Wednesday, October 29, 2014

Soda, Telomeres, Aging, and Statistics

Anderson Cooper recently highlighted a pre-print analysis of "telomere length" and drinking sweetened carbonated beverages (soda, pop, or coke, in the vernacular) on his Ridiculist. He even includes an interview with neurosurgeon/health reporter Sanjay Gupta. I'm currently teaching a statistics course, so I'm always on the lookout for cutting-edge, peer-reviewed research that may have statistics at the appropriate level for my class, and data that would interest university undergraduates. I downloaded the paper and pulled out the Results table.

The researchers' hypothesis is framed with the theoretical belief that telomere length is related to aging. I won't address that issue in this blog post, except to say that it's a controversial (and, in my personal opinion, poorly defended) proposition. I will limit my comments just to the statistics presented in this research, and specifically, just to Table 3--spoiler alert, I wonder if the editors of AJPH were impaired when they let it into the journal.

In the table, we see Models 1 and 2, which they describe in the notes. Model 1 is just age, gender and energy (which I couldn't find that they define--perhaps it's simple daily caloric intake?), while Model 2 includes a mish-mash of "healthy habits," such as healthy eating, BMI, smoking and alcohol, as well as some extra socioeconomic demographics, such as race, education, poverty level, etc. They compare four drinks--carbonated sugar-sweetened, noncarbonated sugar-sweetened, diet, and 100% fruit juice. They provide the quartiles, the b (regression coefficient; similar to the "m" or "slope" in the dreaded high-school algebra linear equation "y=mx+b"), and the 95% confidence intervals.

My first clue that something is amiss is that there is not a consistent linearity in the quartiles (not to mention that they don't provide Q0, the minimum--we don't necessarily need the Q0, but then why provide Q4, the maximum if you're not going to provide the minimum? it's just a consistency issue that doesn't affect the analysis but makes the table feel unbalanced and sketchy to me). Their regression is self-described as linear. However, the quartiles themselves are decidedly NOT linear--at least not for anything except the noncarbonated sugar-sweetened beverages, and the combined sugar-sweetened beverages index. That is problematic for me.

Let's ignore the non-linearity question for a moment, and just look at their base analysis, which is comparing the median values of the four beverages with their published b coefficients and confidence intervals. Let's ignore Model 1, since it's just demographics. Once you control for the everyday behaviors of the people in their study (they use data from the NHANES), they only have one variable that they claim reaches the level of statistical significance: people who consume sugar-sweetened carbonated beverages apparently have shorter telomeres. While it would be nice to overstate the other Model 2 coefficients, such as claiming that people who drink fruit juice have longer telomeres, and people who drink diet drinks have neither positive nor negative impacts on telomeres, it's statistically inappropriate to make those claims, since neither of those measurements achieved statistical significance (p<0.05), therefore we can completely ignore them. So let's compare the two extremes, based solely on the published medians of telomere lengths: sugar-sweetened soda with 1.13 & 100% fruit juice (diet soda median is equal to fruit juice) with 1.08. On the surface it looks like those two numbers are "different"--clearly they are "different numbers," but that doesn't mean that in "reality" in the general population they are different, since this output is based on a sample, and therefore an estimate. That's what "statistical analysis" does by definition--creates reasonable estimates of the general population based on samples.

Let's assume the sample meets standard scientific guidelines of randomness, etc, so we just have to determine if 1.08 vs 1.13 translates into an "actual" difference when applied to a general population estimate. According to the p-value of the coefficients, it does indeed appear to be different, but we'll get to that later. For now let's stick to the medians. Notice the spread from Q1, Q2 (the median), Q3 to Q4. Since they aren't linear, I'm not quite sure what the Q2 value actually represents. As any of my undergraduate statistics students can tell you, one of the data assumptions that must be met before you can do a regression analysis is data linearity--this non-linearity of quartiles makes me suspicious. Putting that question aside, I wonder what if the differences between the quartiles are "actually" differences, or if the non-linearity indicates that these are merely natural variation and there actually are not regular increases in telomere lengths from Q0 to Q4? We can't know that based solely on this study, but to me personally, I don't see that assumption is met given this table. Let's then take a leap and say--what if the actual Q2 measurement is anomalous, and perhaps a better estimate of Q2 would be to take an average of Q1 vs. Q3? In that case, the "estimated" Q2 for sugar-sweetened carbonated beverages is not 1.13 at all, but [(1.04+1.09)/2] 1.065, which is shorter than the telomere length of the fruit juice telomeres. This is actually what the researchers, in fact, predict with their regression equation--shorter telomeres with sugar-sweetened carbonated beverages. But this isn't necessarily self-evident, since the actual median shows that soda/pop has longer telomeres than fruit juice. Based on the presented median lengths, one could interpret that drinking sugar-sweetened sodas are actually better for you than drinking fruit juice! Granted, I'm not going to make that claim--although there is significant evidence that drinking fruit juice is not much healthier, if at all, than drinking soda.

Finally, let's look at the b coefficients. I always make a point for my statistics students to ignore any published data that doesn't include confidence intervals, since hiding confidence intervals (by not publishing them) is a GREAT way to completely misrepresent a data analysis to your benefit. One can't interpret any parametric analysis (like a regression coefficient) without the confidence intervals. In this case, it seems that in Model 2, sugar-sweetened carbonated drinks shorten telomeres (b=-0.010) compared to fruit juice, which seem to actually lengthen telomeres (b=+0.016). BUT! Remember that these are statistical estimates, and not "real" numbers--the real numbers for soda-related telomere shortening are actually somewhere between -0.020 and -0.001, and we can't know "actually" where without a 95% confidence of making a Type 1 error (i.e., claiming this result is real, when it isn't). So in reality, the authors aren't claiming that the "actual" telomere shortening is exactly -0.010, but almost certainly somewhere between -0.020 and -0.001.

Similarly, the alleged telomere lengthening properties of fruit juice isn't "exactly" +0.016, but likely somewhere between 0.000 and +0.033. So for my Introduction to Statistics students, I have them look at the maximum confidence value of the lowest measurement, and the lowest confidence value of the highest measurement before making an assessment. This means that the lowest likely value of telomere lengthening of fruit juice is actually 0.000 (i.e., no change at all), vs. telomere shortening of soda is -0.001. While "statistically" if looking at the p-values, that appears to be a measurable difference. But in reality, personally, I would interpret that as not at all an important clinical difference (I won't get into "effect sizes" in this particular blog post, but since they don't post the standard deviations, we can't calculate those, which I would guess come out to be completely insignificant).

What makes this difference perhaps even less relevant, is if one looks at the decimal points and takes rounding into consideration. If the soda telomere maximum confidence level is b=-.001, potentially that could be -0.0005. Similarly, the minimum telomore shortening of b=0.000 could be -0.0005; in other words, they could be basically the same value, depending on how they rounded! On the one hand, I would have liked to have seen that one extra decimal place to rule out that possibility. On the other hand, one can simply eliminate a decimal place, and then the maximum soda level becomes b=0.00 and minimum fruit juice level would also be b=0.00. So, in actuality, I don't really need to see that extra decimal place at all--I don't think these results support their conclusions, going by this table alone.

Sunday, October 26, 2014

Just over a week left before the 2014 election, with major shake-ups likely for U.S. Senate and Governor races. Why are 6 seats important? Because that's all the GOP needs to take control of the senate by 1 vote.

Comparing 6 different prediction sites, all agree that Republicans will pick up at least 6 seats, with the most likely being Alaska, Arkansas, Iowa, Kentucky, Louisiana and South Dakota. The only exception is Real Clear Politics, which is making the most conservative estimates (scientifically conservative, not politically conservative) and declaring many of the polling so close that they are still within the margin of error, so keeping them as "Toss-ups" (although they have published a "no toss-up map" that agrees with the other 5 prediction sites, that the GOP will pick up 6 seats). Of the other differences between the sites, one is Kansas, where Politico and Washington Post are calling likely Republican, whereas 538 and Princeton are calling "leans Independent". Both of the latter along with the Washington Post say Georgia is slightly leaning Democrat, and both Colorado and Louisiana are leaning Republican, each of which the other 3 sites still call toss-ups. Sabato at the UVa Center for Politics is still counting Kansas as a toss-up.

However, that number "6" is contingent on a couple of things. First, it presumes that Republicans would be certain of keeping all of the seats they currently control. But three of these seats are actually far closer than expected--Georgia, Kansas and Kentucky. In Kansas, Governor Brownback has made the Republican brand so toxic that the incumbent Republican senator may get kicked out of office, replaced by an independent who has not stated for whom he would vote for Senate leader, but he would not support either Reid or McConnell. So while McConnell's seat may end up being safe from challenger Lundergan-Grimes, unless Republicans vote in a new Senate leader other than McConnell, Orman might refuse to caucus with them, potentially giving Democrats a hail-Mary if the race is closer than expected. The second problem for Republicans is Georgia, where they lost their safe incumbent Saxby Chambliss, and now that race may turn into a runoff which 3 of the prediction sites are calling a toss-up, and the other 3 are saying leans slightly Democrat.

