PSID summary
Fatma Mahmoud
July 29, 2023
1 Overview
1.1 Summary
This report provides an introductory examination of the Food Stamps participation trends from 1968 to 1988 using the PSID data, which assesses economic, social, and health aspects throughout the life span of families across multiple generations. First, data cleaning was performed so that the analysis is focused on the subset of survey takers who participated in the Food Stamps program. The variables I looked at are only those relevant to Food Stamps program such as income, state, family size, annual amount paid for food stamps, annual amount saved from using food stamps, annual food standards, and annual food bill. The number of observations is not constant across the years as families kept expanding.
1.2 Variable descriptions
| Variable | Description |
|---|---|
| participation | Number of food stamps participants in each year from 1969 to 1988 except 1973 |
| weight | Weight of the sruvey |
| size | Family size |
| state | State where the family currently lives recoded to be the actual states names |
| food_std | Annual food needs based on the USDA Low Cost plan estimates of the weekly food costs |
| food_bill | Annual food expenditures excluding alcohol and cigarettes in nominal value |
| saved_FS | Money saved by using food stamps, which is the difference between the money spent on food stamps and the value of the food stamps purchased |
| income | Total annual family Money Income |
| paid_monthly_FS | Money spent on food stamps last month |
| saved_monthly_FS | Money saved using food stamps last month |
| avg_size | Weighted average family size across the years from 1968 to 1988 except 1973 |
| avg_income | Weighted average nominal income across the years from 1967 to 1988 except 1973 |
| avg_food_bill | Weighted average annual food expenditures across the years from 1968 to 1988 except 1972, 1987, and 1988 |
| avg_saved_FS | Weighted average money saved annually from using food stamps across the years from 1967 to 1988 except 1972 |
| avg_paid_FS | Weighted average money spent annually on food stamps across the years from 1974 to 1978 |
| avg_paid_monthly_FS | Weighted average money spent monthly on food stamps across the years from 1975 to 1979 |
| avg_saved_monthly_FS | Weighted average money saved monthly from using food stamps across the years from 1975 to 1987 |
| year | Year from 1968 to 1988 |
2 Data Cleaning
# Data Cleaning for all datasets and renaming variables
#participation vector to store number of food stamps participants
participation<- c()
#1968----
PSID_1968$weight = na.omit(weights$Weight1968)
PSID_1968$STATE <-recode(PSID_1968$STATE, "1"= "Alabama", "2"= "Arizona", "3"= "Arkansas", "4"= "California", "5"= "Colorado", "6"= "Connecticut", "7"= "Delaware", "8"= "District of Columbia", "9"= "Florida", "10"= "Georgia", "11"= "Idaho", "12"= "Illinois", "13"= "Indiana", "14"= "Iowa", "15"= "Kansas", "16"= "Kentucky", "17"= "Louisiana", "18"= "Maine", "19"= "Maryland", "20"= "Massachusetts", "21"= "Michigan", "22"= "Minnesota", "23"= "Mississippi", "24"= "Missouri", "25"= "Montana", "26"= "Nebraska", "27"= "Nevada", "28"= "New Hampshire", "29"= "New Jersey", "30"= "New Mexico", "31"= "New York", "32"= "North Carolina", "33"= "North Dakota", "34"= "Ohio", "35"= "Oklahoma", "36"= "Oregon", "37"= "Pennsylvania", "38"= "Rhode Island", "39"= "South Carolina", "40"= "South Dakota", "41"= "Tennessee", "42"= "Texas", "43"= "Utah", "44"= "Vermont", "45"= "Virginia", "46"= "Washington", "47"= "West Virginia", "48"= "Wisconsin", "49"= "Wyoming", "50"= "Alaska", "51"= "Hawaii")
STATE_68 <- PSID_1968$STATE
PSID_68 <- subset(PSID_1968, PSID_1968$`YRLY FOOD STAMPS` > 0)
weight_68 <- as.numeric(PSID_68$weight)
participation <- append(participation, nrow(PSID_68))
size_68<- as.numeric(PSID_68$"FAMILY SIZE")
food_std_68= as.numeric(PSID_68$"YRLY FOOD STD.")
food_bill_68= as.numeric(PSID_68$"YRLY FOOD BILL")
saved_FS_67= as.numeric(PSID_68$"YRLY FOOD STAMPS")
income_67=as.numeric(PSID_68$"TOTAL INCOME")
state_68= as.factor(na.omit(PSID_68$"STATE"))
#Computing 1968 averages ----
avg_size_68= weighted.mean(size_68, weight_68)
avg_food_std_68= weighted.mean(food_std_68, weight_68)
avg_food_bill_68= weighted.mean(food_bill_68, weight_68)
avg_saved_FS_67= weighted.mean(saved_FS_67, weight_68)
avg_income_67= weighted.mean(income_67, weight_68)
#1969----
PSID_1969$weight = na.omit(weights$Weight1969)
PSID_1969$STATE <-recode(PSID_1969$STATE, "1"= "Alabama", "2"= "Arizona", "3"= "Arkansas", "4"= "California", "5"= "Colorado", "6"= "Connecticut", "7"= "Delaware", "8"= "District of Columbia", "9"= "Florida", "10"= "Georgia", "11"= "Idaho", "12"= "Illinois", "13"= "Indiana", "14"= "Iowa", "15"= "Kansas", "16"= "Kentucky", "17"= "Louisiana", "18"= "Maine", "19"= "Maryland", "20"= "Massachusetts", "21"= "Michigan", "22"= "Minnesota", "23"= "Mississippi", "24"= "Missouri", "25"= "Montana", "26"= "Nebraska", "27"= "Nevada", "28"= "New Hampshire", "29"= "New Jersey", "30"= "New Mexico", "31"= "New York", "32"= "North Carolina", "33"= "North Dakota", "34"= "Ohio", "35"= "Oklahoma", "36"= "Oregon", "37"= "Pennsylvania", "38"= "Rhode Island", "39"= "South Carolina", "40"= "South Dakota", "41"= "Tennessee", "42"= "Texas", "43"= "Utah", "44"= "Vermont", "45"= "Virginia", "46"= "Washington", "47"= "West Virginia", "48"= "Wisconsin", "49"= "Wyoming", "50"= "Alaska", "51"= "Hawaii")
STATE_69 <- PSID_1969$STATE
particip_68 <- nrow(subset(PSID_1969, PSID_1969$`FOOD STAMPS?` == 1))
PSID_69 <- subset(PSID_1969, PSID_1969$`SAVD FOOD STMP` > 0)
weight_69 <- as.numeric(PSID_69$weight)
participation<- append(participation, nrow(PSID_69))
size_69<- as.numeric(PSID_69$"FAMILY SIZE")
food_std_69= as.numeric(PSID_69$"YRLY FOOD STD")
food_bill_69_est= as.numeric(PSID_69$"YRLY FOOD BILL")
saved_FS_68= as.numeric(PSID_69$"SAVD FOOD STMP")
income_68=as.numeric(PSID_69$"TOTAL INCOME")
used_FS_68= as.numeric(PSID_69$"FOOD STAMPS?")
