we currently have highly detailed information about a large set of variables that
affect the quality of living, we are proposing to obtain a larger set of variables that could
complement this analysis.
2.1.2
City wide information required
We need to obtain additional information at the city level in order to estimate our quality
of life indices for the totality of urban areas. Specifically, we need to obtain or generate
more information about environmental variables, crime variables, and public goods. To
obtain some of these variables, we will need to visit primary sources but for others, we
will use Geographic Information Systems with already available information.
Environmental variables
Within this group of environmental factors, we include variables related to contamination
and natural characteristics.
•
Contamination: we have GIS maps that show where the main roads are located within
the city. These roads are associated with high contamination due to the number of
vehicles that circulate daily. We will complement this with information from models
of contamination for the metropolitan area of San Jose (Estado de la Nacion 2005).
•
Natural characteristics: we are going to use weather and terrain characteristics, which
affect the standards of leaving directly, but also, indirectly through the effects in the
propensity to disasters. Among the characteristics we are going to use are: proximity
to rivers, amount of precipitation, altitude and slope of the terrain. We already have
this information in GIS format. We need to compute the values of these variables for
each of the neighborhoods that we are going to use in the analysis.
Crime variables
The information about crime is the more challenging for Costa Rica. The Department of
Justice generates statistics at the national level about crimes (Poder Judicial 2006 and
2007). However, the department currently does not generate statistics by neighborhood.
We are planning to take a random sample of all the crimes committed in 2000 and locate
them geographically.
Public goods
Here, we present the variables that we will use as the indicators of access to public goods
for each neighborhood. We will consider education, health, access to fire protection and
recreation areas.
•
Education: we currently have information about the geographic location of secondary
schools across Costa Rica. We will also determine the location of elementary schools.
Then, we will need to compute how many secondary and elementary schools are
present in each neighborhood. Using GIS, we could also determine how many
education centers are close by the neighborhood.
•
Fire Departments: we currently have the location of all fire departments across Costa
Rica. We will be able to use this GIS map to establish how protected each
neighborhood is from the event of a fire.
•
Health Facilities: we have the location of clinics which will be easily associated to
neighborhoods using GIS. However, we propose to obtain the location of the
hospitals within the area of analysis, also.
•
Recreation areas: For the year 2000, we have the location of all the protected areas
that include national parks, biological reserves and national monuments. We will
compute the distances to these places from each of the neighborhoods. Also, we have
a forest cover map that can be used to determine how far forested areas are from each
of the neighborhoods. Additionally, we are planning to mark the location of the main
recreational parks within the city and then compute also the distance to those parks.
Political participation
At the district level, we have information of the fraction of individuals that voted in the
election for local government and central government for 1998 and 2002. The difference
between the fraction of individuals that voted for local government and for the central
government might reflect how involve the citizens of that community are in solving local
problems relative to country wide problems.
As previously mentioned, in the HHS, we have information about the participation of
individuals in community organizations and cooperatives as well as in unions. All these
information can help us to establish how public participation affects the quality of living
in urban neighborhoods.
2.2 Statistical Analysis
2.2.1
City Limits defined by household survey
The census classifies each house in four categories: urban, urban periphery, rural and
rural sparse. We will classify neighborhoods as within the city if those neighborhoods are
attached to the specific city and more than 95% of the houses were classified as being in
an urban area. Additionally, to compute the variables of interest from the census, we will
only use those households that were classified as urban.
For Great Metropolitan Area of San Jose, there are well defined borders that we’ll also be
used as alternative definition of city borders for robustness checks, as presented in Figure
2.
2.2.2
Level of Aggregation
For the purpose of this study, we will use the census track as our definition of
neighborhood when using the census. However, we expect that some of the variables
will only be available at the district level. When considering these variables in the
analysis, we will use districts as neighborhoods.
2.2.3
Construction of the index
As described previously, data on house rents is available from the 2000 census as well as
the characteristics of the houses. Additionally, we will obtain a series of variables that
reflect neighborhood amenities using geographic information systems and global
positioning systems GPS units. This will permits us to establish the index as proposed by
Gyourkuo et al (1999).
Possible violations of the assumptions that are required to apply Gyourko et al. (1999)’s
strategy might appear. We discuss how these violations affect our estimates of the index
of quality of life and what we can do to address them.
Orthogonolaity between the error term and the variables
Some of the variables that affect rents might be correlated to unobservable variables that
are also correlated to rents. If this is the case, the estimated parameters can be biased and
the estimates of quality of life might be misleading. For instance, if the amount of green
areas and quality of air are correlated and the researchers only observes green areas, they
are going to conclude that the effect that green areas has on the quality of life is larger
than what really is and neighborhoods that have large green areas and bad air quality will
appear to generate a larger quality of life than what they actually do. Our data is as
disaggregated as possible using and the amount of variables that we have and their spatial
precision due to GIS will allow eliminating this potential problem. Note that other studies
that do not have the level of precision and/or a reduce number of variables will not be
albe to estimate correctly the parameters of the model nor the right ranking of QoL of
neighborhoods and cities.
