Category Archives: English

This contains all blogs primarily written in English.

The Electoral College and the Tidewater Nation

The author of American Nations: A History of the Eleven Rival Regional Cultures of North America, tries to show us why we should not view policy positions as simply “Democrat” or “Republican”.  According to Woodard, we live in a country of 11 nations that form coalitions based upon various issues.  The objective of each nation is to preserve their identity and to be influential in national politics.



Woodard (c)2011

The author suggests that contrary to popular notion of the United States being a melting pot, new arrivals either specifically moved to one of the 11 nations because the nation encompassed their values or the newcomers were assimilated, adopting the pre-existing values of a nation.   In this second scenario, the original founders of a community set the framework for that nation and new arrivals conform to or otherwise reinforce that culture.

Colin Woodard also explains that different nations in the United States held different conceptions of democracy.  The Yankeedom nation held the Nordic or Germanic conception of democracy, which encouraged near universal male suffrage.  Yankeedom was founded primarily by middle-class, well-educated Puritans.  Immigrants came in family units and they valued community structure and shared values.  When migrants settled other parts of the United States, they carried these tendencies and traditions with them.  When confronting other nations, such as New Amsterdam, the Midlands, and Greater Appalachia, they sought to impose their Puritanism.

Other nations were founded by deep inequalities.  The Tidewater and Deep South treasured Greek or Roman democratic system, where the existence of slaves coincided with their perception of democracy.   The Greek of Roman democracy exists to benefit the few, allowing a select group of men to become “enlightened” and guide their societies.  This benefit is seen as outweighing the agonies of those enslaved.  They viewed slavery as more humane than the treatment of the urban poor in the northern nations.  They reasoned that at least the slaves had a master that was supposed to care for them.  “Enlightened” Tidewater and Deep South gentry also argued that Yankeedom was a society of shopkeepers, which prevented individuals from becoming educated enough to advance their societies.

The Tidewater and Deep South were also not founded by equal proportions of men and women and tended to support the Royalists back in the United Kingdom.  During the English Civil War, then tended to side with the King.  The Tidewater saw themselves as an extension of the Norman culture while Yankeedom was Anglo-Saxon.  Things changed for the Tidewater when the British Empire sought to homogenize control over their empire.  The King redefined the rights of his British subjects.  Only those living in England had full rights.  This  clarification of who was considered an Englishman did not go over well for the gentry of the Tidewater.

It should be interesting to note that other nations did not value the democratic system at all.  New Netherland (New York) preferred a hegemonic system and hoped to be reabsorbed by the Dutch or British monarchies on several occasions.   Autocracy worked given that citizens showed tolerance towards one another.

It should not be surprising which cultures would support the continued use of the Electoral College system.  The National Constitution Center features a podcast from December 1, 2016 titled “Should we abolish the Electoral College?”.  The two panelists have biographies included on the website.  From this limited information, we might conclude that the one panelist is from either Yankeedom or the Left Coast while the other is from the Tidewater.  Given that Woodward’s theory is correct, both natives and migrants become assimilated by their nations.  In turn, panelists eventually will advocate the ideals of their nations.

This perspective is interesting because “Yankeedom” or “the Left Coast” could be considered “Democrat” in this past election cycle.  They will be on the defensive when faced with the new administration.  The representative from the “Tidewater” may or may not be considered a “Democrat”, but they come from a dying nation.   The Tidewater nation may not exist in the future. The growth of the DC metropolitan area into Maryland and northern Virginia essentially divides this nation.  Incremental growth from the Midlands also reduces its power.  With rising sea levels, the region will also loose geography to the east.  Essentially, the representative from the Tidewater seeks to preserve any formerly established advantage at all costs.

Both panelists introduce us to the history of the Electoral College.  Some of the original founders envisioned the electors to choose the President and Vice President that were most qualified for the position.  Initially the most qualified person would become the President and the second-most qualified would be Vice President.  Electors were supposed to deliberate and select candidates to run for the final election.

The Electoral College was one of the last systems established during the Constitutional Convention.  The framers were concerned about the excesses of democracy and the emergence of demagogues, but showed “haste and fatigue” by the time they got around to the Electoral College.  Modern campaigns were also not envisioned.  Founding fathers thought the President should be determined based on their reputation and history of service, not by their cleverness, or radicalism, during a campaign.

According to Alex, the electoral college was supposed to serve as a nominating board to send candidates to the House of Representatives.  From this cohort we would end up with the best candidate.  However, by the 1820s, the responsibility for narrowing down the candidate list was being usurped from the Electoral College and handed over to the political parties.

During the 19th century a series of reforms were advocated.  Since several nations exist in the same state, district elections were advocated versus the “winner-take-all”. Some also wanted to eliminate human electors.  Andrew Jackson, an Appalachian, was one of these strong advocates for changing the system.

The moderator and President of the National Constitution Center reminds us that when elections are close, the Electoral College provides us with a clear winner.  A series of small differences in certain states are magnified by the electoral system.  In effect, there is “no room for doubt”.

The Tidewater representative suggests that the smaller states look favorably on keeping the Electoral College.  Its existence helps preserve the Federation; all consistences matter.  The Yankeedom or Left Coast representative refutes this idea, starting that two strong advocates for ditching the Electoral College in favor of a popular vote came from small states, Rhode Island and North Dakota.  Candidates do not campaign in these states under the Electoral College system and they probably still would not if we switched to a Popular Election system.

Both representatives do agree that a popular vote system would lead to increased role for the federal government, since national standards for registration and voting would need to be set and enforced.  The Tidewater representative shows deep concern over this possibility.

It is important to put this concern in its proper context.  As previously mentioned, the Tidewater nation is the only one today that is at risk of extinction.  During the expansion of the Deep South, the values of the Tidewater were eroded and made more extreme, especially its policy towards slavery.  Tidewater leaders eventually followed the lead of the Deep South.  The tobacco industry declined in the Tidewater just as the cotton industry became prosperous in the Deep South.  The Deep South was also able to expand west whereas the Tidewater was cut off by a new nation, Greater Appalachia.

american-nations-advancingWoodard (c)2011

The Yankeedom representative tells us that the conception of “democracy” has changed over time.  The Electoral College does not conform with people’s every day notions of democracy.  He uses our gubernatorial and student body elections as classic examples.  In these instances the popular vote installs the new leader.

This argument rests on the belief that all people in the 11 nations share this belief.  We might question if the Deep South uses wealth and race, or if Greater Appalachia uses strength, in place of popular elections as their preferred method for finding a new leader.

The panelists also discuss the geography of states.  The blue oases in red states do not count.  Woodard addresses this issue by analyzing nations at the county level.

They also discuss the implications of a Popular Vote system.  The Tidewater representative reminds us that having “run off elections” creates an entirely different system.  Other “fringe” political parties would have a stronger initiative to enter the contest.  These “fringe” parties would be able to form coalitions and run for a second round.  The Tidewater representative also warns us that with more than two political parties, there would be less of a “moderating” influence.  It is also uncertain if third parties would increase or reduce the emergence of demagogues.  Regardless of how many exist, political parties were not viewed favorably by most of the founding fathers.


Voting Characteristics in Philadelphia’s Second Ward

The following is from information about the 27 divisions in the 2nd ward of Philadelphia.  As we seek to understand the results of the general presidential election of 2016, it helps to look at the data and trends at the local level.  Whoever best understands what happens locally can help sway the overall results on the state level. 

