To What Extent Does Income Affect Voting Behavior When Accounting For Race?

Abstract

In heavily Hispanic Californian neighborhoods, wealth had no relationship with voting patterns in the 2008 United States presidential election and the 2010 California gubernatorial election. An area’s voting patterns are often considered to be linked with its wealth. Analysis of this relationship, however, is confounded by the fact that the affluence of an area is also linked with its racial composition (and ethnicity in itself is a very important predictor of voting patterns). This study broke the endogeneity problem by looking at Californian Census Block Groups in which Hispanics compose more than 95% of the overall population. Using 2007-2011 American Community Survey data, the study regressed the Democratic share of the vote won in a Census Block Group during the 2008 United States presidential election and the 2010 California gubernatorial election to the Census Block Group’s median household income. Median household income was not found to be statistically significant. Further regressions using poverty rates, quadratic regressions, dummy variables, controls for country of origin, and heavily white neighborhoods did not find a consistently significant relationship between wealth and voting patterns.

1 Introduction

Democrats represent the poor and Republicans represent the rich. Except they don’t. In the United States lower-income areas do tend to be more Democratic. But factor in race and the link is much more tenuous.

This paper analyzes the effect of income on voting behavior, accounting for race. Specifically, it looks at the extent to which lower-income Hispanics vote more Democratic than upper-income Hispanics. The results have significant electoral implications.

2 Literature Review

Voting patterns by race and income as separate categories are well-known, although there has been surprisingly little academic work done on both. Exit polls are the primary source of analysis for political analysts. Political analysts also often analyze other types of polls, generally telephone surveys conducted by companies such as Gallup.

Academic research has also examined both income and the Hispanic community in American politics. Most academic research works with data from the American National Election Studies (ANES), a survey of voters before and after elections.

Exit polling generally shows that lower-income voters are more Democratic. The 2008 United States presidential election exit polls indicated that voters making under $15,000 a year gave 73% of their vote to President Barack Obama, whereas voters making more than $200,000 a year gave only 52% of their vote to Obama. Interestingly, however, Obama support amongst voters making more than $200,000 a year was higher than Obama support amongst voters making $50,000 to $200,000 a year.

Exit polling also shows that Hispanics vote Democratic, giving Obama 67% of their vote compared with the 53% he received nationwide.

There are also exit poll data that adjust for race; the 2008 United States presidential election exit poll indicates that whites making under $50,000 gave Senator John McCain 51% of the vote compared to the 56% of the vote he obtained with whites making over $50,000. The well-regarded Pew Research Center’s analysis of the 2012 United States presidential election exit poll indicates that 82% of Hispanics making less than $50,000 a year voted Democratic, compared with 59% of Hispanics making more than $50,000 a year. Other studies, however, argue that income does not have a significant effect on Hispanic partisanship (Alvarez and Bedolla 2003, de la Garza and Cortina 2007).

One of the most well-known academics working with electoral politics is Professor Andrew Gelman. He has found that on an individual level higher-income individuals vote more Republican, and that this effect is stronger in more Republican states (Gelman, Shor, Bafumi, and Park 2007). Other studies agree that class continues to play an important role in American politics, both in differing voting patterns (Brooks and Manza 1997; Brooks and Brady 1999) and different turn-out rates (Leighley and Nagler 1992).

Much academic research has also been done on the Hispanic vote, especially during the Bush years when Republicans made strong but temporary inroads into the Hispanic vote. This research has found that Hispanic turn-out is much lower than overall turn-out. National origin and generational differences play important roles in the Hispanic vote (Alvarez and Bedolla 2003). The role of racial identity in the Hispanic vote is also important; Hispanics, especially those of non-European descent, are more likely to vote for Latino candidates (Stokes-Brown 2006). Some studies, however, are more equivocal about this point (Abrajano, Nagler, and Alvarez 2005). Much attention has been focused on Republican attempts to gain Hispanic votes. Republican-leaning Hispanics generally put high trust in the government relative to other Republicans. Additionally, they have a more “American” identity relative to other Hispanics and are more educated (Dutwin, Brodie, Herrmann, and Levin 2005). Republican appeals to moral values and national security played well amongst Hispanics during the 2004 United States presidential election (Abrajano, Alvarez, and Nagler 2008). On the other hand, Garza and Cortina (2007) argue that “Latinos are not Republicans and they know it.”

It is often argued that racial voting patterns are different in the South due to its distinct history. This was true in the past, especially during the 1950s and the Civil Rights era. Since then, however, Southern and non-Southern voting patterns have converged to an extent (Aistrup 2010). This is true in terms both of race and class. This convergence and the increasing role of class in the South have been argued as factors behind the decline of the Democratic Party in the South (Brewer 2001).

There are significant methodological problems with all this research. First, there is far too much reliance on polling rather than actual election results. This is especially true with respect to exit polls. Exit polls are good for getting a general idea of voting groups in the United States. However, exit polls are unreliable and often flat-out wrong (Silver 2008). For instance, raw, unadjusted exit polls consistently overestimate the Democratic share of the vote.

Exit polls are especially bad with immigrant groups, who are more difficult to contact due to language and cultural barriers. In the 2004 United States presidential election, the national exit poll famously stated that President George W. Bush won 44% of the Hispanic vote. This statistic is almost certainly wrong and a function of oversampling Cubans; it is contradicted by other exit polls and results from heavily Hispanic counties (Leal, Barreto, Lee, and de la Garza 2005). Differing polls often find radically different levels of Democratic support amongst Hispanics, based on differing population assumptions and interviewing methods. Of particular note is the polling firm Latino Decisions, which argues that normal exit polls – such as the national exit poll for the 2010 United States mid-term elections – consistently overestimate Republican support amongst Hispanics (Segura and Barreto 2010). In Latino Decisions polls, Hispanic support for the Democratic Party is consistently higher than the level of support found amongst other pollsters, which do not focus on the Hispanic community. Unfortunately, there is no way to know who is right.

Academic research also relies far too heavily on the ANES. With respect to the ANES, Garza 2004 argues that the survey needs to be modernized to incorporate Hispanic viewpoints more reliably:

The first step in implementing this approach will be to modernize NES so that it includes a representative sample of the nation’s new demography and questions that address the new issues that affect the political life of these new populations. This means more than having Latinos statistically represented; it means restructuring NES sampling procedures so that it is regularly possible to understand Latino perspectives and their impact on national political life.

There are many studies that look at the Hispanic vote and many studies that look at class in American politics. But there are few studies that look at income controlled for race, which leaves a substantial endogeneity problem since race changes income. Brooks, Clem and Brady (1999) do try to adjust their income statistics for race; however, they only do so for blacks and do not account for Hispanics as a separate racial category. In addition, there is disagreement as to whether or not income has an effect on the Hispanic vote at all. Exit poll results indicate that there is an effect; however, other studies running regressions based off ANES data indicate that there is no effect.

Disappointingly, only one study in this literature review looked at actual election results rather than polling. Election results have the advantage of being perfectly accurate. On the other hand, polling is subject to the assumptions of those conducting the poll and the difficulty of contacting Hispanics. I therefore looked at actual election results at the Census Block Group level to determine the effect of income on the Hispanic vote.

3 Data

I used Census Block Group data for California.

Income: Income data were derived from the 2007 to 2011 five-year American Community Survey (ACS) estimates.

Voting: Voting results were taken from Census Block Group results for the 2008 United States presidential election and 2010 California gubernatorial election. The Democratic candidates were President Barack Obama (facing Senator John McCain) and Governor Jerry Brown (facing businesswoman Meg Whitman), respectively. Census Block Group data were downloaded using Dave’s redistricting application.

The data were compiled by Steve Gerontakis. Since California has a substantial absentee ballot vote that is mailed in, it is often difficult to assign absentee ballots to the exact Census Block Group in which the voter who cast the absentee ballot lives. Thus out of all the votes cast in the 2008 United States presidential election, 97.8% of them are assigned to Census Block Groups and accounted for in this analysis. Out of all the votes cast in the 2010 California gubernatorial election, 99.4% are assigned to Census Block Groups and accounted for in this analysis. The problem is most acute in populous Los Angeles County and San Mateo County. In the former, 94.9% of the total 2008 United States presidential election vote is assigned to Census Block Groups and 98.5% of the total 2010 gubernatorial vote is assigned to Census Block Groups. In San Mateo County only 93.2% of the total 2008 United States presidential election vote is assigned; fortunately the county was not used in this study.

