Final Project

An Explanation to African Systemic Crises

Code

library(tidyverse)

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✔ readr   2.1.3      ✔ forcats 0.5.2 

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Code

library(countrycode)

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Code

library(summarytools)

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Code

library(htmlTable)
library(lessR)

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Attaching package: 'lessR'

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Code

library(RColorBrewer)
library(corrplot)

corrplot 0.92 loaded

Code

load("Final_project- Said Arslan.RData")

knitr::opts_chunk$set(echo = TRUE, warning=FALSE)

Introduction

The primary data I used for this project is extracted from Kaggle: https://www.kaggle.com/chirin/africa-economic-banking-and-systemic-crisis-data. The dataset is a derivative of Reinhart et al’s Global Crises dataset. It was downloaded from: https://www.hbs.edu/behavioral-finance-and-financial-stability/data/Pages/global.aspx Non-African countries were filtered out from the dataset. And, variables containing notes or sparse data were removed. Years with no data were also filtered out.

The dataset specifically focuses on the Banking, Debt, Financial, Inflation and Systemic Crises that occurred, from 1860 to 2014, in 13 African countries, including: Algeria, Angola, Central African Republic, Ivory Coast, Egypt, Kenya, Mauritius, Morocco, Nigeria, South Africa, Tunisia, Zambia and Zimbabwe. Each country-year observation (row) includes macroeconomic indicators of the country. I also web scraped additional import and export price index data for these countries, from Commodity of Terms of Trade Database of IMF: https://data.imf.org/?sk=2CDDCCB8-0B59-43E9-B6A0-59210D5605D2&sId=1434492942851, which I think will play essential role in my analysis.

The main purpose of the project is to understand what patterns and trends are associated with mainly systemic crises and other type of economic crises as well as how they differ by country.

Import and transform into tidy data

Read the primary dataset

Code

# ac <-
#    read_csv("C:/Users/saida/Documents/african_crises.csv",
#     col_types = cols(
#         case = col_integer(),
#         cc3 = col_factor(ordered = TRUE),
#         country = col_character(),
#         year = col_date(format = "%Y"),
#         exch_usd = col_double(),
#         gdp_weighted_default = col_double(),
#         inflation_annual_cpi = col_double(),
#         systemic_crisis = col_integer(),
#         domestic_debt_in_default = col_integer(),
#         sovereign_external_debt_default = col_integer(),
#         independence = col_integer(),
#         currency_crises = col_integer(),
#         inflation_crises = col_integer(),
#         banking_crisis = col_character()
#     )
#   )
# 
# sample_n(ac, 10) %>% htmlTable()

In the main dataset, each variable has its own column, each observation has its own row, and each value has its own cell.

The variables in the primary dataset

1. case is a factor variable that identifies the index for each country from the original, global dataset.

2. cc3 is a factor variable that identifies each country with a three letter code.

3. country is a character variable that names each country.

4. year is a date variable that identifies the year of the observation.

5. systemic_crisis is a binary variable that identifies whether a systemic crisis is observed in that year or not. Systemic crisis means the nationwide banking crisis which its impact spreads to the other sectors.

6. exch_usd is a numeric variable that gives exchange rate of USD to the country’s currency. For a country, the lower the exchange rate the more valuable its currency.

7. domestic_debt_in_default is a binary variable that shows if the government failed in repaying its domestic debts or not.

8. sovereign_external_debt_default is a binary variable that identifies if sovereign external debt default occurred or not. It is a failure of a government to honor some or all of its debt obligations to the external lenders.

9. gdp_weighted_default is a numeric variable which gives the ratio of debt to gdp when debt default is observed.

10. inflation_annual_cpi is numeric variable that shows inflation rate based on consumer price index.

11. independence is a binary variable that indicates if the country is independent or not as of observation year.

12. currency_crises is a binary variable that identifies currency crisis occured or not in the observation year.

13. inflation_crises is a binary variable that identifies inflation crisis occured or not in the observation year.

14. banking_crisis is a binary variable that identifies banking crisis occured or not in the observation year.

For consistency I will first change column names with “crises” to “crisis” as well as “inflation_annual_cpi” to “inflation_rate. Second, I will remove case number that is not needed in my analysis. Then, I will recode”banking_crisis” variable which is differently coded in the dataset than the other crisis indicator variables.

Code

cat(unique(ac$banking_crisis), sep = "\n")

1
0

Code

# ac <- ac %>% dplyr::rename("inflation_crisis" = "inflation_crises", 
#                            "currency_crisis" = "currency_crises",
#                            "inflation_rate" = "inflation_annual_cpi") %>% 
#   select(-case) %>% 
#   mutate(banking_crisis = case_when(
#          banking_crisis == "crisis" ~ 1L,
#          banking_crisis == "no_crisis" ~ 0L)
#   )

Initial analysis

I would like to supplement different economic indicators to the original dataset to better analyze the economic crises, however, there is no financial and economic data available for African countries that goes back to the 1800s or early 1900s.

That’s why, I wanted to first examine timeline of crisis in these African countries along with dates of their gaining independency.

Code

#I'll create a timeline that shows independence of each country and include point for years of crises in the plot

filt_crs <- ac %>% 
  filter(if_any(ends_with("crisis"), ~ .x == 1)) %>%
  pivot_longer(cols = ends_with("crisis"), 
               names_to = "type_of_crisis",
               values_to = "crisis_or_not") %>% 
  filter(crisis_or_not == 1)


ac %>% ggplot(aes(year, independence)) + 
  geom_line() +
  geom_area(fill = "#FFDB6D", alpha = 0.3) +
  geom_point(data = filt_crs, 
             mapping = aes(year, crisis_or_not, color=type_of_crisis)) +
  guides(color=guide_legend("Types of Crisis", override.aes = list(size = 3))) +
  scale_y_continuous(name = "Independence", breaks = c(0,1)) +
  scale_x_date(name = "Year", 
               date_breaks = "20 years", 
               date_labels = "%Y", 
               limits = c(as.Date("1860-01-01"), NA), 
               expand=c(0,0)) +
  facet_wrap(vars(country), ncol = 2) +
  theme_light()

The plots above reveal crucial situation for my further analysis. As we can see, most of the economic crisis in African countries actually took place after they gained independence and most of the countries got their independency around 1960s. Especially, all the systemic crises are after1960s. Therefore, I decided to focus on the data after 60s in analysis. Also, it appears that including additional economic indicators, which are usually start from 60s, to the main dataset would comply with this perspective

Merging Commodity Import & Export Price Index obtained by web scraping with the main dataset

In order to expand the features of the main dataset, I scrape additional data provided by the Commodity of Terms of Trade Database of International Monetary Fund (IMF). While the IMF provides many formats including excel and csv file formats but I instead wanted to invest time on scraping the rendered html using their online table viewer: https://data.imf.org/?sk=2CDDCCB8-0B59-43E9-B6A0-59210D5605D2&sId=1434492942851.

Using the viewer I selected the desired data and used inspect in Chrome to view the rendered html for the current page. Then copied the div representing the entire table in html and pasted the text into local files.

First I wrote a function to convert the html, into a data frame matching the online table viewer.

