who knows this political research

country2008

structure(list(code = structure(c(1L, 3L, 48L, 2L, 6L, 7L, 8L, 9L, 10L, 18L, 17L, 15L, 24L, 20L, 12L, 21L, 13L, 26L, 22L, 19L, 27L, 23L, 25L, 16L, 14L, 11L, 87L, 34L, 29L, 39L, 28L, 154L, 31L, 32L, 37L, 38L, 36L, 35L, 40L, 33L, 70L, 41L, 42L, 43L, 46L, 45L, 47L, 49L, 50L, 144L, 65L, 51L, 53L, 54L, 56L, 55L, 57L, 58L, 63L, 60L, 44L, 61L, 66L, 67L, 62L, 64L, 68L, 71L, 69L, 72L, 78L, 74L, 73L, 76L, 77L, 75L, 79L, 80L, 81L, 83L, 82L, 84L, 85L, 130L, 88L, 89L, 86L, 90L, 99L, 91L, 96L, 92L, 93L, 97L, 98L, 105L, 102L, 113L, 114L, 103L, 106L, 107L, 111L, 112L, 104L, 101L, 109L, 100L, 110L, 108L, 115L, 121L, 119L, 4L, 122L, 118L, 116L, 117L, 120L, 133L, 123L, 124L, 125L, 128L, 132L, 126L, 127L, 129L, 131L, 134L, 135L, 136L, 137L, 94L, 172L, 147L, 138L, 140L, 146L, 143L, 141L, 149L, 150L, 142L, 145L, 174L, 52L, 95L, 139L, 148L, 152L, 151L, 30L, 153L, 157L, 163L, 156L, 155L, 159L, 160L, 161L, 162L, 158L, 164L, 165L, 5L, 59L, 167L, 166L, 168L, 171L, 169L, 170L, 173L, 175L, 176L), .Label = c("AFG", "AGO", "ALB", "ANT", "ARE", "ARG", "ARM", "AUS", "AUT", "AZE", "BDI", "BEL", "BEN", "BFA", "BGD", "BGR", "BHR", "BHS", "BIH", "BLR", "BLZ", "BOL", "BRA", "BRB", "BRN", "BTN", "BWA", "CAF", "CAN", "CHE", "CHL", "CHN", "CIV", "CMR", "COD", "COG", "COL", "COM", "CPV", "CRI", "CUB", "CYP", "CZE", "DEU", "DJI", "DNK", "DOM", "DZA", "ECU", "EGY", "ERI", "ESP", "EST", "ETH", "FIN", "FJI", "FRA", "GAB", "GBR", "GEO", "GHA", "GIN", "GMB", "GNB", "GNQ", "GRC", "GTM", "GUY", "HND", "HRV", "HTI", "HUN", "IDN", "IND", "IRL", "IRN", "IRQ", "ISL", "ISR", "ITA", "JAM", "JOR", "JPN", "KAZ", "KEN", "KGZ", "KHM", "KOR", "KWT", "LAO", "LBN", "LBR", "LBY", "LCA", "LKA", "LSO", "LTU", "LUX", "LVA", "MAR", "MDA", "MDG", "MDV", "MEX", "MKD", "MLI", "MLT", "MMR", "MNG", "MOZ", "MRT", "MUS", "MWI", "MYS", "NAM", "NER", "NGA", "NIC", "NLD", "NOR", "NPL", "NZL", "OMN", "PAK", "PAN", "PER", "PHL", "PNG", "POL", "PRK", "PRT", "PRY", "PSE", "QAT", "ROM", "RUS", "RWA", "SAU", "SDN", "SEN", "SGP", "SLB", "SLE", "SLV", "SOM", "SRB", "STP", "SUR", "SVK", "SVN", "SWE", "SWZ", "SYR", "TCD", "TGO", "THA", "TJK", "TKM", "TON", "TTO", "TUN", "TUR", "TZA", "UGA", "UKR", "URY", "USA", "UZB", "VEN", "VNM", "VUT", "WSM", "YEM", "ZAF", "ZMB", "ZWE"), class = "factor"), country = structure(c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L, 50L, 51L, 52L, 53L, 54L, 55L, 56L, 57L, 58L, 59L, 60L, 61L, 62L, 63L, 64L, 65L, 66L, 67L, 68L, 69L, 70L, 71L, 72L, 73L, 74L, 75L, 76L, 77L, 78L, 79L, 80L, 81L, 82L, 83L, 117L, 146L, 84L, 85L, 86L, 87L, 88L, 89L, 90L, 91L, 92L, 93L, 94L, 95L, 96L, 97L, 98L, 99L, 100L, 101L, 102L, 103L, 104L, 105L, 106L, 107L, 108L, 109L, 110L, 111L, 112L, 113L, 114L, 115L, 116L, 118L, 121L, 119L, 120L, 122L, 123L, 124L, 125L, 126L, 127L, 128L, 129L, 130L, 131L, 132L, 133L, 134L, 135L, 136L, 137L, 138L, 139L, 140L, 141L, 142L, 143L, 144L, 145L, 147L, 148L, 149L, 150L, 151L, 152L, 153L, 154L, 155L, 156L, 157L, 158L, 159L, 160L, 161L, 162L, 163L, 164L, 165L, 166L, 167L, 168L, 169L, 170L, 171L, 172L, 173L, 174L, 175L, 176L), .Label = c("Afghanistan", "Albania", "Algeria", "Angola", "Argentina", "Armenia", "Australia", "Austria", "Azerbaijan", "Bahamas", "Bahrain", "Bangladesh", "Barbados", "Belarus", "Belgium", "Belize", "Benin", "Bhutan", "Bolivia", "Bosnia and Herzegovina", "Botswana", "Brazil", "Brunei", "Bulgaria", "Burkina Faso", "Burundi", "Cambodia", "Cameroon", "Canada", "Cape Verde", "Central African Republic", "Chad", "Chile", "China", "Colombia", "Comoros", "Congo", "Congo, Democratic Republic of the", "Costa Rica", "Cote d'Ivoire", "Croatia", "Cuba", "Cyprus", "Czech Republic", "Denmark", "Djibouti", "Dominican Republic", "Ecuador", "Egypt", "El Salvador", "Equatorial Guinea", "Eritrea", "Estonia", "Ethiopia", "Fiji", "Finland", "France", "Gabon", "Gambia", "Georgia", "Germany", "Ghana", "Greece", "Guatemala", "Guinea", "Guinea-Bissau", "Guyana", "Haiti", "Honduras", "Hungary", "Iceland", "India", "Indonesia", "Iran", "Iraq", "Ireland", "Israel", "Italy", "Jamaica", "Japan", "Jordan", "Kazakhstan", "Kenya", "Kuwait", "Kyrgyzstan", "Laos", "Latvia", "Lebanon", "Lesotho", "Liberia", "Libya", "Lithuania", "Luxembourg", "Macedonia", "Madagascar", "Malawi", "Malaysia", "Maldives", "Mali", "Malta", "Mauritania", "Mauritius", "Mexico", "Moldova", "Mongolia", "Morocco", "Mozambique", "Myanmar", "Namibia", "Nepal", "Netherlands", "Netherlands Antilles", "New Zealand", "Nicaragua", "Niger", "Nigeria", "North