Monday, July 22, 2013

Health Care Satisfaction and Finance Source

This is not a full post, just a quick data note for a Facebook conversation I am having, regarding the issues around universal payer health care systems vs. health care satisfaction rankings. The data comes from two sources: health care financing comes from OECD data, and satisfaction comes from Commonwealth Fund. The former has data from 34 OECD (basically high income) countries, while the latter has survey results from 11 of those countries from 2010. The satisfaction measure asked how much change their health care system needs, ranging from very little change, a moderate level of change, to "rebuild completely."

For the financing data, I looked at 4 different programs: Individual health programs, Individual health + all medical goods, and Total health expenditure, averaging the US$ PPP per capita expenditurefor 2003-2012 for each country. The correlations with satisfaction were almost identical for all three measures--I present the results for Total Expenditure. The financing number I show in the table represents "public financing/private financing." Therefore the higher the number, the greater the proportion of public funding vs. private financing. Public financing represents federal and local sources, and private financing includes private insurance and out of pocket expenses. In this case, Australia has a value of 2.2, meaning that, on average, the government spends just over 2x per person what the individual spends on health care, while the 0.9 value for the U.S. indicates that the government spends less than individuals do for health care. The country that spend the most, Norway, has government expenditure more than 5x what individuals spend privately on health care.

For the countries surveyed, individuals with greater government contribution were strongly more likely to be satisfied with their health care system, with a correlation of 0.61. Typically, correlations above 0.3 indicate moderate relationship, and above .6 indicate a strong relationship. In the table below, the data is ordered based on financing agent, with the lowest public financing at the top, and the greatest public financing at the bottom.

CountryTotal Health Care Expenditure, Public vs. Private in US$PPPRebuild health care system completely, % of those surveyed
United States0.927
Switzerland1.68
Australia2.220
Canada2.310
Germany3.414
France3.511
United Kingdom4.13
Netherlands4.37
Sweden4.58
New Zealand4.511
Norway5.312

Tuesday, June 25, 2013

Paula Deen, Race Relations, and the Southern Plantation Culture

I do not know Paula Deen, I have never seen her show, I have never read any of her books. On the other hand, I do not cook unless it is microwavable, I am a vegetarian, and I do not watch the Food Network or QVC. I knew very little about the 66-year old Deen until the recent controversy about racist statements she admits to have made 20 years ago. In a country that seems to easily forgive past mistakes, including an governor's adulterous affair supported by public money for trips to Argentina, what is the big deal about casual references made 20 years ago by an old lady who grew up in a Southern slave state prior to the civil rights movement?

For me, if it was simply that, I too would likely easily forgive, rolling my eyes and exchanging knowing looks with other under-40 year olds at the quaint racism of the elderly. However, it is not simply a 20-year old statement. There have been more recent examples of this larger pattern, as well as her broader response to these old statement. Take, for instance, the recent lawsuit by her former employees alleging racial discrimination. Deen is innocent until proven guilty, so employment discrimination proper is yet to be determined. But statements contained in the complaint speak to the broader narrative that surrounds the mystique of an older, racist South that clearly seems to continue to infuse Deen's psyche. The plaintiff references the following discussion with a wedding planner from 2007:

"I want a true southern plantation-style wedding." Asked by Ms. Jackson what type of uniforms she preferred servers to wear, Paula Deen stated, "Well what I would really like is a bunch of little niggers to wear long-sleeve white shirts, black shorts and black bow ties, you know in the Shirley Temple days, they used to tap dance around."
Perhaps she simply imported language from her girlhood, and in her excitement about the vision for the wedding uniforms, failed to self-censor, as we all sometimes do in a moment of emotional exuberance. Inappropriate, but by itself, perhaps excusable, if an isolated incident. We do not know the broader context of the conversation, except that Ms. Jackson expressed dismay and disapproval at the suggestion.

Let us assume these incidents were rarities, and her public behavior has generally been exemplary, other than contributing to the skyrocketing rates of heart disease and diabetes with her lard and sugar-based recipes. What seems to me more indicative of her beliefs about race are her recent, overt, conscious statements about her race comments. Take her interview with the left-leaning New York Times just last year:

“Back then, black folk were such an integral part of our lives,” said Deen. “They were like our family, and for that reason we didn’t see ourselves as prejudiced.” She also called up an employee to join her onstage, noting that Hollis Johnson was “as black as this board” — pointing to the dark backdrop behind her. “We can’t see you standing in front of that dark board!” Deen quipped, drawing laughter from the audience.
The New York audience reaction itself is problematic, finding amusement in Deen’s race-laden remark. What is more damning is the fact that Deen does not seem to find the statement problematic, and makes it in the context of being asked by “Lefties” about her history of racially insensitive language. If she has a “spin” team, I would hope they would have encouraged her to approach the issue with gravitas and race-neutral language, not to infuse such discussions with racist jokes.

But certainly she does not perceive the remark as racist, or she would not have made it. It is that perception that, in my opinion, is more problematic than the remark itself—the failure to recognize the importance of race inequality that still subsumes our cultural practices, not to mention legal, political and economic practices. Keep in mind, I have a white friend, Natalie, who tells inappropriate jokes to lighten the mood when she gets anxious, and Deen’s statement sounds identical to statements Natalie would make. In fact, I have heard Natalie joke about how she can only tell her black friend is in an unlit room when he smiles and she can see his teeth. However, in this case, her black friend is her husband, not an employee, and she is saying that to close friends who share her vision of racial justice, not to an audience for whom is attempting to defend and explain her race-neutral position.

Take another example, not from Deen herself, but from her sons, defending their mother. Granted, they would be poor excuses for sons if they did not defend their mother and their family, so one expects neutral objectivity from them. What is interesting is not their support of their mother, but the language they use to support her. In a June 2013 joint interview of Bobby and Jamie, they make these separate statements:

"That is not her heart, it is certainly not the home we were raised in. We were raised in a family with love, and of faith, in a house where God lived. Neither one of our parents ever taught us to be bigoted towards any other person for any reason. This is so saddening to me, because our mother is one of the most compassionate, good-hearted, empathetic people you'd ever meet. ... Frankly, I'm disgusted by the entire thing, because it started out as extortion, and it became character assassination."

"We care very much about our community, I'm raising two boys right now, this is ridiculous, it's completely absurd to think there is an environment of racism in our business. It's really disrespectful to the people we work with, we have strong, educated men and women of character that have been with us for 5, 10, 15, 20 years. To think that they would allow themselves to be in this position is simply baloney. It’s ridiculous"

For Whites who grew up in the South, the Deen boys’ language sounds very positive. However, for Blacks, and younger Whites, the language is recognizably coded with phrases that signify not racial sensitivity and justice, but phrases that signify anchors to the perception of a genteel and noble Southern past, a past demolished by the perceived atheistic, anti-family, anti-tradition, liberal civil rights movement of the 1960s. In their defense of maternal non-racism, absent are statements about how she has worked for racial equality. Absent is their recognition of a pervasive and crippling history of racism in the United States, especially in the South, and specifically in their home state of Georgia. If their goal was to defend that their mother was not a racist, such affirmative statements would seem to be required to support such a narrative.

Rather, the references to the importance of family and community were mentioned by both. Who would believe that a pro-family, pro-community parent could possibly be a racist?! The notion seems ridiculous to them. Strike that—not “seems,” since they clearly state that the notion is ridiculous. Similarly, they refer to their family's Christian faith. Again, the notion that good Christian folk could be racist is an absurdity to them. Besides, their mother is compassionate, who hires educated staff of good character. Her own statements about how "black folk were such an integral part of our lives,... they were like our family," import the paternalistic belief that Whites and Blacks were equally satisfied and benefiting from the structures of inequality, and that there was mutual love between them.

