Tag: Data

A quick view of an economic system

By Matt Johnson

In this short blog, I will illustrate one way an urban dynamicist, i.e., systems scientist, looks at an economic system and its data.

Diagram 1

Diagram 1 is hierarchical, derives from the U.S. Census Bureau, and represents a few of the many levels of an economic system. Moreover, each level of the economic system in Diagram 1 is further a sub-system, or sub-economy, of the general United States economy.

This means that a zip code, for example, can be examined as an economic system, and then it can be compared and contrasted with a city’s economic system. And this examination will illustrate similarities and differences between a sub-system, a zip code, and a general system, a city, for instance.

Thus, an urban dynamicist can partition out each level of the economic system and analyze each level as a distinct entity, although one system is still a sub-system of the one superior to it in the hierarchy. Within each level, differences, relationships, perspectives, dynamics, and models can be examined through data.

As stated before, each level of the system can be analyzed against the other levels of the system through data, because data provides a picture at each level of the system. For example, the State can be illustrated and compared to the Division, Zip Code, or Census Tract via crime densities, demographic comparisons and migration patterns, and economic variables such as median household incomes, unemployment rates, the labor force and labor participation rates.

Here is the stochastic (probabilistic) behavior of the labor force in Minneapolis over the past 10 years as seen here in Graph 1.

Graph 1

And here is the stochastic (probabilistic) behavior of the Minnesota labor force over the past 10 years as illustrated in Graph 2.

Future articles will delve deeper into the specifics of the behavior and dynamics of these two systems and their respective data sets. For now, the main point is that data can provide a picture of the economic systems at their respective levels of the system.

One last thought, Diagram 1 does not illustrate the interactions or dynamics that take place within each level of the system by itself, nor does it account for a lot of things. This is why the data is needed. So assumptions and conclusions should be limited.

As this focus on data continues, I will be utilizing the hierarchical model and other systems models to help illustrate and explain how economic systems can be better understood. In addition, I will be using systems theory along with applied mathematics to explore the complexity of systems. But I will also be working diligently and meticulously to convey this information to you the best I can.

As I get better at explaining this stuff to you, I hope your knowledge of systems, mathematics, and economics increases as well.

 

Matt Johnson is a writer for the Urban Dynamics blog; and is a mathematical scientist. He has also contributed to the Iowa State Daily and Our Black News.

You can connect with him directly in the comments section, and follow him on Facebook

Photo credit: Pixabay

 

 

 

 

 

Copyright ©2017 – The Systems Scientist

 

 

With new technology, mathematicians turn numbers into art

Once upon a time, mathematicians imagined their job was to discover new mathematics and then let others explain it.

Today, digital tools like 3-D printing, animation, and virtual reality are more affordable than ever, allowing mathematicians to investigate and illustrate their work at the same time. Instead of drawing a complicated surface on a chalkboard, we can now hand students a physical model to feel or invite them to fly over it in virtual reality.

Last year, a workshop called “Illustrating Mathematics” at the Institute for Computational and Experimental Research in Mathematics (ICERM) brought together an eclectic group of mathematicians and digital art practitioners to celebrate what seems to be a golden age of mathematical visualization. Of course, visualization has been central to mathematics since Pythagoras, but this seems to be the first time it had a workshop of its own.

The atmosphere was electric. Talks ran the gamut, from wildly creative thinkers who apply mathematics in the world of design to examples of pure mathematical results discovered through computer experimentation and visualization. It shed light on how powerful visualization has become for studying and sharing mathematics.

Reimagining math

Visualization plays a growing role in mathematical research. According to John Sullivan at the Technical University of Berlin, mathematical thinking styles can be roughly categorized into three groups: “the philosopher,” who thinks purely in abstract concepts; “the analyst,” who thinks in formulas; and “the geometer,” who thinks in pictures.

Mathematical research is stimulated by collaboration between all three types of thinkers. Many practitioners believe teaching should be calibrated to connect with different thinking styles.


Borromean Rings, the logo of the International Mathematical Union.
John Sullivan

Sullivan’s own work has benefited from images. He studies geometric knot theory, which involves finding “best” configurations. For example, consider his Borromean rings, which won the logo contest of the International Mathematical Union several years ago. The rings are linked together, but if one of them is cut, the others fall apart, which makes it a nice symbol of unity.

