Predicting Robotic Grasps Using Surrogate Datasets

One of the tasks that continues to prove difficult in robotics is the ability to grasp objects of varying shapes. It is time-consuming to acquire large amounts of real-world data in order to train accurate classifiers that can predict the success or failure of a grasp. To solve this issue, we examine using artificially generated surrogate, or substitute, datasets as replacement training data for more expensive physically-tested training data. By dividing up the grasp space using kd-trees, we demonstrate that surrogate datasets can be efficiently leveraged to produce high-precision data in specific areas of the grasp space. This greatly eases the burden of collecting data by only requiring physical testing in areas where surrogate datasets have little expertise.Key Words: robotic grasping, kd-tree, Gaussian Process, Logistic Regression, classificatio

Essays on the Earned Income Tax Credit

This dissertation investigates three questions related to the Earned Income Tax Credit, the largest cash-based, means-tested program in the United States. I study whether the EITC changes how much (as opposed to whether) workers choose to earn, whether increasing awareness of the program can increase participation, and to what extent eligible households take up the California supplement to the federal credit. In Chapter 1, I propose a new strategy for identifying workers’ intensive-margin labor supply elasticity using within-year variation in anticipated year-end tax rates. I modify the standard non-linear budget set approach to include uncertainty about future employment. With uncertainty, households must forecast their annual income in order to anticipate the average and marginal tax rates that apply to their earnings. Using survey and administrative data, I find that low-income households’ labor supply responds more to expected tax rates at the end of the year, when certainty about annual income is greatest. I use the excess sensitivity to tax incentives near the end of the year, relative to other periods, to estimate an intensive margin labor supply elasticity between .08 and .18. This response is identified largely from non-linearity in the EITC schedule and implies a larger intensive margin response to this program than previous estimates.In Chapter 2, my co-authors and I summarize six pre-registered, large-scale field experiments involving over one million subjects testing whether “nudges” could increase take-up of the Earned Income Tax Credit (EITC). Despite varying the content, design, messenger, and mode of our messages, we find no evidence that they affected households’ likelihood of filing a tax return or claiming the credit. We conclude that even the most behaviorally informed low-touch outreach efforts cannot overcome the barriers faced by low-income households who do not file returns.In Chapter 3, my co-authors and I use administrative data from California on the population of Supplemental Nutrition Assistance Program (SNAP) recipients, linked to state tax records, to estimate the number of households who are eligible for California’s supplement to the federal EITC but do not claim it. We find that nearly half a million households who receive SNAP benefits and who were eligible for the state EITC in 2017 did not receive the credit. This includes approximately 42,000 eligible households who claimed the federal EITC but not the state credit; 110,000 eligible households who filed a state tax return but did not claim the state credit; and 290,000 eligible households who did not file a state tax return. The corresponding take-up rate for the CalEITC among eligible SNAP-enrolled households was 53%. Altogether, these households left on the table a total of $75 million in state EITC funds. If received, these credits would have increased incomes among these households by 2.6% and increased total state EITC outlays by 38.8%

Increasing Tax Filing and Take Up of Earned Income Tax Credit via Texting and Social Media

The Golden State Opportunity Center (GSOC) is conducting a statewide outreach campaign to increase tax filing and receipt of the Earned Income Tax Credit (EITC) during the 2018 tax season. The California Policy Lab at UC Berkeley (CPL) will partner with GSOC to evaluate this effort

High-frequency labor market measures for workers at small businesses

As evidenced by both the Pandemic Recession and Great Recession, labor markets can change rapidly. More granular data on the time-path of these changes, and the role played by firm closures, layoffs, hours changes, and worker turnover can help us better understand how the labor market is evolving. On this site, we will post weekly updates of labor market information from Homebase’s timecard data to shed light on the details of a rapid evolving labor market. We aim to measure the short- and medium-term evolution of the size of the small business sector and of the health of employers in this sector, by tracking whether firms in Homebase’s userbase are expanding or contracting the number of hours that they use each week and the rate of turnover among their workers.This work has been supported, in part, by the University of California Multicampus Research Programs and Initiatives grants MRP-19-600774 and M21PR3278

Measuring the labor market at the onset of the COVID-19 crisis

We use traditional and non-traditional data to measure the collapse and partial recovery of the U.S. labor market from March to early July, contrast this downturn to previous recessions, and provide preliminary evidence on the effects of the policy response. For hourly workers at both small and large businesses, nearly all of the decline in employment occurred between March 14 and 28. It was driven by low-wage services, particularly the retail and leisure and hospitality sectors. A large share of the job losses in small businesses reflected firms that closed entirely, though many subsequently reopened. Firms that were already unhealthy were more likely to close and less likely to reopen, and disadvantaged workers were more likely to be laid off and less likely to return. Most laid off workers expected to be recalled, and this was predictive of rehiring. Shelter-in-place orders drove only a small share of job losses. Last, states that received more small business loans from the Paycheck Protection Program and states with more generous unemployment insurance benefits had milder declines and faster recoveries. We find no evidence that high UI replacement rates drove job losses or slowed rehiring.This work has been supported, in part, by the University of California Multicampus Research Programs and Initiatives grant MRP-19-600774

Measuring the labor market at the onset of the COVID-19 crisis

We use traditional and non-traditional data to measure the collapse and partial recovery of the U.S. labor market from March to early July, contrast this downturn to previous recessions, and provide preliminary evidence on the effects of the policy response. For hourly workers at both small and large businesses, nearly all of the decline in employment occurred between March 14 and 28. It was driven by low-wage services, particularly the retail and leisure and hospitality sectors. A large share of the job losses in small businesses reflected firms that closed entirely, though many subsequently reopened. Firms that were already unhealthy were more likely to close and less likely to reopen, and disadvantaged workers were more likely to be laid off and less likely to return. Most laid off workers expected to be recalled, and this was predictive of rehiring. Shelter-in-place orders drove only a small share of job losses. Last, states that received more small business loans from the Paycheck Protection Program and states with more generous unemployment insurance benefits had milder declines and faster recoveries. We find no evidence that high UI replacement rates drove job losses or slowed rehiring.This work has been supported, in part, by the University of California Multicampus Research Programs and Initiatives grant MRP-19-600774