The Relational Database Software Market size was estimated at USD 21.97 Billion in 2024 and is projected to reach USD 45.23 Billion by 2031, growing at a CAGR of 9.4 % from 2024 to 2031

Global Relational Database Software Market Drivers

Rising Demand for Efficient Data Management: Organizations across industries are generating and collecting ever-increasing volumes of data. This necessitates efficient and secure data management solutions. Relational databases, with their structured format and robust querying capabilities, offer a valuable tool to organize, manage, and analyze this data, leading to increased demand for this software.

Cloud Adoption and Scalability: The proliferation of cloud computing has significantly impacted the relational database market. Cloud-based database solutions offer scalability, flexibility, and reduced IT infrastructure burden for businesses. This makes them particularly attractive for small and medium-sized enterprises (SMEs) and facilitates easier data access for geographically dispersed teams.

Growing Importance of Data Security and Compliance: Data breaches and cyberattacks pose significant threats to businesses. Relational database software vendors are constantly innovating to enhance security features like encryption and access controls. Additionally, stringent data privacy regulations like GDPR and CCPA are driving the need for compliant data storage and management solutions, further propelling the market for secure relational databases.

Brazil Central Government: Sources: IB: Relationship TN/Bacen data was reported at -84,926.784 BRL mn in Dec 2024. This records a decrease from the previous number of -71,384.739 BRL mn for Nov 2024. Brazil Central Government: Sources: IB: Relationship TN/Bacen data is updated monthly, averaging -991.465 BRL mn from Jan 2001 (Median) to Dec 2024, with 288 observations. The data reached an all-time high of 265,424.634 BRL mn in Sep 2023 and a record low of -147,760.248 BRL mn in Dec 2020. Brazil Central Government: Sources: IB: Relationship TN/Bacen data remains active status in CEIC and is reported by Central Bank of Brazil. The data is categorized under Global Database’s Brazil – Table BR.FA024: Public Sector: Uses and Sources: Federal Government.

The Cloud Database and DBaaS Market Report is Segmented by Component (Solution, Services), by Type (NoSQL, Relational Database), by Deployment (Public, Private, Hybrid), by Enterprise Size (SMEs, Large Enterprises), by End-User (BFSI, IT and Telecom, Retail, Healthcare, Government, Other End-Users), by Geography (North America, Europe, Asia-Pacific, Latin America, Middle East and Africa). The Market Sizes and Forecasts are Provided in Terms of Value (USD) for all the Above Segments.

The size of the Relational Database Market was valued at USD 19942.01 million in 2023 and is projected to reach USD 45481.69 million by 2032, with an expected CAGR of 12.50% during the forecast period. This growth trajectory is primarily driven by the advent of hybrid seeds, which offer superior yield and improved disease resistance. Government initiatives aimed at promoting food security and the adoption of advanced technologies further fuel market expansion. Key applications for hybrid seeds encompass field crops, horticulture, and fodder crops. Leading players in the market include Monsanto, DuPont Pioneer, Syngenta, and Bayer CropScience. Recent developments include: October 2022: Oracle released latest advancements in database technology with the announcement of Oracle Database 23c Beta. It accommodates diverse data types, workloads, and development styles. The release incorporates numerous innovations across Oracle's database services and product portfolio., October 2023: Microsoft has launched a public preview of a new Azure SQL Database free offering, marking a significant addition to its cloud services. Users can access a 32 GB general purpose, serverless Azure SQL database with 100,000 vCore seconds of compute free monthly..

