Functions

# Share of households
count_over_zero100 <- function(x) { 100 * sum(x>0, na.rm = TRUE) / length(na.omit(x)) }

# Net present value
npv <- function(cf0, cf, times, i){
  cf0 <- as.numeric(cf0)
  cf <- as.numeric(cf)
  vectordiscount <- rep((1+i), times)
  t <- 1:times
  discountrates <- vectordiscount^t
  discount <- cf / discountrates
  cf0 + sum(discount)
}

Setup surveys

NL 598 employed

df.NL598 <- read.csv("data/NL598.csv") %>% select(-X)

# net figures
df.NL598$net_income <- df.NL598$A - rowSums(df.NL598[c("A1", "A2", "A3", "A4", "A5")] )
df.NL598$net_savings <- df.NL598$E - df.NL598$B
df.NL598$net_credit <- df.NL598$F - df.NL598$C

# consumption categories
df.NL598$food <- df.NL598$D1_16
df.NL598$shelter <- rowSums(df.NL598[c("D17", "D18", "D19", "D20", "D21a", "D21b", "D21c")])
df.NL598$clothing <- rowSums(df.NL598[c("D25", "D26", "D22")])
df.NL598$leisure <- rowSums(df.NL598[c("D32", "D33")])
df.NL598$other <- rowSums(df.NL598[c("D23a", "D23b", "D24", "D27", "D28", "D29", "D30", "D31", "D34", "D35",  "D36", "D37", "D38")])

# fragility
df.NL598 <- df.NL598 %>% 
  mutate(fm = net_income - food - shelter - clothing) %>%
  mutate(fm2 = net_income + A4 - food - shelter - clothing) 

# share of consumption
df.NL598 <- df.NL598 %>% 
  mutate(fffm2 = ifelse(fm2 < quantile(fm2, probs = c(0.33)), 1, 
                       ifelse(fm2 > quantile(fm2, probs = c(0.66)), 0, NaN))) %>%
  mutate(fffm.con = ifelse(fm < quantile(fm, probs = c(0.33)), fm,
                           ifelse(fm > quantile(fm, probs = c(0.66)), fm, NaN)))

# HH size
df.NL598 <- df.NL598 %>%
  mutate(temp = ifelse(is.na(age_m) & !is.na(age_f), 1,
                       ifelse(is.na(age_f) & !is.na(age_m), 1,2))) %>%
  mutate(HH_size = temp + visitor + kids) %>%
  select(-temp)

# Healthcare
df.NL598 <- df.NL598 %>%
  mutate(healthcare = D28 / (net_income + A4)) %>%
  mutate(healthcare_shock = ifelse(healthcare > quantile(healthcare, probs = c(0.66)), 1, 0))

Wealth NL 598 employed

Home value #### data manipulation Mean income per income group without farmers (work_class = 4). These average income figures match CBS (1938)

A.1 <- mean(df.NL598[df.NL598$A<1400 & df.NL598$work_class != 4,"A"],na.rm=TRUE)
A.2 <- mean(df.NL598[df.NL598$A>1400 & df.NL598$A<1800 & df.NL598$work_class != 4,"A"],na.rm=TRUE)
A.3 <- mean(df.NL598[df.NL598$A>1800 & df.NL598$A<2300 & df.NL598$work_class != 4,"A"],na.rm=TRUE)
A.4 <- mean(df.NL598[df.NL598$A>2300 & df.NL598$A<3000,"A"],na.rm=TRUE)
A.5 <- mean(df.NL598[df.NL598$A>3000 & df.NL598$A<4000,"A"],na.rm=TRUE)
A.6 <- mean(df.NL598[df.NL598$A>4000 & df.NL598$A<6000,"A"],na.rm=TRUE)
A.7 <- mean(df.NL598[df.NL598$A>6000,"A"],na.rm=TRUE)

shares of premiums per income class.

Pension and old-age

P1 <- 8.28  # < 1400
P2 <- 41.21 # > 1400 & < 1800
P3 <- 60.52 # > 1800 & < 2300
P4 <- 62.03 # > 2300 & < 3000
P5 <- 58.27 # > 3000 & < 4000
P6 <- 59.97 # > 4000 & < 6000
P7 <- 48.37 # > 6000

Funeral and life-insurance

P11 <- 78.91 # < 1400
P21 <- 50.29 # > 1400 & < 1800
P31 <- 35.14 # > 1800 & < 2300
P41 <- 34.33 # > 2300 & < 3000
P51 <- 38.47 # > 3000 & < 4000
P61 <- 37.31 # > 4000 & < 6000
P71 <- 44.84 # > 6000

Health and other

P12 <- 12.81  # < 1400
P22 <-  8.5   # > 1400 & < 1800
P32 <-  4.34  # > 1800 & < 2300
P42 <-  3.64  # > 2300 & < 3000
P52 <-  3.26  # > 3000 & < 4000
P62 <-  2.72  # > 4000 & < 6000
P72 <-  6.79  # > 6000

setup

How I set the fraction spent on pension and old-age insurance:

Up until A.1 (mean income of HH with income < 1400) P1;
From A.1 apply growh rate S1 until A.2 (mean income of HH with income > 1400 & income < 1800);
From A.2 apply growth rate S2 until A.3;
From A.3 until and including A.7, take the weighted average of the share P_weighted.
From weighted to A.7 take the growth rate

Weighted average share between A3 and A7

Pension and old-age

P_weighted <- (nrow(subset(df.NL598, df.NL598$A>A.3 & df.NL598$A<=3000 & df.NL598$work_class != 4)) * P4/100 +
              nrow(subset(df.NL598, df.NL598$A>3000 & df.NL598$A<=4000 & df.NL598$work_class != 4)) * P5/100 +
              nrow(subset(df.NL598, df.NL598$A>4000 & df.NL598$A<=A.7& df.NL598$work_class != 4)) * P6/100) /
              nrow(subset(df.NL598,df.NL598$A > A.3 & df.NL598$A <= A.7 & df.NL598$work_class != 4))
P_weighted