The problem with the Deen boys’ language, and Ms. Deen's, is that it is the same language commonly used by racists to defend this racist, Southern Plantation culture. Race relations in the South, and the broader old Southern culture, have long been understood as framed by faith, respect for tradition, respect for hierarchy, good-heartedness, and strong character. Very little of the internal narrative of the pre-civil rights movement White Southerner is characterized by a recognition of “hatred” or even “antagonism” for Blacks. Jason Sokol, historian at University of New Hampshire, began his academic career with an insightful book on White Southern culture, titled, “There Goes My Everything” (Vintage, 2006). In it, he interviews older Whites from the South, who self-narrate their experiences of the transformation of their own lives, and the South, from before and after the civil-rights movement. In it, Sokol documents a pre-civil rights worldview that hangs together on many of the ideas encapsulated by the Deen boys’ language as they defend their mother.

For example, the issue of religion, which many today perceive as a stalwart defending racial freedom and equality of all people, has not always been so, and still acts as a lens through which to defend racial (and gender) inequality in parts of the South. Churches in both the North and the South long used the Bible to vigorously defend the practice of slavery, and even after slavery was making its legal way out, several Bible verses were deployed to scold “slaves to obey their masters,” and to encourage Blacks to remember their place.

Southern Whites perceived the structures of the South benefitted both Blacks and Whites, and that all race groups were satisfied with their stations in life. They also believed that they solely understood appropriate race relations, and that “their Blacks” were happy with the way the South treated them, presuming they were “good Blacks” who weren’t trying to disrupt the social order. One representative interviewee says, “We in the South are the ones in the whole United States who love the colored people. … Down here we understand the colored people” (44). However, while Sokol demonstrates that the common perception that “southern whites possessed care, affection, and even love for blacks,” Southern Blacks themselves have lived experiences that demonstrate that “the whites gravely missed the point. In a society democratic only in name, shot through with discrimination and layered with inequality, emotional bonds were never enough” (113).

Many of the Southern Whites that Sokol interviewed were remorseful about the previous ways of life embodied in Southern culture, even though they often still identified it as having respectable traditions, filled with faith, family, and mutual benefit to all members of that society, specifically in contrast to the "liberal North." Sokol’s study was not to “expose” contemporary racism, or investigate legal discrimination. We already understand that history, and recognize that continued present, especially in the South. Sokol’s purpose was to explore the internal narratives of the older Southern White, to understand both their pre-civil-rights consciousness, and to understand their transformation post-civil-rights. As I listen to Deen’s explanations of her racial beliefs, and her sons’ defense of their belief of their non-racist family life, I was reminded of the following extended passage from Sokol (58-59).

“In our inmost [ears], we knew we were wrong. And so… we didn’t talk about justice, we talked about love. But love unsupported by justice becomes sentimentality.” … It was another weight that white southerners had long balanced. Many thought themselves sincere when they said they cared deeply for blacks. But it was a care based upon inequality, rooted in oppression, layered with discrimination, and willfully blind to those very facts. Natives of the east Tennessee town of Clinton believed that race relations had always been good. According to a Newsweek background report, “What the ‘good’ relations seem to amount to is absence of trouble and submissive acceptance of the part of Negroes of a social system that excludes them from everything except menial job opportunities in the community, occasional friendly exchanges on the streets, access to downtown stores, and the annual exchange of church choirs.” Whites interpreted lack veneers of deference as actual friendship.
The Deen family sins, from the perspective of Northerners, and us under 40-year olds, is that they seem to believe that faith, family, and good-heartedness are enough to create moral goodness. What they seem to fail to recognize, that others in the South have begun to learn, is that racial justice is equally as important as good-heartedness, and the latter does not automatically create (or negate the need for) the former. Rather, it takes repentance from former ways of life, including those carried from previous generations, and it requires a recognition of the fact that radical inequalities still exist between Blacks and Whites—legal, political, and economic---and that these problems cannot be solved by good-heartedness, but by actively living the change and supporting policies of justice. These goals are likely not achieved by glamorizing the Southern plantation culture, such as, by specifically desiring to re-create it in a wedding, where “a bunch of little niggers… [in] white shirts, black shorts and black bow ties… tap dance around.”

Saturday, June 22, 2013

Choosing a Range (Cooktop and Oven): by the Numbers

I have been designing my kitchen. I have only had a kitchen for about a year, defined as a room that contained a kink with running water and drain, a full-size refrigerator, a dishwasher and a microwave. Previously I had a room in the house with a mini-fridge, a microwave and a sink with running water (no drain). After I reconstructed the joists under the previous kitchen floor, put in subfloor and tile, I finally had the beginnings of a real kitchen. I recently made a built-in for my dining room, and learned how to make drawers and cabinet doors. With these skills I should be able to make my kitchen cabinets. But I can't do that until I have my final appliance--a range.

I went to Consumer Reports, where I compiled their list of 161 reviewed ranges from May 2013. I won't reproduce the entire list here, but I will produce a statistical summary I created. Rather than focus on a specific range to purchase, I wanted to evaluate the companies that made them. Consumer Reports listed these 161 ranges, along with the list price for each, and a "score" of their quality based on 5 specific features. These scores ranged from 48 to 87 on a 100pt scale. I analyzed the percent of ranges that each company produced that scored above 75 on this Consumer Reports Scale, along with the average price for the evaluated ranges. The list is sorted from best to worst based on the percent of evaluated ranges with a score above 75. This list does not represent all 161 Consumer Reports evaluated ranges, but just the smooth-top ranges (n=65).

CompanyAverage ScorePercent of Ranges Above 75ptsAverage PricePercent of Ranges Below 70pts
Electrolux81.7100% $ 2,000 0%
LG79.688% $ 1,279 13%
GE80.980% $ 1,505 0%
Kenmore77.367% $ 1,155 17%
Frigidaire72.255% $ 1,030 27%
Maytag68.840% $ 1,010 60%
Samsung73.733% $ 967 17%
KitchenAid71.033% $ 1,483 33%
Whirlpool66.911% $ 833 78%
Jenn-Air70.00% $ 1,600 0%
Amana54.70% $ 533100%

(Notes: Jenn-Air just had one reviewed range, at 70pts. Also, while Kenmore scored 4th on the list with the average highest ratings, they had the top 2 scoring ranges on the list, at 87 pts each.)

Sunday, June 16, 2013

Dining Room Built-In Cabinet

The 1895 Victorian house I purchased 4 years ago had been abandoned for about a decade, and the infrastructure of the back 1/3 of the house was destroyed from wood rot. The first 2 years was primarily tearing out walls and floors, then rebuilding what I could. The third year was one of the biggest projects (compared to other big projects, like tearing out and rebuilding 3 entire exterior walls, from the bottom to the top), which was replacing one of the center beams that held up the back of the house.
I had already built 2 new support walls in the basement to help carry the load. The center beam, and everything above it on the first floor was covered with a terrifying, dense network of wood rot. For example, there was a beautiful hardwood pantry and cabinet that was overgrown with the mold, as seen here in picture one. In addition to the pantry, in the dining room there was a built-in with a pass-through, as seen in picture two. While this built-in looks salvageable, once you opened the doors, it was covered in wood rot. It was very sad, but it all had to be excavated and thrown out. Nothing could be saved. In this photo, the entry to the pantry is just to the right of the built-in.

Picture 3 is the space where the wall and pantry used to be, all the way back to the rear wall. As you can see, the floors were also rotten and had to be removed. For the first 3 years I lived with no floor in the back 1/3 of the house, just an open hole into the basement that I had to hope the dogs never fell into.

Several times I fell over the course of working in that area, fortunately only resulting in a couple of broken ribs and a broken toe. Things could have been worse.

After the original pantry and built-in were torn out, along with everything else for about 15 feet behind it to the rear wall of the house, the process of slowly reconstructing new walls was the next task. The space in the farthest corner, which used to be a tiny downstairs bathroom, is becoming a small breakfast nook, and the space where the pantry used to be is now the laundry room. I will save those stories for later. This post is about the dining-room built-in cabinet. In the above picture of the original built-in, it is on the left of the wall with the doorway to the pantry to the right. I have put the new built-in on the right. Right behind it is the laundry room.

It took several weeks of planning to get the measurements for the space itself. I already knew how to put up interior walls, but in this case, I wasn't putting up a wall as such, with drywall, but was used 3/4" oak plywood to make the walls, floor and ceiling of the built-in cabinet. I used the same to make the shelves. The difficulty here was in the planning of measurements, getting the wood into the tight spaces, and cutting heavy 8x4 sheets of plywood with my little table saw.

