Macroeconomics Q4

Kenji Sato
2016-12-07

Welcome to Macroeconomics (Q4)!

Name:
- Kenji Sato
- PLEASE DO NOT call me “Professor!”
- “Kenji” or “Sato-san” are preferable. “Sato-sensei” is acceptable.
Fields of Interest:
- Macroeconomic Dynamics
- Economics of Innovation
- Comparative Statics/Dynamics
For course mechanics, take a look at the handout.

Welcome to Macroeconomics (Q4)!

Office hours
Textbook
Schedule
Grading
Course websites

Welcome to Macroeconomics (Q4)!

Macroeconomics is a study of such aggregate economic evariables as …
- Gross Domestic Product (GDP)
- Growth Rate
- Business Cycles
- Unemployment Rate
- Inflation Rate
- Income Inequality

Welcome to Macroeconomics (Q4)!

In this course, you are going to study …
- basic machinery of graduate-level macroeconomics;
- how to code computer programs to compare theory with data;
- theories on how GDP and growth rates move.
To learn how to solve growth models, you will need to learn
- differential equations and difference equations, and
- dynamic optimization.

Limit

Because of the limited time, this course will focus on a study of economic growth theory.
- The foundation of modern macroeconomic research.
- If we have time, we will move on to the study of innovation or business cycles.
- No money, unemployment or inequality.

Call for Enrollment

You are highly recommended to take more advanced/applied courses together with this course.
- Inequality and Macroeconomics by Professor Dr. Reto Föllmi, from January 26 to 31.
- Please go to Kyoumu for enrollment!

Programing Languages

After taking Watkins's courses, I believe you are familiar with R programing language.

I will also use R when I show you data.
- Highly recommended to type yourself to reproduce plots.
- Building muscle memory is a necessary part of climbing up the learning curve.
Sometimes I will use Python for performing simulations.
- Because I like Python.

Penn World Table, Ver. 8

Feenstra, Inklaar and Timmer (2015) “The next generation of the Penn World Table,” American Economic Review 105(10). 3150–82.

library(readstata13)
library(dplyr)
library(ggplot2)
pwt8 = read.dta13("pwt90.dta")

Penn World Table, Ver. 8 (cont'd)

You can easily analyse the data with R + dplyr.

pwt8 %>% 
  filter(country == "Japan", year > 2010) %>% 
  mutate(rgdp_per_capita = rgdpo / pop) %>%
  select(year, rgdp_per_capita)

  year rgdp_per_capita
1 2011        34979.34
2 2012        35191.24
3 2013        35476.46
4 2014        35566.22

Unit: 2011US$

Note: With R it is difficult to see the descriptive labels that STATA variables have. Install gretl to work around.

Real GDP per capita: Data processing

We are interested in evolution of real GDP per person (per capita). Becase it is a nice statistic to measure the standard of living for a country.

Let's compare those of three countries since 1980.

rgdp <- pwt8 %>% 
  filter(countrycode %in% c("JPN","USA", "KOR"), 
         year > 1980) %>% 
  mutate(rgdp_pc = rgdpo / pop) %>%
  select(country, year, rgdp_pc) %>%
  as.data.frame

Real GDP per capita: Plotting

ggplot(rgdp, aes(x=year, y=rgdp_pc, color=country)) + 
  geom_point() + geom_line(aes(group=country))

plot of chunk unnamed-chunk-4

GDP Growth

When studying the growth rates, semi-log scale is helpful.

rgdp %>% ggplot(aes(x=year, y=rgdp_pc, color=country)) + geom_point() + 
         scale_y_log10() +  # Plot in semi-log scale
         geom_smooth(method = "lm", se = FALSE)

plot of chunk unnamed-chunk-5

Why log-scale?

\[ \begin{aligned} & \text{Gross annual growth rate from 2000 to 2010} \\ &= \left(\frac{GDP_{2010}}{GDP_{2000}}\right)^{1/10} \end{aligned} \]

And so, ….

\[ \begin{aligned} & \log (\text{Gross annual growth rate from 2000 to 2010}) \\ &= \frac{\log GDP_{2010} - \log GDP_{2000}}{2010 - 2000} \end{aligned} \]

The slopes of the lines you observed in the semi-log plot correspond to the growth rates of the countries.

Change in growth rates.

The growth rates change over time.

rgdp %>% ggplot(aes(x=year, y=rgdp_pc, color=country)) + geom_point() + 
         scale_y_log10() +  # Plot in semi-log scale
         geom_smooth(method="lm", se=FALSE) + 
         geom_smooth(data=filter(rgdp, year > 1995), 
                     method="lm", se=FALSE)

Observations and Questions

There is a growing trend in per capita GDP.
- What drives the growth?
The US growth rate seems to be very stable.
- What determines the growth rate?
- Is the long-run stability of the growth rate common to all countries?
Korea experienced rapid growth recently but seems to be slowing down.
- Is this a common feature? Will every rapidly growing country slow down its growth?
- Or, will Korea accelerate the growth again?

Cross Country Differences

cross_country <- pwt8 %>% filter(year == 2014) %>% 
  mutate(rgdp_pc = rgdpo / pop) %>% 
  select(countrycode, rgdp_pc) %>% as.data.frame

ggplot(cross_country, aes(x=rgdp_pc)) + 
  geom_histogram() +  labs(x="GDP per Capita", y="Number of Countries")

plot of chunk unnamed-chunk-7

Observation

There is variation in per capita income across economies.
- What makes rich countries rich?
As we saw before, Korea is going to overtake Japan.
- Why do the growth rates change over time?