American Community Survey Analysis Report Example
AUSTIN ENERGY
DISTRIBUTED ENERGY SERVICES
MARKET RESEARCH & PRODUCT DEVELOPMENT
RESIDENTIAL
E L E C T R I C I T Y BU R D E N
AN INVESTIGATION OF AMERICAN
COMMUNITY SURVEY DATA
-)
AUSTIN ENERGY
NOVEMBER 5, 2010
RESIDENTIAL
ELECTRICITY BURDEN
E X E C U T I V E S U M M A RY
In this study, Austin Energy examines the burden on households living in Travis County arising from
their expenditures on electricity. The “Electricity Burden” of a household is defined as the share of a
household’s income spent on electricity service. Information provided in this report is based on a
variety of data sources, including American Community Survey (ACS) data and American Housing
Survey data (both available from the U.S. Census Bureau); Center for Public Policy Priorities family
economic security data; Travis County Appraisal District data; Austin Energy Program data, and
Energy Efficiency Literature / Case Studies. This comprehensive data set allows for a broader view
of electricity burden across many different sources.
Current analysis indicates that AE provides some of the most affordable residential customer electric
rates and electric bills of major metropolitan areas examined in Texas, as well as generous customerassistance discount policies and programs. Although the ACS data may suggest Austin fares
relatively well in comparison with other areas, this does not discount the fact that households in this
area, as in other communities in Texas, struggle with electricity costs and are vulnerable to potential
increases in these costs.
Within literature focused on the plight of households regarding electricity or other energy costs, one
series of reports are often cited as particularly relevant. On the Brink: The Home Energy Affordability Gap
are state-level analyses conducted on an annual basis which seek to inform policy by identifying and
analyzing the impact of energy costs on households, particularly those at the lower end of the income
distribution 1 . In this report, we attempt to replicate the methodology found in these reports where
possible. One clarification should be provided up front: throughout this report, the term “electricity
burden” will be used, which is slightly different from “energy burden” which is generally referred to
in policy related analyses. Electricity burden is focused solely on the costs of electricity while energy
burden generally includes the cost of using gas heat. While this report does provide some indication
of this burden as well, the primary focus is on electricity.
The current analysis found similar findings to that provided in the On the Brink reports in that utility
burden (or the percentage a household pays for all utilities) is correlated with electricity burden. This
means a household that is struggling with electricity costs is also more likely to be struggling with
water/sewer, gas, or other utility costs. This finding demonstrates that the driving factor for high
energy burden is primarily household income rather than simply energy costs. Therefore, when
considering policies aimed at reducing energy burden, a holistic approach that evaluates income in
relation to poverty levels is due as well as an analysis of costs of electricity.
Nonmonetary programs targeting vulnerable households can help to reduce household electricity
burden. Preliminary analysis suggests that homes that participate under free weatherization achieved
an average savings of about 800 kWh per year. Assuming the current residential rate structure, that
would equate to a savings of roughly $80-$100 per year.
1
For more information, see http://www.homeenergyaffordabilitygap.com/index.html.
2
I N T RO D U C T I O N
The following information provides an analysis of key variables that impact the affordability of
electric bills. The majority of this analysis relates to electricity cost burden, a metric that is based on
the cost of electric service relative to household income, poverty, and demographic information that
identifies the most vulnerable households with respect to electricity burden.
As readers of this report may know, the impetus for this report is based on direction from City
Council in April 2010 as they deliberated Austin Energy’s Generation and Resource Plan, which
provides a roadmap for the utility as it relates to future generation priorities and goals. At that time,
Austin Energy was directed to produce what was termed an “affordability matrix” which sought to
highlight the potential impact of any new generation resource on the community and in particular,
those segments most vulnerable to potential cost increases. This report provides one component of
the affordability matrix in providing a basis for future policy discussions. It should be noted,
however, that the primary role of the current analysis is not to assign value to various economic,
environmental, and societal outcomes of the Generation and Resource Plan. Rather, this analysis is
meant to detail the landscape and characteristics of residential energy usage.
