89 Revisiting Employment Vulnerability Index Using Principal Component Analysis GLECIL JOY L. DALUPO anakgwapo14319@gmail.com Southern Leyte State University— San Juan Campus San Juan, Philippines GARY D. GARCIA garychmich@yahoo.com.ph Southern Leyte State University— San Juan Campus San Juan, Philippines Originality: 100% • Grammar Check: 95% • Plagiarism: 0% ABSTRACT The share of workers in vulnerable employment is directly linked to the share of people living in poverty. The statement recalls why understanding the employment vulnerability index (EVI) of a nation based on the present situations is very relevant. In view of that, The International Labor Organization (ILO) designed a parameter to predict the possible increase or decrease of the employment vulnerability index. However, the said formula does not capture the general issue of employment vulnerability, specifically in terms of the principal component. Hence this study was purposely conducted to develop a unique formula in computing EVI as a form of resolution in the development of vulnerability indices using the Principal Component Analysis. The principal component analysis develops indicators of vulnerability in employment using the Vol. 42 · October 2020 DOI: https://doi.org/10.7719/jpair.v41i3.797 Print ISSN 2012-3981 Online ISSN 2244-0445 This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. mailto:anakgwapo14319@gmail.com mailto:garychmich@yahoo.com.ph https://creativecommons.org/licenses/by-nc/4.0/ https://creativecommons.org/licenses/by-nc/4.0/ 9090 JPAIR Multidisciplinary Research United Nations Development Program (UNDP) data. The results identified two principal components that determine 82.60% of the variance of Employment Vulnerability. The generated formula concludes that the Employment vulnerability index is a function of types of employment and unemployment rate. Types of employment included the employment in agriculture and employment in services, and unemployment rate comprised of employment to population ratio, unemployment labor force, unemployment youth, and labor force participation. Further, types of employment should be given more emphasis when it comes to EVI Plan than unemployment rate hence, type of employment shows greater influence to EVI with 66.30% compared to unemployment rate with only 33.70%. Keywords — Social Science, Types of Employment Unemployment rate, EVI ranking, Philippines INTRODUCTION The concept of vulnerability was used for the analysis of environmental risks and hazards as well as for demographic  and economic conditions. The United Nations Development Programme of the Economic Commission for Latin America and the Caribbean (UNDP-ECLAC)  considers that vulnerability and poverty are converging phenomena, describing, among other things, that “the current social scenario simultaneously records an increasing uncertainty regarding work as the main way of building the future of persons and their families (Mac Donald & Simioni, 2000) Concerning on vulnerability, the International Labor Organization (ILO) designed a parameter to predict the possible increase or decrease of the employment vulnerability index (EVI). In the said model, the share of vulnerable employment is calculated as the sum of contributing family workers and own‐ account workers as a percentage of total employment. Generally, based on this formula, EVI was considered as a function of Employment. Employment, as defined by the Parliament of Australia (2003) and Azpitarte (2012), is an effective barrier against abject poverty, so being excluded from employment brings with it significant financial concerns for individuals and their families. In terms of the growth of poverty and social exclusion, welfare agencies are quick to point to the problems imposed by unemployment and labor market disadvantage. 