PSND: by Conditioning Factor: Acknowledgement of Debts: Internal Net Debt: Federal Government: Relationship with Central Bank of Brazil data was reported at 0.000 BRL mn in Apr 2019. This records an increase from the previous number of -25,973.338 BRL mn for Mar 2019. PSND: by Conditioning Factor: Acknowledgement of Debts: Internal Net Debt: Federal Government: Relationship with Central Bank of Brazil data is updated monthly, averaging 0.000 BRL mn from Jun 2006 (Median) to Apr 2019, with 155 observations. The data reached an all-time high of 40,000.000 BRL mn in Jan 2016 and a record low of -42,565.826 BRL mn in Mar 2016. PSND: by Conditioning Factor: Acknowledgement of Debts: Internal Net Debt: Federal Government: Relationship with Central Bank of Brazil data remains active status in CEIC and is reported by Central Bank of Brazil. The data is categorized under Brazil Premium Database’s Government and Public Finance – Table BR.FB018: Public Sector Net Debt: by Conditioning Factor: Asset and Methodological Adjustments. Banco Central do Brasil (Bacen)

This paper introduces the African Relational Pro-Government Militia Dataset (RPGMD). Recent research has improved our understandings of how pro-government forces form, under what conditions they are most likely to act, and how they affect the risk of internal conflict, repression, and state fragility. In this paper, we give an overview of our dataset that identifies African pro-government militias (PGMs) from 1997 to 2014. The dataset shows the wide proliferation and diffusion of these groups on the African continent. We identify 149 active PGMs, 104 of which are unique to our dataset. In addition to descriptive information about these PGMs, we contribute measures of PGM alliance relationships, ethnic relationships, and context. We use these variables to examine the determinants of the presence and level of abusive behavior perpetrated by individual PGMs. Results highlight the need to consider nuances in PGM-government relationships in addition to PGM characteristics.

PSND: by Indexing Factor: IPCA: Internal Net Debt: Central Bank of Brazil: Relationship with Federal Government data was reported at -491,928.714 BRL mn in Apr 2019. This records a decrease from the previous number of -486,151.826 BRL mn for Mar 2019. PSND: by Indexing Factor: IPCA: Internal Net Debt: Central Bank of Brazil: Relationship with Federal Government data is updated monthly, averaging -312,462.237 BRL mn from Aug 2006 (Median) to Apr 2019, with 153 observations. The data reached an all-time high of 0.000 BRL mn in Feb 2007 and a record low of -491,928.714 BRL mn in Apr 2019. PSND: by Indexing Factor: IPCA: Internal Net Debt: Central Bank of Brazil: Relationship with Federal Government data remains active status in CEIC and is reported by Central Bank of Brazil. The data is categorized under Brazil Premium Database’s Government and Public Finance – Table BR.FB025: Public Sector Net Debt: by Indexing Factor: Broad Consumer Price Index - IPCA. Banco Central do Brasil (Bacen)

During a survey conducted in the United Kingdom (UK) on May 1st, 2020, 63 percent of respondents said that a government data strategy would have helped in the fight against COVID-19. For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

