This dataset contains information about Africa's Infrastructure National Data for 1990-2008.Data from The World Bank.Notes:The Africa Infrastructure Country Diagnostic (AICD) has data collection and analysis on the status of the main network infrastructures. The AICD database provides cross-country data on network infrastructure for nine major sectors: air transport, information and communication technologies, irrigation, ports, power, railways, roads, water and sanitation. The indicators are defined as to cover key areas for policy making: affordability, access, pricing as well as institutional, fiscal and financial aspects. The analysis encompasses public expenditure trends, future investment needs and sector performance reviews. It offers users the opportunity to view AICD results, download documents and materials, search databases and perform customized analysis.

The Private Participation in Infrastructure (PPI) Project Database has data on over 6,400 infrastructure projects in 137 low- and middle-income countries. The database is the leading source of PPI trends in the developing world, covering projects in the energy, transport, water and sewerage, ICT backbone, and Municipal Solid Waste (MSW) sectors (MSW data includes projects since 2008) Projects include management or lease contracts, concessions, greenfield projects, and divestitures.

This data set includes 5-minute time series runoff and precipitation data of neighborhood catchments with a variety of stormwater control measures, and definition of individual precipitation-runoff events and associated runoff metrics. Also included are geospatial data that delineates the neighborhood catchments with their land use/land cover and stormwater infrastructure. This dataset is associated with the following publication: Woznicki, S., K. Hondula, and T. Jarnagin. Effectiveness of landscape‐based green infrastructure for stormwater management in suburban catchments. Hydrological Processes. John Wiley & Sons, Ltd., Indianapolis, IN, USA, 32(15): 2346-2361, (2018).

Abstract

Estache and Goicoechea present an infrastructure database that was assembled from multiple sources. Its main purposes are: (i) to provide a snapshot of the sector as of the end of 2004; and (ii) to facilitate quantitative analytical research on infrastructure sectors. The related working paper includes definitions, source information and the data available for 37 performance indicators that proxy access, affordability and quality of service (most recent data as of June 2005). Additionally, the database includes a snapshot of 15 reform indicators across infrastructure sectors.

This is a first attempt, since the effort made in the World Development Report 1994, at generating a database on infrastructure sectors and it needs to be recognized as such. This database is not a state of the art output—this is being worked on by sector experts on a different time table. The effort has however generated a significant amount of new information. The database already provides enough information to launch a much more quantitative debate on the state of infrastructure. But much more is needed and by circulating this information at this stage, we hope to be able to generate feedback and fill the major knowledge gaps and inconsistencies we have identified.

