The Corporate Tax Rate in the United States stands at 21 percent. This dataset provides - United States Corporate Tax Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

A historical tabulation of selected tax return statistics by counties and other geographic areas reported every year by the Research Section at the Department of Revenue. Source data comes from Tables A through D in the "Returns by county, other states, and city" spreadsheet that accompanies every annual Personal Income Tax publication. Data are reported for all 36 Oregon counties as well as five areas outside Oregon, based on the mailing address on the return when it was filed. Clark County, Washington, is reported separately from the remainder of Washington because so many Clark County residents work in Portland. Idaho and California also have individual tables. Returns from all other states (and outside of the US) are grouped together as "Other". For full-year resident returns, Oregon AGI is the same as federal AGI. For part-year resident and nonresident returns, Oregon AGI is determined from Oregon sourced income and adjustments. Note that some rows have blank cells indicating that data has been omitted, often for disclosure reasons. See "data limitations" below.

Taxes are grouped into six major categories: property, general sales, personal income, business income, real estate-related, and other. We also separate non-exported and exported taxes, that is, taxes levied on New York City resident households and businesses and taxes levied on nonresidents. Taxes in the former category enter into the calculation of New York City tax effort. The latter category includes sales and other taxes on hotel occupancy, city income taxes paid by commuters into the city, and portions of state and MTA auto rental taxes remitted in the city. We could not, however, estimate and net out non-hotel sales and other taxes paid by visitors to the city. Nor could we account, as we did in our previous report, for any New York City tax imports, that is, taxes of other, non-overlapping jurisdictions paid by city residents.1 For brief descriptions of the tables and figures along with methodological notes please see the Tax Effort Background and Methodology document.

Context

Form 990 (officially, the "Return of Organization Exempt From Income Tax"1) is a United States Internal Revenue Service form that provides the public with financial information about a nonprofit organization. It is often the only source of such information. It is also used by government agencies to prevent organizations from abusing their tax-exempt status. Source: https://en.wikipedia.org/wiki/Form_990

Content

Form 990 is used by the United States Internal Revenue Service to gather financial information about nonprofit/exempt organizations. This BigQuery dataset can be used to perform research and analysis of organizations that have electronically filed Forms 990, 990-EZ and 990-PF. For a complete description of data variables available in this dataset, see the IRS’s extract documentation: https://www.irs.gov/uac/soi-tax-stats-annual-extract-of-tax-exempt-organization-financial-data.

Update Frequency: Annual

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Acknowledgements

https://bigquery.cloud.google.com/dataset/bigquery-public-data:irs_990

https://cloud.google.com/bigquery/public-data/irs-990

Dataset Source: U.S. Internal Revenue Service. This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - http://www.data.gov/privacy-policy#data_policy - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

Banner Photo by @rawpixel from Unplash.

Inspiration

What organizations filed tax exempt status in 2015?

What was the revenue of the American Red Cross in 2017?

The Withholding Tax Rate in the United States stands at 30 percent. This dataset includes a chart with historical data for the United States Withholding Tax Rate.

This annual study provides selected income and tax items classified by State, ZIP Code, and the size of adjusted gross income. These data include the number of returns, which approximates the number of households; the number of personal exemptions, which approximates the population; adjusted gross income; wages and salaries; dividends before exclusion; and interest received. Data are based who reported on U.S. Individual Income Tax Returns (Forms 1040) filed with the IRS. SOI collects these data as part of its Individual Income Tax Return (Form 1040) Statistics program, Data by Geographic Areas, ZIP Code Data.

Very Important

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Overview:

Explore the dataset and potentially gain valuable insight into your data science project through interesting features. The dataset was developed for a portfolio optimization graduate project I was working on. The goal was to the monetize risk of company deleveraging by associated with changes in economic data. Applications of the dataset may include. To see the data in action visit my analytics page. Analytics Page & Dashboard and to access all 295,000+ records click here.

• Mortgage-Backed Securities
• Geographic Business Investment
• Real Estate Analysis

For any questions, you may reach us at research_development@goldenoakresearch.com. For immediate assistance, you may reach me on at 585-626-2965. Please Note: the number is my personal number and email is preferred

Statistical Themes:

Note: in total there are 75 fields the following are just themes the fields fall under Home Owner Costs: Sum of utilities, property taxes.

