Once again there is another article pointing out that crime is on the decline in the United States.Once again socilogists and criminologists are seeking to explain why there is a decline in crime. The study concluded:

…during the current downturn, the unemployment rate rose as the crime rate fell. Between 2008 and 2009 violent crime fell by 5.3% and property crime by 4.6%; between 2009 and 2010, according to the preliminary Uniform Crime Report released by the FBI on May 23rd, violent crime fell by another 5.5% and property crime by 2.8%. Robberies-precisely the crime one might expect to rise during tough economic times-fell by 9.5% between 2009 to 2010.

We here at strictlynumbers.com hypothesized there is a simple explanation for this correlation. Unemployment rates have a direct affect on the crime rate in a given city. The correlation relates to the fact that as unemployment rates rise less people chose to spending time out in a given city going to bars, clubs, restaurants, and other night time venues due to a lack of revenue. If there are less people out and about in a given city then logic suggests there are less people to be victims of crime. In other words, crime isn’t declining it is just becoming harder to commit.

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When you look at food stamp (now called SNAP) participation rates, California as a state ranks 4th from the bottom. And if you look at the food stamp participation rates of the 24 largest metropolitan areas in the nation, San Diego ranks dead last. This means hungry people don’t eat, but it also means that San Diego county loses $144 million annually. And that’s $144 million in the form of the very best economic stimulus the government can give us – each dollar of food stamps generates about $1.80 in economic activity.

Let’s take a look at San Diego as a case study: Why aren’t San Diegoans getting food stamps? And what can we learn from San Diego that might help us increase the participation rate nationally.

First up, those eligible for food stamps don’t all participate at the same rate. Take a look at this:

Food Stamp Participation in 2003

56% of total eligible population

74% of eligible children

28% of eligible elderly individuals

62% of individuals in households with no earnings

47% of individuals in households with earnings

Source: Sources of Variation in State-Level Food Stamp Participation (PDF)

So when you see the HUGE discrepancy between the 89.5% of eligible food stamp recipients who participated in Missouri in 2003 and the miserably low 29% of those who participate in San Diego, that explains part of what’s going on. If San Diego’s eligible population is made up of demographics that are less likely to participate, then naturally San Diego’s participation rate will be lower as a result.

That explains SOME of the discrepancy but not all. Another possible explanation is that differing state policies make it more or less likely for those eligible to apply or receive food stamps. For example:

• Certification period – How frequently must an applicant reapply (between 3-12 months)
• Reporting requirement – Are applicants required to report any changes in income? (And if so, how frequently?)
• Categorical eligibility – Is any group of people automatically eligible for food stamps if they are eligible for another government program?
• Fingerprinting – Are applicants subject to fingerprinting, which might discourage some from applying?
• Application page length
• Work requirements – Are able bodied adults required to work?
• Number of visits required to apply
• State outreach – Does the state engage in any outreach activities?

I can imagine that if your state makes it a real pain in the butt to apply for food stamps, you might just give up. Especially if you wouldn’t receive very much in benefits anyway. Maybe you’d make that first trip to apply but if subsequent visits were required, they want your fingerprint, and the application’s long, maybe you don’t bother. Or maybe you bother the first time, but three months later when they want you to re-certify, it’s just not worth the hassle.

The USDA crunched the numbers to see if the make-up of the population accounted for the differences in participation rates (it did some, but not too significantly), or if different state policies explained the discrepancies. The answer? Well, they couldn’t find any statistically significant difference in participation rates based on the policies.

However, they also say that they doubt that the variation in participation rates is totally random. And it’s hard to believe that a state that makes its application process difficult and obnoxious wouldn’t have any effect on its participation rate.

The USDA suspects that their inability to account for differences in participation may be due to lack of sufficient data or overly imprecise data, or perhaps similar policies are implemented differently, making statistical comparisons between them impossible. (For example, if two states had an identical policy but implemented it differently. When the USDA does its number crunching these states would be lumped into the same category but in reality food stamp applicants in either state would have very different experiences.) Another possibility is that “aggregate measures may mask meaningful local variations.” Last, perhaps state procedures – how the states actually do what they do – are more important than state policies.

