I teach university-level sociology and statistics, and use this blog to post data notes about social inequalities and social movements (and sometimes photos of renovations I do on my Victorian house in downtown Indianapolis)
Tuesday, February 4, 2014
Nye-Ham Creation Origins Debate, 2/4/14, Creation Museum, KY
Thursday, January 30, 2014
House Vote Patterns on HJR3-Gay Marriage Constitutional Amendment Ban in Indiana
Analysis of the representatives voting patterns show that 12 voted to strip the 2nd sentence, the ban on protections similar to marriage, indicating a concern about the extreme nature of the language, but voted for the amendment as a whole, indicating a preference to constitutionally define marriage as exclusively male-female--all of these were Republican. There were 11 Republicans who voted both to strip the 2nd sentence and to reject the entire amendment. All Democrats voted to strip the 2nd sentence, and to kill the amendment. Forty-three Republicans voted to keep the 2nd sentence, and to push the amendment forward, while 5 members who were excused from one or both of these votes. Below I post the voting record for selected members, along with the citizen voting percent results from the 2012 election from the representatives' districts: Republicans and Democrats who won their elections with less than 10% margins; all representatives who split their votes (all of whom were Republicans); Republicans who voted both for stripping the 2nd sentence, and against the amendment.
| Representative | HJR3-2nd Reading | HJR3-3rd Reading | District | 2012 Election Results | Party |
| Hamm | n | y | 56 | 49.3% | R |
| Morrison | n | y | 42 | 50.2% | R |
| Lutz | n | y | 35 | 50.8% | R |
| Slager | n | y | 15 | 51.0% | R |
| Carbaugh | n | y | 81 | 51.7% | R |
| Ubelhor | n | y | 62 | 52.9% | R |
| Karickhoff | n | y | 30 | 54.0% | R |
| Mayfield | n | y | 60 | 54.2% | R |
| Soliday | y | y | 4 | 51.7% | R |
| Arnold | y | y | 74 | 52.7% | R |
| Mahan | y | y | 31 | 53.1% | R |
| McNamara | y | y | 76 | 56.4% | R |
| Bacon | y | y | 75 | 57.5% | R |
| Beumer | y | y | 33 | 57.8% | R |
| Lucas | y | y | 69 | 57.8% | R |
| Negele | y | y | 13 | 59.0% | R |
| Cox | y | y | 85 | 64.3% | R |
| Neese | y | y | 48 | 65.2% | R |
| Leonard | y | y | 50 | 67.4% | R |
| Sullivan | y | y | 78 | 100.0% | R |
| Truitt | y | n | 26 | 53.2% | R |
| Clere | y | n | 72 | 54.4% | R |
| Ziemke | y | n | 55 | 60.6% | R |
| Eberhart | y | n | 57 | 67.1% | R |
| Heuer | y | n | 83 | 72.1% | R |
| Saunders | y | n | 54 | 73.1% | R |
| Kubacki | y | n | 22 | 75.6% | R |
| Torr | y | n | 39 | 90.2% | R |
| Braun | y | n | 24 | 100.0% | R |
| Huston | y | n | 37 | 100.0% | R |
| Kirchhofer | y | n | 89 | 100.0% | R |
| Hale | y | n | 87 | 50.1% | D |
| Battles | y | n | 45 | 50.1% | D |
| Candelaria | y | n | 12 | 53.8% | D |
| Macer | y | n | 92 | 54.0% | D |
| Klinker | y | n | 27 | 54.5% | D |
| Dvorak | y | n | 8 | 54.8% | D |
ADDENDUM
Indiana Senate Rules Committee Members with Elections Data
| 2014 ReElection | District | Vote % | Representative | Party |
| * | 26 | 52.4% | Eckerty,Douglas | R |
| * | 48 | 100.0% | Hume,Lindel | D |
| * | 14 | 100.0% | Kruse,Dennis | R |
| * | 25 | 53.4% | Lanane,Tim | D |
| * | 31 | 53.8% | Merritt,James | R |
| * | 15 | 59.9% | Wyss,Thomas | R |
| 8 | 58.3% | Arnold,Jim | D | |
| 34 | 100.0% | Breaux,Jean | D | |
| 5 | 54.8% | Charbonneau,Ed | R | |
| 7 | 63.6% | Hershman,Brandt | R | |
| 16 | 65.1% | Long,David | R | |
| 44 | 100.0% | Steele,Brent | R |
Saturday, January 25, 2014
Economic and Jobs Impact of HJR-3, Indiana Constitutional Amendment Banning Same-Sex Marriage
Introduction
In Indiana, and the United States as a whole, the rights of sexual minorities, such as gays/lesbians, asexuals, and transgenders, continue to be debated by legislatures and courts. In Indiana, January, 2014, the bill HJR-3 purports to reinforce Indiana’s current definition of “marriage” as solely between one man and one woman by amending the state constitution to prevent future changes to this definition. Additionally, the proposed amendment is believed by many to limit alternative forms of “marriage” such as domestic partnerships and civil unions. These limitations have been demonstrated in Michigan, Kentucky and Ohio because of similar constitutional amendments, as testified in the January Indiana Assembly hearings on HJR-3.