Race: Racial data by Census Block Group are available at Dave’s redistricting application. They were derived from the 2010 Census.

One should note two other things about the data set. First, voting results in Del Norte County are actually compiled at a more detailed level than Census Block Groups; however, this county was not used in this study. Second, both the 2008 United States presidential election and 2010 California gubernatorial election results do not include third-party votes. In general third-party votes are negligible due to the two-party, first-past-the-post structure of United States politics. Third-party votes accounted for 2.72% of the overall 2008 United States presidential election vote in the state of California. They accounted for 5.35% of the overall 2010 California gubernatorial election vote.

4 Results

I first looked at the effect of income on partisanship without accounting for any other variables.

There are 23,212 Census Block Groups in California. These Census Block Groups consist of groups of (generally) several thousand people. Thus the mean is the average of all these groups aggregated together; it does not reflect the true mean. For instance, the mean share of the two-party vote that Barack Obama gained in these Census Block Groups is not the same thing as the actual percentage of the vote that the president won in California in the 2008 United States presidential election.

Table 1: Descriptive Statistics, Overall Dataset

Variable Number of

Observations

Mean Median Standard

Deviation

Minimum Maximum
Population

23,212

1,604.9

1,420

929.2

0

37.452

Median

Household

Income

23,068

68.3

61.2

35.5

2.5

250.0

Obama Percent

of Two-Party Vote

23,146

64.9%

65.9%

17.1%

0%

100%

Brown Percent

of Two-Party Vote

23,129

61.0%

61.4%

18.5%

0%

100%

Values are missing for slightly more than one hundred of these Census Block Groups. This is because not all Census Block Groups are populated; some are airports, deserts, ocean, or unpopulated islands. These Census Block Groups have neither election results nor income statistics. Other Census Block Groups are very lightly populated and contain less than a dozen people (or even just one or two individuals). Some are parks populated by the homeless (or pranksters lying to the Census). Others are large estates owned by a single wealthy family. Since the American Community Survey is a statistical sample, these Census Block Groups lack data on income. It is also possible that data have been withheld out of privacy concerns. Election data are still available for Census Block Groups with only one or two people, which implies that you could find out how certain individuals voted if you know that they live in a certain Census Block Group.

To find the effect of income on partisanship without accounting for any other variables, I ran the simple regression:

Democratic sharei = β0 + β1 * income + ε

Note that:

Democratic sharei = percentage of the two-party vote received by the Democratic candidate

Income = median household income (in thousands of dollars)

As Table 2 indicates, median household income is indeed a statistically significant predictor of the Democratic vote share. However, income only accounts for 5% of the variation in the vote that the Democratic candidate won in the 2008 United States presidential election. The link is stronger but still weak in the 2010 California gubernatorial election; there income accounts for 10% of the variation in the vote that the Democratic candidate won.

Table 2: General OLS Regressions for Overall Dataset, on Obama and Brown Percent of Two-Party Vote

Variable

(1)

(2)

 

Obama

Brown

Median Household Income, Thousands

-0.001

-0.002

(-34.45)***

(-50.11)***

Constant

0.721

0.722

(304.68)***

(289.00)***

Observations

23,059

23,058

R-squared

0.05

0.10

Absolute value of t-statistics in parentheses
* significant at 5% level; ** significant at 1% level; *** significant at 0.1% level

A Breusch-Pagan test was performed to test for heteroskedasticity and found a chi-squared value of 0.23, corresponding to a 0.6323 chance under the null distribution. Accordingly, we cannot reject the null hypothesis of homoskedasticity.

To control for race and look specifically at the Hispanic vote, I excluded all California Census Block Groups that are less than 95% Hispanic. I also changed the variable overall median household income to median household income for Hispanics only.

There are 483 observations for Census Block Groups that are more than 95% Hispanic. Of these Census Block Groups, Table 3 describes the median household income for Hispanics only, the percent Hispanic, the Obama share of the two-party vote, and the Brown share of the two-party vote.

Table 3: Descriptive Statistics, Heavily Hispanic Census Block Groups

Variable
Number of

Observations

Mean Median Standard

Deviation

Minimum Maximum
Population

483

1,755.8

1,630

837.8

7

8,704

Median Household

Income for

Hispanics Only,

Thousands

483

39.7

38.6

12.6

14.8

88.1

Percent Hispanic

483

97.0%

97.0%

1.1%

95%

100%

Obama Percent

of Two-Party Vote

481

85.2%

85.8%

5.8%

50%

100%

Brown Percent

of Two-Party Vote

483

85.2%

86.1%

6.4%

40.9%

100%

Then I ran the simple linear regression model again:

Democratic sharei = β0 + β1 * income + ε

Note that:

Democratic sharei = percentage of the two-party vote received by the Democratic candidate

Income = median household income of Hispanics only (in thousands of dollars)

At this point one might ask, why not go with the obvious solution? Why not simply add a variable incorporating the percent Hispanic of the overall population to the regression?

There are several problems with this, however. The percentage Hispanic of the overall population is often a poor predictor of the percentage Hispanic of the electorate. Hispanics vote at far lower rates than their overall numbers, for a variety of reasons. First, the Hispanic population is skewed young; relatively more Hispanics are children ineligible to vote. Thus the voting-age population (VAP) is less Hispanic than the overall population. Second, many Hispanics are immigrants who have not attained American citizenship and thus are ineligible to vote. The government attempts to estimate the citizen voting-age population (CVAP) of the electorate, but these figures are highly inaccurate. Finally, even Hispanic immigrants who have attained American citizenship vote at far lower rates than native citizens. To make things worse, VAP, CVAP, and voting participation rates differ greatly amongst different parts of the Hispanic community and reflect the extent to which the community has established itself. Thus a 70% Hispanic Census Block Group in California’s rural Central Valley might have a majority-white electorate. On the other hand, a 70% Hispanic Census Block Group in Miami would probably have an electorate close to 70% Hispanic. Interestingly, the same general factors apply to the Asian vote.

I regressed median household income for Hispanics only to find the percentage of the vote that Barack Obama received in California during the 2008 United States presidential election and the percentage of the vote that Jerry Brown received in the 2010 California gubernatorial election results.

Table 4: General OLS Regressions for Heavily Hispanic Census Block Groups, on Obama and Brown Percent of Two-Party Vote

Variable

(1)

(2)

 

Obama

Brown

Median Household Income for Hispanics Only, Thousands

0.00231

-0.0180

(1.10)

(-0.77)

Constant

0.843

0.859

(97.12)***

(87.65)***

Observations

481

483

R-squared

0.06

0.06

Absolute value of t-statistics in parentheses
* significant at 5% level; ** significant at 1% level; *** significant at 0.1% level

Income is not statistically significant for either election; the p value for the 2008 United States presidential election is 0.27 while the p value for the 2010 California gubernatorial election is 0.44. With p values of 0.27 and 0.44, there is not enough evidence to conclude that there is a relationship between median household income and partisanship.

Surprisingly, the results seem to hint that Hispanic areas with higher incomes voted more Democratic in the 2008 United States presidential election. Additionally, the fit for the 2010 California gubernatorial election is very poor. Indeed, the coefficients for both elections are not even the same sign. The coefficient for median household income for Hispanics only in the 2008 United States presidential election is positive, which might imply that wealthier Hispanic areas voted more Democratic. On the other hand, the coefficient for median household income for Hispanics only in the 2010 California gubernatorial election is negative, which might imply that wealthier Hispanic areas voted more Republican. With such low p-values, however, it’s difficult to evaluate the practical significance of these variables.

Below are two figures graphing the relationship between the two variables in a two-way scatter plot:

Figure 1: Relationship Between Median Household Income for Hispanics Only and Obama Percent of Two-Party Vote in the 2008 United States Presidential Election

To What Extent Does Income Affect Voting Behavior Once Accounting For Race Figure 1

Figure 2: Relationship Between Median Household Income for Hispanics Only and Brown Percent of Two-Party Vote in the 2010 California Gubernatorial Election

To What Extent Does Income Affect Voting Behavior Once Accounting For Race Figure 2 Visually Figure 1 and Figure 2 show strikingly the relationship – or lack thereof – between income and partisanship amongst Hispanics.

5 Further Analysis

There are several ways to extend this analysis and confirm the robustness of these results.

Poverty Rates

While median household income is generally considered the gold standard for measuring income, it’s possible that measurement error or some other problem makes it unsuitable for this type of regression.