## [1] 0.6092213

Funeral and life

P_weighted1 <- (nrow(subset(df.NL598, df.NL598$A>A.3 & df.NL598$A<=3000 & df.NL598$work_class != 4)) * P41/100 +
              nrow(subset(df.NL598, df.NL598$A>3000 & df.NL598$A<=4000 & df.NL598$work_class != 4)) * P51/100 +
              nrow(subset(df.NL598, df.NL598$A>4000 & df.NL598$A<=A.7& df.NL598$work_class != 4)) * P61/100) /
              nrow(subset(df.NL598,df.NL598$A > A.3 & df.NL598$A <= A.7 & df.NL598$work_class != 4))
P_weighted1

## [1] 0.3573915

Change of P over change of income

Pension and old-age

S1 <- (P2 - P1)/(A.2-A.1)
S2 <- (P_weighted*100 - P2)/(A.3-A.2)
S3 <- (P4 - P3)/(A.4-A.3)
S4 <- (P5 - P4)/(A.5-A.4)
S5 <- (P6 - P5)/(A.6-A.5)
S6 <- (P7 - P6)/(A.7-A.6)

Funeral and life

S11 <- (P21 - P11)/(A.2-A.1)
S21 <- (P_weighted1*100 - P21)/(A.3-A.2)
S31 <- (P41 - P31)/(A.4-A.3)
S41 <- (P51 - P41)/(A.5-A.4)
S51 <- (P61 - P51)/(A.6-A.5)
S61 <- (P71 - P61)/(A.7-A.6)

combine

Pension and old-age

df.NL598$ins.frac <- 
  ifelse(df.NL598$A<A.1, P1/100, 
         ifelse(df.NL598$A>=A.1 & df.NL598$A<A.2,(P1 + ((df.NL598$A-A.1) * S1))/100, 
                ifelse(df.NL598$A>=A.2 & df.NL598$A < A.3, (P2 + ((df.NL598$A - A.2) *S2))/100 ,
                       ifelse(df.NL598$A >= A.3  &df.NL598$A < A.6, P_weighted ,
                              ifelse(df.NL598$A>= A.6 & df.NL598$A < A.7, (P6 + ((df.NL598$A - A.6) *S6))/100,
                                     ifelse(df.NL598$A >=A.7,P7/100,"")))))) %>% as.numeric()

Funeral and life

df.NL598$ins.frac1 <- 
  ifelse(df.NL598$A<A.1, P11/100, 
         ifelse(df.NL598$A>=A.1 & df.NL598$A<A.2,(P11 + ((df.NL598$A-A.1) * S11))/100, 
                ifelse(df.NL598$A>=A.2 & df.NL598$A < A.3, (P21 + ((df.NL598$A - A.2) *S21))/100 ,
                       ifelse(df.NL598$A >= A.3  &df.NL598$A < A.6, P_weighted1 ,
                              ifelse(df.NL598$A>= A.6 & df.NL598$A < A.7, (P61 + ((df.NL598$A - A.6) *S61))/100,
                                     ifelse(df.NL598$A >=A.7,P71/100,"")))))) %>% as.numeric()

Original CBS distribution

Pension and old-age

df.NL598$ins.frac.CBS <- 
  ifelse(df.NL598$A<1400 , P1/100, 
          ifelse(df.NL598$A>1400 & df.NL598$A<1800,(P2)/100, 
              ifelse(df.NL598$A>1800 & df.NL598$A<2300, P3/100 ,
                     ifelse(df.NL598$A>2300 & df.NL598$A<3000, P4/100,
                            ifelse(df.NL598$A>3000 & df.NL598$A<4000, P5/100,
                                   ifelse(df.NL598$A>4000 & df.NL598$A<6000,P6/100,
                                          ifelse(df.NL598$A>6000,P7/100,""
                                                 ))))))) %>% as.numeric()

Funeral and life

df.NL598$ins.frac.CBS1 <- 
  ifelse(df.NL598$A<1400 , P11/100, 
          ifelse(df.NL598$A>1400 & df.NL598$A<1800,P21/100, 
              ifelse(df.NL598$A>1800 & df.NL598$A<2300, P31/100 ,
                     ifelse(df.NL598$A>2300 & df.NL598$A<3000, P41/100,
                            ifelse(df.NL598$A>3000 & df.NL598$A<4000, P51/100,
                                   ifelse(df.NL598$A>4000 & df.NL598$A<6000,P61/100,
                                          ifelse(df.NL598$A>6000,P71/100,""
                                                 ))))))) %>% as.numeric()

Health and other

df.NL598$ins.frac.CBS2 <- 
  ifelse(df.NL598$A<1400 , P12/100, 
          ifelse(df.NL598$A>1400 & df.NL598$A<1800,P22/100, 
              ifelse(df.NL598$A>1800 & df.NL598$A<2300, P32/100 ,
                     ifelse(df.NL598$A>2300 & df.NL598$A<3000, P42/100,
                            ifelse(df.NL598$A>3000 & df.NL598$A<4000, P52/100,
                                   ifelse(df.NL598$A>4000 & df.NL598$A<6000,P62/100,
                                          ifelse(df.NL598$A>6000,P72/100,""
                                                 ))))))) %>% as.numeric()

Net Present Value

Now I will calculate NPV following my own fractions and CBS fractions, both for pension and life insurance wealth.

