Filter Results
35508 results
  • I study expertise acquisition in a model of trading under asymmetric information. I propose and implement a method to measure r, the ratio of the marginal social value to the marginal private value of expertise. This can be decomposed into three sufficient statistics: traders' average profits, the fraction of bad assets among traded assets, and the elasticity of good assets traded with respect to capital inflows. I measure r = 0.16 for the junk bond underwriting market. Since this is less than 1, it implies that marginal investments in expertise destroy surplus.
    Data Types:
    • Dataset
  • This is the replication package for "Six Centuries of Real Wages in Francefrom Louis IX to Napoleon III: 1250–1860." ,Male workers: agricultural laborers, building laborers, building craftsmen.,
    Data Types:
    • Dataset
  • The National Surveys on Energy and Environment (NSEE), a core activity in CLOSUP's Energy and Environmental Policy Initiative, reflects a formal partnership between the Muhlenberg Institute of Public Opinion at Muhlenberg College and the Center for Local, State, and Urban Policy at the University of Michigan's Gerald R. Ford School of Public Policy. NSEE surveys include twice per year national opinion surveys on issues directly related to climate change and energy policy, as well as other surveys conducted on a range of topics such as hydraulic fracturing ("fracking"), the Great Lakes, and wider issues of energy and environment. NSEE is co-directed by professor Barry Rabe at the University of Michigan, and professor Christopher Borick at Muhlenberg College. For more information on the collaboration between the University of Michigan and Muhlenberg College, please see the recent article from Muhlenberg Magazine. For more information about the NSEE, contact CLOSUP staff at 734-647-4091 or closup@umich.edu. From 2008-2012 the survey was called the â¿¿National Survey of American Public Opinion on Climate Change⿝ (NSAPOOC); starting in 2013 the survey was renamed to the â¿¿National Surveys on Energy and Environment⿝ (NSEE). The NSEE is committed to transparency in all facets of our work, including timely release and posting of data from each survey wave. A grant from the Office of the Provost at the University of Michigan has allowed us to provide online access to earlier waves of the NSEE, including frequency tables, survey instruments, and datasets. Users can see a list of topics covered by the NSEE, and search for questions by text, variable name, or variable category on CLOSUP's website. Although the datasets are listed by survey wave, the NSEE is a valuable source of longitudinal public-opinion data on climate change and energy policy. Many questions have been asked over multiple waves, including questions about belief in global warming that have been asked in every wave of the NSEE. Consult the NSEE Crosswalk to see which questions have been asked in prior and subsequent waves of the NSEE. To facilitate longitudinal analysis, the NSEE datasets use a longitudinal variable naming scheme to facilitate longitudinal analysis. Variable names include two parts: a subject category for the question, and a description of the contents of the question. When a question has been asked with the same text and response options over multiple waves, the same variable name will be used in each dataset. For more information on the longitudinal naming scheme users should consult the codebooks for the datasets.
    Data Types:
    • Dataset
  • These files include data access information and analysis scripts. The abstract is below: "High school students from lower–socioeconomic status (SES) backgrounds are less likely to enroll in advanced mathematics and science courses compared to students from higher-SES backgrounds. The current longitudinal study draws on identity-based and expectancy-value theories of motivation to explain the SES and mathematics and science course-taking relationship. This was done by gathering reports from students and their parents about their expectations, values, and future identities for science, technology, engineering, and mathematics (STEM) topics beginning in middle school through age 20. Results showed that parental education predicted mathematics and science course taking in high school and college, and this relationship was partially mediated by students’ and parents’ future identity and motivational beliefs concerning mathematics and science. These findings suggest that psychological interventions may be useful for reducing social class gaps in STEM course taking, which has critical implications for the types of opportunities and careers available to students."
    Data Types:
    • Dataset
  • This project uses restricted data from the TIMSS 2007 dataset to investigate the link between inquiry-based pedagogical practices and students' attitudes.