FS_participants_68= sum(used_FS_68 == 1)
state_69= as.factor(PSID_69$"STATE")
#Computing 1969 averages ----
avg_size_69= weighted.mean(size_69, weight_69)
avg_food_std_69= weighted.mean(food_std_69, weight_69)
avg_saved_FS_68= weighted.mean(saved_FS_68, weight_69)
avg_income_68= weighted.mean(income_68, weight_69)
#1970----
PSID_1970$weight = na.omit(weights$Weight1970)
PSID_1970$STATE <-recode(PSID_1970$STATE, "1"= "Alabama", "2"= "Arizona", "3"= "Arkansas", "4"= "California", "5"= "Colorado", "6"= "Connecticut", "7"= "Delaware", "8"= "District of Columbia", "9"= "Florida", "10"= "Georgia", "11"= "Idaho", "12"= "Illinois", "13"= "Indiana", "14"= "Iowa", "15"= "Kansas", "16"= "Kentucky", "17"= "Louisiana", "18"= "Maine", "19"= "Maryland", "20"= "Massachusetts", "21"= "Michigan", "22"= "Minnesota", "23"= "Mississippi", "24"= "Missouri", "25"= "Montana", "26"= "Nebraska", "27"= "Nevada", "28"= "New Hampshire", "29"= "New Jersey", "30"= "New Mexico", "31"= "New York", "32"= "North Carolina", "33"= "North Dakota", "34"= "Ohio", "35"= "Oklahoma", "36"= "Oregon", "37"= "Pennsylvania", "38"= "Rhode Island", "39"= "South Carolina", "40"= "South Dakota", "41"= "Tennessee", "42"= "Texas", "43"= "Utah", "44"= "Vermont", "45"= "Virginia", "46"= "Washington", "47"= "West Virginia", "48"= "Wisconsin", "49"= "Wyoming", "50"= "Alaska", "51"= "Hawaii")
STATE_70 <- PSID_1970$STATE
particip_70 <- nrow(subset(PSID_1970, PSID_1970$`AMT PAID FOOD STAMPS`>0 & PSID_1970$`AMT PAID FOOD STAMPS` < 4681))
PSID_70 <- subset(PSID_1970, PSID_1970$`$ FOOD BOUGHT W/ STAMPS`>0)
weight_70 <- as.numeric(PSID_70$weight)
participation<- append(participation, nrow(PSID_70))
size_70<- as.numeric(PSID_70$"FAMILY SIZE")
food_std_70= as.numeric(PSID_70$"YRLY FOOD STD")
food_bill_69= as.numeric(PSID_70$"YRLY FOOD BILL")
saved_FS_69= as.numeric(PSID_70$"SAVD.FOOD.STMP")
income_69=as.numeric(PSID_70$"TOTAL INCOME")
paid_FS_69=as.numeric(PSID_70$"AMT PAID FOOD STAMPS")
value_FS_69=as.numeric(PSID_70$"$ FOOD BOUGHT W/ STAMPS")
FS_participants_70= sum(paid_FS_69>0)
state_70= as.factor(PSID_70$STATE)
#Computing 1970 averages ----
avg_size_70= weighted.mean(size_70, weight_70)
avg_food_std_70= weighted.mean(food_std_70, weight_70)
avg_food_bill_69= weighted.mean(food_bill_69, weight_70)
avg_saved_FS_69= weighted.mean(saved_FS_69, weight_70)
avg_income_69= weighted.mean(income_69, weight_70)
avg_paid_FS_69= weighted.mean(paid_FS_69, weight_70)
avg_value_FS_69= weighted.mean(value_FS_69, weight_70)
#1971----
PSID_1971$weight = na.omit(weights$Weight1971)
PSID_1971$STATE <-recode(PSID_1971$STATE, "1"= "Alabama", "2"= "Arizona", "3"= "Arkansas", "4"= "California", "5"= "Colorado", "6"= "Connecticut", "7"= "Delaware", "8"= "District of Columbia", "9"= "Florida", "10"= "Georgia", "11"= "Idaho", "12"= "Illinois", "13"= "Indiana", "14"= "Iowa", "15"= "Kansas", "16"= "Kentucky", "17"= "Louisiana", "18"= "Maine", "19"= "Maryland", "20"= "Massachusetts", "21"= "Michigan", "22"= "Minnesota", "23"= "Mississippi", "24"= "Missouri", "25"= "Montana", "26"= "Nebraska", "27"= "Nevada", "28"= "New Hampshire", "29"= "New Jersey", "30"= "New Mexico", "31"= "New York", "32"= "North Carolina", "33"= "North Dakota", "34"= "Ohio", "35"= "Oklahoma", "36"= "Oregon", "37"= "Pennsylvania", "38"= "Rhode Island", "39"= "South Carolina", "40"= "South Dakota", "41"= "Tennessee", "42"= "Texas", "43"= "Utah", "44"= "Vermont", "45"= "Virginia", "46"= "Washington", "47"= "West Virginia", "48"= "Wisconsin", "49"= "Wyoming", "50"= "Alaska", "51"= "Hawaii")
STATE_71 <- PSID_1971$STATE
PSID_71 <- subset(PSID_1971, PSID_1971$SAVD.FOOD.STMP> 0)
weight_71 <- as.numeric(PSID_71$weight)
participation<- append(participation, nrow(PSID_71))
size_71<- as.numeric(PSID_71$"FAMILY SIZE")
food_std_71= as.numeric(PSID_71$"YRLY FOOD STD")
food_bill_70= as.numeric(PSID_71$"YRLY FOOD BILL")
saved_FS_70= as.numeric(PSID_71$"SAVD.FOOD.STMP")
income_70=as.numeric(PSID_71$"TOTAL INCOME")
state_71= as.factor(PSID_71$STATE)
#Computing 1971 averages ----
avg_size_71= weighted.mean(size_71, weight_71)
avg_food_std_71= weighted.mean(food_std_71, weight_71)
avg_food_bill_70= weighted.mean(food_bill_70, weight_71)
avg_saved_FS_70= weighted.mean(saved_FS_70, weight_71)
avg_income_70= weighted.mean(income_70, weight_71)
#1972----
PSID_1972$weight = na.omit(weights$Weight1972)
PSID_1972$STATE <-recode(PSID_1972$STATE, "1"= "Alabama", "2"= "Arizona", "3"= "Arkansas", "4"= "California", "5"= "Colorado", "6"= "Connecticut", "7"= "Delaware", "8"= "District of Columbia", "9"= "Florida", "10"= "Georgia", "11"= "Idaho", "12"= "Illinois", "13"= "Indiana", "14"= "Iowa", "15"= "Kansas", "16"= "Kentucky", "17"= "Louisiana", "18"= "Maine", "19"= "Maryland", "20"= "Massachusetts", "21"= "Michigan", "22"= "Minnesota", "23"= "Mississippi", "24"= "Missouri", "25"= "Montana", "26"= "Nebraska", "27"= "Nevada", "28"= "New Hampshire", "29"= "New Jersey", "30"= "New Mexico", "31"= "New York", "32"= "North Carolina", "33"= "North Dakota", "34"= "Ohio", "35"= "Oklahoma", "36"= "Oregon", "37"= "Pennsylvania", "38"= "Rhode Island", "39"= "South Carolina", "40"= "South Dakota", "41"= "Tennessee", "42"= "Texas", "43"= "Utah", "44"= "Vermont", "45"= "Virginia", "46"= "Washington", "47"= "West Virginia", "48"= "Wisconsin", "49"= "Wyoming", "50"= "Alaska", "51"= "Hawaii")
STATE_72 <- PSID_1972$STATE
PSID_72 <- subset(PSID_1972, PSID_1972$`$ SAVD FD STMP`> 0)
PSID_72 <- filter(PSID_72, STATE != 0)
weight_72 <- as.numeric(PSID_72$weight)
participation<- append(participation, nrow(PSID_72))
size_72<- as.numeric(PSID_72$"FAMILY SIZE")
food_std_72= as.numeric(PSID_72$"YRLY FOOD STD")
food_bill_71= as.numeric(PSID_72$"YRLY FOOD BILL")
saved_FS_71= as.numeric(PSID_72$"$ SAVD FD STMP")
income_71=as.numeric(PSID_72$"TOTAL INCOME")
state_72= as.factor(PSID_72$"STATE")
#Computing 1972 averages ----
avg_size_72=weighted.mean(size_72, weight_72)
avg_food_std_72=weighted.mean(food_std_72, weight_72)
avg_food_bill_71=weighted.mean(food_bill_71, weight_72)
avg_saved_FS_71=weighted.mean(saved_FS_71, weight_72)
avg_income_71=weighted.mean(income_71, weight_72)
#1973----
PSID_1973$weight = na.omit(weights$Weight1973)
participation<- append(participation, NA)
size_73<- as.numeric(PSID_1973$"FAMILY SIZE")
food_std_73= as.numeric(PSID_1973$"YRLY FOOD STD")
income_72=as.numeric(PSID_1973$"TOTAL INCOME")
state_73= as.factor((PSID_1973$"STATE"))
state_73 <-recode(state_73, "1"= "Alabama", "2"= "Arizona", "3"= "Arkansas", "4"= "California", "5"= "Colorado", "6"= "Connecticut", "7"= "Delaware", "8"= "District of Columbia", "9"= "Florida", "10"= "Georgia", "11"= "Idaho", "12"= "Illinois", "13"= "Indiana", "14"= "Iowa", "15"= "Kansas", "16"= "Kentucky", "17"= "Louisiana", "18"= "Maine", "19"= "Maryland", "20"= "Massachusetts", "21"= "Michigan", "22"= "Minnesota", "23"= "Mississippi", "24"= "Missouri", "25"= "Montana", "26"= "Nebraska", "27"= "Nevada", "28"= "New Hampshire", "29"= "New Jersey", "30"= "New Mexico", "31"= "New York", "32"= "North Carolina", "33"= "North Dakota", "34"= "Ohio", "35"= "Oklahoma", "36"= "Oregon", "37"= "Pennsylvania", "38"= "Rhode Island", "39"= "South Carolina", "40"= "South Dakota", "41"= "Tennessee", "42"= "Texas", "43"= "Utah", "44"= "Vermont", "45"= "Virginia", "46"= "Washington", "47"= "West Virginia", "48"= "Wisconsin", "49"= "Wyoming", "50"= "Alaska", "51"= "Hawaii")