This data exploration can be enhanced by looking at information from the other wards.  This is a map of all the wards in Philadelphia.  This is the map of the 2nd ward which is subject to this analysis.

02.26.17 Philadelphia 2nd ward.jpg

Below is a summary of the following information from the 27 divisions. 

The original variables is this dataset are:

  • Democrats
  • Republicans
  • Independents
  • Other Party
  • Total Population
  • White
  • Black
  • Hispanic
  • Other Race
  • Male
  • Female
  • Gender Unreported

Other proportion variables will be created when appropriate.

> summary(second[,2:13])
      Dem             Rep              Ind          Other Party      Total Pop.         White      
 Min.   :293.0   Min.   : 41.00   Min.   : 3.000   Min.   : 42.0   Min.   : 379.0   Min.   : 54.0  
 1st Qu.:475.5   1st Qu.: 67.50   1st Qu.: 4.000   1st Qu.: 81.0   1st Qu.: 648.5   1st Qu.:166.0  
 Median :547.0   Median : 80.00   Median : 9.000   Median : 93.0   Median : 711.0   Median :225.0  
 Mean   :552.3   Mean   : 88.96   Mean   : 9.444   Mean   :102.0   Mean   : 752.7   Mean   :211.7  
 3rd Qu.:630.0   3rd Qu.:100.50   3rd Qu.:13.000   3rd Qu.:118.5   3rd Qu.: 846.0   3rd Qu.:246.0  
 Max.   :852.0   Max.   :203.00   Max.   :20.000   Max.   :212.0   Max.   :1252.0   Max.   :405.0  
     Black           Hispanic       Other Race         Male           Female      Gender Unreported
 Min.   :  7.00   Min.   : 6.00   Min.   :10.00   Min.   :122.0   Min.   :148.0   Min.   :112.0    
 1st Qu.: 15.00   1st Qu.:10.00   1st Qu.:20.50   1st Qu.:218.5   1st Qu.:246.5   1st Qu.:163.0    
 Median : 38.00   Median :13.00   Median :24.00   Median :256.0   Median :284.0   Median :193.0    
 Mean   : 58.11   Mean   :13.52   Mean   :27.11   Mean   :261.9   Mean   :288.1   Mean   :205.4    
 3rd Qu.: 66.00   3rd Qu.:16.50   3rd Qu.:30.00   3rd Qu.:311.5   3rd Qu.:317.0   3rd Qu.:241.0    
 Max.   :209.00   Max.   :26.00   Max.   :56.00   Max.   :413.0   Max.   :487.0   Max.   :365.0 


Are there associations between some of these variables?

1.)    Does the proportion of female voters in a division help explain the variation in the gross amount of Democratic voters?

A proportion variable is created for the female population in each division.

> second$FemaleProp <- second$Female/second$`Total Pop.`

> summary(lm(second$Dem ~ second$FemaleProp))


lm(formula = second$Dem ~ second$FemaleProp)

     Min       1Q   Median       3Q      Max

-256.340  -66.267   -1.328   75.866  302.028


                  Estimate Std. Error t value Pr(>|t|)

(Intercept)          711.4      418.8   1.699    0.102

second$FemaleProp   -415.0     1090.1  -0.381    0.707

Residual standard error: 136.8 on 25 degrees of freedom

Multiple R-squared:  0.005765, Adjusted R-squared:  -0.034

F-statistic: 0.1449 on 1 and 25 DF,  p-value: 0.7066

The percentage of females of each division’s total population does a very bad job explaining the variability in the number of democratic voters in each division.  The coefficient of determination is almost zero and the p-value is very large.

 It might be more appropriate to look at the number of Democrats in each division as a proportion rather than in total persons.

 I make another variable to represent this new proportion.


> second$DemProp <- second$Dem/second$`Total Pop.`

> summary(lm(second$DemProp ~ second$FemaleProp))


lm(formula = second$DemProp ~ second$FemaleProp)


      Min        1Q    Median        3Q       Max 

-0.075952 -0.019556  0.002954  0.029052  0.058959 



                  Estimate Std. Error t value Pr(>|t|)    

(Intercept)         0.6205     0.1146   5.415 1.28e-05 ***

second$FemaleProp   0.3045     0.2983   1.021    0.317    


Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.03744 on 25 degrees of freedom

Multiple R-squared:  0.04003, Adjusted R-squared:  0.001632 

F-statistic: 1.043 on 1 and 25 DF,  p-value: 0.317

The p-value halves, but the coefficient of determination is still very low.  The association is not even close to being statistically significant. 




2.)    Is the male proportion of the population indicative of the total Republicans in the same division? 

A new variable is created to represent the male proportion of the population of each division.

> second$MaleProp <- second$Male/second$`Total Pop.`

> summary(lm(second$Rep ~ second$MaleProp))


lm(formula = second$Rep ~ second$MaleProp)


    Min      1Q  Median      3Q     Max

-54.447 -19.993  -9.245  14.653 108.770


                Estimate Std. Error t value Pr(>|t|) 

(Intercept)       173.71      89.37   1.944   0.0633 .

second$MaleProp  -243.12     255.59  -0.951   0.3506 


Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 36.46 on 25 degrees of freedom

Multiple R-squared:  0.03493, Adjusted R-squared:  -0.003676

F-statistic: 0.9048 on 1 and 25 DF,  p-value: 0.3506


There is no statistically significant association between these the proportion of males and the amount of Republicans in a division for the same reasons stated in the first example.

Another new variable is created to represent the Republican proportion of the population of each division.

> second$RepProp <- second$Rep/second$`Total Pop.`

> summary(lm(second$RepProp ~ second$MaleProp))


lm(formula = second$RepProp ~ second$MaleProp)


      Min        1Q    Median        3Q       Max

-0.049654 -0.013996 -0.004151  0.016322  0.057392


                Estimate Std. Error t value Pr(>|t|)  

(Intercept)      0.17521    0.06203   2.825  0.00916 **

second$MaleProp -0.16729    0.17740  -0.943  0.35472  


Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.02531 on 25 degrees of freedom

Multiple R-squared:  0.03435, Adjusted R-squared:  -0.00428

F-statistic: 0.8892 on 1 and 25 DF,  p-value: 0.3547


This time the creation of a proportion versus total does almost nothing to change the coefficient of determination and p-value of the regressions.  There is no statistically significant association between the two variables at any reasonable level of significance.

However, we must also consider the quality of the data we have received.


Gender Unreported

Min.   :112.0    

1st Qu.:163.0    

Median :193.0    

Mean   :205.4    

3rd Qu.:241.0    

Max.   :365.0 


To put this in percentage terms of the total population in each division:


We do not know the gender of a sizeable percentage of voters in each division!  22% to 32% of each division does not have an identified gender.  We might have found associations between the variables in the first two examples if we had more complete data.


3.)    Do populations with a higher proportion of white voters help explain the variation in the amount of Independent party registrants? 

A proportion is created for the white voters over the total population in the division.

> second$WhiteProp <- second$White/second$`Total Pop.`

> summary(lm(second$Ind ~ second$WhiteProp))


lm(formula = second$Ind ~ second$WhiteProp)


   Min     1Q Median     3Q    Max

-7.465 -3.085  0.759  2.726  9.918


                 Estimate Std. Error t value Pr(>|t|) 

(Intercept)         2.385      3.917   0.609   0.5480 

second$WhiteProp   25.531     13.746   1.857   0.0751 .


Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 4.92 on 25 degrees of freedom

Multiple R-squared:  0.1213,  Adjusted R-squared:  0.08612

F-statistic:  3.45 on 1 and 25 DF,  p-value: 0.07507

We are much closer to finding an association between the proportion of white voters and their tendency to register as Independent versus our attempts to find associations between political parties and genders.  However, the white proportion is still not a statistically significant indicator of independent party registrants if we define alpha at 0.05.  The p-value is found as 0.07507 and the coefficient of determination is very low at 12.13%.

As we did before, we can now create a new object for the percentage of Independent party registrants from the total number of registrants per division.

> second$IndProp <- second$Ind/second$`Total Pop.`

> summary(lm(second$IndProp ~ second$WhiteProp))


lm(formula = second$IndProp ~ second$WhiteProp)


       Min         1Q     Median         3Q        Max

-0.0081644 -0.0033878 -0.0001117  0.0032048  0.0082518


                 Estimate Std. Error t value Pr(>|t|) 

(Intercept)      0.004396   0.003759   1.170   0.2532 

second$WhiteProp 0.027473   0.013191   2.083   0.0477 *


Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.004721 on 25 degrees of freedom

Multiple R-squared:  0.1479,  Adjusted R-squared:  0.1138

F-statistic: 4.338 on 1 and 25 DF,  p-value: 0.04766


As a proportion, we find the association to be significant, even though the R-squared value is only 14.79%.  We should now check the residual plots and consider the possibility of adding other variables to the model to improve the coefficient of determination.

op = par(mfrow = c(2,2))> plot(lm(IndProp ~ WhiteProp, data = second))


The residuals versus fits plot and the normal probability plot look good.  The errors are distributed normally with an approximate mean of zero and constant variance.


4.)    Does the total population of a division help explain the variation in the proportion of residents that register as another party other than Republican, Democrat, or “Independent”?

> summary(lm(second$`Other Party`~second$`Total Pop.`))


lm(formula = second$`Other Party` ~ second$`Total Pop.`)


    Min      1Q  Median      3Q     Max

-30.163 -10.212  -0.958   5.314  39.011


                     Estimate Std. Error t value Pr(>|t|)   

(Intercept)         -26.74068   12.18433  -2.195   0.0377 * 

second$`Total Pop.`   0.17109    0.01567  10.918 5.29e-11 ***


Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 15.87 on 25 degrees of freedom

Multiple R-squared:  0.8266,  Adjusted R-squared:  0.8197

F-statistic: 119.2 on 1 and 25 DF,  p-value: 5.295e-11

The total population does a good job at explaining the variability in the number of individuals that register as “other party”.  The coefficient of determination is larger at 80.97% and the predictor variable is significant at any level of significance.  This is our best result!

> op = par(mfrow = c(2,2))> plot(lm(second$`Other Party`~second$`Total Pop.`))


The residuals seem to bounce randomly about the residual = 0 line, but there are three outliers flagged by R.  These are the divisions 15, 20, and 25.  On our normal probability plot we also see that the errors are normally distributed for middle values, but not for lower and higher values.  We may need to transform the variable and/or consider another regression type besides linear.

5.)    Is there an association between those who register as another party and the amount individuals in a population that identify as white?

summary(lm(second$`Other Party`~second$White))

lm(formula = second$`Other Party` ~ second$White)
    Min      1Q  Median      3Q     Max 
-45.492 -13.703  -3.486  14.109  60.357 
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)  28.81628   13.79156   2.089    0.047 *  
second$White  0.34592    0.06099   5.672 6.63e-06 ***
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 25.21 on 25 degrees of freedom
Multiple R-squared:  0.5627,  Adjusted R-squared:  0.5452 
F-statistic: 32.17 on 1 and 25 DF,  p-value: 6.631e-06

The number of those who identify as white does a good job at explaining the variability in the number of individuals that register as “other party”.  The coefficient of determination is 56.27% and we reject the null hypothesis that there is no association at any level of significance.

This is good news.  Below are the results of the residual v. fits and normal probability plot.


Once again there are three outliers.  Division 25 appears again as an outlier, but now we should further examine the data for divisions 6 and 26.  R depicts a pattern in the residuals, or that they do no bounce randomly around the residual = 0 line.  However, we should also consider the possibility that more data would eliminate this slight pattern or appearance of “non-randomness”.

The normal probability plot is once again good for middle values, but loses its utility at lower and higher values for the divisions 6, 25, and 26.  We could try a transformation of this variable, possibly a squared version of the White variable.

Squared and cubed versions of the WhiteProp, White, and TotalPop do not enhance the models once we look at the residuals versus fits and normal probability plots.

We can attempt to regression two predictor variables on the response variable OtherParty.  Since the total population and white variables were both found to be significant individually, we can see if they together can help to explain the variability in the number of other party registrants.

> summary(lm(second$`Other Party`~second$`Total Pop.` + second$White))
lm(formula = second$`Other Party` ~ second$`Total Pop.` + second$White)
    Min      1Q  Median      3Q     Max 
-30.108 -10.240  -0.993   5.313  39.035 
                      Estimate Std. Error t value Pr(>|t|)    
(Intercept)         -2.671e+01  1.276e+01  -2.092   0.0472 *  
second$`Total Pop.`  1.708e-01  2.826e-02   6.045 3.05e-06 ***
second$White         8.462e-04  6.925e-02   0.012   0.9904    
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 16.2 on 24 degrees of freedom
Multiple R-squared:  0.8266,  Adjusted R-squared:  0.8122 
F-statistic: 57.22 on 2 and 24 DF,  p-value: 7.374e-10

When both predictor variables are included in the same model, only the total population is both to be statistically significant.


NAFTA – agricultural trends and the future of the trade agreement

Almost 7 years ago, I drafted this capstone paper (nafta_peter_r_abraldes) at the University of Pittsburgh about agricultural subsidies and the development of NAFTA.

A lot has changed since 2010.  Ironically, in 2008 it was former President Obama’s election that caused “uncertainty” around the continued development of NAFTA.  Protectionism was in the air, but NAFTA served as a stabilizer against these protectionist forces and political cycles.  Canada was able to pressure the U.S. to scrap some the “Buy American” provisions in the Stimulus Bill after the Great Recession.

If NAFTA is renegotiated, it seems unlikely that this will include provisions for the environment and labor.  The Democrats sought to add these provisions incrementally.  They were unable to include these in the agreement even though the bill was promoted by former President Clinton.

Mexico could seek to improve NAFTA in two ways.  Mexico could require that the NAFTA market price discriminate between white and yellow corn.  In the world market, white corn is priced higher, but in NAFTA both types are priced the same.  Ironically it is Mexico that “specializes” in white corn while the U.S. produces mostly yellow corn.

Mexico could also renegotiate the Rio Grande Treaty of 1944.  The northern stretch of Mexico has most of the corporate farms.  The treaty is outdated and Mexico’s water requirements are much higher than they were in 1944.

I would like to investigate a few of the topics discussed in the original draft:

  1. What has happened to the tomato industry in all three countries since 2010?  Have changes been made regarding Canada’s “bulk water transfer” prohibitions?  I believe the footnote on page 14 to now be false.  Canadian exports of tomatoes in the winter seem to have increased and some U.S. states have entered the domestic market such as Michigan and New York.  I would like to further investigate this trend.  It could possibly be limited to what I see in the Philadelphia Metropolitan Region.
  2. What are the recent rulings of the Canadian International Trade Tribunal (CITT).  What other controversies besides lumber are being discussed?
  3. Do international and regional commissions help dismantle protectionism and help policy makers overcome the influence of big business?  We should pay attention to their rulings in the face of the current U.S. administration.
  4. What were the major changes in the Agricultural Act (2014) from the Food, Conversation, and Energy Act (2008)?
  5. What were the implications for the U.S. cotton industry since 2001?  How has the WTO ruling reduced cotton production in the United States?  Which regions of the country were most effected?  Does the cotton industry still receive 13% of U.S. agricultural subsidies as it did in 2001?