[Note: Integrated as part of FoodData Central, April 2019.] The database consists of several sets of data: food descriptions, nutrients, weights and measures, footnotes, and sources of data. The Nutrient Data file contains mean nutrient values per 100 g of the edible portion of food, along with fields to further describe the mean value. Information is provided on household measures for food items. Weights are given for edible material without refuse. Footnotes are provided for a few items where information about food description, weights and measures, or nutrient values could not be accommodated in existing fields. Data have been compiled from published and unpublished sources. Published data sources include the scientific literature. Unpublished data include those obtained from the food industry, other government agencies, and research conducted under contracts initiated by USDA’s Agricultural Research Service (ARS). Updated data have been published electronically on the USDA Nutrient Data Laboratory (NDL) web site since 1992. Standard Reference (SR) 28 includes composition data for all the food groups and nutrients published in the 21 volumes of "Agriculture Handbook 8" (US Department of Agriculture 1976-92), and its four supplements (US Department of Agriculture 1990-93), which superseded the 1963 edition (Watt and Merrill, 1963). SR28 supersedes all previous releases, including the printed versions, in the event of any differences. Attribution for photos: Photo 1: k7246-9 Copyright free, public domain photo by Scott Bauer Photo 2: k8234-2 Copyright free, public domain photo by Scott Bauer Resources in this dataset:Resource Title: READ ME - Documentation and User Guide - Composition of Foods Raw, Processed, Prepared - USDA National Nutrient Database for Standard Reference, Release 28. File Name: sr28_doc.pdfResource Software Recommended: Adobe Acrobat Reader,url: http://www.adobe.com/prodindex/acrobat/readstep.html Resource Title: ASCII (6.0Mb; ISO/IEC 8859-1). File Name: sr28asc.zipResource Description: Delimited file suitable for importing into many programs. The tables are organized in a relational format, and can be used with a relational database management system (RDBMS), which will allow you to form your own queries and generate custom reports.Resource Title: ACCESS (25.2Mb). File Name: sr28db.zipResource Description: This file contains the SR28 data imported into a Microsoft Access (2007 or later) database. It includes relationships between files and a few sample queries and reports.Resource Title: ASCII (Abbreviated; 1.1Mb; ISO/IEC 8859-1). File Name: sr28abbr.zipResource Description: Delimited file suitable for importing into many programs. This file contains data for all food items in SR28, but not all nutrient values--starch, fluoride, betaine, vitamin D2 and D3, added vitamin E, added vitamin B12, alcohol, caffeine, theobromine, phytosterols, individual amino acids, individual fatty acids, or individual sugars are not included. These data are presented per 100 grams, edible portion. Up to two household measures are also provided, allowing the user to calculate the values per household measure, if desired.Resource Title: Excel (Abbreviated; 2.9Mb). File Name: sr28abxl.zipResource Description: For use with Microsoft Excel (2007 or later), but can also be used by many other spreadsheet programs. This file contains data for all food items in SR28, but not all nutrient values--starch, fluoride, betaine, vitamin D2 and D3, added vitamin E, added vitamin B12, alcohol, caffeine, theobromine, phytosterols, individual amino acids, individual fatty acids, or individual sugars are not included. These data are presented per 100 grams, edible portion. Up to two household measures are also provided, allowing the user to calculate the values per household measure, if desired.Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/ Resource Title: ASCII (Update Files; 1.1Mb; ISO/IEC 8859-1). File Name: sr28upd.zipResource Description: Update Files - Contains updates for those users who have loaded Release 27 into their own programs and wish to do their own updates. These files contain the updates between SR27 and SR28. Delimited file suitable for import into many programs.

The Online Transaction Processing (OLTP) market is experiencing robust growth, driven by the increasing adoption of cloud-based solutions, the expanding digital economy, and the imperative for real-time data processing across diverse sectors. The market's expansion is fueled by the need for high-performance databases capable of handling massive transaction volumes, particularly within sectors like Smart Government, Information Security, and Digital Industrialization. The preference for agile and scalable NoSQL databases is growing, challenging the traditional dominance of Relational Database Management Systems (RDBMS). However, the legacy systems still hold a significant market share, particularly in established industries, leading to a dynamic market landscape with both established players and innovative newcomers vying for dominance. We estimate the 2025 market size at $150 billion, based on observable market trends and growth patterns within adjacent technology sectors. A compound annual growth rate (CAGR) of 12% is projected through 2033, indicating a substantial increase in market value and influence over the next decade. This growth is further segmented by database type (RDBMS and NoSQL), application (Smart Government, Information Security, etc.), and geographic region. The restraints on market growth primarily stem from concerns regarding data security and compliance, the complexities of data migration, and the high initial investment required for implementing advanced OLTP solutions. Despite these challenges, the overall trend demonstrates significant potential. The increasing reliance on real-time data analytics, coupled with the rising adoption of Internet of Things (IoT) technologies, will further accelerate the demand for robust and scalable OLTP systems. This necessitates a focus on developing advanced security measures, streamlined integration processes, and cost-effective cloud-based solutions to overcome existing limitations and unlock the full potential of the OLTP market. North America currently holds a leading market share due to high technological adoption and established digital infrastructure, but Asia Pacific is expected to witness significant growth in the coming years due to rapid digitalization efforts in major economies like India and China.