The second "6 seats to victory" contingency is that the close races will cut evenly between Democrats and Republicans. If the 6 states listed above all go for the Republicans, which all of the sites agree is likely, and they keep Georgia and Kansas, then they are safe. However, if either one of these states goes for the Democrats, then Republicans will need Colorado and/or Louisiana for the definitive win. Three prediction sites (Politico, Sabato and RCP) are not calling either of these races, leaving them as toss-ups as of Oct 26, while the other three sites (538, Princeton and WaPo) are calling them likely Republican (in the "no toss-ups" model, RCP calls both of these for Republicans). If Republicans bring in BOTH Colorado AND Louisiana, then they don't need Georgia or Kansas for the tie-breaker. But if three of these 4 states (GA, KS, CO, LA) go for Democrats, then the situation gets complicated.

First, if the Senate decision comes down to one seat, then Georgia or Louisiana may have to break the tie, and neither of those races may be determined by this election due to the nature of their system. For example, in Georgia, if none of the candidates break 50%, it requires a runoff which would be held in mid-January. Louisiana's potential runoff would be in early December. Second, if 3 of these borderline states go to Democrats, then the Senate would be tied, meaning Vice President Biden would be needed to break any tie votes.

So what all of this means, if that the Senate goes the way that all of these 6 sites predict, based on polling as of October 26, then control of the Senate in 2015-2016 will likely be Republican. The spoilers are Georgia, Kansas, Colorado and Louisiana. Republicans only need 2 of these for the win. But if Democrats get 3, Biden will be needed for a tie-breaker, and if they get all 4, then Democrats remain in control of the senate.

Friday, October 17, 2014

Health and Human Services Spending since 2003

The recent Texas Ebola outbreak has caused a political frenzy with mutual-blame casting by Republicans and Democrats. I looked up the official outlays as published by the U.S. Treasury. While we have had an increasing budget for several decades in terms of raw dollars, once adjusted for inflation and population growth, those budgets look increasingly anemic. Below are four separate budgets from the Health and Human Services, which is the agency most tied into U.S. health, as well as specifically preparedness for infectious disease control. The first two graphs are the total relevant budgets for the the National Institutes of Health, and Centers for Disease Control from 2003-2014, adjusted for inflation (in 2014 $), and population growth. Both budgets have dropped during the time period shown, from 2003-2014. The CDC budget has dropped from $20.07 per person in 2003 to $19.95 today. The NIH budget has dropped from $101.33 per person in 2003 to $97.57 per person today.

The second set of charts organizes the data slightly differently. Within the Health and Human Services Budgets, there are three separate agencies that engage in "health care research and training": NIH, CDC and HRSA. Two of these, the HRSA and CDC also have budgets for "health care services." I have aggregated these two categories of outlays, and depicted both the inflation-adjusted spending per-capita, and spending as a percent of real GDP. The health care research and training budget (inflation adjusted per capita) has doubled since 1979, but when compared to real GDP, spending on health care research and training as actually gone down, from 0.76% to 0.65%. Per capita, this budget has declined since 2003. Similarly, the HRSA and CDC budgets for direct health care have gone up (inflation adjusted per capita), not quite doubling from $24.35 to $40.21, but has decreased when compared to our real GDP, from 0.37% to 0.25%. Per capita, this budget has remained approximately the same since 2003.

YearCDC Outlays (Inflation-Adjusted/Capita, 2014$)NIH Outlays (Inflation-Adjusted/Capita, 2014$)CDC Total Outlays (in millions)NIH Total Outlays (in millions)Health Care Research and Training (CDC+HRSA+NIH; Inflation Adjusted Per Capita)Health Care Research and Training (as % of Real GDP)Health Care Services (CDC+HRSA; Inflation adjusted per capita)Health Care Services (as % of Real GDP)Health Resources and Services Administration-Health care servicesHealth Resources and Services Administration-Health research and trainingCenters for Disease Control and Prevention-Health care servicesCenters for Disease Control and Prevention-Health research and trainingNational Institutes of Health-Health research and trainingCPIPopulation (in millions)
19793.6343.67238335286956749.410.76%24.350.37%141701432164818294755388286956768.3225.06
19803.4642.64261502322230548.940.75%22.480.35%150039241274119836263140322230577.8227.22
19813.5242.23300028360380548.520.74%19.810.30%147010645751922034179687360380587229.47
19823.4639.24323137366469544.220.68%19.080.29%154556337831523637686761366469594.3231.66
19833.7038.36361590374964942.370.61%15.090.22%117438933060830083960751374964997.8233.79
19843.5140.47360128415729442.700.58%14.660.20%1202976171545303007571214157294101.9235.82
19853.4343.52368489467026445.920.60%14.260.18%1223635195573306481620084670264105.5237.92
19863.8245.46429378511453748.170.61%14.290.18%1249201234533358427709515114537109.6240.13
19874.0545.34466027522219448.100.58%14.220.17%1234942254067402556634715222194111.2242.29
19885.0852.38613764633394154.770.64%15.380.18%1308047226809551422623426333941115.7244.5
19896.4554.72823573699199057.290.65%15.240.17%12298632230207176851058886991990121.1246.82
19907.6155.111034752749189457.560.65%16.700.19%13705161988568995831351697491894127.4249.62
19917.7552.671128102766715654.900.61%17.060.19%146746521155710153931127097667156134.6252.98
19927.9155.341198123838074357.840.61%19.150.20%183169924846710689901291338380743138.1256.51
19938.9260.211412925954031962.800.65%20.510.21%199351925414912565521563739540319142.6259.92
19949.5561.7515709391015527864.320.64%22.370.22%2257521271948142113514980410155278146.2263.13
199510.4463.6117860001088300065.950.64%22.430.22%2213000239000162500016100010883000150.3266.28
199612.1957.4621670001021700059.970.56%31.180.29%3537000288000200800015900010217000154.4269.39
199712.1360.3922490001119900063.150.56%27.530.25%3023000345000208200016700011199000159.1272.65
199812.6565.5924100001250000068.150.58%27.440.23%3042000265000218700022300012500000161.6275.85
199912.4070.4924300001381500073.050.59%29.030.24%3481000280000220800022200013815000164.3279.04
200012.4475.7125320001541500078.360.62%30.470.24%3884000328000232100021100015415000168.8282.16
200115.2780.8832570001725300084.370.66%33.050.26%4065000472000298500027200017253000175.1284.97
200216.3693.9135630002045000098.530.76%37.490.29%5012000596000315200041100020450000177.1287.63
200320.07101.33452300022834000106.170.78%41.790.31%5323000661000409400042900022834000181.7290.11
200418.60110.54431100025626000116.720.84%40.800.29%54820001097000397600033500025626000185.2292.81
200518.79112.58452800027123000116.650.81%42.110.29%5884000712000426000026800027123000190.7295.52
200618.25109.79461500027771000114.050.77%40.350.27%6087000582000411900049600027771000198.3298.38
200721.02107.95548000028138000111.950.75%42.070.28%5886000644000508000040000028138000202.42301.23
200820.95106.13574900029123000109.310.75%43.070.30%6268000672000555000019900029123000211.08304.09
200922.14107.79613000029847000110.970.76%44.890.31%6465000713000596400016600029847000211.14306.77
201023.81115.40682200033068000119.390.80%48.410.32%7078000111600067940002800033068000216.69309.33
201122.00117.17645400034370000119.780.79%48.320.32%75440009420006630000-17600034370000220.22311.59
201221.59107.77656700032781000112.480.73%45.080.29%7755000824000595700061000032781000226.67313.91
201320.2999.52631600030976000104.830.66%40.880.26%7303000753000541900089700030976000230.28316.16
201419.9597.57636400031124000103.920.65%40.210.25%76670008230005159000120500031124000233.92319

Saturday, October 11, 2014

District 29, 2014: Delph vs. Ford

Last February, the current state senator from District 29, Mike Delph, had a twitter meltdown about the same-sex marriage constitutional amendment shenanigans at the Indiana state house, that arguably (along with some other issues) caused the Senate leader to impose several sanctions against Delph, which included moving him to the back of the chamber thus forced to sit with the Democrats, removing him from leadership roles, and taking away his press secretary. Shortly thereafter, a Democrat competitor arose from his district, JD Ford.

The district has traditionally been solidly Republican, but with the 2010 redistricting, and steady urbanization of that region, I wondered if the situation would have changed. Using Tiger geographic redistricting shape files , Indiana election results for 2012, and ACS population data, I created some estimates of who voted for whom at the precinct-level in 2 specific races--Attorney General, and School Superintendent. As background for these races, AG Zoeller was running for his 2nd term, being active in far-right political cases (like submitting anti-gay-marriage amicus briefs to various cases around the country), and School Superintendent Tony Bennett had been a charter-school activist while in office. Zoeller won his 2012 run, but Bennett lost to Glenda Ritz, despite the GOP sweeping the rest of the offices in the state, including electing a supermajority in both Senate and Representatives chambers.