U.S. States Agricultural Exports between 2000 and 2015

The USDA provides data on the value of agricultural goods exported by each U.S. state from 2000 to 2015.  The export value is measured in millions of dollars.

I observed the aggregate amount of agricultural goods exported by each state.  A more detailed analysis by type of commodity might yield more insight into why certain states changed rankings over the course of 15 years.

These other categories are:

Animal Products: Beef, Pork, Hides, Other Livestock, Dairy, Broilers, Other Poultry

Plant Products: Veggies (Fresh), Veggies (Processed), Fruits (Fresh), Fruits (Processed), Tree Nuts, Rice, Wheat, Corn, Feeds, Grain Products, Soybeans, Soymeal, Vegetable Oils, Other Oilseed Products, Cotton, Tobacco, Other Plant Products

(I would also be interested in measuring a state’s geographic proximity to a NAFTA neighbor, using an indicator variable for if this state trades with a NAFTA neighbor, or considering the size of arable land in each state.)

agricultural.exports$BASE represents the vector values for 2010

agricultural.exports$YEAR15 represents the vector values for 2015

 > weighted <- (agricultural.exports$`YEAR15`/1.38)


I wanted to analyze the 2015 values after they were adjusted for inflation.  According to the U.S. Inflation Calculator website, $1.38 in 2015 equates to $1 in 2000.  In other words, there was 38% inflation over these 15 years.

> boxplot(agricultural.exports$BASE, notch = TRUE, col = "blue", main = "M($) exports - 2000")
 > boxplot(agricultural.exports$weighted, notch = TRUE, col = "red", main = "M($) exports - 2015 inflation adj.")


As we see, there are two exporting states that represent the outliers beyond the upper whisker of each boxplot.  These states are California and Iowa.  Illinois remains in third place over the 15 years, but is not considered an outlier, even though its exports an aggregate value a little less than that of Iowa.

Over the 15 years, the first quartile showed less variance.  The same occurs in the upper whisker.  This could maybe be described as a harmonization among some states.

It may be interested to group these states by regions or indicate whether they share a border with a NAFTA country.  We would also group them as being inside or outside a certain distance from a NAFTA neighbor.

Not considering the agriculture export values as inflation adjusted can lead to form overly optimistic projects.  We can see “exponential” growth in the exporting values of the top producing states.  See the steam charts below for BASE (2000), YEAR15 (2015), and weighted (2015 adjusted for inflation).


  The decimal point is 3 digit(s) to the right of the |
  0 | 00001111111222334555566677888999
1 | 233333447999
2 | 237
3 | 14
4 |
5 |
6 | 9
> stem(agricultural.exports$YEAR15)

  The decimal point is 3 digit(s) to the right of the |

   0 | 00111222333445688122455556999
2 | 018891556678
4 | 116
6 | 134
8 | 0
10 | 0
12 |
14 |
16 |
18 |
20 |
22 | 5
> stem(agricultural.exports$weighted)
  The decimal point is 3 digit(s) to the right of the |
   0 | 0011111222233455689901111244455
2 | 0013666677903
4 | 4668
6 | 2
8 |
10 |
12 |
14 |
16 | 3

The summary of agricultural exports gives us a nice breakdown of how the value of agricultural exports changed over 15 years.  Alaska is the state that exported the least, measured in millions of dollars of agricultural product, whatever that product might be.  Adjusting for inflation, Alaska doubled the value of its agricultural exports.  California almost tripled the value of its agricultural exports.  The value of agricultural exports has at least doubled in real terms for all 50 states.

> summary(agricultural.exports)
    STATE                BASE              YEAR15            weighted       
 Length:50          Min.   :   6.204   Min.   :   16.78   Min.   :   12.16  
 Class :character   1st Qu.: 182.337   1st Qu.:  441.42   1st Qu.:  319.87  
 Mode  :character   Median : 698.612   Median : 1570.87   Median : 1138.31  
                    Mean   :1025.309   Mean   : 2661.05   Mean   : 1928.30  
                    3rd Qu.:1327.824   3rd Qu.: 3589.07   3rd Qu.: 2600.77  
                    Max.   :6853.875   Max.   :22546.99   Max.   :16338.40


Now we can compare how some states “gain ground” over others.

TX NE -2
KS IN -1
FL KS -7
NC ND -5
OH SD -1
GA OH -5
KY GA -3
CO PA -4
OK OR -5
AL NY -3
VA TN -4
MD NM -1
ME WY -1
DE CT -1
MA VT -1
NV MA -2
*The place movement is indicated for each state in relation to the place in occupied in the first column (year 2000).

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Model building: What accounts for the variation in Brazilian state GDP?

This is the first attempt to build a model that accounts for some of the observed variability in the Brazilian state GDP values.  The investigation is attached as a PDF because some of the Minitab output does not retain its original format when it is pasted into WordPress.

Here are the potential predictor variables, their type, measurement, and the source used to obtain the data:

State contribution to national GDP Quantitative Year 2010, measured in R$ 1,000,000 IBGE
Population Quantitative Year 2010, measured by individuals IBGE
Municipalities Quantitative Year 2010, measured by individual municipalities Fundação Joaquim Nabuco
Region Qualitative Year 2010, the federal government identifies five regions (more details below) Fundação Joaquim Nabuco


Foreign Border Qualitative Year 2010, border is defined as “yes” or “no” Consult any modern day geopolitical map of Brazil.
Border with MERCOSUR country Qualitative Year 2010, border is defined as “yes” or “no”; MERCOSUR is defined as sharing a border with Argentina, Uruguay, Paraguay, or Venezuela.  Venezuela was admitted to MERCOSUR in 2012, but its inclusion was considered important for the economic impact it could have had on border states Consult any modern day geopolitical map of Brazil.
Foreign Presence Quantitative This is defined as the number of consulates in a given state.  This would probably be more meaningful if we exclude the Federal District. Wikipedia
Ocean Port Quantitative The number of ports in the state (measured by various sources between the years 2009 and 2015) Wikipedia



River Port Quantitative The number of ports in the state (measured by various sources between the years 2009 and 2015).  I also considered making the predictor variables for ocean and river port qualitative. Wikipedia


Soy Quantitative The amount of thousand tons exported during the two harvests of year of the growing year 2010/2011. CONAB
Corn Quantitative The amount of thousand tons exported during the two harvests of year of the growing year 2010/2011. CONAB

Corn and soy account for about 80% of the grains produced in Brazil.  Soy has “immediate liquidity” since it is an international commodity while corn historically was used for internal supply (EMBRAPA).

When I look for significance of “region”, I should be aware that the sample size of each region is very small.  There are 3 to 9 observations per group, with 9 for the Northeast being more the exception than the rule.  I suspect this variable might have been significant if we coded municipalities per region and used their relative GDP instead of that for the state.  If we opted to include this variable in the model, we would have included four dummy variables and chosen one baseline region.

After looking at associations, I could be interesting to see if the deleted residual for the Federal District makes the point influential.  This unit is an anomaly in a few categories, especially in regards to the “foreign presence”.