• Second Mortgage: Households with a second mortgage statistics.
• Home Equity Loan: Households with a Home equity Loan statistics.
• Debt: Households with any type of debt statistics.
• Mortgage Costs: Statistics regarding mortgage payments, home equity loans, utilities and property taxes
• Home Owner Costs: Sum of utilities, property taxes statistics
• Gross Rent: Contract rent plus the estimated average monthly cost of utility features
• Gross Rent as Percent of Income Gross rent as the percent of income very interesting
• High school Graduation: High school graduation statistics.
• Population Demographics: Population demographic statistics.
• Age Demographics: Age demographic statistics.
• Household Income: Total income of people residing in the household.
• Family Income: Total income of people related to the householder.

Sources, if you wish to get the data your self :)

2012-2016 ACS 5-Year Documentation was provided by the U.S. Census Reports. Retrieved May 2, 2018, from

• Census estimate, error, and location data
• Census location information

Access All 325,258 Location of Our Most Complete Database Ever:

Providing you the potential to monetize risk and optimize your investment portfolio through quality economic features at unbeatable price. Access all 295,000+ records on an incredibly small scale, see links below for more details:

• Full Dataset: View Full Dataset
• Real Estate Research: View Research

This U.S. Census Bureau American Community Survey (ACS) five-year estimates data set contains median household income estimates during the past 12 months and in inflation-adjusted dollars. The data is available over a number of geographies ranging from statewide to census tract level. The data is available for year 2009-2016. This includes the median income of the householder and all other individuals 15 years old and over in the household, whether they are related to the householder or not. Income is based on “money” income–income received on a regular basis before payments for personal income taxes, social security, union dues, etc. Money income does not include noncash benefits that may be received.

US E-Cig Tax Regulations

1995-2016 State Taxation Policies

By Health [source]

About this dataset

This dataset, provided by the Centers for Disease Control and Prevention (CDC) through the State Tobacco Activities Tracking and Evaluation (STATE) System, contains information on state-level legislative data on tobacco use prevention and control policies related to e-cigarette taxes. It captures various measures of state excise taxes for e-cigarettes implemented over a span of almost two decades. The STATE System stores comprehensive historical data which can be used to track changes in these policies at the state level over time. This dataset includes fields such as location abbreviations, topic descriptions, measure descriptions, provision value, provision description, citations and more that provide valuable insight into understanding how these measures have evolved overtime across states in the US

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How to use the dataset

This dataset contains information on state-level legislative data on tobacco use prevention and control policies related to e-cigarette taxes from 1995-2016. It includes the following columns: location abbreviations, location descriptions, topic descriptions, measure descriptions, data sources, provision group descriptions, provision descriptions, provision values, citations for the provisions cited in the dataset as well as alternative values for those provisions if they are used. Additionally it contains dates when certain provisions become effective or enacted and also geographic locations of the data which can be used as a helpful reference point.

In order to best use this dataset you should familiarize yourself with its columns and their definitions. This will help you better understand how each element relates to others within the set and give you an idea of what type of analyses can be conducted using it. You should also take note of any relevant comments that may shed light on specific elements or provide additional information not captured in other columns. After understanding the contents of this dataset it is suggested that individuals analyze it according to their individual needs and interests but some general uses may include exploring trends in e-cigarette taxation over time by examining yearly changes in tax rates or seeing how tax regulation varies among states depending on location abbreviations provided in each row entry etc.. With these tools one could potentially make meaningful connections between different variables within this set and gain valuable insights into how US states legislate taxes related to tobacco use prevention methods

Research Ideas

• Analyzing the impact of e-cigarette taxes on usage rates in different states, in order to inform tax policy decisions.
• Examining the differences between enacted and effective dates for legislations by state and across the country, in order to gain a better understanding of how long it takes for new laws to become implemented.
• Tracking changes of e-cigarette regulation over time and studying how they correlate with measures such as number of youth users or youth perception on risk associated with e-cigarettes by state

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Open Database License (ODbL) v1.0 - You are free to: - Share - copy and redistribute the material in any medium or format. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices. - No Derivatives - If you remix, transform, or build upon the material, you may not distribute the modified material. - No additional restrictions - You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.