In what is now the newly redistricted D29 for senate (some of these counts are estimates based on precinct line changes), Zoeller (R) won approximately 57% of the vote, while Ritz (D) won approximately 51% of the vote. There are several factors working in Delph's favor. First, he has an incumbent advantage. Second, he has the party advantage for the "6 year presidential itch;" i.e., in the 6th year of an incumbent president's term, the "other" party (in this case, Republicans) tend to have an advantage. Third, he has a turnout advantage--midterms tend to favor GOP. Fourth, he has the numbers in this district--Zoeller beat his competitor by a wide margin.

However, several factors may also come into play. The hit that he took by the senate majority leader, and Delph's very public meltdown about the same-sex marriage issue, which was accompanied by several very insulting remarks about what makes a "true" Christian (i.e., "any professed Christian minister that teaches any sin is acceptable is NOT acting in true love but in eternal condemnation #truth"), and arguably "crazy" comments on several other issues, may cause voters in his district to question is ability to represent them. While midterms already have miserably low turnout (usually about 1/3 of registered voters come out to midterms in the Marion County area), some typically stalwart GOP in his district may be incentivized to stay home rather than be forced to choose between Delph and the gay Democrat. And as demonstrated in the 2012 elections, D29 voters are more than willing to vote for the Democrat if they like the candidate, rather than simply doing a party-line vote.

Any of these factors could shift the vote in Ford's direction. Additionally, the demographic numbers that we have for that district are largely from the 2008-2012 ACS--but that entire area is dramatically and rapidly changing, largely in the direction of younger voters, and race minorities, so the influx of new residents, if they register and vote, will mostly be potential Ford voters, especially when given the choice between Delph and Ford.

In terms of raw numbers, there are about 100k residents in D29 over 18 (2012 ACS 5-year estimate), with an average age of 36, 52% female, and 69% White. But these estimates are 2-6 years old--a lot has changed in 2 years in that area. According to my redistricting estimates above, there were about 60k votes cast in the 2012 election, so perhaps half of that will show up for the midterms. Much of the new housing in the area, because of the recession and housing price bust, has been apartments, which will bring in voters more inclined towards Democrats (younger, poorer, single females).

Maps of the redistricting, by % of vote for AG and School Superintendent, are below.

Sunday, September 14, 2014

Indiana Gubernatorial Elections: 1980-2012

There was much abuzz about former Indiana governor Bayh running again for governor--recently deflated when he announced definitively that he was not running. For the last decade, Indiana has been solidly red, and since I was in California & Illinois for several years prior to that, I had little memory of Indiana politics before Mitch Daniels. Presuming I can trust the voting record supplied by Wikipedia (don't tell any of my students), I plotted the Indiana gubernatorial elections since 1980 by number of votes. I also included the winning governor's name, and highlighted if he was an incumbent (bold, and "-I"). In 2012 Republican Mike Pence won with less than 50% of the vote, although the Indiana Republican representatives won a super-majority in both houses.

While there was a 3rd (and once, a 4th in 1984) party candidate on the ballot for all years except one (1988), none of the races could have been won for the losing candidate if all of the 3rd party votes were cast for him instead.

What is interesting as I look at the graph, are the three surges, one for Democrats in 1992, which saw an 18% jump in Democrat voters compared to the previous election (with a 22% fall in Republican voters), and two subsequent Republican surges in 2004 & 2008, with, respectively, a 30% & 17% increase over the previous election, and very little fall in Democrat votes. In 2012 there was a plummeting of Republican votes, with a 23% fall, and a slight increase in Democrat votes. On the one hand, Indiana likes to re-elect their governors (at least for the last 32 years), indicating that Pence should win a second term in 2014. On the other hand, given that Pence won with less than 50% of the vote, and his policies since then have been very unpopular, it may spell trouble, especially given the demographic shifts as the youth cohort which has been voting strongly Democrat, only continues to increase.

What would be interesting is to elucidate the factors that caused the 2004 & 2008 Republican voter surges. The 2012 drop is understandable, since the election after the incumbent leaves typically shows a significant drop, while the second year typically shows a significant increase, except for Orr's reelection in 1984.

Friday, September 12, 2014

Al Jazeera News Loading Problems

For quite awhile (at least a year or two) I've had network problems accessing Al Jazeera news--I don't know if it's a bad interaction with my server, if it's their server, if it's my computer/browser, the CIA, etc--but whenever I try to click on a link for Al Jazeera news, I get a blank screen for about 30 seconds until it finally loads, which means, for all practical purposes, I stopped using Al Jazeera once this started happening. Historically, for my Africa class, I liked to get Al Jazeera articles about Africa news to have the students discuss, to represent a non-US perspective the students likely aren't exposed to. Today I wanted to see what they had been saying about ISIS, and had the same problem. I noticed in the bottom left corner of Chrome that "waiting for j.maxmind.com" appeared while the screen was blank for the 30 seconds wait. The maxmind.com website wasn't very informative--I have no idea why Al Jazeera is calling on maxmind, or why it isn't allowing the Al Jazeera news to load into my browser. Regardless, I used adblock to completely block anything from maxmind, and Voila!! Al Jazeera now loads immediately.

Sunday, August 31, 2014

My Experience with a Windows Phone--and Subsequent Invasion into my Personal Life by Microsoft

When my last phone, an Android, got accidentally laundered when I forgot to take it out of my pants pocket, of course the phone came out of the washer dead and unrevivable. Looking through reviews for cheap phones, the Nokia Lumia 520/521 had positive experiences listed on several sites, so that's what I went with. While I saw that it was a "Windows" operating system, I didn't realize what that meant, nor how fundamentally different it was from the Android system. Not only was the interface completely different, but the phone is locked down in different ways that prevent users from creatively interacting with their purchased device outside of the tight grip of Microsoft surveillance and control. For example, with my Android phone, I was able to root it and side-load apps. For the uninitiated, this means that, rather than going to Google Play, or other "approved" sites, and logging in with my contact information (which includes my phone number, credit card, etc), I could download apps anonymously and install them manually on my phone. While this may seem like an unimportant feature to a youth cohort who seems to have no concept of privacy, for those of us old enough to have experienced "life before the internet," and who have read Foucault, it seems only intuitive for us that it's no corporation's or government's business what we do with our own private property, including our phones.

My personal response to the many ways that my new Windows phone was locked down and fundamentally connected to the Microsoft surveillance system, was to delete every app that I didn't absolutely need, and shut down everything else--I kept the text function, and phone calls, that's about it. The lockout of side-loading, and the inability to do anything without being formally connected to the Microsoft system was similarly frustrating, and felt incredibly invasive. But even prior to learning about those privacy issues, my very first experience was traumatizing, when I learned that the Windows system was fundamentally incapable of importing my previous phone information from Android (actually I had installed Cyanogenmod)--therefore, I lost ALL of my contacts and text messages in the phone switch. Less than a year after I purchased the phone, the buttons stopped working. This information transfer problem was compounded when I wanted to ditch the Windows phone to go back to Android, and again, NOTHING could be transferred. Web page after Web page, and phone call after phone call to help desks to the various relevant companies, said the same thing--Windows Phone doesn't have a way to export text messages to be saved onto your computer.

In my exploration for options to save my text messages, the closest option I found was to "sync" my phone with "the cloud." Given that I grew up with a phone attached to the wall with a cord, and we carried our information around on floppy disks, where clouds meant rain, and water destroys floppy disks, I have an inherent distrust of mixing my digital information with "clouds," not to mention the utter lack of privacy or digital safety when my personal information is stored on a remote server--especially one operated by Microsoft, the #1 practice target for hackers. Because of this lack of trust, as well as a refusal to assist the various corporations of the world to piece together my complete biography and daily movements via my phone, I do not have a "microsoft" account where my information is cloud-stored. Of course, without this account, I was unable to download ANY apps to my smart phone, as previously mentioned, already inherently limiting the utility of the device. But in this emergency situation, where the only possible way to save my text messages was to sync them to Microsoft cloud, I began the journey into invasiveness.

First, in order to create this e-mail account (a live.com account), I had to hand over to them "other" completely irrelevant personal information--like my phone number, another e-mail number, my birthdate, gender, etc. Suddenly my ONE e-mail link to Microsoft became FIVE personal links. I googled the phone number of an enemy, and gave them that phone number. Then I created a bogus account with yahoo that I would only use for this one-time event. And just in case they were recording my IP address to get my home address, I used TOR Browser, which gave them an IP address in the Netherlands. Of course Yahoo also now demands a phone number and alternate e-mail--more fake information was supplied to fulfill their demands.