#Computing 1973 averages ----
avg_size_73=weighted.mean(size_73, PSID_1973$weight)
avg_food_std_73=weighted.mean(food_std_73, PSID_1973$weight)
avg_income_72=weighted.mean(income_72, PSID_1973$weight)
#1974----
PSID_1974$weight = na.omit(weights$Weight1974)
PSID_1974$STATE <-recode(PSID_1974$STATE, "1"= "Alabama", "2"= "Arizona", "3"= "Arkansas", "4"= "California", "5"= "Colorado", "6"= "Connecticut", "7"= "Delaware", "8"= "District of Columbia", "9"= "Florida", "10"= "Georgia", "11"= "Idaho", "12"= "Illinois", "13"= "Indiana", "14"= "Iowa", "15"= "Kansas", "16"= "Kentucky", "17"= "Louisiana", "18"= "Maine", "19"= "Maryland", "20"= "Massachusetts", "21"= "Michigan", "22"= "Minnesota", "23"= "Mississippi", "24"= "Missouri", "25"= "Montana", "26"= "Nebraska", "27"= "Nevada", "28"= "New Hampshire", "29"= "New Jersey", "30"= "New Mexico", "31"= "New York", "32"= "North Carolina", "33"= "North Dakota", "34"= "Ohio", "35"= "Oklahoma", "36"= "Oregon", "37"= "Pennsylvania", "38"= "Rhode Island", "39"= "South Carolina", "40"= "South Dakota", "41"= "Tennessee", "42"= "Texas", "43"= "Utah", "44"= "Vermont", "45"= "Virginia", "46"= "Washington", "47"= "West Virginia", "48"= "Wisconsin", "49"= "Wyoming", "50"= "Alaska", "51"= "Hawaii")
STATE_74 <- PSID_1974$STATE
PSID_74 <- subset(PSID_1974, PSID_1974$`AMT SAVD FD STMP`> 0)
PSID_74 <- filter(PSID_74, STATE != 0)
weight_74 <- as.numeric(PSID_74$weight)
participation<- append(participation, nrow(PSID_74))
particip_73 <- nrow(subset(PSID_1974, PSID_1974$`FREQ USE FD STAMPS` != 0 & PSID_1974$`FREQ USE FD STAMPS` != 9 & PSID_1974$`FREQ USE FD STAMPS` != 5))
#yielded a different number of observations
size_74<- as.numeric(PSID_74$"FAMILY SIZE")
food_std_74= as.numeric(PSID_74$"YRLY FOOD STD")
food_bill_73= as.numeric(PSID_74$"YRLY FOOD BILL")
income_73=as.numeric(PSID_74$"TOTAL INCOME")
saved_FS_73= as.numeric(PSID_74$"AMT SAVD FD STMP")
frequency_FS_73= as.numeric(PSID_74$"FREQ USE FD STAMPS")
FS_participants_73= sum((frequency_FS_73) != 0 & (frequency_FS_73) != 5)
state_74= as.factor((PSID_74$STATE))
#Computing 1974 averages ----
avg_size_74=weighted.mean(size_74, weight_74)
avg_food_std_74=weighted.mean(food_std_74, weight_74)
avg_food_bill_73=weighted.mean(food_bill_73, weight_74)
avg_income_73=weighted.mean(income_73, weight_74)
avg_saved_FS_73=weighted.mean(saved_FS_73, weight_74)
#1975----
PSID_1975$weight = na.omit(weights$Weight1975)
PSID_1975$STATE <-recode(PSID_1975$STATE, "1"= "Alabama", "2"= "Arizona", "3"= "Arkansas", "4"= "California", "5"= "Colorado", "6"= "Connecticut", "7"= "Delaware", "8"= "District of Columbia", "9"= "Florida", "10"= "Georgia", "11"= "Idaho", "12"= "Illinois", "13"= "Indiana", "14"= "Iowa", "15"= "Kansas", "16"= "Kentucky", "17"= "Louisiana", "18"= "Maine", "19"= "Maryland", "20"= "Massachusetts", "21"= "Michigan", "22"= "Minnesota", "23"= "Mississippi", "24"= "Missouri", "25"= "Montana", "26"= "Nebraska", "27"= "Nevada", "28"= "New Hampshire", "29"= "New Jersey", "30"= "New Mexico", "31"= "New York", "32"= "North Carolina", "33"= "North Dakota", "34"= "Ohio", "35"= "Oklahoma", "36"= "Oregon", "37"= "Pennsylvania", "38"= "Rhode Island", "39"= "South Carolina", "40"= "South Dakota", "41"= "Tennessee", "42"= "Texas", "43"= "Utah", "44"= "Vermont", "45"= "Virginia", "46"= "Washington", "47"= "West Virginia", "48"= "Wisconsin", "49"= "Wyoming", "50"= "Alaska", "51"= "Hawaii")
STATE_75 <- PSID_1975$STATE
PSID_75 <- subset(PSID_1975, PSID_1975$`$ SAVD FDST`> 0)
PSID_75 <- filter(PSID_75, STATE != 0)
weight_75 <- as.numeric(PSID_75$weight)
participation<- append(participation, nrow(PSID_75))
size_75<- as.numeric(PSID_75$"FAMILY SIZE")
food_std_75= as.numeric(PSID_75$"YRLY FOOD STD")
food_bill_74= as.numeric(PSID_75$"YRLY FOOD BILL")
income_74=as.numeric(PSID_75$"TOTAL INCOME")
monthly_paid_FS_75= as.numeric(PSID_75$`$FD STMP LASTMO`)
monthly_saved_FS_75= as.numeric(PSID_75$`$ SAVD LASMO FDST`)
paid_FS_74=as.numeric(PSID_75$"$ FD STMP")
saved_FS_74= as.numeric(PSID_75$"$ SAVD FDST")
state_75= as.factor((PSID_75$STATE))
#Computing 1975 averages ----
avg_size_75=weighted.mean(size_75, weight_75)
avg_food_std_75=weighted.mean(food_std_75, weight_75)
avg_food_bill_74=weighted.mean(food_bill_74, weight_75)
avg_income_74=weighted.mean(income_74, weight_75)
avg_paid_FS_74=weighted.mean(paid_FS_74, weight_75)
avg_saved_FS_74=weighted.mean(saved_FS_74, weight_75)
avg_monthly_paid_FS_75=weighted.mean(monthly_paid_FS_75, weight_75)
avg_monthly_saved_FS_75=weighted.mean(monthly_saved_FS_75, weight_75)
#1976----
PSID_1976$weight = na.omit(weights$Weight1976)
PSID_1976$STATE <-recode(PSID_1976$STATE, "1"= "Alabama", "2"= "Arizona", "3"= "Arkansas", "4"= "California", "5"= "Colorado", "6"= "Connecticut", "7"= "Delaware", "8"= "District of Columbia", "9"= "Florida", "10"= "Georgia", "11"= "Idaho", "12"= "Illinois", "13"= "Indiana", "14"= "Iowa", "15"= "Kansas", "16"= "Kentucky", "17"= "Louisiana", "18"= "Maine", "19"= "Maryland", "20"= "Massachusetts", "21"= "Michigan", "22"= "Minnesota", "23"= "Mississippi", "24"= "Missouri", "25"= "Montana", "26"= "Nebraska", "27"= "Nevada", "28"= "New Hampshire", "29"= "New Jersey", "30"= "New Mexico", "31"= "New York", "32"= "North Carolina", "33"= "North Dakota", "34"= "Ohio", "35"= "Oklahoma", "36"= "Oregon", "37"= "Pennsylvania", "38"= "Rhode Island", "39"= "South Carolina", "40"= "South Dakota", "41"= "Tennessee", "42"= "Texas", "43"= "Utah", "44"= "Vermont", "45"= "Virginia", "46"= "Washington", "47"= "West Virginia", "48"= "Wisconsin", "49"= "Wyoming", "50"= "Alaska", "51"= "Hawaii")
STATE_76 <- PSID_1976$STATE
#Why is this yielding a different number of participants
particip_75 <- nrow(subset(PSID_1976, PSID_1976$`# MO FDSTAMP IN 75`> 0))
PSID_76 <- subset(PSID_1976, PSID_1976$`SAVD FDSTA 75`> 0)
PSID_76 <- filter(PSID_76, STATE != 0)
weight_76 <- as.numeric(PSID_76$weight)
participation<- append(participation, nrow(PSID_76))
size_76<- as.numeric(PSID_76$"FAMILY SIZE")
food_std_76= as.numeric(PSID_76$"YRLY FOOD STD")
food_bill_75= as.numeric(PSID_76$"YRLY FOOD BILL")
income_75=as.numeric(PSID_76$"TOTAL INCOME")
paid_FS_75=as.numeric(PSID_76$"SPENT FDSTA 75")
saved_FS_75= as.numeric(PSID_76$"SAVD FDSTA 75")
monthly_paid_FS_76= as.numeric(PSID_76$"$PAID FOR STAMPS")
monthly_saved_FS_76= as.numeric(PSID_76$"$SAV FDSTAM LASTMO")
frequency_FS_75= as.numeric(PSID_76$"# MO FDSTAMP IN 75")
FD_recipients_family_75= as.numeric(PSID_76$"NUMBER FD RECIPIENTS")
FS_participants_75= sum(((frequency_FS_75) != 0 & (frequency_FS_75) != 5))
state_76= as.factor((PSID_76$STATE))
#computing 1976 averages----
avg_size_76=weighted.mean(size_76, weight_76)
avg_food_std_76=weighted.mean(food_std_76, weight_76)
avg_food_bill_75=weighted.mean(food_bill_75, weight_76)
avg_income_75=weighted.mean(income_75, weight_76)
avg_paid_FS_75=weighted.mean(paid_FS_75, weight_76)
avg_saved_FS_75=weighted.mean(saved_FS_75, weight_76)
avg_monthly_paid_FS_76=weighted.mean(monthly_paid_FS_76, weight_76)
avg_monthly_saved_FS_76=weighted.mean(monthly_saved_FS_76, weight_76)
avg_frequency_FS_75= weighted.mean(frequency_FS_75, weight_76)
#1977----
PSID_1977$weight = na.omit(weights$Weight1977)
