f1 <- plot_ly(df, y = ~gdpr, x = ~FY, type = "bar",
text= ~gdpr,
name='Real GDP (producer prices)')
f2 <- plot_ly(df, y = ~rgdpgro, x = ~FY, type = "scatter",mode='markers+lines',name='Real GDP Growth',
text=~rgdpgro, color='salmon') %>%
add_lines(y=mean(df$rgdpgro),name='Average Growth rate',line=list(dash='dash',color='salmon'))
fig1 <- subplot(f1, f2,nrows=1) %>%
layout(title = '')
fig1Real GDP has kept rising through this period although at different rates and with the exception of 2019/20 when real GDP contracted at -2.37%. Real GP growth rates were highest in the year 2016/17. Although annual growth rates fluctuated, the average growth rate was 4.29%. GDP rose from NPR 1689.57 billion in FY 2012/13 to NPR 2576.25 billion in 2022/23 an increase of 52.48% compared to 2012/13. In 2022/23, however, real GDP growth rates were well below the average at 2%.
f1 <- plot_ly(df, y = ~cpi, x = ~FY, type = "bar",
text=~cpi, name='CPI') %>%
add_lines(y=mean(df$cpi),name='Average CPI',line=list(dash='dash',color='salmon'))%>%
layout(yaxis=list(title="CPI (% change y-o-y)"),
xaxis=list(title="",
zerolinecolor='white'))
f1The average year-on-year (y-o-y) change in the CPI (Consumer Price Index) was 6.52% while the maximum inflation rates of 9.94% were observed in 2015/16. The lowest inflation levels observed was 3.6% observed in 2020/21. More recently inflation has been trending near its long-term average.
f1 <- plot_ly(df, y = ~gdpn, x = ~FY, type = "bar",
text=~gdpn,
name='GDP (purchaser prices)')
f2 <- plot_ly(df, y = ~ngdpgro, x = ~FY, type = "scatter",
mode='markers+lines',
text=~ngdpgro,
name='GDP Growth') %>%
add_lines(y=mean(df$ngdpgro),name='Average',line=list(dash='dash',color='salmon'))
fig1 <- subplot(f1, f2) %>%
layout(title = '',
yaxis=list(title=''),
xaxis=list(title=''))
fig1Nominal GDP (purchaser prices) has kept rising through this period. Although annual growth rates fluctuated, the average growth rate was 11.01%. GDP rose from NPR 1949.29 billion in FY 2012/13 to NPR 5381.34 billion in 2022/23 an increase of 176.07% compared to 2012/13.
f1<-plot_ly(df,y=~pvtinvgdp, x=~FY, type='bar',name='Private',text=~pvtinvgdp) %>%
add_trace(y=~pubinvgdp, x=~FY, type='bar', name='Public', text=~pubinvgdp) %>%
layout(yaxis=list(title='%GDP'),
xaxis=list(title=''),
barmode='stack')
f1An increasing trend can be seen from 2012/13 to 2018/19 when Gross Fixed Capital Formation as a share of GDP rose from 24.73 % to 33.82 % driven mainly by rising private sector investment along with public sector investment. Since then as a share of GDP they have both declined notably to 25.21% in 2022/23.
f1<-plot_ly(df,y=~recexpgdp, x=~FY, type='bar',
name='Recurrent', text=~recexpgdp,
marker=list(color='darkblue')) %>%
add_trace(df,y=~capexpgdp,x=~FY, type='bar',
name='Capital', text=~capexpgdp,
marker=list(color='darkturquoise')) %>%
add_trace(df,y=~fingdp, x=~FY,type='bar',
name='Financing', text=~fingdp,
marker=list(color='lightblue')) %>%
add_lines(y=mean(df$expgdp),name='Exp/GDP',line=list(dash='dash',color='salmon')) %>%
layout(yaxis=list(title='% GDP'),
xaxis=list(title=''),
barmode='stack')
f2<-plot_ly(df,y=~recexpgro,x=~FY,type='scatter',mode='markers+lines',
name='Rec Exp Growth',line=list(color='darkblue'))%>%
add_trace(df,y=~capexpgro,x=~FY,type='scatter',mode='markers+lines',
name='Cap Exp Growth',line=list(color='darkturquoise'))%>%
add_trace(df,y=~fingro, x=~FY, type='bar',
name='Fin Exp Growth',marker=list(color='lightblue'))%>%
add_lines(y=mean(df$expgro),name='Exp Growth',line=list(dash='dash',color='coral'))
fig<-subplot(f1,f2)
figPublic expenditure as a share of GDP reached a maximum 2017/18 hitting its highest value of 31%. Since then, there has been a decline in expenditures as a share of GDP reaching 27% in 2022/23. Expenditures are heavily influenced by recurrent expenditures which have averaged 17% of GDP. Financing (debt service) obligations have averaged 3% of GDP and capital expenditures have averaged 5% of GDP, implying total expenditures have averaged been 25% of GDP.