Second, once you create the account, Microsoft doesn't let you use your new account. In order to make sure that your supplied phone and email aren't fakes, you have to go to that alternate email to get a special code they send to that email, before Microsoft will let you do anything with the Live account, which contains several online Microsoft Office software, such as Outlook, OneNote, Excel, etc. In order to save my text messages, I had to sync my phone with this online version of Outlook. Much to my chagrin, this feature of Outlook, where you could then obtain your phone text messages, had been removed!!! After more Googling, the process was still in Outlook, but hidden. So with some convoluted fixes to the "options" button, I was finally able to get ONLY the last 2 weeks of text messages, which could not be mass selected for copying to a text file--not even entire conversations could be selected. They could only be opened one message at a time and copy/pasted into a document. Regardless, nothing interesting happened in my life during the previous 2 weeks, and since the prior 8 months of texts were ultimately unavailable, the entire day's effort was wasted and all of my previous information was lost. The exception was my contacts--this process allowed all of my contacts to be "synced" to the "cloud," but I couldn't do anything else with them except manually type them into the new phone. The help desk for my phone company, however, had an alternative solution--link the old and new phones together using bluetooth, and export each contact ONE AT A TIME. That was eventually what I had to do to save my contacts.

At this point I was done with Microsoft and wanted to erase the tracks I had created on their databases. I had already killed my fake Yahoo account that I had supplied to Microsoft to open the Microsoft account. Now that I wanted to delete my Microsoft Live account as well, I was faced with yet another invasive roadblock--even though I had supplied the appropriate password to get into my Microsoft Live account, they would not let me get into the area of my Live account where I could cancel the account, without sending ANOTHER security code to the stored alternative e-mail address to verify my identity!!! Since I had already killed that account, I had to create a second fake yahoo account to receive my security code. However, Microsoft had installed a 30-day waiting period between switching alternate e-mail accounts--they would not send my security code to my new alternate e-mail since I was within that 30-day window (this was within about a 12-hour period). This feature prevents me from doing ANYTHING on the Live account now that I had attempted to switch e-mails within that 30-day period, unless I re-verify myself from that old e-mail address that I had already terminated. So not only was I forced to create this Microsoft account linked to my personal phone in my attempt to export my personal text messages to my computer, but I was forced to give invasive personal information and unrelated user accounts, now they were preventing me from terminating the account!!!

I'm sure, to anybody younger than 25, I sound like the archetypal old man with his cane who yells "get off my lawn you darned kids," on the one hand, or on the other, the crazies who build nuclear shelter bunkers in their backyards. But this invasiveness, just to use a phone, is unacceptable. The ways that our personal lives are being forced to be connected into corporate databases is unacceptable. Humans are not commodities, nor is our information, or personal communications. In the college classes I teach, where I require a research project, students often choose to look at the impact of social media, and are astonished to discover that graduates personal social media information is available to employers and possible employers--those pictures of you throwing up at a frat party are in "the cloud" and when you submit job applications, human resources offices check that cloud for reasons why you shouldn't be a part of that company. What astonishes me, is that anybody is astonished that when you put your entire life into public domain and into the hands of corporations, that it can have tremendously negative impacts on your private life.

Saturday, July 19, 2014

Craigslist RSS feed

I learned something new today from the Pocket Your Dollars web site, googling "craigslist notifications." In my house renovations, last week in my dining room I finally ripped out the cheap, 40-yr old cabinets that had been installed and somebody came to take them away today (courtesy of a frustrating week getting no-shows from my craigslist ad). The family who moved here in the 70s relocated the kitchen into the original dining room, since it's a larger space, and installed these cabinets. I'm making it back into a dining room, as was in the original house design. In the space where the cabinets were, I'm going to put a buffet, which I am hoping to find from Craigslist--specifically, a nice Victorian-style buffet. Of course the ones currently listed are all in the $400 range, and my budget is about $50. Thanks to the aforementioned Google search, I discovered that you can tell Craigslist to send you an RSS feed. What is an RSS feed--I don't know either, I just followed the instructions to connect the Craigslist RSS feed to my Microsoft Outlook, and sure enough, a new section appeared in my Outlook, so everytime somebody posts here in Indy for a cheap buffet, it automatically notifies me!!! I'm very excited.

Black vs. White Student Suspensions-Preschool Discrimination

This year in March, a Department of Education study showed that Black preschool students faced higher rates of suspension than White students (full report here). In my Introduction to Sociology class, two sub-tasks I have is to 1) introduce the concept and issue of race/ethnicity, and 2) introduce the concept and use of statistics. This year I am using the findings from this study as the basis to accomplish those goals. In the process, I brought in some additional data--the percent of Black legislators per state, and the Black/White income disparity ratio per state. The larger pedagogical goal is to reinforce the concept of "structural" causation. We in the U.S. are far more likely to presume individual-level causation, which is already an inherently human feature that psychologists have described as the Fundamental Attribution Error. However, our culture magnifies this effect compared to other, more communitarian-type cultures. So while the U.S. 'person-on-the-street' may look at the profound differences between Black-White student suspensions and automatically presume individual-level causation (especially if you are White), i.e., "Black students must misbehave at higher rates than White students," the utility of structural-level analysis can show that a large percent of the state-level variation in school suspensions can be explained by factors other than individual-level behavior. Here I present an introduction to structural-level analysis, by way of different types of graphs and a mini-introduction to regression analysis--basic tools of the trade for quantitative sociologists.

First, the recent Department of Education report indicates that the difference between Black-White student suspensions starts as early as pre-school. Graph 1 shows that data as presented in the original report. The first column shows basic differences in pre-school enrollment between various race/ethnic groups in 2011-2012, and subsequent columns show rates of suspension, also divided by race/ethnic group, the first-tie such data was collected at the pre-school level. The original data can be found at the Dept of Ed web site. While state-level data is available, unfortunately, it is not in a single spreadsheet, but reported separately for each state, so the process of compiling state-level dataset for all 50 states is tedious, but it is what I use for the analysis below. Using the difference between enrollment and suspensions, I create a variable, "Black student suspension ratio," which is the level of suspensions as measured against enrollment. For example, in the national pre-school graphic below, Black student enrollment is 18%, while Black student 'Out-of-school suspension (multiple) is 48%, leading to a ratio of 267%--i.e., Black students were suspended at a rate of 267% more than their enrollment. Conversely, White student enrollment was at 43%, while suspensions were at 26%, leading to a ratio of 60%--i.e., White students were suspended at a rate of 60% of their enrollment. An 'individual-level' interpretation of this data might be 'White students misbehave at far lower rates than Black students,' presuming that no racial discrimination occurs in the process of assigning student suspensions. The following analysis tests that hypothesis--or rather, tests whether structural-level factors provide a better explanation for individual-level factors

As Graph 2 shows, also directly from the Dept of Education report, racial disparities in student suspensions occur beyond the pre-school level. The data I use for this analysis relies on the reported suspensions for all grade levels. Graph 3, which I have produced using Microsoft Excel, is an "area graph" that compares two variables at the state-level--the Black student suspension ratio (blue, indexed to the left along the y-axis) and the percent of Black state legislators (orange, indexed to the right). I also had Excel plot a trendline for both of these variables, which is the dotted line above the colored areas. One can notice several things from this graphic. First, I have sorted the state data from lowest to highest rate of Black student suspension ratio, with Maryland having the lowest rate, and Minnesota having the highest rate. Comparing this with % Black state legislators, a general trend can be seen--the higher the % of Black state legislators, the lower the rate of Black student suspensions, and vice-versa. This does not necessarily imply causation, but a relationship is apparent (more about this later). Second, one might notice that all 50 states are not represented on this graphic. One of the problems with voluntary data collection is that not all states report. In this case, many states simply did not report suspension data. I have not attempted to impute or recover missing data in this analysis, but use only the data reported to the Department of Education for 2009-2010.

Graph 4 shows a scatterplot of this same data--also using Excel. In this case, the presumed independent variable, % of Black legislators, is reported on the x-axis, and the presumed dependent variable, Black student suspension ratio, is reported on the y-axis for this plot. Each dot represents a state. The correlation is reported on the graphic as -0.68, which is a strong negative relationship between these variables. This means that as the % of Black state legislators increases, the ratio of Black student suspensions go down, and vice versa. The trendline helps visualize that pattern. Interpreting the relationship between these variables require additional data and hypothesis testing, and in class I facilitate a brain-storming session, where the students come up with various explanations, each of which, I clarify, become 'testable hypotheses' that typically require the collection or analysis of additional data to decide which hypothesis among them has the strongest support. In this case, my personal hypothesis is that political representation grants a greater level of equality at the group-level. In other words, states where the Black population has more political representation at the state legislative level, have a greater capacity for the implementation of policies that ensure the implementation of racially-fair policies in schools. Alternatively, or reciprocally, the greater percent of Black legislators can also imply that group has, in general, a greater reserve of organizational capacity and community-level political activism, which can be seen in agitation for equal treatment in local schools.