One of the arguments against the proposed amendments is an economic assertion by Cummins, Lilly, the Indy Chamber of Commerce, and others, that this amendment will make it more difficult for them to recruit candidates into the state. Governor Pence, and the state GOP committee, prior to the last state-wide election in 2012, explicitly dropped the issue of same-sex marriage from their state platform, saying that their focus would be on jobs creation and attracting younger voters to the party. The rationale for the latter is found in all of the polling data that suggests that voters of both parties under 40 years of age are overwhelmingly opposed to marriage discrimination laws, including voters here in Indiana. Testifying at the Indiana Assembly January hearings, were a number of active GOP members, predominantly young people (under 40 years old) who were vigorously opposed to HJR-3, arguing that it violates traditional GOP principles of liberty, and affirming previous testimony, that it would impede the process of attracting young professionals to Indiana.
This study looks at 9 Midwestern states from 2003-2012, and finds that the testimony of business representatives is supported by time-series analysis—that for every anti-gay legislation passed by a state, and supported by court rulings, there is a subsequent loss of professional firms in that state, and negative impact to the state economy, measured by real GDP per capita. These effects are statistically significant on a number of different firms and jobs to the p<0.01 level (99% confidence). Further, not only raw job numbers are impacted, but also annualized rates of growth are impacted, as anti-gay legislation seems to slow the rate of growth of firms and GDP in subsequent years. This study documents that these changes occur at lags of 1-4 years, i.e., the loss of firms and jobs begins to occur the year after anti-gay legislation is enacted (lag=1), and causes increasing economic damage at least as of 4 years afterwards (lag=4).
Theoretically, these effects are supported by urban studies research that indicates that young professionals who move to cities from other areas, or who remain in their cities of origin, often do so because those cities offer a climate of diversity and tolerance. For example, Richard Florida documents these effects in Cities and the Creative Class (2005, Routledge Press), indicating that cities where public policies support racial equality, equality of sexual minorities, and the arts, are growing at faster rates than cities that lag behind on these cultural trends, and that policies of diversity precede and speed growth.
Methods
This study uses time-series analysis of publicly-available data for 9 Midwestern states. Based on claims by several large employers and chambers of commerce in Indiana that anti-gay legislation has a negative impact on candidate recruitment, data was collected from the U.S. Bureau of Economic Analysis, the American Community Survey (U.S. Census), and the County Business Patterns (U.S. Census) to obtain information about firms by sector, and economic growth, in terms of real GDP per capita. Documentation about state-by-state legislation and court cases relating to same-sex marriage or domestic partnerships/civil unions, was obtained from various journalistic, organizational, and government online sources. I specifically looked at firms and GDP, as well as narrowly focused state-wide legislation and court cases relating to marriage or domestic partnerships/civil unions, and did not factor in other types of sexual minority protections, such as adoption law, employment protections, city-specific protections, transgender protections, etc.
Given that each region of the country faces different types of economic influences, this study focuses only on Midwestern/Great Lakes states. While the Census counts states farther west (Kansas, Nebraska, the Dakotas) as Midwestern, they were excluded from this study to focus on states closest to Indiana. States such as Iowa, Minnesota, and Missouri, while not adjacent to Indiana, are close enough to share many of the same economic and cultural variables to warrant inclusion. While arguably, Pennsylvania, West Virginia and Tennessee are similar in distance to Indiana compared to Minnesota, other geographical factors arguably exclude them from this study, since both north-eastern and southern states have widely divergent characteristics from the typical Midwestern state. Table 1 documents the specific state-level events that led to the annualized scores for each state presented here. Scores range from 0-5, with 0 being a score representing the highest level of same-sex marriage equality, and 5 being a score representing the most restrictive legislation. Major anti-gay events, such as passage of a restrictive bill into law, or a higher court ruling opposing the legal recognition of same-sex relationships, count as a +2, while other events, such as the proposal of an anti-gay bill, or lower court ruling, count as a +1. Consecutive events, such as the requirement of multiple-year passage of bills, are often not added, as such state processes would give those states an inadvertently high number compared to states with processes that do not require consecutive passage of bills for the movement into law.
In 1996, following the federal passage of DOMA, most Midwestern states, from 1996-1998, passed a series of state-level laws that prohibited the recognition of any same-sex marriages that may have been recognized in other states, with Iowa and Wisconsin being notable exceptions. After this period, there are relatively few major events until 2004. The subsequent events are documented in Table 2, which lists the specific events that create the state-level scoring system. The existence of DOMA-related laws in most states from the 1990s start them at a score of 2 as of 2003, the starting point for this analysis. In 2004, several state passed constitutional amendments banning same-sex marriage, specifically, Kentucky, Ohio and Michigan. In 2005, the Indiana court of appeals upheld the Indiana statute defining marriage as between one man and one woman—this event increased Indiana’s score from 2 to 3. In 2008, the Michigan Supreme Court decided that their constitutional amendment banning same-sex marriage also bans recognition of domestic partnerships, raising the Michigan score from 4 to 5. In 2009, Iowa’s Supreme Court struck down their anti-gay legislation, lowering their score from 2 to 0, indicating that gay marriage is fully legal in this state. In the same year, Wisconsin passed a law allowing for domestic partnerships, despite their constitutional amendment defining marriage as between one man and one woman, lowering their score from 4 to 2. In 2011, Illinois passed a civil unions law, lowering its score from 2 to 1, the same year that a Minnesota constitutional amendment ban on same sex marriage passed the legislature, raising its score from 4 to 5. However, the following year, Minnesota voters failed to support that amendment ban, lowering its score from 5 to 3. In 2013, Minnesota passed a law allowing for same sex marriage, which would drop their score from 3 to 0, except that this dataset stops at 2012 for the purposes of scoring gay rights by state, since this study looks at subsequent impact to the economy of gay rights legislation—since 2014 economic and firms data is not available, 2013 gay rights events are not pertinent here.
