Residential Energy Consumption Survey (RECS)
1993 RECS Survey Data 2009 | 2005 | 2001 | 1997 | 1993 | Previous
Housing Characteristics Tables
| Topical Sections | Entire Section |
|---|---|
| All Detailed Tables | |
| Tables: HC1 Household Characteristics, Million U.S. Households Presents data relating to location, type, ownership, age, size, construction, and householder demographic and income characteristics. |
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| Tables: HC2 Space Heating, Million U.S. Households Presents data describing the types of heating fuel and equipment used for main and secondary heating purposes. |
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| Tables: HC3 Air-Conditioning, Million U.S. Households Presents data describing selected household characteristics including location, number of rooms and area cooled and air-conditioning usage. |
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| Tables: HC4 Appliances, Million U.S. Households Presents data describing the frequency and characteristics of energy-intensive appliances found in most households. |
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| Tables: HC5 Light Usage, Million U.S. Households Presents data describing the number and usage of incandescent and fluorescent indoor lights and outdoor lights. |
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| Tables: HC6 Usage Indicators, Million U.S. Households Presents data describing usage of heating and cooling equipment, including thermostat settings at various times of the day, equipment using hot water, and cooking appliances. |
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| Tables: HC7 Conservation, Million U.S. Households Presents data describing conservation measures taken by the household, participation in demand-side management programs, and types of windows in the housing unit. |
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| Tables: HC8 Equipment Purchase, Million U.S. Households Presents data describing the purchase and replacement of heating and cooling equipment, refrigerators, and water heaters in the past 4 years and purchase considerations such as price and energy efficiency. |
Within each section, except for Air-conditioning and Light Usage, four sets of tables are presented. Each set of tables is comprised of data presented in terms of counts of millions of U.S. households and in terms of percent of U.S. households. Each count table is paired with its comparable percent table so that for each topic or variable counts are presented on the left-hand page and percentages presented on the facing right-hand page. The four sets of tables are entitled:
- Census Region and Climate Zone
- Year of Construction
- Type and Ownership of Housing Unit
- Family Income.
In addition to the four sets of tables listed above, the Household Characteristics and Space Heating sections also include tables that present data as a function of average and total floorspace. The three tables in the Air-conditioning tables use a unique format that presents data as a function of cooled floorspace and air-conditioning usage. Finally, the Light Usage section includes a table that describes indoor light usage by type of bulb. This section does not present data by the Year of Construction and Family Income headings formats
Categories of Data in the Tables:
+ Column Categories
The column categories most commonly classify data by the four sets of headings described above. The following, listed in alphabetical order, are explanations of some of the column categories that may require clarification.
Below Poverty Line (100 Percent and 125 Percent)—Low income classifications to which certain households are assigned. "Below 100 percent of poverty line includes households with incomes below the poverty level as defined by the U.S. Bureau of the Census and the Office of Management and Budget. "Below 125 percent of poverty" includes households with incomes below 125 percent of the poverty level. These groups of the poor and near-poor represent alternative levels for defining poverty. The poverty line varies with the number of family members in the household and the income of the entire family. (See Eligible for Federal Assistance below.)
Census Region—Four regions as defined by the U.S. Bureau of Census. For a map showing the four Census regions (and nine Census divisions), see Appendix E. For a listing of the States included in each Census region (and division), see the Glossary.
Climate Zone—One of five climatically distinct areas, defined by long-term weather conditions affecting the heating and cooling loads in buildings. The zones were developed by the Energy End Use and Integrated Statistics Division (EEUISD) from seven distinct climate categories originally identified by the American Institute of Architects (AIA) for the U.S. Department of Energy and the U.S. Department of Housing and Urban Development. The zones were determined according to the 30-year average (1961-1990) of the annual heating and cooling degree-days (base 65 degrees Fahrenheit). For additional details, see the Glossary.
Cooled Floorspace—Computed as heated floorspace times the percentage of rooms that are cooled over total rooms. If the housing unit has no heated floorspace then total floorspace is substituted for heated floorspace in the computation of cooled floorspace.
