Minimum Wages and Employment: A Case Study of the Fast-Food Industry in New Jersey and Pennsylvania Replication
Avery Davis
2024-09-26
1.0 Introduction
This in document I have attempted the replication of the very famous Card and Kruger 1994 study of minimum wage.
The impact of minimum wage increases on employment has long been a contentious issue in labor economics. This seminal study by Card & Kruger examined New Jersey’s wage hike from $4.25 to $5.05 per hour, surveying 410 fast-food restaurants in New Jersey and eastern Pennsylvania. The study found no significant evidence that this wage increase led to job losses, challenging prevailing economic theories.
This replication study utilizes the same dataset as the original research to explore the impact of the minimum wage increase. By doing so, I aim to refine my data analysis skills and enhance my proficiency in coding with R. This exercise not only provides an opportunity to validate the original findings but also allows me to practice and develop my analytical capabilities in a real-world context.
Note: This project is an ongoing side project that is not finished yet.
1.1 upload data
The data sets for this project come from David Cards website. The replication folder has several files but we will only need two of them. The data has come in withought column names luckly we have the code book so that we can know what each column means.Because the column names follow a consistent pattern we are able to use a regular expression to grab our column names. Then we will replace the data frame column names with the vector of new names.
df <- read.table("~/Documents/Rpubs_Projects/njmin/public.dat")
codebook <- read_lines("~/Documents/Rpubs_Projects/njmin/codebook")
kable(df[1:8,])| V1 | V2 | V3 | V4 | V5 | V6 | V7 | V8 | V9 | V10 | V11 | V12 | V13 | V14 | V15 | V16 | V17 | V18 | V19 | V20 | V21 | V22 | V23 | V24 | V25 | V26 | V27 | V28 | V29 | V30 | V31 | V32 | V33 | V34 | V35 | V36 | V37 | V38 | V39 | V40 | V41 | V42 | V43 | V44 | V45 | V46 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 46 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 30.00 | 15.00 | 3.00 | . | 19.0 | . | 1 | . | 2 | 6.5 | 16.5 | 1.03 | 1.03 | 0.52 | 3 | 3 | 1 | 1 | 111792 | 1 | 3.50 | 35.00 | 3.00 | 4.30 | 26.0 | 0.08 | 1 | 2 | 6.50 | 16.50 | 1.03 | . | 0.94 | 4 | 4 |
| 49 | 2 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 6.50 | 6.50 | 4.00 | . | 26.0 | . | 0 | . | 2 | 10.0 | 13.0 | 1.01 | 0.90 | 2.35 | 4 | 3 | 1 | 1 | 111292 | . | 0.00 | 15.00 | 4.00 | 4.45 | 13.0 | 0.05 | 0 | 2 | 10.00 | 13.00 | 1.01 | 0.89 | 2.35 | 4 | 4 |
| 506 | 2 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 3.00 | 7.00 | 2.00 | . | 13.0 | 0.37 | 0 | 30.0 | 2 | 11.0 | 10.0 | 0.95 | 0.74 | 2.33 | 3 | 3 | 1 | 1 | 111292 | . | 3.00 | 7.00 | 4.00 | 5.00 | 19.0 | 0.25 | . | 1 | 11.00 | 11.00 | 0.95 | 0.74 | 2.33 | 4 | 3 |
| 56 | 4 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 20.00 | 20.00 | 4.00 | 5.00 | 26.0 | 0.10 | 1 | 0.0 | 2 | 10.0 | 12.0 | 0.87 | 0.82 | 1.79 | 2 | 2 | 1 | 1 | 111492 | . | 0.00 | 36.00 | 2.00 | 5.25 | 26.0 | 0.15 | 0 | 2 | 10.00 | 12.00 | 0.92 | 0.79 | 0.87 | 2 | 2 |
| 61 | 4 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 6.00 | 26.00 | 5.00 | 5.50 | 52.0 | 0.15 | 1 | 0.0 | 3 | 10.0 | 12.0 | 0.87 | 0.77 | 1.65 | 2 | 2 | 1 | 1 | 111492 | . | 28.00 | 3.00 | 6.00 | 4.75 | 13.0 | 0.15 | 0 | 2 | 10.00 | 12.00 | 1.01 | 0.84 | 0.95 | 2 | 2 |
| 62 | 4 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 2 | 0.00 | 31.00 | 5.00 | 5.00 | 26.0 | 0.07 | 0 | 45.0 | 2 | 10.0 | 12.0 | 0.87 | 0.77 | 0.95 | 2 | 2 | 1 | 1 | 111492 | . | . | . | . | . | 26.0 | . | 0 | 2 | 10.00 | 12.00 | . | 0.84 | 1.79 | 3 | 3 |
| 445 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 50.00 | 35.00 | 3.00 | 5.00 | 26.0 | 0.10 | 0 | 0.0 | 2 | 6.0 | 18.0 | 1.04 | 0.88 | 0.94 | 3 | 3 | 1 | 1 | 110792 | . | 15.00 | 18.00 | 5.00 | 4.75 | 26.0 | 0.15 | 0 | 2 | 6.00 | 18.00 | 1.04 | 0.86 | 0.94 | 3 | 3 |
| 451 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 10.00 | 17.00 | 5.00 | 5.00 | 52.0 | 0.25 | 0 | 0.0 | 2 | 0.0 | 24.0 | 1.05 | 0.84 | 0.96 | 6 | 4 | 1 | 1 | 111792 | 2 | 26.00 | 9.00 | 6.00 | 5.00 | 26.0 | 0.20 | 0 | 2 | 0.00 | 24.00 | 1.11 | 0.84 | 0.94 | 6 | 3 |
print(codebook)## [1] " Code Book for New Jersey-Pennsylvania Data Set"
