Showing posts with label Economy. Show all posts
Showing posts with label Economy. Show all posts

Saturday, December 14, 2019

Holiday Poverty Estimates from the Census Bureau: Johnstown SD Ranks 84th Nationally.


The Census Bureau has come out with its annual Small Income and Poverty Estimates for each county and school District in the US for 2018.  The above graph shows the 20 year trend in estimates of the percentage of students in poverty for the US, Pennsylvania, Greater Johnstown (GJSD), Ferndale, Richland, and Westmont school districts.  These districts are chosen as they are adjacent to GJSD.  The top 10 school districts in terms of poverty rate in Pennsylvania are listed below.

Greater Johnstown once again has the highest poverty rate for children ages 5-17 in the state out of 500 school districts at 46.7%.  Nationally it ranks 84th out of 13,207 school districts or in the 99th percentile in poverty.  The above graph shows that this trend has been increasing for GJSD.  The rates for the US, Pennsylvania, and the other school districts have remained relatively stable over the last 10 years by comparison.  

The Ferndale SD is also above the state (15.9%) and national (17.0%) rates for 2018 at 26.2%.  Richland and Westmont were virtually identical at 10.7% and 11.0% respectively.  Next I will comment on overall poverty trends for Cambria County and the City of Johnstown.  The 2020 census is coming soon and they are looking to hire census takers.  Whether or not you want to work on it you should participate to ensure that they will have the best possible estimates of the US, state and local populations.


District Name
Grade range of responsibility
Total Population
Relevant Ages 5 to 17 Population
Relevant Ages 5 to 17 in Families in Poverty
Relevant age 5 to 17 Ratio %
Greater Johnstown School District (PA)
KG-12
25,338
3,392
1,584
46.7
Aliquippa School District (PA)
KG-12
9,117
1,249
582
46.6
Farrell Area School District (PA)
PK-12
5,299
811
356
43.9
Harrisburg City School District (PA)
KG-12
51,186
8,835
3,620
41
Clairton City School District (PA)
KG-12
6,771
857
344
40.1
Salisbury-Elk Lick School District (PA)
KG-12
2,824
461
185
40.1
New Castle Area School District (PA)
PK-12
23,013
3,272
1,228
37.5
Sharon City School District (PA)
KG-12
13,299
2,003
752
37.5
Duquesne City School District (PA)
PK-12
5,540
939
349
37.2
Steelton-Highspire School District (PA)
KG-12
8,672
1,498
552
36.8
**Related Posts**

 

Friday, August 17, 2018

Trump is Popular on the Economy but not Foreign Policy


Much has been written about how Trump's popularity inched up from a low of 37% (according to the Real Clear Politics (RCP) poll average) in December 2017 to 43% in June of 2018.  It has stubbornly remained around 43% ever since as can be seen in the graph above.  His disapproval ratings have fluctuated between 52% and 54% over the same period.




Much less reported is Trump's approval ratings on the economy.  The RCP average of polls on this question is 50.8%, a slight majority.  The polls used to create this average over the last two months can be seen in the image above.  The approval ratings of these polls range from 49% to 55%.  The letter RV next to the sample size for the poll means that they limited their sample to registered voters.  The A next to the sample size means that all Americans were included in the sample.  There is no graph showing how this rating has changed over time but the few times I have looked at this average has been consistent.  His disapproval ratings on the economy average to 42% and range from 36% to 47%.  There aren't as many polls on this question as there are on his overall popularity.  

One poll that is absent from the above table in Rasmussen Reports.  They come out almost daily with overall approval ratings for Trump ranging from 46% to 50%.  They restrict their sample to likely voters (the only ones in the RCP average to do so) and their estimates are consistently the most generous to Trump.



There are even fewer polls asking about Trump's approval on Foreign Policy.  Not surprisingly the RCP average on this is lower than his overall approval rating and his approval rating on the economy at 40.7%.  Rasmussen's polls are not on this question either.  