9191 International Peer Reviewed Journal In relation to this, Dutiro (2010) reported that unemployment is a growing problem over the past decade, the narrow and broad unemployment rates have increased from 22.5% to 29.0% and from 29.7% to 38.5%, respectively.  However, the latest edition of the World Employment and Social Outlook compiled by the International Labour Organization (2018) recounted that global unemployment in 2018 remains at a similar level to 2917. Peaking at 5.9% in 2009, the world unemployment rate started slowly decreasing. After 2014, it has essentially stabilized around the 5.5% mark, with the total number of estimated unemployed persons exceeding 192 million. Going back to the ILO concept on EVI, these reports indicate concerns on employment vulnerability. Similarly, Garzon-Duque et al. (2017) identified the following conditions as evidence of employment vulnerability: accentuation of productive heterogeneity that affects the occupation, the segmentation of the work and greater precariousness, employment deregulation (or flexibilization) without unemployment insurance, the reduction of the quantitative weight of unions, and the sustained growth of labor informality, especially for city workers. Finally, the share of workers in vulnerable employment is directly linked to the share of people living in poverty (Human, 2009 ). The statement recalls why understanding the employment vulnerability index (EVI) of a nation based on the present situations is very relevant. Moreover, the ILO formula for calculating EVI does not capture the general issue of employment vulnerability, specifically in terms of the principal component. Hence this study was proposed to develop a unique formula in computing EVI as a form of resolution in the development of vulnerability indices. FRAMEWORK The concept of this study was anchored on the theoretical reduction. In the twentieth century, most philosophers considered the question of the reduction of theories to be prior to the question of the reduction of entities or phenomena. The reduction was primarily understood to be a way to unify the sciences. The general goal of a theoretical reduction is to promote the  unity of science. All of these models provide some sense in which science may become more unified. For sciences may become unified by being expressed in the same language. This allows one to see that there is only one language that is required to express all truths in the theories. Sciences may also become unified when the laws of one theory are shown to be derivable from those of another theory. This allows one to 9292 JPAIR Multidisciplinary Research see that there is only one basic set of principles that are required to account for the other truths in the theories. Finally, sciences may become unified when the observations explained by one theory are shown to be also explainable by another theory (Ney, 2008). Similarly, the original EVI was a weighted composite index constructed using spatial statistical analysis of 2006 Census data. The index was developed from an initial theoretical and empirical conceptualization of the drivers and indicators of employment vulnerability. The summary indicator measured a suburb’s potential for increasing joblessness or employment vulnerability rather than the actual level of joblessness or vulnerability (Mitchel, 2015). From this, the ILO developed another formula in calculating EVI in which the share of vulnerable employment is calculated as the sum of contributing family workers and own‐account workers as a percentage of total employment. From the cited scenario, a formula/model which will be considered as a general