This paper using panel data of 2008-2019 Shanghai and Shenzhen A-share listed companies as the research sample and employing the multiple regression method to tests the relationship between executive compensation incentives and R&D investment of listed companies in China, further investigates the path of the relationship between the two and the influence of government subsidy to the relationship. In this paper, the selected samples are excluded according to the following criteria: ①Companies with incomplete data on financial indicators and corporate governance indicators are excluded. ②Eliminate companies with negative asset-liability ratio or greater than 1. ③Exclude companies in the financial and insurance industry. ④Exclude listed companies less than 1 year. ⑤Exclude companies containing S, ST and *ST. ⑥Exclude the companies with extreme sample data. The risk-taking data involved in this paper came from the WIND database. Other data come from the CSMAR database.

There is a newer version of this dataset available online. Click this link for the hosting site.

We have included a snapshot of the documentation file here to help with future use along with an Excel version of the file for non-STATA users. This document also includes information on submitting edits and corrections to the open source data, which we welcome and encourage. We will acknowledge the participation of editors in the versioning changes at the bottom of the documentation file.

This version updates the set to the current turnovers as of May 1, 2023 version of Execucomp database and adds/clarifies several variables. Please check the documentation for the change log. The file was shared and completed on November 9, 2023

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This is a relational database schema for a sales and order management system, designed to track customers, employees, products, orders, and payments. Below is a detailed breakdown of each table and their relationships:

1. productlines Table (Product Categories)

• Represents different product categories.
• Primary Key: productLine
• Attributes:
  • textDescription: A short description of the product line.
  • htmlDescription: A detailed HTML-based description.
  • image: Associated image (if applicable).
• Relationships:
  • One-to-Many with products: Each product belongs to one productLine.

2. products Table (Product Information)

• Stores details of individual products.
• Primary Key: productCode
• Attributes:
  • productName: Name of the product.
  • productLine: Foreign key linking to productlines.
  • productScale, productVendor, productDescription: Additional product details.
  • quantityInStock: Number of units available.
  • buyPrice: Cost price per unit.
  • MSRP: Manufacturer's Suggested Retail Price.
• Relationships:
  • Many-to-One with productlines (each product belongs to one category).
  • One-to-Many with orderdetails (a product can be part of many orders).

3. orderdetails Table (Line Items in an Order)

• Stores details of each product within an order.
• Composite Primary Key: (orderNumber, productCode)
• Attributes:
  • quantityOrdered: Number of units in the order.
  • priceEach: Price per unit.
  • orderLineNumber: The sequence number in the order.
• Relationships:
  • Many-to-One with orders (each order has multiple products).
  • Many-to-One with products (each product can appear in multiple orders).

4. orders Table (Customer Orders)

• Represents customer orders.
• Primary Key: orderNumber
• Attributes:
  • orderDate: Date when the order was placed.
  • requiredDate: Expected delivery date.
  • shippedDate: Actual shipping date (can be NULL if not shipped).
  • status: Order status (e.g., "Shipped", "In Process", "Cancelled").
  • comments: Additional remarks about the order.
  • customerNumber: Foreign key linking to customers.
• Relationships:
  • One-to-Many with orderdetails (an order contains multiple products).
  • Many-to-One with customers (each order is placed by one customer).

5. customers Table (Customer Details)

• Stores customer information.
• Primary Key: customerNumber
• Attributes:
  • customerName: Name of the customer.
  • contactLastName, contactFirstName: Contact person.
  • phone: Contact number.
  • addressLine1, addressLine2, city, state, postalCode, country: Address details.
  • salesRepEmployeeNumber: Foreign key linking to employees, representing the sales representative.
  • creditLimit: Maximum credit limit assigned to the customer.
• Relationships:
  • One-to-Many with orders (a customer can place multiple orders).
  • One-to-Many with payments (a customer can make multiple payments).
  • Many-to-One with employees (each customer has a sales representative).