## [2] ""
## [3] "Note: there are 410 observations in the data set"
## [4] ""
## [5] " Column Location"
## [6] " Name: Start End Format Explanation"
## [7] "\xc4\xc4\xc4\xc4\xc4\xc4\xc4\xc4\xc4\xc4\xc4\xc4\xc4\xc4\xc4\xc4\xc4\xc4\xc4\xc4\xc4\xc4\xc4\xc4\xc4\xc4\xc4\xc4\xc4\xc4\xc4\xc4\xc4\xc4\xc4\xc4\xc4\xc4\xc4\xc4\xc4\xc4\xc4\xc4\xc4\xc4\xc4\xc4\xc4\xc4\xc4\xc4\xc4\xc4\xc4\xc4\xc4\xc4\xc4\xc4\xc4\xc4\xc4\xc4\xc4\xc4\xc4\xc4\xc4\xc4\xc4\xc4\xc4\xc4\xc4"
## [8] "SHEET 1 3 3.0 sheet number (unique store id)"
## [9] "CHAIN 5 5 1.0 chain 1=bk; 2=kfc; 3=roys; 4=wendys"
## [10] "CO_OWNED 7 7 1.0 1 if company owned"
## [11] "STATE 9 9 1.0 1 if NJ; 0 if Pa "
## [12] ""
## [13] "Dummies for location:"
## [14] "SOUTHJ 11 11 1.0 1 if in southern NJ"
## [15] "CENTRALJ 13 13 1.0 1 if in central NJ"
## [16] "NORTHJ 15 15 1.0 1 if in northern NJ"
## [17] "PA1 17 17 1.0 1 if in PA, northeast suburbs of Phila"
## [18] "PA2 19 19 1.0 1 if in PA, Easton etc"
## [19] "SHORE 21 21 1.0 1 if on NJ shore"
## [20] ""
## [21] "First Interview"
## [22] "NCALLS 23 24 2.0 number of call-backs*"
## [23] "EMPFT 26 30 5.2 # full-time employees"
## [24] "EMPPT 32 36 5.2 # part-time employees"
## [25] "NMGRS 38 42 5.2 # managers/ass't managers"
## [26] "WAGE_ST 44 48 5.2 starting wage ($/hr)"
## [27] "INCTIME 50 54 5.1 months to usual first raise"
## [28] "FIRSTINC 56 60 5.2 usual amount of first raise ($/hr)"
## [29] "BONUS 62 62 1.0 1 if cash bounty for new workers"
## [30] "PCTAFF 64 68 5.1 % employees affected by new minimum"
## [31] "MEALS 70 70 1.0 free/reduced price code (See below)"
## [32] "OPEN 72 76 5.2 hour of opening"
## [33] "HRSOPEN 78 82 5.2 number hrs open per day"
## [34] "PSODA 84 88 5.2 price of medium soda, including tax"
## [35] "PFRY 90 94 5.2 price of small fries, including tax"
## [36] "PENTREE 96 100 5.2 price of entree, including tax"
## [37] "NREGS 102 103 2.0 number of cash registers in store"
## [38] "NREGS11 105 106 2.0 number of registers open at 11:00 am"
## [39] ""
## [40] "Second Interview"
## [41] "TYPE2 108 108 1.0 type 2nd interview 1=phone; 2=personal"
## [42] "STATUS2 110 110 1.0 status of second interview: see below"
## [43] "DATE2 112 117 6.0 date of second interview MMDDYY format"
## [44] "NCALLS2 119 120 2.0 number of call-backs*"
## [45] "EMPFT2 122 126 5.2 # full-time employees"
## [46] "EMPPT2 128 132 5.2 # part-time employees"
## [47] "NMGRS2 134 138 5.2 # managers/ass't managers"
## [48] "WAGE_ST2 140 144 5.2 starting wage ($/hr)"
## [49] "INCTIME2 146 150 5.1 months to usual first raise"
## [50] "FIRSTIN2 152 156 5.2 usual amount of first raise ($/hr)"
## [51] "SPECIAL2 158 158 1.0 1 if special program for new workers"
## [52] "MEALS2 160 160 1.0 free/reduced price code (See below)"
## [53] "OPEN2R 162 166 5.2 hour of opening"
## [54] "HRSOPEN2 168 172 5.2 number hrs open per day"
## [55] "PSODA2 174 178 5.2 price of medium soda, including tax"
## [56] "PFRY2 180 184 5.2 price of small fries, including tax"
## [57] "PENTREE2 186 190 5.2 price of entree, including tax"
## [58] "NREGS2 192 193 2.0 number of cash registers in store"
## [59] "NREGS112 195 196 2.0 number of registers open at 11:00 am"
## [60] ""
## [61] "Codes:"
## [62] ""
## [63] "Free/reduced Meal Variable:"
## [64] "0 = none"
## [65] "1 = free meals"
## [66] "2 = reduced price meals"
## [67] "3 = both free and reduced price meals"
## [68] ""
## [69] ""
## [70] "Second Interview Status"
## [71] "0 = refused second interview (count = 1)"
## [72] "1 = answered 2nd interview (count = 399)"
## [73] "2 = closed for renovations (count = 2)"
## [74] "3 = closed \"permanently\" (count = 6)"
## [75] "4 = closed for highway construction (count = 1)"
## [76] "5 = closed due to Mall fire (count = 1)"
## [77] ""
## [78] ""
## [79] "*Note: number of call-backs = 0 if contacted on first call"
## [80] ""1.2 Data Cleaning
# Use regex to match the beginning of each line followed by a space
pattern <- "^\\S+(?= )"
# Find the matches using gregexpr
matches <- gregexpr(pattern, codebook, perl = TRUE)
# Extract the matches
matched_words <- regmatches(codebook, matches)
# Convert list to vector for easier manipulation
matched_words <- unlist(matched_words)
# Remove blank or whitespace-only elements
new_column_names <- matched_words[matched_words != "" & !grepl("^\\s*$", matched_words)]
# Display the non-blank words
print(new_column_names)## [1] "SHEET" "CHAIN" "CO_OWNED" "STATE" "SOUTHJ" "CENTRALJ"
## [7] "NORTHJ" "PA1" "PA2" "SHORE" "NCALLS" "EMPFT"
## [13] "EMPPT" "NMGRS" "WAGE_ST" "INCTIME" "FIRSTINC" "BONUS"
## [19] "PCTAFF" "MEALS" "OPEN" "HRSOPEN" "PSODA" "PFRY"
## [25] "PENTREE" "NREGS" "NREGS11" "TYPE2" "STATUS2" "DATE2"
## [31] "NCALLS2" "EMPFT2" "EMPPT2" "NMGRS2" "WAGE_ST2" "INCTIME2"
## [37] "FIRSTIN2" "SPECIAL2" "MEALS2" "OPEN2R" "HRSOPEN2" "PSODA2"
## [43] "PFRY2" "PENTREE2" "NREGS2" "NREGS112"# replace column names
colnames(df) <- new_column_nameshead(df)## SHEET CHAIN CO_OWNED STATE SOUTHJ CENTRALJ NORTHJ PA1 PA2 SHORE NCALLS EMPFT