METHODOLOGY
Information provided in this summary is based in varying degree on the following sources:
•
•
•
•
•
•
American Community Survey (ACS): Census Bureau
American Housing Survey (AHS): Census Bureau
Family Budget Estimator Tables: Center for Public Policy Priorities (CPPP)
Travis County Appraisal District Data
Austin Energy Program Data & Collateral
Energy Efficiency Literature / Case Studies
The American Community Survey is conducted by the U.S. Census Bureau (within the
Department of Commerce). It represents the largest source of data used in this report 2 . Data
from the American Community Survey (ACS) represents actual individual-level responses from a
nation-wide household survey conducted on a monthly basis with identical questions and a
consistent methodology. For the purposes of this analysis, the most recent three-year -) pooled compilation of this data is being utilized. A data set consisting of three years of
pooled data is being used for two reasons: 1) the larger sample size (compared to a single year
file) provides a greater level of precision when calculating electricity cost burden; and 2) weather
differences inherent in a single year data file might produce less than reliable results than a threeyear pooled data file.
The American Community Survey has been used to generate estimates of electricity cost burden.
Electricity cost burden, or simply electricity burden is defined as the median monthly cost of
2
For public access to the data, See http://factfinder.census.gov/home/en/acs_pums_2008_3yr.html
3
electricity divided by median monthly household income, either for all households or a particular
segment of households. The “median” represents the point at which 50 percent of households
fall below and 50 percent above; for example, if the median monthly electric bill was $100, 50
percent of households would pay less than $100 and 50 percent would pay greater than $100.
Where presented, “utility burden” is defined as the median total monthly expenditures for all
utilities as collected by Census (including electricity, gas, water/sewer, and other fuel) divided by
median monthly household income. Through analysis of the ACS data, electricity cost burden
can be viewed based on many different characteristics singularly or in combination including
type of housing structure, family composition, income (including distribution and poverty
status), labor force and disability status.
As the ACS data is the primary source of information related to electricity cost burden, it is
important to provide the context under which this survey is conducted. Based on the actual
questionnaire provided to households, information is collected first about the residents living
within the household and the relationships among each person within the household 3 .
Following this section of the questionnaire, the respondent is asked to provide information
about their housing characteristics. This includes the following questions related to the cost of
utilities:
1. LAST MONTH, what was the cost of electricity for this house, apartment, or mobile
home?
2. This same question is asked with reference to gas, water/sewer, and other fuel (e.g. oil,
coal, kerosene, wood, etc.) 4 .
Additional questions are related to the types of income being secured by the household. The
ACS survey asks these questions based on the most recent 12-month period (relative to the
month in which the household is being interviewed). Collection of this information includes an
accounting of the receipt of income from the following eight sources:
•
•
•
•
•
•
•
•
Wages, salaries, commissions, bonuses, or tips from all jobs;
Self-employment income from own non-farm businesses or farm businesses, including
proprietorships and partnerships;
Interest, dividends, net rental income, royalty income, or income from estates and trusts;
Social Security or Railroad Retirement;
Supplemental Security Income (SSI);
Any public assistance or welfare payments from the state or local welfare office;
Retirement, survivor, or disability pensions;
Any other sources of income received regularly such as Veterans’ (VA) payments,
unemployment compensation, child support, or alimony.
These questions allow for the summation of the eight sources to provide total household income
upon which electricity cost burden is calculated. It also allows for classification of households
according to the types of income they receive (i.e., wage/salary vs. fixed income).
For a copy (pdf) of the actual American Community Survey questionnaire (2008 version), see
http://www.census.gov/acs/www/Downloads/SQuest08.pdf
4 Information regarding expenditures for water/sewer and other fuel are based on the previous 12
months (relative to the month in which the interview takes place).
3
4
All dollar amounts collected by the ACS survey are based on the nominal value at the time they
were collected. All dollar values for 2006 and 2007 have been adjusted to represent 2008
constant dollars according to adjustment factors provided by Census and based on changes in
the Consumer Price Index (CPI).
Travis Central Appraisal District (TCAD) data was used to provide general information on local
housing characteristics, particularly square footage. However, the TCAD data cannot be merged
in any practical fashion with the ACS data due to the lack of specific identifying information (i.e.
physical address) in the ACS data. For that reason, analysis of electricity cost burden based on
square footage cannot be included. Nevertheless, AE data and program information have been
used to analyze the impact of energy efficiency on customer bills and, potentially, cost burden.