Columns

File: CDC_STATE_System_E-Cigarette_Legislation_-_Tax.csv | Column name | Description | |:-----------------------|:------------------------------------------------------------------| | YEAR | Year of the policy (Integer) ...

Context

The United States Census is a decennial census mandated by Article I, Section 2 of the United States Constitution, which states: "Representatives and direct Taxes shall be apportioned among the several States ... according to their respective Numbers."
Source: https://en.wikipedia.org/wiki/United_States_Census

Content

The United States census count (also known as the Decennial Census of Population and Housing) is a count of every resident of the US. The census occurs every 10 years and is conducted by the United States Census Bureau. Census data is publicly available through the census website, but much of the data is available in summarized data and graphs. The raw data is often difficult to obtain, is typically divided by region, and it must be processed and combined to provide information about the nation as a whole.

The United States census dataset includes nationwide population counts from the 2000 and 2010 censuses. Data is broken out by gender, age and location using zip code tabular areas (ZCTAs) and GEOIDs. ZCTAs are generalized representations of zip codes, and often, though not always, are the same as the zip code for an area. GEOIDs are numeric codes that uniquely identify all administrative, legal, and statistical geographic areas for which the Census Bureau tabulates data. GEOIDs are useful for correlating census data with other censuses and surveys.

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Acknowledgements

https://bigquery.cloud.google.com/dataset/bigquery-public-data:census_bureau_usa

https://cloud.google.com/bigquery/public-data/us-census

Dataset Source: United States Census Bureau

Use: This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - http://www.data.gov/privacy-policy#data_policy - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

Banner Photo by Steve Richey from Unsplash.

Inspiration

What are the ten most populous zip codes in the US in the 2010 census?

What are the top 10 zip codes that experienced the greatest change in population between the 2000 and 2010 censuses?

https://cloud.google.com/bigquery/images/census-population-map.png" alt="https://cloud.google.com/bigquery/images/census-population-map.png"> https://cloud.google.com/bigquery/images/census-population-map.png

As shown by the recent crisis, tax evasion poses a significant problem for countries such as Greece, Spain and Italy. While these societies certainly possess weaker fiscal institutions as compared to other EU members, might broader cultural differences between northern and southern Europe also help to explain citizens’ (un)willingness to pay their taxes? To address this question, we conduct laboratory experiments in the UK and Italy, two countries which straddle this North-South divide. Our design allows us to examine citizens’ willingness to contribute to public goods via taxes while holding institutions constant. We report a surprising result: when faced with identical tax institutions, redistribution rules and audit probabilities, Italian participants are significantly more likely to comply than Britons. Overall, our findings cast doubt upon “culturalist” arguments that would attribute cross-country differences in tax compliance to the lack of morality amongst southern European taxpayers.

This annual study provides migration pattern data for the United States by State or by county and are available for inflows (the number of new residents who moved to a State or county and where they migrated from) and outflows (the number of residents who left a State or county and where they moved to). The data include the number of returns filed, number of personal exemptions claimed, total adjusted gross income, and aggregate migration flows at the State level, by the size of adjusted gross income (AGI) and by age of the primary taxpayer. Data are collected and based on year-to-year address changes reported on U.S. Individual Income Tax Returns (Form 1040) filed with the IRS. SOI collects these data as part of its Individual Income Tax Return (Form 1040) Statistics program, Data by Geographic Areas, U.S. Population Migration Data.

This spreadsheet includes Minnesota Income Tax information for the years 2003-2008. Data includes number of tax returns and the median, mean and sum of Minnesota Taxable Income (MTI). Data are unduplicated and reported in one of four general categories: 1. Records outside of Minnesota are reported at the state level and include territories, APO's, foreign and unassigned. Within Minnesota, records are assigned to one of three geographic levels (blockgroup, city or county): 2. Within the 7-county Twin Cities metro area, records are assigned to blockgroups. 3. Outside of the 7-county Twin Cities metro area, records are assigned to cities with populations greater than 2,000 residents. 4. All other unassigned Minnesota records are tabulated by county. For additional information and statistical reports, please visit http://www.revenue.state.mn.us/research_stats/Pages/Whats-New.aspx

This dataset includes all 7 metro counties that have made their parcel data freely available without a license or fees.

