Unemployment

Abstract

This essay represents the vision of Divyosmi Goswami, a data reporter, who has sought to develop a research agenda in response to the massive Indian unemployment crisis that has been evoked by the COVID-19 pandemic. The research agenda includes exploring how this unemployment crisis may differ from previous unemployment periods; predicting the trajectory of unemployment in different states of India; How better/worse the scenario shall be in the coming years; Ask some of the most intriguing questions.

Keywords: Unemployment, Precarious work, Work-family interface, Inequality, Youth unemployment, COVID-19, Unemployment intervention, Unemployment in India, Poverty.

Small note from the researcher

The following are the results of my research. It is disheartening to see that though the world is advancing into greater and wider ranges of technologies, people still suffer from hunger, poverty, gender inequality and unemployment. Children suffer from child labour. I am making this for the people to know and for people to drive changes. Let us see how the economy will be scaling up, we need to prepare ourselves accordingly. If my report does not match with any other report or government records then i have nothing to do than showing regret. Thank you.

Library

library(tidyverse)

## Warning: package 'tidyverse' was built under R version 4.0.5

## -- Attaching packages --------------------------------------- tidyverse 1.3.0 --

## v ggplot2 3.3.3     v purrr   0.3.4
## v tibble  3.1.0     v dplyr   1.0.5
## v tidyr   1.1.3     v stringr 1.4.0
## v readr   1.4.0     v forcats 0.5.1

## Warning: package 'ggplot2' was built under R version 4.0.5

## Warning: package 'tibble' was built under R version 4.0.5

## Warning: package 'tidyr' was built under R version 4.0.5

## Warning: package 'readr' was built under R version 4.0.5

## Warning: package 'purrr' was built under R version 4.0.5

## Warning: package 'dplyr' was built under R version 4.0.5

## Warning: package 'stringr' was built under R version 4.0.5

## Warning: package 'forcats' was built under R version 4.0.5

## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

library(waffle)

## Warning: package 'waffle' was built under R version 4.0.5

library(mlbench)

## Warning: package 'mlbench' was built under R version 4.0.5

library(ggthemes)

## Warning: package 'ggthemes' was built under R version 4.0.5

library(lubridate)

## Warning: package 'lubridate' was built under R version 4.0.5

## 
## Attaching package: 'lubridate'

## The following objects are masked from 'package:base':
## 
##     date, intersect, setdiff, union

Load Data

urt_uemp = read.csv(url("https://bit.ly/haesunemp"))
head(urt_uemp)

##           Region        Date Frequency Estimated.Unemployment.Rate....
## 1 Andhra Pradesh  31-01-2016         M                           17.91
## 2 Andhra Pradesh  29-02-2016         M                            9.98
## 3 Andhra Pradesh  31-03-2016         M                           10.72
## 4 Andhra Pradesh  30-04-2016         M                            5.51
## 5 Andhra Pradesh  31-05-2016         M                            6.29
## 6 Andhra Pradesh  30-06-2016         M                            4.51
##   Estimated.Employed Estimated.Labour.Participation.Rate....
## 1           20626291                                   62.67
## 2           19334918                                   53.50
## 3           19646970                                   54.73
## 4           18929583                                   49.76
## 5           21163390                                   56.02
## 6           18431598                                   47.81

Data Cleaning

urt_uemp$unemp.rate = urt_uemp[,4]
urt_uemp$employed = urt_uemp[,5]
urt_uemp$part.rate = urt_uemp[,6]
modeld = urt_uemp[,c(1,2,7,8,9)]
viz_data = urt_uemp[,c(1,2,7,8,9)]
rm(urt_uemp)

Visualization for good, Crucial Truths

p <- function(..., sep='') {
    paste(..., sep=sep, collapse=sep)
}
ind = c(67,134,201,268,335,401,468,535,602,668,735,802,869,936,1004,1046,1113,1180,1247,1314,1346,1412,1480,1526,1593,1660,1727)#hard-coded
c = 1
head(viz_data)