6. payments Table (Customer Payments)

• Stores payment transactions.
• Composite Primary Key: (customerNumber, checkNumber)
• Attributes:
  • paymentDate: Date of payment.
  • amount: Payment amount.
• Relationships:
  • Many-to-One with customers (each payment is linked to a customer).

7. employees Table (Employee Information)

• Stores details of employees, including reporting hierarchy.
• Primary Key: employeeNumber
• Attributes:
  • lastName, firstName: Employee's name.
  • extension, email: Contact details.
  • officeCode: Foreign key linking to offices, representing the employee's office.
  • reportsTo: References another employeeNumber, establishing a hierarchy.
  • jobTitle: Employee’s role (e.g., "Sales Rep", "Manager").
• Relationships:
  • Many-to-One with offices (each employee works in one office).
  • One-to-Many with employees (self-referential, representing reporting structure).
  • One-to-Many with customers (each employee manages multiple customers).

8. offices Table (Office Locations)

• Represents company office locations.
• Primary Key: officeCode
• Attributes:
  • city, state, country: Location details.
  • phone: Office contact number.
  • addressLine1, addressLine2, postalCode, territory: Address details.
• Relationships:
  • One-to-Many with employees (each office has multiple employees).

Conclusion

This schema provides a well-structured design for managing a sales and order system, covering: ✅ Product inventory
✅ Order and payment tracking
✅ Customer and employee management
✅ Office locations and hierarchical reporting

Database Management Services Market Outlook

The global database management services market size was estimated at USD 20.5 billion in 2023 and is projected to reach USD 40.8 billion by 2032, growing at a compound annual growth rate (CAGR) of 7.6% during the forecast period. A significant growth factor propelling this market includes the increasing digital transformation initiatives across various industries, driving the need for robust database management solutions.

One of the primary growth drivers for the database management services market is the exponential growth of data generated globally. Enterprises are increasingly digitizing their operations, generating massive volumes of data that need efficient management. Furthermore, the proliferation of cloud computing has made the storage and management of data more flexible and scalable, fueling the adoption of cloud-based database management services. Another critical aspect is the advent of big data analytics, which demands advanced database management systems to handle and process large datasets effectively.

The increasing adoption of advanced technologies such as artificial intelligence (AI), machine learning (ML), and the Internet of Things (IoT) is also contributing significantly to the market's growth. These technologies require robust database management systems to store and analyze the vast amounts of data they generate. Businesses are recognizing the value of data-driven insights for making informed decisions, thereby accelerating the demand for sophisticated database management services. Additionally, regulatory requirements for data storage and management are becoming more stringent, compelling organizations to adopt advanced database management systems to ensure compliance.

The growing trend of remote work and the need for real-time data access also play a crucial role in the market's expansion. With more employees working remotely, the demand for seamless and secure data access has surged, leading to a higher need for effective database management solutions. Moreover, the rise of e-commerce and online services has led to an increased demand for efficient and scalable database management systems to handle customer data, transactions, and other critical information.

From a regional perspective, North America holds a significant share of the database management services market, primarily due to the presence of major technology companies and early adoption of advanced technologies. The Asia-Pacific region is expected to witness the highest growth rate during the forecast period, driven by rapid industrialization, increasing digitalization, and growing investments in IT infrastructure. Europe and Latin America are also experiencing steady growth, with organizations in these regions increasingly adopting database management solutions to enhance operational efficiency and drive business growth.

Service Type Analysis

Database management services can be segmented by service type into consulting, implementation, maintenance, and support. Consulting services involve providing expert advice and strategies for database management tailored to an organization’s specific needs. As businesses strive to integrate more sophisticated data solutions, the demand for consulting services is expected to grow. Consultants help identify the most suitable database management systems, optimize existing infrastructure, and ensure that data policies comply with regulatory standards, thus driving the segment's growth.

Implementation services encompass the deployment of database management systems and solutions within an organization. This segment is poised for significant growth as companies move towards modernizing their IT infrastructures. Implementation services ensure seamless integration of new systems with existing technologies, minimizing disruption and enhancing data accessibility and security. With the rise of cloud computing, implementation services are increasingly focused on migrating on-premises databases to cloud-based solutions, which offers scalability and cost-efficiency.

