HW5

Briefly answer this question

a. What is the Causal link the paper is trying to reveal?

The paper is trying to reveal the causal link between passage of the minimum wage law on establishment level employment outcomes.

b. What would be the ideal experiment to test this causal link?

The ideal experiment would be to take states that are very similar to each other, record wages before change in minimum wages, randomly pass minimum wage policy in some states and mark them as treated while not changing things in remaining states. Finally, we compare changes in outcomes. The ideal experiment would be to randomly assign minimum wages to a large sample of workers and measure the wages

c. What is the identification strategy?

The identification strategy is exploiting the plausible exogenous variation in the the laws passed. Assuming that passing of minimum wage law by NJ is exogenous to the model creates a quasi-experimental situation where the changes in employment outcomes can be attributed to wage changes due to minimum wage law. The idea is to compare employment outcomes of two states where only minimum wage differs.

d. What are the assumptions / threats to this identification strategy?

First, the states themselves may not be very similar.
Second, there could be significant pre trends. The outcomes may not be similar in states in absence of treatment.

Part 2 Replication analysis

a. Load Ashenfelter and Krueger AER 1994 data. You can load it directly from my website here. Variable names should be self-explanatory if you read the paper.

#install.packages(c("dplyr", "stringr", "ggplot2", "readxl", "stargazer", "plm"), repos="http://cran.us.r-project.org")

I load the data here.

library(dplyr)
library(magrittr)
library(readr)
library(plm)
library(stargazer)
CK<- read_csv("../HW5/CardKrueger1994_fastfood.csv")
head(CK)

## # A tibble: 6 x 12
##      id state emptot emptot2   demp chain    bk   kfc  roys wendys wage_st
##   <dbl> <dbl>  <dbl>   <dbl>  <dbl> <dbl> <dbl> <dbl> <dbl>  <dbl>   <dbl>
## 1    46     0   40.5    24   -16.5      1     1     0     0      0    NA  
## 2    49     0   13.8    11.5  -2.25     2     0     1     0      0    NA  
## 3   506     0    8.5    10.5   2        2     0     1     0      0    NA  
## 4    56     0   34      20   -14        4     0     0     0      1     5  
## 5    61     0   24      35.5  11.5      4     0     0     0      1     5.5
## 6    62     0   20.5    NA    NA        4     0     0     0      1     5  
## # ... with 1 more variable: wage_st2 <dbl>

meansStores <- CK %>%
  group_by(state) %>%
    summarize(BK=mean(bk)*100, KFC=mean(kfc)*100, ROYS=mean(roys), WENDYS=mean(wendys), FTE_1=mean(emptot, na.rm=TRUE), FTE_2=mean(emptot2, na.rm=TRUE))
meansStores

## # A tibble: 2 x 7
##   state    BK   KFC  ROYS WENDYS FTE_1 FTE_2
## * <dbl> <dbl> <dbl> <dbl>  <dbl> <dbl> <dbl>
## 1     0  44.3  15.2 0.215  0.190  23.3  21.2
## 2     1  41.1  20.5 0.248  0.136  20.4  21.0

Use OLS to get their Diff-Diff Estimator

library(plm)
library(stargazer)
olsreg<-lm(demp~state, CK)
stargazer(olsreg,
          title="OLS Diff-Diff Estimator.",
          covariate.labels = "Minimum Wage",
          type="text",
          keep.stat=c("n", "rsq"), 
          column_labels=c("OLS"))

## 
## OLS Diff-Diff Estimator.
## ========================================
##                  Dependent variable:    
##              ---------------------------
##                         demp            
## ----------------------------------------
## Minimum Wage           2.750**          
##                        (1.154)          
##                                         
## Constant              -2.283**          
##                        (1.036)          
##                                         
## ----------------------------------------
## Observations             384            
## R2                      0.015           
## ========================================
## Note:        *p<0.1; **p<0.05; ***p<0.01
## 
## OLS Diff-Diff Estimator.
## ===
## OLS
## ---

HW5

2/1/2021

Part 1