The “bubble” version of the configuration, shown below, is minimal, in the sense that it is the shortest possible shape where the tubes around the rings do not overlap. It’s as if you were to blow a soap bubble around each of the rings in the configuration. Techniques for proving that configurations like this are optimal often involve concepts of flow: If a given configuration is not the best, there are often ways to tell it to move in a direction that will make it better. This topic has great potential for visualization.

At the workshop, Sullivan dazzled us with a video of the three bands flowing into their optimal position. This animation allowed the researchers to see their ideas in action. It would never be considered as a substitute for a proof, but if an animation showed the wrong thing happening, people would realize that they must have made an error in their mathematics.


In this version of the Borromean Rings, a virtual ‘soap bubble’ is blown around the wire-frame configuration.
John Sullivan

The digital artists

Visualization tools have helped mathematicians share their work in creative and surprising ways – even to rethink what the job of a mathematician might entail.

Take mathematician Fabienne Serrière, who raised US$124,306 through Kickstarter in 2015 to buy an industrial knitting machine. Her dream was to make custom-knit scarves that demonstrate cellular automata, mathematical models of cells on a grid. To realize her algorithmic design instructions, Serrière hacked the code that controls the machine. She now works full-time on custom textiles from a Seattle studio.

Edmund Harriss of the University of Arkansas hacked an architectural drilling machine, which he now uses to make mathematical sculptures from wood. The control process involves some deep ideas from differential geometry. Since his ideas are basically about controlling a robot arm, they have wide application beyond art. According to his website, Harriss is “driven by a passion to communicate the beauty and utility of mathematical thinking.”

Mathematical algorithms power the products made by Nervous System, a studio in Massachusetts that was founded in 2007 by Jessica Rosenkrantz, a biologist, and architect, and Jess Louis-Rosenberg, a mathematician. Many of their designs, for things like custom jewelry and lampshades, look like naturally occurring structures from biology or geology.

Their first 3-D printed dress consists of thousands of interlocking pieces designed to fit a particular model. In order to print the dress, the designers folded up their virtual version, using protein-folding algorithms. A selective laser sintering process fused together parts of a block of powder to make the dress, then let all the unwanted powder fall away to reveal its shape.

Meanwhile, a delightful collection called Geometry Games can help everyone, from elementary school students to professional mathematicians, explore the concept of space. The project was founded by mathematician Jeff Weeks, one of the rock stars of the mathematical world. The iOS version of his “Torus Games” teaches children about multiply-connected spaces through interactive animation. According to Weeks, the app is verging on one million downloads.

Mathematical wallpaper

My own work, described in my book “Creating Symmetry: The Artful Mathematics of Wallpaper Patterns,” starts with a visualization technique called the domain coloring algorithm.

I developed this algorithm in the 1990s to visualize mathematical ideas that have one dimension too many to see in 3-D space. The algorithm offers a way to use color to visualize something seemingly impossible to visualize in one diagram: a complex-valued function in the plane. This is a formula that takes one complex number (an expression of the form a+_b_i, which has two coordinates) and returns another. Seeing both the 2-D input and the 2-D output is one dimension more than ordinary eyes can see, hence the need for my algorithm. Now, I use it to create patterns and mathematical art.


A curve with pleasing 5-fold symmetry, constructed using Fourier techniques.
Frank A Farris

My main pattern-making strategy relies on a branch of mathematics called Fourier theory, which involves the superposition of waves. Many people are familiar with the idea that the sound of a violin string can be broken down into its fundamental frequencies. My “wallpaper functions” break down plane patterns in just the same way.

My book starts with a lesson in making symmetric curves. Taking the same idea into a new dimension, I figured out how to weave polyhedral solids – think cube, dodecahedron, and so on – from symmetric bands made from these waves. I staged three of these new shapes, using Photoshop’s 3-D ray-tracing capacity, in the “Platonic Regatta” shown below. The three windsails display the symmetries of Platonic solids: the icosahedron/dodecahedron, cube/octahedron and tetrahedron.