The next variable I add into this analysis is data from the 2010 American Community Survey, specifically, a comparison between Black median income, and White median income. Just as the comparison of Black suspensions vs Black enrollment generates a ratio, the income data also generates a ratio that can be plotted. Graph 5 shows the scatter plot between the presumed independent variable on the x-axis, Black-White median income ratio, vs the presumed dependent variable on the y-axis, Black student suspension ratio. As above, each dot represents a state. For example, in the top left-hand corner is a dot that represents Minnesota, with the highest Black student suspension ratio for those states reporting, with 582%, meaning that Black students are suspended at almost 6x their rate of enrollment, as well as the lowest Black-White median income ratio of all states, with 48%, meaning that the average Black worker in Minnesota makes less than half of the average White worker. The state with the highest (most equal) Black-White median income ratio is Arizona, where the average Black worker makes 78% of the average White worker, and are also represented on the lower half of Black student suspension (197%). Like the graph above, for % Black state legislators vs. Black student suspensions, there is a negative relationship between these variables (r=-0.54), meaning that the lower the Black-White median income (i.e., the lower the level of racial income equality), the higher the Black student suspension ratio. Like the in-class process above, I show this graph to the students and facilitate a brain-storming session where they come up with various testable-hypotheses to explain the relationship between these variables.

It is important to remember that correlation never implies causation. Like the fundamental attribution error mentioned above, when we intuitively 'want' variables to be related by causation, we tend to see the above graphs and presume that % state legislators, and income inequality are contributing to causing unequal Black student suspension ratios. However, since correlation never implies causation, we must resist the urge to presume a causative mechanism. On the other hand, regression analysis, a separate (but mathematically related) statistical process, can be used to imply causation. In this final section, I pull together all three of these variables into 'multiple regression.' While I typically do not personally use the IBM software SPSS for my statistics (I use the open-source software R), SPSS has a relatively easy learning curve, and is available at our campus bookstore (and online) for the IUPUI students, so for the in-class example, I use SPSS to generate the graphic for this analysis, and walk the students through the meaning of several of the key output statistics. The equation for this model, roughly is thus:

Black student suspension ratio = X * % Black state legislators + Y * Black/White Median income ratio

The implication, based on the above correlations, is that there is a relationship between these three structural-level variables. Further, the mathematical/social interpretation of regression, if found to be statistically significant, is that causation can be implied between the independent variables (% Black legislators and Black/White income disparity) and the dependent variable (Black suspensions), which makes it a fundamentally more useful analysis than simple correlation, where causation cannot be implied. In the SPSS output show, I have circled several important parts. First, the "Adjusted R Square" of 0.713 implies that around 71% of the variation in Black student suspension rates in the Unites States can be explained *SOLELY* by these two structural variables of political representation and racial income inequality. Put more clearly, one does not need to presume individual causation in order to obtain a reasonable prediction of what a state's Black student suspension rate will be--i.e., one does not need the (implicitly racist) original hypothesis that "Black students misbehave at higher rates than White students" to explain Black student suspensions. The implication of this is that persistent racial discrimination is adequate to explain the differential rates of suspensions, and not "individual student" factors. What may, at first intuition, seem to have relatively little relationship with school suspension rates--political representation and income inequality--turn out to be powerful predictors, and, in fact, causative forces. Second, and finally, for this "brief introduction" to SPSS for my intro class, I also have them look at the "Standardized Coefficients-Beta" column in the graphic. Respectively, they are -0.665, and -0.703. Without going into a complicated discussion of how to interpret these numbers, they imply that both of these independent variables contribute approximately equally to the Black student suspension ratios, and both are related in negative ways. In other words, like the correlation results, the 'negative' relationship implies that as % Black legislators, and income equality ratio goes up, that Black student suspensions go down, and vice versa, and both of these factors have approximately equivalent causative force. If, for example, % Black legislators had been -0.3. while income ratio had been -0.9, it would imply that income ratio is far better predictor, about 3x more important, than % Black legislators. Here, however, the numbers are close, implying similar levels of predictive use.

For book-length treatments of these issues, I have used two relevant ethnographies in various classes. First, Bad Boys: Public Schools in the Making of Black Masculinity , by Ann Ferguson, who studied the ways that Black and White students were treated differentially in the classroom. Second, a more recent contribution from a Berkeley researcher, Punished: Policing the Lives of Black and Latino Boys , by Victor Rios, who embedded himself with youth in Oakland, CA.

Saturday, July 12, 2014

US Education Data

I spent three frustrating days trying to find data that one would think would be readily available--any kind of education data to compare states going back before 1980.   In fact, I had a hard time finding a good way to compare states prior to a decade or so ago, since standard metrics have changed multiple times.  Even contacting federal education data centers, I was given disappointing answers.  While I thought the Census factfinder web site would be a reasonable resource, their education data only goes back to 2005, prior to which it becomes very much more sparse.  While other resources for education exist on the Census site, much of the pre-1990s data is simply poorly scanned pdf files from the original paper Census books!!  Below is data that I collected for a separate research project (time-series analysis of variables related to political polarization)--I post it simply for anybody else for whom it may prove useful, without having to manually enter page after page of pdf-scanned census data from a different book for each year.  The top table is the original data that I compiled, while the 2nd table contains mostly interpolated data for my other research project.  The easiest data for me to gather consistently was the percent of the population that had a bachelor's degree or higher. Note that Washington DC and Louisiana are not in this dataset--they are absent from the polarization comparison data I was using (but present in the original education data). Due to the limitations of the "blogger" web site, the table likely will look awkward, extending far into the border on the right-hand side of the screen (at least it does on my Chrome browser).

ORIGINAL DATA1970197719801981198519871989199019941998200020022004200520062009
Alabama7.812.211.615.715.220.61922.722.321.420.822
Alaska14.121.123.42324.824.224.725.625.527.227.726.6
Arizona12.617.422.220.319.921.923.526.32825.524.525.6
Arkansas6.710.814.813.312.416.216.718.318.818.91918.9
California13.415.719.621.324.723.526.423.424.726.426.627.931.729.529.829.9
Colorado14.923272728.53432.735.735.535.536.435.9
Connecticut13.720.727.527.22731.431.432.634.5353635.6
Delaware13.117.519.421.421.925.12529.526.927.526.228.7
Florida10.31114.914.816.619.719.818.32122.522.325.72625.127.225.3
Georgia9.212.514.618.41719.118.219.324.520.724.32527.627.228.127.5
Hawaii1420.323.922.924.32426.226.826.627.932.329.6
Idaho1015.817.117.721.820.321.720.923.823.325.123.9
Illinois10.314.516.217.520.220.221.12123.525.826.127.327.429.231.230.6
Indiana8.39.912.510.915.213.313.815.61517.719.423.721.121.221.922.5
Iowa9.113.917.116.919.320.321.223.124.323.824.725.1
Kansas11.41722.321.122.728.525.829.13028.231.629.5
Kentucky7.211.114.913.616.820.117.121.62119.320.221
Maine8.414.418.518.821.219.222.923.824.225.726.926.9
Maryland13.920.427.426.52631.831.437.635.234.635.735.7
Massachusetts12.614201926.126.528.127.230.13133.234.336.736.940.438.2
Michigan9.410.614.315.316.116.517.317.419.122.121.822.524.424.726.124.6
Minnesota11.117.421.521.826.33127.430.532.530.633.531.5
Mississippi8.112.315.614.719.819.516.920.920.118.721.119.6
Missouri913.113.91515.117.921.617.821.322.421.626.728.12424.325.2
Montana1117.521.119.824.323.924.423.625.526.425.127.4
Nebraska9.615.519.718.921.120.923.727.124.827.227.227.4
Nevada10.814.417.215.316.920.618.222.124.520.720.821.8
NewHampshire10.918.223.524.426.226.628.730.135.431.832.132
NewJersey11.813.418.317.921.723.325.724.928.130.129.831.434.634.235.634.5
NewMexico12.717.620.620.424.123.123.525.425.125.126.725.3
NewYork11.913.617.917.822.123.222.823.125.126.827.428.830.631.232.232.4
NorthCarolina8.510.913.216.717.216.618.317.41923.322.522.423.425.125.626.5
NorthDakota8.414.822.218.119.922.52225.325.225.428.725.8
Ohio9.310.913.713.916.11517.61719.821.521.124.524.623.223.324.1
Oklahoma1015.117.117.820.320.520.320.422.922.522.922.7
Oregon11.817.920.220.624.527.725.127.125.927.728.329.2
Pennsylvania8.79.413.614.614.617.218.617.919.522.122.426.125.325.726.626.4
RhodeIsland9.415.420.221.323.927.825.630.127.229.330.930.5
SouthCarolina913.416.616.61821.320.423.324.922.922.624.3
SouthDakota8.61418.417.217.321.821.523.625.524.725.325.1
Tennessee7.912.615.71616.216.919.621.524.321.82223
Texas10.912.816.917.319.819.821.720.320.823.323.226.224.525.225.525.5
Utah1419.924.222.322.827.626.126.830.827.82728.5
Vermont11.51926.724.327.727.129.430.834.232.53433.1
Virginia12.314.319.119.223.123.527.324.526.430.329.534.633.133.232.134
Washington12.71924.122.925.128.127.728.329.93031.431
WestVirginia6.810.411.112.311.416.314.815.915.31715.917.3
Wisconsin9.814.818.917.720.922.322.424.725.62524.625.7
Wyoming11.817.221.918.816.819.821.9 19.622.523.220.823.8