Finally, I understand that with more time I would have consulted original sources instead of using data from second-hand sources.

I begin by a simple matrix plot of each variable regressed against the other.


Visually we can identify a few associations between variables:

  • Population and number of municipalities
  • Population and state GDP
  • Soy and corn exports

Correlation: Population, Municipalities, GDP, Soy, Corn


                    Population     Municipalities         GDP             Soy

Municipalities           0.762

GDP                      0.953           0.629

Soy                      0.072           0.292           0.064

Corn                     0.353           0.607           0.313           0.860


Cell Contents: Pearson correlation


To a lesser degree, associations exist between:

·         Foreign Presence and GDP

·         Soy and GDP

·         Corn and GDP

Correlation: Foreign Presence, GDP, Soy, Corn


                 Foreign Presence               GDP               Soy

GDP                          0.357

Soy                         -0.067             0.064

Corn                        -0.032             0.313             0.860


Cell Contents: Pearson correlation

 The rest of this investigation can be found via this link: 12-00-16-brazilian-gdp-project.



U.S. state fertilizer indices and growth of factor productivity levels

I use USDA data from 1960 and 2004 to create a brief exploratory analysis about what makes some states more agriculturally productive than others.

Is a higher fertilizer index associated with higher factor productivity levels?

H0: There is no correlation between fertilizer indices and the growth of factor productivity.

Ha: There is a correlation between fertilizer indices and the growth of factor productivity.


Both growth of factor productivity levels and fertilizer consumption indices are relative to Alabama in 1998.  Alaska and Hawaii are the only states excluded.

I run the regression analysis for the 1960 and 2004 data.  A small p-value for the 1960 data has us reject the null hypothesis and conclude the alternative hypothesis that a correlation between the two variables exists.  A larger p-value for the 2004 data has us fail to reject the null hypothesis. 

We should note that the fertilizer index variable explains such a small percentage of the variability in the response variable.  The data points are scattered far from the regression line.  We see this by the value of the R-sq value.  Over time, the fertilizer index predictor variable explains even less of the variability in the response variable. 

Regression Analysis: Factor Productivity (1960) versus Fertilizer Indices in 1960

 Analysis of Variance

Source                        DF   Adj SS    Adj MS  F-Value  P-Value

Regression                     1  0.07532  0.075324     7.75    0.008

  Fertilizer Indices in 1960   1  0.07532  0.075324     7.75    0.008

Error                         46  0.44736  0.009725

Total                         47  0.52269


Model Summary 

        S    R-sq  R-sq(adj)  R-sq(pred)

0.0986168  14.41%     12.55%       1.83%



Term                          Coef  SE Coef  T-Value  P-Value   VIF

Constant                    0.4969   0.0222    22.40    0.000

Fertilizer Indices in 1960  0.0499   0.0179     2.78    0.008  1.00


Regression Equation

 Factor Productivity (1960) = 0.4969 + 0.0499(Fertilizer Indices in 1960)


 Fits and Diagnostics for Unusual Observations



Obs        (1960)     Fit    Resid  Std Resid

  2        0.7057  0.5104   0.1953       2.02  R         [Arizona]

  4        0.8643  0.6561   0.2082       2.34  R  X      [California]

  8        0.8649  0.5997   0.2652       2.78  R          [Florida]

 33        0.4673  0.6438  -0.1765      -1.94     X        [Ohio]


R  Large residual

X  Unusual X



Regression Analysis: Factor Productivity (2004) versus Fertilizer Indices in 2004

 Analysis of Variance

Source                        DF  Adj SS   Adj MS  F-Value  P-Value

Regression                     1  0.1356  0.13556     2.12    0.152

  Fertilizer Indices in 2004   1  0.1356  0.13556     2.12    0.152

Error                         46  2.9435  0.06399

Total                         47  3.0791


Model Summary

       S   R-sq  R-sq(adj)  R-sq(pred)

0.252961  4.40%      2.32%       0.00%



Term                          Coef  SE Coef  T-Value  P-Value   VIF

Constant                    1.1049   0.0493    22.39    0.000

Fertilizer Indices in 2004  0.0184   0.0127     1.46    0.152  1.00

 Regression Equation

 Factor Productivity (2004) = 1.1049 + 0.0184(Fertilizer Indices in 2004)

 Fits and Diagnostics for Unusual Observations



Obs        (2004)     Fit    Resid  Std Resid

  1        1.7979  1.1305   0.6674       2.67  R     [Alabama]

  2        1.6304  1.1162   0.5142       2.07  R     [Arizona]

  4        1.5297  1.2817   0.2480       1.06     X  [California]

 13        1.3554  1.3211   0.0343       0.15     X  [Iowa]

 47        0.5777  1.1679  -0.5902      -2.36  R     [Wisconsin]

 48        0.5712  1.1103  -0.5391      -2.17  R     [Wyoming]


R  Large residual

X  Unusual X


09.06.16 fitted line plot #2.png


We could create a prediction interval for the 1960 data, but the low R-sq value indicates that this interval will be wider than desired.

We may want to include other variables in the linear regression to see if we can better capture the changes of variability of the response variable.

 Data for this brief exploratory analysis was gathered from the USDA website, specifically this page:






Sugarcane Millage in Six Brazilian States

Over time the production and millage of sugarcane in Brazil has gained importance in different states.  Historically sugarcane production was concentrated in the northeast, eastern coastal regions, and São Paulo.  Even though São Paulo has remained the definitive leader in sugarcane millage between 1980 and 2015, there have been a few interesting developments.

Certain states in the Center-West (Centro-Oeste), Southeast (Sudeste), and South (Sul) have become more important in the millage of sugarcane.

regional map of Brazil

The Programa Nacional do Álcool was implemented in 1975.  It is generally believed that this served as an impetus for increased sugarcane production, and subsequently, sugarcane millage.

For the first part of the analysis, I used data collected by the União da Indústria de Cana-de-Açúcar (UNICA).  UNICA is known as the Brazilian Sugarcane Industry Association in English.  I was particularly interested in seeing how the millage production increased between 1980 and 2015 in the following states: Goiás, Mato Grosso, Mato Grosso do Sul, Minas Gerais, Paraná, and Rio de Janeiro.

I choose not to include the state of São Paulo in the analysis.  During the time period, São Paulo remained the dominant miller of sugarcane in the country, accounting for over half of the national production.  Even though the Paulista millage of sugarcane increased by an impressive 271,813 thousand tons between the harvests of 1980/1981 and 2014/2015, the overall national millage percentage captured by the state rose only 0.13% over 25 years.

In more concrete numbers, São Paulo milled 65,967 thousand tons of sugarcane out of a national total of 123,681 thousand tons for the 1980/1981 harvest.  Following the 2014/2015 harvest, São Paulo milled 337,780 thousand tons of a national total of 632,127 thousand tons.

São Paulo is clearly the key player, but I am interested in identifying states that were once insignificant players in the millage of sugarcane in 1980 that have since captured a larger share of the market.  Including São Paulo in the graphs would have not allowed us to clearly see the significant divergences that occurred between other states.

Here is a table showing the winners and losers of the national millage of sugarcane.

state millage percentages in Brazil

[This is a link to the workable data.]

The states for the analysis are highlighted in light blue.  The main sugarcane millage losers are highlighted in yellow.  Negative ratio differences for state millage production over the national millage production during the time period are shown by highlighted red cells.

