Sub-Saharan Africa Trade
Nsovo Glory Ntuli
2026-03-14
The Importance Of Trade In Sub-Saharan Africa
#load packages
library(readxl)
library(tidyverse)
library(rstatix)
library(plm)#load dataset
P_Popular_Indicators <- read_excel("P_Popular Indicators.xlsx")
View(P_Popular_Indicators)
sub_saharan_trade <- P_Popular_Indicators#Go through the data
head(sub_saharan_trade, 5)## # A tibble: 5 × 55
## `Series Name` `Series Code` `Country Name` `Country Code` `1975 [YR1975]`
## <chr> <chr> <chr> <chr> <chr>
## 1 Population growth… SP.POP.GROW Angola AGO 3.4125582897881
## 2 Population growth… SP.POP.GROW Benin BEN 2.431774167343…
## 3 Population growth… SP.POP.GROW Botswana BWA 4.970621147575…
## 4 Population growth… SP.POP.GROW Burkina Faso BFA 2.015517348022…
## 5 Population growth… SP.POP.GROW Burundi BDI 2.480647057955…
## # ℹ 50 more variables: `1976 [YR1976]` <chr>, `1977 [YR1977]` <chr>,
## # `1978 [YR1978]` <chr>, `1979 [YR1979]` <chr>, `1980 [YR1980]` <chr>,
## # `1981 [YR1981]` <chr>, `1982 [YR1982]` <chr>, `1983 [YR1983]` <chr>,
## # `1984 [YR1984]` <chr>, `1985 [YR1985]` <chr>, `1986 [YR1986]` <chr>,
## # `1987 [YR1987]` <chr>, `1988 [YR1988]` <chr>, `1989 [YR1989]` <chr>,
## # `1990 [YR1990]` <chr>, `1991 [YR1991]` <chr>, `1992 [YR1992]` <chr>,
## # `1993 [YR1993]` <chr>, `1994 [YR1994]` <chr>, `1995 [YR1995]` <chr>, …str(sub_saharan_trade, 5)## tibble [1,349 × 55] (S3: tbl_df/tbl/data.frame)
## $ Series Name : chr [1:1349] "Population growth (annual %)" "Population growth (annual %)" "Population growth (annual %)" "Population growth (annual %)" ...