Table 1: Midwestern/Great Lakes Scores by State, Gay-Marriage or Relationship Legislation or Court Decisions. A score of 0 implies that state fully recognizes gay marriage, while a score of 5 implies the most restrictive laws against gay relationships.
2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
IA 2 2 2 2 2 2 0 0 0 0
IL 2 2 2 2 2 2 2 2 1 1
IN 2 2 3 4 4 4 4 4 4 4
KY 2 4 4 4 4 4 4 4 4 4
MI 2 4 4 4 4 5 5 5 5 4
MN 2 3 3 4 4 4 4 4 5 3
MO 1 3 3 3 3 3 3 3 3 3
OH 1 3 3 3 3 3 3 2 2 2
WI 1 2 3 4 4 4 2 3 3 2
Economic, jobs and firms data are publicly available from federal sources. This study looked at firms by industry by state and jobs by sector by state, available from the U.S. Census (factfinder2.gov), and real gross domestic product per capita by state, available from the U.S. Bureau of Economic Analysis (bea.gov), using data collected January 23, 2014, from the years 2003-2013. Given the testimony of corporations about the negative impact of anti-gay legislation on candidate recruitment, four published firm sub-types were analyzed: “Professional, Science, and Technology” firms, “Educational Services” firms, “Health and Social Services” firms, “Finance” firms, as well as “Total” firms. Each of these came from the one-year samples of the County Business Patterns for the years of 2003-2012 (U.S. Census, not all data available for all years, but all available data from these years was used for each variable). In addition to the raw firms numbers, a rate of change variable was calculated for each variable ([current year firms – previous year firms]/previous year firms). Job data is available from the American Community Survey (U.S. Census, not all data available for all years, but all available data from these years was used for each variable). In order to provide a contrast with the “professional” firms analyzed, two of the largest industry categories were chosen for the jobs analysis: manufacturing jobs, and construction jobs. Gross domestic product can be measured in several ways, but in order to compensate for the different population sizes for each state, this study uses real gross domestic product per capita from 2003-2013, as published by the BEA.
Table 2: Major and Minor Events by Midwestern/Great Lakes State, Gay-Marriage or Relationship Legislation or Court Decisions (ssm=same sex marriage)
2004 Approved state ban on ssm: KY, OH, MI
2004 WI constitutional amendment passed both houses
2004 MN ssm ban proposed, failed
2005 IN appeals court upholds ssm ban
2005 WI constitutional amendment passed both houses
2006 WI ban on ssm
2006 MN ssm ban proposed, failed
2006 IN fails to pass ssm ban
2007 MI appeals court says ssm ban also prohibits domestic partnerships
2007 MN ssm ban proposed, failed
2008 MI supreme court says ssm ban also prohibits domestic partnerships
2009 IA court rules ssm ban unconstitutional
2009 WI passes law allowing for domestic partnerships
2009 MN ssm ban proposed, failed
2010 OH lawsuit challenging domestic partnership registries fails
2011 MI bans domestic partnerships
2011 IL passes civil unions approval
2011 MN ssm ban amendment passed legislature
2011 IN constitutional amendment ssm ban passes assembly
2011 WI supreme court upholds ssm ban
2012 MN ssm ban rejected by voters
2012 WI court upholds domestic partnership
To perform this analysis, this study uses the software R, an open source (free and publicly available) statistical analysis project. The specific package used for this time-series, panel data analysis was PLM. All of the data from 2003-2013 were formatted according to the PLM requirements for the 9 Midwestern/Great Lakes states. Because the question at hand is the impact that each individual state’s legislation has on jobs and the economy for each state, a “within” (fixed) approach was used. This approach was confirmed using a Hausman test, which can be used to determine the validity of a random versus fixed approach. In this case, the variable relationships in question achieved statistical significance (p<0.05), with the null being that random effects are likely—in this case, the null was rejected, implying that the most relevant approach uses a fixed effects model. Two types of regression relationships were tested, the first being the bivariate relationship of the outcome variables (firms, jobs and GDP), to the predictor variable alone, the same-sex marriage related legislation/court events, and the second, being a multivariate analysis between either the firms or jobs variables alone, regressed to the combined same-sex marriage legislation/court events and GDP. The rationale for the first approach, is to determine the relationship on either firms or GDP of anti-gay legislation. This approach ignores the potential impact of GDP on firms, or firms on GDP. This establishes a baseline of relationship between these factors and legislative/court events. The rationale for the second approach, is that in reality, GDP does impact subsequent firms—i.e., a downturn of the GDP can itself produce a loss of professional firms, or an upturn of the GDP can subsequently produce a growth in professional firms. The second approach looks at the impact on professional firms of the combined GDP + legislative/court events. The resulting regression equations follow the typical pattern for estimator prediction, where y is the outcome variable, in this case, sector-level firms or GDP, and x is the predictor variable estimator, in this case either GDP, or the legislative/court events. For the combined GDP + legislative/court events analysis, only additive effects are evaluated, not combined effects, and none of the variables are transformed (i.e., the logarithm is not generated, nor are quadratic, or other non-linear effects evaluated).
