# Presentation: 079408

## Practical Considerations in Selecting Statistical Disclosure Methodology for Tabular Data

**Practical Considerations in Selecting Statistical Disclosure Methodology for Tabular Data**

- BTS Confidentiality Seminar Series, March 2003

## Examples in the Presentation are Based on Actual Occurrences

## Data have been changed to protect the innocent

## Goals:

- Discuss Decision Factors
- Discuss Alternatives
- Present Illustrations
- Provide a Perspective for Future Decisions

## DECISION FACTORS - 1

- What is your data structure?
- Household, establishment, opinion, census data, sample results, etc...

## DECISION FACTORS - 2

- What are your population characteristics?
- Limited variation, extremely clustered, highly skewed, etc...

## DECISION FACTORS - 3

- Who are your customers?
- 	Casual  readers
- 	In-depth analysts
- 	People (companies) from 	the data population
- 	Etc...

## DECISION FACTORS - 4

- What decisions will be made by your customers?
- Social Research Studies
- Economic Investment Decisions
- Implementation of Government Programs
- Etc...

## BASIC SUPPRESSION APPROACHES

- (n, p) Rules
- (p, k) Rules
- (n, p, t) Rules

## Should You Publish Your Suppression Rule?

- Conventional wisdom says  NO!
- It might be essential for certain situations

## BASIC NASS APPROACHES

- (p, k) Rule for Ag Census
- (n, p) Rule for Periodic Surveys
- Special Techniques

## SPECIAL TECHNIQUES

- Signed Release Agreements (Waivers)
- Geographic Combinations
- Size Group Combinations
- Reclassification of Categories

## WAIVERS

- Large Operations
- Permission to Publish is Requested
- Must Agree in Writing
- Updated Annually

## Waiver Example

| Hypothetical Pecan Production Example 1/ |  |
| --- | --- |
| State | Production – 1,000lbs |
| AL | 15,000 |
| GA | 80,000 |
| LA | 18,000 |
| NM | 35,000 |
| TX | 30,000 |
| All Other 2/ | 31,850 |
| US | 209,850 |

- 1/  Assumes One person controls all Arizona Production
- 2/  Includes AZ, AR, CA, FL, KS, MS, NC, OK, SC

## Waiver Example

| Pecan Production Example, Actual Results |  |
| --- | --- |
| State | 2000 Production/1,000lbs |
| AL | 15,000 |
| AZ | 14,500 |
| AR | 900 |
| CA | 3,400 |
| FL | 3,300 |
| GA | 80,000 |
| KS | 550 |
| LA | 18,000 |
| MS | 3,500 |
| NM | 35,000 |
| NC | 1,600 |
| OK | 2,500 |
| SC | 1,600 |
| TX | 30,000 |
| US | 209,850 |

## Geographic Combinations

| Hypothetical Olive Acreage |  |  |
| --- | --- | --- |
| States | Growers | Acres |
| CA | 1,363 | 37,714 |
| FL | 2 | 2 |
| U.S. Total | 1,365 | 37,716 |
| Traditional Approach |  |  |
| CA | 1,363 | (D) |
| FL | 2 | (D) |
| U.S. Total | 1,365 | (D) |
| (D)  Withheld to avoid disclosing data for individual farms. |  |  |

## Geographic Combinations

| Handling hypothetical Olive data |  |  |
| --- | --- | --- |
| Situation by moving Florida Olives to “Other” |  |  |
| Olive Acreage |  |  |
| States | Growers | Acres |
| CA | 1363 | 37,714 |
| U.S. Total | 1363 | 37,714 |

## Size Group Example

| Percent of Farms, by Economic Sales Class, U.S. 2001 |  |
| --- | --- |
| Economic Sales Class | Percent of Total Farms |
| $1,000 - $2,499 | 25.3 |
| $2,500 - $4,999 | 15.3 |
| $5,000 - $9,999 | 13.4 |
| $10,000 - $24,999 | 12.5 |
| $25,000 - $49,999 | 8.7 |
| $50,000 - $99,999 | 8.6 |
| $100,000 - $249,999 | 8.9 |
| $250,000 - $499,999 | 4.1 |
| $500,000 - $999,999 | 2.0 |
| $1,000,000+ | 1.2 |
|  |  |
| Total | 100.0 |