I’m glad the USDA is looking into this, and I hope they can find an answer that explains why 70% of those eligible for food stamps in San Diego do not receive them.

Participation Rate (%) by State (2003)

Missouri 89.5

Oregon 85.7

Tennessee 83.1

Hawaii 79.1

DC 74.3

Oklahoma 73.0

Maine 69.9

Kentucky 68.9

Mississippi 67.9

Georgia 67.5

Louisiana 66.2

South Carolina 65.9

Ohio 65.2

Michigan 65.1

Arizona 65.0

West Virginia 64.9

Indiana 63.6

Minnesota 63.1

Alaska 61.5

Vermont 60.8

Illinois 60.6

Nebraska 60.5

Arkansas 60.1

North Dakota 57.8

Iowa 57.2

Delaware 54.8

South Dakota 54.3

Idaho 54.2

Alabama 54.1

Pennsylvania 54.0

Connecticut 53.8

Wisconsin 53.3

Kansas 53.0

New Mexico 53.0

Rhode Island 51.9

Virginia 51.5

Washington 51.4

Utah 50.9

New York 50.2

New Hampshire 49.7

Wyoming 49.2

Florida 48.9

New Jersey 48.7

Maryland 48.1

Texas 47.4

Colorado 45.5

North Carolina 45.4

California 45.3

Montana 44.6

Nevada 41.0

Massachusetts 40.1

Based on a Post 4/11/2008 6:02 PM PDT on MyDesert.com in BluePalmSpringsBoyz blog

My first exposure to Statistics was in undergraduate school. One of our textbooks was Huff’s “How to Lie With Statistics.”  (Before you question my Statistical knowledge, I also had two semesters, six credits, of Statistics in my Master’s program, and another two semesters, six credits, of same in my doctoral program.)  Oh, the perils of using statistics in either a naive manner or purposefully misrepresenting these little guys.  Seems that bethcaskie, aka soyinkafan, the former a new blogger to mydesert.com has intimate knowledge of this text and put it to good use in her first posting re the Binder Poll recently released. Shame on bethcaskie for her misleading blog title and for the loose interpretation of the Binder Poll.

Bethcaskie entitled her piece, dramatically claiming that Manuel Perez, Vice-President of the failed Coachella Valley Unified School District board and Democratic candidate for the CA 80th Assembly District, leads in the Binder polling. Then she articulated that Perez and Greg Pettis, Cathedral City Councilman, were actually tied in the poll! How then is Perez ahead? This is a shameful misrepresentation of the facts.

More below the flip…

According to Pettis campaign officials, the actual Binder Poll shows Pettis ahead of his nearest Democratic challenger by 7 percentage points amongst likely primary and general election voters.  Is this the same poll that bethcaskie referenced?  Seems a little askew.  In the Pettis campaign press release, Petts has 22 percent of the total vote and his nearest Democratic challenger has only 15 percent.  Supposing that it is Perez with the 15 percent, and this is a hasty assumption since I do not have access to the polling date, this is hardly either a statistical tie or a manifestation of Perez leading in the poll, even should he be the second-place finisher.

Then, bethcaskie claimed that with ‘education’ the voters dutifully voted for Perez’ candidacy to the detriment of Pettis’.  Just what ‘education’ meant, bethcaskie failed to enunciate.  Given the failures of Perez as Vice-President of the Coachella Valley Unified School District as board Vice-President, the threatened State sanctions due to CVUSD’s failure to meet the requirements of ‘No Child Left Behind,’ and the State placing CVUSD under trusteeship under Perez’ leadership, the Perez campaign is hardly the Great Decider in determining what ‘education’ might be.