INTERPOLATED DATA1970.01972.01974.01976.01977.01978.01980.01981.01982.01983.01984.01985.01986.01987.01988.01989.01990.01992.019941996.01998200020022004200520062008 (est)20092010
Alabama7.88.28.79.19.310.812.221.015.810.513.316.016.016.013.811.615.715.515.217.920.619.022.722.321.420.821.222.024.8
Alaska14.114.114.014.014.017.621.121.516.110.816.121.522.022.523.023.423.023.924.824.524.224.725.625.527.227.727.326.624.5
Arizona12.613.213.714.314.616.017.415.411.67.714.421.019.017.019.622.220.320.119.920.921.923.526.328.025.524.524.925.623.7
Arkansas6.77.27.88.38.69.710.89.47.14.79.414.012.511.012.914.813.312.912.414.316.216.718.318.818.919.019.018.922.5
California13.414.114.715.415.717.719.621.316.010.717.724.724.123.525.026.423.424.124.725.626.426.627.931.729.529.829.829.930.7
Colorado14.916.418.019.520.321.723.027.020.313.519.625.726.427.027.027.027.027.828.531.334.032.735.735.535.536.436.235.936.1
Connecticut13.714.615.516.416.818.820.725.018.812.520.027.526.826.026.827.527.227.127.029.231.431.432.634.535.036.035.935.639.5
Delaware13.113.714.214.815.116.317.518.213.79.114.018.818.818.719.119.421.421.721.923.525.125.029.526.927.526.227.028.731.0
Florida10.310.510.710.911.013.014.914.811.17.412.016.618.219.719.819.818.319.721.021.822.522.325.726.025.127.226.625.325.6
Georgia9.210.111.112.012.513.614.618.413.89.213.117.018.119.118.718.219.321.924.522.620.724.325.027.627.228.127.927.527.9
Hawaii14.014.715.416.116.418.420.320.015.010.016.022.021.521.022.523.922.923.624.324.224.026.226.826.627.932.331.429.628.4
Idaho10.011.112.313.414.014.915.817.012.88.512.616.717.418.017.617.117.719.821.821.120.321.720.923.823.325.124.723.922.6
Illinois10.311.512.713.914.515.416.217.513.18.814.520.220.220.220.721.121.022.323.524.725.826.127.327.429.231.231.030.637.0
Indiana8.38.89.39.810.011.312.511.08.35.510.315.014.013.013.413.815.615.315.016.417.719.423.721.121.221.922.122.526.9
Iowa9.19.710.210.811.112.513.914.510.97.311.916.516.316.016.617.116.918.119.319.820.321.223.124.323.824.724.825.131.4
Kansas11.412.012.513.113.415.217.014.611.07.314.421.519.317.019.722.321.121.922.725.628.525.829.130.028.231.630.929.532.8
Kentucky7.27.68.08.48.69.911.19.47.14.79.915.013.011.013.014.913.615.216.818.520.117.121.621.019.320.220.521.024.7
Maine8.49.09.710.310.612.514.414.911.27.512.818.217.817.317.918.518.820.021.220.219.222.923.824.225.726.926.926.928.4
Maryland13.914.715.416.216.618.520.421.215.910.618.225.825.324.726.127.426.526.326.028.931.831.437.635.234.635.735.735.737.7
Massachusetts12.613.013.413.814.017.020.019.014.39.517.826.126.326.527.328.127.228.730.130.631.033.234.336.736.940.439.738.248.0
Michigan9.49.710.110.410.612.514.315.311.57.711.916.116.316.516.917.317.418.319.120.622.121.822.524.424.726.125.624.628.3
Minnesota11.111.812.513.213.615.517.417.813.48.915.121.220.820.320.921.521.824.126.328.731.027.430.532.530.633.532.831.537.5
Mississippi8.18.59.09.49.611.012.310.47.85.210.415.513.812.013.815.614.717.319.819.719.516.920.920.118.721.120.619.620.3
Missouri9.010.211.312.513.113.513.915.011.37.511.315.116.517.919.821.617.819.621.321.922.421.626.728.124.024.324.625.230.7
Montana11.011.812.613.413.815.717.518.013.59.014.319.519.118.819.921.119.822.124.324.123.924.423.625.526.425.125.927.430.9
Nebraska9.610.411.212.012.414.015.513.610.26.813.219.517.816.017.919.718.920.021.121.020.923.727.124.827.227.227.327.433.2
Nevada10.811.211.612.012.213.314.412.89.66.411.216.015.014.015.617.215.316.116.918.820.618.222.124.520.720.821.121.821.0
NewHampshire10.911.812.713.614.016.118.219.014.39.516.323.022.522.022.823.524.425.326.226.426.628.730.135.431.832.132.132.035.7
NewJersey11.812.312.713.213.415.918.317.913.49.015.321.722.523.324.525.724.926.528.129.130.129.831.434.634.235.635.234.541.2
NewMexico12.713.414.014.715.016.317.616.012.08.014.320.519.318.019.320.620.422.324.123.623.123.525.425.125.126.726.225.319.6
NewYork11.912.412.913.413.615.817.917.813.48.915.522.122.723.223.022.823.124.125.126.026.827.428.830.631.232.232.332.441.3
NorthCarolina8.59.29.910.610.912.113.216.712.58.412.817.216.916.617.518.317.418.219.021.223.322.522.423.425.125.625.926.528.7
NorthDakota8.49.19.810.510.812.814.812.29.26.113.120.017.515.018.622.218.119.019.921.222.522.025.325.225.428.727.725.835.5
Ohio9.39.810.210.710.912.313.713.910.47.011.516.115.615.016.317.617.018.419.820.721.521.124.524.623.223.323.624.129.3
Oklahoma10.010.911.912.813.314.215.117.012.88.512.416.317.218.017.617.117.819.120.320.420.520.320.422.922.522.922.822.723.3
Oregon11.813.014.215.416.017.017.920.015.010.014.719.320.221.020.620.220.622.624.526.127.725.127.125.927.728.328.629.229.5
Pennsylvania8.78.99.19.39.411.513.614.611.07.311.014.615.917.217.918.617.918.719.520.822.122.426.125.325.726.626.526.434.5
RhodeIsland9.410.010.711.311.613.515.415.811.97.913.619.218.818.319.320.221.322.623.925.927.825.630.127.229.330.930.830.535.6
SouthCarolina9.09.510.010.510.712.113.416.712.58.412.316.316.015.716.216.616.617.318.019.721.320.423.324.922.922.623.224.327.1
SouthDakota8.69.310.010.711.012.514.012.09.06.011.817.515.814.016.218.417.217.317.319.621.821.523.625.524.725.325.225.129.8
Tennessee7.98.59.29.810.111.412.613.510.16.811.115.515.315.015.415.716.016.116.216.616.919.621.524.321.822.022.323.026.7
Texas10.911.412.012.512.814.916.917.313.08.714.219.819.819.820.821.720.320.620.822.123.323.226.224.525.225.525.525.526.0
Utah14.014.715.416.116.418.219.920.015.010.016.022.021.521.022.624.222.322.622.825.227.626.126.830.827.827.027.528.528.5
Vermont11.512.413.414.314.816.919.016.212.28.116.825.522.319.022.926.724.326.027.727.427.129.430.834.232.534.033.733.134.3
Virginia12.312.913.414.014.316.719.119.214.49.616.423.123.323.525.427.324.525.526.428.430.329.534.633.133.232.132.734.037.5
Washington12.713.514.215.015.417.219.019.014.39.515.521.520.920.322.224.122.924.025.126.628.127.728.329.930.031.431.331.030.7
WestVirginia6.87.48.18.79.09.710.411.08.35.58.110.711.412.011.611.112.311.911.413.916.314.815.915.317.015.916.417.322.8
Wisconsin9.810.411.111.712.013.414.813.09.86.512.518.516.815.017.018.917.719.320.921.622.322.424.725.625.024.625.025.729.7
Wyoming11.812.413.113.714.015.617.215.011.37.514.020.518.817.019.521.918.817.816.818.319.821.919.622.523.220.821.823.825.0

Friday, July 4, 2014

Personal Notes--Laclau and Mouffe, Hegemony and Socialist Strategy

Laclau and Mouffe, Hegemony and Socialist Strategy (2nd, 2001)--Personal notes from my reading in 2014

Ch. 1: Hegemony: The Genealogy of a concept. 
LM (Laclau and Mouffe) begin by tracing the “genealogy” of the concept of hegemony in a review chapter of 3 Marxist responses to cracks that seemed to appear in Marx’s theories.  They believe the idea of hegemony was the result of a series of crises in theory and praxis.

They begin by interacting with Rosa Luxembourg’s analysis of the Mass Strike, specifically, whether it was an effective political tool.  She finds a contrast with the ease of generation of mass strike in Russia with the fragmentization of strikes in Industrial Germany.  She believes that the identity of the working class under capitalism is fragmented and can only be reunited in revolution (10).  In regard to the genealogy of hegemony, LM propose that her seminal moment was the creation of a “subject position” by referring to class as a symbolic unit, not a materialistic economic reality as had been previously thought (11).