## $ Series Code : chr [1:1349] "SP.POP.GROW" "SP.POP.GROW" "SP.POP.GROW" "SP.POP.GROW" ...
## $ Country Name : chr [1:1349] "Angola" "Benin" "Botswana" "Burkina Faso" ...
## $ Country Code : chr [1:1349] "AGO" "BEN" "BWA" "BFA" ...
## $ 1975 [YR1975]: chr [1:1349] "3.4125582897881" "2.43177416734328" "4.97062114757515" "2.01551734802293" ...
## $ 1976 [YR1976]: chr [1:1349] "3.33014644361355" "2.48777341182847" "5.15744953032927" "2.1414370897999" ...
## $ 1977 [YR1977]: chr [1:1349] "3.37937480483443" "2.54383914171627" "5.27257399739864" "2.33065392660895" ...
## $ 1978 [YR1978]: chr [1:1349] "3.4768536806595" "2.59097540280167" "6.02615685121168" "2.49250252307762" ...
## $ 1979 [YR1979]: chr [1:1349] "3.50754362150738" "2.65970887557255" "5.99877937542467" "2.5897435791745" ...
## $ 1980 [YR1980]: chr [1:1349] "3.58793986406709" "2.75652901926142" "3.887230961075" "2.68393653376526" ...
## $ 1981 [YR1981]: chr [1:1349] "3.64246045254538" "2.83952465028822" "3.40911900610212" "2.73112751689755" ...
## $ 1982 [YR1982]: chr [1:1349] "3.67803500207566" "2.88709253402082" "3.96896376390478" "2.76149506900293" ...
## $ 1983 [YR1983]: chr [1:1349] "3.71606373976317" "2.90177259558927" "3.58780257751429" "2.83314070020022" ...
## $ 1984 [YR1984]: chr [1:1349] "3.70603601409782" "2.92292209235418" "3.52398684718508" "2.88633812219102" ...
## $ 1985 [YR1985]: chr [1:1349] "3.67864774172346" "2.95456137340361" "3.47195856096711" "2.88729469195998" ...
## $ 1986 [YR1986]: chr [1:1349] "3.61842556125761" "2.98299675866442" "3.39164067201576" "2.8212491022274" ...
## $ 1987 [YR1987]: chr [1:1349] "3.47374802918502" "3.01407725589672" "3.26469560707317" "2.73614817791528" ...
## $ 1988 [YR1988]: chr [1:1349] "3.40339476992955" "3.04249160427487" "2.982917184193" "2.68703394002769" ...
## $ 1989 [YR1989]: chr [1:1349] "3.41457162860406" "3.0593332626888" "2.77763043077559" "2.65499355256176" ...
## $ 1990 [YR1990]: chr [1:1349] "3.39240335400398" "3.07281526239198" "2.75036067045791" "2.6348206163474" ...
## $ 1991 [YR1991]: chr [1:1349] "3.35905461487339" "3.0846342257467" "2.72429719004269" "2.53625917894951" ...
## $ 1992 [YR1992]: chr [1:1349] "3.27414246108306" "3.08954460200877" "2.81282080899369" "2.46694750828756" ...
## $ 1993 [YR1993]: chr [1:1349] "3.19559442992564" "4.43675552850148" "2.86304423228008" "2.48654572072589" ...
## $ 1994 [YR1994]: chr [1:1349] "3.24168920160086" "3.74003044963406" "2.77143347713386" "2.52154952786592" ...
## $ 1995 [YR1995]: chr [1:1349] "3.33998873703717" "2.11424008856257" "2.62366178434921" "2.56393697670826" ...
## $ 1996 [YR1996]: chr [1:1349] "3.3816089218138" "2.6166771873555" "2.44368367103611" "2.55894111772828" ...
## $ 1997 [YR1997]: chr [1:1349] "3.39551504166387" "2.95210198459104" "2.30264662254376" "2.57799245224128" ...
## $ 1998 [YR1998]: chr [1:1349] "3.34679546644871" "3.0829668212606" "2.21541249490062" "2.76403220911076" ...
## $ 1999 [YR1999]: chr [1:1349] "3.29527663572252" "3.09369584671073" "2.16069492392568" "2.9303462196778" ...
## $ 2000 [YR2000]: chr [1:1349] "3.31228746945312" "3.07665265205002" "2.13210606291764" "2.98414422391968" ...