Korea", "Norway", "Oman", "Pakistan", "Palestinian Territories", "Panama", "Papua New Guinea", "Paraguay", "Peru", "Philippines", "Poland", "Portugal", "Qatar", "Romania", "Russian Federation", "Rwanda", "Saint Lucia", "Samoa", "Sao Tome and Principe", "Saudi Arabia", "Senegal", "Serbia", "Sierra Leone", "Singapore", "Slovakia", "Slovenia", "Solomon Islands", "Somalia", "South Africa", "South Korea", "Spain", "Sri Lanka", "Sudan", "Suriname", "Swaziland", "Sweden", "Switzerland", "Syria", "Tajikistan", "Tanzania", "Thailand", "Togo", "Tonga", "Trinidad and Tobago", "Tunisia", "Turkey", "Turkmenistan", "Uganda", "Ukraine", "United Arab Emirates", "United Kingdom", "United States", "Uruguay", "Uzbekistan", "Vanuatu", "Venezuela", "Viet Nam", "Yemen", "Zambia", "Zimbabwe"), class = "factor"), europe = structure(c(1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("0", "1"), class = "factor"), americas = structure(c(1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L), .Label = c("0", "1"), class = "factor"), asia = structure(c(2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 1L), .Label = c("0", "1"), class = "factor"), africa = structure(c(1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L), .Label = c("0", "1"), class = "factor"), income.f = c(373L, 3728L, 3546L, 1787L, 10063L, 3893L, 26311L, 18397L, 3960L, 14656L, 10496L, 1282L, 12868L, 6236L, 22182L, 4022L, 732L, 2141L, 2059L, 2864L, 5913L, 6204L, 15658L, 7176L, 966L, 611L, 2332L, 1519L, 25448L, 3087L, 933L, 1126L, 6871L, 5220L, 5680L, 1337L, 841L, 488L, 6983L, 795L, 10587L, 4268L, 16805L, 13992L, 28766L, 1422L, 4907L, 3102L, 1635L, 3043L, 4635L, 689L, 12112L, 796L, 3928L, 26795L, 23945L, 5049L, 1327L, 1731L, 21823L, 2056L, 16738L, 2267L, 1876L, 558L, 2665L, 1146L, 2160L, 14058L, 28637L, 1620L, 2410L, 4475L, 374L, 21076L, 20497L, 18501L, 3107L, 17802L, 2566L, 6141L, 1126L, 920L, 12531L, 12623L, 1414L, 1385L, 10951L, 2701L, 2340L, 234L, 4054L, 12000L, 20446L, 4676L, 758L, 565L, 5751L, 3992L, 833L, 12834L, 1489L, 7407L, 6039L, 1634L, 1413L, 1846L, 1115L, NA, 5527L, 1038L, 25625L, 12500L, 20666L, 1773L, 561L, 652L, 30749L, NA, 4516L, 1059L, 5537L, 2140L, 2358L, 4269L, 3883L, 10414L, 15294L, 9211L, 7443L, 8476L, 1031L, 4501L, 3338L, 1022L, 4031L, 1256L, NA, 507L, 20044L, 11777L, 