ScPoEconometrics

.title[
# ScPoEconometrics
]
.subtitle[
## Introduction
]
.author[
### Mylène Feuillade, Gustave Kenedi, Florian Oswald and Junnan He
]
.date[
### Sciences Po Paris </br> 2023-02-09
]

---

---

# Welcome to ScPoEconometrics!

- In this course you will learn the core tools of ***econometrics***.
 
--

- You will also learn to use the `R` programming language!

## What is *econometrics*?

- A set of ***techniques and methods*** to answer (economic) questions with ***data***.

- Some examples!

---

# Answering Important Questions with Econometrics

[<ru-blockquote>
Does immigration *lead to* lower wages and/or higher unemployment for locals?
</ru-blockquote>](http://davidcard.berkeley.edu/papers/mariel-impact.pdf)

[<ru-blockquote>
Does raising the minimum wage *reduce* employment for the low-skilled?
</ru-blockquote>](http://davidcard.berkeley.edu/papers/njmin-aer.pdf)

[<ru-blockquote>
Does getting a college degree *afford* higher wages?
</ru-blockquote>](http://davidcard.berkeley.edu/papers/causal_educ_earnings.pdf)

[<ru-blockquote>
Do higher public debt levels *lead to* lower economic growth?
</ru-blockquote>](https://www.imf.org/external/pubs/ft/wp/2014/wp1434.pdf)

[<ru-blockquote>
Does the neighborhood you grew up in have an *impact* on your life outcomes?
</ru-blockquote>](https://academic.oup.com/qje/article/133/3/1107/4850660)

---

# Causality

* Notice that ***many other factors could have caused*** each of the outcomes mentioned.

* Often, we'll want to focus on the ***causal impact*** of just one of these factors (immigration, minimum wage, education ,etc.)

* Econometrics is about spelling out ***conditions*** under which we can ***claim to measure causal relationships***.

* We will encounter the most basic of those conditions, and talk about some potential pitfalls.

* ["Credibility Revolution"](https://www.aeaweb.org/articles?id=10.1257/jep.24.2.3) in econometrics over the past 30 years ([2022 Economics Nobel](https://www.nobelprize.org/prizes/economic-sciences/2021/press-release/) awarded to some of the main protagonists of this "revolution")

---

# R

---

---

## What is `R`?

`R` is a __programming language__ with powerful statistical and graphic capabilities.

## Why are we using `R`?<sup>1</sup>

.footnote[
[1]: This list has been inspired by [Ed Rubin's](https://github.com/edrubin/EC421S19).  
<span style="visibility:hidden">[2]: Learning `R` definitely requires time and effort but it's worth it, trust me! .</span>
]

1. `R` is __free__ and __open source__—saving both you and the university 💰💵💰.

1. `R` is very __flexible and powerful__—adaptable to nearly any task, (data cleaning, data visualization, econometrics, spatial data analysis, machine learning, web scraping, etc.)

1. `R` has a vibrant, [thriving online community](https://stackoverflow.com/questions/tagged/r) that will (almost) always have a solution to your problem.

1. If you put in the work<sup>2</sup>, you will come away with a __very valuable and useful__ tool.

.footnote[
<span style="visibility:hidden">[1]: This list has been inspired by [Ed Rubin's](https://github.com/edrubin/EC421S19).</span>  
[2]: Learning `R` definitely requires time and effort but it's worth it, trust me! 
]

---

# First Taste of R

---

---

# In Practice: Data Wrangling

* You will spend a lot of time preparing data for further analysis.

* The `gapminder` dataset contains data on life expectancy, GDP per capita and population by country between 1952 and 2007.

* Suppose we want to know the average life expectancy and average GDP per capita for each continent in each year.