An alternative way of measuring income is through the poverty rate of an area. The poverty rate of each Census Block Group can be found from 2007 to 2011 five-year American Community Survey (ACS) estimates.

Table 5: Descriptive Statistics, Poverty Rates

Variable Number of

Observations

Mean Median Standard

Deviation

Minimum Maximum
Poverty Rate

23,098

14.3%

10.2%

13.4%

0%

100%

Poverty Rate

(Heavily Hispanic

Census Blocks)

482

27.0%

25.4%

13.6%

0%

74.0%

Instead of looking at median household income for Hispanics only, the next regression examined the poverty rate. Using all 23,212 Census Block Groups, I regressed the poverty rate to find the Democratic percentage of the vote (in both the 2008 United States presidential election and the 2010 California gubernatorial election). This regression used the overall dataset.

Table 6: General OLS Regressions for Overall Dataset Using Alterative Measure of Income, on Obama and Brown Percent of Two-Party Vote

Variable

(1)

(2)

 

Obama

Brown

Poverty Rate

0.367

0.464

(45.30)***

(54.45)***

Constant

0.597

0.544

(380.43)***

(326.14)***

Observations

23,059

23,058

R-squared

0.08

0.11

Absolute value of t-statistics in parentheses
* significant at 5% level; ** significant at 1% level; *** significant at 0.1% level

As with median household income, the poverty rate is statistically significant in this regression using the overall dataset. For both elections examined, it explains a slightly higher degree of the variation in the Democratic vote than median household income.

I then controlled for the effect of ethnicity by excluding all California Census Block Groups that were less than 95% Hispanic and ran the same regression as in the previous part.

Table 7: General OLS Regressions for Heavily Hispanic Census Block Groups Using Alterative Measure of Income, on Obama and Brown Percent of Two-Party Vote

Variable

(1)

(2)

 

Obama

Brown

Poverty Rate

-0.041

-0.043

(-2.19)

(-2.01)

Constant

0.863

0.863

(149.95)***

(133.32)***

Observations

480

482

R-squared

0.06

0.06

Absolute value of t-statistics in parentheses
* significant at 5% level; ** significant at 1% level; *** significant at 0.1% level

The results, shown in Table 7, are quite surprising. As with median household income, the poverty rate is not statistically significant. What is more surprising is that the coefficient for both elections is negative. The implication would be that as the poverty rate of a heavily Hispanic community decreases, Democrats gain more support. This is contrary to what most political science would predict. Nevertheless, the lack of statistical significance in these results indicates that caution is needed before making any conclusions.

Non-Linear Regressions

It is also possible that income has a non-linear relationship with partisanship. For instance, it might argued that wealthy areas would be exponentially more likely to vote Republican for varying reasons, such as income gains from tax cuts espoused by the Republican Party. On the other hand, it might also be argued that the wealthiest neighborhoods are more Democratic-leaning than middle-class neighborhoods. This is based on the perception that the modern Democratic Party appeals to low-income and high-income constituencies (high school graduates and graduate-degree holders), whereas the Republican Party is supposedly dominant amongst middle-income constituencies.

If these arguments are valid, the traditional linear regression used so far in this analysis might not capture such a non-linear relationship.

To test for such a possibility, I ran two types of regressions. First, I ran a quadratic equation using both poverty rates and median household income for Hispanics only. The quadratic regression was run under the following formula:

Democratic sharei = β0 + β1 * income + β2 * income2 + ε

Note that:

Democratic sharei = percentage of the two-party vote received by the Democratic candidate

Income = median household income of Hispanics only (in thousands of dollars) or the poverty rate

Under this model, the results indicate that income does not have a statistically significant effect on partisanship amongst heavily Hispanic Census Block Groups.

Table 8: General OLS Regressions for Heavily Hispanic Census Block Groups Using Quadratic Model, on Obama and Brown Percent of Two-Party Vote

Variable

(1)

(2)

(3)

(4)

 

Obama

Brown

Median Household Income

for Hispanics Only, Thousands

0.003

0.002

(2.53)

(1.62)

(Median Household Income

for Hispanics Only, Thousands)2

0.000

0.000

(-2.35)

(-1.81)

Poverty Rate

0.015

0.001

(0.22)

(0.01)

(Poverty Rate)2

-0.094

-0.074

(-0.86)

(-0.59)

Constant

0.795

0.857

0.818

0.858

(35.70)***

(86.56)***

(33.15)***

(76.39)***

Observations

481

481

483

483

R-squared

0.06

0.06

0.06

0.06

Absolute value of t-statistics in parentheses
* significant at 5% level; ** significant at 1% level; *** significant at 0.1% level

I also ran two regressions using dummy variables. These regressions used the poverty rate of heavily Hispanic Census Block Groups to create these dummies. Under the first regression, a dummy variable was created measuring whether or not the poverty rate of the Census Block Group is greater than the national poverty rate (14.3%). If the Census Block Group’s poverty rate is below 14.3%, then the value “1” was used for the Census Block Group. If not, the value “0” was used for the Census Block Group. 82 out of the 483 heavily Hispanic Census Block Groups have poverty rates below the national poverty rate. The model used for this was:

Democratic sharei = β0 + β1 * income + ε

Note that:

Democratic sharei = percentage of the two-party vote received by the Democratic candidate

Income = a dummy variable indicating if the poverty rate of the Census Block Group is below 14.3%

Under the second regression, the heavily Hispanic Census Block Groups were further divided into different categories using dummy variables. In addition to the previous dummy variable, two other dummy variables were created. One classified the heavily Hispanic Census Block Groups by whether or not the poverty rate in them is more than the national poverty rate but less than twice the national poverty rate. If the Census Block Group’s poverty rate is between 14.3% and 28.6%, then the value “1” was used for the Census Block Group. Otherwise the value “0” was used. The other dummy variable classified the heavily Hispanic Census Block Groups by whether or not the poverty rate in them is greater than twice the national poverty rate but less than 50%. If the Census Block Group’s poverty rate is between 28.6% and 50%, then the value “1” was used for the Census Block Group. Otherwise the value “0” was used.

The model used was:

Democratic sharei = β0 + β1 * income1 + β2 * income2 + β3 * income3 + ε

Note that:

Democratic sharei = percentage of the two-party vote received by the Democratic candidate

Income1 = a dummy variable indicating if the poverty rate of the Census Block Group is below 14.3%

Income2 = a dummy variable indicating if the poverty rate of the Census Block Group is between 14.3% and 28.6%

Income3 = a dummy variable indicating if the poverty rate of the Census Block Group is between 28.6% and 50%

In the first regression, the dummy variable for income is not statistically significant in either election analyzed. However, under the second regression Census Block Groups with poverty rates above the national poverty rate but less than twice the national poverty rate pass the 5% statistical significance test. If a Census Block Group has a poverty rate between 14.3% and 28.6%, it gave Barack Obama 2.5% more of the vote and Jerry Brown 2.9% more of the vote under the elections analyzed.

Table 9: General OLS Regressions for Heavily Hispanic Census Block Groups Using Dummy Variables, on Obama and Brown Percent of Two-Party Vote

Poverty Rate Is…

(1)

(2)

(3)

(4)

Obama

Brown

Below National Poverty Rate

0.002

0.020

0.001

0.022

(0.30)

(1.64)

(0.18)

(1.58)

Above National Poverty Rate,

But Less Than Twice National Poverty Rate

0.025

0.029

(2.22)*

(2.30)*

More Than Twice National Poverty Rate,

But Less Than 50%

0.013

0.013

(1.15)

(1.05)

Constant

0.852

0.834

0.852

0.0831

(299.79)*** (80.71)*** (262.44)*** (70.55)***
Observations

481

481

483

483

R-squared

0.00

0.06

0.00

0.06

Absolute value of t-statistics in parentheses
* significant at 5% level; ** significant at 1% level; *** significant at 0.1% level

Puzzlingly, however, Census Block Groups with even higher rates of poverty were not statistically significantly more likely to vote Democratic.

Hispanics by Country of Origin

Another possible critique is that the previous analysis lumps together all Hispanics in one group. In reality, however, differences by country of origin are a key part of immigrant voting patterns. Mexicans vote differently from Cubans, who vote differently from Puerto Ricans. Is it possible that income is statistically significant once accounting for both ethnicity and country of origin amongst Hispanics?

To explore this possibility, I looked at the 2007-2011 American Community Survey estimates of Hispanics by country of origin.

