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This paper documents a set of facts about the dramatic decline in birth rates in the United States between 2007 and 2020 and explores possible explanations for it. The overall reduction in the birth rate reflects both very large declines within certain groups of women, including teens and Hispanic women ��� and smaller declines among demographic groups that comprise a large population share, including college-educated white women. We explore potential economic, policy, and social factors that might be responsible for the overall decline. We conclude from our empirical examination of possible factors that there is not a readily identifiable economic or policy factor or set of factors this is likely responsible for a substantial share of the decline. Instead, the patterns observed suggest that widespread, hard to quantify changes in preferences for having children, aspirations for life, and the nature of parenting are more likely behind the recent decline in US births. We conclude with a brief discussion about the societal consequences for a declining birth rate and what the United States might do about it. The universe of recorded births in the United States from 1980-2020 (main analysis focuses on the period from 2007-2020), among women 15-44 and within selected age and race or ethnicity groups.. Smallest Geographic Unit: United States, and by State

The Repertory Dataset is the third public-use dataset in the Dunham's Data series, a unique data collection created by Kate Elswit (Royal Central School of Speech and Drama, University of London) and Harmony Bench (The Ohio State University) to explore questions and problems that make the analysis and visualization of data meaningful for dance history through the case study of choreographer Katherine Dunham. The Repertory Dataset catalogues the various titles and descriptions in Dunham's repertory by which a piece might be known, the years in which it was performed, and all of the singers, dancers, and drummers who were listed as performing in it. The Repertory dataset documents other aspects of each work such as composers of the music, the varying numbers of performers, places of inspiration where available, and whether pieces were performed in concert venues, nightclubs, or both. It also tracks fluid relationships among nearly 300 numbers identified in Dunham's repertory from the 1930s onwards by examining the various scales at which Dunham repurposed choreographic elements over time and for different performance venues, and therefore the alternative ways that works might connect individual performers. Dunham's Data: Digital Methods for Dance Historical Inquiry is funded by the United Kingdom Arts and Humanities Research Council (AHRC AH/R012989/1, 2018-2022) and is part of a larger suite of ongoing digital collaborations by Bench and Elswit, Movement on the Move. The Dunham's Data team also includes digital humanities postdoctoral research assistant Antonio Jiménez-Mavillard and dance history postdoctoral research assistants Takiyah Nur Amin and Tia-Monique Uzor. This dataset was created and audited as follows: The initial 1947-60 repertory dataset was built alongside the Everyday Itinerary and Personnel datasets, with the structure emerging through the process. The resulting structure was built a dataset from scratch using the same archival information. Further nuance was added to the data structure as part of this second iteration. Then the information from both datasets were compared and manually compiled a new, third dataset that reconciled the two. This reconciled dataset was subsequently expanded to include repertory material from 1937-1962, alongside an expanded draft of the Personnel Check-Ins. Finally, it's then conducted a second process of auditing by programmatically extracting every archival reference present in the dataset into a list and then manually confirming the data against the archival material. The purpose of this study is to explore the kinds of questions and problems that make the analysis and visualization of data meaningful for dance history. Using digital research methods and data visualization in the context of dance history can catalyze a better understanding of how dance movements over time. mixed modeFor further information, please see the Dunham's Data website.To view the interactive visualizations, please visit the Dunham's Data research blog. Datasets: DS1: Dunham's Data: Katherine Dunham and Digital Methods for Dance Historical Inquiry, Repertory, 1937-1962 For the description of variables, please see the User Guide. Smallest Geographic Unit: country

This Repository contains the code related to the AERA Open Major Forecasting Paper Abstract:This Repository contains the code related to the AERA Open Major Forecasting PaperAbstract: Commitment to a major is a fateful step in an undergraduate education, yet the relationship between courses taken early in an academic career and ultimate major issuance remains little studied at scale. Using transcript data capturing the academic careers of 26,892 undergraduates enrolled at a private university between 2000 and 2020, we describe enrollment histories using natural-language methods and vector embeddings to forecast terminal major on the basis of course sequences beginning at college entry. We find that (I) a student's very first enrolled course predicts their major thirty times better than random guessing and more than a third better than majority-class voting, (II) modeling strategies substantially influence forecasting metrics, and (III) course portfolios vary substantially within majors, such that students with the same major exhibit relatively modest overlap.Due to the PII nature of the data as well as to protect the underlying institution, data will not be available in this repository. If interested in obtaining the data, contact Mitchell Stevens stevens4@stanford.edu.see the readme in the code folder for additional details on how to execute the code.