17022L, 1345L, 477L, 6927L, 18335L, 2647L, 832L, 4426L, 2187L, 29044L, 25056L, 1907L, 992L, 627L, 6695L, 907L, 5243L, 9307L, 3748L, 4385L, 6108L, 1199L, 4970L, 8329L, 26242L, 25005L, 7203L, 1547L, 2601L, 4560L, 2540L, 424L, 725L, 1499L), income.m = c(1119L, 6930L, 10515L, 2898L, 18686L, 6150L, 37414L, 40000L, 6137L, 20803L, 29796L, 2792L, 20309L, 9835L, 40000L, 10117L, 1543L, 4463L, 3584L, 4341L, 19094L, 10664L, 37506L, 11010L, 1458L, 791L, 3149L, 3086L, 40000L, 8756L, 1530L, 1735L, 17293L, 8213L, 8966L, 2643L, 1691L, 944L, 13271L, 2472L, 15687L, 9489L, 27808L, 27440L, 39288L, 2935L, 11465L, 5572L, 7024L, 7543L, 10814L, 1544L, 19430L, 1316L, 8103L, 37739L, 37169L, 8876L, 2525L, 5188L, 37461L, 2893L, 30184L, 6990L, 2734L, 1103L, 6467L, 2195L, 4680L, 22098L, 40000L, 5194L, 5280L, 11363L, 1925L, 40000L, 31345L, 39163L, 5503L, 40000L, 8270L, 9723L, 1354L, 2000L, 31476L, 36403L, 2455L, 2692L, 16842L, 8585L, 4480L, 471L, 13460L, 17349L, 40000L, 9734L, 1090L, 771L, 15861L, 7946L, 1234L, 25623L, 2996L, 18098L, 15680L, 2608L, 2799L, 7297L, 1378L, NA, 9679L, 2072L, 39845L, 22340L, 29479L, 5577L, 991L, 1592L, 40000L, NA, 23880L, 3607L, 9636L, 2960L, 6892L, 7791L, 6375L, 17493L, 25881L, 37774L, 10761L, 13581L, 1392L, 8805L, 8797L, 3357L, 25678L, 2346L, NA, 1114L, 39150L, 20218L, 27779L, 2672L, 1000L, 15446L, 36324L, 6479L, 3317L, 11029L, 7659L, 36059L, 40000L, 5684L, 1725L, 863L, 10732L, 2119L, 10981L, 20053L, 12924L, 12368L, 9596L, 1708L, 9067L, 33555L, 40000L, 40000L, 12890L, 2585L, 3830L, 8683L, 3604L, 1422L, 1319L, 2585L), inc.parity = c(0.33, 0.54, 0.34, 0.62, 0.54, 0.63, 0.7, 0.46, 0.65, 0.7, 0.35, 0.46, 0.63, 0.63, 0.55, 0.4, 0.47, 0.48, 0.57, 0.66, 0.31, 0.58, 0.42, 0.65, 0.66, 0.77, 0.74, 0.49, 0.64, 0.35, 0.61, 0.65, 0.4, 0.64, 0.63, 0.51, 0.5, 0.52, 0.53, 0.32, 0.67, 0.45, 0.6, 0.51, 0.73, 0.48, 0.43, 0.56, 0.23, 0.4, 0.43, 0.45, 0.62, 0.6, 0.48, 0.71, 0.64, 0.57, 0.53, 0.33, 0.58, 0.71, 0.55, 0.32, 0.69, 0.51, 0.41, 0.52, 0.46, 0.64, 0.72, 0.31, 0.46, 0.39, 0.19, 0.53, 0.65, 0.47, 0.56, 0.45, 0.31, 0.63, 0.83, 0.46, 0.4, 0.35, 0.58, 0.51, 0.65, 0.31, 0.52, 0.5, 0.3, 0.69, 0.51, 0.48, 0.7, 0.73, 0.36, 0.5, 0.68, 0.5, 0.5, 0.41, 0.39, 0.63, 0.5, 0.25, 0.81, NA, 0.57, 0.5, 0.64, 0.56, 0.7, 0.32, 0.57, 0.41, 0.77, NA, 