Table 10: Descriptive Statistics, Country of Origin

Variable Number of

Observations

Mean Median Standard

Deviation

Minimum Maximum
Percent

Mexican

483

86.0%

86.9%

7.3%

47.5%

98.8%

Percent

Puerto Rican

483

0.3%

0.6%

4.3%

Percent

Cuban

483

0.3%

0.7%

6.6%

Percent

Dominican

483

0.0%

0.3%

3.2%

Percent

Central American

483

7.2%

6.1%

5.8%

28.7%

Percent

Costa Rican

483

0.0%

0.2%

1.3%

Percent

Guatemalan

483

2.1%

1.0%

2.6%

13.1%

Percent

Honduran

483

0.5%

1.4%

16.4%

Percent

Nicaraguan

483

0.4%

0.9%

6%

Percent

Panamanian

483

0.0%

0.2%

1.9%

Percent

Salvadorean

483

4.0%

3.2%

3.6%

20.5%

Percent Other

Central American

483

0.1%

0.3%

3.2%

Percent

South American

483

0.4%

0.8%

4.1%

Percent

Argentinean

483

0.1%

0.2%

2.0%

Percent

Bolivian

483

0.0%

0.1%

1.7%

Percent

Chilean

483

0.0%

0.2%

1.6%

Percent

Colombian

483

0.1%

0.3%

2.9%

Percent

Ecuadorian

483

0.1%

0.4%

4.0%

Percent

Paraguayan

483

Percent

Peruvian

483

0.0%

0.4%

3.4%

Percent

Uruguayan

483

0.0%

0.0%

0.4%

Percent

Venezuelan

483

0.0%

0.0%

0.6%

Percent Other

South American

483

0.0%

0.1%

1.0%

Percent Other

Hispanic or Latino

483

1.1%

0.9%

1.1%

7.2%

Percent

Spaniard

483

0.1%

0.3%

3.0%

Percent

Spanish

483

0.1%

0.2%

2.3%

Percent

Spanish American

483

0.0%

0.0%

0.3%

Percent All Other

Hispanic or Latino

483

0.1%

0.7%

1.0%

6.6%

It should be noted that there is a difference between “Percent Other Hispanic or Latino” and “Percent All Other Hispanic or Latino.” “Percent Other Hispanic or Latino” is a grouping encompassing the categories “Percent Spaniard,” “Percent Spanish,” “Percent Spanish American,” and “Percent All Other Hispanic or Latino.” This is simply how a function of how the government classifies its data.

Additionally, a value of 0.0% indicates that the value for the mean or standard deviation rounds to zero (but is not exactly zero). A value of “–” indicates that the value for the mean, median, or minimum is exactly zero.

Unfortunately, the data for country of origin only go as deep as the Census Tract level. The data on election results and income are on the Census Block Group level; the data on Hispanics by country of origin are on the Census Tract level. Each Census Tract is composed of several Census Block Groups.  This makes the results of the following regressions somewhat less rigorous.

Finally, it’s useful to note that the Hispanic population in California is heavily Mexican and Central American; other groupings (even Cubans and Puerto Ricans) are negligible.

Several regressions were run, using differing methods of measuring country by origin. The first regression simply added a variable incorporating the percent Mexican of a Census Tract to the regression. This regression was run due to the fact that Mexicans compose the great majority of Hispanics in this dataset. The second regression added variables incorporating the percent of Hispanics with origins in each continent (Central America, South America, and other). Finally, the third regression replaced the continental variables with variables incorporating the percent of Hispanics from each country with data available in the 2007 to 2011 American Community Survey. Regressions were ran using both the 2008 United States presidential election and the 2010 California gubernatorial election, as well as using median household income for Hispanics only and the poverty rate. These are shown below:

Table 11: General OLS Regressions for Heavily Hispanic Census Block Groups by Country of Origin, on Obama Percent of Two-Party Vote in the 2008 United States Presidential Election

Variable

(1)

(2)

(3)

(4)

(5)

(6)

 

Obama Percent of Two-Party Vote

Median Household

Income for

Hispanics Only,

Thousands

0.000

0.000

0.000

(1.39)

(1.60)

(1.83)

Poverty Rate

-0.044

-0.031

-0.032

(-2.32)**

(-1.69)

(-1.72)

Percent

Mexican

0.111

0.387

0.371

0.108

0.373

0.386

(3.11)***

(7.17)***

(6.60)***

(3.07)***

(6.90)***

(6.33)***

Percent

Central American

0.425

0.412

(6.59)***

(6.35)***

Percent

South American

0.086

0.025

(0.26)

(0.07)

Percent Other

Hispanic or Latino

0.57

0.5444

(2.33)*

(2.23)*

Percent

Puerto Rican

-0.986

-0.951

(-2.16)*

(-2.08)*

Percent

Cuban

0.986

0.079

(0.25)

(0.20)

Percent

Dominican

0.920

0.949

(0.96)

(0.99)

Percent

Costa Rican

-2.40

-2.350

(-1.44)

(-1.41)

Percent

Guatemalan

0.653

0.647

(5.43)***

(5.37)***

Percent

Honduran

0.346

0.290

(1.66)

(1.40)

Percent

Nicaraguan

0.145

0.099

0.45

(0.30)

Percent

Panamanian

-0.823

-0.749

(-0.53)

(-0.48)

Percent

Salvadorean

0.355

0.35

(3.93)***

(3.88)***

Percent Other

Central American

0.141

0.058

(0.19)

(0.08)

Percent

Argentinean

-0.690

-0.709

(-0.53)

(-0.55)

Percent

Bolivian

0.249

0.338

(0.08)

(0.11)

Percent

Chilean

1.125

1.083

(0.76)

(0.74)

Percent

Colombian

0.447

0.503

(0.53)

(0.60)

Percent

Ecuadorian

0.297

0.215

(0.48)

(0.35)

Percent

Peruvian

0.247

0.196

(0.39)

(0.31)

Percent

Uruguayan

-10.488

-9.695

(-0.76)

(-0.70)

Percent

Venezuelan

-0.778

-0.192

(-0.13)

(-0.03)

Percent Other

South American

-1.781

-1.934

(-0.81)

(-0.88)

Percent

Spaniard

-0.344

-0.203

(-0.38)

(-0.23)

Percent

Spanish

0.505

-0.588

(-0.47)

(-0.55)

Percent

Spanish American

9.390

8.456

(0.94)

(0.84)

Percent All Other

Hispanic or Latino

0.778

0.725

(2.92)**

(2.77)**

Constant

0.745

0.470

0.483

0.771

0.504

0.519

(22.91)***

(9.02)***

(8.97)***

(25.00)***

(9.73)***

(9.50)***

Observations

481

481

481

481

481

481

R-squared

0.06

0.10

0.10

0.06

0.05

0.10

Absolute value of t-statistics in parentheses
* significant at 5% level; ** significant at 1% level; *** significant at 0.1% level

Table 12: General OLS Regressions for Heavily Hispanic Census Block Groups by Country of Origin, on Brown Percent of Two-Party Vote in the 2010 California Gubernatorial Election

Variable

(7)

(8)

(9)

(10)

(11)

(12)

 

Brown Percent of Two-Party Vote

Median Household

Income for

Hispanics Only,

Thousands

0.000

0.000

0.000

(-0.30)

(-0.23)

-0.05

Poverty Rate

-0.048

-0.025

-0.247

(-2.28)**

(-1.26)

(-1.23)

Percent

Mexican

0.191

0.646

0.631

0.195

0.641

0.622

(4.77)***

(11.30)***

(10.50)***

(4.93)***

(11.21)***

(10.29)***

Percent

Central American

0.722

0.713

(10.57)***

(10.38)***

Percent

South American

0.192

0.118

-0.55

(0.34)

Percent Other

Hispanic or Latino

0.542

0.544

(2.09)*

(2.10)*

Percent

Puerto Rican

-0.137

-0.155

(-0.28)

(-0.32)

Percent

Cuban

0.369

0.354

(0.86)

(0.83)

Percent

Dominican

1.062

1.031

(1.03)

(1.00)

Percent

Costa Rican

-0.298

-0.297

(-0.17)

(-0.17)

Percent

Guatemalan

0.716

0.702

(5.64)***

(5.52)***

Percent

Honduran

0.573

0.575

(2.57)**

(2.62)**

Percent

Nicaraguan

0.738

0.696

(2.13)*

(2.01)*

Percent

Panamanian

-1.220

-1.358

(-0.73)