## $ 2001 [YR2001]: chr [1:1349] "3.35370941359163" "3.05435506649292" "2.05763865830609" "3.05099351122556" ...
## $ 2002 [YR2002]: chr [1:1349] "3.40747615681454" "3.04010801597376" "1.88080986109573" "3.09039308299234" ...
## $ 2003 [YR2003]: chr [1:1349] "3.49334277776736" "3.04920259714654" "1.75962550604573" "3.11812174449527" ...
## $ 2004 [YR2004]: chr [1:1349] "3.59445752400652" "3.06173106096489" "1.75338203751141" "3.14512608695879" ...
## $ 2005 [YR2005]: chr [1:1349] "3.64626893356444" "3.22061199704122" "1.79499163132533" "3.14680149143287" ...
## $ 2006 [YR2006]: chr [1:1349] "3.68489201610335" "3.12066888297415" "1.86517712065057" "3.12034537509581" ...
## $ 2007 [YR2007]: chr [1:1349] "3.74258021023639" "2.95509554727715" "1.9381347493906" "3.03277637197036" ...
## $ 2008 [YR2008]: chr [1:1349] "3.77828976645524" "3.01959398464251" "2.0082147581123" "2.93816522811728" ...
## $ 2009 [YR2009]: chr [1:1349] "3.80156187494836" "2.99903642214246" "2.06587846755776" "2.91916025918168" ...
## $ 2010 [YR2010]: chr [1:1349] "3.85109824897149" "2.98424162798508" "2.10943597528371" "2.92577947499021" ...
## $ 2011 [YR2011]: chr [1:1349] "3.8879459971363" "2.97782202797765" "1.96877479635498" "2.95657078370668" ...
## $ 2012 [YR2012]: chr [1:1349] "3.88358355528674" "2.96766540040992" "1.67774021157009" "3.0171707485343" ...
## $ 2013 [YR2013]: chr [1:1349] "3.84998068971631" "2.94885160135819" "1.50921875015756" "3.00083639274971" ...
## $ 2014 [YR2014]: chr [1:1349] "3.73272806660513" "2.9550187594558" "1.45441898422222" "2.9733794200807" ...
## $ 2015 [YR2015]: chr [1:1349] "3.60507162233632" "2.95391627666724" "1.42753223737773" "2.96196306547686" ...
## $ 2016 [YR2016]: chr [1:1349] "3.57644143982026" "2.92460216679542" "1.41970172619868" "2.92508251378454" ...
## $ 2017 [YR2017]: chr [1:1349] "3.54061221486272" "2.88117869411855" "1.42047647332447" "2.85291595529266" ...
## $ 2018 [YR2018]: chr [1:1349] "3.45323300897802" "2.8136418840483" "1.41898108514606" "2.69713675384815" ...
## $ 2019 [YR2019]: chr [1:1349] "3.38788422184303" "2.73538842831436" "1.42262847077389" "2.52989803984158" ...
## $ 2020 [YR2020]: chr [1:1349] "3.26795864914978" "2.66259875153288" "1.4394103578493" "2.43522959287393" ...
## $ 2021 [YR2021]: chr [1:1349] "3.18132259995095" "2.59230167510348" "1.49130106425067" "2.37649218888363" ...
## $ 2022 [YR2022]: chr [1:1349] "3.14302612044601" "2.54740926342667" "1.58848021878269" "2.30907147387783" ...
## $ 2023 [YR2023]: chr [1:1349] "3.08065530722915" "2.52274772277722" "1.64031664035479" "2.26973666268599" ...
## $ 2024 [YR2024]: chr [1:1349] "3.04419972245653" "2.46175361912328" "1.63538411022867" "2.24597728439097" ...
## $ 2025 [YR2025]: chr [1:1349] ".." ".." ".." ".." ...Clean dataset
#Clean the colomn and pivot the dataset into longer format
sub_saharan_trade <- sub_saharan_trade %>%
pivot_longer(
cols = matches("^197\\d|^198\\d|^199\\d|^200\\d|^201\\d|^202\\d"),
names_to = "Years",
values_to = "Values") %>%
mutate(Years = sub("\\s*\\[YR\\d+\\]", "", Years))
# year and value should be numeric
sub_saharan_trade <- sub_saharan_trade %>%