Geographic coverage

The database covers the following countries: - Afghanistan - Albania - Algeria - American Samoa - Andorra - Angola - Antigua and Barbuda - Argentina - Armenia - Aruba - Australia - Austria - Azerbaijan - Bahamas, The - Bahrain - Bangladesh - Barbados - Belarus - Belgium - Belize - Benin - Bermuda - Bhutan - Bolivia - Bosnia and Herzegovina - Botswana - Brazil - Brunei - Bulgaria - Burkina Faso - Burundi - Cambodia - Cameroon - Canada - Cape Verde - Cayman Islands - Central African Republic - Chad - Channel Islands - Chile - China - Colombia - Comoros - Congo, Dem. Rep. - Congo, Rep. - Costa Rica - Cote d'Ivoire - Croatia - Cuba - Cyprus - Czech Republic - Denmark - Djibouti - Dominica - Dominican Republic - Ecuador - Egypt, Arab Rep. - El Salvador - Equatorial Guinea - Eritrea - Estonia - Ethiopia - Faeroe Islands - Fiji - Finland - France - French Polynesia - Gabon - Gambia, The - Georgia - Germany - Ghana - Greece - Greenland - Grenada - Guam - Guatemala - Guinea - Guinea-Bissau - Guyana - Haiti - Honduras - Hong Kong, China - Hungary - Iceland - India - Indonesia - Iran, Islamic Rep. - Iraq - Ireland - Isle of Man - Israel - Italy - Jamaica - Japan - Jordan - Kazakhstan - Kenya - Kiribati - Korea, Dem. Rep. - Korea, Rep. - Kuwait - Kyrgyz Republic - Lao PDR - Latvia - Lebanon - Lesotho - Liberia - Libya - Liechtenstein - Lithuania - Luxembourg - Macao, China - Macedonia, FYR - Madagascar - Malawi - Malaysia - Maldives - Mali - Malta - Marshall Islands - Mauritania - Mauritius - Mayotte - Mexico - Micronesia, Fed. Sts. - Moldova - Monaco - Mongolia - Morocco - Mozambique - Myanmar - Namibia - Nepal - Netherlands - Netherlands Antilles - New Caledonia - New Zealand - Nicaragua - Niger - Nigeria - Northern Mariana Islands - Norway - Oman - Pakistan - Palau - Panama - Papua New Guinea - Paraguay - Peru - Philippines - Poland - Portugal - Puerto Rico - Qatar - Romania - Russian Federation - Rwanda - Samoa - San Marino - Sao Tome and Principe - Saudi Arabia - Senegal - Seychelles - Sierra Leone - Singapore - Slovak Republic - Slovenia - Solomon Islands - Somalia - South Africa - Spain - Sri Lanka - St. Kitts and Nevis - St. Lucia - St. Vincent and the Grenadines - Sudan - Suriname - Swaziland - Sweden - Switzerland - Syrian Arab Republic - Tajikistan - Tanzania - Thailand - Togo - Tonga - Trinidad and Tobago - Tunisia - Turkey - Turkmenistan - Uganda - Ukraine - United Arab Emirates - United Kingdom - United States - Uruguay - Uzbekistan - Vanuatu - Venezuela, RB - Vietnam - Virgin Islands (U.S.) - West Bank and Gaza - Yemen, Rep. - Yugoslavia, FR (Serbia/Montenegro) - Zambia - Zimbabwe

Kind of data

Aggregate data [agg]

Mode of data collection

Face-to-face [f2f]

Response rate

Sector Performance Indicators

Energy The energy sector is relatively well covered by the database, at least in terms of providing a relatively recent snapshot for the main policy areas. The best covered area is access where data are available for 2000 for about 61% of the 207 countries included in the database. The technical quality indicator is available for 60% of the countries, and at least one of the perceived quality indicators is available for 40% of the countries. Price information is available for about 41% of the countries, distinguishing between residential and non residential.

Water & Sanitation Because the sector is part of the Millennium Development Goals (MDGs), it enjoys a lot of effort on data generation in terms of the access rates. The WHO is the main engine behind this effort in collaboration with the multilateral and bilateral aid agencies. The coverage is actually quite high -some national, urban and rural information is available for 75 to 85% of the countries- but there are significant concerns among the research community about the fact that access rates have been measured without much consideration to the quality of access level. The data on technical quality are only available for 27% of the countries. There are data on perceived quality for roughly 39% of the countries but it cannot be used to qualify the information provided by the raw access rates (i.e. access 3 hours a day is not equivalent to access 24 hours a day).

Information and Communication Technology The ICT sector is probably the best covered among the infrastructure sub-sectors to a large extent thanks to the fact that the International Telecommunications Union (ITU) has taken on the responsibility to collect the data. ITU covers a wide spectrum of activity under the communications heading and its coverage ranges from 85 to 99% for all national access indicators. The information on prices needed to make assessments of affordability is also quite extensive since it covers roughly 85 to 95% of the 207 countries. With respect to quality, the coverage of technical indicators is over 88% while the information on perceived quality is only available for roughly 40% of the countries.

Transport The transport sector is possibly the least well covered in terms of the service orientation of infrastructure indicators. Regarding access, network density is the closest approximation to access to the service and is covered at a rate close to 90% for roads but only at a rate of 50% for rail. The relevant data on prices only cover about 30% of the sample for railways. Some type of technical quality information is available for 86% of the countries. Quality perception is only available for about 40% of the countries.