Code

imf_html_to_df <-  function (html_filename){

  # getting column labels by looking at the custom filename
  filename_pieces = str_split(html_filename, "_")[[1]]

  # read all the html in the file
  html_str = readLines(html_filename)

  # function returning the elements matched by the regex
  html_to_byRowList = function(full_file_str){
    regex_exp = "<div class=\"PPTSCellConText.*?>(.*?)<"
    result = stringr::str_match_all(full_file_str, regex_exp)
    return(result[[1]][, 2])
  }

  # all elements in the df by row
  elements = html_to_byRowList(html_str)

  columns = c("country", seq(as.integer(filename_pieces[3]), as.integer(filename_pieces[4]), by=1))
  num_columns = length(columns)
  schema = matrix(ncol = num_columns, nrow = 0)
  df = data.frame(schema)
  colnames(df) = columns

  # grab elements, ignoring column headers
  elements = elements[num_columns:length(elements)]

  # populate df iterating through element by df row
  start = 1
  while(start + num_columns - 1 <= length(elements)){
    curr = elements[start:(start+num_columns-1)]
    curr = do.call(c, list(list(curr[1]), as.double(curr[2:(length(curr))])))
    df[nrow(df) + 1,] = curr
    start = start + num_columns
  }

  # add cc3 labels used in joining later
  df$cc3 = as_factor(countrycode(df$country, origin = 'country.name', destination = 'iso3c'))
  return(df)
}

Applying the transformer function on html export price index files. They are in 20 year grouping since at most 20 columns render.

Joining them together.

Code

export_df <-  merge(x=export_df_1, y=export_df_2, by=c("cc3", "country")) %>% 
  as_tibble()
htmlTable(export_df)

	cc3	country	1965	1966	1967	1968	1969	1970	1971	1972	1973	1974	1975	1976	1977	1978	1979	1980	1981	1982	1983	1984	1985	1986	1987	1988	1989	1990	1991	1992	1993	1994	1995	1996	1997	1998	1999	2000	2001	2002	2003	2004	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014
1	AGO	Angola	40.83	40.36	40.41	40.57	40.36	39.65	40.86	40.79	40.92	61.84	58.33	60.86	62.37	59.86	78.01	81.05	77.91	76.25	73.29	71.88	71.08	52.01	55.29	48.22	51.89	57.39	52.33	52.04	49.36	48.07	48.8	52.2	51.85	44.63	51.52	62.81	59.69	60.81	64.12	70.06	80.31	86.37	89.05	99.55	82.15	90.83	100.7	100	100.87	97.7
2	CAF	Central African Republic	99.9	99.88	99.96	100.01	99.71	100.03	99.6	99.4	100.37	100.08	99.24	100.93	101.43	100.54	101.13	100.69	99.51	99.64	99.68	100.08	99.3	99.45	99.65	99.15	99.01	98.67	98.51	98.22	99.45	100.46	100.2	99.5	99.78	98.94	98.79	98.48	97.65	97.79	98.33	98.29	98.61	99.03	99.36	99.6	99.26	99.88	100.99	100	99.65	99.79
3	CIV	Côte d'Ivoire	92.84	94.73	95.44	96.71	98.11	95.79	92.49	93.04	98.01	102.28	97.72	104.17	110.29	106.54	108.21	104.6	100.47	99.06	100.58	101.51	100.17	96.25	95.4	91.42	89.6	89.87	88.03	87.2	87.5	90.65	90.77	90.29	91.5	89.98	87.91	87.44	87.49	91.67	92.44	92.11	93.66	95.28	97.34	100.54	99.01	101.95	103.6	100	100.09	101.64
4	DZA	Algeria	59.16	58.8	58.99	59.06	58.87	58.13	58.65	58.55	58.94	73.33	71.72	73.73	76.05	75.29	85.38	87.85	87.27	87.6	86.12	85.24	84.64	72.86	72.43	67.65	69.39	72.97	69.81	69.07	68.21	66.69	66.99	70.31	69.96	64.81	69.02	79.31	77.66	76.94	81.18	84.43	92.25	94.6	95.7	102.89	90.26	94.47	100.26	100	100.79	99.54
5	EGY	Egypt	94	93.9	94.01	94.04	93.97	93.8	93.9	93.87	94.12	96.92	96.34	96.73	97.02	96.77	98.5	98.79	98.43	98.18	98.05	97.87	97.61	95.37	95.83	94.96	95.4	96.05	95.29	95.1	94.74	94.72	94.93	95.31	95.24	94.19	94.97	96.5	96.11	96.17	96.73	97.34	98.36	98.98	99.21	100.11	98.43	99.43	100.37	100	100.06	99.81
6	KEN	Kenya	100.22	100.12	100.01	99.71	99.57	99.79	99.41	99.39	99.36	100.01	99.56	100.36	101.6	100.63	100.85	100.43	99.81	99.82	100.11	100.75	99.84	99.34	98.7	98.38	98.39	98.31	97.82	97.8	97.72	98.28	98.09	97.98	98.65	98.39	98.37	98.53	97.9	97.87	98.13	98.24	98.78	99.18	99.11	99.63	99.39	99.8	100.28	100	99.56	99.51
7	MAR	Morocco	100.54	100.44	100.51	100.57	100.86	100.78	100.35	100.67	101.5	101.16	100.19	100.81	101.3	101	101.24	100.72	100.51	100.65	100.48	100.45	100.35	99.87	100.1	99.92	99.6	99.66	99.15	99.22	98.96	99.06	99.11	98.96	98.85	98.88	98.81	99	98.82	98.87	98.9	98.96	99.46	100.23	100.17	99.94	99.51	100.27	100.52	100	100.46	100.61
8	MUS	Mauritius	97.15	95.56	95.72	95.51	100.29	100.92	101	105.49	107.96	116.83	110.86	105.49	101.99	100.17	100.79	109.86	103.87	97.64	97.6	93.12	91.15	94.14	94.32	96.74	98.14	97.87	94.32	94.63	95.26	96.82	97.15	95.86	95.74	94.01	91.03	93.27	93.42	91.26	91.92	92.19	94.63	98.24	94.24	95.79	99.35	100.86	102.57	100	99.07	98.73
9	NGA	Nigeria	66.78	66.47	66.48	66.61	66.52	65.89	66.69	66.67	66.91	80.46	78.3	79.9	80.88	79.48	89.46	90.94	89.31	88.41	86.96	86.22	85.73	74.55	76.57	72.01	74.34	77.75	74.58	74.38	72.67	71.82	72.35	74.6	74.33	69.47	73.99	80.97	79.2	79.92	81.96	85.25	90.65	93.67	94.98	99.91	91.51	95.86	100.42	100	100.4	98.94
10	TUN	Tunisia	90.58	90.5	90.48	90.55	90.56	90.34	90.58	90.64	90.88	95.07	94.2	94.78	95.07	94.58	97.53	97.78	97.24	97.06	96.65	96.38	96.23	92.85	93.51	92.11	92.82	93.83	92.77	92.74	92.18	91.94	92.13	92.77	92.73	91.14	92.6	94.74	94.18	94.36	94.95	95.92	97.45	98.43	98.75	99.96	97.69	98.96	100.19	100	100.21	99.89
11	ZAF	South Africa	96.79	96.63	96.54	96.57	96.78	96.89	96.58	96.6	96.93	97.68	97.73	98.01	98.18	97.8	98.14	98.06	97.76	97.81	97.45	97	96.96	96.25	96.14	96.43	96.54	96.66	96.16	96.11	95.64	95.77	96.13	96.03	95.95	95.48	95.4	95.87	96.05	95.92	96.11	97.18	97.73	98.46	98.86	99.93	98.82	100.22	100.85	100	99.97	99.46
12	ZMB	Zambia	98.46	101.68	94.67	96.79	99.69	97.81	89.51	87.97	96.39	95.77	83.13	84.89	82.63	81.15	85.89	84.96	79.64	77.2	78.65	75.88	76.44	74.76	77.5	82.54	84	82.38	79.05	78.69	75.66	78.83	82.53	77.82	78.37	73.48	72.71	75.25	73.43	73.47	75.33	82.71	86.84	99.46	100.03	98.29	92.1	100.23	103.24	100	98.75	97.31
13	ZWE	Zimbabwe	98.88	98.72	98.98	99.17	99.6	100.04	99.61	99.89	100.64	100.71	99.94	100.32	100.15	99.23	99.68	99.76	98.68	97.72	97.86	97.59	97.12	96.15	96.75	99.2	99.35	98.24	97.37	96.69	96.01	96.97	97.76	97.12	97.02	95.71	95.91	97.15	96.15	96.37	97.56	98.48	98.69	100.53	101.47	100.06	98.92	100.96	101.71	100	99.59	99.8