The Check-In Dataset is the second public-use dataset in the Dunham's Data series, a unique data collection created by Kate Elswit (Royal Central School of Speech and Drama, University of London) and Harmony Bench (The Ohio State University) to explore questions and problems that make the analysis and visualization of data meaningful for dance history through the case study of choreographer Katherine Dunham. The Check-In Dataset accounts for the comings and goings of Dunham's nearly 200 dancers, drummers, and singers and discerns who among them were working in the studio and theatre together over the fourteen years from 1947 to 1960. As with the Everyday Itinerary Dataset, the first public-use dataset from Dunham's Data, data on check-ins come from scattered sources. Due to information available, it has a greater level of ambiguity as many dates are approximated in order to achieve accurate chronological sequence. By showing who shared time and space together, the Check-In Dataset can be used to trace potential lines of transmission of embodied knowledge within and beyond the Dunham Company. Dunham's Data: Digital Methods for Dance Historical Inquiry is funded by the United Kingdom Arts and Humanities Research Council (AHRC AH/R012989/1, 2018-2022) and is part of a larger suite of ongoing digital collaborations by Bench and Elswit, Movement on the Move. The Dunham's Data team also includes digital humanities postdoctoral research assistant Antonio Jiménez-Mavillard and dance history postdoctoral research assistants Takiyah Nur Amin and Tia-Monique Uzor. For more information about Dunham's Data, please see the Dunham's Data website. Also, visit the Dunham's Data research blog to view the interactive visualizations based on the Dunham's Data. Personnel Check-In 1947-60 Data: Date: The data are organized chronologically from January 1st, 1947 to December 31st, 1960 (one date per row). Source: The sources for materials from which information contained in that row was drawn. Box and folder-level information is provided to document the researchers' process and to aid future researchers. Source type: The type of archival evidence that underpins the row, categorized as "travel", "stage", "administration", and "correspondence". Notes: Description of the source document and/or key information drawn from it, including commentary around conflicting pieces of evidence. Confidence: Confidence in each check-in date's accuracy. Lower numbers represent higher thresholds of confidence that the check-in occurred on the date listed. Comprehensiveness: A comprehensive check-in is based on archival evidence that includes all performers present at the time. A non-comprehensive check-in is based on evidence that only includes some performers. Performers are sorted alphabetically by last name and only includes those who appeared onstage with Dunham. "Y" indicates a performer was there, whereas "N" indicates a performer was specifically not there. First and Last Check-Ins Data: This dataset compiles every performer's first and last check-in data. Names are consistent with those used in the main Personnel Check-In data. Dates are formatted by "day, month, year". Performer Passport Nationality by Year Data: This dataset aggregates the number of checked-in performers that traveled with Dunham's company during each year by passport nationality. Public AKAs Data: All key alternative names for performers encountered in the archives are listed. Creation and auditing for this dataset was done in the following phases. First, the preliminary draft of the 1947-60 dataset was curated from archival sources, in tandem with the Everyday Itinerary and Repertory datasets. A second pass was done to revisit each piece of evidence identified as a source, and assign confidence levels to each check-in. During this second phase, some check-ins were re-dated to better represent their position chronologically in relation to other evidence. In the third phase, names from the expanded 1937-62 Repertory dataset were added to the Personnel Attributes dataset, and reconciled as AKAs where appropriate. In the fourth phase, an expanded Personnel Check-In dataset was drafted from select programs to encompass 1937-1962, which supported the discovery and disambiguation of further AKAs (not part of this release). In the final phase, performer check-ins were audited in two ways: 1) by comparing performers with similar first and last names across 1937-62; and 2) by spot-checking any performer with a gap in check-ins of more than two years. As part of a larger case study of choreographer Katherine Dunham, the purpose of these data is to account for the movements over time of the nearly 200 dancers, drummers, and singers in Dunham's employ between 1947 and 1960. Datasets: DS0: Study-Level Files DS1: Personnel Check-In 1947-60 Data DS2: First and Last Check-Ins Data DS3: Performer Passport Nationality by Year Data DS4: Public AKAs Data mixed modeFor further information, please see the Dunham's Data website.To view the interactive visualizations, please visit the Dunham's Data research blog.

This file contains data that can be used to replicate the figures and tables reported in the main text of "Socialist growth revisited: Insights from Yugoslavia".