Group effects in the error (spatially correlated errors)
Also, it is possible that variables that affect quality of life at the neighborhood level are
not observed. These factors could affect the estimation of the standard errors and
therefore, lead to incorrect conclusions about the significance of the results (Gyourko et
al. 1999, Anselin 1988, Conley and Topa 2002, Glaeser and Scheinkman 2001 and Hall
2007).
We will test for spatial correlation of the errors. In order to do that, we are going to use
parametric and non parametric tests.
•
Parametric Tests: We are going to test if the variables are spatially correlated using
the I-Moran test for spatial correlation and the Spatial Autoregressive Model
(Anselin, 1988). Both of these techniques require an explicit assumption of how each
neighborhood is related to other neighborhoods. We will use to strategies to
determine the spatial relationship between neighborhoods: contiguity and distance.
•
Non-Parametric Tests: This non-parametric test developed by Conley and Topa 2002
requires fewer assumptions about the relationship between neighborhoods. Using
distances, strategy shows the distance at which neighborhood variables are correlated
(see for example Conley and Topa 2002 for an application of this method to
unemployment in Chicago and Robalino and Pfaff 2005 for an application of this
method to deforestation in Costa Rica).
If we find evidence that the errors are spatially correlated, we will use two strategies to
solve address this problem. The Spatial Error Model developed in Anselin 1988 assumes
explicitly the interdependence or similarity of errors within a neighborhood. Using a
maximum likelihood function that assumes the correlation between the error terms he
presents the methodology to obtain unbiased and efficient estimates. Our second strategy
to address this problem is using the General Methods of Moments with Cross Sectional
Dependence developed by Conley 1999. Again, assuming that correlation exists between
the errors of the observations he presents a strategy to estimate robustly the standard
errors. We will also use this method to test the robustness of our results. As Gyourko et
al. 1999 suggests, it is prudent to analyze how assuming the presence of these group
effects (as they call it) affects the quality of life rankings.
Selection bias due to ownership
One problem that we face is the fact that we only have information about rents for those
houses that are rented. This type of houses might generate systematic biases due to this
sample selection issue. In order to address this problem, we plan to use Heckman’s
correction of the sample selection problem. This strategy will help us determine if the
coefficients will change significantly by also considering the households that owned their
houses and, therefore, if our rankings of quality of living vary.
2.2.4
Sources of the variation of the index of Quality of Life
There is compelling evidence of a high degree of variation across cities in different aspect
of life such as criminality (Glaeser and Scheinkman 2001), unemployment (Topa (1999)),
education (Hall (2007)), and pregnancy (Case and Katz (1991)). Therefore, we should
expect a high degree of variation in the index of QoL across neighborhoods. Moreover,
as Glaeser and Scheinkman (2001) suggest this large degree of dispersion is hardly
justified by differences in tastes and endowments and require of further explanations and
that this variation might be explained by social interactions (defined as the presence of
effects of neighbors’ decisions on individuals’ decisions). We plan to establish the degree
of social interactions in the variables we will use to estimate the QoL indicator.
Explaining the source of variability is important for policy responses. The set of policy
recommendations to reduce differences and improve quality of life depend on the nature
of the forces driving these differences. If for example interactions are present in
schooling decisions (as shown by Hall 2007), an increase of number of schools will not
necessarily lead to a more educated neighborhood, but an increase in the education level
of the population by bringing more educated people could have a significant multiplying
effect.
3 Analysis of the Survey Results and Expected Results
We will apply a survey as proposed in the terms of reference of this project to extend our
previous quantitative analysis. For the purpose of this study, we will select four
representative urban neighbors. We will determine how representative each neighborhood
is using the neighborhood characteristics available.
Our sample will be composed of 700 individuals that will be selected using random
sampling with stratification within the neighborhoods using neighbor population weights.
Phone interviews will be conducted.
The survey will first include the set of questions proposed in the terms of reference of this
study and will be extended to include other variable that we consider relevant for the case
of Costa Rica. We will extend the survey with question related to religious affiliation,
entertainment networks, location of extended family members, and valuation of
neighborhood members.
We will ask on the relative importance ascribed by the interviewee to the quality of life
factors in his/her neighbor and the weights of these factors. We will also include direct
questions about the importance of a series of factors in the quality of life. Other question
along these lines can be used to compare the results we obtain from the census estimates.
Information about the intentions of the individuals to move will also be perused.
This information will allow us determining if the person feels that is in equilibrium at the
current allocation. We will also add questions about the potential access barriers
perceived by the individual. This information will also help us to determine how accurate
the methodology to estimate the quality of life indicators is. If we find that a significant
amount of neighbors are off equilibrium, based on the characteristics of the individuals in
the neighborhood and the neighborhood, we could establish in what neighborhoods the
estimates of quality of life might be more accurate.