A) Firms or Jobs or GDP ~ Same sex marriage legislative/court event
B) Firms or Jobs ~ Same sex marriage legislative/court event + GDP
Results
The results of this panel analysis are statistically significant and robust. Take, for example, the regression equation generated for the simple relationship between “Professional, Science, and Technical” firms as impacted solely by the legislative/court events, as seen in Table 3. “Lagged years” represents how length of time between the legislative/court events and the firms measure. For example, for “lagged years”=2, the model suggests with a certainty of p=0.07 (within a 90% confidence that these results are not due to chance), that at two years after an anti-gay legislative/court event, there is a subsequent decrease in the number of residents of that state employed in the professional, scientific, and technical fields at a rate of 108.6 firms for every increase of anti-gay score of “1.” At the third year after the anti-gay legislative event, there is a measurable decrease of 191.1 professional firms at a confidence of p=99.9% that these results are not due to chance. The results are equally strong at the fourth year after the event, and demonstrate an even greater loss of firms. Table 4 shows a similar pattern, but for “Finance” firms. While the impact on the 2nd year after the legislative/court events is not statistically significant, the impact is significant at the 3rd and fourth year, with an even greater impact on raw firms than professional/science/technical firms, at a loss of 309 firms for every anti-gay legislative/court event, with the fourth year events showing confidence at the 99.9% level that these results are not due to chance.
Table 3: Professional, science, and technical establishments, in raw firms, regressed against legislative/court events impacting same-sex marriage or relationships
Legislation/Court Event lagged years Firms Estimator (in firms per state per event/score) Statistical Significance (p)
2 -108.6 0.07 *
3 -191.1 0.00 ***
4 -209.0 0.00 ***
Table 4: Finance establishments, in raw firms, regressed against legislative/court events impacting same-sex marriage or relationships
Legislation/Court Event lagged years Firms Estimator (in firms per state per event/score) Statistical Significance (p)
2 -17.7 0.82
3 -205.0 0.08 *
4 -309.9 0.00 ***
These impacts are not limited to professional firms. Anti-gay legislative/court events were also compared to total firms numbers. As shown in Table 5, these negative total firms impacts are even stronger than the sector-level approach above. While the sector-level effects are not statistically significant until the 2nd or 3rd year out, total firms are impacted the immediate year after the legislative/court events. In this case, all four years after the anti-gay events have statistically significant effects at the 95-99.9% confidence level, costing each state thousands of firms per year for each anti-gay legislative/court event score. As a frame of reference, in 2011, for these nine states, the average number of total firms was 169,282. So for the 4th year after an anti-gay legislative/court event, the average firm loss would be approximately 2.1% for each event-score.
Table 5: Total establishments, in raw numbers, regressed against legislative/court events impacting same-sex marriage or relationships
Legislation/Court Event lagged years Firms Estimator (in firms per state per event/score) Statistical Significance (p)
1 - 1, 425 0.05 **
2 - 2,342 0.00 ***
3 - 3,294 0.00 ***
4 - 3,642 0.00 ***
A second approach used rates of change, rather than raw firm numbers, in some cases, the apparent loss of firms could be attributed to broader historical trends, despite the statistical significance at the 99.9% levels. If these trends have been occurring for over a decade, then such patterns might be masked when looking at raw firm numbers. For example, Indiana, and several Midwestern states, have a persistent “brain drain” as college graduates move to larger cities with more desirable natural amenities, such as mountains and oceans, or jobs that can offer higher rates of pay, advancement or influence. Rates of change can be a way to filter some of those effects. In Table 6, rates of changes are used to look at the impact of the anti-gay legislative/court decisions on health and social services firms in the Midwestern/Great Lakes states. As shown above, these firms are also negatively impacted by anti-gay events. In this case, the immediate year following an anti-gay event leads to a decrease in the rate of health/social services firms by 0.4% from the previous year, with a confidence of greater than 95% that these results are not due to chance. At the fourth year out, firm growth decreases by 3.3% from the previous year, at a confidence level of 99.9%. Table 7 shows these same effects for education firms.