Maintenance services involve the ongoing management and upkeep of database systems to ensure their optimal performance. This includes regular updates, security patches, and troubleshooting to prevent downtime and data loss. As businesses become more reliant on data-driven operations, the importance of maintenance services cannot be overstated. These services ensure that databases remain functional, secure, and efficient, thereby supporting continuous business operations and data availabilit

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We present a dataset created from merged secondary sources of ExecuComp and CompuStat and then augmented with manual data collection through searches of news stories related to CEO turnover.

We start dataset construction with the ExecuComp executive-level data for the period from 1992 through 2020. These data are merged with the CompuStat dataset of financial variables. As the dataset is intended for research on CEO turnover, we exclude observations in which the CEO at the start of the fiscal year is not well-defined; these are cases when there were co-CEOs and cases when the CEO was shared across different firms. The data set also excludes firm/year combinations that involve a restructuring of the firm – spinoff, buyout, merger, or bankruptcy.

We identify the CEO at the start of each year for each firm. This also helps identify the last year an individual served as CEO. In order to identify CEO turnover based on changes in the CEO from year to year, we require firm observations to extend over at least six contiguous years for the firm to remain in the sample. Cases involving the last year the firm is in the sample are excluded. We also exclude from the dataset cases when there was an interim CEO who stayed in the position for less than 2 years. This results in a sample of 3,100 firms reflecting 41,773 firm/year combinations.

For this sample, we examine news articles related to CEO turnover to confirm the reasons for each CEO departure case. We use the ProQuest full-text news database and search for the company name, the executive name, and the departure year. We identify news articles mentioning the turnover case and then classify the explanation of each CEO departure case into one of five categories of turnover. These categories represent CEOs who resigned, were fired, retired, left due to illness or death, and those who left the position but stayed with the firm in a change of duties, respectively.

The published data file does not include proprietary data from ExecuComp and CompuStat such as executive names and firm financial data. These data fields may be merged with the current data file using the provided ExecuComp and CompuStat identifiers.

The dataset consists of a single table containing the following fields: • gvkey – unique identifier for the firms retrieved from CompuStat database • firmid – unique firm identifier to distinguish distinct contiguous time periods created by breaks in a firm’s presence in the dataset • coname – company name as listed in the CompuStat database • execid – unique identifier for the executives retrieved from ExecuComp database • year – fiscal year • reason – reason for the eventual departure of the CEO executive from the firm, this field is blank for executives who did not leave the firm during the sample period • ceo_departure – dummy variable that equals 1 if the executive left the firm in the fiscal year, and 0 otherwise

Sidewalk Management System is used to track and organize inspections, volitions and the status of New York City sidewalks. A reinspection can be requested by the property owner if they do not agree with the initial inspection. A different DOT inspector performs a second sidewalk inspection. For more information please visit NYC DOT website: www.nyc.gov/sidewalks

There is a newer version of this dataset available online. Click the link for the hosting site.

There is a new version of this data available on Zenodo. Look in the version log on this site.

We have included a snapshot of the documentation file here to help with future use along with an Excel version of the file for non-STATA users. This document also includes information on submitting edits and corrections to the open source data, which we welcome and encourage. We will acknowledge the participation of editors in the versioning changes in the documentation file.