As you can see, Rio de Janeiro is included within the cohort of six states, yet its share of national millage production decreased during the 25 years.

Below are two more graphs.  The first shows the gradual decline of relevance of Rio de Janeiro in the cohort.  By the late 1980’s Paraná emerges as the leader of the pack, but falls to second place during the harvest of 2009/2010 to Minas Gerais.  Paraná falls to third place during the harvest of 2010/2011 assuming its place after Goiás.  Mato Grosso do Sul seems poised to overtake Paraná’s third place position in the near future.

annual sugarcane millage per state (thousand tons)

The next graph shows the same data in a different way.  We see what percentage of millage each state captured out of the total millage for only these six states (as opposed to the national total).  Goiás captures a remarkable market share from its peers.  In contrast, Rio’s fall is just as pronounced.  Data for Mato Grosso do Sul seems to be missing until the harvest of 1984/1985.  Possibly data for the state was still maintained by Mato Grosso for a few years after the later state’s “desmembramento”.

sugarcane millage percentage per state within the cohort between 1980 and 2015

After seeing the changes at the state level, I wanted to analyze the changes at the municipal level.  I used a report produced by the Conjuntura Econômica Goiana in 2012 written by four authors.  This report was titled “Produção e preço de cana-de-açúcar em Goiás”.

This report does not measure the sugarcane millage, but rather how the price of the commodity effects the decision to produce it.  I plan on reading this report in detail, but now I want to focus on a few areas.

Portions of the Brazilian savanna (cerrado) were previously not used to cultivate sugarcane.  Over time the agricultural frontier has expanded into different portions of Goiás.

The authors claim that Goiás benefited from certain advantages:

  • Farming costs are relatively lower;
  • The harvest is almost entirely mechanized;
  • High technological investments were made;
  • The sugarcane crop varieties chosen are relatively more productive;
  • Land prices are relatively low.

If valid, these advantages may shed light over why the millage in certain states stagnated or declined, while the millage in others has increased.

It is not necessarily true that sugarcane is harvested close to the location it is milled.  However, there has been an increase in the amount of sugar mills in Goiás to accompany the increased production.  The association will be analyzed in the next blog post.

The authors included two maps of the production of sugarcane in the state.  The two maps depict the production of sugarcane in 2000 and 2010 respectively.  Sugar production is measured in thousand tons.  In 2000, there were 11 sugar mills which increased to 36 in 2010.  This number does not include the 15 additional sugar mills that were still under construction.

The key for the sugar production remained consistent between the two maps.  Production increased most dramatically in the south and southwest portions of the state.

Below are two maps showing the changes of sugarcane production in Goiás between 2000 and 2010.

2000 Goias sugarcane production map

2000 Goias sugarcane production key

2010 Goias sugarcane production map

2010 Goias sugarcane production key



Agricultural Trends in the state of Paraná

From 1970 to 2012, there have been some general tendencies in Paranaense agriculture.  I was particularly interested in the amount of land (measured in hectares) used to produce a crop and the amount of each crop produced (measured in tons).

The data was produced by the Secretaria de Agricultura e do Abastecimento (SEAB) and the Departamento de Economia Rural (DERAL) and was made available on the Paranaense government portal. Eighteen crops were considered significant.  They are listed below:

  • Cotton (bush and tree)
  • Peanut (with shell)
  • Rice (with shell)
  • Oat (with husk)
  • Potato (the sum of three harvests annually)
  • Coffee (refined)
  • Sugarcane
  • Onion
  • Rye
  • Barley
  • Beans (the sum of three harvests annually)
  • Tabaco (in sheets)
  • Ricinus
  • Manioc
  •  Corn (the sum of two harvests annually)
  • Soy (the sum of the regular harvest and interim harvest annually)
  • Sorghum
  • Wheat

Certain crops such as soy and corn have become more popular, occupying more hectares relative to other crops.  The amount of hectares sown with wheat has been extremely volatile during the time period.  Farmers’ choice of sorghum experienced a jump during the middle of the period, but was relatively insignificant towards the beginning and end.  Sugarcane has gradually occupied more hectares, but has lost relative importance to crops such as soy and corn.  Beans and coffee have seen a relative decline in land used since 1970.

In general, the state of Paraná is more specialized agriculturally than it was in 1970.  However, sixteen of the eighteen crops are still produced on a significant scale.

01.26.16 Area cultivada em hectares por cada cultivo no Parana

To put these changes in proper context, here is how land use increased per hectare during the years studied.  The planting of soy is clearly occurring as the total area cultivated increases.

01.26.16 total area cultivada ao longo do tempo.png

I next wanted to see if the production of each commodity experienced a similar trend.  Below is a graph for the total production (measured in tons) for the aggregate of the eighteen commodities.

01.26.16 rendimento total ao longo do tempo.png

If we look at the production (measured in tons) per crop, a different reality emerges.  Sugarcane emerges as selling the highest yield.  Next corn and soy occupy their predictable places.  A sizeable amount of manioc and wheat are also produced.

01.26.16 production per ton.png

In general, Paranaense farmers today are more productive than they were in 1970.  Although land use has increased, productivity has increased by a higher factor.  In the interest of conserving the environment, the government may be able to provide economic incentives to cease the expansion of, or even revert, the agricultural frontier.  This may be achieved with minimal losses in agricultural production (measured in tons).  Higher efficiencies can be achieved with less land.

01.26.16 rendimento v. area cultivada.png

Another interesting note is that only two crops were essentially phased out of production – ricinus and sorghum.  Neither of these crops are commonly used for human consumption.  This may be an encouraging sign for food security in the state.  It could be true that farmers still cultivate other crops for local consumption, while taking advantage of their competitive advantages to produce other crops for export (nationally and internationally).





Sobering the euphoria behind the Pacific Alliance

Every day I receive dozens of articles praising developments of the Pacific Alliance.  Over time, I became disillusioned with what I read.  Each author seemed to regurgitate previous articles instead of critically analyzing new regional and global developments.

One transcending element of most reports is juxtaposing the Pacific Alliance with MERCOSUR. Arguments focus on GDP growth, future trade agreements, and foreign direct investment.

MERCOSUR has grown slow and steady, focusing first on integration within the regional trade block. Lately everyone is a critic of MERCOSUR. MERCOSUR is depicted as a “protectionist” block with socialist leanings. In reality, MERCOSUR has the scale it needs to truly develop from within and should develop strong regional industries.  My objective behind this post is to paint a sobering image of the Pacific Alliance in relation to MERCOSUR.

1.) When the economy is small, it is easy for it to grow in large percentage points.

Assumption: GDP growth is a reliable indicator for the general well-being of a country’s economy.

Suppose the following as the status quo:

Country A has one factory that produces “one unit” of GDP. This “one unit” also happens to be the only unit produced in the entire country.

Country B has four factories that each produce “one unit” of GDP. These “four units” also happen to be the only units produced in the entire country.

Suppose the following as the change to the status quo:

Country A adds two factories that each produce two more units. The first factory continues to produce the same unit as before. These are the only unit-producing identities in the entire country. The GDP effectively grew at a rate of 200%.

Country B also adds two factories that each produce two more units. The first four factories continue to produce the same units as before. These six factories are the only unit-producing identities in the entire country. The GDP effectively grew at a rate of 50%.

This growth rate may be interesting for a short-term investor, but this is less important for those committed to investing for longer periods. To get the pulse of the economy, it might be more important to know other quantitative data such as the number of new patents licensed, dispersion of factories over geographic area, and growth of median net disposable income.  Qualitative factors such popular fields of study at the university level or a happiness index rating might also be worthy of consideration.