For the generic congressional race polls the Democrats have a 6.8% lead in the RCP average.  Only Rasmussen limits their sample to likely voters the rest use registered voters on this question.  These numbers have been more volatile than the approval ratings for Trump.  Gerrymandering in many states gives the GOP an advantage in states where the two parties have an equal number of voters.  Pennsylvania just had it's congressional districts redrawn and it remains to be seen what impact it will have.

The Republicans running this year probably will stress the economy while Democrats should be stressing Trump's foreign policy as his approval ratings are weaker there. This doesn't mean that Democrats should ignore domestic/economic issues such as health care, immigration, climate change, and income inequality.  Foreign policy provides a fuller picture.


**Related Posts**

Will Trump be Impeached? Damned if I know



Saturday, October 21, 2017

Veterans, the Living Wage, and the McNamara Fallacy


In the first post for the eighth year of the blog I was going to reflect on the Ken Burns film about Vietnam.  My first impression was how little things have changed since then with all of the protests.  The second thing that jumped out at me was President Johnson's defense secretary's obsession with collecting data (mostly body counts) to determine who was winning the war.  This is called the McNamara fallacy and is discussed below.



In my blog and my other writings I use statistics to enlighten people and to shed light on various social phenomena.  For example, for the Hill Talk, I looked at various variables which may predict the magnitude of the increase in the living wage for the cities/counties that have passed such ordinances.  

The graph below shows the strongest predictor which is the percentage of veterans in that city/county.  As the percentage of the veteran population increases by one percent, the amount of the living wage decreases by an expected 59 cents.  This relationship accounts for 28% of the variability in the amount of the living wage passed. 


The mean % veterans of the 38 living wage entities is 5% while the US as a whole has 6.2% of its population who are veterans.  Case in point Seattle, WA passed a $15/hour living wage ordinance while nearby Tacoma, WA passed a $12/hr wage.  Tacoma has 9.34% of its population as veterans while Seattle has less than half at 4.54%.  All of the cities that have passed a $15/hour wage or higher have % veterans that are lower than the US as a whole.  Six of the nine cities/counties with wages $15/hr or higher are in California.  

If one spends too much time looking at the leaves and the twigs on a tree, one can miss the surrounding forest.  This is basically what the McNamara fallacy is.  New findings with statistics can reveal important features of the forest as I believe this analysis has with regard to the forest activists must navigate to pass a living wage ordinance.  

The percentage of veterans in a city/county was the most robust variable negatively associated with the amount of the living wage increase after considering the % poverty, the % foreign born, the % change in the population, the % uninsured, the % in poverty, median household income, median housing value, and the % with a high school education or higher.  The full data set used in this analysis can be seen here.  The amounts of the minimum wage increases were found from the National Employment Law Project or NELP.  The demographic information on the cities/counties that have passed these ordinances was found from the Census Bureau at Census.gov.

Unlike McNamara and later Donald Rumsfeld and their ilk, I do not claim to have a full grasp of the whole forest surrounding the Fight for $15.  Further research is needed to fully understand the forest.  An argument could be made that it was the arrogance of men like McNamara and Rumsfeld that created the large population of veterans in the US.  One would think that if anyone could use a raise the veterans could.  A significant portion of the homeless population are veterans.


**Related Posts**


What do Living Wage Cities Have in Common?





Wednesday, September 27, 2017

Pennsylvania Lags Behind US in Startup Job Creation


Last Thursday I attended the Startup Alleghenies launch event to network with those interested in learning how to create one's own company.  The presentations were interesting.  I went to offer my statistical services to anyone who had such a need.  

Looking at the census bureau's website I came across this article 


Startup Firms Created Over 2 Million Jobs in 2015

The map below shows the % change in startup job creation by state.  The states with the largest increases are in the west the southeast, and Michigan.  These numbers are encouraging but the report states that "this level of startup activity is well below the pre-Great Recession average of 524,000 startup firms and 3.3 million new jobs per year for the period 2002-2006." 


The growth in startups in different states may be reflective of the business climate there. The article does not explicitly state what the definition of a startup company is.  It speaks of old (> 25 years old) and young companies (< 6 years old) but does not state how startups fit into this picture.  It also provides no information on how long startup companies tend to last. 

