In thomascrines/labourmarketreports: Fetch data from APIs and generate markdown reports
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```r
library(readxl) # Uses the function read_excel
library(data.table) # Uses the functions data.table & dcast
library(flextable) # Uses the function flextable
library(stringr) # Uses the funtion str_sub
library(plyr) # Uses the function count
library(lubridate) # Uses the functions month & year
library(officer) # Uses function fp_border
library(ggplot2) # Uses the function ggplot
Date <- as.Date("01/10/2020")
Key_Stats <- data.table(labourmarketreports::lm_data_key_stats())
Country_Stats <- data.table(labourmarketreports::lm_data_country_stats())
Region_Stats <- data.table(labourmarketreports::lm_data_region_stats())
Time_Stats <- data.table(labourmarketreports::lm_data_time_stats())
Time_Stats[,Period := as.Date(paste("01", str_sub(Period, 5, 7), str_sub(Period, -4), sep = " "), format = "%d %b %Y")]
TimeN <- paste(month(Date %m-% months(4), label = T, abbr = F), " to ",
month(Date %m-% months(2), label = T, abbr = F), " ",
year(Date %m-% months(2)), sep = "")
TimeQ <- paste(month(Date %m-% months(7), label = T, abbr = F), " to ",
month(Date %m-% months(5), label = T, abbr = F), " ",
year(Date %m-% months(5)), sep = "")
Timeq <- paste(month(Date %m-% months(7), label = T, abbr = T), "-",
month(Date %m-% months(5), label = T, abbr = T), " ",
year(Date %m-% months(5)), sep = "")
Timey <- paste(month(Date %m-% months(4), label = T, abbr = T), "-",
month(Date %m-% months(2), label = T, abbr = T), " ",
year(Date %m-% months(14)), sep = "")
Timed <- paste(month(Date %m-% months(4), label = T, abbr = T), "-",
month(Date %m-% months(2), label = T, abbr = T), " 2008", sep = "")
# A useful function for adding comma to data.
Commaed <- function(INPUT){
OUTPUT <- prettyNum(INPUT, big.mark = ",", scientific = F)
return(OUTPUT)
}
# LABOUR MARKET STATISTICS
### `r paste(month(Date, label = T, abbr = F), year(Date), sep = " ")`
In r TimeN, compared to r TimeQ (the quarter before):
The employment rate was r Key_Stats[Area == "Scotland" & Status == "Employment" & Type == "Rate" & Sex == "People" & Statistic == "Estimate",]$Value per cent, r if(Key_Stats[Area == "Scotland" & Status == "Employment" & Type == "Rate" & Sex == "People" & Statistic == "Quarter",]$Value > 0){"higher than"}else if(Key_Stats[Area == "Scotland" & Status == "Employment" & Type == "Rate" & Sex == "People" & Statistic == "Quarter",]$Value < 0){"lower than"} else {"the same as"} the quarter before (r abs(Key_Stats[Area == "Scotland" & Status == "Employment" & Type == "Rate" & Sex == "People" & Statistic == "Quarter",]$Value) percentage points) and r if(Key_Stats[Area == "Scotland" & Status == "Employment" & Type == "Rate" & Sex == "People" & Statistic == "Estimate",]$Value > Key_Stats[Area == "United Kingdom" & Status == "Employment" & Type == "Rate" & Sex == "People" & Statistic == "Estimate",]$Value){"above"}else if(Key_Stats[Area == "Scotland" & Status == "Employment" & Type == "Rate" & Sex == "People" & Statistic == "Estimate",]$Value < Key_Stats[Area == "United Kingdom" & Status == "Employment" & Type == "Rate" & Sex == "People" & Statistic == "Estimate",]$Value){"below"} else {"the same as"} the UK rate of r Key_Stats[Area == "United Kingdom" & Status == "Employment" & Type == "Rate" & Sex == "People" & Statistic == "Estimate",]$Value per cent. r Commaed(Key_Stats[Area == "Scotland" & Status == "Employment" & Type == "Level" & Sex == "People" & Statistic == "Estimate",]$Value) people were in employment, r Commaed(abs(Key_Stats[Area == "Scotland" & Status == "Employment" & Type == "Level" & Sex == "People" & Statistic == "Quarter",]$Value)) r if(Key_Stats[Area == "Scotland" & Status == "Employment" & Type == "Level" & Sex == "People" & Statistic == "Quarter",]$Value > 0){"more than"}else if(Key_Stats[Area == "Scotland" & Status == "Employment" & Type == "Level" & Sex == "People" & Statistic == "Quarter",]$Value < 0){"less than"} else {"the same as"} r TimeQ.
The unemployment rate was r Key_Stats[Area == "Scotland" & Status == "Unemployment" & Type == "Rate" & Sex == "People" & Statistic == "Estimate",]$Value per cent, r if(Key_Stats[Area == "Scotland" & Status == "Unemployment" & Type == "Rate" & Sex == "People" & Statistic == "Quarter",]$Value > 0){"higher than"}else if(Key_Stats[Area == "Scotland" & Status == "Unemployment" & Type == "Rate" & Sex == "People" & Statistic == "Quarter",]$Value < 0){"lower than"} else {"the same as"} the quarter before (r abs(Key_Stats[Area == "Scotland" & Status == "Unemployment" & Type == "Rate" & Sex == "People" & Statistic == "Quarter",]$Value) percentage points) and r if(Key_Stats[Area == "Scotland" & Status == "Unemployment" & Type == "Rate" & Sex == "People" & Statistic == "Estimate",]$Value > Key_Stats[Area == "United Kingdom" & Status == "Unemployment" & Type == "Rate" & Sex == "People" & Statistic == "Estimate",]$Value){"above"}else if(Key_Stats[Area == "Scotland" & Status == "Unemployment" & Type == "Rate" & Sex == "People" & Statistic == "Estimate",]$Value < Key_Stats[Area == "United Kingdom" & Status == "Unemployment" & Type == "Rate" & Sex == "People" & Statistic == "Estimate",]$Value){"below"} else {"the same as"} the UK rate of r Key_Stats[Area == "United Kingdom" & Status == "Unemployment" & Type == "Rate" & Sex == "People" & Statistic == "Estimate",]$Value per cent. r Commaed(Key_Stats[Area == "Scotland" & Status == "Unemployment" & Type == "Level" & Sex == "People" & Statistic == "Estimate",]$Value) people were unemployed, r Commaed(abs(Key_Stats[Area == "Scotland" & Status == "Unemployment" & Type == "Level" & Sex == "People" & Statistic == "Quarter",]$Value)) r if(Key_Stats[Area == "Scotland" & Status == "Unemployment" & Type == "Level" & Sex == "People" & Statistic == "Quarter",]$Value > 0){"more than"}else if(Key_Stats[Area == "Scotland" & Status == "Unemployment" & Type == "Level" & Sex == "People" & Statistic == "Quarter",]$Value < 0){"less than"} else {"the same as"} r TimeQ.
