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e4f4b02 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 | # 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). “NBER-CES Manufacturing Industry Database (1958-2018, version 2021a).” National Bureau of Economic Research.
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## 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 |
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## 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** | Δ 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** | Δ Total Factor Productivity (4-factor model) | Alternative measure of productivity growth |
| **TFP4** | Total Factor Productivity (4-factor model) | Measures multi-factor efficiency |
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