Economic Data Literacy

This is the course web for W203(2): Economic Data Literacy/ E206-4b: Global Business b.

• The objective of this course is for students to acquire basic concepts and skills of economic data analysis and to learn how to apply these skills to real-world problems. In this course, we utilize free statistical software, R and RStudio, to learn concepts and skills for analyzing economic data.

• There is no required textbook for this course. But I will follow closely the following textbook:

  • Elena Llaudet and Kosuke Imai, Data Analysis for Social Science: A Friendly and Practical Introduction, Princeton Univ. Press, 2022.

  • The Japanese translation was published by Iwanami Publishers. (You can download the quick guide from this web page.)

  • Student resources for the textbook can be downloaded from https://press.princeton.edu/student-resources/data-analysis-for-social-science.

Notice

• There will be no class on October 7 due to administrative work outside of the KC campus. 

• We will meet on October 14. However, note the change in the starting time to 1:30 PM due to the 150th anniversary ceremony.

• From the next class, we can change the language of instruction to Japanese because two foreign students will drop the course. 

• If you installed R and RStudio on your laptop computer, you may bring your computer to the class without applying for the use of CSL domain.

R and RStudio are available from the following links:

• R is the statistical program that will perform the calculations and create the graphics for us, available at https://cran.r-project.org/.

• RStudio is the user-friendly interface we will use to communicate with R, available at https://posit.co/download/rstudio-desktop/#download

Preparation

• You can download all data and script files used in this class from the link, DSS folder, in Moodle. Download all the files in ZIP format to your network drive (Y:/ if you use the computer in the lab) or to your desktop (if you use your own laptop), then unzip them into the folder named DSS. 

• Every time you create a new script file, include the following command at the beginning so that the working directory is always set to the DSS folder above. 

  • For the computer users in the lab: setwd("Y:/DSS") or setwd("Y:/DSS/DSS"), depending on whether you include the folder name DSS when unzipping the files.

  • For users of your own laptop: setwd("c:/Users/username/Desktop/DSS")

    • Replace \ with yen mark. Don't forget to replace "username" with your own user name.

Lecture Slides (password-protected)

1. Introduction to Data Analysis 1 (corrected and updated on Oct. 14)

2. Introduction to Data Analysis 2

3.  Estimating Causal Effects with Randomized Experiments 1

4. Estimating Causal Effects with Randomized Experiments 2

5.  Inferring Population Characteristics via Survey Research 1

6.  Inferring Population Characteristics via Survey Research 2

7.  Predicting Outcomes Using Linear Regression 1

8.  Predicting Outcomes Using Linear Regression 2

Assignments (password-protected)

• Assignment #1 (due on November 25 December 2, by 5:00 PM)

  • india.csv

  • Important notice in Japanese as I send via Moodle:

大学のコンピュータでは、csv形式のデータ・ファイルの読み込みがうまくいかない場合があるようです。Question 1の答えを教えることになってしまいますが、下記が今まで使ってきたコマンドです。

india <- read.csv("india.csv")

このコマンドでエラーが出る場合は、下記のコマンドを使ってください。それでも問題が発生する場合は、山崎まで連絡を下さい。

india <- read.csv("india.csv", fileEncoding="UTF-8-BOM")

• • 課題に関する補足説明：上記の問題に対処する際の詳しい説明です。11月18日の授業で配付予定。

• Assignment #2 (due on January 20, 2026, by 5:00 PM)

  • Assignment #2のQuestion 1に関する質問がありました。この問題は、計算を何もする必要はなく、単に推定量の名前（the name of the estimator）を答える質問です。

質問した人は、おそらくestimator（推定量）の意味が分からなかったのだと思います。Estimator（推定量）とは、統計学で「母集団の特徴」を「標本データ」から推測するために使われる統計量、つまり計算方法や関数のことで、その結果に特定の標本データを当てはめて得られた具体的な数値をEstimate（推定値）と呼びます。

今までのスライドを検索してみましたが、おそらく何々Estimator（推定量）という言い回しは、一つしか出てきていません。復習をして、見つけてみてください。