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Version 2 release notes: Makes columns showing the number of beers, glasses of wine, shots of liquor, and total drinks consumed based on the amount of ethanol consumed for each category that was already included. This data set contains the per capita (persons aged 14+) consumption of ethanol (in gallons) for each state, Washington D.C., and totals for census regions and the United States as a whole, for the years 1977-2016. This includes total ethanol consumed as well as consumption by three categories: beer, wine, and shots of liquor ("spirits"). The PDF includes a method to convert the ethanol variables into total drinks of each type. I used this method to create columns for how many beers, glasses of wine, shots of liquor, and total drinks were consumed. The PDF doesn't say how many ounces of fluid is in each drink type (except for the number_of_drinks_total variable) so I used the information provided by the National Institute on Alcohol Abuse and Alcoholism here - https://www.niaaa.nih.gov/alcohol-health/overview-alcohol-consumption/what-standard-drink. Please note that the number_of_drinks_total variable is based on the conversion formula provided, not by adding the individual drink categories together and therefore will be slightly different than that way of measuring it. This data comes from a report by Sarah P. Haughwout and Dr. Megan E. Slater at the National Institute on Alcohol Abuse and Alcoholism (downloaded here https://pubs.niaaa.nih.gov/publications/surveillance110/CONS16.htm). That report is one of the files available to download and is included as it explains the methodology the two authors used for the data. I am not affiliated with the original report at all. If you do use this data please also cite the original report. For the code used to scrape and clean the data, and the tests to ensure my code is accurate, please see my GitHub file here: https://github.com/jacobkap/alcohol. When using this data consider that it is rate per capita (persons aged 14+) based on the population in that state so states that experience lots of visitors (e.g. Nevada and Washington D.C.) may have incorrect numbers. ,Persons aged 14 and over living in the United States Smallest Geographic Unit: State, Region, Country,
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This is a replication package for "the Persistence of the Harvest in Medieval England." Notes for replicating results from “The Persistence of the Harvest in Medieval England.” The two data files contain yields per acre and per seed, the rest of the control variables are the same for both files. Format is STATA data file. File 1: yieldsperacre.dta File 2: yieldperseed.dta File 1 is used to produce Panel A for most tables (i.e., those tables having two panels), file 2 produces Panel B for most tables. List of variables in data files: yldw: wheat yields (per seed or acre) with date stubyldo: oat yields (per seed or acre) with date stubyldb: barley yields (per seed or acre) with date stubsow: typical sow rates for wheat by periodsowo: typical sow rates for oats by periodsow: typical sow rates for barley by periodani: typical # of draft animals by manor by periodacres: mean acres sown by period by manorsheep: typical # of sheep by manor by periodprecip: measured precipitation by yearpreciptr: 30 year moving average of precipitationtemp: measured temperature by yeartempr: 30 year moving average of temperaturedpop: change in manor’s population density over periodpop: population level in perioddis: distance of manor to Londoncroptype: code for crop mix, from Campbellhustype: code for husbandry, from Campbellfarmtype: code for farming strategy, from Campbellsoiltype: code for soil make up, geology of manor, from CampbellThe provided STATA *.do files are listed by the relevant table or figure in the text that they produce. Each do-file uses either data file 1 or data file 2 depending on which panel of the relevant table is produced. To use the do file replace wd with your relevant data and working directory. All of the do files contain redundant code, they can be used for “stand alone” analysis in addition to producing the relevant table/figure.
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Citation for Published Paper:Curran, F. C., & Kitchin, J. (2018). Why are the early elementary race/ethnicity test score gaps in science larger than those in reading or mathematics? National evidence on the importance of language and immigration context in explaining the gap-in-gaps. Science Education. Online First. Abstract:Recent work examining science test performance in the earliest grades of school has demonstrated that science test score gaps by race/ethnicity are apparent as early as kindergarten and that, in a number of cases, the racial/ethnic test score gaps in science are significantly larger than the corresponding gaps in reading or mathematics. This study explores the factors that explain the differences in the magnitudes of racial/ethnic disparities in performance on science standardized tests as compared to those in reading/mathematics. Drawing on nationally representative data from over 10,000 kindergartners in the 2010⿿2011 school year, this study employs regression models that examine the explanatory power of nine conceptual domains for explaining the ⿿gap-in-gaps⿝ or test score gap differences in science relative to mathematics or reading. Results indicate that the gap-in-gaps is relatively unchanged by the inclusion of many conceptual domains but that students' language and immigration contexts do explain substantial portions of the gap-in-gaps for Hispanic and Asian students. Implications for policy and practice are discussed.