Institutional Reform Indicators

Electricity The data on electricity policy reform were collected from the following sources: ABS Electricity Deregulation Report (2004), AEI-Brookings telecommunications and electricity regulation database (2003), Bacon (1999), Estache and Gassner (2004), Estache, Trujillo, and Tovar de la Fe (2004), Global Regulatory Network Program (2004), Henisz et al. (2003), International Porwer Finance Review (2003-04), International Power and Utilities Finance Review (2004-05), Kikukawa (2004), Wallsten et al. (2004), World Bank Caribbean Infrastructure Assessment (2004), World Bank Global Energy Sector Reform in Developing Countries (1999), World Bank staff, and country regulators. The coverage for the three types of institutional indicators is quite good for the electricity sector. For regulatory institutions and private participation in generation and distribution, the coverage is about 80% of the 207 counties. It is somewhat lower on the market structure with only 58%.

Water & Sanitation The data on water policy reform were collected from the following sources: ABS Water and Waste Utilities of the World (2004), Asian Developing Bank (2000), Bayliss (2002), Benoit (2004), Budds and McGranahan (2003), Hall, Bayliss, and Lobina (2002), Hall and Lobina (2002), Hall, Lobina, and De La Mote (2002), Halpern (2002), Lobina (2001), World Bank Caribbean Infrastructure Assessment (2004), World Bank Sector Note on Water Supply and Sanitation for Infrastructure in EAP (2004), and World Bank staff. The coverage for institutional reforms in W&S is not as exhaustive as for the other utilities. Information on the regulatory institutions responsible for large utilities is available for about 67% of the countries. Ownership data are available for about 70% of the countries. There is no information on the market structure good enough to be reported here at this stage. In most countries small scale operators are important private actors but there is no systematic record of their existence. Most of the information available on their role and importance is only anecdotal.

Information and Communication Technology The report Trends in Telecommunications Reform from ITU (revised by World Bank staff) is the main source of information for this sector. The information on institutional reforms in the sector is however not as exhaustive as it is for its sector performance indicators. While the coverage on the regulatory institutions is 100%, it varies between 76 and 90% of the countries for more of the other indicators. Quite surprisingly also, in contrast to what is available for other sectors, it proved difficult to obtain data on the timing of reforms and of the creation of the regulatory agencies.

Transport Information on transport institutions and reforms is not systematically generated by any agency. Even though more data are needed to have a more comprenhensive picture of the transport sector, it was possible to collect data on railways policy reform from Janes World Railways (2003-04) and complement it with

The Bipartisan Infrastructure Law (BIL) is the largest long-term investment in our infrastructure in nearly a century. Signed into law by President Joe Biden on Nov. 15, 2021, this historic act will make life better for millions of Connecticut residents and create economic growth. To date, Connecticut has received $6.4 billion in Bipartisan Infrastructure Law (BIL) funding with over 167 specific projects identified for funding. Many more projects will be added in the coming months, as funding opportunities become grant awards and as formula funds become specific projects. By reaching communities across Connecticut – including rural communities and historically underserved populations – the law makes critical investments that will improve the lives of Connecticut residents and position the state for success.

The Oil and Gas Infrastructure Mapping (OGIM) database is a global, spatially explicit, and granular database of oil and gas infrastructure, developed by Environmental Defense Fund (EDF) (www.edf.org) and MethaneSAT, LLC (www.methanesat.org) – a wholly owned subsidiary of EDF. The OGIM database is developed to support the quantification and source characterization of oil and gas methane emissions. The database development is based on the acquisition, analysis, curation, integration, and quality-assurance, performed at EDF and MethaneSAT, LLC, of publicly available geospatial data sources of oil and gas facilities reported by official government sources, industry, academic, and other non-government entities.

OGIM is a collection of data tables within a GeoPackage, an open-source geospatial database format. Each data table within the GeoPackage includes locations and facility attributes of oil and gas infrastructure types that are important sources of methane emissions, including oil and gas production wells, offshore production platforms, natural gas compressor stations, oil and natural gas processing facilities, liquefied natural gas facilities, crude oil refineries, and pipelines. All location data have been transformed to a common spatial reference system (WGS 1984, EPSG:4326). The GeoPackage also includes a “Data Catalog” table which lists each primary data source utilized during OGIM database development. Each source in the Data Catalog is assigned a Source Reference ID (‘SRC_ID’) and each record in the OGIM database has a 'SRC_REF_ID' attribute that can be used to join the record to its original source(s).