## 1 46 1 0 0 0 0 0 1 0 0 0 30.00
## 2 49 2 0 0 0 0 0 1 0 0 0 6.50
## 3 506 2 1 0 0 0 0 1 0 0 0 3.00
## 4 56 4 1 0 0 0 0 1 0 0 0 20.00
## 5 61 4 1 0 0 0 0 1 0 0 0 6.00
## 6 62 4 1 0 0 0 0 1 0 0 2 0.00
## EMPPT NMGRS WAGE_ST INCTIME FIRSTINC BONUS PCTAFF MEALS OPEN HRSOPEN PSODA
## 1 15.00 3.00 . 19.0 . 1 . 2 6.5 16.5 1.03
## 2 6.50 4.00 . 26.0 . 0 . 2 10.0 13.0 1.01
## 3 7.00 2.00 . 13.0 0.37 0 30.0 2 11.0 10.0 0.95
## 4 20.00 4.00 5.00 26.0 0.10 1 0.0 2 10.0 12.0 0.87
## 5 26.00 5.00 5.50 52.0 0.15 1 0.0 3 10.0 12.0 0.87
## 6 31.00 5.00 5.00 26.0 0.07 0 45.0 2 10.0 12.0 0.87
## PFRY PENTREE NREGS NREGS11 TYPE2 STATUS2 DATE2 NCALLS2 EMPFT2 EMPPT2 NMGRS2
## 1 1.03 0.52 3 3 1 1 111792 1 3.50 35.00 3.00
## 2 0.90 2.35 4 3 1 1 111292 . 0.00 15.00 4.00
## 3 0.74 2.33 3 3 1 1 111292 . 3.00 7.00 4.00
## 4 0.82 1.79 2 2 1 1 111492 . 0.00 36.00 2.00
## 5 0.77 1.65 2 2 1 1 111492 . 28.00 3.00 6.00
## 6 0.77 0.95 2 2 1 1 111492 . . . .
## WAGE_ST2 INCTIME2 FIRSTIN2 SPECIAL2 MEALS2 OPEN2R HRSOPEN2 PSODA2 PFRY2
## 1 4.30 26.0 0.08 1 2 6.50 16.50 1.03 .
## 2 4.45 13.0 0.05 0 2 10.00 13.00 1.01 0.89
## 3 5.00 19.0 0.25 . 1 11.00 11.00 0.95 0.74
## 4 5.25 26.0 0.15 0 2 10.00 12.00 0.92 0.79
## 5 4.75 13.0 0.15 0 2 10.00 12.00 1.01 0.84
## 6 . 26.0 . 0 2 10.00 12.00 . 0.84
## PENTREE2 NREGS2 NREGS112
## 1 0.94 4 4
## 2 2.35 4 4
## 3 2.33 4 3
## 4 0.87 2 2
## 5 0.95 2 2
## 6 1.79 3 3# find all charter vectors in df and change to numeric
df <- df|>
mutate(across(where(is.character), ~ as.numeric(replace(., . == ".", NA))))df$DATE2 <- as.Date(strptime(df$DATE2, format = "%m%d%y"))df |>
psych::describe() |>
kable(caption = "Table 0: Summary Statistics")|>
kable_classic()| vars | n | mean | sd | median | trimmed | mad | min | max | range | skew | kurtosis | se | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| SHEET | 1 | 410 | 246.5073171 | 148.2317989 | 237.50 | 244.1646341 | 186.807600 | 1.00 | 522.00 | 521.00 | 0.1079612 | -1.1698454 | 7.3206467 |
| CHAIN | 2 | 410 | 2.1170732 | 1.1104972 | 2.00 | 2.0213415 | 1.482600 | 1.00 | 4.00 | 3.00 | 0.4099750 | -1.2668755 | 0.0548435 |
| CO_OWNED | 3 | 410 | 0.3439024 | 0.4755893 | 0.00 | 0.3048780 | 0.000000 | 0.00 | 1.00 | 1.00 | 0.6548374 | -1.5750117 | 0.0234877 |
| STATE | 4 | 410 | 0.8073171 | 0.3948880 | 1.00 | 0.8841463 | 0.000000 | 0.00 | 1.00 | 1.00 | -1.5526808 | 0.4118399 | 0.0195021 |
| SOUTHJ | 5 | 410 | 0.2268293 | 0.4192929 | 0.00 | 0.1585366 | 0.000000 | 0.00 | 1.00 | 1.00 | 1.2998286 | -0.3111868 | 0.0207074 |
| CENTRALJ | 6 | 410 | 0.1536585 | 0.3610617 | 0.00 | 0.0670732 | 0.000000 | 0.00 | 1.00 | 1.00 | 1.9137822 | 1.6666450 | 0.0178316 |
| NORTHJ | 7 | 410 | 0.4268293 | 0.4952214 | 0.00 | 0.4085366 | 0.000000 | 0.00 | 1.00 | 1.00 | 0.2947864 | -1.9177606 | 0.0244572 |
| PA1 | 8 | 410 | 0.0878049 | 0.2833567 | 0.00 | 0.0000000 | 0.000000 | 0.00 | 1.00 | 1.00 | 2.9022768 | 6.4389330 | 0.0139940 |
| PA2 | 9 | 410 | 0.1048780 | 0.3067706 | 0.00 | 0.0060976 | 0.000000 | 0.00 | 1.00 | 1.00 | 2.5697266 | 4.6147684 | 0.0151503 |
| SHORE | 10 | 410 | 0.0853659 | 0.2797667 | 0.00 | 0.0000000 | 0.000000 | 0.00 | 1.00 | 1.00 | 2.9569123 | 6.7598353 | 0.0138167 |