The next difficult task for the shelving, were similar--learning the precision to get the measurements exactly right, doing the precise cutting, and getting the shelving into the tight spaces. It was also challenging affixing the shelves in place, since I only had access to the other side of one of the walls--i.e., the other side of the left side of the cabinet is what will become the new pantry, and I was able to screw the shelves into place on that one wall. However, the other two sides I couldn't access, so the shelving is partly held into place by the tightness of the fit, and partly by small trim pieces nailed to the back of the cabinet wall on the underside of the shelving. I thought I was 75% finished at this point. Wow, was I mistaken. That was last summer (2012).

Then last fall I tackled the main side trim, which I mostly used from reclaimed trim from the excavated doors in the house. In this picture you can see that I've framed the sides and top with these pieces. Part of this process was buying a router, and learning how to make the decorative grooves in the top horizontal piece. This matches the pattern from the rest of the house, but I didn't have another piece long enough for the top of the built-in. The plinth blocks, the blocks of wood at the bottom sides, as well as the vertical pieces, and the top corner blocks were all reclaimed from other areas of the house, cut to size, sanded and re-stained. This was all reasonably easy, just tedious, and it took about a week.

This summer I tackled the drawers, the trim, and the cabinet doors, which I thought would be quick and easy. I hand-wrung for several months over the pattern of the drawers and cabinet door. Finally I realized that I should just use the existing pattern of the interior doors of the house, which were a reasonably simple raised panel design. The router I had was a cheap one I got from Craigslist, and after several practice attempts, I realized this was insufficient for cabinet work. I had to invest in good equipment--higher quality table saw, router, router table, and specialized bits that made the cabinet doors. Learning how to do this was a several week process. Far longer and more expensive than I expected. I also didn't realize that all of the pieces had to be solid oak. If I were using a flat panel I could have used the cheaper oak plywood. However, when you cut out the raised portion, it exposes all of the inside of the boards, so it has to be solid oak. I was shocked how expensive oak is. I had to practice quite a lot before I was brave enough to carve out the actual oak pieces for the final panels. The door has several rails and stiles, as well as the raised panels themselves. Despite triple-checking measurements, after I put the pieces together, the door was still 1/4" too big! I had to recut a couple of pieces. After I glued them all together, it was still 1/8" too big, and both the door and the cabinet opening were out of square, so I had to use my plane and a belt sander to get everything to the right size. The drawer faces were far easier, since I basically just cut them like a door panel, but didn't put the stiles/rails on them. All of this took about a week.

Another big challenge was what I call the "inside trim," distinguishing the solid oak pieces that line the inside opening of the cabinet, from the reclaimed exterior framing trim I put up the previous year. This was also far more complicated than I expected, partly because I didn't even think about These pieces. Since I used plywood shelving, I had to put up a decorative molding on the exposed side. That was reasonably simple. But all around the rest of the cabinet I put a 1.5x3/4" trim. I also had to make the base of the cabinet, which were several 3x3/4" boards making small boxes on the floor. All of this took about a week. Staining wasn't as time-consuming. I resanded the entire finished piece, used a wood-conditioner product, and my own color mixture using oil-based stain, all of which took about a day. Installing the cabinet door was a pain, and eventually the door will have to be remade. The euro-style flush hinges were larger than the stile, so when I bore out the hole for the hinge, I went all the way through the thin area of the raised panel.

There wasn't extensive damage, so I was able to use wood fill and restain it. Unless you look close, you can't tell, but it's certainly an area of structural weakness for the door, which I'm sure is already unstable and weak since it's my first effort. But it seems fairly sturdy when I carried it around. Here is the final book case, about 2 months worth of work extended over about a year.

Right now this room is basically my tool storage room. The family who bought the house in the 1970s made this room into their kitchen. Originally, the room was the dining room. I am in the process of making it back into a dining room. It still looks mostly like my tool storage room, but finally, after 4 years, it's slowly starting to look like the beginnings of a dining room. Over the last several years I have received fancy kitchen display items from my family for Christmas, but I have had no place to put them, so they have all been put in a box. Now I finally have a place to put them.

Friday, May 3, 2013

BLS Hiring and Firing Data

In discussing welfare with conservative friends, the discussion inevitably turns to all the people they know who are "able-bodied" but who simply refuse to work so they can collect welfare, in addition to all of the people they know who scam the welfare system. I'm not sure where these folks hang out that they personally know all of these people scamming the welfare system, apparently bleeding taxpayers dry, despite the fact that welfare is only 3% of the federal budget.

When I point out the BLS high rates of unemployment, especially for those under 30 and race minorties, conservatives typically critique the unemployment measure as "how many people aren't working because they would rather collect unemployment benefits." While federal data doesn't support this assumption, it is true that the "unemployment rate" isn't a masure of how many people are "unemployed who want work," which is important to my argument, since I claim the high rates of poverty and welfare use are because there are no jobs available, not because Americans are lazy and like to leech of our neighbors. This is the typical sociological frame--that poverty is due to structural forces that prevent people from access to skills, or access to jobs.

Based on a friend's question, "is there a 'jobs opportunity index,' an alternative to the unemployment index, but which shows actual availability of jobs?," I did some digging, but found little that adequately addressed that important question. In 2010, a pair of economists won the Nobel Prize for developing the Matching Theory Problem, that there is a severe structural problem with matching job seekers with job opportunities. There have been several nice studies that look at the lack of available jobs, and the subsequent rise in poverty; conversely, when job opportunities rise, then poverty and welfare use decreases. But all of these studies are micro-level studies of communities, based on researchers spending months canvassing from street-to-street to find employers, or utilizing online databases like Craigslist or Monster.com. There is no national database where all employers input available jobs, so therefore, there is no "available jobs index" to measure national, regional, or local employment opportunity rates.

The closest I could find was the BLS JOLTS database, the Job Openings and Labor Turnover Survey, which began in late 2000. The title sounds promising, but it actually doesn't measure "job openings." It takes a random sampling of 16,000 employers a month, primarily firms (larger, stable businesses), and asks how many people they have hired, and how many they have let go, or separations. The assumption is that separations minus hiring should represent jobs available. But later researchers found that when firms layoff workers and simply make businesses smaller, then the separations-hiring number is not job openings, but an actual contraction of the market.

In the table above, I track three measures: national unemployment rate (blue), hiring in the Midwest (gray), and layoffs in the Midwest (yellow). I purposefully left out "quits", to remove those people who leave the job market willfully, to emphasize the number of people who are taken out of the employment numbers due to structural factors. You can see the impact of the 2008/09 recession by the long-term spike in lay-offs, and the decreases in hiring that began in 2007, which is also when the unemployment rate began the slow incline. Compounding the lay-offs and decrease in hiring, you see the tremendous spike in unemployment rates.

Regardless, neither of these measure what my friend asked--whether there is a "jobs available index." But it seems to be about as close as we have for the time being, except for individual studies doing micro-level survey research.

Thursday, May 2, 2013

Why Teenagers Should have Anti-Pregnancy Tools

Obama's Justice Department today filed notice that it will appeal a Federal court's decision to allow 15-yr olds access to so-called "Plan B," a form of emergency contraception, which the FDA recently ruled was safe for girls of that age and older (Washington Post). While few want 15-year olds to be having sex, unless you are part of the Warren Jeff's clan, the issue isn't whether you want "your" daughter to have sex, or an "abstract" girl, but how can we, as a society, decrease the teen pregnancy rate. Arguments about abstinence education aside, scolds of poor parenting, and the older generation complaining about the wild, thoughtless youth of today, there are very practical steps that we can take to limit the risks that youth pose to themselves, one of which is to provide them access to contraception. Studies are convergent--providing access decreases rates of STDs and teen birth rates. It is naive to hand-wave at the loose morals of society, and believe that if we just yell louder about kids having sex too young, that the problem will go away. We have been trying that method for decades with little success, especially in the states that are least progressive when it comes to sex education. In fact, teen birth rates have gone down in the US, but primarily only in states that provide access to contraception, which includes a knowledge of how to use it, i.e., comprehensive sex education (Hall and Hall, 2011; Kohler and Manhart, 2008).