This dataset is a compilation of tax parcel polygon and point layers assembled into a common coordinate systems from Twin Cities, Minnesota metropolitan area counties. No attempt has been made to edgematch or rubbersheet between counties. A standard set of attribute fields is included for each county. (See section 5 of the metadata). The attributes are the same for the polygon and points layers. Not all attributes are populated for all counties. Summary attribute information is in the Attributes Overview. Detailed information about the attributes can be found in the MetroGIS Regional Parcels Attributes document.

The polygon layer contains one record for each real estate/tax parcel polygon within each county's parcel dataset. Some counties have polygons for each individual condominium, and others do not. (See Completeness in Section 2 of the metadata for more information.) The points layer includes the same attribute fields as the polygon dataset. The points are intended to provide information in situations where multiple tax parcels are represented by a single polygon. One primary example of this is the condominium, though some counties stacked polygons for condos. Condominiums, by definition, are legally owned as individual, taxed real estate units. Records for condominiums may not show up in the polygon dataset. The points for the point dataset often will be randomly placed or stacked within the parcel polygon with which they are associated.

The polygon layer is broken into individual county shape files. The points layer is provided as both individual county files and as one file for the entire metro area.

In many places a one-to-one relationship does not exist between these parcel polygons or points and the actual buildings or occupancy units that lie within them. There may be many buildings on one parcel and there may be many occupancy units (e.g. apartments, stores or offices) within each building. Additionally, no information exists within this dataset about residents of parcels. Parcel owner and taxpayer information exists for many, but not all counties.

This is a MetroGIS Regionally Endorsed dataset.

Additional information may be available from each county at the links listed below. Also, any questions or comments about suspected errors or omissions in this dataset can be addressed to the contact person at each individual county.

Anoka = http://www.anokacounty.us/315/GIS
Caver = http://www.co.carver.mn.us/GIS
Dakota = http://www.co.dakota.mn.us/homeproperty/propertymaps/pages/default.aspx
Hennepin = http://www.hennepin.us/gisopendata
Ramsey = https://www.ramseycounty.us/your-government/open-government/research-data
Scott = http://opendata.gis.co.scott.mn.us/
Washington: http://www.co.washington.mn.us/index.aspx?NID=1606

This dataset is a compilation of tax parcel polygon and point layers from the seven Twin Cities, Minnesota metropolitan area counties of Anoka, Carver, Dakota, Hennepin, Ramsey, Scott and Washington. The seven counties were assembled into a common coordinate system. No attempt has been made to edgematch or rubbersheet between counties. A standard set of attribute fields is included for each county. (See section 5 of the metadata). The attributes are the same for the polygon and points layers. Not all attributes are populated for all counties.

This is an annual version of the MetroGIS Regional Parcel Dataset that can be used with other annual versions to do change analysis and time series investigations. This dataset is intended to contain all updates to each county's parcel data through the end of 2004. It was originally published as the 'January 1, 2005' version of the dataset. See the Currentness Reference below and the Entity and Attribute information in Section 5 for more information about the dates for specific aspects of the dataset.

The polygon layer contains one record for each real estate/tax parcel polygon within each county's parcel dataset. Some counties will polygons for each individual condominium, and others do not. (See Completeness in Section 2 of the metadata for more information.) The points layer includes the same attribute fields as the polygon dataset. The points are intended to provide information in situations where multiple tax parcels are represented by a single polygon. The primary example of this is the condominium. Condominiums, by definition, are legally owned as individual, taxed real estate units. Records for condominiums may not show up in the polygon dataset. The points for the point dataset often will be randomly placed or stacked within the parcel polygon with which they are associated.

The polygon layer is broken into individual county shape files. The points layer is one file for the entire metro area.