Cooling Degree-Days (CDD)—A measure of how hot a location was over a period of time, relative to a base temperature. In this report, the base temperature is 65 degrees Fahrenheit, and the period of time is 1 year. The cooing degree-days for a single day is the difference between that day's average temperature (the mean of the maximum and minimum temperature for a 24-hour period) and the base temperature if the daily average is greater than the base; it is zero if the daily average temperature is less than or equal to the base temperature.
Eligible for Federal Assistance—Households are categorized as eligible for federal energy assistance if their income is below the federal maximum standard. The Federal standard is 150 percent of the poverty line or 60 percent of statewide median income, whichever is the higher income. Individual States can set the standard at a lower level than the federal maximum. (See Below Poverty Line above.)
Family Income—The total combined income (before taxes and deductions) of all members of the family from all sources, for the 12 months prior to the interview. This definition includes the total income of all family members who lived in the household during the 12 months prior to the interview, regardless of whether they were living there at the time of the interview. For additional details, see Family Income Category in the Glossary.
Floorspace (square feet)—The floor area of the housing unit that is enclosed from the weather. Basements are included, whether or not they contain finished space. The finished space and the heated space in attics are included. Garages are included if they share a wall with the house. Crawl spaces, even if they are enclosed from the weather, are not included. Sheds and other buildings that are not attached to the house are not included.
Heated Floorspace—The portion of floorspace that is heated during most of the winter season. Rooms that are shut off during the heating season to save fuel are not counted as heated square footage. Attached garages that are unheated and unheated areas in basements and attics are not counted as heated floorspace.
Lights Used One or More Hours per Day—All the light bulbs controlled by one switch are counted as one light. For example, a chandelier with multiple lights controlled by one switch is counted as one light. A floor lamp with two separate bulbs controlled by two separate switches would be counted as two lights. Indoor and outdoor lights were counted only if they were under the control of the householder.
"Incandescent Lights" are the most common household lamps. Electricity runs through a tungsten filament that glows and produces a soft, warm light. Because so much of the energy used is lost as heat, these are highly inefficient sources of light. These common general-service bulbs emit light in all directions.
"Fluorescent Lights" are usually long, narrow, white tubes connected to a fixture at both ends of the lamp; some are circular tubes. The inner surface of the tube is coated with a material that fluoresces (emits visible light) when bombarded with secondary radiation generated by a gaseous discharge within the tube. These lights are typically found in kitchen and basement work areas. Newer types ("compact" fluorescent lamps), looking somewhat more like a conventional incandescent bulb, are being made, which can be screwed into fixtures.
Type and Ownership of Housing Unit—"Single-Family Housing Unit" is a unit that provides living space for one household or family. The structure may be detached or attached to another unit. Attached houses are considered single-family houses as long as the house itself is not divided into more than one housing unit and has an independent outside entrance. A single-family house is contained within walls that go from the basement or the ground floor (if there is no basement) to the roof. (A mobile home with one or more rooms added is classified as a single-family home.) Townhouses, rowhouses, and duplexes are considered single-family attached housing units, as long as there is no household living above another one within the walls that go from the basement to the roof to separate the units.
"Multifamily (two to four units)" is a housing unit in a building with two to four housing units—a structure that is divided into living quarters for two, three, or four families or households and in which one household lives above another. This category also includes houses originally intended for occupancy by one family (or for some other use) that have been converted into separate dwellings for two to four families. Typical arrangements in these types of living quarters are separate apartments downstairs and upstairs or one apartment on each of three or four floors.
"Multifamily (five or more units)" is a housing unit in a building with five or more housing units—a structure that is divided into living quarters for five or more families or households and in which one household lives above another.
"Mobile Home" is a housing unit built on a movable chassis and moved to the site. It may be placed on a permanent or temporary foundation and may contain one or more rooms. If rooms are added to the structure, it is considered a single-family housing unit. A manufactured house assembled on site is a single-family housing unit, not a mobile home.
"Owned/Rented" describes the relationship of a housing unit's occupants to the structure itself, not the land on which the structure is sited. "Owned" means the owner or co-owner is a member of the household and the housing unit is either fully paid for or mortgaged. A household is classified "rented" even if the rent is paid by someone not living in the unit. "Rent-free" means the unit is not owned and no money is paid or contracted for rent. Such units are usually provided in exchange for services rendered or as an allowance or favor from a relative or friend not living in the unit. Unless shown separately, rent-free households are grouped with rented households.