* We need to group the data by continent *and* year, then compute the average life expectancy and average GDP per capita

```r
# load gapminder package
library(gapminder)
# load the dataset from the gapminder package
data(gapminder, package = "gapminder") 
# show first 4 lines of this dataframe
head(gapminder,n = 4)
```
]

```
## # A tibble: 4 × 6
##   country     continent  year lifeExp      pop gdpPercap
##   <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
## 1 Afghanistan Asia       1952    28.8  8425333      779.
## 2 Afghanistan Asia       1957    30.3  9240934      821.
## 3 Afghanistan Asia       1962    32.0 10267083      853.
## 4 Afghanistan Asia       1967    34.0 11537966      836.
```
]

---

# In Practice: Data Wrangling

* There are always several ways to achieve a goal. (As in life 😁)

* Here we will only focus on the `dplyr` way:

```r
# compute the required statistics
gapminder_dplyr <- gapminder %>% 
  group_by(continent, year) %>% 
  summarise(count = n(),
            mean_lifeexp = mean(lifeExp),
            mean_gdppercap = mean(gdpPercap))
```

```r
# show first 5 lines of the new data
head(gapminder_dplyr, n = 5)
```

```
## # A tibble: 5 × 5
## # Groups:   continent [1]
##   continent  year count mean_lifeexp mean_gdppercap
##   <fct>     <int> <int>        <dbl>          <dbl>
## 1 Africa     1952    52         39.1          1253.
## 2 Africa     1957    52         41.3          1385.
## 3 Africa     1962    52         43.3          1598.
## 4 Africa     1967    52         45.3          2050.
## 5 Africa     1972    52         47.5          2340.
```

---

# Visualisation

.pull-left[
* Now we could *look* at the result in `gapminder_dplyr`, or compute some statistics from it.

* Nothing beats a picture, though:

```r
ggplot(data = gapminder_dplyr, 
       mapping = aes(x = mean_lifeexp,
                     y = mean_gdppercap,
                     color = continent,
                     size = count)) +
  geom_point(alpha = 1/2) +
  labs(x = "Average life expectancy",
       y = "Average GDP per capita",
       color = "Continent",
       size = "Nb of countries") +
  theme_bw()
```
]

.pull-right[
<img src="chapter_intro_files/figure-html/gampminder_plot-1.svg" style="display: block; margin: auto;" />
]

---

# Animated Plotting 👌 <sup>1</sup>

.footnote[
[1]: This animation is taken from [Ed Rubin](https://raw.githack.com/edrubin/EC421S19/master/LectureNotes/01Intro/01_intro.html#40).
]

---

# R 101: Here Is Where You Start

---

---

# Start your `RStudio`!

## First Glossary of Terms

* `R`: a programming language.

* `RStudio`: an integrated development environment (IDE) to work with `R`.

* *command*: user input (text or numbers) that `R` *understands*.

* *script*: a list of commands collected in a text file, each separated by a new line, to be run one after the other.

* To run a script, you need to highlight the relevant code lines and hit `Ctrl`+`Enter` (Windows) or `Cmd`+`Enter` (Mac).

---

# `RStudio` Layout

---

# R as a Calculator

* You can use the `R` console like a calculator

* Just type an arithmetic operation after `>` and hit `Enter`!

* Some basic arithmetic first:

```r
4 + 1
```

```
## [1] 5
```

```r
8 / 2
```

```
## [1] 4
```

* Great! What about this?

```r
2^3
```

```
## [1] 8
```

```r
# by the way: this is a comment! R therefore disregards it
```

---

# Task 1

```r
countdown(minutes = 5, top = 0)
```

1. Create a new R script (File `$\rightarrow$` New File `$\rightarrow$` R Script). Save it somewhere as `lecture_intro.R`.

1. Type the following code in your script and run it. To run the code press `Ctrl` or `Cmd` + `Enter` (you can either highlight the code or just put your cursor at the end of the line)
    
    ```r
    4 * 8
    ```

1. Type the following code in your script and run it. What happens if you only run the first line of the code?
    
    ```r
    x = 5 # equivalently x <- 5
    x
    ```
Congratulations, you have created your first `R` "object"! Everything is an object in R! Objects are assigned using `=` or `<-`.