Some households don’t have a male household head so then we work with female age. Here I calculate the number of years applied for discounting (time to retirement, ttr).

df.NL598$ttr <- ifelse(is.na(df.NL598$age_m),
                      ifelse(df.NL598$age_f < 60, 60 - df.NL598$age_f, 0),
                      ifelse(df.NL598$age_m < 60, 60 - df.NL598$age_m, 0))

Fraction * premium spent (D35)

df.NL598$pension <- df.NL598$ins.frac * df.NL598$D35
df.NL598$pension.CBS <- df.NL598$ins.frac.CBS * df.NL598$D35

df.NL598$life <- df.NL598$ins.frac1 * df.NL598$D35
df.NL598$life.CBS <- df.NL598$ins.frac.CBS1 * df.NL598$D35

NPV

Run the NPV command

b  <- NULL
b1 <- NULL
b2 <- NULL
b3 <- NULL

#Calculating the NPV
for(i in 1:nrow(df.NL598)) {
  if (is.na(df.NL598$ttr[i]) ){
    a <- print(NA)
  }
  else{
    a <- (npv(0,df.NL598$pension[i],df.NL598$ttr[i], 0.0332))
    a1 <- (npv(0,df.NL598$pension.CBS[i],df.NL598$ttr[i], 0.0332))
    a2 <- (npv(0,df.NL598$life[i],df.NL598$ttr[i], 0.0332))
    a3 <- (npv(0,df.NL598$life.CBS[i],df.NL598$ttr[i], 0.0332))
  }
  b <- c(b,a)
  b1 <- c(b1,a1)
  b2 <- c(b2,a2)
  b3 <- c(b3,a3)
}

Combine the data.

df.NL598 <- cbind(df.NL598, npv.pension = b, npv.pension.CBS = b1, npv.life = b2, npv.life.CBS = b3)

Land wealth

# land & pacht
df.land <- read.csv("data/land.csv") %>%
  subset(!is.na(pacht)) %>%
  select(provincie, pacht) %>%
  as.data.frame()

df.NL598 <- df.NL598 %>%
  inner_join(df.land, by = "provincie")

df.NL598 <- df.NL598 %>%
  mutate(land_value = (land * pacht)/ (0.0332 * 1000) * 1000)

Home value

df.NL598 <- df.NL598 %>% mutate(home_value = D17 * own_house / 0.0332)

clean up

# clean up
rm(list = ls.str(mode = 'numeric'))
rm(df.land)
df.NL598 <- df.NL598 %>% select(-matches("ins.frac"), -matches("CBS"), -pension, -life)

NL 700 unemployed

df.NL700 <- read.csv("data/NL700.csv") %>% select(-X)

# towards yearly figures
df.NL700 <- df.NL700 %>%
  mutate(support = support * 52) %>%
  mutate(public_employment = public_employment * 52) %>%
  mutate(fuel_subsidy = fuel_subsidy * 52) %>%
  mutate(food_distr = food_distr * 52) %>%
  mutate(income.HH.head = income.HH.head * 52) %>%
  mutate(income.own.company = income.own.company * 52) %>%
  mutate(other.HH.income = other.HH.income * 52) %>%
  mutate(gifts = gifts * 52) %>%
  mutate(stocks = stocks * 52) %>%
  mutate(income = income * 52) %>%
  mutate(food = food * 52) %>%
  mutate(housing = housing * 52) %>%
  mutate(fire = fire * 52) %>%
  mutate(clothing = clothing * 52) %>%
  mutate(insurance = insurance * 52) %>%
  mutate(other = other * 52) %>%
  mutate(consumption = consumption * 52)

# add financial margin and net income
df.NL700 <- df.NL700 %>%
  mutate(housing = housing + fire) %>%
  mutate(fm = income - stocks - food - housing - clothing) %>%
  mutate(net_income = support + public_employment + fuel_subsidy + food_distr + income.HH.head + income.own.company + other.HH.income) %>% 
  mutate(social_security = support + public_employment + fuel_subsidy + food_distr) %>%
  mutate(HHincome = income.HH.head + income.own.company + other.HH.income)

Amsterdam 1934

df.A1934 <- read.csv("data/Amsterdam1934.csv") %>% 
  mutate(HHincome = Man + Vrouw + Kinderen) %>%
  mutate(social_security = Werkloosheidssteun + `Andere.steun` + Brandstoffentoeslag) %>%
  mutate(financial = `Invaliditeits..of.weezenrente..Pensioen.e.d.`) %>%
  mutate(other_income = Onderhuur + Overige) %>%
  mutate(gifts = `Diverse.voorwerpen`+ Schoolvoeding + Schoolkleding) %>%
  mutate(credit = `Geleend.of.uitbeleening`) %>%
  mutate(income = select(., HHincome, social_security,financial,other_income, gifts) %>% rowSums(na.rm = TRUE)) %>%
  mutate(food = Food) %>% select(-Food) %>%
  mutate(shelter = Rent + `Gas.El.Fuel`) %>% select(-Rent, -`Gas.El.Fuel`) %>%
  mutate(clothing = Clothing + Footwear) %>% select(-Clothing, -Footwear) %>%
  mutate(insurance = `Funeral.Health`+Insurance) %>%
  mutate(other_cons = Other) %>% select(-Other) %>%
  mutate(consumption = select(., food, shelter, clothing,insurance,other_cons) %>% rowSums(na.rm = TRUE)) %>%
  mutate(fm = income - consumption) %>%
  mutate_at(vars(income, consumption, HHincome, social_security, financial, other_income, gifts, 
                 credit,fm, food, shelter, clothing, insurance, other_cons), funs(. / `X.Weeks` * 52))

Den Haag 1932

df.DH1932 <- read.csv("data/DH1932.csv") %>% 
mutate(HHincome = Man_loon + Vrouw_loon + Zoonloon + Dochter_loon) %>%
  mutate(social_security = Totaal_Steun) %>%
  mutate(financial = Ouderdomsrente + Dividend + Restitutie + `Kerst.uitkering`) %>%
  mutate(other = `Overig.inkomen`) %>%
  mutate(gifts = Voeding + Kleding + Schoeisel + Huisraad + Rookwaren + Overig) %>%
  mutate(stocks = Voedsel + Brandstof) %>%
  mutate(credit = `Lening.1`) %>%
  mutate(income = select(., HHincome, social_security,financial,other, gifts, stocks) %>% rowSums(na.rm = TRUE)) %>%
  mutate(consumption = Uitgaven) %>%
  mutate(fm = income - consumption) %>%
  mutate_at(vars(income, consumption, HHincome, social_security, financial, other, gifts, stocks, credit,fm), funs(. / `X.weken` * 52))

