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Post Secondary Completions Total Awards/Degrees SY 2015-16 - Current Education
data.pa.gov | Last Updated 2023-06-14T18:42:27.000ZA listing of completion counts by Institution name, School year and the type of Degrees.
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College and Career Readiness Indicators Current, Annual, Education
data.pa.gov | Last Updated 2023-05-17T19:22:00.000ZThese data describe the percentages of students that attained specific education milestones and objectives. The indictor-level definitions can be found here: <a href="https://futurereadypa.org/Home/Glossary">Future Ready Glossary</a> The Industry Based measured are for the 2020-2021 school year, and all other measures are for the 2018-2019 school year,
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Uninsured Population Census Data CY 2009-2014 Human Services
data.pa.gov | Last Updated 2022-10-18T14:19:11.000ZThis data is pulled from the U.S. Census website. This data is for years Calendar Years 2009-2014. Product: SAHIE File Layout Overview Small Area Health Insurance Estimates Program - SAHIE Filenames: SAHIE Text and SAHIE CSV files 2009 – 2014 Source: Small Area Health Insurance Estimates Program, U.S. Census Bureau. Internet Release Date: May 2016 Description: Model‐based Small Area Health Insurance Estimates (SAHIE) for Counties and States File Layout and Definitions The Small Area Health Insurance Estimates (SAHIE) program was created to develop model-based estimates of health insurance coverage for counties and states. This program builds on the work of the Small Area Income and Poverty Estimates (SAIPE) program. SAHIE is only source of single-year health insurance coverage estimates for all U.S. counties. For 2008-2014, SAHIE publishes STATE and COUNTY estimates of population with and without health insurance coverage, along with measures of uncertainty, for the full cross-classification of: •5 age categories: 0-64, 18-64, 21-64, 40-64, and 50-64 •3 sex categories: both sexes, male, and female •6 income categories: all incomes, as well as income-to-poverty ratio (IPR) categories 0-138%, 0-200%, 0-250%, 0-400%, and 138-400% of the poverty threshold •4 races/ethnicities (for states only): all races/ethnicities, White not Hispanic, Black not Hispanic, and Hispanic (any race). In addition, estimates for age category 0-18 by the income categories listed above are published. Each year’s estimates are adjusted so that, before rounding, the county estimates sum to their respective state totals and for key demographics the state estimates sum to the national ACS numbers insured and uninsured. This program is partially funded by the Centers for Disease Control and Prevention's (CDC), National Breast and Cervical Cancer Early Detection ProgramLink to a non-federal Web site (NBCCEDP). The CDC have a congressional mandate to provide screening services for breast and cervical cancer to low-income, uninsured, and underserved women through the NBCCEDP. Most state NBCCEDP programs define low-income as 200 or 250 percent of the poverty threshold. Also included are IPR categories relevant to the Affordable Care Act (ACA). In 2014, the ACA will help families gain access to health care by allowing Medicaid to cover families with incomes less than or equal to 138 percent of the poverty line. Families with incomes above the level needed to qualify for Medicaid, but less than or equal to 400 percent of the poverty line can receive tax credits that will help them pay for health coverage in the new health insurance exchanges. We welcome your feedback as we continue to research and improve our estimation methods. The SAHIE program's age model methodology and estimates have undergone internal U.S. Census Bureau review as well as external review. See the SAHIE Methodological Review page for more details and a summary of the comments and our response. The SAHIE program models health insurance coverage by combining survey data from several sources, including: •The American Community Survey (ACS) •Demographic population estimates •Aggregated federal tax returns •Participation records for the Supplemental Nutrition Assistance Program (SNAP), formerly known as the Food Stamp program •County Business Patterns •Medicaid •Children's Health Insurance Program (CHIP) participation records •Census 2010 Margin of error (MOE). Some ACS products provide an MOE instead of confidence intervals. An MOE is the difference between an estimate and its upper or lower confidence bounds. Confidence bounds can be created by adding the margin of error to the estimate (for the upper bound) and subtracting the margin of error from the estimate (for the lower bound). All published ACS margins of error are based on a 90-percent confidence level.
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Post Secondary Enrollment Un-duplicated SY 2002-2003 - Current Education
data.pa.gov | Last Updated 2023-06-14T18:41:31.000ZThe purpose of the 12-Month Enrollment component of IPEDS is to collect unduplicated enrollment counts of all students enrolled for credit and instructional activity data in postsecondary institutions for an entire 12-month period. Data are collected by level of student and by race/ethnicity and gender. Instructional activity is collected as total credit and/or contact hours attempted at the undergraduate, graduate, and doctor's professional levels. Using the instructional activity data reported, a full-time equivalent (FTE) student enrollment at the undergraduate and graduate level is estimated.
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Educational Program Report 2017 - Current Education
data.pa.gov | Last Updated 2023-06-14T18:35:35.000ZA report completed for the type of credentialed awards at each Administrative Unit by type of Major or Program area of study including the type of instruction.