(-0.83)

Percent

Salvadorean

0.713

0.705

(7.41)***

(7.32)***

Percent Other

Central American

1.551

1.558

(1.97)*

(1.98)*

Percent

Argentinean

0.548

0.449

(0.39)

(0.32)

Percent

Bolivian

2.02

1.654

(0.59)

(0.49)

Percent

Chilean

0.096

0.036

(0.06)

(0.02)

Percent

Colombian

0.158

0.091

(0.18)

(0.11)

Percent

Ecuadorian

0.340

0.325

(0.51)

(0.49)

Percent

Peruvian

-0.798

-0.854

(-1.18)

(-1.27)

Percent

Uruguayan

-5.14

-5.526

(-0.35)

(-0.37)

Percent

Venezuelan

0.095

0.525

(0.16)

(0.08)

Percent Other

South American

0.240

0.192

(0.10)

(0.08)

Percent

Spaniard

-0.740

-0.801

(-0.76)

(-0.83)

Percent

Spanish

0.489

-0.438

(-0.42)

(-0.38)

Percent

Spanish American

19.231

18.373

(1.80)

(1.72)

Percent All Other

Hispanic or Latino

0.680

0.672

(2.40)*

(2.38)*

Constant

0.690

0.239

0.251

0.697

0.249

0.267

(18.83)***

(4.32)***

(4.35)***

(20.18)***

(4.54)***

(4.59)***

Observations

483

483

483

483

483

483

R-squared

0.04

0.06

0.06

0.06

0.06

0.06

Absolute value of t-statistics in parentheses
* significant at 5% level; ** significant at 1% level; *** significant at 0.1% level

Median household income for Hispanics only is never statistically significant. The poverty rate, however, has a statistically significant negative effect when regressing with the term percent Mexican in both elections. This means that once taking into account the percent Mexican of heavily Hispanic Census Block Groups, rising poverty indicates a smaller Democratic vote share. Again, this is contrary to what most political analysis indicates.

Whites

One can also run the same analysis with non-Hispanic whites in California. Here I took all Census Block Groups that are more than 95% white by voting-age population (VAP). Despite being the largest racial group in California, however, there are surprisingly only 21 Census Block Groups that are more than 95% VAP white.

Table 13: Descriptive Statistics, Heavily White Census Block Groups

Variable Number of

Observations

Mean Median Standard

Deviation

Minimum Maximum
Population

21

474.2

228

566.5

1

1,860

Median Household

Income for

Whites Only,

Thousands

13

74.0

59.3

58.5

6.8

250.0

Poverty Rate

16

11.0%

6.1%

17.4%

0.0%

70.8%

Percent White

21

97.6%

96.6%

2.1%

95.0%

100%

Obama Percent

of Two-Party Vote

18

57.2%

62.5%

24.7%

0%

100%

Brown Percent

of Two-Party Vote

19

42.0%

44.6%

26.5%

0%

100%

For these Census Block Groups, I then used regressions to examine the relationship between income (using both ways of measuring income) and the Democratic share of the two-party vote in both elections.

Table 14: General OLS Regressions for Heavily White Census Block Groups, on Obama and Brown Percent of Two-Party Vote

Variable

(1)

(2)

(3)

(4)

 

Obama

Brown

Median Household Income for Whites Only, Thousands

0.001

0.000

(0.96)

(0.48)

Poverty Rate

0.007

0.503

(0.02)

(1.31)

Constant

0.483

0.540

0.432

0.503

(6.42)***

(7.41)***

(5.38)***

(4.95)***

Observations

13

16

13

16

R-squared

-0.01

-0.07

-0.07

0.04

Absolute value of t-statistics in parentheses
* significant at 5% level; ** significant at 1% level; *** significant at 0.1% level

Income is not statistically significant. The low t-statistics of the above regressions do not imply a relationship between affluence and the Democratic percentage of the two-party vote.

Surprisingly, amongst non-Hispanic whites the coefficient for income is positive. This hints that as income increases amongst white neighborhoods, the Democratic share of the vote increases. Again, however, the low p-values make such a conclusion very tentative.

6 Discussion and Conclusion

The income of a neighborhood has little to no effect on partisanship in California. Income is statistically significant without controlling for any other factor. However, even then, it only accounts for 5-10% of the variation in the Democratic share of the vote in the two California elections analyzed. This holds true whether the income of an area is measured by median household income or the poverty rate.

Once accounting for race, however, income tends to lose all statistical significance. The wealth of heavily Hispanic Census Block Groups in California appears to have no effect on whether or not the area votes Democratic. This holds true whether or not one uses median household income or the poverty rate to measure wealth, whether or not one uses a linear or quadratic model to measure wealth, and whether or not one looks at the 2008 United States presidential election or the 2010 California gubernatorial election.

There are two puzzling exceptions in which income was found to be statistically significant. These are puzzling because they lend to opposite conclusions. Under a dummy variable model, heavily Hispanic Census Block Groups with poverty rates less than twice the national poverty rate, but still above the national average, were found to be more likely to vote Democratic.

Each Census Block Group is located in a Census Tract. Under an analysis in which the percent Mexican of the Census Tract was added to the original regression, as poverty rates increased the Democratic share of the vote was found to decrease. The former result implies that the income of an area is positively correlated with the Democratic share of the vote; the latter result implies that the income of an area is negatively correlated with the Democratic share of the vote. Indeed, throughout this analysis the coefficients on income do not remain the same.

Amongst whites, one finds that the affluence of a neighborhood also has no effect. However, great caution is warranted in this analysis due to the low sample size of the extremely white Census Block Groups. Nevertheless, the white vote also appears unaffected by whether or not one uses median household income or the poverty rate to measure income, and whether or not one looks at the 2008 United States presidential election or the 2010 California gubernatorial election.

It is often taken for granted that higher-income areas vote more Republican, whereas lower-income areas vote more Democratic. The party platforms appear to substantiate these assumptions. Democrats argue for the need of a more progressive income tax, for instance, while Republicans emphasize the advantages of job-creating (small) business owners. The most recent 2012 United States presidential election was marked by Democratic attacks on the Republican candidate as a very wealthy boss disconnected from the working class. Republicans argued that the Democratic candidate was a tax-and-spend believer in socialism. These appeals are not unique to the 2012 United States presidential election; rather they are typical of the rhetoric and strategy employed in political campaigns in the United States.

These arguments are also heavily weighted in appeals to income. And yet – at least in the state of California – they appear to have little effect. At most 10% of the variation in the Democratic share of the vote in either the 2008 United States presidential election or the 2010 California gubernatorial election is explained by the affluence of a neighborhood. Once accounting for race the effect disappears altogether.

One caveat is that California may have differences compared to the rest of the United States. California is more diverse, more populous, more urbanized, more western, and more Democratic than the typical state. This may affect voting patterns in the state. The last factor is particularly important; research indicates that the income gap is greater amongst more Republican states. Nevertheless, California is important. More than one in ten Americans live in California. The state is heavily Hispanic, and Hispanics are a rapidly growing and much-analyzed political group in the United States.

The findings of this study have important political implications. Both parties will take important lessons from this point. Republicans can conclude that income is not destiny. The fact that Hispanics generally tend to live in poorer communities need not make them an inevitably Democratic group. Hispanics, especially immigrants, are often said (especially by Republicans) to be more conservative than the norm. A Republican strategist might be heartened by the evidence from this study, which implies that economics are not the determinate factor in the Hispanic vote. The Hispanic community might be wide open to appeals based on social values.

Democrats, on the other hand, can take heart in another fact. Despite the results of this study, Hispanics are undeniably still voting strongly Democratic. It therefore must be another factor that is drawing Hispanics to the Democratic Party. A Democratic strategist might argue that immigration and racial issues are drawing the Hispanic community to his or her party. The Democratic Party has traditionally been seen as more sympathetic than the Republican Party to immigrants and a path to citizenship for undocumented immigrants. This is a concern that resonates heavily with the Hispanic community. As long as the Republican Party remains hostile to immigration reform, a Democrat might claim, Hispanics will continue to vote Democratic even if their incomes increase. Similarly, the Republican Party is often accused of being more hostile to minorities, such as Hispanics, than the Democratic Party. This may continue to drive Hispanics to the Democratic Party. Democrats might derive comfort from the belief that the growing Hispanic community will strengthen Democratic dominance even as it integrates into the American fabric.