A M E R I C A N C O M M U N I T Y S U RV E Y DA TA L I M I TA T I O N S
As a caution with regard to how one may interpret the information presented in this report, it is
critical that a number of data limitations be recognized. Some of these limitations are based on
the structure of the data itself, how it is collected, or other constraints.
1) Information collected from households is self-reported. In other words, with some
exceptions, there is no independent verification that the amount stated by the
respondent is indeed what appeared on their electric bill. As the result of conversations
with Census technical personnel, the fact that a number of other utilities are included in
a single bill may be a potential issue. When averages of the electricity cost reported by
households in Travis County are compared with Austin Energy internal billing data,
there is a difference which may suggest an overestimation of electric cost by households.
Nevertheless, Austin Energy is not comfortable proposing an adjustment factor given
the lack of certainty on how this effect is distributed across all households.
2) For the purposes of this report, the Austin region is defined as Travis County. While
AE provides electric service to a large portion of Travis County, the ACS sample
includes some Travis County customers served by other electric service providers.
Therefore, electricity expenditures may be based on bills from these other providers.
3) Due to the structure of the ACS household-level data, the month in which the interview
took place is not provided with the public versions of this data. This makes it
impossible to view electricity cost burden across different months of the year. This is a
significant limitation given that customer electric bills vary significantly between the
winter and summer, primarily attributed to the variation in heating and cooling costs.
Therefore, estimates of electricity cost burden represent a monthly average taken over a
36-month period from January 2006 through December 2008.
4) The ACS data provides no indication of the level of energy usage exhibited by each
household. It is important to keep in mind that a customer’s electric bill is based on two
primary components (other than administrative or other surcharges): one, the rate being
paid for the electricity; and two, the amount of electricity consumed or used. The first is
typically constant while the second is not. Understanding the level of usage a particular
household exhibits is critical to understanding what options exist to mitigate any cost
impacts. Understanding average usage per household across different areas of the state
5
also informs the impact of conservation activities and how these actions, at the aggregate
level, contribute to lower usage, and subsequently, to lower bills.
5) The ACS data does not collect information on square footage, which would be useful in
analyzing impacts of electricity cost as they relate to square footage, an indicator of the
energy efficiency or energy conservation behaviors of a home.
6) The ACS data is based solely on information collected during the 36 months from
January 2006 through December 2008; therefore, this analysis does not attempt to
project what costs may be in 2009, 2010, or any future projections.
COST OF LIVING INFORMATION
Cost of living information provides insight into how variation in the cost of goods and services
essential for a basic standard of living among different cities impacts cost burden. Cost of living
information for residential households used in this study is based upon household budget
information provided by the Center for Public Policy Priorities (CPPP). The CPPP is a nonpartisan, non-profit policy institute based in Austin that provides research-based and rigorous
estimates of costs paid across different areas of the State of Texas. The actual data comes from
the center’s Family Budget Estimator5 tables for metro areas across Texas, including those
chosen for comparison within this analysis. While the exact county definitions used by CPPP
are somewhat different from those used in our analysis of the ACS data, these differences are
minor and we feel do not impact the overall results.
Household budget information developed by CPPP is provided under two scenarios: one in which
employer-sponsored health insurance is available and one in which it is not available. Households
contained in the ACS data are classified according to whether they have the ability to afford
household expenditures as indicated by CPPP information. Due to differences in how household
budget items are defined by CPPP and Census, the data from CPPP is only used to provide estimates
on the necessary minimum monthly income required to meet basic household necessities overall,
rather than for specific household items such as electricity or other utilities. This provides a manner
by which one can make comparisons across metropolitan areas based on cost of living.
G E O G R A P H I C C OV E R A G E
In this document, Austin Energy staff has provided a high-level summary of where the Austin area
falls in relation to other metropolitan areas chosen. Metropolitan areas were chosen for comparison
based on population; these include: Dallas-Fort Worth-Arlington, Houston-Sugar Land-Baytown,
San Antonio-New Braunfels, Austin-Round Rock, El Paso, Corpus Christi, and BrownsvilleHarlingen. Given that the Austin region was defined as close as possible to the Austin Energy
service territory, the selection of ACS counties from Metropolitan Statistical Areas (MSAs) has been
narrowed to include only “core” urban counties. It was felt under this method that comparisons
between Austin and other areas would be more precise.