reference in measuring the employment vulnerability index of a country was developed. The general concept was to revisit the existing EVI formula and identify variables that were considered in the calculation of EVI to form new formulas in the simplest form. Only identified variables with available data common to all countries from UNDP data banks were included in the data analysis. The developed model was considered unique due to its composition of parameters in which principal components were derived from the selected variables using Principal Component Analysis. The PCA provides the related factor loadings for the identified indicators in which results were used to develop a simple weighted index. OBJECTIVES OF THE STUDY This study was purposely conducted to develop a unique formula in computing Employment Vulnerability Index (EVI) as a form of resolution in the development of vulnerability indices using principal component analysis since the International Labor Organization (ILO) designed a parameter to predict the possible increase or decrease of the employment vulnerability that does not capture the general issue of employment vulnerability in terms of principal components; thus, this study is conducted. 9393 International Peer Reviewed Journal METHODOLOGY Research Design This study utilized descriptive design to describe the generated data and to generate an index using six (6) indicators of employment vulnerability. The utilization of secondary data from the United Nations Development Program (UNDP) for the discovery of useful information was applied; hence, it should be cleaned and free from error to ensure the validity of the results. The variables include Employment in Services consists of wholesale and retail trade and restaurants and hotels; transport, storage, and communications; financing, insurance, real estate, and business services; and community, social, and personal services, Employment in Agriculture consists of activities in agriculture, hunting, forestry and fishing, Employment to Population Ratio which is calculated by dividing the number of people employed by the total number of people of working age, Unemployment Labor Force defined as the share of the labor force that is jobless, Unemployment Youth defined as the share of the youth that is jobless and Labor Force Participation Rate which is a measure of an economy’s active workforce. Instrumentation The Scree Plot and the Eigen analysis of the Principal Component Analysis were used to reduce the variables into few components without losing any valuable information. Thus, a new indicator was determined that was used as a new component for the ranking. Data Analysis After the generated PC, the employment vulnerability index was derived by computing the sum of the product of the factor loading, and the scores are computed for each component. And lastly, the computed sum of each component and ranked the country according to scores. RESULTS AND DISCUSSION The Scree plot of the Components of Employment Vulnerability The figure below showed that there are two (2) Principal Components that will determine the variability of the components. Thus, it is supported by the Eigen analysis result in table 1 that 82.60% of the variance will be explained by the two components identified. 9494 JPAIR Multidisciplinary Research Figure 1. Scree Plot of the Components of Employment Vulnerability Table 1. Eigen analysis of the Correlation Matrix Eigenvalue 3.2834 1.6709 0.9289 0.0598 0.0554 0.0016 Proportion 0.547 0.278 0.155 0.010 0.009 0.000 Cumulative 0.547 0.826 0.981 0.990 1.000 1.000 Factor Loading of the Principal Components Based on Table 2, which is the result of the PCA, there are two components of employment Vulnerability, which will determine 82.60% of the variance. The two components were identified as Type of Employment, which includes employment in services and agriculture, and another is the unemployment rate, which is comprised of employment to population ratio, Unemployment of Labor force, and Youth and Labor force Participation. 