Veterans, The Elderly, and Living Wage Cities/Counties

I asked others in the field of demographics on my last post on the percentage of veterans being a negative predictor of the amount of living wage enacted in the 38 cities/counties that have passed living wage ordinances.  One expert in the field suggested one variable that I hadn't considered.  

Chris Briem over at the blog Nullspace suggested I look at age as a possible variable that could mediate this relationship.  He stated that there are higher concentrations of veterans among the elderly.  This makes sense as the draft existed before 1970.  I did obtain the % of the population over the age of 65 for cities in the 2010 census and added it to the model seen below.

	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%
Intercept	14.70	1.58	9.30	0.00	11.49	17.91
% veteran	-0.60	0.17	-3.61	0.00	-0.94	-0.26
% over 65	0.03	0.13	0.22	0.83	-0.24	0.30

The % of veterans in the city/county still significantly negatively predicted the amount of the living wage passed while the % over the age of 65 did not predict it in either direction.  These cities did have lower percentage of veterans (mean=4.95%) than the US (6.22%).  Likewise these cities did have lower percentages of those over 65 (mean=11.77%) than the US (13.00%).  

I looked at the correlation between the % of veterans and the % over 65.  There was a non-significant positive correlation between the variables as can be seen in the graph below.  Only 8% of the variability in the % over 65 was accounted for by the % veterans for these cities.  There are cities with high elderly populations and low veteran populations such as Palo Alto and El Cerrito, CA

It may be more informative to look at the % of elderly veterans vs. younger veterans as a predictor of the amount of the living wage.  I'm not sure where that data is available but it is a good area of inquiry.

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Veterans, the Living Wage, and the McNamara Fallacy

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.


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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

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