Not a very clear argument for Perez being ahead in the polls since Pettis is clearly ahead in the poll amongst likely primary and general election voters, ahead of each of his Democratic rivals, and ahead of the presumptive Republican nominee, Gary Jeandron, another failed leader of another school district threatened with sanctions.  (Pettis leads Jeandron 41% – 39% amongst these likely voters in the Binder poll, according to the Pettis campaign staffer.)

How to Lie with Statistics? Ask the Perez campaign.

For those who may not know how the California Democratic delegate delegation system works, let me give a brief explanation as to how the delegates are divvied up. The 241 district delegates are proportioned by the congressional district’s vote. The 129 at-large delegates are divvied up by the statewide percentage vote, though adjustments were necessary since the sum of Clinton’s and Obama’s percentages was less than 100%. I summed up their totals (93.9%) and divided Clinton’s 52.3% and Obama’s 41.6% by the 93.9% to obtain 55.7% for Clinton and 44.3% for Obama. Then I distributed the delegates based on those percentages.

Update: I readjusted some districts as well as the statewide delegates when I found out about the 2-block system for the statewide delegates. I don’t know how PLEO delegates are distributed, so I just took Hermit9’s word and gave Clinton 71 statewide delegates and Obama 58. The raw numbers of delegates changed slightly in Obama’s favor, though Hillary still takes about 55% of the delegates.

Over the flip is the table of results based on the numbers as of 3:30 AM Pacific Standard Time. I will have one more update when all results are in.

District	Delegates	C %	C Delegates	O %	O Delegates
1	5	45.7	2	46.1	3
2	4	46.9	2	40.9	2
3	4	48.2	2	44.4	2
4	5	46.5	2	43.1	3
5	5	44.9	2	50.1	3
6	6	41.7	3	50.9	3
7	5	49.7	3	44.8	2
8	6	43.5	3	53.4	3
9	6	34.2	2	61.5	4
10	5	49.3	3	44.3	2
11	4	54.6	2	38.9	2
12	6	52.6	3	42.3	3
13	5	57.9	3	31.0	2
14	6	44.7	3	49.9	3
15	5	56.1	3	37.6	2
16	4	59.8	2	34.8	2
17	5	48.8	3	45.4	2
18	4	60.5	2	32.6	2
19	4	55.0	2	35.2	2
20	3	64.5	2	28.8	1
21	4	59.4	2	31.5	2
22	4	50.9	2	38.3	2
23	5	46.4	2	47.2	3
24	5	51.4	3	41.9	2
25	4	53.9	2	39.7	2
26	4	54.7	2	39.2	2
27	5	59.6	3	36.2	2
28	5	60.0	3	36.8	2
29	5	52.2	3	44.3	2
30	6	49.9	3	46.7	3
31	4	64.1	3	33.5	1
32	4	71.4	3	24.4	1
33	5	36.8	2	61.4	3
34	4	73.3	3	23.3	1
35	5	39.1	2	58.6	3
36	5	52.2	3	43.4	2
37	5	43.8	2	53.8	3
38	4	72.5	3	24.0	1
39	4	66.9	3	29.3	1
40	4	57.4	2	35.3	2
41	4	58.5	2	31.9	2
42	4	56.3	2	37.3	2
43	4	63.2	3	31.2	1
44	4	55.2	2	38.3	2
45	4	59.8	3	32.8	1
46	4	53.0	2	40.1	2
47	3	67.9	2	26.8	1
48	4	50.9	2	43.3	2
49	4	54.6	2	37.4	2
50	5	47.7	3	46.3	2
51	4	59.7	3	35.7	1
52	4	50.1	2	41.8	2
53	5	47.8	3	47.5	2
State	129	52.0	71	42.4	58

Based on the results so far, Clinton receives 202 delegates (71 at-large and 131 district) for about 55% of California’s 370 delegates up for grabs on Super Tuesday, while Obama receives 168 (58 at-large and 110 district) for about 45%.