# Comparing Minneapolis wages to wages in North Minneapolis

As Aristotle explored in his Metaphysics: Book Delta, the parts of something, say the parts of a city, are divisions of the whole that can be differentiated from one another by quantification or by qualification. In the sense of quantifying, North Minneapolis can be differentiated from Minneapolis by observational data, for example, unemployment rates, education rates, and wages.

In the sense of qualifying, North Minneapolis can be differentiated by recognition of area. But it should be noted that the geography of North Minneapolis is still the geography of Minneapolis. It is just a recognition of a specified area, which is not Northeast Minneapolis, South Minneapolis, or Southwest Minneapolis.

Furthermore, North Minneapolis is broken down further by quantification and qualification into area codes: 55411 and 55412. Thus, the 55411 and 55412 zip codes are distinguishable by name and specific geography, this is obvious, and by observational data.

For example, previous articles in this blog have shown the 55411 zip code to be the zip code with the highest number of reported crimes in North Minneapolis; whereas, previous articles in this blog have shown the 55412 zip code to be the zip code with the highest number of foreclosures over the past decade.

Utilizing this systemic approach, the wages between Minneapolis and North Minneapolis, specifically the 55411 zip code, can be differentiated and analyzed.

Thus, are the dynamics of the wages (how wages change over time) shown to be relatively equal to one another? Are the dynamics of the wages of the 55411 zip code shown to be greater than Minneapolis? Or are the dynamics of the wages of the 55411 zip code shown to be less than Minneapolis?

As Graph 1 illustrates, we can see that the wage rate of Minneapolis is steeper than the wage rate of the 55411 zip code in Graph 2. And we’re not just eyeing this. We can see this distinctly via the linearization equations in Graph 1 and Graph 2.

The linearization equation in Graph 1 (y = 6.4152x + 1083.1) shows a rate of 6.4 and the linearization equation in Graph 2 (y = 2.2805x + 823.6) shows a rate of 2.3, if both rates of change are rounded-off. Obviously, 6.4 is greater than 2.3, and by quite a bit. Why is this important?

Dynamically (how wages change over time), this shows the wages of Minneapolis are growing at a greater rate than the wages of the 55411 zip code. Of course, these equations also show that the average weekly wages of Minneapolis are between \$250 and \$300 higher than the 55411 zip code.

This little bit of information ought to provide policy makers with some much-needed direction to create and apply economic policy. Of course the operative modal verb is “ought to.”

So do you think local policy makers would consider differentiating between the part and the whole when creating economic policy? Or do you think local policy makers would just create and apply the same policy for both the part and the whole?

Photo credit: Wikimedia Commons

# Some Thoughts on Systems

By Matt Johnson

We’ve spent some time talking about elements of systems, but what do we mean by the elements of system? And what are they?

Well, we’ve discussed the foreclosure rates, condemned and vacant buildings (CVBs), the median household incomes, and unemployment rates in North and South Minneapolis, and Minneapolis in general. These are elements of the system of Minneapolis. But more importantly, these are, as this systems scientist believes, the most important elements in the system that have been identified thus far and as of this writing. This is because they play a significant role in the lives of residents (please note that I am a scientist, so I open to changing my mind as the data is presented to me).

And what have we learned from these elements? Well in the case of the 4th Ward, specifically 55412, we’ve learned that the foreclosures rates are higher relative to the rest of the city except when compared to the 55411. 55412 also has a higher than average population of “black” residents, it has a lower than average level of median household income, the education level is lower than average when compared to the rest of the city, and the unemployment rate is higher than average. According to City Data, the unemployment rate was 16 percent in 2013, only second to the 55411’s 21.9 percent.

In contrast, these elements look very different in other parts of the city. As we have learned from the data, the 2nd Ward in Southeast Minneapolis and the 10th Ward in Southwest Minneapolis have much lower foreclosure numbers and percentages than Wards 4 and 5 in North Minneapolis. These areas are also mostly populated by “white” residents and have higher levels of education. In the case of the 2nd Ward, there is an anomaly. The zip code 55455 is in the 2nd Ward and it has an unemployment level of more than 33 percent. But it also has a poverty level of less than 1 percent. What the…???

This is because this is one of the zip codes for the University of Minnesota. They are students my dear Watson. And yes, we should expect that type of rate with a large population of students, which is why we throw it out as an anomaly.

The element of the median household income also looks different in the 55406 and 55419 in South Minneapolis. In 2013, almost 40 percent of households in the 55406 had a median household income greater than \$75 thousand and just under 60 percent of the households in the 55419 had a median household income greater than \$75 thousand.

