# Presentation: 343163

## Post-stratification Methodology for 2002 Manufacturing Energy Consumption Survey

- Richard Hough and Stacey Cole
- Manufacturing and Construction Division
- Census Bureau
- October 29, 2004

## Acknowledgements	

**Census Bureau**
**  ****Survey Analysts**
-   Susan Bucci
-   Vicki Haitot
-   Lacey Loftin
-   Eva Snap
-   **Methodologists**
-   Jeff Dalzell
-   Cathy Gregor
-   John Slanta
-   Bob Struble

**EIA**
- Bob Adler
- Dwight French
- Tom Lorenz

## Overview

- Background and goals of MECS
- Description of Economic Census
- MECS sample design
- Post-stratification methodology and results
- Questions for the panel

## Manufacturing Energy Consumption Survey (MECS)

**Goals and Background**
- Provide detailed data on energy consumption for the manufacturing sector
- Initiated in 1985 and conducted periodically since then

## The Economic Census

**General Information**
- Provides a detailed portrait of the Nation's economy
- Conducted every five years
- Conducted using the North American Industry Classification System (NAICS)
- Provides a comprehensive update to the classifications in the Census Bureau’s Business Register

## 2002 Economic CensusManufacturing Sector

**Non-mailed File**
- Single-unit manufacturing establishments with less than 5 employees were not mailed questionnaires
- Contained approximately 160,000 establishments
- Administrative data from other Government agencies are used to estimate
- Account for approximately 3% of published totals

## 2002 Economic CensusManufacturing Sector (cont.)

**Mail File**
- Single-unit establishments with more than 5 employees and all multi-unit manufacturing establishments were mailed questionnaires
- Contained approximately 200,000 manufacturing establishments

## 2002 Economic CensusManufacturing Sector (cont.)

**Data**
- Economic Census questionnaires collect operational data on Employment, Receipts, Inventories, Costs, Assets, etc...
- Among the Cost data are Cost of Fuels and Cost of Electricity
- ‘Cost of Energy’ = Cost of Fuels + Cost of Electricity

## MECS Sample Design

**Goal is to produce detailed energy estimates for the manufacturing sector **
- Industry x US level
- Industry x Census Bureau region
**Target population**
- Single-location companies with 5 or more employees
- All establishments of multi-location companies

## Sample Frame

- Source:  2002 Economic Census mail file
- 200,000 mailed establishments
- 		Assigned to a specific industry
- 		Assigned an estimated ‘cost of 	 	     	     energy’ value

## Stratification Strategy

**Industry**
- 473 Manufacturing industries collapsed to 67 groups
- 	37 Specific industries
- 	30 Groups of industries
**Geographic**
- Four Census Bureau regions
**267 Industry group x region strata**

## Sample Allocation Strategy

- Total sample size constraint 15,000 establishments
- A sliding scale of reliability constraints was used
- 		Large cells had a tighter reliability 		      constraint than small cells

## Sample Selection

- Independent sample selected within each stratum
- Probability-proportionate-to-size strategy
- 		Establishment probability was based on 	      historical ‘cost of energy’
- 		Minimum probability of 0.02

## Estimation

- Non-response weight adjustment
- Post-stratification weight adjustment
- Coverage adjustment

## Non-response Weight Adjustment

## Post-stratification:  Frame Concerns

- Coverage of the target population
- Classification of the eligible establishments
- ‘Cost of energy’ data used as a measure-of-size

## Post-stratification

- 2002 Economic Census provides updated information on:
- 		Classifications for all establishments
- 		‘Cost of energy’ data for all 			      manufacturing establishments

## Post-stratification Process

**For each stratum:**
- The strata population was re-assembled
- A population control total for ‘cost of energy’ was derived
- An estimate of the ‘cost of energy’ control total was derived

## Slide 19

## 

**Estimate less than population**

**Estimate greater than population**

**K = 0**

**Original Weight**

**K =**

****

**K = 1**

**Adjusted**
**Weight**

**(0,0)**

**(1,1)**

## Post-stratification Weight Adjustment

## Post-stratification Weight Adjustment

| Original Weight | Revised Weight |
| --- | --- |
|           2.0 |   1.8     (10%) |
|         10.0 |   8.2     (18%) |
|         20.0 | 16.2     (19%) |

**WT****ADJ ****= 1 + K(W****i**** – 1)**

- Example for K= 0.8

## Properties of the Adjustment Process

- Establishments with a sample weight of 1.00 are not adjusted
- Establishments with a relatively small sample weight receive less adjustment than those with large weights

## Impact on the Estimates

- 956 establishments switched strata due to reclassification
- The overall estimate decreased by approximately 1.8%
- 	136 strata were adjusted upward
- 	114 strata were adjusted downward
- The average absolute percentage change was approximately 3.7%

## Coverage Adjustment

**Sample frame did not include:**
- New operations (births) identified after mailout
- Transfers into manufacturing from other sectors

## Slide 26

## Slide 27

## Slide 28

## Coverage Weight Adjustment

- There were approximately 15,000 births and sector transfers into manufacturing
- For each establishment a ‘cost of energy’ value was available
- A revised stratum ‘cost of energy’ control total was derived

## Coverage Weight Adjustment (cont.)

-       ADJj = _Revised Control Total_
- 			   Original Control Total
- Final weight of establishment:
- 			wF(i) = ADJj (wi)
- 3⁄4 of the strata were adjusted upward
- The average upward adjustment was approximately 3%

## Questions for the Panel

- Is the methodology consistent with established practices?
- Are there any material methodological issues we may have overlooked?
- Can you suggest alternative methods that may improve the quality of the results?
- What metrics regarding the overall impact on the estimates should be developed and provided to EIA and data users?