| NCALLS | 11 | 410 | 1.1414634 | 1.3860950 | 0.00 | 0.9573171 | 0.000000 | 0.00 | 8.00 | 8.00 | 1.2248531 | 2.1240241 | 0.0684544 |
| EMPFT | 12 | 404 | 8.2029703 | 8.6195226 | 6.00 | 6.7916667 | 5.930400 | 0.00 | 60.00 | 60.00 | 1.8746681 | 5.1452723 | 0.4288373 |
| EMPPT | 13 | 406 | 18.8312808 | 10.0817269 | 17.00 | 17.9723926 | 10.378200 | 0.00 | 60.00 | 60.00 | 0.9252540 | 1.0792019 | 0.5003477 |
| NMGRS | 14 | 404 | 3.4202970 | 1.0184082 | 3.00 | 3.3512346 | 1.482600 | 1.00 | 10.00 | 9.00 | 1.5797983 | 6.9777038 | 0.0506677 |
| WAGE_ST | 15 | 390 | 4.6156410 | 0.3470151 | 4.50 | 4.5816026 | 0.370650 | 4.25 | 5.75 | 1.50 | 0.6591875 | -0.3515297 | 0.0175718 |
| INCTIME | 16 | 379 | 18.3100264 | 11.2046053 | 13.00 | 17.3393443 | 13.343400 | 2.00 | 52.00 | 50.00 | 1.0781286 | 1.4317377 | 0.5755419 |
| FIRSTINC | 17 | 367 | 0.2252861 | 0.1079813 | 0.22 | 0.2121695 | 0.074130 | 0.05 | 0.75 | 0.70 | 1.6422167 | 4.3241097 | 0.0056366 |
| BONUS | 18 | 410 | 0.2463415 | 0.4314062 | 0.00 | 0.1829268 | 0.000000 | 0.00 | 1.00 | 1.00 | 1.1730934 | -0.6253593 | 0.0213056 |
| PCTAFF | 19 | 366 | 48.8696721 | 35.1159517 | 50.00 | 48.5928571 | 44.478000 | 0.00 | 100.00 | 100.00 | 0.0412889 | -1.3665141 | 1.8355402 |
| MEALS | 20 | 410 | 1.9073171 | 0.5336548 | 2.00 | 1.8902439 | 0.000000 | 0.00 | 3.00 | 3.00 | -0.1811396 | 0.6623298 | 0.0263553 |
| OPEN | 21 | 410 | 8.0414634 | 2.1608854 | 7.00 | 8.0259146 | 1.482600 | 0.00 | 11.50 | 11.50 | -0.2181176 | 0.3990436 | 0.1067185 |
| HRSOPEN | 22 | 410 | 14.4390244 | 2.8099873 | 15.50 | 14.4771341 | 2.223900 | 7.00 | 24.00 | 17.00 | 0.0761353 | 0.0591687 | 0.1387754 |
| PSODA | 23 | 402 | 1.0448756 | 0.0886873 | 1.06 | 1.0426398 | 0.074130 | 0.73 | 1.49 | 0.76 | 0.3476042 | 1.5595291 | 0.0044233 |
| PFRY | 24 | 393 | 0.9219847 | 0.1058813 | 0.93 | 0.9214603 | 0.118608 | 0.67 | 1.27 | 0.60 | 0.2517357 | 0.3211310 | 0.0053410 |
| PENTREE | 25 | 398 | 1.3221859 | 0.6430845 | 1.02 | 1.2297187 | 0.118608 | 0.49 | 3.95 | 3.46 | 1.2931530 | 0.3478130 | 0.0322349 |
| NREGS | 26 | 404 | 3.5940594 | 1.2576572 | 3.00 | 3.4907407 | 1.482600 | 1.00 | 8.00 | 7.00 | 0.7581589 | 0.5461304 | 0.0625708 |
| NREGS11 | 27 | 398 | 2.7160804 | 0.8878087 | 3.00 | 2.6718750 | 1.482600 | 1.00 | 6.00 | 5.00 | 0.4547008 | 0.3926864 | 0.0445018 |
| TYPE2 | 28 | 410 | 1.0951220 | 0.2937417 | 1.00 | 1.0000000 | 0.000000 | 1.00 | 2.00 | 1.00 | 2.7499703 | 5.5759543 | 0.0145069 |
| STATUS2 | 29 | 410 | 1.0487805 | 0.3532053 | 1.00 | 1.0000000 | 0.000000 | 0.00 | 5.00 | 5.00 | 7.3333510 | 61.1614907 | 0.0174436 |
| DATE2 | 30 | 410 | NaN | NA | NA | NaN | NA | Inf | -Inf | -Inf | NA | NA | NA |
| NCALLS2 | 31 | 161 | 2.2236025 | 1.7641399 | 2.00 | 1.8372093 | 1.482600 | 1.00 | 9.00 | 8.00 | 2.1858518 | 4.9389853 | 0.1390337 |
| EMPFT2 | 32 | 398 | 8.2751256 | 7.9707627 | 6.00 | 7.1421875 | 7.413000 | 0.00 | 40.00 | 40.00 | 1.1262055 | 0.8229380 | 0.3995382 |
| EMPPT2 | 33 | 400 | 18.6775000 | 10.6996355 | 17.00 | 17.9796875 | 10.378200 | 0.00 | 60.00 | 60.00 | 0.6387888 | 0.1363367 | 0.5349818 |
| NMGRS2 | 34 | 404 | 3.4839109 | 1.1398984 | 3.00 | 3.4336420 | 1.482600 | 0.00 | 8.00 | 8.00 | 0.4569002 | 1.7412189 | 0.0567121 |
| WAGE_ST2 | 35 | 389 | 4.9962725 | 0.2531904 | 5.05 | 5.0329393 | 0.000000 | 4.25 | 6.25 | 2.00 | -1.1329012 | 4.4272551 | 0.0128373 |