All in all, it is striking the extent to which an area’s affluence and partisanship are unrelated. This is a unique and not necessarily intuitive finding. It goes strongly against common strains of thought in political science. Indeed, in many countries income is strongly predictive of one’s vote. To take one example, in the United Kingdom the Labour Party is very strong amongst the working class, whereas the Conservative Party has a strong hold on the upper classes. Yet apparently neither in California nor the United States is this true (if California is a good representation of the United States). At least in California, whether you live in a rich or poor neighborhood has absolutely no effect on whether or not you live in a Democratic or Republican neighborhood.

References

Abrajano, Marisa A., Jonathan Nagler, and Michael R. Alvarez (2005). “A Natural Experiment of Race-Based and Issue Voting: The 2001 City of Los Angeles Elections”. Political research quarterly (1065-9129), 58 (2), p. 203-218.

Abrajano, Marisa A., Michael R. Alvarez, and Jonathan Nagler (2008). “The Hispanic Vote in the 2004 Presidential Election: Insecurity and Moral Concerns”. The Journal of politics (0022-3816), 70 (02), p. 368.

Aistrup, Joseph A. (2010). “Southern Political Exceptionalism? Presidential Voting in the South and Non-South* Presidential Voting in the South and Non-South”. Social science quarterly (0038-4941), 91 (4), p. 906-927.

Alvarez, Michael R. and Lisa García Bedolla (2003). “The foundations of Latino voter partisanship: Evidence from the 2000 election”. The Journal of politics (0022-3816), 65 (1), p. 31-49.

Brewer, Mark D. and Jeffrey M. Stonecash (2001). “Class, race issues, and declining white support for the Democratic Party in the South”. Political behavior (0190-9320), 23 (2), p. 131-155.

Brooks, Clem and David Brady (1999). “Income, Economic Voting, and Long-Term Political Change in the U.S., 1952- 1996”. Social forces (0037-7732), 77 (4), p. 1339-1374.

Brooks, Clem and Jeff Manza (1997). “Class Politics and Political Change in the United States, 1952-1992”. Social forces (0037-7732), 76 (2), p. 379-408.

De la Garza, Rodolfo O. (2004). “Latino Politics”. Annual review of political science (1094-2939), 7 (1), p. 91-123.

De la Garza, Rodolfo O. and Jeronimo Cortina. (2007). “Are Latinos Republicans But Just Don’t Know It?: The Latino Vote in the 2000 and 2004 Presidential Elections”. American politics research (1532-673X), 35 (2), p. 202.

Dutwin, David, Mollyann Brodie, Melissa Herrmann, and Rebecca Levin. (2005). “Latinos and Political Party Affiliation”. Hispanic journal of behavioral sciences (0739-9863), 27 (2), p. 135-160.

Edison Research. “ Election Center 2008: Exit Polls.” CNN. CNN, 4 11 2008. Web. 10 Feb 2013. <http://www.cnn.com/ELECTION/2008/results/polls/ >

Gelman, Andrew, Boris Shor, Joseph Bafumi, and David Park. “Rich State, Poor State, Red State, Blue State: What’s the Matter with Connecticut?” Quarterly Journal of Political Science. 2. (2007): 345-367. Web. 10 Feb. 2013. <http://www.stat.columbia.edu/~gelman/research/unpublished/redblue11.pdf >.

Leal, David L., Matt A. Barreto, Jongho Lee, and Rodolfo O. de la Garza (2005). “The Latino vote in the 2004 election”. PS, political science & politics (1049-0965), 38 (1), p. 41.

Leighley, Jan E. and Jonathon Nagler (1992). “Socioeconomic Class Bias in Turnout, 1964-1988: The Voters Remain the Same”. The American political science review (0003-0554), 86 (3), p. 725-736.

Lopez, Mark Hugo, and Paul Taylor. “ Latino Voters in the 2012 Election .” Pew Research Hispanic Center. Pew Research Hispanic Center, 07 11 2012. Web. 10 Feb 2013. <http://www.pewhispanic.org/2012/11/07/latino-voters-in-the-2012-election/ >.

Macartney, Suzanne, Alemayehu Bishaw, and Kayla Fontenot. “Poverty Rates for Selected Detailed Race and Hispanic Groups by State and Place: 2007–2011.” American Community Survey Briefs. 11.17 (2013): 2. Web. 15 Apr. 2013. <http://www.census.gov/prod/2013pubs/acsbr11-17.pdf >.

Segura, Gary, and Matt Barreto. “How the National Exit Poll Badly Missed the Latino Vote in 2010.” Latino Decisions. Latino Decisions, 04 11 2010. Web. 10 Feb 2013.

Silver, Nate. “Ten Reasons Why You Should Ignore Exit Polls.” FiveThirtyEight. FiveThirtyEight, 04 11 2008. Web. 10 Feb 2013. <http://www.fivethirtyeight.com/2008/11/ten-reasons-why-you-should-ignore-exit.html >.

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How Japanese and Americans Save Differently

In America a dollar today is worth much less than a dollar in 1980. Americans marvel at how much cheaper things used to be in the past, when Coca-Cola and movies only cost five cents.

In Japan the story is quite different. For long periods in the past two decades, nominal prices have in fact declined.

These facts reflect a conscious decision made by the authorities of both countries. The United States, like most of the world, has accepted and even encouraged a slight degree of inflation. The belief is that this will encourage spending, providing a continuous boost to the economy.

Japan – for better or worse, probably for worse – has done things differently. It has not encouraged inflation; prices have stayed the same or even decreased in nominal terms. This reflects the strong power of the often elderly Japanese saver, who doesn’t want her savings to lose value. Today Japan’s new government is undertaking a bold course by forcing the Central Bank to print as much money as possible until inflation hits 2%.

These different policies have created different attitudes towards saving in both countries. In the United States saving is often associated with the stock market. Americans ought to invest their money in the stock market for retirement because “the stock market will always go up.” Japan’s stock market has done poorly over the past few decades, however. As of this writing, the Nikkei 225 is at 14,937.56 – far below its high of 38,957.44 in 1989. A Japanese saver who invested all his earnings in stocks during the 1980s would have lost a lot of money. Instead, Japanese households keep their earnings in cash. This graphic, from the Wall Street Journal, shows this:

Household Financial AssetsCash is a much safer way of saving in Japan than in the United States; it doesn’t lose as much value over time, unlike here.

There is much to be said about the Japanese way of saving. The American method is complicated. Investing in stocks requires lots of tough decisions: how much money to put in, how conservatively to invest it, which mutual fund or plan is best, how to diversify, etc. There are lots of big words which the typical person might not understand. The whole process often takes a considerable amount of time and effort. The poor often lack this.

In a sense, then, the American system of saving exacerbates income inequality. Wealthy Americans save in stocks and bonds and are shielded from inflation. Poor Americans save in cash and bear the brunt of the blow. Is it a coincidence that Japan is much more equal than America?

For all its disadvantages, however, the American system is probably better than the Japanese system. It is Japan which is trying to create inflation and make itself more like America, not the other way around. What is more, Shinzo Abe’s government is wildly popular for doing so.

Still, few policymakers consider how a system of saving based on the stock market, due to inflationary pressures encouraged by the government, widens income inequality. Perhaps the overall economic advantages still outweigh the disadvantages. But more people ought to pay attention to the disadvantages.

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Do Tourists Get Sick When They Visit the United States?

It’s a common thing when Americans visit other countries. They get sick. An American visits a friend in a foreign country, consumes everything on the plate her friend has prepared (out of politeness, not because she actually likes the food), and spends the rest of her week sitting on top of a toilet.

It’s the different environment and the lower sanitary conditions, people say. Your immune system just has to get used to the bacteria there, that’s all.

It makes one wonder, however. Do foreign tourists ever get sick when they visit the United States? Does an Indian visiting her American friend ever eat everything on the plate her friend has prepared (out of politeness, not because she actually likes the food), and spend the rest of her week sitting on top of a toilet?

I’ve never thought about this until now, and I honesty don’t know. Does anybody reading this know?

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Did You Know that the United Kingdom Censored Animal Farm? Here’s Why:

Animal Farm is one of the most famous tracts of the 20th century. It’s a prescient warning against totalitarianism. Its influence is such that phrases such as “All animals are equal, but some animals are more equal than others” are still used today.

Yet for several years before its publication in the United Kingdom, Animal Farm was censored. Why did this happen? Well, the answer is quite simple.

It criticized the Soviet Union.

