Data Across the Curriculum: Using Geospatial Data to Illustrate Historical Change

History is a discipline that is founded on looking at changes over time, and for Sarah Purcell, Professor of History, data is an essential tool in measuring that change. More specifically, Purcell employs geospatial data to investigate historical change in both time and space for her Civil War & Reconstruction class, which focuses on the causes, progress, and consequences of the Civil War and Reconstruction with an emphasis on race, politics, economics, gender, and military conflict.

Purcell uses a stair-step approach in getting students exposed to geospatial data, first by using Google Maps to compare Civil War battleground locations to the locations of students’ hometowns, then investigating how other historians have used data, especially economic and demographic data, in tandem with historical narrative. Finally, Purcell has her students work with ArcGIS, an analytical map-making software, to visualize geographic trends in various historical data. For example, students in the class explore on black soldiers who enlisted in the U.S. Army during the Civil War in an in-class exercise (Figure 1) that encourages them to think critically about military data.

 

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To Sarah Purcell, data is important due to its wide applicability: using data in the context of history teaches a valuable lesson about how data can enhance just about any discipline. Moreover, in the history field, there exists a broad array of different types of data to be utilized, both qualitative and quantitative. While Purcell admits that some students have easier facility with working with data than others, she stresses that the struggle is important in internalizing quantitative literacy and getting accustomed to confronting data, an essential skill. The amount of involvement with data students get in her courses has impacted her students in a variety of ways: some students have gone on to get further training in ArcGIS via formal coursework, and others have been able to secure jobs, citing that employers are largely attracted to data skills in historical work.

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Mapping State Tax Expenditures to Demonstrate that All Else Really is Equal

Typically, when a business invests in a new piece of equipment, it cannot immediately deduct the full purchase price from its taxable income in the first year.  Instead, according to federal tax regulations, it deducts a percentage of the price in each of 2, 5, or 7 years depending on the type of equipment.  Businesses, of course, would prefer the tax deduction to happen in the first year so they have lower current taxes and therefore increased current cashflow which can be used to make additional investments that will pay off in the future.

In an effort to help small businesses, the federal government has long allowed for all investment costs below a specified threshold by any given firm to be immediately deducted.  This threshold, is specified in Section 179 of the tax code and is generally referred to as the Section 179 allowance.   For example, in 2002, all investment costs below $20,000 could be immediately deducted from taxable income but investment costs beyond $20,000 were subject to normal rules.

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Since 2003, the government has worked to encourage business investment by significantly increased this threshold (see figure 1). Interestingly, as the government has increased the threshold, many states have made equivalent alterations to their state tax policies.  Other states have increased their Section 179 allowance some.  Still others have not increased Section 179 generosity at all.  In new research, I attempt to use this state-level variation in Section 179 generosity to estimate how manufacturing investment and employment respond to state Section 179 conformity.

An important step in this research process has been demonstrating that states that do and do not conform to the federal threshold are not substantially different in other ways that would affect investment or employment trends.  One major concern, in particular, is that conforming states might be concentrated in a single region.  If investment and employment is changing in this region for reasons other than Section 179 conformity, then the research design, which compares conforming and non-conforming states, would inappropriately attribute investment and employment effects to state 179 conformity when, in fact, these effects are really due to regional trends.

To allay this concern, I enlisted the help of Bonnie Brooks in DASIL to create an interactive ArcGIS application which shows the evolution of state 179 conformity during the years 2000 to 2011.  From the application, it is immediately apparent that state conformity or non-conformity is not concentrated in any region.  Thus, the ArcGIS application simply and elegantly allays concerns that regional trends may undermine the key assumption in this and all applied microeconometrics research project: that all else really is equal.

To use the map:

  • Drag the second ticker to the beginning of the timeline to start the visualization from the year 2000

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Mental Health Mortality, by Gender and Race

US President Barack Obama announced on January 5th that he would be taking executive action on gun control in light of a tragic trend of mass shootings in the last several years. Among the details of his gun control plan, he mentioned an increase in mental health services. While the expansion of mental health support may help in ameliorating the mass shootings epidemic, it may also has positive implications for reducing the number of Americans who die due to mental health causes. Using DASIL’s United States Mortality by Cause of Death, Race, and Gender visualization, one can see how deaths due to mental illness have been on the rise since the 1990s, and how the trend has had varying effects on every demographic:

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When looking at strictly male versus female deaths due to mental health causes, males in recent years are slightly more affected than females, at an average 3.84 deaths compared to 3.50 as of 2009. However, the 90s saw the reverse, with female fatalities at 1.92 compared to 1.37 in 1994.

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When breaking down within each gender by race, a much different story emerges. For females, the sharp rise in deaths due to mental health is observed after the year 2000, which differs from the trend for all races and all genders. In addition, while each race follows the same sharp increase after the year 2000, white women are more adversely affected, at an average 5.92 deaths compared to 4.09 for blacks and 3.68 for other races in 2009. For males, on the other hand, the same sharp increase also appears after the year 2000, however the averages for each race are much less in comparison to their female counterparts. White males are also more adversely affected in comparison to other races, at 3.11 deaths, while black males are averaging 2.41 deaths and other races 2.33 deaths.