Year of Construction—The year the structure was originally completed or the year any part of the structure was first occupied. For mobile homes, year of construction is the model year.
+ Row Categories
The row categories classify data by specific features of the households as described by the section headings described above. All of the column categories already described also are employed as row categories. The large majority of the row categories presented are not particularly technical in nature, e.g., number and percent of color televisions in U.S. households. The Glossary provides detailed definitions of the more technical terms used as row categories.
Statistical Significance of Data
Row and Column Factors
The tables provide row factors in the far-right column and column factors on the top line of each table. These factors are to be used to determine the Relative Standard Error (RSE) for each estimate, which in turn can be used to determine the standard error and the confidence level of the estimate and to determine whether the difference between any two figures is statistically significant. However, since the RSE's are only approximate, standard errors, confidence intervals, and statistical tests must also be regarded as only approximate. For more details about the derivation of the row and column RSE factors, see Appendix B, "Quality of the Data."
To calculate the RSE for a specific estimate, multiply the row factor by the column factor, as illustrated in Figure 3.1, an excerpt from Table 3.1a of this report. This table shows that 10.2 million housing units in the Midwest were located in suburban areas. Multiplying 4.2 (the row factor) by 0.9 (the column factor) yields an approximate RSE of 3.8 percent.
Figure 3.1. Use of RSE Row and Column Factors
Table 3.1a. Household Characteristics by Census Region and Climate Zone, Million U.S. Households, 1993
Source: Energy Information Administration, Office of Energy Markets and End Use, the 1993 Residential Energy Consumption Survey. Standard Errors
Since the estimates presented in the following tables are based on a sample of residential housing units, they are subject to sampling error, or standard error. To determine the standard error for an estimate in these tables, multiply the approximate RSE by the estimate. For example, to determine the standard error of 10.2 million housing units located in the suburbs of the Midwest in 1993, multiply 10.2 million housing units by .0378 (the approximate RSE). The result, 0.39 million housing units, is the approximate standard error for the estimate.
Confidence Levels:
+ Confidence Levels
For each of the estimates given in the tables, a 95-percent confidence range can be determined with the estimate at the mid-point. To calculate the 95-percent confidence range for a given figure:
- Multiply the RSE row factor by the RSE column factor to determine the approximate RSE.
- Multiply the approximate RSE (divided by 100) by the estimate given in the table to determine the approximate standard error.
- Multiply the result by 1.96 to determine approximate 2 standard errors.
- Subtract the result of Step 3 from the given estimate to determine the bottom of the range.
- Add the result of Step 3 to the given estimate to determine the top of the range.
The result of these steps will yield a range with the property that, in repeated surveys, the estimate would fall in the range constructed in this way 95 percent of the time.
For example, to determine the confidence range for the estimated 10.2 million midwestern housing units located in the suburbs in 1993:
- Multiply 4.2 (the RSE row factor) by 0.9 (the RSE column factor), which yields 3.78 percent (the approximate RSE).
- Multiply .0378 (the approximate RSE) by 10.2 million households (the estimate), which yields 0.4 million housing units (the approximate standard error).
- Multiply 0.4 million housing units by 1.96, which yields 0.8 million housing units (approximate 2 standard errors).
- To determine the bottom of the range, subtract 0.8 million housing units from 10.2 million housing units, which yields 9.4 million housing units.
To determine the top of the range, add 0.8 million housing units to 10.2 million housing units, which yields 11.0 million housing units.
It can then be said with 95-percent confidence that, in 1993, between 9.4 million and 11.0 million of the midwestern housing units were located in the suburbs. For each of the estimates......
+ Statistical Significance Between Two Statistics
The difference between any two estimates given in the detailed tables may or may not be statistically significant. Statistical significance for the difference between two independent variables is computed as:
where S is the standard error, x1 is the first estimate, and x2 is the second estimate. The result of this computation is to be multiplied by 1.96, and if this result is less than the difference between the two estimates, the difference is statistically significant.