A quick look at recent CDC data on teen birth rates (Table 1 below), reveals the staggering details of the problem that many in our society fail to grasp. This CDC table indicates not only the rates of teen birth by age, but by how many children by age. Look at the table for age 16, 3rd child--your eyes are not deceiving you--there were 69 live births to 16 year olds that were the 3rd child just in 2010 alone here in the U.S, and 8 births that were the 4th child. To a 16-yr old. None of this data even considers abortions figures. If social conservatives want to decrease abortion, then call Obama and, as you have for the last 5 years, oppose him--ask him to reconsider his directive to appeal the Plan B decision. You can continue to work on various "moral" programs to limit teen pregnancies. But in the meantime, let's agree that providing every reasonable tool contributes to the solution.

Age of Motherunder 1515 yrs16 yrs17 yrs18 yrs
Total4,49713,47533,36262,336104,052
1st child4,37212,97131,11654,83684,632
2nd child743861,9186,45716,355
3rd child713695381,990
4th child23833202
5th child-131120
6th child-1-17
7th child----1
8+ children---23
Source: CDC, Vital and Health Statistics, 2012

Tuesday, April 30, 2013

Student Plagiarists Who Watch too Many Crime Dramas

Every year I discover a plagiarist in one of my classes. Half of those have clearly watched too many crime dramas, where the guy who 10 witnesses saw murder somebody in clear daylight, gets off on a technicality, just because his lawyer filed every possible appeal for every possible loophole, or because of behind-the-scenes politics, where the judge just doesn't like the prosecuting attorney, so lets the perpetrator walk. Apparently this belief in the combination of tenacity and luck as the road to success for any good capitalist, drives them to appeal my decision to give them a 0 on their assignment which they clearly copied word-for-word from another source. When I find plagiarism, I gather the evidence, present it to the student, tell them the consequence, and inform them of their rights: “if you want to appeal my decision you can go to the Dean’s office,” which is the basic procedure at all of universities where I have taught.

The best stories about my plagiarists come from the most extreme cases. In two instances, I had a student who, as it turned out, were dating a student who had taken my class during a previous semester, or from a different section of the course, and the plagiarist submitted papers identical to what the other student had submitted to me earlier. Unfortunately for one of these students, he might have got away with it, except he referenced a textbook from a previous semester that I had stopped using. An obvious give-away. In several instances, the students literally copy/pasted an entire Wikipedia article and submitted them as-is. In each of these cases, when I confronted the students with the evidence, they were shocked at my accusation, having no idea where I would get the idea they had copied somebody else’s work, despite the identical papers sitting in front of them—theirs and somebody else’s.

When I inform them of their right to appeal, I strongly urge them not to appeal the decision because as it stands, they are facing a 0, and usually they can pull out a passing grade. Sometimes it only drops them one letter grade. I explain that if they appeal, they enter the Dean’s radar as a cheater, and an official report is filed in their permanent record. Not to mention the fact that the Dean’s office reserves the right to take further action—such as expel the student. Here’s where the stories get frustrating, more so than the original plagiarism itself. The students, who watch too many crime dramas, “hear” my warning as, “this professor is afraid that the Dean’s office will overturn his decision, so if I appeal, not only will I get my grade back, but this professor will get into trouble, so if I just keep appealing, I’ll get off on some kind of technicality, or a brain-dead administrator who doesn’t like this professor.”

As one particular of these students later confessed to me, “how could I trust that you were looking out for my best interest, when you were giving me a zero!?” This confession occurred after they had appealed my decision, and the Dean confirmed to them, “Yes, now you have a permanent record of cheating on file, and if it happens again, you are expelled. You should have listened to your professor,” followed by the administrator's very stern lecture about the stupidity of copy/pasting a Wikipedia article, and asking the student how they had got this far in their college career?! I tend not to yell at students. Some Deans seem to relish it in certain instances--dealing with plagiarists is one of those times.

Senators Who Voted Against Gun Background Checks Face Public Blowback

The recent senate vote that killed common sense background checks for gun purchases has had major consequences for those voting against the bill. Those senators have seen approval ratings collapse--not only in the change from current approval to disapproval ratings, but the change is especially marked when comparing their prior approval ratings. Below are three tables that represent recent polling. The rows highlighted in green represent current polling, while the row below that represents the prior most recent polling (if available), and the number in the far right-hand column represents the change from previous polling to current.

StateVoted against background
checks for guns
ApprovalDisapprovalDifferenceChange since
prior poll
AZFlake3251- 19
AKMurkowski4641+5- 16
Prior rating5433+21
AKBegich4137+4- 6
Prior rating4939+10
OHPortman2634- 8-18
Prior rating3525+10
NVHeller4441+3- 2
Prior rating4742+5
NHAyotte4446- 2-15
Prior rating4835+13

On the other had, senator Toomey helped create the background checks bill. Senator McCain supported the bill, and is from the same state as senator Flake above. The most recent PPP question specifically asks about trust ratings comparing McCain to Flake.

StateVoted FOR background
checks for guns
ApprovalDisapprovalDifferenceChange since
prior poll
PAToomey4830+18 +7
Prior rating4332+11
AZMccain4524+21

Even a Fox News poll indicates collapsing support for those senators who voted against background checks for gun purchases: "Likely to support a politician who voted AGAINST expanding background checks"

More LikelyLess Likely
23%61%

Sources
Apr 29 PPP poll: More backlash against Senators on gun vote
Atlantic Wire: How Jeff Flake Became the Most Unpopular Senator in America
Business Insider: We're Starting To See Some Very Real Ramifications From Senators' Votes On Gun Control

Monday, April 29, 2013

Indiana GOP Legislature Micro-Manipulates Marion County Political Structure

In case you weren't paying attention, the GOP controlled Indiana legislature reshaped the Marion County city-couny council with the passage of SB621. Currently we have 4 "at large" councilors. Originally, back in 1969, these seats were created to consolidate GOP hold on the city, when it was believed a simple by-district representation would not give them the majority, even though, by-population, the mayor would always be expected to be Republican. The process was quite heated, and very obviously a political move to consolidate power. 30 years later, the removal of these seats is again a subversive political move--all 4 at-large councilors are currently Democrat, as is the council majority, while the mayor is Republican. Removing the at-large councilors, however, will give the council back to Republican majority.

Presuming this was a political move, it was incredibly short-sighted, in terms of strategy. Large urban cities across the country, including the Midwest, trend towards Democrat. Indianapolis is no different. Removing these at-large seats will not consolidate Republican power, as they hope. Presuming trends hold in the future, and the current state of the GOP nationally gives no indication of a change, Indianapolis will continue to move Democratic, and SB621 will serve only as a reminder of manipulative political tricks by the GOP in Indiana now that they have a trifecta supermajority.

http://www.theindychannel.com/news/politics/lawmakers-eliminate-at-large-indianapolis-city-county-council-seats

Friday, April 26, 2013

Testing for Data Normality in R

This tutorial uses the free, open-source statistics software R. It uses primarily command-line entry, not as simple as SPSS, but far more powerful.

For regression analysis, your univariate data (all of the continuous variables used, independent and dependent) has to be normal, equivalent to a bell-shaped, gaussian distribution. You can visualize the curve of this distribution with a histogram, verification of which can help you establish the normality of your data. Visualization with a Q-Q plot can also help verify normality. However, these "eye-ball" tests have never satisfied my empirical instincts, since what seems to be "normal enough" for one person, may not be normal to another.