2.) Mexico has the highest number of free trade agreements in Latin America, currently numbering twelve (Mexican Embassy in Singapore). Currently it also has preferential commercial status with the world’s largest economy, the United States.

This fact alone distorts the accomplishments of the Pacific Alliance and makes it hard to isolate which trade agreement leads to Mexican success, however you wish to define it. Mexico also acts as a Trojan horse for American or Canadian countries that seek to use Mexico as an export to countries with have free trade agreements with Mexico.

3.) Chile has been a model economy in Latin America since the 1990’s. It is well poised to guide the Pacific Alliance from the south.

However, what works for Chile may not be the answer for Colombia or Peru. When Pinochet allowed his country to transition to democracy, the Congress remained conservative. Economists were trained at the University of Chicago and returned to implement neoliberal policies. This occurred at a time when neoliberal policies were also being implemented in the “developed” world.

Historically Chile was also isolated from its Latin American neighbors. Argentina and Chile have had disputes over Patagonia and the extreme south. Chile also won a war and seized territory from Bolivia and Peru. The situation emerged since British guano businesses sought refuge in Chile as Bolivia and Peru sought policies that were more nationalistic.

Population density and social inequality in Chile now seem to be less problematic in comparison to its South American neighbors.

4.) What are the most important reasons for economic growth in Colombia?

The United States also has a free trade agreement with Colombia. Is this agreement more, equally, or less important than the Pacific Alliance?

President Santos has also taken efforts to make peace with the FARC and unite the country through infrastructure projects. The benefits from these initiatives are not observed in a bubble.

5.) There is a difference between economic development and exploitation.

As history has taught us, Latin America has often been the stage for the massive exploitation of natural resources. Today the developed world will continue to exploit the region and its labor, both skilled and unskilled.

It is rational for a country to seek to limit the perverse effects of foreign direct investment. There is a difference between long-term and speculative foreign investor. The first provides stability while the later creates the opposite. Stability has multiplier effects for society beyond the initial investment.

Any country with a coherent economic development strategy should recognize that fewer dedicated stakeholders are better than a thousand fickle ones.

6.) Some other questions include:

  • Does the Pacific Alliance partake in large development bank activity like Brazil? Is there a Banco do Desenvolvimento or BNDES? Does the Pacific Alliance seek to attract investment through foreign lenders, on its new stock exchange, or through the Inter-American Bank?
  • Will the Pacific Alliance loose its identity if it accepts extra hemispherical countries as full members that do not have cultural and linguistical similarities?
  • Will Chilean and Mexican companies dominate the investment projects in the other Pacific Alliance countries?
  • Is the group seeking neoliberal principles in an age where these are already out of use in much of the “developed” world?

I used the traditional measurement for economic growth, GDP, to determine how MERCOSUR and Pacific Alliance countries have grown over the last 15 years. The individual country data comes from the World Bank. The MERCOSUR countries include Argentina, Bolivia, Brazil, Paraguay, Uruguay, and Venezuela. The Pacific Alliance countries include Chile, Colombia, Costa Rica, Mexico, Panama, and Peru.

There are problems with the graph below. The first year of MERCOSUR is not included (1991). In contrast, there is a clear jump in 2011 when the Pacific Alliance was formed. It might be more appropriate to compare trade blocks with more equal maturities. An example would be to compare successes and failures of MERCOSUR to those of NAFTA or the European Union.

I also include all of the above-mentioned countries as if they were always members. We cannot appreciate the GDP growth in anticipation of joining an economic trade zone. I also did not account for the natural pro or anticyclical tendencies of individual Latin American economies in comparison to the United States. Weights or dummies would have been appropriate for a more thorough analysis.

10.19.15 annual GDP growth of all countries in current US$

Growth Rate Comparison Chart


Simple GDP data in Excel for individual countries and groups

Countries to watch in the future are Bolivia and Paraguay. It would be interesting to study the origin of foreign direct investment into these countries.

Bolivia GDP growth between 2000 and 2014

Paraguay GDP growth between 2000 and 2014

The Role of Public Reason in the clash between LGBT advocates and true believers

Typically, we assume that our character traits must be consistent across all aspects of our lives. A devoted partner is also a loyal employee. The same person must also be a dedicated pet owner and a steadfast patron to their favorite establishments. In the real world, this is not how it works. We are not consistent across all domains. We are all “hypocrites”.

Often times we watch people in the spotlight fail to be “consistent”. Politicians that espouse religious doctrines find themselves caught in the crossfire when they violate a sacred doctrine. Their opponents eagerly tear down the “façade” while denouncing the perpetrator as a hypocrite. This approach is misguided since we are not the perfect wholes that we imagine.

Rather than consider these character inconsistencies as a weakness, Libby Newman believes this is what allows us to engage and cooperate with greater society. Newman explores how we can harness cognitive dissonance, or “domain-differentiation”, and use it as a tool to teach public reason. The pedagogy of public reason requires at least a dispositional commitment to engage with others different from yourself in order to find common ground. This discourse requires a foundation of civic friendship or mutual goodwill” (97). Newman considers the skills of civility, sincerity, and patience as necessary conditions for exercising public reason (95).

Our nuances of character allow us to push beyond a single doctrine in order to find this common ground. Newman believes that through domain-differentiation, we are able to include “true believers”, who often find themselves marginalized in our pluralistic society. True believers can preserve their worldviews while actively engaging in the political process[1]

I thought it was interesting how Welton says that some worldviews can only be described in faith-based terms.  Also, he mentions the boundary between secularism and religion is violated depending on if the society is pre or post-Enlightenment.


As she discusses liberalism and its justification, Newman reminds us of the role of “true believers” in a democratic society. She challenges us to embrace these sectors of society and not dismiss them as remainders whose beliefs cannot be incorporated into our system. In this sense, she builds upon and deviates from the work done by Rawles and Honig (15).

As a proponent of liberalism, she also reminds us that the true believers are not the only ones unwilling to compromise.

“Secularism has its own true believers – those unwilling to compromise with citizens of faith whose processes of ethical judgment they regard as little more than superstition. There is no monopoly on intransigence!” (21).

By engaging with persons of faith, we make our liberal society stronger, including members most prone to be excluded (27).

This exchange of ideas can sometimes be contentious, but this is okay. A tranquil society is not a success sign of liberalism, and possibly indicates the breakdown of democracy, and the path towards suppression and despotism. “Public reason welcomes moral argumentation as the basis for identifying shared values. It offers the possibility of more productive conflicts, rather than the elimination of conflict” [emphasis added] (17). Public reason is “consensus seeking”, not “consensus establishing” (77).

Libby also follows the lead of O’Neill who seeks the “possible consent of actual agents” as justification for liberalism and as a corollary for the pedagogy of public reason (32). This third road is an alternative to the empirical public justification and normative public justification which both are insufficient on their own (xvi) and eventually self-defeating (xvii).

To find “possible consent”, citizens must be trained and institutions must be formed. This implies an obligation for both the State and citizen. Citizens must be “active” agents in society. Institutions must be designed so that citizens can “offer or refuse consent” of the current arrangements. In essence, these institutions are helping citizens develop the “wherewithal” of public reason. Ultimately, these citizens are then later able to reason away the existence of the very same institutions that “developed” these skills (32). The cultivation process of “wherewithal” or “public reason” is an active one, which is not always in the best interests of the institutions that foster them (40).