PSID_1977$STATE <-recode(PSID_1977$STATE, "1"= "Alabama", "2"= "Arizona", "3"= "Arkansas", "4"= "California", "5"= "Colorado", "6"= "Connecticut", "7"= "Delaware", "8"= "District of Columbia", "9"= "Florida", "10"= "Georgia", "11"= "Idaho", "12"= "Illinois", "13"= "Indiana", "14"= "Iowa", "15"= "Kansas", "16"= "Kentucky", "17"= "Louisiana", "18"= "Maine", "19"= "Maryland", "20"= "Massachusetts", "21"= "Michigan", "22"= "Minnesota", "23"= "Mississippi", "24"= "Missouri", "25"= "Montana", "26"= "Nebraska", "27"= "Nevada", "28"= "New Hampshire", "29"= "New Jersey", "30"= "New Mexico", "31"= "New York", "32"= "North Carolina", "33"= "North Dakota", "34"= "Ohio", "35"= "Oklahoma", "36"= "Oregon", "37"= "Pennsylvania", "38"= "Rhode Island", "39"= "South Carolina", "40"= "South Dakota", "41"= "Tennessee", "42"= "Texas", "43"= "Utah", "44"= "Vermont", "45"= "Virginia", "46"= "Washington", "47"= "West Virginia", "48"= "Wisconsin", "49"= "Wyoming", "50"= "Alaska", "51"= "Hawaii")
STATE_77 <- PSID_1977$STATE
PSID_77 <- subset(PSID_1977, PSID_1977$`BONUS $ FD STAMPS 76` > 0)
particip_76 <- nrow(subset(PSID_1977, PSID_1977$"WTR FD STMPS IN 76" == 1))
particip_76_mos <- nrow(subset(PSID_1977, PSID_1977$`# MONTHS USED STAMPS 76`> 0 & PSID_1977$`# MONTHS USED STAMPS 76`< 13))
particip_76_paid <- nrow(subset(PSID_1977, PSID_1977$`$ PAID FD STAMPS 76` >0))
PSID_77 <- filter(PSID_77, STATE != 0)
weight_77 <- as.numeric(PSID_77$weight)
participation<- append(participation, nrow(PSID_77))
size_77<- as.numeric(PSID_77$"FAMILY SIZE")
food_std_77= as.numeric(PSID_77$"YRLY FOOD STD")
food_bill_76= as.numeric(PSID_77$"ANNUAL FOOD$ EXCL FDSTMP")
income_76=as.numeric(PSID_77$"TOTAL FAMILY INCOME")
paid_FS_76=as.numeric(PSID_77$"$ PAID FD STAMPS 76")
saved_FS_76= as.numeric(PSID_77$"BONUS $ FD STAMPS 76")
monthly_paid_FS_77= as.numeric(PSID_77$"$ PAID FD STAMP LAST MO")
monthly_saved_FS_77= as.numeric(PSID_77$"$ BONUS FD STAMP LAST MO")
frequency_FS_76= as.numeric(PSID_77$"# MONTHS USED STAMPS 76")
FS_participants_76= sum((frequency_FS_76) != 0)
state_77= as.factor((PSID_77$STATE))
eligibility_76= as.factor(PSID_1977$"WTR ELIG FD STMPS")
#computing 1977 averages ----
avg_size_77=weighted.mean(size_77, weight_77)
avg_food_std_77=weighted.mean(food_std_77, weight_77)
avg_food_bill_76=weighted.mean(food_bill_76, weight_77)
avg_income_76=weighted.mean(income_76, weight_77)
avg_paid_FS_76=weighted.mean(paid_FS_76, weight_77)
avg_saved_FS_76=weighted.mean(saved_FS_76, weight_77)
avg_monthly_paid_FS_77=weighted.mean(monthly_paid_FS_77, weight_77)
avg_monthly_saved_FS_77=weighted.mean(monthly_saved_FS_77, weight_77)
#1978----
PSID_1978$weight = na.omit(weights$Weight1978)
PSID_1978$STATE <-recode(PSID_1978$STATE, "1"= "Alabama", "2"= "Arizona", "3"= "Arkansas", "4"= "California", "5"= "Colorado", "6"= "Connecticut", "7"= "Delaware", "8"= "District of Columbia", "9"= "Florida", "10"= "Georgia", "11"= "Idaho", "12"= "Illinois", "13"= "Indiana", "14"= "Iowa", "15"= "Kansas", "16"= "Kentucky", "17"= "Louisiana", "18"= "Maine", "19"= "Maryland", "20"= "Massachusetts", "21"= "Michigan", "22"= "Minnesota", "23"= "Mississippi", "24"= "Missouri", "25"= "Montana", "26"= "Nebraska", "27"= "Nevada", "28"= "New Hampshire", "29"= "New Jersey", "30"= "New Mexico", "31"= "New York", "32"= "North Carolina", "33"= "North Dakota", "34"= "Ohio", "35"= "Oklahoma", "36"= "Oregon", "37"= "Pennsylvania", "38"= "Rhode Island", "39"= "South Carolina", "40"= "South Dakota", "41"= "Tennessee", "42"= "Texas", "43"= "Utah", "44"= "Vermont", "45"= "Virginia", "46"= "Washington", "47"= "West Virginia", "48"= "Wisconsin", "49"= "Wyoming", "50"= "Alaska", "51"= "Hawaii")
STATE_78 <- PSID_1978$STATE
PSID_78 <- subset(PSID_1978, PSID_1978$`BONUS FDSTMP 77`> 0)
particip_77 <- nrow(subset(PSID_1978, PSID_1978$`# MONTHS USED FDSTMP 77` > 0))
particip_77_paid <- nrow(subset(PSID_1978, PSID_1978$`PAID FDSTMP 77` > 0))
PSID_78 <- filter(PSID_78, STATE != 0)
weight_78 <- as.numeric(PSID_78$weight)
participation<- append(participation, nrow(PSID_78))
size_78<- as.numeric(PSID_78$"FAMILY SIZE" )
food_std_78= as.numeric(PSID_78$"YRLY FOOD STD")
food_bill_77= as.numeric(PSID_78$"ANN FD $ EXCL FDSTMP")
income_77=as.numeric(PSID_78$"TOTAL FAMILY INCOME")
paid_FS_77=as.numeric(PSID_78$"PAID FDSTMP 77")
saved_FS_77= as.numeric(PSID_78$"BONUS FDSTMP 77")
monthly_paid_FS_78= as.numeric(PSID_78$"$ PAID FD ST LAST MO")
monthly_saved_FS_78= as.numeric(PSID_78$"$ BONUS PD ST LAST MO")
frequency_FS_77= as.numeric(PSID_78$"# MONTHS USED FDSTMP 77")
FD_recipients_family_78= as.numeric(PSID_78$"# FD ST ISSUED FOR")
FS_participants_77= sum((frequency_FS_77) != 0)
state_78= as.factor((PSID_78$STATE))
#computing 1978 averages ----
avg_size_78=weighted.mean(size_78, weight_78)
avg_food_std_78=weighted.mean(food_std_78, weight_78)
avg_food_bill_77=weighted.mean(food_bill_77, weight_78)
avg_income_77=weighted.mean(income_77, weight_78)
avg_paid_FS_77=weighted.mean(paid_FS_77, weight_78)
avg_saved_FS_77=weighted.mean(saved_FS_77, weight_78)
avg_monthly_paid_FS_78=weighted.mean(monthly_paid_FS_78, weight_78)
avg_monthly_saved_FS_78=weighted.mean(monthly_saved_FS_78, weight_78)
#1979----
PSID_1979$weight = na.omit(weights$Weight1979)
PSID_1979$STATE <-recode(PSID_1979$STATE, "1"= "Alabama", "2"= "Arizona", "3"= "Arkansas", "4"= "California", "5"= "Colorado", "6"= "Connecticut", "7"= "Delaware", "8"= "District of Columbia", "9"= "Florida", "10"= "Georgia", "11"= "Idaho", "12"= "Illinois", "13"= "Indiana", "14"= "Iowa", "15"= "Kansas", "16"= "Kentucky", "17"= "Louisiana", "18"= "Maine", "19"= "Maryland", "20"= "Massachusetts", "21"= "Michigan", "22"= "Minnesota", "23"= "Mississippi", "24"= "Missouri", "25"= "Montana", "26"= "Nebraska", "27"= "Nevada", "28"= "New Hampshire", "29"= "New Jersey", "30"= "New Mexico", "31"= "New York", "32"= "North Carolina", "33"= "North Dakota", "34"= "Ohio", "35"= "Oklahoma", "36"= "Oregon", "37"= "Pennsylvania", "38"= "Rhode Island", "39"= "South Carolina", "40"= "South Dakota", "41"= "Tennessee", "42"= "Texas", "43"= "Utah", "44"= "Vermont", "45"= "Virginia", "46"= "Washington", "47"= "West Virginia", "48"= "Wisconsin", "49"= "Wyoming", "50"= "Alaska", "51"= "Hawaii")
STATE_79 <- PSID_1979$STATE
PSID_79 <- subset(PSID_1979, PSID_1979$`BONUS FDSTMP 78`> 0)
particip_78 <- nrow(subset(PSID_1979, PSID_1979$`# MOS USED FDSTMP 78`> 0 & PSID_1979$`# MOS USED FDSTMP 78` < 13))
particip_78_paid <-nrow(subset(PSID_1979, PSID_1979$`PAID FDSTMP 78`> 0 ))
PSID_79 <- filter(PSID_79, STATE != 0)
weight_79 <- as.numeric(PSID_79$weight)
participation<- append(participation, nrow(PSID_79))
size_79<- as.numeric(PSID_79$"FAMILY SIZE")
food_std_79= as.numeric(PSID_79$"YRLY FOOD STD")
food_bill_78= as.numeric(PSID_79$"ANN FD $ EXCL FDSTMP")
income_78=as.numeric(PSID_79$"TOTAL FAMILY INCOME")
paid_FS_78=as.numeric(PSID_79$"PAID FDSTMP 78")
saved_FS_78= as.numeric(PSID_79$"BONUS FDSTMP 78")
monthly_paid_FS_79= as.numeric(PSID_79$"$ PD FD ST LAST MO" )
monthly_saved_FS_79= as.numeric(PSID_79$"$ BONUS PD LAST MO")
frequency_FS_78= as.numeric(PSID_79$"# MOS USED FDSTMP 78")
FD_recipients_family_79=as.numeric(PSID_79$"# FD ST ISSUED FOR")
FS_participants_78= sum(frequency_FS_78 != 0)
state_79= as.factor(PSID_79$STATE)
#computing 1979 averages ----
avg_size_79=weighted.mean(size_79, weight_79)
avg_food_std_79=weighted.mean(food_std_79, weight_79)
avg_food_bill_78=weighted.mean(food_bill_78, weight_79)
avg_income_78=weighted.mean(income_78, weight_79)