9595 International Peer Reviewed Journal Table 2. Factor Loading of the Principal Components Variable PC1 PC2 Employment in Services -0.321 -0.605 Employment in Agriculture 0.305 0.617 Employment to Population Ratio -0.497 0.150 Unemployment (Labor force) 0.413 -0.374 Unemployment (Youth) 0.445 -0.298 Labor Force Participation -0.434 0.046 Principal Component Model The generated formula concludes that the Employment vulnerability index is a function of types of employment and unemployment rate. PC 1 (Type of Employment) PC1 = (-0.605*Employment in Services) + (0.617*Employment in Agriculture) The results show that the Types of Employment is a function of Employment in Services and Agriculture. It implies that the decrease of Employment in Services determines an increase in the Type of Employment, and the increase in employment in agriculture determines an increase in the Type of Employment and vice versa. PC 2 (Unemployment Rate) PC2 = (-0.497*Employment to Population ratio) + (0.413*Unemployment (Labor Force)) + (0.445*Unemployment (Youth)) – (0.434*Labor Force Participation) Unemployment Rate is a function of Employment to Population Ratio, Unemployment Labor force, and youth and Labor Force Participation. The equation implies that the increase of Employment to population Ratio and Labor Force participation determines decreases in Unemployment Rate. Otherwise, an increase in Unemployment on both the Labor Force and the youth determines an increase in the Unemployment Rate. 9696 JPAIR Multidisciplinary Research Scores According to Identified Components Table 3 showed the indicators of employment vulnerability of different countries. On the first indicator, which is the Type of Employment, Hong Kong scored the highest among the countries while Bosnia and Herzegovina Qatar scored highest in the unemployment rate. Table 3. Country’s Scores According to Identified Components Country Type of Employment Unemployment Rate Afghanistan -1.49102 0.80622 Albania -0.66191 1.57884 Algeria -0.02624 2.16260 Angola -0.91920 -1.08279 Argentina 1.35183 0.59749 Armenia -0.26061 2.00046 Australia 1.36751 -0.52768 Austria 1.07948 -0.25982 Azerbaijan -0.36907 -0.66363 Bahamas 1.51454 -0.24877 Bahrain 0.97221 -1.82220 Bangladesh -0.66915 -0.01608 Barbados 1.34822 0.36514 Belarus 0.61008 -1.38389 Belgium 1.37172 0.80446 Belize 0.79387 -0.38399 Benin -0.81617 -1.61178 Bhutan -1.28857 -1.10334 Bolivia (Plurinational State of ) -0.05514 -1.21252 Bosnia and Herzegovina 0.07802 4.28512 Botswana 0.25639 0.97376 Brazil 0.89570 0.90539 Brunei Darussalam 1.52357 -0.01532 Bulgaria 0.86255 0.51961 Burkina Faso -0.41186 -0.75064 Burundi -2.95042 -2.45535 9797 International Peer Reviewed Journal Country Type of Employment Unemployment Rate Cabo Verde -1.79532 0.45845 Cambodia -0.17746 -3.16938 Cameroon -1.55851 -1.73792 Canada 1.38498 -0.50154 Central African Republic -2.79918 -1.11562 Chad -2.79255 -1.11557 Chile 0.87642 -0.04994 China 0.33351 -1.02834 Colombia 0.62636 -0.44696 Comoros -1.35425 1.02668 Congo -0.71374 -0.04492 Congo (Democratic Republic of the) -2.69193 -1.54394 Costa Rica 0.87582 0.53237 Côte d’Ivoire 0.86075 1.51886 Croatia 0.57816 -0.22081 Cuba 1.37896 0.50796 Cyprus 0.78783 -0.53423 Czechia -0.72818 -0.52891 Denmark 1.37654 -0.41394 Djibouti -0.42693 -0.07859 Dominican Republic 0.88727 -0.71691 Ecuador 0.06477 -0.95173 Egypt -0.03225 2.20536 El Salvador 0.42570 -0.46174 Equatorial Guinea -1.33556 