0.19, 0.29, 0.57, 0.72, 0.34, 0.55, 0.61, 0.6, 0.59, 0.24, 0.69, 0.62, 0.74, 0.51, 0.38, 0.3, 0.16, 0.54, NA, 0.45, 0.51, 0.58, 0.61, 0.5, 0.48, 0.45, 0.5, 0.41, 0.25, 0.4, 0.29, 0.81, 0.63, 0.34, 0.57, 0.73, 0.62, 0.43, 0.48, 0.46, 0.29, 0.35, 0.64, 0.7, 0.55, 0.25, 0.66, 0.63, 0.56, 0.6, 0.68, 0.53, 0.7, 0.3, 0.55, 0.58), lifexp.f = c(42.8, 79.5, 73, 43.3, 78.6, 74.9, 83.3, 82.2, 70.8, 75, 77, 64, 79.3, 74.9, 81.8, 79.1, 56.5, 66.5, 66.9, 77.1, 48.4, 75.5, 79.3, 76.4, 52.9, 49.8, 60.6, 50.2, 82.6, 73.8, 45, 51.8, 81.3, 74.3, 76, 66.3, 55.2, 47.1, 80.9, 48.3, 78.8, 79.8, 81.5, 79.1, 80.1, 55.2, 74.8, 77.7, 73, 74.3, 51.6, 59, 76.8, 53.1, 70.6, 82, 83.7, 56.9, 59.9, 74.5, 81.8, 59.5, 80.9, 73.2, 56.4, 47.5, 68.1, 61.3, 73.1, 77, 83.1, 65.3, 71.6, 71.8, 59.9, 80.9, 82.3, 83.2, 74.9, 85.7, 73.8, 71.5, 53.1, 68.8, 81.5, 79.6, 69.6, 64.5, 77.3, 73.7, 42.9, 45.6, 76.3, 78, 81.4, 76.3, 60.1, 46.7, 76.1, 67.6, 55.3, 81.1, 65, 75.8, 78, 72, 69.2, 72.7, 43.6, 64.2, 52.2, 62.9, 81.4, 78.7, 81.8, 75, 54.9, 47.1, 82.2, 74.4, 76.7, 64.8, 77.8, 60.1, 73.4, 73.3, 73.3, 79.4, 80.9, 75.8, 75.6, 72.1, 46.7, 75, 74.2, 66.7, 74.6, 64.4, 76, 43.4, 81.4, 78.2, 81.1, 63.8, 48.2, 52, 83.8, 75.6, 58.9, 73, 41.4, 82.7, 83.7, 75.5, 69, 52, 74.5, 59.6, 73.8, 71.2, 75.6, 73.9, 67, 50.2, 73.6, 81, 81.2, 80.4, 79.4, 70, 71.3, 76.3, 75.7, 63.1, 40.6, 40.2), lifexp.m = c(42.9, 73.1, 70.4, 40.1, 71.1, 68.2, 78.5, 76.5, 63.5, 69.6, 73.9, 62.3, 73.6, 62.7, 75.8, 73.1, 54.1, 63.1, 62.6, 71.8, 47.6, 68.1, 74.6, 69.2, 49.8, 47.1, 55.2, 49.4, 77.9, 67.5, 42.3, 49, 75.3, 71, 68.7, 62, 52.8, 44.4, 76.2, 46.5, 71.8, 75.8, 76.6, 72.7, 75.5, 52.6, 68.6, 71.8, 68.5, 68.2, 49.1, 54, 65.5, 50.5, 66.1, 75.6, 76.6, 55.6, 57.7, 66.7, 76.2, 58.7, 76.7, 66.2, 53.2, 44.2, 62.4, 57.7, 65.8, 68.8, 79.9, 62.3, 67.8, 68.7, 55.7, 76, 78.1, 77.2, 69.6, 78.7, 70.3, 60.5, 51.1, 64.6, 74.3, 75.7, 61.7, 61.9, 66.5, 69.4, 42.1, 43.8, 71.1, 66.9, 75.4, 71.4, 56.7, 46, 71.4, 66.6, 50.8, 76.8, 61.5, 69.1, 73.1, 64.7, 62.8, 68.3, 42, 57.6, 50.9, 62.1, 76.9, 71.2, 77.7, 69, 56.7, 46, 77.3, 71.3, 73.6, 64.3, 72.7, 54.3, 69.2, 68.2, 68.9, 71, 74.5, 74.6, 68.4, 58.6, 43.6, 71.3, 67.8, 63, 70.3, 60.4, 71.3, 40.2, 77.5, 70.3, 73.6, 62.2, 45.9, 49.5, 77.2, 67.9, 56, 66.4, 40.4, 78.3, 78.5, 71.8, 