5
To access this data and information, see http://www.cppp.org/fbe/.
6
R E S E A RC H F I N D I N G S / S TA T E W I D E C O M PA R A T I V E A NA LY S I S
Comparative results indicated in Table 1 (page 8) demonstrate that Austin Energy provides some of
the most affordable residential customer electricity bills of major metropolitan areas examined in
Texas, as well as arguably one of the more generous customer-assistance discount policies and
programs. As Table 1 demonstrates, the Austin region fares favorably compared to other areas of
the State with regard to electricity burden.
Table 1 also provides an overview of some of the key characteristics for each community under study
in this report. We begin by examining general characteristics of the income distribution of
households. In addition to the relationship between household income and official poverty
indicators, we analyze household income as it relates to the CPPP family budget tables described on
the previous page. The CPPP information provides an advantage over the official poverty definition
because it takes into account household costs not considered in determining the federal poverty
threshold. For instance, poverty definitions account for food costs borne by households but do not
take into account the following costs (which are considered by CPPP): housing, child care, medical
care, transportation, other necessary costs (such as phone service, clothing, and other household
items that most observers would consider necessities). The goal of the Family Economic Budget
exercise undertaken by CPPP is to estimate a more realistic measure of what it takes to maintain a
basic standard of living.
There are a number of takeaways from Table 1 which inform policy decisions surrounding not only
electricity burden but the general economic burden of households. The difference between the
average monthly household income that should be obtained to stay above the poverty line and the
estimated amount produced by CPPP indicates the inadequacies inherent in the federal definition of
poverty. On a rounded basis, the CPPP estimates are approximately double the federal definition
threshold 6 . This is further highlighted by the percentage of households that fall below the CPPP
threshold.
As will be reiterated later in this report, there is a general correlation between electricity burden and
other burdens related to household necessities (such as housing), meaning policy prescriptions
seeking to mitigate the impacts of electricity burden should consider a holistic approach. While both
average monthly and median monthly electric bills are presented in Table 1, the differences between
these two statistics should be clarified. The average is simply the total monthly amount paid by
households for electricity service divided by the number of households. The median is the point at
which 50% of households pay below and 50% pay above. The median is not impacted by extreme
values and is generally considered a better measure of central tendency. For that reason, unless
otherwise noted, the median is used in this report as a primary indicator.
It should be noted that the CPPP produces two sets of estimates for each area; one in which health
insurance is provided by the employer and one in which health insurance is not provided. For the
purposes of this report, we have used the latter.
6
7
Measure
Total Households
Households Below Poverty:
% of All Households
Households by
Percent of Poverty
0-50 %
51-100 %
101-150 %
151-200%
201-250%
251-400%
401-500%
> 500 %
Median Annual
Household Income
CPPP Income
Requirement (Month)
% 500 %
70%
Electricity Burden (%)
60%
50%
40%
30%
20%
10%
0%
Austin
Brownsville
Corpus
Christi
Dallas
El Paso
Houston San Antonio
Texas
Area
A key application of this analysis is that the data, and hence the results, can be used to examine more closely
the most vulnerable households in our community as it relates to electricity consumption. Figure 1 9 provides
an indication of electricity burden according to where households fall relative to the Federal Poverty
Threshold 10 . With some variation, most areas exhibit quite high electricity burdens for those households
earning between 0 percent and 50 percent of the poverty threshold, with burden decreasing as income rises.
Austin finds itself somewhere in the middle of areas around the state with regard to poverty status and
electricity burden. While it may seem odd to find Brownsville and El Paso among areas characterized by
lower electricity burdens, this is likely due to a higher percentage of households within lower income
segments. Households at 0-50% or 51-100% of the federal poverty threshold, based on national data, are
more likely to live in housing that is not air conditioned space 11 . As Table 1 on page 8 indicates, Brownsville
and El Paso have a higher proportion of these households than any other area under study (32% and 26%
respectively compared to 14% for the state overall). Given that air conditioning usage contributes greatly to
higher usage, it is possible lower usage is dampening electricity burden in these areas.
Source: U.S. Census Bureau, American Community Survey -); based on tabulation of PUMS micro-data,
Austin Energy MRPD.
9
Federal Poverty Threshold information was used for each year (2006, 2007, and 2008) and is based on the
number of people within the household.