| INCTIME2 | 36 | 344 | 22.0319767 | 12.8292950 | 26.00 | 20.6123188 | 16.308600 | 2.00 | 52.00 | 50.00 | 0.8325025 | 0.4946323 | 0.6917092 |
| FIRSTIN2 | 37 | 330 | 0.2170909 | 0.1057957 | 0.20 | 0.2029167 | 0.074130 | 0.00 | 1.00 | 1.00 | 2.2566788 | 10.0619748 | 0.0058239 |
| SPECIAL2 | 38 | 392 | 0.2091837 | 0.4072456 | 0.00 | 0.1369427 | 0.000000 | 0.00 | 1.00 | 1.00 | 1.4245678 | 0.0294880 | 0.0205690 |
| MEALS2 | 39 | 399 | 1.7769424 | 0.5471440 | 2.00 | 1.7788162 | 0.000000 | 0.00 | 3.00 | 3.00 | -0.2576902 | 0.1158165 | 0.0273915 |
| OPEN2R | 40 | 399 | 8.1109023 | 2.1579612 | 7.00 | 8.1253894 | 1.482600 | 0.00 | 11.50 | 11.50 | -0.3889423 | 0.9599420 | 0.1080332 |
| HRSOPEN2 | 41 | 399 | 14.4655388 | 2.7524948 | 15.00 | 14.4306854 | 2.965200 | 8.00 | 24.00 | 16.00 | 0.2890415 | 0.4151100 | 0.1377971 |
| PSODA2 | 42 | 388 | 1.0449485 | 0.0935670 | 1.05 | 1.0394231 | 0.074130 | 0.41 | 1.40 | 0.99 | 0.0387418 | 6.9057394 | 0.0047501 |
| PFRY2 | 43 | 382 | 0.9412304 | 0.1093037 | 0.94 | 0.9422549 | 0.111195 | 0.69 | 1.37 | 0.68 | 0.1734571 | 0.3395168 | 0.0055925 |
| PENTREE2 | 44 | 386 | 1.3540674 | 0.6497019 | 1.04 | 1.2697097 | 0.185325 | 0.41 | 2.85 | 2.44 | 1.0409515 | -0.6428443 | 0.0330690 |
| NREGS2 | 45 | 388 | 3.6082474 | 1.2435400 | 3.00 | 3.5128205 | 1.482600 | 1.00 | 8.00 | 7.00 | 0.6415350 | 0.0090107 | 0.0631312 |
| NREGS112 | 46 | 383 | 2.6605744 | 0.8860092 | 3.00 | 2.6026059 | 1.482600 | 0.00 | 7.00 | 7.00 | 0.6469254 | 1.6504314 | 0.0452730 |
2.0 Sample Tabulations:
# Create Tabulations for all data
table1_col1 <- df |>
summarise(Num_stores = length(SHEET),
Refused_Second_Interview = sum(STATUS2 == 0),
Num_Closed = sum(STATUS2 == 3),
Num_Renovations = sum(STATUS2 == 2),
Num_Interviewed = sum(STATUS2 == 1),
Num_temp_closed = sum(STATUS2 == 4),
Num_Close_due_to_fire = sum(STATUS2 == 5)) |>
select(Num_stores,
Num_Interviewed,
Num_Renovations,
Num_Closed,
Num_temp_closed,
Num_Close_due_to_fire,
Refused_Second_Interview
)
# Tabulate for each state
table1_col2 <- df |>
group_by(STATE)|>
summarise(Num_stores = length(SHEET),
Refused_Second_Interview = sum(STATUS2 == 0),
Num_Closed = sum(STATUS2 == 3),
Num_Renovations = sum(STATUS2 == 2),
Num_Interviewed = sum(STATUS2 == 1),
Num_temp_closed = sum(STATUS2 == 4),
Num_Close_due_to_fire = sum(STATUS2 == 5))|>
select(Num_stores,
Num_Interviewed,
Num_Renovations,
Num_Closed,
Num_temp_closed,
Num_Close_due_to_fire,
Refused_Second_Interview
)
# combine data, then transpose for correct orientation
table1 <- as.data.frame(t(rbind(table1_col1,table1_col2)))
# rename columns
table1 <- table1 |>
rename(All = V1,
NJ = V3,
PA = V2)
# use Kable to make pretty table
kable(table1, caption = "Table 1 - Sample Design and Responce Rates")|>
kable_classic(full_width = FALSE , html_font = "Cambria")| All | PA | NJ | |
|---|---|---|---|
| Num_stores | 410 | 79 | 331 |
| Num_Interviewed | 399 | 78 | 321 |
| Num_Renovations | 2 | 0 | 2 |
| Num_Closed | 6 | 1 | 5 |
| Num_temp_closed | 1 | 0 | 1 |
| Num_Close_due_to_fire | 1 | 0 | 1 |
| Refused_Second_Interview | 1 | 0 | 1 |
In the public data the stores that dropped out after the first wave are no longer in the data set. This is why this table looks diffrent than the one in the orgnal paper
