At the IDBG Annual Meeting in 2016, the Board of Governors resolved to endorse the goal of increasing the financing of climate change related projects in LAC to 30% of the IDB’s and IIC’s combined total approvals operations by 2020, subject to demand from borrowing countries and clients and access to external sources of concessional financing.

During 2017, the IDB Group invested approximately US$4.3 billion in climate finance; that is, in development activities carried out by the public and private sectors that reduce greenhouse gas (GHG) emissions and thus mitigate climate change, and/or that reduce vulnerability to climate change and contribute to an adaptation process. This amount represented 28.5% of the IDB Group’s total approvals.
Please note that only data from IDB and IDB Lab are available in this dataset.

At the IDBG Annual Meeting in 2016, the Board of Governors resolved to endorse the goal of increasing the financing of climate change related projects in LAC to 30% of the IDB’s and IIC’s combined total approvals operations by 2020, subject to demand from borrowing countries and clients and access to external sources of concessional financing.

During 2016, the IDB Group invested approximately US$2.7 billion in climate finance; that is, in development activities carried out by the public and private sectors that reduce greenhouse gas (GHG) emissions and thus mitigate climate change, and/or that reduce vulnerability to climate change and contribute to an adaptation process. This amount represented 22% of the IDB Group’s total approvals.
Please note that only data from IDB and IDB Lab are available in this dataset.

This dataset consists of indicators capturing how much students know and are able to do. It includes statistics from international learning assessments such as OECD’s PISA, UNESCO’s SERCE and TERCE, and CSEC of the Caribbean Examination Council. Main indicators: Average score, passing rate, percentage of top performers, percentage of low achievers.


Click here to access the data: https://mydata.iadb.org/d/sqwh-9zsr

This dataset consists of statistics measuring the educational attainment and average years of schooling of the adult population, how many children are actively participating in the school system, the efficiency of the school system, and youth inactivity. Main indicators: mean years of schooling, educational attainment, attendance rates, out of school children, overage rates, years in school vs schooling years, and youth inactivity.

This dataset contains information on Programa Nacional Cuna Más (Cuna Mas, hereinafter), Peru’s largest early childhood development program established in 2012. It focuses on one of the two services provided by Cuna Mas known as Servicio de Acompañamiento a Familias (SAF), a home visiting program that operates in rural areas and provides one-hour weekly home visits to children aged 0-36 months and their caregiver.

This dataset consists of statistics measuring the financial resources available to fund the education system. Main indicators: Government expenditure on education as % of GDP, in PPP, as % of total government expenditure, expenditure on salaries as % of education expenditure, expenditure on education per student as % of pc gdp.


Click here to access the data: https://mydata.iadb.org/d/y35v-i2ux/

This dataset contains information on Programa Nacional Cuna Más (Cuna Mas, hereinafter), Peru’s largest early childhood development program established in 2012. It focuses on one of the two services provided by Cuna Mas known as Servicio de Acompanamiento a Familias (SAF), a home visiting program that operates in rural areas and provides one-hour weekly home visits to children aged 0-36 months and their caregiver.

The objective of the study was to compare different instruments to measure the quality of home visiting programs. Between August and October 2015, three instruments were administered to a sample of 554 children enrolled in Cuna Mas and receiving home visits at the time of data collection, and on their 176 home visitors who regularly work with 80 supervisors.

The following quality instruments were administered:
• Home Visit Rating Scale, version A+2 (HOVRS; Roggman et al. 2010)
• Home Visit Content and Characteristics Form (HVCCF; Boller et al. 2009)
• A supervisor checklist

The first two quality instruments are widely used among home visiting services in the United States. For each child in the sample, one home visit was filmed and the videos were later coded to create the scores, following the instruments’ manuals. The checklist, on the other hand, was designed based on record forms used by Cuna Mas and other home visiting programs in low- and middle- income countries, and was administered live during the visit by the home visitor’s supervisor.

The final, multilevel dataset includes:
• 554 visits with an identical number of distinct children
• 176 home visitors
• 80 supervisors •

Methodology available at: https://idbg.sharepoint.com/teams/EZ-RG-ESW/RG-K1423/25%20Results%20Management/Publicaciones%20-%20Base%20de%20Datos/measuring-quality-and-characterizing-cuna-mas-home-visits.pdf

This benchmarking index is a learning tool that evaluates the legal, regulatory and operating environments for Public Private Partnerships (PPPs) in transport, water/sanitation and electricity infrastructure for 19 Latin American and Caribbean countries. The analysis and scores in the model provide a comparable assessment of individual countries' readiness and facility for PPP project design and implementation. For the purpose of this index, PPP refers specifically to projects which involve a long-term contract between a public-sector body and a private-sector entity for the design, construction (or upgrading), operation and maintenance of public infrastructure; with finance usually provided by, and significant construction, operation and maintenance risks transferred to, the private-sector, which also bears either availability or demand risk; but with the public sector remaining responsible for policy oversight and regulation; and the infrastructure generally reverting to public-sector control at the end of the contract term.

This benchmarking index is a learning tool that evaluates the legal, regulatory and operating environments for Public Private Partnerships (PPPs) in transport, water/sanitation and electricity infrastructure for 19 Latin American and Caribbean countries. The analysis and scores in the model provide a comparable assessment of individual countries' readiness and facility for PPP project design and implementation. For the purpose of this index, PPP refers specifically to projects which involve a long-term contract between a public-sector body and a private-sector entity for the design, construction (or upgrading), operation and maintenance of public infrastructure; with finance usually provided by, and significant construction, operation and maintenance risks transferred to, the private-sector, which also bears either availability or demand risk; but with the public sector remaining responsible for policy oversight and regulation; and the infrastructure generally reverting to public-sector control at the end of the contract term.

The database allows estimating structural fiscal balances for 20 countries in the region under different assumptions regarding the output gap and commodity structural prices. It is a unique database of its kind since: 1) It takes into consideration the distinct responsiveness of different types of revenues to changes in the output gap: In order to adjust for the impact of the business cycle on revenues, we calculate individual elasticities for each source of revenue (i.e. direct taxes, indirect taxes, revenues from non-renewable resources, etc.). Since the different types of revenues in the region have different sensitivities to changes in the output gap, this disaggregated approach allows for a more fine-tuned adjustment. 2) It includes estimations of SFBs based on output gaps’ projections available in “real time”. In addition to giving estimations of the actual SFBs, we provide with estimations of the SFBs that would have resulted should the projections on output gaps available to policymakers at the time of designing fiscal policy (data in “real time”) have been correct. This is in contrast to much of the existing work on structural fiscal balances that makes only an “ex post” analysis using actual and revised information on the output gaps. 3) It allows assessing the response of fiscal policy to the business cycle. We provide with measures of the fiscal impulse, assessing not only the actual but also the intentional fiscal stance, as well as the degree of procyclicality of fiscal policy.

Click here to access the data: https://mydata.iadb.org/idb/dataset/3itg-avtz