[Note: Integrated as part of FoodData Central, April 2019.] The USDA National Nutrient Database for Standard Reference (SR) is the major source of food composition data in the United States and provides the foundation for most food composition databases in the public and private sectors. This is the last release of the database in its current format. SR-Legacy will continue its preeminent role as a stand-alone food composition resource and will be available in the new modernized system currently under development. SR-Legacy contains data on 7,793 food items and up to 150 food components that were reported in SR28 (2015), with selected corrections and updates. This release supersedes all previous releases. Resources in this dataset:Resource Title: USDA National Nutrient Database for Standard Reference, Legacy Release. File Name: SR-Leg_DB.zipResource Description: Locally stored copy - The USDA National Nutrient Database for Standard Reference as a relational database using AcessResource Title: USDA National Nutrient Database for Standard Reference, Legacy Release. File Name: SR-Leg_ASC.zipResource Description: ASCII files containing the data of the USDA National Nutrient Database for Standard Reference, Legacy Release.Resource Title: USDA National Nutrient Database for Standard Reference, Legacy Release. File Name: SR-Leg_ASC.zipResource Description: Locally stored copy - ASCII files containing the data of the USDA National Nutrient Database for Standard Reference, Legacy Release.

Public Sector: Year to Date: % of GDP: Federal Government: Sources: Internal Borrowing: Relationship of National Treasury & Central Bank of Brazil data was reported at 7.220 % in Apr 2019. This records an increase from the previous number of 4.741 % for Mar 2019. Public Sector: Year to Date: % of GDP: Federal Government: Sources: Internal Borrowing: Relationship of National Treasury & Central Bank of Brazil data is updated monthly, averaging 2.535 % from Jan 2001 (Median) to Apr 2019, with 220 observations. The data reached an all-time high of 30.500 % in Jan 2011 and a record low of -8.065 % in Mar 2005. Public Sector: Year to Date: % of GDP: Federal Government: Sources: Internal Borrowing: Relationship of National Treasury & Central Bank of Brazil data remains active status in CEIC and is reported by Central Bank of Brazil. The data is categorized under Brazil Premium Database’s Government and Public Finance – Table BR.FA026: Public Sector: Uses and Sources: Federal Government: % of Nominal GDP: Year to Date. Banco Central do Brasil (Bacen)

This data set is a digital soil survey and generally is the most detailed level of soil geographic data developed by the National Cooperative Soil Survey. The information was prepared by digitizing maps, by compiling information onto a planimetric correct base and digitizing, or by revising digitized maps using remotely sensed and other information. This data set consists of georeferenced digital map data and computerized attribute data. The map data are in a soil survey area extent format and include a detailed, field verified inventory of soils and miscellaneous areas that normally occur in a repeatable pattern on the landscape and that can be cartographically shown at the scale mapped. A special soil features layer (point and line features) is optional. This layer displays the location of features too small to delineate at the mapping scale, but they are large enough and contrasting enough to significantly influence use and management. The soil map units are linked to attributes in the National Soil Information System relational database, which gives the proportionate extent of the component soils and their properties.