Den Haag 1935

df.DH1935 <- read.csv("data/DH1935.csv") %>% 
  mutate(income = Ontvangsten + `Waarde.van.schenkingen.in.natura`) %>%
  mutate(kind = `Waarde.van.schenkingen.in.natura`) %>%
  mutate(social_security = `steunbedrag.per.week.bij.aanvang.onderzoek`) %>%
  mutate(consumption = Uitgaven) %>%
  mutate(fm = income - consumption) %>%
  mutate_at(vars(income, consumption, fm, social_security, kind), funs(. * 52))

Figures

Figure 1

fig1 <- read.csv("data/savings.csv") %>%
  subset(year<1939) %>%
  subset(year > 1899) %>%
  # gather(asset, value, "savings", factor_key=TRUE) %>%
  ggplot(aes(x=year, y=savings)) + geom_point(shape=1) +
  geom_point()  +
  scale_color_manual(name = "", labels = c("")) +
  xlab("Jaar") +  ylim(0,350) + scale_x_continuous(breaks = seq(1900, 1940, by=10), limits=c(1900,1940))+
  guides(colour = guide_legend(reverse=T)) + 
  ylab("1936 Guilders")  + xlab("year")  + labs(title = "Household real savings per capita ") +
  theme_bw() 

fig2 <- read.csv("data/unemployment.csv")  %>%
  subset(jaar<1939) %>%
  mutate(unemp = unemp/100) %>%
  as.data.frame() %>%
  ggplot(aes(x=jaar, y=unemp)) + 
  geom_point()  +
  scale_color_manual(name = "", labels = c("")) +
  xlab("Jaar") +  scale_x_continuous(breaks = seq(1900, 1940, by=10), limits=c(1900,1940))+ 
  scale_y_continuous(labels = percent_format(accuracy = 1), breaks = seq(0, 0.2, by = 0.05), limits= c(0,0.20)) +
  guides(colour = guide_legend(reverse=T)) + 
  ylab("")    + xlab("year") +  labs(title = "Unemployment") +
  theme_bw() 

fig3 <- read.csv("data/hhbudget.csv") %>%
  subset(year<1939) %>%
  as.data.frame() %>%
  ggplot(aes(x=year, y=rw)) + geom_point(shape=1) +
  geom_point()  +
  scale_color_manual(name = "", labels = c("")) +
  xlab("year") +  scale_x_continuous(breaks = seq(1900, 1940, by=10), limits=c(1900,1940))+
  scale_y_continuous(breaks = seq(0, 50, by = 10), limits= c(0,50)) +
    guides(colour = guide_legend(reverse=T)) + 
  ylab("Guilders")    + labs(title = "Real wages building labourer") +
  theme_bw() 

fig4 <- read.csv("data/hhbudgetprimary.csv")  %>%
  subset(year<1939) %>%
  as.data.frame() %>% select(year, group2, percentage) %>%
  ggplot(aes(x=year, y=percentage, group=group2)) +
  scale_shape_manual(values=c(1, 2, 3)) +
  geom_point(aes(shape = group2))  +
  xlab("Jaar") +  scale_x_continuous(breaks = seq(1900, 1940, by=10), limits=c(1900,1940))+ 
  scale_y_continuous(labels = percent_format(accuracy = 1), breaks = seq(0, 1, by = 0.2), limits= c(0,1)) +
  guides(colour = guide_legend(reverse=T)) + guides(color=guide_legend("labour type")) +
  ylab("") + xlab("year")    + labs(title = "Share of primary expenses", shape = "Labour type") + scale_size_area(max_size = 20) +
  theme_bw() + theme(legend.position = c(0.25, 0.25),legend.box.margin = margin(t = 0.5, unit='cm'), 
                     legend.margin = margin(t = -0.5, unit='cm'), 
                     legend.background = element_rect(fill = "white", color = "black")) 

ggarrange(fig2, fig3, fig4, fig1,
              labels = c("A", "B", "C", "D"),
              ncol = 2, nrow = 2)

rm(fig1, fig2, fig3, fig4)

Figure 4

ggplot(st_read("data/shapefile598.shp", stringsAsFactors = F)) +
  geom_sf(aes(fill = Freq), size = .1, colour = "grey90") +
  labs(fill = "N") +
  theme_minimal() +
  scale_fill_gradient(high = "red", low = "yellow", na.value = "grey70") +
  theme(axis.text.y = element_blank()) +
  theme(axis.text.x = element_blank()) +  
  theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())

## Reading layer `shapefile598' from data source 
##   `/Users/timvdvalk/Documents/Economics/finkwets/R/howitsmade/data/shapefile598.shp' 
##   using driver `ESRI Shapefile'
## replacing null geometries with empty geometries
## Simple feature collection with 1125 features and 6 fields (with 4 geometries empty)
## Geometry type: POLYGON
## Dimension:     XY
## Bounding box:  xmin: 13240 ymin: 306980 xmax: 278070 ymax: 618430
## CRS:           NA

ggplot(st_read("data/shapefile700.shp", stringsAsFactors = F)) +
  geom_sf(aes(fill = Steekproef), size = .1, colour = "grey90") +
  labs(fill = "N") +
  theme_minimal() +
  scale_fill_gradient(high = "red", low = "yellow", na.value = "grey70") +
  theme(axis.text.y = element_blank()) +
  theme(axis.text.x = element_blank()) +  
  theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())

## Reading layer `shapefile700' from data source 
##   `/Users/timvdvalk/Documents/Economics/finkwets/R/howitsmade/data/shapefile700.shp' 
##   using driver `ESRI Shapefile'
## Simple feature collection with 1121 features and 7 fields
## Geometry type: POLYGON
## Dimension:     XY
## Bounding box:  xmin: 13240 ymin: 306980 xmax: 278070 ymax: 618430
## CRS:           NA

Tables

Table 1

df.income <- read.csv("data/income_distribution.csv")