Lastly I need to join IMF dataframes to the main dataset, ac. There are two complexities:

1. reformatting the year columns as a single new ‘year’ column
2. join on year & cc3 code, since Ivory Coast has different names in ac and export_df.

Wrote a function to transform the IMF data frame into a joinable format:

Code

imf_df_transform <- function(df, col_name){
  
  df = df %>% arrange(cc3)

  # select each grouping of data I need
  just_cpi_df = subset(df, select = -c(cc3, country))
  cpi = unlist(just_cpi_df, use.names=FALSE)
  cc3 = unlist(subset(df, select = c(cc3)), use.names=FALSE)
  country = unlist(subset(df, select = c(country)), use.names=FALSE)
  year = colnames(just_cpi_df)

  row_repeats = length(cpi) / length(cc3)
  col_repeats = length(cpi) / length(year)
  as.date=function(x){
    year_starts = paste(as.character(x), rep("01", length(x)), rep("01", length(x)), sep="-" )
    return(as.Date(year_starts, format="%Y-%m-%d"))
  }

  # reconstruct the joinable df
  df_joinable = data.frame(
    country=rep(country, times=row_repeats),
    cc3=rep(cc3, times=row_repeats),
    year=rep(as.date(year),each=col_repeats)
  )
  df_joinable[col_name] = cpi

  # remove the country column as not needed for join
  df_joinable = subset (df_joinable, select = -country)
  return(df_joinable)
}

Defining new object to distinguish original dataset from the merged and truncated one.

Lastly, I use this function and inner join the export price data to main dataset.

Code

export_df_joinable <- imf_df_transform(export_df, "exp_pi")
htmlTable(head(export_df_joinable, n=10))

	cc3	year	exp_pi
1	DZA	1965-01-01	59.16
2	AGO	1965-01-01	40.83
3	CAF	1965-01-01	99.9
4	CIV	1965-01-01	92.84
5	EGY	1965-01-01	94
6	KEN	1965-01-01	100.22
7	MUS	1965-01-01	97.15
8	MAR	1965-01-01	100.54
9	NGA	1965-01-01	66.78
10	ZAF	1965-01-01	96.79

Code

ac_imf <- inner_join(ac_imf, export_df_joinable, by = c("cc3", "year"))

NOw repeating the same scraping process for the import price index data:

Merging two import data and then converting into a joinable format.

Code

import_df <- merge(x=import_df_1, y=import_df_2, by=c("cc3", "country"))
htmlTable(import_df)