avg_paid_FS_78=weighted.mean(paid_FS_78, weight_79)
avg_saved_FS_78=weighted.mean(saved_FS_78, weight_79)
avg_monthly_paid_FS_79=weighted.mean(monthly_paid_FS_79, weight_79)
avg_monthly_saved_FS_79=weighted.mean(monthly_saved_FS_79, weight_79)
#1980----
PSID_1980$weight = na.omit(weights$Weight1980)
PSID_1980$STATE <-recode(PSID_1980$STATE, "1"= "Alabama", "2"= "Arizona", "3"= "Arkansas", "4"= "California", "5"= "Colorado", "6"= "Connecticut", "7"= "Delaware", "8"= "District of Columbia", "9"= "Florida", "10"= "Georgia", "11"= "Idaho", "12"= "Illinois", "13"= "Indiana", "14"= "Iowa", "15"= "Kansas", "16"= "Kentucky", "17"= "Louisiana", "18"= "Maine", "19"= "Maryland", "20"= "Massachusetts", "21"= "Michigan", "22"= "Minnesota", "23"= "Mississippi", "24"= "Missouri", "25"= "Montana", "26"= "Nebraska", "27"= "Nevada", "28"= "New Hampshire", "29"= "New Jersey", "30"= "New Mexico", "31"= "New York", "32"= "North Carolina", "33"= "North Dakota", "34"= "Ohio", "35"= "Oklahoma", "36"= "Oregon", "37"= "Pennsylvania", "38"= "Rhode Island", "39"= "South Carolina", "40"= "South Dakota", "41"= "Tennessee", "42"= "Texas", "43"= "Utah", "44"= "Vermont", "45"= "Virginia", "46"= "Washington", "47"= "West Virginia", "48"= "Wisconsin", "49"= "Wyoming", "50"= "Alaska", "51"= "Hawaii")
STATE_80 <- PSID_1980$STATE
PSID_80 <- subset(PSID_1980, PSID_1980$`VALUE FD ST 79`> 0)
particip_79 <- nrow(subset(PSID_1980, PSID_1980$`# MOS USED FD STMP 79`> 0 & PSID_1980$`# MOS USED FD STMP 79`< 13 ))
PSID_80 <- filter(PSID_80, STATE != 0)
weight_80 <- as.numeric(PSID_80$weight)
participation<- append(participation, nrow(PSID_80))
size_80<- as.numeric(PSID_80$"FAMILY SIZE")
food_std_80= as.numeric(PSID_80$"YRLY FOOD STD")
food_bill_79= as.numeric(PSID_80$"ANN FD $ EXCL FD STMP")
income_79=as.numeric(PSID_80$"TOTAL FAMILY INCOME")
saved_FS_79= as.numeric(PSID_80$"VALUE FD ST 79")
monthly_saved_FS_80= as.numeric(PSID_80$"$ VLUE FD ST LST MO")
frequency_FS_79= as.numeric(PSID_80$"# MOS USED FD STMP 79")
FD_recipients_family_80=as.numeric(PSID_80$"# FD ST ISSUED FOR")
FS_participants_79= sum(frequency_FS_79 != 0)
eligibility_79= as.factor(PSID_1980$"ELGBL FOR FD STMPS")
state_80= as.factor(PSID_80$STATE)
#computing 1980 averages ----
avg_size_80=weighted.mean(size_80, weight_80)
avg_food_std_80=weighted.mean(food_std_80, weight_80)
avg_food_bill_79=weighted.mean(food_bill_79, weight_80)
avg_income_79=weighted.mean(income_79, weight_80)
avg_saved_FS_79=weighted.mean(saved_FS_79, weight_80)
avg_monthly_saved_FS_80=weighted.mean(monthly_saved_FS_80, weight_80)
#1981----
PSID_1981$weight = na.omit(weights$Weight1981)
PSID_1981$STATE <-recode(PSID_1981$STATE, "1"= "Alabama", "2"= "Arizona", "3"= "Arkansas", "4"= "California", "5"= "Colorado", "6"= "Connecticut", "7"= "Delaware", "8"= "District of Columbia", "9"= "Florida", "10"= "Georgia", "11"= "Idaho", "12"= "Illinois", "13"= "Indiana", "14"= "Iowa", "15"= "Kansas", "16"= "Kentucky", "17"= "Louisiana", "18"= "Maine", "19"= "Maryland", "20"= "Massachusetts", "21"= "Michigan", "22"= "Minnesota", "23"= "Mississippi", "24"= "Missouri", "25"= "Montana", "26"= "Nebraska", "27"= "Nevada", "28"= "New Hampshire", "29"= "New Jersey", "30"= "New Mexico", "31"= "New York", "32"= "North Carolina", "33"= "North Dakota", "34"= "Ohio", "35"= "Oklahoma", "36"= "Oregon", "37"= "Pennsylvania", "38"= "Rhode Island", "39"= "South Carolina", "40"= "South Dakota", "41"= "Tennessee", "42"= "Texas", "43"= "Utah", "44"= "Vermont", "45"= "Virginia", "46"= "Washington", "47"= "West Virginia", "48"= "Wisconsin", "49"= "Wyoming", "50"= "Alaska", "51"= "Hawaii")
STATE_81 <- PSID_1981$STATE
PSID_81 <- subset(PSID_1981, PSID_1981$`VALUE FOOD STMPS 80`> 0)
particip_80 <- nrow(subset(PSID_1981, PSID_1981$`# MOS USED FD ST`> 0 & PSID_1981$`# MOS USED FD ST`< 13))
PSID_81 <- filter(PSID_81, STATE != 0)
weight_81 <- as.numeric(PSID_81$weight)
participation<- append(participation, nrow(PSID_81))
size_81= as.numeric(PSID_81$"FAMILY SIZE")
food_std_81= as.numeric(PSID_81$"YRLY FOOD STD")
food_bill_80= as.numeric(PSID_81$"ANN FD $ $ EXC FD STMP")
income_80=as.numeric(PSID_81$"TOTAL FAMILY INCOME")
saved_FS_80= as.numeric(PSID_81$"VALUE FOOD STMPS 80")
monthly_saved_FS_81= as.numeric(PSID_81$"$ $ VALU FD ST LST MO")
frequency_FS_80= as.numeric(PSID_81$"# MOS USED FD ST")
FD_recipients_family_81=as.numeric(PSID_81$"# PERSONS GOT FOOD STAMP")
eligibility_80= as.factor(PSID_1981$"WTR ELGBL FOOD STAMP")
FS_participants_80= sum(frequency_FS_80 != 0)
state_81= as.factor(PSID_81$STATE)
#computing 1981 averages ----
avg_size_81=weighted.mean(size_81, weight_81)
avg_food_std_81=weighted.mean(food_std_81, weight_81)
avg_food_bill_80=weighted.mean(food_bill_80, weight_81)
avg_income_80=weighted.mean(income_80, weight_81)
avg_saved_FS_80=weighted.mean(saved_FS_80, weight_81)
avg_monthly_saved_FS_81=weighted.mean(monthly_saved_FS_81, weight_81)
#1982----
PSID_1982$weight = na.omit(weights$Weight1982)
PSID_1982$STATE <-recode(PSID_1982$STATE, "1"= "Alabama", "2"= "Arizona", "3"= "Arkansas", "4"= "California", "5"= "Colorado", "6"= "Connecticut", "7"= "Delaware", "8"= "District of Columbia", "9"= "Florida", "10"= "Georgia", "11"= "Idaho", "12"= "Illinois", "13"= "Indiana", "14"= "Iowa", "15"= "Kansas", "16"= "Kentucky", "17"= "Louisiana", "18"= "Maine", "19"= "Maryland", "20"= "Massachusetts", "21"= "Michigan", "22"= "Minnesota", "23"= "Mississippi", "24"= "Missouri", "25"= "Montana", "26"= "Nebraska", "27"= "Nevada", "28"= "New Hampshire", "29"= "New Jersey", "30"= "New Mexico", "31"= "New York", "32"= "North Carolina", "33"= "North Dakota", "34"= "Ohio", "35"= "Oklahoma", "36"= "Oregon", "37"= "Pennsylvania", "38"= "Rhode Island", "39"= "South Carolina", "40"= "South Dakota", "41"= "Tennessee", "42"= "Texas", "43"= "Utah", "44"= "Vermont", "45"= "Virginia", "46"= "Washington", "47"= "West Virginia", "48"= "Wisconsin", "49"= "Wyoming", "50"= "Alaska", "51"= "Hawaii")
STATE_82 <- PSID_1982$STATE
PSID_82 <- subset(PSID_1982, PSID_1982$`VALUE FD ST 81`> 0)
particip_81 <- nrow(subset(PSID_1982, PSID_1982$`# MOS USED FD ST 81`> 0 & PSID_1982$`# MOS USED FD ST 81`< 13 ))
PSID_82 <- filter(PSID_82, STATE != 0)
weight_82 <- as.numeric(PSID_82$weight)
participation<- append(participation, nrow(PSID_82))
size_82= as.numeric(PSID_82$"FAMILY SIZE")
food_std_82= as.numeric(PSID_82$"ANNUAL FOOD STD")
food_bill_81= as.numeric(PSID_82$"ANN FOOD COST EXC FD ST" )
income_81=as.numeric(PSID_82$"TOTAL FAMILY INCOME")
saved_FS_81= as.numeric(PSID_82$"VALUE FD ST 81")
monthly_saved_FS_82= as.numeric(PSID_82$"VALUE FD ST LAST MO")
frequency_FS_81= as.numeric(PSID_82$"# MOS USED FD ST 81")
FD_recipients_family_82=as.numeric(PSID_82$"# PERSONS GOT FOOD STAMP")
FS_participants_81= sum(frequency_FS_81 != 0)
state_82= as.factor(PSID_82$STATE)
#computing 1982 averages ----
avg_size_82=weighted.mean(size_82, weight_82)
avg_food_std_82=weighted.mean(food_std_82, weight_82)
avg_food_bill_81=weighted.mean(food_bill_81, weight_82)
avg_income_81=weighted.mean(income_81, weight_82)
avg_saved_FS_81=weighted.mean(saved_FS_81, weight_82)
avg_monthly_saved_FS_82=weighted.mean(monthly_saved_FS_82, weight_82)
#1983----
PSID_1983$weight = na.omit(weights$Weight1983)
