# This file reads in replication data from
# Carvalho, L. S., Meier, S., & Wang, S. W. (2016). Poverty and economic decision-making: Evidence from changes in financial resources at payday. American economic review, 106(2), 260-84.
# Farbmacher, H., Kögel, H., & Spindler, M. (2021). Heterogeneous effects of poverty on attention. Labour Economics, 71, 102028.
# and saves processed covariate/treatment/outcome info for use in the grf package.

# Data license copy/paste:

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# Step 1: download publicly available replication data for Carvalho et al. from https://www.aeaweb.org/articles?id=10.1257/aer.20140481
# Step 2: download publicly available replication code for Farbmacher et al. from http://www.farbmacher.de/codes/FKS_2019_replication.zip
# Step 3: run "KP_replication_script.R" from Farbmacher et al. to produce the data.frame "full.kp.mod"

data = full.kp.mod # See step 3 above

Y1 = data[, 1] # Correct answers per second
Y2 = data[, 2] # Number of correct answers, lower outcome = worse
Y3 = data[, 3] # Total response time, a higher outcome = worse
W = data[, 4] # W = 1 if survey before payday
X = data[, -(1:4)]

# Table 1 Farbmacher
mean(Y2)
sd(Y2)
mean(W)
sd(W)
# Table 2 Farbmacher
print(as.matrix(apply(X, 2, mean)), digits=2)

# Rename variables
colnames(X) = c(
  "live.paycheck.by.paycheck",
  "caloric.crunch",
  "liquidity.constrained",
  "financial.hardship",
  "current.income",
  "pay.day.amount.fraction.of.income",
  "age",
  "male",
  "married",
  "divorced",
  "widowed",
  "never.married",
  "white",
  "black",
  "hispanic",
  "other.race",
  "working",
  "unemployed",
  "disabled",
  "retired",
  "other.employment.status",
  "household.size",
  "college.graduate",
  "some.college",
  "high.school",
  "less.high.school",
  "income_less_5k",
  "income_btw_5k_10k",
  "income_btw_10k_15k",
  "income_btw_15k_20k",
  "income_btw_20k_25k",
  "income_btw_25k_30k",
  "income_btw_30k_35k",
  "income_btw_35k_40k",
  "household.head",
  "children.in.household",
  "metropolitan.area"
)

# Re-encode the income-dummies to an ordinal level (income range) to make it easier for forests to split
inc.dummies = c("income_less_5k", "income_btw_5k_10k", "income_btw_10k_15k", "income_btw_15k_20k", "income_btw_20k_25k",
                "income_btw_25k_30k", "income_btw_30k_35k", "income_btw_35k_40k")
inc.colnum = which(colnames(X) %in% inc.dummies)
inc.dummies.income = c(5000, 10000, 15000, 20000, 25000, 30000, 35000, 40000)
all(rowSums(X[inc.dummies]) == 1)
income = inc.dummies.income[max.col(X[inc.dummies])]
X = X[-inc.colnum]
X[, "household.income"] = income

# Do the same with education dummies
edu.dummies = c("college.graduate", "some.college", "high.school", "less.high.school")
edu.colnum = which(colnames(X) %in% edu.dummies)
edu.level = c(4, 3, 2, 1)
all(rowSums(X[edu.dummies]) == 1)
edu = edu.level[max.col(X[edu.dummies])] # high = college, low = less than high school
X = X[-edu.colnum]
X[, "education"] = edu

# No need to split on at several digits of precision
X$pay.day.amount.fraction.of.income = round(X$pay.day.amount.fraction.of.income, 2)

# Remove some redundant (perfectly colinear) variables
summary(lm(Y2 ~as.matrix(X)))
X$never.married=NULL
X$other.race=NULL
X$other.employment.status=NULL

out = cbind(outcome.correct.ans.per.second = Y1, outcome.num.correct.ans = Y2, outcome.response.time = Y3,
            treatment = W, X)
write.csv(out, "carvalho2016.csv", row.names = FALSE)