	cc3	country	1965	1966	1967	1968	1969	1970	1971	1972	1973	1974	1975	1976	1977	1978	1979	1980	1981	1982	1983	1984	1985	1986	1987	1988	1989	1990	1991	1992	1993	1994	1995	1996	1997	1998	1999	2000	2001	2002	2003	2004	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016
1	AGO	Angola	99.53	99.5	99.47	99.35	99.47	99.49	99.34	99.39	100.11	100.8	100.07	99.89	99.83	99.78	100.04	100.11	99.83	99.47	99.52	99.43	99.22	98.67	98.63	98.73	98.78	98.76	98.48	98.51	98.46	98.59	98.63	98.67	98.56	98.34	98.19	98.36	98.31	98.48	98.59	98.78	98.93	99.26	99.44	99.93	99.48	99.74	100.15	100	99.98	99.81	99.28	99.35
2	CAF	Central African Republic	98.57	98.52	98.6	98.55	98.58	98.5	98.43	98.53	99.19	100.24	99.55	99.48	99.32	99.22	99.81	100.06	99.64	99.27	99.23	99.06	98.76	98.1	98.31	98.22	98.44	98.45	98.1	98.17	98.19	98.32	98.42	98.47	98.33	97.8	97.85	98.24	98.08	98.22	98.4	98.58	98.88	99.34	99.48	99.97	99.4	99.73	100.27	100	99.94	99.78	99.02	99.02
3	CIV	Côte d'Ivoire	92.85	92.79	92.88	92.75	92.75	92.35	92.18	92.43	93.76	97.7	96.06	96.35	96.63	96.29	98.61	99.09	98.54	97.77	97.28	96.88	96.61	93.45	94.08	92.9	93.42	94.2	93.19	93.19	92.45	92.35	92.59	93.14	92.9	91.49	92.53	94.27	93.56	93.96	94.47	95.5	97.04	98.09	98.45	100.29	98.06	99.15	100.31	100	100.28	99.74	96.74	96.52
4	DZA	Algeria	99.56	99.53	99.49	99.37	99.47	99.5	99.25	99.38	100.81	101.62	100.5	100.31	99.92	99.7	100.05	100.08	99.45	98.99	99.12	98.84	98.36	97.75	97.45	97.87	98.12	97.78	97.27	97.43	97.45	97.86	98.09	98.18	97.86	97.16	96.75	97	97.06	97.4	97.54	97.78	97.93	98.67	99.11	99.87	98.92	99.36	100.33	100	99.82	99.53	98.65	98.59
5	EGY	Egypt	97.98	97.94	97.81	97.64	97.7	97.69	97.33	97.31	98.94	100.08	98.78	98.84	98.65	98.46	99.09	98.92	98.32	98.07	98.16	97.91	97.38	96.08	95.82	95.95	96.26	96.17	95.59	95.79	95.7	95.84	96.01	96.35	96.01	95.17	95.02	95.71	95.75	96.13	96.45	96.93	97.32	98.14	98.72	99.77	98.41	99.19	100.35	100	99.94	99.52	98.09	97.86
6	KEN	Kenya	93.97	93.81	93.8	93.66	93.7	93.67	93.73	93.66	94.24	97.49	96.51	96.6	96.75	96.46	98.32	98.56	98.09	97.62	97.48	97.41	97.01	94.46	94.9	94.23	94.67	95.15	94.44	94.51	94.09	94.15	94.42	94.82	94.73	93.68	94.31	95.54	95.17	95.54	95.99	96.66	97.55	98.37	98.89	99.93	98.25	99.2	100.23	100	99.91	99.55	97.39	97.14
7	MAR	Morocco	90.21	90.09	89.98	89.88	89.9	89.76	89.72	89.76	91.14	95.89	94.32	94.62	94.76	94.21	97.1	97.45	96.52	96.03	95.79	95.42	94.77	91.16	91.32	90.47	91.29	92	90.62	90.68	90.27	90.3	90.66	91.42	91.1	89.06	89.95	92.37	91.97	92.27	93.18	94.38	95.87	97.32	98.11	100.12	96.86	98.44	100.5	100	99.91	99.2	95.59	94.92
8	MUS	Mauritius	91.84	91.73	91.58	91.34	91.48	91.19	90.77	91.31	93.8	97.96	95.44	96.18	96.53	95.96	98.87	99.19	98.23	97.35	96.81	96.27	95.74	91.79	92.5	91.25	91.69	92.56	90.99	90.97	90.19	90.28	90.5	91.04	90.75	88.78	89.78	92.05	91.32	91.89	92.74	94.05	95.72	97.16	97.91	100.27	96.99	98.91	100.9	100	100.43	99.76	95.76	95.63
9	NGA	Nigeria	99.52	99.47	99.44	99.39	99.49	99.41	99.29	99.45	100.01	100.52	100.01	100.02	99.98	99.89	100.16	100.31	100.06	99.81	99.77	99.61	99.49	99.12	99.18	99.13	99.16	99.15	98.88	98.94	98.8	98.9	98.99	99	98.9	98.62	98.61	98.86	98.75	98.85	98.94	99.08	99.34	99.69	99.73	100.05	99.68	99.92	100.19	100	100.07	99.96	99.45	99.5
10	TUN	Tunisia	91.69	91.69	91.43	91.33	91.41	91.2	90.98	91.13	92.82	96.68	94.86	95.32	95.48	95.05	97.59	97.6	96.65	96.17	96.19	95.89	95.19	91.99	92.1	91.47	92.13	92.69	91.38	91.41	91.14	91.24	91.6	92.31	91.98	90.05	90.7	92.99	92.66	92.96	93.9	94.92	96.22	97.64	98.44	100.14	96.95	98.53	100.43	100	100.02	99.41	96.02	95.32
11	ZAF	South Africa	94.08	94.02	93.98	93.98	93.99	93.88	93.94	93.94	94.31	97.02	96.36	96.67	96.87	96.55	98.33	98.49	98.1	97.82	97.6	97.45	97.25	95.06	95.47	94.75	95.2	95.75	95.05	95.02	94.64	94.58	94.76	95.16	95.09	94.01	94.82	96.14	95.78	95.99	96.42	97.16	98.08	98.74	99.1	100.03	98.47	99.36	100.2	100	100.03	99.74	97.76	97.45
12	ZMB	Zambia	94.39	94.44	94.09	94.12	94.24	94.08	93.75	93.6	94.47	97.18	95.84	96.11	96.12	95.77	97.71	97.82	97.19	96.75	96.68	96.4	96.21	93.88	94.37	93.97	94.47	94.92	94.09	94.07	93.52	93.64	94.03	94.23	94.09	92.83	93.48	94.81	94.4	94.68	95.17	96.24	97.26	98.52	98.98	99.9	98.04	99.23	100.3	100	99.93	99.49	97.38	97.02
13	ZWE	Zimbabwe	91.59	91.51	91.33	91.25	91.39	91.35	91.24	91.09	92.06	96.07	94.79	95.04	95.02	94.44	97.06	97.28	96.58	95.95	95.87	95.58	95.21	91.83	92.23	91.85	92.52	93.12	92.01	91.92	91.29	91.3	91.72	92.4	92	90.26	91.34	93.32	92.76	93.2	93.94	95.12	96.37	97.78	98.58	99.87	97.24	98.74	100.38	100	99.87	99.2	96.18	95.65

Code

import_df_joinable <- imf_df_transform(import_df, "imp_pi")
htmlTable(head(import_df_joinable, n=10))

	cc3	year	imp_pi
1	DZA	1965-01-01	99.56
2	AGO	1965-01-01	99.53
3	CAF	1965-01-01	98.57
4	CIV	1965-01-01	92.85
5	EGY	1965-01-01	97.98
6	KEN	1965-01-01	93.97
7	MUS	1965-01-01	91.84
8	MAR	1965-01-01	90.21
9	NGA	1965-01-01	99.52
10	ZAF	1965-01-01	94.08

Attaching import price data to the main data frame.

Code

ac_imf <- inner_join(ac_imf, import_df_joinable, by = c("cc3", "year"))
sample_n(ac_imf, 10) %>% htmlTable()

	cc3	country	year	systemic_crisis	exch_usd	domestic_debt_in_default	sovereign_external_debt_default	gdp_weighted_default	inflation_rate	independence	currency_crisis	inflation_crisis	banking_crisis	exp_pi	imp_pi
1	EGY	Egypt	1987-01-01	0	0.7	0	0	0	25.185	1	0	1	0	95.83	95.82
2	CIV	Ivory Coast	1984-01-01	0	486.5	0	1	0	4.281	1	0	0	0	101.51	96.88
3	DZA	Algeria	1982-01-01	0	4.6355	0	0	0	6.593	1	0	0	0	87.6	98.99
4	ZMB	Zambia	1975-01-01	0	0.0006429	0	0	0	10.16949153	1	0	0	0	83.13	95.84
5	CAF	Central African Republic	2009-01-01	0	455.3359711	0	1	0	3.522	1	0	0	0	99.26	99.4
6	KEN	Kenya	1995-01-01	1	55.9389	0	1	0	1.554	1	0	0	1	98.09	94.42
7	NGA	Nigeria	1996-01-01	0	21.8861	0	0	0	29.292	1	0	1	0	74.6	99
8	MUS	Mauritius	1966-01-01	0	4.77862	0	0	0	2.982014118	1	0	0	0	95.56	91.73
9	NGA	Nigeria	2007-01-01	0	117.968	0	0	0	5.413	1	0	0	0	94.98	99.73
10	EGY	Egypt	1999-01-01	0	3.405	0	0	0	3.745	1	0	0	0	94.97	95.02

Creating new variables

I have now export price index and import price index data included in the data set. I will also create yearly change rates for export&import indices so that I could compare them with inflation rate for my analysis. I will use lag function to calculate change rates.