In many places a one-to-one relationship does not exist between these parcel polygons or points and the actual buildings or occupancy units that lie within them. There may be many buildings on one parcel and there may be many occupancy units (e.g. apartments, stores or offices) within each building. Additionally, no information exists within this dataset about residents of parcels. Parcel owner and taxpayer information exists for many, but not all counties.

Polygon and point counts for each county are as follows (based on the January, 2005 dataset):

Anoka = 124,042 polygons, 124,042 points
Carver = 32,910 polygons, 32,910 points
Dakota = 130,989 polygons, 141,444 points
Hennepin = 353,759 polygons, 399,184 points
Ramsey = 148,266 polygons, 163,376 points
Scott = 49,958 polygons, 49,958 points
Washington = 93,794 polygons, 96,570 points

This is a MetroGIS Regionally Endorsed dataset.

Each of the seven Metro Area counties has entered into a multiparty agreement with the Metropolitan Council to assemble and distribute the parcel data for each county as a regional (seven county) parcel dataset.

A standard set of attribute fields is included for each county. The attributes are identical for the point and polygon datasets. Not all attributes fields are populated by each county. Detailed information about the attributes can be found in the MetroGIS Regional Parcels Attributes 2004 document.

Additional information may be available in the individual metadata for each county at the links listed below. Also, any questions or comments about suspected errors or omissions in this dataset can be addressed to the contact person listed in the individual county metadata.

Anoka = http://www.anokacounty.us/315/GIS

Caver = http://www.co.carver.mn.us/GIS

Dakota = http://www.co.dakota.mn.us/homeproperty/propertymaps/pages/default.aspx

Hennepin: http://www.hennepin.us/gisopendata

Ramsey = https://www.ramseycounty.us/your-government/open-government/research-data

Scott = http://www.scottcountymn.gov/1183/GIS-Data-and-Maps

Washington = http://www.co.washington.mn.us/index.aspx?NID=1606

🔗 Check out my notebook here: Link

This dataset includes malnutrition indicators and some of the features that might impact malnutrition. The detailed description of the dataset is given below:

• Percentage-of-underweight-children-data: Percentage of children aged 5 years or below who are underweight by country.

• Prevalence of Underweight among Female Adults (Age Standardized Estimate): Percentage of female adults whos BMI is less than 18.

• GDP per capita (constant 2015 US$): GDP per capita is gross domestic product divided by midyear population. GDP is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in constant 2015 U.S. dollars.

• Domestic general government health expenditure (% of GDP): Public expenditure on health from domestic sources as a share of the economy as measured by GDP.

• Maternal mortality ratio (modeled estimate, per 100,000 live births): Maternal mortality ratio is the number of women who die from pregnancy-related causes while pregnant or within 42 days of pregnancy termination per 100,000 live births. The data are estimated with a regression model using information on the proportion of maternal deaths among non-AIDS deaths in women ages 15-49, fertility, birth attendants, and GDP measured using purchasing power parities (PPPs).

• Mean-age-at-first-birth-of-women-aged-20-50-data: Average age at which women of age 20-50 years have their first child.

• School enrollment, secondary, female (% gross): Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Secondary education completes the provision of basic education that began at the primary level, and aims at laying the foundations for lifelong learning and human development, by offering more subject- or skill-oriented instruction using more specialized teachers.

A novel dataset for bankruptcy prediction related to American public companies listed on the New York Stock Exchange and NASDAQ is provided. The dataset comprises accounting data from 8,262 distinct companies recorded during the period spanning from 1999 to 2018.

According to the Security Exchange Commission (SEC), a company in the American market is deemed bankrupt under two circumstances. Firstly, if the firm's management files for Chapter 11 of the Bankruptcy Code, indicating an intention to "reorganize" its business. In this case, the company's management continues to oversee day-to-day operations, but significant business decisions necessitate approval from a bankruptcy court. Secondly, if the firm's management files for Chapter 7 of the Bankruptcy Code, indicating a complete cessation of operations and the company going out of business entirely.