Table 6: Health and Social Services establishments, in rate of change, regressed against legislative/court events impacting same-sex marriage or relationships
Legislation/Court Event lagged years Health/Social Services Firms Estimator ( in % rate of change per event/score) Statistical Significance (p)
1 -0.4 % 0.02 **
2 -0.7 % 0.34
3 -0.4 % 0.07 *
4 -3.3 % 0.00 ***
Table 7: Educational services firms, in rate of change, regressed against legislative/court events impacting same-sex marriage or relationships
Legislation/Court Event lagged years Educational Services Firms Estimator ( in % rate of change per event/score) Statistical Significance (p)
1 -0.5 % 0.10 *
2 -0.8 % 0.02 **
3 -1.1 % 0.01 ***
4 -1.8 % 0.00 ***
In addition to the negative impact to firms, anti-gay legislative/court events also have a negative impact on the number of jobs in the state. The results of this third approach can be seen in Tables 8 and 9, using calculations similar to those above, but regressed against construction jobs and manufacturing jobs (data from the American Community Survey, U.S. Census). In this case, the loss of jobs in both of these industries is evident. For example, in the case of manufacturing, the job loss is not statistically significant the first year after a major anti-gay legislative/court event. However, by each of the 2nd-4th years, job losses are evident with 99.8-99.99% confidence levels that these results are not from chance. The job losses per year average between 20,000-25,000 manufacturing jobs for each anti-gay legislative/court event-score. Construction job loss follows a similar pattern. As a frame of reference, in 2012, the average number of construction jobs in these nine states was 181,340, and the average number of manufacturing jobs was 511,422. Thus the average job loss per negative event-score the third year after the event, would, respectively, be approximately 8.2% of construction jobs, and 4.9% of manufacturing jobs.
Table 8: Manufacturing jobs, regressed against legislative/court events impacting same-sex marriage or relationships
Legislation/Court Event lagged years Manufacturing Jobs Estimator (in jobs per state per event/score) Statistical Significance (p)
1 -9,526 0.31
2 -20,780 0.002 ***
3 -25,080 0.000 ***
4 -23,908 0.000 ***
Table 9: Construction jobs, regressed against legislative/court events impacting same-sex marriage or relationships
Legislation/Court Event lagged years Construction Jobs Estimator (in jobs per state per event/score) Statistical Significance (p)
1 -3,424 0.53
2 -11,997 0.002 ***
3 -14,859 0.000 ***
4 -15,023 0.000 ***
A fourth approach looks specifically at the impact of the economy on these anti-gay legislative/court events, by comparing subsequent years of real gross domestic product per capita. As shown in Table 8, these impacts are significant and negative. While this study does not test mechanisms for the impact on GDP, it is reasonable to infer that these negative GDP effects are related to the loss of firms and jobs described above. These effects are statistically significant to the 99.0-99.9% levels through the first three years. As a frame of reference, in 2012, for these nine states, the average real GDP/capita was $39,677.
Table 10: Real gross domestic product, regressed against legislative/court events impacting same-sex marriage or relationships
Legislation/Court Event lagged years Real GDP/Capita Estimator (per event/score) Statistical Significance (p)
1 - $384 0.01 ***
2 - $521 0.00 ***
3 - $518 0.00 ***
4 - $318 0.10 *
Finally, a comparison was made between establishments and jobs, separately, regressed against the combination of GDP and anti-gay legislative/court events. The results of this analysis can be seen in Table 9, which looks at total establishments as a combination of these two effects. As expected, real GDP per capita has a strong positive effect on the number of firms in each state. For example, for real per capita GDP, every measured dollar is related to an increase in almost 3 firms the following year, and the second year out, just over 2 firms per dollar. However, by the 3rd year, the impact of GDP/capita drops out of statistical significance. On the other hand, even when taking real GDP/capita into consideration, the competing impact of anti-gay legislative/court events is statistically significant for all four yours after any event. For every major anti-gay event, there is a measurable loss of 963 firms the first year out, a loss of 2,104 firms the second year out, and the 3rd and 4th year have a loss of over 3,000 firms per year for each anti-gay event. These effects are measurable at a 99.9% confidence level, far overpowering the impact of GDP by the 3rd and 4th years after any given anti-gay legislative/court event. While not presented here, these results are evident at the sectoral level as well—i.e., even when taking GDP into account, there is a negative impact on professional job loss from each anti-gay legislative/court event in any given state (results available on request). Similarly, the loss to manufacturing jobs are also robust to the inclusion of GDP into the calculation. The first year after the event-score has no statistical impact on job loss, but each of the subsequent three years show a loss of tens of thousands of jobs per event-score at the 99.9% confidence level, overshadowing any GDP impact.