A Platonic Regatta. Mathematical art by Frank A. Farris shows off three types of polyhedral symmetry: icosahedral/dodecahedral, cube/octaheral and tetrahedral.
Frank Farris

About an hour after I spoke at the workshop, mathematician Mikael Vejdemo-Johansson had posted a Twitter bot to animate a new set of curves every day!

Mathematics in the 21st century has entered a new phase. Whether you want to crack an unsolved problem, teach known results to students, design unique apparel or just make beautiful art, new tools for visualization can help you do it better.

This article was updated on April 5, 2017 with the full name of Mikael Vejdemo-Johansson.

Frank A. Farris, Associate Professor of Mathematics, Santa Clara University

Photo Credit:  Frank Farris

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This article was originally published on The Conversation. Read the original article.

How I used math to develop an algorithm to help treat diabetes

When people ask me why I, an applied mathematician, study diabetes, I tell them that I am motivated for both scientific and human reasons.

Type 2 diabetes runs in my family. My grandfather died of complications related to the condition. My mother was diagnosed with the disease when I was 10 years old, and my Aunt Zacharoula suffered from it. I myself am pre-diabetic.

As a teen, I remember being struck by the fact that my mother and her sister received different treatments from their respective doctors. My mother never took insulin, a hormone that regulates blood sugar levels; instead, she ate a limited diet and took other oral drugs. Aunt Zacharoula, on the other hand, took several injections of insulin each day.

Though they had the same heritage, the same parental DNA and the same disease, their medical trajectories diverged. My mother died in 2009 at the age of 75 and my aunt died the same year at the age of 78, but over the course of her life dealt with many more serious side effects.

When they were diagnosed back in the 1970s, there were no data to show which medicine was most effective for a specific patient population.

Today, 29 million Americans are living with diabetes. And now, in an emerging era of precision medicine, things are different.

Increased access to troves of genomic information and the rising use of electronic medical records, combined with new methods of machine learning, allow researchers to process large amounts data. This is accelerating efforts to understand genetic differences within diseases – including diabetes – and to develop treatments for them. The scientist in me feels a powerful desire to take part.

Using big data to optimize treatment

My students and I have developed a data-driven algorithm for personalized diabetes management that we believe has the potential to improve the health of the millions of Americans living with the illness.

It works like this: The algorithm mines patient and drug data, finds what is most relevant to a particular patient based on his or her medical history and then makes a recommendation on whether another treatment or medicine would be more effective. Human expertise provides a critical third piece of the puzzle.

After all, it is the doctors who have the education, skills and relationships with patients who make informed judgments about potential courses of treatment.

We conducted our research through a partnership with Boston Medical Center, the largest safety net hospital in New England that provides care for people of lower income and uninsured people. And we used a data set that involved the electronic medical records from 1999 to 2014 of about 11,000 patients who were anonymous to us.

These patients had three or more glucose level tests on record, a prescription for at least one blood glucose regulation drug, and no recorded diagnosis of type 1 diabetes, which usually begins in childhood. We also had access to each patient’s demographic data, as well their height, weight, body mass index, and prescription drug history.

Next, we developed an algorithm to mark precisely when each line of therapy ended and the next one began, according to when the combination of drugs prescribed to the patients changed in the electronic medical record data. All told, the algorithm considered 13 possible drug regimens.

For each patient, the algorithm processed the menu of available treatment options. This included the patient’s current treatment, as well as the treatment of his or her 30 “nearest neighbors” in terms of the similarity of their demographic and medical history to predict potential effects of each drug regimen. The algorithm assumed the patient would inherit the average outcome of his or her nearest neighbors.

If the algorithm spotted substantial potential for improvement, it offered a change in treatment; if not, the algorithm suggested the patient remain on his or her existing regimen. In two-thirds of the patient sample, the algorithm did not propose a change.

The patients who did receive new treatments as a result of the algorithm saw dramatic results. When the system’s suggestion was different from the standard of care, an average beneficial change in the hemoglobin of 0.44 percent at each doctor’s visit was observed, compared to historical data. This is a meaningful, medically material improvement.

Based on the success of our study, we are organizing a clinical trial with Massachusetts General Hospital. We believe our algorithm could be applicable to other diseases, including cancer, Alzheimer’s, and cardiovascular disease.