##           Region        Date unemp.rate employed part.rate
## 1 Andhra Pradesh  31-01-2016      17.91 20626291     62.67
## 2 Andhra Pradesh  29-02-2016       9.98 19334918     53.50
## 3 Andhra Pradesh  31-03-2016      10.72 19646970     54.73
## 4 Andhra Pradesh  30-04-2016       5.51 18929583     49.76
## 5 Andhra Pradesh  31-05-2016       6.29 21163390     56.02
## 6 Andhra Pradesh  30-06-2016       4.51 18431598     47.81

for(eind in ind){
    if(c == 1){
        reg = viz_data[c(1:eind),]
        c = c+1
    }
    else{
        reg = viz_data[c(((ind[[c-1]]+1):eind)),]
        c = c+1
    }
    p1 = ggplot(data = reg, aes(x = Date, y = unemp.rate,group=1))+
        geom_line()+
        geom_point()+
        labs(x = "Date",
             y = "Estimated Unemployment Rate",
             title = p("Estimated Unemployment Rate of ",reg[1,1]),
             subtitle = p(reg[1,1],"; 2016-21"))
    p2 = ggplot(data = reg, aes(x = Date, y = employed,group=1))+
        geom_point()+
        geom_line()+
        labs(x = "Date",
             y = "Estimated employed",
             title = p("Estimated employed of ",reg[1,1]),
             subtitle = p(reg[1,1]," ; 2016-21"))
    p3 = ggplot(data = reg, aes(x = Date, y = part.rate,group=2))+
        geom_line()+
        geom_point()+
        labs(x = "Date",
             y = "Estimated participation rate",
             title = p("Estimated participaton rate of ",reg[1,1]),
             subtitle = p(reg[1,1]," ; 2016-21"))
    print(p1+theme_dark()+theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 1)))
    print(p2+theme_dark()+theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 1)))
    print(p3+theme_dark()+theme(axis.text.x = element_text(angle = 90, hjust = 1,vjust = 1)))
    print(head(reg))
    rm(reg)
}

##           Region        Date unemp.rate employed part.rate
## 1 Andhra Pradesh  31-01-2016      17.91 20626291     62.67
## 2 Andhra Pradesh  29-02-2016       9.98 19334918     53.50
## 3 Andhra Pradesh  31-03-2016      10.72 19646970     54.73
## 4 Andhra Pradesh  30-04-2016       5.51 18929583     49.76
## 5 Andhra Pradesh  31-05-2016       6.29 21163390     56.02
## 6 Andhra Pradesh  30-06-2016       4.51 18431598     47.81

##    Region        Date unemp.rate employed part.rate
## 68  Assam  31-01-2016       5.89 11359949     51.38
## 69  Assam  29-02-2016      12.68 11099822     54.01
## 70  Assam  31-03-2016       4.31 10185318     45.13
## 71  Assam  30-04-2016       0.65 10034857     42.74
## 72  Assam  31-05-2016       3.78 10742464     47.15
## 73  Assam  30-06-2016      10.23 10091196     47.38

##     Region        Date unemp.rate employed part.rate
## 135  Bihar  31-01-2016       4.36 28057185     41.53
## 136  Bihar  29-02-2016       6.71 30498497     46.19
## 137  Bihar  31-03-2016       3.46 27221171     39.75
## 138  Bihar  30-04-2016       6.38 29658522     44.57
## 139  Bihar  31-05-2016      14.02 28478123     46.50
## 140  Bihar  30-06-2016       9.85 30522107     47.43

##           Region        Date unemp.rate employed part.rate
## 202 Chhattisgarh  31-01-2016       2.38  8062186     41.83
## 203 Chhattisgarh  29-02-2016       4.90  8160104     43.37
## 204 Chhattisgarh  31-03-2016       3.07  8964452     46.64
## 205 Chhattisgarh  30-04-2016       3.09  8310606     43.15
## 206 Chhattisgarh  31-05-2016       5.41  8584637     45.56
## 207 Chhattisgarh  30-06-2016       4.60  8525708     44.77