63.8, 50, 65, 56, 71.8, 67.2, 71.5, 69, 58.5, 49.1, 62, 76.8, 76.7, 75.2, 72.2, 63.6, 67.5, 70.4, 71.9, 60, 40.3, 41.4), literacy.f = c(12.6, 98.3, 60.1, 54.2, 97.2, 99.2, NA, NA, 98.2, 95, 83.6, 40.8, 99.7, 99.4, NA, 94.6, 23.3, NA, 80.7, 94.4, 81.8, 88.8, 90.2, 97.7, 16.6, 52.2, 64.1, 59.8, NA, 75.5, 33.5, 12.8, 95.6, 86.5, 92.9, 63.9, 79, 54.1, 95.1, 38.6, 97.1, 99.8, 95.1, NA, NA, 79.9, 87.2, 89.7, 59.4, 79.2, 80.5, 71.5, 99.8, 22.8, 95.9, NA, NA, 79.7, 49.9, NA, NA, 49.8, 94.2, 63.3, 18.1, 60, 99.2, 56.5, 80.2, NA, NA, 47.8, 86.8, 76.8, 64.2, NA, 97.7, 98, 85.9, NA, 87, 99.3, 70.2, NA, NA, 91, 98.1, 60.9, 99.7, 93.6, 90.3, 45.7, 74.8, 99.6, NA, 94.1, 65.3, 54, 85.4, 96.4, 15.9, 89.2, 43.4, 80.5, 90.2, 98.6, 97.5, 39.6, 25, 86.4, 83.5, 34.9, NA, 96, NA, 76.6, 15.1, 60.1, NA, 88, 73.5, 35.4, 91.2, 50.9, 92.7, 82.5, 93.6, NA, 92, 88.6, 96.3, 99.2, 59.8, NA, 98.3, 77.9, 76.3, 29.2, 94.1, 24.2, 88.6, NA, 99.6, NA, NA, 80.9, NA, 89.1, 51.8, 87.2, 78.3, NA, NA, 73.6, 99.2, 62.2, 90.5, 38.5, 99, 97.8, 65.3, 79.6, 98.3, 57.7, 99.2, 87.8, NA, NA, 97.3, 99.6, NA, 92.7, 86.9, 34.7, 59.8, 86.2), literacy.m = c(43.1, 99.2, 79.6, 82.9, 97.2, 99.7, NA, NA, 99.5, 95, 88.6, 53.9, 99.7, 99.8, NA, 94.6, 47.9, NA, 93.1, 99, 80.4, 88.4, 95.2, 98.7, 31.4, 67.3, 84.7, 77, NA, 87.8, 64.8, 40.8, 95.8, 95.1, 92.8, 63.9, 90.5, 80.9, 94.7, 60.8, 99.3, 99.8, 98.6, NA, NA, 79.9, 86.8, 92.3, 83, 82.1, 93.4, 71.5, 99.8, 50, 95.9, NA, NA, 88.5, 49.9, NA, NA, 66.4, 97.8, 75.4, 42.6, 60, 99.2, 56.5, 79.8, NA, NA, 73.4, 94, 88, 84.1, NA, 97.7, 98.8, 74.1, NA, 95.2, 99.8, 77.7, NA, NA, 94.4, 99.3, 77, 99.8, 93.6, 73.7, 58.3, 92.8, 99.6, NA, 98.2, 76.5, 74.9, 92, 96.2, 32.7, 86.4, 59.5, 88.2, 93.2, 99.6, 98, 65.7, 54.8, 93.9, 86.8, 62.7, NA, 96.1, NA, 76.8, 42.9, 78.2, NA, 96.7, 86.9, 64.1, 92.5, 63.4, 94.3, 93.7, 91.6, NA, 95.8, 89.1, 98.4, 99.7, 71.4, NA, 98.9, 92.2, 87.5, 51.1, 98.9, 46.7, 96.6, NA, 99.7, NA, NA, 84.1, NA, 92.3, 71.1, 92, 80.9, NA, NA, 87.8, 99.7, 77.5, 94.9, 68.7, 98.8, 98.9, 83.4, 95.3, 99.3, 76.8, 99.7, 89, NA, NA, 96.2, 99.6, NA, 93.3, 93.9, 73.1, 76.3, 92.7), marr1519.f = c(NA, 8L, 4L, NA, 12L, 9L, 1L, 3L, 13L, 4L, 7L, 48L, 1L, 6L, 1L, 8L, 29L, 27L, 12L, NA, 5L, 17L, 8L, 16L, 35L, 7L, 12L, 36L, 3L, 7L, 42L, 49L, 12L, 1L, 18L, 