# now create the table for 598 households
b <- NULL
a <- length(df.NL598$net_income[ df.NL598$net_income < 1400      & df.NL598$net_income >= 800]) 
  b <- c(b,a)
a <- length(df.NL598$net_income[ df.NL598$net_income < 2000      & df.NL598$net_income >= 1400]) 
  b <- c(b,a)
a <- length(df.NL598$net_income[ df.NL598$net_income < 3000      & df.NL598$net_income >= 2000]) 
  b <- c(b,a)
a <- length(df.NL598$net_income[ df.NL598$net_income < 5000      & df.NL598$net_income >= 3000]) 
  b <- c(b,a)
a <- length(df.NL598$net_income[ df.NL598$net_income < 10000     & df.NL598$net_income >= 5000]) 
  b <- c(b,a)
a <- length(df.NL598$net_income[ df.NL598$net_income < 20000     & df.NL598$net_income >= 10000]) 
  b <- c(b,a)
a <- length(df.NL598$net_income[ df.NL598$net_income < 30000     & df.NL598$net_income >= 20000]) 
  b <- c(b,a)
a <- length(df.NL598$net_income[ df.NL598$net_income < 100000    & df.NL598$net_income >= 30000]) 
  b <- c(b,a)
a <- length(df.NL598$net_income[ df.NL598$net_income >= 100000  ])  
  b <- c(b,a)
  
# now combine
df.income$N2 <- b
df.income$share2 <- df.income$N2 / sum(df.income$N2)

b <- NULL
a <- length(df.NL700$income[ df.NL700$income < 1400      & df.NL700$income >= 800]) 
b <- c(b,a)
a <- length(df.NL700$income[ df.NL700$income < 2000      & df.NL700$income >= 1400]) 
b <- c(b,a)
a <- length(df.NL700$income[ df.NL700$income < 3000      & df.NL700$income >= 2000]) 
b <- c(b,a)
a <- length(df.NL700$income[ df.NL700$income < 5000      & df.NL700$income >= 3000]) 
b <- c(b,a)
a <- length(df.NL700$income[ df.NL700$income < 10000     & df.NL700$income >= 5000]) 
b <- c(b,a)
a <- length(df.NL700$income[ df.NL700$income < 20000     & df.NL700$income >= 10000]) 
b <- c(b,a)
a <- length(df.NL700$income[ df.NL700$income < 30000     & df.NL700$income >= 20000]) 
b <- c(b,a)
a <- length(df.NL700$income[ df.NL700$income < 100000    & df.NL700$income >= 30000]) 
b <- c(b,a)
a <- length(df.NL700$income[ df.NL700$income >= 100000  ])  
b <- c(b,a)

# now combine
df.income$N3 <- b
df.income$share3 <- df.income$N3 / sum(df.income$N3) 

rm(a,b)

colnames(df.income) <- c("range", "NL N", "NL share", "598 N", "598 share", "700 N", "700 share")

df.income

##            range    NL N     NL share 598 N  598 share 700 N   700 share
## 1      800-1,400 595,384 0.4634940010   154 0.30738523   382 0.957393484
## 2    1,400-2,000 349,502 0.2720800027   162 0.32335329    16 0.040100251
## 3    2,000-3,000 180,867 0.1408011796   102 0.20359281     1 0.002506266
## 4    3,000-5,000  97,444 0.0758581175    55 0.10978044     0 0.000000000
## 5   5,000-10,000  43,846 0.0341331947    22 0.04391218     0 0.000000000
## 6  10,000-20,000  12,492 0.0097247609     6 0.01197605     0 0.000000000
## 7  20,000-30,000   2,730 0.0021252479     0 0.00000000     0 0.000000000
## 8 30,000-100,000   2,084 0.0016223504     0 0.00000000     0 0.000000000
## 9       >100,000     207 0.0001611452     0 0.00000000     0 0.000000000

Table 2

Income shares for Den Haag and Utrecht 1935 are derived from the original report

df.A1934 %>%
  select(income, social_security , HHincome, gifts, other_income, financial) %>%
  mutate(HHincome = HHincome / income, social_security = social_security / income, gifts = gifts / income,  other_income = (financial +other_income) / income ) %>% select(-financial) %>%
  stargazer(summary.stat = c("n","mean"), type = "text",
            title = "Domestic accounts Amsterdam 1934-1935 (N = 78)")

## 
## Domestic accounts Amsterdam 1934-1935 (N = 78)
## ==========================
## Statistic       N   Mean  
## --------------------------
## income          78 977.559
## social_security 78  0.820 
## HHincome        78  0.121 
## gifts           78  0.039 
## other_income    78  0.020 
## --------------------------

df.DH1932 %>% 
  select(income, social_security , HHincome, gifts, other, financial) %>%
  mutate(HHincome = HHincome / income, social_security = social_security / income, gifts = gifts / income,  other_income = (financial +other) / income ) %>% select(-other, -financial) %>%
  stargazer(summary.stat = c("n","mean"), type = "text",
            title = "Domestic accounts The Hague 1932 (N = 90)")

## 
## Domestic accounts The Hague 1932 (N = 90)
## ============================
## Statistic       N    Mean   
## ----------------------------
## income          90 1,103.925
## social_security 90   0.867  
## HHincome        90   0.051  
## gifts           90   0.049  
## other_income    90   0.032  
## ----------------------------

df.DH1935 %>% 
  select(income) %>%
  stargazer(summary.stat = c("n","mean"), type = "text",
            title = "Domestic accounts The Hague 1935 (N = 129)")

## 
## Domestic accounts The Hague 1935 (N = 129)
## =====================
## Statistic  N   Mean  
## ---------------------
## income    129 935.639
## ---------------------

Table 3

Consumption shares for Den Haag 1932 and 1935 and Utrecht 1935 are derived from the original report

df.A1934 %>% select(consumption, food, shelter, clothing, insurance, other_cons) %>%    
  mutate(food = food / consumption, shelter = shelter / consumption, clothing = clothing / consumption,  other_cons = (other_cons +insurance) / consumption ) %>% select(-insurance) %>%
  stargazer(summary.stat = c("n","mean"), type = "text",
            title = "Domestic accounts Amsterdam 1934-1935 (N = 78)")