While many in the social sciences still are not implementing, or at least reporting, these tests, without using normal data, the regression analysis may be invalid. Several tests exist for demonstrating data normality, with the follow-up procedures for non-normality being data transformation and/or removal of outliers. The Law of Large Numbers and the Central Limit Theorem provide some protection for your analysis if you have a large sample (n>30) even if your data is not normal, as do certain kinds of tests that are considered robust. However, it is good practice to try to get your data as normal as possible, since the math behind these types of analyses assume univariate data normality. The primary normality tests are as follows:

  1. Visual tests: Histogram and Q-Q plots
  2. Z-score tests for skew and kurtosis (Statistic/Standard Error for that Statistic)
  3. Specialized tests: Shapiro-Wilk, Kolmogorov-Smirnov, Jarque-Bera, etc.
  4. Filliben's test for Q-Q plot correlation
For the numerical tests, significance of the model typically requires your p-value > 0.05. This causes my students immeasurable confusion, since they have been taught in other classes, and in most instances of my class, that they should look for p < 0.05 to determine if a test is significant. The latter is for tests of means, such as ANOVA, or significance for their regression model and coefficient. In those cases, the null hypothesis is: the means are equal. If you are testing that one intervention is effective, and another one isn't, or that one group is different from another, you want to reject the null hypothesis, therefore, you are looking for p < 0.05. On the other hand, for data normality tests, the null hypothesis is: the data is normal, or the data is equivalent to a normal distribution. In this case, you want your data to be normal, since otherwise you cannot even proceed to the means test. You want to be able to "fail to reject the null hypothesis," i.e., you want p > 0.05.

USING DATASET “WORLD” VARIABLE “HIV” (UPDATED 5/26/2013)

Find the optimal transformation using Box-Cox procedure: generates the optimal λ (power)
(Note: The first version of this post recommended the command box.cox.powers, which has since been deprecated, so is no longer available.)
A handy way to obtain a good univariate lambda power value is from the AID package, that you will need to install.
library(AID)
>boxcoxnc(world$hiv)
The output is a best-guess lambda, plus the normality values for each of 7 common normality tests. Be certain to look at the plots that are generated. If the software can generate a reasonable lambda, then the plots will show a "peak" (either minimum or maximum). Sometimes a reasonable lambda cannot be determined within the -2 to 2 boundaries, and the plots will not peak for any of the normality tests, but will simply appear linear. In this case, it may be that the data cannot be transformed.

Generating a Box-Cox transformation: Generates [(xλ-1)/λ], where x is each value, and λ is a power
>bcPower(world$hiv, λ)

Produce histogram
>hist(world$hiv)
Importing text files seems to create non-numeric data problems. Import from SPSS works OK, as does importing from the clipboard using x.num <- as.numeric(readClipboard())

Produce qq-plot
>qqPlot(world$hiv)

QQ-Plot Correlation Coefficient (Filliben, 1975)
>qqp <- qqnorm(hivt) [also produces a qq-plot, but less informative than qqPlot]
>cor(qqp$x,qqp$y)
Interpretation for sample size at http://www.dm.unibo.it/~simoncin/QQCritVal.pdf

Remove missing data—some tests won’t work with missing data
>hivt <- na.omit(world$hiv)

Skewness and Kurtosis
Some argue that any skew and kurtosis < 1 represent normal data (assuming k-3, the value most software provides, since for normal distribution, unmodified k=3). Most use the Z-score of skew and kurtosis. If n < 200, then most argue that normal data is given by Z < 1.96, and if n>200, then Z < 2.58 likely represents normal data. For n > 2,000, these numbers often produce unreliable results. Z-Skew = [skew/standard error of skew], and Z-Kurtosis = [kurtosis/standard error of kurtosis].

In R, package e1071 produces skew and kurtosis, so will need to be installed and then activated. The default for these commands is "Type 3", which is the standard skewness formula adjusted for the sample size, called the "adjusted Fisher-Pearson standardized moment coefficient," modified by multiplying skew by: n/(n-1)(n-2). If you change this to "Type=1" then you generate the results from the standard formula, and if you change this to "Type=2" then you get the values produced by SPSS. You can also load the package "moments" to get a skew and kurtosis command that matches the hand-calculated values. Package e1071, Type=1, uses sample size = n-1, presuming you are using a "sample," while package Moments uses sample size = n, presuming you are using a population. Each of these methods used to calculate skew and kurtosis can produce different values, but which are relatively close to each other, and should be statistically insignificant.

Skewness and Z-skew
>skewness(hivt)
>skewness(hivt)/sqrt(6/n) [n=sample size]
or >skewness(hivt)/sqrt(6/length(hivt))

Kurtosis and Z-kurtosis
>kurtosis(hivt)
>kurtosis(hivt)/sqrt(24/n) [n=sample size]
or >kurtosis(hivt)/sqrt(24/length(hivt))

Specialized Numerical Tests: Empirical Distribution Functions
Kolmogorov-Smirnov test-Liliefors Correction (need to install library nortest)
lillie.test(hivt)

Kolmogorov-Smirnov (uncorrected)
ks.test(hivt,pnorm)

Shapiro-Wilk test (Royston correction)
>shapiro.test(hivt)

Jarque-Bera test (need to install library tseries)
>jarque.bera.test(hivt)

Robust Jarque-Bera test (need to install library lawstat)
>rjb.test(hivt)

Anderson-Darling test (from nortest)
>ad.test(hivt)

Cramer-von Mises test (from nortest)
>cvm.test(hivt)

Shapiro-Francia test (from nortest)
>sf.test(hivt)

Sunday, April 21, 2013

Comparing Between Regression Models: Aikaike Information Criterion (AIC)

In preparing for my final week of sociological statistics class, the textbook takes us to "nested regression models," which is simply a way of comparing various multiple regression models with one or more independent variables removed. In the example I'll be using in my class, we'll be looking at a dataset of Florida crime by county as a dependent variable, with the independent variables of urbanization, education, and average income. To evaluate the reliability of the independent variables to be able to predict crime rates, we can generate any of several regression equations, using all of the three variables, two of them, or just one.

Distinguishing the "best" equation is somewhat subjective, but statisticians have developed some criteria to evaluate whether one model is likely better than another. For my class we are using SPSS as our statistical software, since that's the licensed software on our campus (IUPUI). It's expensive, and even with our campus license, you have to "rent" it every semester you want to use it. I personally don't use it for my research, since, while it's a reasonable GUI option, there are many advanced functions that it just can't do, and its flexibility to alter parameters is limited. I use the free, open-source software R, which has a steep learning curve, since it is command-line, but far more powerful and flexible. I don't have my students to learn it because of the learning curve, and since most of them in their future careers will just want simple software that they can point-and-click to get reasonable results. They likely won't want to do any programming to get their results.

In my search for a way to allow my students compare "nested regression models" using SPSS, I spent a great deal of time Googling ways to get SPSS to generate AIC, and it just won't, except for logistic regression, or using the advanced Generalized Regression Models feature--both of which are great options, but the former is a specialized technique for probability outcomes, while the latter is not necessarily a good option for an introductory sociological stats class.

I found 5 ways to get SPSS to give me AIC, and I will teach the students 2 of those ways--one formula, and manually forcing SPSS to produce the regression AIC using syntax. I reproduce the 5 methods below, since there is no simple "checkbox" for regular linear regression in SPSS. Recognize that the linear regression method and the GZM (generalized linear regression) AIC produce different numbers. The absolute AIC number is not relevant, but only the difference in the AICs of different models--then choose the model that produces the smallest AIC.

In the equations below, n = sample size, k = number of parameters, SSE = sum of squares error (or residual sum of squares as listed in SPSS output)

  1. AIC formula #1 (same result as SPSS linear regression syntax)
    n*Ln(SSE/n) + 2*(k+1)

  2. AIC formula #2 (same result as SPSS GZM)
    2k + n [Ln(2(pi) SSE/n ) + 1]

  3. AIC formula #3 (same result as SPSS GZM)
    Requires you to obtain the log-likelihood, which in SPSS, you can only get using GZM (generalized linear model, see option #5 below)
    2*k – 2* loglikelihood

  4. SPSS method #1: Use linear regression syntax
    REGRESSION
    /DESCRIPTIVES MEAN STDDEV CORR SIG N
    /MISSING LISTWISE
    /STATISTICS COEFF OUTS R ANOVA Selection
    /CRITERIA=PIN(.05) POUT(.10)
    /NOORIGIN
    /DEPENDENT DependentVariable
    /METHOD=ENTER IndependentVariables separated by a space.