4 Model of City-Level Monitoring and Dissemination
From the economic policy point of view, our research will produce two outputs. First, we
will provide an index of life. This first generation of the index will be based on available
data that is produced on regular basis in Costa Rica in the household survey. This will
permit updating the index over time and across neighbors or districts. We will also
introduce a second generation index that will depend on the potential inclusion of new
variables and mechanism to generate such data in the country. Given the importance of
this index, we plan to produce a methodological summary document that will be freely
available and permit the implementation and revaluation of the index.
To gain feedback and participation of the relevant institutions, we plan to concert the
relevant institutions that will use and produce this index. For concerting these
institutions, we plan to have a seminar to present the results of this project with potential
participation of other members or directors of the project depending on availability
Additionally, we will plot the results using GIS maps of the main urban centers of Costa
Rica. This will facilitate the communication of our results to policy makers and the public
in general.
We believe that eventually the index should be produced by a governmental institution
such as the Ministry of Planning in Costa Rica that has produced the Human
Development Index and should incorporate other entities such as the “Estado de la
Nacion” an institution primarily focused on the continuing evaluation of the performance
of social indicators in Costa Rica with an annual publication on this topic for the last
fifteen years.
In the EfD Initiative at CATIE, there is a strong support for the dissemination of the
results. We will use these available resources to help us construct documents with our the
results of our analyses that will have an accessible format to policy and decision makers
and the public in general.
5 Host Institution and Research Team
The host institution of this research will be the Environment for Development Initiative
hosted at CATIE. CATIE is an international organization for the management of natural
resources, dedicated to sustainable development and poverty reduction in the tropical
America. Its mission is to contribute to poverty reduction by promoting competitive and
sustainable natural resource management, through higher education, research and
technical cooperation.
The Environment for Development (EfD) initiative builds on 15 years of Sida-supported
capacity building in environmental economics. EfD signals environment as an important
resource for development rather than as a constraint. It aims to tap the potential of
environmental economics by supporting research, training and policy advice. There are
six EfD centers in all over the world, incluiding EfD at CATIE.
These centers are characterized by i) a good environment for applied research in
environment and poverty-related issues; ii) strong connections to institutions with
influence on national policy processes; iii) involvement in national and regional Master
and PhD programs.
The long-term goal of the initiative is to improve policy making in developing countries.
A major activity will be international research collaboration in areas such as design of
policy instruments, non-market valuation, management of natural resource, behavioral
and experimental economics with applications to equity, governance and social capital.
This creates the ideal conditions to develop this proposed research within these
institutions.
Project Leader: Juan Robalino, Ph.D.
Mr. Robalino is currently the deputy director of the Latin American and Caribbean
Environmental Economics Program (LACEEP) and a research fellow at the Environment
for Development Center at CATIE and at the Earth Institute at Columbia University. His
fields of specialization are applied microeconomics, environmental economics and
development economics. His current research deals with spatial econometrics and
program evaluation, more specifically, he looks at the causal effects of endogenous
development and environmental policies in Brazil and Costa Rica. He has been a
consultant for the Development Economics Research Group at the World Bank. His
research has been funded for NASA and LACEEP among other institutions. He received
his Ph.D. from Columbia University in May 2005. His dissertation titled Essays on
Environmental Economics and Development Economics deals with spatial models of
deforestation and land policies. His research has been published in Environment and
Development Economics, the Journal of Regional Science and Conservation Biology
Roger Madrigal, M.Sc.
Mr. Madrigal is an assistant professor in the Department of Natural Resources at CATIE
and Environment for Development Center Research Fellow. He received his MSc in
Environmental Economics from CATIE. His main field of research is the design and
application of policy instruments to water issues, with special interest to economic
valuation methods, payments for ecosystem services and institutional arrangements.
Mr.Madrigal has extensive experience in survey design and application in developing
countries.
Luis Hall, Ph.D.
Mr Hall is an assistant professor in the Department of Economics of the University of
Alicante. He received his Ph.D in Economics from New York University. His main field
of research is on applied microeconomics with special emphasis to public finance,
economic development and corporate finance. He wrote his dissertation on differentiated
social interactions in the US schooling race gap under the guidance of Alberto Bisin. Mr
Hall has a solid experience in using and constructing data from Costa Rica at the social
level. His dissertation paper also combines several sources of data in Costa Rica from the
Household survey, geography data and school level data.
6 Budget
Item
Human Capital
3 Researchers
Field Work and Others
Survey
Travel and other expenses
Total
Overhead (25%)
Contribution: IADB
1 Secretary
3 Computers
Photocopy
Lease
Communications
Office Materials
Dissemination Seminar
Contributions CATIE-Costa Rica
Time
(Months)
Cost
9
$800
10
10
10
10
10
10
$400
$240
$50
$300
$400
$200
Total Cost
$21,600
$6000
$2,400
$30,000
$10,000
$40,000
$4000
$2400
$500
$3000
$4000
$2000
$3000
$18,900
7 References
Anselin, L. 1988. Spatial econometrics : methods and models.Boston: Kluwer Academic
Publishers
Case, A. and L. Katz [1991]: ”The Company You Keep: The Effects of Family and
Neighborhood on Disadvantaged Families,” working paper 3705, NBER.