See http://www.census.gov/hhes/www/poverty/data/threshld/index.html for actual thresholds.
11 Based on 2005 EIA Residential Usage Indicators (the latest available); of the 16.6 million households
classified as below the poverty line at that time, 10.2 million households (61%) had zero square feet of
conditioned space.
10
9
While Figure 1 highlights some differences across the metropolitan areas of the state, there are some general
observations that can be made which apply to all areas:
As indicated to the right
Figure 2: Electricity Burden by Housing Costs (% of Income)
(Figure 2) and clarified in the
subsequent section focusing on
Owner
Renter
Austin-specific data, other
economic
burdens
(in
16%
particular housing) tend to be
14%
correlated
with
energy
12%
Figure 2 is
burden 12 .
10%
developed
as
follows:
8%
households are selected into
6%
three groups based upon the
4%
percentage
of
income
2%
households spend on housing.
0%
In this case, these groups
Up to 25%
25‐50%
> 50%
include households which
spend up to 25% of their
Percent of Household Income Spent on Housing Costs
household income on housing
costs, those that spend between 25-50%, and those households that spend greater than 50% on housing
costs. As the figure indicates, those households that pay greater than 50% of their income for housing
costs are also impacted by a higher electricity burden. Stated plainly, if a household struggles with
housing burden, they are more likely to struggle with electricity burden. The same is true of other utility
burdens (e.g. gas, water/sewer). This finding demonstrates that the driving factor of electricity burden is
primarily income rather than simply energy costs. Therefore, with regard to policy prescriptions,
affordability is not only an issue with regard to the cost of electricity, but requires a holistic approach that
seeks to address other burdens that exacerbate the impact of electricity burden.
Tenure (e.g. owner or renter) appears to be a general predictor of whether a household struggles with
energy burden. While median bills tend to be lower for renter-occupied households, the percentage of
their income spent on electricity tends to be higher. This result is apparent in every area under study,
though the difference in electricity burden is small. This effect is likely masking income effects since – all
things being equal – renter-occupied households have lower average incomes than do owner-occupied
households.
Larger households tend to have higher energy burdens; in particular, those with children. Single-headed
households with children are the most vulnerable with regard to electricity burden. Statewide, median
electricity burden for single-headed households and three children is 2.5 times greater than electricity
burden for households with one person.
As will be covered in more detail later in this report, electricity burden is not based simply on what rate is
paid for electricity, but rather is dependent upon the level of usage exhibited by households in each
region. All else being equal, two separate areas could be subject to the exact same rates yet have average
or median bills very different from each other; the difference between the two areas would be based
solely on the inherent level of conservation or energy efficiency.
Electricity Burden (%)
•
•
•
•
Housing costs defined as follows: owner: mortgage, property taxes, insurance, and utility costs; renter: gross
rent, in addition to utility costs.
12
10
R E S E A RC H F I N D I N G S / AU S T I N - S P E C I F I C A NA LY S I S
As noted previously, while the ACS data may suggest Austin fares relatively well in comparison with other
areas, this should not diminish the fact that households in this area struggle with electricity burden, and are
vulnerable to potential increases in electricity costs. This section provides some details regarding the
experience of households in the Austin area. This description focuses on analyzing those items that
contribute most to electricity burden, denoted here as key contributors.
Key Contributor: Household Income
This analysis attempts to replicate work provided to Austin Energy staff and conducted by Fisher, Sheehan,
and Colton in their widely recognized annual report among researchers entitled On the Brink 13 . Fisher,
Sheehan, and Colton’s research examines home energy affordability and the gap between actual costs and
Low Income Home Energy Assistance Program (LIHEAP) allocations 14 . The main goal of the analysis in
this section is to identify the most vulnerable households that may be impacted by any potential cost changes
in electric service by examining household incomes in detail.