The inactivity rate was r Key_Stats[Area == "Scotland" & Status == "Inactivity" & Type == "Rate" & Sex == "People" & Statistic == "Estimate",]$Value per cent, r if(Key_Stats[Area == "Scotland" & Status == "Inactivity" & Type == "Rate" & Sex == "People" & Statistic == "Quarter",]$Value > 0){"higher than"}else if(Key_Stats[Area == "Scotland" & Status == "Inactivity" & Type == "Rate" & Sex == "People" & Statistic == "Quarter",]$Value < 0){"lower than"} else {"the same as"} the quarter before (r abs(Key_Stats[Area == "Scotland" & Status == "Inactivity" & Type == "Rate" & Sex == "People" & Statistic == "Quarter",]$Value) percentage points) and r if(Key_Stats[Area == "Scotland" & Status == "Inactivity" & Type == "Rate" & Sex == "People" & Statistic == "Estimate",]$Value > Key_Stats[Area == "United Kingdom" & Status == "Inactivity" & Type == "Rate" & Sex == "People" & Statistic == "Estimate",]$Value){"above"}else if(Key_Stats[Area == "Scotland" & Status == "Inactivity" & Type == "Rate" & Sex == "People" & Statistic == "Estimate",]$Value < Key_Stats[Area == "United Kingdom" & Status == "Inactivity" & Type == "Rate" & Sex == "People" & Statistic == "Estimate",]$Value){"below"} else {"the same as"} the UK rate of r Key_Stats[Area == "United Kingdom" & Status == "Inactivity" & Type == "Rate" & Sex == "People" & Statistic == "Estimate",]$Value per cent. r Commaed(Key_Stats[Area == "Scotland" & Status == "Inactivity" & Type == "Level" & Sex == "People" & Statistic == "Estimate",]$Value) people were in economically inactive, r Commaed(abs(Key_Stats[Area == "Scotland" & Status == "Inactivity" & Type == "Level" & Sex == "People" & Statistic == "Quarter",]$Value)) r if(Key_Stats[Area == "Scotland" & Status == "Inactivity" & Type == "Level" & Sex == "People" & Statistic == "Quarter",]$Value > 0){"more than"}else if(Key_Stats[Area == "Scotland" & Status == "Inactivity" & Type == "Level" & Sex == "People" & Statistic == "Quarter",]$Value < 0){"less than"} else {"the same as"} r TimeQ.
Over the quarter
From r TimeQ, the unemployment rate r if(Key_Stats[Area == "Scotland" & Status == "Unemployment" & Type == "Rate" & Sex == "People" & Statistic == "Quarter",]$Value > 0){"increased to"}else if(Key_Stats[Area == "Scotland" & Status == "Unemployment" & Type == "Rate" & Sex == "People" & Statistic == "Quarter",]$Value < 0){"decreased to"} else {"remained the same at"} r Key_Stats[Area == "Scotland" & Status == "Unemployment" & Type == "Rate" & Sex == "People" & Statistic == "Estimate",]$Value per cent, the employment rate r if(Key_Stats[Area == "Scotland" & Status == "Employment" & Type == "Rate" & Sex == "People" & Statistic == "Quarter",]$Value > 0){"increased to"}else if(Key_Stats[Area == "Scotland" & Status == "Employment" & Type == "Rate" & Sex == "People" & Statistic == "Quarter",]$Value < 0){"decreased to"} else {"remained the same at"} r Key_Stats[Area == "Scotland" & Status == "Employment" & Type == "Rate" & Sex == "People" & Statistic == "Estimate",]$Value per cent, and the inactivity rate r if(Key_Stats[Area == "Scotland" & Status == "Inactivity" & Type == "Rate" & Sex == "People" & Statistic == "Quarter",]$Value > 0){"increased to"}else if(Key_Stats[Area == "Scotland" & Status == "Inactivity" & Type == "Rate" & Sex == "People" & Statistic == "Quarter",]$Value < 0){"decreased to"} else {"remained the same at"} r Key_Stats[Area == "Scotland" & Status == "Inactivity" & Type == "Rate" & Sex == "People" & Statistic == "Estimate",]$Value per cent.
Over the year
The unemployment rate r if(Key_Stats[Area == "Scotland" & Status == "Unemployment" & Type == "Rate" & Sex == "People" & Statistic == "Year",]$Value > 0){"increased"}else if(Key_Stats[Area == "Scotland" & Status == "Unemployment" & Type == "Rate" & Sex == "People" & Statistic == "Year",]$Value < 0){"decreased"} else {"remained the same "}, the employment rate r if(Key_Stats[Area == "Scotland" & Status == "Employment" & Type == "Rate" & Sex == "People" & Statistic == "Year",]$Value > 0){"increased"}else if(Key_Stats[Area == "Scotland" & Status == "Employment" & Type == "Rate" & Sex == "People" & Statistic == "Year",]$Value < 0){"decreased"} else {"remained the same"}, and the inactivity rate r if(Key_Stats[Area == "Scotland" & Status == "Inactivity" & Type == "Rate" & Sex == "People" & Statistic == "Year",]$Value > 0){"increased"}else if(Key_Stats[Area == "Scotland" & Status == "Inactivity" & Type == "Rate" & Sex == "People" & Statistic == "Year",]$Value < 0){"decreased"} else {"remained the same"}.
Compared with the UK
Scotland’s unemployment rate was r if(Key_Stats[Area == "Scotland" & Status == "Unemployment" & Type == "Rate" & Sex == "People" & Statistic == "Estimate",]$Value > Key_Stats[Area == "United Kingdom" & Status == "Unemployment" & Type == "Rate" & Sex == "People" & Statistic == "Estimate",]$Value){"higher"}else if(Key_Stats[Area == "Scotland" & Status == "Unemployment" & Type == "Rate" & Sex == "People" & Statistic == "Estimate",]$Value < Key_Stats[Area == "United Kingdom" & Status == "Unemployment" & Type == "Rate" & Sex == "People" & Statistic == "Estimate",]$Value){"lower"} else {"the same"}, the employment rate was r if(Key_Stats[Area == "Scotland" & Status == "Employment" & Type == "Rate" & Sex == "People" & Statistic == "Estimate",]$Value > Key_Stats[Area == "United Kingdom" & Status == "Employment" & Type == "Rate" & Sex == "People" & Statistic == "Estimate",]$Value){"higher"}else if(Key_Stats[Area == "Scotland" & Status == "Employment" & Type == "Rate" & Sex == "People" & Statistic == "Estimate",]$Value < Key_Stats[Area == "United Kingdom" & Status == "Employment" & Type == "Rate" & Sex == "People" & Statistic == "Estimate",]$Value){"lower"} else {"the same"} and the inactivity rate was r if(Key_Stats[Area == "Scotland" & Status == "Inactivity" & Type == "Rate" & Sex == "People" & Statistic == "Estimate",]$Value > Key_Stats[Area == "United Kingdom" & Status == "Inactivity" & Type == "Rate" & Sex == "People" & Statistic == "Estimate",]$Value){"higher"}else if(Key_Stats[Area == "Scotland" & Status == "Inactivity" & Type == "Rate" & Sex == "People" & Statistic == "Estimate",]$Value < Key_Stats[Area == "United Kingdom" & Status == "Inactivity" & Type == "Rate" & Sex == "People" & Statistic == "Estimate",]$Value){"lower"} else {"the same"}.