Code

ac_imf <- ac_imf %>% group_by(cc3) %>% arrange(year) %>%
  mutate(change_epi= round((exp_pi-lag(exp_pi))/lag(exp_pi)*100, digits=2)) %>%
  mutate(change_ipi=round((imp_pi-lag(imp_pi))/lag(imp_pi)*100, digits=2)) %>% 
  ungroup()


ac_imf %>% group_by(country) %>% sample_n(2) %>% arrange(year) %>% htmlTable()

	cc3	country	year	systemic_crisis	exch_usd	domestic_debt_in_default	sovereign_external_debt_default	gdp_weighted_default	inflation_rate	independence	currency_crisis	inflation_crisis	banking_crisis	exp_pi	imp_pi	change_epi	change_ipi
1	MAR	Morocco	1967-01-01	0	5.0276	0	0	0	-1.126519599	1	0	0	0	100.51	89.98	0.07	-0.12
2	TUN	Tunisia	1969-01-01	0	0.52	0	0	0	2.804694188	1	0	0	0	90.56	91.41	0.01	0.09
3	ZWE	Zimbabwe	1969-01-01	0	0.00000000000000000000000000714	1	1	0	0.364644862	1	0	0	0	99.6	91.39	0.43	0.15
4	MUS	Mauritius	1971-01-01	0	5.22362	0	0	0	1.886196037	1	0	0	0	101	90.77	0.08	-0.46
5	MAR	Morocco	1973-01-01	0	3.8851	0	0	0	7.193348119	1	0	0	0	101.5	91.14	0.82	1.54
6	KEN	Kenya	1974-01-01	0	7.1429	0	0	0	16.41376144	1	0	0	0	100.01	97.49	0.65	3.45
7	KEN	Kenya	1976-01-01	0	8.27	0	0	0	9.527607326	1	1	0	0	100.36	96.6	0.8	0.09
8	DZA	Algeria	1979-01-01	0	3.7555	0	0	0	14.60957788	1	0	0	0	85.38	100.05	13.4	0.35
9	CAF	Central African Republic	1979-01-01	1	200.9998578	0	0	0	9.210526316	1	0	0	1	101.13	99.81	0.59	0.59
10	CIV	Ivory Coast	1980-01-01	0	221	0	0	0	8.81	1	0	0	0	104.6	99.09	-3.34	0.49
11	NGA	Nigeria	1980-01-01	0	0.544454729	0	0	0	9.97	1	0	0	0	90.94	100.31	1.65	0.15
12	CIV	Ivory Coast	1984-01-01	0	486.5	0	1	0	4.281	1	0	0	0	101.51	96.88	0.92	-0.41
13	CAF	Central African Republic	1986-01-01	0	322.7497717	0	1	0	2.411	1	0	0	0	99.45	98.1	0.15	-0.67
14	NGA	Nigeria	1986-01-01	0	3.316749585	0	0	0	6.25	1	1	0	0	74.55	99.12	-13.04	-0.37
15	ZMB	Zambia	1987-01-01	0	0.0077	0	1	0	47.028	1	0	1	0	77.5	94.37	3.67	0.52
16	ZWE	Zimbabwe	1989-01-01	0	0.0000000000000000000000000222	0	0	0	12.8	1	1	0	0	99.35	92.52	0.15	0.73
17	DZA	Algeria	1991-01-01	1	21.3919	0	1	0.23	25.9	1	1	1	1	69.81	97.27	-4.33	-0.52
18	EGY	Egypt	1992-01-01	0	3.33861	0	0	0	21.142	1	0	1	1	95.1	95.79	-0.2	0.21
19	ZMB	Zambia	1994-01-01	0	0.699	0	1	0	54.614	1	1	1	0	78.83	93.64	4.19	0.13
20	ZAF	South Africa	1996-01-01	0	4.6825	0	0	0	7.32	1	1	0	0	96.03	95.16	-0.1	0.42
21	AGO	Angola	2000-01-01	0	16.81784	1	1	0	325.029	1	1	1	0	62.81	98.36	21.91	0.17
22	ZAF	South Africa	2005-01-01	0	6.325	0	0	0	3.393	1	0	0	0	97.73	98.08	0.57	0.95
23	AGO	Angola	2007-01-01	0	75.023	0	0	0	12.249	1	0	0	0	89.05	99.44	3.1	0.18
24	MUS	Mauritius	2007-01-01	0	28.2162	0	0	0	8.827	1	0	0	0	94.24	97.91	-4.07	0.77
25	TUN	Tunisia	2008-01-01	0	1.3099	0	0	0	4.913	1	0	0	0	99.96	100.14	1.23	1.73
26	EGY	Egypt	2013-01-01	0	6.94	0	0	0	6.914	1	0	0	0	100.06	99.94	0.06	-0.06

Exploratory Data Analysis

Before producing summary tables, I would like to convert inflation rate and exchange rate values to two-decimal format for consistency with values of other variables in the dataset. Also, it the tables will look more tidy with this way.

Code

ac_imf <- ac_imf %>% 
  mutate(exch_usd = round(exch_usd, digits=2),
         inflation_rate = round(inflation_rate, digits=2)
         )

Summary statistics of the columns

Code

print(dfSummary(ac_imf, 
                varnumbers= FALSE, 
                plain.ascii= FALSE, 
                style= "grid", 
                graph.magnif= 0.80, 
                valid.col= TRUE),
      method= 'render', 
      table.classes= 'table-condensed')