OGIM v2.5.1 includes approximately 6.7 million features, including 4.5 million point locations of oil and gas wells and over 1.2 million kilometers of oil and gas pipelines. This work and the OGIM database, which we anticipate updating on a regular cadence, helps fill a crucial oil and gas geospatial data need, in support of the quantification and attribution of global oil and gas methane emissions at high resolution. Please see the PDF document in the ‘Files’ section of this page for a description of all attribute columns present within the OGIM database. Full details on database development and related analytics can be found in the following Earth System Science Data (ESSD) journal paper. Please cite the paper when using any version of the database:

Omara, M., Gautam, R., O'Brien, M., Himmelberger, A., Franco, A., Meisenhelder, K., Hauser, G., Lyon, D., Chulakadabba, A., Miller, C., Franklin, J., Wofsy, S., and Hamburg, S.: Developing a spatially explicit global oil and gas infrastructure database for characterizing methane emission sources at high resolution, Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-15-3761-2023, 2023.

Important note: While the results section of the manuscript is specific to v1 of the OGIM, the methods described therein are the the same methods used to develop and update OGIM_v2.5.1. Additionally, while we describe our data sources in detail in the manuscript above, and include maps for all acquired datasets, this open-access version of the OGIM database does not include the locations of about 300 natural gas compressor stations in Russia. Future updates may include these datasets when appropriate permissions to make them publicly accessible are obtained.

OGIM_v2.5.1.gpkg. Key changes since v1.1:

• Significant updates have been made to the oil and natural gas wells layer. In most US states, v2.5.1 relies upon well location data reported by state agencies rather than the Dept. of Homeland Security’s Homeland Infrastructure Foundation-Level Data (HIFLD). Across all countries, about 50% of well records in v2.5.1 were published by their primary source 1-April-2024 or later.
  
  
• The earlier OGIM_v1.1 version included a layer of publicly sourced, spatially explicit oil and natural gas production volumes for the year 2022. This production layer is not present in v2.5.1 as we continue to develop and improve that data product.

OGIM v2.5.1 is based on public-domain datasets reported on or prior to April 2024. Each record in OGIM indicates a source date (SRC_DATE) when the original source of the data was last updated. Some records may have out-of-date information, for example, if facility status has changed since we last acquired the data. We are continuing to update the OGIM database as new public-domain datasets become available.

---

Point of Contact at Environmental Defense Fund and MethaneSAT, LLC: Mark Omara (momara@edf.org) and Ritesh Gautam (rgautam@edf.org).

The data set comprises the infrastructure of transport including roads (in KM), number of motor vehicles per 1000 population and railways in countries around the world.

The infrastructure database or POI database of the city of Gelsenkirchen offers you extensive information about infrastructures in Gelsenkirchen. You currently have access to over 100 different types of infrastructure, as well as over 7,000 data sets from the areas of family, education, leisure, infrastructure, culture, administration, social affairs and economy. In addition to the spatial location, information on contact details and other specialist information is stored. The offer is constantly being expanded and maintained by the responsible services.

Big Data Infrastructure Market Outlook

The global Big Data Infrastructure market size was valued at approximately $98 billion in 2023 and is projected to grow to around $235 billion by 2032, exhibiting a compound annual growth rate (CAGR) of about 10.1% during the forecast period. This impressive growth can be attributed to the increasing demand for big data analytics across various sectors, which necessitates robust infrastructure capable of handling vast volumes of data effectively. The need for real-time data processing has also been a significant driver, as organizations seek to harness data to gain competitive advantages, improve operational efficiencies, and enhance customer experiences.