Why has mental health been more fatal for women across all demographics? One reason may be eating disorders. Women are more likely to contract an eating disorder than men (although that does not mean men do not develop eating disorders), and eating disorders have the highest mortality rate of any mental illness. For example, according to the South Carolina Department of Mental Health, the mortality rate associated with anorexia nervosa, one type of eating disorder, is 12 times higher than the death rate associated with all causes of death for females between the ages of 15-24 years old.

President Obama’s plan for enforced support and better resources for those suffering with mental illness will not only help in tackling the gun violence epidemic, but also larger instances of mental illness fatalities.

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Highlighting the Importance of Intersectionality in the Gender Pay Gap

The gender pay gap is again receiving much-needed publicity in recent years as a topic of debate between US presidential hopefuls for 2016 and information uncovered from Sony’s email hack this time last year. While the phrase “women get paid 78% of what men are paid” is touted frequently in discussion, the 78% figure is static in dimension. Do all women get paid 78% of what men are paid, or is it just a subset of the female working population?

There is a lot more to the 78% figure than meets the eye, and the intersection of race and gender is important to telling the fuller story behind the 78%, and the wider issue of gender parity in earnings.

Using DASIL’s Pay by Race & Gender visualization, we can see that race plays a significant role in the pay of a full-time working woman and reveals the nuances to the widely-cited 78% figure. Asian women working full-time in the US are (and have been) the subset of women getting paid closest to what all men are getting paid throughout history, at 86% that of men in 2013. However, Asian women were only paid 75% that of Asian men in 2013. On the other end of the spectrum, Hispanic women were disproportionately getting paid only 60% of men’s wages in 2013, the lowest of all recorded races. Hispanic males also earn the lowest in comparison to all men, at 64% of what all men earn (not shown) in 2013. As the graph indicates, the asymmetric trends for Hispanic and Black women have remained relatively constant for the past twenty years.

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With regard to part-time labor, however, there is virtually complete gender parity in 2013 when focusing on average figures, with “all women” receiving 99% of what a man earns. When filtering by race, part-time working White and Asian women even get paid more than that of average men; white women receive 106% of what a man earns, and Asian women 101% in 2013. However, racial disparity still persists: both Black and Hispanic women in part-time labor received 85% of what part-time men were paid in 2013, and the closest Black and Hispanic women have been in achieving pay parity with the average man was in 1994.

As this infographic suggests, one reason for full-time and part-time pay disparity can be due to industry: black women are more likely to work in less-lucrative jobs (e.g. service, healthcare) than high-lucrative jobs (e.g. STEM, management). Relatedly, education can be a contributing factor: Hispanic and Black women are less likely to graduate than whites. Yet, even if women of color have the same education levels as their white peers, they are still paid less; there is more contributing to pay disparity than the educational attainment of women of color.

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While there is clear cause for more work to be done in bridging the pay gap between men and women, recognizing the multiple dimensions of the issue will be key to creating meaningful and effective policy changes.

Explore more trends with our Pay by Gender and Race visualization here.

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Exploring Racial Disparities in New York City’s Stop-and-Frisk Policies

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A comparison of the two maps above yields a surprising conclusion: African-Americans are much less likely to be arrested in areas with higher African-American populations!

One of the best examples of the use of statistics in policy research is in the controversy about New York City’s Stop-Question-and-Frisk policies, which give police officers the right to stop, search, or arrest any suspicious person with reasonable grounds for action. These policies were an effort to reduce crime rates, under the philosophy that stopping suspicious persons will prevent smaller crimes from escalating into more violent ones. In recent years, the NYPD had been under fire for alleged racial discrimination in their stops. Research on approximately 175,000 stops from January 1998 through March 1999, for example, showed that Blacks and Hispanics represented 51% and 33% of the stops, while only representing 26% and 24% of the New York City population respectively. The NYPD defended their practices, saying that since crimes mostly happen in black neighborhoods, it is natural that more black people would be found suspicious of crimes.

Using the stop-and-frisk dataset provided by the NYPD and 2010 census data, numbers were compiled into an interactive heat map of arrests directly related to the stop-and-frisk policy in New York City, as an aid to visualizing this disparity in race.

For each precinct, the visualization allows you to compare the racial make-up of the population with the proportion of arrests by race. For instance, this shows that in Precinct 104 while less than 2 % of the population in this precinct is African-Americans, over 15% of the arrests were of African-Americans.

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Evidence of racial disparity is clear.  African-Americans are consistently overrepresented in arrests compared to the population in each precinct.

The exception to this trend which was alluded to at the beginning of the post:  in areas with high African-American populations, the disparity disappears, and even reverses in a few precincts! Thus African-Americans are much less likely to be arrested in neighborhoods with high African-American populations.

Use this visualization to explore the trends in arrests due to the stop-and-frisk policies in New York:

Another visualization on stops and arrests in New York City can be accessed here. You can also go here for more information on these data visualizations.  These visualizations were created as part of a Grinnell College Mentored Advanced Project with Ying Long and Zachary Segall under the direction of Shonda Kuiper.

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