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.

Open-source software is widely used in commercial applications. Pair that with the fact that when choosing open-source software for a new problem, developers often use social proof as a cue. These two facts raise concern that bad actors can game social proof metrics to induce the use of malign software. We study the question using two field experiments. On the largest developer platform, GitHub, we buy ‘stars’ for a random set of GitHub repositories of new Python packages and estimate their impact on package downloads. We find no discernible impact. In another field experiment, we manipulate the number of human downloads for Python packages. Again, we find little effect.

StatQA is a modern Python framework for automatically extracting structured facts, statistical insights, and multimodal Q/A pairs from tabular datasets. It converts raw columns and values into clear, human-readable statements paired with rich visualizations, enabling rapid knowledge discovery, CLIP-style multimodal RAG corpus construction, and LLM training.

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.

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.

Roush and Sood (2023) use a dataset of 162,083 responses to 187 items on 47 surveys to find that partisan gaps are smaller and less frequent than commonly understood. The average is a mere six and a half points and gaps’ “signs” run counter to expectations roughly 30% of the time. However, one exception is the size of gaps on retrospection items on the ANES, which are considerably bigger. These retrospection items use vague response options, e.g., ‘About the same.’ Vague response options can inflate partisan gaps by offering partisans the opportunity to interpret the same data differently. We test this assumption with a novel survey experiment. We present partisans data indicating a small improvement in economic indicators and manipulate the partisan tint of the change by manipulating who is responsible for the change. We find that significantly fewer partisans pick the option that ’[things] got better’ when presented with an out-partisan cue than a co-partisan cue. Our findings suggest that vague options can induce knowledge gaps even when partisans have the same information.

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.