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

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

The Public Management Evaluation Tool (PET) evaluates five “pillars” of the public policies' management cycle that are considered important for the implementation of Management for Development Results (MfDR): (i) results-based planning, (ii) results-based budgeting, (iii) public financial management (including auditing and procurement), (iv) program and project management (including the public investment system), and (v) monitoring and evaluation of public management. These pillars are broken down into components that track the maturity of institutional systems. The components are in turn composed of indicators and minimum requirements that these systems must have in an MfDR environment. All of these measures (minimum requirements, indicators, components, and pillars) are scored on a scale from 0 to 5, where a 5 indicates an ideal institutional situation.

Click here to access the data: https://mydata.iadb.org/d/c89t-dh9r

This dataset consists of statistics on basic infrastructure resources and other resources that support student learning within schools. Main indicators: access to water, bathrooms, electricity, phone, internet, art rooms, auditoriums, library, gymnasium, science lab, and number of books per student.

Click here to access the data: https://mydata.iadb.org/d/b2gd-27ej

This dataset consists of indicators and descriptions used in Learning Improvement Information Center.

Click here to access the data: https://mydata.iadb.org/d/wjh3-9awh/