Table 11: Total firms, regressed against legislative/court events impacting same-sex marriage or relationships combined with real GDP/capita
Legislation/Court Event lagged years Anti-Gay Legislative/Court Events (p) Real GDP/Capita Estimator (p)
1 - 963 firms (0.06) * 2.9 (0.00) ***
2 - 2,104 firms (0.00) *** 2.1 (0.00) ***
3 - 3,378 firms (0.00) *** 0.7 (0.37)
4 - 3,415 firms (0.00) *** -0.8 (0.32)
Table 12: Manufacturing jobs, regressed against legislative/court events impacting same-sex marriage or relationships combined with real GDP/capita
Legislation/Court Event lagged years Anti-Gay Legislative/Court Events (p) Real GDP/Capita Estimator (p)
1 - 3,985 jobs (0.49) 32.9 (0.00) ***
2 - 18,473 jobs (0.00) *** 14.5 (0.00) ***
3 - 24,882 jobs (0.00) *** 1.7 (0.70)
4 - 23,168 jobs (0.00) *** -6 (0.37
Conclusion
U.S. federal, state, and local public policy is rapidly changing to affirm different family types than the one-man, one-woman ideal that has been dominant in white, middle class culture since WWII. Setting aside religious, moral and social questions of the impact of these changes to legal and cultural recognition of a diversity of family types, the economic impact of states deciding not to affirm diversity can have measurable impacts on both firms and economic growth. This study used a time-series, panel data approach to determine the relationship between state-level legislation and court decisions that impact either same-sex marriage or other types of legal same-sex relationship, such as domestic partnerships and civil unions. Since the 1996 federal DOMA law, various states have taken divergent approaches to these legal affirmations of diversity. Most of the Midwestern/Great lakes states have taken approaches that restrict the rights of same-sex couples. This study looked at nine of these states that are arguably similar in cultural and economic features to Indiana, where the legislature is in the process of evaluating a constitutional amendment banning same-sex marriage and similar legal affirmations of such relationships, to the citizens of Indiana for passage. This study seeks to contribute to that discussion by proposing a negative impact to the Indiana economy because of the passage of such a constitutional amendment ban.
Four different measures were examined in relation to the univariate regression of antigay legislative/court events: sector level and total firms by raw numbers, rates of growth for sector-level firms, jobs and economic growth (real GDP per capita). Further, firms and jobs were examined in relation to the combination measure of anti-gay legislative/court events and economic growth. For all of these measures, a statistically significant finding was that each anti-gay event leads to a negative impact on firms and the economy, often at the 99.9% confidence level, and at the cost of thousands of firms per anti-gay event per state. Given that the current GOP-dominated legislature has previously emphasized their interest in creating jobs versus the disruption caused by social issues debates in their 2012 state platform, it seems unreasonable for the state assembly to pursue this divisive bill, that will undoubtedly hurt jobs growth in Indiana.
One can visualize these effects to produce a more striking representation of the negative impact that this amendment will have on Indiana’s economy. Chart 1 shows the magnitude of this relationship when looking at total firms, with a 3-year lag, comparing three groups of states: States with a score of 0-1 (strongest laws that protect same-sex relationships), states with a score of 2-3 (moderate laws restricting same-sex relationships), and states with a score of 4-5 (strongest laws restricting same-sex relationships). For this chart, growth of total firms is measured for the 3rd year after a given state score. On average, when comparing states with a score of 0-1, those are the only Midwestern/Great Lakes states that show total firms growth. States with moderately restrictive laws show slight negative total firms growth. States with the strongest regulations against same-sex marriage have by far the strongest negative growth rates for total firms. Chart 2 shows the same effect, but measured as a factor of real GDP per capita growth over a 2-year lag. The same progression is evident—the states with the strongest protection for same-sex relationships have the strongest economic growth, measured 2 years out, while the states with the most restrictive laws, not only do poorer, but actually have negative growth. Finally, in Chart 3 one sees the same relative impact on the growth of manufacturing jobs of anti-gay legislative/court events. As states evidence greater respect for the rights of sexual minorities, they see a subsequent impact on jobs growth, in this case, manufacturing jobs at a 3-year lag. On the other hand, states that are the most restrictive in limiting the legal affirmation of the relationships of gays and lesbians, those states evidence a strong jobs decline.
Saturday, January 4, 2014
Screenshot to Clipboard--Ubuntu, KDE
xfce4-screenshooter -fo gimp
This sends the fullscreen screenshot to Gimp so I can edit it, which is typically (although not always) what I want to do with my screenshot.
Tuesday, December 24, 2013
Education Spending Disparities--international Comparison
The claim of "more spending per student than any other country" is often followed by the complaint about teacher's unions forcing us to pay more than any other country for teacher salaries, yet preventing us from firing bad teachers. While the former claim is partially true, the latter is not. Leaving that debate for another discussion, it is important to understand the spending data. First, Graph 1 shows "all" education spending, which includes pre-primary all the way through tertiary spending, including vocational spending. It also includes all funding sources--local government through federal, and includes private funding (spending is converted from national currency to PPP by GDP for interstate comparison).
However, separating tertiary spending from primary/secondary spending produces a slightly different picture (note also that this data is just from a one-year snapshot of 2010).
No longer the biggest spender in these categories, we are clearly in the top spending cluster, although all of the top 8-12 countries are relatively close to each other (except the top-spending country, Luxembourg, whose GDP/capita is just less than twice that of the US, by far the largest of the OECD countries). Looking at tertiary education, i.e., university and college spending (excludes vocational training), the comparison looks quite a bit different.
What we can now see, is that while our national conversation about education is typically centered around primary and secondary failings, linked to high levels of comparative spending, our actual spending compared to other countries is greatest for tertiary education. I will come back to the issue of primary/secondary school funding, to suggest a clarification for how we can still be at the top of the cluster of spenders, but still be producing poor results. As for tertiary spending--where is all that money going? While an excellent question, a number of recent analyses have indicated that US spending on college sports (and here, and here), as well as administration/bureaucratic costs (and here) far outpaces other countries, and has little benefit to student learning, which is arguably the main reason for the existence of the university. Note that the skyrocketing US tuition is not going to most faculty, especially the adjunct faculty who comprise over 50% of teachers in most state schools--many of those part-time faculty are on food stamps and receive no benefits or job security.
