There is a light at the end of the tunnel and Texas will likely be the first to see it, according to state and national leaders in the home building industry.

While there were no predictions on when the current building and housing downturn may come to an end, Joe Robson, the chairman of the National Association of Homebuilders, said he thinks the state, and the Dallas and Fort Worth area in particular, is a step ahead in the recovery process.

“Real estate is all local,” Robson said while speaking at the Homebuilders Association of Greater Dallas in Plano. “It gets a little crazy when all you hear about is the national market, everyone starts thinking that its their own town that’s struggling, but Dallas, and Texas in general, has held up real well.”

Robson, a builder from Tulsa, Oklahoma, said he has seen the inventory levels throughout the area remain steady. Keeping the number of homes on the market “in check” has helped the Metroplex avoid the massive declines in property value and abundance of unsold homes that plague the housing industries in California and Florida.

Bob Morris, executive director of the Homebuilders Association of Greater Dallas, estimates the area currently has about six months worth of unsold inventory. That amounts to less than half of what some of the hardest hit markets on the West and East Coast are trying to unload.

“Our inventory level is very manageable," Morris said." We’re sitting strong right now and six months from now we could be better.”

Much of the optimism comes from a spike in buying interest nationally and a steady local economy. In the first two months since the government issued a $8,000 tax credit to first time homebuyers, Robson estimates that more than 600,000 builders across the country have taken advantage of it.

“Certainly it’s working in Tulsa, where I’m from, and here in Texas. How much is it working in California, Florida and some of the other places underwater that are really priced higher? I don’t know,” he said.

Ron Connally, president of the Texas Association of Builders, agreed with the assessment that steady income levels and a lower than national average unemployment rate has helped the area, and state, avoid some of the housing pitfalls seen everywhere else.

According to the US Department of Labor, the unemployment rate for the Metroplex was 6.7 percent in March, well below the 8.7 percent national rate. At the same time, according to First American Core Logic’s monthly home price index, housing prices in the Forth Worth/Arlington area rose 1.18 percent and 1.59 percent in the Dallas/Plano/Irving area compared to the same time a year ago. The rise went against a 12.2 percent drop in housing prices across the country.

“There’s no doubt we’re in great shape in comparison to the rest of the country,” Connally said.

Yet lower than average inventory levels and slightly rising home prices haven’t kept the Metroplex from its own suffering. Three local builders (Arlington-based Wall Homes, Plano-based Sotherby Homes and Irving-based Choice Homes) have either closed or filed for bankruptcy in the past six months. There are countless numbers of unfinished community developments by these and other builders scattered throughout Dallas, Collin and Tarrant counties.

Connally sites a lack of lending to those that qualify to purchase a home and Robson mentioned people scared or unwilling to commit to a home purchase in this shaky economy as reasons for the problems on a local and national level. It’s also a situation that both believe needs to be kept under watch to avoid further housing problems down the road.

“Our biggest obstacle is getting financing,” Connally said. “We very well could wake up and see shortages in the housing market by the time this thing sorts itself out.”

Mistakes have been made and lessons have been learned, but the housing industry fallout could liger a while longer. Still industry leaders believe that the signs point to a steady improvement locally and statewide, which in turn, means better news for the rest of the country.

“The affordability factor is higher than it’s been since statistics have been held. We are at historic lows on mortgage rates,” Robson said. “[The industry] is going to come back.”

Wednesday, May 06, 2009 - By Mario Zavala
Special to the Business Press

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Dallas home prices may be down 4.5 percent when comparing D-FW’s February home prices to the same period last year, but Dallas is still faring better than the rest of the nation, according to Standard & Poor’s latest S&P/Case-Shiller Home Price Indices.

S&P’s latest report also indicates Dallas home prices in February dropped 0.3 percent from the previous month.

The S&P report says in considering all of the data, Dallas has suffered the least in the nation, with the area’s home prices down only 11.1 percent from peak prices that were reached in June of 2007. Dallas’ price drops are relatively minor when compared to cities like Phoenix, which saw home prices drop 50.8 percent from their June 2006 peak.

When comparing year-over-year declines in February, the report says Dallas, Denver and Boston fared the best with their prices declining 4.5 percent, 5.7 percent and 7.2 percent, respectively.

Metropolitan areas in the Sunbelt are feeling the most heat in terms of rapidly declining home prices. Among those hurting the most are the cities of Phoenix, Las Vegas and San Francisco — all of which saw their home prices drop more than 30 percent when comparing February numbers to the same period last year.

Standard & Poor’s did report some positive news about the declines. The report concluded, “While the declines in residential real estate continued into February, we witnessed some deceleration in the rate of decline in some of the markets,” said David Blitzer, chairman of the Index Committee at Standard & Poor’s. “All 20 metro areas recorded a monthly decline in February, but 16 of the 20 metro areas saw an improvement in their monthly returns compared to January.”

Metropolitan Home Price Statistics from the S&P/Case-Shiller Home Price Indices

Percentage change in February when compared to same month last year


Atlanta: -15.3 percent
Boston: -7.2 percent
Charlotte: -9.4 percent
Chicago: -17.6 percent
Cleveland: -8.5 percent
Dallas: -4.5 percent
Denver: -5.7 percent
Detroit: -23.6 percent
Las Vegas: -31.7 percent
Los Angeles: -21.4 percent
Miami: -29.5 percent
Minneapolis: -20.3 percent
New York: -10.2 percent
Phoenix: -35.2 percent
Portland: -14.4 percent
San Diego: -22.9 percent
San Francisco: -31 percent
Seattle: -15.4 percent
Tampa: -23 percent
Washington: -19.2 percent

 

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Green living is catching on; at least that’s one thing we can thank Al Gore for.  Home builders are building Energy Efficient Homes and green communities, and individuals across the nation are taking steps to make their homes conserve more.  The best advice I can give you is buy a brand spanking new green built home.  That is, of course, self serving, as I’m a home builder, and  Certified Green Home Builder to boot.  But, if you aren’t in a position to buy a new green-built home just yet, here are some suggestions for living greener…until you can call me a for a new green home.

1.    CFL Light Bulbs – Compact florescent light bulbs.  Probably the most popular (and easy) thing to do in your home TODAY. By replacing five of the most frequently used incandescent bulbs with CFL bulbs you could save about $100 a year on your electric bill. And going for Energy Star qualified CFL bulbs can last up to 10 times longer than regular bulbs and use 75% less energy. This means that just one bulb can save you $30 or more in electricity costs in its lifetime.   Yahoo has a Green Website that is tracking CFL light bulb installations and the Green rating of communities

2.    High Efficiency Toilets – Toilets make up about 30% of your home’s indoor water usage.   Advancements in technology have helped produce toilets which use 20% less water than standard and past models.  Models like those found at Kohler and TOTO offer both high water efficiency and high performance.

3.    Water Efficient Faucets – Using water efficient faucets throughout your home can save about 3-5% of your indoor water usage. Doesn’t sound like much, but energy savings though reduced hot-water use will repay the cost of the faucet within the year. According to the EPA, if every American home used a Water Sense faucet, we would save 60 billion Gallons of water annually.

4.    Energy Star Appliances – From the water heater and fridge to the DVD player and computer you use every day, even the smallest plug-in appliances waste energy and electricity. Using Energy Star certified appliances guarantees the appliance is as energy efficient as possible. Energy Star certified computers can use 70% less energy than one that is not certified. Certified televisions can use about 25% less. When making a new purchase, check for the Energy Star label/certification.

5.    Efficient Washing Machines – Washing machines can be big water wasters. Replacing your old washer with a newer, more efficient model can decrease your water usage by up to 60%, saving your family about 7,000 gallons of water each year. You will also save on detergent and energy. In addition to saving water, the newer front load washers get clothes cleaner than the standard washers.

These things will, of course, help, but they come up decidedly short of the technology which is built in to new green built homes.  Take a look at the Green Initiatives section of our website for a glimpse of just some of the green features and systems employed by Lexington Luxury Builders in our new homes.  At Lexington, we have even developed and built an entirely green neighborhood, of which we are quite proud.  Lexington Park at Rice Field is a sustainable, new urban neighborhood in Downtown Plano where every home is Certified Green Built by and Energy Star.  So, if you really want a Green home, Lexington Park is the place.

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Lexington Luxury Builders today announced a new price incentive plan for its new green-built homes at Lexington Park  in Plano.  For a limited time, buyers at Lexington Park  get $40,000 discount from list.  Contact Scott Schaefer 214-354-2228.

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A recent study and findings report released by the US Census Bureau entitled Income, Earnings and Poverty Data from the 2007 American Community Survey reports that Plano Texas is now the Most Affluent big city in America.  The criteria to be considered a large city is a population which is greater than 250,000, which hurdle Plano nominally clears.  The Report also indicates that Plano also has the lowest incidence of poverty of all large cities in the country.  I have embedded the full report below.  Given the well-known affluence of some coastal areas in this country, I found this rating for Plano to be particularly impressive. 

Read the full report and you’ll very quickly understand why the real estate in Plano has remained so desirable and stable, despite the economic downturn.  To further emphasize this point, the average number of days a home spends on the market for sale in Plano is 72 days, down from last year.  This bodes well for two neighborhoods in which Lexington Luxury Builders is building, Avignon Windhaven in West Plano and Lexington Park at Rice Field in Downtown Plano.  New homes at Lexington Park also come with $40,000 in price incentives for a limited time.