0.10154 Eritrea -2.67686 -1.85556 Estonia 0.97161 -0.21011 Eswatini (Kingdom of ) -2.03695 3.77580 Ethiopia -1.89775 -2.21078 Fiji -0.45885 0.28313 Finland 1.20059 0.55094 France 1.31508 1.02293 9898 JPAIR Multidisciplinary Research Country Type of Employment Unemployment Rate Gabon -0.56490 2.70534 Gambia 0.15335 0.29306 Georgia -0.51067 0.42636 Germany 1.19396 -0.58935 Ghana -0.54803 -2.13366 Greece 0.96681 2.98325 Guatemala -0.14311 -0.83277 Guinea -1.79230 -0.85071 Guinea-Bissau -2.65215 -1.08111 Guyana 0.55886 1.12143 Haiti -0.52332 0.86616 Honduras -0.10033 -1.07711 Hong Kong, China (SAR) 1.67843 -0.53261 Hungary 0.90292 0.04464 Iceland 1.38860 -2.06836 India -0.94874 0.08961 Indonesia -0.26182 -0.67843 Iran (Islamic Republic of ) 0.18045 2.44744 Iraq 0.47632 1.38322 Ireland 1.21566 -0.03515 Israel 1.50308 -0.79001 Italy 1.08008 2.17579 Jamaica 0.61130 0.55887 Japan 1.12286 -0.71763 Jordan 1.07586 3.34322 Kazakhstan 0.47873 -1.40124 Kenya -0.40462 0.47997 Korea (Democratic People’s Rep. of ) -2.08214 -1.93353 Korea (Republic of ) 1.07104 -0.60435 Kuwait 1.08972 -1.12745 Kyrgyzstan -0.03285 -0.00532 Lao People’s Democratic Republic -1.53260 -2.54117 9999 International Peer Reviewed Journal Country Type of Employment Unemployment Rate Latvia 0.95414 0.29876 Lebanon 1.29881 1.10304 Lesotho 0.29975 2.39172 Liberia -0.60647 -0.42716 Libya 0.60707 2.78661 Lithuania 0.90775 -0.01862 Luxembourg 1.67120 0.19287 Madagascar -2.22796 -3.05909 Malawi -2.76242 -1.65391 Malaysia 0.66190 -0.78912 Maldives 0.93004 -0.63913 Mali -1.29580 -0.66089 Malta 1.42897 0.09037 Mauritania -2.24904 1.35280 Mauritius 0.90473 0.49568 Mexico 0.59622 -0.66121 Moldova (Republic of ) -0.23711 1.19166 Mongolia -0.13708 0.26894 Montenegro 1.12648 2.47994 Morocco -0.53718 1.28462 Mozambique -2.02067 1.42103 Myanmar -1.12227 -1.42514 Namibia 0.39799 2.60717 Nepal -2.04839 -2.72603 Netherlands 1.46753 -0.63676 New Zealand 1.11443 -0.95911 Nicaragua -0.04067 -0.95161 Niger -2.24483 -2.67129 Nigeria -0.25036 0.40729 Norway 1.38558 -0.71871 Oman 0.58054 1.34104 Pakistan -0.90776 -0.01344 100100 JPAIR Multidisciplinary Research Country Type of Employment Unemployment Rate Palestine, State of 0.68058 4.15228 Panama 0.71674 -0.65366 Papua New Guinea 0.73784 -1.52093 Paraguay 0.34677 -0.96680 Peru 0.06478 -1.83127 Philippines 0.14070 -0.77555 Poland 0.56609 0.14896 Portugal 0.96499 0.70029 Qatar 0.35336 -3.35903 Romania -0.03466 0.57722 Russian Federation 0.91016 -0.34209 Rwanda -1.76639 -3.11842 Saint Lucia 0.73301 1.86926 Saint Vincent and the Grenadines 1.36209 1.31248 Samoa 1.32051 2.59191 Sao Tome and Principe 0.73181 1.27163 Saudi Arabia 1.06441 0.97548 Senegal -1.41752 -0.23886 Serbia 0.31844 1.89897 Sierra Leone -1.38618 -0.20859 Singapore 1.58744 -1.48391 Slovakia 0.83302 0.29062 Slovenia 0.83603 0.30427 Solomon Islands -1.98573 -1.73184 Somalia -2.81967 0.96402 South Africa 1.07827 3.96102 South Sudan -2.00803 -0.37424 Spain 1.27411 2.11335 Sri Lanka -0.13528 0.53176 Sudan -1.38498 2.17354 Suriname 1.21144 0.74256 Sweden 1.43560 -0.15291 101101 International Peer Reviewed Journal Country Type of Employment Unemployment Rate Switzerland 1.27049 -1.06552 Syrian Arab Republic -0.14312 3.04171 Tajikistan -1.20843 0.58607 Tanzania (United Republic of ) -1.71397 -2.72129 Thailand -0.37269 -1.53446 The former Yugoslav Republic of Macedonia 0.29675 3.12201 Timor-Leste 0.27025 1.35658 Togo -0.48717 -2.32282 Tonga -0.58420 -0.87853 Trinidad and Tobago 1.08188 -0.43707 Tunisia 0.05752 2.62111 Turkey 0.22444 1.34046 Turkmenistan 0.28348 -1.02686 Uganda -1.86581 -1.71849 Ukraine 0.51367 1.07357 United Arab Emirates 0.89026 -2.42796 United Kingdom 1.46994 -0.49531 United States 1.42535 -0.52512 Uruguay 1.03971 0.13229 Uzbekistan -0.23954 -0.37911 Vanuatu -1.61334 -1.11344 Venezuela (Bolivarian Republic of ) 0.82881 -0.01899 Viet Nam -0.88728 -2.19708 Yemen -0.71795 2.82763 Zambia -1.16504 -1.02993 Zimbabwe -1.85376 -2.15955 102102 JPAIR Multidisciplinary Research Weights of the Indices Weights of Indices are computed by getting the percentage of the proportion of each component Index Weight Type of Employment 66.30% Unemployment Rate 33.70% Total 100.00% EVI Equation Type of Employment*66.30%)+(Unemployment Rate*33.70%) The combination of Components as predictor variables is quite useful in Employment Vulnerability. Further, the generated formula determines which variables are the strongest predictors or contributed much in terms of influences in employment vulnerability. Among the two identified major components on EVI, Types of Employment shows greater influence, with 66.30% compared to the unemployment rate with only 33.70%. Correspondingly, Baum et al. (2013) reported that the Employment Vulnerability Index (EVI) is an indicator that identifies those suburbs that have higher proportions of the types of jobs thought to be at risk in the current economic climate. Ranking Based Weighted Indices The new ranking in terms of the employment vulnerability index of different countries is shown in Table 4 based on the unified index. The top 5 countries with high employment vulnerability are South Africa, Palestine, Jordan, Samoa & Greece. 103103 International Peer Reviewed Journal Table 5. Country’s Ranking Based on the Weighted Indices Country Employment Vulnerability Index Rank South Africa 2.04976 1 Palestine, State of 1.850543 2 Jordan 1.83996 3 Samoa 1.748972 4 Greece 1.646347 5 Montenegro 1.582599 6 Spain 1.556931 7 Bosnia and Herzegovina 1.495809 8 Italy 1.449332 9 Saint Vincent and the Grenadines 1.345372 10 Libya 1.341574 11 The former Yugoslav Republic of Macedonia 1.248862 12 Lebanon 1.232837 13 France 1.216626 14 Belgium 1.180555 15 Luxembourg 1.173004 16 Namibia 1.142483 17 Saint Lucia 1.115927 18 Argentina 1.097621 19 Cuba 1.08543 20 Côte d’Ivoire 1.08253 21 Suriname 1.053425 22 Saudi Arabia 1.034442 23 Barbados 1.016925 24 Brunei Darussalam 1.004965 25 Lesotho 1.004742 26 Finland 0.98166 27 Malta 0.97786 28 Iran (Islamic Republic of ) 0.944425 29 104104 JPAIR Multidisciplinary Research Country Employment Vulnerability Index Rank Hong Kong, China (SAR) 0.933311 30 Syrian Arab Republic 0.930165 31 Tunisia 0.921448 32 Bahamas 0.920304 33 Sao Tome and Principe 0.913728 34 Sweden 0.900269 35 Brazil 0.898965 36 Portugal 0.875784 37 Serbia 0.851081 38 Oman 0.836832 39 United Kingdom 0.807651 40 Ireland 0.794138 41 Iraq 0.781944 42 Denmark 0.773152 43 United States 0.768043 44 Mauritius 0.766881 45 Costa Rica 0.760075 46 Netherlands 0.758385 47 Canada 0.749223 48 Guyana 0.74845 49 Bulgaria 0.746979 50 Uruguay 0.733911 51 Latvia 0.733281 52 Israel 0.730312 53 Australia 0.728827 54 Egypt 0.721826 55 Algeria 0.711398 56 Ukraine 0.702357 57 Norway 0.676436 58 Slovenia 0.65683 59 105105 International Peer Reviewed Journal Country Employment Vulnerability Index Rank Slovakia 0.650231 60 Timor-Leste 0.636342 61 Austria 0.628135 62 Hungary 0.613681 63 Turkey 0.600541 64 Lithuania 0.595561 65 Jamaica 0.59363 66 Germany 0.592984 67 Estonia 0.573372 68 Trinidad and Tobago 0.569999 69 Chile 0.564234 70 Singapore 0.552397 71 Venezuela (Bolivarian Republic of ) 0.543104 72 Gabon 0.537175 73 Korea (Republic of ) 0.506433 74 Japan 0.502615 75 Armenia 0.501373 76 Botswana 0.498145 77 Russian Federation 0.488148 78 Switzerland 0.483256 79 Yemen 0.476912 80 Poland 0.425518 81 New Zealand 0.415645 82 Maldives 0.401231 83 Belize 0.396931 84 Dominican Republic 0.346659 85 Kuwait 0.342533 86 Cyprus 0.342294 87 Croatia 0.308905 88 Colombia 0.26465 