1. Create a new object named `x_3` to which you assign the cube of `x`. Note that to assign you need to use `=` or `<-`. Use code to compute the cube, not a calculator.

---

# Where to get Help?

```r
?log #? in front of function
help(lm)   # help() is equivalent
??plot  # get all help on keyword "plot"
```
]

---

# Collaborate!

---

# R Packages

* `R` users contribute add-on data and functions as *packages*

* Installing packages is easy! Just use the `install.packages` function:
    
    ```r
    install.packages("ggplot2")
    ```

* To *use* the contents of a packge, we must load it from our library using `library`:
    
    ```r
    library(ggplot2)
    ```

---

# Vectors

.pull-left[
* The `c` function creates vectors, i.e. *one-dimensional arrays*.
    
    ```r
    c(1, 3, 5, 7, 8, 9)
    ```
    
    ```
    ## [1] 1 3 5 7 8 9
    ```
    
* Coercion to unique types:
    
    ```r
    (v <- c(42, "Statistics", TRUE))
    ```
    
    ```
    ## [1] "42"         "Statistics" "TRUE"
    ```
]

* Creating a *range*
    
    ```r
    1:10
    ```
    
    ```
    ##  [1]  1  2  3  4  5  6  7  8  9 10
    ```

* get vector elements with square bracket operator `[index]`:
    
    ```r
    v[c(1,3)]
    ```
    
    ```
    ## [1] "42"   "TRUE"
    ```
]

---

# `data.frame`'s

`data.frame`s represent **tabular data**. Like spreadsheets.

```r
example_data = data.frame(x = c(1, 3, 5, 7),
                          y = c(rep("Hello", 3), "Goodbye"),
                          z = c("one", 2, "three", 4))
example_data
```

```
##   x       y     z
## 1 1   Hello   one
## 2 3   Hello     2
## 3 5   Hello three
## 4 7 Goodbye     4
```

* A `data.frame` has 2 dimensions: *rows* and *columns*. Like a *matrix*. Can get elements with `[row_index,col_index]`.

* In practice, you will be importing files that contain the data into `R` rather than creating `data.frame`s by hand.

---

# Task 2

1. Find out (using `help()` or google) how to import a .csv file. Do NOT use the "Import Dataset" button, nor install a package.

1. Import [gun_murders.csv](https://www.dropbox.com/s/zuk0qcfm3kyzs4e/gun_murders.csv?dl=1)<sup>1</sup> in a new object `murders`. This file contains data on gun murders by US state in 2010. (Hint: objects are created using `=` or `<-`).

1. Ensure that `murders` is a data.frame by running:
    
    ```r
    class(murder) # check class
    ```

1. Find out what variables are contained in `murders` by running:
    
    ```r
    names(murders) # obtain variable names
    ```

1. View the contents of `murders` by clicking on `murders` in your workspace. What does the `total` variable correspond to?

---

# `data.frame`s

Useful functions to describe a dataframe:

```r
str(murders) # `str` describes structure of any R object
```

```
## 'data.frame':	51 obs. of  5 variables:
##  $ state     : chr  "Alabama" "Alaska" "Arizona" "Arkansas" ...
##  $ abb       : chr  "AL" "AK" "AZ" "AR" ...
##  $ region    : chr  "South" "West" "West" "South" ...
##  $ population: int  4779736 710231 6392017 2915918 37253956 5029196 3574097 897934 601723 19687653 ...
##  $ total     : int  135 19 232 93 1257 65 97 38 99 669 ...
```

```r
names(murders) # column names
```

```
## [1] "state"      "abb"        "region"     "population" "total"
```

```r
nrow(murders) # number of rows
```

```
## [1] 51
```

```r
ncol(murders) # number of columns
```

```
## [1] 5
```