US Census Report on Affluent Cities

Income, Earnings, and Poverty Data From the 2007 American Community Survey American Community Survey Reports Issued August 2008 ACS-09 By Alemayehu Bishaw Jessica Semega U.S. Department of Commerce Economics and Statistics Administration Helping You Make Informed Decisions U.S. CENSUS BUREAU Suggested Citation Bishaw, Alemayehu and Jessica Semega, U.S. Census Bureau, American Community Survey Reports, ACS-09, Income, Earnings, and Poverty Data From the 2007 American Community Survey, U.S. Government Printing Office, Washington, DC, 2008. Economics and Statistics Administration Cynthia A. Glassman, Under Secretary for Economic Affairs U.S. CENSUS BUREAU Steve H. Murdock, Director Thomas L. Mesenbourg, Acting Deputy Director and Chief Operating Officer Arnold A. Jackson, Associate Director for Decennial Census Daniel H. Weinberg, Assistant Director for ACS and Decennial Census Susan Schechter, Chief, American Community Survey Office Howard R. Hogan, Associate Director for Demographic Programs David S. Johnson, Chief, Housing and Household Economic Statistics Division Contents TEXT Income, Earnings, and Poverty Data From the 2007 American Community Survey ……………………………………………………. 1 Introduction ………………………………………………………………………….. 1 What Is the American Community Survey? ………………………………….. 1 Additional Source of State and Local Estimates of Income and Poverty ………………………………………………………… 2 How Is Income Collected and Measured in the 2007 ACS?……………… 2 Household Income…………………………………………………………………….. 3 Median Household Income for the United States by Race and Hispanic Origin ……………………………………………………………. 3 Median Household Income for States …………………………………………. 3 Median Household Income for Counties and Places ……………………… 7 Median Income in Larger Areas ……………………………………………. 7 Median Income in Smaller Areas …………………………………………… 8 Income Inequality for the United States and the States………………….. 9 What Are Shares of Aggregate Household Income and a Gini Index? ……………………………………………………………………… 9 Earnings of Men and Women …………………………………………………… 12 Men’s and Women’s Earnings by State ………………………………………. 12 What Are “Earnings”? …………………………………………………………….. 12 Median Earnings by Race and Hispanic Origin……………………………. 14 Median Earnings by Educational Attainment ……………………………… 16 Median Earnings by Industry and Class of Worker ……………………… 16 Median Earnings by Occupation ………………………………………………. 17 Poverty ……………………………………………………………………………………. 19 How Is Poverty Calculated in the ACS? ……………………………………… 19 Poverty Status for the United States by Race and Hispanic Origin………………………………………………………………… 20 Poverty Status for States………………………………………………………… 20 Depth of Poverty ………………………………………………………………….. 22 Poverty Status for Counties and Places …………………………………….. 24 Poverty in Larger Areas …………………………………………………….. 24 Poverty in Smaller Areas …………………………………………………… 25 Source of the Estimates …………………………………………………………… 27 Accuracy of the Estimates ………………………………………………………. 27 Income, Earnings, and Poverty Data From the 2007 American Community Survey U.S. Census Bureau iii TEXT TABLES 1. 2. 3. 4. 5. 6. 7. 8. 9. Median Household Income in the Past 12 Months by Race and Hispanic Origin: 2007 ………………………………… 3 Median Household Income in the Past 12 Months by State: 2006 and 2007 ………………………………………………. 4 Median Household Income in the Past 12 Months for Ten of the Highest and Lowest Income Counties and Places With 250,000 or More People: 2007 ………………………………………………………………………………….. 7 Median Household Income in the Past 12 Months for Ten of the Highest and Lowest Income Counties and Places With 65,000 to 249,999 People: 2007 ………………………………………………………………………………. 8 Gini Coefficients and Shares of Income by Quintile in the Past 12 Months by State: 2007 ………………………….. 10 Median Earnings in the Past 12 Months of Full-Time, Year-Round Workers 16 and Older by Sex and Women’s Earnings as a Percentage of Men’s Earnings by State: 2007 ………………………………………………. 13 Median Earnings in the Past 12 Months of Workers by Sex and Women’s Earnings as a Percentage of Men’s Earnings by Selected Characteristics for the United States: 2007 ………………………………………………… 15 Median Earnings in the Past 12 Months of Workers by Sex and Women’s Earnings as a Percentage of Men’s Earnings by Occupation for the United States: 2007 …………………………………………………………………………. 18 Number and Percentage of People in Poverty in the Past 12 Months by Race and Hispanic Origin: 2007 ……… 20 10. Number and Percentage of People in Poverty in the Past 12 Months by State: 2006 and 2007 ……………………. 21 11. Percentage in Poverty in the Past 12 Months for Ten of the Highest and Lowest Poverty-Rate Counties and Places With 250,000 or More People: 2007 …………………………………………………………………… 25 12. Percentage in Poverty in the Past 12 Months for Ten of the Highest and Lowest Poverty-Rate Counties and Places With 65,000 to 249,999 People: 2007 ……………………………………………………………….. 26 FIGURES 1. 2. 3. 4. 5. 6. Difference in Median Household Income by State: 2006 to 2007 …………………………………………………………….. 5 Median Household Income in the Past 12 Months by State: 2007…………………………………………………………….. 6 Gini Index of Income Inequality in the Past 12 Months by State: 2007 …………………………………………………… 11 Women’s Earnings as a Percentage of Men’s Earnings in the Past 12 Months by State: 2007 ……………………….. 14 Percentage of People in Poverty in the Past 12 Months by State: 2007 ……………………………………………………. 22 Percentage of People by Income-to-Poverty Ratio in the Past 12 Months by State: 2007 ……………………………. 23 APPENDIX A: INCOME AND EARNINGS Figure A-1. Median Household Income in the Past 12 Months With Margins of Error by State: 2007 ……………………. 29 Table A-1. Median Household Income in the Past 12 Months by Metropolitan or Micropolitan Statistical Area Status and State: 2007…………………………………………………………………………………………………………….. 30 Table A-2. Median Earnings in the Past 12 Months of Workers by Sex and Women’s Earnings as a Percentage of Men’s Earnings by Detailed Occupation for the United States: 2007 ……………………………… 32 APPENDIX B: POVERTY Figure B-1. Percentage of People in Poverty in the Past 12 Months With Margins of Error by State: 2007 …………….. 43 Table B-1. Percentage of People Below Poverty Level in the Past 12 Months by Metropolitan or Micropolitan Statistical Area Status and State: 2007 …………………………………………………………………………….. 44 Table B-2. Number and Percentage of People by Income-to-Poverty Ratio in the Past 12 Months by State: 2007 ………………………………………………………………………………………………………………………………. 46 iv Income, Earnings, and Poverty Data From the 2007 American Community Survey U.S. Census Bureau Income, Earnings, and Poverty Data From the 2007 American Community Survey INTRODUCTION This report presents data on income, earnings, and poverty by detailed socioeconomic characteristics for the United States, states, and lower levels of geography based on information collected in the 2006 and 2007 American Community Surveys (ACS). A description of the ACS is provided in the text box “What Is the American Community Survey?”1 The U.S. Census Bureau also reports income, earnings, and poverty data based on the Current Population Survey Annual Social and Economic Supplement (CPS ASEC). Following the standard specified by the Office of Management and Budget (OMB) in Statistical Policy Directive 14, the Census Bureau computes official national poverty rates using the CPS ASEC and reports the 2007 data in the publication Income, Poverty, and Health Insurance Coverage in the United States: 2007. The 2007 ACS is the second year of the survey’s implementation 1 The text of this report discusses data for the United States, including the 50 states and the District of Columbia. Data for the Commonwealth of Puerto Rico, collected with the Puerto Rico Community Survey first introduced in 2005, are shown in Tables 2, 5, 6, and 10; in Appendix Tables A-1, B-1, and B-2; and in Figures 1, 2, 3, 4, and 5. including both housing units and group quarters in its sample.2 The ACS is designed to provide detailed estimates of housing, demographic, social, and economic characteristics for the states, counties, places, and From 2000 to 2004, the ACS was in the demonstration phase, which consisted of a housing unit sample of approximately 800,000 addresses per year and produced estimates for the United States, states, and essentially all places, counties, and metropolitan areas with at least 250,000 people. In 2005, the ACS went to full implementation with its sample of housing units. The 2005 ACS produced annual estimates for the United States, states, and all places, counties, and metropolitan areas with at least 65,000 people. The 2006 and the 2007 ACS samples also included people in group quarters. For guidance on comparing 2007 ACS data with 2006 ACS data and data from other sources, see . 2 other localities. This report makes state-level comparisons over the 2006 to 2007 time period. Such comparisons should be interpreted with caution because of overlapping income reference periods.3 3 As described in the text box “How Is Income Collected and Measured in the 2007 ACS?” the reference period for income data collected in the ACS is the past 12 months. As ACS data are collected in every month of the year, adjacent years have some reference months in common. Hence, comparing the 2007 with the 2006 estimates is not an exact comparison of the economic conditions in 2007 with those in 2006. Although the ACS will show trends over time, precise year-toyear comparisons are difficult to interpret. For a discussion of this and related issues, see Howard Hogan, “Measuring Population Change Using the American Community Survey,” Applied Demography in the 21st Century, Steve H. Murdock and David A. Swanson eds., Springer Netherlands, 2008. What Is the American Community Survey? The American Community Survey (ACS) is the largest survey in the United States, with an annual sample size of about 3 million addresses across the United States and Puerto Rico, and is conducted in every county throughout the nation (including every municipio in Puerto Rico). As part of the 2010 Decennial Census Program, the ACS has replaced the traditional decennial census long form. The ACS collects detailed social, economic, housing, and demographic information previously collected by the decennial census long form, but it provides up-to-date information every year rather than once a decade. Beginning in 2006, ACS data for 2005 were released for geographic areas with populations of 65,000 and higher. In 2008, the first set of multiyear period estimates will be released for data collected between January 2005 and December 2007. These 3-year period estimates will include geographic areas with populations from 20,000 and up. The Census Bureau is currently planning to release the first 5-year period estimates in 2010 for the smallest geographic areas—down to the tract and block group level—based on data collected between January 2005 and December 2009. The data contained in this report are based on the ACS sample interviewed in 2006 and 2007 and include only geographic areas with populations of 65,000 and higher. For information on the ACS sample design and other ACS topics, visit . Income, Earnings, and Poverty Data From the 2007 American Community Survey U.S. Census Bureau 1 Additional historical trend data on median household income and poverty from the CPS ASEC are available on the Internet.4 The Census Bureau also produces annual estimates of median household income and poverty for states, as well as for counties and school districts, as part of the Small Area Income and Poverty Estimates program (SAIPE). For more information about estimates for smaller geographic areas, see the text box “Additional Source of State and Local Estimates of Income and Poverty.” This report has three main sections: household income, earnings of men and women, and poverty. The income and poverty estimates in this report are based solely on money income received (exclusive of certain money receipts, such as 4 See . Additional Source of State and Local Estimates of Income and Poverty While the ACS produces annual single-year estimates of income and poverty for counties and places with population of 65,000 or more, the Census Bureau’s Small Area Income and Poverty Estimates (SAIPE) program produces single-year estimates of median household income and poverty for states and all counties, as well as population and poverty estimates for school districts. These estimates are based on models using data from a variety of sources, including current surveys, administrative records, and personal income data published by the U.S. Bureau of Economic Analysis. In general, the SAIPE estimates have lower variance than the ACS estimates, but they are released later because they incorporate ACS data in the models. Estimates for 2005 are available on the Internet at . Estimates for 2006 and 2007 will be available later in 2008. capital gains) before deductions are made for items such as personal income taxes, social security, union dues, and Medicare. Money income does not include the value of noncash benefits such as food stamps; health benefits; subsidized housing; payments by employers for retirement programs, medical, and educational expenses; and goods produced and consumed on the farm. How Is Income Collected and Measured in the 2007 ACS? The information on income and earnings presented from the 2007 ACS was collected between January and December 2007. People 15 years and older were asked about income for the previous 12-month period (the reference period), yielding a total time span covering 23 months. For example, data collected in January 2007 had a reference period from January 2006 to December 2006, while data collected in December 2007 had a reference period from December 2006 to November 2007. All income was inflation-adjusted to reflect calendar year 2007 dollars. That is, the 12 different reference periods were adjusted to reflect a fixed reference period, in this case January 2007 through December 2007, using the Consumer Price Index (CPI). This adjustment took the sum of the 2007 CPI monthly indexes, divided by the sum of the CPI monthly indexes for the income reference period, and multiplied the result by the income. Example: Consider a household surveyed in June of 2007 with a household income of $40,000. The sum of the CPI monthly indexes for 2007 was 3,653.7. The sum of the CPI monthly indexes for the reference period June 2006 to May 2007 was 3,589.4. Dividing 3,653.7 by 3,589.4 creates an adjustment factor of 1.0179. Multiplying the reported household income of $40,000 by this adjustment factor results in a 2007 inflationadjusted household income of $40,716. For more information on income in the ACS and how it differs from the Current Population Survey Annual Social and Economic Supplement (CPS ASEC), which also collects information on income, visit or . For a comparison of median household income data from the ACS and the CPS ASEC, visit . 2 Income, Earnings, and Poverty Data From the 2007 American Community Survey U.S. Census Bureau HOUSEHOLD INCOME Household income includes the income of the householder and all other people 15 years and older in the household, whether or not they are related to the householder. For comparisons of household income, this report focuses on the median— the point that divides the household income distribution into halves, one half with income above the median and the other with income below the median. The median is based on the income distribution of all households, including those with no income. In the 2007 ACS, information on income was collected between January and December 2007. All income data were inflation-adjusted to reflect calendar year 2007 values and are referred to in this report as 2007 ACS income. See the text box “How Is Income Collected and Measured in the 2007 ACS?” for more information on data collection and income adjustment. Median Household Income for the United States by Race and Hispanic Origin5 The discussion of race groups in this report refers to people who indicated only one race among the six categories in the survey: White, Black or African American, American Indian or Alaska Native, Asian, This report uses the characteristics of the householder to describe the household. The householder is the person (or one of the people) in whose name the home is owned or rented and the person to whom the relationship of other household members is recorded. If a married couple owns the home jointly, either the husband or the wife may be listed as the householder. Since only one person in each household is designated as the householder, the number of householders is equal to the number of households. 5 Table 1. Median Household Income in the Past 12 Months by Race and Hispanic Origin: 2007 (In 2007 inflation-adjusted dollars. Data are limited to the household population and exclude the population living in institutions, college dormitories, and other group quarters. For information on confidentiality protection, sampling error, nonsampling error, and definitions, see www.census.gov/acs/www/) Median household income (dollars) Race and Hispanic origin Estimate All households . . . . . . . . . . . . . . . . . . . . . . . . . . White alone . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . White alone, not Hispanic . . . . . . . . . . . . . . . . . . . . . . . . Black alone . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . American Indian and Alaska Native alone. . . . . . . . . . . . Asian alone . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Native Hawaiian and Other Pacific Islander alone. . . . . Some Other Race alone . . . . . . . . . . . . . . . . . . . . . . . . . . . Two or More Races . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hispanic (any race) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Margin of error1 (±) 75 109 106 208 714 465 2,660 270 610 182 50,740 53,714 55,096 34,001 35,343 66,935 55,273 40,755 44,626 40,766 Data are based on a sample and are subject to sampling variability. A margin of error is a measure of an estimate’s variability. The larger the margin of error in relation to the size of the estimate, the less reliable the estimate. When added to and subtracted from the estimate, the margin of error forms the 90-percent confidence interval. Source: U.S. Census Bureau, 2007 American Community Survey. Native Hawaiian or Other Pacific Islander, and Some Other Race.6 In the 2007 ACS, median household income in the United States for all households was $50,740.7 Table 1 shows that Asian households had the highest median household income ($66,935). While not statistically different from each other, the median household incomes for Native Hawaiian and Other Pacific Islander households ($55,273) and 6 Because federal surveys, including the ACS, allow people to report one or more races, two ways of defining a group such as Asian are possible. The first includes those who reported Asian and no other race (Asian alone); the second includes everyone who reported Asian regardless of whether they also reported another race (Asian alone or in combination with one or more other races). The use of the single-race population in this report does not imply that it is the preferred method of presenting or analyzing data. The Census Bureau uses a variety of approaches. Some Other Race was selected by respondents who did not identify with the five Office of Management and Budget race categories. 7 The estimates in this report (which may be shown in text, figures, and tables) are based on responses from a sample of the population and may differ from actual values because of sampling variability or other factors. As a result, apparent differences between the estimates for two or more groups may not be statistically significant. All comparative statements have undergone statistical testing and are significant at the 90-percent confidence level unless otherwise noted. non-Hispanic White households ($55,096) were less than that of Asian households and higher than that of Some Other Race households ($40,755). American Indian and Alaska Native households ($35,343) and Black households ($34,001) had lower median household income than the other race groups. Median income for Hispanic households was $40,766 in the 2007 ACS.8 Median Household Income for States Table 2 shows the median household incomes of states from the 2006 ACS and the 2007 ACS. The median household income estimates in the 2007 ACS varied from state to state, ranging from a median of $68,080 for Maryland to $36,338 for Mississippi.9 8 The median household income of Hispanic households was not statistically different from the median household income of Some Other Race households. Because Hispanics may be any race, data for Hispanics overlap with data for racial groups. 9 The median household income for the state of Mississippi was not statistically different from the median household income for West Virginia. The median household income for Puerto Rico was $17,741. Income, Earnings, and Poverty Data From the 2007 American Community Survey U.S. Census Bureau 3 Table 2. Median Household Income in the Past 12 Months by State: 2006 and 2007 (In 2007 inflation-adjusted dollars. Data are limited to the household population and exclude the population living in institutions, college dormitories, and other group quarters. For information on confidentiality protection, sampling error, nonsampling error, and definitions, see www.census.gov/acs/www/) 2006 median household income 2007 median household income (dollars) (dollars) Area Estimate United States . . . . . . . . . Alabama. . . . . . . . . . . . . . . . . . . . . Alaska . . . . . . . . . . . . . . . . . . . . . . Arizona. . . . . . . . . . . . . . . . . . . . . . Arkansas . . . . . . . . . . . . . . . . . . . . California . . . . . . . . . . . . . . . . . . . . Colorado . . . . . . . . . . . . . . . . . . . . Connecticut . . . . . . . . . . . . . . . . . . Delaware . . . . . . . . . . . . . . . . . . . . District of Columbia . . . . . . . . . . . . Florida . . . . . . . . . . . . . . . . . . . . . . Georgia . . . . . . . . . . . . . . . . . . . . . Hawaii . . . . . . . . . . . . . . . . . . . . . . Idaho . . . . . . . . . . . . . . . . . . . . . . . Illinois. . . . . . . . . . . . . . . . . . . . . . . Indiana . . . . . . . . . . . . . . . . . . . . . . Iowa . . . . . . . . . . . . . . . . . . . . . . . . Kansas . . . . . . . . . . . . . . . . . . . . . . Kentucky . . . . . . . . . . . . . . . . . . . . Louisiana . . . . . . . . . . . . . . . . . . . . Maine . . . . . . . . . . . . . . . . . . . . . . . Maryland . . . . . . . . . . . . . . . . . . . . Massachusetts . . . . . . . . . . . . . . . . Michigan. . . . . . . . . . . . . . . . . . . . . Minnesota . . . . . . . . . . . . . . . . . . . Mississippi . . . . . . . . . . . . . . . . . . . Missouri . . . . . . . . . . . . . . . . . . . . . Montana . . . . . . . . . . . . . . . . . . . . . Nebraska . . . . . . . . . . . . . . . . . . . . Nevada . . . . . . . . . . . . . . . . . . . . . New Hampshire . . . . . . . . . . . . . . . New Jersey . . . . . . . . . . . . . . . . . . New Mexico . . . . . . . . . . . . . . . . . . New York . . . . . . . . . . . . . . . . . . . . North Carolina . . . . . . . . . . . . . . . . North Dakota . . . . . . . . . . . . . . . . . Ohio . . . . . . . . . . . . . . . . . . . . . . . . Oklahoma. . . . . . . . . . . . . . . . . . . . Oregon . . . . . . . . . . . . . . . . . . . . . . Pennsylvania . . . . . . . . . . . . . . . . . Rhode Island . . . . . . . . . . . . . . . . . South Carolina . . . . . . . . . . . . . . . . South Dakota . . . . . . . . . . . . . . . . . Tennessee . . . . . . . . . . . . . . . . . . . Texas . . . . . . . . . . . . . . . . . . . . . . . Utah . . . . . . . . . . . . . . . . . . . . . . . . Vermont . . . . . . . . . . . . . . . . . . . . . Virginia . . . . . . . . . . . . . . . . . . . . . . Washington . . . . . . . . . . . . . . . . . . West Virginia . . . . . . . . . . . . . . . . . Wisconsin. . . . . . . . . . . . . . . . . . . . Wyoming . . . . . . . . . . . . . . . . . . . . Puerto Rico . . . . . . . . . . . . . . . . . . 49,807 39,870 61,098 48,622 37,511 58,277 53,539 65,312 54,312 53,363 46,603 48,065 62,926 44,211 53,444 46,488 45,668 46,496 40,331 40,301 44,646 66,750 61,415 48,546 55,527 35,411 44,063 41,562 46,575 54,543 61,242 66,159 41,540 52,656 43,820 43,010 45,664 39,765 47,388 47,389 53,394 41,964 43,851 41,199 46,013 52,636 49,090 57,869 54,149 36,006 50,052 49,006 18,184 Margin of error1 (±) 80 500 1,652 533 602 275 592 733 1,339 1,392 264 471 1,283 906 356 430 494 524 441 473 772 787 631 371 444 613 392 618 594 946 1,099 615 676 330 462 1,272 316 588 600 311 1,409 459 1,093 449 269 726 1,207 492 460 607 381 1,484 386 Estimate 50,740 40,554 64,333 49,889 38,134 59,948 55,212 65,967 54,610 54,317 47,804 49,136 63,746 46,253 54,124 47,448 47,292 47,451 40,267 40,926 45,888 68,080 62,365 47,950 55,802 36,338 45,114 43,531 47,085 55,062 62,369 67,035 41,452 53,514 44,670 43,753 46,597 41,567 48,730 48,576 53,568 43,329 43,424 42,367 47,548 55,109 49,907 59,562 55,591 37,060 50,578 51,731 17,741 Margin of error1 (±) 75 428 1,594 508 739 295 650 815 1,581 1,984 341 488 1,923 755 370 378 577 640 522 457 710 740 510 386 605 686 489 1,028 689 936 1,147 573 677 349 432 1,205 304 395 681 297 1,353 635 944 345 308 762 1,176 589 501 760 364 1,322 390 Change in median income (2007 less 2006) Dollars Estimate *933 *684 *3,235 *1,267 623 *1,671 *1,673 655 298 954 *1,201 *1,071 820 *2,042 *680 *960 *1,624 *955 –64 625 *1,242 *1,330 *950 *–596 275 *927 *1,051 *1,969 510 519 1,127 *876 –88 *858 *850 743 *933 *1,802 *1,342 *1,187 174 *1,365 –427 *1,168 *1,535 *2,473 817 *1,693 *1,442 *1,054 526 *2,725 -443 Margin of error1 (±) 109 658 2,295 737 953 404 879 1,096 2,072 2,424 431 678 2,312 1,179 514 572 760 827 683 658 1,049 1,081 811 535 750 920 627 1,200 910 1,331 1,588 840 957 480 633 1,752 439 709 907 430 1,953 784 1,444 566 409 1,052 1,685 767 680 972 527 1,988 549 Percent Estimate *1.9 *1.7 *5.2 *2.6 1.6 *2.8 *3.1 1.0 0.5 1.8 *2.5 *2.2 1.3 *4.5 *1.3 *2.0 *3.5 *2.0 –0.2 1.5 *2.7 *2.0 *1.5 *–1.2 0.5 *2.6 *2.4 *4.6 1.1 0.9 1.8 *1.3 –0.2 *1.6 *1.9 1.7 *2.0 *4.4 *2.8 *2.5 0.3 *3.2 –1.0 *2.8 *3.3 *4.6 1.7 *2.9 *2.6 *2.9 1.0 *5.4 –2.5 Margin of error1 (±) 0.2 1.6 3.7 1.5 2.5 0.7 1.6 1.7 3.8 4.5 0.9 1.4 3.7 2.6 1.0 1.2 1.6 1.8 1.7 1.6 2.3 1.6 1.3 1.1 1.3 2.6 1.4 2.8 1.9 2.4 2.6 1.3 2.3 0.9 1.4 4.0 1.0 1.7 1.9 0.9 3.7 1.8 3.3 1.4 0.9 2.0 3.4 1.3 1.2 2.7 1.0 3.9 3.1 * Statistically different from zero at the 90-percent confidence level. 1 Data are based on a sample and are subject to sampling variability. A margin of error is a measure of an estimate’s variability. The larger the margin of error in relation to the size of the estimate, the less reliable the estimate. When added to and subtracted from the estimate, the margin of error forms the 90-percent confidence interval. Source: U.S. Census Bureau, 2006 and 2007 American Community Surveys, and 2006 and 2007 Puerto Rico Community Surveys. 4 Income, Earnings, and Poverty Data From the 2007 American Community Survey U.S. Census Bureau Figure 1. AK Difference in Median Household Income by State: 2006 to 2007 (In 2007 inflation-adjusted dollars) WA MT OR ID SD WY NE UT CA CO KS MO KY NC AZ NM OK TN AR MS TX AL GA SC IA IL IN OH WV VA ND MN WI MI PA NJ DE MD DC* CT NY MA RI VT NH ME NV Difference in median household income Increased Not statistically different Decreased LA FL HI PR * DC is represented at 4.5 times the scale of other continental states. Source: U.S. Census Bureau, 2006 and 2007 American Community Surveys, and 2006 and 2007 Puerto Rico Community Surveys. Table 2 and Figure 1 show that real median household income rose between the 2006 ACS and the 2007 ACS in 33 states, while one state—Michigan—experienced a decline.10 (All year-to-year comparisons using ACS data should be viewed with caution. See footnote 3 for more information.) For the states that experienced increases, ten states were in the West (Alaska, Arizona, California, Colorado, Idaho, Montana, Oregon, Utah, Washington, and Wyoming), twelve states were in the South 10 All income values are adjusted to reflect 2007 dollars. “Real” refers to income after adjusting for inflation. The adjustment is based on percentage changes in prices between 2006 and 2007 and is computed by dividing the annual average Consumer Price Index Research Series (CPI-U-RS) for 2007 by the annual average for 2006. The CPI-U-RS values for 1947 to 2007 are available on the Internet at . Inflation between 2006 and 2007 was 2.8 percent. (Alabama, Florida, Georgia, Maryland, Mississippi, North Carolina, Oklahoma, South Carolina, Tennessee, Texas, Virginia, and West Virginia), six states were in the Midwest (Iowa, Illinois, Indiana, Kansas, Missouri, and Ohio), and five states were in the Northeast (Maine, Massachusetts, New Jersey, New York, and Pennsylvania).11 Figure 2 displays the relationships of state median household incomes to the median for the United States. Median incomes in 18 states and the District of Columbia were above the U.S. median, while 29 state medians were below it. Three states had median household incomes that were not statistically different from the U.S. median. The states in the Northeast tended to have median incomes above the U.S. median. Six of the nine Northeast states—Connecticut, Massachusetts, New Hampshire, New Jersey, New York, and Rhode Island—had median household incomes above the U.S. median, while Maine and Pennsylvania were below the U.S. median. Vermont’s median household income was not statistically different from the U.S. median. 11 The Northeast region includes the states of Connecticut, Maine, Massachusetts, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, and Vermont. The Midwest region includes the states of Illinois, Indiana, Iowa, Kansas, Michigan, Minnesota, Missouri, Nebraska, North Dakota, Ohio, South Dakota, and Wisconsin. The South region includes the states of Alabama, Arkansas, Delaware, Florida, Georgia, Kentucky, Louisiana, Maryland, Mississippi, North Carolina, Oklahoma, South Carolina, Tennessee, Texas, Virginia, West Virginia, and the District of Columbia, a state equivalent. The West region includes the states of Alaska, Arizona, California, Colorado, Hawaii, Idaho, Montana, Nevada, New Mexico, Oregon, Utah, Washington, and Wyoming. Income, Earnings, and Poverty Data From the 2007 American Community Survey U.S. Census Bureau 5 Figure 2. AK Median Household Income in the Past 12 Months by State: 2007 (In 2007 inflation-adjusted dollars) WA MT OR ID SD WY NE UT CA CO KS MO KY NC AZ NM OK TN AR MS TX AL GA SC IA IL IN OH WV VA ND MN WI MI PA NJ DE MD DC* CT NY MA RI VT NH ME NV Median household income Higher than U.S. median Not statistically different from U.S. median LA FL Below U.S. median 2007 U.S. median household income = $50,740 PR HI * DC is represented at 4.5 times the scale of other continental states. Source: U.S. Census Bureau, 2007 American Community Survey and 2007 Puerto Rico Community Survey. Similarly, states in the West were likely to be above the U.S. median, with 7 of the 13 states having household incomes above the U.S. median. They were Alaska, California, Colorado, Hawaii, Nevada, Utah, and Washington. Those below the U.S. median in the West were Arizona, Idaho, Montana, New Mexico, and Oregon. Wyoming had a median household income that was not statistically different from the U.S. median. The majority of states in the Midwest (9 out of 12) and the South (13 out of 17) had median incomes that were below the U.S. median. Illinois and Minnesota in the Midwest and Delaware, Maryland, Virginia, and the District of Columbia in the South had incomes above the national median. Wisconsin, in the Midwest, had a median income that was not statistically different from the U.S. median. Incomes were generally higher on the East and West coasts than they were in the rest of the country in the 2007 ACS. Figure 2 shows that 13 out of the 18 states with median household incomes higher than the U.S. median were East and West coast states. Of the 5 states bordering the Pacific Ocean—Alaska, California, Hawaii, Oregon, and Washington—only Oregon had a median income that was lower than the U.S. median. Of the 14 states bordering the Atlantic Ocean, 9 had medians above the U.S. median. In the 2007 ACS, the total U.S. median household income in metropolitan or micropolitan statistical areas was $51,658 (see Appendix Table A-1). For households in principal cities within metropolitan statistical areas, the median was $45,590; for households in principal cities within micropolitan statistical areas, the median was $35,962; and for households not in metropolitan or micropolitan statistical areas, the median was $37,844. Among the states, while not statistically different from Alaska and New Jersey, Maryland had the highest median household income in metropolitan or micropolitan statistical areas ($68,512), and Mississippi, while not statistically different from West Virginia, had the lowest at $38,380. Median incomes were lower for households in principal cities of metropolitan statistical areas than households not in principal cities in all states except Alaska, where they were not statistically different.12 12 The District of Columbia, Massachusetts, New Jersey, and Rhode Island do not contain any micropolitan statistical areas. 6 Income, Earnings, and Poverty Data From the 2007 American Community Survey U.S. Census Bureau Median Household Income for Counties and Places One of the strengths of the ACS is its ability to produce estimates for substate geographic areas. Because smaller geographic areas differ from larger ones in many ways, this report divides counties and places into two groups—those with populations of 250,000 or more (larger areas) and those with populations from 65,000 to 249,999 (smaller areas).13 Table 3 identifies some of the larger counties and places that have high and low median 13 Population size is based on the 2007 population estimates released as part of the Census Bureau’s Population Estimates Program. household incomes, while Table 4 does the same for smaller counties and places.14 Median Income in Larger Areas For counties with 250,000 or more people, median household income estimates ranged from $107,207 for Loudoun County, VA, to $29,347 for Cameron County, TX.15 For places 14 Because of sampling error, the estimates for the high- and low-income counties and places shown in Tables 3 and 4 may not be statistically different from one another or from counties and places not shown. 