It is professionally satisfying and personally gratifying to work on a breakthrough project like this one. By reading a person’s medical history, we are able to tailor specific treatments to specific patients and provide them with more effective therapeutic and preventive strategies. Our goal is to give everyone the greatest possible opportunity for a healthier life.

Best of all, I know my mom would be proud.

Dimitris Bertsimas, Professor of Applied Mathematics, MIT Sloan School of Management

Photo Credit: Shutterstock.com

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This article was originally published on The Conversation. Read the original article.

School bus routes are expensive and hard to plan. We calculated a better way

Here’s a math problem even the brightest school districts struggle to solve: getting hordes of elementary, middle and high school students onto buses and to school on time every day. The Conversation

Transporting all of these pupils presents a large and complex problem. Some school districts use existing software systems to develop their bus routes. Others still develop these routes manually.

In such problems, improving operational efficiency even a little could result in great advantages. Each school bus costs school districts somewhere between US$60,000 and $100,000. So, scheduling the buses more efficiently will result in significant monetary savings.

Over the past year, we have been working with the Howard County Public School System (HCPSS) in Maryland to analyze its transportation system and recommend ways to improve it. We have developed a way to optimize school bus routes, thanks to new mathematical models.

Finding the optimal solution to this problem is very valuable, even if that optimal solution is only slightly better than the current plan. A solution that is only one percent worse would require a considerable number of additional buses due to the size of the operation.

By optimizing bus routes, schools can cut down on costs, while still serving all of the children in their district. Our analysis shows that HCPSS can save between five and seven percent on the number of buses needed.

Route planning

A bus trip in the afternoon starts from a given school and visits a sequence of stops, dropping off students until the bus is empty. A route is a sequence of trips from different schools that are linked together to be served by one bus.

Our goal was to reduce both the total time buses run without students on board – also known as aggregate deadhead time – as well as the number of routes. Fewer routes require fewer buses since each route is assigned to a single bus. Our approach uses data analysis and mathematical modeling to find the optimal solution in a relatively short time.

To solve this problem, a computer algorithm considers all of the bus trips in the district. Without modifying the trips, the algorithm assigns them to routes such that the aggregate deadhead time and the number of routes are minimized. Individual routes become longer, allowing the bus to serve more trips in a single route.

Since the trips are fixed, in this way we can decrease the total time the buses are en route. Minimizing the deadhead travel results in cost savings and reductions in air pollution.

The routes that we generated can be viewed as a lower bound to the number of buses needed by school districts. We can find the optimal solution for HCPSS in less than a minute.

Serving all students

While we were working on routes, we decided to also tackle the problem of the bus trips themselves. To do this, we needed to determine what trips are required to serve the students for each school in the system, given bus capacities, stop locations and the number of students at each stop. This has a direct impact on how routes are chosen.

Most existing models aim to minimize either the total travel time or the total number of trips. The belief in such cases is that, by minimizing the number of trips, you can minimize the number of buses needed overall.

However, our work shows that this is not always the case. We found a way to cut down on the number of buses needed to satisfy transportation demands, without trying to minimize either of the above two objectives. Our approach considers not only minimizing the number of trips but also how these trips can be linked together.

New start times

Last October, we presented our work at the Maryland Association of Pupil Transportation conference. An audience member at that conference suggested that we analyze school start and dismissal times. By changing the high school, middle school and elementary school start times, bus operations could potentially be even more efficient. Slight changes in school start times can make it possible to link more trips together in a single bus route, hence decreasing the number of buses needed overall.

We developed a model that optimizes the school bell times, given that each of the elementary, middle and high school start times fall within a prespecified time window. For example, the time window for elementary school start times would be from 8:15 to 9:25 a.m.; for middle schools, from 7:40 to 8:30 a.m.; and all high schools would start at 7:25 a.m.

Our model looks at all of the bus trips and searches for the optimal combination of school dismissal time such that the number of school buses, which is the major contributing factor to costs, is minimized. We found that, in most cases, optimizing the bell times results in significant savings regarding the number of buses.

Next steps

Using our model, we ran many different “what if?” scenarios using different school start and dismissal times for the HCPSS. Four of these are currently under consideration by the Howard County School Board for possible implementation.

We are also continuing to enhance our current school bus transportation models, as well developing new ways to further improve efficiency and reduce costs.