The Allegheny County 2000-2010 Census Tract Relationship File shows how 2010 Census tracts in Allegheny County, Pennsylvania relate to the 2000 Census tracts. Each record (row) consists of one unique relationship between a 2000 Census tract/2010 Census tract spatial set where a spatial set is the unique area shared by the record’s 2000 and 2010 tracts. Changes in tracts involve area, land area, population, and/or housing unit counts. Specifically, each record specifies the area, land area, population, and housing unit counts that were transferred to the record’s 2010 tract (TRACT10) from the record’s 2000 tract. The 2000 area (AREA00), land area (AREALAND00), population (POP00), and housing unit count (HU00) for the record is the standardized 2010 value for the record’s 2000 tract (TRACT00). These are the values to use when comparing 2010 population and housing unit counts to those values in the 2000 Census for a particular tract. The relationship file provides the information necessary to conduct a decennial analysis between the 2000 and 2010 Censuses for a particular tract. Steps to use this file for a decennial analysis follow: Obtain the population and/or housing unit count from the 2000 Census for the tract of interest. Find a record in the relationship file where the tract of interest appears in the TRACT00 field. Compare the population (POP00) and housing unit count (HU00) from the relationship file record to the 2000 Census population and housing unit count found in step 1. This data was prepared by Lisa Over as a course project for LIS 2970 Open Government Data in the Master of Library Science Program at the University of Pittsburgh.

Database Automation Market size was worth around USD 1.74 Billion in 2023 and is predicted to grow to around USD 16.52 Billion by 2032

In September 2023, the national debt of the United States had risen up to 33.17 trillion U.S. dollars. The national debt per capita had risen to 85,552 U.S. dollars in 2021. As represented by the statistic above, the public debt of the United States has been continuously rising.

U.S. public debt Public debt, also known as national and governmental debt, is the debt owed by a nations’ central government. In the case of the U.S., national debt is owed by the federal government to Treasury security holders. Generally speaking, government debt increases with government spending, and can be decreased through taxes. During the COVID-19 pandemic, the U.S. government increased spending significantly to finance virus infrastructure, aid, and various forms of economic relief.

International public debt

Venezuela leads the global ranking of the 20 countries with the highest public debt in 2021. In relation to the Gross Domestic Product (GDP), Venezuela's public debt amounted to around 306.95 percent of GDP. Eritrea was ranked fifth, with an estimated debt of 170 percent of the Gross Domestic Product.

The national debt of the United Kingdom is forecasted to grow from 87 percent in 2022 to 70 percent in 2027, in relation to the Gross Domestic Product. These figures include England, Wales, Scotland as well as Northern Ireland.

Greece had the highest national debt among EU countries as of the 4th quarter of 2020 in relation to the Gross Domestic Product. Germany ranked 13th in the EU, with its national debt amounting to 69 percent of GDP in the same time period.

Tuvalu was one of the 20 countries with the lowest national debt in 2021 in relation to the GDP, while Macao had an estimated level of national debt of zero percent, the lowest of any country. The data refer to the debts of the entire state, including the central government, the provinces, municipalities, local authorities and social insurance.

PSND: Internal Net Debt: Federal Government: Relationship with Central Bank of Brazil data was reported at 675,121.454 BRL mn in Apr 2019. This records an increase from the previous number of 607,408.180 BRL mn for Mar 2019. PSND: Internal Net Debt: Federal Government: Relationship with Central Bank of Brazil data is updated monthly, averaging 283,672.516 BRL mn from Dec 2000 (Median) to Apr 2019, with 221 observations. The data reached an all-time high of 805,988.020 BRL mn in Jan 2018 and a record low of 31,269.257 BRL mn in Jun 2007. PSND: Internal Net Debt: Federal Government: Relationship with Central Bank of Brazil data remains active status in CEIC and is reported by Central Bank of Brazil. The data is categorized under Brazil Premium Database’s Government and Public Finance – Table BR.FB011: Public Sector Net Debt. Banco Central do Brasil (Bacen)

Abstract

This study examines various dimensions of primary health care delivery in Uganda, using a baseline survey of public and private dispensaries, the most common lower level health facilities in the country.

The survey was designed and implemented by the World Bank in collaboration with the Makerere Institute for Social Research and the Ugandan Ministries of Health and of Finance, Planning and Economic Development. It was carried out in October - December 2000 and covered 155 local health facilities and seven district administrations in ten districts. In addition, 1617 patients exiting health facilities were interviewed. Three types of dispensaries (both with and without maternity units) were included: those run by the government, by private for-profit providers, and by private nonprofit providers, mainly religious.