15 For the discussion of the ten highest and lowest income counties and in the release of county-level data, parishes in Louisiana and incorporated cities in several states are treated as county equivalents. The median household income for Loudoun County, VA, is not statis- with 250,000 people or more, median household incomes ranged from $84,492 for Plano city, TX, to $28,097 for Detroit city, MI.16 All of the counties in Table 3 with high median household income estimates are found in states with incomes above the U.S. median. Eight of the ten counties in Table 3 with lower incomes are in states tically different from the median household income for Fairfax County, VA. The median household income for Cameron County, TX, is not statistically different from the median household income for Hidalgo County, TX. 16 The median household income for Detroit city, MI, is not statistically different from the median household income for Miami city, FL; Buffalo city, NY; or Cleveland city, OH, nor is it statistically different from the median household income for Cameron County, TX. Table 3. Median Household Income in the Past 12 Months for Ten of the Highest and Lowest Income Counties and Places With 250,000 or More People: 2007 (In 2007 inflation-adjusted dollars. Data are limited to the household population and exclude the population living in institutions, college dormitories, and other group quarters. For information on confidentiality protection, sampling error, nonsampling error, and definitions, see www.census.gov/acs/www/) Ten of the highest median incomes (dollars) Area Estimate Counties2 Loudoun County, VA . . . . . . . . . . . Fairfax County, VA . . . . . . . . . . . . Howard County, MD . . . . . . . . . . . Somerset County, NJ . . . . . . . . . . Morris County, NJ . . . . . . . . . . . . . Douglas County, CO . . . . . . . . . . Montgomery County, MD . . . . . . . Nassau County, NY. . . . . . . . . . . . Prince William County, VA . . . . . . Santa Clara County, CA. . . . . . . . Places2 Plano city, TX . . . . . . . . . . . . . . . . . San Jose city, CA . . . . . . . . . . . . . Anchorage municipality, AK . . . . San Francisco city, CA . . . . . . . . San Diego city, CA . . . . . . . . . . . . Virginia Beach city, VA . . . . . . . . Seattle city, WA . . . . . . . . . . . . . . . Anaheim city, CA . . . . . . . . . . . . . Riverside city, CA . . . . . . . . . . . . . Honolulu CDP, HI . . . . . . . . . . . . . 84,492 76,963 68,726 68,023 61,863 61,462 57,849 57,059 55,999 55,536 4,929 2,580 3,329 3,283 1,332 1,699 2,274 2,623 2,860 2,737 107,207 105,241 101,672 97,658 94,684 92,824 91,835 89,782 87,243 84,360 3,203 1,822 3,594 4,270 2,695 2,752 2,126 1,987 3,644 1,608 Margin of error1(±) Counties2 Marion County, FL . . . . . . . . . . . . Mobile County, AL . . . . . . . . . . . . Baltimore city, MD . . . . . . . . . . . . Philadelphia County, PA . . . . . . . El Paso County, TX . . . . . . . . . . . Caddo Parish, LA. . . . . . . . . . . . . St. Louis city, MO. . . . . . . . . . . . . Bronx County, NY . . . . . . . . . . . . Hidalgo County, TX . . . . . . . . . . Cameron County, TX . . . . . . . . . Places2 Toledo city, OH . . . . . . . . . . . . . . Memphis city, TN . . . . . . . . . . . . Newark city, NJ . . . . . . . . . . . . . . St. Louis city, MO . . . . . . . . . . . . Cincinnati city, OH . . . . . . . . . . . Pittsburgh city, PA . . . . . . . . . . . . Buffalo city, NY . . . . . . . . . . . . . . Miami city, FL . . . . . . . . . . . . . . . Cleveland city, OH . . . . . . . . . . . Detroit city, MI . . . . . . . . . . . . . . . 35,216 35,143 34,452 34,191 33,006 32,363 29,706 29,075 28,512 28,097 1,866 1,238 1,922 1,789 1,678 1,394 1,544 1,916 1,654 1,138 39,294 37,391 36,949 35,365 34,980 34,744 34,191 34,156 30,295 29,347 2,037 1,544 896 870 1,479 2,383 1,789 1,138 1,711 1,701 Area Estimate Margin of error1(±) Ten of the lowest median incomes (dollars) 1 Data are based on a sample and are subject to sampling variability. A margin of error is a measure of an estimate’s variability. The larger the margin of error in relation to the size of the estimate, the less reliable the estimate. When added to and subtracted from the estimate, the margin of error forms the 90-percent confidence interval. 2 Population size is based on the 2007 population estimates released as part of the U.S. Census Bureau’s Population Estimates Program. Note: Because of sampling variability, some of the estimates in this table may not be statistically different from one another or from estimates for other geographic areas not listed in the table. Source: U.S. Census Bureau, 2007 American Community Survey. Income, Earnings, and Poverty Data From the 2007 American Community Survey U.S. Census Bureau 7 Table 4. Median Household Income in the Past 12 Months for Ten of the Highest and Lowest Income Counties and Places With 65,000 to 249,999 People: 2007 (In 2007 inflation-adjusted dollars. Data are limited to the household population and exclude the population living in institutions, college dormitories, and other group quarters. For information on confidentiality protection, sampling error, nonsampling error, and definitions, see www.census.gov/acs/www/) Ten of the highest median incomes (dollars) Area Estimate Counties2 Hunterdon County, NJ . . . . . . . . . Calvert County, MD. . . . . . . . . . . . Arlington County, VA . . . . . . . . . . . Stafford County, VA. . . . . . . . . . . . Fauquier County, VA . . . . . . . . . . . Forsyth County, GA. . . . . . . . . . . . Putnam County, NY . . . . . . . . . . . Marin County, CA . . . . . . . . . . . . . Charles County, MD . . . . . . . . . . . Carroll County, MD . . . . . . . . . . . . Places2 Pleasanton city, CA. . . . . . . . . . . . Newton city, MA. . . . . . . . . . . . . . . Newport Beach city, CA . . . . . . . Yorba Linda city, CA . . . . . . . . . . Flower Mound town, TX . . . . . . . Highlands Ranch CDP, CO . . . . Irvine city, CA . . . . . . . . . . . . . . . . West Bloomfield Township CDP, MI. . . . . . . . . . . . . . . . . . . . . Chino Hills city, CA . . . . . . . . . . . . Naperville city, IL . . . . . . . . . . . . . . 113,345 110,885 110,511 109,681 105,812 99,066 98,923 98,832 96,733 96,548 8,196 10,361 5,967 7,142 6,366 5,052 4,821 100,327 95,134 94,876 87,629 84,888 84,872 84,624 83,870 83,412 82,492 4,282 8,091 4,154 5,720 13,752 4,067 3,803 4,851 4,582 5,651 Margin of error1(±) Counties2 Imperial County, CA . . . . . . . . . . Orangeburg County, SC . . . . . . . Potter County, TX . . . . . . . . . . . . Cabell County, WV . . . . . . . . . . . Scioto County, OH . . . . . . . . . . . Fayette County, PA . . . . . . . . . . . Lauderdale County, MS . . . . . . . Robeson County, NC . . . . . . . . . Apache County, AZ . . . . . . . . . . St. Landry Parish, LA . . . . . . . . . Places2 Passaic city, NJ . . . . . . . . . . . . . . Hartford city, CT . . . . . . . . . . . . . Gainesville city, FL. . . . . . . . . . . . Gary city, IN . . . . . . . . . . . . . . . . . Macon city, GA . . . . . . . . . . . . . . Flint city, MI . . . . . . . . . . . . . . . . . . Reading city, PA . . . . . . . . . . . . . Camden city, NJ . . . . . . . . . . . . . 8,442 Bloomington city, IN. . . . . . . . . . . 8,745 Youngstown city, OH . . . . . . . . . . 4,752 27,691 27,654 27,479 26,725 26,555 26,143 25,536 25,389 25,225 24,941 5,937 2,780 3,523 2,438 4,065 1,869 2,141 2,435 3,939 2,349 31,912 31,877 31,788 31,592 31,446 31,344 31,054 30,882 30,534 26,275 2,685 3,948 2,250 2,915 3,987 1,888 2,331 1,569 4,004 2,777 Area Estimate Margin of error1(±) Ten of the lowest median incomes (dollars) 1 Data are based on a sample and are subject to sampling variability. A margin of error is a measure of an estimate’s variability. The larger the margin of error in relation to the size of the estimate, the less reliable the estimate. When added to and subtracted from the estimate, the margin of error forms the 90-percent confidence interval. 2 Population size is based on the 2007 population estimates released as part of the U.S. Census Bureau’s Population Estimates Program. Note: Because of sampling variability, some of the estimates in this table may not be statistically different from one another or from estimates for other geographic areas not listed in the table. Source: U.S. Census Bureau, 2007 American Community Survey. with median household incomes below the U.S. median. The two exceptions are Bronx County, NY, and Baltimore city, MD. Both Maryland and New York have counties (or county equivalents) on both the high and the low median household income lists. Median household incomes in the state of Maryland for larger counties ranged from $101,672 for Howard County to $36,949 for Baltimore city, while in the state of New York, incomes ranged from $89,782 for Nassau County to $34,156 for Bronx County. Unlike the county ranking, one of the ten places with a high median income—Plano city, TX—is not in a state with a median household income above the U.S. median. Eight of the ten lower-income large places are in lower-income states. The two exceptions are Newark city, NJ, and Buffalo city, NY, which are both in states with median incomes above the U.S. level. Median Income in Smaller Areas Table 4 lists smaller counties and places with both high and low median incomes. For counties with 65,000 to 249,999 people, median household incomes ranged from $100,327 for Hunterdon County, NJ, to $26,275 for St. Landry Parish, LA.17 Median household incomes for places with 65,000 to 249,999 people ranged from $113,345 for Pleasanton city, CA, to $24,941 for Youngstown city, OH.18 17 The median household income for Hunterdon County, NJ, is not statistically different from the median household income for Calvert County, MD, or Arlington County, VA. The median household income for St. Landry Parish, LA, is not statistically different from the median household income for Apache County, AZ. 18 The median household income for Pleasanton city, CA, is not statistically different from the median household income for Newton city, MA; Newport Beach city, CA; Yorba Linda city, CA; or Flower Mound town, TX. The median household income for Youngstown city, OH, is not statistically different from any of the ten lowest-income places with 65,000 people to 249,999 people in Table 4, nor is it statistically different from the median household income for St. Landry Parish, LA. 8 Income, Earnings, and Poverty Data From the 2007 American Community Survey U.S. Census Bureau Nine of the ten counties with high median household incomes are found in states with median incomes above the U.S. median; the exception is Forsyth County, GA. Nine of the ten counties with lower incomes in Table 4 are in states with incomes below the U.S. median; the exception is Imperial County, CA. California has counties on both the high and the low median household income lists. Median household incomes for smaller counties in California ranged from $83,870 for Marin County to $31,912 for Imperial County. Eight of the ten places with high median household incomes are in states with median incomes above the U.S. median; the exceptions are Flower Mound town, TX, and West Bloomfield Township CDP, MI. At the place level, seven of the ten lower-income places are in lower income states. The exceptions are Passaic city, NJ; Hartford city, CT; and Camden city, NJ, which are in states with medians above the U.S. level. Michigan had smaller places on both the high and the low lists. Median household incomes for smaller places in Michigan ranged from $98,832 for West Bloomfield Township CDP to $26,143 for Flint city.19 19 The median household income for Flint city, MI, is not statistically different from the median household incomes for Pontiac city, MI, and Kalamazoo, MI. Income Inequality for the United States and the States This section focuses on two widely used measures of income inequality —the Gini index and shares of aggregate household income by quintile. These estimates were calculated for the first time in the 2006 ACS. The definitions of these measures and their calculation methods are discussed in the text box “What Are Shares of Aggregate Household Income and a Gini Index?” National estimates of these measures are also calculated using CPS ASEC data, and they are included in the publication Income, Poverty, and Health Insurance Coverage in the United States: 2007, along with historical data. The Gini index in the 2007 ACS was .467 for the United States. As shown in Table 5, the Gini index varied by state, ranging from .542 for the District of Columbia to .409 for both Utah and Alaska.20 Figure 3 displays the relationship of state Gini indexes to the Gini index for the United States. Five states and the District of Columbia showed more income inequality (a higher Gini index) than the nation, while 33 states showed less income inequality (a lower Gini index). 20 The Gini index for the District of Columbia is not statistically different from the Gini index for Puerto Rico. The Gini indexes for Utah and Alaska are not statistically different from the Gini index for New Hampshire, and the Gini index for Alaska is also not statistically different from the Gini indexes for Hawaii and South Dakota. What Are Shares of Aggregate Household Income and a Gini Index? Income inequality measures look at how income is being distributed across a population. Two of the most widely used measures of income inequality are the shares of aggregate household income by quintile and the Gini index. This report presents these two measures for the household population. The share of aggregate income by quintile is the amount of aggregate income that households within each fifth of the income distribution receive as a percentage of overall aggregate income of all households. The Gini index is a summary measure of income inequality. It indicates how much the income distribution differs from a proportionate distribution (one where everyone would have the same income; for example, 20 percent of the population would hold 20 percent of the income, 40 percent of the population would hold 40 percent of the income, etc.). The Gini index varies from 0 to 1, where 0 indicates perfect equality (a proportional distribution of income), and 1 indicates perfect inequality (where one person has all the income and no one else has any). For more information on income inequality measures, see Current Population Reports, P60-204, The Changing Shape of the Nation’s Income Distribution: 1947–1998. Income, Earnings, and Poverty Data From the 2007 American Community Survey U.S. Census Bureau 9 Table 5. Gini Coefficients and Shares of Income by Quintile in the Past 12 Months by State: 2007 (Data are limited to the household population and exclude the population living in institutions, college dormitories, and other group quarters. For information on confidentiality protection, sampling error, nonsampling error, and definitions, see www.census.gov/acs/www/) Shares of income by quintile Gini coefficients Area Estimate United States . . . . . Alabama . . . . . . . . . . . . . . . . Alaska . . . . . . . . . . . . . . . . . . Arizona . . . . . . . . . . . . . . . . . Arkansas . . . . . . . . . . . . . . . . California . . . . . . . . . . . . . . . . Colorado . . . . . . . . . . . . . . . . Connecticut . . . . . . . . . . . . . . Delaware . . . . . . . . . . . . . . . . District of Columbia . . . . . . . . Florida . . . . . . . . . . . . . . . . . . Georgia . . . . . . . . . . . . . . . . . Hawaii . . . . . . . . . . . . . . . . . . Idaho . . . . . . . . . . . . . . . . . . . Illinois . . . . . . . . . . . . . . . . . . Indiana. . . . . . . . . . . . . . . . . . Iowa. . . . . . . . . . . . . . . . . . . . Kansas . . . . . . . . . . . . . . . . . Kentucky . . . . . . . . . . . . . . . . Louisiana . . . . . . . . . . . . . . . . Maine. . . . . . . . . . . . . . . . . . . Maryland . . . . . . . . . . . . . . . . Massachusetts. . . . . . . . . . . . Michigan . . . . . . . . . . . . . . . . Minnesota . . . . . . . . . . . . . . . Mississippi . . . . . . . . . . . . . . . Missouri . . . . . . . . . . . . . . . . . Montana . . . . . . . . . . . . . . . . Nebraska . . . . . . . . . . . . . . . . Nevada . . . . . . . . . . . . . . . . . New Hampshire . . . . . . . . . . . New Jersey . . . . . . . . . . . . . . New Mexico. . . . . . . . . . . . . . New York . . . . . . . . . . . . . . . . North Carolina . . . . . . . . . . . . North Dakota . . . . . . . . . . . . . Ohio. . . . . . . . . . . . . . . . . . . . Oklahoma . . . . . . . . . . . . . . . Oregon . . . . . . . . . . . . . . . . . Pennsylvania . . . . . . . . . . . . . Rhode Island . . . . . . . . . . . . . South Carolina. . . . . . . . . . . . South Dakota. . . . . . . . . . . . . Tennessee . . . . . . . . . . . . . . . Texas . . . . . . . . . . . . . . . . . . . Utah. . . . . . . . . . . . . . . . . . . . Vermont . . . . . . . . . . . . . . . . . Virginia . . . . . . . . . . . . . . . . . Washington . . . . . . . . . . . . . . West Virginia . . . . . . . . . . . . . Wisconsin . . . . . . . . . . . . . . . Wyoming . . . . . . . . . . . . . . . . Puerto Rico . . . . . . . . . . . . . . 0.467 0.471 0.409 0.448 0.462 0.469 0.452 0.481 0.435 0.542 0.469 0.464 0.422 0.436 0.466 0.429 0.426 0.444 0.465 0.478 0.440 0.442 0.467 0.448 0.436 0.480 0.450 0.443 0.429 0.437 0.417 0.464 0.459 0.500 0.465 0.440 0.448 0.463 0.442 0.460 0.457 0.459 0.423 0.471 0.473 0.409 0.428 0.456 0.444 0.454 0.428 0.437 0.544 Margin of error1 (±) 0.0001 0.0050 0.0125 0.0044 0.0069 0.0026 0.0055 0.0052 0.0103 0.0129 0.0033 0.0036 0.0093 0.0100 0.0033 0.0042 0.0056 0.0064 0.0057 0.0044 0.0094 0.0043 0.0043 0.0027 0.0042 0.0065 0.0047 0.0104 0.0055 0.0070 0.0080 0.0033 0.0077 0.0031 0.0032 0.0122 0.0032 0.0058 0.0049 0.0033 0.0089 0.0051 0.0085 0.0041 0.0024 0.0067 0.0095 0.0037 0.0044 0.0075 0.0040 0.0223 0.0061 Lowest quintile Estimate 3.4 3.2 4.2 3.8 3.5 3.4 3.6 3.3 3.8 2.1 3.7 3.4 3.9 4.3 3.4 4.0 4.1 4.0 3.3 3.0 3.9 3.7 3.1 3.6 3.9 3.2 3.7 3.8 4.1 4.2 4.3 3.3 3.4 2.9 3.4 3.8 3.5 3.5 3.8 3.6 3.2 3.4 4.2 3.4 3.3 4.6 4.1 3.5 3.8 3.5 4.1 4.2 1.5 Margin of error1 (±) 0.01 0.22 0.20 0.17 0.13 0.01 0.18 0.20 0.28 0.25 0.14 0.01 0.22 0.20 0.01 0.16 0.11 0.19 0.07 0.16 0.18 0.13 0.10 0.15 0.12 0.16 0.18 0.28 0.15 0.17 0.24 0.17 0.20 0.01 0.04 0.24 0.23 0.07 0.06 0.17 0.19 0.22 0.24 0.08 0.20 0.16 0.21 0.10 0.17 0.16 0.10 0.31 0.09 Second quintile Estimate 8.8 8.5 10.5 9.4 8.8 8.7 9.3 8.7 9.7 6.8 8.9 9.0 10.2 9.8 8.9 9.9 9.8 9.4 8.6 8.2 9.5 9.5 8.7 9.2 9.8 8.2 9.2 9.4 9.8 9.9 10.2 8.9 8.9 8.0 8.8 9.3 9.3 8.9 9.5 8.9 8.8 9.0 10.0 8.8 8.6 10.6 9.9 9.1 9.5 8.8 9.9 9.6 6.6 Margin of error1 (±) 0.01 0.19 0.34 0.21 0.19 0.04 0.17 0.18 0.29 0.36 0.16 0.07 0.32 0.25 0.13 0.12 0.20 0.26 0.20 0.16 0.24 0.21 0.16 0.07 0.16 0.22 0.16 0.30 0.17 0.21 0.24 0.21 0.27 0.07 0.18 0.34 0.06 0.20 0.18 0.19 0.31 0.13 0.29 0.15 0.04 0.21 0.28 0.17 0.15 0.24 0.16 0.47 0.18 Third quintile Estimate 14.7 14.7 16.4 15.1 14.8 14.6 15.2 14.3 15.6 12.5 14.4 14.8 16.1 15.3 14.9 15.8 15.9 15.1 14.9 14.5 15.5 15.5 15.1 15.3 15.6 14.3 15.2 15.5 15.7 15.2 16.1 14.9 14.9 13.9 14.8 15.7 15.4 14.8 15.4 14.9 15.4 15.0 15.8 14.7 14.4 16.0 15.7 15.0 15.4 15.1 15.9 15.4 12.8 Margin of error1 (±) 0.01 0.19 0.43 0.13 0.25 0.05 0.20 0.19 0.37 0.42 0.23 0.17 0.33 0.35 0.21 0.16 0.23 0.26 0.20 0.22 0.30 0.22 0.17 0.11 0.12 0.25 0.24 0.35 0.25 0.28 0.28 0.19 0.31 0.21 0.12 0.43 0.14 0.20 0.23 0.18 0.32 0.24 0.26 0.21 0.04 0.24 0.37 0.20 0.19 0.28 0.23 0.67 0.25 Fourth quintile Estimate 22.8 23.4 24.0 23.1 23.2 22.8 23.1 21.8 23.7 21.6 22.2 22.8 23.7 22.6 22.7 23.5 23.6 23.1 23.5 23.5 23.2 23.2 23.2 23.6 23.1 23.2 23.2 23.3 23.5 22.5 23.5 22.9 23.5 22.1 22.9 23.7 23.4 23.0 23.3 23.0 24.0 23.4 23.4 22.5 22.8 23.1 23.3 23.0 23.2 24.0 23.4 23.0 22.8 Margin of error1 (±) 0.01 0.27 0.60 0.23 0.32 0.16 0.24 0.28 0.39 0.61 0.21 0.19 0.39 0.38 0.15 0.21 0.26 0.25 0.28 0.26 0.38 0.25 0.17 0.19 0.19 0.28 0.21 0.49 0.29 0.32 0.36 0.20 0.35 0.17 0.15 0.48 0.12 0.28 0.24 0.18 0.41 0.25 0.38 0.23 0.18 0.28 0.42 0.19 0.21 0.36 0.18 0.94 0.32 Highest quintile Estimate 50.3 50.2 44.9 48.7 49.7 50.5 48.9 51.9 47.3 56.9 50.8 50.0 46.1 48.1 50.1 46.8 46.6 48.4 49.7 50.7 47.8 48.0 49.9 48.4 47.6 51.2 48.7 48.0 46.9 48.1 45.9 49.9 49.3 53.2 50.1 47.6 48.4 49.8 48.0 49.6 48.7 49.2 46.6 50.7 50.9 45.7 46.9 49.4 48.2 48.6 46.8 47.7 56.2 Margin of error1 (±) 0.09 0.47 1.19 0.41 0.64 0.20 0.52 0.54 0.95 1.26 0.32 0.36 0.86 0.93 0.33 0.37 0.55 0.60 0.55 0.47 0.87 0.39 0.41 0.31 0.35 0.64 0.43 0.96 0.54 0.65 0.73 0.36 0.75 0.29 0.33 1.13 0.31 0.56 0.49 0.29 0.86 0.48 0.77 0.39 0.25 0.60 0.92 0.38 0.40 0.73 0.38 2.09 0.66 1 Data are based on a sample and are subject to sampling variability. A margin of error is a measure of an estimate’s variability. The larger the margin of error in relation to the size of the estimate, the less reliable the estimate. When added to and subtracted from the estimate, the margin of error forms the 90-percent confidence interval. Source: U.S. Census Bureau, 2007 American Community Survey and 2007 Puerto Rico Community Survey. 10 Income, Earnings, and Poverty Data From the 2007 American Community Survey U.S. Census Bureau Figure 3. AK Gini Index of Income Inequality in the Past 12 Months by State: 2007 WA MT OR ID SD WY NE UT CA CO KS MO KY NC AZ NM OK TN AR MS TX AL GA SC IA IL IN OH WV VA ND MN WI MI PA NJ DE MD DC* CT NY MA RI VT NH ME NV Gini index Higher than U.S. index Not statistically different from U.S. index LA FL Below U.S. index United States =.467 PR HI * DC is represented at 4.5 times the scale of other continental states. Source: U.S. Census Bureau, 2007 American Community Survey and 2007 Puerto Rico Community Survey. Twelve states had Gini indexes that were not statistically different from the national estimate. Also included in Table 5 are shares of aggregate income by quintile for the United States, states, and the District of Columbia. The shares of aggregate income held by the lowest quintile of households ranged from 4.6 percent for Utah to 2.1 percent for the District of Columbia. The shares of aggregate income held by the highest quintile of households ranged from 56.9 percent for the District of Columbia to 44.9 percent for Alaska.21 21 The share of aggregate income for the highest quintile for Alaska was not statistically different from the shares of aggregate income for the highest quintile for Hawaii, New Hampshire, and Utah. Income, Earnings, and Poverty Data From the 2007 American Community Survey U.S. Census Bureau 11 EARNINGS OF MEN AND WOMEN This section examines the earnings of men and women by geography, race and Hispanic origin, educational attainment, industry and occupation, and class of worker. Median earnings are calculated only for people 16 years and older with earnings. The tables and figures focus on various aspects of earnings. Table 6 presents earnings by state for full-time, year-round workers. Table 7 includes earnings by race and Hispanic origin for full-time, year-round workers; earnings by educational attainment for people 25 years and older (regardless of hours and weeks worked); and earnings by type of industry and class of worker for full-time, year-round civilian workers. Table 8 includes earnings by occupation for full-time, year-round civilian workers. For most individuals, earnings are the largest component of their total income. The text box “What Are ‘Earnings’?” describes this income category. Men’s and Women’s Earnings by State Table 6 shows earnings data by state and the District of Columbia in the 2007 ACS for men and women who worked full-time, yearround. Some of the states that had high median household incomes, as shown in Table 2, such as Connecticut, New Jersey, Maryland, Massachusetts, New Hampshire, and Alaska, also had high median earnings for men, that is, earnings above $50,000. No state had median earnings for women above $50,000, but in the District of Columbia, Maryland, New Jersey, Massachusetts, and Connecticut, median earnings for women were above $40,000. The median earnings of men in the United States in the 2007 ACS were $44,255; for women median earnings were $34,278, or 77.5 percent of men’s earnings. The District of Columbia had the highest ratio of women’s-to-men’s earnings (93.4 percent), and there was no statistically significant difference between women’s median earnings and men’s median earnings.22 In each of the 50 states, women’s median earnings were less than men’s median earnings. 22 The ratio of women’s to men’s earnings for the District of Columbia and Puerto Rico was not statistically different from 100 percent. The median earnings for men in Puerto Rico were $20,242, and the median earnings for women were $19,812. The median earnings for men in Puerto Rico were not statistically different from the median earnings for women in Puerto Rico. What Are “Earnings”? “Earnings” are the sum of wage and salary income and self-employment income. Earnings are often the largest part of overall income. The 2007 ACS showed that 81 percent of aggregate household income came from earnings. This report presents information on year-round, full-time workers 16 years or older, unless noted otherwise. “Year-round” means an individual worked 50 or more weeks in the past 12 months, including paid time off for sick leave or vacation. “Full-time” means that the individual usually worked 35 or more hours per week. The text of the two 2007 ACS questions used to determine earnings is: 41. INCOME IN THE PAST 12 MONTHS. Mark (X) the “Yes” box for each type of income this person received, and give your best estimate of the TOTAL AMOUNT during the PAST 12 MONTHS. (NOTE: The “past 12 months” is the period from today’s date one year ago through today.) Mark (X) the “No” box to show types of income NOT received. If net income was a loss, mark the “Loss” box to the right of the dollar amount. For income received jointly, report the appropriate share for each person—or, if that’s not possible, report the whole income for only one person and mark the “No” box for the other person. a. Wages, salary, commissions, bonuses, or tips from all jobs. Report amount before deductions for taxes, bonds, dues, or other items. b. Self-employment income from own nonfarm businesses or farm businesses, including proprietorships and partnerships. Report NET income after business expenses. ACS questionnaires can be found at . 12 Income, Earnings, and Poverty Data From the 2007 American Community Survey U.S. Census Bureau Table 6. Median Earnings in the Past 12 Months of Full-Time, Year-Round Workers 16 and Older by Sex and Women’s Earnings as a Percentage of Men’s Earnings by State: 2007 (In 2007 inflation-adjusted dollars. For information on confidentiality protection, sampling error, nonsampling error, and definitions, see www.census.gov/acs/www/) Median earnings (dollars) Area Men Estimate United States . . . . . . . . . . . . . Alabama . . . . . . . . . . . . . . . . . . . . . . Alaska . . . . . . . . . . . . . . . . . . . . . . . . Arizona . . . . . . . . . . . . . . . . . . . . . . . Arkansas . . . . . . . . . . . . . . . . . . . . . . California . . . . . . . . . . . . . . . . . . . . . . Colorado . . . . . . . . . . . . . . . . . . . . . . Connecticut . . . . . . . . . . . . . . . . . . . . Delaware . . . . . . . . . . . . . . . . . . . . . . District of Columbia . . . . . . . . . . . . . . Florida . . . . . . . . . . . . . . . . . . . . . . . . Georgia . . . . . . . . . . . . . . . . . . . . . . . Hawaii . . . . . . . . . . . . . . . . . . . . . . . . Idaho . . . . . . . . . . . . . . . . . . . . . . . . . Illinois . . . . . . . . . . . . . . . . . . . . . . . . Indiana. . . . . . . . . . . . . . . . . . . . . . . . Iowa. . . . . . . . . . . . . . . . . . . . . . . . . . Kansas . . . . . . . . . . . . . . . . . . . . . . . Kentucky . . . . . . . . . . . . . . . . . . . . . . Louisiana . . . . . . . . . . . . . . . . . . . . . . Maine. . . . . . . . . . . . . . . . . . . . . . . . . Maryland . . . . . . . . . . . . . . . . . . . . . . Massachusetts. . . . . . . . . . . . . . . . . . Michigan . . . . . . . . . . . . . . . . . . . . . . Minnesota . . . . . . . . . . . . . . . . . . . . . Mississippi . . . . . . . . . . . . . . . . . . . . . Missouri . . . . . . . . . . . . . . . . . . . . . . . Montana . . . . . . . . . . . . . . . . . . . . . . Nebraska . . . . . . . . . . . . . . . . . . . . . . Nevada . . . . . . . . . . . . . . . . . . . . . . . New Hampshire . . . . . . . . . . . . . . . . . New Jersey . . . . . . . . . . . . . . . . . . . . New Mexico. . . . . . . . . . . . . . . . . . . . New York . . . . . . . . . . . . . . . . . . . . . . North Carolina . . . . . . . . . . . . . . . . . . North Dakota . . . . . . . . . . . . . . . . . . . Ohio. . . . . . . . . . . . . . . . . . . . . . . . . . Oklahoma . . . . . . . . . . . . . . . . . . . . . Oregon . . . . . . . . . . . . . . . . . . . . . . . Pennsylvania . . . . . . . . . . . . . . . . . . . Rhode Island . . . . . . . . . . . . . . . . . . . South Carolina. . . . . . . . . . . . . . . . . . South Dakota. . . . . . . . . . . . . . . . . . . Tennessee . . . . . . . . . . . . . . . . . . . . . Texas . . . . . . . . . . . . . . . . . . . . . . . . . Utah. . . . . . . . . . . . . . . . . . . . . . . . . . Vermont . . . . . . . . . . . . . . . . . . . . . . . Virginia . . . . . . . . . . . . . . . . . . . . . . . Washington . . . . . . . . . . . . . . . . . . . . West Virginia . . . . . . . . . . . . . . . . . . . Wisconsin . . . . . . . . . . . . . . . . . . . . . Wyoming . . . . . . . . . . . . . . . . . . . . . . Puerto Rico . . . . . . . . . . . . . . . . . . . . 1 Women’s earnings as a percentage of men’s earnings Women Margin of error1 (±) 147 370 873 346 449 256 574 904 1,879 4,534 206 269 1,552 1,046 549 586 294 417 713 423 549 976 822 609 607 616 365 1,568 864 1,176 525 772 1,312 346 675 1,158 430 907 598 411 1,983 397 799 606 215 926 712 779 375 829 583 1,711 461 Estimate 34,278 29,756 37,835 33,723 26,815 38,903 36,827 41,868 38,543 49,364 32,150 33,351 35,471 28,846 35,638 31,158 30,925 31,145 29,957 27,469 31,496 44,022 42,062 34,849 36,707 26,838 30,827 26,598 30,406 34,164 35,722 42,221 30,188 38,830 31,738 27,554 32,853 29,378 32,538 33,438 37,475 30,124 26,965 30,178 31,845 31,001 34,341 36,971 37,454 26,719 32,265 28,540 19,812 Margin of error1 (±) 85 572 1,913 765 522 353 468 514 1,501 2,451 195 557 780 835 324 263 299 387 402 623 551 716 355 411 343 512 289 635 512 809 694 395 573 438 245 753 372 580 679 343 1,231 362 646 264 205 467 1,091 435 527 538 247 1,967 414 Estimate 77.5 72.9 73.8 81.6 73.7 83.8 79.7 75.6 80.4 93.4 79.9 79.7 79.2 73.2 73.4 71.8 74.7 74.1 75.0 65.4 75.5 80.8 78.5 71.8 77.1 72.9 74.6 69.6 77.8 79.8 69.5 77.0 78.7 82.3 80.5 68.8 73.9 77.5 76.8 74.7 77.3 75.0 73.4 77.0 78.9 72.0 84.1 76.8 74.5 66.6 73.2 63.0 97.9 Margin of error1 (±) 0.3 1.5 3.9 2.0 1.7 0.9 1.4 1.5 4.4 9.3 0.6 1.4 3.2 2.9 1.1 1.1 0.9 1.2 1.7 1.6 1.7 2.0 1.4 1.2 1.2 1.8 1.0 3.3 2.2 2.9 1.5 1.3 3.1 1.1 1.5 2.7 1.1 2.4 1.9 1.0 4.1 1.2 2.4 1.4 0.7 1.9 3.0 1.5 1.2 1.9 1.1 5.0 3.0 44,255 40,829 51,275 41,308 36,379 46,404 46,230 55,394 47,964 52,860 40,238 41,837 44,802 39,413 48,562 43,410 41,375 42,041 39,920 41,980 41,704 54,501 53,602 48,512 47,602 36,819 41,347 38,230 39,070 42,787 51,385 54,846 38,366 47,198 39,447 40,028 44,443 37,884 42,389 44,755 48,492 40,139 36,726 39,207 40,344 43,035 40,834 48,142 50,269 40,126 44,105 45,310 20,242 Data are based on a sample and are subject to sampling variability. A margin of error is a measure of an estimate’s variability. The larger the margin of error in relation to the size of the estimate, the less reliable the estimate. When added to and subtracted from the estimate, the margin of error forms the 90-percent confidence interval. Source: U.S. Census Bureau, 2007 American Community Survey and 2007 Puerto Rico Community Survey. Income, Earnings, and Poverty Data From the 2007 American Community Survey U.S. Census Bureau 13 Figure 4. AK Women’s Earnings as a Percentage of Men’s Earnings in the Past 12 Months by State: 2007 WA MT OR ID SD WY NE UT CA CO KS MO KY TN AR MS TX LA FL AL GA SC NC IA IL IN OH WV VA ND MN WI MI PA NJ DE MD DC* CT NY MA RI VT NH ME NV Percentage 80.0 or more 77.5 to 79.9 73.4 to 77.4 Less than 73.4 United States = 77.5 percent PR AZ NM OK HI * DC is represented at 4.5 times the scale of other continental states. Source: U.S. Census Bureau, 2007 American Community Survey and 2007 Puerto Rico Community Survey. Figure 4 displays the relationship between men’s and women’s earnings for all states and the District of Columbia. The Northeast, the South, and the West have states in which women’s earnings as a percentage of men’s earnings are relatively high (falling into the highest category in Figure 4). Every region has states in which the percentage was relatively low (falling into the two lower categories). In the South, five states (Maryland, North Carolina, Florida, Georgia, and Texas) and the District of Columbia had ratios higher than the national ratio, as did three states in the West (California, Arizona, and Colorado). Two states in the Northeast (Vermont and New York) had ratios higher than the national ratio. There were no states in the Midwest that had ratios higher than the national ratio. As a result, women’s earnings were closer to men’s in more states in the South and the West than in the Northeast and the Midwest. Median Earnings by Race and Hispanic Origin Table 7 shows that Asian men working full-time, year-round had higher median earnings ($51,174) in the 2007 ACS than men in any of the other single-race groups. Non-Hispanic White men ($50,139) had higher earnings than Native Hawaiian and Other Pacific Islander men ($36,624), Black men ($35,652), and American Indian and Alaska Native men ($34,833).23 The lowest median earnings for men were for those who reported Some Other Race ($28,462). For Hispanic men, $29,239 was the median earnings. The pattern observed for women by race was similar to that of men. Asian women had the highest median earnings ($40,664). NonHispanic White women ($36,398) had higher earnings than Black women ($31,035), Native Hawaiian and Other Pacific Islander women ($29,835), and American Indian and 23 The median earnings of Native Hawaiian and Other Pacific Islander men were not statistically different from those of Black men and those of American Indian and Alaska Native men. The median earnings of Black men were not statistically different from those of American Indian and Alaska Native men. 14 Income, Earnings, and Poverty Data From the 2007 American Community Survey U.S. Census Bureau Table 7. Median Earnings in the Past 12 Months of Workers by Sex and Women’s Earnings as a Percentage of Men’s Earnings by Selected Characteristics for the United States: 2007 (In 2007 inflation-adjusted dollars. For information on confidentiality protection, sampling error, nonsampling error, and definitions, see www.census.gov/acs/www/) Median earnings (dollars) Selected characteristic Men Estimate Race and Hispanic Origin Full-time, year-round workers 16 years and older with earnings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . White alone . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . White alone, not Hispanic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Black alone . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . American Indian and Alaska Native alone . . . . . . . . . . . . . . . . . . . . Asian alone . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Native Hawaiian and Other Pacific Islander alone . . . . . . . . . . . . . . Some Other Race alone . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Two or More Races . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hispanic (any race) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Educational Attainment Population 25 years and older with earnings . . . . . . . . . . . . . . . Less than high school graduate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . High school graduate (includes equivalency). . . . . . . . . . . . . . . . . . . Some college or associate’s degree . . . . . . . . . . . . . . . . . . . . . . . . . Bachelor’s degree . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Graduate or professional degree . . . . . . . . . . . . . . . . . . . . . . . . . . . . Industry Full-time, year-round civilian workers 16 years and older with earnings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Agriculture, forestry, fishing, and hunting . . . . . . . . . . . . . . . . . . . . . . Mining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Construction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Manufacturing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Wholesale trade. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Retail trade . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Transportation and warehousing . . . . . . . . . . . . . . . . . . . . . . . . . . . . Utilities. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Information. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Finance and insurance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Real estate and rental and leasing . . . . . . . . . . . . . . . . . . . . . . . . . . Professional, scientific, and technical services . . . . . . . . . . . . . . . . . Management of companies and enterprises . . . . . . . . . . . . . . . . . . . Administrative and support and waste management services . . . . . . Educational services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Health care and social assistance . . . . . . . . . . . . . . . . . . . . . . . . . . . Arts, entertainment, and recreation . . . . . . . . . . . . . . . . . . . . . . . . . . Accommodation and food services . . . . . . . . . . . . . . . . . . . . . . . . . . Other services (except public administration) . . . . . . . . . . . . . . . . . . Public administration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Class of Worker2 Full-time, year-round civilian workers 16 years and older with earnings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Employee of private company workers . . . . . . . . . . . . . . . . . . . . . . . Self-employed in own incorporated business workers . . . . . . . . . . . . Private not-for-profit wage and salary workers . . . . . . . . . . . . . . . . . Local government workers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . State government workers. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Federal government workers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Self-employed in own unincorporated business workers . . . . . . . . . . Margin of error1 (±) Women Estimate Margin of error1 (±) Women’s earnings as a percentage of men’s earnings Margin of error1 (±) Estimate 44,255 47,113 50,139 35,652 34,833 51,174 36,624 28,462 40,353 29,239 40,481 22,602 32,435 41,035 57,397 77,219 147 93 63 179 932 292 2,068 350 548 268 53 137 63 83 227 347 34,278 35,542 36,398 31,035 28,837 40,664 29,835 24,801 32,976 25,454 27,276 14,202 21,219 27,046 38,628 50,937 85 64 64 127 693 317 1,515 234 678 143 46 116 54 69 156 133 77.5 75.4 72.6 87.1 82.8 79.5 81.5 87.1 81.7 87.1 67.4 62.8 65.4 65.9 67.3 66.0 0.3 0.2 0.1 0.5 2.7 0.7 5.5 1.1 1.8 0.8 0.1 0.7 0.2 0.2 0.4 0.4 44,627 27,854 55,533 38,823 45,954 45,767 35,721 46,052 60,617 58,964 71,422 43,314 75,320 76,630 31,706 47,308 50,258 35,953 25,611 35,504 54,545 151 604 1,015 332 191 367 211 240 351 1,282 406 1,024 491 3,691 223 289 299 447 207 271 348 34,393 23,621 47,146 36,593 32,535 36,187 25,959 37,145 45,539 43,614 39,390 36,959 47,292 47,715 28,973 40,100 33,477 30,293 20,708 26,166 41,936 85 811 2,007 328 262 402 131 381 841 691 297 396 278 1,598 444 138 179 331 155 205 222 77.1 84.8 84.9 94.3 70.8 79.1 72.7 80.7 75.1 74.0 55.2 85.3 62.8 62.3 91.4 84.8 66.6 84.3 80.9 73.7 76.9 0.3 3.2 3.9 1.2 0.6 1.0 0.6 1.0 1.5 2.1 0.6 2.2 0.5 3.6 1.6 0.6 0.5 1.3 0.9 0.8 0.6 44,627 42,215 61,549 46,420 47,915 48,778 57,377 38,564 151 81 230 413 439 440 432 729 34,393 32,035 41,395 37,918 39,729 38,584 50,329 25,003 85 60 398 285 264 303 214 448 77.1 75.9 67.3 81.7 82.9 79.1 87.7 64.8 0.3 0.2 0.7 1.0 0.8 0.8 0.8 1.8 1 Data are based on a sample and are subject to sampling variability. A margin of error is a measure of an estimate’s variability. The larger the margin of error in relation to the size of the estimate, the less reliable the estimate. When added to and subtracted from the estimate, the margin of error forms the 90-percent confidence interval. 2 Data from unpaid family workers are excluded from this table. Source: U.S. Census Bureau, 2007 American Community Survey. Income, Earnings, and Poverty Data From the 2007 American Community Survey U.S. Census Bureau 15 Alaska Native women ($28,837).24 The lowest median earnings ($24,801) of any race group were for women of Some Other Race. Hispanic women had median earnings of $25,454. For each of the race groups and Hispanics, as shown in Table 7, men had higher earnings than women. The group with the lowest femaleto-male earnings ratio was nonHispanic Whites, where women’s earnings were 72.6 percent of men’s earnings. The median earnings of women were at least 85 percent of men’s for the Some Other Race group, Blacks, and Hispanics.25 Median Earnings by Educational Attainment Data on median earnings by educational attainment in Table 7 are for all individuals 25 years and older with earnings and are not limited to full-time, year-round workers. A person’s level of education is a predictor of earnings—in general, the more education, the larger the earnings potential. Table 7 shows that this was true for both men and women in the 2007 ACS. The median earnings of men who were not high school graduates were $22,602. Median earnings were higher for male high school graduates ($32,435) and higher still for men with some college or an associate’s degree ($41,035). 24 The median earnings for Native Hawaiian and Other Pacific Islander women were not statistically different from those of Black women and those of American Indian and Alaska Native women. 25 The female-to-male earnings ratio was not statistically different from 85 percent for Native Hawaiians and Other Pacific Islanders and American Indians and Alaska Natives. Men who completed college and received a bachelor’s degree earned a median of $57,397. The highest median earnings among education groups, $77,219, were for men with a graduate or professional degree. Women who did not complete high school reported median earnings of $14,202 in the 2007 ACS, while women who graduated from high school earned $21,219. Attending but not completing college, or receiving an associate’s degree, resulted in median earnings of $27,046, while women who completed a bachelor’s degree had median earnings of $38,628. As with men, women who received a graduate or professional degree earned the most, $50,937. While both men and women showed higher earnings with higher levels of education, at each level of education, men earned more than women. The ratio of female-to-male earnings was lowest for those with less than a high school education, where women earned 62.8 percent of men. The ratio was higher among people with more education, up to the completion of a bachelor’s degree. For men and women with a high school education, women earned 65.4 percent of what men earned, while women earned 65.9 percent when both had some college or an associate’s degree. The ratio was higher still when both men and women had bachelor’s degrees. At that educational level, women earned 67.3 percent of what men earned. Additional education beyond a bachelor’s degree resulted in a lower earnings ratio. Women earned 66.0 percent of men’s earnings when both had a graduate or professional degree.26 Median Earnings by Industry and Class of Worker Data on earnings by type of industry and class of worker are limited to full-time, year-round civilian workers 16 years or older. Industry refers to the kind of business conducted by a person’s employing organization. The industries for which data are collected in the ACS are commonly grouped into sectors. Table 7 shows the 20 major industry sectors. Men earned the most in the 2007 ACS in two of those sectors: the management of companies and enterprises sector ($76,630) and the professional, scientific, and technical services sector ($75,320).27 Men in the accommodation and food services sector had the lowest median earnings ($25,611). For women, several sectors had relatively high median earnings in the 2007 ACS. In the following sectors, women’s median earnings were $45,000 or higher: management of companies and enterprises ($47,715); professional, scientific, and technical services ($47,292); mining ($47,146); and utilities 26 The female-to-male earnings ratio for workers with graduate or professional degrees was not statistically different from the ratio for workers with some college or associate’s degrees. 27 The median earnings for men in the management of companies and enterprises sector were not statistically different from the median earnings for men in the professional, scientific, and technical services sector. 16 Income, Earnings, and Poverty Data From the 2007 American Community Survey U.S. Census Bureau ($45,539).28 As with men, the sector with the lowest earnings for women was accommodation and food services ($20,708). In each of the 20 industry sectors, men earned more than women. The sector where the ratio between women’s and men’s earnings was the lowest was finance and insurance, where women’s earnings were 55.2 percent of men’s, while the highest ratio was in the construction sector, where women’s earnings were 94.3 percent of men’s. Class of worker analysis categorizes employees according to the type of ownership of the organization employing them. Men who were employed in their own incorporated business and worked full-time, yearround had the highest median earnings at $61,549. Men employed in their own unincorporated business had the lowest median earnings ($38,564). For women, those employed by the federal government had the highest median earnings at $50,329. Similar to men, those employed in their own unincorporated business had the lowest median earnings ($25,003). For each of the class of worker categories shown in Table 7, men had higher earnings than women. The ratio of women’s to men’s earnings was highest for men and women 28 The median earnings of women in the management of companies and enterprises industry were not statistically different from the median earnings of women in the professional, scientific, and technical services industry and the mining industry, nor were the median earnings of women in the professional, scientific, and technical services industry statistically different from the median earnings of women in the mining industry. The median earnings of women in the utilities industry were not statistically different from the median earnings of women in the mining industry. employed by the federal government (87.7). The ratio was lowest for women and men employed in their own businesses. When that business was unincorporated, women earned 64.8 percent of what men earned; when it was incorporated, they earned 67.3 percent of what men earned. Median Earnings by Occupation Occupation describes the kind of work that a person does on the job. Table 8 shows 26 occupation groups for full-time, year-round civilian workers. The large sample size of the ACS allows further examination of the earnings of men and women for many detailed occupations (see Appendix Table A-2).29 Men earned the most in the legal occupations ($105,233) and the least in the food preparation and serving related occupations ($21,765). Women who worked in computer and mathematical occupations had the highest median earnings ($61,957). Women’s median earnings in food preparation and serving related occupations ($18,060) were lower than all occupations except farming, fishing, and forestry occupations ($18,564).30 For women and men in the broad occupational groups shown in Table 8, men had higher median earnings than women. Installation, maintenance, and repair occupations and community and social services occupations had among the highest women’s-to-men’s earnings ratios, with a ratio of women’s earnings to men’s earnings higher than 90 29 Appendix Table A-2 is restricted to occupations with 100 or more sample cases and shows ratios for 283 out of 466 total occupations. 30 The difference in women’s median earnings between farming, fishing, and forestry occupations and building and grounds cleaning and maintenance occupations was not statistically significant. percent. Within the community and social service occupations, the women’s-to-men’s earnings ratios ranged from 78.6 percent for religious workers to 94.2 percent for counselors.31 In contrast, women’s earnings as a percentage of men’s earnings were 70 percent or less for legal occupations, health diagnosing, transportation supervisors and material moving workers, sales and related occupations, production occupations, motor vehicle operators, and personal care and service occupations. The legal occupation group had the lowest ratio of women’s earnings to men’s earnings (51.1 percent). There was less difference between women’s and men’s earnings among the detailed legal occupations. For example, Appendix Table A-2 shows that female paralegal and legal assistants earned 93.2 percent of what men earned and, for lawyers, the ratio was 77.8 percent.32 Personal care and service workers, all other, was among the occupations with a high women’sto-men’s earnings ratio, shown in Appendix Table A-2, at 111.3 percent, and the paper goods machine setters, operators, and tenders occupation was among the occupations with a low women’s-to-men’s earnings ratio, at 54.4 percent.33 31 The women’s-to-men’s earnings ratio for religious workers was not statistically different from the ratio for community workers, clergy, or religious activities directors, nor was the ratio for counselors statistically different from that of social workers, clergy, and religious activities directors. 32 The women’s-to-men’s earnings ratio for paralegal and legal assistants was not statistically different from the ratio for counselors. The women’s-to-men’s earnings ratio for lawyers was not statistically different from the ratio for religious workers. 33 The personal care and service workers occupation was not statistically different from 41 other occupations with high earnings ratios. The paper goods machine setters, operators, and tenders occupation was not statistically different from 13 other lowearnings-ratio occupations. Income, Earnings, and Poverty Data From the 2007 American Community Survey U.S. Census Bureau 17 Table 8. Median Earnings in the Past 12 Months of Workers by Sex and Women’s Earnings as a Percentage of Men’s Earnings by Occupation for the United States: 2007 (In 2007 inflation-adjusted dollars. For information on confidentiality protection, sampling error, nonsampling error, and definitions, see www.census.gov/acs/www/) Median earnings (dollars) Occupation Men Estimate Full-time, year-round civilian workers 16 years and older with earnings . . . . . . . . . . . . . . . . . . . . . . . Management occupations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Business and financial operations occupations . . . . . . . . . . . . Computer and mathematical occupations. . . . . . . . . . . . . . . . . Architecture and engineering occupations . . . . . . . . . . . . . . . . Life, physical, and social science occupations . . . . . . . . . . . . Community and social services occupations . . . . . . . . . . . . . . Legal occupations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Education, training, and library occupations. . . . . . . . . . . . . . . Arts, design, entertainment, sports, and media occupations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Health diagnosing and treating practitioners and other technical occupations. . . . . . . . . . . . . . . . . . . . . . . . . . . . Health technologists and technicians occupations . . . . . . . . . Healthcare support occupations . . . . . . . . . . . . . . . . . . . . . . . . . Fire fighting and prevention and other protective service workers, including supervisors occupations . . . . . . . . . . . . . . Law enforcement workers, including supervisors occupations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Food preparation and serving related occupations. . . . . . . . . Building and grounds cleaning and maintenance occupations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Personal care and service occupations . . . . . . . . . . . . . . . . . . Sales and related occupations . . . . . . . . . . . . . . . . . . . . . . . . . . Office and administrative support occupations . . . . . . . . . . . . Farming, fishing, and forestry occupations. . . . . . . . . . . . . . . . Construction and extraction occupations . . . . . . . . . . . . . . . . . Installation, maintenance, and repair occupations . . . . . . . . . Production occupations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Supervisors, transportation, and material moving workers, and other transportation workers except motor vehicle operators occupations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Motor vehicle operators occupations . . . . . . . . . . . . . . . . . . . . . Material moving workers occupations . . . . . . . . . . . . . . . . . . . . Margin of error1 (±) Women Estimate Margin of error1 (±) Women’s earnings as a percentage of men’s earnings Margin of error1 (±) Estimate 44,627 71,949 64,965 71,980 70,606 63,235 40,677 105,233 51,225 50,013 100,451 42,323 28,095 40,266 52,159 21,765 26,291 30,575 48,392 36,466 23,117 35,771 41,472 36,565 50,979 37,425 28,690 151 227 700 297 296 1,451 325 1,998 231 761 507 473 832 422 307 165 191 369 534 175 411 169 128 137 491 225 361 34,393 52,510 46,974 61,957 56,627 53,389 37,173 53,790 40,567 41,799 59,318 35,719 24,855 31,997 42,950 18,060 19,093 21,256 30,777 31,173 18,564 32,011 40,325 24,722 32,409 25,783 22,325 85 316 240 402 830 1,127 264 1,168 145 332 483 237 185 528 1,220 222 200 154 165 65 791 657 982 191 1,505 624 268 77.1 73.0 72.3 86.1 80.2 84.4 91.4 51.1 79.2 83.6 59.1 84.4 88.5 79.5 82.3 83.0 72.6 69.5 63.6 85.5 80.3 89.5 97.2 67.6 63.6 68.9 77.8 0.3 0.5 0.9 0.7 1.2 2.6 1.0 1.5 0.5 1.4 0.6 1.1 2.7 1.6 2.4 1.2 0.9 1.0 0.8 0.4 3.7 1.9 2.4 0.6 3.0 1.7 1.4 1 Data are based on a sample and are subject to sampling variability. A margin of error is a measure of an estimate’s variability. The larger the margin of error in relation to the size of the estimate, the less reliable the estimate. When added to and subtracted from the estimate, the margin of error forms the 90-percent confidence interval. Source: U.S. Census Bureau, 2007 American Community Survey. 18 Income, Earnings, and Poverty Data From the 2007 American Community Survey U.S. Census Bureau POVERTY This section discusses poverty status for the nation, states, counties, and places and makes year-toyear comparisons in poverty-rate estimates between 2006 and 2007 for the states.34 This section also discusses the depth of poverty for the nation and the states using the 34 The poverty universe is a subset of the total population covered by the ACS. Specifically, the universe excludes unrelated children under 15 years, people living in institutional group quarters, and those living in college dormitories or military barracks. distribution of the population by income-to-poverty ratio. Official poverty, as defined by OMB’s Statistical Policy Directive 14, uses a set of money income thresholds that vary by family size and composition but do not vary geographically—the Census Bureau uses the same threshold regardless of where a person or family resides.35 The 35 The National Academy of Sciences Panel on Poverty and Family Assistance stated that the cost of housing varied across geographic areas, and the panel encouraged researchers to examine adjustments to poverty thresholds based on differences in housing costs. For text box “How Is Poverty Calculated in the ACS?” provides a more detailed explanation of the poverty definition. examples of this work, see Charles Nelson and Kathleen Short, “The Distributional Implications of Geographic Adjustment of Poverty Thresholds” and Kathleen Short, “Where We Live—Geographic Differences in Poverty Thresholds,’’ both available at . In March 2008, Alemayehu Bishaw presented some preliminary estimates for adjusting the ACS poverty thresholds using a geographic index based on ACS gross rent data. The materials from the presentation at the Southern Regional Science Association Annual Meeting are available from the author on request. How Is Poverty Calculated in the ACS? Poverty statistics presented in this report and other ACS products adhere to the standards specified by the Office of Management and Budget in Statistical Policy Directive 14. The Census Bureau uses a set of dollar value thresholds that vary by family size and composition to determine who is in poverty. Further, poverty thresholds for people living alone or with nonrelatives (unrelated individuals) vary by age (under 65 years or 65 years and older). The poverty thresholds for two-person families also vary by the age of the householder. If a family’s total income is less than the dollar value of the appropriate threshold, then that family and every individual in it are considered to be in poverty. Similarly, if an unrelated individual’s total income is less than the appropriate threshold, then that individual is considered to be in poverty. The poverty thresholds do not vary geographically. They are updated annually to allow for changes in the cost of living (inflation factor) using the Consumer Price Index (CPI). Since the ACS is a continuous survey, people respond throughout the year. Because the income items specify a period covering the last 12 months, the appropriate poverty thresholds are determined by multiplying the base-year poverty thresholds (1982) by the monthly inflation factor based on the 12 monthly CPIs and the base-year CPI.* Example: Consider a family of three with one child under 18 years of age, interviewed in July 2007 and reporting a total family income of $14,000 for the previous 12 months (July 2006 to June 2007). The base-year (1982) threshold for such a family is $7,765, while the average of the 12 inflation factors is 2.11529. Multiplying $7,765 by 2.11529 determines the appropriate poverty threshold for this family, which is $16,425. Comparing the family’s income of $14,000 with the poverty threshold shows that the family and all people in the family are considered to have been in poverty. The only difference for determining poverty status for unrelated individuals is that the person’s individual total income is compared with the threshold. For further information on poverty data in the ACS, visit the Census Bureau’s Web site at . For information on poverty estimates from the ACS and how they differ from those based on the Current Population Survey Annual Social and Economic Supplement (CPS ASEC), which is the official source of poverty statistics for the United States, see “Guidance on Differences in Income and Poverty Estimates from Different Sources” at . For a comparison of poverty rates and analysis of differences between the ACS and the CPS ASEC, see “A Comparison of the American Community Survey and the Current Population Survey” at . * In 1982, the Census Bureau adopted a new poverty threshold matrix (as described above) that included the following changes from the original matrix: it eliminated the distinction between farm and nonfarm families and removed the separate thresholds for families with a female householder, no husband present. Income, Earnings, and Poverty Data From the 2007 American Community Survey U.S. Census Bureau 19 Table 9. Number and Percentage of People in Poverty in the Past 12 Months by Race and Hispanic Origin: 2007 (Numbers in thousands. For information on confidentiality protection, sampling error, nonsampling error, and definitions, see www.census.gov/acs/www/) 2007 Race and Hispanic origin All people for whom poverty status is determined1 293,744 217,751 193,759 35,681 2,278 13,000 422 18,330 6,283 44,471 Below poverty Number 38,052 22,284 17,404 8,807 576 1,376 66 3,890 1,054 9,219 Margin of error2 (±) 233 166 142 77 19 35 7 63 23 89 Percentage 13.0 10.2 9.0 24.7 25.3 10.6 15.7 21.2 16.8 20.7 Margin of error2 (±) 0.1 0.1 0.1 0.2 0.8 0.3 1.5 0.3 0.4 0.2 All races . . . . . . . . . . . . . . . . . . . . . . . . . . . . White alone. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . White alone, not Hispanic. . . . . . . . . . . . . . . . . Black alone . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . American Indian and Alaska Native alone . . . . . . . Asian alone. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Native Hawaiian and Other Pacific Islander alone . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Some Other Race alone . . . . . . . . . . . . . . . . . . . . . . Two or More Races. . . . . . . . . . . . . . . . . . . . . . . . . . . Hispanic (any race) . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Poverty status is determined for individuals in housing units and noninstitutional group quarters except people living in college dormitories or military barracks. Unrelated individuals under 15 years old are also excluded from the poverty universe. 2 Data are based on a sample and are subject to sampling variability. A margin of error is a measure of an estimate’s variability. The larger the margin of error in relation to the size of the estimate, the less reliable the estimate. When added to and subtracted from the estimate, the margin of error forms the 90-percent confidence interval. Source: U.S. Census Bureau, 2007 American Community Survey. Poverty Status for the United States by Race and Hispanic Origin The 2007 ACS data show that an estimated 13.0 percent of the U.S. population had income below the poverty threshold in the past 12 months. Table 9 shows the number and percentage of people in poverty by race and Hispanic origin for 2007 with the margins of error. At 9.0 percent, non-Hispanic Whites had the lowest percentage of people in poverty of all the groups presented in Table 9. The poverty rate for Asians was 10.6 percent. Native Hawaiians and Other Pacific Islanders had a poverty rate of 15.7 percent, which was lower than the rates for Blacks (24.7 percent) and American Indians and Alaska Natives (25.3 percent).36 The ACS includes reporting by people who chose Some Other Race or Two or More Races rather than The poverty rates for Blacks and for American Indians and Alaska Natives were not statistically different from each other. 36 one of the five single races named above. As presented in Table 9, the poverty rates for people who identified themselves as Some Other Race and Two or More Races were 21.2 percent and 16.8 percent, respectively.37 In the 2007 ACS, 20.7 percent of Hispanics (who may be any race) were in poverty. Poverty Status for States Table 10 shows the number and percentage of people in poverty in the past 12 months by state for the 2007 ACS. The map in Figure 5 displays the variation in poverty rates by state, while Appendix Figure B-1 shows a comparison of poverty rates by state with margins of error. In the 2007 ACS, poverty rates among the 50 states and the District of Columbia varied from a low of 7.1 percent to a high of 20.6 37 The poverty rate for Native Hawaiians and Other Pacific Islanders was not statistically different from the poverty rate for people who identified themselves as Two or More Races. percent (Table 10 and Appendix Figure B-1). Twenty-nine states had poverty rates lower than the estimated rate for the nation, while 17 states and the District of Columbia had rates higher than that of the nation.38 At 7.1 percent, the poverty rate for New Hampshire was the lowest among all the states and the District of Columbia. At the other end of the distribution, Mississippi had the highest poverty rate, 20.6 percent, among all the states and the District of Columbia. As shown in Table 10, twelve states (Alaska, California, Florida, Hawaii, Kansas, Missouri, New Hampshire, New York, Oklahoma, Pennsylvania, Texas, and Utah) and the District of Columbia had lower poverty rates in the 2007 ACS than in the 2006 ACS.39 Ten of the states (Alaska, California, Florida, Hawaii, 38 The poverty rates for Oregon, Missouri, South Dakota, and Ohio were not statistically different from the estimated poverty rate for the nation. 39 All year-to-year comparisons using ACS data should be viewed with caution. See footnote 3 for more information. 20 Income, Earnings, and Poverty Data From the 2007 American Community Survey U.S. Census Bureau Table 10. Number and Percentage of People in Poverty in the Past 12 Months by State: 2006 and 2007 (Numbers in thousands. For information on confidentiality protection, sampling error, nonsampling error, and definitions, see www.census.gov/acs/www/) 2006 All people for whom poverty status is determined1 291,531 4,482 652 6,052 2,729 35,675 4,653 3,393 829 551 17,686 9,083 1,252 1,432 12,516 6,126 2,878 2,680 4,087 4,165 1,286 5,476 6,236 9,853 5,037 2,815 5,674 921 1,715 2,461 1,277 8,540 1,912 18,770 8,591 606 11,156 3,462 3,627 12,015 1,026 4,183 753 5,878 22,887 2,509 604 7,404 6,261 1,771 5,401 500 3,865 Below poverty All people for whom poverty Margin status is 2 of error deter(±) mined1 0.1 0.5 1.1 0.4 0.6 0.2 0.4 0.4 1.1 1.4 0.2 0.3 0.7 0.6 0.3 0.4 0.4 0.5 0.5 0.6 0.7 0.3 0.3 0.3 0.3 0.8 0.4 0.8 0.6 0.5 0.6 0.3 0.7 0.2 0.3 0.8 0.3 0.5 0.5 0.2 0.8 0.5 0.9 0.4 0.2 0.5 0.7 0.3 0.3 0.8 0.3 1.0 0.8 293,744 4,507 667 6,225 2,754 35,768 4,756 3,388 838 560 17,847 9,286 1,255 1,464 12,541 6,145 2,882 2,689 4,121 4,167 1,281 5,478 6,245 9,833 5,067 2,822 5,709 933 1,719 2,529 1,275 8,506 1,926 18,775 8,793 613 11,151 3,498 3,670 11,999 1,019 4,270 768 5,997 23,284 2,601 600 7,466 6,338 1,763 5,447 509 3,878 2007 Below poverty Change in poverty (2007 less 2006) Area Number 38,757 742 71 857 471 4,690 556 280 92 108 2,227 1,334 116 180 1,539 778 316 331 693 793 166 428 620 1,332 492 593 770 126 197 254 102 742 354 2,662 1,261 69 1,486 588 481 1,448 114 656 102 952 3,869 265 62 709 737 307 592 47 1,753 Margin of error2 Percent(±) age 222 21 7 27 16 69 18 13 9 8 42 28 9 8 34 24 12 12 20 24 9 17 19 29 14 21 23 7 10 13 7 24 13 41 29 5 36 18 18 27 9 20 7 27 53 13 4 22 20 14 19 5 31 13.3 16.6 10.9 14.2 17.3 13.1 12.0 8.3 11.1 19.6 12.6 14.7 9.3 12.6 12.3 12.7 11.0 12.4 17.0 19.0 12.9 7.8 9.9 13.5 9.8 21.1 13.6 13.6 11.5 10.3 8.0 8.7 18.5 14.2 14.7 11.4 13.3 17.0 13.3 12.1 11.1 15.7 13.6 16.2 16.9 10.6 10.3 9.6 11.8 17.3 11.0 9.4 45.4 Number 38,052 760 60 881 492 4,433 569 269 88 92 2,159 1,324 100 178 1,496 758 318 300 714 775 154 454 621 1,377 482 582 742 132 193 270 90 729 349 2,570 1,259 74 1,464 557 474 1,393 122 642 101 954 3,791 251 61 743 725 298 588 44 1,763 Margin of error2 Percent(±) age 223 23 5 31 16 63 20 13 8 8 39 31 7 9 35 20 14 13 22 20 8 21 21 28 15 18 20 8 9 17 8 23 16 42 29 5 29 17 19 33 9 20 6 29 49 13 5 23 20 11 18 6 27 13.0 16.9 8.9 14.2 17.9 12.4 12.0 7.9 10.5 16.4 12.1 14.3 8.0 12.1 11.9 12.3 11.0 11.2 17.3 18.6 12.0 8.3 9.9 14.0 9.5 20.6 13.0 14.1 11.2 10.7 7.1 8.6 18.1 13.7 14.3 12.1 13.1 15.9 12.9 11.6 12.0 15.0 13.1 15.9 16.3 9.7 10.1 9.9 11.4 16.9 10.8 8.7 45.5 Margin of error2 (±) 0.1 0.5 0.8 0.5 0.6 0.2 0.4 0.4 0.9 1.4 0.2 0.3 0.5 0.6 0.3 0.3 0.5 0.5 0.5 0.5 0.6 0.4 0.3 0.3 0.3 0.7 0.4 0.8 0.5 0.7 0.6 0.3 0.8 0.2 0.3 0.9 0.3 0.5 0.5 0.3 0.9 0.5 0.8 0.5 0.2 0.5 0.9 0.3 0.3 0.6 0.3 1.2 0.7 Number *–705 18 *–11 24 21 *–257 13 –11 –4 *–16 *–68 –10 *–16 –2 –43 –20 2 *–31 21 –18 –12 26 1 *45 –10 –11 –28 6 –4 16 *–12 –13 –5 *–92 –2 5 –22 *–31 –7 *–55 8 –14 –1 2 *–78 –14 –1 *34 –12 –9 –4 –3 10 Percentage *–0.3 0.3 *–2.0 0.0 0.6 *–0.7 0.0 –0.4 –0.6 *–3.2 *–0.5 –0.4 *–1.3 –0.5 –0.4 –0.4 0.0 *–1.2 0.3 –0.4 –0.9 0.5 0.0 *0.5 –0.3 –0.5 *–0.6 0.5 –0.3 0.4 *–0.9 –0.1 –0.4 *–0.5 –0.4 0.7 –0.2 *–1.1 –0.4 *–0.5 0.9 –0.7 –0.5 –0.3 *–0.6 *–0.9 –0.2 0.3 –0.4 –0.4 –0.2 –0.7 0.1 United States . . . . . . Alabama . . . . . . . . . . . . . . . . . Alaska . . . . . . . . . . . . . . . . . . Arizona . . . . . . . . . . . . . . . . . . Arkansas . . . . . . . . . . . . . . . . . California . . . . . . . . . . . . . . . . . Colorado . . . . . . . . . . . . . . . . . Connecticut . . . . . . . . . . . . . . . Delaware . . . . . . . . . . . . . . . . . District of Columbia . . . . . . . . . Florida . . . . . . . . . . . . . . . . . . . Georgia . . . . . . . . . . . . . . . . . . Hawaii . . . . . . . . . . . . . . . . . . . Idaho . . . . . . . . . . . . . . . . . . . . Illinois . . . . . . . . . . . . . . . . . . . Indiana . . . . . . . . . . . . . . . . . . Iowa . . . . . . . . . . . . . . . . . . . . Kansas . . . . . . . . . . . . . . . . . . Kentucky . . . . . . . . . . . . . . . . . Louisiana . . . . . . . . . . . . . . . . . Maine . . . . . . . . . . . . . . . . . . . Maryland . . . . . . . . . . . . . . . . . Massachusetts. . . . . . . . . . . . . Michigan . . . . . . . . . . . . . . . . . Minnesota . . . . . . . . . . . . . . . . Mississippi . . . . . . . . . . . . . . . . Missouri . . . . . . . . . . . . . . . . . . Montana . . . . . . . . . . . . . . . . . Nebraska . . . . . . . . . . . . . . . . . Nevada . . . . . . . . . . . . . . . . . . New Hampshire. . . . . . . . . . . . New Jersey . . . . . . . . . . . . . . . New Mexico. . . . . . . . . . . . . . . New York . . . . . . . . . . . . . . . . . North Carolina . . . . . . . . . . . . . North Dakota . . . . . . . . . . . . . . Ohio . . . . . . . . . . . . . . . . . . . . Oklahoma . . . . . . . . . . . . . . . . Oregon . . . . . . . . . . . . . . . . . . Pennsylvania . . . . . . . . . . . . . . Rhode Island . . . . . . . . . . . . . . South Carolina. . . . . . . . . . . . . South Dakota . . . . . . . . . . . . . Tennessee . . . . . . . . . . . . . . . . Texas. . . . . . . . . . . . . . . . . . . . Utah . . . . . . . . . . . . . . . . . . . . Vermont. . . . . . . . . . . . . . . . . . Virginia . . . . . . . . . . . . . . . . . . Washington . . . . . . . . . . . . . . . West Virginia . . . . . . . . . . . . . . Wisconsin . . . . . . . . . . . . . . . . Wyoming . . . . . . . . . . . . . . . . . Puerto Rico . . . . . . . . . . . . . . . * Statistically different from zero at the 90-percent confidence level. 1 Poverty status is determined for individuals in housing units and noninstitutional group quarters except people living in college dormitories or military barracks. Unrelated individuals under 15 years old are also excluded from the poverty universe. 2 Data are based on a sample and are subject to sampling variability. A margin of error is a measure of an estimate’s variability. The larger the margin of error in relation to the size of the estimate, the less reliable the estimate. When added to and subtracted from the estimate, the margin of error forms the 90-percent confidence interval. Source: U.S. Census Bureau, 2006 and 2007 American Community Surveys, and 2006 and 2007 Puerto Rico Community Surveys. Income, Earnings, and Poverty Data From the 2007 American Community Survey U.S. Census Bureau 21 Figure 5. AK Percentage of People in Poverty in the Past 12 Months by State: 2007 WA MT OR ID SD WY NE UT CA CO KS MO KY NC AZ NM OK TN AR MS TX AL GA SC IA IL IN OH WV VA ND MN WI MI PA NJ DE MD DC* CT NY MA RI VT NH ME NV Percentage of people living below poverty 16.0 or more 13.0 to 15.9 11.0 to 12.9 Less than 11.0 LA FL United States = 13.0 percent HI PR * DC is represented at 4.5 times the scale of other continental states. Source: U.S. Census Bureau, 2007 American Community Survey and 2007 Puerto Rico Community Survey. Kansas, New Hampshire, New York, Oklahoma, Pennsylvania, and Texas) and the District of Columbia also had decreases in the number of people in poverty. Michigan was the only state that had a higher poverty rate in the 2007 ACS than in the 2006 ACS, while Michigan and Virginia were the only states with an increase in the estimated number of people in poverty. At the same time, the changes in poverty rates for the rest of the states (37 states) were statistically undetectable. For people living inside a metropolitan statistical area, the percentage of people in poverty was 12.4 percent in the 2007 ACS (Appendix Table B-1). For people in principal cities within metropolitan statistical areas, the poverty rate was 17.2 percent, while the rate for those not in principal cities within metropolitan statistical areas was 9.4 percent. Among the states, poverty rates for people living in principal cities within metropolitan statistical areas ranged from 6.5 percent to 23.7 percent, while the poverty rate for people within metropolitan statistical areas and not in principal cities ranged from 5.1 percent to 17.1 percent.40 The ratio of the poverty rate for people not in principal cities within a metropolitan area to those in principal cities ranged from about 0.3 to about 1.1. Two states, Alaska and New Mexico, had ratios not 40 Two states (New Jersey and Rhode Island) and the District of Columbia have all of their population living in metropolitan statistical areas. Among the states, the lowest poverty rate for people living in principal cities within metropolitan statistical areas (6.5 percent) was not statistically different from the lowest poverty rate for people within metropolitan statistical areas and not in principal cities (5.1 percent). statistically different from 1— meaning the poverty rate for people living inside the principal cities was not statistically different from the poverty rate for people not living in principal cities within metropolitan statistical areas.41 Depth of Poverty The poverty rate provides a measure of the proportion of people with family or individual income that is below the established poverty thresholds. The income-topoverty ratio provides a measure to gauge the depth of poverty and to calculate the size of the population who might be eligible for government-sponsored assistance 41 The ratio of the poverty rate for people not in principal cities within metropolitan statistical areas to those in principal cities for Alaska was not statistically different from those of three other states (New Mexico, Idaho, and Hawaii). 