Labour market statistics Scotland and UK, May to July 2020
X <- data.table(dcast(Key_Stats[Sex == "People"], Status+Area~Type+Statistic, value.var = "Value"))
X[,Level_Estimate := Commaed(Level_Estimate/1000)]
X[,Level_Quarter := Commaed(Level_Quarter/1000)]
X[,Level_Year := Commaed(Level_Year/1000)]
BE <- data.table(Status = "", Area = "Employment",
Rate_Estimate = "", Rate_Quarter = "", Rate_Year = "",
Level_Estimate = "", Level_Quarter = "", Level_Year = "")
BU <- data.table(Status = "", Area = "Unemployment",
Rate_Estimate = "", Rate_Quarter = "", Rate_Year = "",
Level_Estimate = "", Level_Quarter = "", Level_Year = "")
BI <- data.table(Status = "", Area = "Inactivity",
Rate_Estimate = "", Rate_Quarter = "", Rate_Year = "",
Level_Estimate = "", Level_Quarter = "", Level_Year = "")
X <- rbind(BE, X[Status == "Employment",], BU, X[Status == "Unemployment",], BI, X[Status == "Inactivity",])
X <- X[, Status := NULL]
Y <- flextable(X,
col_keys = c("Area",
"Rate_Estimate", "Rate_Quarter", "Rate_Year", "Col2",
"Level_Estimate", "Level_Quarter", "Level_Year"))
Y <- autofit(Y)
Y <- colformat_num(Y, j = c("Rate_Estimate", "Rate_Quarter", "Rate_Year"), digits = 1)
Y <- colformat_num(Y,
j = c("Level_Estimate", "Level_Quarter", "Level_Year"),
big.mark = ",",
digits = 0)
Y <- empty_blanks(Y)
Y <- set_header_labels(Y,
Area = "",
Rate_Estimate = "Rate",
Rate_Quarter = "Change on quarter",
Rate_Year = "Change on year",
Level_Estimate = "Level",
Level_Quarter = "Change on quarter",
Level_Year = "Change on year")
Y <- add_header_row(Y, top = T,
values = c("", "Percentage Point", "", "Level (thousands)"),
colwidths = c(1, 3, 1, 3))
Y <- add_header_row(Y, top = F,
values = c("", "(per cent)", Timeq, Timey, "", "(thousands)", Timeq, Timey),
colwidths = c(1, 1, 1, 1, 1, 1, 1, 1))
Y <- hline_top(Y, j = c(1, 2:4, 6:8), border = fp_border(color = "#81358D", width = 2), part = "all")
Y <- hline(Y, i = 1, j = c(1, 2:4, 6:8), border = fp_border(color = "#81358D", width = 2), part = "header")
Y <- hline(Y, i = 2, border = fp_border(color = "#FFFFFF", width = 0), part = "header")
Y <- hline(Y, border = fp_border(color = "#FFFFFF", width = 0), part = "body")
Y <- hline_bottom(Y, j = c(1, 2:4, 6:8), border = fp_border(color = "#81358D", width = 2), part = "all")
Y <- bg(Y,
~ Area %in% c("Employment", "Unemployment", "Inactivity", "Scotland"),
j = c(1, 2:4, 6:8),
bg = "#EBE6E6",
part = "body")
Y <- bold(Y, part = "header")
Y <- bold(Y, ~ Area %in% c("Employment", "Unemployment", "Inactivity"))
Y <- align(Y, i = 1, align = "center", part = "header")
Y <- align(Y, i = 3, align = "right", part = "header")
Y <- align(Y, j = "Area", align = "left", part = "all")
Y <- compose(Y, i = 1, j = 1, part = "body",
value = as_paragraph("Employment",
as_chunk("1", props = fp_text(vertical.align = "superscript"))))
Y <- compose(Y, i = 4, j = 1, part = "body",
value = as_paragraph("Unemployment",
as_chunk("2", props = fp_text(vertical.align = "superscript"))))
Y <- compose(Y, i = 7, j = 1, part = "body",
value = as_paragraph("Inactivity",
as_chunk("3", props = fp_text(vertical.align = "superscript"))))
Y
**Source:** Labour Force Survey, seasonally adjusted
Rates and change over time based on unrounded data.
Level:
1,2: All persons aged 16+;
3: All persons aged 16-64.
Denominator:
1,3: all persons 16-64 years;
2: economically active 16+.
##### **Employment Rate**
wzxhzdk:2
##### **Unemployment Rate**
wzxhzdk:3
##### **Inactivity Rate**
wzxhzdk:4
##### **Country/Region Comparisons**
wzxhzdk:5
Out of the 4 UK nations, Scotland has the `r if(data.table(count(CU, "Rank"))[Rank == CU[Area == "Scotland",]$Rank, ]$freq != 1){"joint"}` `r if(floor(CU[Area == "Scotland",]$Rank) == 4){"highest"} else if (floor(CU[Area == "Scotland",]$Rank) == 3){"2nd highest"} else if (floor(CU[Area == "Scotland",]$Rank) == 2){"2nd lowest"} else if (floor(CU[Area == "Scotland",]$Rank) == 1){"lowest"}` unemployment, the `r if(data.table(count(CE, "Rank"))[Rank == CE[Area == "Scotland",]$Rank, ]$freq != 1){"joint"}` `r if(floor(CE[Area == "Scotland",]$Rank) == 4){"highest"} else if (floor(CE[Area == "Scotland",]$Rank) == 3){"2nd highest"} else if (floor(CE[Area == "Scotland",]$Rank) == 2){"2nd lowest"} else if (floor(CE[Area == "Scotland",]$Rank) == 1){"lowest"}` employment rate, and the `r if(data.table(count(CI, "Rank"))[Rank == CI[Area == "Scotland",]$Rank, ]$freq != 1){"joint"}` `r if(CI[Area == "Scotland",]$Rank == 4){"highest"} else if (CI[Area == "Scotland",]$Rank == 3){"2nd highest"} else if (CI[Area == "Scotland",]$Rank == 2){"2nd lowest"} else if (CI[Area == "Scotland",]$Rank == 1){"lowest"}` inactivity rate.
Out of the 12 regions of the UK, Scotland has the`r if(data.table(count(RU, "Rank"))[Rank == RU[Area == "Scotland",]$Rank, ]$freq != 1){"joint"}` `r if(floor(RU[Area == "Scotland",]$Rank) == 12){"highest"} else if (floor(RU[Area == "Scotland",]$Rank) == 11){"2nd highest"} else if (floor(RU[Area == "Scotland",]$Rank) == 10){"3rd highest"} else if (floor(RU[Area == "Scotland",]$Rank) %in% c(9, 8, 7)){paste(12 - floor(RU[Area == "Scotland",]$Rank), "th highest", sep = "")} else if (floor(RU[Area == "Scotland",]$Rank) %in% c(6, 5, 4)){paste(floor(RU[Area == "Scotland",]$Rank), "th lowest", sep = "")} else if (floor(RU[Area == "Scotland",]$Rank) == 3){"3rd lowest"} else if (floor(RU[Area == "Scotland",]$Rank) == 2){"2nd lowest"} else if (floor(RU[Area == "Scotland",]$Rank) == 1){"lowest"}` unemployment rate, `r if(data.table(count(RE, "Rank"))[Rank == RE[Area == "Scotland",]$Rank, ]$freq != 1){"joint"}` `r if(floor(RE[Area == "Scotland",]$Rank) == 12){"highest"} else if (floor(RE[Area == "Scotland",]$Rank) == 11){"2nd highest"} else if (floor(RE[Area == "Scotland",]$Rank) == 10){"3rd highest"} else if (floor(RE[Area == "Scotland",]$Rank) %in% c(9, 8, 7)){12 - paste(floor(RE[Area == "Scotland",]$Rank), "th highest", sep = "")} else if (floor(RE[Area == "Scotland",]$Rank) %in% c(6, 5, 4)){paste(floor(RE[Area == "Scotland",]$Rank), "th lowest", sep = "")} else if (floor(RE[Area == "Scotland",]$Rank) == 3){"3rd lowest"} else if (floor(RE[Area == "Scotland",]$Rank) == 2){"2nd lowest"} else if (floor(Re[Area == "Scotland",]$Rank) == 1){"lowest"}` employment rate, and the `r if(data.table(count(RI, "Rank"))[Rank == RI[Area == "Scotland",]$Rank, ]$freq != 1){"joint"}` `r if(floor(RI[Area == "Scotland",]$Rank) == 12){"highest"} else if (floor(RI[Area == "Scotland",]$Rank) == 11){"2nd highest"} else if (floor(RI[Area == "Scotland",]$Rank) == 10){"3rd highest"} else if (floor(RI[Area == "Scotland",]$Rank) %in% c(9, 8, 7)){paste(12 - floor(RI[Area == "Scotland",]$Rank), "th highest", sep = "")} else if (floor(RI[Area == "Scotland",]$Rank) %in% c(6, 5, 4)){paste(floor(RI[Area == "Scotland",]$Rank), "th lowest", sep = "")} else if (floor(RI[Area == "Scotland",]$Rank) == 3){"3rd lowest"} else if (floor(RI[Area == "Scotland",]$Rank) == 2){"2nd lowest"} else if (floor(RI[Area == "Scotland",]$Rank) == 1){"lowest"}` inactivity rate.