Animal Farm may have been about animals, but the farm was quite obviously the Soviet Union. Napoleon, the dictator pig, was obviously Stalin. It was in the midst of World War II at that time, and the Soviet Union was bearing the brunt of the Nazi attack. The Russians were a vital ally of the United Kingdom.

No reputable publishing organization was willing to lay hands on a text which tarred the Soviet Union so blatantly. One publisher wrote:

I mentioned the reaction I had had from an important official in the Ministry of Information with regard to Animal Farm. I must confess that this expression of opinion has given me seriously to think … I can see now that it might be regarded as something which it was highly ill-advised to publish at the present time. If the fable were addressed generally to dictators and dictatorships at large then publication would be all right, but the fable does follow, as I see now, so completely the progress of the Russian Soviets and their two dictators, that it can apply only to Russia, to the exclusion of the other dictatorships. Another thing: it would be less offensive if the predominant caste in the fable were not pigs. I think the choice of pigs as the ruling caste will no doubt give offence to many people, and particularly to anyone who is a bit touchy, as undoubtedly the Russians are.

George Orwell, in protest, included an introduction arguing for the need for free speech. He wrote a preface to the novel which can be found here. The preface included the argument that:

At this moment what is demanded by the prevailing orthodoxy is an uncritical admiration of Soviet Russia. Every-one knows this, nearly everyone acts on it. Any serious criticism of the Soviet régime, any disclosure of facts which the Soviet government would prefer to keep hidden, is next door to unprintable. And this nation-wide conspiracy to flatter our ally takes place, curiously enough, against a background of genuine intellectual tolerance. For though you are not allowed to criticize the Soviet government, at least you are reasonably free to criticize our own. Hardly anyone will print an attack on Stalin, but it is quite safe to attack Churchill, at any rate in books and periodicals…

Stalin is sacrosanct and certain aspects of his policy must not be seriously discussed. This rule has been almost universally observed since 1941, but it had operated, to a greater extent than is sometimes realized, for ten years earlier than that…You could, indeed, publish anti-Russian books, but to do so was to make sure of being ignored or misrepresented by nearly the whole of the highbrow press. Both publicly and privately you were warned that it was ‘not done’. What you said might possibly be true, but it was ‘inopportune’ and ‘played into the hands of’ this or that reactionary interest. This attitude was usually defended on the ground that the international situation, and the urgent need for an Anglo-Russian alliance, demanded it; but it was clear that this was a rationalization. The English intelligentsia, or a great part of it, had developed a nationalistic loyalty towards the USSR, and in their hearts they felt that to cast any doubt on the wisdom of Stalin was a kind of blasphemy.

Times were different back then. It takes a very different frame of mind to think of a world in which this “nationalistic loyalty towards the USSR” exists amongst the British intellectual elite. The censorship of Animal Farm would probably not have happened in the United States, where anti-communism sentiment was much greater. Yet it happened in the same world we live in, albeit one different in surprising ways.

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Japan’s Strange Isolation, and How It Hurts Us and Japan

Japan is one of the world’s most advanced economies. In some ways, however, its economy is quite isolated. This is a result of both geography and history. Japan is both an island alone and a former colonial power heartily disliked by its immediate neighbors.

This is different from most other advanced economies. Any Western country has close links with the rest of the West. Take the United States. American culture is fairly similar to Western culture, and there is a deep degree of mixing between the Anglosphere, the United States, and Western Europe. In that sense the market for American companies is not just 300 million people, but rather 800 million people.

Japan is different. Japan should have just as close a connection with East and Southeast Asia as the United States has with Western Europe and the Anglosphere. But due to its colonial past, it doesn’t. In a sense Japan is like Israel, surrounded by hostile neighbors. The irony is that there are a lot of similarities between Japan’s culture and its path of development compared to those of its neighbors. But its neighbors would rather pretend that this similarity doesn’t exist.

So Japan is left isolated. But Japan is also a big and wealthy country, with well over 100 million people. It can afford to remain splendidly isolated. Japanese companies can market to the 125 million Japanese consumers and ignore the rest of the world.

This is bad for both Japan and the rest of us. Japan, in its isolation, has developed a number of unique practices and products. Some are better than the rest of the world’s; some are worse. In an ideal world we would copy what Japan does well, and Japan would copy what we do well.

This happens, but not enough. For instance, Japan has incredibly advanced cellphones. Long before Apple came up with the iPhone, you could check your e-mail, pay for your groceries, surf the web, and watch TV on a Japanese cellphone. In fact, in the United States you still can’t do some of these things with the iPhone! Unfortunately, consumer tastes in the West were too different for Japanese electronic companies to penetrate. The natural thing for them to do would have been to market to China and Korea, and from there spread the technology to the rest of the world. Too bad China and Korea hate Japan.

The opposite story holds true with something surprising, the fax machine. Today faxing is almost a relic in most of the world. Few young people even know how to use a fax machine, and I am not one of them. In their splendid isolation, however, Japanese companies are still highly dependent on the fax machine. An enormous amount of energy, paper, and time is wasted faxing in Japan. Here Japan would do well to copy from the rest of the world and abandon this increasingly unique Japanese practice.

In an ideal world, Japanese companies would have spread the smart-phone to us years before Apple came up with the idea, and our advances in technology would have convinced Japanese companies to abandon the wasteful fax machine years ago. Both sides would have been better off. Alas, this hasn’t happened. Instead, Japan and the rest of the world continue to remain too far apart, to the detriment of both.

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The Hillary Clinton Gender Gap

Four years before the 2016 presidential election, and before we even know who is running, some enterprising pollsters have released polls matching Hillary Clinton against an assorted group of potential Republican candidates. Clinton does well; she leads in a number of red states.

In these polls, one interesting constant is the massive gender gap that Hillary Clinton opens up.

Here are as many 2016 polls that I could find. They show the gender gap in hypothetical match-ups between Clinton and various Republicans.

Date State Hillary Clinton (Overall) Hillary Clinton (♀) Hillary Clinton (♂) Republican Gender Gap
25-Apr NH 52 61 42 41 19
25-Apr NH 52 60 43 38 17
23-Apr CO 48 53 41 45 12
23-Apr CO 48 53 42 44 11
19-Apr NC 49 54 43 42 11
19-Apr NC 52 57 46 40 11
19-Apr NY 59 64 53 32 11
11-Apr KY 45 50 40 45 10
11-Apr KY 46 51 41 40 10
3-Apr US 46 51 41 42 10
3-Apr US 49 54 45 43 9
3-Apr US 49 53 46 42 7
3-Apr US 50 53 46 43 7
3-Apr US 46 47 45 43 2
3-Apr US 54 56 52 38 4
3-Apr US 52 57 46 40 11
3-Apr US 52 59 44 41 15
21-Mar FL 53 58 47 40 11
21-Mar FL 56 61 50 40 11
21-Mar FL 54 60 49 41 11
21-Mar FL 51 56 45 40 11
21-Mar FL 52 55 48 41 7
14-Mar PA 47 55 39 42 16
14-Mar PA 54 62 45 36 17
14-Mar PA 55 63 45 38 18
13-Mar PA 52 55 49 37 6
13-Mar PA 52 56 49 40 7
13-Mar PA 55 58 52 38 6
8-Mar MI 51 57 44 37 13
8-Mar MI 52 59 44 41 15
7-Mar US 45 51 38 37 13
7-Mar US 50 56 43 34 13
7-Mar US 49 49 36 42 13
28-Feb WI 52 56 47 38 9
28-Feb WI 51 56 45 43 11
28-Feb WI 54 59 48 41 11
27-Feb KS 42 49 34 47 15
27-Feb KS 42 52 34 50 18
21-Feb MT 42 47 37 50 10
21-Feb MT 44 47 41 51 6
20-Feb NJ 49 60 35 45 25
20-Feb GA 49 54 42 46 12
20-Feb GA 50 55 44 45 11
14-Feb LA 48 56 40 45 16
14-Feb LA 46 54 39 43 15
14-Feb LA 46 52 40 46 12
8-Feb AK 42 47 35 43 12
8-Feb AK 53 60 46 37 14
8-Feb AK 44 49 37 43 12
7-Feb US 49 54 44 43 10
7-Feb US 46 53 38 41 15
7-Feb US 49 54 44 41 10
7-Feb US 50 55 45 44 10
31-Jan TX 45 48 41 43 7
31-Jan TX 50 54 45 42 9
31-Jan TX 46 51 40 45 11
24-Jan MN 44 51 38 38 13
24-Jan MN 50 57 43 37 14
17-Jan FL 49 54 43 44 11
17-Jan FL 50 56 43 46 13
10-Jan US 51 54 47 38 7
10-Jan US 44 52 37 42 15
10-Jan US 51 55 47 37 8
10-Jan US 53 58 48 39 10

Comparatively, in 2012 women gave Barack Obama 55% of the vote, compared to the 45% that he won amongst men. The gender gap in that year was 10 points. In 2008 it was 7 points (Obama won 49% of males and 56% of females). The largest ever gender gap in recorded exit polling occurred in the 2000 presidential election, when Al Gore won only 42% of men but 54% of women.