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Dangerous Dogs 1996-Current County Agriculture
data.pa.gov | Last Updated 2020-02-27T14:35:08.000ZHistorical results of Dangerous Dogs in Pennsylvania. A dangerous dog is one that has: (1) Inflicted severe injury on a human being without provocation on public or private property. (2) Killed or inflicted severe injury on a domestic animal, dog or cat without provocation while off the owner’s property. (3) Attacked a human being without provocation. (4) Been used in the commission of a crime. And the dog has either or both of the following: (1) A history of attacking human beings and/or domestic animals, dogs or cats without provocation. (2) A propensity to attack human beings and/or domestic animals, dogs or cats without provocation. *A propensity to attack may be proven by a single incident. Severe injury is defined as, [3 P.S. § 459-102] “Any physical injury that results in broken bones or disfiguring lacerations requiring multiple sutures or cosmetic surgery.” More information can be found here - https://www.agriculture.pa.gov/Animals/DogLaw/Dangerous%20Dogs/Pages/default.aspx More information on Chapter 27 Regulations - https://www.agriculture.pa.gov/Animals/DogLaw/Dangerous%20Dogs/Documents/Chapter%2027%20Dangerous%20Dogs.pdf PDF's for Chapter 27 and Pennsylvania Dog Laws are attached to the metadata
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Industry Certifications Offered By Career and Technical Education Programs SY 2016-2022 - Current Education
data.pa.gov | Last Updated 2022-07-24T16:33:38.000ZThis dataset contains the Industry Certifications offered by each secondary Career and Technical Education (CTE) program for 2016 school year to the current period. For additional information on Career and Technical Education visit <a href="https://www.education.pa.gov/K-12/Career%20and%20Technical%20Education/Pages/default.aspx">Career and Technical Education</a>.
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Emissions Inventory System (EIS) Emissions 2017 - Current Semi-Annual County Environmental Protection
data.pa.gov | Last Updated 2021-07-27T14:37:45.000ZEPA's Emissions Inventory System (EIS) contains information about sources that emit criteria air pollutants (CAPs) and hazardous air pollutants (HAPs). The EIS includes estimates of annual air pollutant emissions from point, non-point, and mobile sources in the Pennsylvania counties. EPA collects information about emission sources and releases an updated version of the NEI database every three years. The data made available in the NEI are used for air dispersion modeling, regional strategy development, setting regulations, air toxins risk assessment, and tracking trends in emissions over time. The data derived in the State of Pennsylvania is published and searchable online on the www.pa.gov website. This data will be updated annually for the prior calendar year in the first Quarter of the following year.
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Workforce Development Training Through Industry Partnerships PY 2014-2017 Labor And Industry
data.pa.gov | Last Updated 2022-02-21T17:46:29.000ZThe data depicts each training opportunity completed by individuals through Industry Partnership training funding by Program Year (PY). The file includes all training and the number of individuals that benefited from the training and the workforce development area in which the industry partnership is organized. The data show the amount of training that is driven by employer demand to ensure PA’s employers remain competitive and workers retain employment and enhance their career opportunities. This is Department of Labor and Industry(DLI) dataset. There are 5 other Workforce training files from Department of Community and Economic Development (DCED) that when combined with this file support the Governor's Workforce Development Goal of training 340,000 individuals by 2020
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Industry Partnership Wage Program PY 2013-2017 Labor And Industry
data.pa.gov | Last Updated 2024-03-14T13:32:55.000ZThe data represents the percent change in wages for an individual who has wages recorded in the Unemployment Compensation (UC) wage record file in the quarter in which they completed Industry Partnership training and wages found in the UC wage record file for that individual four quarters later. The change could be an increase or a decrease in wages. For example, if an individual completed training in the third quarter of 2013 and earned $5,000 in that quarter and earned $7,500 in the third quarter of 2014 the percent change for that individual would be 50%. The file incudes a count of all individuals who benefited from industry partnership training, the workforce development area of the industry partnership, the training program completed and the percentage change in wages per individual training. The top line of the file includes the overall percentage change for all trainings. *The goal for Labor & Industry is based on receiving $10 million to fund Industry Partnerships. This dataset is for Program Year 2013-2017 and will be updated annually due to federal release schedule. There are many reasons why an individual’s wage may have changed dramatically. Some of the reasons for negative wage changes or large increases in wages are listed below (not an exhaustive list). • An individual may have left the job, was laid off, or retired within the year after they were trained. • An individual may have become ill and left work. • An individual may have accepted a job in or moved to another state. • An individual may have been working two jobs and switched to one, or vice versa. • An individual’s hours may have been reduced/increased during a quarter. • Overtime hours may have been reduced/increased during a quarter. • An individual may have taken family leave. • A bonus could have been paid right after training was completed. • Wage records may not have been reported. • An employer may have closed and laid off all of their employees.