##     Region        Date unemp.rate employed part.rate
## 269  Delhi  31-01-2016      10.61  5482637     44.00
## 270  Delhi  29-02-2016      10.84  4829996     38.78
## 271  Delhi  31-03-2016      11.05  5513829     44.26
## 272  Delhi  30-04-2016       6.30  5954845     45.28
## 273  Delhi  31-05-2016      17.30  5523948     47.48
## 274  Delhi  30-06-2016      15.68  5029744     42.30

##     Region        Date unemp.rate employed part.rate
## 336    Goa  31-01-2016      12.31   597685     56.13
## 337    Goa  29-02-2016      10.75   582685     53.71
## 338    Goa  31-03-2016       5.11   615303     53.29
## 339    Goa  31-05-2016      16.04   590215     57.64
## 340    Goa  30-06-2016      13.91   586814     55.83
## 341    Goa  31-07-2016       5.25   623983     53.88

##      Region        Date unemp.rate employed part.rate
## 402 Gujarat  31-01-2016       3.44 21488124     46.35
## 403 Gujarat  29-02-2016       4.12 21120270     45.80
## 404 Gujarat  31-03-2016       4.07 21774339     47.10
## 405 Gujarat  30-04-2016       3.30 22470289     48.13
## 406 Gujarat  31-05-2016       2.63 23071104     48.98
## 407 Gujarat  30-06-2016       2.92 21713869     46.15

##      Region        Date unemp.rate employed part.rate
## 469 Haryana  31-01-2016      14.82  7990833     46.62
## 470 Haryana  29-02-2016      11.83  7984203     44.91
## 471 Haryana  31-03-2016      10.40  8061205     44.53
## 472 Haryana  30-04-2016      13.57  7932826     45.33
## 473 Haryana  31-05-2016      10.78  8213227     45.36
## 474 Haryana  30-06-2016      11.67  7666571     42.68

##               Region        Date unemp.rate employed part.rate
## 536 Himachal Pradesh  31-01-2016       3.16  2252583     41.60
## 537 Himachal Pradesh  29-02-2016       3.32  2220379     41.01
## 538 Himachal Pradesh  31-03-2016       5.54  2335451     44.08
## 539 Himachal Pradesh  30-04-2016       4.24  1982465     36.85
## 540 Himachal Pradesh  31-05-2016       4.11  2279919     42.25
## 541 Himachal Pradesh  30-06-2016       1.38  2270199     40.84

##              Region        Date unemp.rate employed part.rate
## 603 Jammu & Kashmir  31-01-2016      12.31  3377251     41.10
## 604 Jammu & Kashmir  29-02-2016      17.97  3197946     41.52
## 605 Jammu & Kashmir  31-03-2016      13.80  3393838     41.84
## 606 Jammu & Kashmir  30-04-2016      10.49  3720119     44.08
## 607 Jammu & Kashmir  31-05-2016      22.02  3330402     45.20
## 608 Jammu & Kashmir  30-06-2016      21.30  3301976     44.31

##        Region        Date unemp.rate employed part.rate
## 669 Jharkhand  31-01-2016      13.05  8064211     38.75
## 670 Jharkhand  29-02-2016      11.13  9382717     44.02
## 671 Jharkhand  31-03-2016       8.97  8987924     41.07
## 672 Jharkhand  30-04-2016       7.97 10355816     46.71
## 673 Jharkhand  31-05-2016      11.16  8368601     39.01
## 674 Jharkhand  30-06-2016       9.64  9580041     43.82

##        Region        Date unemp.rate employed part.rate
## 736 Karnataka  31-01-2016       6.38 23141471     49.05
## 737 Karnataka  29-02-2016       5.27 22647222     47.35
## 738 Karnataka  31-03-2016       3.26 22105452     45.17
## 739 Karnataka  30-04-2016       4.94 22235918     46.15
## 740 Karnataka  31-05-2016      11.49 23357728     51.96
## 741 Karnataka  30-06-2016       3.07 23269877     47.18

##     Region        Date unemp.rate employed part.rate
## 803 Kerala  31-01-2016      18.40 10188518     46.30
## 804 Kerala  29-02-2016      23.53 10223651     49.54
## 805 Kerala  31-03-2016      22.91  9605152     46.12
## 806 Kerala  30-04-2016      18.38 10078897     45.67
## 807 Kerala  31-05-2016      11.96 10320728     43.32
## 808 Kerala  30-06-2016      10.34 10135488     41.73