## Size Group Example

| Number of Farms:  Economic Sales Class by State and Region, 2001 |  |  |  |
| --- | --- | --- | --- |
| State and Region | Economic Sales Class |  |  |
|  | $1,000 - $9,999 | $10,000 - $99,999 | $100,000 & Over |
|  | Number | Number | Number |
| Northeast |  |  |  |
| CT | 2,450 |  |  |
| ME | 4,200 |  |  |
| MA | 3,200 |  |  |
| NH | 2,100 |  |  |
| NJ | 6,400 |  |  |
| NY | 17,800 | 11,000 | 8,700 |
| PA | 32,800 | 16,500 | 9,700 |
| RI | 350 |  |  |
| VT | 3,500 |  |  |
| Other States |  | 10,600 | 3,800 |
|  |  |  |  |
| Total | 72,800 | 38,100 | 22,200 |

## Reclassification Example

| Hypothetical Potato Variety Data, 1 State |  |
| --- | --- |
| Fall Potatoes:  Percent of Major Varieties Planted, |  |
| State and Varieties | Percent of Planted Acres |
|  |  |
|    R Burbank | 71.0 |
|    Ranger R | 12.0 |
|    R Norkotah | 7.5 |
|    Shepody | 3.4 |
|    Silverton R | 2.4 |
|    De Wolf | 1.0 |
|    Other | 2.7 |
| TOTAL | 100.0 |

- However, assume that deWolf is a new variety planted only on one large farms

## Reclassification Example

| Actual Potato Variety Data, 1 State |  |
| --- | --- |
| Fall Potatoes:  Percent of Major Varieties Planted, |  |
| State and Varieties | Percent of Planted Acres |
|  |  |
|    R Burbank | 71.0 |
|    Ranger R | 12.0 |
|    R Norkotah | 7.5 |
|    Shepody | 3.4 |
|    Silverton R | 2.4 |
|    Other | 3.7 |
| TOTAL | 100.0 |

## CASE STUDY

- Agricultural Marketing Service implementation of mandatory price reporting law illustrates basic decision factor principles.

## OUTLINE

- Define the problem
- Explain the approach
- Describe the solution
- Present some results

## Large meat packers must report details of all purchase transactions.

## Data must be reported by fixed times each day.ANDSummaries published an hour later.

## AMS must protect confidentiality in issuing summaries.

## 90% of slaughteris in 114 plants, owned by 65 companies.

## 4 Companies:

**80% of fed cattle**
**80% of fed lambs**
**55% of all hogs****	****	**

## AMS adopted a 3/60 standard for each data cell.

## At least three companies and no company exceeding 60%.

## AMS never identified how many companies were in an aggregate.

## AMS did not distinguish 
blank cells from suppressed cells.

**blank cells from suppressed cells.**

## Many (24 %) daily reports were suppressed plus many cells in released reports.

## Some observers calculated 90% suppression.

## When in Doubt – 
Examine the Data!

**Examine the Data!**

## SEQUENCE DAY

| Plant | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| A | X |  |  | X | X | X |  |  | X | X |  | X |
| B |  |  |  |  |  |  |  | X |  |  |  |  |
| C | X | X | X | X | X | X | X |  | X | X | X | X |
| D | X | X | X | X |  |  |  |  | X | X | X |  |
| E |  |  |  | X |  | X |  |  | X |  |  |  |
| F | X | X | X | X | X | X | X |  | X | X | X | X |
| G | X |  | X | X | X | X | X |  | X |  |  |  |
| H | X |  | X | X | X | X | X | X | X | X | X | X |
| I |  |  | X |  |  |  |  |  |  |  |  |  |

**SEQUENCE DAY**

## When are proprietary datanot unique?

## A new 3/70/20 confidentiality standard based on 60-days of data.

**A new 3/70/20 confidentiality standard based on 60-days of data.**

## At least 3 companies operated 50% of the time.

**At least **_**3**_** companies operated 50% of the time.**

## No company had more than 70 percent of the volume.

**No company had more than **_**70**_** percent of the volume.**

## No company would be exposed more than 20% of the time.

**No company would be exposed more than **_**20%**_** of the time.**

## Nearly all cattle daily reports and all cells now released.

## EXAMPLE TALLY Cattle Open Market, One Region, Daily

| 
3 or more	       | 
68% |
| --- | --- |
| Concen. | 47% |
| Exposure | 2% |

## EXAMPLE TALLYCattle Forward Contracts, National, Daily

| 3 or more | 41% |
| --- | --- |
| Concen. | 51% |
| Exposure | 6% |

## Modification 1

- Cattle forward contracts are now published weekly

## Modification 2

- _All _swine packer sales alternatives weighted together daily.

## Modification 3

- Lamb purchases accumulated until the 3/70/20 is reached.

## Author Contact Information

- Rich Allen
- Deputy Administrator, NASS
- Room 5905 South Building
- 1400 Independence Ave., S.W.
- Washington, D.C. 20250-2001
- 202-690-8141  Fax 202-690-1311
- rich_allen@nass.usda.gov