    Data Types:
    • Dataset
  • We examine the impact of "big data" on firm performance in the context of forecast accuracy using proprietary retail sales data obtained from Amazon. We measure the accuracy of forecasts in two relevant dimensions: the number of products (N), and the number of time periods for which a product is available for sale (T). Theory suggests diminishing returns to larger N and T, with relative forecast errors diminishing at rate 1/sqrt(N)+1/sqrt(T). Empirical results indicate gains in forecast improvement in the T dimension but essentially flat N effects.
    Data Types:
    • Dataset
  • This article analyzes the integration of the Spanish money market in the nineteenth century. We use a Band-Threshold autoregression model of prices of bills-of-exchange in ten cities to measure market convergence and efficiency in 1825–1875. While price gaps generally decreased during the period, progress in efficiency was concentrated in a small group of cities. We suggest that convergence was associated to the reduction in transaction costs, which started well before the railways through improvements in roads and postal services. By contrast, the heterogeneous behavior of efficiency might be associated to economic geography changes and their effects on monetary leadership.
    Data Types:
    • Dataset
  • The 2015 American Housing Survey marks the first release of a newly integrated national sample and independent metropolitan area samples. The 2015 release features many variable name revisions, as well as the integration of an AHS Codebook Interactive Tool available on the U.S. Census Bureau We site. This data collection provides information on the characteristics of a national sample of housing units in 2015, including apartments, single-family homes, mobile homes, and vacant housing units. Data from the 15 largest metropolitan areas in the United States are included in the national sample survey (the AHS 2015 Metropolitan Data are also available as ICPSR 36805). The data are presented in three separate parts: Part 1, Household Record (Main Record), Part 2, Person Record, and Part 3, Project Record. Household Record data includes questions about household occupancy and tenure, household exterior and interior structural features, household equipment and appliances, housing problems, housing costs, home improvement, neighborhood features, recent moving information, income, and basic demographic information. The household record data also features four rotating topical modules: Arts and Culture, Food Security, Housing Counseling, and Healthy Homes. Person Record data includes questions about personal disabilities, income, and basic demographic information. Finally, the Project Record data includes questions about home improvement projects. Specific questions were asked about the types of projects, costs, funding sources, and year of completion.,The purpose of the AHS is to provide a current and continuous series of data on selected housing and demographic characteristics.,The 2015 American Housing Survey (AHS) integrated national sample was obtained through a two-stage sample selection process: (1) First Stage of Sample Selection: Select Primary Sampling Units The first stage of the sample selection was to determine which representative areas within the United States to include in the sample. To accomplish this, the United States was divided into areas made up of counties or groups of counties known as primary sampling units (PSUs), of which there were two types: self-representing PSUs and non-self representing PSUs. The end result of the first stage of sample selection was an AHS sample spread over 309 PSUs. The 85 self-representing PSUs included 547 counties and county equivalents, and the 224 non-self-representing PSUs consist of 353 counties and county equivalents. (2) Second Stage of Sample Selection: Select Housing Units Within PSUs The second stage of sample selection involved selecting housing units from each of the 309 PSUs. The housing units were selected from a list of all housing units in the United States known as the Master Address File (MAF). The MAF is maintained by the U.S. Census Bureau and based on updates from the prior decennial census and semiannual updates from the United States Postal Service (USPS) Delivery Sequence File, which itself consists of the addresses and mail routes serviced by the USPS. The MAF is updated semiannually in January and July using information provided by the USPS. The 2015 AHS sample was based on the July 2014 MAF. The 2015 integrated national sample consisted of 85,393 housing units. To ensure the sample was representative of all housing units within the PSU, the U.S. Census Bureau stratified all housing units in each PSU into different housing categories. Please see the 2015 American Housing Survey integrated national sample: Sample Design, Weighting, and Error