mutate(Values = as.numeric(Values),
Years = as.numeric(Years)) %>% # remove null values
drop_na()#rename some colomns
sub_saharan_trade <- sub_saharan_trade %>%
rename("Indicators" = `Series Name`,
"Country" = `Country Name`,
)#filter the variables that are useful for trading in the dataset
Trade_African_Sub <- sub_saharan_trade %>%
filter(Indicators %in% c("GDP growth (annual %)",
"Trade (% of GDP)",
"Foreign direct investment, net inflows (% of GDP)",
"Industry (including construction), value added (% of GDP)",
"Labor force, total",
"Inflation, consumer prices (annual %)"
)) %>% # select colomns that are needed in the dataset
select(Indicators, `Series Code`, Country, Years, Values) %>% #remove null values
drop_na()# pivot the dataset into wider format so itll be easy for analyses
Trade_African_Sub <- Trade_African_Sub %>%
pivot_wider(id_cols = c(Country, Years),
names_from = Indicators,
values_from = Values) %>% # remove the null values
drop_na()#skim the dataset
head(Trade_African_Sub, 10)## # A tibble: 10 × 8
## Country Years `GDP growth (annual %)` Industry (including construction), va…¹
## <chr> <dbl> <dbl> <dbl>
## 1 Angola 2002 13.7 51.9
## 2 Angola 2003 3.49 48.9
## 3 Angola 2004 11.4 50.3
## 4 Angola 2005 14.2 56.1
## 5 Angola 2006 11.8 54.2
## 6 Angola 2007 13.0 57.9
## 7 Angola 2008 10.8 56.2
## 8 Angola 2009 2.00 41.0
## 9 Angola 2010 5.29 48.6
## 10 Angola 2011 3.59 52.2
## # ℹ abbreviated name:
## # ¹`Industry (including construction), value added (% of GDP)`
## # ℹ 4 more variables:
## # `Foreign direct investment, net inflows (% of GDP)` <dbl>,
## # `Inflation, consumer prices (annual %)` <dbl>, `Labor force, total` <dbl>,
## # `Trade (% of GDP)` <dbl>tail(Trade_African_Sub, 10)## # A tibble: 10 × 8
## Country Years `GDP growth (annual %)` Industry (including construction), v…¹
## <chr> <dbl> <dbl> <dbl>
## 1 Tanzania 2015 6.16 24.5
## 2 Tanzania 2016 6.87 24.9
## 3 Tanzania 2017 6.73 25.0
## 4 Tanzania 2018 5.50 27.0
## 5 Tanzania 2019 5.80 28.7
## 6 Tanzania 2020 1.99 27.1
## 7 Tanzania 2021 4.32 27.3
## 8 Tanzania 2022 4.57 27.7
## 9 Tanzania 2023 5.07 28.0
## 10 Tanzania 2024 5.53 28.6
## # ℹ abbreviated name:
## # ¹`Industry (including construction), value added (% of GDP)`
## # ℹ 4 more variables:
## # `Foreign direct investment, net inflows (% of GDP)` <dbl>,
## # `Inflation, consumer prices (annual %)` <dbl>, `Labor force, total` <dbl>,
## # `Trade (% of GDP)` <dbl># rename the colomns
Trade_African_Sub <- Trade_African_Sub %>%
rename(
gdp_growth = `GDP growth (annual %)`,
industry_value = `Industry (including construction), value added (% of GDP)`,
fdi_inflows = `Foreign direct investment, net inflows (% of GDP)`,
inflation = `Inflation, consumer prices (annual %)`,
labor_force = `Labor force, total`,
trade = `Trade (% of GDP)`
)#remove duplicate
Trade_African_Sub %>%
distinct()## # A tibble: 1,123 × 8
## Country Years gdp_growth industry_value fdi_inflows inflation labor_force
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Angola 2002 13.7 51.9 10.1 109. 7311174