For the Austin region, analysis of the ACS data indicates findings similar to those explored in On The Brink
regarding poverty level of the household and electricity burden. As noted, utility burden, as opposed to electricity
burden covered to this point, includes not only electricity but other utilities such as natural gas, water/sewer
and other fuels 15 . As described earlier, households were classified according to their annual household
income in relation to federal poverty thresholds for each of the years under study -). As indicated,
burden for those households at 0-50% of the poverty level was highest, experiencing an average electricity
burden of 39% and an overall utility burden of 72%. Median utility burdens decline as income relative to
poverty increases, with average utility burdens of 21%, 13%, 9%, and 8% for the 51-100, 101-150, 151-200,
and 201-250 household segments respectively. The current analysis had similar findings as provided in On the
Brink in that utility burdens are correlated with electricity burden, meaning a household that is struggling with
electricity cost is also more likely to be struggling with water/sewer, gas, or other utility costs. Table 3 on the
following page highlights some of the relevant data related to these findings, including the number of
households impacted within the Austin area.
On The Brink: Home Energy Affordability Gap. For example report (National & Census Division), see
http://portal.hud.gov/portal/page/portal/HUD/topics/energy/On%20the%20Brink.pdf.
14 LIHEAP (Low Income Home Energy Assistance Program) is a federal source of funds allocated as a block
grant or contingency (FY 2010 appropriations equaled $5.1 billion for block grants and $490 million for
contingency).
15 Other fuels are defined as oil, coal, kerosene, wood, etc; the use of these fuels are rare and based on the
data being analyzed, impact less than 5% of households statewide.
13
11
Table 3: Austin (Travis County), Percent of Poverty Threshold by Median Utility Burden
- Averages)
Percent of
Federal Poverty
Threshold
Income
Limit
(Family of 4)
0-50 %
51-100%
101-150%
151-200%
201-250%
251-400%
401-500%
> 500%
Total
Households
$10,514
$21,027
$31,541
$42,054
$52,568
$84,108
$105,135
N/A
Households
Number
Percent
Electricity
Burden
Cost Burdens
Gas
Water &
Burden
Sewer
Burden
Other
Fuel
Burden
Utility
Burden
21,105
25,251
27,680
28,801
29,617
80,155
39,976
128,723
-
-
-
-
-
-
381,307
100
2.7
0.9
0.7
0.5
4.8
Source: U.S. Census Bureau, American Community Survey -); based on tabulation of PUMS micro-data, Austin Energy MRPD. Note: Not
all households are included in the electricity, gas, water/sewer, or utility burden (See Footnote 6, Page 7). Income limit for family of four based on
federal poverty thresholds for 2007, and values reflect point at upper end of percent range (i.e. 50%, 100%, 150%, etc).
Table 3 provides information on electricity burden as presented earlier, as well as describing burdens
associated with other utilities. For instance, other utilities such as natural gas, water & sewer, other fuel and a
summation of all four utilities (indicated by column labeled “Utility Burden”). Other fuel, as defined by the
Census, includes heating oil, kerosene, wood, etc and the use of these fuels is rare (only impacts 5% of
households statewide). At any rate, it has been included here for demonstration purposes. Electricity burden,
as Table 3 suggests, is the main driver of overall utility burden, accounting for nearly half of median utility
burden across all income segments.
An examination of these results provokes a question researchers interested in energy costs and their impact
on households have discussed, which is: At what level does any of these utility-related burdens become a
public policy issue? With regard to electricity, a number of observers have used 6% as a potential normative
threshold upon which households paying above this amount are faced with electricity burden. As Table 3
indicates, households above 150% of the poverty threshold would be below this threshold. Below 150% of
the poverty threshold, households experience a very high electricity burden and perhaps what a reasonable
observer would suggest is deserving of public policy intervention.
12
Key Contributor: Household Composition
Figure 3: Electricity Burden by Household Composition
12%
Electricity Burden (%)
10%
8%
6%
4%
2%
0%
1 Adult, No
Children
2 Adult, No
Children
1 Adult, 1 Child 1 Adult, 2 Children 1 Adult, 3 Children 2 Adult, 1 Child 2 Adult, 2 Children 2 Adult, 3 Children
Household Composition
Related to information provided earlier regarding necessary monthly income for households according to
composition, electricity burdens are more likely to occur in households with certain adult and child
compositions. Differences in electricity burden are based on the number of adults and children in the
household, as indicated in Figure 3. As the figure suggests, households with either single parents and/or
higher numbers of children are more likely to experience a higher electricity burden. One reason is that these
households face many challenges economically in addition to electricity burden. Though not presented
graphically here, additional analysis into electricity burden by household composition indicates that burden is
also further impacted by attachment to the labor market, as those households whose largest source of income
is fixed (i.e., not based on wage and salary employment) have energy burdens greater than that of households
whose income is based solely on wage and salary employment.