##             Region        Date unemp.rate employed part.rate
## 870 Madhya Pradesh  31-01-2016       4.07 25565126     48.60
## 871 Madhya Pradesh  29-02-2016       4.36 21964907     41.79
## 872 Madhya Pradesh  31-03-2016       6.62 20672491     40.20
## 873 Madhya Pradesh  30-04-2016       8.73 21505008     42.69
## 874 Madhya Pradesh  31-05-2016       6.63 25480320     49.33
## 875 Madhya Pradesh  30-06-2016       2.86 22389513     41.58

##          Region        Date unemp.rate employed part.rate
## 937 Maharashtra  31-01-2016       4.62 41175830     46.77
## 938 Maharashtra  29-02-2016       3.69 41947738     47.11
## 939 Maharashtra  31-03-2016       5.92 41426005     47.53
## 940 Maharashtra  30-04-2016       9.92 42200931     50.47
## 941 Maharashtra  31-05-2016       9.05 43656285     51.60
## 942 Maharashtra  30-06-2016       6.58 44556278     51.18

##         Region        Date unemp.rate employed part.rate
## 1005 Meghalaya  28-02-2018       7.92  1138690     56.50
## 1006 Meghalaya  31-03-2018       6.72  1067546     52.16
## 1007 Meghalaya  30-04-2018       4.04  1168761     55.38
## 1008 Meghalaya  31-05-2018      10.54  1149825     58.30
## 1009 Meghalaya  30-06-2018       6.73  1111337     53.91
## 1010 Meghalaya  31-07-2018       3.51  1178817     55.14

##      Region        Date unemp.rate employed part.rate
## 1047 Odisha  31-01-2016       7.06 15086246     49.41
## 1048 Odisha  29-02-2016      10.45 13815790     46.89
## 1049 Odisha  31-03-2016      12.64 15149389     52.62
## 1050 Odisha  30-04-2016       8.67 13731170     45.55
## 1051 Odisha  31-05-2016       7.85 14699097     48.25
## 1052 Odisha  30-06-2016      10.60 13928832     47.05

##          Region        Date unemp.rate employed part.rate
## 1114 Puducherry  31-01-2016      12.53   477055     49.91
## 1115 Puducherry  29-02-2016      16.32   520965     56.84
## 1116 Puducherry  31-03-2016       4.77   481553     46.05
## 1117 Puducherry  30-04-2016       1.42   527271     48.59
## 1118 Puducherry  31-05-2016       5.96   473039     45.59
## 1119 Puducherry  30-06-2016      13.55   521557     54.54

##      Region        Date unemp.rate employed part.rate
## 1181 Punjab  31-01-2016       7.66  9754023     46.27
## 1182 Punjab  29-02-2016      12.38  9262659     46.23
## 1183 Punjab  31-03-2016       8.32  9643108     45.92
## 1184 Punjab  30-04-2016       7.28  9656125     45.38
## 1185 Punjab  31-05-2016       5.07 10078832     46.19
## 1186 Punjab  30-06-2016      10.58  9560266     46.43

##         Region        Date unemp.rate employed part.rate
## 1248 Rajasthan  31-01-2016       6.72 19348695     40.49
## 1249 Rajasthan  29-02-2016       3.82 19918070     40.34
## 1250 Rajasthan  31-03-2016       7.56 18609185     39.12
## 1251 Rajasthan  30-04-2016      11.45 18644477     40.82
## 1252 Rajasthan  31-05-2016       8.82 18690639     39.65
## 1253 Rajasthan  30-06-2016       7.16 19780871     41.12

##      Region        Date unemp.rate employed part.rate
## 1315 Sikkim  30-09-2018      11.25   280884     58.62
## 1316 Sikkim  31-10-2018      12.14   207652     43.66
## 1317 Sikkim  30-11-2018       4.69   258027     49.87
## 1318 Sikkim  31-01-2019      13.24   274484     57.97
## 1319 Sikkim  28-02-2019       8.30   227220     45.28
## 1320 Sikkim  31-03-2019       6.80   265777     51.97