89 106106 JPAIR Multidisciplinary Research Country Employment Vulnerability Index Rank Panama 0.254916 90 Moldova (Republic of ) 0.244388 91 Iceland 0.223602 92 Gambia 0.200434 93 Malaysia 0.172905 94 Mexico 0.172467 95 Romania 0.171543 96 El Salvador 0.126636 97 Albania 0.093221 98 Sri Lanka 0.089511 99 Morocco 0.076762 100 Bahrain 0.030495 101 Mongolia -0.00026 102 Papua New Guinea -0.02337 103 Kyrgyzstan -0.02357 104 Nigeria -0.02873 105 Haiti -0.05506 106 Belarus -0.06189 107 Eswatini (Kingdom of ) -0.07805 108 Paraguay -0.0959 109 Kenya -0.10651 110 China -0.12544 111 Kazakhstan -0.15482 112 Turkmenistan -0.15811 113 Philippines -0.16808 114 Sudan -0.18576 115 Georgia -0.19489 116 Fiji -0.2088 117 United Arab Emirates -0.22798 118 Ecuador -0.27779 119 107107 International Peer Reviewed Journal Country Employment Vulnerability Index Rank Uzbekistan -0.28657 120 Djibouti -0.30954 121 Nicaragua -0.34766 122 Guatemala -0.37553 123 Indonesia -0.40221 124 Honduras -0.4295 125 Bolivia (Plurinational State of ) -0.44518 126 Bangladesh -0.44906 127 Azerbaijan -0.46833 128 Congo -0.48835 129 Burkina Faso -0.52603 130 Liberia -0.54604 131 Comoros -0.55188 132 Peru -0.57419 133 India -0.59881 134 Tajikistan -0.60368 135 Pakistan -0.60638 136 Czechia -0.66103 137 Tonga -0.68339 138 Afghanistan -0.71685 139 Thailand -0.7642 140 Equatorial Guinea -0.85126 141 Mozambique -0.86082 142 Qatar -0.89771 143 Angola -0.97433 144 Sierra Leone -0.98933 145 Senegal -1.02031 146 Mauritania -1.03522 147 Cabo Verde -1.0358 148 Mali -1.08183 149 108108 JPAIR Multidisciplinary Research Country Employment Vulnerability Index Rank Ghana -1.08238 150 Benin -1.08429 151 Togo -1.10578 152 Zambia -1.11951 153 Cambodia -1.18574 154 Myanmar -1.22434 155 Bhutan -1.22615 156 Viet Nam -1.32868 157 Vanuatu -1.44487 158 South Sudan -1.45744 159 Guinea -1.47498 160 Somalia -1.54456 161 Cameroon -1.61897 162 Uganda -1.81617 163 Lao People’s Democratic Republic -1.87249 164 Solomon Islands -1.90017 165 Zimbabwe -1.95681 166 Ethiopia -2.00324 167 Korea (Democratic People’s Rep. of ) -2.03206 168 Tanzania (United Republic of ) -2.05344 169 Guinea-Bissau -2.12271 170 Rwanda -2.22203 171 Chad -2.22741 172 Central African Republic -2.23182 173 Nepal -2.27676 174 Congo (Democratic Republic of the) -2.30506 175 Niger -2.38855 176 Malawi -2.38885 177 Eritrea -2.40008 178 Madagascar -2.50805 179 Burundi -2.78358 180 109109 International Peer Reviewed Journal CONCLUSION Calculation of employment vulnerability index using the newly generated formula that focuses on determining the strongest predictors or contribution in increasing and decreasing the employment vulnerability index changes the world EVI ranking. Further, types of employment should be given more emphasis when it comes to EVI Plan than the unemployment rate; hence, the type of employment shows greater influence with 66.30% compared to the unemployment rate with only 33.70%. LITERATURE CITED Anderson, B. (2010). Migration, immigration controls and the fashioning of precarious workers. Work, employment and society, 24(2), 300-317. Retrieved from https://doi.org/10.1177/0950017010362141 Azpitarte, F. (2012). Social exclusion monitor bulletin December 2012. Retrieved from https://bit.ly/3d9AHpF Baum, S., Mitchell, W., & Flanagan, M. (2013). Employment Vulnerability in Australian Suburbs. Red alert suburbs: An employment vulnerability index for Australia’s major urban regions. In 14th Path to Full Employment Conference 19th National Conference on Unemployment (pp. 1-31). Centre for Full Employment and Equity, The University of Newcastle. Retrieved from https://bit.ly/2TLVMPh Dutiro, I. (2019).  Workplace Solutions Company Adcorp. 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