Graph and download economic data for Total Unemployed, Plus Discouraged Workers, Plus All Other Marginally Attached Workers, as a Percent of the Civilian Labor Force Plus All Marginally Attached Workers for Pennsylvania (U5UNEM5PA) from Q4 2003 to Q3 2024 about discouraged, marginally attached, labor underutilization, PA, workers, civilian, 16 years +, labor force, labor, household survey, unemployment, rate, and USA.

Graph and download economic data for Persons Unemployed 15 Weeks or Longer, as a Percent of the Civilian Labor Force for Pennsylvania (U1UNEM1PA) from Q4 2003 to Q3 2024 about 15 weeks +, labor underutilization, PA, civilian, 16 years +, labor force, labor, household survey, unemployment, rate, and USA.

This is the replication package for "Churches as Social Insurance: Oil Risk and Religion in the U.S. South". Abstract: Religious communities are important providers of social insurance. We show that risk associated with oil dependence facilitated the proliferation of religious communities throughout the U.S. South during the 20th century. Known oil abundance predicts higher rates of church membership, which are not driven by selective migration or local economic development. Consistent with a social insurance channel, greater oil price volatility increases effects, while greater access to credit, state-level social insurance, and private insurance crowds out effects. Religious communities limit spillovers of oil price shocks across sectors, reducing increases in unemployment following a negative shock by about 30%.

This data set contains the data used in the research project "Cognitive Biases in Consumer Sentiment: the Peak-End Rule and Herding". The following files and items are includedICSdata.xlsx: Index of Consumer Sentiment and its constituents (sheet 1), and PAGO per region (sheet 2); original source University of Michigan, Survey of Consumers, https://data.sca.isr.umich.edu/ALFRED_data: macro economic series related to economic growth, inflation, (un)employment and consumption, including publication date; original source ArchivaL Federal Reserve Economic Data (ALFRED), https://alfred.stlouisfed.org/; for each series a README sheet is included with metadataFREDdata: financial and economic series related to stock, bond, housing markets, interest rates,gasoline prices and regional unemployment rates; each sheet contains the mnemonic of the donwloaded series.MicroData_20220113: demographic information of each respondent in the Survey of Consumers conducted by the University of Michigan; downloaded from University of Michigan, Survey of Consumers, https://data.sca.isr.umich.edu/Prelim_PA.xlsx: the Index of Consumer Sentiment and its constituent series, as reported in the preliminary annoucement by the University of Michigan (prelim), and the series constructed based on the surveys after the preliminary announcements. The prelim series are publicly available via https://data.sca.isr.umich.edu/ . The pa series have been constructed based on interview datas obtains from the University of Michigan. These data are proprietory and cannot be shared freely.DemographicDifferences.xlsx: average differences between the prelim and pa monthly subsample in the demographic statistics available in MicroData_20220113.xlsx. The difference have been constructed based on interview datas obtains from the University of Michigan. These data are proprietory and cannot be shared freely.Methodology: Linear regressions and time-series methods.Findings: We show that two heuristics, the peak-end rule and herding, generate biases in indexes of consumer sentiment. Both affect respondents' assessment of changes in their financial position over the past year. Conform the peak-end rule, their answers relate more to extreme detrimental monthly than to yearly changes in key financial and macro variables. These effects are stronger for more salient variables. As for herding, we document that respondents interviewed in the second round about past financial changes rely too strongly on future expectations from first-round respondents. These effects persist when we account for structural differences in sample composition or for the effect of other predictive variables. Our research shows the presence of both biases outside controlled environments and sheds new light on the relevance of sentiment indexes.