For example, in 1993, 10.2 million of the midwestern housing units were located in the suburbs, while 6.4 million midwestern households were located in the central city, for an estimated difference of 3.8 million housing units. The standard error for the 10.2 million suburban housing units estimate (x1) is 0.39, and the standard error for the 6.4 million central city housing units estimate (x2) is 0.31:
Multiplying .50 by 1.96 yields 1.0 million housing units. Since 1.0 housing units is less than the 3.8 million housing units difference between the 1993 midwestern suburban and central city estimates, the difference is statistically significant.
Quick-Reference Guide
The Quick-Reference Guide below lists topical sections and table headings covered in the detailed tables and shows the table numbers for each of the tables. To assist the reader in locating a particular table, the topical section title is printed along the outside edge of each table page. The suffix "a" that accompanies the table number refers to the table that presents data in terms of counts of millions of U.S. households; the suffix "b" refers to the table that presents data in terms of the percent of U.S. households.
You have the option of downloading "all tables" or the tables that relate to each "topical section."
Specific questions on this product may be directed to:
Chip Berry
James.Berry@eia.gov
RECS Survey Manager
Phone: (202) 586-5543
Fax: (202) 586-0018
Consumption & Expenditures Tables
Energy End Uses Ranked by Energy Consumption, 1989
The following 28 tables present detailed data describing the consumption of and expenditures for energy used by households in the residential sector. The data are presented at the national level, Census region and division levels, for climate zones and for the most populous States, as well as for other selected characteristics of households. This section provides assistance in reading the tables by explaining some of the headings for the categories of data. It also explains the use of the row and column factors to compute the relative standard error of the estimates given in the tables.
Organization of the Tables
The tables cover consumption and expenditures for six topical areas:
- Major Energy Source
- Space-Heating End Use
- Air-Conditioning End Use
- Water-Heating End Use
- Refrigerator End Use
- Appliance End Use
The tables displaying data by major source (Tables 5.1 through 5.10) present household energy consumption and expenditure data that were obtained from the energy suppliers of the households. The tables present the average consumption and expenditures for all energy sources, followed by tables displaying statistics on individual energy sources. Statistics are provided both for the aggregate of all households and by per-household averages.
The tables presenting data by total end use and by space heating, air-conditioning, water heating, refrigerators, and appliances (Tables 5.11 through 5.28) contain nonlinear regression estimates of energy consumption and expenditures. Details concerning the methodology used for the end-use estimates are in Appendix C, "End-Use Estimation Methodology." Data are presented for a total of all energy sources, followed by tables displaying statistics for each energy source. Statistics are provided both by all households and per household averages.
Statistical Significance of Data
Row and Column Factors
The tables provide row factors in the far-right column and column factors on the top line of each table. These factors are to be used to determine the Relative Standard Error (RSE) for each estimate, which, in turn, can be used to determine the standard error and the confidence level of the estimate and to determine whether the difference between any two figures is statistically significant. However, since the RSE's are only approximate, standard errors, confidence intervals, and statistical tests must also be regarded as only approximate. For more details about the derivation of the row and column RSE factors, see Appendix B, "Quality of the Data."
To calculate the RSE for a specific estimate, multiply the row factor by the column factor, as illustrated in Figure 5.1, an excerpt from Table 5.10 of this report. This table shows that the average expenditure for natural gas in 1993 among U. S. households that were located in suburban areas was $6.03 per million Btu. Multiplying 1.9 (the row factor) by 0.8 (the column factor) yields an approximate RSE of 1.52 percent.
Figure 5.1. Use of RSE Row and Column Factor
Standard Errors
Since the estimates presented in the following tables are based on a sample of residential housing units, they are subject to sampling error, or standard error. To determine the standard error for an estimate in these tables, multiply the approximate RSE by the estimate. For example, to determine the standard error of the average expenditures for natural gas in 1993 among U.S. households located in suburban areas, multiply $6.03 per million Btu by .0152 (the approximate RSE). The result, $0.09 per million Btu, is the approximate standard error for the estimate.