Data Frame Summary

ac_imf

Dimensions: 629 x 17
Duplicates: 0

Variable	Stats / Values	Freqs (% of Valid)	Graph	Valid	Missing
cc3 [factor]	1. DZA 2. AGO 3. CAF 4. CIV 5. EGY 6. KEN 7. MUS 8. MAR 9. NGA 10. ZAF [ 3 others ]	47 ( 7.5% ) 35 ( 5.6% ) 50 ( 7.9% ) 50 ( 7.9% ) 50 ( 7.9% ) 50 ( 7.9% ) 50 ( 7.9% ) 50 ( 7.9% ) 49 ( 7.8% ) 49 ( 7.8% ) 149 ( 23.7% )		629 (100.0%)	0 (0.0%)
country [character]	1. Central African Republic 2. Egypt 3. Ivory Coast 4. Kenya 5. Mauritius 6. Morocco 7. Tunisia 8. Zambia 9. Nigeria 10. South Africa [ 3 others ]	50 ( 7.9% ) 50 ( 7.9% ) 50 ( 7.9% ) 50 ( 7.9% ) 50 ( 7.9% ) 50 ( 7.9% ) 50 ( 7.9% ) 50 ( 7.9% ) 49 ( 7.8% ) 49 ( 7.8% ) 131 ( 20.8% )		629 (100.0%)	0 (0.0%)
year [Date]	min : 1965-01-01 med : 1990-01-01 max : 2014-01-01 range : 49y 0m 0d	50 distinct values		629 (100.0%)	0 (0.0%)
systemic_crisis [integer]	Min : 0 Mean : 0.1 Max : 1	0 : 550 ( 87.4% ) 1 : 79 ( 12.6% )		629 (100.0%)	0 (0.0%)
exch_usd [numeric]	Mean (sd) : 63 (132.7) min ≤ med ≤ max: 0 ≤ 5.2 ≤ 744.3 IQR (CV) : 33.7 (2.1)	420 distinct values		629 (100.0%)	0 (0.0%)
domestic_debt_in_default [integer]	Min : 0 Mean : 0.1 Max : 1	0 : 587 ( 93.3% ) 1 : 42 ( 6.7% )		629 (100.0%)	0 (0.0%)
sovereign_external_debt_default [integer]	Min : 0 Mean : 0.2 Max : 1	0 : 475 ( 75.5% ) 1 : 154 ( 24.5% )		629 (100.0%)	0 (0.0%)
gdp_weighted_default [numeric]	Mean (sd) : 0 (0) min ≤ med ≤ max: 0 ≤ 0 ≤ 0.4 IQR (CV) : 0 (6)	0.00 : 607 ( 96.5% ) 0.06 : 4 ( 0.6% ) 0.13 : 6 ( 1.0% ) 0.23 : 6 ( 1.0% ) 0.36 : 5 ( 0.8% ) 0.40 : 1 ( 0.2% )		629 (100.0%)	0 (0.0%)
inflation_rate [numeric]	Mean (sd) : 35098 (876785.1) min ≤ med ≤ max: -8.8 ≤ 8 ≤ 21989695 IQR (CV) : 10.1 (25)	561 distinct values		629 (100.0%)	0 (0.0%)
independence [integer]	Min : 0 Mean : 1 Max : 1	0 : 5 ( 0.8% ) 1 : 624 ( 99.2% )		629 (100.0%)	0 (0.0%)
currency_crisis [integer]	Mean (sd) : 0.2 (0.4) min ≤ med ≤ max: 0 ≤ 0 ≤ 2 IQR (CV) : 0 (2.2)	0 : 516 ( 82.0% ) 1 : 110 ( 17.5% ) 2 : 3 ( 0.5% )		629 (100.0%)	0 (0.0%)
inflation_crisis [integer]	Min : 0 Mean : 0.2 Max : 1	0 : 531 ( 84.4% ) 1 : 98 ( 15.6% )		629 (100.0%)	0 (0.0%)
banking_crisis [integer]	Min : 0 Mean : 0.1 Max : 1	0 : 538 ( 85.5% ) 1 : 91 ( 14.5% )		629 (100.0%)	0 (0.0%)
exp_pi [numeric]	Mean (sd) : 92.3 (12.1) min ≤ med ≤ max: 39.6 ≤ 97.1 ≤ 116.8 IQR (CV) : 9 (0.1)	512 distinct values		629 (100.0%)	0 (0.0%)
imp_pi [numeric]	Mean (sd) : 96.4 (3) min ≤ med ≤ max: 88.8 ≤ 97.1 ≤ 101.6 IQR (CV) : 4.9 (0)	460 distinct values		629 (100.0%)	0 (0.0%)
change_epi [numeric]	Mean (sd) : 0.4 (4.5) min ≤ med ≤ max: -35.4 ≤ 0 ≤ 51.1 IQR (CV) : 1.4 (10.2)	375 distinct values		616 (97.9%)	13 (2.1%)
change_ipi [numeric]	Mean (sd) : 0.1 (1) min ≤ med ≤ max: -4.1 ≤ 0 ≤ 5.2 IQR (CV) : 0.8 (9.4)	265 distinct values		616 (97.9%)	13 (2.1%)

Generated by summarytools 1.0.1 (R version 4.2.1)
2022-12-23

My last form of the dataset is composed from 629 observations and 17 variables. Each observation includes country-year economic indicators. 17 variables consists of: - 2 categorical variable - 1 date variable - 7 numeric variable - 7 binary variable

Univariate analysis

Year

Dataset covers 50 years from 1965 to 2014. Since we have 13 countries, each year should have frequency of 13.

Code

ac_imf %>% mutate(Year= format(year, format="%Y")) %>% 
  group_by(Year) %>% 
  summarise(number_of_obs=n()) %>% 
  arrange(number_of_obs) %>% 
  filter(number_of_obs < 13) %>% 
  htmlTable()

	Year	number_of_obs
1	1965	11
2	1966	11
3	1967	11
4	2014	11
5	1968	12
6	1969	12
7	1981	12
8	1982	12
9	1983	12
10	1984	12
11	1985	12
12	1986	12
13	1987	12
14	1988	12
15	1989	12
16	1990	12
17	1991	12

So, only for 4 years there is no observation for 2 countries and 13 years have missing information for only 1 country. All other 37 years include data from all countries, which is good.

Country

According to the summary table, 8 of 13 countries have frequency of 50 which is as expected, however 5 countries have less than 50 frequency, which means that there is no observation for those countries for some years.

Code

ac_imf %>% ggplot(aes(x= fct_rev(fct_infreq(country)))) + 
  geom_bar(stat= 'count', alpha= 0.6, width= 0.5) + 
  xlab("Country") +
  ylab("Number of observations") +
  coord_flip() +
  theme_bw()

It looks that Angola has significant amount of missing data with no observation for 15 years. Other countries look good.

Exchange Rate

Let me compare and analyze exchange rate of the countries with box plots.

Code

ac_imf %>% ggplot() + 
  geom_boxplot(aes(x= reorder(country, exch_usd, FUN= median, decreasing= F), 
                   y= exch_usd, fill= country), show.legend= F) +
  xlab("Country") +
  ylab("Exchange Rate") +
  coord_flip() +
  theme_bw()

Median exchange rate of Central African Republic and Ivory Coast are significantly higher than exchange rates of other countries. HOwever, their box plots are not highly right skewed, they disperse evenly over their values. On the other hand, Nigeria and Angola have significantly right skewed box plots, which might be a sign that these countries experienced more currency crises than other countries. Similarly. Zimbabwe have the lowest median exchange rate but it has huge outlier points, which poses a bad evidence for Zimbabwe currency.

Currency Crises

currency_crisis variable should only consist of 0 and 1 values but When we look at the summary table, we see that there are three cells with value of 2. I think they are wrong entries and their true value is 1. So let me fix them first.

Code

ac_imf$currency_crisis[ac_imf$currency_crisis > 1] <- 1L

Now I will create a table that shows how many currency crisis observed by each country over 50 years.

Code

ac_imf %>% group_by(country) %>% 
  summarise(total_currency_crises= sum(currency_crisis)) %>% 
  arrange(desc(total_currency_crises)) %>% 
  htmlTable()

	country	total_currency_crises
1	Zimbabwe	21
2	Angola	19
3	Zambia	18
4	South Africa	12
5	Nigeria	10
6	Kenya	9
7	Algeria	6
8	Egypt	6
9	Mauritius	5
10	Tunisia	4
11	Central African Republic	1
12	Ivory Coast	1
13	Morocco	1

As I expected in previous part, Zimbabwe and Angola had currency crises more often than other countries. Even though Central African Republic and Ivory Coast have higher exchange rate generally, they experienced a currency crisis only once.

GDP weighted default

According to the summary table, gdp weighted default is 0 majority of the time. When it is not 0, it is between 0 and 1 because it gives debt default and gdp ratio of the country. I will rescale gdp weighted default values so it shows percentage of gdp and then visualize the distribution by histogram.

Code

ac_imf <- ac_imf %>% 
  mutate(gdp_weighted_default= gdp_weighted_default*100)

Instead of distribution for single gdp weighted default values, I would like to know distribution of aggregate values of 13 countries for each year.