##          Region        Date unemp.rate employed part.rate
## 1347 Tamil Nadu  31-01-2016       7.59 30805567     55.23
## 1348 Tamil Nadu  29-02-2016       4.41 31278490     54.13
## 1349 Tamil Nadu  31-03-2016      11.60 30359801     56.73
## 1350 Tamil Nadu  30-04-2016       9.77 30660799     56.04
## 1351 Tamil Nadu  31-05-2016       9.11 30645647     55.52
## 1352 Tamil Nadu  30-06-2016       6.79 30171453     53.22

##          Region        Date unemp.rate employed part.rate
## 1413 Tamil Nadu  31-07-2021       4.78 24651814     38.66
## 1414  Telangana  31-01-2016       7.36 17815321     66.91
## 1415  Telangana  29-02-2016       4.67 16508460     60.14
## 1416  Telangana  31-03-2016       4.76 15830168     57.62
## 1417  Telangana  30-04-2016       6.49 17066847     63.15
## 1418  Telangana  31-05-2016       6.30 16361651     60.31

##       Region        Date unemp.rate employed part.rate
## 1481 Tripura  30-09-2017      19.41  1218132     49.47
## 1482 Tripura  31-10-2017       8.51  1468082     52.43
## 1483 Tripura  30-11-2017       8.35  1266700     45.08
## 1484 Tripura  31-01-2018      31.11  1399231     66.00
## 1485 Tripura  28-02-2018      30.23  1416297     65.85
## 1486 Tripura  31-03-2018      15.19  1301630     49.70

##             Region        Date unemp.rate employed part.rate
## 1527 Uttar Pradesh  31-01-2016      14.91 55745996     44.88
## 1528 Uttar Pradesh  29-02-2016      15.45 55421160     44.80
## 1529 Uttar Pradesh  31-03-2016      14.39 56956539     45.37
## 1530 Uttar Pradesh  30-04-2016      15.38 56075058     45.10
## 1531 Uttar Pradesh  31-05-2016      16.42 57530487     46.74
## 1532 Uttar Pradesh  30-06-2016      17.98 56695242     46.83

##           Region        Date unemp.rate employed part.rate
## 1594 Uttarakhand  31-01-2016       1.31  4445674     57.37
## 1595 Uttarakhand  29-02-2016       4.53  3639695     48.45
## 1596 Uttarakhand  31-03-2016       2.42  2979208     38.72
## 1597 Uttarakhand  30-04-2016       1.26  3983774     51.06
## 1598 Uttarakhand  31-05-2016       1.08  4800244     61.28
## 1599 Uttarakhand  30-06-2016       2.85  3465939     44.96

##           Region        Date unemp.rate employed part.rate
## 1661 West Bengal  31-01-2016       9.04 31812990     47.11
## 1662 West Bengal  29-02-2016       6.19 30811585     44.16
## 1663 West Bengal  31-03-2016       8.24 31201247     45.64
## 1664 West Bengal  30-04-2016       7.54 31812979     46.09
## 1665 West Bengal  31-05-2016       6.60 32086784     45.93
## 1666 West Bengal  30-06-2016       6.85 30067878     43.08

ind = seq(67,1794,67)#imbalanced
ind = c(67,134,201,268,335,401,468,535,602,668,735,802,869,936,1004,1046,1113,1180,1247,1314,1346,1412,1480,1526,1593,1660,1727)#hard-coded
data = viz_data[ind,c(1,2,3,4,5)]
head(data)

##             Region        Date unemp.rate employed part.rate
## 67  Andhra Pradesh  31-07-2021       8.72 15473355     38.51
## 134          Assam  31-07-2021       1.90 11247012     42.81
## 201          Bihar  31-07-2021      13.02 25373093     35.86
## 268   Chhattisgarh  31-07-2021       3.25  8798367     39.76
## 335          Delhi  31-07-2021      10.70  5013429     34.40
## 401            Goa  31-07-2021      21.43   398738     38.86

p1 = ggplot(data=data,aes(x=Region,y=unemp.rate))+
    geom_bar(stat = "identity")+
    labs(x="Region",y="Unemployment Rate",title = "Recent Unemployment rates of all states in India",subtitle = "31-7-21")+
    theme(axis.text.x = element_text(angle = 60, hjust = 1,vjust = 1))