Confidence Levels
Statistical Significance Between Two Statistics
| All Tables - (file size 665,893 bytes) pages: 97 | |
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| Energy Consumption and Expenditures tables - (file size 198,280 bytes) pages: 31. Table 5.1: Average of All Major Source Table 5.2: Consumption by Each Major Source Table 5.3: Expenditures by Each Major Source Table 5.4: Electricity, per Household Table 5.5: Natural Gas, per Household Table 5.6: Fuel Oil, per Household Table 5.7: Kerosene, per Household Table 5.8: Liquefied Petroleum Gas (LPG), per Household Table 5.9: Wood Consumption Table 5.10: Average Expenditures by Each Major Energy Source |
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| Energy End Use tables - (file size 69,589 bytes) pages: 9. Table 5.11: Consumption and Expenditures by End Use Table 5.12: Consumption by End Use, per Household Table 5.13: Expenditures by End Use, per Household |
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| Space-Heating Consumption and Expenditures tables - (file size 152,578 bytes) pages: 21. Table 5.14: Electricity and Natural Gas Table 5.15: Fuel Oil, Kerosene, and LPG Table 5.16: Electricity, per Household Table 5.17: Natural Gas, per Household Table 5.18: Fuel Oil, per Household Table 5.19: LPG, per Household |
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| Air-Conditioning Consumption and Expenditures tables - (file size 63,033 bytes) pages: 7 Table 5.20: Electricity for all A/C and Central A/C Table 5.21: Electricity for Room A/C |
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| Water-Heating Consumption and Expenditures tables - (file size 100,233 bytes) pages: 14. Table 5.22: Electricity and Natural Gas Table 5.23: Fuel Oil and Natural Gas Table 5.24: Electricity and Natural Gas, per Household Table 5.25: Fuel Oil and Natural Gas, per Household |
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| Appliances and Refrigerator Consumption and Expenditures tables - (file size 74,699 bytes) pages: 10. Table 5.26: Electricity, Natural Gas, and LPG Table 5.27: Electricity, per Household Table 5.28: Natural Gas and LPG, per Household |
The following tables are unpublished Census Region and State-level data based on the energy consumption data collected from the1993 Residential Energy Consumption Survey. The State data are based on the four largest populated States: California, Florida, New York, and Texas.
| Region | State |
|---|---|
| Northeast | California |
| Midwest | Florida |
| West | New York |
| South | Texas |
Specific questions on this product may be directed to:
Chip Berry
James.Berry@eia.gov
RECS Survey Manager
Phone: (202) 586-5543
Fax: (202) 586-0018
FINISH FIXING TABLE LINKS IN TABLES BELOW
1993 Public Use Data Files (ASCII Format)
WHAT IS RECS?
The Residential Energy Consumption Survey (RECS) is a national sample survey of housing units. The survey collects statistical information on the consumption of and expenditures for energy in housing units along with data on energy-related characteristics of the housing units and occupants. The survey is restricted to housing units that are the primary residence of the occupants; the RECS does not cover vacant housing units, second homes, or vacation units. RECS is conducted by the Energy Information Administration of the U.S. Department of Energy. The RECS was conducted in 1978, 1979, 1980, 1981, 1982, 1984, 1987, 1990, and 1993. For the 1993 RECS, data were obtained for 7,111 housing units. Energy-related characteristics of the housing units and occupants are obtained in an on-site personal interview with the occupants. Energy consumption and expenditures information are obtained from the energy suppliers to the responding households during the Energy Suppliers Survey that follows the household personal interview.
WHAT ARE THE RECS PUBLIC USE FILES?
The 1993 RECS Public Use Files are microdata files that contain 7,111 records, representing housing units from the 50 States and the District of Columbia. Each record corresponds to a single responding, in-scope sampled housing unit and contains information for that unit about the size, year constructed, types of energy used, energy-using equipment, conservation features, energy consumption and expenditures (electricity, natural gas, fuel oil, kerosene, and LPG), and the amount of energy used for five end uses: space heating, air-conditioning, water heating, refrigeration, and other.
WHAT IS THE GEOGRAPHIC LEVEL OF DATA AVAILABLE?
RECS data are available for the four Census regions and nine Census divisions. State-level data are available for the four most populated States (California, Texas New York, and Florida).
WHAT IS THE FORMAT OF THE PUBLIC USE FILES?
The Public Use Files are comma-delimited ASCII files.
HOW ARE THE PUBLIC USE FILES ORGANIZED?