22 Income, Earnings, and Poverty Data From the 2007 American Community Survey U.S. Census Bureau Figure 6. Percentage of People by Income-to-Poverty Ratio in the Past 12 Months by State: 2007 (For information on confidentiality protection, sampling error, nonsampling error, and definitions, see www.census.gov/acs/www/) New Hampshire Hawaii Connecticut Maryland New Jersey Wyoming Minnesota Massachusetts Virginia Alaska Utah Nevada Vermont Delaware Wisconsin Iowa Washington Kansas Rhode Island Pennsylvania Nebraska North Dakota Colorado Illinois Maine Indiana Florida Idaho Ohio California United States Oregon Missouri New York South Dakota Michigan Georgia Arizona Montana North Carolina South Carolina District of Columbia Tennessee Oklahoma Texas Alabama Kentucky West Virginia Arkansas New Mexico Louisiana Mississippi 0 3.4 3.8 3.6 3.8 3.9 3.6 3.9 4.4 4.2 3.7 3.8 4.6 3.9 5.2 4.5 4.7 5.1 4.8 5.2 5.1 4.8 5.3 5.5 5.3 4.5 5.8 5.0 4.7 6.0 5.1 5.6 5.7 5.7 6.1 5.6 6.5 6.4 6.6 5.8 6.0 6.7 8.4 6.5 6.7 6.8 6.9 7.1 7.0 6.5 7.6 7.8 3.7 4.1 4.4 4.5 4.7 5.0 5.6 5.5 5.8 5.2 5.9 2.7 2.6 2.7 2.7 2.8 3.2 3.2 2.8 3.1 4.4 4.0 6.0 6.2 5.3 6.3 6.4 6.4 6.4 6.8 6.5 6.4 6.8 6.5 6.6 7.5 6.5 7.1 7.4 7.1 7.3 7.4 7.3 7.3 7.6 7.5 7.5 7.9 7.5 8.3 8.4 8.3 8.0 9.4 9.2 9.5 10.0 10.2 9.9 11.4 10.5 10.8 11.9 3.5 4.1 3.8 3.8 3.6 3.7 4.0 3.5 4.0 4.5 3.6 3.7 3.9 4.4 4.1 4.6 5.0 4.2 4.9 4.3 4.6 4.6 4.0 4.7 4.2 4.5 4.8 5.0 5.0 5.2 4.2 4.8 5.5 5.5 5.1 4.7 5.7 5.6 5.4 5.5 6.5 Income-to-poverty ratio Under 50 percent 50.0 to 99.9 percent 100.0 to 124.9 percent 8.7 5 10 15 20 25 Note: Details may not sum to totals because of rounding. Source: U.S. Census Bureau, 2007 American Community Survey. Income, Earnings, and Poverty Data From the 2007 American Community Survey U.S. Census Bureau 23 programs, such as Temporary Assistance for Needy Families (TANF), Medicaid, food stamps, and the Low-Income Home Energy Assistance Program (LIHEAP). The income-to-poverty ratio is reported as a percentage, which compares a family’s or individual’s income relative to their poverty threshold. For example, a family or an individual with an income-to-poverty ratio of 110 percent has income that is 10 percent above their poverty threshold. Appendix Table B-2 provides statelevel estimates for the proportions of people with an income-to-poverty ratio that is less than 50 percent, less than 100 percent, and less than 125 percent. Figure 6 displays the percentage of the population with an income-to-poverty ratio less than 50 percent, 50 percent to less than 100 percent, and 100 percent to less than 125 percent. For purposes of comparison, both include estimates for the nation. The 2007 ACS data show that 17.3 percent of the U.S. population had income below 125 percent of their poverty threshold. In Figure 6, people with income below 125 percent of their threshold are further divided into three groups based on their income-to-poverty ratios. Nationally, about 5.6 percent of people had income below 50 percent of their poverty threshold, 7.4 percent of people had income at or above 50 percent and less than 100 percent of their threshold, and 4.3 percent were at or above 100 percent of their threshold but below 125 percent of their threshold. While not statistically different from each other, the following states were among the states with the lowest proportion of people with incomes less than 50 percent of their thresholds in the 2007 ACS: New Hampshire (3.4 percent), Connecticut (3.6 percent), Wyoming (3.6 percent), Alaska (3.7 percent), Utah (3.8 percent), Hawaii (3.8 percent), and Vermont (3.9 percent). At the other end of the distribution, the proportion of people with income-to-poverty ratios less than 50 percent was 8.7 percent in Mississippi, higher than the other 49 states but not statistically different from the District of Columbia.42 About 17.3 percent of the population of the United States had an income-to-poverty ratio less than 125 percent, placing them in or near poverty (Appendix Table B-2). Although not statistically different from Hawaii, New Hampshire (9.8 percent) had a lower proportion of people with income-to-poverty ratios less than 125 percent than the other 48 states and the District of Columbia.43 On the other hand, Mississippi (27.1 percent) had the highest proportion of people with income less than 125 percent of their threshold. Poverty Status for Counties and Places This section discusses poverty rates for counties and places with populations of 65,000 or more. Using the same two population-size categories as the household income section, larger areas are those with populations of 250,000 or more, and smaller areas are those with populations of 65,000 to less than 250,000.44 Poverty in Larger Areas Table 11 shows counties or county equivalents and places with populations of 250,000 or more. This table 42 The percentage of people with incometo-poverty ratios under 50 percent for the District of Columbia was not statistically different from the proportions for New Mexico and Louisiana. 43 The percentages of people with incometo-poverty ratios under 125 percent for Hawaii, Connecticut, Maryland, and Wyoming were not statistically different from each other. 44 Population size is based on the 2007 population estimates released as part of the Census Bureau’s Population Estimates Program. contains a list of the counties and places with ten of the highest and lowest poverty rates, together with their margins of error. In this table, the poverty rates for counties and places may not be statistically different from each other or from areas that are not shown. Among the counties with a population of 250,000 or more, Cameron County, TX, (34.7 percent) and Hidalgo County, TX, (34.3 percent) had the highest proportions of people with income below their poverty thresholds in the past 12 months.45 Among these large counties, the proportion of people with income below the poverty threshold in the past 12 months was lower for Douglas County, CO, (1.8 percent) than for all but one other county in the same size category. At 2.6 percent, the poverty rate for Somerset County, NJ, is not statistically different from Douglas County, CO.46 Other counties included in the list of relatively low poverty rates had poverty rates that were, in many cases, not statistically different from each other. For example, the poverty rate for Loudon County, VA, at 3.1 percent, was not statistically different from those of Morris County, NJ, and Hamilton County, IN, both at 3.9 percent; Waukesha County, WI, (4.0 percent); Rockingham County, NH, (4.1 percent); and St. Charles County, MO, (4.2 percent)—all of which were not statistically different from each other. Table 11 also shows that New York and Missouri had at least one county on the highest list and one on the lowest list. Among the large counties in New York, Nassau County (4.4 percent) and Suffolk County (5.0 percent) had lower poverty rates than other counties in 45 The poverty rates for Hidalgo County, TX, and Cameron County, TX, were not statistically different from each other. 46 The poverty rate for Somerset County, NJ, was not statistically different from that of Loudoun County, VA. 24 Income, Earnings, and Poverty Data From the 2007 American Community Survey U.S. Census Bureau Table 11. Percentage in Poverty in the Past 12 Months for Ten of the Highest and Lowest Poverty-Rate Counties and Places With 250,000 or More People: 2007 (For information on confidentiality protection, sampling error, nonsampling error, and definitions, see www.census.gov/acs/www/) Ten of the highest rates Area Estimate1 Counties3 Cameron County, TX . . . . . . . . . . Hidalgo County, TX . . . . . . . . . . . . El Paso County, TX. . . . . . . . . . . . Bronx County, NY . . . . . . . . . . . . . Philadelphia County, PA . . . . . . . . Tulare County, CA . . . . . . . . . . . . . Caddo Parish, LA . . . . . . . . . . . . . St. Louis city, MO . . . . . . . . . . . . . Kings County, NY . . . . . . . . . . . . . Mobile County, AL . . . . . . . . . . . . . Places3 Detroit city, MI . . . . . . . . . . . . . . . . Cleveland city, OH. . . . . . . . . . . . . Buffalo city, NY . . . . . . . . . . . . . . . El Paso city, TX . . . . . . . . . . . . . . . Memphis city, TN . . . . . . . . . . . . . . Miami city, FL . . . . . . . . . . . . . . . . . Milwaukee city, WI. . . . . . . . . . . . . Newark city, NJ . . . . . . . . . . . . . . . Philadelphia city, PA . . . . . . . . . . . Cincinnati city, OH. . . . . . . . . . . . . 33.8 29.5 28.7 27.4 26.2 25.5 24.4 23.9 23.8 23.5 1.4 2.1 2.5 1.8 1.9 2.2 1.4 2.6 1.3 2.1 34.7 34.3 28.7 27.1 23.8 23.7 23.5 22.4 21.9 21.1 2.5 2.6 1.7 1.1 1.3 2.3 2.6 2.0 0.8 1.9 Margin of error2 (±) Counties3 Nassau County, NY . . . . . . . . . . . Johnson County, KS . . . . . . . . . . St. Charles County, MO . . . . . . . Rockingham County, NH . . . . . . Waukesha County, WI. . . . . . . . . Hamilton County, IN . . . . . . . . . . Morris County, NJ . . . . . . . . . . . . Loudoun County, VA . . . . . . . . . . Somerset County, NJ . . . . . . . . . Douglas County, CO . . . . . . . . . . Places3 San Diego city, CA . . . . . . . . . . . Las Vegas city, NV . . . . . . . . . . . Colorado Springs city, CO . . . . . San Francisco city, CA . . . . . . . . Mesa city, AZ . . . . . . . . . . . . . . . . San Jose city, CA . . . . . . . . . . . . Honolulu CDP, HI . . . . . . . . . . . . . Anchorage municipality, AK . . . . Virginia Beach city, VA . . . . . . . . Plano city, TX . . . . . . . . . . . . . . . . 12.1 11.9 11.8 10.5 10.2 9.9 8.6 7.3 6.4 5.9 0.9 1.5 1.5 0.8 1.4 1.0 1.1 1.4 1.1 1.4 4.4 4.2 4.2 4.1 4.0 3.9 3.9 3.1 2.6 1.8 0.5 0.6 0.8 0.9 0.7 1.0 1.0 0.8 0.6 0.6 Area Estimate1 Ten of the lowest rates Margin of error2 (±) 1 Poverty status is determined for individuals in housing units and noninstitutional group quarters except people living in college dormitories or military barracks. Unrelated individuals under 15 years old are also excluded from the poverty universe. 2 Data are based on a sample and are subject to sampling variability. A margin of error is a measure of an estimate’s variability. The larger the margin of error in relation to the size of the estimate, the less reliable the estimate. When added to and subtracted from the estimate, the margin of error forms the 90-percent confidence interval. 3 Population size is based on the 2007 population estimates released as part of the U.S. Census Bureau’s Population Estimates Program. Note: Because of sampling variability, some of the estimates in this table may not be statistically different from one another or from estimates for other geographic areas not listed in the table. Source: U.S. Census Bureau, 2007 American Community Survey. the state, while Bronx County (27.1) had the highest poverty rate among similar-sized counties in the state.47 The poverty rate for large counties in Missouri ranged from a low of 4.2 percent in St. Charles County to a high of 22.4 percent for St. Louis city. Table 11 also shows that Detroit city, MI, had a higher proportion of people in poverty, at 33.8 percent, in the past 12 months than other places with populations of 250,000 or more. While not statistically different from each other, the poverty 47 The poverty rate for Nassau County, NY, was not statistically different from the poverty rate for Suffolk County, NY, and the poverty rates for Nassau and Suffolk Counties in New York were not statistically different from that of St. Charles County, MO. rates for Cleveland city, OH, (29.5 percent); Buffalo city, NY, (28.7 percent); and El Paso city, TX, (27.4 percent), were higher than most other large places.48 Among all the large places, Plano city, TX, (5.9 percent); Virginia Beach city, VA, (6.4 percent); and Anchorage municipality, AK, (7.3 percent) had percentages of people in poverty lower than other places of the same size.49 The poverty rates for large places in Texas ranged from a low of 5.9 percent in Plano city to a high of 27.4 percent in El Paso city. Poverty in Smaller Areas Table 12 presents data for ten of the highest and ten of the lowest poverty rates among counties and places with a population of 65,000 to less than 250,000. As noted with Table 11, the poverty rates for counties and places may not be statistically different from each other or from areas that are not shown. Of the counties with 65,000 to 249,999 people, Apache County, AZ, (33.8 percent); St. Landry Parish, LA, (32.8 percent); Webb County, TX, (31.1 percent); and Robeson County, NC, (28.7 percent) had The poverty rate for Buffalo city, NY, was not statistically different from the rate for El Paso city, TX; Memphis, TN; and Miami city, FL. 49 The poverty rate for Plano city, TX; Virginia Beach city, VA; and Anchorage municipality, AK, were not statistically different from each other. Also, the poverty rate for Anchorage municipality, AK, was not statistically different from Honolulu CDP HI. , 48 Income, Earnings, and Poverty Data From the 2007 American Community Survey U.S. Census Bureau 25 Table 12. Percentage in Poverty in the Past 12 Months for Ten of the Highest and Lowest Poverty-Rate Counties and Places With 65,000 to 249,999 People: 2007 (For information on confidentiality protection, sampling error, nonsampling error, and definitions, see www.census.gov/acs/www/) Ten of the highest rates Area Estimate1 Counties3 Apache County, AZ . . . . . . . . . . . . St. Landry Parish, LA . . . . . . . . . . Webb County, TX . . . . . . . . . . . . . Clarke County, GA . . . . . . . . . . . . Robeson County, NC . . . . . . . . . . Monroe County, IN . . . . . . . . . . . . Brazos County, TX . . . . . . . . . . . . Forrest County, MS . . . . . . . . . . . . Putnam County, TN. . . . . . . . . . . . Bulloch County, GA. . . . . . . . . . . . Places3 Bloomington city, IN . . . . . . . . . . . Camden city, NJ . . . . . . . . . . . . . . Brownsville city, TX . . . . . . . . . . . . Gainesville city, FL . . . . . . . . . . . . Kalamazoo city, MI . . . . . . . . . . . . Flint city, MI . . . . . . . . . . . . . . . . . . Reading city, PA . . . . . . . . . . . . . . Macon city, GA. . . . . . . . . . . . . . . . Youngstown city, OH. . . . . . . . . . . Pontiac city, MI. . . . . . . . . . . . . . . . 41.6 38.2 36.5 36.0 35.5 35.5 34.5 33.0 32.6 32.4 4.3 5.6 3.6 4.1 4.1 3.9 4.7 4.2 4.9 5.8 33.8 32.8 31.1 28.7 28.7 27.3 27.3 26.2 26.0 25.8 4.0 4.8 3.3 3.0 3.9 2.9 2.8 5.3 5.8 4.8 Margin of error2 (±) Counties3 Sussex County, NJ . . . . . . . . . . . Delaware County, OH . . . . . . . . . Carroll County, MD . . . . . . . . . . . Rockwall County, TX . . . . . . . . . . Hancock County, IN. . . . . . . . . . . Scott County, MN . . . . . . . . . . . . . Hunterdon County, NJ. . . . . . . . . Kendall County, IL . . . . . . . . . . . . Carver County, MN . . . . . . . . . . . Stafford County, VA . . . . . . . . . . . Places3 Overland Park city, KS . . . . . . . . Troy city, MI. . . . . . . . . . . . . . . . . . Lakewood city, CA . . . . . . . . . . . . Weston city, FL. . . . . . . . . . . . . . . Pleasanton city, CA . . . . . . . . . . . Flower Mound town, TX . . . . . . . Folsom city, CA . . . . . . . . . . . . . . Yorba Linda city, CA . . . . . . . . . . Chino city, CA . . . . . . . . . . . . . . . . Highlands Ranch CDP, CO . . . . 3.1 3.1 3.0 2.3 2.1 1.9 1.8 1.8 1.7 0.8 1.1 1.2 1.9 1.5 1.2 1.2 1.2 0.9 1.7 0.6 4.6 4.5 4.5 4.5 4.2 4.2 4.1 3.9 3.5 3.4 1.2 1.2 1.2 2.0 1.7 1.1 0.8 1.4 1.8 1.2 Area Estimate1 Ten of the lowest rates Margin of error2 (±) 1 Poverty status is determined for individuals in housing units and noninstitutional group quarters except people living in college dormitories or military barracks. Unrelated individuals under 15 years old are also excluded from the poverty universe. 2 Data are based on a sample and are subject to sampling variability. A margin of error is a measure of an estimate’s variability. The larger the margin of error in relation to the size of the estimate, the less reliable the estimate. When added to and subtracted from the estimate, the margin of error forms the 90-percent confidence interval. 3 Population size is based on the 2007 population estimates released as part of the U.S. Census Bureau’s Population Estimates Program. Note: Because of sampling variability, some of the estimates in this table may not be statistically different from one another or from estimates for other geographic areas not listed in the table. Source: U.S. Census Bureau, 2007 American Community Survey. among the higher point estimates of the proportion of people in poverty in the past 12 months. These estimates were not statistically different from each other. Furthermore, the poverty rates for Webb County, TX, and Robeson County, NC, were not statistically different from the rates of any other counties of comparable size presented in Table 12.50 With point estimates ranging from 3.4 percent to 4.6 percent, the poverty rates for ten of the low-poverty small counties shown in Table 12 were not statistically different from each other. Texas had counties on both lists, with Rockwall County 50 The poverty rate for Webb County, TX, was not statistically different from St. Landry Parish, LA; Clarke County, GA; Robeson County, NC; Monroe County, IN; Brazos County, TX; Forrest County, MS; Putnam County, TN; and Bulloch County, GA. having the lowest poverty rate (4.5 percent) among smaller counties in Texas, and Webb County (31.1 percent) and Brazos County (27.3 percent) having higher poverty rates than all counties of similar size in Texas.51 Table 12 also presents data for places with a population of 65,000 to less than 250,000 people. While not statistically different from Camden city, NJ; Brownsville city, TX; and Gainesville city, FL, the poverty rate for Bloomington city, IN, (41.6 percent) was higher than other smaller places. Table 12 51 The poverty rates for Webb County and Brazos County in Texas were not statistically different from each other, and the poverty rates for Brazos County and Potter County in Texas were not statistically different from each other. shows 10 smaller places with low poverty rates that range from 0.8 percent to 3.1 percent. The apparent differences among these rates were not statistically significant except the poverty rate for Highlands Ranch CDP CO, which , was statistically different from those of Lakewood city, CA; Troy city, MI; and Overland Park city, KS. The poverty rate for Michigan, which has at least one place on the list of the highest and the list of the lowest poverty rates for small places, ranged from 3.1 percent in Troy city to 35.5 percent in both Kalamazoo city and Flint city.52 52 The poverty rate for Troy city, MI, is not statistically different from the rates for Livonia city, MI, and West Bloomfield Township CDP , MI. The poverty rate for Kalamazoo city, MI, and Flint city, MI, were not statistically different from Pontiac city, MI. 26 Income, Earnings, and Poverty Data From the 2007 American Community Survey U.S. Census Bureau SOURCE OF THE ESTIMATES The data in this report are from the 2006 and 2007 ACS and the 2006 and 2007 Puerto Rico Community Surveys. The population covered in this report (the population universe) includes the population living in both households and group quarters. As described briefly in the introduction, different units of analysis are used for income and poverty in the different sections of this report. The section on household income does not include the group quarters population. The section on earnings includes all people 16 years and older regardless of living quarters (including people in households and all types of group quarters). The poverty universe excludes unrelated individuals under 15 years of age, people living in institutional group quarters, and people living in college dormitories and military barracks. The 2007 ACS estimated that 8.2 million people, or 2.7 percent of the total population, in the 50 states and the District of Columbia lived in group quarters. Of this population, 4.2 million lived in places classified as institutions and 2.3 million lived in college dormitories. Among people in group quarters, 15.5 percent were part of the poverty universe. ACCURACY OF THE ESTIMATES Statistics from surveys are subject to sampling and nonsampling error. Data from the ACS are based on a sample and are estimates of the actual figures that would have been obtained by interviewing the entire population using the same methodology. All comparisons presented in this report have taken sampling error into account and are significant at the 90-percent confidence level unless noted otherwise. This means the 90-percent confidence interval for the difference between the estimates being compared does not include zero. In this report, the 90-percent margins of error for the estimates are included in the tables in the columns labeled “Margin of error” and in Appendix Figures A-1 and B-1. Nonsampling error in surveys may be attributed to a variety of sources, such as how the survey is designed, how respondents interpret questions, how able and willing they are to provide correct answers, and how accurately the answers are keyed, coded, edited, and classified. Nonsampling errors in the ACS may affect the data in two ways. Errors that are introduced randomly increase the variability of the estimates. Systematic errors consistent in one direction introduce bias into the results. The Census Bureau protects against systematic errors by conducting extensive research and evaluation programs on sampling techniques, questionnaire design, and data collection and processing procedures. The final ACS population estimates are adjusted in the weighting procedure for coverage error by controlling specific survey estimates to independent population controls by sex, age, race, and Hispanic origin. This weighting partially corrects for bias due to over- or undercoverage, but biases may still be present, for example, when people who were missed differ from those interviewed in ways other than sex, age, race, and Hispanic origin. How this weighting procedure affects other variables in the survey is not precisely known. All of these considerations affect comparisons across different surveys or data sources. For information on sampling and estimation methods, confidentiality protection, and sampling and nonsampling errors, please see the “2007 ACS Accuracy of the Data” document located at . Measures of ACS quality—including sample size and number of interviews, response and nonresponse rates, coverage rates, and item allocation rates—are available at . Income, Earnings, and Poverty Data From the 2007 American Community Survey U.S. Census Bureau 27 APPENDIX A. INCOME AND EARNINGS Figure A-1. Median Household Income in the Past 12 Months With Margins of Error by State: 2007 Maryland New Jersey Connecticut Alaska Hawaii New Hampshire Massachusetts California Virginia Minnesota Washington Colorado Utah Nevada Delaware District of Columbia Illinois Rhode Island New York Wyoming United States Wisconsin Vermont Arizona Georgia Oregon Pennsylvania Michigan Florida Texas Kansas Indiana Iowa Nebraska Ohio Idaho Maine Missouri North Carolina North Dakota Montana South Dakota South Carolina Tennessee Oklahoma New Mexico Louisiana Alabama Kentucky Arkansas West Virginia Mississippi 2007 estimate Margin of error $30,000 $35,000 $40,000 $45,000 $50,000 $55,000 $60,000 $65,000 $70,000 $75,000 Source: U.S. Census Bureau, 2007 American Community Survey. Income, Earnings, and Poverty Data From the 2007 American Community Survey U.S. Census Bureau 29 30 In metropolitan or micropolitan statistical area In metropolitan statistical area In micropolitan statistical area Total In principal city Not in principal city Total Total In principal city Not in principal city Not in metropolitan or micropolitan statistical area Margin of error1 (±) Median Median 45,590 37,644 66,577 46,951 40,018 56,977 46,101 51,973 38,203 54,317 43,753 40,695 55,536 43,879 47,615 39,863 43,373 44,796 41,345 38,252 42,135 46,438 49,963 39,041 51,921 33,727 38,049 43,123 46,048 51,917 56,171 1,274 1,139 765 895 2,517 1,164 1,896 1,091 928 3,462 74,455 67,785 55,506 65,179 48,217 55,097 50,125 65,727 58,907 74,644 908 742 495 866 1,554 632 2,304 2,126 1,284 2,420 62,332 (X) 43,077 46,561 31,875 38,126 46,428 42,223 50,280 54,279 827 2,737 1,992 757 792 1,056 982 1,003 1,029 2,993 55,770 75,621 51,666 62,837 55,586 60,234 61,945 49,381 47,751 53,109 491 2,450 998 607 678 1,085 1,126 942 1,041 1,255 36,744 60,879 42,365 41,035 43,815 43,964 40,398 35,114 33,657 44,398 1,189 2,156 1,555 813 887 1,643 1,210 1,167 1,182 3,528 4,265 (X) 1,099 1,132 1,077 1,095 1,790 1,221 2,714 2,400 29,811 60,096 40,085 33,273 35,808 40,039 37,604 31,545 27,274 (B) (B) (X) 34,246 40,716 26,420 33,698 41,906 39,892 48,776 48,324 2,123 3,801 3,080 1,843 950 1,374 1,984 1,749 2,502 (B) (B) (X) 1,874 1,732 1,607 1,354 2,654 1,667 3,280 5,011 993 2,814 798 1,462 362 844 1,214 2,865 1,984 512 45,891 68,867 55,415 44,932 62,682 65,621 73,801 60,547 (X) 50,554 984 2,340 892 1,201 411 895 1,163 2,342 (X) 381 37,182 (B) 40,082 35,046 43,450 51,224 63,023 50,976 (X) 39,664 1,312 (B) 2,300 1,016 1,610 2,879 2,197 2,532 (X) 1,419 34,521 (B) 41,349 32,952 36,436 44,191 (B) (B) (X) 40,010 1,903 (B) 3,695 1,676 3,056 5,040 (B) (B) (X) 3,202 148 58,772 157 41,367 195 35,962 328 44,136 38,291 (B) 39,041 36,357 45,951 53,940 67,700 51,276 (X) 39,603 40,448 61,123 43,542 45,961 49,092 49,945 43,351 37,218 36,802 49,653 67,701 (X) 45,966 51,162 35,539 40,596 51,473 47,252 52,439 56,424 Median Median 53,066 42,491 67,509 50,697 42,309 60,363 56,369 66,313 55,841 54,317 48,489 52,143 65,367 48,223 56,710 48,861 50,724 52,766 46,048 43,458 50,449 68,852 62,334 50,148 60,931 43,940 49,532 45,535 51,989 55,412 69,027 791 506 367 668 1,083 615 2,000 868 932 2,066 395 1,776 904 385 507 586 923 699 696 912 598 2,280 448 777 203 681 963 2,372 1,984 360 112 78 490 2,179 437 613 215 689 815 1,581 1,984 343 370 1,924 874 361 400 801 791 710 463 965 748 506 425 716 894 510 1,411 813 929 1,374 Margin of error1 (±) Margin of error1 (±) Median Margin of error1 (±) Margin of error1 (±) Margin of error1 (±) Margin of error1 Median (±) 240 1,685 (B) 2,715 1,338 2,158 5,445 3,073 2,455 (X) 1,578 1,524 2,588 2,233 1,055 1,068 1,977 2,316 1,479 1,384 2,181 2,444 (X) 1,045 948 1,506 923 4,746 1,922 3,763 2,075 Median 37,844 31,315 55,395 36,061 30,400 44,563 45,541 (X) (X) (X) 39,091 35,038 (B) 42,384 41,240 42,904 43,464 40,324 31,317 33,637 38,149 48,546 (B) 38,055 43,374 30,315 34,804 38,941 39,756 (B) (B) Margin of error1 (±) 267 1,287 2,558 3,001 948 2,818 1,660 (X) (X) (X) 2,778 1,219 (B) 2,553 1,155 1,476 881 1,020 893 2,113 1,349 4,202 (B) 755 812 902 1,020 1,530 1,227 (B) (B) Table A-1. Median Household Income in the Past 12 Months by Metropolitan or Micropolitan Statistical Area Status and State: 2007 (In 2007 inflation-adjusted dollars. For information on confidentiality protection, sampling error, nonsampling error, and definitions, see www.census.gov/acs/www/) Area Median United States . . . . . 51,658 Alabama . . . . . . . . . . . . . . . . . Alaska . . . . . . . . . . . . . . . . . . Arizona . . . . . . . . . . . . . . . . . . Arkansas . . . . . . . . . . . . . . . . California . . . . . . . . . . . . . . . . Colorado . . . . . . . . . . . . . . . . Connecticut . . . . . . . . . . . . . . Delaware . . . . . . . . . . . . . . . . District of Columbia . . . . . . . . Florida . . . . . . . . . . . . . . . . . . 41,507 67,272 50,263 40,443 60,090 56,129 65,967 54,610 54,317 48,010 Georgia . . . . . . . . . . . . . . . . . Hawaii . . . . . . . . . . . . . . . . . . Idaho . . . . . . . . . . . . . . . . . . . Illinois . . . . . . . . . . . . . . . . . . . Indiana . . . . . . . . . . . . . . . . . . Iowa . . . . . . . . . . . . . . . . . . . . Kansas . . . . . . . . . . . . . . . . . . Kentucky . . . . . . . . . . . . . . . . Louisiana . . . . . . . . . . . . . . . . Maine . . . . . . . . . . . . . . . . . . . 50,667 63,757 46,822 55,035 47,795 49,090 49,250 43,050 41,421 49,472 Maryland . . . . . . . . . . . . . . . . Massachusetts . . . . . . . . . . . . Michigan . . . . . . . . . . . . . . . . . Minnesota . . . . . . . . . . . . . . . Mississippi . . . . . . . . . . . . . . . Missouri . . . . . . . . . . . . . . . . . Montana . . . . . . . . . . . . . . . . . Nebraska . . . . . . . . . . . . . . . . Nevada . . . . . . . . . . . . . . . . . . New Hampshire . . . . . . . . . . . 68,512 62,334 49,250 58,252 38,380 47,132 45,988 49,388 55,052 63,209 See footnotes at end of table. Income, Earnings, and Poverty Data From the 2007 American Community Survey U.S. Census Bureau Table A-1. Median Household Income in the Past 12 Months by Metropolitan or Micropolitan Statistical Area Status and State: 2007—Con. U.S. Census Bureau (In 2007 inflation-adjusted dollars. For information on confidentiality protection, sampling error, nonsampling error, and definitions, see www.census.gov/acs/www/) In metropolitan or micropolitan statistical area In metropolitan statistical area In micropolitan statistical area Total In principal city Not in principal city Total Total Margin of error1 (±) Median 67,035 43,985 54,974 47,438 45,346 47,740 44,884 51,277 50,378 53,568 45,778 47,488 45,779 49,319 56,569 56,879 63,559 57,903 40,480 52,427 49,598 17,990 399 20,174 761 17,395 400 13,895 637 1,407 433 325 774 2,858 662 714 883 496 2,670 39,329 45,636 40,111 42,924 41,917 (B) 51,747 50,292 34,678 41,325 47,496 1,423 2,015 734 426 1,272 (B) 807 851 2,469 770 2,889 47,274 50,119 51,140 57,477 62,327 61,570 70,462 62,998 41,877 60,988 (B) 762 2,123 700 589 932 2,715 699 815 914 585 (B) 39,197 40,787 36,444 37,675 46,565 47,918 42,466 44,314 35,237 49,239 58,890 1,670 1,866 827 859 2,171 1,712 1,908 1,872 1,822 1,196 3,247 1,700 38,895 35,956 32,014 34,095 42,100 (B) 39,449 36,085 32,931 39,958 54,811 (B) 3,475 3,588 1,832 1,478 3,374 (B) 2,693 2,240 4,621 1,823 4,788 (B) 573 1,160 373 594 1,722 462 622 556 241 1,353 45,437 42,758 45,748 45,749 38,958 35,043 40,691 46,193 33,784 46,662 1,847 1,515 513 856 2,351 606 835 958 693 2,999 69,892 45,792 67,381 48,907 58,826 54,237 49,147 55,709 56,054 57,896 759 1,766 525 715 2,515 569 923 964 375 2,311 (X) 37,400 42,466 40,373 45,353 42,497 37,293 40,931 41,555 (X) (X) 1,371 791 613 1,868 835 945 1,129 682 (X) (X) 37,140 34,355 34,360 42,678 35,372 34,166 37,907 30,643 (X) (X) 1,778 1,758 1,742 3,563 1,450 1,131 2,229 1,693 (X) Median Median Median Median Median (X) 37,817 45,941 42,102 (B) 46,318 40,732 42,715 43,575 (X) 39,285 48,868 38,240 42,309 51,394 50,771 43,592 48,633 35,757 52,954 64,958 15,017 573 700 345 468 1,291 324 547 701 317 1,353 669 1,183 457 324 749 1,169 431 506 1,006 404 2,573 383 Margin of error1 (±) Margin of error1 (±) Margin of error1 (±) Margin of error1 (±) Margin of error1 (±) In principal city Not in principal city Margin of error1 (±) (X) 2,163 992 734 (B) 884 929 1,563 786 (X) 1,909 2,358 1,182 1,564 3,093 2,365 2,851 1,635 2,153 1,245 5,507 2,262 Not in metropolitan or micropolitan statistical area Area Median Median (X) 27,429 44,824 34,758 40,377 40,791 35,498 39,056 38,357 (X) 29,556 38,664 33,170 36,664 40,878 45,122 38,634 39,487 31,723 42,913 46,715 (B) Margin of error1 (±) (X) 3,678 1,350 1,248 1,533 864 982 1,901 870 (X) 2,028 1,969 1,588 732 2,027 2,004 1,363 1,835 1,107 906 2,718 (B) Income, Earnings, and Poverty Data From the 2007 American Community Survey New Jersey . . . . . . . . . . . . . . New Mexico . . . . . . . . . . . . . . New York . . . . . . . . . . . . . . . . North Carolina . . . . . . . . . . . . North Dakota . . . . . . . . . . . . . Ohio . . . . . . . . . . . . . . . . . . . . Oklahoma . . . . . . . . . . . . . . . Oregon . . . . . . . . . . . . . . . . . . Pennsylvania . . . . . . . . . . . . . Rhode Island . . . . . . . . . . . . . 67,035 41,907 53,834 45,629 45,349 46,887 42,719 49,235 49,026 53,568 South Carolina . . . . . . . . . . . . South Dakota . . . . . . . . . . . . . Tennessee . . . . . . . . . . . . . . . Texas . . . . . . . . . . . . . . . . . . . Utah . . . . . . . . . . . . . . . . . . . . Vermont . . . . . . . . . . . . . . . . . Virginia . . . . . . . . . . . . . . . . . . Washington . . . . . . . . . . . . . . West Virginia . . . . . . . . . . . . . Wisconsin . . . . . . . . . . . . . . . Wyoming . . . . . . . . . . . . . . . . 44,570 45,258 43,732 48,376 55,911 51,414 62,323 56,356 39,033 51,908 55,317 Puerto Rico . . . . . . . . . . . . . . 17,833 (B) Data for territories below the population of 65,000 are not published as single-year estimates by the American Community Survey. (X) Not applicable. Indicates states that do not contain any territory in micropolitan statistical areas and/or do not contain any territory outside of metropolitan or micropolitan statistical areas. 1 Data are based on a sample and are subject to sampling variability. A margin of error is a measure of an estimate’s variability. The larger the margin of error in relation to the size of the estimate, the less reliable the estimate. When added to and subtracted from the estimate, the margin of error forms the 90-percent confidence interval. Source: U.S. Census Bureau, 2007 American Community Survey and 2007 Puerto Rico Community Survey. 