#there is huge imbalance in data so need to code it out in the hard way.
print(p1+theme(axis.text.x = element_text(angle = 60, hjust = 1, vjust = 1)))

p2 = ggplot(data=data,aes(x=Region,y=employed))+
    geom_bar(stat = "identity")+
    labs(x="Region",y="All employed",title = "Recent employed of all states in India",subtitle = "31-7-21")+
    theme(axis.text.x = element_text(angle = 60, hjust = 1,vjust = 1))

#there is huge imbalance in data so need to code it out in the hard way.
print(p2+theme(axis.text.x = element_text(angle = 60, hjust = 1)))

p3 = ggplot(data=data,aes(x=Region,y=part.rate))+
    geom_bar(stat = "identity")+
    labs(x="Region",y="Participation Rate",title = "Recent Participation rates of all states in India",subtitle = "31-7-21")+
    theme(axis.text.x = element_text(angle = 60, hjust = 1))

#there is huge imbalance in data so need to code it out in the hard way.
print(p3+theme(axis.text.x = element_text(angle = 60, hjust = 1)))

modeld$day = sapply(strsplit(as.character(modeld$Date), '-'), function(x) x[1])
modeld$month = sapply(strsplit(as.character(modeld$Date), '-'), function(x) x[2])
modeld$year = sapply(strsplit(as.character(modeld$Date), '-'), function(x) x[3])
modeld$day = as.numeric(modeld$day)
modeld$month = as.numeric(modeld$month)
modeld$year = as.numeric(modeld$year)