PSID_1983$STATE <-recode(PSID_1983$STATE, "1"= "Alabama", "2"= "Arizona", "3"= "Arkansas", "4"= "California", "5"= "Colorado", "6"= "Connecticut", "7"= "Delaware", "8"= "District of Columbia", "9"= "Florida", "10"= "Georgia", "11"= "Idaho", "12"= "Illinois", "13"= "Indiana", "14"= "Iowa", "15"= "Kansas", "16"= "Kentucky", "17"= "Louisiana", "18"= "Maine", "19"= "Maryland", "20"= "Massachusetts", "21"= "Michigan", "22"= "Minnesota", "23"= "Mississippi", "24"= "Missouri", "25"= "Montana", "26"= "Nebraska", "27"= "Nevada", "28"= "New Hampshire", "29"= "New Jersey", "30"= "New Mexico", "31"= "New York", "32"= "North Carolina", "33"= "North Dakota", "34"= "Ohio", "35"= "Oklahoma", "36"= "Oregon", "37"= "Pennsylvania", "38"= "Rhode Island", "39"= "South Carolina", "40"= "South Dakota", "41"= "Tennessee", "42"= "Texas", "43"= "Utah", "44"= "Vermont", "45"= "Virginia", "46"= "Washington", "47"= "West Virginia", "48"= "Wisconsin", "49"= "Wyoming", "50"= "Alaska", "51"= "Hawaii")
STATE_83 <- PSID_1983$STATE
PSID_83 <- subset(PSID_1983, PSID_1983$`VALUE FD ST 82`> 0)
particip_82 <- nrow(subset(PSID_1983, PSID_1983$`# MOS USED FD ST 82`> 0 & PSID_1983$`# MOS USED FD ST 82`< 13))
PSID_83 <- filter(PSID_83, STATE != 0)
weight_83 <- as.numeric(PSID_83$weight)
participation<- append(participation, nrow(PSID_83))
size_83= as.numeric(PSID_83$"FAMILY SIZE")
food_std_83= as.numeric(PSID_83$"ANNUAL FOOD STD")
food_bill_82= as.numeric(PSID_83$"ANN FOOD COST EXC FD ST")
income_82=as.numeric(PSID_83$"TOTAL FAMILY INCOME")
saved_FS_82= as.numeric(PSID_83$"VALUE FD ST 82")
monthly_saved_FS_83= as.numeric(PSID_83$"VALUE FD ST LAST MO" )
frequency_FS_82= as.numeric(PSID_83$"# MOS USED FD ST 82")
FD_recipients_family_83=as.numeric(PSID_83$"# PERSONS GOT FOOD STAMP")
FS_participants_82= sum(frequency_FS_82 != 0)
eligibility_82= as.factor(PSID_1983$"IG FD STMP 82")
state_83= as.factor(PSID_83$STATE)
#computing 1983 averages ----
avg_size_83=weighted.mean(size_83, weight_83)
avg_food_std_83=weighted.mean(food_std_83, weight_83)
avg_food_bill_82=weighted.mean(food_bill_82, weight_83)
avg_income_82=weighted.mean(income_82, weight_83)
avg_saved_FS_82=weighted.mean(saved_FS_82, weight_83)
avg_monthly_saved_FS_83=weighted.mean(monthly_saved_FS_83, weight_83)
#1984----
PSID_1984$weight = na.omit(weights$Weight1984)
PSID_1984$STATE <-recode(PSID_1984$STATE, "1"= "Alabama", "2"= "Arizona", "3"= "Arkansas", "4"= "California", "5"= "Colorado", "6"= "Connecticut", "7"= "Delaware", "8"= "District of Columbia", "9"= "Florida", "10"= "Georgia", "11"= "Idaho", "12"= "Illinois", "13"= "Indiana", "14"= "Iowa", "15"= "Kansas", "16"= "Kentucky", "17"= "Louisiana", "18"= "Maine", "19"= "Maryland", "20"= "Massachusetts", "21"= "Michigan", "22"= "Minnesota", "23"= "Mississippi", "24"= "Missouri", "25"= "Montana", "26"= "Nebraska", "27"= "Nevada", "28"= "New Hampshire", "29"= "New Jersey", "30"= "New Mexico", "31"= "New York", "32"= "North Carolina", "33"= "North Dakota", "34"= "Ohio", "35"= "Oklahoma", "36"= "Oregon", "37"= "Pennsylvania", "38"= "Rhode Island", "39"= "South Carolina", "40"= "South Dakota", "41"= "Tennessee", "42"= "Texas", "43"= "Utah", "44"= "Vermont", "45"= "Virginia", "46"= "Washington", "47"= "West Virginia", "48"= "Wisconsin", "49"= "Wyoming", "50"= "Alaska", "51"= "Hawaii")
STATE_84 <- PSID_1984$STATE
PSID_84 <- subset(PSID_1984, PSID_1984$`VALUE FD ST 83`> 0)
particip_83 <- nrow(subset(PSID_1984, PSID_1984$`# MOS USED FD ST 83`> 0 & PSID_1984$`# MOS USED FD ST 83`< 13))
PSID_84 <- filter(PSID_84, STATE != 0)
weight_84 <- as.numeric(PSID_84$weight)
participation<- append(participation, nrow(PSID_84))
size_84= as.numeric(PSID_84$"FAMILY SIZE")
food_std_84= as.numeric(PSID_84$"ANNUAL FOOD STD")
food_bill_83= as.numeric(PSID_84$"ANN FOOD COST EXC FD ST")
income_83=as.numeric(PSID_84$"TOTAL FAMILY INCOME")
saved_FS_83= as.numeric(PSID_84$"VALUE FD ST 83")
monthly_saved_FS_84= as.numeric(PSID_84$"VALUE FD ST LAST MO")
frequency_FS_83= as.numeric(PSID_84$"# MOS USED FD ST 83")
FD_recipients_family_84=as.numeric(PSID_84$"# PERS GOT FD ST LAST MO")
FS_participants_83= sum(frequency_FS_83 != 0)
state_84= as.factor(PSID_84$STATE)
#computing 1984 averages ----
avg_size_84=weighted.mean(size_84, weight_84)
avg_food_std_84=weighted.mean(food_std_84, weight_84)
avg_food_bill_83=weighted.mean(food_bill_83, weight_84)
avg_income_83=weighted.mean(income_83, weight_84)
avg_saved_FS_83=weighted.mean(saved_FS_83, weight_84)
avg_monthly_saved_FS_84=weighted.mean(monthly_saved_FS_84, weight_84)
#1985----
PSID_1985$weight = na.omit(weights$Weight1985)
PSID_1985$STATE <-recode(PSID_1985$STATE, "1"= "Alabama", "2"= "Arizona", "3"= "Arkansas", "4"= "California", "5"= "Colorado", "6"= "Connecticut", "7"= "Delaware", "8"= "District of Columbia", "9"= "Florida", "10"= "Georgia", "11"= "Idaho", "12"= "Illinois", "13"= "Indiana", "14"= "Iowa", "15"= "Kansas", "16"= "Kentucky", "17"= "Louisiana", "18"= "Maine", "19"= "Maryland", "20"= "Massachusetts", "21"= "Michigan", "22"= "Minnesota", "23"= "Mississippi", "24"= "Missouri", "25"= "Montana", "26"= "Nebraska", "27"= "Nevada", "28"= "New Hampshire", "29"= "New Jersey", "30"= "New Mexico", "31"= "New York", "32"= "North Carolina", "33"= "North Dakota", "34"= "Ohio", "35"= "Oklahoma", "36"= "Oregon", "37"= "Pennsylvania", "38"= "Rhode Island", "39"= "South Carolina", "40"= "South Dakota", "41"= "Tennessee", "42"= "Texas", "43"= "Utah", "44"= "Vermont", "45"= "Virginia", "46"= "Washington", "47"= "West Virginia", "48"= "Wisconsin", "49"= "Wyoming", "50"= "Alaska", "51"= "Hawaii")
STATE_85 <- PSID_1985$STATE
PSID_85 <- subset(PSID_1985, PSID_1985$`VALUE FD ST 84`> 0)
particip_84 <- nrow(subset(PSID_1985, PSID_1985$`# MOS USED FD ST 84`> 0 & PSID_1985$`# MOS USED FD ST 84`< 13))
weight_85 <- as.numeric(PSID_85$weight)
participation<- append(participation, nrow(PSID_85))
size_85= as.numeric(PSID_85$"FAMILY SIZE")
food_std_85= as.numeric(PSID_85$"YRLY FOOD STD")
food_bill_84= as.numeric(PSID_85$"ANN FOOD COST EXC FD ST")
income_84=as.numeric(PSID_85$"TOTAL FAMILY INCOME")
saved_FS_84= as.numeric(PSID_85$"VALUE FD ST 84")
monthly_saved_FS_85= as.numeric(PSID_85$"VALUE FD ST LAST MO")
frequency_FS_84= as.numeric(PSID_85$"# MOS USED FD ST 84")
FD_recipients_family_85=as.numeric(PSID_85$"# PERSONS GOT FOOD STAMP")
FS_participants_84= sum(frequency_FS_84 != 0)
state_85= as.factor(PSID_85$STATE)
#computing 1985 averages ----
avg_size_85=weighted.mean(size_85, weight_85)
avg_food_std_85=weighted.mean(food_std_85, weight_85)
avg_food_bill_84=weighted.mean(food_bill_84, weight_85)
avg_income_84=weighted.mean(income_84, weight_85)
avg_saved_FS_84=weighted.mean(saved_FS_84, weight_85)
avg_monthly_saved_FS_85=weighted.mean(monthly_saved_FS_85, weight_85)
#1986----
PSID_1986$weight = na.omit(weights$Weight1986)
PSID_1986$STATE <-recode(PSID_1986$STATE, "1"= "Alabama", "2"= "Arizona", "3"= "Arkansas", "4"= "California", "5"= "Colorado", "6"= "Connecticut", "7"= "Delaware", "8"= "District of Columbia", "9"= "Florida", "10"= "Georgia", "11"= "Idaho", "12"= "Illinois", "13"= "Indiana", "14"= "Iowa", "15"= "Kansas", "16"= "Kentucky", "17"= "Louisiana", "18"= "Maine", "19"= "Maryland", "20"= "Massachusetts", "21"= "Michigan", "22"= "Minnesota", "23"= "Mississippi", "24"= "Missouri", "25"= "Montana", "26"= "Nebraska", "27"= "Nevada", "28"= "New Hampshire", "29"= "New Jersey", "30"= "New Mexico", "31"= "New York", "32"= "North Carolina", "33"= "North Dakota", "34"= "Ohio", "35"= "Oklahoma", "36"= "Oregon", "37"= "Pennsylvania", "38"= "Rhode Island", "39"= "South Carolina", "40"= "South Dakota", "41"= "Tennessee", "42"= "Texas", "43"= "Utah", "44"= "Vermont", "45"= "Virginia", "46"= "Washington", "47"= "West Virginia", "48"= "Wisconsin", "49"= "Wyoming", "50"= "Alaska", "51"= "Hawaii")