The above chart clearly shows that we in the US are spending far more than any other country on tertiary education--but why is that? Are there more of us going to college? Are we going to college longer? It's definitely not the former. In fact, we have one of the lowest college-participation rates of all of the OECD countries.
So we are spending far more than any other country on tertiary education, but sending almost the lowest proportion to tertiary education. The problem would seem to be the costs themselves. Indeed, most OECD countries provide free, or almost free, tertiary education: France, Denmark, Sweden, Iceland, Finland, Norway, Belgium, Spain, Italy, Austria, Poland, Turkey, Mexico, and Slovenia. The graph below separates the tertiary institutions into two categories--public and private--with average cost per student for each (countries with no bar data either are completely free, as listed above, have no private colleges, or did not supply data). As can be seen, the cost of a U.S. education, especially for private colleges, far supersedes any other OECD country.
Finally, getting back to the question of primary/secondary funding, if we are spending near the top of the cluster, there is the persistent issue that our students are performing at a mediocre level compared to other countries. Intuitively, this must be an issue of how the money is being spent. But perhaps a better question is "where" the money is being spent, speaking geographically. A recent analysis showed that, unlike almost every other OECD country, our money is being spent where our wealthiest students reside, while we strip funding for our poorest students. The OECD data shown below supports this proposal, to the extent that we have created an educational system whereby the majority of funding comes from local sources, predominantly property taxes, whereas other countries have far greater input from federal sources for more equitable distribution of national resources. Unlike the graph above for primary/secondary spending, which was only for 2010, the graph below is an average of 2006-2010, to generate a broader representation of spending. Here it is evident that we are close to Luxembourg for spending, far above the other countries. However, it is also clear that we shift the majority of our funding to local sources, with relatively little federal funding.
One might notice that several countries with high PISA scores also have a large percent of their education budget from local sources, such as Norway, Denmark, Finland, Iceland, Canada and the UK. The critical difference is that in all of these cases, their levels of inequality (GINI) are far lower than that of the US. So while all countries have areas that are poorer, and some are wealthier, in the US there is tremendous geographic inequality, large islands of poverty, and large islands of wealth. In areas of poverty, where education is funded by property taxes, there is very little money coming into schools, and relatively little federal money to make up the difference. On the contrary, areas of wealth have the ability to collect sufficient funding for a wide variety of educational supplements, infrastructure/development investment, and recruiting of the best teachers. In countries with low-GINI there are far higher levels of equality throughout the population and the geography, with far higher spending on social safety net systems designed to generate equality of opportunity and access to resources. I will leave this for another time to demonstrate myself--in the meantime, others have already analyzed the OECD and US data, arriving at the same conclusion.
Sunday, December 8, 2013
Indiana State Legislative Districts vs Census Population Density
The first two images represent a "block-group" population density mapping of Indiana. The first of these images is an actual population density, derived directly from Census. While there is no variable for "population density," it can be readily calculated using "total population" divided by "land area." Population comes from the factfinder2 Census site linked above, and land area is embedded in the Tiger shapefile. There are various levels of measurement available from the Census, ranging from the entire US-level, all the way down to the block-level. In this case I have used the 2nd smallest unit available, the "block group" level, which is one step smaller than the "census tract" level. The calculated value can be transferred to the original Tiger block-group level shapefile and mapped through QGis. The Census definition of an urban space is one with 1,000 or more people per square mile. The Tiger shapefile gives the land area in square meters, so in order to get a square miles value, you must include a conversion factor. There are two primary sources in the Census for population--the annual ACS sample, and the decennial Census. The latter includes the entire population, while the former contains just a few individuals from each locality. The decennial Census allows more accurate reporting, and far smaller localities to be used. For this data I used the decennial Census.
The second image in this set is urban density, also derived solely from Census data. One of the values you can choose from the factfinder2 site, in addition to "population," is "urban" vs "rural." Within this set of information you can find 3 distinct values, in addition to the total population of the area. The first is "urbanized area" population, which is the number of people who live in regions with 50,000 or more people. The second is "urban cluster" population, with between 2,500-50,000 people. The third is "rural" population, with less than 2,500 people. The Census pre-defines these values based on the localization of the population density. This particular map is by block-group, and indicates the percent of people in each block group that occupy an "urbanized area." This is different from population density, in that the latter represents the total population/land area. Urban percent represents the percent of the population of an mapped feature (in this case a block-group) whose total incorporated area represents 50,000 or more people.