## 2 Angola 2003 3.49 48.9 17.6 98.2 7582160
## 3 Angola 2004 11.4 50.3 8.14 43.5 7870095
## 4 Angola 2005 14.2 56.1 -3.15 23.0 8170696
## 5 Angola 2006 11.8 54.2 -0.0643 13.3 8483347
## 6 Angola 2007 13.0 57.9 -1.22 12.3 8810729
## 7 Angola 2008 10.8 56.2 1.70 12.5 9150465
## 8 Angola 2009 2.00 41.0 2.70 13.7 9502781
## 9 Angola 2010 5.29 48.6 -3.38 14.5 9873442
## 10 Angola 2011 3.59 52.2 -2.41 13.5 10270426
## # ℹ 1,113 more rows
## # ℹ 1 more variable: trade <dbl>Data Analysis
#Summary statistics
summary_stat <- Trade_African_Sub %>%
group_by(Country) %>%
get_summary_stats(trade, gdp_growth, type = "mean_sd")
print(summary_stat, n = Inf)## # A tibble: 82 × 5
## Country variable n mean sd
## <chr> <fct> <dbl> <dbl> <dbl>
## 1 Angola trade 23 74.4 19.9
## 2 Angola gdp_growth 23 4.93 5.32
## 3 Benin trade 32 51.7 8.30
## 4 Benin gdp_growth 32 4.83 1.71
## 5 Botswana trade 35 93.7 12.2
## 6 Botswana gdp_growth 35 3.82 5.31
## 7 Burkina Faso trade 35 45.8 13.1
## 8 Burkina Faso gdp_growth 35 5.22 2.59
## 9 Cabo Verde trade 35 89.7 11.3
## 10 Cabo Verde gdp_growth 35 6.16 6.98
## 11 Cameroon trade 35 42.9 6.66
## 12 Cameroon gdp_growth 35 2.85 3.25
## 13 Central African Republic trade 16 44.6 8.05
## 14 Central African Republic gdp_growth 16 0.7 10.2
## 15 Chad trade 35 56.4 19.0
## 16 Chad gdp_growth 35 4.73 7.93
## 17 Comoros trade 24 39.1 4.09
## 18 Comoros gdp_growth 24 2.88 1.71
## 19 Congo, Dem. Rep. trade 23 55.9 17.3
## 20 Congo, Dem. Rep. gdp_growth 23 2.87 5.18
## 21 Congo, Rep. trade 33 110. 25.3
## 22 Congo, Rep. gdp_growth 33 2.01 5.04
## 23 Cote d'Ivoire trade 35 56.6 7.91
## 24 Cote d'Ivoire gdp_growth 35 3.58 4.21
## 25 Djibouti trade 11 275. 35.7
## 26 Djibouti gdp_growth 11 5.63 1.81
## 27 Equatorial Guinea trade 19 102. 23.3
## 28 Equatorial Guinea gdp_growth 19 0.217 7.96
## 29 Eswatini trade 7 87.6 2.28
## 30 Eswatini gdp_growth 7 2.98 2.02
## 31 Ethiopia trade 14 32.5 9.53
## 32 Ethiopia gdp_growth 14 8.32 1.98
## 33 Gabon trade 35 85.8 8.76
## 34 Gabon gdp_growth 35 2.27 3.46
## 35 Gambia, The trade 35 53.2 16.1
## 36 Gambia, The gdp_growth 35 3.36 3.42
## 37 Ghana trade 35 74.0 17.6
## 38 Ghana gdp_growth 35 5.20 2.42
## 39 Guinea trade 19 85.5 18.2
## 40 Guinea gdp_growth 19 5.01 2.70
## 41 Guinea-Bissau trade 35 47.2 6.27
## 42 Guinea-Bissau gdp_growth 35 3.68 5.61
## 43 Kenya trade 35 50.4 11.1
## 44 Kenya gdp_growth 35 3.75 2.28
## 45 Lesotho trade 17 144. 10.6
## 46 Lesotho gdp_growth 17 1.6 3.70
## 47 Madagascar trade 17 61.3 6.47
## 48 Madagascar gdp_growth 17 2.58 3.42
## 49 Mali trade 35 47.4 6.51
## 50 Mali gdp_growth 35 4.04 4.06
## 51 Mauritania trade 35 77.2 18.0
## 52 Mauritania gdp_growth 35 3.33 4.37
## 53 Mauritius trade 35 125. 10.6
## 54 Mauritius gdp_growth 35 4.03 3.59
## 55 Mozambique trade 20 96.1 21.1
## 56 Mozambique gdp_growth 20 5.32 2.68
## 57 Namibia trade 22 96.5 13.3
## 58 Namibia gdp_growth 22 3.40 3.94
## 59 Niger trade 35 38.6 6.98
## 60 Niger gdp_growth 35 4.18 3.61
## 61 Rwanda trade 33 39.8 13.1
## 62 Rwanda gdp_growth 33 6.68 4.84
## 63 Senegal trade 35 55.4 8.81
## 64 Senegal gdp_growth 35 3.77 2.13
## 65 Sierra Leone trade 18 39.2 10.8
## 66 Sierra Leone gdp_growth 18 4.14 5.09
## 67 South Africa trade 35 50.2 8.90
## 68 South Africa gdp_growth 35 