---

# Accessing `data.frame` Columns
    
* To extract one column **as a vector** we can use the `$` operator (as in `murders$state`), or the square bracket operator `[which_index]` with name or position index:
    
    ```r
    first5 <- murders[1:5, ]  # take first 5 states only
    first5$state  # extract with $ operator
    ```
    
    ```
    ## [1] "Alabama"    "Alaska"     "Arizona"    "Arkansas"   "California"
    ```
    
    ```r
    first5[ ,"state"]  # extract with column name
    ```
    
    ```
    ## [1] "Alabama"    "Alaska"     "Arizona"    "Arkansas"   "California"
    ```
    
    ```r
    first5[ ,1] # get first column
    ```
    
    ```
    ## [1] "Alabama"    "Alaska"     "Arizona"    "Arkansas"   "California"
    ```

.pull-left[
* Check `class` of an object:
    
    ```r
    class(murders)
    ```
    
    ```
    ## [1] "data.frame"
    ```
]

.pull-right[
* `typeof` gives the R-internal data type:
    
    ```r
    typeof(murders)
    ```
    
    ```
    ## [1] "list"
    ```
]

---

# Subsetting `data.frames`

* Subsetting a data.frame: `murders[row condition, column number]` or `murders[row condition, "column name"]`
    
    ```r
    # Only keep states with over 500 gun murders and keep only the "state" and "total" variables
    murders[murders$total > 500, c("state", "total")]
    ```
    
    ```
    ##         state total
    ## 5  California  1257
    ## 10    Florida   669
    ## 33   New York   517
    ## 44      Texas   805
    ```
    
    ```r
    # Only keep California and Texas and keep only the "state" and "total" variables
    murders[murders$state %in% c("California", "Texas"), c("state", "total")]
    ```
    
    ```
    ##         state total
    ## 5  California  1257
    ## 44      Texas   805
    ```

---

# Task 3

1. How many observations are there in `murders`?

1. How many variables? What are the data types of each variable?

1. Remember that the colon operator `1:10` is just short for *construct a sequence from `1` to `10`* (i.e. 1, 2, 3, etc). Create a new object `murders_2` containing the rows 10 to 25 of `murders`.

1. Create a new object `murders_3` which only contains the columns `state` and `total`. (Recall that `c` creates vectors.)

1. Create a `total_percap` variable equal to the number of murders per 10,000 inhabitants by running the following code.
    
    ```r
    murders$total_percap = (murders$total / murders$population) * 10000
    ```

Congratulations, you've created your first variable! Click on the `murders` object to see the new variable.

---

# Course Details

---

---

# This Course

- Teach you the basics of ***linear regression***, ***statistical inference*** and ***impact evaluation***.

- Equip you with a framework to think more deeply about ***causality***.

- Introduce you to the `R` software environment.

- ⚠️ This is *not* a course about `R`.

## Grading

1. There will be ***five quizzes*** on Moodle roughly every two weeks `$\rightarrow$` 40%

1. There will be ***two take home exams / case studies*** `$\rightarrow$` 60%

1. There will be _no_ final exam 😅

---

# Syllabus 🤞

*Quiz 1 (after lecture 2)*

Lectures 2 and 3: ***Tidying, Visualising and Summarising Data***

*Quiz 2*

Lecture 4: ***Simple Linear Regression***

Lecture 5: ***Introduction to Causality***

*Midterm Project*

Lecture 6: ***Multiple Linear Regression***

Lecture 7: ***Linear Regression Extensions***
]

Lecture 8: ***Sampling***

Lecture 9: ***Confidence Intervals and Hypothesis Testing***

*Quiz 4*

Lecture 10: ***Statistical Inference***

Lecture 11: ***Regression Discontinuity***

*Quiz 5*

Lecture 12: ***Recap***

*Final Project*
]