The growth rates of all components of expenditures show fluctuations. The average growth rate of public expenditures of 15%, is heavily influenced by the growth rates of recurrent expenditures, financing expenditures and capital expenditures. Capital expenditures reached its zenith in 2016/17 growing at 69% and then plunging to -22% in 2019/20 implying an average growth rates of 17%. The growth in financing obligations averaged 16% but in 2022/23 reached 36%. The growth in recurrent expenditures has averaged 14% and has been considerably lower after reaching its maximum in 2016/17. In the last 5 years, the average growth has been 8%.
f1<-plot_ly(df,y=~taxgdp, x=~FY, type='bar',
name='Tax', text=~taxgdp,
marker=list(color='darkviolet')) %>%
add_trace(df,y=~ntaxgdp,x=~FY, type='bar',
name='Non-tax', text=~ntaxgdp,
marker=list(color='violet')) %>%
add_lines(y=mean(df$trevgdp),name='Mean Revenue',line=list(dash='dash',color='salmon')) %>%
layout(yaxis=list(title='% GDP'),
xaxis=list(title=''),
barmode='stack')
f2<-plot_ly(df,y=~taxgro,x=~FY,type='scatter',mode='markers+lines',
name='Tax',line=list(color='darkviolet'))%>%
add_trace(df,y=~ntaxgro,x=~FY,type='scatter',mode='markers+lines',
name='Non-tax',line=list(color='violet'))%>%
add_lines(y=mean(df$revgro),name='Rev',
line=list(dash='dash',color='deeppink'))
fig<-subplot(f1,f2)
figRevenues (tax and non-tax) grew at an average rate of 14% and averaged 19% of GDP. After rising from 2012/13 to 2018/19 and reaching 22% of GDP, there was a dip in 2019/20. There was also a sharp drop in 2022/23 to 18% of GDP as the growth in tax revenues hit a historic low.
df$defgdp<-round((df$expgdp-df$trevgdp),0)
f1<-plot_ly(df,y=~trevgdp, x=~FY, type='scatter',mode='markers+lines',
name='Rev', text=~trevgdp,
line=list(color='darkgreen')) %>%
add_trace(df,y=~expgdp,x=~FY, type='scatter', mode='markers+lines',
name='Exp', text=~expgdp,
line=list(color='darkblue')) %>%
add_trace(df,y=~defgdp,x=~FY, type='bar',name='Deficit',
marker=list(color='purple'),
text=~defgdp)%>%
add_lines(y=mean(df$defgdp),line=list(dash='dash',color='purple'),name='Deficit')%>%
add_lines(y=mean(df$expgdp),line=list(dash='dash',color='darkblue'),name='Exp')%>%
add_lines(y=mean(df$trevgdp),line=list(dash='dash',color='darkgreen'),name='Rev')
f2<-plot_ly(df,y=~revgro, x=~FY, type='scatter',mode='markers+lines',
name='Rev', text=~revgro,
line=list(color='dodgerblue')) %>%
add_trace(df,y=~expgro,x=~FY, type='scatter', mode='markers+lines',
name='Exp', text=~expgro,
line=list(color='darkblue')) %>%
add_lines(y=mean(df$revgro),line=list(dash='dash',color='dodgerblue'),name='Rev')%>%
add_lines(y=mean(df$expgro),line=list(dash='dash',color='darkblue'),name='Exp')
fig1<-subplot(f1,f2)
fig1<-fig1%>%layout(yaxis=list(title='% GDP'),
xaxis=list(title=''))
fig1The expenditure to GDP ratio has averaged around 25% while revenues as a share of GDP have averaged 19% resulting in an average deficit of 6%. However, in 2022/23 deficits increased to 9% on account of the fall in revenues. The difference between the average growth rates of expenditures and revenues is 1% which is the average growth rate of the deficit.