wzxhzdk:6
Women (r TimeN)
Scotland’s women’s employment rate is r if(Key_Stats[Area == "Scotland" & Status == "Employment" & Type == "Rate" & Sex == "Female" & Statistic == "Estimate",]$Value > Key_Stats[Area == "United Kingdom" & Status == "Employment" & Type == "Rate" & Sex == "Female" & Statistic == "Estimate",]$Value){"higher"}else if(Key_Stats[Area == "Scotland" & Status == "Employment" & Type == "Rate" & Sex == "Female" & Statistic == "Estimate",]$Value < Key_Stats[Area == "United Kingdom" & Status == "Employment" & Type == "Rate" & Sex == "Female" & Statistic == "Estimate",]$Value){"lower"} else {"the same"} than the UK (r Key_Stats[Area == "Scotland" & Status == "Employment" & Type == "Rate" & Sex == "Female" & Statistic == "Estimate",]$Value% v r Key_Stats[Area == "United Kingdom" & Status == "Employment" & Type == "Rate" & Sex == "Female" & Statistic == "Estimate",]$Value%), women’s unemployment rate is
r if(Key_Stats[Area == "Scotland" & Status == "Unemployment" & Type == "Rate" & Sex == "Female" & Statistic == "Estimate",]$Value > Key_Stats[Area == "United Kingdom" & Status == "Unemployment" & Type == "Rate" & Sex == "Female" & Statistic == "Estimate",]$Value){"higher"}else if(Key_Stats[Area == "Scotland" & Status == "Unemployment" & Type == "Rate" & Sex == "Female" & Statistic == "Estimate",]$Value < Key_Stats[Area == "United Kingdom" & Status == "Unemployment" & Type == "Rate" & Sex == "Female" & Statistic == "Estimate",]$Value){"lower"} else {"the same"} than the UK (r Key_Stats[Area == "Scotland" & Status == "Unemployment" & Type == "Rate" & Sex == "Female" & Statistic == "Estimate",]$Value% v r Key_Stats[Area == "United Kingdom" & Status == "Unemployment" & Type == "Rate" & Sex == "Female" & Statistic == "Estimate",]$Value%) and women’s inactivity rate is r if(Key_Stats[Area == "Scotland" & Status == "Inactivity" & Type == "Rate" & Sex == "Female" & Statistic == "Estimate",]$Value > Key_Stats[Area == "United Kingdom" & Status == "Inactivity" & Type == "Rate" & Sex == "Female" & Statistic == "Estimate",]$Value){"higher"}else if(Key_Stats[Area == "Scotland" & Status == "Inactivity" & Type == "Rate" & Sex == "Female" & Statistic == "Estimate",]$Value < Key_Stats[Area == "United Kingdom" & Status == "Inactivity" & Type == "Rate" & Sex == "Female" & Statistic == "Estimate",]$Value){"lower"} else {"the same"} than the UK (r Key_Stats[Area == "Scotland" & Status == "Inactivity" & Type == "Rate" & Sex == "Female" & Statistic == "Estimate",]$Value% v r Key_Stats[Area == "United Kingdom" & Status == "Inactivity" & Type == "Rate" & Sex == "Female" & Statistic == "Estimate",]$Value%).
wzxhzdk:7
**Source:** Labour Force Survey, seasonally adjusted
Rates and change over time based on unrounded data.
Level:
1,2: All persons aged 16+;
3.:All persons aged 16-64.
Denominator:
1,3: all persons 16-64 years;
2: economically active 16+.
Men (r TimeN)
Scotland’s men’s employment rate is r if(Key_Stats[Area == "Scotland" & Status == "Employment" & Type == "Rate" & Sex == "Male" & Statistic == "Estimate",]$Value > Key_Stats[Area == "United Kingdom" & Status == "Employment" & Type == "Rate" & Sex == "Male" & Statistic == "Estimate",]$Value){"higher"}else if(Key_Stats[Area == "Scotland" & Status == "Employment" & Type == "Rate" & Sex == "Male" & Statistic == "Estimate",]$Value < Key_Stats[Area == "United Kingdom" & Status == "Employment" & Type == "Rate" & Sex == "Male" & Statistic == "Estimate",]$Value){"lower"} else {"the same"} than the UK (r Key_Stats[Area == "Scotland" & Status == "Employment" & Type == "Rate" & Sex == "Male" & Statistic == "Estimate",]$Value% v r Key_Stats[Area == "United Kingdom" & Status == "Employment" & Type == "Rate" & Sex == "Male" & Statistic == "Estimate",]$Value%), men’s unemployment rate is r if(Key_Stats[Area == "Scotland" & Status == "Unemployment" & Type == "Rate" & Sex == "Male" & Statistic == "Estimate",]$Value > Key_Stats[Area == "United Kingdom" & Status == "Unemployment" & Type == "Rate" & Sex == "Male" & Statistic == "Estimate",]$Value){"higher"}else if(Key_Stats[Area == "Scotland" & Status == "Unemployment" & Type == "Rate" & Sex == "Male" & Statistic == "Estimate",]$Value < Key_Stats[Area == "United Kingdom" & Status == "Unemployment" & Type == "Rate" & Sex == "Male" & Statistic == "Estimate",]$Value){"lower"} else {"the same"} than the UK (r Key_Stats[Area == "Scotland" & Status == "Unemployment" & Type == "Rate" & Sex == "Male" & Statistic == "Estimate",]$Value% v r Key_Stats[Area == "United Kingdom" & Status == "Unemployment" & Type == "Rate" & Sex == "Male" & Statistic == "Estimate",]$Value%) and men’s inactivity rate is r if(Key_Stats[Area == "Scotland" & Status == "Inactivity" & Type == "Rate" & Sex == "Male" & Statistic == "Estimate",]$Value > Key_Stats[Area == "United Kingdom" & Status == "Inactivity" & Type == "Rate" & Sex == "Male" & Statistic == "Estimate",]$Value){"higher"}else if(Key_Stats[Area == "Scotland" & Status == "Inactivity" & Type == "Rate" & Sex == "Male" & Statistic == "Estimate",]$Value < Key_Stats[Area == "United Kingdom" & Status == "Inactivity" & Type == "Rate" & Sex == "Male" & Statistic == "Estimate",]$Value){"lower"} else {"the same"} than the UK (r Key_Stats[Area == "Scotland" & Status == "Inactivity" & Type == "Rate" & Sex == "Male" & Statistic == "Estimate",]$Value% v r Key_Stats[Area == "United Kingdom" & Status == "Inactivity" & Type == "Rate" & Sex == "Male" & Statistic == "Estimate",]$Value%).
wzxhzdk:8
**Source:** Labour Force Survey, seasonally adjusted
Rates and change over time based on unrounded data.
Level:
1,2: All persons aged 16+;
3.:All persons aged 16-64.
Denominator:
1,3: all persons 16-64 years;
2: economically active 16+.
About this Page
This page is updated monthly and summarises employment, unemployment and economic inactivity sourced from the Labour Force Survey for Scotland and the UK.
This release follows the ONS monthly releases of:
Regional Labour Market Statistics in the UK
Further statistics relating to Scotland's Labour Market can be found on the Scottish Government website:
Scottish Labour Market Statistics
Sources of information
All estimates are sourced from the labour force survey. The Labour Force Survey is a survey of households collected and published by the Office for Nationals Statistics. Information are obtained from a sample survey and are therefore subject to some error.
thomascrines/labourmarketreports documentation built on Jan. 28, 2021, 9:47 a.m.