In that same year, less than 25 percent of households in the 55412 had a median household income greater than \$75 thousand and a bit more than 16 percent of the households in the 55411 had a median household income greater than \$75 thousand. Obviously the median household income element shows different faces in different wards and zip codes.

We’ve taken our first step in understanding systems. To recap, we know what the primary elements of the system are for this particular system and we know that they show different faces in different parts of the system. We must also understand that they perpetuate different interactions with other elements of the system, which gives rise to the purpose of this particular system. but that is another discussion for another day.

We are indeed moving towards a greater understanding of systems and Urban Dynamics my dear readers.

By Matt Johnson

As Figure 1 illustrates, the 4th Ward has had the highest percentage of foreclosures in the City of Minneapolis since at least the fourth quarter of 2006 with the exception of the second quarter of 2007 when the 5th Ward held the highest frequency.

But is every zip code and neighborhood created equal when it comes to the numbers and percentages within the 4th Ward?

As we can see from Figure 2, there are distinct differences between the zip codes of the 4th Ward in North Minneapolis – 55411, 55412, and 55430. It is clear that 55412 contains the highest numbers of foreclosures from quarter to quarter throughout 2014.

And the distinction is fantastically highlighted when we look at the differences in relative frequency. As Figure 3 indicates, between 70 to 80 percent of the foreclosures in the 4th Ward were concentrated in the 55412 zip code throughout the respective quarters of 2014.

But there are a couple of differences to keep in mind while we continue to analyze these three zip codes over the course of Urban Dynamic’s analysis of the north side.

55412 is the largest zip code by area in the 4th Ward at 3.6 square miles and it comprises most of the 4th Ward’s land area.  55430 comprises the next most land area for the 4th Ward at 1.23 square miles. This is because only 22.07 percent of 55430 resides in Minneapolis. But there are some other differences between 55430 and 55412. According to City Data, 55430 has a higher proportion of “white” to “black” residents, a lower unemployment rate, and a higher level of education from high school through graduate school than 55411, 55412. How much of this difference between “white” and “black” residents in the 55430 zip code exists? That’s an additional question that will require a bit more digging.

Continuing our decomposition of the 4th Ward, 55411 is the smallest zip code in area in the 4th Ward. This is because most of 55411 resides in the 5th Ward, so in this analysis, 55411 and 55412 won’t be compared.

55411 will be considered in future articles when we analyze the 5th Ward and when we directly compare the 4th Ward to the 5th Ward. However, it should be noted that 55411 and 55412 contribute most of the foreclosures in the 4th and 5th Wards.

Moreover, future articles will explain how the number and percent of foreclosures in North Minneapolis increase as one moves from north to south (or from south to north depending on your reference point); that is, from 55430 to 55412 to 55411 and to 55405 and 55401. Future articles will also attempt to illustrate this same thinking and pattern for unemployment, median household income, education level, and “race.” Remember, as each article is published, another part of the painting is created.

We have established some differences between different parts of the City of Minneapolis. For example, we know that the foreclosure numbers and rates are the highest in Wards 4 and 5 in North Minneapolis as Figure 2 demonstrates. We know that both of those wards also have the highest concentration of condemned and vacant buildings. But we now have some additional information to add to this difference – Median Household Income.

Figure 1 provides us an opportunity to compare and contrast two zip codes in North Minneapolis and two zip codes in South Minneapolis. As we can see, the percentage of median household incomes in the two zip codes in South Minneapolis – 55406 and 55419 – are higher than the two zip codes on the north side.

Whereas the median household income greater than \$75 thousand is above 35 percent in the 55406 and almost 60 percent in the 55419, we see that the median household income greater than \$75 thousand is only about 15 percent for the 55411 and about 25 percent for the 55412. Clearly, there is a difference ranging from 10 percent to 45 percent depending on what zip codes we compare.

Moving forward, this data will add to our already accumulated knowledge of North Minneapolis and Minneapolis in general. But what are some questions we may want to ask from this new information? Since we have some knowledge about foreclosure rates and condemned and vacant buildings, perhaps we will want to see if there is a correlation between median household incomes and foreclosures and condemned and vacant buildings.

Or what about knowing the levels and rates of home ownership in the respective zip codes? How does the north side compare to the south side in home ownership? These questions are important because we agree that home ownership is a part of the American dream, right? And finally, these questions are important because eventually they will lend to future policies.

Author’s Notes:

1. Data from Figure 1 is drawn from http://www.city-data.com and represents the year 2013.
2. Data from Figure 2 is drawn from the City of Minneapolis’ Minneapolis Trends Report.