This repository compares models that estimate race and ethnicity labels from name patterns, using voter-registration data and external validation datasets. It includes preprocessing, character-based and neural models, and evaluation notebooks; these are statistical estimates from source labels rather than observations of a person's identity.

We compare name classification using phonetic encodings with classification using character sequences. In the reported experiments, an LSTM trained on Metaphone encodings performs worse, suggesting that spellings contain information lost by the sound encoding.

An exploratory method that combines image-search results for first names with image tags to estimate gender associations. Validation uses labeled name data, while the project notes that search results are not representative population samples and names need not be unique to one gender.

Naampy estimates aggregate patterns associated with Indian first names using calibrated model scores or exact source-data lookups. Results include abstention and artifact provenance; the scores describe patterns in recorded labels, not an individual's gender.

Pranaam estimates how strongly a name follows patterns associated with Muslim names in its training data. It returns a calibrated score, abstention information, and artifact provenance for aggregate research, rather than establishing a person's religion.

A character-level LSTM trained on Florida voter-registration names generates synthetic name-like strings. The outputs are intended for demonstrations, testing, and exploration, rather than verified names or representative samples of a population.

Parsernaam uses character-level models to distinguish first-name from last-name tokens and infer their order in multi-token strings. It helps parse combined name fields, while its limited label set cannot represent every naming convention.

Instate estimates state composition associated with Indian surnames using electoral-roll lookups and a calibrated character model. It combines state shares with census mother-tongue shares to derive aggregate language compositions; these are name-pattern estimates, not a person's location or language.

To what extent are users surveilled on the web, by what technologies, and by whom? We answer these questions by combining passively observed, anonymized browsing data of a large, representative sample of Americans with domain-level data on tracking from Blacklight. We find that nearly all users (>99%) encounter at least one ad tracker or third-party cookie over the observation window. More invasive techniques like session recording, keylogging, and canvas fingerprinting are less widespread, but over half of the users visited a site employing at least one of these within the first 48 hours of the start of tracking. Linking trackers to their parent organizations reveals that a single organization, usually Google, can track over 50% of web activity of more than half the users. Demographic differences in exposure are modest and often attenuate when we account for browsing volume. However, disparities by age and race remain, suggesting that what users browse, not just how much, shapes their surveillance risk.

News about massive data breaches is increasingly common. But what proportion of Americans are exposed in these breaches is still unknown. We combine data from a large, representative sample of American adults (n = 5,000), recruited by YouGov, with data from Have I Been Pwned to estimate the lower bound of the number of times Americans’ private information has been exposed. We find that at least 82.84% of Americans have had their private information, such as account credentials, Social Security Number, etc., exposed. On average, Americans’ private information has been exposed in at least three breaches. The better educated, the middle-aged, women, and Whites are more likely to have had their accounts breached than the complementary groups.

The package infers the kind of content hosted by a domain using the domain name, the textual content, and the screenshot of the homepage. We use domain category labels from Shallalist and build our own training dataset by scraping and taking screenshots of the homepage.

We train character-level models on leaked passwords to study predictable password choices. Comparisons with Have I Been Pwned and randomly generated strings show how learned patterns, including common starting characters, can help diagnose password weakness.

Using 12,384 email addresses of politicians from 59 countries, we examine exposure to breaches recorded by Have I Been Pwned. A third of the politicians have at least one recorded breach, and more than one in five have sensitive information exposed; incomplete coverage of their email addresses makes these conservative estimates.

We combine a month of observed browsing by over a thousand Americans with malicious-domain classifications. About half visited a malicious domain; demographic differences in median exposure disappear after accounting for how much people use the internet.

A Python toolkit for enriching IP addresses with registration, network, geolocation, and reputation information from multiple providers. It reconciles provider outputs while recording provenance, caching results, and handling rate limits and partial failures.

An R client for VirusTotal API v3, covering reports on files, URLs, domains, and IP addresses, as well as submissions for analysis. The package includes request pacing, retries, and structured error handling.

A Model Context Protocol server that lets compatible assistants invoke R tools for statistical analysis. Its documented tools cover regression, econometrics, time series, statistical testing, and data exploration.

An R client for searching and retrieving YouTube videos, channels, playlists, comments, and captions. It also supports selected authenticated operations, including uploads, moderation, and playlist updates.

Repaper converts photographed forms into editable digital forms. The documented workflow includes generating a Google Form from an image through a Python interface or command-line tool.

A transliteration toolkit for converting between Indic scripts and English-script representations. It supports composable word-table, local-model, and language-model backends, with batch processing and structured output.

Mortality rate is puzzling to mortals. A better number is the expected number of years lost. (A yet better number would be quality-adjusted years lost.) To make it easier to calculate the expected years lost, lost_years provides a convenient way to join to the SSA actuarial data, HLD data, and WHO life table data.

A Python package and web interface for retrieving historical daily weather by US ZIP code or coordinates. It uses NOAA station data, selects nearby stations, and returns weather measures in metric or imperial units.