This research is a Quantitative Service Delivery Survey (QSDS). It collected microlevel data on service provision and analyzed health service delivery from a public expenditure perspective with a view to informing expenditure and budget decision-making, as well as sector policy.

Objectives of the study included: 1) Measuring and explaining the variation in cost-efficiency across health units in Uganda, with a focus on the flow and use of resources at the facility level; 2) Diagnosing problems with facility performance, including the extent of drug leakage, as well as staff performance and availability;
3) Providing information on pricing and user fee policies and assessing the types of service actually provided; 4) Shedding light on the quality of service across the three categories of service provider - government, for-profit, and nonprofit; 5) Examining the patterns of remuneration, pay structure, and oversight and monitoring and their effects on health unit performance; 6) Assessing the private-public partnership, particularly the program of financial aid to nonprofits.

Geographic coverage

The study districts were Mpigi, Mukono, and Masaka in the central region; Mbale, Iganga, and Soroti in the east; Arua and Apac in the north; and Mbarara and Bushenyi in the west.

Analysis unit

• local dispensary with or without maternity unit

Universe

The survey covered government, for-profit and nonprofit private dispensaries with or without maternity units in ten Ugandan districts.

Kind of data

Sample survey data [ssd]

Sampling procedure

The survey covered government, for-profit and nonprofit private dispensaries with or without maternity units in ten Ugandan districts.

The sample design was governed by three principles. First, to ensure a degree of homogeneity across sampled facilities, attention was restricted to dispensaries, with and without maternity units (that is, to the health center III level). Second, subject to security constraints, the sample was intended to capture regional differences. Finally, the sample had to include facilities in the main ownership categories: government, private for-profit, and private nonprofit (religious organizations and NGOs). The sample of government and nonprofit facilities was based on the Ministry of Health facility register for 1999. Since no nationwide census of for-profit facilities was available, these facilities were chosen by asking sampled government facilities to identify the closest private dispensary.

Of the 155 health facilities surveyed, 81 were government facilities, 30 were private for-profit facilities, and 44 were nonprofit facilities. An exit poll of clients covered 1,617 individuals.

The final sample consisted of 155 primary health care facilities drawn from ten districts in the central, eastern, northern, and western regions of the country. It included government, private for-profit, and private nonprofit facilities. The nonprofit sector includes facilities owned and operated by religious organizations and NGOs. Approximately one third of the surveyed facilities were dispensaries without maternity units; the rest provided maternity care. The facilities varied considerably in size, from units run by a single individual to facilities with as many as 19 staff members.

Ministry of Health facility register for 1999 was used to design the sampling frame. Ten districts were randomly selected. From the selected districts, a sample of government and private nonprofit facilities and a reserve list of replacement facilities were randomly drawn. Because of the unreliability of the register for private for-profit facilities, it was decided that for-profit facilities would be identified on the basis of information from the government facilities sampled. The administrative records for facilities in the original sample were first reviewed at the district headquarters, where some facilities that did not meet selection criteria and data collection requirements were dropped from the sample. These were replaced by facilities from the reserve list. Overall, 30 facilities were replaced.

The sample was designed in such a way that the proportion of facilities drawn from different regions and ownership categories broadly mirrors that of the universe of facilities. Because no nationwide census of for-profit health facilities is available, it is difficult to assess the extent to which the sample is representative of this category. A census of health care facilities in selected districts, carried out in the context of the Delivery of Improved Services for Health (DISH) project supported by the U.S. Agency for International Development (USAID), suggests that about 63 percent of all facilities operate on a for-profit basis, while government and nonprofit providers run 26 and 11 percent of facilities, respectively. This would suggest an undersampling of private providers in the survey. It is not clear, however, whether the DISH districts are representative of other districts in Uganda in terms of the market for health care.

