Pakistan economy is suffering from twin deficit-current account deficit (CAD) and fiscal deficit. Current deficit consists of trade deficit, primary income and secondary income balnce. CAD is mainly due to Balance of trade (gap between export and import) . I am plotting the data over time for import and export in the following section to have an overview when this trade gap started growing.

library(readxl)
setwd("C:/Users/Dr.Zahid/OneDrive - Higher Education Commission")
XM<-read_xlsx("Export_Import.xlsx",col_names = TRUE)
Import_major_categories<-read_xlsx("Import_categories.xlsx",col_names=TRUE)
XM<-data.frame(XM)  ## As data are mixed nature (date and numbers)

library(ggplot2)
##ggplot(XM, aes(Date)) + 
  #geom_line(aes(y = Exports, colour = "Exports")) + 
  #geom_line(aes(y = Imports, colour = "Imports"))

 p<-ggplot(XM, aes(Date)) + 
  geom_line(aes(y = Exports, colour = "Exports")) + 
  geom_line(aes(y = Imports, colour = "Imports"))
p+ggtitle("Export and Import 1986-2017 (Million of USD")

One can notice from this plot that trade deficit from 1986 till 2004 was almost constant. After 2005, increase in import was far greater than export. This has caused serious implications for CAD and successive governments have been under great pressure to overcome the issue. Question is which of the import items mainly led to increase the import bill. For this I am plotting here import over the last decade by major categories. ### Import by Major Categories

Import_major_categories<-read_xlsx("Import_categories.xlsx",col_names=TRUE)
M_cat<-data.frame(Import_major_categories)

str(M_cat)  ## brief overview of basic data structure
## 'data.frame':    17 obs. of  18 variables:
##  $ Date                           : POSIXct, format: "1999-01-01" "2000-01-01" ...
##  $ petroleum                      : num  145238 195611 172578 179317 182322 ...
##  $ other.imports                  : num  97145 106554 118831 128470 189287 ...
##  $ chemicals                      : num  72797 80106 82263 90954 119684 ...
##  $ machinery_nonelectric          : num  66207 88551 96833 119260 140906 ...
##  $ transport_equipment            : num  29202 24918 30587 39984 87374 ...
##  $ vegetables_oil                 : num  22577 22050 25252 35663 39112 ...
##  $ food                           : num  19639 7987 11636 9290 6338 ...
##  $ iron_steel_manufactures        : num  18864 18739 24633 28813 35942 ...
##  $ Drugs_medicine                 : num  13429 13965 13988 12964 15812 ...
##  $ tea                            : num  10895 12030 9611 10095 11078 ...
##  $ chemical_fertilizer            : num  10227 9842 10904 14068 16405 ...
##  $ electrical.goods               : num  8026 7695 7835 12661 14682 ...
##  $ Dyes.and.colors                : num  6950 7346 7775 8419 9218 ...
##  $ paper..paper.board...stationery: num  6352 7646 8608 10457 12138 ...
##  $ non.ferous.metals              : num  5016 5964 6757 8430 10544 ...
##  $ art.silk.yarn                  : num  2460 3509 5054 5375 6793 ...
##  $ sugar.refined                  : num  769 14488 1485 153 188 ...
ggplot(M_cat, aes(Date)) + 
 geom_line(aes(y = M_cat$petroleum, colour = "petroleum")) + 
geom_line(aes(y = M_cat$other.imports, colour = "other.imports"))+
geom_line(aes(y=M_cat$chemicals,colour="chemicals"))+
geom_line(aes(y=M_cat$machinery_nonelectric,colour="machinery_nonelectric"))+
 geom_line(aes(y=M_cat$transport_equipment,colour="transport_equiment"))+
  geom_line(aes(y=M_cat$vegetables_oil,colour="vegetables_oil"))+
  geom_line(aes(y=M_cat$food,colour="food"))

One can add more groups but 4 main categories make 70-75% of total import. There is not any major change in import pattern other than couple of major categories, therefore, I shall now plot only top 4 import categories.

ggplot(M_cat, aes(Date)) + 
 geom_line(aes(y = M_cat$petroleum, colour = "petroleum")) + 
geom_line(aes(y = M_cat$other.imports, colour = "other.imports"))+
geom_line(aes(y=M_cat$chemicals,colour="chemicals"))+
geom_line(aes(y=M_cat$machinery_nonelectric,colour="machinery_nonelectric"))

This graph indicates that petroleum import bill has significantly increased after 2006 onward. “Other imports”" share has also increased significantly over time. Increase in oil import bill after 2006 can be due to increase in demand for oil consumption but mainly it was exactly the time when oil prices in the world hit very high point. One can notice the dip at the end of this purple line 2015-16 when world oil prices showed reverse trend. Oil prices which are not in our control are main driver of our import. Therefore, one needs to work out serously on this issue since any unusual jump in oil prices internationally can put Pakistan economy under stress and can detrack any government for doing any long term reforms.