# Calculations for table 2
table2 <- df|>
group_by(STATE)|>
summarise(`a. Burger King` = sum(CHAIN == 1),
`b. KFC` = sum(CHAIN == 2),
`c. Roy Rogers` = sum(CHAIN == 3),
`d. Wendys` = sum(CHAIN == 4),
`e. CompanyOwned` = sum(CO_OWNED == 1),
`f. FTE Employment` = mean(EMPFT + NMGRS + .5 * EMPPT , na.rm = TRUE),
`g. Percentage full-time employees` = mean((EMPFT + NMGRS) / (EMPFT + EMPPT + NMGRS) ,na.rm = TRUE ),
`h. Starting Wage` = mean(WAGE_ST, na.rm = TRUE),
`i. Wage = $4.25 (percentage)` = mean(PCTAFF, na.rm = TRUE),
`j. Price of full meal` = mean(PENTREE + PFRY + PSODA, na.rm = TRUE),
`k. Hours open on week days` = mean(HRSOPEN, na.rm = TRUE),
`l. FTE Employment` = mean(EMPFT2 + NMGRS2 + .5 * EMPPT2 , na.rm = TRUE),
`m. Percentage full-time employees` = mean((EMPFT2 + NMGRS2) / (EMPFT2 + EMPPT2 + NMGRS2),na.rm = TRUE),
`n. Starting Wage` = mean(WAGE_ST2, na.rm = TRUE),
`o. Price of full meal` = mean(PENTREE2 + PFRY2 + PSODA2, na.rm = TRUE),
`p. Hours open on week days` = mean(HRSOPEN2, na.rm = TRUE)
) |>
mutate(across(everything(), ~ round(., 2)))
# combine data, then transpose for correct orentation
table2 <- as.data.frame(t(table2))
# Rename columns for table
table2 <- table2 |>
rename(PA = V1,
NJ = V2)
# Kable for pretty table
kable(table2, caption = "Table2 - Means of Key Variables") |>
kable_classic(full_width = F, html_font = "Cambria") |>
pack_rows("1. Distribution of Store Types", 2, 6) |>
pack_rows("2. Means in Wave 1:", 7, 12) |>
pack_rows("3. Means in Warve2:",13,17)| PA | NJ | |
|---|---|---|
| STATE | 0.00 | 1.00 |
| 1. Distribution of Store Types | ||
|
35.00 | 136.00 |
|
12.00 | 68.00 |
|
17.00 | 82.00 |
|
15.00 | 45.00 |
|
28.00 | 113.00 |
| 2. Means in Wave 1: | ||
|
23.33 | 20.44 |
|
0.39 | 0.38 |
|
4.63 | 4.61 |
|
45.91 | 49.62 |
|
3.04 | 3.35 |
|
14.53 | 14.42 |
| 3. Means in Warve2: | ||
|
21.17 | 21.03 |
|
0.36 | 0.41 |
|
4.62 | 5.08 |
|
3.03 | 3.41 |
|
14.65 | 14.42 |
# this is the count of stores and we want to use the precentage
df |>
mutate(state = factor(STATE ,
labels = c("Pennsylvania", "New Jersey"))) |>
ggplot(aes(x = WAGE_ST, fill = state))+
geom_histogram(aes(y = after_stat(count) / sum(after_stat(count)) * 100),
position = "dodge", bins = 14) +
labs(title = "Febuary 1992",x = "Wage Range", y = "Precentage of Stores")+
theme_classic()
df |>
mutate(state = factor(STATE ,
labels = c("Pennsylvania", "New Jersey"))) |>
ggplot(aes(x = WAGE_ST2, fill = state))+
geom_histogram(aes(y = after_stat(count) / sum(after_stat(count)) * 100),
position = "dodge", bins = 14) +
labs(title = "November 1992",x = "Wage Range", y = "Precentage of Stores") +
theme_classic()
3.0 Diffrence In Diffrence Estimation
3.1 Simple Comparison
# Column i, ii, & iii
stores_by_state <- df |>
group_by(STATE)|>
summarise(FTE_EMP1 = mean(EMPFT + NMGRS + .5 * EMPPT, na.rm = TRUE),
FTE_EMP2 = mean(EMPFT2 + NMGRS2 + .5 * EMPPT, na.rm = TRUE),
Change_in_mean_FTE = FTE_EMP2 - FTE_EMP1)|>
select(FTE_EMP1,FTE_EMP2,Change_in_mean_FTE) |>
mutate(across(everything(), ~ round(., 2)))
# transpose data
stores_by_state <- as.data.frame(t(stores_by_state))
# rename columns for table
stores_by_state <- stores_by_state |>