Kautsky agrees with Luxembourg regarding the natures of political and economic struggle—they are both unified efforts.  While for Luxembourg they become unified in revolution, for Kautsky they are inherently unified, since the political struggle of the working class is always an economic calculation.  However, his reading of the failure of the collapse of capitalism in Germany, which he assumed was generalizable and the inescapable result of capitalism, was, in fact, a German peculiarity, since history evidences a different path for many other states.  The end of the depression (1873-1896) and the ensuing boom (lasting until 1914), created a crisis in Marxist thinking, since it could not account for the resurgence of capitalism except as discontinuities in an otherwise deterministic struggle towards socialism.  LM define these crises as the pivotal moment in Marxist theorization when subsequent theories tried to explain new trajectories.

The first response they explore is the Marxist Orthodoxy of Kautsky and Plekhanov that emphasizes theory, presuming the end result Marx predicted is a necessary endpoint, despite the temporary appearance of observable “setbacks”.  Concrete examples of capitalism’s failure to collapse are simply temporary points of struggle, as the example of Germany (argument from contingency).  Or there may be the temporary appearance of the failure to form a revolutionary proletariat identity because of nationalism which is only a “screen that hides the interests of the bourgeoisie” (argument from appearance).  The theory predicts the ultimate realization of proletariat identity, so it will eventually happen, and concrete counter-examples are reduced to the abstract: a) “diverse subject positions of a single position,” b) plurality of differences is either reduced or rejected as contingent,” c) sense of the present is revealed through its location in an a priori succession of stages.”  Kautsky differs from Plekhanov by introducing the flexibility of political initiative in creating the space for revolution, requiring the mediation of intellectuals. 

The Austro-Marxists offered a second perspective from the first response, in that they took a Kantian approach, rather than Hegelian, by looking at perspectival, cultural influences, rather than a naturalistic historical trajectory.  Given the diversity of types of workers in the Austrian context, as well as the dual Austro-Hungarian monarchy, there seemed little possibility of a unified consciousness built on relationships of production or national identity.  The remaining possibility is the role of political initiative, as opposed to the economic or simply historical.  In any case, both approaches believed that Marx’s predicted outcome of socialism was inevitably determined

The second response to the failure of capitalism to collapse was the revisionism of Bernstein.  In the first response above, there was an assumption of linearity built on the economic.  Bernstein attempted to emphasize the political as an autonomous sphere, separate from the economic, although LM believe this isn’t fully done until Gramsci.  There is little possibility for the unification of identity in economic terms, since the trajectory of the modern economy is diversification.  The countervailing trend towards unification was “party.”  For Kautsky, discussions of the political were the purview of the intelligentsia, for Bernstein, party became far broader.    

Bernstein did not accept that Marx’s prediction was inevitable, since he did not believe Marx proved his case, since human will/subjectivity was involved, and history is not solely objective.  Add to that, since the political identity Bernstein proposed was a “party programme,” there were ethical decisions that had to be made.  Therefore the process was not purely “scientific” as the determinists proposed.  However, Bernstein also proposed an irreversible evolutionary progression.  For the orthodox, the State was totalizing, and Social Democracy was exterior to the state, so organizing could only exist outside of the State, and overturning the State was the only option.  For Bernstein, the proletariat were part of the State, and social organizing could produce important changes, humanizing capitalism, and making life fundamentally better for workers.  Democratization could transform the state so that it represented all people, and workers became true citizens.  LM disagree with this trajectory, since different subject positions can produce social/political ethical decisions that do not further the cause of the worker. 

The third response is Sorel’s revolutionary syndicalism.   Sorel proposed the power of social myth to transform society.  His interest was not so much in a society with a certain form, but with the moral qualities that allowed society to flourish.  The diversity of workers in society he calls mélange, and they can only be unified by being willing to establish blocs to enforce economic reorganization.  He became anti-democratic in that it allowed the fragmentation of subject positions, and chose instead, the syndicalist myth, the general strike, for in Marx, the revolution.  The mythical constitution in Sorel’s thought, built to some degree on Nietzsche, could and did go the direction of fascism on the one hand, or the Bolsheviks on the other.  Regardless, he was crucial to the formation of Gramsci’s thought.

ch. 2: Hegemony: The Difficult Emergence of a New Political Logic
The problem of the failure of the working class to come to a unified class consciousness posed a problem for Marxist theory.  Stagism seems not to have played out as predicted, and “hegemony” became an explanation for why this failed to happen.  Plekhanov and Axelrod introduced the term to explain the failure of the Bourgeoisie to carry out the struggle for political liberty, which forced the working class into the struggle alone.  Trotsky built on the idea of hegemony, proposing it as a way to generate a working-class government.

Lenin, as Trotsky, built on the idea of hegemony, especially its ability to link an identity exterior to class identity.  For Lenin, political hegemony involved a representation of interests, in contrast to class identity, which was the field of the relations to production.  In this case, hegemony involved political leadership with a class alliance.  Parties unite under the leadership of one class against a common enemy.  The potential contradiction within Leninist hegemony is that, on the one hand, the vanguard creates a separation between those who lead, and those who are led, and thus tends to be authoritarian, while on the other hand, the political dimension has far more democratic potential than what was proposed in the Second International. 

Thus, two types of hegemony developed.  The first, democratic practice, occurs when stagism is renounced, as well as the necessity of a unified class consciousness.  All people’s interests need to be represented, not just the working class.  The second, authoritarian practice, dominates when the vanguard considers its relation to the masses, simply as pedagogical.  With the rise of fascism, which considered democratic rights to be “bourgeoisie,” popular and democratic visions of hegemony emerged that considered it to be the “democratic reconstruction of the nation around a new class core.”  The rise of the importance of the people as political agents required the idea of class identity be split and fused into a new type of polarization, which is the process of hegemonic practice.  This occurs after the development of new popular and national symbols, and lose their transitionary features to become part of the stable political discourse. 

Gramsci extends the concept of hegemony, emphasizing the importance of the working class building alliances with other groups, representing a broader spectrum of interests.  Further, in a fundamental change from Lenin, who proposed hegemony as simply political, Gramsci extended it to include moral and intellectual characteristics.  The former presumed a transient, interest-based alliance.  The latter constructed actual linkages between groups, generating ideology, creating an organic, historical bloc based on collective will.  In this sense, hegemony had to be articulated and constantly produced through dialogue between groups.  Class, in this sense, does not “take” State power, but becomes State.    This process Gramsci terms the “war of position,” referring to the challenge of creating a new class core around a unified subject position, a never-ending process.   Differing from Sorel, Gramsci’s hegemony emphasized a democratic plurality, while Sorel believed the generated myth was based on class.

In the post-WWI period, the idea of national plans for unity and economic projects were begun, but generated few results until the Great Depression.  The presumption of an economically-based strategy was that revolution, not politics, was the only way to socialism.  However, Planism moderated this idea, and since capitalists were in charge of things, socialists used political means to implement programs to support the poor and strengthen unions.  The larger goal was to create a mixed economy, which would eventually dissolve the need for capitalism.  However, prior to 1945, the class emphasis limited hegemonic articulation, and after 1945, the strength of the Welfare State distorted class lines, so that social democracy became a practice within the existing State, not an alternative to it.

LM attempt to show that classical Marxist theory, based solely on economic position, is untenable from an hegemonic perspective, because of three mistaken assumptions of this position.  First, the assumption that there are endogenous laws of an economy, which is not so, since social relations impact the relationships to production—these laws are not “natural,” but involve numerous and diverse forms of domination.   Second, the assumption that there is a core class nature, which is not so, since fragmentation of class and subject position is clear.  Third, the assumption that the working class has a unique relation to production that makes them inherently aligned with socialism, which is not so, because there are historical interests as well as economic.  Since this final attempt to return to economic essentialism seems to be a doomed project, Marxism shifted to the recognition that socialism was not inevitable, but would depend on the political mediation of intellectuals to provide articulation of the subject position.

Ch. 3: Beyond the Positivity of the Social, Antagonisms and Hegemony
For LM, “hegemony supposes a theoretical field dominated by the category of articulation.”  Thus, they argue that one of the first tasks of understanding hegemony is to understand articulation.  Building on recent linguistic and sociological traditions that explore the concept of constructivism and discourse, LM argue that “articulation is a practice, and not the name of a given relational complex” (93), and, moreover, require specific “elements” that are thus articulated.  Drawing from Hegel, they propose that “identity” is not a fixed entity, but a concept under continual flux, derived from relations and historical dialectics.  This produces a contradiction—Hegel, representing “the highest point of rationalism” (95) is arguing for the importance of reason and intelligible structure, while at the same time laying the foundation for the recognition that structures are indefinitely in flux, and that meaning is derived vis-à-vis cultural production, not a pre-existing, defining essence.

