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NBER-CES Manufacturing Industry Database Overview
1. Dataset Overview
- Source: NBER-CES Manufacturing Industry Database
- Time Span: 1958 to 2018 (Annual Data)
- Region: U.S.-based manufacturing industries (sectoral level)
- Industries Covered: Over 450 manufacturing industries
- Key Features: Revenue, employment, capital investment, R&D spending, productivity, and energy usage
2. Data Coverage
The NBER-CES database is a comprehensive resource for analyzing various aspects of the U.S. manufacturing sector. It includes:
- Industry Classification:
- 1987 SIC (Standard Industrial Classification): 459 four-digit industries.
- 1997 NAICS (North American Industry Classification System): 473 six-digit industries.
- 2012 NAICS: 364 six-digit industries.
- Key Variables:
- Output Measures: Value of Shipments, Value Added
- Input Measures: Employment, Payroll, Cost of Materials, Energy Consumption
- Investment and Capital: Capital Expenditures, Capital Stocks
- Productivity Metrics: Total Factor Productivity (TFP), Labor Productivity
- Price Indexes: Industry-specific price deflators
3. Applications
This dataset can be utilized for:
- Economic Research: Analyzing trends in manufacturing output, productivity, and employment.
- Policy Analysis: Assessing the impact of policy changes on different manufacturing industries.
- Business Strategy: Supporting investment, production, and resource allocation decisions.
4. Data Format
The dataset is available in multiple formats for ease of analysis:
- Stata
- SAS
- Excel
- CSV
5. Documentation
Comprehensive documentation is provided, including:
- Variable Descriptions & Summary Statistics: Explains each variable and its statistical properties.
- Technical Notes: Details methodology used in data collection and processing.
- Industry Concordances: Helps navigate industry classification changes over time.
6. Citation
If using this database, please cite:
Becker, Randy A., Wayne B. Gray, and Jordan Marvakov. (2021). โNBER-CES Manufacturing Industry Database (1958-2018, version 2021a).โ National Bureau of Economic Research.
7. Key Features for Turnover Forecasting
The dataset includes several features that are critical for turnover forecasting:
| Feature | Description | Importance |
|---|---|---|
| VSHIP (Value of Shipments) | Total revenue from shipments of goods | Primary revenue indicator for turnover forecasting |
| EMP (Employment) | Number of employees in the sector | Correlates labor with revenue growth |
| CAPEX (Capital Expenditure) | Investment in machinery and assets | Higher CAPEX often leads to future revenue growth |
| ENERGY (Energy Usage) | Power consumption in industry | Signals productivity and operational efficiency |
| MATCOST (Materials Cost) | Cost of raw materials used | Higher costs may impact profit margins and revenue |
| RD (R&D Expenditure) | Investment in innovation and new tech | High R&D leads to long-term revenue growth |
| WAGE (Wages) | Total wages paid in the industry | Useful for modeling cost-revenue relationships |
| PROD (Productivity) | Output per worker or per machine | Efficiency metric to predict revenue shifts |
8. Additional Variables for Turnover Forecasting
| Column | Description | Importance |
|---|---|---|
| NAICS | NAICS Industry Code | Unique identifier for each industry classification |
| Year | Year of observation | Used for time-series analysis and forecasting trends |
| PRODE | Productivity (Output per employee) | Measures efficiency; affects turnover growth |
| PRODH | Productivity (Output per hour worked) | Higher values indicate better labor efficiency |
| PRODW | Productivity (Output per wage dollar) | Helps in measuring cost efficiency |
| VADD | Value Added (Revenue - Input Costs) | Represents economic contribution of the industry |
| INVEST | Investment (Capital Expenditure - CAPEX) | High investments often lead to future revenue growth |
| INVENT | Inventory Levels | Impacts supply chain and demand forecasting |
| ENERGY | Energy Costs | Higher energy costs reduce profit margins |
| CAP | Capital Stock | Total capital assets; influences production capacity |
| EQUIP | Equipment Stock | Investment in machinery; affects manufacturing output |
| PLANT | Plant Stock | Investment in physical infrastructure |
| PISHIP | Price Index for Shipments | Adjusts revenue for inflation effects |
| PIMAT | Price Index for Materials | Adjusts material costs for inflation |
| PIINV | Price Index for Inventory | Adjusts inventory value for inflation |
| PIEN | Price Index for Energy | Adjusts energy costs for inflation |
| DTFP5 | ฮ Total Factor Productivity (5-factor model) | Measures efficiency improvements over time |
| TFP5 | Total Factor Productivity (5-factor model) | Higher values indicate better overall efficiency |
| DTFP4 | ฮ Total Factor Productivity (4-factor model) | Alternative measure of productivity growth |
| TFP4 | Total Factor Productivity (4-factor model) | Measures multi-factor efficiency |