Recently, Laffer and Hartley proposed a negative payroll tax as a more desirable means of moving money into individual pockets than the current multi-pronged response to the health and economic impact of coronavirus. These responses include the Economic Impact Payments, Pandemic Emergency Unemployment Compensation and Pandemic Unemployment Assistance, the Paycheck Protection Program, among others. Laffer and Hartley’s recommendation is to, instead, take some of the approximately 9.40% employees and employers both pay in payroll taxes,1 and turn it into a negative tax received by employees and employers.
In a recent post, I shared my machine learning skills with a TicTacToe program based on Monte Carlo Tree Search. This post lets you play against the program.
Remember, it’s still learning. Nonetheless, it’s actually already a very sophisticated player and so far I’ve only found one weakness. If you find it, go ahead and pummel the program with as many losses as you want until it eventually racks up enough simulated games to change its behavior.
COVID-19 cases are sweeping the globe. One potential hotspot seems to be New York City. Here I show the historical case count and present a simple forecast.
I’m not a professional epidemiologist nor forecaster, I merely present a simple forecast for the coming week based on past observations. Here we have the actual data gathered from the historical timemap of New York City’s Department of Health and Mental Health’s Coronavirus Disease 2019 (COVID-19) page as seen at archive.
I wanted to add my own custom tags to my Zotero citation management database based on the term frequency—inverse document frequency (tf-idf) formula. While not perfect, this is a nice way to pick out what is distinct and important about individual texts.
“external access to the SQLite database (including direct access via the mozStorage API) should be done only in a read-only manner. Modifying the database while Zotero is running can easily result in a corrupted database, as mozStorage caching breaks the normal file-locking in SQLite that allows for safe concurrent file access.
I recently saw the AlphaGo documentary film and it blew my mind. Essentially, a computer was able to beat a world champion in a best of five match of Go, a game with an overwhelmingly large search space—a 19 x 19 board. One of the principal techniques of its success was Monte Carlo Tree Search (MCTS). While it has received adequate exposition elsewhere,1 I thought that a video could be a nice addition to what’s already out there.
Each year, hundreds of thousands of kids apply1 to New York City’s public school system with a ranking of up to twelve schools. The Department of Education then matches all of these students up with a single offer from one of NYC’s 1,800 public schools,2 ensuring maximum satisfaction for the applicants. How does it work?
At first glance, my intuition was that this was a really complex problem. After watching this fantastic video, I then sketched out the algorithm in a notebook and came to believe that it was pretty simple.
This is a follow-up post to The Three ’I’s of Poverty in NYC, with the main difference being the inclusion of all of New York State rather than New York City alone. After the maps below, I’ve also included the tables to facilitate an understanding of the full rankings on each poverty measure.
The typical story about poverty focuses on what fraction of individuals are poor.
While the Framers of the American Constitution were “pretty smart,” they had no experience with the type of representational inequality of today’s Senate. The Gini index of representational inequality in the Senate was 0.342 in 1790 and was 0.506 in 2015, the latest date from the data. This is an enormous change—in the income inequality literature, a change of 0.03 is considered “salient.”1
Another way to think about this, rather than with the difficult to interpret Gini, is the “social tables” approach preferred by Piketty.
The typical story about poverty focuses on what fraction of individuals are poor. Economists are interested in more than this, however, and the classical ways of measuring poverty are the:1
incidence of poverty (what proportion of the population is poor?) intensity of poverty (how poor are the poor?), and inequality of poverty (how unequal is the distribution of mild, moderate, and severe poverty?
Here are the top 10 ways the poor in Brownsville / Ocean Hill differs from the rest of New York. The data behind the visualizations comes from the 0 New Yorkers who responded to the 2018 American Community Survey.
Table of Contents Top 10 Differences between the Poor in Brownsville / Ocean Hill and the rest of New York City Proportions 10. Workers in Family 9. Household Language 8.