## 
## Domestic accounts Amsterdam 1934-1935 (N = 78)
## ========================
## Statistic   N    Mean   
## ------------------------
## consumption 78 1,003.922
## food        78   0.403  
## shelter     78   0.422  
## clothing    78   0.045  
## other_cons  78   0.129  
## ------------------------

df.DH1932 %>% 
  select(consumption) %>%
  stargazer(summary.stat = c("n","mean"), type = "text",
            title = "Domestic accounts The Hague 1932 (N = 90)")

## 
## Domestic accounts The Hague 1932 (N = 90)
## ========================
## Statistic   N    Mean   
## ------------------------
## consumption 90 1,077.354
## ------------------------

df.DH1935 %>% 
  select(consumption) %>%
  stargazer(summary.stat = c("n","mean"), type = "text",
            title = "Domestic accounts The Hague 1935 (N = 129)")

## 
## Domestic accounts The Hague 1935 (N = 129)
## =======================
## Statistic    N   Mean  
## -----------------------
## consumption 129 892.705
## -----------------------

Table 4

This table is directly taken from the original report.

Table 5

modelsummary::datasummary(data = df.NL700, escape = FALSE,
                          (Income = income)  +
                            (`   Net income` = HHincome) +
                            (`   Social sec.` = social_security) +
                            (`   Gifts` = gifts) +
                            (`   Stocks` = stocks) +
                            (`Consumption` = consumption) +
                            (`   Food` = food) +
                            (`   Shelter` = housing) +
                            (`   Clothing` = clothing) +
                            (`   Insurance` = insurance) +
                            (`   Other` = other)  ~
                            (`Share (%)`= count_over_zero100) +
                            (Mean = mean) + 
                            (`Std. Dev.` = sd) + 
                            (Min = min) + 
                            (Max = max) + 
                            (Median = median) )

	Share (%)	Mean	Std. Dev.	Min	Max	Median
Income	100.00	852.63	222.58	409.24	2048.80	834.08
Net income	59.29	138.13	206.04	0.00	1382.16	40.30
Social sec.	100.00	645.65	184.14	15.60	1235.00	660.92
Gifts	70.29	32.68	48.21	0.00	323.44	11.96
Stocks	79.00	35.27	46.39	0.00	250.12	17.94
Consumption	100.00	891.65	230.47	390.52	2437.76	857.48
Food	100.00	446.95	145.18	163.80	1347.84	428.48
Shelter	100.00	241.05	76.22	46.28	528.32	242.32
Clothing	99.29	67.11	51.26	0.00	367.64	53.56
Insurance	97.86	57.58	31.50	0.00	171.60	56.68
Other	99.29	78.05	74.54	0.00	1296.36	61.10

Table 6

This table comes from the reported sources in the paper

Table 7

modelsummary::datasummary(data = df.NL598 %>% filter(work != "landbouwer"), escape = FALSE,
                            (Income = A)  +
                            (`   Net income` = net_income) +
                            (`   Home prod.` = A4) +
                            (`   Impl. rent` = A3) +
                            (`   Inc. (kind)` = A1) +
                            (`   Sup. (kind)` = A2) +
                            (`   Lodgers` = A5) +
                            (`Consumption` = D) +
                            (`   Food` = food) +
                            (`   Shelter` = shelter) +
                            (`   Clothing` = clothing) +
                            (`   Other` = other) +
                            (`Savings (net)` = net_savings) +
                            (`Credit (net)` = net_credit) ~
                            (`Share (%)`= count_over_zero100) +
                            (Mean = mean) +
                            (`Std. Dev.` = sd) + 
                            (Min = min) + 
                            (Max = max) + 
                            (Median = median) )

	Share (%)	Mean	Std. Dev.	Min	Max	Median
Income	100.00	2255.46	1863.15	720.29	19553.20	1740.25
Net income	100.00	2176.17	1839.08	574.51	19553.20	1692.32
Home prod.	37.90	12.93	39.30	0.00	566.12	0.00
Impl. rent	22.29	29.39	80.38	0.00	652.52	0.00
Inc. (kind)	25.52	18.18	55.62	0.00	700.00	0.00
Sup. (kind)	26.86	7.61	20.10	0.00	159.58	0.00
Lodgers	7.05	11.17	98.62	0.00	1553.80	0.00
Consumption	100.00	2198.52	1702.30	739.48	19835.26	1727.92
Food	100.00	630.43	211.16	276.95	1651.35	579.51
Shelter	100.00	491.24	281.87	118.74	2554.20	422.99
Clothing	100.00	231.60	182.04	23.94	1698.20	180.44
Other	100.00	736.23	1041.69	29.96	12255.76	399.02
Savings (net)	52.76	61.71	432.60	−1073.32	6163.92	3.13
Credit (net)	38.86	−4.87	190.47	−2454.38	1409.29	0.00

Table 8

This table directly comes from the reported sources in the paper

Table 9

The Estimated Wealth of 525 Households in the Survey of 598 (1936-1937)
	Share (%)	Mean	Median	Max
Housing wealth	24.76	2316.68	0.00	30120.48
Land wealth	40.19	103.61	0.00	8148.19
LI wealth	93.33	925.71	664.62	11175.71
Pension wealth	93.33	1088.14	477.19	12590.88

Table 10

df.DH1932 %>% 
  select(fm) %>%
    stargazer(covariate.labels = c("Financial margin", summary = TRUE),
            summary.stat = c("n","mean","min", "p25","median", "p75","max"), type = "text",
            title = "Financial margin The Hague 1932 (N = 90)")

## 
## Financial margin The Hague 1932 (N = 90)
## ====================================================================
## Statistic        N   Mean    Min    Pctl(25) Median Pctl(75)   Max  
## --------------------------------------------------------------------
## Financial margin 90 26.571 -194.240 -16.690  8.180   83.850  231.010
## --------------------------------------------------------------------

df.A1934 %>%
  select(fm) %>%
  stargazer(covariate.labels = c("Financial margin", summary = TRUE),
            summary.stat = c("n","mean","min", "p25","median", "p75","max"), type = "text",
            title = "Financial margin Amsterdam 1934-1935 (N = 78)")

