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2024/TA8_HW3/HW_TEMPLATE.R

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+#Author: YOURNAME
+# Date 03-13-24
+
+# A. Import data
+df = read.csv('ipumsi_00008 (1).csv')
+
+# A.1 Truncate/subset data
+df = df %>% filter(
+  # YOUR CODE HERE
+)
+
+# B. Create dummies
+## B.1 Labor
+# Explore
+df %>% 
+  select(LABFORCE) %>% 
+  unique()
+
+# Create
+df = df %>% 
+  mutate(dummy_work = as.numeric(LABFORCE==2)
+  )
+
+## B.2 Age buckets
+cutoffs = c(seq(15, 70, by = 5))
+df %>% 
+  mutate(
+    age_bucket = cut(AGE, cutoffs, include.lowest=TRUE, include.highest=FALSE)
+  ) %>% 
+  select(age_bucket, AGE) %>% 
+  head(5)
+
+
+# 1.1 What is the labor market participation of men and women aged 16 to 65? 
+df %>% 
+  group_by(
+    # YOUR CODE HERE
+           ) %>% 
+  summarise(
+    mean(
+      # YOUR CODE HERE
+         )
+  )
+
+# 1.2 Provide labor market participation rates by 5 year age ranges for men and women.
+df %>% 
+  group_by(SEX, age_bucket) %>% 
+  summarise(
+    mean(dummy_work)
+  )
+
+# 2. Construct at least two different definitions of the informal sector to answer the following questions.
+## 2.A # Create informal dummy
+df %>% 
+  select(EMPSECT) %>% 
+  unique()
+
+df %>% mutate(
+    dummy_inform = as.numeric(EMPSECT == 20 | EMPSECT == 0)
+  ) %>% 
+  select(
+    dummy_inform, EMPSECT
+  ) %>% 
+  head(10)
+
+emp_list = c(20, 0)
+df %>% mutate(
+  dummy_inform = as.numeric(EMPSECT %in% emp_list)
+) %>% 
+  select(
+    dummy_inform, EMPSECT
+  ) %>% 
+  head(10)
+
+
+
+# 2.1 What proportion of men and women who work are in the informal sector?
+
+
+
+# 2.2 Compare total earnings, average earnings per hour and median earnings per hour in the formal and informal sector for men and women.
+
+
+
+# 2.3 Compare total earnings, average earnings per hour and median earnings per hour in the formal and informal sector for men and women.
+
+
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