As a premise for the pedagogy of public reason, we must assume that “people are the basic units of deliberation and responsibility”, even though this is not universally seen as a truth (37). The role of the State and its institutions is to create a favorable environment to encourage the development and continuation of the use of public reason (42).

Newman finds support for character domain-differentiation in all subfields of psychology, minus from personality psychologists (Chapter 3). We commit the fundamental attribution error when we express ourselves using “global” characteristic traits (46).

Ie: Mark donates a lot of money to build a new cathedral. Mark must be a generous and charitable man. When Mark’s child asks him to fund his missionary trip, Mark refuses.

Mark may be hypocritical, but this is the usual conclusion if we view character traits as existing in purely “global” terms. It would be most accurate to describe our character in situational terms using “…if…then..” scenarios (59). Furthermore, most individuals cannot “fully integrate” their moral character without assuming extreme costs that would be unbearable for most (69).

Newman also considers other researchers who take an agnostic approach towards character and virtues. Scholars like Vranas (2005) “suggest that people are neither good nor bad, but morally indeterminate, that is, capable of great harm or good, depending on the situation” (51). In addition, character and virtues are always evolving. “Both co-regulation and moral network theory suggest that development involves interaction  between internal and external forces in our lives – it is neither the case that we are independent of our surroundings, nor that we are completely determined by them (see also Johnson 1993, 134). Rather, through networks and feedback loops, we create information and settle upon commitments that are unique consequences of particular interactions and experiences” (55).

We also change depending on our environment, the places where we are and the people who surround us. Lev Vygotsky paints this phenomenon in a positive light, calling is the “zone of proximal development” (56). Vygostky shows how we can perform better when surrounded by individuals with stronger skills in a given area, even if this exposure is short-term.

To further complicate the analysis, individual construal is also necessary to comprehend our world (61). Each one of us derives different points from the same situation, fixating on some, while ignoring or forgetting others. This process is supported by our previous experience.

Our first moral code is adopted without conscious thought (73). This includes all methods of thinking, even if not considered “religious”. However, the pedagogy of public reason helps us challenge this initial foundation, ultimately to build a stronger structure (our ability to reason). At a minimum, it helps us better understand those with contrasting views.

As a prerequisite of compromise, there must be a least some shared criteria. When we cannot think beyond our own ideas and listen to contrary ones, our “moral utterances are little more than statements of unfounded preference. Attempts to dress up these utterances as appeals to objective moral standards are merely manipulative efforts to induce others to share our unfounded preferences” (quote of Alasadir MacIntyre on page 73).

Commitment to public reason is essential for the development of a vibrant, liberal society. Newman traces Rawls’ argument for how instrumental commitment becomes principled commitment. Once actors realize liberalism can be used to their advantage, they become stakeholders in the system. Their initial self-motivated commitment matures to a principled commitment, believing in public reason as an end to its own. This is similar to Adam’s Smith argument of the invisible hand establishing temporary equilibriums to balance actions, desires, and resources.

Newman does not think we should expect all members in society to adopt a principled commitment to public reason. This is where she suggests an intermediary step between simple instrumental commitment and principled commitment. Newman calls this dispositional commitment.

“A dispositional commitment allows individuals to adopt the habits of public reason in a way that grants the everyday benefits of social cooperation and political deliberation (beyond simple political entrée and power) without requiring a larger dogmatic commitment to public reason” (78). Sometimes promoting dispositional commitments to public reason may be our best bet. It certainly is when there is a risk of excluding “true believers” (79).

Newman offers this alternative even though the “integralist position” of true believers may be exaggerated (82). Efforts to integrate character traits and moral values across all domains run up against limits, an important one being the differentiated nature of our minds (86). Another limit is the principal of noncontradiction, which we must accept if we settle on an integralist position instead of domain-differentiation.

In a separate post, I will use a specific contention point to highlight the need for public reason in contemporary society. The recent Scotus decision from the Supreme Court was seen as the victory for the LGBT movement over religious conservative groups. I argue now that there is a danger for the LGBT movement to push too hard and in effect violate the religious liberties of others. What the LGBT community needs to do now is step back and redefine itself.

I next will predict an eventual schism within the LGBT community. This fault line will occur between the “hardliners” and the younger generation who seeks complete integration into larger society. Younger members seek not just the advantages of marriage, but also its obligations. They will be worried about the image they portray to society. Most importantly, they will seek to distinguish the “right to marry” from “sexual liberty”.  Eventually this will mean the demise of symbols such as the rainbow flag and organizations that have exclusively LGBT objectives.

This post appears here.

Libby Newman’s book is Liberalism in Practice the Psychology and Pedagogy of Public Reason. Here is the summary from the MIT Press. I was also very fortunate to have Newman as a professor as the University of Pittsburgh in 2009.

[1] Michael Welton's article on Jurgen Habermas discusses how some worldviews can only be described in faith-based terms.  Habermas considers this an undue burden on religious communities in post-Enlightenment societies.  In pre-Enlightenment societies, it is the secularists that are unduly burdened by having to express their findings in non-secular terms.

Specialization and the Lost Art of Public Reason

In order to teach public reason, students should have an engaging classroom experience. They should have to defend their views and learn to master multiple domains.

Cláudio Amir Dalbosco argues that the increased specialization and “technification” of our curriculums are making students think less about the collective experience, social cooperation, common good, and public defense.   Even though we need students with this type of training, it does not serve our society well if it does not have a solid base in the social sciences. With a purely technical educational training, graduates are not necessarily taught solidarity and are unable to find common ground with others from different backgrounds. The lack of critical thinking and imaginative skills can effectively prevent them from becoming good “universal citizens”. In this critique, I will consider “universal students” in a national context (United States).

Without the ability to critically analyze their surroundings, there is a regression to the average (pressão do pares) and the blind submission to authority (submissão cega à autoridade). Dalbosco considers these two obstacles for a functioning democracy. Their absence eventually lead to the incapacity to think and the lack of responsibility to act.

As we seek consensus through productive conflicts, we learn public reason through a dispositional or principled commitment. This result is not a luxury, but rather a necessity for a democratic society. As we debate, we learn of our own vulnerability no matter how well learned we are in a given area. As Socrates stated “the only true wisdom is knowing that you know nothing”. This realization puts in check our own arrogance and diminishes the probability of an individual seeking an authoritarian resolution.

Dalbosco says this skill can be learned through a “cosmopolitan” education. This is similar to Newman’s idea of an education that teaches “public reason”. Dalbosco thinks that the lessons learned from a cosmopolitan education and challenge doctrines from traditional education which are based on the idea that a good citizen blindly obeys traditions and prefers unconditional subordination to critical examination and debate.

To be fair, some of the main ideas from both authors also have major divergences. This is mostly due to their backgrounds. The “cosmopolitan” education is mainly highlights the goal for Brazilians to be globally conscious. As expected, there is added emphasis on foreign language acquisition alongside pragmatism. This is what makes Brazilians natural arbitrators in the geopolitical arena.

One novelty that Dalbosco discusses is an additional advantage of language learning and translation. This is the humbling experience one has when translating from one language to another. The more you know, the more you question, the more you hesitate, and in turn, the more nuanced your answers. This leads to a greater respect for the language learning process.

These ideas will be explored in more depth here.

English version: College Education and the challenges of shaping for a democratic citizenship

O texto em português: Educação superior e os desafios da formação para a cidadania democrática