One of the primary growth factors driving the Big Data Infrastructure market is the exponential increase in data generation from digital sources. With the proliferation of connected devices, social media, and e-commerce, the volume of data generated daily is staggering. Organizations are realizing the value of this data in gaining insights and making informed decisions. Consequently, there is a growing demand for infrastructure solutions that can store, process, and analyze this data effectively. Additionally, developments in cloud computing have made big data technology more accessible and affordable, further fueling market growth. The ability to scale resources on-demand without significant upfront capital investment is particularly appealing to businesses.

Another critical factor contributing to the growth of the Big Data Infrastructure market is the advent of advanced technologies such as artificial intelligence, machine learning, and the Internet of Things (IoT). These technologies require sophisticated data management solutions capable of handling complex and large-scale data sets. As industries across the spectrum from healthcare to manufacturing integrate these technologies into their operations, the demand for capable infrastructure is scaling correspondingly. Moreover, regulatory requirements around data management and security are prompting organizations to invest in reliable infrastructure solutions to ensure compliance and safeguard sensitive information.

The role of data analytics in shaping business strategies and operations has never been more pertinent, driving organizations to invest in Big Data Infrastructure. Businesses are keenly focusing on customer-centric approaches, understanding market trends, and innovating based on data-driven insights. The ability to predict trends, consumer behavior, and potential challenges offers a significant strategic advantage, further pushing the demand for robust data infrastructure. Additionally, strategic partnerships between technology providers and enterprises are fostering an ecosystem conducive to big data initiatives.

From a regional perspective, North America currently holds the largest share in the Big Data Infrastructure market, driven by the early adoption of advanced technologies and the presence of major technology companies. The region's strong digital economy and a high degree of IT infrastructure sophistication are further bolstering its market position. Europe is expected to follow suit, with significant investments in data infrastructure to meet regulatory standards and drive digital transformation. The Asia Pacific region, however, is anticipated to witness the highest growth rate, attributed to rapid digitalization, the proliferation of IoT devices, and increasing awareness of the benefits of big data analytics among businesses. Other regions like Latin America and the Middle East & Africa are also poised for growth, albeit at a relatively moderate pace, as they continue to embrace digital technologies.

Component Analysis

In the realm of Big Data Infrastructure, the component segment is categorized into hardware, software, and services. The hardware segment consists of the physical pieces needed to store and process big data, such as servers, storage devices, and networking equipment. This segment is crucial because the efficiency of data processing depends significantly on the capabilities of these physical components. With the rise in data volumes, there’s an increased demand for scalable and high-performance hardware solutions. Organizations are investing heavily in upgrading their existing hardware to ensure they can handle the data influx effectively. Furthermore, the development of advanced processors and storage systems is enabling faster data processing and retrieval, which is critical for real-time analytics.

The software segment of Big Data Infrastructure encompasses analytics soft

The data covers road length, railway line length, electricity generating capacity, and number of telephone main lines. This data is an update of Canning (1998). Details of definitions and data construction can be found there. We give the infrastructure data set constructed by Canning (1998) together with the infrastructure data from the World Bank’s World Development Indicators 2006. The data sets are merged to give our best estimates of infrastructure over the period 1950-2005. The World Bank usually does not give any figures for infrastructure before 1980. Canning (1998) only goes up to 1995, so that by combining the two datasets we get a longer time series.