c = 1
for(eind in ind){
    if(c == 1){
        reg = modeld[c(1:eind),]
        c = c+1
    }
    else{
        reg = modeld[c((ind[[(c-1)]]+1):eind),]
        c = c+1
    }
    unemp.rate.mod = lm(formula=unemp.rate~day+month+year,data = reg)
    xtest = data.frame(Date=c("31-01-2022","28-02-2022","31-03-2022","30-04-2022","31-05-2022","30-06-2022","31-07-2022","31-08-2022","31-09-2022","30-10-2022","31-11-2022","30-12-2022","31-01-2023","28-02-2023","31-03-2023","30-04-2023","31-05-2023","30-06-2023","31-07-2023","31-08-2023","31-09-2023","30-10-2023","31-11-2023","30-12-2023","31-01-2024","29-02-2024","31-03-2024","30-04-2024","31-05-2024","30-06-2024","31-07-2024","31-08-2024","31-09-2024","30-10-2024","31-11-2024","30-12-2024","31-01-2025","28-02-2025","31-03-2025","30-04-2025","31-05-2025","30-06-2025","31-07-2025","31-08-2025","31-09-2025","30-10-2025","31-11-2025","30-12-2025","31-01-2026","28-02-2026","31-03-2026","30-04-2026","31-05-2026","30-06-2026","31-07-2026","31-08-2026","31-09-2026","30-10-2026","31-11-2026","30-12-2026","31-01-2027","28-02-2027","31-03-2027","30-04-2027","31-05-2027","30-06-2027","31-07-2027","31-08-2027","31-09-2027","30-10-2027","31-11-2027","30-12-2027"),
                       day = c(31,28,31,30,31,30,31,31,31,30,31,30,31,28,31,30,31,30,31,31,31,30,31,30,31,29,31,30,31,30,31,31,31,30,31,30,31,28,31,30,31,30,31,31,31,30,31,30,31,28,31,30,31,30,31,31,31,30,31,30,31,28,31,30,31,30,31,31,31,30,31,30),
                       month = c(1,2,3,4,5,6,7,8,9,10,11,12,1,2,3,4,5,6,7,8,9,10,11,12,1,2,3,4,5,6,7,8,9,10,11,12,1,2,3,4,5,6,7,8,9,10,11,12,1,2,3,4,5,6,7,8,9,10,11,12,1,2,3,4,5,6,7,8,9,10,11,12),
                       year = c(2022,2022,2022,2022,2022,2022,2022,2022,2022,2022,2022,2022,2023,2023,2023,2023,2023,2023,2023,2023,2023,2023,2023,2023,2024,2024,2024,2024,2024,2024,2024,2024,2024,2024,2024,2024,2025,2025,2025,2025,2025,2025,2025,2025,2025,2025,2025,2025,2026,2026,2026,2026,2026,2026,2026,2026,2026,2026,2026,2026,2027,2027,2027,2027,2027,2027,2027,2027,2027,2027,2027,2027))
    reg1 = reg[,c(2,6,7,8)]
    extest = bind_rows(reg1,xtest)
    unemp.sy = data.frame(unemp.rate=predict(unemp.rate.mod,newdata = extest))
    extest = bind_cols(extest,unemp.sy)
    p1 = ggplot(data = extest,aes(x=Date,y=unemp.rate,group=1,colour=unemp.rate))+
        geom_point()+
        geom_line()+
        labs(x="timeline",y="trajectory of unemployment rates",title=reg[c(1),c(1)],subtitle ="2016-2027" )
    print(p1+theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 1)))
    emp.mod = lm(formula=employed~day+month+year,data = reg)
    xtest = data.frame(Date=c("31-01-2022","28-02-2022","31-03-2022","30-04-2022","31-05-2022","30-06-2022","31-07-2022","31-08-2022","31-09-2022","30-10-2022","31-11-2022","30-12-2022","31-01-2023","28-02-2023","31-03-2023","30-04-2023","31-05-2023","30-06-2023","31-07-2023","31-08-2023","31-09-2023","30-10-2023","31-11-2023","30-12-2023","31-01-2024","29-02-2024","31-03-2024","30-04-2024","31-05-2024","30-06-2024","31-07-2024","31-08-2024","31-09-2024","30-10-2024","31-11-2024","30-12-2024","31-01-2025","28-02-2025","31-03-2025","30-04-2025","31-05-2025","30-06-2025","31-07-2025","31-08-2025","31-09-2025","30-10-2025","31-11-2025","30-12-2025","31-01-2026","28-02-2026","31-03-2026","30-04-2026","31-05-2026","30-06-2026","31-07-2026","31-08-2026","31-09-2026","30-10-2026","31-11-2026","30-12-2026","31-01-2027","28-02-2027","31-03-2027","30-04-2027","31-05-2027","30-06-2027","31-07-2027","31-08-2027","31-09-2027","30-10-2027","31-11-2027","30-12-2027"),
                       day = c(31,28,31,30,31,30,31,31,31,30,31,30,31,28,31,30,31,30,31,31,31,30,31,30,31,29,31,30,31,30,31,31,31,30,31,30,31,28,31,30,31,30,31,31,31,30,31,30,31,28,31,30,31,30,31,31,31,30,31,30,31,28,31,30,31,30,31,31,31,30,31,30),