Because of the size of the RECS database, the variables were grouped into 9 files by section of Household Questionnaire:
- Section A: Preinterview Observation
Section B: Housing Type - Section C: Home Heating
- Section D: Air Conditioning
Section E: Water heating
Section F: Lights - Section G: Appliances
- Section H: Conservation Measures and Usage
Section I: Demand Side Management - Section J: Fuel Used
- Section K: Fuel Bills
- Section L: Background Information
Section N: Vehicles - Section M: Program Participation
- Characteristics of Energy Supplier Data
- Energy Consumption
- Energy Expenditures
VARIABLES ON EVERY FILE
Several variables are frequently used in the analysis of residential energy data. These include the type of housing unit, the geographic location of the unit, and weather data for the location of the unit. The nine variables on all 9 files are:
- HHID (unique housing unit identifier)
- NWEIGHT (household weight)
- QMAIL (mail questionnaire identifier)
- TYPEHUQ (type of housing unit)
- REGIONC (Census region)
- DIVISION (Census division)
- LRGSTATE (indicator for California, Texas, New York, and Florida)
- HDD65 (heating degree-days to 65 degrees for 1993)
- CDD65 (cooling degree-days to 65 degrees for 1993)
HOW TO MERGE FILES
Each of these 9 files can be used by itself or be merged with other files. By merging files together, a new file can be created that contains, for each respondent, variables from two or more files. The variable HHID can be used to link the files.
HOW TO USE WEIGHTS
The RECS sample was designed so that survey responses could be used to estimate characteristics of the national stock of occupied housing units. In order to arrive at national estimates from the RECS sample, base sampling weights for each housing unit, which were the reciprocal of the probability of that building being selected into the sample, were calculated. Therefore, a housing unit with a base weight of 10,000 represents itself and 9,999 similar, but unsampled housing units in the total stock of occupied residential housing units. The base weight is further adjusted to account for nonresponse bias. Finally, ratio adjustments were used to ensure that the RECS weights add up to Current Population Survey estimates of the number of households. The variable NWEIGHT in the data file is the final weight.
- EXAMPLE 1: SINGLE RESPONSE
The respondent with HHID = 5198 has NWEIGHT = 13,292. Hence this respondent represents a total of 13,292 households. The site of the respondent home was 1,600 square feet. Hence, the respondent contributed 1,600 times 13,292 = 21,267,200 square feet to the estimated national total square footage.
- EXAMPLE 2: USING NWEIGHT TO ESTIMATE NUMBER OF HOUSEHOLDS
There were 865, out of the 7,111 RECS respondents, that used fuel oil in their homes (USEFO = 1). Most, but not all, of these households use fuel oil for space heating. The sum of NWEIGHT over these 865 cases is 10,791,313. Hence, the estimated number of households that use fuel oil is 10,800,000.
- EXAMPLE 3: USING NWEIGHT TO ESTIMATE PERCENTAGE OF HOUSEHOLDS
The sum of NWEIGHT over all 7,111 cases is 96,631,492. This is also an estimate of the total number of households as of July 1993. Hence, the estimated percent of households that use fuel oil (for any use in the home) is (10,791,313/96,631,492) times 100 equals 11.2 percent.
- EXAMPLE 4: USING NWEIGHT TO ESTIMATE TOTAL SQUARE FEET
To estimate total square feet, multiply NWEIGHT times HOMEAREA for the 865 cases where fuel oil is used in the home (USEFO = 1), then sum the product over the cases where USEFO = 1. The resulting estimate is 24,413,335,370 square feet. This should be rounded to 24.4 billion square feet or 24,413 million square feet.
- EXAMPLE 5: USING NWEIGHT TO ESTIMATE AVERAGE SQUARE FEET
The sum of NWEIGHT over cases where USEFO =1 is 10,791,313. Hence the estimated average square feet in homes that use fuel oil, is 24,413,335,370/10,791,313 = 2,262 square feet.
MAIL RESPONSES
If the field interviewers were not successful in obtaining a personal interview, a short mail questionnaire was mailed to the housing unit. Variables not on the mail questionnaire were then imputed for the housing unit using a hot deck procedure. There were 115 observations obtained via a mail questionnaire. These 115 records can be identified using the variable QMAIL.