Conley, T. 1999 “GMM Estimation with Cross Sectional Dependence” Journal of
Econometrics 92 (1) 1-45.
Conley, T. G. and Topa, G. 2002. “Socio-economic distance and spatial patterns in
unemployment”, Journal of Applied Econometrics 17 (4): 303-327
Estado de la Nación 2005 “Situación de la Calidad del Aire en Costa Rica” San José
Costa Rica
Epple,D. and H. Sieg (1999), “Estimating Equilibrium Models of Local Jurisdictions”,
Journal of Political Economy, 107, 645-681.
Epple, D., Romer, T. and H. Sieg (2001), Interjurisdictional Sorting and Majority Rule:
An Empirical Analysis, Econometrica, 69, 1437-1465.
Glaeser, E. and J. Scheinkman (2001) “Measuring Social Interactions” in Social
Dynamics, ed. by S. Durlauf and P. Young. Cambridge: MIT Press.
Gyourko, J, Khan, M, and J. Tracy (1999) “Quality of Life and Environmental
Comparisons” Handbook of Regional and Urban Economics, Volume 3, edited by Paul
Cheshire and Edwin Mills 1413-1454 New York, North-Holland.
Hall, L. (2007) “Differentiated Social Interaction in the US Schooling Gap,” Chapter on
Phd Dissertation, New York University, May 2007.
MIDEPLAN 2000, “Sistema de Indicadores sobre Desarollo Sostenible” San Jose Costa
Rica.
Poder Judicial 2006 “Compendio de Indicadores Judiciales 2000-2004” Departamento de
Planificación, Sección Estadística, San José Costa Rica
Poder Judicial 2007 “Compendio de Indicadores Judiciales 2001-2005” Departamento de
Planificación, Sección Estadística, San José Costa Rica
Robalino, J., and Pfaff A., (2005) “Contagious Development: Neighbors’ Interactions in
Deforestation” Mimeo Columbia University.
Tiebout Charles M. “A Pure Theory of Local Public Expenditures.” Journal of Political
Economy, October 1956, 64(5), 416-424
Topa, G. (1997) ”Social Interactions, Local Spillovers and Unemployment,” working
paper, New York University.
Appendix
Table1:
Already Available Information
Variables
Source
Period
Area
Spatial Reference
Aggregation
Household characteristics
Educational level of head of household
School attainment (6-12 years old)
Size of the household
Reads and writes?
County of Residence 5 years ago
Housing Basic necessities satisfied
Health Basic necessities satisfied (water)
Consumption Basic necessities satisfied
Income
Census
Census
Census
Census
Census
Census
Census
Census
HHS
2000
2000
2000
2000
2000
2000
2000
2000
2005
Country
Country
Country
Country
Country
Country
Country
Country
Country*
Census Track
Census Track
Census Track
Census Track
Census Track
Census Track
Census Track
Census Track
Census Track
Individual
Individual
Individual
Individual
Individual
Individual
Individual
Individual
Individual
Housing Infrastructure
Type of house
Type of zone rural-urban-disperse-periph
Ownership
Amount of rent if rented
Type of materials (roof, floor and walls)
Rooms, Bedrooms and Bathrooms
Water Access/Source
Electricity
Television
Fridge
Phone
Microwave
Hot Water
Washing Machine
Computer
Car
Estado de la casa
Haccinamiento
Numero de Hogares en la casa
Internet Access
Shanty town
Census
Census
Census
Census
Census
Census
Census
Census
Census
Census
Census
Census
Census
Census
Census
Census
Census
Census
Census
HHS
Census
2000
2000
2000
2000
2000
2000
2000
2000
2000
2000
2000
2000
2000
2000
2000
2000
2000
2000
2000
2005
2000
Country
Country
Country
Country
Country
Country
Country
Country
Country
Country
Country
Country
Country
Country
Country
Country
Country
Country
Country
Country*
Country
Census Track
Census Track
Census Track
Census Track
Census Track
Census Track
Census Track
Census Track
Census Track
Census Track
Census Track
Census Track
Census Track
Census Track
Census Track
Census Track
Census Track
Census Track
Census Track
Census Track
Census Track
House
House
House
House
House
House
House
House
House
House
House
House
House
House
House
House
House
House
House
House
House
Participation in Organizations
Member of Cooperatives
Member of Union
Member of solidarism association
Member of a gremial association
Member of community association
HHS
HHS
HHS
HHS
HHS
2005
2005
2005
2005
2005
Country*
Country*
Country*
Country*
Country*
Census Track
Census Track
Census Track
Census Track
Census Track
Individual
Individual
Individual
Individual
Individual
*sample
Figure 1
Census Tracks within Provinces
Figure 2
Great Metropolitan Area as Defined by the Department of Planning
with Schools and Clinics
ROGER MADRIGAL BALLESTERO
Curriculum vitae
PERSONAL DETAILS
Place of birth: San José, Costa Rica
Date of birth: December 28th, 1972
Marital status: Single
Tel: (506) 558-24 51 / (506) 224-7903
WORKING ADDRESS
Tropical Agricultural Research and Higher Education Center (CATIE)
Socioeconomics of Environmental Goods and Services (SEBSA)
7170 CATIE, Turrialba, Costa Rica
Tel: (506) 558 2406
Fax: (506) 556 8514
E-mail: [email protected]