LayoutLens captures screenshots, page structure, styles, and geometry to measure changes between interface versions. Local comparisons report element-level differences and possible layout defects; model-based explanations are optional.

Preen helps Python packages adopt and maintain the py-canon development standard. Its command-line tools scaffold repositories, update shared templates, check conformance, apply fixes, and guide tag-based releases.

A Jekyll theme for academic personal websites, with a single text column and publications rendered from structured data. It uses self-hosted fonts and system-controlled dark mode, without requiring JavaScript.

A GitHub Action that adds related-repository recommendations to a README. It ranks repositories using shared topics, README similarity, or a combination, with controls for exclusions and the number of recommendations.

A GitHub Action that selects a repository from a curated list, generates a short description, and posts it with a link to Bluesky. It supports automated or manually triggered promotion of open-source projects.

How good are the public services and the public infrastructure? Does their quality vary by income? These are vital questions—they shed light on how well the government is doing its job, the consequences of disparities in local funding, etc. But there is little good data on many of these questions. We fill this gap by describing a scalable method of getting data on one crucial piece of public infrastructure: roads. We assess the quality of roads and sidewalks by exploiting data from Google Street View. We randomly sample locations on major roads, query Google Street View images for those locations and code the images using Amazon’s Mechanical Turk. We apply this method to assess the quality of roads in Bangkok, Jakarta, Lagos, and Wayne County, Michigan. Jakarta’s roads have nearly four times the potholes than roads of any other city. Surprisingly, the proportion of road segments with potholes in Bangkok, Lagos, and Wayne is about the same, between .06 and .07. Using the data, we also estimate the relation between the condition of the roads and local income in Wayne, MI. We find that roads in more affluent census tracts have somewhat fewer potholes.

A collection of public-employee salary data and supporting acquisition tools. The project aims to compare compensation across places and over time, with contextual information for analyses of local public spending and pay.

Two national survey experiments test whether partisan gaps in reports of economic conditions reflect differences in knowledge or how people answer. Monetary incentives and appeals for accuracy reduce the gaps, suggesting that ordinary survey responses mix factual beliefs with partisan expression.

Three experiments use accuracy incentives to distinguish biased learning from biased reporting of learned information. Incentives increase accurate reporting of politically uncongenial results, but also increase skepticism about their credibility, suggesting that bias can shift from one judgment to another.

Across 162,083 responses to 187 questions on 47 surveys, partisan knowledge gaps are smaller and less consistently aligned with partisan interests than often assumed. Vague response options inflate some gaps, suggesting a role for motivated responding rather than differences in stored knowledge alone.

Misinformation is a confidently held false belief, distinct from knowledge, mere belief, and ignorance. Multiple-choice items cannot tell it from guessing; asking how definitely true or false each statement is can. In four surveys that randomly assigned the two formats, the scale finds far less misinformation about well-publicized falsehoods, and much smaller partisan gaps, than multiple-choice items do.

Revisionist studies argue that surveys understate political knowledge. Randomized experiments in three online surveys, and a recount of ANES and NAES items and don't-know probes, find little hidden knowledge: photos lower correct identification of officials, menus and probes add little beyond guessing, and asking how sure people are finds far less knowledge than multiple choice.

Media-poll misinformation items rarely offer a 'don't know' and usually ask what respondents think. In a survey experiment, offering and encouraging 'don't know' cut incorrect answers from 32% to 20%, repeating the false claim in the question changed nothing, and asking how definitely true or false each statement is cut them to 7%.

Two national surveys distinguish incorrect answers about the Affordable Care Act from confidently held incorrect beliefs. Most respondents are uncertain; confident misperceptions are less common and are concentrated on provisions about which elites repeatedly made false claims.

We develop a latent class model to estimate learning when correct answers can result from guessing. Under an assumption of no knowledge loss over short periods, simulations recover learning more accurately than raw score differences; an application to Deliberative Polls raises learning estimates and reduces measured gender gaps.

Observed knowledge gain is a poor measure of what people learn from campaigns and deliberative forums: questionnaires ask easy items, so those who learn the most have the least room to show it. When the knowledgeable learn more, post-process knowledge is guaranteed to track true learning and observed gain is not. In simulations matched to 21 Deliberative Polls, observed gain barely correlates with true learning, and the choice of measure changes who appears to learn.

Across 27 Deliberative Polls, factual knowledge increased by an average of 14.3 percentage points, a relative gain of 39.3%. In two polls with control groups, attendees gained an average of 11.2 percentage points more than controls. Measurements before arrival, on arrival, and after deliberation in two studies show substantial gains before participants arrive. The gender gap narrows but remains. Across 20 polls with poll-specific education cutoffs, the gap between more and less educated attendees widens.

Across 19 misinformation-type items in seven Deliberative Polls, incorrect answers fell from 23% to 10%, and participants who began wrong were as likely to end right as those who began not knowing. In polls with control groups, attending cut overestimates of the number of undocumented immigrants by 15 points, but reduced confident denial of human-caused warming only slightly and not durably.