rename(`PA (i)` = V1,
`NJ (ii)` = V2) |>
mutate(`Diffrence in NJ - PA (iii)` = `NJ (ii)` - `PA (i)`)# Column iv
stores_NJ_iv <- df |>
filter(STATE == 1 & WAGE_ST == 4.25 ) |>
summarise(FTE_EMP1 = mean(EMPFT + NMGRS + .5 * EMPPT, na.rm = TRUE),
FTE_EMP2 = mean(EMPFT2 + NMGRS2 + .5 * EMPPT, na.rm = TRUE),
Change_in_mean_FTE = FTE_EMP2 - FTE_EMP1)|>
select(FTE_EMP1,FTE_EMP2,Change_in_mean_FTE) |>
mutate(across(everything(), ~ round(., 2)))
# Column V
stores_NJ_v <- df |>
filter(STATE == 1 & between(WAGE_ST, 4.26,4.99)) |>
summarise(FTE_EMP1 = mean(EMPFT + NMGRS + .5 * EMPPT, na.rm = TRUE),
FTE_EMP2 = mean(EMPFT2 + NMGRS2 + .5 * EMPPT, na.rm = TRUE),
Change_in_mean_FTE = FTE_EMP2 - FTE_EMP1)|>
select(FTE_EMP1,FTE_EMP2,Change_in_mean_FTE) |>
mutate(across(everything(), ~ round(., 2)))
# Column vi
stores_NJ_vi <- df |>
filter(STATE == 1 & WAGE_ST >= 5) |>
summarise(FTE_EMP1 = mean(EMPFT + NMGRS + .5 * EMPPT, na.rm = TRUE),
FTE_EMP2 = mean(EMPFT2 + NMGRS2 + .5 * EMPPT, na.rm = TRUE),
Change_in_mean_FTE = FTE_EMP2 - FTE_EMP1)|>
select(FTE_EMP1,FTE_EMP2,Change_in_mean_FTE) |>
mutate(across(everything(), ~ round(., 2)))
# transpose Data
stores_NJ <- as.data.frame(t(rbind(stores_NJ_iv, stores_NJ_v, stores_NJ_vi)))
# rename columns for table
stores_NJ <- stores_NJ |>
rename(`Wage = $4.25 (iv)` = V1,
`Wage = $4.26-$4.99 (v)`= V2,
`Wage = $5.00 (vi)` = V3)# colum vii
columnVII <- df |>
filter(STATE == 1) |>
summarise(FTEMP1_low = mean(ifelse(WAGE_ST == 4.25, EMPFT + NMGRS + .5 *
EMPPT, NA_real_), na.rm = TRUE),
FTEMP1_high = mean(ifelse(WAGE_ST >= 5 , EMPFT + NMGRS + .5 *
EMPPT, NA_real_), na.rm = TRUE),
`low-high FTE Before` = FTEMP1_low - FTEMP1_high,
FTEMP2_low = mean(ifelse(WAGE_ST == 4.25, EMPFT2 + NMGRS2 + .5 *
EMPPT2, NA_real_), na.rm = TRUE),
FTEMP2_high = mean(ifelse(WAGE_ST >= 5 , EMPFT2 + NMGRS2 + .5 *
EMPPT2, NA_real_), na.rm = TRUE),
`low-high FTE After` = FTEMP2_low - FTEMP2_high,
`Change in mean FTE` = `low-high FTE After` -`low-high FTE Before`
) |>
select(`low-high FTE Before`, `low-high FTE After`, `Change in mean FTE`) |>
rename( FTE_EMP1 = `low-high FTE Before`,
FTE_EMP2 = `low-high FTE After`,
Change_in_mean_FTE = `Change in mean FTE` )
# colum viii
columnVIII <- df |>
filter(STATE == 1) |>
summarise(FTEMP1_mid = mean(ifelse(between(WAGE_ST, 4.26,4.99),
EMPFT + NMGRS + .5 *EMPPT,
NA_real_), na.rm = TRUE),
FTEMP1_high = mean(ifelse(WAGE_ST >= 5 , EMPFT + NMGRS + .5 *
EMPPT, NA_real_), na.rm = TRUE),
`mid-high FTE Before` = FTEMP1_mid - FTEMP1_high,
FTEMP2_mid = mean(ifelse(between(WAGE_ST, 4.26,4.99),
EMPFT2 + NMGRS2 + .5 *EMPPT2,
NA_real_), na.rm = TRUE),
FTEMP2_high = mean(ifelse(WAGE_ST >= 5 ,
EMPFT2 + NMGRS2 + .5 *EMPPT2,
NA_real_), na.rm = TRUE),
`mid-high FTE After` = FTEMP2_mid - FTEMP2_high,
`Change in mean FTE` = `mid-high FTE After` -`mid-high FTE Before`
) |>
select(`mid-high FTE Before`, `mid-high FTE After`, `Change in mean FTE`) |>
rename( FTE_EMP1 = `mid-high FTE Before`,
FTE_EMP2 = `mid-high FTE After`,
Change_in_mean_FTE = `Change in mean FTE` )
Diffrence_NJ <- as.data.frame(t(rbind(columnVII, columnVIII)))
Diffrence_NJ <- Diffrence_NJ |>
rename(`Low-High (vii)` = V1,
`Mid-High (viii)` = V2)# combine data frames for table