STUDIES
2003. M.Sc. Environmental Socioeconomics. CATIE.
1997. Licentiate. Economics. University of Costa Rica.
1996. B.A. Economics. University of Costa Rica.
PROFESSIONAL EXPERIENCE
2007 (ongoing). Environment for Development Center Research Fellow. (CATIE). Tasks: In
charge of the project: Decentralization in water resource management: exploring the
determinants of success.
2004-2007 (ongoing). Researcher. FOCUENCAS Program. (CATIE). Tasks: In charge of
designing and implementing a PES schemes for hydrological ecosystem services and in charge
of a two-year project related to decentralized water management in Costa Rica.
2004-2007 (ongoing). Capacity building coordinator. (CATIE). Tasks: In charge of technical
designing and teaching of capacity building courses on economic foundations and institutions
for managing the environment.
1999-2001. Researcher, Institute of Economic Research, University of Costa Rica. Tasks:
Conducting research related to micro credit and agricultural economics.
1997-1998. Assistant Researcher, Institute of Economic Research, University of Costa Rica.
Tasks: Handling of data bases, collecting primary information.
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PROFESSIONAL EXPERIENCE (SHORT CONSULTANCIES)
2006. Inter-American Development Bank. Consultancy: “Environmental ecosystem services:
non-traditional markets, financial mechanisms and good practices in Latin-American and
Caribbean”.
2004. The Nature Conservancy-Central America. Consultancy: “Sustainable tourism in natural
protected areas: strategies for financial sustainability”.
2004. The Nature Conservancy-Central America. Consultancy: “Linking agricultural
development and biodiversity loss in Central America: historical overview and future
perspectives”.
TEACHING EXPERIENCE
2004-2007 (ongoing). Professor, CATIE. Courses taught at M.Sc level: Introduction to
Environmental Economics and Basic Economics for Valuation and Management of
Ecosystem Services.
2007. Invited Professor, FUSAI, Fundación Böll, El Salvador. II International Course on
Institutional Diversity and Commons.
2006. Invited Professor, FUSAI, Fundación Böll, El Salvador. I International Course on
Common-Pool Resources and Public Policy.
2006. Invited Professor, National Technological University, Argentina. I International Course
on Payments for Environmental Services.
2001. Professor, Department of Business. Panamerican University. Courses taught:
Microeconomics I and Macroeconomics I.
1994-2000. Teaching Assistant, Department of Economics. University of Costa Rica.
PUBLICATIONS
2006. Madrigal, R. An institutional analysis of cooperation in a small irrigation community.
Revista de Ciencias Sociales, Universidad de Costa Rica, Costa Rica. I (106) 181-190 pp.
2005. Alpízar, F; Chacón, M; Harvey, C; Madrigal, R. Assessing linkages between agriculture
and biodiversity in Central America: historical overview and future perspectives. The Nature
Conservancy, San José, Costa Rica. 138 p.
2003. Madrigal, R. The effect of economic incentives and collective action on water use
efficiency in agriculture. MSc Thesis, CATIE, Turrialba, Costa Rica. 209 p.
WORKING PAPERS
2007. Madrigal, R; Alpízar, F. Collective action and PES schemes in Central America. SEBSACATIE-FOCUENCAS II. 17 p.
2007. Madrigal, R; Alpízar, F. Adaptative management of a PES scheme in Copán Ruinas,
Honduras. REDIPASA, San José. 18 p.
2007. Alpízar, F; Madrigal, R. “Ecosystem services in Latin America: good practices, financial
mechanisms and governmental role”. Inter-American Development Bank (IADB). 180 p .
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2007. Retamal, R; Madrigal, R; Alpízar, F; Jiménez, F. Supply of ecosystem services in Copán
Ruinas, Honduras: Where are they? How to protect them? How much to pay? FOCUENCAS
II-CATIE, Turrialba, CR. 40 p.
2006. Madrigal, R; Alpízar. Is cooperation feasible in a rural community that uses a common
pool-resource? Empirical evidence from experimental economics. Submitted to Revista de
Economía Institutional, Universidad Externado, Colombia.