1.98 2.40
## 69 South Sudan trade 4 63.2 8.79
## 70 South Sudan gdp_growth 4 -10.1 25.9
## 71 Sudan trade 33 22.0 8.95
## 72 Sudan gdp_growth 33 2.61 5.76
## 73 Tanzania trade 35 37.6 8.59
## 74 Tanzania gdp_growth 35 5.17 1.91
## 75 Togo trade 33 59.7 8.50
## 76 Togo gdp_growth 33 3.29 5.49
## 77 Uganda trade 31 38.0 5.71
## 78 Uganda gdp_growth 31 6.20 2.24
## 79 Zambia trade 31 68.2 8.03
## 80 Zambia gdp_growth 31 4.63 3.64
## 81 Zimbabwe trade 13 58.7 14.8
## 82 Zimbabwe gdp_growth 13 5.20 8.09# trade trend over time
trade_trends <- Trade_African_Sub %>%
group_by(Years) %>%
summarise(avg_trend = mean(trade, na.rm = TRUE))
ggplot(trade_trends, aes(x = Years, y = avg_trend))+
geom_line()+
labs(title = "Trade Openness in Sub-Saharan Africa",
x = "Year",
y = "Trade (% of GDP)")
#The year where trade was the highest
trade_trends %>%
filter(avg_trend == max(avg_trend, na.rm = TRUE))## # A tibble: 1 × 2
## Years avg_trend
## <dbl> <dbl>
## 1 2014 74.0# The year where trade was the lowest
trade_trends %>%
filter(avg_trend == min(avg_trend, na.rm = TRUE))## # A tibble: 1 × 2
## Years avg_trend
## <dbl> <dbl>
## 1 1993 51.2# correlation analysis to see which variables are correlated
cor_matrix <- Trade_African_Sub %>%
select(gdp_growth, fdi_inflows,trade, inflation, industry_value)%>%
cor(use = "complete.obs")
print(cor_matrix)## gdp_growth fdi_inflows trade inflation industry_value
## gdp_growth 1.00000000 0.10958227 0.04182410 -0.05512160 -0.05254132
## fdi_inflows 0.10958227 1.00000000 0.26464441 -0.02276895 0.09012893
## trade 0.04182410 0.26464441 1.00000000 -0.02555055 0.35597147
## inflation -0.05512160 -0.02276895 -0.02555055 1.00000000 -0.02546744
## industry_value -0.05254132 0.09012893 0.35597147 -0.02546744 1.00000000Panel data model
panel_data <- pdata.frame(Trade_African_Sub, index = c("Country", "Years"))
trade_panel_fe <- plm(gdp_growth ~ fdi_inflows + trade +inflation +industry_value,
data = panel_data, model = "within")
summary(trade_panel_fe)## Oneway (individual) effect Within Model
##
## Call:
## plm(formula = gdp_growth ~ fdi_inflows + trade + inflation +
## industry_value, data = panel_data, model = "within")
##
## Unbalanced Panel: n = 41, T = 4-35, N = 1123
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -37.81696 -1.85530 0.29444 2.20740 25.66718
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## fdi_inflows 0.06801791 0.03506412 1.9398 0.052663 .
## trade 0.05373613 0.01156471 4.6466 3.792e-06 ***
## inflation -0.00023578 0.00019560 -1.2055 0.228286
## industry_value 0.09612132 0.02665500 3.6061 0.000325 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 23308
## Residual Sum of Squares: 21946
## R-Squared: 0.058448
## Adj. R-Squared: 0.020017
## F-statistic: 16.7294 on 4 and 1078 DF, p-value: 2.5948e-13hausman test
trade_panel_re <- plm(gdp_growth ~ fdi_inflows + trade + inflation + industry_value,
data = panel_data, model = "random")
phtest(trade_panel_fe, trade_panel_re)##
## Hausman Test
##
## data: gdp_growth ~ fdi_inflows + trade + inflation + industry_value
## chisq = 54.721, df = 4, p-value = 3.717e-11
## alternative hypothesis: one model is inconsistent