United States US: Total Inland Transport Infrastructure Investment: %: Road Infrastructure data was reported at 91.097 % in 2021. This records an increase from the previous number of 90.587 % for 2020. United States US: Total Inland Transport Infrastructure Investment: %: Road Infrastructure data is updated yearly, averaging 86.586 % from Dec 1995 (Median) to 2021, with 23 observations. The data reached an all-time high of 91.097 % in 2021 and a record low of 83.429 % in 2015. United States US: Total Inland Transport Infrastructure Investment: %: Road Infrastructure data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s United States – Table US.OECD.ITF: Transport Infrastructure, Investment and Maintenance: OECD Member: Annual. [COVERAGE] Investment expenditure on rail, road and inland waterways infrastructure: capital expenditure on new infrastructure or extension of existing infrastructure, including reconstruction, renewal (major substitution work on the existing infrastructure which does not change its overall performance) and upgrades (major modification work improving the original performance or capacity of the infrastructure). Infrastructure includes land, permanent way constructions, buildings, bridges and tunnels, as well as immovable fixtures, fitting and installations connected with them (signalisation, telecommunications, catenaries, electricity sub-stations, toll collection installations, navigation locks, mooring equipment, etc.) as opposed to rolling stock or road vehicles or inland waterways vessels. [COVERAGE] Road infrastructure expenses include investment in motorways, urban roads and installation of traffic service facilities. Road infrastructure expenses do not include investment carried out by the private sector. Road infrastructure expenses include periodic capital maintenance. Since 2004, road infrastructure expenses do not include investment in highways and streets associated with non-road infrastructure (such as road in a hospital complex). TOTAL INLAND INFRASTRUCTURE INVESTMENT Between 2004 and 2007, data do not include inland waterways infrastructure expenses since they are not reported (in 2008 inland waterways infrastructure expenses represent 0.44% of the total inland infrastructure expenses). Since 2004, rail infrastructure expenses include investment in Class 1 Railroads only that accounts for roughly 94% of total rail capital expenditures. Until 2003, inland waterways infrastructure expenses include investment in both inland and maritime water facilities.

The Infrastructure Access Area data set is a composite data set that consists of Water Service Areas, Water Districts, and Urban Service Areas (USAs). Census designated Urbanized Areas and Urban Clusters were used as a proxy for infrastructure access in areas without adequate infrastructure data available. The Census Bureau identifies two types of urban areas:

Urbanized Areas (UAs) of 50,000 or more people; Urban Clusters (UCs) of at least 2,500 and less than 50,000 people.

The global Data Center Infrastructure Management (DCIM) market size reached USD 2.08 Billion in 2022 and is expected to reach USD 6.01 Billion in 2032 registering a CAGR of 11.2%. Data Center Infrastructure Management market growth is primarily driven owing to rapid adoption of cloud computing along...

The infrastructure database or POI database of the city of Gelsenkirchen offers you extensive information about infrastructures in Gelsenkirchen. You currently have access to over 100 different types of infrastructure, as well as over 7,000 data sets from the areas of family, education, leisure, infrastructure, culture, administration, social affairs and economy. In addition to the spatial location, information on contact details and other specialist information is stored. The offer is constantly being expanded and maintained by the responsible services.

The infrastructure database or POI database of the city of Gelsenkirchen offers you extensive information about infrastructure in Gelsenkirchen. You currently have the option of accessing over 100 different types of infrastructure and over 7000 data sets from the areas of family, education, leisure, infrastructure, culture, administration, social affairs and business. In addition to the spatial location, information on contact details and other specialist information is stored. The range is constantly being expanded and maintained by the responsible departments.

The infrastructure database or POI database of the city of Gelsenkirchen offers you extensive information about infrastructure in Gelsenkirchen. You currently have the option of accessing over 100 different types of infrastructure and over 7000 data sets from the areas of family, education, leisure, infrastructure, culture, administration, social affairs and business. In addition to the spatial location, information on contact details and other specialist information is stored. The range is constantly being expanded and maintained by the responsible departments.

Data here contain and describe an open-source structured query language (SQLite) portable database containing high resolution mass spectrometry data (MS1 and MS2) for per- and polyfluorinated alykl substances (PFAS) and associated metadata regarding their measurement techniques, quality assurance metrics, and the samples from which they were produced. These data are stored in a format adhering to the Database Infrastructure for Mass Spectrometry (DIMSpec) project. That project produces and uses databases like this one, providing a complete toolkit for non-targeted analysis. See more information about the full DIMSpec code base - as well as these data for demonstration purposes - at GitHub (https://github.com/usnistgov/dimspec) or view the full User Guide for DIMSpec (https://pages.nist.gov/dimspec/docs). Files of most interest contained here include the database file itself (dimspec_nist_pfas.sqlite) as well as an entity relationship diagram (ERD.png) and data dictionary (DIMSpec for PFAS_1.0.1.20230615_data_dictionary.json) to elucidate the database structure and assist in interpretation and use.

