World Bank Data - Livestock Production Index and Cereal Production

The following is a graph of the of the livestock production indicies for Agintina, Australia, Brazil, the United States, and Uruguay. The light blue line is the start of the TPP discussion talks in 2005. The black line is 2008 when the negotiating counties reached 12. The last dark blue line is 2015, and the start of the US 2016 presidential election cycle. This year is hilighted since most of the candidates, regardless of their party, had a stance of pulling out of the TPP.

library(wbstats)
livestock_use <- wb(country = c("USA", "ARG", "AUS", "BRA", "UY"), indicator = "AG.PRD.LVSK.XD")
library(ggplot2)
g<-ggplot(livestock_use, aes(x=as.numeric(date), y=value, color= country)) + 
    geom_line() + ggtitle("Livestock Production Index 1961-2014")+
    theme(plot.title = element_text(size = 11, face = "bold")) +
    theme( plot.title = element_text(hjust = 0.5)) +
    ylab("Livestock Production Index (2004-2006 = 100") + xlab("Year") +
    theme(axis.title = element_text(size = 8))+ theme_classic()+
    geom_vline(xintercept = 2008, size=0.25) + geom_vline(xintercept = 2015, color = "blue4",size = 0.25)+
    geom_vline(xintercept = 2005,  color="steelblue", size=0.25)
g

These countries in perticular were chosen since they are the major livestock exporters in the pacific arena. There was signifigant worry over the US pulling out of the TPP and losing ground in the Asian consumer markets, especially China (who was not one of the orginal signers of the agreement but who was assusmed to sign on after it was enacted), as well as Japan (which would be a major win for the US agriculture). The country which was expected to fill the gap is the US stepped out of the agreement was Australia, although Brazil was also expected to become a larger competitor as well.

Looking at the above graph, I was hoping to see more of the production index increase for the US when it joined the TPP discussions (when it actually decreased). I was also hoping when the candidates were discission pulling out of the agreement that the other countries would increase in anticipation of filling the trade gap. That, however did not happen as all countries’ livestock production index decreased after the 2015 line.

I then looked to see if cereal production (which would be used for livestock feed) would better reflect the pattern I was expecting above. The lines correspond to the same years as previous.

cereal_use <- wb(country = c("USA", "ARG", "AUS", "BRA", "UY"), indicator = "AG.PRD.CREL.MT")
g_c<-ggplot(cereal_use, aes(x=as.numeric(date), y=value, color= country)) + 
  geom_line() + ggtitle("Cereal Production, 1961-2014")+
  theme(plot.title = element_text(size = 11, face = "bold")) +
  theme( plot.title = element_text(hjust = 0.5)) +
  ylab("Production(metric tons)") + xlab("Year") +
  theme(axis.title = element_text(size = 8))+ theme_classic()+
  geom_vline(xintercept = 2008, size=0.25) + geom_vline(xintercept = 2015, color = "blue4",size = 0.25)+
  geom_vline(xintercept = 2005,  color="steelblue", size=0.25)
g_c

The graph above does show a bit more of the pattern I was looking for. The US cereal production does increase around the time they joined the TPP discussions, then there is a subsequent decrease after the start of the widthdrawl discussions during the election. The other countries, as well, have a dip after the TPP countries increase to 12, and then increase after the widthdrawl discussion started in the US.

Google Data - Generational Name Searches

library(gtrendsR)
library(reshape2)
gens = gtrends(c("Boomer", "Millennials", "Generation Z", "Generation X"), gprop = "web", time = "2017-01-01 2020-01-01")[[1]]
g2<-ggplot(gens, aes(x=date, y=hits, color=keyword)) + 
  geom_line() + ggtitle("Searches for different Generational Names 2017-2020")+
  theme(plot.title = element_text(size = 11, face = "bold")) +
  theme( plot.title = element_text(hjust = 0.5)) +
  ylab("Searches") + xlab("date") +
  theme(axis.title = element_text(size = 8))+ theme_classic()
g2

The above graph is a comparison of the the different names for various generations. Millennials are clearly more popular until the large spike (which is around November 2019) where boomer is searched signifigantly more. This is the “OK boomer” viral video that was popular during that time. Searches for the other generation names also increased around that time.