For the exit poll, 10 interviews per facility were carried out in approximately 85 percent of the facilities. In the remaining facilities the target of 10 interviews was not met, as a result of low activity levels.

Sampling deviation

In the first stage in the sampling process, eight districts (out of 45) had to be dropped from the sample frame due to security concerns. These districts were Bundibugyo, Gulu, Kabarole, Kasese, Kibaale, Kitgum, Kotido, and Moroto.

Mode of data collection

Face-to-face [f2f]

Research instrument

The following survey instruments are available:

• District Health Team Questionnaire;
• District Facility Data Sheets;
• Uganda Health Facility Survey Questionnaire;
• Facility Data Sheets;
• Facility Patient Exit Poll Questionnaire.

The survey collected data at three levels: district administration, health facility, and client. In this way it was possible to capture central elements of the relationships between the provider organization, the frontline facility, and the user. In addition, comparison of data from different levels (triangulation) permitted cross-validation of information.

At the district level, a District Health Team Questionnaire was administered to the district director of health services (DDHS), who was interviewed on the role of the DDHS office in health service delivery. Specifically, the questionnaire collected data on health infrastructure, staff training, support and supervision arrangements, and sources of financing.

The District Facility Data Sheet was used at the district level to collect more detailed information on the sampled health units for fiscal 1999-2000, including data on staffing and the related salary structures, vaccine supplies and immunization activity, and basic and supplementary supplies of drugs to the facilities. In addition, patient data, including monthly returns from facilities on total numbers of outpatients, inpatients, immunizations, and deliveries, were reviewed for the period April-June 2000.

At the facility level, the Uganda Health Facility Survey Questionnaire collected a broad range of information related to the facility and its activities. The questionnaire, which was administered to the in-charge, covered characteristics of the facility (location, type, level, ownership, catchment area, organization, and services); inputs (staff, drugs, vaccines, medical and nonmedical consumables, and capital inputs); outputs (facility utilization and referrals); financing (user charges, cost of services by category, expenditures, and financial and in-kind support); and institutional support (supervision, reporting, performance assessment, and procurement). Each health facility questionnaire was supplemented by a Facility Data Sheet (FDS). The FDS was designed to obtain data from the health unit records on staffing and the related salary structure; daily patient records for fiscal 1999-2000; the type of patients using the facility; vaccinations offered; and drug supply and use at the facility.

Finally, at the facility level, an exit poll was used to interview about 10 patients per facility on the cost of treatment, drugs received, perceived quality of services, and reasons for using that unit instead of alternative sources of health care.

Cleaning operations

Detailed information about data editing procedures is available in "Data Cleaning Guide for PETS/QSDS Surveys" in external resources.

STATA cleaning do-files and the data quality reports on the datasets can also be found in external resources.

The panel data of 50 new energy vehicle enterprises in Shanghai and Shenzhen A-shares from 2012 to 2021 are selected to empirically analyze the impact of government subsidies on the innovation of new energy vehicle enterprises and to further discuss the differences between such an impact in different forms and regions. The study finds that, first, government subsidies have a certain promotion effect on the innovation of new energy vehicle enterprises, and an inverted U-shaped relationship exists thereof. Second, at the enterprise level, government subsidies have a significant effect on the innovation of non-state enterprises, downstream vehicle enterprises, and enterprises with lower establishment years, and the inverted-U trend is evident. Third, at the regional level, government subsidies have a more significant effect on the innovation of enterprises in non-eastern regions and low-environmental regulation regions, and the inverted-U-shaped relationship trend is more apparent. The study establishes the nonlinear relationship between government subsidies and the innovation of new energy vehicle enterprises through empirical research, which expands the theory of enterprise innovation and has a certain guiding significance for improving the innovation capability of new energy vehicle enterprises in the future.

Web-based Customer Relationship Management database, used by Planning portal team