Key Contributor: Housing Characteristics
Given the relationship between electricity usage and structural conditions, it should come as no surprise that
housing characteristics can contribute a great deal to the outcome of electricity burden. The following section
focuses on two main variables that explain some of the variation in electricity burden between households on
the basis of housing characteristics: Tenure and the year in which the structure was built. While tenure (i.e.
owner or renter) is not specifically regarded as a housing unit characteristic, it is included here to highlight
potential energy efficiency issues related to renter-occupied households. On average, renters experience a
higher median electricity burden than do owners. This difference appears regardless of the type of housing in
which the renter is residing (e.g. single vs. multi-family). This outcome may be the result of less than energyefficient renter-occupied housing; however, the data does not allow us to answer that question sufficiently.
Table 4 shows that for a given age of residence, the electricity burden of a renter-occupied household is
13
higher than that of an owner-occupied household. 16 While these differences may not be significant, an
examination of median household income between these two groups reveals some obvious differences.
Table 4: Housing Tenure by Year Structure Built (Households & Electricity Burden)
Housing Tenure
Owner
Number of Households
(% of Owner-Occupied Households)
Median Monthly Income
Median Monthly Electric Bill
Electricity Burden (%)
Renter
Number of Households
(% of Renter-Occupied Households)
Median Monthly Income
Median Monthly Electric Bill
Electricity Burden (%)
Total Households
2000-Present
-
-
Pre-1980
Total
46,400
40,400
42,100
76,700
(23)
(20)
(20)
(37)
205,600
$8,020
$160
2.00
$8,062
$160
1.99
$6,436
$140
2.18
$5,751
$145
2.53
$6,709
$150
2.24
32,600
30,100
40,000
73,000
(19)
(17)
(23)
(42)
175,700
$3,525
$-,000
$3,272
$-,600
$2,885
$-,100
$2,631
$-,700
$2,945
$-,300
Source: U.S. Census Bureau, American Community Survey -); based on tabulation of PUMS micro-data, Austin Energy MRPD. Note:
Median Income, Electric Bill and Electricity burden calculated on a subset of households (See Footnote 8, Table 1, page 8).
Electricity burden appears to be have a negative relation to the year the housing structure was built; in other
words, older homes tend to have higher electricity burdens. This may be a function of income as well as
construction codes in place at the time the housing was developed.
Table 5: Income Source by Year Structure Built
(Households, Median Electricity Burden, and Median Age of Head of Household)
Income Source
2000-Present-
Pre-1980
HH
(000s)
Electric
Burden
(%)
Median
Age
HH
(000s)
Wage/Salary (100%)
Wage/Salary (50-99%)
Wage/Salary (1-49%)
Self-Employed (> 50%)
Fixed Income 17
Total Households
Electric
Burden
(%)
Median
Age
-
Source: U.S. Census Bureau, American Community Survey -); based on tabulation
Electricity burden calculated on a subset of households (See Footnote 8, Table 1, page 8).
HH
(000s)
Electric
Burden
(%)
Median
Age
HH
(000s)
Electric
Burden
(%)
Median
Age
-
of PUMS micro-data, Austin Energy MRPD.
-
Note:
Table 5 adds to the component of year the structure was built the impact of types of income and their impact
on electricity burden. The ACS data provides information on the types of income that people receive. In
Table 5, households are classified according to income source and developed the following categories:
Wage/Salary (100%) represents those households in which 100 percent of the household’s income is based
on earnings related to employment; Wage/Salary (50-99%) is defined the same, except the percentage of
income based on employment is between 50 and 99 percent (with the residual supplemented by some other
form). Wage/Salary (1-49%) is identical, except the percentage is 1 to 49. The category Self-Employed (>
50%) indicates households where the majority of earnings are accrued through self-employment. Finally,
Fixed Income represents households which receive no employment earnings (either self-employed or
Note: With regard to the time periods defining year structure built, optimally we would be able to classify
according to time period categories aligned with significant changes in building codes which has an impact on
energy costs. Unfortunately, the ACS data is collected based on pre-defined categories.
