# Presentation: 150909

## 
David A. Swanson
University of California Riverside
George C. Hough, Jr.
Portland State University

ACS Multi-Year  Studies Meeting 
US Census Bureau 
Suitland, MD
November 15, 2007

- David A. Swanson
- University of California Riverside
- George C. Hough, Jr.
- Portland State University
- ACS Multi-Year  Studies Meeting
- US Census Bureau
- Suitland, MD
- November 15, 2007

**An Evaluation of Persons Per Household (PPH) Data Generated By the American Community Survey: The Demographic Perspective**

## OUTLINE

-     **INTRODUCTION**
**   ****DATA**
**   ****RESULTS**
**   ****DISCUSSION**
**   ****CONCLUSIONS & FUTURE RESEARCH**

**Notes:**
Case studies were developed as a way to show other data users, as well as key decision makers, how data from the American Community Survey can be used to solve “real” problems that the community identifies. Case studies are a little like “model homes”.  The Census Bureau been talking to you and other groups for several years about the “blue prints” for the American Community Survey – how great it’s going to be, what you are going to be able to do with it, what it will do for your community.  With these case studies, we are now able to show you “model homes”,  the idea being that you customize this “model home” to suit your needs -- by using data for your community to solve this kind of problem.
We were concerned that our “model homes” reflect the needs of our data users so we selected an array of topics that would have appeal to most communities.  Those topics included:

	Aging/the elderly
	The well-being of children
	Transportation
	Education
	Health Care
	Rural concerns

## INTRODUCTION

- The American Community Survey (ACS) provides annual estimates of persons per household (PPH) that are subject to sample error. This means that they can fluctuate from year to year in a given population, which reflects a “Statistical Perspective.”
-  Demographers tend to view PPH as a population attribute that has demographic determinants.
- This implies that demographers view PPH as an attribute that changes systematically over time (the “Demographic Perspective”), not randomly (the “Statistical Perspective”).

**Does the ACS produce PPH (Persons Per Household) **
**estimates that applied demographers will use?**

## INTRODUCTION

- Applied demographers need PPH in making population estimates with the housing unit method (HUM).
- Do PPH values generated by the ACS values fit sufficiently into the demographic perspective that they are likely to be used by applied local demographers in conjunction with the HUM?

**Does the ACS produce PPH (Persons Per Household) **
**estimates that applied demographers will use?****(Cont.)**

## INTRODUCTION

-       What generated this research?
-     We were looking at the Multnomah County PPH values from the ACS for 2001 to 2006 and saw some “jumping around,” which got our attention as demographers who use the housing unit method to do estimates. We then compared this to a set of PPH values generated by extrapolating the change in PPH between the 1990 and 2000 censuses for Multnomah County.

## INTRODUCTION

- N

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- W

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- Portland Metropolitan Area

##  INTRODUCTION

- ACS PPH  v.  AD PPH: SINGLE YEAR ACS DATA

##  INTRODUCTION

** **INTRODUCTION

- ACS PPH  v. AD PPH: THREE YEAR ACS DATA

##  INTRODUCTION

-                        P  =  GQ + (PPH)( H)(OR)
- where
-   P        = Total Population
-  GQ      =  Population in Groups Quarters
-  PPH    =  Persons Per Household
-  H        =  Total Number of Housing Units
-  OR     = Occupancy Rate
** ****Note that (H)(OR) = Total Number of Households**

** **INTRODUCTION

## INTRODUCTION

## INTRODUCTION

## DATA

**  ****The 18 (of 36) 1999 ACS test counties used in this study**
**Pima County, AZ ****	****	****       Madison County, MS **
**Jefferson County, AR             Douglas County, NE**
**San Francisco County, CA     Bronx County, NY**
**Tulare County, CA                   Rockland County, NY**
**Broward County, FL                Franklin County, OH**
**Lake County, IL                        Multnomah County, OR**
**Black Hawk County, IA           Schuylkill County, PA**
**Calvert County, MD                 Sevier County, TN**
**Hampden County, MA             Yakima County, WA  **
**	****        **

**Notes:**
“Real” people, not only researchers and academics, are using current data from a few American Community Survey sites to solve “real” problems identified by community leaders in a series of case studies conducted on behalf of the Census Bureau.
The problems identified by community leaders are current and vital to the well-being of a particular sector of the community and will benefit greatly from annual data supplied by the American Community Survey.  