Estimation Documentation for more detailed information.,ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Created variable labels and/or value labels.; Created online analysis version with question text.; Checked for undocumented or out-of-range codes..,Response Rates: The weighted overall response rate was 85 percent and the unweighted overall response rate was also 85 percent.,Datasets: DS0: Study-Level Files DS1: Household Record DS2: Person Record DS3: Project Record,computer-assisted personal interview (CAPI),computer-assisted telephone interview (CATI),Residential housing units in the United States that exist at the time the survey is conducted. The universe includes both occupied and vacant units but excludes group quarters, businesses, hotels, and motels. Smallest Geographic Unit: Core Based Statistical Areas (CBSA),For the 2015 survey year, HUD and the U.S. Census Bureau selected an entirely new sample for the American Housing Survey (AHS). The 2015 AHS sample was composed of an integrated national sample and independent metropolitan area samples. The national sample is described as integrated because it incorporated multiple sample types, including: (1) a representative sample of the nation; (2) representative oversamples of each of the 15 largest metropolitan areas; and (3) a representative oversample of HUD-assisted housing units. HUD and the U.S. Census Bureau intend to survey the entire integrated national sample once every 2 years. As such, it is a longitudinal panel with a 2-year survey cycle. The 2015 AHS integrated national sample originally selected 85,393 housing units for interview. Approximately, 3,382 of the 85,393 total units selected for interview were found to be ineligible because the units either no longer existed or did not meet the AHS definition of a housing unit. Each sample unit in the 2015 integrated national sample was asked a core set of questions. The sample was also randomly split into two sample groups that were each asked a separate set of additional questions from four rotating topical modules. One sample group was asked questions on the topical modules of housing counseling, arts and culture, and food security, while the other group was asked questions on the topical module of healthy homes. Please see the 2015 American Housing Survey Integrated National Sample: Sample Design, Weighting, and Error Estimation document for more detailed information.,
    Data Types:
    • Dataset
  • This is the thirteenth edition of the Global Peace Index (GPI), which ranks 163 independent states and territories according to their level of peacefulness. Produced by the Institute for Economics and Peace (IEP), the GPI is the world’s leading measure of global peacefulness. This report presents the most comprehensive data-driven analysis to date on peace, its economic value, trends, and how to develop peaceful societies.
    Data Types:
    • Dataset
  • In April 2006, Massachusetts passed a comprehensive health care reform bill entitled An Act Providing Access To Affordable, Quality, Accountable Health Care (Chapter 58 of the Acts of 2006), that sought to move the state to near universal coverage. In order to track the impacts of Chapter 58, the Blue Cross Blue Shield of Massachusetts Foundation began funding an annual telephone survey of nonelderly adults in the Commonwealth in fall 2006, just prior to the implementation of key elements of the law. That survey, called the Massachusetts Health Reform Survey (MHRS), was fielded in the fall of 2006-2010, 2012, 2013, 2015, and 2018. This data collection comprises data from the 2018 round of the Massachusetts Health Reform Survey (MHRS). Topics covered by the survey include health insurance status; specific types of health insurance coverage held by the survey respondents; insurance premiums and covered services for those with insurance; access to and use of health care; out-of-pocket health care costs and medical debt; health and disability status; mental health and substance use disorders. Demographic variables include income, race, and employment status.,ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Checked for undocumented or out-of-range codes..,Response Rates: 13.6 percent,Datasets: DS0: Study-Level Files DS1: Public Use DS2: Restricted Use,Health Reform Monitoring Survey (HRMS) Series,computer-assisted telephone interview (CATI),Adults aged 19-64 in Massachusetts households with a landline telephone and/or a cellphone. Smallest Geographic Unit: State,The MHRS sample was designed by Marketing Systems Group (MSG), using the GENESYS IDplus procedures to eliminate non-working and business landline numbers from the sample. Households were selected using stratified random-digit dialing. One respondent was randomly selected from each eligible household. Uninsuredand low- and moderate-income adults were oversampled.,
    Data Types:
    • Dataset
4