# Code for table does not recognize a variable | Doing Economics EMPIRICAL PROJECT 8

Hi everyone,

I have the following issue. I'm currently working on the following project 8. Measuring the non-monetary cost of unemployment – Doing Economics. The thing is, I have to create a table with countries (3 of them) as the rows and the differences in the means of certain variables (namely, life satisfaction for three conditions of employment - full time, unemployed, retired), the standard errors of these differences and the confidence interval width for these differences. The code for doing this is given. It is the following

<

# List chosen countries

country_list <- c("Turkey", "Spain", "Great Britain")
df.employment.se <- lifesat_data %>%

# Select Wave 4

subset(S002EVS == "2008-2010") %>%

# Select the employment types we are interested in

subset(X028 %in% employment_list) %>%

# Select countries

subset(S003 %in% country_list) %>%

# Group by country and employment type

group_by(S003, X028) %>%

# Calculate the standard error of each group mean

summarize(se = sd(A170) / sqrt(n())) %>%

# Calculate the SE of difference

mutate(D1_SE = sqrt(`Full time`^2 + Unemployed^2),
D2_SE = sqrt(`Full time`^2 + Retired^2))

df.employment <- df.employment %>%

# Select chosen countries

subset(S003 %in% country_list) %>%

# We only need the differences.

select(-`Full time`, -Retired, -Unemployed) %>%

# Join the means with the respective SEs

inner_join(., df.employment.se, by = "S003") %>%
select(-`Full time`, -Retired, -Unemployed) %>%

# Compute confidence interval width for both differences

mutate(CI_1 = 1.96 * D1_SE, CI_2 = 1.96 * D2_SE) %>%
print()

However, when running the second chunk of code I get the following error "Error: object 'D1_SE' not found". Now, what's weird is that the original code works. However, the error occurs when I attempt to change the 3 countries mentioned in the 1st chunk above. The code chunks related to the variable D1 are the following:

<
1)

# Set the employment types that we want to report

employment_list = c("Full time", "Retired", "Unemployed")
df.employment <- lifesat_data %>%

# Select Wave 4

subset(S002EVS == "2008-2010") %>%

# Select only observations with these employment types

subset(X028 %in% employment_list) %>%

# Group by country and then employment type

group_by(S003, X028) %>%

# Calculate the mean by country/employment type group

summarize(mean = mean(A170)) %>%

# Create the difference in means

mutate(D1 = `Full time` - Unemployed,
D2 = `Full time` - Retired)

df.employment %>%

# Combine with the average work ethic data

inner_join(., df.work_ethic, by = "S003") %>%
ggplot(., aes(y = D1, x = mean_work)) +
geom_point(stat = "identity") +
xlab("Work ethic") + ylab("Difference") +
ggtitle("Difference in wellbeing
between the full-time employed and the unemployed") +
theme_bw() +

# Rotate the country names

theme(axis.text.x = element_text(
angle = 90, hjust = 1),
plot.title = element_text(hjust = 0.5))

# Full-time vs unemployed

cor(df.employment\$D1, df.work_ethic\$mean_work)

and the two given above