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```r library(readxl) # Uses the function read_excel library(data.table) # Uses the functions data.table & dcast library(flextable) # Uses the function flextable library(stringr) # Uses the funtion str_sub library(plyr) # Uses the function count library(lubridate) # Uses the functions month & year library(officer) # Uses function fp_border library(ggplot2) # Uses the function ggplot Date <- as.Date("01/10/2020") Key_Stats <- data.table(labourmarketreports::lm_data_key_stats()) Country_Stats <- data.table(labourmarketreports::lm_data_country_stats()) Region_Stats <- data.table(labourmarketreports::lm_data_region_stats()) Time_Stats <- data.table(labourmarketreports::lm_data_time_stats()) Time_Stats[,Period := as.Date(paste("01", str_sub(Period, 5, 7), str_sub(Period, -4), sep = " "), format = "%d %b %Y")] TimeN <- paste(month(Date %m-% months(4), label = T, abbr = F), " to ", month(Date %m-% months(2), label = T, abbr = F), " ", year(Date %m-% months(2)), sep = "") TimeQ <- paste(month(Date %m-% months(7), label = T, abbr = F), " to ", month(Date %m-% months(5), label = T, abbr = F), " ", year(Date %m-% months(5)), sep = "") Timeq <- paste(month(Date %m-% months(7), label = T, abbr = T), "-", month(Date %m-% months(5), label = T, abbr = T), " ", year(Date %m-% months(5)), sep = "") Timey <- paste(month(Date %m-% months(4), label = T, abbr = T), "-", month(Date %m-% months(2), label = T, abbr = T), " ", year(Date %m-% months(14)), sep = "") Timed <- paste(month(Date %m-% months(4), label = T, abbr = T), "-", month(Date %m-% months(2), label = T, abbr = T), " 2008", sep = "") # A useful function for adding comma to data. Commaed <- function(INPUT){ OUTPUT <- prettyNum(INPUT, big.mark = ",", scientific = F) return(OUTPUT) }
# LABOUR MARKET STATISTICS
### `r paste(month(Date, label = T, abbr = F), year(Date), sep = " ")`
In r TimeN, compared to r TimeQ (the quarter before):
The employment rate was r Key_Stats[Area == "Scotland" & Status == "Employment" & Type == "Rate" & Sex == "People" & Statistic == "Estimate",]$Value per cent, r if(Key_Stats[Area == "Scotland" & Status == "Employment" & Type == "Rate" & Sex == "People" & Statistic == "Quarter",]$Value > 0){"higher than"}else if(Key_Stats[Area == "Scotland" & Status == "Employment" & Type == "Rate" & Sex == "People" & Statistic == "Quarter",]$Value < 0){"lower than"} else {"the same as"} the quarter before (r abs(Key_Stats[Area == "Scotland" & Status == "Employment" & Type == "Rate" & Sex == "People" & Statistic == "Quarter",]$Value) percentage points) and r if(Key_Stats[Area == "Scotland" & Status == "Employment" & Type == "Rate" & Sex == "People" & Statistic == "Estimate",]$Value > Key_Stats[Area == "United Kingdom" & Status == "Employment" & Type == "Rate" & Sex == "People" & Statistic == "Estimate",]$Value){"above"}else if(Key_Stats[Area == "Scotland" & Status == "Employment" & Type == "Rate" & Sex == "People" & Statistic == "Estimate",]$Value < Key_Stats[Area == "United Kingdom" & Status == "Employment" & Type == "Rate" & Sex == "People" & Statistic == "Estimate",]$Value){"below"} else {"the same as"} the UK rate of r Key_Stats[Area == "United Kingdom" & Status == "Employment" & Type == "Rate" & Sex == "People" & Statistic == "Estimate",]$Value per cent. r Commaed(Key_Stats[Area == "Scotland" & Status == "Employment" & Type == "Level" & Sex == "People" & Statistic == "Estimate",]$Value) people were in employment, r Commaed(abs(Key_Stats[Area == "Scotland" & Status == "Employment" & Type == "Level" & Sex == "People" & Statistic == "Quarter",]$Value)) r if(Key_Stats[Area == "Scotland" & Status == "Employment" & Type == "Level" & Sex == "People" & Statistic == "Quarter",]$Value > 0){"more than"}else if(Key_Stats[Area == "Scotland" & Status == "Employment" & Type == "Level" & Sex == "People" & Statistic == "Quarter",]$Value < 0){"less than"} else {"the same as"} r TimeQ.
The unemployment rate was r Key_Stats[Area == "Scotland" & Status == "Unemployment" & Type == "Rate" & Sex == "People" & Statistic == "Estimate",]$Value per cent, r if(Key_Stats[Area == "Scotland" & Status == "Unemployment" & Type == "Rate" & Sex == "People" & Statistic == "Quarter",]$Value > 0){"higher than"}else if(Key_Stats[Area == "Scotland" & Status == "Unemployment" & Type == "Rate" & Sex == "People" & Statistic == "Quarter",]$Value < 0){"lower than"} else {"the same as"} the quarter before (r abs(Key_Stats[Area == "Scotland" & Status == "Unemployment" & Type == "Rate" & Sex == "People" & Statistic == "Quarter",]$Value) percentage points) and r if(Key_Stats[Area == "Scotland" & Status == "Unemployment" & Type == "Rate" & Sex == "People" & Statistic == "Estimate",]$Value > Key_Stats[Area == "United Kingdom" & Status == "Unemployment" & Type == "Rate" & Sex == "People" & Statistic == "Estimate",]$Value){"above"}else if(Key_Stats[Area == "Scotland" & Status == "Unemployment" & Type == "Rate" & Sex == "People" & Statistic == "Estimate",]$Value < Key_Stats[Area == "United Kingdom" & Status == "Unemployment" & Type == "Rate" & Sex == "People" & Statistic == "Estimate",]$Value){"below"} else {"the same as"} the UK rate of r Key_Stats[Area == "United Kingdom" & Status == "Unemployment" & Type == "Rate" & Sex == "People" & Statistic == "Estimate",]$Value per cent. r Commaed(Key_Stats[Area == "Scotland" & Status == "Unemployment" & Type == "Level" & Sex == "People" & Statistic == "Estimate",]$Value) people were unemployed, r Commaed(abs(Key_Stats[Area == "Scotland" & Status == "Unemployment" & Type == "Level" & Sex == "People" & Statistic == "Quarter",]$Value)) r if(Key_Stats[Area == "Scotland" & Status == "Unemployment" & Type == "Level" & Sex == "People" & Statistic == "Quarter",]$Value > 0){"more than"}else if(Key_Stats[Area == "Scotland" & Status == "Unemployment" & Type == "Level" & Sex == "People" & Statistic == "Quarter",]$Value < 0){"less than"} else {"the same as"} r TimeQ.