f1<-plot_ly(df,y=~oddgdp, x=~FY, type='bar',
name='Domestic', text=~oddgdp,
marker=list(color='magenta')) %>%
add_trace(df,y=~ofdgdp,x=~FY, type='bar',
name='Foreign', text=~ofdgdp,
marker=list(color='darkmagenta')) %>%
add_lines(y=mean(df$tpdgdp),line=list(dash='dash',color='darkturquoise'),name='Total')%>%
layout(yaxis=list(title='% GDP'),
xaxis=list(title=''),
barmode='stack')
f1Total outstanding public debt, averaged 31% of GDP. Since 2019/20 both domestic and foreign debt have increased with domestic debt beginning to comprise a significant share of total outstanding debt. As of 2022/23, total public outstanding debt had reached 42% with outstanding domestic debt rising to 21% and outstanding foreign debt rising to 22%.
f1<-plot_ly(df,y=~importgdp, x=~FY, type='scatter',name='Imports',
mode='markers+lines',line=list(color='darkgreen')) %>%
add_trace(df,y=~exportgdp,x=~FY, type='scatter',name='Exports',
mode='markers+lines',line=list(color='darkblue')) %>%
add_lines(y=mean(df$importgdp),name='Avg Imports', line=list(color='darkgreen',dash='dash')) %>%
add_lines(y=mean(df$exportgdp),name='Avg Exports', line=list(color='darkblue',dash='dash'))
f2<-plot_ly(df,y=~tbalgdp, x=~FY, type='bar',name='Trade Gap as % GDP', markers=list(color='purple')) %>%
add_lines(y=mean(df$tbalgdp),name='Avg Trade Gap', line=list(color='purple',dash='dash'))
fig1<-subplot(f1,f2)
fig1Imports have averaged 33% of GDP per year, although there are notable fluctuations on an annual basis. Exports have averaged 3% of GDP per year. Hence on average a trade gap of 30% of GDP has persisted.
df$tbalgdp<--df$tbalgdp
f1<-plot_ly(df,y=~remitgdp, x=~FY, type='scatter',
name='Remits', mode='markers+lines',
lines=list(color='darkgreen')) %>%
add_trace(df,y=~forexgdp,x=~FY, type='scatter',
name='Forex',mode='markers+lines',
lines=list(color='darkblue')) %>%
add_trace(df,y=~cabgdp, x=~FY, type='scatter',
name='CAB',mode='markers+lines',
lines=list(color='darkcyan'))%>%
add_trace(df,y=~bopgdp, x=~FY, type='scatter',
name='BOP',mode='markers+lines',
lines=list(color='darkmagenta'))%>%
add_trace(df,y=~tbalgdp, x=~FY, type='scatter',
name='Trade Gap',
mode='markers+lines',
markers=list(color='grey'))%>%
layout(yaxis=list(title='% GDP'),
xaxis=list(title=''))
f1Foreign exchange (Forex) reserves averaged 31% of GDP with some years showing higher reserves and more recently lower. Remittances have averaged around 23%. The trade gap has averaged around -30%. The current account balance was on average -2% while the balance of payments averaged 3%. These averages hide annual fluctuations. In those years where the trade gap was increasing, the current account balance, balance of payments and foreign exchange reserves also decreased. Similarly remittances also play an important role in shaping external sector outcomes. In those years where remittances were growing, foreign exchange reserves were also growing. In the most recent FY, 2012/13, there was an improvement in the trade gap and an uptick in remittances which saw the current account balance, balance of payments and foreign exchange reserves also improve.