For example, we are building models that can help schools select the right vendors for their transportation needs, as well as minimize the number of hours that buses run per day.

In the future, the type of models we are working on could be bundled into a software system that schools can use by themselves. There is really no impediment in using these types of systems as long as the school systems have an electronic database of their stops, trips, and routes.

Such software could potentially be implemented in all school districts in the nation. Many of these districts would benefit from using such models to evaluate their current operations and determine if any savings can be realized. With many municipalities struggling with budgets, this sort of innovation could save money without degrading service.

Ali Haghani, Professor of Civil & Environmental Engineering, University of Maryland and Ali Shafahi, Ph.D. Candidate in Computer Science, University of Maryland

Photo Credit: Dean Hochman


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This article was originally published on The Conversation. Read the original article.

Can Trump resist the power of behavioral science’s dark side like other politicians?

More than two dozen governments, including the U.S., now have a team of behavioral scientists tasked with trying to improve bureaucratic efficiency to “nudge” their citizens toward what they deem to be higher levels of well-being.

A few recent examples include a push by the socialist French government to increase the numbers of organ donors, a conservative UK government plan to prevent (costly) missed doctor appointments, and efforts by the Obama White House to boost voter turnout on Election Day.

While the government’s use of our psychological quirks to affect behavior rubs some people the wrong way, most of us can agree that the above examples achieve positive ends. More organ donors mean more lives saved, fewer missed doctor appointments mean the government or health industry is more efficient, and increased voting means stronger citizen engagement in democracy.

But “nudges” themselves are value neutral. That is, they can be used to both achieve altruistic ends or more malicious ones. Just as behavioral science can be used to increase voter turnout, it can also be used to suppress the votes of specific individuals likely to favor the opposing side, as reportedly happened in the recent U.S. presidential election.

The nudge, in other words, has a dark side.

My research explores how behavioral science can help people follow through on their intentions where they make better or longer-term choices that increase their well-being. Because choices are influenced by the environment in which they are made, changing the environment can change decision outcomes.

This can be positive to the extent that those designing interventions have good intentions. But what happens when someone uses these insights to systematically influence others’ behavior to favor his or her own interests – even at the expense of everyone else’s?

That’s my concern with President Donald Trump, whose campaign appears to have exploited behavioral science to suppress the vote of Hillary Clinton supporters.

What’s in a nudge?

Behavioral science is a relatively young field, and governments have only recently begun using its insights to inform public policy.

The UK was the first in 2010 when it created its Behavioral Insights Team. In subsequent years, dozens of governments around the world followed, including Canada with its Behavioral Insights Unit and the U.S., which in 2015 officially launched the White House Social and Behavioral Sciences Team.

The teams’ missions are all relatively similar: to leverage insights from behavioral science to make public services more cost-effective and easier to use, to help people make better choices for themselves, and to improve well-being.

In the UK, for example, the Behavioral Insights Team was able to persuade about 100,000 more people a year to donate their organs by tweaking a message people received when renewing their car tax. Here in the U.S., the Social and Behavioral Science Team helped the Department of Defense increase the amount of retirement savings accounts for service members by 8.3 percent.

These kinds of interventions have been criticized for unjustly interfering with an individual’s autonomy. Some even compare it with mind-control.

However, as I have pointed out elsewhere, our environment (and the government) is always exerting some influence on our behavior, so we’re always being nudged. The question is therefore not whether we will be nudged, but how and in what direction.

For example, when you sit down to dinner, the size of your plate can make a big difference in how much you eat. Studies show you’re more likely to consume less food if you use a smaller plate. So if the government is handing out the dinnerware, and if most us wanted to avoid overeating, why not set the default plate to a small one?

But now let’s consider the dark side: a restaurant might hand out a small plate if it means it can charge more for less food and thus make more money. The owner likely doesn’t care about your waist size.

Any intervention based on behavioral science is therefore neither good nor bad. What matters is the intention behind it, the aim which the nudge is ultimately supposed to help achieve.

Potential for abuse

Take the case of what Cambridge Analytica – a company founded in 2013 and reportedly funded by the family of billionaire conservative donor Robert Mercer – did during the election. This team of data scientists and behavioral researchers claims to have collected thousands of data points on 220 million Americans in order to “model target audience groups and predict the behavior of like-minded people.”