FUEL USAGE INDICATORS
The variables USEEL, USEFO, USEKERO, USELP, and USENG are indicator variables for the use electricity, fuel oil, kerosene, LPG, and natural gas in the housing unit. They are on three files. They were obtained using section J of the questionnaire and they are indicator variables that equal 1 if the households uses the corresponding fuel and 0 otherwise.
HOW ARE THE VARIABLES THAT BEGIN WITH A Z DIFFERENT FROM THE NON-Z VARIABLES?
The "Z variables" are also referred to as "imputation flags." Imputation is a statistical procedure used to fill in missing values for respondents that are otherwise considered to be complete. Missing values for many, but not all, of the variables were imputed in 1997. The imputation flag indicates whether the corresponding non-Z variable was based upon reported data (Z variable = 0) or was imputed (Z variable = 1). There are no corresponding "Z variables" for variables from the RECS questionnaire that were not imputed, variables where there was no missing data, and variables that are not from the questionnaire.
HOW IS THE SURVEY RESPONDENT'S CONFIDENTIALITY PROTECTED?
There are no respondent names and address on these files. EIA does not receive nor take possession of the names or addresses of individual respondents or any other individually identifiable energy data that could be specifically linked with a housing unit. Local geographic identifiers and National Oceanic and Atmospheric Administration Weather Division identifiers are not included on these data files.
In addition, values for HDD65, CDD65, ELECRATE, and UGASRATE were altered slightly to mask the exact geographic location of the housing unit.
LlNKS TO EACH DATA FILE AND SUPPORTING DOCUMENTATION
For each data file, a codebook is provided (both files are in ASCII format). For files based upon the Household Questionnaire, the corresponding section of the questionnaire is provided (PDF format). To view and/or print PDF files (requires Adobe Acrobat Reader) Download Adobe Acrobat Reader .
Note: To DOWNLOAD one of the Text or PDF files below, click on the file of your choice to open it, then select FILE and SAVE AS, save file to your hard drive or a disk.
| File 1: Preinterview Observation and Housing Type | |
| Date Released: 07/08/03 | |
| Date Released: 07/08/03 | |
| (PDF Format - 18 kilobytes) | |
| File 2: Home Heating | |
| Date Released: 07/08/03 | |
| Date Released: 07/08/03 | |
| (PDF Format - 19 kilobytes) | |
| File 3: Air Conditioning, Water Heating, and Lights | |
| Date Released: 07/08/03 | |
| Date Released: 07/08/03 | |
| (PDF Format - 14 kilobytes) | |
| File 4: Appliances | |
| Date Released: 07/08/03 | |
| Date Released: 07/08/03 | |
| (PDF Format - 20 kilobytes) | |
| File 5: Conservaton Measures and Usage and Demand Side Management |
|
| Data File (Size - 917 kilobytes) | Date Released: 07/08/03 |
| Codebook (Size - 49 kilobytes) |
|
| Questionnaire - Sections H and I | (PDF Format - 19 kilobytes) |
| File 6: Fuels Used | |
| Date Released: 07/08/03 | |
| Date Released: 07/08/03 | |
| (PDF Format - 21 kilobytes) | |
| File 7: Fuel Bills | |
| Date Released: 07/08/03 | |
| Date Released: 07/08/03 | |
| (PDF Format - 14 kilobytes) | |
| File 8: Background Information and Vehicles | |
| Date Released: 07/08/03 | |
| Date Released: 07/08/03 | |
| (PDF Format - 13 kilobytes) | |
| File 9: Program Participation | |
| Date Released: 07/08/03 | |
| Date Released: 07/08/03 | |
| (PDF Format - 14 kilobytes) | |
| File 10: Characteristics of Energy Supplier Data | |
| Date Released: 03/13/07 | |
| Date Released: 03/13/07 | |
| File11: Energy Consumption |
| Date Released: 03/13/07 | |
| Date Released: 03/13/07 |
| File12: Energy Expenditures |
| Date Released: 03/13/07 | |
| Date Released: 03/13/07 |
| Microdata Files | ||||
|---|---|---|---|---|
| by Topic | Data Files | Codebooks | Questionnaire | Release Date |
| File 1: Preinterview Observation and Housing Type | TXT | TXT | Section A | 07/08/03 |
| File 2: Home Heating | TXT | TXT | Section B | 07/08/03 |
| File 3: Air Conditioning, Water Heating, and Lights | TXT | TXT | Section C | 07/08/03 |
| File 4: Appliances | TXT | TXT | Section D | 07/08/03 |
| File 5: Conservaton Measures and Usage and Demand Side Management | TXT | TXT | Sections E, F and G | 07/08/03 |
| File 6: Fuels Used | TXT | TXT | Section H | 07/08/03 |