The inactivity rate was r Key_Stats[Area == "Scotland" & Status == "Inactivity" & Type == "Rate" & Sex == "People" & Statistic == "Estimate",]$Value per cent, r if(Key_Stats[Area == "Scotland" & Status == "Inactivity" & Type == "Rate" & Sex == "People" & Statistic == "Quarter",]$Value > 0){"higher than"}else if(Key_Stats[Area == "Scotland" & Status == "Inactivity" & Type == "Rate" & Sex == "People" & Statistic == "Quarter",]$Value < 0){"lower than"} else {"the same as"} the quarter before (r abs(Key_Stats[Area == "Scotland" & Status == "Inactivity" & Type == "Rate" & Sex == "People" & Statistic == "Quarter",]$Value) percentage points) and r if(Key_Stats[Area == "Scotland" & Status == "Inactivity" & Type == "Rate" & Sex == "People" & Statistic == "Estimate",]$Value > Key_Stats[Area == "United Kingdom" & Status == "Inactivity" & Type == "Rate" & Sex == "People" & Statistic == "Estimate",]$Value){"above"}else if(Key_Stats[Area == "Scotland" & Status == "Inactivity" & Type == "Rate" & Sex == "People" & Statistic == "Estimate",]$Value < Key_Stats[Area == "United Kingdom" & Status == "Inactivity" & Type == "Rate" & Sex == "People" & Statistic == "Estimate",]$Value){"below"} else {"the same as"} the UK rate of r Key_Stats[Area == "United Kingdom" & Status == "Inactivity" & Type == "Rate" & Sex == "People" & Statistic == "Estimate",]$Value per cent. r Commaed(Key_Stats[Area == "Scotland" & Status == "Inactivity" & Type == "Level" & Sex == "People" & Statistic == "Estimate",]$Value) people were in economically inactive, r Commaed(abs(Key_Stats[Area == "Scotland" & Status == "Inactivity" & Type == "Level" & Sex == "People" & Statistic == "Quarter",]$Value)) r if(Key_Stats[Area == "Scotland" & Status == "Inactivity" & Type == "Level" & Sex == "People" & Statistic == "Quarter",]$Value > 0){"more than"}else if(Key_Stats[Area == "Scotland" & Status == "Inactivity" & Type == "Level" & Sex == "People" & Statistic == "Quarter",]$Value < 0){"less than"} else {"the same as"} r TimeQ.
Over the quarter
From r TimeQ, the unemployment rate r if(Key_Stats[Area == "Scotland" & Status == "Unemployment" & Type == "Rate" & Sex == "People" & Statistic == "Quarter",]$Value > 0){"increased to"}else if(Key_Stats[Area == "Scotland" & Status == "Unemployment" & Type == "Rate" & Sex == "People" & Statistic == "Quarter",]$Value < 0){"decreased to"} else {"remained the same at"} r Key_Stats[Area == "Scotland" & Status == "Unemployment" & Type == "Rate" & Sex == "People" & Statistic == "Estimate",]$Value per cent, the employment rate r if(Key_Stats[Area == "Scotland" & Status == "Employment" & Type == "Rate" & Sex == "People" & Statistic == "Quarter",]$Value > 0){"increased to"}else if(Key_Stats[Area == "Scotland" & Status == "Employment" & Type == "Rate" & Sex == "People" & Statistic == "Quarter",]$Value < 0){"decreased to"} else {"remained the same at"} r Key_Stats[Area == "Scotland" & Status == "Employment" & Type == "Rate" & Sex == "People" & Statistic == "Estimate",]$Value per cent, and the inactivity rate r if(Key_Stats[Area == "Scotland" & Status == "Inactivity" & Type == "Rate" & Sex == "People" & Statistic == "Quarter",]$Value > 0){"increased to"}else if(Key_Stats[Area == "Scotland" & Status == "Inactivity" & Type == "Rate" & Sex == "People" & Statistic == "Quarter",]$Value < 0){"decreased to"} else {"remained the same at"} r Key_Stats[Area == "Scotland" & Status == "Inactivity" & Type == "Rate" & Sex == "People" & Statistic == "Estimate",]$Value per cent.
Over the year
The unemployment rate r if(Key_Stats[Area == "Scotland" & Status == "Unemployment" & Type == "Rate" & Sex == "People" & Statistic == "Year",]$Value > 0){"increased"}else if(Key_Stats[Area == "Scotland" & Status == "Unemployment" & Type == "Rate" & Sex == "People" & Statistic == "Year",]$Value < 0){"decreased"} else {"remained the same "}, the employment rate r if(Key_Stats[Area == "Scotland" & Status == "Employment" & Type == "Rate" & Sex == "People" & Statistic == "Year",]$Value > 0){"increased"}else if(Key_Stats[Area == "Scotland" & Status == "Employment" & Type == "Rate" & Sex == "People" & Statistic == "Year",]$Value < 0){"decreased"} else {"remained the same"}, and the inactivity rate r if(Key_Stats[Area == "Scotland" & Status == "Inactivity" & Type == "Rate" & Sex == "People" & Statistic == "Year",]$Value > 0){"increased"}else if(Key_Stats[Area == "Scotland" & Status == "Inactivity" & Type == "Rate" & Sex == "People" & Statistic == "Year",]$Value < 0){"decreased"} else {"remained the same"}.
Compared with the UK
Scotland’s unemployment rate was r if(Key_Stats[Area == "Scotland" & Status == "Unemployment" & Type == "Rate" & Sex == "People" & Statistic == "Estimate",]$Value > Key_Stats[Area == "United Kingdom" & Status == "Unemployment" & Type == "Rate" & Sex == "People" & Statistic == "Estimate",]$Value){"higher"}else if(Key_Stats[Area == "Scotland" & Status == "Unemployment" & Type == "Rate" & Sex == "People" & Statistic == "Estimate",]$Value < Key_Stats[Area == "United Kingdom" & Status == "Unemployment" & Type == "Rate" & Sex == "People" & Statistic == "Estimate",]$Value){"lower"} else {"the same"}, the employment rate was r if(Key_Stats[Area == "Scotland" & Status == "Employment" & Type == "Rate" & Sex == "People" & Statistic == "Estimate",]$Value > Key_Stats[Area == "United Kingdom" & Status == "Employment" & Type == "Rate" & Sex == "People" & Statistic == "Estimate",]$Value){"higher"}else if(Key_Stats[Area == "Scotland" & Status == "Employment" & Type == "Rate" & Sex == "People" & Statistic == "Estimate",]$Value < Key_Stats[Area == "United Kingdom" & Status == "Employment" & Type == "Rate" & Sex == "People" & Statistic == "Estimate",]$Value){"lower"} else {"the same"} and the inactivity rate was r if(Key_Stats[Area == "Scotland" & Status == "Inactivity" & Type == "Rate" & Sex == "People" & Statistic == "Estimate",]$Value > Key_Stats[Area == "United Kingdom" & Status == "Inactivity" & Type == "Rate" & Sex == "People" & Statistic == "Estimate",]$Value){"higher"}else if(Key_Stats[Area == "Scotland" & Status == "Inactivity" & Type == "Rate" & Sex == "People" & Statistic == "Estimate",]$Value < Key_Stats[Area == "United Kingdom" & Status == "Inactivity" & Type == "Rate" & Sex == "People" & Statistic == "Estimate",]$Value){"lower"} else {"the same"}.