Essentially, all that data can be used to deduce individual’s personality traits and then send them messages that match their personality, which are more likely to be persuasive. For example, highly neurotic Jane will be more receptive to a political message that promises safety, as opposed to financial gains, which may be more compelling to conscientious Joe.

So what’s the problem? In and of itself, this analysis can be a neutral tool. A government might want to use this approach to provide helpful information to at-risk populations, for example by providing suicide prevention hotlines to severely depressed individuals, as Facebook is currently doing. One might even argue that Cambridge Analytica, first hired by the Cruz campaign and later by Trump, was not acting unethically when it sent such personalized messaging to convince undecided voters to support the eventual Republican nominee. After all, this is what all marketing campaigns set out to do.

But there is a fine ethical line here that behavioral science can make easier to cross. In the same way that people can be influenced to engage in a behavior, they may also be discouraged from doing so. Bloomberg reported that Cambridge Analytica identified likely Clinton voters such as African-Americans and tried to dissuade them from going to the ballot box. The company denies discouraging any Americans from casting their vote.

Beyond hiring the company, the Trump administration has a direct tie to Cambridge Analytica through chief strategist Steve Bannon, who sits on its board.

Alexander Nix, CEO of Cambridge Analytica, talks about what his company does.

How might Trump nudge?

So far, it’s unclear whether or how the Trump administration might use behavioral science in the White House.

Trump, like most Republicans, has emphasized his desire to make government more efficient. Since behavioral science is generally a low-cost intervention strategy that provides tangible, measurable gains that should appeal to a business-minded president, Trump may very well turn to its insights to accomplish this goal. After all, the UK’s Behavioral Insights Team was kicked off under conservative leadership.

The White House Social and Behavioral Science Team’s impressive interventions have led to hundreds of millions of dollars in savings across a variety of departments and at the same time increased the well-being of millions of citizens. The future of the team is now unclear. Some members are worried that Trump will use their skills in less benevolent ways.

Trump’s apparent use of Cambridge Analytica to suppress Clinton turnout, however, is not a good sign. More broadly, the president does not seem to value ethics. Despite repeated warnings from government ethics watchdogs, he refuses to seriously deal with his innumerable conflicts of interest. Without the release of his tax returns, the true extent of his conflicts remain unknown.

And as we know from behavioral science, people frequently underestimate the influence conflicts of interests have on their own behavior.

In addition, studies show that people can easily set aside moral concerns in the pursuit of efficiency or other specific goals. People are also creative in rationalizing unethical behavior. It doesn’t seem to be a stretch to imagine that Trump, given his poor track record where ethics is concerned, could cross the fine ethical line and abuse behavioral science for self-serving ends.

A virus and a cure

Behavioral science has been heralded as part of the solution to many societal ills.

Behavioral economists Richard Thaler and Cass Sunstein, co-authors of the book “Nudge” coining the term, have been strong advocates of using the field’s tools to improve government policy – when the intentions are transparent and in the public interest.

But might the current administration use them in ways that go against our own interests? The problem is that we may not even be aware when it happens. People are often unable to tell whether they are being nudged and, even if they are, may be unable to tell how it’s influencing their behavior.

Governments around the world have found success using the burgeoning field of behavioral science to improve the efficiency of their policies and increase citizens’ well-being. While we should continue to find new ways to do this, we also need clear guidelines from Congress on when and how to use behavioral science in policy. That would help ensure the current or a future occupant of the White House doesn’t cross the line into the dark side of nudges.

The Conversation

Jon M Jachimowicz, PhD Student in Management, Columbia University

Photo Credit: Keyword Suggest

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This article was originally published on The Conversation. Read the original article.

What are some labor market challenges for black Americans?

By Matt Johnson

The mainstream media tends to focus on the simple things when it comes to discrepancies between black and white folks. However, it is always much more complex than they report.

In this blog, I have reported crime rates in depressed neighborhoods and wards of urban environments. I have also reported high unemployment rates and low education attainment rates. And I have also reported on decaying housing conditions such as foreclosures and condemned and vacant buildings.

I have illustrated in many articles that these areas of depression tend to be areas that are predominantly black. Sometimes I have done this explicitly and in other times I have done it implicitly. But either way, I have always included and highlighted these issues as multi-variable problems. That is, I have demonstrated that it is not just one problem; I have demonstrated it is a multitude of problems.