---

# Course Materials and Useful Resources

&nbsp; &nbsp; &nbsp; &nbsp; [Book](https://scpoecon.github.io/ScPoEconometrics/) &nbsp; &nbsp; &nbsp; &nbsp; | &nbsp; &nbsp; &nbsp; &nbsp; [Slides](https://github.com/ScPoEcon/ScPoEconometrics-Slides) &nbsp; &nbsp; &nbsp; &nbsp; | &nbsp; &nbsp; &nbsp; &nbsp; [Reading Suggestions](https://github.com/ScPoEcon/ScPoEconometrics-Slides/blob/master/syllabus.md) &nbsp; &nbsp; &nbsp; &nbsp; | &nbsp; &nbsp; &nbsp; &nbsp;  [Interactive R Apps](https://github.com/ScPoEcon/ScPoApps) &nbsp; &nbsp; &nbsp; &nbsp;
-- | -- | --

.pull-left[
## Econometrics
- [*Mastering Metrics* by Angrist and Pischke](https://uspc-spo.primo.exlibrisgroup.com/permalink/33USPC_SPO/1d4eftp/alma991003947949705808)
- [*Introductory Econometrics: A Modern Approach* by Wooldridge](https://uspc-spo.primo.exlibrisgroup.com/permalink/33USPC_SPO/mgsi7h/alma991003442469705808)
- [*Introduction to Econometrics* by Stock and Watson](https://uspc-spo.primo.exlibrisgroup.com/permalink/33USPC_SPO/mgsi7h/alma991003436429705808)
- [*Causal Inference: The Mixtape* by Cunningham](https://mixtape.scunning.com/)
- [Ben Lambert's youtube channel](https://www.youtube.com/user/SpartacanUsuals)
]

.pull-right[
## Metrics and `R`
- [ModernDive](https://moderndive.com/)
- [Introduction to Econometrics with R](https://www.econometrics-with-r.org/)
- [R for Data Science](https://r4ds.had.co.nz/)
- [Awesome R Learning Resources](https://github.com/iamericfletcher/awesome-r-learning-resources)
]

---

# Course Policies

*Be nice. Be honest. Don't cheat.*

(BTW you should apply these principles outside of the classroom as well ***#lifeadvice*** 😉)

We will ***exclusively*** use Slack for our interactions.

Please ***DO NOT*** contact me by email (unless there's a legitimate reason to).

I will be checking Slack sparingly so please help one another!
]

*Late work:* Won't be accepted unless you have a very good reason.

*Cheating:* You will get 0. Just don't cheat, it's honestly not worth it.

*Work in groups:* You can/should work in groups of 2-3 on the quizzes.
]

---

# Mental Health

> ***Pay attention to your mental health in the same way you would your physical health.***

Seeking help is a sign of perceptive self-awareness, [not weakness](https://www.nytimes.com/2020/09/24/well/mind/cord-jefferson-emmy-black-mental-health.html).

Don't hesitate to ***reach out*** to your friends, family, any professor (including myself!) if you feel the need.

***Sciences Po*** offers free and confidential [psychological support services](https://www.sciencespo.fr/students/en/living/health/health-centre) with professionals.

---

# Last But Not Least: Class Rep

---

class: title-slide-final, middle
background-image: url(../img/logo/ScPo-econ.png)
background-size: 250px
background-position: 9% 19%

# SEE YOU NEXT WEEK!

|                                                                                                            |                                   |
| :--------------------------------------------------------------------------------------------------------- | :-------------------------------- |
| <a href="https://github.com/ScPoEcon/ScPoEconometrics-Slides">.ScPored[<i class="fa fa-link fa-fw"></i>] | Slides |
| <a href="https://scpoecon.github.io/ScPoEconometrics">.ScPored[<i class="fa fa-link fa-fw"></i>] | Book |
| <a href="http://twitter.com/ScPoEcon">.ScPored[<i class="fa fa-twitter fa-fw"></i>]                          | @ScPoEcon                         |
| <a href="http://github.com/ScPoEcon">.ScPored[<i class="fa fa-github fa-fw"></i>]                          | @ScPoEcon                       |