2006. Campos, J.J., Alpízar F., Louman, B., Parrotta, J., Madrigal, R. An integral approach to
PES schemes. Paper presented at Second Latin-American Congress IUFRO-LAT. 26 p.
2006. Cisneros, J; Alpízar, F; Madrigal, R. Designing of a payments for environmental services
scheme in Copán Honduras. CATIE- SEBSA. 24 p.
2006. Alpízar, F; Madrigal, R. Valuation of hydrological ecosystem services in Esparza, Costa
Rica. Submitted to Revista Desarrollo y Ambiente, Colombia. 18 p.
2005. Alpízar, F; Madrigal, R. Design of a land use index for hydrological ecosystem services.
CATIE-SEBSA. 22 p.
2004. Alpízar, F; Madrigal, R. A methodological approach to define entrance user fees to
protected areas. The Nature Conservancy, 125 p.
SEMINARS (PRESENTING)
May 2006. Quito, Ecuador. I International Congress on Valuation. Presentation: Use of
economic valuation methods in designing payments for ecosystem services (PES).
January 2006. Siguatepeque, Honduras. I Congress: Framework for collaborative management
in Honduras Watersheds. Presentation: Design and implementation of a payments for
ecosystem services (PES) scheme in Copán, Honduras.
March 2005. Oaxaca, México. II Latin-American and Caribbean Congress of Environmental
Economists. Presentation: Is cooperation feasible in a rural community that uses a common
pool-resource? Empirical evidence from experimental economics.
December 2004. San José, Costa Rica. Universidad de Costa Rica. I Meeting on Social Studies,
Technology and Environment. Presentation: The effect of institutional setting, physical
characteristics and social attributes on water use patterns in a peasant settlement in Bagatzí,
Costa Rica.
June 2004. Workshop on the Workshop 3, Indiana University, US. Presentation: Effect of
social context and group conformation on cooperation levels: Application of IAD framework
and experimental evidence from an irrigation system in Costa Rica.
OTHER TRAINING AND SEMINARS ATTENDED
2006. I International Conference on Commons and Citizenship. DF, México.
2006. Welfare and public economics course. Latin American and Caribean Environmental
Economics Program. LACEEP-CATIE. Given by David Shogren, Wyoming University, US.
2006. Environmental Economics and Policy Course. Latin American and Caribean
Environmental Economics Program. LACEEP-CATIE. Given by David Zilberman, Berkeley
University, US.
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2005. Collective action and rural development in Latin-America and Caribbean Course. ACIFAO-FODEPAL.
2004. The Tenth Biennial Conference of the International Association for the Study of
Common Property. Oaxaca, Mexico.
2003. Seoul, South Korea. Training Course on Rice Production Technology. Korean
International Cooperation Agency.
2002. Topics on environmental economics course. CATIE/Göteborg University (Sweeden)
FELLOWSHIPS AND AWARDS
2003. Best Student Award 2002-2003 Class, CATIE.
2003. Distinguished MSc. dissertation. 2003. CATIE.
2001. Fellowship.Göteborg University and the Swedish International Development
Cooperation Agency.
2001. Fellowship. National Council of Science and Technology, Costa Rican Ministry of
Technology.
PROFESSIONAL AFFILIATIONS
International Association for the Study of the Commons (IASC)
RESEARCH INTERESTS
Institutional economics, common pool resources theory, experimental economics, rural
development, PES.
LANGUAGES
Spanish (mother tongue)
English (good). Toefl: 587, TWE: 4.0.
COMPUTER EXPERIENCE
P.C. environments: Windows and related software.
Econometric software: SAS, LIMDEP, SPSS.
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LUIS J. HALL
868 Carleton Road
Westfield, New Jersey 07090
(908)-370-2400
[email protected]
TEACHING EXPERIENCE
Ž
Ž
Ž
Ž
Assistant Professor, University of Alicante, Spain, September 2007
Professor for course on Risk Management and Credit Derivatives, Executive
International Program, Baruch College, CUNY, course offered in Taiwan, July 2007.
Full time instructor, New York University, Department of Economics, May 2005-May
2007: Corporate Finance, Public Economics, Advanced Micro Theory, Ownership and
Corporate Control, and Statistics.
Professor, University of Costa Rica, Department of Economics, 1997-2001.
PROFESSIONAL EXPERIENCE
New York State Banking Department, NY, NY
Oct 2005 – Sept 2006
Research Assistant
Ž Econometric analysis to identify the implications of changes in regulatory policy particularly
on changes in bank capital requirements.
Ž Use Call Reports dataset to construct the individual banking level data to identify transitional
failure-success and merger states for US banks.
New York University, NY, NY
Summer 2003
Research Assistant
Ž Construct cross country data from UN and IMF datasets for a project on Social Security and
Demographics.