But what I have not yet written about is the labor market for this demographic group. I have not yet highlighted or discussed the importance of an education and how that would provide opportunity; and I have not yet highlighted or discussed the importance of skills and acquiring skills and how that would provide opportunity.

Moreover, I have not yet highlighted or discussed the importance of industry and how picking an industry will lead to greater wages and job security and how that would provide opportunity; and I have not yet highlighted or discussed geography and how that would provide access to a greater number of jobs and opportunity.

In this article, I will provide a short explanation of 4 factors that affect entrance into the labor market for a worker along with data illustrating the current location of black Americans.

Education

One could make an argument that this is the beginning of the road. Why is this? Because success in life is correlated with education, and ridiculously so. In almost every economic measure, a person who has a degree has a higher probability of making more money and a higher probability of job security, although there is variation between industries.

On June 6, 2016, The Brookings Institute published an article on 7 findings that illustrate racial disparities in education. Here is the list of those findings:

  1. School readiness gaps are improving, except for black kids
  2. Misbehavior in school can pay off for white, but not black students
  3. Teacher-student racial mismatch harms black kids
  4. White and Asian students are more likely to be exposed to advanced classes
  5. Gaps remain in high school completion rates
  6. Similar college enrollment rates mask unequal degree completion rates
  7. Black and white students do not attend colleges of equal quality

As with all science, more research is needed in these subsequent areas. In addition, one ought to ask the question, where are the issues more likely to take place?

Is a researcher more likely to find these disparities in a stable economic system with low unemployment, high education and income attainments, and low crime rates? Or is a researcher more than likely to find these disparities in an unstable system with high unemployment, low education and income attainments, and high crime rates?

Skills

As a person goes through life, their skill set will increase. And as their skill set increases so will their pay, which means a person will attain greater earning and purchasing power as they get older. And this is the case for all racial groups. So what are some factors that may influence earning potential?

First, an initial job during teenage years will increase one’s earnings potential over the course of a life-time. This is because teenagers will begin to learn basic market skills and an intuition of how the market works. However, the unemployment rates among racial groups between the ages 16 and 24 are divergent.

If one is black and male, or hispanic and male, then his unemployment rate is higher than the national average. Essentially, both groups are starting from the rear of the market earnings race. In contrast, if one is Asian and female, or white and female, then her unemployment rate is lower than the national average.

Here are the statistics:

employment-status-of-16-to-24-2013-to-2016-dwm

Education will also affect skills. The market is built on science and math, and how science and math perpetuate market engines.

We published an article last summer titled Top 10 Paying Bachelor’s Degrees. In it, we shared with our readers which degrees were the top 10 earners straight out of college. All 10 were engineering degrees:

Rank Major Degree Type Early Career Pay
1 Petroleum Engineering Bachelor’s $101,000
2 Mining Engineering Bachelor’s $71,500
3 Chemical Engineering Bachelor’s $69,500
4 Computer Science Bachelor’s $69,100
5 Computer Engineering Bachelor’s $68,400
6 Nuclear Engineering Bachelor’s $68,200
7 Systems Engineering Bachelor’s $67,100
8 Electrical & Computer Engineering Bachelor’s $67,000
9 Electrical Engineering Bachelor’s $66,500
10 Aeronautical Engineering Bachelor’s $65,100

 
This of course doesn’t mean other degrees don’t pay well straight out of college or don’t have high potential earnings over the course of a life-time. However, what it does show is degrees in science, technology, engineering, and mathematics will pay dividends for those who obtain those degrees. In addition, degrees in economics and finance are competitive degrees in the marketplace. For example, those in finance usually have the highest weekly average wages of all industries in the Minneapolis/St. Paul market.

Industry
Industry can dramatically decide the potential earnings for a worker in the marketplace. As previously stated, those who work in the mathematical sciences, engineering, and finance industries are earning more out of the gate and over a life-time.

And so the question is, how do black Americans compare by industry? With a little help from BlackDemographics.com, the reader can see that black Americans

are again overrepresented in government jobs such as education, social assistance, and public administration. African-Americans also have a large presence in the health care industry which is expected to see substantial job growth for the foreseeable future.