17 Fixed Income households defined as those which receive no wage/salary, self-employed or
interest/dividend earnings. Income sources include Social Security/Railroad Retirement, SSI, Public
Assistance, Retirement/Disability, or temporary income such as Unemployment Insurance.
16
14
otherwise) or interest/dividend earnings. As indicated in Table 5 (preceding page), these households
experience an electricity burden more than three times the rate for all households. These are also households
which are characterized by older individuals, as indicated by the median age of the head of household.
Key Contributor: Electricity Usage
The concluding section covering the analysis of census data provides a good transition into a brief discussion
of how energy efficiency can impact household electricity use and potentially mitigate cost increases. While
ACS data does not collect information on usage, we have calculated an estimate of usage based on a few
assumptions. The first is that a majority of the respondents to the survey within Travis County – it may be
assumed – are Austin Energy customers. The service territory of Austin Energy, while larger than the City of
Austin political limits, is smaller than Travis County. The geographical identifiers within the ACS data do not
allow for the selection of households only in the Austin Energy service territory so all households in Travis
County were selected.
Usage information is estimated based on converting the dollar amounts households have reported to usage
(kWh) based on known rate information. In order to view differences in usage according to income, income
percentiles from 0 to 100 in intervals of 5 were created and households grouped according to these percentile
definitions. Usage and energy burden were then examined across these different households. In order to
achieve a measure of energy intensity, kWh estimates were converted to kWh per room; Figure 4 indicates
results incorporating electricity burden and trend for kWh/room.
Figure 4: Usage & Electricity Burden by Income Percentile
kWh/Room
Energy Burden
Trend
350
50
45
300
40
Electricity Burden (%)
30
200
25
150
20
15
kWh per Room
250
35
100
10
50
5
0
0
5
10
15
20
25
30
35
40
-
Income Percentile
70
75
80
85
90
95 100
Source: U.S. Census Bureau, American Community Survey -); based on tabulation of PUMS micro-data, Austin Energy MRPD.
Figure 4 indicates that electricity burden is the highest for those households at the lowest end of the income
distribution. The “Income Percentile” as noted in Figure 4 is defined as where households fall relative to all
households. The manner in which the income percentiles are determines is as follows: household income for
all households is organized in descending order, or more simply put, with households earning the most at the
top and those earning the least at the bottom. Percentiles are based on determining the point at which a
certain percentage of households is above and below that point. For example, the 50th percentile (or median)
15
indicates the point at which 50 percent of the households are above and 50 percent are below. The 50th
percentile based on the data analyzed is $54,230, which is the same (though unrounded) as the median
household income indicated in Table 1 (page 8). This process can be completed for different percentiles, as
indicated in Figure 4, with the lowest income households on the left of the x axis and the highest income
households on the right. Figure 4 clearly demonstrates the relationship between income and electricity
burden. The usage information also reveals that, with some exceptions, kWh per room among the household
population at the lower end of the income distribution is higher than average. Extending this analysis to take
into account housing stock, nearly half of all homes at the lower end of the income distribution (from 0-15th
percentile) were built before 1980. All else equal, generally older homes (particularly those constructed prior
to conservation-focused building codes) use more electricity per square foot.
P OT E N T I A L I M PA C T S O F E N E RG Y E F F I C I E N C Y
In order to assess the potential of weatherization to increase the energy efficiency of housing units and reduce
costs, Austin Energy staff analyzed energy use before and after the installation of energy efficiency measures
for a sample of 1,071 households. This sample is drawn from participants in the free weatherization
program 18 administered by Austin Energy Distributed Energy Services.
Free weatherization is an energy efficiency program conducted by AE which provides eligible customers with
improvements that can lead to energy savings (and in turn, actual dollar savings). Potential measures include
attic insulation, minor duct repair and sealing, caulking around plumbing penetrations (where air leakage may
exist), weather stripping around doors, and solar screens.
Households are required to meet eligibility requirements to qualify for free weatherization. Table 6 below
indicates the income limits used to determine qualification.
Table 6: Free Weatherization Eligibility Requirements
Household
Size
•
•
•-+
Occupant 60 Years of Age or Older OR
With Physical or Mental Disability AND
Gross Annual Household Income Below:
$41,050
$46,900
$52,800
$58,650
$63,350
$68,050
$72,750
$77,400
•
•
Head of Household