  5. SPSS method #2: Use GZM
    Click the following:
    Analyze --> Generalized Linear Models --> Generalized Linear Models
    Under the "Response" tab, put the outcome variable into the "Dependent Variable" box.
    Under the "Predictors" tab, put all continuous independent variables into the “Covariates” box--if you have any categorical predictors, those go into the "Factors" box.
    Under the "Models" tab, put all listed variables (independents) into the “model” box and make sure that as "Type" they are listed as "Main effects"

Saturday, April 20, 2013

Cookies

Indystar paywall cookies: collective-media.net etweb.indystar.com forum.whatismyip.com indystar.com

Thursday, April 18, 2013

Senate Vote Kills Gun Safety Bill--Did they Vote their People's Wishes?

The Senate just voted down a proposal to create a system of universal background checks on gun purchases, thus closing the so-called "gun show loophole" and "straw purchases." On an issue where 85-90% of the public supports such a bill, did those senators vote the wishes of the people they were elected to support? The vote was highly partisan--in a 54-46 vote, 4 Democrats voted against the bill, while 39 Republicans voted against it. Those senators are listed below, by state.

Some have argued that while the high support for background checks represent national majorities, they do not represent the states from where these senators come. However, that is simply not the case--the high support for background checks has held across states, in those polls that list results by state. For example, this recent poll shows that even in states that went for Romney in the last election, there is still high support for background checks. Republican senators in those states still voted against the bill.

SenatorStatePopulation supporting background checks for gun purchases
Alexander TNNo data
Ayotte NH89%
Barrasso WYNo data
Baucus MT79%
Begich AK90%
Blunt MO85%
Boozman AR84%
Burr NC90%
Chambliss GA91%
Coats IN89%
Coburn OK87%
Cochran MSNo data
Corker TNNo data
Cornyn TXNo data
Crapo IDNo data
Cruz TXNo data
Enzi WYNo data
Fischer NENo data
Flake AZ90%
Graham SCNo data
Grassley IA88%
Hatch UT83%
Heitkamp ND94%
Heller NV86%
Hoeven ND94%
Inhofe OK87%
Isakson GA91%
Johanns NENo data
Johnson WINo data
Lee UT83%
McConnell KY82%
Moran KSNo data
Murkowski AK60%
Paul KY82%
Portman OH83%
Pryor AR84%
Reid NV86%
Risch IDNo data
Roberts KSNo data
Rubio FL91%
Scott SCNo data
Sessions ALNo data
Shelby ALNo data
Thune SDNo data
Vitter LA85%
Wicker MSNo data

Tuesday, March 19, 2013

Published Again--Journal of the American Academy of Religion-Queer Sects in Rom 1

My second research article came out this month in the Journal of the American Academy of Religion. The first, from winter 2011, was in Journal of Biblical Literature. It looked at the historical and linguistic context of Roans 1:26-27, proposing that Rom 1:26, the only alleged reference in the Bible to "lesbians", is not actually a reference of lesbians at all. The entire chapter is Paul's attack on non-Yahwistic religions, and many of the references can be interpeted as allusions to the goddess cults. This most recent article builds on that theory, but demonstrates that the first millennium church's interpretation of this passage shows little indication of a "gay or lesbian" interpretation outside of the goddess religions context.

http://jaar.oxfordjournals.org/content/current "Queer Sects in Patristic Commentaries on Romans 1:26–27: Goddess Cults, Free Will, and “Sex Contrary to Nature”?"
J Am Acad Relig (2013) 81 (1): 56-79.

Abstract

This article provides evidence that Romans 1:26b–27 was interpreted by the early Christian church as a reference to the sexual practices of the goddess cults, and was used as an attack on polytheistic religions, not a reference to homosexuality. I clarify the rhetorical usefulness of the goddess cults for the early church in making the antipolytheistic case in relation to the Patristic contrast between free will and determinism. While the early church did not originally interpret Romans 1:26b as female homogenitality, a transition to this view is apparent as the Western Roman Empire began to collapse. A queer theory lens is incorporated into the discussion about Romans 1 by introducing cultural practices of gender, sexuality, and religion uncommon today.

Tuesday, March 12, 2013

Scientists vs. Creationism

I often find myself spending hours googling the same information, so I thought I'd post it here so I can save time in the future. Scientists do not support creationism. The following are polls of scientists on the issue. It doesn't matter that half of the U.S. "public" supports it, especially when we rank near the bottom of scientific and math literacy of all the industrialized countries (all of which have publics that reject young earth creationism, by the way).

Texas, 2008, "1,019 biologists and biological anthropologists on the faculty of all 35 public and the 15 largest private colleges and universities in Texas" (97.7%)
http://www.tfn.org/site/News2?page=NewsArticle&id=5621

Gallup, 1997, "Scientists," 1997 (95%-evolution)
http://ncse.com/rncse/18/2/do-scientists-really-reject-god

Pew, 2009, "Scientists," (97%) "humans have evolved over time"
http://people-press.org/reports/pdf/528.pdf

NSTA, 2007, "HS Biology Teachers" (16%-short earth creationism)
http://science.nsta.org/nstaexpress/10.1371_journal.pbio.0060124-L.pdf

Supreme Court, 1986, "Amicus Curiae Brief Of 72 Nobel Laureates, 17 State Academies Of Science"
http://www.talkorigins.org/faqs/edwards-v-aguillard/amicus1.html

American Scientific Affiliation, 2010, Scientists by speciality
100% of geologists and astronomers agree to old earth theory
88% of biologists affirmed evolution (7% did not answer the question, so only 5% affirmed short-earth creationism)
http://www.asa3online.org/Voices/2010/07/16/asa-origins-survey-with-correction/

Univ Cincinnati, 2002, "Ohio PhDs in natural or physical science", 90% "no scientific evidence at all for the idea of "intelligent design".
http://www.uc.edu/news/idpoll.htm

Friday, March 8, 2013

Forget your Yahoo Messenger Password?

Did you forget your Yahoo messenger password because you saved it years ago and have it automatically entered? You tried the "password recovery", only to discover that your "alternate e-mail" is no longer a valid email (or was fake to begin with), you don't remember your "secret questions", and you used a fake birthday and address (since it's none of Yahoo's business and you know they sell your private information). But now you are moving to a new computer and without the old password, you'll lose all of your old contacts and e-mails from that account?!

This happened to me, and there is an easy, if not incomplete solution. The problem is that none of the "password" recovery methods work, and Yahoo's "help" services are useless. After Yahoo started requiring personal information, like phone numbers, address, other e-mail accounts, etc, to gain access to your years worth of e-mails and Messenger chats, I found ways to bypass the security questions. The consequence is that the only way I have access to my Yahoo e-mail is to log into Messenger with the stored password, when the "you have mail" icon appears, I can click it, and it opens into my Yahoo email account. However, I cannot log directly into my Yahoo e-mail, because it says that my password is wrong, or expired. I don't quite understand how the "stored" Messenger password works, but I can't manually type it in and it doesn't work, but regardless, that's the situation.

So when I moved to a new computer and tried to set-up Yahoo, Messenger wouldn't accept the password. I KNOW the password, but the software doesn't accept it. Googling for ways to hack the software or the password was useless, and it appears that the current versions of Messenger are not "hackable." This is how I transferred my old stored password from my old computer and Yahoo Messenger to my new computer and updated Messenger.

1) Install Yahoo Messenger on the new computer. I was moving from Windows 7 to Windows 8, and had Yahoo v. 11 on the original and new computer.
2) Copy the old Yahoo files to the new computer--these were in ProgramFiles directory--my profile, old chats, etc.
3) The stored password is saved in the registry, so this has to be copied over.
a) On your original computer, open registry by clicking the start menu and "regedit"
b) Navigate to HKEY_CURRENT_USER\Software\Yahoo\pager -- right click, "export" and save it someplace handy.
c) Copy this file to your new computer and double-click.

That's it--it should automatically install. That's what worked for me. Now the username and password automatically populates the Yahoo Messenger fields again, and I can again access my Yahoo e-mail when the e-mail icon appears. It doesn't seem that you can extract the original password from the registry key (ETS), but copying the entire key to the new computer is sufficient to make Yahoo Messenger work again.