                       month = c(1,2,3,4,5,6,7,8,9,10,11,12,1,2,3,4,5,6,7,8,9,10,11,12,1,2,3,4,5,6,7,8,9,10,11,12,1,2,3,4,5,6,7,8,9,10,11,12,1,2,3,4,5,6,7,8,9,10,11,12,1,2,3,4,5,6,7,8,9,10,11,12),
                       year = c(2022,2022,2022,2022,2022,2022,2022,2022,2022,2022,2022,2022,2023,2023,2023,2023,2023,2023,2023,2023,2023,2023,2023,2023,2024,2024,2024,2024,2024,2024,2024,2024,2024,2024,2024,2024,2025,2025,2025,2025,2025,2025,2025,2025,2025,2025,2025,2025,2026,2026,2026,2026,2026,2026,2026,2026,2026,2026,2026,2026,2027,2027,2027,2027,2027,2027,2027,2027,2027,2027,2027,2027))
    reg1 = reg[,c(2,6,7,8)]
    extest = bind_rows(reg1,xtest)
    emp = data.frame(emp=predict(emp.mod,newdata = extest))
    extest = bind_cols(extest,emp)
    p2 = ggplot(data = extest,aes(x=Date,y=emp,group=1,colour=emp))+
        geom_point()+
        geom_line()+
        labs(x="timeline",y="trajectory of total employments",title=reg[c(1),c(1)],subtitle ="2016-2027" )
    print(p2+theme(axis.text.x = element_text(angle = 90, hjust = 1,vjust = 1)))
part.mod = lm(formula=part.rate~day+month+year,data = reg)
    xtest = data.frame(Date=c("31-01-2022","28-02-2022","31-03-2022","30-04-2022","31-05-2022","30-06-2022","31-07-2022","31-08-2022","31-09-2022","30-10-2022","31-11-2022","30-12-2022","31-01-2023","28-02-2023","31-03-2023","30-04-2023","31-05-2023","30-06-2023","31-07-2023","31-08-2023","31-09-2023","30-10-2023","31-11-2023","30-12-2023","31-01-2024","29-02-2024","31-03-2024","30-04-2024","31-05-2024","30-06-2024","31-07-2024","31-08-2024","31-09-2024","30-10-2024","31-11-2024","30-12-2024","31-01-2025","28-02-2025","31-03-2025","30-04-2025","31-05-2025","30-06-2025","31-07-2025","31-08-2025","31-09-2025","30-10-2025","31-11-2025","30-12-2025","31-01-2026","28-02-2026","31-03-2026","30-04-2026","31-05-2026","30-06-2026","31-07-2026","31-08-2026","31-09-2026","30-10-2026","31-11-2026","30-12-2026","31-01-2027","28-02-2027","31-03-2027","30-04-2027","31-05-2027","30-06-2027","31-07-2027","31-08-2027","31-09-2027","30-10-2027","31-11-2027","30-12-2027"),
                       day = c(31,28,31,30,31,30,31,31,31,30,31,30,31,28,31,30,31,30,31,31,31,30,31,30,31,29,31,30,31,30,31,31,31,30,31,30,31,28,31,30,31,30,31,31,31,30,31,30,31,28,31,30,31,30,31,31,31,30,31,30,31,28,31,30,31,30,31,31,31,30,31,30),
                       month = c(1,2,3,4,5,6,7,8,9,10,11,12,1,2,3,4,5,6,7,8,9,10,11,12,1,2,3,4,5,6,7,8,9,10,11,12,1,2,3,4,5,6,7,8,9,10,11,12,1,2,3,4,5,6,7,8,9,10,11,12,1,2,3,4,5,6,7,8,9,10,11,12),
                       year = c(2022,2022,2022,2022,2022,2022,2022,2022,2022,2022,2022,2022,2023,2023,2023,2023,2023,2023,2023,2023,2023,2023,2023,2023,2024,2024,2024,2024,2024,2024,2024,2024,2024,2024,2024,2024,2025,2025,2025,2025,2025,2025,2025,2025,2025,2025,2025,2025,2026,2026,2026,2026,2026,2026,2026,2026,2026,2026,2026,2026,2027,2027,2027,2027,2027,2027,2027,2027,2027,2027,2027,2027))
    reg1 = reg[,c(2,6,7,8)]
    extest = bind_rows(reg1,xtest)
    part = data.frame(part=predict(part.mod,newdata = extest))
    extest = bind_cols(extest,part)
    p3 = ggplot(data = extest,aes(x=Date,y=part,group=1,colour=part))+
        geom_point()+
        geom_line()+
        labs(x="timeline",y="trajectory of Participation Rates",title=reg[c(1),c(1)],subtitle ="2016-2027" )
    print(p3+theme(axis.text.x = element_text(angle = 90, hjust = 1,vjust = 1)))
}

Ending notes

This research was made so that we can accustom ourselves with the future. Mere numbers cannot decide whether we shall succeed or fail. The numbers are probabilistic. Our success lies in our skills but the future preperation is necessary. Let us work constantly to drive ourselves towards a brighter and better future. Thank you. Take care.