31 Table A-2. Median Earnings in the Past 12 Months of Workers by Sex and Women’s Earnings as a Percentage of Men’s Earnings by Detailed Occupation for the United States: 2007 (In 2007 inflation-adjusted dollars. For information on confidentiality protection, sampling error, nonsampling error, and definitions, see www.census.gov/acs/www/) Median earnings (dollars) Occupation Men Estimate Management Occupations Chief executives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . General and operations managers . . . . . . . . . . . . . . . . . . . . . . Legislators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Advertising and promotions managers . . . . . . . . . . . . . . . . . . . Marketing and sales managers . . . . . . . . . . . . . . . . . . . . . . . . . Public relations managers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Administrative services managers . . . . . . . . . . . . . . . . . . . . . . Computer and information systems managers . . . . . . . . . . . . Financial managers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Human resources managers . . . . . . . . . . . . . . . . . . . . . . . . . . . Industrial production managers . . . . . . . . . . . . . . . . . . . . . . . . . Purchasing managers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Transportation, storage, and distribution managers . . . . . . . . Farm, ranch, and other agricultural managers . . . . . . . . . . . . Farmers and ranchers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Construction managers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Education administrators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Engineering managers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Food service managers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Funeral directors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Gaming managers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lodging managers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Medical and health services managers . . . . . . . . . . . . . . . . . . Natural sciences managers . . . . . . . . . . . . . . . . . . . . . . . . . . . . Postmasters and mail superintendents . . . . . . . . . . . . . . . . . . Property, real estate, and community association managers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Social and community service managers . . . . . . . . . . . . . . . . Managers, all other . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Business and Financial Operations Occupations Agents and business managers of artists, performers, and athletes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Purchasing agents and buyers, farm products . . . . . . . . . . . . Wholesale and retail buyers, except farm products . . . . . . . Purchasing agents, except wholesale, retail, and farm products . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Claims adjusters, appraisers, examiners, and investigators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Compliance officers, except agriculture, construction, health and safety, and transportation . . . . . . . . . . . . . . . . . . . Cost estimators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Human resources, training, and labor relations specialists . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Logisticians . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Management analysts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Meeting and convention planners . . . . . . . . . . . . . . . . . . . . . . . Other business operations specialists . . . . . . . . . . . . . . . . . . . Accountants and auditors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Appraisers and assessors of real estate . . . . . . . . . . . . . . . . . Budget analysts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . See footnotes at end of table. Margin of error2 (±) Women Estimate Margin of error2 (±) Women’s earnings as a percentage of men’s earnings1 Margin of error2 (±) Estimate 116,800 75,200 66,300 70,800 90,300 81,100 63,100 91,200 86,000 71,500 70,500 73,900 46,300 40,500 30,400 61,800 73,800 106,900 40,500 55,700 48,700 44,500 76,500 108,700 65,800 55,700 60,600 76,000 3,000 1,594 8,574 4,879 1,186 2,765 1,949 826 1,393 962 1,653 3,423 1,342 406 145 1,449 1,821 2,597 270 3,670 5,053 4,175 1,640 21,622 2,586 2,907 1,936 308 90,300 55,500 (B) 50,300 60,200 61,600 52,300 80,800 51,300 60,600 57,100 56,000 44,600 31,900 17,600 53,000 54,200 96,700 30,400 35,700 (B) 35,600 60,500 (B) 53,500 40,100 46,900 56,200 4,381 1,589 (B) 4,300 2,020 3,639 2,862 1,504 428 765 5,167 1,230 5,540 3,075 2,064 1,444 1,740 8,421 192 2,385 (B) 1,688 630 (B) 2,025 1,522 1,711 474 77.3 73.8 (X) 71.1 66.7 76.0 82.8 88.6 59.7 84.8 81.1 75.8 96.3 78.7 57.9 85.7 73.4 90.5 75.0 64.2 (X) 80.0 79.0 (X) 81.3 72.0 77.5 74.0 4.2 2.6 (X) 7.8 2.4 5.2 5.2 1.8 1.1 1.6 7.6 3.9 12.3 7.6 6.8 3.1 3.0 8.2 0.7 6.0 (X) 8.4 1.9 (X) 4.4 4.6 3.8 0.7 51,400 50,900 45,400 52,200 51,400 60,600 58,900 57,700 57,300 82,100 (B) 53,900 69,900 55,200 68,800 3,851 3,270 972 2,058 1,555 1,306 2,945 2,492 4,076 1,290 (B) 3,700 1,892 4,043 6,017 43,700 (B) 39,100 44,700 41,300 50,900 45,000 46,200 46,300 64,900 46,100 40,600 47,600 40,100 60,800 6,113 (B) 1,653 1,772 1,379 975 5,305 479 4,395 2,236 614 426 824 3,260 1,660 84.9 (X) 86.0 85.7 80.3 84.0 76.4 80.1 80.8 79.0 (X) 75.4 68.0 72.7 88.4 13.5 (X) 4.1 4.8 3.6 2.4 9.8 3.6 9.6 3.0 (X) 5.2 2.2 8.0 8.1 32 Income, Earnings, and Poverty Data From the 2007 American Community Survey U.S. Census Bureau Table A-2. Median Earnings in the Past 12 Months of Workers by Sex and Women’s Earnings as a Percentage of Men’s Earnings by Detailed Occupation for the United States: 2007—Con. (In 2007 inflation-adjusted dollars. For information on confidentiality protection, sampling error, nonsampling error, and definitions, see www.census.gov/acs/www/) Median earnings (dollars) Occupation Men Estimate Business and Financial Operations Occupations—Con. Credit analysts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Financial analysts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Personal financial advisors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Insurance underwriters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Financial examiners . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Loan counselors and officers . . . . . . . . . . . . . . . . . . . . . . . . . . . Tax examiners, collectors, and revenue agents . . . . . . . . . . . Tax preparers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Financial specialists, all other . . . . . . . . . . . . . . . . . . . . . . . . . . Computer and Mathematical Occupations Computer scientists and systems analysts . . . . . . . . . . . . . . . Computer programmers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Computer software engineers . . . . . . . . . . . . . . . . . . . . . . . . . . Computer support specialists . . . . . . . . . . . . . . . . . . . . . . . . . . . Database administrators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Network and computer systems administrators . . . . . . . . . . . Network systems and data communications analysts . . . . . . Actuaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Operations research analysts . . . . . . . . . . . . . . . . . . . . . . . . . . Statisticians . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Architecture and Engineering Occupations Architects, except naval . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Surveyors, cartographers, and photogrammetrists . . . . . . . . Aerospace engineers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Biomedical engineers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chemical engineers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Civil engineers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Computer hardware engineers . . . . . . . . . . . . . . . . . . . . . . . . . Electrical and electronic engineers . . . . . . . . . . . . . . . . . . . . . . Environmental engineers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Industrial engineers, including health and safety . . . . . . . . . . Marine engineers and naval architects . . . . . . . . . . . . . . . . . . Materials engineers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mechanical engineers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Petroleum engineers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Engineers, all other . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Drafters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Engineering technicians, except drafters . . . . . . . . . . . . . . . . . Surveying and mapping technicians . . . . . . . . . . . . . . . . . . . . . Life, Physical, and Social Science Occupations Agricultural and food scientists . . . . . . . . . . . . . . . . . . . . . . . . . Biological scientists . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conservation scientists and foresters . . . . . . . . . . . . . . . . . . . Medical scientists . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Astronomers and physicists . . . . . . . . . . . . . . . . . . . . . . . . . . . . Atmospheric and space scientists . . . . . . . . . . . . . . . . . . . . . . . Chemists and materials scientists . . . . . . . . . . . . . . . . . . . . . . . See footnotes at end of table. Margin of error2 (±) Women Estimate Margin of error2 (±) Women’s earnings as a percentage of men’s earnings1 Margin of error2 (±) Estimate 49,900 92,200 88,600 66,200 76,700 61,500 50,700 63,200 71,000 70,800 72,000 86,900 50,700 76,900 63,500 63,400 101,800 77,700 81,800 71,400 53,200 87,300 73,100 87,600 76,700 84,500 82,100 72,900 71,300 77,700 70,200 71,900 102,800 81,800 45,800 51,100 41,100 61,500 55,100 55,200 70,700 95,300 70,800 66,500 4,550 11,454 5,438 5,453 10,285 711 3,547 12,624 4,667 778 773 689 566 2,691 1,735 2,454 12,546 3,576 5,980 977 4,458 1,425 6,736 4,586 712 4,853 1,533 5,308 620 8,569 2,727 671 7,317 610 452 257 648 4,042 2,647 2,757 3,501 6,741 12,662 3,203 40,800 60,800 51,900 45,400 (B) 42,700 41,500 40,500 41,100 61,400 67,500 75,700 47,600 59,400 56,500 55,200 (B) 60,800 65,400 55,400 (B) 76,300 (B) 75,700 64,000 70,200 72,000 (B) 59,000 (B) (B) 68,800 (B) 71,900 38,700 40,500 (B) (B) 51,000 (B) 64,200 (B) (B) 59,200 1,477 2,806 2,027 1,760 (B) 944 2,255 3,176 3,566 620 2,245 1,135 2,221 3,619 2,453 2,652 (B) 804 3,879 3,359 (B) 4,195 (B) 7,546 2,287 16,332 2,963 (B) 4,178 (B) (B) 4,057 (B) 3,747 1,416 859 (B) (B) 2,158 (B) 4,011 (B) (B) 3,751 81.8 65.9 58.5 68.6 (X) 69.4 81.9 64.1 58.0 86.7 93.8 87.1 93.8 77.3 89.0 87.0 (X) 78.3 80.0 77.5 (X) 87.5 (X) 86.5 83.4 83.1 87.7 (X) 82.9 (X) (X) 95.7 (X) 87.9 84.4 79.2 (X) (X) 92.6 (X) 90.8 (X) (X) 89.1 8.0 8.7 4.3 6.2 (X) 1.7 7.3 13.8 6.3 1.3 3.3 1.5 4.5 5.4 4.6 5.4 (X) 3.7 7.5 4.8 (X) 5.0 (X) 9.7 3.1 19.9 4.0 (X) 5.9 (X) (X) 5.7 (X) 4.6 3.2 1.7 (X) (X) 5.9 (X) 7.2 (X) (X) 7.1 Income, Earnings, and Poverty Data From the 2007 American Community Survey U.S. Census Bureau 33 Table A-2. Median Earnings in the Past 12 Months of Workers by Sex and Women’s Earnings as a Percentage of Men’s Earnings by Detailed Occupation for the United States: 2007—Con. (In 2007 inflation-adjusted dollars. For information on confidentiality protection, sampling error, nonsampling error, and definitions, see www.census.gov/acs/www/) Median earnings (dollars) Occupation Men Estimate Life, Physical, and Social Science Occupations—Con. Environmental scientists and geoscientists . . . . . . . . . . . . . . . Physical scientists, all other . . . . . . . . . . . . . . . . . . . . . . . . . . . . Economists . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Market and survey researchers . . . . . . . . . . . . . . . . . . . . . . . . . Psychologists . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Urban and regional planners . . . . . . . . . . . . . . . . . . . . . . . . . . . Miscellaneous social scientists and related workers . . . . . . . Agricultural and food science technicians . . . . . . . . . . . . . . . . Biological technicians . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chemical technicians . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Geological and petroleum technicians . . . . . . . . . . . . . . . . . . . Other life, physical, and social science technicians . . . . . . . Community and Social Services Occupations Counselors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Social workers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Miscellaneous community and social service specialists . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Clergy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Directors, religious activities and education . . . . . . . . . . . . . . Religious workers, all other . . . . . . . . . . . . . . . . . . . . . . . . . . . . Legal Occupations Lawyers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Judges, magistrates, and other judicial workers . . . . . . . . . . Paralegals and legal assistants . . . . . . . . . . . . . . . . . . . . . . . . . Miscellaneous legal support workers . . . . . . . . . . . . . . . . . . . . Education, Training, and Library Occupations Postsecondary teachers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Preschool and kindergarten teachers . . . . . . . . . . . . . . . . . . . . Elementary and middle school teachers . . . . . . . . . . . . . . . . . Secondary school teachers . . . . . . . . . . . . . . . . . . . . . . . . . . . . Special education teachers . . . . . . . . . . . . . . . . . . . . . . . . . . . . Other teachers and instructors . . . . . . . . . . . . . . . . . . . . . . . . . Archivists, curators, and museum technicians . . . . . . . . . . . . Librarians . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Library technicians . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Teacher assistants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Other education, training, and library workers . . . . . . . . . . . . Arts, Design, Entertainment, Sports, and Media Occupations Artists and related workers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Designers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Producers and directors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Athletes, coaches, umpires, and related workers . . . . . . . . . Musicians, singers, and related workers . . . . . . . . . . . . . . . . . Announcers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . News analysts, reporters, and correspondents . . . . . . . . . . . Public relations specialists . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Editors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Technical writers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . See footnotes at end of table. Margin of error2 (±) Women Estimate Margin of error2 (±) Women’s earnings as a percentage of men’s earnings1 Margin of error2 (±) Estimate 73,100 81,800 102,800 77,500 71,000 61,600 56,000 39,900 47,600 51,800 50,900 41,600 40,500 41,100 41,500 39,400 40,400 39,200 120,400 108,100 45,700 56,000 66,000 (B) 47,300 49,600 45,300 46,600 48,100 50,400 (B) 27,600 51,200 4,159 2,221 6,082 4,530 1,759 4,341 9,324 5,104 4,963 2,020 9,039 2,186 388 753 1,387 1,045 2,099 3,960 2,071 6,825 1,469 3,989 911 (B) 531 957 2,425 2,395 4,344 1,701 (B) 2,528 1,601 52,400 62,800 (B) 58,700 57,800 56,100 45,800 32,400 39,900 42,800 (B) 37,500 38,100 38,300 34,800 35,100 35,900 30,800 93,600 69,500 42,600 40,700 51,900 22,900 43,000 44,700 42,900 35,200 42,800 45,800 29,600 18,400 47,700 3,438 2,525 (B) 3,736 2,788 4,710 7,509 5,529 7,355 2,351 (B) 2,316 813 485 999 1,629 2,101 2,782 3,897 4,159 475 544 1,101 847 168 1,082 1,306 1,087 3,566 561 1,793 186 3,721 71.6 76.8 (X) 75.7 81.4 91.1 81.8 81.3 84.0 82.5 (X) 90.1 94.2 93.1 84.0 89.1 88.8 78.6 77.8 64.3 93.2 72.7 78.6 (X) 91.0 90.1 94.7 75.6 89.0 91.0 (X) 66.8 93.1 6.2 3.7 (X) 6.5 4.4 10.0 19.1 17.3 17.8 5.6 (X) 7.3 2.2 2.1 3.7 4.8 7.0 10.7 3.5 5.6 3.2 5.3 2.0 (X) 1.1 2.8 5.8 4.5 10.9 3.3 (X) 6.1 7.8 47,600 50,500 56,500 42,300 39,000 40,300 51,300 65,900 52,900 63,700 3,512 638 6,258 3,267 3,716 3,711 2,191 5,772 2,527 4,233 39,800 39,100 52,500 35,100 35,200 (B) 42,300 50,700 46,300 57,300 3,106 1,296 3,772 2,642 1,063 (B) 3,163 821 1,995 4,121 83.5 77.5 92.9 83.0 90.1 (X) 82.4 76.9 87.5 89.9 9.0 2.7 12.3 9.0 9.0 (X) 7.1 6.9 5.6 8.8 34 Income, Earnings, and Poverty Data From the 2007 American Community Survey U.S. Census Bureau Table A-2. Median Earnings in the Past 12 Months of Workers by Sex and Women’s Earnings as a Percentage of Men’s Earnings by Detailed Occupation for the United States: 2007—Con. (In 2007 inflation-adjusted dollars. For information on confidentiality protection, sampling error, nonsampling error, and definitions, see www.census.gov/acs/www/) Median earnings (dollars) Occupation Men Estimate Arts, Design, Entertainment, Sports, and Media Occupations—Con. Writers and authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Miscellaneous media and communication workers . . . . . . . . Broadcast and sound engineering technicians and radio operators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Photographers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Television, video, and motion picture camera operators and editors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Healthcare Practitioner and Technical Occupations Chiropractors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dentists . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dietitians and nutritionists . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Optometrists . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pharmacists . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Physicians and surgeons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Physician assistants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Registered nurses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Audiologists . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Occupational therapists . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Physical therapists . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Radiation therapists . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Respiratory therapists . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Speech-language pathologists . . . . . . . . . . . . . . . . . . . . . . . . . . Therapists, all other . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Veterinarians . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Clinical laboratory technologists and technicians . . . . . . . . . . Dental hygienists . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Diagnostic related technologists and technicians . . . . . . . . . Emergency medical technicians and paramedics . . . . . . . . . Health diagnosing and treating practitioner support technicians . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Licensed practical and licensed vocational nurses . . . . . . . . Medical records and health information technicians . . . . . . . Opticians, dispensing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Miscellaneous health technologists and technicians . . . . . . . Other healthcare practitioners and technical occupations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Healthcare Support Occupations Nursing, psychiatric, and home health aides . . . . . . . . . . . . . Physical therapist assistants and aides . . . . . . . . . . . . . . . . . . Massage therapists . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dental assistants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Medical assistants and other healthcare support occupations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . See footnotes at end of table. Margin of error2 (±) Women Estimate Margin of error2 (±) Women’s earnings as a percentage of men’s earnings1 Margin of error2 (±) Estimate 53,500 40,700 47,200 41,000 45,500 81,800 150,500 (B) 102,700 103,000 181,200 77,000 63,100 (B) 63,900 69,400 (B) 55,400 (B) 48,600 96,100 45,700 (B) 55,400 41,800 34,700 35,900 (B) 39,100 42,900 51,400 26,500 37,700 (B) (B) 30,300 3,803 2,568 2,683 2,013 2,494 6,939 9,334 (B) 5,351 1,657 2,959 6,463 2,617 (B) 5,068 2,104 (B) 1,826 (B) 4,374 7,909 1,489 (B) 3,152 1,987 1,724 1,387 (B) 3,196 3,976 3,038 724 6,363 (B) (B) 355 48,300 35,600 (B) 28,800 (B) 45,700 102,500 41,300 (B) 100,300 115,000 49,300 57,900 60,200 56,900 61,200 68,200 47,700 55,700 41,100 71,100 40,700 50,800 49,100 32,600 28,200 35,700 25,600 31,400 32,700 43,200 23,300 35,600 28,400 28,800 25,900 3,213 1,943 (B) 1,856 (B) 12,336 9,678 2,548 (B) 1,397 7,203 6,070 668 8,781 2,132 571 3,688 2,405 893 1,387 2,425 214 595 735 1,719 757 286 441 2,846 1,477 4,780 169 1,275 3,144 1,331 665 90.4 87.5 (X) 70.3 (X) 55.9 68.1 (X) (X) 97.4 63.5 64.0 91.7 (X) 89.1 88.2 (X) 86.2 (X) 84.5 74.0 89.1 (X) 88.7 78.0 81.3 99.6 (X) 80.3 76.2 84.0 88.1 94.4 (X) (X) 85.5 8.8 7.3 (X) 5.7 (X) 15.8 7.7 (X) (X) 2.1 4.1 9.5 3.9 (X) 7.8 2.8 (X) 5.2 (X) 8.1 6.6 2.9 (X) 5.2 5.5 4.6 3.9 (X) 9.8 7.8 10.5 2.5 16.3 (X) (X) 2.4 Income, Earnings, and Poverty Data From the 2007 American Community Survey U.S. Census Bureau 35 Table A-2. Median Earnings in the Past 12 Months of Workers by Sex and Women’s Earnings as a Percentage of Men’s Earnings by Detailed Occupation for the United States: 2007—Con. (In 2007 inflation-adjusted dollars. For information on confidentiality protection, sampling error, nonsampling error, and definitions, see www.census.gov/acs/www/) Median earnings (dollars) Occupation Men Estimate Protective Service Occupations First-line supervisors/managers of correctional officers . . . . First-line supervisors/managers of police and detectives . . . First-line supervisors/managers of fire fighting and prevention workers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Supervisors, protective service workers, all other . . . . . . . . . Fire fighters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fire inspectors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bailiffs, correctional officers, and jailers . . . . . . . . . . . . . . . . . . Detectives and criminal investigators . . . . . . . . . . . . . . . . . . . . Police and sheriff’s patrol officers . . . . . . . . . . . . . . . . . . . . . . . Animal control workers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Private detectives and investigators . . . . . . . . . . . . . . . . . . . . . Security guards and gaming surveillance officers . . . . . . . . . Lifeguards and other protective service workers . . . . . . . . . . Food Preparation and Serving Related Occupations Chefs and head cooks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . First-line supervisors/managers of food preparation and serving workers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cooks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Food preparation workers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bartenders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Combined food preparation and serving workers, including fast food . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Counter attendants, cafeteria, food concession, and coffee shop . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Waiters and waitresses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Food servers, nonrestaurant . . . . . . . . . . . . . . . . . . . . . . . . . . . Dining room and cafeteria attendants and bartender helpers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dishwashers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hosts and hostesses, restaurant, lounge, and coffee shop . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Building and Grounds Cleaning and Maintenance Occupations First-line supervisors/managers of housekeeping and janitorial workers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . First-line supervisors/managers of landscaping, lawn service, and groundskeeping workers . . . . . . . . . . . . . Janitors and building cleaners . . . . . . . . . . . . . . . . . . . . . . . . . . Maids and housekeeping cleaners . . . . . . . . . . . . . . . . . . . . . . Pest control workers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Grounds maintenance workers . . . . . . . . . . . . . . . . . . . . . . . . . See footnotes at end of table. Margin of error2 (±) Women Estimate Margin of error2 (±) Women’s earnings as a percentage of men’s earnings1 Margin of error2 (±) Estimate 48,500 70,800 73,100 44,200 57,100 51,000 41,000 68,900 54,400 (B) 54,600 30,100 34,300 30,600 27,600 20,200 18,400 25,600 20,300 18,400 22,100 23,600 17,900 16,300 (B) 3,277 1,175 4,361 3,007 2,099 4,330 411 3,696 1,328 (B) 4,030 540 4,336 326 1,568 94 518 844 528 2,716 892 2,374 957 222 (B) 45,500 51,000 (B) 40,800 51,300 (B) 35,300 51,300 50,600 28,800 40,900 28,300 25,000 25,200 21,400 16,500 16,800 20,500 16,300 15,400 17,900 19,400 16,400 15,200 18,100 5,835 4,316 (B) 697 2,444 (B) 740 2,731 1,839 2,739 2,257 1,122 2,005 924 892 513 951 719 538 885 704 1,096 1,020 571 933 93.9 72.0 (X) 92.3 89.9 (X) 86.2 74.5 93.0 (X) 74.9 93.9 72.8 82.5 77.4 81.3 91.5 80.1 80.2 83.7 81.0 82.4 91.8 93.1 (X) 13.6 6.2 (X) 6.5 5.4 (X) 2.0 5.6 4.1 (X) 6.9 4.1 10.9 3.1 5.5 2.6 5.8 3.9 3.4 13.3 4.6 9.5 7.5 3.7 (X) 38,700 36,600 26,400 22,200 32,900 21,900 1,117 1,691 178 701 1,811 890 25,300 29,100 20,100 17,400 (B) 20,400 398 3,598 155 265 (B) 844 65.5 79.6 76.1 78.6 (X) 93.1 2.2 10.5 0.8 2.8 (X) 5.4 36 Income, Earnings, and Poverty Data From the 2007 American Community Survey U.S. Census Bureau Table A-2. Median Earnings in the Past 12 Months of Workers by Sex and Women’s Earnings as a Percentage of Men’s Earnings by Detailed Occupation for the United States: 2007—Con. (In 2007 inflation-adjusted dollars. For information on confidentiality protection, sampling error, nonsampling error, and definitions, see www.census.gov/acs/www/) Median earnings (dollars) Occupation Men Estimate Personal Care and Service Occupations First-line supervisors/managers of gaming workers . . . . . . . First-line supervisors/managers of personal service workers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Animal trainers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Nonfarm animal caretakers . . . . . . . . . . . . . . . . . . . . . . . . . . . . Gaming services workers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Miscellaneous entertainment attendants and related workers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Barbers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hairdressers, hairstylists, and cosmetologists . . . . . . . . . . . . Miscellaneous personal appearance workers . . . . . . . . . . . . . Baggage porters, bellhops, and concierges . . . . . . . . . . . . . . Tour and travel guides . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Transportation attendants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Child care workers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Personal and home care aides . . . . . . . . . . . . . . . . . . . . . . . . . Recreation and fitness workers . . . . . . . . . . . . . . . . . . . . . . . . . Residential advisors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Personal care and service workers, all other . . . . . . . . . . . . . Sales and Related Occupations First-line supervisors/managers of retail sales workers . . . . First-line supervisors/managers of non-retail sales workers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cashiers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Counter and rental clerks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Parts salespersons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Retail salespersons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Advertising sales agents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Insurance sales agents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Securities, commodities, and financial services sales agents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Travel agents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sales representatives, services, all other . . . . . . . . . . . . . . . . Sales representatives, wholesale and manufacturing . . . . . . Models, demonstrators, and product promoters . . . . . . . . . . . Real estate brokers and sales agents . . . . . . . . . . . . . . . . . . . Sales engineers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Telemarketers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Door-to-door sales workers, news and street vendors, and related workers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sales and related workers, all other . . . . . . . . . . . . . . . . . . . . . Office and Administrative Support Occupations First-line supervisors/managers of office and administrative support workers . . . . . . . . . . . . . . . . . . . . . . . . Switchboard operators, including answering service . . . . . . . Telephone operators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bill and account collectors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Billing and posting clerks and machine operators . . . . . . . . . Bookkeeping, accounting, and auditing clerks . . . . . . . . . . . . Payroll and timekeeping clerks . . . . . . . . . . . . . . . . . . . . . . . . . See footnotes at end of table. Margin of error2 (±) Women Estimate Margin of error2 (±) Women’s earnings as a percentage of men’s earnings1 Margin of error2 (±) Estimate 40,800 40,500 29,300 28,300 37,500 24,700 25,200 32,800 21,300 28,300 28,200 41,000 23,100 23,100 35,700 25,400 23,500 42,300 56,400 23,600 33,600 33,700 35,900 60,400 63,600 85,900 36,300 61,300 61,100 (B) 60,400 85,800 25,400 30,500 60,400 869 535 6,175 3,652 2,956 1,545 1,030 3,227 3,604 2,722 4,395 1,163 2,709 1,469 1,043 2,663 3,446 658 658 1,110 3,356 1,109 239 2,783 4,787 5,285 2,331 503 339 (B) 2,102 5,226 4,057 1,226 2,134 33,800 26,300 28,000 21,900 33,400 21,000 20,900 22,500 20,400 26,400 (B) 33,900 17,300 19,600 26,700 24,500 26,200 30,400 46,300 18,100 20,500 25,200 23,600 45,700 37,600 49,400 32,200 50,300 50,500 26,400 43,200 (B) 21,500 24,600 42,800 2,584 893 4,371 3,302 2,875 2,173 1,783 676 253 3,958 (B) 2,706 257 564 1,633 2,242 2,162 120 1,267 173 1,918 1,392 648 1,614 1,513 3,104 1,194 1,336 627 1,630 2,724 (B) 1,762 2,686 1,972 82.9 65.0 95.5 77.6 89.0 85.3 82.9 68.7 95.8 93.3 (X) 82.7 74.8 85.1 74.9 96.6 111.3 71.9 82.1 76.9 61.0 74.6 65.9 75.7 59.1 57.5 88.6 82.1 82.6 (X) 71.6 (X) 84.6 80.9 70.9 6.6 2.4 25.1 15.4 10.4 10.3 7.9 7.1 16.3 16.6 (X) 7.0 8.9 5.9 5.1 13.4 18.7 1.2 2.4 3.7 8.4 4.8 1.9 4.4 5.0 5.1 6.6 2.3 1.1 (X) 5.2 (X) 15.2 9.4 4.1 48,900 (B) (B) 32,700 35,900 38,400 37,700 748 (B) (B) 972 1,923 1,426 3,175 38,600 25,400 26,700 30,400 29,800 32,100 34,400 380 1,165 2,352 229 505 360 931 78.9 (X) (X) 92.9 83.1 83.7 91.1 1.4 (X) (X) 2.8 4.7 3.2 8.0 Income, Earnings, and Poverty Data From the 2007 American Community Survey U.S. Census Bureau 37 Table A-2. Median Earnings in the Past 12 Months of Workers by Sex and Women’s Earnings as a Percentage of Men’s Earnings by Detailed Occupation for the United States: 2007—Con. (In 2007 inflation-adjusted dollars. For information on confidentiality protection, sampling error, nonsampling error, and definitions, see www.census.gov/acs/www/) Median earnings (dollars) Occupation Men Estimate Office and Administrative Support Occupations—Con. Procurement clerks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tellers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Court, municipal, and license clerks . . . . . . . . . . . . . . . . . . . . . Credit authorizers, checkers, and clerks . . . . . . . . . . . . . . . . . Customer service representatives . . . . . . . . . . . . . . . . . . . . . . . Eligibility interviewers, government programs . . . . . . . . . . . . . File clerks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hotel, motel, and resort desk clerks . . . . . . . . . . . . . . . . . . . . . Interviewers, except eligibility and loan . . . . . . . . . . . . . . . . . . Library assistants, clerical . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Loan interviewers and clerks . . . . . . . . . . . . . . . . . . . . . . . . . . . New accounts clerks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Order clerks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Human resources assistants, except payroll and timekeeping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Receptionists and information clerks . . . . . . . . . . . . . . . . . . . . Reservation and transportation ticket agents and travel clerks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Information and record clerks, all other . . . . . . . . . . . . . . . . . . Cargo and freight agents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Couriers and messengers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dispatchers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Meter readers, utilities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Postal service clerks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Postal service mail carriers . . . . . . . . . . . . . . . . . . . . . . . . . . . . Postal service mail sorters, processors, and processing machine operators . . . . . . . . . . . . . . . . . . . . . . . . Production, planning, and expediting clerks . . . . . . . . . . . . . . Shipping, receiving, and traffic clerks . . . . . . . . . . . . . . . . . . . . Stock clerks and order fillers . . . . . . . . . . . . . . . . . . . . . . . . . . . Weighers, measurers, checkers, and samplers, recordkeeping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Secretaries and administrative assistants . . . . . . . . . . . . . . . . Computer operators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Data entry keyers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Word processors and typists . . . . . . . . . . . . . . . . . . . . . . . . . . . Insurance claims and policy processing clerks . . . . . . . . . . . Mail clerks and mail machine operators, except postal service . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Office clerks, general . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Office machine operators, except computer . . . . . . . . . . . . . . Statistical assistants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Office and administrative support workers, all other . . . . . . . Farming, Fishing, and Forestry Occupations First-line supervisors/managers of farming, fishing, and forestry workers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Agricultural inspectors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Graders and sorters, agricultural products . . . . . . . . . . . . . . . Miscellaneous agricultural workers . . . . . . . . . . . . . . . . . . . . . . Fishers and related fishing workers . . . . . . . . . . . . . . . . . . . . . Forest and conservation workers . . . . . . . . . . . . . . . . . . . . . . . Logging workers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . See footnotes at end of table. Margin of error2 (±) Women Estimate Margin of error2 (±) Women’s earnings as a percentage of men’s earnings1 Margin of error2 (±) Estimate 55,300 24,300 41,600 43,700 34,900 42,100 31,400 22,900 32,600 (B) 40,700 (B) 30,700 (B) 30,300 39,000 36,400 40,400 38,900 38,900 36,500 50,200 51,000 50,700 50,900 29,500 25,500 35,900 36,200 42,500 30,800 32,700 35,800 28,600 34,600 30,700 (B) 41,300 3,469 985 1,601 8,562 957 4,649 2,291 1,453 5,517 (B) 1,367 (B) 436 (B) 786 3,310 5,415 888 1,974 1,505 3,775 477 265 387 620 666 160 2,342 1,344 2,341 1,109 2,441 1,171 1,408 1,778 1,308 (B) 1,546 37,500 22,600 31,400 32,300 30,100 37,000 28,500 20,400 27,400 25,300 35,300 29,600 28,200 33,700 25,300 32,600 30,700 (B) 30,300 30,900 (B) 48,000 47,100 48,300 35,700 25,200 23,400 25,600 32,000 33,800 28,700 30,400 30,900 25,500 30,100 25,800 35,100 34,500 1,926 235 1,365 1,755 252 1,515 507 342 1,326 582 956 2,877 943 2,319 156 1,694 438 (B) 2,088 676 (B) 1,068 951 525 883 260 360 1,622 293 1,841 426 196 760 513 268 1,115 1,531 748 67.8 92.9 75.6 74.0 86.2 87.8 90.9 89.1 84.2 (X) 86.6 (X) 91.9 (X) 83.5 83.6 84.4 (X) 78.0 79.4 (X) 95.5 92.4 95.4 70.2 85.5 91.6 71.1 88.5 79.6 93.3 92.9 86.2 89.3 87.0 84.1 (X) 83.7 5.5 3.9 4.4 15.1 2.5 10.3 6.8 5.8 14.8 (X) 3.7 (X) 3.3 (X) 2.2 8.3 12.6 (X) 6.7 3.5 (X) 2.3 1.9 1.3 1.9 2.1 1.5 6.5 3.4 6.2 3.6 7.0 3.5 4.8 4.5 5.1 (X) 3.6 36,700 45,900 26,600 20,900 30,200 35,600 30,000 2,671 2,627 6,863 722 4,113 8,886 1,675 (B) (B) 18,400 17,300 (B) (B) (B) (B) (B) 2,091 360 (B) (B) (B) (X) (X) 69.3 82.5 (X) (X) (X) (X) (X) 19.6 3.3 (X) (X) (X) 38 Income, Earnings, and Poverty Data From the 2007 American Community Survey U.S. Census Bureau Table A-2. Median Earnings in the Past 12 Months of Workers by Sex and Women’s Earnings as a Percentage of Men’s Earnings by Detailed Occupation for the United States: 2007—Con. (In 2007 inflation-adjusted dollars. For information on confidentiality protection, sampling error, nonsampling error, and definitions, see www.census.gov/acs/www/) Median earnings (dollars) Occupation Men Estimate Construction and Extraction Occupations First-line supervisors/managers of construction trades and extraction workers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Boilermakers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Brickmasons, blockmasons, and stonemasons . . . . . . . . . . . Carpenters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Carpet, floor, and tile installers and finishers . . . . . . . . . . . . . Cement masons, concrete finishers, and terrazzo workers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Construction laborers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Paving, surfacing, and tamping equipment operators . . . . . . Operating engineers and other construction equipment operators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Drywall installers, ceiling tile installers, and tapers . . . . . . . . Electricians . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Glaziers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Insulation workers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Painters, construction and maintenance . . . . . . . . . . . . . . . . . Pipelayers, plumbers, pipefitters, and steamfitters . . . . . . . . Plasterers and stucco masons . . . . . . . . . . . . . . . . . . . . . . . . . Roofers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sheet metal workers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Structural iron and steel workers . . . . . . . . . . . . . . . . . . . . . . . Helpers, construction trades . . . . . . . . . . . . . . . . . . . . . . . . . . . Construction and building inspectors . . . . . . . . . . . . . . . . . . . . Elevator installers and repairers . . . . . . . . . . . . . . . . . . . . . . . . Fence erectors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hazardous materials removal workers . . . . . . . . . . . . . . . . . . . Highway maintenance workers . . . . . . . . . . . . . . . . . . . . . . . . . Rail-track laying and maintenance equipment operators . . . Septic tank servicers and sewer pipe cleaners . . . . . . . . . . . Miscellaneous construction and related workers . . . . . . . . . . Derrick, rotary drill, and service unit operators, oil, gas, and mining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Earth drillers, except oil and gas . . . . . . . . . . . . . . . . . . . . . . . . Explosives workers, ordnance handling experts, and blasters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mining machine operators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Other extraction workers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Installation, Maintenance, and Repair Occupations First-line supervisors/managers of mechanics, installers, and repairers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Computer, automated teller, and office machine repairers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Radio and telecommunications equipment installers and repairers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Avionics technicians . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Electric motor, power tool, and related repairers . . . . . . . . . . Electrical and electronics repairers, industrial and utility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . See footnotes at end of table. Margin of error2 (±) Women Estimate Margin of error2 (±) Women’s earnings as a percentage of men’s earnings1 Margin of error2 (±) Estimate 50,800 49,400 30,500 31,900 30,500 30,200 27,700 31,800 39,000 26,500 42,200 35,500 32,300 27,200 40,400 26,500 26,500 36,900 42,400 21,300 48,700 66,500 26,300 36,400 34,500 48,700 35,400 33,400 53,500 40,600 40,700 50,800 45,600 234 3,849 541 670 308 1,666 847 6,239 1,351 750 883 972 3,070 1,255 299 1,750 1,895 1,301 3,093 1,209 1,344 6,799 3,377 2,907 1,120 5,358 2,449 3,601 5,756 1,798 7,220 480 3,170 43,000 (B) (B) 27,400 (B) (B) 25,100 (B) (B) (B) 33,900 (B) (B) 25,200 (B) (B) (B) (B) (B) (B) 45,300 (B) (B) (B) (B) (B) (B) (B) (B) (B) (B) (B) (B) 5,307 (B) (B) 4,122 (B) (B) 1,415 (B) (B) (B) 4,912 (B) (B) 873 (B) (B) (B) (B) (B) (B) 4,687 (B) (B) (B) (B) (B) (B) (B) (B) (B) (B) (B) (B) 84.8 (X) (X) 86.0 (X) (X) 90.5 (X) (X) (X) 80.4 (X) (X) 92.9 (X) (X) (X) (X) (X) (X) 93.1 (X) (X) (X) (X) (X) (X) (X) (X) (X) (X) (X) (X) 10.5 (X) (X) 13.1 (X) (X) 5.8 (X) (X) (X) 11.8 (X) (X) 5.4 (X) (X) (X) (X) (X) (X) 10.0 (X) (X) (X) (X) (X) (X) (X) (X) (X) (X) (X) (X) 51,700 42,500 52,200 48,600 45,800 60,800 1,671 1,048 1,852 5,859 1,406 1,313 48,100 40,100 52,700 (B) (B) (B) 3,821 2,112 3,042 (B) (B) (B) 93.0 94.4 101.0 (X) (X) (X) 8.0 5.5 6.8 (X) (X) (X) Income, Earnings, and Poverty Data From the 2007 American Community Survey U.S. Census Bureau 39 Table A-2. Median Earnings in the Past 12 Months of Workers by Sex and Women’s Earnings as a Percentage of Men’s Earnings by Detailed Occupation for the United States: 2007—Con. (In 2007 inflation-adjusted dollars. For information on confidentiality protection, sampling error, nonsampling error, and definitions, see www.census.gov/acs/www/) Median earnings (dollars) Occupation Men Estimate Installation, Maintenance, and Repair Occupations—Con. Electronic equipment installers and repairers, motor vehicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Electronic home entertainment equipment installers and repairers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Security and fire alarm systems installers . . . . . . . . . . . . . . . . Aircraft mechanics and service technicians . . . . . . . . . . . . . . Automotive body and related repairers . . . . . . . . . . . . . . . . . . Automotive glass installers and repairers . . . . . . . . . . . . . . . . Automotive service technicians and mechanics . . . . . . . . . . . Bus and truck mechanics and diesel engine specialists . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Heavy vehicle and mobile equipment service technicians and mechanics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Small engine mechanics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Miscellaneous vehicle and mobile equipment mechanics, installers, and repairers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Control and valve installers and repairers . . . . . . . . . . . . . . . . Heating, air conditioning, and refrigeration mechanics and installers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Home appliance repairers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Industrial and refractory machinery mechanics . . . . . . . . . . . Maintenance and repair workers, general . . . . . . . . . . . . . . . . Maintenance workers, machinery . . . . . . . . . . . . . . . . . . . . . . . Millwrights . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Electrical power-line installers and repairers . . . . . . . . . . . . . Telecommunications line installers and repairers . . . . . . . . . . Precision instrument and equipment repairers . . . . . . . . . . . . Coin, vending, and amusement machine servicers and repairers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Locksmiths and safe repairers . . . . . . . . . . . . . . . . . . . . . . . . . . Riggers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Helpers—installation, maintenance, and repair workers . . . Other installation, maintenance, and repair workers . . . . . . . Production Occupations First-line supervisors/managers of production and operating workers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Electrical, electronics, and electromechanical assemblers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Engine and other machine assemblers . . . . . . . . . . . . . . . . . . Structural metal fabricators and fitters . . . . . . . . . . . . . . . . . . . Miscellaneous assemblers and fabricators . . . . . . . . . . . . . . . Bakers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Butchers and other meat, poultry, and fish processing workers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Food batchmakers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Computer control programmers and operators . . . . . . . . . . . Extruding and drawing machine setters, operators, and tenders, metal and plastic . . . . . . . . . . . . . . . . . . . . . . . . . . . . See footnotes at end of table. Margin of error2 (±) Women Estimate Margin of error2 (±) Women’s earnings as a percentage of men’s earnings1 Margin of error2 (±) Estimate 45,600 36,500 40,800 50,900 36,000 33,300 34,600 40,300 44,100 31,500 25,300 44,000 39,800 35,600 45,600 40,400 42,700 50,600 56,100 46,600 46,200 31,300 37,100 43,700 22,500 36,000 6,682 2,800 790 462 1,202 4,512 745 403 1,587 1,759 714 3,701 1,319 1,160 299 299 3,152 564 1,939 2,324 3,149 1,642 4,843 4,481 1,995 1,324 (B) (B) (B) 39,100 (B) (B) 26,700 (B) (B) (B) (B) (B) (B) (B) 38,500 32,600 (B) (B) (B) 46,400 (B) (B) (B) (B) (B) 35,500 (B) (B) (B) 12,976 (B) (B) 5,488 (B) (B) (B) (B) (B) (B) (B) 4,099 3,000 (B) (B) (B) 8,712 (B) (B) (B) (B) (B) 6,384 (X) (X) (X) 76.8 (X) (X) 77.2 (X) (X) (X) (X) (X) (X) (X) 84.3 80.7 (X) (X) (X) 99.5 (X) (X) (X) (X) (X) 98.6 (X) (X) (X) 25.5 (X) (X) 15.9 (X) (X) (X) (X) (X) (X) (X) 9.0 7.5 (X) (X) (X) 19.3 (X) (X) (X) (X) (X) 18.1 50,200 30,700 36,600 39,000 30,800 26,400 26,700 29,400 40,100 33,800 334 939 4,144 3,053 156 821 1,112 1,879 1,661 3,458 35,700 24,100 (B) (B) 24,600 20,600 20,400 22,300 (B) (B) 431 893 (B) (B) 595 1,054 520 1,925 (B) (B) 71.2 78.5 (X) (X) 80.0 78.0 76.3 75.8 (X) (X) 1.0 3.8 (X) (X) 2.0 4.7 3.7 8.1 (X) (X) 40 Income, Earnings, and Poverty Data From the 2007 American Community Survey U.S. Census Bureau Table A-2. Median Earnings in the Past 12 Months of Workers by Sex and Women’s Earnings as a Percentage of Men’s Earnings by Detailed Occupation for the United States: 2007—Con. (In 2007 inflation-adjusted dollars. For information on confidentiality protection, sampling error, nonsampling error, and definitions, see www.census.gov/acs/www/) Median earnings (dollars) Occupation Men Estimate Production Occupations—Con. Forging machine setters, operators, and tenders, metal and plastic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Rolling machine setters, operators, and tenders, metal and plastic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cutting, punching, and press machine setters, operators, and tenders, metal and plastic . . . . . . . . . . . . . . . . . . . . . . . . Grinding, lapping, polishing, and buffing machine tool setters, operators, and tenders, metal and plastic . . . . Lathe and turning machine tool setters, operators, and tenders, metal and plastic . . . . . . . . . . . . . . . . . . . . . . . . Machinists . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Metal furnace and kiln operators and tenders . . . . . . . . . . . . Molders and molding machine setters, operators, and tenders, metal and plastic . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tool and die makers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Welding, soldering, and brazing workers . . . . . . . . . . . . . . . . . Plating and coating machine setters, operators, and tenders, metal and plastic . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tool grinders, filers, and sharpeners . . . . . . . . . . . . . . . . . . . . Metalworkers and plastic workers, all other . . . . . . . . . . . . . . Bookbinders and bindery workers . . . . . . . . . . . . . . . . . . . . . . . Job printers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Prepress technicians and workers . . . . . . . . . . . . . . . . . . . . . . Printing machine operators . . . . . . . . . . . . . . . . . . . . . . . . . . . . Laundry and dry-cleaning workers . . . . . . . . . . . . . . . . . . . . . . Pressers, textile, garment, and related materials . . . . . . . . . . Sewing machine operators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shoe and leather workers and repairers . . . . . . . . . . . . . . . . . Tailors, dressmakers, and sewers . . . . . . . . . . . . . . . . . . . . . . . Upholsterers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Textile, apparel, and furnishings workers, all other . . . . . . . . Cabinetmakers and bench carpenters . . . . . . . . . . . . . . . . . . . Furniture finishers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sawing machine setters, operators, and tenders, wood . . . . Woodworking machine setters, operators, and tenders, except sawing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Woodworkers, all other . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Power plant operators, distributors, and dispatchers . . . . . . Stationary engineers and boiler operators . . . . . . . . . . . . . . . Water and liquid waste treatment plant and system operators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Miscellaneous plant and system operators . . . . . . . . . . . . . . . Chemical processing machine setters, operators, and tenders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Crushing, grinding, polishing, mixing, and blending workers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cutting workers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Extruding, forming, pressing, and compacting machine setters, operators, and tenders . . . . . . . . . . . . . . . . . . . . . . . . Furnace, kiln, oven, drier, and kettle operators and tenders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . See footnotes at end of table. Margin of error2 (±) Women Estimate Margin of error2 (±) Women’s earnings as a percentage of men’s earnings1 Margin of error2 (±) Estimate 36,500 35,700 30,700 30,800 34,500 41,000 40,100 35,500 50,600 35,200 28,900 37,600 33,500 33,400 32,800 40,300 37,000 22,400 20,900 20,600 27,300 29,400 27,100 28,700 30,600 30,400 25,200 28,300 28,800 71,600 50,800 40,600 56,600 51,100 34,200 28,700 32,800 40,900 1,989 1,477 926 1,612 4,877 229 2,244 1,566 413 480 3,308 3,826 1,036 2,191 4,516 3,162 1,622 1,349 1,813 1,061 3,405 3,974 2,152 3,816 674 3,540 1,181 3,532 2,949 1,209 640 683 4,532 1,189 1,716 1,798 1,500 6,134 (B) (B) 24,500 (B) (B) 28,700 (B) 25,200 (B) 25,400 (B) (B) 24,600 25,600 26,200 27,700 24,600 17,500 16,700 18,600 (B) 22,500 (B) (B) (B) (B) (B) (B) (B) (B) (B) (B) (B) (B) 25,700 21,500 (B) (B) (B) (B) 1,798 (B) (B) 2,069 (B) 3,318 (B) 403 (B) (B) 628 1,619 1,291 3,570 1,451 1,179 729 1,158 (B) 1,364 (B) (B) (B) (B) (B) (B) (B) (B) (B) (B) (B) (B) 6,365 1,357 (B) (B) (X) (X) 79.7 (X) (X) 70.0 (X) 70.8 (X) 72.2 (X) (X) 73.4 76.4 79.8 68.8 66.5 77.8 80.1 90.0 (X) 76.5 (X) (X) (X) (X) (X) (X) (X) (X) (X) (X) (X) (X) 75.0 75.0 (X) (X) (X) (X) 6.3 (X) (X) 5.1 (X) 9.8 (X) 1.5 (X) (X) 2.9 7.0 11.7 10.4 4.9 7.0 7.8 7.3 (X) 11.3 (X) (X) (X) (X) (X) (X) (X) (X) (X) (X) (X) (X) 19.0 6.7 (X) (X) Income, Earnings, and Poverty Data From the 2007 American Community Survey U.S. Census Bureau 41 Table A-2. Median Earnings in the Past 12 Months of Workers by Sex and Women’s Earnings as a Percentage of Men’s Earnings by Detailed Occupation for the United States: 2007—Con. (In 2007 inflation-adjusted dollars. For information on confidentiality protection, sampling error, nonsampling error, and definitions, see www.census.gov/acs/www/) Median earnings (dollars) Occupation Men Estimate Production Occupations—Con. Inspectors, testers, sorters, samplers, and weighers . . . . . . Jewelers and precious stone and metal workers . . . . . . . . . . Medical, dental, and ophthalmic laboratory technicians . . . . Packaging and filling machine operators and tenders . . . . . Painting workers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Photographic process workers and processing machine operators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Molders, shapers, and casters, except metal and plastic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Paper goods machine setters, operators, and tenders . . . . . Tire builders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Helpers—production workers . . . . . . . . . . . . . . . . . . . . . . . . . . . Production workers, all other . . . . . . . . . . . . . . . . . . . . . . . . . . . Transportation and Material Moving Occupations Supervisors, transportation and material moving workers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Aircraft pilots and flight engineers . . . . . . . . . . . . . . . . . . . . . . . Air traffic controllers and airfield operations specialists . . . . Bus drivers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Driver/sales workers and truck drivers . . . . . . . . . . . . . . . . . . . Taxi drivers and chauffeurs . . . . . . . . . . . . . . . . . . . . . . . . . . . . Motor vehicle operators, all other . . . . . . . . . . . . . . . . . . . . . . . Locomotive engineers and operators . . . . . . . . . . . . . . . . . . . . Railroad brake, signal, and switch operators . . . . . . . . . . . . . Railroad conductors and yardmasters . . . . . . . . . . . . . . . . . . . Sailors and marine oilers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ship and boat captains and operators . . . . . . . . . . . . . . . . . . . Parking lot attendants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Service station attendants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Transportation inspectors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Other transportation workers . . . . . . . . . . . . . . . . . . . . . . . . . . . Crane and tower operators . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dredge, excavating, and loading machine operators . . . . . . Industrial truck and tractor operators . . . . . . . . . . . . . . . . . . . . Cleaners of vehicles and equipment . . . . . . . . . . . . . . . . . . . . Laborers and freight, stock, and material movers, hand . . . Machine feeders and offbearers . . . . . . . . . . . . . . . . . . . . . . . . Packers and packagers, hand . . . . . . . . . . . . . . . . . . . . . . . . . . Pumping station operators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Refuse and recyclable material collectors . . . . . . . . . . . . . . . . Material moving workers, all other . . . . . . . . . . . . . . . . . . . . . . Margin of error2 (±) Women Estimate Margin of error2 (±) Women’s earnings as a percentage of men’s earnings1 Margin of error2 (±) Estimate 41,100 32,300 38,100 26,400 32,100 29,500 30,500 41,100 41,100 25,100 32,900 326 2,160 3,255 1,691 1,355 5,606 1,199 3,163 12,635 1,425 521 28,200 25,300 30,100 20,500 22,400 20,400 (B) 22,400 (B) (B) 25,300 791 4,167 1,726 398 1,219 604 (B) 2,249 (B) (B) 163 68.7 78.3 79.0 77.9 69.8 69.1 (X) 54.4 (X) (X) 77.0 2.0 13.9 8.1 5.2 4.8 13.3 (X) 6.9 (X) (X) 1.3 46,900 87,100 77,000 34,800 38,900 26,400 23,300 66,700 48,700 60,600 35,200 51,000 22,200 21,100 55,100 35,000 50,600 38,000 29,400 22,300 28,400 28,700 23,300 46,100 29,300 35,400 2,532 6,763 11,030 977 313 918 3,348 1,364 6,962 2,648 1,546 5,116 3,473 1,130 2,645 4,034 823 2,569 714 1,166 308 3,130 1,380 2,938 2,706 2,449 35,600 (B) (B) 25,400 28,200 21,500 (B) (B) (B) (B) (B) (B) (B) 17,900 (B) (B) (B) (B) 28,100 20,300 23,300 20,500 20,200 (B) (B) (B) 957 (B) (B) 387 2,032 1,716 (B) (B) (B) (B) (B) (B) (B) 1,479 (B) (B) (B) (B) 1,594 637 489 2,393 202 (B) (B) (B) 76.0 (X) (X) 73.0 72.5 81.7 (X) (X) (X) (X) (X) (X) (X) 84.7 (X) (X) (X) (X) 95.8 90.9 82.0 71.3 86.7 (X) (X) (X) 4.6 (X) (X) 2.3 5.3 7.1 (X) (X) (X) (X) (X) (X) (X) 8.4 (X) (X) (X) (X) 5.9 5.5 1.9 11.4 5.2 (X) (X) (X) (B) Less than 100 sample cases. Base figure too small to meet statistical standards for reliability of a derived figure. (X) Not applicable. 1 Ratios calculated from unrounded data. 2 Data are based on a sample and are subject to sampling variability. A margin of error is a measure of an estimate’s variability. The larger the margin of error in relation to the size of the estimate, the less reliable the estimate. When added to and subtracted from the estimate, the margin of error forms the 90-percent confidence interval. Source: U.S. Census Bureau, 2007 American Community Survey. 42 Income, Earnings, and Poverty Data From the 2007 American Community Survey U.S. Census Bureau APPENDIX B. POVERTY Figure B-1. Percentage of People in Poverty in the Past 12 Months With Margins of Error by State: 2007 New Hampshire Connecticut Hawaii Maryland New Jersey Wyoming Alaska Minnesota Utah Virginia Massachusetts Vermont Delaware Nevada Wisconsin Iowa Kansas Nebraska Washington Pennsylvania Illinois Colorado Rhode Island Maine North Dakota Florida Idaho Indiana California Oregon United States Missouri South Dakota Ohio New York Michigan Montana Arizona Georgia North Carolina South Carolina Tennessee Oklahoma Texas District of Columbia Alabama West Virginia Kentucky Arkansas New Mexico Louisiana Mississippi 2007 estimate Margin of error 0 5 10 15 20 25 Source: U.S. Census Bureau, 2007 American Community Survey. Income, Earnings, and Poverty Data From the 2007 American Community Survey U.S. Census Bureau 43 44 In metropolitan or micropolitan statistical area In metropolitan statistical area In micropolitan statistical area Total Margin of error2 Percent(±) age1 Percentage1 13.1 16.2 (B) 17.3 17.9 13.8 9.9 4.4 9.0 (X) 15.3 1.2 1.1 1.6 0.7 0.8 1.2 1.1 1.5 1.3 1.9 0.4 0.3 0.3 0.4 1.0 0.4 2.0 0.8 0.8 0.7 8.1 (X) 13.9 10.3 24.5 15.1 13.6 11.9 11.7 7.9 1.6 (X) 0.7 0.7 1.2 1.1 1.7 1.1 2.4 0.9 27.6 10.8 18.2 20.6 17.2 15.9 16.6 23.0 30.5 (B) (B) (X) 21.5 13.8 30.7 19.8 19.0 14.0 12.4 9.1 2.5 2.7 2.5 1.5 1.6 1.9 1.6 2.3 2.8 (B) (B) (X) 1.8 1.3 2.3 2.0 3.1 1.6 3.7 2.0 16.3 8.2 12.3 11.1 10.0 9.0 11.6 19.3 21.0 8.2 6.9 (X) 11.9 7.8 20.9 12.4 10.2 9.2 11.3 7.5 0.2 1.1 (B) 2.3 1.3 1.5 1.9 0.9 1.4 (X) 1.3 21.8 (B) 18.3 24.0 18.0 17.9 (B) (B) (X) 12.7 2.1 (B) 4.5 2.4 2.9 4.0 (B) (B) (X) 2.6 20.1 0.3 Margin of error2 (±) In principal city Not in principal city Total Total Margin of error2 Percent(±) age1 Percentage1 9.4 13.7 7.9 11.2 14.3 10.9 8.1 5.6 8.6 (X) 10.7 10.8 7.0 10.3 7.8 8.6 5.6 6.5 11.9 14.1 9.1 6.4 7.5 9.4 6.6 12.7 8.9 9.8 6.0 9.1 5.1 0.4 0.9 1.2 0.3 0.4 0.6 0.7 0.7 0.7 1.0 19.9 8.8 14.4 14.5 12.5 12.7 14.5 20.3 23.8 12.1 0.7 1.7 0.7 0.9 0.3 0.6 0.4 1.0 (X) 0.2 17.9 (B) 17.7 20.0 14.9 12.4 6.1 9.2 (X) 14.9 0.1 15.4 0.1 0.5 0.9 0.5 0.7 0.2 0.4 0.4 0.9 1.4 0.2 0.4 0.5 0.7 0.3 0.3 0.5 0.5 0.6 0.5 0.8 0.4 0.3 0.3 0.3 0.7 0.4 1.0 0.5 0.7 0.6 8.2 10.0 13.9 9.1 15.5 11.8 12.6 10.6 10.5 6.4 0.4 0.3 0.3 0.3 1.0 0.4 1.3 0.7 0.7 0.7 16.0 17.2 23.4 14.7 23.3 18.7 14.1 13.6 12.0 10.6 1.0 0.8 0.6 0.7 2.1 1.0 1.8 1.1 1.0 2.2 13.1 7.6 11.1 11.7 12.3 11.1 10.3 14.0 16.8 10.5 0.4 0.7 0.8 0.3 0.4 0.6 0.6 0.6 0.6 0.9 21.8 8.6 12.0 17.7 18.0 15.9 14.3 17.1 20.7 14.8 1.0 1.1 1.1 0.5 0.7 1.0 0.9 1.0 1.2 2.0 16.0 7.4 13.6 16.5 12.4 11.8 8.1 10.8 16.4 11.9 0.6 1.0 0.5 0.8 0.2 0.5 0.4 1.0 1.4 0.2 19.8 7.2 15.5 19.0 13.9 16.6 13.8 23.6 16.4 15.1 1.0 1.2 0.7 1.5 0.3 0.8 1.0 4.2 1.4 0.5 12.4 0.1 17.2 0.1 Margin of error2 Percent(±) age1 Margin of error2 (±) Margin of error2 Percent(±) age1 In principal city Not in principal city Not in metropolitan or micropolitan statistical area Margin of error2 (±) 0.2 1.3 (B) 2.4 1.6 1.8 2.3 0.9 1.4 (X) 1.5 1.4 1.3 1.8 0.9 0.8 1.3 1.4 1.9 1.4 1.5 1.7 (X) 0.7 0.7 1.6 1.1 2.2 1.3 2.8 0.9 16.6 21.3 14.5 26.8 20.0 12.9 13.8 (X) (X) (X) 17.7 19.2 (B) 13.7 12.7 11.9 9.8 10.1 23.0 24.1 15.1 12.7 (B) 15.3 10.9 24.8 17.7 16.2 12.2 (B) (B) 0.2 1.6 1.3 2.4 1.4 1.7 1.4 (X) (X) (X) 1.9 1.4 (B) 1.9 0.9 1.4 0.8 0.9 1.3 2.0 1.1 2.5 (B) 0.7 0.7 1.6 1.1 1.4 0.9 (B) (B) Margin of error2 Percent(±) age1 Table B-1. Percentage of People Below Poverty Level in the Past 12 Months by Metropolitan or Micropolitan Statistical Area Status and State: 2007 (For information on confidentiality protection, sampling error, nonsampling error, and definitions, see www.census.gov/acs/www/) Area Percentage1 United States . . . . 12.7 Alabama . . . . . . . . . . . . . . . . . Alaska . . . . . . . . . . . . . . . . . . Arizona . . . . . . . . . . . . . . . . . . Arkansas . . . . . . . . . . . . . . . . California . . . . . . . . . . . . . . . . Colorado . . . . . . . . . . . . . . . . Connecticut . . . . . . . . . . . . . . Delaware . . . . . . . . . . . . . . . . District of Columbia . . . . . . . . Florida . . . . . . . . . . . . . . . . . . 16.3 7.2 13.7 17.3 12.4 11.8 7.9 10.5 16.4 12.0 Georgia . . . . . . . . . . . . . . . . . Hawaii . . . . . . . . . . . . . . . . . . Idaho . . . . . . . . . . . . . . . . . . . Illinois . . . . . . . . . . . . . . . . . . . Indiana . . . . . . . . . . . . . . . . . . Iowa . . . . . . . . . . . . . . . . . . . . Kansas . . . . . . . . . . . . . . . . . . Kentucky . . . . . . . . . . . . . . . . Louisiana . . . . . . . . . . . . . . . . Maine . . . . . . . . . . . . . . . . . . . 13.8 8.0 11.9 11.9 12.4 11.5 11.4 15.5 18.2 10.8 Maryland . . . . . . . . . . . . . . . . Massachusetts . . . . . . . . . . . . Michigan . . . . . . . . . . . . . . . . . Minnesota . . . . . . . . . . . . . . . Mississippi . . . . . . . . . . . . . . . Missouri . . . . . . . . . . . . . . . . . Montana . . . . . . . . . . . . . . . . . Nebraska . . . . . . . . . . . . . . . . Nevada . . . . . . . . . . . . . . . . . . New Hampshire . . . . . . . . . . . 8.2 10.0 13.9 9.3 19.4 12.3 13.1 11.0 10.6 6.9 See footnotes at end of table. Income, Earnings, and Poverty Data From the 2007 American Community Survey U.S. Census Bureau Table B-1. Percentage of People Below Poverty Level in the Past 12 Months by Metropolitan or Micropolitan Statistical Area Status and State: 2007—Con. U.S. Census Bureau (For information on confidentiality protection, sampling error, nonsampling error, and definitions, see www.census.gov/acs/www/) In metropolitan or micropolitan statistical area In metropolitan statistical area In micropolitan statistical area Total Margin of error2 Percent(±) age1 Percentage1 (X) 20.1 11.9 14.4 (B) 10.8 13.5 12.4 10.4 (X) 18.3 16.9 25.0 23.0 16.6 (B) 18.9 21.8 26.8 16.1 8.6 0.8 55.8 3.7 (B) 2.7 1.8 2.2 1.6 3.7 (B) 4.1 2.4 4.1 1.8 1.7 (B) 17.1 8.9 15.0 16.0 11.5 9.1 11.8 11.8 16.8 7.3 9.8 53.1 (X) 1.6 0.8 0.7 1.6 0.6 1.1 1.1 0.6 (X) 1.3 1.5 1.0 1.1 2.0 1.3 1.7 1.1 1.6 0.8 1.9 (X) 20.4 21.1 21.8 11.0 20.0 22.2 17.7 22.2 (X) (X) 2.0 2.3 1.7 2.3 1.6 1.9 2.1 2.3 (X) Margin of error2 (±) In principal city Not in principal city Total Total Margin of error2 Percent(±) age1 Percentage1 7.5 17.1 6.9 11.2 6.3 8.9 11.7 10.6 7.6 9.2 12.6 5.8 11.0 12.0 6.2 6.0 6.9 8.9 13.4 6.5 (B) 46.1 0.5 1.4 0.5 0.4 0.6 1.3 0.3 0.4 1.1 0.4 (B) 17.3 13.7 17.6 19.4 13.8 10.2 13.8 14.9 19.0 9.8 9.1 0.3 1.5 0.2 0.4 1.7 0.3 0.8 0.7 0.2 0.9 (X) 20.2 14.2 16.2 10.2 13.4 18.2 14.3 12.1 (X) 0.3 0.9 0.2 0.3 1.1 0.3 0.5 0.5 0.3 0.9 0.5 0.9 0.5 0.2 0.5 1.0 0.3 0.3 0.8 0.4 1.3 0.7 45.0 0.7 41.7 1.3 13.7 9.1 15.0 16.0 9.1 7.9 9.1 10.9 14.9 10.9 6.8 0.5 1.1 0.5 0.2 0.5 1.4 0.3 0.3 1.1 0.4 1.6 18.5 11.4 19.6 19.5 17.3 (B) 14.0 14.5 20.4 18.0 6.5 1.3 1.7 0.9 0.3 1.4 (B) 0.6 0.7 2.4 0.8 1.9 8.6 16.8 13.7 13.2 12.3 13.0 14.3 12.4 11.5 12.0 0.3 1.0 0.2 0.4 1.3 0.3 0.6 0.6 0.3 0.9 19.0 16.5 19.3 15.9 15.5 23.4 17.6 14.7 23.7 18.2 1.4 1.3 0.4 0.7 1.8 0.8 1.0 0.8 0.9 2.3 Margin of error2 Percent(±) age1 Margin of error2 (±) Margin of error2 Percent(±) age1 In principal city Not in principal city Not in metropolitan or micropolitan statistical area Margin of error2 (±) (X) 2.3 0.7 0.8 (B) 0.5 1.2 1.2 0.6 (X) 1.4 2.3 1.1 1.3 2.5 1.3 2.0 1.3 1.8 0.7 3.4 4.6 (X) 24.3 12.8 18.6 13.2 14.2 19.6 17.0 13.3 (X) 25.1 19.2 19.8 17.5 14.6 12.6 15.2 16.9 19.6 11.4 9.9 (B) (X) 3.7 1.0 1.3 1.2 1.3 1.2 2.6 1.1 (X) 2.7 1.8 1.5 1.0 2.1 1.5 1.1 1.8 1.5 0.7 2.4 (B) Area Percentage1 Margin of error2 Percent(±) age1 Income, Earnings, and Poverty Data From the 2007 American Community Survey New Jersey . . . . . . . . . . . . . . New Mexico . . . . . . . . . . . . . . New York . . . . . . . . . . . . . . . . North Carolina . . . . . . . . . . . . North Dakota . . . . . . . . . . . . . Ohio . . . . . . . . . . . . . . . . . . . . Oklahoma . . . . . . . . . . . . . . . Oregon . . . . . . . . . . . . . . . . . . Pennsylvania . . . . . . . . . . . . . Rhode Island . . . . . . . . . . . . . 8.6 17.9 13.7 13.9 11.6 13.1 15.3 12.8 11.6 12.0 South Carolina . . . . . . . . . . . . South Dakota . . . . . . . . . . . . . Tennessee . . . . . . . . . . . . . . . Texas . . . . . . . . . . . . . . . . . . . Utah . . . . . . . . . . . . . . . . . . . . Vermont . . . . . . . . . . . . . . . . . Virginia . . . . . . . . . . . . . . . . . . Washington . . . . . . . . . . . . . . West Virginia . . . . . . . . . . . . . Wisconsin . . . . . . . . . . . . . . . Wyoming . . . . . . . . . . . . . . . . 14.4 10.8 15.5 16.2 9.4 9.2 9.3 11.2 16.0 10.7 8.2 Puerto Rico . . . . . . . . . . . . . . 45.4 (B) Data for territories below the population of 65,000 are not published as single-year estimates by the American Community Survey. (X) Not applicable. Indicates states that do not contain any territory in micropolitan statistical areas and/or do not contain any territory outside of metropolitan or micropolitan statistical areas. 1 Poverty status is determined for individuals in housing units and noninstitutional group quarters except people living in college dormitories or military barracks. Unrelated individuals under 15 years old are also excluded from the poverty universe. 2 Data are based on a sample and are subject to sampling variability. A margin of error is a measure of an estimate’s variability. The larger the margin of error in relation to the size of the estimate, the less reliable the estimate. When added to and subtracted from the estimate, the margin of error forms the 90-percent confidence interval. Source: U.S. Census Bureau, 2007 American Community Survey and 2007 Puerto Rico Community Survey. 45 Table B-2. Number and Percentage of People by Income-to-Poverty Ratio in the Past 12 Months by State: 2007 (For information on confidentiality protection, sampling error, nonsampling error, and definitions, see www.census.gov/acs/www/) Percentage of people with income-to-poverty ratio less than— Area All people for whom poverty status is determined1 (thousands) 293,744 4,507 667 6,225 2,754 35,768 4,756 3,388 838 560 17,847 9,286 1,255 1,464 12,541 6,145 2,882 2,689 4,121 4,167 1,281 5,478 6,245 9,833 5,067 2,822 5,709 933 1,719 2,529 1,275 8,506 1,926 18,775 8,793 613 11,151 3,498 3,670 11,999 1,019 4,270 768 5,997 23,284 2,601 600 7,466 6,338 1,763 5,447 509 3,878 50 percent Estimate 5.6 6.9 3.7 6.6 6.5 5.1 5.5 3.6 5.2 8.5 5.0 6.4 3.8 4.7 5.3 5.8 4.7 4.8 7.1 7.8 4.6 3.8 4.4 6.5 3.9 8.7 5.7 5.8 4.8 4.6 3.4 3.9 7.6 6.1 6.0 5.3 6.0 6.7 5.7 5.1 5.2 6.7 5.6 6.5 6.8 3.8 3.9 4.2 5.1 7.0 4.5 3.6 26.0 Margin of error2 (±) 0.1 0.3 0.5 0.3 0.4 0.1 0.3 0.3 0.6 1.1 0.1 0.3 0.4 0.5 0.2 0.3 0.3 0.3 0.3 0.4 0.4 0.2 0.2 0.2 0.2 0.5 0.3 0.6 0.4 0.3 0.4 0.2 0.5 0.2 0.2 0.6 0.2 0.3 0.4 0.2 0.7 0.3 0.6 0.3 0.2 0.3 0.4 0.2 0.3 0.5 0.2 0.8 0.6 100 percent Estimate 13.0 16.9 8.9 14.2 17.9 12.4 12.0 7.9 10.5 16.4 12.1 14.3 8.0 12.1 11.9 12.3 11.0 11.2 17.3 18.6 12.0 8.3 9.9 14.0 9.5 20.6 13.0 14.1 11.2 10.7 7.1 8.6 18.1 13.7 14.3 12.1 13.1 15.9 12.9 11.6 12.0 15.0 13.1 15.9 16.3 9.7 10.1 9.9 11.4 16.9 10.8 8.7 45.5 Margin of error2 (±) 0.1 0.5 0.8 0.5 0.6 0.2 0.4 0.4 0.9 1.4 0.2 0.3 0.5 0.6 0.3 0.3 0.5 0.5 0.5 0.5 0.6 0.4 0.3 0.3 0.3 0.7 0.4 0.8 0.5 0.7 0.6 0.3 0.8 0.2 0.3 0.9 0.3 0.5 0.5 0.3 0.9 0.5 0.8 0.5 0.2 0.5 0.9 0.3 0.3 0.6 0.3 1.2 0.7 125 percent Estimate 17.3 22.0 13.3 18.9 23.5 17.3 15.7 10.7 14.3 20.6 16.7 18.8 10.5 17.1 15.8 16.4 14.7 15.2 22.0 24.1 16.4 11.0 12.7 18.2 12.7 27.1 17.6 19.1 15.7 14.1 9.8 11.4 23.5 17.7 19.4 15.7 17.3 21.4 17.6 15.6 15.5 20.2 17.8 20.7 21.8 13.7 14.2 13.1 15.2 22.6 14.6 11.8 54.1 Margin of error2 (±) 0.1 0.5 1.0 0.5 0.7 0.2 0.5 0.4 1.0 1.4 0.3 0.4 0.6 0.7 0.3 0.4 0.5 0.5 0.5 0.6 0.7 0.4 0.3 0.3 0.3 0.8 0.4 1.0 0.6 0.8 0.7 0.3 0.9 0.2 0.3 0.9 0.3 0.6 0.6 0.3 1.0 0.5 0.8 0.5 0.2 0.6 0.9 0.3 0.4 0.7 0.4 1.2 0.7 United States . . . . . . . . . Alabama . . . . . . . . . . . . . . . . . . . . Alaska . . . . . . . . . . . . . . . . . . . . . . Arizona . . . . . . . . . . . . . . . . . . . . . Arkansas . . . . . . . . . . . . . . . . . . . . California . . . . . . . . . . . . . . . . . . . . Colorado . . . . . . . . . . . . . . . . . . . . Connecticut . . . . . . . . . . . . . . . . . . Delaware . . . . . . . . . . . . . . . . . . . . District of Columbia . . . . . . . . . . . . Florida . . . . . . . . . . . . . . . . . . . . . . Georgia . . . . . . . . . . . . . . . . . . . . . Hawaii . . . . . . . . . . . . . . . . . . . . . . Idaho . . . . . . . . . . . . . . . . . . . . . . . Illinois . . . . . . . . . . . . . . . . . . . . . . Indiana . . . . . . . . . . . . . . . . . . . . . Iowa . . . . . . . . . . . . . . . . . . . . . . . Kansas . . . . . . . . . . . . . . . . . . . . . Kentucky . . . . . . . . . . . . . . . . . . . . Louisiana . . . . . . . . . . . . . . . . . . . Maine . . . . . . . . . . . . . . . . . . . . . . Maryland . . . . . . . . . . . . . . . . . . . . Massachusetts . . . . . . . . . . . . . . . Michigan . . . . . . . . . . . . . . . . . . . . Minnesota . . . . . . . . . . . . . . . . . . . Mississippi . . . . . . . . . . . . . . . . . . Missouri . . . . . . . . . . . . . . . . . . . . Montana . . . . . . . . . . . . . . . . . . . . Nebraska . . . . . . . . . . . . . . . . . . . Nevada . . . . . . . . . . . . . . . . . . . . . New Hampshire . . . . . . . . . . . . . . New Jersey . . . . . . . . . . . . . . . . . . New Mexico . . . . . . . . . . . . . . . . . New York . . . . . . . . . . . . . . . . . . . North Carolina . . . . . . . . . . . . . . . . North Dakota . . . . . . . . . . . . . . . . . Ohio . . . . . . . . . . . . . . . . . . . . . . . Oklahoma . . . . . . . . . . . . . . . . . . . Oregon . . . . . . . . . . . . . . . . . . . . . Pennsylvania . . . . . . . . . . . . . . . . . Rhode Island . . . . . . . . . . . . . . . . . South Carolina . . . . . . . . . . . . . . . South Dakota . . . . . . . . . . . . . . . . Tennessee . . . . . . . . . . . . . . . . . . Texas . . . . . . . . . . . . . . . . . . . . . . Utah . . . . . . . . . . . . . . . . . . . . . . . Vermont . . . . . . . . . . . . . . . . . . . . Virginia . . . . . . . . . . . . . . . . . . . . . Washington . . . . . . . . . . . . . . . . . . West Virginia . . . . . . . . . . . . . . . . . Wisconsin . . . . . . . . . . . . . . . . . . . Wyoming . . . . . . . . . . . . . . . . . . . . Puerto Rico . . . . . . . . . . . . . . . . . . 1 Poverty status is determined for individuals in housing units and noninstitutional group quarters except people living in college dormitories or military barracks. Unrelated individuals under 15 years old are also excluded from the poverty universe. 2 Data are based on a sample and are subject to sampling variability. A margin of error is a measure of an estimate’s variability. The larger the margin of error in relation to the size of the estimate, the less reliable the estimate. When added to and subtracted from the estimate, the margin of error forms the 90-percent confidence interval. Source: U.S. Census Bureau, 2007 American Community Survey and 2007 Puerto Rico Community Survey. 46 Income, Earnings, and Poverty Data From the 2007 American Community Survey U.S. Census Bureau