Sunday, February 17, 2013

Health Care Worker Deficits in Indiana

I recently became ill and tried to get an urgent appointment to my insurance assigned clinic. The scheduler told me they could squeeze me in, in three weeks. When I explained I was hoping for the next few days since I was in distress, I was told I could try to get a waiver to go to the emergency room. Not wanting to clog the already over-burdened Wishard ER, I offered to see anybody who was available, since I didn't have a preferred primary care physician. In fact, the last several times I have been to the clinic in the last 5 years, I have randomly seen whomever was available, including a nurse practitioner. Apparently now I was only able to see the exact physician I saw 2 years ago, whose first available appointment was in three weeks. After spending several hours on the phone complaining to every administrator I could find, I was finally given an appointment in 4 days (2 if you don't count the weekend).

This inspired me to investigate the geography of the physician shortage in the U.S. As a baseline, I looked at the international data about physician and nurse distribution. According to OECD data, the U.S. ranks 18th of top 20 OECD countries for number of physicians per 1,000 population (see Table 1). I obtained the physician and nursing data by averaging 2005-2010, and ranked countries by physician density.

TABLE 1: HEALTH CARE WORKERS BY COUNTRY (OECD)

CountryRankPhysicians
/1,000
Nurses
/1,000
Austria14.67.4
Norway23.913.9
Switzerland33.814.7
Spain43.74.5
Sweden53.610.9
Germany63.510.5
Italy73.46.2
Denmark83.414.5
France93.38.0
Israel103.35.0
Ireland112.912.7
Belgium122.914.8
Australia132.910.0
Netherlands142.88.3
Luxembourg152.711.0
Finland162.79.4
UK172.69.7
US182.410.6
New Zealand192.49.4
Canada202.39.0

Then I examined the U.S. by state, since research indicates that physicians tend to congregate geographically unevenly, typically based on compensation rates, so there is likely to be greater physician density in some states, and greater physician deficit in other states. Similarly nursing and other non-MD health care professionals are not evenly distributed. I used American Community Survey data (2011, 3-year estimate) to obtain number of health care workers by state for three specific occupations: physicians, registered nurses, and "other health care diagnosing and treating practitioners." I divided these by the 2010 Census total population for each state. Additionally, I obtained the number of residents per state over the age of 60 years, presuming this population will tend to utilize health care services far more frequently than younger populations. I list the relevant data for each of these groups per state (Table 2).

Additionally, I ranked each state by an index formula based on these four populations. Using a Z-score for each state for each group, I added the health care workers, and subtracted the older population, creating a composite score indicating the accessibility of health care workers per population in each state. In the composite formula, it seemed that physician totals were the most important for general health care access, so doubled that number in the equation. Each state's ranking is listed in Table 2, from least available health care workers per state, to most, per population.

Indiana ranks 40th (Graph 1). A 2012 study by the Association of American Medical Colleges states that, for Indiana, "These already severe shortages are going to become even more prevalent when considering that the number of Indiana residents over age 65 will double between 2000 and 2030, the segment of the population that uses health care services the most." The study specifically identifies, as of 2007, a 5,000 physician shortage for Indiana alone.

As a check on my health care worker index, I used CDC death rates to generate a comparison for validity. I used two measures, CDC infant mortality rates from 2007-2008 by state (CDC Wonder), and death rates (age adjusted) for all causes from 1998-2010 by state (CDC Wonder). My health care worker index had a correlation of -0.46 with infant mortality, and -0.55 for all-age death rates (see Graph 2), indicating a reasonably strong relationship--as the density of health care workers increases, both infant mortality and death rates go down, and vice versa. The correlation with the composite index is greater than any of the individual measures.

GRAPH 1: PHYSICIAN DENSITY BY STATE

GRAPH 2: RATES OF DEATH VS HEALTH CARE WORKER INDEX (CDC, 1998-2010, ALL CAUSES, AGE ADJUSTED)

TABLE 2: HEALTH CARE WORKERS BY STATE

StateRankPhysicians &
surgeons
/1,000
Registered
nurses
/1,000
Other health
practitioners
/1,000
60+ PopulationZ-Index
Florida502.58.62.723%-1.96
Nevada492.16.82.417%-1.55
West Virginia482.2102.923%-1.5
Arkansas471.99.42.720%-1.48
Oklahoma4627.92.619%-1.43
Arizona452.27.62.619%-1.38
South Carolina442.48.52.620%-1.25
Alabama432.19.72.719%-1.12
Mississippi4129.52.618%-1
New Mexico412.37.42.819%-1
Indiana402.2102.618%-0.95
Idaho381.97.13.117%-0.67
Hawaii382.77.13.120%-0.67
Georgia362.17.42.616%-0.63
Louisiana362.68.52.618%-0.63
Texas352.37.32.515%-0.58
Delaware342.9112.620%-0.47
Kentucky332.610.22.819%-0.45
Tennessee322.79.12.919%-0.38
Missouri312.89.82.920%-0.28
Oregon3037.53.120%-0.25
Michigan292.98.9320%-0.18
Illinois272.89.12.718%-0.16
Ohio272.910.52.920%-0.16
Maine263.410.5322%-0.14
California252.67.12.916%-0.13
Virginia242.78.2318%-0.1
Wyoming231.37.43.616%-0.09
Montana222.49.83.421%-0.02
Wisconsin212.410.23.219%0.01
North Carolina202.79.13.119%0.07
Iowa182.111.23.620%0.14
Kansas182.710.42.918%0.14
Utah172.26.62.813%0.36
South Dakota162.211.63.419%0.45
Pennsylvania153.510.73.421%0.67
New Jersey143.48.93.219%0.69
New Hampshire132.912.13.319%0.75
North Dakota12211.33.719%0.76
Rhode Island11410.32.920%0.79
Washington102.98.53.518%0.92
Connecticut93.910.23.320%1.2
Minnesota83.110.73.518%1.28
New York7493.419%1.31
Colorado62.88.33.716%1.39
Nebraska52.710.73.818%1.41
Maryland449.93.218%1.64
Massachusetts34.3113.319%1.77
Vermont23.311420%1.85
Alaska12.58.73.210%1.98

Tuesday, February 12, 2013

Gun Ownership and Firearm Homicide Rates

While tracking down a Facebook meme from MoveOn.org about the relationship between gun ownership and firearm murder rates, I fitfully realized that we don't have gun ownership data, thanks to legislation that forbids tracking of firearms. Research that claims to look at the relationship between gun ownership and crime base their analysis on 2001 data from the CDC's Behavioral Risk Factor Surveillance System survey from 2001, with a sample size of 201,881. Presuming people told the truth when asked if they have guns, and the CDC was able to contact a representative sample of people, they provide a margin of error rate averaging 1.8 (range 1-2.9)--however, given the nature of this population, I am skeptical of both of these presumptions. The CDC asked three questions--gun ownership, whether their guns were loaded, and whether their guns were kept in an unlocked place + loaded. Summary results can be found in the journal Pediatrics. Not to mention that this study is over 10 years old, and the world has become a very different place since 9/11, economic disaster, and a Black Democrat was elected to the presidency.

I found only one other possible source of data for gun ownership, a proxy measure from internet news, The Daily Beast, which, from 2011-2012, tracked the number of NICS background checks for firearms by state. The primary problem with this data is that each state can create its own policies for tracking and reporting background checks, so it is difficult to compare between state rates. For example, they mention that Kentucky mandates monthly checks for concealed weapons, creating a presumably inflated number, and indeed, the largest of background checks relative to the other states. Further, this is only a measure of background checks, which neither considers illegal weapons, nor whether the individual actually purchased the weapon. One might propose that both of these are random sources of error, equally distributed through states. However, given the vast differences between states, I doubt this can be considered equally distributed variance. There is a 0.38 correlation between the 2001 BRFSS gun ownershp study, and the 2012 Daily Beast background checks data. This is a moderate correlation, indicating that the data may have some reasonable level of consistency in predicting actual gun ownership, especially if combined into an index score.