Institute of Economic Research, Costa Rica
July 1996 – July 2001
Economic Researcher
• Consulting:
Ž Construct an implement a credit scoring model for a large bank in Costa Rica for the consumer
and real state sector. Coordinate and develop the project at all levels from the collection and
building of the data to the econometric analysis and its implementation.
Ž Econometric analysis based on consumer-level loan data to study the determinants of default
for a large bank in Costa Rica.
Ž Economic sector studies for bank-making decisions focused particularly on the manufacturing,
educational and real state sectors.
• Academic:
Ž Design and develop a study on the determinants of loan default in the banking sector of Costa
Rica for the Inter-American Development Bank (IADB). The project was funded, directed and
published by the IADB.
Ž Design and develop a study on the access to credit of firms in the manufacturing sector of
Costa Rica. The paper was funded, directed and published by the IADB.
Ž Study on the impact of Macroeconomic fluctuations in Central America using aggregate data at
the country level. Project funded and coordinated by the World Bank.
LUIS J. HALL (908)-370-2400
PAGE 2
EDUCATION
Ž PhD, New York University, Economics, May 2007
•
Corporate Finance, Finance, Applied Microeconomics
Ž MA, New York University, Economics, 2005
Ž MA, New School for Social Research, Economics, Honors, 1995
Ž BA, Universidad de Costa Rica, Economics, 1990
HONORS
Ž MacCracken Fellowship, New York University, 2001-2005
Ž Dean Minority Fellowship, New York University, 2001-2005
Ž Graduation with Distinction, New School for Social Research, 1995
Ž New School Scholarship and Student Advisor, New School for Social Research, 1994
PUBLICATIONS
Ž Dynamic Outside Equity (2006), mimeo, New York University.
Ž Economic Implications of the Prompt Corrective Action for the US Banking Sector
(2006), mimeo, New York University.
Ž Differentiated Social Interactions in US Schooling Decisions (2005), mimeo, New
York University.
Ž Access to credit and the Effect of Credit Constraints: Firms in the Manufacturing
Sector in Costa Rica (2003), Joint with Alexander Monge, in “Credit Constraints in
Latin America” edited by Arturo Galindo and Fabio Schiantarelli.
Ž Enforcement Mechanisms, Default, and Contract Design in Credit Markets: Exploring
Costa Rica (2001). Joint with Alexander Monge and Javier Cascante, in “Defusing
Default: Incentive and Institutions” edited by Marco Pagano.
Ž Aggregate Fluctuations in Central America, Mexico and USA (1999), with A. Monge
and E. Robles, and Transmission of U.S. and Mexican Shocks in Central America
(1999), with A. Hoffmaister, coordinated and directed by Norman Loayza for the
World Bank.
CONFERENCE - PRESENTATIONS
Ž Applied Microeconomics Seminar, New York University, 2006
Ž Latin American and Caribbean Economic Association (LACEA), San Jose (2005) and
Buenos Aires (2000)
Ž Inter-American Developing Bank – Latin American Research Project Network;
Washington (2001), Mexico (2000), Buenos Aires (1999) and Bogota (1997)
OTHERS
Ž Software – Matlab, STATA, Limdep
Ž Costa Rican and USA citizen, Spanish, English, French (Intermediate)
Ž References upon request
Juan A. Robalino
2910 Broadway MC 3277
The Earth Institute
Columbia University
New York, NY10025
212 854-5182 / 646 229 3520
[email protected]
www.columbia.edu/~jar101
CURRENT POSITIONS
Sept. 2007
Deputy Director of the Latin American and Caribbean Environmental
Economics Program, CATIE
2005-Present
Postdoctoral Research Scholar, The Earth Institute, Columbia University
EDUCATION
2002-2005
Ph.D. Department of Economics, Columbia University
2001-2002
M.Phil. Department of Economics, Columbia University
1999-2001
M.A. Department of Economics, Columbia University
1994-1998
B.Sc. Departamento de Economía, Universidad de Costa Rica
HONORS & AWARDS
2002-2004
Institute for Social and Economic Research and Policy Fellow,
Columbia University
2002-2003
Center for Economy, Environment and Society Fellow,
The Earth Institute, Columbia University
2000-2002
Teaching Fellow, Department of Economics, Columbia University
1996-1998
Academic Fellowship, Universidad de Costa Rica, San José, Costa Rica
RESEARCH FIELDS
Environmental & Res. Economics
Development Economics
Applied Microeconomics
PUBLICATIONS
Robalino, J. 2007 “Land Conservation Policies and Income Distribution: Who Bears the Burden
of our Environmental Efforts?” Forthcoming, Environment and Development Economics 12 (4)
Robalino, J., Pfaff, A., and Sánchez, A., 2007 “Estimating Spatial Interactions in Deforestation
Decisions” (Forthcoming, In A. Kantolean, U. Pascual and T. Swanson Eds. Biodiversity