Here’s the data provided by BlackDemographics.com:

blackdemographics-2012

Geography

This last category can also dramatically affect someone’s entrance in the marketplace. This is because American neighborhoods are still relatively segregated by racial group. For example, Milwaukee’s segregation was highlighted after the police shooting death of Sylville Smith in August of 2016. According to Business Insider, Milwaukee is the most segregated city in the United States. As this map illustrates, black Americans are highly concentrated in two distinct areas:

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Milwaukee. Photo: Courtesy of the University of Virginia Weldon Cooper Center for Public Service.

The green dots are black Americans while the blue dots are white Americans. Of course a thorough geographical analysis of each American city would send this point home. But immediately, research by this publication has demonstrated correlation between black Americans and depressed environments which include adverse socio-economic factors such as high unemployment, relatively low earnings compared to other racial groups, low education rates, high crime rates, and disparate housing issues in the form of foreclosures and condemned and vacant buildings.

One last thought to consider, black businesses are more than likely to hire black employees while white businesses are more than likely to hire white employees despite federal laws. And this hiring behavior seems to make sense based off of geographical data.

Matt Johnson is a writer for The Systems Scientist and the Urban Dynamics blog; and is a mathematical scientist. He has also contributed to the Iowa State Daily and Our Black News.

You can connect with him directly in the comments section, and follow him on Twitter or on Facebook

You can also follow The Systems Scientist on Twitter or Facebook.

Photo credit: Pixabay

 

 

Copyright ©2017 – The Systems Scientist

In 2016, crime was ‘Up’ overall in Minneapolis but…

By Matt Johnson

After a first pass through the Minneapolis crime data, it appears reported crimes in Minneapolis increased in 2016. However, they didn’t increase by much. In total numbers, reported crimes increased from 21,341 in 2015 to 21,485 in 2016. That’s 144 reported crimes.

As a percent, that’s less than 1 percent. But of course, this crime data only tells us about the total number of crimes for the city of Minneapolis. It doesn’t tell us anything about where the majority of these crimes happened nor does it tell us anything about the types of crimes that are most prominent in these locations. And of course, it really does depend on location.

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For example, North Minneapolis has some of the highest crime rates per square mile in the city. But not all neighborhoods and zip codes are created equal when it comes to crime. There are certain neighborhoods that experience much higher crime rates than others.

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The Jordan neighborhood, which resides in the central part of North Minneapolis in the 5th Ward, experienced the highest number of crimes and the highest number of crimes per square mile on the north side in 2015 and 2016. But the number of crimes and the crimes per square mile in the Jordan neighborhood decreased in 2016. In other words, they were higher in 2015.

The Jordan neighborhood is the only neighborhood in Minneapolis that is predominantly black.

In contrast, the crimes per square mile on average are much lower in the Harrison and Sumner Glenwood neighborhoods in the 5th Ward on the north side. And of course, the 4th Ward, which also resides on the north side, has its neighborhoods that are relatively quiet when it comes to crime and others that are active with higher numbers of crimes.

Harrison and Sumner Glenwood are predominantly white.

As my regular readers know, crime is usually associated with other adverse socio-economic factors such as higher rates of unemployment, lower rates of education, and housing issues. Sometimes this is referred to as urban decay or urban blight. But in the case of my research, I am using mathematical methods to lead me to this knowledge of urban environments and their respective discrepancies. But there are instances where I have found crime does exist on its own.

For Minneapolis, this happens in downtown Minneapolis, specifically in the Downtown West neighborhood, which experiences the highest number of crimes in the city month after month.

Why is this so? Well this is a question I will leave for you to ponder. Other questions you might think about as well are, why do these adverse socio-economic factors exist together with very few exceptions? Are policy makers aware of these facts? And if they are, why haven’t they done anything about it?

Matt Johnson is a writer for The Systems Scientist and the Urban Dynamics blog; and is a mathematical scientist. He has also contributed to the Iowa State Daily and Our Black News.

You can connect with him directly in the comments section, and follow him on Twitter or on Facebook

You can also follow The Systems Scientist on Twitter or Facebook.

Photo credit: Tony Webster

 

 

 

Copyright ©2017 – The Systems Scientist