People love to quote how Apple computer was founded in a garage but often they are the exception to the rule.  Would there be differences between the states in how long startups last?  Usually one discovery raises more questions than it answers.  I'm sure the data exists elsewhere.

On a side note I have begun to write columns for the The Hill Talk online publication.  I have written two posts there so far.  My next post will be the seventh anniversary post where I take stock of the blog.

**Related Posts**

Personal Bankruptcy Rates as a Measure of Underinsurance (Cross Post with PUSH)



Tuesday, August 15, 2017

What do Living Wage Cities Have in Common?

On Saturday, August 12, the there is a profile of me in the local Johnstown, PA newspaper (the Tribune Democrat) on my campaign for city council. I was hoping to push for a living wage ordinance for the city but the State of Pennsylvania, along with 24 other states, has a law preventing the city from raising it's minimum wage as the map from the National Employment Law Project (NELP) shows.  I thought I would take a look at the characteristics of the cities/counties that have passed these ordinances.


NELP came out with a list of cities and counties that have passed minimum wage laws above the state or federal minimum wage. There are 38 cities and or counties that have passed such laws.  17 of these entities are in California, five were in New Mexico, three were in Washington state, two were in Maine, Missouri (which now has a law forcing them to go back to the federal wage), Maryland, and Kentucky (where there are lawsuits challenging these laws). Alabama along with Missouri has a law preventing municipalities from passing these laws after at least one city has passed them.  

The first cities to pass such laws were San Francisco, CA ($12.25) and Santa Fe, NM ($10.84) in 2003.  San Francisco increased it's wage in 2014 to $15.00 to be fully implemented in 2018.  Mountain View,CA revised it's 2014 increase to $15/hour in 2015 also to be fully implemented in 2018.  The most recent city to pass such a law is Minneapolis, MN at $15/hour to be implemented this year.

There were ten cities with populations less than 100,000 in 2016 and there were four cities with populations less than 40,000 (SeaTac, WA, El Cerrito, CA, Emeryville, CA, and Bangor, ME).  Looking at which variables might predict the amount of the living wage ordinance above the federal minimum of $7.25/hr, there was a borderline significantly regression line with the city population in 2016.  



As an indicator of health of the city or county's economy I looked at the % change in the population from the last decennial census to the estimated population in 2016. Only three of the cites had a net loss of population:  St. Louis, MO (-2.47%), Bangor ME (-3.19%), and Birmingham, AL (0.04%).  St. Louis and Birmingham, AL are required by their states to go back to the federal minimum.  The slope of the regression line was significantly positive with an estimated $0.17 increase in the living wage ordinance for every 1% increase in the population change.  As can be seen in the chart above,  Emeryville, CA had the largest increase in population and one of the largest increases in the minimum wage to $15/hr. 

One can speculate as to the reasons for this relationship.  Are cities with a population increase more receptive to the idea of a living wage ordinance?  Are areas with shrinking populations, and shrinking economies, less receptive to a living wage?  Is there something about the states that have passed laws barring cities from raising their minimum wages that is different from more receptive states like California and New Mexico?

I don't have the answers to the above questions.  This analysis cannot answer the more fundamental question of what impact these laws have on job growth in these areas.  Two studies came out recently looking at the impact of Seattle's living wage ordinance.  One said it's having a positive impact, the other said it's negative.  While it's important to look at Seattle's experience most of the cities on this list are not like Seattle.  The city most similar to my hometown in population trends is Bangor, Maine.  Studying that city would be most relevant for policy makers here, if the state would let us have a living wage.