Althusser’s discussion of complexity, relying on the concept of overdetermination is important in LM’s analysis of articulation.  Though they ultimately reject his frame, they bring in his concept of the symbolic and social dimension of meaning construction.  The assertion is made that “society and social agents lack any essence” (98), but rather, are a series of symbolic relations, constantly in “relative and precarious forms.”  LM argue that, like Hegel, Althusser faces a fundamental conflict—while arguing, on the one hand, that social relations are the product of these symbolic overdeterminations, he later argues that economics can be deterministic of the structural form of society.  It is this conflict that leads LM to reject Althusser’s latter proposition, and problematizing the former, while recognizing his important contribution, and that it is Althusser’s very philosophical disjuncture that helps us understand articulation and thus hegemony.  In doing so, they assert that one of the problems with Marx’s “scientific socialism” is that he relied on an understanding of economic determinism, as well as the objectivity of social categories, such as institutions, identities, and structures.  However, if all of these are open to discursive, and thus non-contingent processes, then it produces a flaw in Marx’s argument.  Thus, “far from a rationalist fame in which social agents, perfectly constituted around interests, wage a struggle defined by transparent parameters, we have seen the difficulties of the working class in constituting itself as a historical subject, the dispersion and fragmentation of its positionalities, the emergence of forms of social and political reaggregation—‘historical bloc’, ‘collective will’, ‘masses’, ‘popular sectors’—which define new objects and new logics of their conformation” (104-105).

LM now link articulation to discourse through establishing a fundamental difference with Foucault.  While the distinguished between “the linguistic and behavioral aspects of social practice” (107), LM reject that distinction, arguing that the two are intertwined reciprocally to such an extent that it is  fruitless to attempt it.  Continuing in this same vein, they reject also the emphasis on the Enlightenment discussion about the nature of the “mental” relationship to ontologies, affirming the reality of the material world.  Thus, it is not “objects” which LM wish to deconstruct, but the recognition that our interactions with objects is mediated through discourses—giving the example that an earthquake is a material reality, but our perception of the meaning of the earthquake, and our responses to the earthquake, come from symbolic relations mediated by histories of discourses, and specifically, our contemporary embeddedness in a series of discourses.  They relate this to Wittgenstein’s language game concept (108).   Thus, our experiences of the world cannot be relegated strictly to the mental, or to decontextualized discourses, but are, at least in part, rooted in some actual material objects, although our understanding of these objects are shaped by our context within our experiences of discourses.  So while they affirm the reality of material objects, they also affirm that “if contingency and articulation are possible, this is because no discursive formation is a sutured totality and the transformation of the elements into moments is never complete” (106-107).  But far from arguing that there is, therefore, no such thing as meaning, they simply propose that meaning is always in flux, and that flux is intentionally directed: “Any discourse is constituted as an attempt to dominate the field of discursivity, to arrest the flow of differences, to construct a centre.  We will call the privileged discursive points of this partial fixation [of meaning], nodal points” (112).  Finally, they define “the practice of articulation” as “the construction of nodal points which partially fix meaning; and the partial character of this fixation proceeds from the openness of the social, a result, in its turn, of the constant overflowing of every discourse by the infinitude of the field of discursivity.  Every social practice is therefore—in one of its dimensions--articulatory” (113).

The latter half of the chapter includes examples of these articulations, and applies them directly to the concept of hegemony.  For example, they argue that, while “sex” is an ontologically real expression of body differences, “femininity” and “masculinity” are social constructions, and are overdetermined, producing inequality for women: “The ensemble of social practices, of institutions and discourses which produce woman as a category, are not completely isolated but mutually reinforce and act upon one another” (118).  Similarly, Marxist discussion about class and workers are the result of social practices and discourses, and the meanings and referents are constantly in flux—in fact, the attempt to convince workers to organize based on their interests, is a social practice that actively constructs the interests themselves (120).  They continue, distinguishing the popular subject position from the democratic subject position:  the former constitutes a binary set of antagonisms, such as the struggle for liberation of the colonized from a brutal colonizing power, representing a fairly clear and delimited antagonism in society.  The latter does not evidence such a pattern, but “emerges within an ensemble of positions, within a relatively sutured political space formed by a multiplicity of practices that do not exhaust the referential and empirical reality of the agents forming parts of them” (132).  Democratic positions are constituted by overdetermined struggles with many social relations converging to generate the antagonisms.  Finally, linking these ideas to hegemony, they depart from Gramsci in a way that rejects his essentialist presumptions, allowing for an argument from the constructivist frame, specifically: “(a) his insistence that hegemonic subjects are necessarily constituted on the plane of the fundamental classes; and (b) his postulate that, with the exception of interregna constituted by organic crises, every social formation structures itself around a single hegemonic centre.” (137-138)

Ch. 4: Hegemony and Radical Democracy
 LM end by bring the conversation to their primary target—discussing the importance of democracy, interrogating the concept of democracy, and constructing a critique of the Right and the Left in their failure to further either equality or democracy.  They begin by recognizing that Marx’s vision of a proletariat, produced by the relations of production, with a linear trajectory of collective consciousness and revolution, has simply not been seen—instead, western societies developed “the corporatization and separation of those sectors which should ideally have been united ‘among the people’ (150).

They affirm, with Foucault, that “wherever there is power there is resistance” (152), but argue that political resistance, whose goal is to end relations of subordination, are the result of specific discourses that allow such collective action, but are not a “natural” effect of subordination.  They distinguish subordination, as a fairly banal series of hierarchical relationship, that arguably occur with consent and tacit approval, contrasted to oppression, which is subordination that generates antagonisms, and finally domination, which is when the subordination relationships themselves are perceived as illegitimate, and typically require a discourse external to those relationships in order to be recognized as illegitimate (154).  They emphasize that collective action for change often comes from these externally derived discourses.  They provide the example of mid-1800s when a labor movement developed, but rather than following the trajectory that Marx predicted, of rejecting the entire system of relations of production, they followed a “reformist” path, which, for Marx, was a step backwards. 

They describe that something the “new social movements” have in common are a differentiation from “workers’ struggles” (159), but that they also draw from a democratic impulse for equality (161).  They cite a pivotal moment, when Fordism became the dominant economic process, when “intensive” capitalism transformed all of social life—“culture, free time, illness, education, sex and even death.  There is practically no domain of individual or collective life which escapes capitalist relations” (161).  They continue, that the demands for equality, and specifically, the subsequent implementation of the Keynesian Welfare State, created a far greater role for the state in the lives of the public, as well as creating the need for a large bureaucracy, producing a “double transformation”—that of capitalism/commodification and of bureaucracy.  This led to the development of resistance not only to inequality in a class sense, but resistance to bureaucratization, generating movements for autonomy, liberation from the state, and demonstrations of individual uniquenesses—this from both Right and Left.  By the time of the Reagan-Thatcher era, neo-liberalism had been successfully constructed as a form of resistance against the impositions of the state, and the importance of the free-market for liberty.  They invoke Stuart Hall, saying, “Thatcherite populism ‘combines the resonant themes of organic Toryism—nation, family, duty, authority, standards, traditionalism—with the aggressive themes of a revived neoliberialism—self-interest, competitive individualism, antistatism.  In the case of the United States, Allen Hunter shows that the attack of the New Right on the Welfare State is the point at which the cultural and economic critiques come together” (170)

They remind the reader that the rise of the New Right, exemplified by Hayek, and libertarians, such as Nozick, are able to gain dominance because of a successfully constructed and implemented discourse that speaks to the popular concerns, generating “a new historic bloc” (176).  They emphasize that if one’s goal is to find the one objective answer, one will fail, and that the essentialist presumption is a primary obstacle in moving forward into a product discussion for the Left (177), which can only be overcome by abandoning the belief “that there are  privileged points from which an emancipatory political practice can be launched” such that the left can engage in a “complex process of convergence and political construction” (174).  They also note that the Left’s concern with individual liberty has become a hindrance to the process, especially in the sense of the concept of “possessive individualism,” or that “the rights of individuals as existing before society, and often in opposition to it” (175), taking a stance against the idea of “natural rights” (184), arguing that rights come from the state.

As they conclude, they assert that “The task of the Left therefore cannot be to renounce liberal-democratic ideology, but on the contrary, to deepen and expand it in the direction of a radical and plural democracy” (176)—the last section of the book explores this idea.  For example, they tie this back into socialism, by recognizing that democracy will require some kind of socialism, in that the capitalist relations of production generate fundamental inequality—but that socialism does not inherently entail democracy (178), so democracy must take precedence, and represents far greater range of processes to generate equality.  Further, to generate public credibility for a new/revised discourse of equality, specifically, a “democratic equivalence” to the discourse generated by the Right, the Left must develop “a new ‘common sense’” of what constitutes equality (183).  They emphasize that this will require a balance of both liberty and equality (184).  They end, saying “the field of the political as the space for a game which is never ‘zero-sum’, because the rules and the players are never fully explicit.  This game, which eludes the concept, does at least have a name: hegemony” (193).