The Operational Database Management (ODBMS) market is experiencing robust growth, driven by the increasing demand for real-time data processing and applications requiring high transaction volumes and low latency. The market, estimated at $50 billion in 2025, is projected to achieve a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $150 billion by 2033. This expansion is fueled by several key factors, including the widespread adoption of cloud computing, the proliferation of Internet of Things (IoT) devices generating massive datasets, and the growing need for businesses to make data-driven decisions in real-time. Furthermore, the shift towards microservices architectures and the rising popularity of NoSQL databases are contributing to the market's dynamism. Major players like Oracle, Microsoft, and SAP continue to dominate the market, but the emergence of innovative open-source and cloud-native solutions from companies like MongoDB and Aerospike is fostering competition and driving innovation. The market's segmentation, while not explicitly provided, is likely to include categories based on deployment model (cloud, on-premises), database type (relational, NoSQL), and industry vertical (finance, healthcare, retail). The restraints on market growth primarily involve the complexities associated with data migration, integration, and security. Ensuring data consistency and reliability across diverse operational databases remains a significant challenge for many organizations. However, the ongoing development of advanced data management tools and security protocols is progressively mitigating these obstacles. Future growth will be further shaped by the advancement of technologies like edge computing and artificial intelligence, which promise to enhance the performance and analytical capabilities of ODBMS. The increasing importance of data sovereignty and compliance regulations will also influence market strategies and product development. Regional growth will vary, with North America and Europe likely maintaining strong market shares, while developing economies in Asia-Pacific and Latin America offer significant untapped potential for expansion.

Sidewalk Management System is used to track and organize inspections, violations and the status of New York City sidewalks. This identifies sidewalk locations by borough, block and lot numbers. For more information please visit NYC DOT website: www.nyc.gov/sidewalks

United States CEO Economic Outlook Index data was reported at 109.300 % in Sep 2018. This records a decrease from the previous number of 111.100 % for Jun 2018. United States CEO Economic Outlook Index data is updated quarterly, averaging 84.700 % from Dec 2002 (Median) to Sep 2018, with 64 observations. The data reached an all-time high of 118.600 % in Mar 2018 and a record low of -5.000 % in Mar 2009. United States CEO Economic Outlook Index data remains active status in CEIC and is reported by Business Roundtable. The data is categorized under Global Database’s United States – Table US.S018: CEO Economic Outlook Survey.

This dataset provides information on 358 in Brazil as of May, 2025. It includes details such as email addresses (where publicly available), phone numbers (where publicly available), and geocoded addresses. Explore market trends, identify potential business partners, and gain valuable insights into the industry. Download a complimentary sample of 10 records to see what's included.

The global database management system market was valued at over USD 89.00 Billion in the year 2024. It is likely to project growth at a CAGR of 10.80% during the forecast years-from 2025 to 2034, to reach a value of USD 248.19 Billion in 2034. The rise in the database management system (DBMS) market can be attributed to the rising amount of digital data produced through various digital platforms.

Sidewalk Management System is used to track and organize inspections, violations and the status of New York City sidewalks. Identifies a Notice of Violation has been issued for a sidewalk defect. For more information please visit NYC DOT website: www.nyc.gov/sidewalks

The size and share of this market is categorized based on Application (Relational database management systems (RDBMS), NoSQL databases, In-memory databases, Cloud databases, Graph databases) and Product (Data storage, Data retrieval, Transaction management, Analytics, Data security) and geographical regions (North America, Europe, Asia-Pacific, South America, Middle-East and Africa).

This statistic shows the results of a survey question asking business executives where they thought companies were and ideally should be focusing on the use of big data in the interest of improving performance. 60 percent of executives surveyed said that they thought companies should be focusing on customer insights, segmentation or targeting.

As of June 2024, the most popular open-source database management system (DBMS) in the world was MySQL, with a ranking score of 1061. Oracle was the most popular commercial DBMS at that time, with a ranking score of 1244.

The authority granted to the County Executive by law to take a certain specific action or the means by which the County Executive exercises his general executive powers. An order generally directs a specific single action rather than establishing rules and standards. Examples of the use of Executive Orders granted by local law include the specific authority of the County Executive under Section 2A-17 of the County Code to issue traffic orders which direct the establishment of stop signs, fire lanes, no parking, etc., at particular designated locations. Examples of Executive Orders arising from his general authority under the Charter of Montgomery County include orders to acquire specific parcels of land for rights-of-way, to direct condemnation by the County Attorney, and to authorize the sale of surplus County property. Update Frequency : Monthly

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