table3 <- cbind(stores_by_state, stores_NJ, Diffrence_NJ )
# use Kable for making pretty table
kable(table3, caption = "Table 3 - AVERAGE EMPLOYMENT PER STORE BEFORE AND
AFTER THE RISE IN NEW JERSEY MINIMUM MAGE") |>
kable_classic(full_width = F, html_font = "Cambria") |>
add_header_above(c(" ","Stores by State" = 3,"Stores in New Jersey" = 3,
"Differences within NJ" = 2))|
Stores by State
|
Stores in New Jersey
|
Differences within NJ
|
||||||
|---|---|---|---|---|---|---|---|---|
| PA (i) | NJ (ii) | Diffrence in NJ - PA (iii) | Wage = $4.25 (iv) | Wage = $4.26-$4.99 (v) | Wage = $5.00 (vi) | Low-High (vii) | Mid-High (viii) | |
| FTE_EMP1 | 23.33 | 20.44 | -2.89 | 19.56 | 20.08 | 22.25 | -2.6932990 | -2.1693431 |
| FTE_EMP2 | 20.89 | 21.31 | 0.42 | 21.18 | 21.32 | 20.29 | 0.6637829 | 0.7417874 |
| Change_in_mean_FTE | -2.44 | 0.87 | 3.31 | 1.63 | 1.24 | -1.96 | 3.3570819 | 2.9111305 |
3.1 Regression Analysis
# Create variables for regression Analysis
final_df <- df |>
mutate(FTE_EMP1 = EMPFT + NMGRS + .5 * EMPPT,
FTE_EMP2 = EMPFT2 + NMGRS2 + .5 * EMPPT2,
delta_emp = FTE_EMP2 - FTE_EMP1,
GAP = ifelse(STATE == 1 & WAGE_ST <= 5.05,((5.05-WAGE_ST)/WAGE_ST),0),
CHAIN1 = ifelse(CHAIN == 1,1,0),
CHAIN2 = ifelse(CHAIN == 2,1,0),
CHAIN3 = ifelse(CHAIN == 3,1,0)) |>
select(FTE_EMP1,FTE_EMP2,delta_emp,GAP,CHAIN1,CHAIN2,CHAIN3, STATE, CO_OWNED,
SOUTHJ, CENTRALJ, NORTHJ, PA1, PA2) |>
drop_na()# Chek-out the mean of non-zero GAP data
final_df|>
filter(GAP > 0) |>
summarise(GAP_MEAN = mean(GAP)) -> test
print(test)## GAP_MEAN
## 1 0.1152347# Regressions
reg1 <- lm(delta_emp ~ STATE, data = final_df)
reg2 <- lm(delta_emp ~ STATE + CHAIN1 + CHAIN2 + CHAIN3 + CO_OWNED, data = final_df)
reg3 <- lm(delta_emp ~ GAP, data = final_df)
reg4 <- lm(delta_emp ~ GAP + CHAIN1 + CHAIN2 + CHAIN3 + CO_OWNED, data = final_df)
reg5 <- lm(delta_emp ~ GAP + SOUTHJ + CENTRALJ + NORTHJ + PA1 + PA2 ,data = final_df)# present htm table of regression results
htmltools::includeHTML("table3.htm")| Dependent variable: | |||||
| delta_emp | |||||
| (1) | (2) | (3) | (4) | (5) | |
| STATE | 2.496** | 2.497** | |||
| (1.127) | (1.130) | ||||
| CHAIN1 | 0.189 | -0.355 | |||
| (1.444) | (1.462) | ||||
| CHAIN2 | 0.839 | 0.453 | |||
| (1.625) | (1.636) | ||||
| CHAIN3 | -1.984 | -2.199 | |||
| (1.636) | (1.637) | ||||
| CO_OWNED | 0.333 | 0.431 | |||
| (1.072) | (1.071) | ||||
| GAP | 16.359*** | 15.877*** | 13.005* | ||
| (5.924) | (6.044) | (7.156) | |||
| SOUTHJ | 0.054 | ||||
| (1.844) | |||||
| CENTRALJ | -1.346 | ||||
| (1.938) | |||||
| NORTHJ | 0.093 | ||||
| (1.688) | |||||
| PA1 | -2.898 | ||||
| (2.013) | |||||
| PA2 | |||||
| Constant | -2.283** | -2.167 | -1.669** | -1.187 | -0.970 |
| (1.006) | (1.521) | (0.672) | (1.313) | (1.355) | |
| Observations | 368 | 368 | 368 | 368 | 368 |
| R2 | 0.013 | 0.026 | 0.020 | 0.032 | 0.031 |
| Adjusted R2 | 0.011 | 0.013 | 0.018 | 0.018 | 0.018 |
| Residual Std. Error | 8.709 (df = 366) | 8.699 (df = 362) | 8.678 (df = 366) | 8.675 (df = 362) | 8.678 (df = 362) |
| F Statistic | 4.906** (df = 1; 366) | 1.958* (df = 5; 362) | 7.626*** (df = 1; 366) | 2.368** (df = 5; 362) | 2.318** (df = 5; 362) |
| Note: | *p<0.1; **p<0.05; ***p<0.01 | ||||