The final four images represent the Indiana state legislative districts, as highlighted by both population density and urban percent. The first two of these images is the lower house districts, while the last two are upper house districts. There are 100 lower house seats, and are analogous to the federal House of Representatives, while there are 50 upper house districts, analogous to the federal Senate. The Census Tiger shapefiles for these districts are drawn from the 2013 legislative maps. The population for the districts come directly from the Indiana web site, but the population density and urban percent had to be calculated by QGis, since the Census has not yet published either of these values for state legislative districts. I calculated these values by downloading the block-group-level population and urban data from factfinder2, and block-group-level shapefiles from Tiger, in addition to the state legislative shapefiles. In QGis there is a vector feature, "join attributes by location," which allows the user to spatially integrate various features. In this case, I used the spatial features of the state legislative districts, both upper and lower, respectively, as the digital shape targets, and "joined" to those shapes the population data in the block-group-level shapes from the second shapefile. Since there are many block-groups per legislative district, a join by "sum" allowed for a total population could be obtained for each of the districts.
The population density values were calculated directly from the land area given in the shapefiles for the legislative districts, and the population from the Indiana data for each legislative district. For this, QGis was not needed for calculations, just for mapping. In this case, as above, I used land area divided by total population, with the inclusion of the square meter to square miles conversion factor. However, for urban percent, I had to use the "join attributes by location" feature. After the join summed the values, the result was, for each legislative district, a sum of the population whom the census determined lived in an "urbanized area," as well as the total population for each district. The map colors thus represent the percent of people living in each district whom the Census has determined lives in an incorporated area of more than 50,000 people. This process sometimes has spatial difficulties--for example, block groups can cross legislative lines, and thus QGIS may produce unpredictable results in those instances. The summed populations did not match the populations given by the State of Indiana for each district. However, even if the population as such wasn't always accurate, the ratio of urban population to total population should be reasonably consistent with the spatial features. To test this, I compared the resulting urban percent values with the population density, which produced a correlation of r=0.78. I also did two separate comparisons, one using the census-tract level, and again with the block-group-level and the results were almost identical. For a finer comparison, a block-level measurement could be used, but that requires downloading separate files for each county, as opposed to one file for the entire state, and then integrating all of those county-level files back into one huge state file. For the purposes of this demonstration, the block-group-level files should be sufficient.
Wednesday, November 27, 2013
Ethnic Violence in the Balkans and the Caucasus
One of the interesting features of this region since the fall of the Soviet Union, is the radically different paths taken by the satellite states. Consider that most of the post-communist countries had relatively peaceful transitions out of Communism, from Poland, to Hungary, to Czechoslavakia, to Germany. Even countries further east, like the Ukraine's Orange Revolution was relatively violence-free, at least on the side of the protesters (those wanting a change in government and political process). Building on a generation of non-violent protests, from Ghandi to Martin Luther King Jr, and many others during this period, non-violence proved a radical weapon in transforming dictatorial governments.
On the other hand, countries with radically ethnically-diverse populations had transitions that brought far more loss of life. As can be seen from the tables, many were killed, for example, as Serbia tried to keep Yugoslavia together, opposing first Slovenia's independence, then Croatia, then Bosnia-Herzogovina, and most recently, Kosovo. From the ethnicity map, it is clear that most of the Kosovars are Albanian. When Kosovo rebelled against Serbia for liberation, Serbia led brutal attacks on the population. Macedonia, to the south, opened its borders in 1991, and let Kosovars come in, dramatically increasing the Albanian-speaking population in northern Macedonia. From the table, one can see a subsequent fatalities marker for Macedonia in 2001, when the Albanian/Kosovar refugees who stayed in Macedonia, later rebelled against Macedonia. Just this month (Nov, 2013), Kosovo held the first national election for local offices, after having entered into an EU-brokered agreement with Serbia, that the northern part of Kosovo would be released from Serbian control back to Kosovo. The continued Kosovo-Serbia conflict is one of the primary factors preventing them from being accepted into the European Union, and the agreement helps pave the way through that impediment.
Turning to the Caucasus, there are at least 40 recognized, distinct languages in the region, and the ethnic divisions map shows the compact space with tremendous diversity. The mountains run from north-west to south-east, dividing the country into north and south. At the intersection of three great empires, Ottoman, Persian, and Russian, they have been at the cross-roads of these rich cultures, as well as the intersection of centuries of battle. While the low-land cultures at the foot of the mountains tend to assimilate reasonably well into the conquering empires, the highland cultures in the mountains, such as the Chechens, Dagestanians, and Ossetians, have posed great difficulties any invader. In the last WWII period (1944), Stalin deported all of the north-Caucasus people to the Gulags, many dying on the way, in what was later recognized as a genocide by the European Parliament. As can be seen in the Caucasus violence table, Chechnya has been rife with violence over the last 20 years.
Thursday, November 14, 2013
RTMP downloading
I used to have occasional success using browser extensions, especially when the video was directly served as an flv or mp4 file, but none of those seem to work any longer as the video is often on a separate server in an RMTP process. The most recent solution I have found is reasonably complicated, so I'm writing this to remind myself of the process. The software rtmpdump works to extract the video. For my system (Windows 8, using Mozilla) I had to download the main file, rtmpdump, and then a second file from NirSoft, rtmpdumphelper. From the rtmpdump download I had to start "rtmpsuck.exe," then from the second download I started "rtmpdumphelper.exe." I then opened my browser, navigated to the page with the video, and started the file. The rtmpsuck program recognizes that an rtmp file has been accessed and is playing, then identifies the source. Rtmpdumphelper imports that information and begins downloading it.