Labour market statistics Scotland and UK, May to July 2020
X <- data.table(dcast(Key_Stats[Sex == "People"], Status+Area~Type+Statistic, value.var = "Value")) X[,Level_Estimate := Commaed(Level_Estimate/1000)] X[,Level_Quarter := Commaed(Level_Quarter/1000)] X[,Level_Year := Commaed(Level_Year/1000)] BE <- data.table(Status = "", Area = "Employment", Rate_Estimate = "", Rate_Quarter = "", Rate_Year = "", Level_Estimate = "", Level_Quarter = "", Level_Year = "") BU <- data.table(Status = "", Area = "Unemployment", Rate_Estimate = "", Rate_Quarter = "", Rate_Year = "", Level_Estimate = "", Level_Quarter = "", Level_Year = "") BI <- data.table(Status = "", Area = "Inactivity", Rate_Estimate = "", Rate_Quarter = "", Rate_Year = "", Level_Estimate = "", Level_Quarter = "", Level_Year = "") X <- rbind(BE, X[Status == "Employment",], BU, X[Status == "Unemployment",], BI, X[Status == "Inactivity",]) X <- X[, Status := NULL] Y <- flextable(X, col_keys = c("Area", "Rate_Estimate", "Rate_Quarter", "Rate_Year", "Col2", "Level_Estimate", "Level_Quarter", "Level_Year")) Y <- autofit(Y) Y <- colformat_num(Y, j = c("Rate_Estimate", "Rate_Quarter", "Rate_Year"), digits = 1) Y <- colformat_num(Y, j = c("Level_Estimate", "Level_Quarter", "Level_Year"), big.mark = ",", digits = 0) Y <- empty_blanks(Y) Y <- set_header_labels(Y, Area = "", Rate_Estimate = "Rate", Rate_Quarter = "Change on quarter", Rate_Year = "Change on year", Level_Estimate = "Level", Level_Quarter = "Change on quarter", Level_Year = "Change on year") Y <- add_header_row(Y, top = T, values = c("", "Percentage Point", "", "Level (thousands)"), colwidths = c(1, 3, 1, 3)) Y <- add_header_row(Y, top = F, values = c("", "(per cent)", Timeq, Timey, "", "(thousands)", Timeq, Timey), colwidths = c(1, 1, 1, 1, 1, 1, 1, 1)) Y <- hline_top(Y, j = c(1, 2:4, 6:8), border = fp_border(color = "#81358D", width = 2), part = "all") Y <- hline(Y, i = 1, j = c(1, 2:4, 6:8), border = fp_border(color = "#81358D", width = 2), part = "header") Y <- hline(Y, i = 2, border = fp_border(color = "#FFFFFF", width = 0), part = "header") Y <- hline(Y, border = fp_border(color = "#FFFFFF", width = 0), part = "body") Y <- hline_bottom(Y, j = c(1, 2:4, 6:8), border = fp_border(color = "#81358D", width = 2), part = "all") Y <- bg(Y, ~ Area %in% c("Employment", "Unemployment", "Inactivity", "Scotland"), j = c(1, 2:4, 6:8), bg = "#EBE6E6", part = "body") Y <- bold(Y, part = "header") Y <- bold(Y, ~ Area %in% c("Employment", "Unemployment", "Inactivity")) Y <- align(Y, i = 1, align = "center", part = "header") Y <- align(Y, i = 3, align = "right", part = "header") Y <- align(Y, j = "Area", align = "left", part = "all") Y <- compose(Y, i = 1, j = 1, part = "body", value = as_paragraph("Employment", as_chunk("1", props = fp_text(vertical.align = "superscript")))) Y <- compose(Y, i = 4, j = 1, part = "body", value = as_paragraph("Unemployment", as_chunk("2", props = fp_text(vertical.align = "superscript")))) Y <- compose(Y, i = 7, j = 1, part = "body", value = as_paragraph("Inactivity", as_chunk("3", props = fp_text(vertical.align = "superscript")))) Y
**Source:** Labour Force Survey, seasonally adjusted
Rates and change over time based on unrounded data.
Level:
1,2: All persons aged 16+;
3: All persons aged 16-64.
Denominator:
1,3: all persons 16-64 years;
2: economically active 16+.
##### **Employment Rate**
wzxhzdk:2
##### **Unemployment Rate**
wzxhzdk:3
##### **Inactivity Rate**
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##### **Country/Region Comparisons**
wzxhzdk:5
Out of the 4 UK nations, Scotland has the `r if(data.table(count(CU, "Rank"))[Rank == CU[Area == "Scotland",]$Rank, ]$freq != 1){"joint"}` `r if(floor(CU[Area == "Scotland",]$Rank) == 4){"highest"} else if (floor(CU[Area == "Scotland",]$Rank) == 3){"2nd highest"} else if (floor(CU[Area == "Scotland",]$Rank) == 2){"2nd lowest"} else if (floor(CU[Area == "Scotland",]$Rank) == 1){"lowest"}` unemployment, the `r if(data.table(count(CE, "Rank"))[Rank == CE[Area == "Scotland",]$Rank, ]$freq != 1){"joint"}` `r if(floor(CE[Area == "Scotland",]$Rank) == 4){"highest"} else if (floor(CE[Area == "Scotland",]$Rank) == 3){"2nd highest"} else if (floor(CE[Area == "Scotland",]$Rank) == 2){"2nd lowest"} else if (floor(CE[Area == "Scotland",]$Rank) == 1){"lowest"}` employment rate, and the `r if(data.table(count(CI, "Rank"))[Rank == CI[Area == "Scotland",]$Rank, ]$freq != 1){"joint"}` `r if(CI[Area == "Scotland",]$Rank == 4){"highest"} else if (CI[Area == "Scotland",]$Rank == 3){"2nd highest"} else if (CI[Area == "Scotland",]$Rank == 2){"2nd lowest"} else if (CI[Area == "Scotland",]$Rank == 1){"lowest"}` inactivity rate.
Out of the 12 regions of the UK, Scotland has the`r if(data.table(count(RU, "Rank"))[Rank == RU[Area == "Scotland",]$Rank, ]$freq != 1){"joint"}` `r if(floor(RU[Area == "Scotland",]$Rank) == 12){"highest"} else if (floor(RU[Area == "Scotland",]$Rank) == 11){"2nd highest"} else if (floor(RU[Area == "Scotland",]$Rank) == 10){"3rd highest"} else if (floor(RU[Area == "Scotland",]$Rank) %in% c(9, 8, 7)){paste(12 - floor(RU[Area == "Scotland",]$Rank), "th highest", sep = "")} else if (floor(RU[Area == "Scotland",]$Rank) %in% c(6, 5, 4)){paste(floor(RU[Area == "Scotland",]$Rank), "th lowest", sep = "")} else if (floor(RU[Area == "Scotland",]$Rank) == 3){"3rd lowest"} else if (floor(RU[Area == "Scotland",]$Rank) == 2){"2nd lowest"} else if (floor(RU[Area == "Scotland",]$Rank) == 1){"lowest"}` unemployment rate, `r if(data.table(count(RE, "Rank"))[Rank == RE[Area == "Scotland",]$Rank, ]$freq != 1){"joint"}` `r if(floor(RE[Area == "Scotland",]$Rank) == 12){"highest"} else if (floor(RE[Area == "Scotland",]$Rank) == 11){"2nd highest"} else if (floor(RE[Area == "Scotland",]$Rank) == 10){"3rd highest"} else if (floor(RE[Area == "Scotland",]$Rank) %in% c(9, 8, 7)){12 - paste(floor(RE[Area == "Scotland",]$Rank), "th highest", sep = "")} else if (floor(RE[Area == "Scotland",]$Rank) %in% c(6, 5, 4)){paste(floor(RE[Area == "Scotland",]$Rank), "th lowest", sep = "")} else if (floor(RE[Area == "Scotland",]$Rank) == 3){"3rd lowest"} else if (floor(RE[Area == "Scotland",]$Rank) == 2){"2nd lowest"} else if (floor(Re[Area == "Scotland",]$Rank) == 1){"lowest"}` employment rate, and the `r if(data.table(count(RI, "Rank"))[Rank == RI[Area == "Scotland",]$Rank, ]$freq != 1){"joint"}` `r if(floor(RI[Area == "Scotland",]$Rank) == 12){"highest"} else if (floor(RI[Area == "Scotland",]$Rank) == 11){"2nd highest"} else if (floor(RI[Area == "Scotland",]$Rank) == 10){"3rd highest"} else if (floor(RI[Area == "Scotland",]$Rank) %in% c(9, 8, 7)){paste(12 - floor(RI[Area == "Scotland",]$Rank), "th highest", sep = "")} else if (floor(RI[Area == "Scotland",]$Rank) %in% c(6, 5, 4)){paste(floor(RI[Area == "Scotland",]$Rank), "th lowest", sep = "")} else if (floor(RI[Area == "Scotland",]$Rank) == 3){"3rd lowest"} else if (floor(RI[Area == "Scotland",]$Rank) == 2){"2nd lowest"} else if (floor(RI[Area == "Scotland",]$Rank) == 1){"lowest"}` inactivity rate.