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The National Surveys on Energy and Environment (NSEE) is an on-going biannual national opinion survey on energy and climate policy. Launched in 2008, over time the NSEE has covered topics such as public policy approaches to address climate change including federal, state, and international action; energy policies such as cap-and-trade, carbon taxes, renewable energy requirements, vehicle emissions standards, and many more; and knowledge and attitudes about global warming, climate adaptation, fracking, and geoengineering. From 2008-2012 the survey was called the “National Survey of American Public Opinion on Climate Change” (NSAPOCC); starting in 2013 the survey was renamed to the “National Surveys on Energy and Environment” (NSEE). NSEE was co-founded by professor Barry Rabe at the University of Michigan and professor Christopher Borick at Muhlenberg College, and is fielded by the Muhlenberg College Institute of Public Opinion. For more information about the NSEE, contact closup-nsee@umich.edu. 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. ,Weight variables are provided for each dataset. Data are weighted by gender, age, race, income, and education, to reflect population characteristics of the United States as reported by the United States Census Bureau.,Presence of Common Scales: Several likert-type scales were used.,computer-assisted telephone interview (CATI)~~,Adult (age 18 or older) residents of the United StatesSmallest Geographic Unit: State,The NSEE is conducted as a telephone survey adult (age 18 or older) residents of the United States. In 2008, only landlines were included in the sampling frame, starting in 2009 both landline and cell phones have been included in the sampling frames. See individual waves for more detailed sampling information.,
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Data collected for the purpose of the psychometric evaluation of the Short Dark Triad Scale (SD3) in Croatian sample. Data set contains information for 375 participants that were included in the final sample. Variables are: Age, gender, NPI-1-NPI-40 (Narcissism Personality Inventory), MACH-1-MACH-20 (MACH-IV), SRP-1-SRP31 (SRP-III), M1-P9 (SD3). All reverse-coded items are re-coded ,Presence of Common Scales: SD3; Mach-IV; SRP-III; NPI-40,Students at the University of Zadar ,Convenient sample ,
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Studies have documented that school bullying victimization (SBV) aroused by negative interpersonal relationships can lead victims to show more depressive symptoms than those who are not bullied, but few studies have explored the relationship among the three variables from a cumulative interpersonal relationship risk (CIRR) perspective and examined how to reduce the negative chain reactions aroused by CIRR from a positive psychological perspective. The present research explored the protective mechanisms of resilience in the relations among CIRR, SBV and depression and tested 2 serial mediation models for the relationships. Self-report data were collected from 742 middle school students (48.38% male) in grades 7-11 (mean age = 14.32 years, SD =1.54 years). The results supported both models. CIRR could increase SBV by decreasing resilience and, therefore, improve a victim’s depressive level. Alternatively, CIRR could also directly increase SBV and further improve depressive level by decreasing resilience. Our results shed light on the idea that it is a good suggestion to decrease SBV and depression that follow multiple interpersonal relationship risk exposures by cultivating adolescence resilience if we cannot reach the goal by decreasing multiple interpersonal relationship risk at the same time.
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Data from this study comes from a theory-driven mHealth intervention which sought to assess the effect of voice SMS on the prevalence of malaria among children under-five living in rural districts of Ghana, Sub-sahara Africa. A quasi-experimental study was conducted from Februray 2016 to March 2017 using a random sample of 332 caregivers with children under-five from two rural health districts, assigned to either an intervention or a control group. A two-stage cluster smpling was used to select the sample. Caregivers in the intervention group received voice short message service (SMS) on malaria prevention based on a behavior change theory to improve their health behaviors and practice, once a week for twelve months, while caregivers in the control group received none. ,Response Rates: For this Survey data, which involved cross-sectional surveys at bseline and endline, all participants (332) participated at baseline. Six (6) participants dropped out at 12 months follow-up, giving a response rate of 98.2%. ,Presence of Common Scales: Includes a Likert-type scale ,face-to-face interview~~on-site questionnaire~~other~~,Caregivers and children under-five from rural districts of Ghana ,A two-stage random cluster sampling method was employed; cluster selection and participants from households ,
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In comparison with younger children, older students tend to be less motivated to read. A literature class that fails to motivate students is one aspect that has often been discussed in this regard. Using data from 405 German ninth graders, we examined how students’ book reading is related to intrinsic situational and intrinsic habitual reading motivation in and out of school. The books that students reported to have read were characterized by LIX readability and text type. Our results first showed that recreational reading motivation exceeded school reading motivation. Second, the reading of classic literature was a negative predictor of intrinsic situational reading motivation. Third, in the school context, students who read more difficult books were less motivated to read them. Fourth, analyses showed that individual book-reading experiences were linked to intrinsic habitual reading motivation. We discuss practical implications for book reading in and out of the literature class.
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Stata data files and code to reproduce the results in Komlos and A'Hearn, "Clarifications of a Puzzle: The Decline in Nutritional Status at the Onset of Modern Economic Growth in the United States," forthcoming JEH 2019.
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We study how goods- and labor-market frictions affect aggregate labor productivity in China. Combining unique data with a general equilibrium model of internal and international trade, and migration across regions and sectors, we quantify the magnitude and consequences of trade and migration costs. The costs were high in 2000, but declined afterward. The decline accounts for 36 percent of the aggregate labor productivity growth between 2000 and 2005. Reductions in internal trade and migration costs are more important than reductions in external trade costs. Despite the decline, migration costs are still high and potential gains from further reform are large.
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