Code

ac_imf %>% group_by(year) %>% 
  summarise(cum_gdp_wght_def=sum(gdp_weighted_default)) %>% 
  ggplot(aes(x=cum_gdp_wght_def, y = after_stat(density))) + 
  geom_histogram(bins = 10, fill="blue", alpha=0.6) + 
  scale_x_continuous(breaks=c(0,10,20,30,40,50,60)) +
  xlab("Yearly cumulative gdp weighted default") +
  theme_bw()

Also let me find out which countries and how many times had positive gdp weighted default as well as their average.

Code

ac_imf %>% filter(gdp_weighted_default > 0) %>% 
  group_by(country) %>% 
  summarise(
    number_of_years=n(), 
    mean_percentage=mean(gdp_weighted_default)) %>% 
  arrange(desc(number_of_years), desc(mean_percentage)) %>% 
  htmlTable()

	country	number_of_years	mean_percentage
1	Algeria	6	23
2	Morocco	6	13
3	South Africa	5	36
4	Tunisia	4	6
5	Egypt	1	40

The worst case is obviously for South Africa with 5 years and with average default of 36% of their gdp.

Independence

Code

ac_imf %>% filter(independence < 1) %>% 
  select(country, year, independence) %>% 
  mutate(year=format(year, format="%Y"))

# A tibble: 5 × 3
  country year  independence
  <chr>   <chr>        <int>
1 Angola  1970             0
2 Angola  1971             0
3 Angola  1972             0
4 Angola  1973             0
5 Angola  1974             0

Over the years that covered by transformed dataset, only Angola was not independent from 1965 to 1975. So, it doesn’t enable us to evaluate the effect of independence on economic crisis.

Inflation Rate

According to the summary table, inflation rate have some extreme values. Let me first find them.

Code

ac_imf %>% select(country, inflation_rate) %>% 
  group_by(country) %>% 
  summarise_at(.vars="inflation_rate", 
               .funs= list(minimum=min, 
                           median=median, 
                           maximum=max)) %>% 
  htmlTable()

	country	minimum	median	maximum
1	Algeria	0.3	6.31	31.7
2	Angola	5.78	46.71	4146.01
3	Central African Republic	-8.82	3.725	27.7
4	Egypt	0	10	25.18
5	Ivory Coast	-1.07	4.25	25.96
6	Kenya	-0.13	8.62	45.98
7	Mauritius	0.01	6.47	33.04
8	Morocco	-1.13	3.065	15.98
9	Nigeria	-4.55	11.77	72.73
10	South Africa	1.39	8.71	18.75
11	Tunisia	1.44	4.66	13.67
12	Zambia	2.52	17.08	183.26
13	Zimbabwe	-7.67	12.55	21989695.22

As we can see from the table that all countries’ median inflation rate is around normal/acceptable values, however, Angola has a very extreme maximum value (4146%). In addition to Angola, Zimbabwe has an astronomic maximum inflation rate with 21989695%. It is either a recording error or there is a very very exceptional thing happened in Zimbabwe for a year because Zimbabwe has median inflation rate of only 12%.

Code

ac_imf %>% select(year, country, inflation_rate) %>% 
  filter(inflation_rate>1000) %>% 
  htmlTable()

	year	country	inflation_rate
1	1993-01-01	Angola	1379.48
2	1995-01-01	Angola	2672.23
3	1996-01-01	Angola	4146.01
4	2006-01-01	Zimbabwe	1281.11
5	2007-01-01	Zimbabwe	66279.89
6	2008-01-01	Zimbabwe	21989695.22

So there are 6 exceptional observations with higher than 1000% inflation rate. To deal with these cases, I will fix them to 1000% for health of my further analysis.

Code

ac_imf$inflation_rate[ac_imf$inflation_rate > 1000] <- 1000

Now I can plot the distribution of inflation rates over countries.

Code

ac_imf %>% filter(inflation_rate < 100) %>% ggplot() + 
  geom_violin(aes(x= reorder(country, inflation_rate, FUN= median, decreasing= F), 
                   y= inflation_rate, fill= country), show.legend= F, alpha=0.8) +
  xlab("Country") +
  ylab("Inflation Rate") +
  coord_flip() +
  theme_bw()

Bivariate and multivarite analysis

Economic crises

My main focus of analysis is systemic crisis. According to the summary table, 79 times systemic crisis were observed over 50 years. In this perspective, the first thing I would like to know is how the distribution of systemic crises is between countries in terms of percentage of occurrence. Also I expect that the countries that had systemic crises also experienced banking crises. According to the summary table, 91 times banking crises observed. To verify my hypothesis I will visualise countries’ systemic crises and banking crises experiences over pie charts

Code

systemic_pie <- ac_imf %>% filter(systemic_crisis==1) %>% select(country)
PieChart(country, 
         hole = 0, 
         values = "%", 
         data = systemic_pie, 
         fill = brewer.pal(n = 12, name = "Paired"), 
         main = "Systemic Crises", 
         quiet = TRUE)

Code

banking_pie <- ac_imf %>% filter(banking_crisis==1) %>% select(country)
PieChart(country, 
         hole = 0, 
         values = "%", 
         data = banking_pie, 
         fill = "rainbow", 
         main = "Banking Crises", 
         quiet = TRUE)

As I expected, countries which had systemic crises more often experienced banking crises more often as well such as Central African Republic, Zimbabwe and Nigeria, These pie charts above also revealed another thing that all of the 13 countries observed banking crises at least once whereas only 10 countries observed systemic crises. So, there are 3 countries that had banking crisis but no systemic crisis.

Let me find out which countries they are.

Code

ac_imf %>% 
  group_by(country) %>% 
  summarize(total_sys_crs=sum(systemic_crisis)) %>% 
  arrange(total_sys_crs) %>% 
  ungroup() %>% 
  slice_head(n=3) %>% 
  htmlTable()

	country	total_sys_crs
1	Angola	0
2	Mauritius	0
3	South Africa	0

These countries did not have systemic crises and also they had banking crises much less frequent than other countries, which makes me think that banking crises are related to systemic crises. To verify that, I will plot timelines for systemic crises and banking crises for each country.

Code

ac_imf %>% select(country, year, systemic_crisis, banking_crisis) %>% 
  pivot_longer(cols= ends_with("crisis"), 
               names_to= "Type_of_crisis", 
               values_to= "occurance") %>% 
  ggplot() +
  geom_jitter(aes(year, occurance, color= Type_of_crisis, shape=Type_of_crisis), width = 0.2, height = 0.2) + 
  geom_line(aes(year, occurance, color= Type_of_crisis)) +
  scale_y_continuous(name = "Occurance of Crisis", breaks = c(0,1)) +
  facet_wrap(vars(country), ncol = 2) +
  theme_minimal()

Plots for Central African Republic, Zimbabwe, Kenya, Algeria, Ivory Coast, Tunusia, Nigeria, Zambia and Morocca pose big evidence that there is very close relationship between banking crises and systemic crises. Indeed, it looks that whenever a country had systemic crisis it had banking crisis as well. The reverse of that not be true though. From the plots above, we see that Angola and South Africa had banking crises for few times but they did not experience systemic crises meanwhile. However,

Besides that, the plots above show that banking and sytemic crises became more frequent after 1980s. I will now investigate it and also include currency and inflation crises to my analysis.