## DATA 

**    ****For these counties we assembled PPH values from the American Community Survey as follows:**
** ****Single Year Estimates, 2001 to 2006; and**
** ****Three-Year Estimates, 1999-2001 to 2003-2005 **
**   ****To compare against PPH values derived analytically.**

**Notes:**
“Real” people, not only researchers and academics, are using current data from a few American Community Survey sites to solve “real” problems identified by community leaders in a series of case studies conducted on behalf of the Census Bureau.
The problems identified by community leaders are current and vital to the well-being of a particular sector of the community and will benefit greatly from annual data supplied by the American Community Survey.  

##  DATA

-       The analytical method for generating PPH in this study is a method commonly used by applied demographers for this purpose, namely, the geometric rate of change benchmarked to two most recent successive census counts and applied to the PPH value found in the most recent census count, which is then extrapolated beyond the most recent census by applying the rate of change to it.

** **DATA

##  DATA

-                       r = (PPHl/PPHb)(1/y) – 1
- where
-   r         = rate of change
-   PPH   = Persons Per Household
-   l         = Launch Year (most recent census)
-   b        = Base Year (Census preceding launch Year
-   y        = Number of years between l and b (10 years)

** **DATA

##  DATA

-                     PPHt = (PPHl) [( 1 + r)(y) ]
- where
-    r       = rate of change (from step 1)
-    PPH = Persons Per Household
-    t       = Target Year
-    l       = Launch Year (most recent census)
-    y      = number of years between t and l

** **DATA

##  DATA

-       To demonstrate that this method works well, here is the result of a test in which estimated PPH values in 2000 are compared to Census 2000 PPH values for the 39 counties of Washington.
-      The estimated PPH values in 2000 are based on applying the changes in PPH values observed between the 1980 census and 1990 census to 1990 census PPH values.

** **DATA

##  DATA

**             ****Accuracy Test of the Geometric Method of Estimating PPH Values                             **

**Notes:**
Case studies were developed as a way to show other data users, as well as key decision makers, how data from the American Community Survey can be used to solve “real” problems that the community identifies. Case studies are a little like “model homes”.  The Census Bureau been talking to you and other groups for several years about the “blue prints” for the American Community Survey – how great it’s going to be, what you are going to be able to do with it, what it will do for your community.  With these case studies, we are now able to show you “model homes”,  the idea being that you customize this “model home” to suit your needs -- by using data for your community to solve this kind of problem.
We were concerned that our “model homes” reflect the needs of our data users so we selected an array of topics that would have appeal to most communities.  Those topics included:

	Aging/the elderly
	The well-being of children
	Transportation
	Education
	Health Care
	Rural concerns

##  DATA

**      ****Accuracy Test of the Geometric Method of Estimating   **
**             ****PPH Values for Counties: Washington State 2000**

**Notes:**
Case studies were developed as a way to show other data users, as well as key decision makers, how data from the American Community Survey can be used to solve “real” problems that the community identifies. Case studies are a little like “model homes”.  The Census Bureau been talking to you and other groups for several years about the “blue prints” for the American Community Survey – how great it’s going to be, what you are going to be able to do with it, what it will do for your community.  With these case studies, we are now able to show you “model homes”,  the idea being that you customize this “model home” to suit your needs -- by using data for your community to solve this kind of problem.
We were concerned that our “model homes” reflect the needs of our data users so we selected an array of topics that would have appeal to most communities.  Those topics included:

	Aging/the elderly
	The well-being of children
	Transportation
	Education
	Health Care
	Rural concerns

** **RESULTS

##   RESULTS

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-       In comparing the single-year ACS PPH values to the model-based PPH values, we find that the ACS PPH values are above the model-based PPH values in seven counties for the entire period, 2001-2006, that they are below the model-based values in two counties for the entire period and cross over the model-based values in nine counties (three of which have two crossovers each).

