## Does the US Federal Reserve Bank Fulfill its Mandate?
In Keynes’ economics and in fiscal policy, a countries’ central bank is responsible for controlling the funds rate, which in turn has influence on the unemployment rate and the inflation rate. Therefore, using this tool, the US Federal Reserve (short: FED), aims to control both at the same time. However, given economic principles, it appears to be difficult to do both at the same time, as inflation is intrically linked with unemployment. During times of economic boom, inflation may rise, coupled with low unemployment; during economic hardships, the inverse is true. Therefore, it is necessary to ask: does the FED actually maintain this goal?
To answer this question, data from the last 25 years for the FED funds rate, unemployment rate, and inflation rate, was extracted from the FED’s database, FRED, and from the Beaureau of Labor Statistics, respectively.
Below, the API for both is used to extract said data, and subsequently visualizations were created in Excel to showcase whether this goal was reached.
#----Call packages----library(rjson)
Warning: package 'rjson' was built under R version 4.3.3
library(blsAPI)library(httr)
Warning: package 'httr' was built under R version 4.3.2
library(dplyr)
Warning: package 'dplyr' was built under R version 4.3.2
Attaching package: 'dplyr'
The following objects are masked from 'package:stats':
filter, lag
The following objects are masked from 'package:base':
intersect, setdiff, setequal, union
#----BLS----#Function to interact quickly with BLS APIbsl_payload =function(seriesid, startyear, endyear, nrow) { payload <-list('seriesid'=c(seriesid),'startyear'= startyear,'endyear'= endyear) response <-blsAPI(payload, 2) json <-fromJSON(response) df <-data.frame(matrix(unlist(json$Results$series[[1]]$data), nrow =length(json$Results$series[[1]]$data), byrow =TRUE))colnames(df) <-c("year", "period", "periodName", "value")return(df)} cpi_1999_2008 =bsl_payload('CUUR0000SA0', 1999, 2008) #Can only pull 10 years at a time it appearscpi_2009_2018 =bsl_payload('CUUR0000SA0', 2009, 2018)cpi_2018_2023 =bsl_payload('CUUR0000SA0', 2018, 2023)ue_1999_2008 =bsl_payload('LNS14000000', 1999, 2008)ue_2009_2018 =bsl_payload('LNS14000000', 2009, 2018)ue_2018_2023=bsl_payload('LNS14000000', 2018, 2023)cpi =bind_rows(cpi_1999_2008, cpi_2009_2018, cpi_2018_2023) #Combine all CPI tablesue =bind_rows(ue_1999_2008, ue_2009_2018, ue_2018_2023) #Combine all UE tables#----FED Rate----#API call for FRED to get fed-funds-ratefred_res =GET("https://api.stlouisfed.org/fred/series/observations",query =list(series_id ='FEDFUNDS',api_key ='1384298aeab27419f2e59acb18554185',file_type ='json',frequency ='m',observation_start ='1999-02-15',observation_end ='2024-02-15'))fred_json =content(fred_res, "text") fred_json =fromJSON(fred_json) #Extract JSON from responsefed =data.frame(matrix(unlist(fred_json$observations), nrow =length(fred_json$observations), byrow =TRUE)) #Extract data tablecolnames(fed) =c('x', 'y', 'Date', 'FFR') #Rename columnfed =select(fed, c(Date, FFR)) #Remove unneeded columns#----Handle Dates in data frames----ue$Date =paste(ue$periodName, ue$year, sep =' ')cpi$Date =paste(cpi$periodName, cpi$year, sep =' ')#----Save Data----#Locally save data frames as CSV#write.csv(cpi, 'cpi.csv')#write.csv(fed, 'fed.csv')#write.csv(ue, 'ue.csv')
The code above returns three separate datasets for the three measures. Using Excel, the below graphs were plotted.
Time Series Analysis
Investigating the graph above, it becomes apparent that the unemployment rate (blue) and the inflation rate (orange) work often times in contrast to each other. When one goes up, the other goes down. Additionally, and more importantly, this is seemingly aligned with the funds rate. There appears to be a slight lag of unemployment and inflation rates moving, and the FED adjusting the funds rate to correct for it. For example, after the COVID-19 pandemic, inflation has risen substantially, and while unemployment was high to begin with, the FED needed to regain control of both, and subsequently started to raise rates in 2021. Reactive to the fiscal measurements. Similarly, during the 2008 financial crisis the opposite happened, a deflation was occurring with very high unemployment, leading the FED to scale down the funds rate to 0, regaining control.
The time series shows that all three rates have a predictable, cyclical pattern.
Correlation Analysis
The scatterplot above depicts the correlation between unemployment and inflation rate for each month of the last 25-years. The correlation appears to be statistically significant (r = -0.424, p < 0.001) and shows a negative correlation, such that when unemployment rate is high, inflation rate is low. However, there is also apparent a bottom floor for the unemployment rate at about 4%, suggesting that the FED has never been able to push below this.
Overall, putting the insights of both plots together, it appears that the FED is able to maintain control of both, unemployment rate and inflation rate, therefore serving the mandate given by congress. However, it does appear that the FED has substantially more control over the inflation rate than the unemployment rate. This makes sense, because the inflation rate reflects spending behavior, and if it is more expensive to borrow money (e.g., funds rate is high), spending will decrease. Unemployment rate seems to be in lag of inflation rate.