## 
## Financial margin Amsterdam 1934-1935 (N = 78)
## =====================================================================
## Statistic        N   Mean     Min    Pctl(25) Median Pctl(75)   Max  
## ---------------------------------------------------------------------
## Financial margin 78 -26.364 -308.956 -35.790  -6.030  4.785   104.880
## ---------------------------------------------------------------------

df.DH1935 %>% 
  select(fm) %>% 
    stargazer(covariate.labels = c("Financial margin", summary = TRUE),
            summary.stat = c("n","mean","min", "p25","median", "p75","max"), type = "text",
            title = "Financial margin The Hague 1935 (N = 129)")

## 
## Financial margin The Hague 1935 (N = 129)
## =====================================================================
## Statistic         N   Mean    Min    Pctl(25) Median Pctl(75)   Max  
## ---------------------------------------------------------------------
## Financial margin 129 42.934 -181.220  -1.560  26.260  66.300  603.460
## ---------------------------------------------------------------------

df.NL700 %>% 
  select(fm) %>% 
    stargazer(covariate.labels = c("Financial margin", summary = TRUE),
            summary.stat = c("n","mean","min", "p25","median", "p75","max"), type = "text",
            title = "Financial margin The Netherlands 1937 (N = 700)")

## 
## Financial margin The Netherlands 1937 (N = 700)
## =====================================================================
## Statistic         N   Mean    Min    Pctl(25) Median Pctl(75)   Max  
## ---------------------------------------------------------------------
## Financial margin 700 62.261 -374.920  5.070   70.720 121.160  629.720
## ---------------------------------------------------------------------

df.NL598 %>% filter(work != "landbouwer") %>% 
  select(fm) %>% 
    stargazer(covariate.labels = c("Financial margin", summary = TRUE),
            summary.stat = c("n","mean","min", "p25","median", "p75","max"), type = "text",
            title = "Financial margin The Netherlands 1936-37 (N = 598)")

## 
## Financial margin The Netherlands 1936-37 (N = 598)
## ==========================================================================
## Statistic         N   Mean     Min    Pctl(25) Median  Pctl(75)    Max    
## --------------------------------------------------------------------------
## Financial margin 525 822.902 -632.510 214.970  464.200 837.340  13,649.450
## --------------------------------------------------------------------------

Table 11

linear.1 <- lm(fffm.con ~ av_age + HH_size + HC2 + HC3 + HC5 ,
             data = df.NL598 %>% filter(work != "landbouwer")) %>%
            coeftest(., vcov = vcovHC(., type = "HC0"))

linear.2 <- lm(fffm.con ~ av_age + HH_size + HC2 + HC3 + HC5 + location21 + location22 ,
               data = df.NL598 %>% filter(work != "landbouwer")) %>%
            coeftest(., vcov = vcovHC(., type = "HC0"))

linear.3 <- lm(fffm.con ~ av_age + HH_size + HC2 + HC3 + HC5 + location21 + location22 +home_value + land_value + npv.life + npv.pension,
               data = df.NL598 %>% filter(work != "landbouwer")) %>%
            coeftest(., vcov = vcovHC(., type = "HC0"))

linear.4 <- lm(fffm.con ~ av_age + HH_size + HC2 + HC3 + HC5 + location21 + location22 + av_age +home_value + land_value + npv.life + npv.pension + death + newborn + healthcare_shock + unemp ,
               data = df.NL598 %>% filter(work != "landbouwer")) %>%
            coeftest(., vcov = vcovHC(., type = "HC0"))



stargazer(linear.1, linear.2, linear.3, linear.4, title="The Determinants of Financial Fragility of Dutch Households in 1936-37", type = "text", align=TRUE, covariate.labels = c("Av. Age", "HH size", "HC2 (d)", "HC3 (d)", "HC5 (d)", "Large (d)", "Industrial (d)", "Housing wealth", "Land wealth", "LI wealth", "Pension wealth", "death (d)", "Newborn (d)", "Healthcare (d)", "Unemployment"))

## 
## The Determinants of Financial Fragility of Dutch Households in 1936-37
## ======================================================================
##                                  Dependent variable:                  
##                -------------------------------------------------------
##                                                                       
##                     (1)           (2)           (3)           (4)     
## ----------------------------------------------------------------------
## Av. Age          14.650**      16.060**      33.652***     28.983***  
##                   (7.190)       (7.572)       (7.922)       (7.430)   
##                                                                       
## HH size          -41.545*      -46.864**    -61.260***    -64.612***  
##                  (21.800)      (22.846)      (20.626)      (20.805)   
##                                                                       
## HC2 (d)        -1,766.206*** -1,769.582***  -920.535***   -920.310*** 
##                  (350.806)     (348.478)     (260.659)     (254.669)  
##                                                                       
## HC3 (d)        -2,714.342*** -2,755.682*** -1,627.673*** -1,655.356***
##                  (332.114)     (338.446)     (247.325)     (245.594)  
##                                                                       
## HC5 (d)        -2,874.741*** -2,839.641*** -1,693.984*** -1,701.765***
##                  (334.485)     (334.488)     (266.609)     (265.403)  
##                                                                       
## Large (d)                      273.216*       -28.426       -38.343   
##                                (156.651)     (134.714)     (133.286)  
##                                                                       
## Industrial (d)                  -54.386       -98.253      -104.157   
##                                (109.012)     (111.200)     (110.201)  
##                                                                       
## Housing wealth                                0.029*        0.029**   
##                                               (0.015)       (0.015)   
##                                                                       
## Land wealth                                   -0.002        -0.016    
##                                               (0.036)       (0.033)   
##                                                                       
## LI wealth                                    1.296***      1.370***   
##                                               (0.431)       (0.433)   
##                                                                       
## Pension wealth                               -0.459**      -0.530**   
##                                               (0.224)       (0.230)   
##                                                                       
## death (d)                                                   420.937   
##                                                            (307.033)  
##                                                                       
## Newborn (d)                                               -266.799**  
##                                                            (131.314)  
##                                                                       
## Healthcare (d)                                            -287.495*** 
##                                                            (90.750)   
##                                                                       
## Unemployment                                                -2.595    
##                                                             (3.104)   
##                                                                       
## Constant       2,672.911***  2,567.659***     446.290       809.374   
##                  (416.609)     (414.650)     (515.391)     (491.540)  
##                                                                       
## ======================================================================
## ======================================================================
## Note:                                      *p<0.1; **p<0.05; ***p<0.01

rm(linear.1, linear.2, linear.3, linear.4)