STATE_86 <- PSID_1986$STATE
PSID_86 <- subset(PSID_1986, PSID_1986$`VALUE FD ST 85`> 0)
particip_85 <- nrow(subset(PSID_1986, PSID_1986$`# MOS USED FD ST 85`> 0 & PSID_1986$`# MOS USED FD ST 85`< 13))
weight_86 <- as.numeric(PSID_86$weight)
participation<- append(participation, nrow(PSID_86))
size_86= as.numeric(PSID_86$"FAMILY SIZE")
food_std_86= as.numeric(PSID_86$"YRLY FOOD STD")
food_bill_85= as.numeric(PSID_86$"ANN FOOD COST EXC FD ST")
income_85=as.numeric(PSID_86$"TOTAL FAMILY INCOME")
saved_FS_85= as.numeric(PSID_86$"VALUE FD ST 85")
monthly_saved_FS_86= as.numeric(PSID_86$"VALUE FD ST LAST MO" )
frequency_FS_85= as.numeric(PSID_86$"# MOS USED FD ST 85")
FD_recipients_family_86=as.numeric(PSID_86$"# PERSONS GOT FOOD STAMP" )
FS_participants_85= sum(frequency_FS_85 != 0)
state_86= as.factor(PSID_86$STATE)
#computing 1986 averages ----
avg_size_86=weighted.mean(size_86, weight_86)
avg_food_std_86=weighted.mean(food_std_86, weight_86)
avg_food_bill_85=weighted.mean(food_bill_85, weight_86)
avg_income_85=weighted.mean(income_85, weight_86)
avg_saved_FS_85=weighted.mean(saved_FS_85, weight_86)
avg_monthly_saved_FS_86=weighted.mean(monthly_saved_FS_86, weight_86)
#1987----
PSID_1987$weight = na.omit(weights$Weight1987)
PSID_1987$STATE <-recode(PSID_1987$STATE, "1"= "Alabama", "2"= "Arizona", "3"= "Arkansas", "4"= "California", "5"= "Colorado", "6"= "Connecticut", "7"= "Delaware", "8"= "District of Columbia", "9"= "Florida", "10"= "Georgia", "11"= "Idaho", "12"= "Illinois", "13"= "Indiana", "14"= "Iowa", "15"= "Kansas", "16"= "Kentucky", "17"= "Louisiana", "18"= "Maine", "19"= "Maryland", "20"= "Massachusetts", "21"= "Michigan", "22"= "Minnesota", "23"= "Mississippi", "24"= "Missouri", "25"= "Montana", "26"= "Nebraska", "27"= "Nevada", "28"= "New Hampshire", "29"= "New Jersey", "30"= "New Mexico", "31"= "New York", "32"= "North Carolina", "33"= "North Dakota", "34"= "Ohio", "35"= "Oklahoma", "36"= "Oregon", "37"= "Pennsylvania", "38"= "Rhode Island", "39"= "South Carolina", "40"= "South Dakota", "41"= "Tennessee", "42"= "Texas", "43"= "Utah", "44"= "Vermont", "45"= "Virginia", "46"= "Washington", "47"= "West Virginia", "48"= "Wisconsin", "49"= "Wyoming", "50"= "Alaska", "51"= "Hawaii")
STATE_87 <- PSID_1987$STATE
PSID_87 <- subset(PSID_1987, PSID_1987$`VALUE FD ST 86`> 0)
particip_86 <- nrow(subset(PSID_1987, PSID_1987$`# MOS USED FD ST 86`> 0 & PSID_1987$`# MOS USED FD ST 86`< 13 ))
weight_87 <- as.numeric(PSID_87$weight)
participation<- append(participation, nrow(PSID_87))
size_87= as.numeric(PSID_87$"FAMILY SIZE")
food_std_87= as.numeric(PSID_87$"YRLY FOOD STD")
food_bill_86= as.numeric(PSID_87$"ANN FOOD COST EXC FD ST")
income_86=as.numeric(PSID_87$"TOTAL FAMILY INCOME")
saved_FS_86= as.numeric(PSID_87$"VALUE FD ST 86")
monthly_saved_FS_87= as.numeric(PSID_87$"VALUE FD ST LAST MO" )
frequency_FS_86= as.numeric(PSID_87$"# MOS USED FD ST 86")
FD_recipients_family_87=as.numeric(PSID_87$"# PERSONS GOT FOOD STAMP")
FS_participants_86= sum(frequency_FS_86 != 0)
state_87= as.factor(PSID_87$STATE)
eligibility_86= as.factor(PSID_1987$"ELIG FOR FD STMPS 86")
#computing 1987 averages ----
avg_size_87=weighted.mean(size_87, weight_87)
avg_food_std_87=weighted.mean(food_std_87, weight_87)
avg_food_bill_86=weighted.mean(food_bill_86, weight_87)
avg_income_86=weighted.mean(income_86, weight_87)
avg_saved_FS_86=weighted.mean(saved_FS_86, weight_87)
avg_monthly_saved_FS_87=weighted.mean(monthly_saved_FS_87, weight_87)
#1988----
PSID_1988$weight = na.omit(weights$Weight1988)
PSID_1988$STATE <-recode(PSID_1988$STATE, "1"= "Alabama", "2"= "Arizona", "3"= "Arkansas", "4"= "California", "5"= "Colorado", "6"= "Connecticut", "7"= "Delaware", "8"= "District of Columbia", "9"= "Florida", "10"= "Georgia", "11"= "Idaho", "12"= "Illinois", "13"= "Indiana", "14"= "Iowa", "15"= "Kansas", "16"= "Kentucky", "17"= "Louisiana", "18"= "Maine", "19"= "Maryland", "20"= "Massachusetts", "21"= "Michigan", "22"= "Minnesota", "23"= "Mississippi", "24"= "Missouri", "25"= "Montana", "26"= "Nebraska", "27"= "Nevada", "28"= "New Hampshire", "29"= "New Jersey", "30"= "New Mexico", "31"= "New York", "32"= "North Carolina", "33"= "North Dakota", "34"= "Ohio", "35"= "Oklahoma", "36"= "Oregon", "37"= "Pennsylvania", "38"= "Rhode Island", "39"= "South Carolina", "40"= "South Dakota", "41"= "Tennessee", "42"= "Texas", "43"= "Utah", "44"= "Vermont", "45"= "Virginia", "46"= "Washington", "47"= "West Virginia", "48"= "Wisconsin", "49"= "Wyoming", "50"= "Alaska", "51"= "Hawaii")
STATE_88 <- PSID_1988$STATE
PSID_88 <- subset(PSID_1988, PSID_1988$`VALUE FD ST 87`> 0)
particip_87 <- nrow(subset(PSID_1988, PSID_1988$`# MOS USED FD ST 87`> 0 & PSID_1988$`# MOS USED FD ST 87`< 13 ))
weight_88 <- as.numeric(PSID_88$weight)
participation<- append(participation, nrow(PSID_88))
size_88= as.numeric(PSID_88$"FAMILY SIZE")
food_std_88= as.numeric(PSID_88$"YRLY FOOD STD")
income_87=as.numeric(PSID_88$"TOTAL FAMILY INCOME")
saved_FS_87= as.numeric(PSID_88$"VALUE FD ST 87")
frequency_FS_87= as.numeric(PSID_88$"# MOS USED FD ST 87")
FS_participants_87= sum(frequency_FS_87 != 0)
state_88= as.factor(PSID_88$STATE)
#computing 1988 averages ----
avg_size_88=weighted.mean(size_88, weight_88)
avg_food_std_88=weighted.mean(food_std_88, weight_88)
avg_income_87=weighted.mean(income_87, weight_88)
avg_saved_FS_87=weighted.mean(saved_FS_87, weight_88)
#1989----
PSID_1989$weight = na.omit(weights$Weight1989)
PSID_89 <- subset(PSID_1989, PSID_1989$`VALUE FD ST 88`> 0)
particip_88 <- nrow(subset(PSID_1989, PSID_1989$`# MOS USED FD ST 88`> 0 & PSID_1989$`# MOS USED FD ST 88`< 13))
weight_89 <- as.numeric(PSID_89$weight)
participation<- append(participation, nrow(PSID_89))
income_88=as.numeric(PSID_89$"TOTAL FAMILY INCOME")
saved_FS_88= as.numeric(PSID_89$"VALUE FD ST 88" )
frequency_FS_88= as.numeric(PSID_89$"# MOS USED FD ST 88")
FS_participants_89= sum(frequency_FS_88 != 0)
#computing 1989 averages ----
avg_income_88=weighted.mean(income_88, weight_89)
avg_saved_FS_88=weighted.mean(saved_FS_88, weight_89)#average family size
avg_size<- c(NA, avg_size_68, avg_size_69, avg_size_70, avg_size_71, avg_size_72, avg_size_73, avg_size_74, avg_size_75, avg_size_76, avg_size_77, avg_size_78, avg_size_79, avg_size_80, avg_size_81, avg_size_82, avg_size_83, avg_size_84, avg_size_85, avg_size_86, avg_size_87, avg_size_88)
#average total family income
avg_income <- c(avg_income_67, avg_income_68, avg_income_69,avg_income_70, avg_income_71, avg_income_72, avg_income_73, avg_income_74, avg_income_75, avg_income_76, avg_income_77, avg_income_78, avg_income_79, avg_income_80, avg_income_81, avg_income_82, avg_income_83, avg_income_84, avg_income_85, avg_income_86, avg_income_87, avg_income_88)
#average annual food bill
avg_food_bill<- c(NA, avg_food_bill_68, avg_food_bill_69, avg_food_bill_70, avg_food_bill_71, NA , avg_food_bill_73, avg_food_bill_74, avg_food_bill_75, avg_food_bill_76, avg_food_bill_77, avg_food_bill_78, avg_food_bill_79, avg_food_bill_80, avg_food_bill_81, avg_food_bill_82, avg_food_bill_83, avg_food_bill_84, avg_food_bill_85, avg_food_bill_86, NA , NA)