City
State
year passed
Year implemented
amount
population when passed
Current population
Pop Change
% pop Change
Lawsuit
State Law
Revised
Albuquerque, NM
NM
2012
2012
$8.75
545852
559277
13425
2.46
0
0
0
Bangor, ME
ME
2015
2019
$9.75
33039
31985
-1054
-3.19
0
0
0
Berkeley, CA
CA
2014
2016
$12.53
112580
121240
8660
7.69
0
0
0
Bernalillo County, NM
NM
2013
2013
$8.65
662564
676953
14389
2.17
0
0
0
Birmingham, AL
AL
2015
2017
$10.10
212237
212157
-80
-0.04
0
1
0
Chicago, IL
IL
2014
2019
$13.00
2695598
2704958
9360
0.35
0
0
0
El Cerrito, CA
CA
2015
2019
$15.00
23549
25400
1851
7.86
0
0
0
Emeryville, CA
CA
2015
2018
$15.00
10080
11671
1591
15.78
0
0
0
Johnson County, IA
IA
2015
2017
$10.10
130882
146547
15665
11.97
0
0
0
Kansas City, MO
MO
2015
2020
$13.00
459787
481420
21633
4.71
0
1
0
Las Cruces, NM
NM
2014
2019
$10.10
97618
101759
4141
4.24
0
0
0
Lexington, KY
KY
2015
2018
$10.10
295803
318449
22646
7.66
1
0
0
Long Beach, CA
CA
2015
2016
$11.00
462257
470130
7873
1.70
0
0
0
Los Angeles County, CA
CA
2015
2021
$15.00
9818605
10137915
319310
3.25
0
0
0
Los Angeles, CA
CA
2015
2020
$15.00
3792621
3976322
183701
4.84
0
0
0
Louisville, KY
KY
2014
2017
$9.00
597337
616261
18924
3.17
1
0
0
Minneapolis, MN
MN
2017
2017
$15.00
382578
413651
31073
8.12
0
0
0
Montgomery County, MD
MD
2013
2017
$11.50
971777
1043863
72086
7.42
0
0
0
Mountain View, CA
CA
2015
2018
$15.00
74066
80447
6381
8.62
0
0
1
Mountain View, CA
CA
2014
2014
$10.30
74066
80447
6381
8.62
0
0
0
Oakland, CA
CA
2014
2014
$12.25
390724
420005
29281
7.49
0
0
0
Palo Alto, CA
CA
2015
2016
$11.00
64403
67024
2621
4.07
0
0
0
Portland, ME
ME
2015
2017
$10.68
66194
66937
743
1.12
0
0
0
Prince Georges County, MD
MD
2013
2017
$11.50
863420
908049
44629
5.17
0
0
0
Richmond, CA
CA
2014
2018
$13.00
103701
109813
6112
5.89
0
0
0
Sacramento, CA
CA
2015
2020
$12.50
466488
495234
28746
6.16
0
0
0
San Diego, CA
CA
2014
2017
$11.50
1307402
1406630
99228
7.59
0
0
0
San Francisco, CA
CA
2014
2018
$15.00
805235
870887
65652
8.15
0
0
1
San Francisco, CA
CA
2003
2003
$12.25
776733
870887
94154
12.12
0
0
0
San Jose, CA
CA
2012
2012
$10.30
945942
1025350
79408
8.39
0
0
0
Santa Clara, CA
CA
2015
2016
$11.00
116468
125948
9480
8.14
0
0
0
Santa Fe County, NM
NM
2014
2014
$10.84
144170
148651
4481
3.11
0
0
0
Santa Fe, NM
NM
2003
2003
$10.84
61109
67947
6838
11.19
0
0
0
Santa Monica, CA
CA
2015
2020
$15.00
89736
92478
2742
3.06
0
0
0
SeaTac, WA
WA
2013
2013
$15.24
26909
28873
1964
7.30
0
0
0
Seattle, WA
WA
2014
2021
$15.00
608660
704352
95692
15.72
0
0
0
St. Louis, MO
MO
2015
2018
$11.00
319294
311404
-7890
-2.47
0
1
0
Sunnyvale, CA
CA
2014
2014
$10.30
140081
152771
12690
9.06
0
0
0
Tacoma, WA
WA
2015
2018
$12.00
198397
211277
12880
6.49
0
0
0
Washington, DC
DC
2013
2016
$11.50
601723
681170
79447
13.20
0
0
0
**Related Posts**

Save Dollars. Save Lives: An evening with Dr. Gerald Friedman


Unemployment: A Universal Underreported Problem



State of Working PA: Slowing Job Growth, Falling Wages Impede Recovery (and The Drop in Uninsured)