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It’s little wonder that Dallas is one of the healthiest real estate markets in the country.  With continued job growth, despite the nationwide recession, and continuing growth to our population, the Dallas Fort Worth Metroplex, along with Houston, will lead the United States out of the doldrums of the housing recession.

The Dallas-Fort Worth Metroplex was one of four major metropolitan areas that grew by more than 100,000 people between the years of 2007 and 2008, according to a new US Census Bureau Report.

View of Downtown Dallas from Across the Trinity River

View of Downtown Dallas from the Trinity River

 The report says in the span of a year, the D/FW metro area added 147,000 people, while Houston added 130,000 people. Phoenix and Atlanta followed, adding 116,000 and 115,000 people, respectively.

Dallas-Fort Worth also was home to the ninth-ranked fastest growing county: Rockwall.

D-FW population grows by 147,000 - Dallas Business Journal:

The Nations Building News.  23 February 2009

Okay, the housing market is lousy, we get it.  This really isn’t breaking news, so I’ll skip over it, for the moment and move the remainder of the factual content below.  What I’d really like to know - and what I really wish someone could finally explain - is this:  Does the release of a gloomy report like this, along with the accompanying bad news which main-stream media is seemingly required to

heap on help or hurt?  Has the impartial media coverage actually become self-fulfilling?  Main stream media is about as impartial as the Texas Stadium crowd when the Cowboys play the Redskins in November. 

But, I digress.  In the actual story subject matter, in free fall, US housing starts and permits hurtled downward in January, the US Commerce Department reported, to seasonally adjusted annual rates of 466,000 and 521,000 units, respectively. Both represented new record lows in a downturn that has gone unbroken for seven straight months.

"Builders are continuing to exercise extreme caution in response to market conditions, particularly weak consumer demand and the large inventory of homes for sale that is being fueled by a constant flow of foreclosures," said NAHB Chairman Joe Robson.

"We are certainly optimistic that the newly signed economic stimulus package — and particularly the enhanced first-time home buyer tax credit — will help spark more consumer demand for homes going forward,” Robson said. “However, until that happens, builders have little choice but to put a hold on new construction."

Okay, I digress again.  Please read Related Post.

Single-family starts fell 12.2% in January to a seasonally adjusted annual rate of 347,000 units, a new record low, for those still tracking record lows, while multifamily starts plunged nearly 28% to a rate of 119,000 units — also a record low.

Regionally, starts plummeted nearly 43% in the Northeast and 29.3% in the Midwest. They were down 12.8% in the South and 6.4% in the West.

January permit issuance, typically an indicator of future building activity, declined 8% to a seasonally adjusted annual rate of 335,000 units on the single-family side and 1.6% to a rate of 186,000 units on the multifamily side.

Regionally, permits were down 3.3% in the Northeast, 2.4% in the Midwest, 6.9% in the South and 1.8% in the West.

Maybe that’s Obama’s solution.  If the economy continues hurtling towards earth, as he reminds us daily, housing starts will actually reach zero, thus forcing the rapid absorption of remaining inventory. Hey, that might just work. Except we’ll all be penniless and working for the government or a government bank by then.

Maybe thinking through the first gnawing question in light of my last smart ass as usual statement has caused enlightenment.  The force that really is at work here is that builders are being prudent.  Yes, those unabashed greedy capitalists are keeping a measured eye on output given economic conditions and demand.  Let’s see main stream media types give them credit for that.

At Lexington Luxury Builders we have deferred plans to start a dozen townhomes and three big custom single family homes in First Quarter 2009.  And the greedy, capitalist pig bankers I’ve heard so much about on NBC can’t be found.  No sir, my bankers are a bunch of conservative, prudent guys reminding me to be cautious.  But CNBC isn’t telling anybody about us.

President Obama last week unveiled details of a $75 billion foreclosure prevention plan designed to help seven to nine million “responsible” home owners remain in their homes with affordable mortgage payments. The official rollout date for the program is March 4.

“We applaud the Obama Administration for unveiling its plan to stem the rising tide of foreclosures that is flooding the market with excess inventory and undermining overall home values,” said NAHB Chairman Joe Robson. “This is an important first step to address the acute supply problems confronting the housing market.”

The plan has three main components:

  1. A refinancing program for borrowers of mortgages held or guaranteed by Fannie Mae and Freddie Mac who are current on their mortgage payments but who have been unable to refinance because the value of their home has declined.
  2. A mortgage modification program for borrowers in default, or at imminent risk of default, that builds on the model established by the Federal Deposit Insurance Corporation by expanding eligibility and establishing incentives for borrowers, mortgage holders and servicers.
  3. Actions to bolster the financial stability and mortgage support capacity of Fannie Mae and Freddie Mac.

Of particular interest to NAHB are provisions that relate to mortgage loan modifications for primary residences.

“We hope this will focus only on those mortgages responsible for the surge in defaults,” said Robson, adding that NAHB looks forward to working with the Administration and Congress to ensure that any legislative change is done in a careful manner that will have a positive impact on the marketplace.

The Administration believes its plan will enable Fannie Mae and Freddie Mac to refinance four million to five million home owners. Currently, these institutions have rules that make it difficult to refinance mortgages valued at more than 80% of the home’s worth.

For example, on a home valued at $300,000 with a mortgage of $270,000, a home owner might have trouble refinancing through Fannie Mae and Freddie Mac. The Administration will remove limitations on Fannie and Freddie so that they can refinance mortgages they already own or guarantee.

The plan would create new incentives for lenders to work with borrowers to modify the terms of loans at risk of default or foreclosure. This would require both borrowers and lenders to do their part. Lenders would be required to reduce payments to no more than 38% of a borrower’s income. The government would provide a subsidy to help further cut the borrower’s mortgage debt-to-income ratio to 31%. 

To encourage lender participation in the program, the plan provides them with additional financial incentives to modify loans prior to default.  The program also encourages borrowers to stay current on their payments. Those who participate will be required to make payments on time in return for this opportunity to reduce their monthly mortgage payments and stay in their homes. Home owners who remain current on their mortgage payments following loan modification will be eligible for an incentive of up to $1,000 a year from the government for five years. The bonus will be applied to the borrower’s mortgage to lower the principal balance.

The mortgage modification program will also be available to home owners who are “underwater” and owe more on their mortgage than their home is worth.

The plan seeks to shore up Fannie Mae and Freddie Mac to help keep mortgage rates low for millions of middle-class families looking to buy a new home or to refinance an existing one.

The Treasury Department will provide additional financial support for Fannie and Freddie and allow them to increase their portfolios, which is designed to help the broader mortgage finance market.

The Treasury and the Federal Reserve will also continue purchasing Fannie Mae and Freddie Mac mortgage-backed securities to lower mortgage rates and to maintain stability and liquidity in the marketplace.

The foreclosure prevention package also calls on Fannie and Freddie to provide support to state housing finance agencies. These agencies are currently frozen out of the credit markets and are unable to provide much-needed support to first-time home buyers.

With Fannie and Freddie helping the state housing finance agencies to increase their liquidity, this will provide a ripple effect to strengthen the mortgage markets, said Robson.

While the Administration’s plan is aimed at helping to ease excess capacity in the market due in large part to an unprecedented wave of foreclosures, Robson said that Congress still needs to take additional measures to stimulate housing demand to get the economy moving forward again.

“Until we move to resolve the housing crisis, we will not be able to pull the nation out of recession,” he said.

I’d add to that folks, that until we pull the government out of the crisis, we’ll never see a sustainable recovery.  For all of you out there who are now looking at the government as the savior, I’d like to remind you that the government struggles to deliver mail, can’t keep illegal aliens from walking across the border and takes 90 days to be able to deliver a passport.  And these are the same brilliant people you want running the most evolved economy in the history of mankind?  Not me brother.

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With most economists and builders expecting a national market decline this year, this may not seem like the best time to be selecting the “healthiest” markets in the country. Virtually every market was down last year. But a close look at the numbers reveals that some markets have way outperformed others during the last four years and are likely to continue to do so this year.

When the housing market stages its official recovery, the markets listed on the following pages are likely to lead the parade. It may take a year or more for the weakest markets–where burgeoning foreclosure sales are still pounding new home values, making building and selling new homes an exercise in futility– to finally stage a turnaround. We’ll present that list next week.

The healthiest markets have many things in common. Most of them are great places to live, either close to the ocean, mountains, or major universities. Most of them didn’t have a huge run-up in prices during the boom and aren’t experiencing rampant deflation during the bust.

To compile these lists, we analyzed the top 75 housing markets in the country. We ranked them based on population trends and job growth, perennial drivers of housing demand. We also examined what’s happened with home prices; many of the healthiest markets have managed to hold the line on home values. And finally, we considered the rate building permits, which may be the single best ongoing indicator of builder confidence in a market. We combined all these metrics to produce a score for each market. Here are the top 15, in reverse order.

READ MORE

The Senate voted overwhelmingly Wednesday night to provide new home purchasers with the largest tax break of its kind ever in US history. The bill, designed to buoy the ailing US housing industry, provides a Federal Income Tax Credit to all home purchasers. Henceforth, 10% of the purchase price of their new home - with a cap of $15,000 - is a tax credit. That’s right, credit, not deduction. This is finally meaningful. Previous tax credits only benefited first-time buyers. Now, you can buy a $300,000 new home and save $15,000 in taxes. This is the same as the IRS providing you with a 5% down payment. No catch.

The bill is seen as a victory for Republicans eager to leave their mark on a mammoth economic stimulus bill at the heart of President Barack Obama’s recovery plan. The tax break was adopted without dissent and came on a day in which Obama pushed back pointedly against Republican critics of the legislation even as he reached across party lines to consider scaling back spending.

Cost of the Housing Stimulus portion of the mammoth economic stimulus package is estimated at a measly $19 billion. It’s not that I’m not appreciative, really, because I am. For selfish reasons, I am a homebuilder. From a selfless point of view (which, at the moment I am maintaining) many people have lost their homes in foreclosure and many more are faced with it, and they need some help. But when housing - the backbone of the American economy and way of life - only merits $19 billion in effort, when more than $1 trillion is being splashed around like so much water at Jones Beach in this spending package, it makes one wonder about the priorities our all knowing President and Congress seem to have. It also begs the question: Where in the hell are they spending the other $981 billion?

I know I sound insensitive and just downright critical, but think, this used to be alot of money.

Jeez, at our new Lexington Park at Rice Field neighborhood in Downtown Plano, the government providing 5% of your down payment while Lexington is providing 4.25% fixed rate mortgages for 30 years. I was going to say with only 5% down, but now it seems the government will pick up that cost for you. You can’t afford not to buy one! I’m buying one tomorrow.

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