It appears that Clinton would open a huge gender gap. The largest gender gap in this table (25 points) comes when she goes head-to-head against Governor Chris Christie of New Jersey. Christie’s brash style is very popular amongst men but much less so with women.

One point of caution is that the vast majority of these polls come from just one polling firm: Public Policy Polling (PPP). It’s possible that this gender gap is simply due to a mistake in the way PPP is polling. Interestingly, the two results with the lowest gender gaps (of two and four points) came from a national poll by McClatchy-Marist. So take caution in interpreting these results.

Nevertheless, as potentially America’s first female president, Clinton appears to be able to count strongly on the support of women.

P.S. Since the full table was too wide for WordPress, I took out a couple of sections. The full table is below.

Date Polling Firm State Hillary Clinton (Overall) Hillary Clinton (Women) Hillary Clinton (Men) Republican Candidate Gender Gap
25-Apr Public Policy Polling New Hampshire 52 61 42 41 19
25-Apr Public Policy Polling New Hampshire 52 60 43 38 17
23-Apr Public Policy Polling Colorado 48 53 41 45 12
23-Apr Public Policy Polling Colorado 48 53 42 44 11
19-Apr Public Policy Polling North Carolina 49 54 43 42 11
19-Apr Public Policy Polling North Carolina 52 57 46 40 11
19-Apr Quinnipiac New York 59 64 53 32 11
11-Apr Public Policy Polling Kentucky 45 50 40 45 10
11-Apr Public Policy Polling Kentucky 46 51 41 40 10
3-Apr Public Policy Polling National 46 51 41 42 10
3-Apr Public Policy Polling National 49 54 45 43 9
3-Apr Public Policy Polling National 49 53 46 42 7
3-Apr Public Policy Polling National 50 53 46 43 7
3-Apr McClatchy-Marist National 46 47 45 43 2
3-Apr McClatchy-Marist National 54 56 52 38 4
3-Apr McClatchy-Marist National 52 57 46 40 11
3-Apr McClatchy-Marist National 52 59 44 41 15
21-Mar Public Policy Polling Florida 53 58 47 40 11
21-Mar Public Policy Polling Florida 56 61 50 40 11
21-Mar Public Policy Polling Florida 54 60 49 41 11
21-Mar Quinnipiac Florida 51 56 45 40 11
21-Mar Quinnipiac Florida 52 55 48 41 7
14-Mar Quinnipiac Pennsylvania 47 55 39 42 16
14-Mar Quinnipiac Pennsylvania 54 62 45 36 17
14-Mar Quinnipiac Pennsylvania 55 63 45 38 18
13-Mar Public Policy Polling Pennsylvania 52 55 49 37 6
13-Mar Public Policy Polling Pennsylvania 52 56 49 40 7
13-Mar Public Policy Polling Pennsylvania 55 58 52 38 6
8-Mar Public Policy Polling Michigan 51 57 44 37 13
8-Mar Public Policy Polling Michigan 52 59 44 41 15
7-Mar Quinnipiac National 45 51 38 37 13
7-Mar Quinnipiac National 50 56 43 34 13
7-Mar Quinnipiac National 49 49 36 42 13
28-Feb Public Policy Polling Wisconsin 52 56 47 38 9
28-Feb Public Policy Polling Wisconsin 51 56 45 43 11
28-Feb Public Policy Polling Wisconsin 54 59 48 41 11
27-Feb Public Policy Polling Kansas 42 49 34 47 15
27-Feb Public Policy Polling Kansas 42 52 34 50 18
21-Feb Public Policy Polling Montana 42 47 37 50 10
21-Feb Public Policy Polling Montana 44 47 41 51 6
20-Feb Quinnipiac New Jersey 49 60 35 45 25
20-Feb Public Policy Polling Georgia 49 54 42 46 12
20-Feb Public Policy Polling Georgia 50 55 44 45 11
14-Feb Public Policy Polling Louisiana 48 56 40 45 16
14-Feb Public Policy Polling Louisiana 46 54 39 43 15
14-Feb Public Policy Polling Louisiana 46 52 40 46 12
8-Feb Public Policy Polling Alaska 42 47 35 43 12
8-Feb Public Policy Polling Alaska 53 60 46 37 14
8-Feb Public Policy Polling Alaska 44 49 37 43 12
7-Feb Public Policy Polling National 49 54 44 43 10
7-Feb Public Policy Polling National 46 53 38 41 15
7-Feb Public Policy Polling National 49 54 44 41 10
7-Feb Public Policy Polling National 50 55 45 44 10
31-Jan Public Policy Polling Texas 45 48 41 43 7
31-Jan Public Policy Polling Texas 50 54 45 42 9
31-Jan Public Policy Polling Texas 46 51 40 45 11
24-Jan Public Policy Polling Minnesota 44 51 38 38 13
24-Jan Public Policy Polling Minnesota 50 57 43 37 14
17-Jan Public Policy Polling Florida 49 54 43 44 11
17-Jan Public Policy Polling Florida 50 56 43 46 13
10-Jan Public Policy Polling National 51 54 47 38 7
10-Jan Public Policy Polling National 44 52 37 42 15
10-Jan Public Policy Polling National 51 55 47 37 8
10-Jan Public Policy Polling National 53 58 48 39 10
Posted in 2016 Presidential Election, Clintons, Gender | Tagged , , , , , | 5 Comments

What Foreign Language Do North Koreans Learn? English!

North Korea has been in the news in the past few days and weeks. Most of the attention has been negative; tensions have risen between North Korea and the United States. Not that this is new. The two countries have had bad relations – to say the least – for many decades now.

Coincidentally, just today I was watching a BBC documentary on life in North Korea.  The documentary is very good. It shows that North Koreans are just humans as well with dreams like the rest of us. On the other hand, there is something strange and broken about the society in the film.

Anyways, skip to 5:04.

It’s an English class, in North Korea! The middle-school children are learning English as their main foreign language.

This was truly shocking to me. English classes in North Korea! The concept is mind-blowing. One would think for sure that the main foreign language taught in North Korea would be Russian or Chinese. But nope, it’s English, the language of the imperialists. Because “foreign language is a weapon for the life and struggle.”

Apparently the children of the elite speak English in North Korea. For instance, the granddaughter of the chairman of North Korea’s parliament is apparently “learning English from British native speakers.” The United Kingdom has a British Council which sends a few English teachers to North Korea. It’s said that Kim Jong-Il once told Madeleine Albright about his desire “to invite Korean Americans to teach English to North Korean students.”

It’s also interesting listening to the lesson itself and the quality of the English being spoken. I had a hard time understanding what the announcers were saying; I got a bit more on the second pass. The man seemed to be a bit worse. The students spoke very quickly, and about half of it was comprehensible.

The teacher, on the other hand, spoke very very good English. She had a British(?) accent. It’s pretty amazing that she learned English in North Korea. I have no idea how she managed this. There were several Americans who deserted to North Korea in the ’70s, but if she had learned from them she would have had an American accent. To gain such an accent, she must have been young when she learned; since the British Council’s endeavors are only a few years old, she couldn’t possibly have learned from them. Perhaps a boarding school in the United Kingdom?

It really speaks to the power of the Anglosphere that even the primary foreign language taught in North Korea is English. And the North Koreans are doing pretty good given the lack of native English speakers willing to work in the country; their English might actually be better than Japan’s. That’s pretty embarrassing for Japan.

All in all, this is probably a good thing for North Korea. It’s impossible to truly learn a language without learning the culture of the countries which speak that language. North Korea may believe that “foreign language is a weapon for the life and struggle,” but foreign language is also a conduit to cultural influence. If the North Korean elite learn English, they’ll be exposed to an incredibly potent firehose of information. Hopefully that will bring positive change to North Korea.

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