**  **RESULTS

##   RESULTS

-       In terms of directional changes, the single-year ACS PPH values change direction three or more times in three counties, twice in nine counties, and once in five counties. Only one county (Blackhawk, Iowa) has no directional changes in its single-year ACS PPH values.

**  **RESULTS

##   RESULTS

-       The three-year ACS PPH values remain above the model-based values for the entire period , 1999-2001 to 2003-2005 in nine counties, while in three counties they remain below the model-based values, and cross over the model based values seven times.

**  **RESULTS

##   RESULTS

-       The three-year ACS PPH values change direction twice in two counties and once in seven counties. In the remaining nine counties no directional changes are observed. The model-based PPH values show a secular decline in 11 counties and an increase in seven.

**  **RESULTS

##   RESULTS

-       In some of the counties with declining model-based PPH values, the trends are very slight (e.g., Pima County, Arizona) and in others, more distinct (Schuylkill County, Pennsylvania).
-      Similarly, some of the counties with increasing model-based PPH values have a very slight upward trend (e.g., San Francisco County, California), in others they are much more pronounced (e.g., Tulare County, California).

**  **RESULTS

##   RESULTS

**  **RESULTS

-      Table 2 provides mean PPH values across the 18 counties (and their standard deviations) by year. Not surprisingly, the single-year ACS PPH values exhibit the least systematic change over time and the most variation each year. In two of the six years, these values are lower than the model-based PPH values while in the remaining four years they exceed the model-based values.  The means of the three-year ACS PPH values show a systematic decline over time with annual variations comparable to the model-based PPH values.

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##  DISCUSSION

** **DISCUSSION

##  DISCUSSION

-       Given this, the results found here are a bit discouraging, given that these same variables are also used as control variables in ACS weighting – with one major change – once beyond the 2000 census, the total household populations and housing units are not enumerated directly, but, instead, estimated.

** **DISCUSSION

##  DISCUSSION

-       Not surprisingly, it is the single-year ACS PPH estimates that are the most discouraging. They jump around a great deal from year to year in many of the counties, a characteristic that is not desirable for both demographers who use the HUM and the stakeholders for whom HUM estimates are done.

** **DISCUSSION

##  DISCUSSION

-       This is because there is an expectation on the part of both these demographers and the stakeholders that PPH values should exhibit systematic changes unless there is compelling substantive evidence (e.g., the PPH values jumped because of a surge of in-migrants with high fertility and large family sizes) to the contrary.

** **DISCUSSION

## DISCUSSION

-       If such PPH values are used in the absence of compelling substantive evidence justifying their temporal instability then it appears to me that the risk of challenges and related administrative and legal actions increases, especially when these estimates are used to allocate resources, which is often the case.

##  DISCUSSION

-       In considering the three-year ACS PPH values, the results are not as discouraging, as those for the single-year values, but neither are they strongly encouraging.  These values change more systematically than do the single-year ACS PPH values, but they still exhibit temporal instability.

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##  CONCLUSIONS 

-  The single Year ACS PPH values viewed here do not appear to be useful to applied demographers who use the Housing Unit Method because of their fluctuation over time.
- The 3-Year ACS PPH values have less fluctuation than the single year values, but they do not appear to be much more useful than the single year values.

##  CONCLUSIONS

** **CONCLUSIONS

-      Our finding that the ACS PPH values are not particularly usable for purposes of making HUM-based population estimates is preliminary in nature.
-      More work needs to be done not only to confirm this finding, but given it is confirmed, we also need to figure out if the ACS PPH values can be modified so that they can be used.

## FUTURE RESEARCH 

-    With this in mind, our suggestions for further analysis include:
- (1) conducting a broader scale comparison, taking into account the full range of counties;
- (2) examining ACS PPH values that are not controlled;
- (3) consideration of a way to utilize sample error (i.e., confidence intervals) in determining ACS PPH changes over time;
- (4) an examination of 5-year ACS PPH values when at least three years of data become available; and
- (5) making adjustments to ACS PPH values (deriving model-based PPH values from the ACS) that may provide more temporal stability.