Table 12

df.NL598.share <- df.NL598 %>%  filter(work != "landbouwer") %>% 
  select(D, net_income, A1, A2, A3, A4, A5, E, B, F, C, fffm2,unemp2) %>%
  mutate(E = -E) %>%
  mutate(F = -F)
df.NL598.share <- cbind(df.NL598.share[,2:11] / df.NL598.share[,1], df.NL598.share[,12:13])
colnames(df.NL598.share) <- c("net_income", "A1", "A2", "A3", "A4", "A5", "E", "B", "F", "C", "fffm2", "unemp2")

mean <- NULL
stat <- NULL
p <- NULL

col <- names(df.NL598.share[,1:10])

for(i in col){
  a <- t.test(data = df.NL598.share, df.NL598.share[[i]] ~ fffm2)$estimate
  b <- t.test(data = df.NL598.share, df.NL598.share[[i]] ~ fffm2)$statistic
  c <- t.test(data = df.NL598.share, df.NL598.share[[i]] ~ fffm2)$p.value
  
  mean <- rbind(mean,a)
  stat <- rbind(stat,b)
  p <- rbind(p,c)
}

tt2.2 <- cbind(mean, stat, p)
rownames(tt2.2) <- c("Net income", "Income in kind", "Support in kind", "Impl. rent (net)", "Home prod.", "Lodgers", "Savings (out)", "Savings (in)" , "Credit (out)", "Credit (in)")
colnames(tt2.2) <- c("ff2 = 0", "ff2 = 1", "t", "p")


tt2.2 <- cbind(mean, stat, p)
rownames(tt2.2) <- c("Net income", "Income in kind", "Support in kind", "Impl. rent (net)", "Home prod.", "Lodgers", "Savings (out)", "Savings (in)" , "Credit (out)", "Credit (in)")
# colnames(tt2.2) <- 

stargazer(tt2.2, title = "Cash Flows to Finance the Consumption of Dutch Households in 1936-1937 (N = 525)", 
          type = "text", align=TRUE)

## 
## Cash Flows to Finance the Consumption of Dutch Households in 1936-1937 (N = 525)
## ===============================================================
##                  mean in group 0 mean in group 1   t      .1   
## ---------------------------------------------------------------
##    Net income         1.027           0.888      11.811    0   
##  Income in kind       0.006           0.011      -2.136  0.034 
## Support in kind      0.0002           0.018      -8.550    0   
## Impl. rent (net)      0.013           0.015      -0.676  0.499 
##    Home prod.         0.002           0.025      -5.773 0.00000
##     Lodgers           0.005           0.005      -0.012  0.991 
##  Savings (out)       -0.074          -0.010      -5.658 0.00000
##   Savings (in)        0.019           0.039      -3.526 0.0005 
##   Credit (out)       -0.018          -0.008      -2.411  0.017 
##   Credit (in)         0.021           0.017      0.510   0.611 
## ---------------------------------------------------------------

rm(df.NL598.share, mean, p, stat, tt2.2, a, b, c, col, i)

Table 13

df.NL598.share <- df.NL598 %>%
  select(D, food, shelter, clothing, leisure, other, fffm2 )
df.NL598.share <- cbind(df.NL598.share[,2:6] / df.NL598.share[,1], df.NL598.share[,7])
colnames(df.NL598.share) <- c("Food", "Shelter", "Clothing", "Leisure", "Other", "fffm2")

mean <- NULL
stat <- NULL
p <- NULL

col <- names(df.NL598.share[,1:5])

for(i in col){
  a <- t.test(data = df.NL598.share, df.NL598.share[[i]] ~ fffm2)$estimate
  b <- t.test(data = df.NL598.share, df.NL598.share[[i]] ~ fffm2)$statistic
  c <- t.test(data = df.NL598.share, df.NL598.share[[i]] ~ fffm2)$p.value
  
  mean <- rbind(mean,a)
  stat <- rbind(stat,b)
  p <- rbind(p,c)
}

ttc22 <- cbind(mean, stat, p)
rownames(ttc22) <- c("Food", "Shelter", "Clothing", "Leisure", "Other")
colnames(ttc22) <- c("ff = 0", "ff = 1", "t", "p")

stargazer(ttc22, title = "Consumption shares of Dutch Households in 1936-1937 (N = 525)",
          type = "text", align=TRUE)

## 
## Consumption shares of Dutch Households in 1936-1937 (N = 525)
## ======================================
##          ff = 0 ff = 1    t       p   
## --------------------------------------
##   Food   0.239  0.452  -24.744    0   
## Shelter  0.216  0.245  -5.066  0.00000
## Clothing 0.108  0.101   2.052   0.041 
## Leisure  0.051  0.034   5.967     0   
##  Other   0.387  0.169  25.672     0   
## --------------------------------------

rm(df.NL598.share, mean, p, stat, ttc22, a, b, c, col, i)

Coping with financial fragility: Dutch households in the Great Depression

Online data Appendix

Oscar Gelderblom and Tim van der Valk

2022-10-08

Functions

Setup surveys

NL 598 employed

Wealth NL 598 employed

shares of premiums per income class.

setup

Change of P over change of income

combine

Original CBS distribution

Net Present Value

Fraction * premium spent (D35)

NPV

Land wealth

Home value

clean up

NL 700 unemployed

Amsterdam 1934

Den Haag 1932

Den Haag 1935

Figures

Figure 1

Figure 4

Tables

Table 1

Table 2

Table 3

Table 4

Table 5

Table 6

Table 7

Table 8

Table 9

Table 10

Table 11

Table 12

Table 13