The infrastructure database or POI database of the city of Gelsenkirchen offers you comprehensive information about infrastructures in Gelsenkirchen. You currently have the possibility to access over 100 different types of infrastructure as well as over 7000 datasets from the areas of family, education, leisure, infrastructure, culture, administration, social affairs and the economy. In addition to the spatial location, information on contact details and other technical information is stored. The offer is constantly expanded and maintained by the responsible services.

This submission offers a link to a web mapping application hosted instance of the Global Oil & Gas Features Database (GOGI), via EDX Spatial. This offers users with the ability to visualize, interact, and create maps with data of their choice, as well as download specific attributes or fields of view from the database. This data can also be downloaded as a File Geodatabse from EDX at https://edx.netl.doe.gov/dataset/global-oil-gas-features-database. Access the technical report describing how this database was produced using the following link: https://edx.netl.doe.gov/dataset/development-of-an-open-global-oil-and-gas-infrastructure-inventory-and-geodatabase” This data was developed using a combination of big data computing, custom search and data integration algorithms, and expert driven search to collect open oil and gas data resources worldwide. This approach identified over 380 data sets and integrated more than 4.8 million features into the GOGI database. Acknowledgements: This work was funded under the Climate and Clean Air Coalition (CCAC) Oil and Gas Methane Science Studies. The studies are managed by United Nations Environment in collaboration with the Office of the Chief Scientist, Steven Hamburg of the Environmental Defense Fund. Funding was provided by the Environmental Defense Fund, OGCI Companies (Shell, BP, ENI, Petrobras, Repsol, Total, Equinor, CNPC, Saudi Aramco, Exxon, Oxy, Chevron, Pemex) and CCAC.

Abstract

The Village Law, enacted in 2014, mandated the transfer of funds to villages with the goals of reducing poverty and improving living standards in villages through village-led development and community empowerment. Village Law (VL) builds on Indonesia’s 17-year history of participatory and community-driven development (CDD) approaches such as under the Kecamatan Development Project (KDP) and Program Nasional Permberdayaan Masyarakat (PNPM). The changes consequent upon the closing down of PNPM and its replacement by Village Law transfers (Dana Desa and Alokasi Dana Desa) and implementation arrangements, form a critical backdrop to the report titled: Indonesia Village Law: Technical Evaluation of Infrastructure Built with Village Funds.

The Technical Evaluation of Village Infrastructure evaluates the development process, quality, costs, and operations and maintenance (O&M) of 168 village infrastructure projects (VIPs) with budgets greater than USD 10,000, from 39 villages in six provinces. The five types of projects assessed were: A) buildings (33); B) bridges (15); C) water supply (14); D) roads and drainage (94); and E) irrigation (12). Assessors evaluated the physical structures and related files (budgets, design, approvals, etc.) implementation methods, and operations and maintenance (O&M) procedures. The technical evaluation covers VIPs in the same provinces as in 2012 under the PMPN program.

This collection of data is comprised of audit results from seven field tools, plus one administrative data file. The technical evaluation team collected data on five types of infrastructure projects, with total observations at 168, as described above. The seven field tools are included in this data deposit, for reference. Data were originally collected and assembled as eight data files; one for administrative data and one for each of the seven field tools. The technical evaluation team stored data primarily in binary format, using hundreds of variables per field tool to accommodate the options available for each question within each of the field tools. These data were reorganized into five data sets, one for each infrastructure type (compare to one for each field tool). The data were also consolidated from many sets of binary variables to encoded numeric variables, where applicable, for efficiency. Responses to open-ended questions were left as string variables. Responses to simple yes/no questions were left as binary numeric variables. The public versions of the datasets included here exclude variables containing PII, including: (1) name of infrastructure project inspector; (2) name or firm of infrastructure project design consultant; (3) narrative description of infrastructure project, in Indonesian; and (4) narrative description of infrastructure project, in English. Total infrastructure variables sum to 736 across all five datasets. All variables are named logically and include descriptions in their labels.

Geographic coverage

This sample includes observations (projects) from the following provinces: Aceh, West Kalimantan, West Java, East Nusa Tenggara, Maluku, West Sulawesi.