#average amount saved by using food stamps
avg_saved_FS<- c(avg_saved_FS_67, avg_saved_FS_68, avg_saved_FS_69, avg_saved_FS_70, avg_saved_FS_71, NA, avg_saved_FS_73, avg_saved_FS_74, avg_saved_FS_75, avg_saved_FS_76, avg_saved_FS_77, avg_saved_FS_78, avg_saved_FS_79, avg_saved_FS_80, avg_saved_FS_81, avg_saved_FS_82, avg_saved_FS_83, avg_saved_FS_84, avg_saved_FS_85, avg_saved_FS_86, avg_saved_FS_87, avg_saved_FS_88)
#average amount paid for food stamps
avg_paid_FS<- c(NA, NA, NA, NA, NA, NA, NA, avg_paid_FS_74, avg_paid_FS_75, avg_paid_FS_76, avg_paid_FS_77, avg_paid_FS_78, NA, 0, 0, 0, 0, 0, 0, 0, 0, 0)
#average amount paid monthly for food stamps
avg_paid_monthly_FS <-c(NA, NA, NA, NA, NA, NA, NA, NA, avg_monthly_paid_FS_75, avg_monthly_paid_FS_76, avg_monthly_paid_FS_77, avg_monthly_paid_FS_78, avg_monthly_paid_FS_79, 0, 0, 0, 0, 0, 0, 0, 0, 0)
#average amount saved monthly by using food stamps
avg_saved_monthly_FS<-c(NA, NA, NA, NA, NA, NA, NA, NA, avg_monthly_saved_FS_75, avg_monthly_saved_FS_76, avg_monthly_saved_FS_77, avg_monthly_saved_FS_78, avg_monthly_saved_FS_79, avg_monthly_saved_FS_80, avg_monthly_saved_FS_81, avg_monthly_saved_FS_82, avg_monthly_saved_FS_83, avg_monthly_saved_FS_84, avg_monthly_saved_FS_85, avg_monthly_saved_FS_86, avg_monthly_saved_FS_87, NA)
#year variable
year<- c(1967:1988)
#creating a data frame for necessary variables
df <- data.frame(year, participation, avg_size, avg_income, avg_food_bill, avg_saved_FS, avg_paid_FS, avg_paid_monthly_FS, avg_saved_monthly_FS)3 Graphs and Figures
# Figures
# participation by year
participation_plot <- ggplot(data=df) +
geom_point(aes(x=year, y=participation)) +
# ggtitle("Number of Food Stamp Participants in the PSID data") +
theme_classic()
#average amount of money paid annually for food stamps
FS_paid_plot_1 <- ggplot(df, aes(year, avg_paid_FS)) +
geom_point(aes(colour = cut(avg_paid_FS, c(-Inf, 0, Inf)))) +
scale_color_manual(values = c("(-Inf,0]" = "red","(0, Inf]" = "black"),
labels = c("estimated", "actual")) +
theme_classic()
#average amount of money paid monthly for food stamps
FS_paid_plot_2 <- ggplot(df, aes(year, avg_paid_monthly_FS)) +
geom_point(aes(colour = cut(avg_paid_monthly_FS, c(-Inf, 0, Inf)))) +
scale_color_manual(values = c("(-Inf,0]" = "red","(0, Inf]" = "black"),
labels = c("estimated", "actual")) +
theme_classic()
#average amount of money saved annualy from stamps by year
FS_saved_plot_1 <- ggplot(data=df) +
geom_point(aes(x=year, y=avg_saved_FS)) +
theme_classic()
#average amount of money saved monthly from stamps by year
FS_saved_plot_2 <- ggplot(data=df) +
geom_point(aes(x=year, y=avg_saved_monthly_FS)) +
theme_classic()3.1 Participation rates by State
#calculate participation rates
# Create an empty list to store participation rates for each year
participation_rates_list <- list()
#define cut off levels
cutoffs <- c(0, 15, 40, 70, Inf)
labels <- c("low", "medium", "high", "very high")
# Iterate over each year from 1968 to 1989
for (year in 68:88) {
if (year != 73) {
freq <- table(get(paste0("state_", year)))
total <- table(get(paste0('STATE_', year)))
data_1 <- data.frame(
state = tolower(names(freq)),
participants = freq
)
data_2 <- data.frame(
state = tolower(names(total)),
population = total
)
merged_data <- merge(data_1, data_2, by = "state", all.x = TRUE)
# Calculate the participation rate for the current year
participation_rate <- (merged_data$participants.Freq / merged_data$population.Freq) * 100
# Store the participation rate in the list
participation_rates_list[[as.character(year)]] <- participation_rate
state_data <- data.frame(
state = tolower(names(freq)),
category = cut(as.numeric(participation_rate), breaks = cutoffs, labels = labels, include.lowest = TRUE)
)
temp <- plot_usmap(regions = "state", data= state_data, values = "category",color="grey")+
theme(panel.background = element_rect(colour = "black")) +
scale_fill_manual(values = c("lightblue2", "steelblue1", "steelblue4", "blue3"))+
theme(legend.position = "right")
assign(paste0("map_19", year), temp)
}
}The following maps show Food Stamps participation rates by State as per the PSID data every year from 1967 to 1988 except 1972 in a chronological order
Food Stamps Participation rates across the US in 1968
Food Stamps Participation rates across the US in 1969
Food Stamps Participation rates across the US in 1970
Food Stamps Participation rates across the US in 1971
Food Stamps Participation rates across the US in 1972
Food Stamps Participation rates across the US in 1974
Food Stamps Participation rates across the US in 1975
Food Stamps Participation rates across the US in 1976
Food Stamps Participation rates across the US in 1977
Food Stamps Participation rates across the US in 1978
Food Stamps Participation rates across the US in 1979
Food Stamps Participation rates across the US in 1980
Food Stamps Participation rates across the US in 1981
Food Stamps Participation rates across the US in 1982
Food Stamps Participation rates across the US in 1983
Food Stamps Participation rates across the US in 1984
Food Stamps Participation rates across the US in 1985
Food Stamps Participation rates across the US in 1986
Food Stamps Participation rates across the US in 1987
Food Stamps Participation rates across the US in 1988
3.2 Food Stamp Participation trends
This plot shows the number of Food Stamps participants across the year. Notice that 1972 is missing its data point since there was no information collected about food stamps for that year.
Number of Food Stamp Participants in the PSID samples each year
Annual Food Stamps Purchases
Monthly Food Stamps Purchases
Average amount of money saved annually by using food stamps
Average amount of money saved monthly by using food stamps
4 Potential limitations to consider
The number of food stamps participants was calculated based on the variables available for each year. For consistency, I consider those who saved an amount of money greater than 0 from using food stamps in a particular year to be food stamps participants for that year. “The SAVD_FS” variable was available in PSID for all years from 1967 to 1990 except 1973.
It is worth mentioning that there were other variables that tracked food stamps participation such as the number of months a family used food stamps or the amount of money spent on food stamps. I expected that calculating the number of participants who saved a positive amount of money from using food stamps in a specific year would yield the the same result as calculating the number of participants who used food stamps in at least one month throughout the same year. However, that was not always the case. I tried comparing the number of participants using other variables in addition to the number of months but there have been some discrepancies, which are illustrated in the table below.
4.1 Food Stamps participants according to different variables
| saved_FS | used_FS? | # Months used FS | paid_FS |
| 291 | 291 | ||
| 834 | 522 | ||
| 730 | 674 | ||
| 931 | 360 | ||
| 892 | 895 | 884 | 854 |
| 811 | 814 | 778 | |
| 847 | 840 | 758 | |
| 1013 | 1003 | ||
| 1067 | 1059 | ||
| 1064 | 1058 | ||
| 1144 | 1124 | ||
| 1090 | 1067 | ||
| 966 | 949 | ||
| 915 | 903 | ||
| 939 | 935 | ||
| 898 | 884 | ||
| 821 | 818 |