| File 7: Fuel Bills | TXT | TXT | Section I | 07/08/03 |
| File 8: Background Information and Vehicles | TXT | TXT | Section J | 07/08/03 |
| File 9: Program Participation | TXT | TXT | Section K and L | 07/08/03 |
| File 10: Characteristics of Energy Supplier Data | TXT | TXT | 07/08/03 | |
| File11: Energy Consumption | TXT | TXT | 07/08/03 | |
| File12: Energy Expenditures | TXT | TXT | 1/10/2000 | |
Specific questions on this product may be directed to:
Chip Berry
James.Berry@eia.gov
RECS Survey Manager
Phone: (202) 586-5543
Fax: (202) 586-0018
1993 Data Quality
| Source | Information provided (preferred source) | Household type | Fallback source |
|---|---|---|---|
| Household survey | Housing-unit and household energy-related characteristics | All housing-units | |
| Supplier survey | Housing-unit consumption and expenditures by fuel type | Households that pay supplier directly for one or more delivered fuels | Household survey |
| Rental agent survey | Main fuel source for space and water heating, cooking, air-conditioning | Households in multi-unit structures with one or more fuels included in rent | |
| NOAA | Weather data for station close to each sample housing-unit | All housing-units | |
| Census Bureau | 1. Data for formulation fo sample data. 2. Household estimates for benchmarking RECS estimates |
||
| Source: Energy Information Administration, Consumption and Expenditures (February 1993) | |||
For those who may be unfamiliar with the nature and principal features of the Residential Energy Consumption Survey, this Data Quality presents some background information. There is a general overview of RECS, its objectives, and the timing of the periodic surveys. Another section describes the design and methodology of the 1993 RECS. The final section identifies significant changes as the survey design and procedures evolved from the initial survey (known as NIECS, the National Interim Energy Consumption Survey) in 1978 through the 1993 RECS.
Contents
- Introduction (file size 215,612 bytes) pages 11.
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Purpose and Scope of This Report
Sources of Information About Data Quality
Relation of This Report to Other RECS Publications
Structure of the Report
- An Overview of RECS (file size 75,720 bytes) pages 18.
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A General Overview of RECS
Overview of the 1993 RECS Design and Procedures
Evolution of the RECS Design: 1978-1993
- Coverage(file size 51,687 bytes) pages 11.
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RECS Target Populations
Frame Development and Sample Selection Procedures
Evaluation of Coverage Based on External Data Sources
- Nonresponse(file size 99,150 bytes) pages 22.
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Nonresponse in the Household Survey
Nonresponse in the Rental Agent Summary
Nonresponse in the Supplier Survey
Summary
- Measurement Error(file size 112,311 bytes) pages 24.
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Special Data Collection Procedures
Comparisons of Individual Household Data from Alternate Sources
Questionnaire and Interviewer Effects on Measurement Error
- Data Processing and Imputation(file size 117,369 bytes) pages 26.
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Data Processing Other than Imputation
Imputation
- Estimation and Sampling Error(file size 120,965 bytes) pages 25.
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Sample Weighting Procedures
End-Use Estimation
Sampling Errors
- Comparisons of RECS Estimates with Other Data(file size 78,356 bytes) pages 17.
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Comparisons of RECS and Supplier Survey Estimates of Consumption
Comparisons of RECS Data on Housing Unit and Household Characteristics with Data from Other Sources
- Summary(file size 88,496 bytes) pages 18.
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Principal Sources of Error
Current Research and Potential Design Changes
Some Suggestions for Data Users
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A. References
B. Related EIA Publications on Energy Consumption
Specific questions on this product may be directed to:
Chip Berry
James.Berry@eia.gov
RECS Survey Manager
Phone: (202) 586-5543
Fax: (202) 586-0018
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