Code

ac_imf %>% select(year, systemic_crisis, banking_crisis, inflation_crisis, currency_crisis) %>% 
  pivot_longer(cols= ends_with("crisis"), 
               names_to= "Type_of_crisis", 
               values_to= "occurance") %>% 
  group_by(year, Type_of_crisis) %>%
  summarize(Number_of_Occurance=sum(occurance), .groups= "drop") %>%
  ggplot(aes(year, Number_of_Occurance)) +
  geom_col(aes(fill=Type_of_crisis), color="white", width= 700, alpha=0.8)

So, the graph tells us that beginning with late 1980s more African countries started to experience crises more often. This trend lasted until 2000s. 1995 is the peak year in terms of overall number of economics crises. Also, systemic crises took place more often during this period.

Domestic & Sovereign external debt in defaults

Now I would like to visualize domestic debt in default and sovereign external debt in default cases varied over 50 year in African countries.

Code

ac_imf %>% mutate(Year= as.numeric(format(year, format="%Y"))) %>% 
  select(Year, contains("debt")) %>% 
  pivot_longer(cols= contains("debt"), 
               names_to= "Debts_in_Deafult", 
               values_to= "occurance") %>% 
  filter(occurance==1) %>% ggplot(aes(x= Year, fill= Debts_in_Deafult)) + 
  geom_histogram(position= "dodge2", alpha = 0.8, bins=15) +
  geom_freqpoly(aes(Year, color=Debts_in_Deafult), size=1, binwidth = 3.5) +
  scale_x_continuous(breaks=c(1960,1970,1980,1990,2000,2010)) +
  theme_bw()

The graph shows that domestic debt in default cases have almost the same trend as economic crises we analyzed in previous part. They started to become more often after mid 80s but they lasted in 2000s as well. On the other hand, there is no specific trend for domestic debt in default cases. So, we can result that in years of systemic crises, debts in default are prevalent as well.

Export & Import Price Index and Inflation Rate

Let me first visualize how export and import price indices change in African countries over 50 years.

Code

ac_imf %>%  dplyr::rename("Export_Price_Index"="exp_pi", "Import_Price_Index"="imp_pi") %>% 
  pivot_longer(cols= c("Export_Price_Index", "Import_Price_Index"), 
                        names_to= "Price_Index",
                        values_to= "Rate") %>% 
  ggplot(aes(x=year, y=Rate)) +
  geom_point(aes(color=Price_Index)) +
  geom_line(aes(color=Price_Index)) +
  guides(color= guide_legend(title = NULL, override.aes= list(size= 3))) +
  scale_y_continuous(name= "Export & Import Price Indices") +
  facet_wrap(vars(country), ncol = 2) +
  theme_minimal()

From the plots, we can see that Algeria, Angola, Nigeria, Zambia had significant drop, Ivory Coast and Tunusia had moderate drop in their export price indices during 80s and 90s, which might cause trade deficits in these countries and trigger other macroeconomic issues.

I also expect that when the gap between Import and Export price indices of a country gets larger, it is more probable that the inflation rate in that country goes up. I would like to test my hypothesis in these African countries. To get better a fit and visualization, I will put an upper limit on inflation rate.

Code

ac_imf %>% filter(inflation_rate <500) %>% mutate(Import_Export= imp_pi/exp_pi*100) %>% filter(Import_Export <200) %>% 
  ggplot(aes(x=Import_Export, y=inflation_rate)) +
  xlab("Import and Export Price Index Ratio") +
  ylab("Inflation Rate") +
  geom_point() +
  geom_smooth(se= FALSE) + 
  theme_bw()

Correlation Between Economic Indicators

Lastly I would like to explore the correlation between the economic indicators of African countries I got in the dataset.

Code

ac_imf %>% select(systemic_crisis, 
                  banking_crisis, 
                  inflation_crisis, 
                  currency_crisis, 
                  inflation_rate, 
                  exch_usd, 
                  domestic_debt_in_default, 
                  sovereign_external_debt_default, 
                  exp_pi, 
                  imp_pi,
                  gdp_weighted_default) %>% 
  dplyr::rename("Systemic Crisis"="systemic_crisis",
                "Banking Crisis"="banking_crisis",
                "Inflation Crisis"="inflation_crisis",
                "Currency Crisis"="currency_crisis",
                "Inflation Rate"="inflation_rate",
                "Exchange Rate"="exch_usd",
                "Domestic Debt"="domestic_debt_in_default",
                "External Debt"="sovereign_external_debt_default",
                "GDP Weighted Default"="gdp_weighted_default",
                "Export Price Index"="exp_pi",
                "Import Price Index"="imp_pi"
                ) %>% 
  cor() %>% 
  corrplot(method="circle", tl.srt= 60)

Thus, the biggest positive correlation is between the banking crises and systemic crises , which is I already pointed in my previous analysis. There is significant positive relationship between inflation rate and domestic debt in default. A new exploration I got form the correlation plot is that there is significant negative correlation between export price index and inflation crisis. It makes sense because these African countries depend on their exports in terms of revenue. If export price index gets lower, they face trade deficits and, as a result of that, their economies get into trouble.

Conclusion and Considerations for Further Analysis

Economic crises in 13 African countries covered in the dataset basically started after 1960s. Additionally, there is not much data collected before 1960s regarding these countries. Since it would not be reliable to interpret the scarce economic indicators older than 50-60 years old, I focused on the data collected after 1960s in my project. The core finding of the project is the strong relationsihp between the systemic crisis and banking crisis. If a country experienced a banking crisis, then their economy got usually through a systemic crisis as well. Most often, they happened at the same time. Besides that, these African countries were exposed to economic crises more often in the period between mid-1980s and 2000s. A possible explanation for that is as following: decline in global commodity prices such as Oil and petroleum products and other agricultural commodities in 1980s impacted the trade/external accounts of these countries because they are heavily depended on import revenues of natural resources. Due to limited diversification of their exports and having low capital inflows, fluctuations in global commodity prices had strong negative impact on their domestic economy.

My considerations for further studies would be:

• More variables should be included such as interest rates, trade deficit, government deficit, growth rate etc.
• More countries should be included to enable to make more reliable interpretations.

References:

• Kaggle dataset: https://www.kaggle.com/datasets/chirin/africa-economic-banking-and-systemic-crisis-data
• Commodity of Terms of Trade: Commodity Import/Export Price Index, Individual Commodities Weighted by Ratio of Imports to GDP Historical, Annual (1965-present), Fixed Weights, Index (2012=100): https://data.imf.org/?sk=2CDDCCB8-0B59-43E9-B6A0-59210D5605D2&sId=1434492942851
• R for Data Science: https://r4ds.had.co.nz/index.html
• https://r-graph-gallery.com/index.html
• ggplot2: Elegant Graphics for Data Analysis: https://ggplot2-book.org/
• DACSS 601 Class resources: https://github.com/DACSS/601_Fall_2022