A third source of estimation about guns is gun laws themselves, while, as above, not a measure of gun ownership as such, but one might presume that a breadth of gun safety laws would be associated with fewer guns. A 2010 report from Mayors Against Guns, Trace the Guns, tracked 10 different specific laws across all states. In the data provided below, I compiled this list into an index score from 0-10, with 10 being the maximum number of gun-safety laws, and 0 being none. There is a -0.36 correlation between gun laws and number of background checks, and -0.73 correlation between gun laws and gun ownership The first correlation is moderate, indicating that the more gun safety laws that a state has enacted, the fewer background checks that were reported. This could indicate either greater subversion of gun laws where they are more tightly regulated, or less interest in purchasing guns in states with more gun safety laws. The latter correlation is quite strong, indicating that in states with more gun safety laws, far fewer people admitted to owning guns in the 2001 CDC survey, than in states where there were fewer gun safety laws. Combined, these three measures are reasonably consistent with each other, although clearly problems exist in attempting to use these to determine actual gun prevalence rates in states.

Firearm death data is more reliable, although problems still exist. The FBI tracks national crime data as it is reported by states, and hosts that data publicly at the Bureau of Justice Statistics Web site. Critics are concerned that this data includes both suicide and self-defense data. I do not know if this assertion is true. However, regarding self-defense, Gary Kleck, a well-known pro-gun activist and researcher, indicates that multiple studies provide a range of 6-12% of firearm homicides that were likely "justifiable" homicides. A second source of firearm death data, from the CDC Web site, filters out suicide data, thus includes only "true homicide" rates, although it likely includes self-defense. However, given the relatively small representation of self-defense homicides, the impact on the data is likely marginal. In the data provided below, I used the total firearm homicides from 2006-2010, to control for specific, outlier years of high or low homicide rates. There is a 0.96 correlation between the 2011 FBI homicide rates, and the 2006-10 CDC firearm homicide rates. This indicates an incredibly high level of consistency between these two separate data sources, thus a substantial likelihood of reliability that these measures accurately represent non-suicide, firearm homicide rates.

Measuring criminally-inflicted death is one approach to the gun safety discussion. However, what seems more relevant are the total number of gun deaths, not just homicide. In terms of a direct, linear correlation, there is no clear relationship between gun ownership, background checks, or gun laws, and firearm homicide (correlations less than abs(0.15)). However, the picture changes dramatically when you combine all firearm deaths: homicide, suicide, and accidental deaths. In this case, correlations for each of the three gun-prevalence measures range from 0.25 for the number of background checks, 0.69 for gun ownership rates, and -0.68 for gun laws (Graph 1). So while there is only a small relationship between the number of background checks and firearm death per state, there is a very strong positive relationship between measured gun ownership rates and firearm death (i.e., as gun ownership goes up, so do firearm deaths), and a very strong negative relationship between gun safety laws and firearm deaths (i.e., as states enact more gun safety laws, firearm deaths go down).

Graph 1: All Firearm Deaths, 2006-2010 vs. Gun Ownership, 2001

Despite the poor bivariate linear relationship between gun-prevalence variables and firearm homicide, controlling for other variables generates a far stronger relationship. Specifically, both poverty and inequality contribute to firearm homicide rates, and controlling for these at the state level allows a strong relationship to appear (I also tested for 2010 urbanization but there seems to be little relationship at the state-level). Explaining the 4-variable relationship is complicated (see below). However, the 3-variable relationship between poverty, gun ownership and firearm homicides is easy to show visually with a scatterplot (Graph 2). Below you can see two trendlines, one for state poverty levels below 15% (blue), and the other for poverty levels above 15% (green). The negative slope of the line representing wealthier states indicates that higher gun ownership rates are related to lower levels of firearm homicide rates (35 states). The positive slope of the line for poorer states indicates that higher rates of gun ownership are related to higher rates of firearm homicide (15 states).

So while there isn't a simple bivariate linear relationship between gun ownership and firearm homicide rates, there is clearly a relationship once you control for poverty, and even moreso once you also control for levels of inequality. Looking just at firearm deaths in total, you don't even have to control for poverty and inequality to demonstrate a very strong relationship between gun ownership and firearm death. (Poverty data is from the 2011 American Community Survey, 5-year estimate, as is the inequality data. Inequality here is measured by the ratio of the number of households in the state living on less than $15,000 to the number of households living on more than $100,000.)

Graph 2: Firearm Homicides, 2006-2010 vs. Gun Ownership, 2001, controlling for poverty

Linear equation for the 4-variable interaction:
Firearm Homicides = -0.073 - 0.678(Inequality) + 1.068(Poverty) - 0.258(Gun Ownership X Poverty) + 0.042(Gun Ownership)
This equation explains 47% of the variance, p<0.000, and the Gun Ownership X Poverty interaction variable is significant at p<0.042.

StateFirearm Murder RateCDC Wonder, 2006-2010, Firearm Homicide (no suicide)CDC, All Firearm Deaths, 2006-10Background Check Rate/PopulationGun Ownership, 2001TraceTheGuns, 2010, All Gun Laws CombinedPoverty, 2011 ACS, 5-year Income Inequality: <15k:>100k, 2011 ACS, 5 year
Alabama717.1838851.7417.61.10452
Alaska0.0225352113.3181106857.809.50.243236
Arizona0.034730914514.8494431.1016.20.633314
Arkansas0.0377229085.215.2796855.3118.41.362871
California0.0327481614.38.6284221.3814.40.363118
Colorado0.0145158082.110.9783534.7412.50.440104
Connecticut0.0263010632.45.1633516.799.50.285685
Delaware0.0311804014.59.5312725.5511.20.385371
Florida4.512.2408124.5214.70.722721
Georgia0.0381915775.212.8444740.3216.50.721028
Hawaii0.0007352940.7312088.7910.20.311204
Idaho0.0108418371.212.3815655.3014.30.850145
Illinois0.0293819664.88.3793720.2813.10.483338
Indiana0.0282233193.811.2686739.1214.10.789097
Iowa0.0062376890.96.5468142.8711.90.709595
Kansas0.0255871012.710.5686442.1012.60.632461
Kentucky0.0230467853.213.25819647.7018.11.234068
Louisiana0.08868299110.119671144.1018.41.000716
Maine0.0090361451.18.6663440.5212.80.842797
Maryland0.0471077246.511223121.3890.241683
Massachusetts0.0186316431.73.5303312.6910.70.373835
Michigan0.045528126511.1412538.4715.70.755491
Minnesota0.0081070891.36.7784441.75110.443991
Mississippi0.0465116287.417.3708255.3121.61.558142
Missouri0.0460844885.313.6800241.7114.30.830348
Montana0.0070778561.615.31278657.7114.60.957503
Nebraska0.0230010952.28434738.65120.684228
Nevada0.0277674944.215.4504733.8012.90.468341
New Hampshire0.0045592710.67897130280.301071
New Jersey0.03059599635.193812.3109.40.262094
New Mexico0.029140359414.6649134.80190.940948
New York0.0229641862.75.11652181014.50.499277
North Carolina0.0351337184.612.2481141.3516.10.864843
North Dakota0.0089153050.68.71190150.7212.30.795394
Ohio0.029817113.59.5510232.4114.80.812913
Oklahoma0.03492402413.9909442.9016.31.003063
Oregon0.0104411381.410.8639839.8514.80.701096
Pennsylvania0.0370020474.210.6721834.7512.60.629322
Rhode Island0.0047483381.64.4210612.8712.80.560825
South Carolina0.0482162165.513.8632242.32171.00953
South Dakota0.0061425060.89.1997756.6013.80.890987
Tennessee0.0384494175.315.2763843.9216.91.044275
Texas0.0277976624.110.7526135.90170.61675
Utah0.0094066571.19.7782743.9311.40.425006
Vermont0.0063897760.99.3522742111.30.594292
Virginia0.025996753.510.6511935.1410.70.341807
Washington0.0117472121.88.9715733.1212.50.419157
West Virginia0.0232056132.713.91167055.4017.51.528704
Wisconsin0.0140671711.98.2843644.42120.615174
Wyoming0.0195035461.2161051759.7210.10.456652