These files contain all the data used in the publication "Poverty or Prosperity in Northern India? New Evidence on Real Wages, 1590s-1870s". This paper introduces a new dataset on wages in northern India (from Gujarat in the West to Bengal in the East) from the 1590s to the 1870s. It follows Allen’s subsistence basket methodology to compute internationally comparable real wages to shed light on developments in Indian living standards over time, as well as to test some of the assumptions underlying the comparative real wage methodology. It adjusts the comparative cost of living indices to take into account differences in caloric intake due to variances in heights. Furthermore, the paper discusses the male/female wage gap in northern India. We demonstrate that the Great Divergence started somewhere in the late seventeenth century. This gap widens further after the 1720s and especially after the 1800s. It is subsequently primarily England’s spurt and India’s stagnation in the first half of the nineteenth century which brought about most serious differences in the standard of living in Eurasia. If the British colonial state is to blame – as often happens in the literature on India’s persistent poverty – it is in their failure to improve the already deteriorated situation after they had become the near-undisputed masters of India since 1820.Note on v2There are two main changes compared with Version 1:1. In the sheet “PricesNEI” from the Excel file “prices_north_india.xlsx”, a faulty comma in the formula of column P, caused the average price of ghi to be calculated over 4 rather than 3 columns. This was corrected and the newly calculated series of ghi were also included in the “BasketNEI” sheet of that same file and the improved CPI was used in the calculations of the real wages. As a consequence of this change, the prices of the overall basket are increased somewhat, causing a slight downward adjustment of real wages. 2. In the sheet “PriceNOI”, for the years 1861-1930, the average price of millet (Column J) was accidentally calculated over columns F-I, rather than just column I. This has been corrected in this file and the newly computed CPI entered in the comparisons and real wages calculations. It has no observable consequences for the results. We thank Joseph Enguehard (l’École normale supérieure de Lyon) for pointing us towards these issues. Note on v3There are two changes compared with v2:1. In the file “7.global_comparisons”, sheet “cpi”, in the calculation of the 10-year averages for Beijing, London, Leipzig and Valencia, the range of years in the formula did not match with the decade in column A. This has been corrected. 2. In that same file, sheets “skilled” and “unskilled”, in the calculation of the 10-year averages, the formula for the 1680s accidently ranged from 1678-1689 instead of 1680-1689. This has been corrected. We thank Tamer Güven (Utrecht University) for pointing us towards these issues.

Curriculum-based measurement of oral reading fluency (CBM-R) is used as an indicator of reading proficiency, and to measure at risk students��� response to reading interventions to help ensure effective instruction. The purpose of this study was to compare model-based WCPM scores (CORE) to Traditional CBM-R WCPM scores to determine which provides more reliable growth estimates and demonstrates better predictive performance of reading comprehension and state reading test scores. Results indicated that in general, CORE had better (a) within-growth properties (smaller SDs of slope estimates and higher reliability), and (b) predictive performance (lower RMSE, and higher R-squared, sensitivity, specificity, and AUC values). These results suggest increased measurement precision for the model-based CORE scores compared to Traditional CBM-R, providing preliminary evidence that CORE can be used for consequential assessment.

This article studies housing rents in St. Petersburg (1880-1917), covering an eventful period of Russian and world history. We digitize over 5,000 rental advertisements from which we construct a state-of-the-art index - the first pre-war and pre-Soviet market data index for any Russian city. In 1915, a rent control and tenant protection policy was introduced in response to soaring prices following the outbreak of WWI. We document official compliance, rising tenure duration and strongly increased affordability for workers. While the immediate prelude to the October Revolution was indeed characterized by economic turmoil, rent affordability was no longer dominating.

These files contain the anonymized data and analysis files used to create the tables found in the manuscript entitled “It’s not That you said it, it’s How you said it: Exploring the Linguistic Mechanisms Underlying Values Affirmation Interventions at Scale”. The abstract for the paper is found below: Over the last decade, psychological interventions, such as the values affirmation intervention, have been shown to alleviate the male-female performance difference when delivered in the classroom, however, attempts to scale the intervention are less successful. This study provides unique evidence on this issue by reporting the observed differences between two randomized controlled implementations of the values affirmation intervention, i) successful in-class and, ii) unsuccessful online implementation at scale. Specifically, we use natural language processing to explore the discourse features that characterize successful female students’ values affirmation essays to gain insight on the underlying mechanisms that contribute to the beneficial effects of the intervention. Our results revealed that linguistic dimensions related to aspects of cohesion, affective, cognitive, temporal, and social orientation, independently distinguished between males and females, as well as more and less effective essays. We discuss implications for the pipeline from theory to practice and for psychological interventions.

A ZIP code to ZCTA crosswalk must be used to combine this dataset with ZIP code geocoded data. Such a crosswalk is available on the UDS Mapper website at https://udsmapper.org/zip-code-to-zcta-crosswalk/. Sample code for merging the UDS Mapper crosswalk with NaNDA datasets is available on the NaNDA repository at https://www.openicpsr.org/openicpsr/project/120088/. This dataset contains measures of the number and per capita density of select types of religious, civic, and social organizations – such as churches, mosques, synagogues, ethnic associations, and veterans’ associations – per ZIP code tabulation area (ZCTA) in the United States from 2003 through 2017. Religious, civic, and social organizations in all ZIP code tabulation areas in the United States, including Puerto Rico and the US Virgin Islands. Smallest Geographic Unit: ZIP code tabulation area