wzxhzdk:6
Women (r TimeN)
Scotland’s women’s employment rate is r if(Key_Stats[Area == "Scotland" & Status == "Employment" & Type == "Rate" & Sex == "Female" & Statistic == "Estimate",]$Value > Key_Stats[Area == "United Kingdom" & Status == "Employment" & Type == "Rate" & Sex == "Female" & Statistic == "Estimate",]$Value){"higher"}else if(Key_Stats[Area == "Scotland" & Status == "Employment" & Type == "Rate" & Sex == "Female" & Statistic == "Estimate",]$Value < Key_Stats[Area == "United Kingdom" & Status == "Employment" & Type == "Rate" & Sex == "Female" & Statistic == "Estimate",]$Value){"lower"} else {"the same"} than the UK (r Key_Stats[Area == "Scotland" & Status == "Employment" & Type == "Rate" & Sex == "Female" & Statistic == "Estimate",]$Value% v r Key_Stats[Area == "United Kingdom" & Status == "Employment" & Type == "Rate" & Sex == "Female" & Statistic == "Estimate",]$Value%), women’s unemployment rate is
r if(Key_Stats[Area == "Scotland" & Status == "Unemployment" & Type == "Rate" & Sex == "Female" & Statistic == "Estimate",]$Value > Key_Stats[Area == "United Kingdom" & Status == "Unemployment" & Type == "Rate" & Sex == "Female" & Statistic == "Estimate",]$Value){"higher"}else if(Key_Stats[Area == "Scotland" & Status == "Unemployment" & Type == "Rate" & Sex == "Female" & Statistic == "Estimate",]$Value < Key_Stats[Area == "United Kingdom" & Status == "Unemployment" & Type == "Rate" & Sex == "Female" & Statistic == "Estimate",]$Value){"lower"} else {"the same"} than the UK (r Key_Stats[Area == "Scotland" & Status == "Unemployment" & Type == "Rate" & Sex == "Female" & Statistic == "Estimate",]$Value% v r Key_Stats[Area == "United Kingdom" & Status == "Unemployment" & Type == "Rate" & Sex == "Female" & Statistic == "Estimate",]$Value%) and women’s inactivity rate is r if(Key_Stats[Area == "Scotland" & Status == "Inactivity" & Type == "Rate" & Sex == "Female" & Statistic == "Estimate",]$Value > Key_Stats[Area == "United Kingdom" & Status == "Inactivity" & Type == "Rate" & Sex == "Female" & Statistic == "Estimate",]$Value){"higher"}else if(Key_Stats[Area == "Scotland" & Status == "Inactivity" & Type == "Rate" & Sex == "Female" & Statistic == "Estimate",]$Value < Key_Stats[Area == "United Kingdom" & Status == "Inactivity" & Type == "Rate" & Sex == "Female" & Statistic == "Estimate",]$Value){"lower"} else {"the same"} than the UK (r Key_Stats[Area == "Scotland" & Status == "Inactivity" & Type == "Rate" & Sex == "Female" & Statistic == "Estimate",]$Value% v r Key_Stats[Area == "United Kingdom" & Status == "Inactivity" & Type == "Rate" & Sex == "Female" & Statistic == "Estimate",]$Value%).
wzxhzdk:7
**Source:** Labour Force Survey, seasonally adjusted
Rates and change over time based on unrounded data.
Level:
1,2: All persons aged 16+;
3.:All persons aged 16-64.
Denominator:
1,3: all persons 16-64 years;
2: economically active 16+.
Men (r TimeN)
Scotland’s men’s employment rate is r if(Key_Stats[Area == "Scotland" & Status == "Employment" & Type == "Rate" & Sex == "Male" & Statistic == "Estimate",]$Value > Key_Stats[Area == "United Kingdom" & Status == "Employment" & Type == "Rate" & Sex == "Male" & Statistic == "Estimate",]$Value){"higher"}else if(Key_Stats[Area == "Scotland" & Status == "Employment" & Type == "Rate" & Sex == "Male" & Statistic == "Estimate",]$Value < Key_Stats[Area == "United Kingdom" & Status == "Employment" & Type == "Rate" & Sex == "Male" & Statistic == "Estimate",]$Value){"lower"} else {"the same"} than the UK (r Key_Stats[Area == "Scotland" & Status == "Employment" & Type == "Rate" & Sex == "Male" & Statistic == "Estimate",]$Value% v r Key_Stats[Area == "United Kingdom" & Status == "Employment" & Type == "Rate" & Sex == "Male" & Statistic == "Estimate",]$Value%), men’s unemployment rate is r if(Key_Stats[Area == "Scotland" & Status == "Unemployment" & Type == "Rate" & Sex == "Male" & Statistic == "Estimate",]$Value > Key_Stats[Area == "United Kingdom" & Status == "Unemployment" & Type == "Rate" & Sex == "Male" & Statistic == "Estimate",]$Value){"higher"}else if(Key_Stats[Area == "Scotland" & Status == "Unemployment" & Type == "Rate" & Sex == "Male" & Statistic == "Estimate",]$Value < Key_Stats[Area == "United Kingdom" & Status == "Unemployment" & Type == "Rate" & Sex == "Male" & Statistic == "Estimate",]$Value){"lower"} else {"the same"} than the UK (r Key_Stats[Area == "Scotland" & Status == "Unemployment" & Type == "Rate" & Sex == "Male" & Statistic == "Estimate",]$Value% v r Key_Stats[Area == "United Kingdom" & Status == "Unemployment" & Type == "Rate" & Sex == "Male" & Statistic == "Estimate",]$Value%) and men’s inactivity rate is r if(Key_Stats[Area == "Scotland" & Status == "Inactivity" & Type == "Rate" & Sex == "Male" & Statistic == "Estimate",]$Value > Key_Stats[Area == "United Kingdom" & Status == "Inactivity" & Type == "Rate" & Sex == "Male" & Statistic == "Estimate",]$Value){"higher"}else if(Key_Stats[Area == "Scotland" & Status == "Inactivity" & Type == "Rate" & Sex == "Male" & Statistic == "Estimate",]$Value < Key_Stats[Area == "United Kingdom" & Status == "Inactivity" & Type == "Rate" & Sex == "Male" & Statistic == "Estimate",]$Value){"lower"} else {"the same"} than the UK (r Key_Stats[Area == "Scotland" & Status == "Inactivity" & Type == "Rate" & Sex == "Male" & Statistic == "Estimate",]$Value% v r Key_Stats[Area == "United Kingdom" & Status == "Inactivity" & Type == "Rate" & Sex == "Male" & Statistic == "Estimate",]$Value%).
wzxhzdk:8
**Source:** Labour Force Survey, seasonally adjusted
Rates and change over time based on unrounded data.
Level:
1,2: All persons aged 16+;
3.:All persons aged 16-64.
Denominator:
1,3: all persons 16-64 years;
2: economically active 16+.
About this Page
This page is updated monthly and summarises employment, unemployment and economic inactivity sourced from the Labour Force Survey for Scotland and the UK.
This release follows the ONS monthly releases of: Regional Labour Market Statistics in the UK
Further statistics relating to Scotland's Labour Market can be found on the Scottish Government website: Scottish Labour Market Statistics
Sources of information
All estimates are sourced from the labour force survey. The Labour Force Survey is a survey of households collected and published by the Office for Nationals Statistics. Information are obtained from a sample survey and are therefore subject to some error.
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