Economics: Principles, Methods and Applications, Cambridge University Press)
Pfaff, A., Robalino, J., Walker, R., Reis, E., Perz, S., Boher, C., Aldrich, S., 2007. “Road
Investment, Spatial Intensification and Deforestation in the Brazilian Amazon,” Journal of
Regional Science 47 (1) 109-123
Sanchez, A., Pfaff, A., Robalino, J., and Boomhower, J., 2007 “Costa Rican Payments for
Ecological Services Program: Intention, Implementation and Impact” (Accepted, Conservation
Biology)
RESEARCH IN PROGRESS
Robalino, J., and Pfaff, A. 2006 “Contagious Development: Neighbors’ Interactions in
Deforestation” (Revision Requested, Journal of Development Economics)
Robalino, J. 2006 “Evaluating Policy Impacts on Deforestation: Spillover Effects of National
Parks in Costa Rica”
Pfaff, A., Robalino, J., and Sánchez, A., 2006 “Payments for Environmental Services: Empirical
Analysis for Costa Rica”
Robalino, J. 2006 “A Note on Exhaustible Resource Extraction and Strategic Behavior in Local
Labor Markets”
RESEARCH AND WORK EXPERIENCE
09/03 - 08/05
Research Assistant, Project “A basin-scale Econometric Model for Projecting
Future Amazonian Landscapes” (funded by NASA), Columbia University
04/03 - 07/03
Consultant for the World Bank, Project “Spatial Basis for Forest Strategies in
Latin America” (for Kenneth Chomitz), Development Economics Research
Group (DECRG) Infrastructure and Environment
01/01 - 04/02
Research Assistant, Project “Applied Economic Analysis of Deforestation and
Poverty in Costa Rica” (for Alexander Pfaff), Columbia University and Food and
Agriculture Organization
01/99 - 08/99
Research Assistant, Project “The Effects of El Niño on Grain Yields in Costa
Rica”, Instituto de Investigaciones de Ciencias Económicas, Universidad de
Costa Rica, San José, Costa Rica
GRANTS AWARDED
2007
Latin American and Caribbean Environmental Economics Program PI Evaluation
Spillover effects of Protected Areas in Costa Rica $15k
2007
Inter-American Institute for Global Change Research Researcher “Conservation
Policy Impact in Tropical Dry Forest.” $80k for 2 years.
TEACHING EXPERIENCE
2007-Present
Instructor, Principles of Economics, Clark University
2000-2002
Teaching Assistant, Principles of Economics, Intermediate Microeconomics and
Environmental Economics, Department of Economics, Columbia University
1996-1998
Teaching Assistant, Mathematics for Economists I, II and IV, School of
Mathematics, Universidad de Costa Rica
SEMINAR AND CONFERENCE PRESENTATIONS
09/2006
Environmental Economics Seminar, Yale University “Evaluating Policy Impacts
on Tropical Deforestation”
09/2006
Department of Economics Seminar, Clark University “Evaluating Policy Impacts
on Deforestation: Spillover Effects of National Parks in Costa Rica”
07/2006
Third World Conference Environmental & Resource Economists, Kyoto, Japan
“Effects of Protected Areas on Neighboring Deforestation: An empirical analysis
of Spillovers in Costa Rica”
09/2005
Large Scale Biosphere-Atmosphere Experiment in Amazonian Meeting, Sao
Paulo, Brazil “Road Investments, Spatial Intensification and Deforestation in the
Brazilian Amazon”
08/2005
Eighth Occasional Conference on Environmental Economics, Bren School of
Environmental Science & Management, University of California Santa Barbara
“Deforestation is contagious: Evidence of spatial interactions from forest clearing
in Costa Rica”
07/2005
Northeast Universities Development Consortium Conference, Brown University
“Estimating Spatial Interactions: Evidence from forest clearing in Costa Rica”
03/2005
Eastern Economic Association Annual Conference, New York “Deforestation is
contagious: Evidence of spatial interactions from forest clearing in Costa Rica”
02/2005
School of Public and International Affairs Seminar, University of Pittsburgh
“Contagious Development: Neighbors’ interactions in Deforestation”
11/2004
Latin American & Caribbean Economic Association Annual Meeting, Costa Rica
“Land Conservation Policies and Income Distribution: Who bears the burden of
our environmental efforts?”
10/2004
Environmental and Resource Economics Workshop, University of Colorado
“Estimating Spatial Interaction in forest clearing”
09/2004
Sixth Annual BIOECON Conference, Cambridge University, England “Spatial
Interactions in Forest Clearing: Deforestation and Fragmentation in Costa Rica”
10/2003
Seventh Occasional Conference on Environmental Economics, Bren School of
Environmental Science & Management, University of California Santa Barbara
“Land Conservation Policies and Income Distribution”
OTHER RELEVANT INFORMATION
Computer skills:
Referee for:
Matlab, Gauss, Stata, Arcview (GIS) and Arcmap (GIS)
Environment and Development Economics and Land Use Policy