In this dataset, the fiscal year prior to the filing of bankruptcy under either Chapter 11 or Chapter 7 is labeled as "Bankruptcy" (1) for the subsequent year. Conversely, if the company does not experience these bankruptcy events, it is considered to be operating normally (0). The dataset is complete, without any missing values, synthetic entries, or imputed added values.

The resulting dataset comprises a total of 78,682 observations of firm-year combinations. To facilitate model training and evaluation, the dataset is divided into three subsets based on time periods. The training set consists of data from 1999 to 2011, the validation set comprises data from 2012 to 2014, and the test set encompasses the years 2015 to 2018. The test set serves as a means to assess the predictive capability of models in real-world scenarios involving unseen cases.

Properties with tax and/or water liens that are potentially eligible to be included in the next lien sale.Tax Lien Sale Lists : Properties with tax, water liens and other charges that are potentially eligible to be included in the next lien sale plus tax liens which were eventually sold.

This dataset includes all 7 metro counties that have made their parcel data freely available without a license or fees. This also includes a combined layer of all points of the 7 counties.

This dataset is a compilation of tax parcel polygon and point layers assembled into a common coordinate system from Twin Cities, Minnesota metropolitan area counties. No attempt has been made to edgematch or rubbersheet between counties. A standard set of attribute fields is included for each county. The attributes are the same for the polygon and points layers. Not all attributes are populated for all counties.

NOTICE: The standard set of attributes changed to the MN Parcel Data Transfer Standard on 1/1/2019.
https://www.mngeo.state.mn.us/committee/standards/parcel_attrib/parcel_attrib.html

See section 5 of the metadata for an attribute summary.

Detailed information about the attributes can be found in the Metro Regional Parcel Attributes document.

The polygon layer contains one record for each real estate/tax parcel polygon within each county's parcel dataset. Some counties have polygons for each individual condominium, and others do not. (See Completeness in Section 2 of the metadata for more information.) The points layer includes the same attribute fields as the polygon dataset. The points are intended to provide information in situations where multiple tax parcels are represented by a single polygon. One primary example of this is the condominium, though some counties stacked polygons for condos. Condominiums, by definition, are legally owned as individual, taxed real estate units. Records for condominiums may not show up in the polygon dataset. The points for the point dataset often will be randomly placed or stacked within the parcel polygon with which they are associated.

The polygon layer is broken into individual county shape files. The points layer is provided as both individual county files and as one file for the entire metro area.

In many places a one-to-one relationship does not exist between these parcel polygons or points and the actual buildings or occupancy units that lie within them. There may be many buildings on one parcel and there may be many occupancy units (e.g. apartments, stores or offices) within each building. Additionally, no information exists within this dataset about residents of parcels. Parcel owner and taxpayer information exists for many, but not all counties.

This is a MetroGIS Regionally Endorsed dataset.

Additional information may be available from each county at the links listed below. Also, any questions or comments about suspected errors or omissions in this dataset can be addressed to the contact person at each individual county.

Anoka = http://www.anokacounty.us/315/GIS
Caver = http://www.co.carver.mn.us/GIS
Dakota = http://www.co.dakota.mn.us/homeproperty/propertymaps/pages/default.aspx
Hennepin = https://gis-hennepin.hub.arcgis.com/pages/open-data
Ramsey = https://www.ramseycounty.us/your-government/open-government/research-data
Scott = http://opendata.gis.co.scott.mn.us/
Washington: http://www.co.washington.mn.us/index.aspx?NID=1606

The Current Population Survey (CPS) is a monthly survey of households conducted by the Bureau of Census for the Bureau of Labor Statistics. The earnings data are collected from one-fourth of the CPS total sample of approximately 60,000 households. Data measures usual hourly and weekly earnings of wage and salary workers. All self-employed persons are excluded, regardless of whether their businesses are incorporated. Data represent earnings before taxes and other deductions and include any overtime pay, commissions, or tips usually received. Earnings data are available for all workers, by age, race, Hispanic or Latino ethnicity, sex, occupation, usual full- or part-time status, educational attainment, and other characteristics. Data are published quarterly. More information and details about the data provided can be found at http://www.bls.gov/cps/earnings.htm