Research suggests that partisan gaps in political knowledge with partisan implications are wide and widespread. Using a series of experiments, we investigate the extent to which partisan gaps in commercial surveys are a result of differences in beliefs than motivated guessing. Knowledge items on commercial surveys often have features that encourage guessing. We find that removing such features yields scales with greater reli- ability and higher criterion validity. More substantively, partisan gaps on scales without these “inflationary” features are roughly 40% smaller. Thus, contrary to Prior, Sood and Khanna (2015), who find that the upward bias is explained by the knowledgeable deliberately marking the wrong answer (partisan cheerleading), our data suggest, in line with Bullock et al. (2015) and Graham and Yair (2023), that partisan gaps on commercial surveys are strongly upwardly biased by motivated guessing by the ignorant. Relatedly, we also find that partisans know less than what toplines of commercial polls suggest.

Two survey experiments present the same small declines in unemployment and inflation under either an Obama cue or a Republican Congress cue. Respondents assess whether conditions got better, stayed about the same, or got worse. On a 0–100 evaluation scale, opposing-party rather than own-party cues change unemployment and inflation evaluations by -9.5 points (95% CI [-12.5, -6.5]) and -6.0 points (95% CI [-9.2, -2.8]) on MTurk, and by -5.2 points (95% CI [-10.6, 0.2]) and -8.6 points (95% CI [-14.2, -3.1]) on Lucid. The Lucid unemployment estimate includes zero. These are effects on evaluations of supplied information, not direct measures of factual knowledge; response-option vagueness was not randomized.

This chapter examines how to measure beliefs about the numerical size of salient and stereotypical groups. It connects systematic overestimation of stereotypical traits to broader questions about group perceptions, prejudice, and the interpretation of survey estimates.

Across more than 34,000 rural local bodies, prior reservation produces small or inconsistent short-run gains in women's success in subsequently open seats. Phone audits document extensive male mediation of access to women elected under quotas, a pattern consistent with, but not proof of, proxy governance limiting independent political careers.

Schooling differences under women's reservation follow distinct patterns across offices and settings. Published village-head studies and our northern village-head estimates show deficits among reserved-seat winners; Kerala's village ward members and councillors in Mumbai and Delhi show no consistent deficit, although the urban estimates do not establish equivalence. We synthesize these groups separately, rather than treating a single overall average as the main finding. Reserved-seat winners are more likely to report no occupation in the village, block and city offices with occupation records, but face fewer pending criminal charges in Mumbai and Delhi. The records do not establish effects on governing performance.

Using electoral-roll records for nearly 70 million couples across Indian states and union territories, we describe spousal age differences. Husbands are generally older, with an average gap of about 4.1 years and substantial variation across states and spouses' ages.

Digitized land records from Bihar reveal concentrated ownership and substantial disparities by gender, religion, and caste. Men make up 76.5% of landowners, and Muslims, Scheduled Castes, and Extremely Backward Classes are underrepresented; the study measures ownership rather than operational agricultural holdings.

Scripts and linked tools collect and parse Indian electoral rolls published as PDFs by state election authorities. The project provides a foundation for research using recorded names, ages, gender labels, family relationships, and electoral geography.

Randomly sampled streets in four Indian cities reveal a large gender imbalance in public space: women account for 12% to 16% of observed adults. Comparisons with residential populations and time-use data suggest that much of the gap arises because women leave home less often and make fewer trips.

We compare businesses named with 'son' or 'sons' and 'daughter' or 'daughters' using state business registries. Across 40 states, the median ratio is 12 to 1, with limitations in registry searches making the analysis a conservative accounting of the imbalance.

We use Indian electoral-roll records for more than 35 million people to estimate adult sex ratios by surname. The analysis describes variation that state-level averages can obscure, using recorded electoral-roll gender labels.

An exploratory project on measuring women's presence in public space using observations of people on streets. It develops sampling and data-collection approaches and discusses the assumptions needed to compare observed street populations with a gender-balance baseline.

A dataset records sons and daughters attributed to key figures across epic and mythological traditions. It documents large differences in the visibility of named sons and daughters, with source and family-structure information for interpreting the counts.

We use official biographies of members of India's 12th through 17th Lok Sabhas to examine the sex ratio of politicians' children. The project compares recorded sons and daughters to assess whether the imbalance observed in the wider population also appears among political elites.

We construct historical monthly Elo ratings for international cricket teams, separately for Tests, one-day internationals, and T20 internationals. The repository provides match outcomes, rating scripts, and downloadable series for comparing team strength over time.

We develop batting and bowling Wins Above Replacement measures for men's and women's one-day international cricket. The project compares simple aggregate measures with context-adjusted approaches, with results depending on the chosen replacement baseline and conversion from runs to wins.

We use cricket's coin toss to estimate the advantage of choosing whether to bat or bowl first. The estimated effect on winning is negligible in daytime international limited-overs matches, but about 3.3 percentage points in day-night matches and 4.9 points in Tests; rain-adjusted matches show no statistically distinguishable effect.