Analysis unit

Across the datasets, an observation represents one piece of infrastructure located fully within one village according to the Indonesia Statistical Agency (BPS) definition for village. Often more than one piece of infrastructure of the same type within the same village was evaluated.

Universe

The population of interest in this survey includes infrastructure projects built using Village Funds in Indonesia.

Kind of data

Sample survey data [ssd]

Sampling procedure

The 2018 technical evaluation assessed 165 VIPs infrastructure built from 2015-2017 with village funds in six of the provinces surveyed in the 2012 study. The evaluation team visited six of the provinces surveyed in 2012 (spread across the archipelago) and included a random selection of the same villages, aiming for a mix of villages considered Remote vs. Not Remote.

The VIPs were randomly chosen with an intention to spread the evaluation sample through the years of Village Law (2015, 2016, 2017). In other words, during the VIP selection process in the villages, the evaluation team made sure to choose a diverse range of VIPs in both type and year of construction. For this evaluation's results to be compared with the 2012 PNPM evaluation, the same classification system for VIP types was used. The VIP types identified for the study are as follows: building, bridge, water supply, road and drainage, and irrigation.

The approach used to identify the sample villages and infrastructure projects for this 2018 evaluation is similar to the approached used in the 2012 study. The assessors (many of whom also participated in the 2012 study) refined their techniques for the current evaluation. The application of a similar methodology in 2018 allows for, in some cases, comparing findings and results. There are some instances where comparison was meaningful and stark. To understand the approach used in 2018 it is helpful to understand the methodology employed in 2012. Instances where the 2018 approach differed from 2012 are noted in the body and relevant section.

PNPM 2012 Sampling vs. Village Law Sampling

The sampling of villages in the 2012 technical evaluation of PNPM was performed randomly within 12 provinces. In total, 1,765 VIPs were assessed in that study. The methodology used included the following steps:

A. A total of 12 provinces were selected ensuring that they would span Indonesia from west to east and north to south;

B. Both rich and poor provinces were included;

C. Sampling of districts (kabupaten) within provinces depended upon the total number of districts within each province;

D. A sampling of three districts was taken for those provinces having ten or more districts. Two districts were selected from those with less than 10 districts. The sole exception to this is Central Java which had four districts selected;

E. Four sub-districts (kecamatan) were sampled within each district. Sub-districts are rated in the BPS system as to level of difficulty of access - normal, hard, very hard and extreme. The random selection process ensured that all levels were represented;

F. The selection of the villages within each of these sub-districts was left to the technical evaluation team to determine at each UPK office in the sub-district.

The 2012 methodology is fully described in that report (Section 4: Site Selection Procedure for Technical Evaluation).

At the villages the evaluators were generally greeted by the Village Head and provided with a list of infrastructure projects financed under the Village Law. From the lists, evaluators chose a variety of VIPs to more closely examine, up to three in each village. Road improvement VIPs tended make up the majority of village lists, followed by buildings. For a more diverse sample, evaluators selected bridges, water supply and irrigation VIPs when they did appear on village lists.

This study did not have access to a master database of all Village Law VIPs and cannot state that this evaluation's relative percentages of infrastructure types is representative.

For additional sampling information, see the report titled Indonesia Village Law: Technical Evaluation of Infrastructure Built with Village Funds, Section 2: Technical evaluation methodology provided under Documentation.

Mode of data collection

Other [oth]

Research instrument

The field tools for the 2018 Village Law Technical Evaluation were structured questionnaires based on the 2012 PNPM questionnaires with some modifications and additions.

A series of field tools was administered for each infrastructure project, which collected for the following: administrative information, indicators for physical quality (evaluation results), beneficiaries (individuals and households), overall project assessment, file inspection and evaluation, environmental and social safeguards, key information and dimensions for unit cost calculations, operation and maintenance/sustainability, and process assessment.

Cleaning operations

Data editing took place at a number of stages throughout the processing, including:

• Office editing and coding • During data entry • Structure checking and completeness • Secondary editing • Structural checking of STATA data files