12L, NA, NA, 20L, 25L, 2L, NA, 8L, 1L, 1L, NA, 29L, 22L, 15L, 16L, NA, 38L, 9L, 30L, 10L, 1L, 0L, 22L, 39L, 16L, NA, 16L, 6L, 26L, 46L, NA, 7L, 19L, 31L, 2L, 1L, 30L, 13L, 18L, 21L, 0L, NA, 1L, 1L, 1L, 8L, 7L, 17L, NA, 1L, 5L, 11L, 27L, 1L, NA, 18L, 36L, 1L, 2L, 2L, NA, 34L, 37L, 5L, 12L, 50L, 3L, 28L, 11L, 17L, 12L, 6L, 13L, 47L, 11L, 8L, 40L, 1L, 1L, 7L, 32L, 62L, 28L, 0L, 24L, 16L, 21L, 22L, 21L, 17L, 13L, 10L, 2L, 6L, 4L, 6L, 11L, 7L, 1L, 8L, 20L, 16L, 29L, 11L, 47L, 1L, 2L, 0L, NA, NA, 3L, 2L, 7L, 21L, NA, 9L, 0L, 1L, NA, 12L, 25L, 15L, 20L, 5L, 9L, 3L, 16L, 6L, 32L, 10L, 19L, 2L, 4L, 13L, 13L, 12L, 18L, 8L, 27L, 24L, 23L), mean.marr.f = c(NA, 23L, 26L, NA, 23L, 23L, 29L, 26L, 24L, 27L, 26L, 19L, 32L, 23L, 28L, 26L, 20L, 21L, 23L, NA, 27L, 23L, 25L, 21L, 19L, 22L, 22L, 20L, 27L, 26L, 20L, 18L, 23L, 23L, 23L, 24L, NA, NA, 21L, 22L, 26L, NA, 23L, 25L, 31L, NA, 21L, 22L, 22L, 22L, NA, 20L, 22L, 21L, 23L, 30L, 30L, 22L, 20L, 24L, NA, 21L, 25L, 20L, 19L, NA, 28L, 22L, 20L, 26L, 31L, 20L, 23L, 22L, 22L, 31L, NA, 28L, 33L, 29L, 25L, 23L, 22L, NA, 26L, 25L, 22L, 21L, 27L, NA, 21L, 20L, 29L, 25L, 26L, NA, 21L, 19L, 25L, 22L, 18L, 22L, 22L, 24L, 23L, 21L, 24L, 25L, 18L, 25L, 26L, 19L, 30L, 30L, 25L, 21L, 18L, 21L, 31L, 22L, 22L, 21L, 22L, 21L, 22L, 23L, 24L, 25L, 24L, 26L, 24L, 22L, 23L, 34L, 24L, 18L, 22L, 22L, 23L, 20L, 27L, 25L, 30L, NA, NA, 28L, 26L, 25L, 23L, NA, 26L, 32L, 29L, NA, 21L, 21L, 24L, 21L, 26L, 27L, 27L, 22L, 23L, 20L, 22L, 23L, 26L, 26L, 23L, 21L, 23L, 22L, 22L, 21L, 21L, 21L), mean.marr.m = c(NA, 27L, NA, NA, 26L, NA, 31L, 29L, 27L, 29L, 28L, NA, 34L, 25L, 30L, 28L, NA, 24L, 25L, NA, 31L, 25L, 27L, 25L, 26L, 26L, 24L, 27L, 30L, 28L, 24L, 24L, 26L, 25L, 27L, 28L, NA, NA, NA, 28L, 30L, NA, 27L, 28L, 33L, NA, 26L, 25L, 28L, 25L, NA, 25L, 25L, 26L, 26L, 32L, 32L, 26L, 28L, NA, NA, 27L, 29L, NA, 28L, NA, 30L, 27L, NA, 29L, 33L, 25L, 26L, 25L, 26L, 32L, NA, 32L, 35L, 31L, NA, 26L, 26L, NA, 29L, 28L, 25L, NA, 29L, NA, 26L, NA, 32L, 27L, 29L, NA, NA, 24L, 29L, 26L, 26L, 26L, 29L, 28L, 25L, 24L, 26L, 30L, 23L, 26L, NA, 23L, 33L, 33L, 27L, 24L, 24L, 27L, 34L, 25L, 26L, 26L, 26L, NA, 26L, 27L, 27L, 28L, 27L, 29L, 27L, 24L, 26L, 35L, 27L, 23L, 26L, NA, 27L, 28L, 30L, 28L, 32L, NA, NA, 30L, 28L, NA, 29L, NA, 29L, 35L, 32L, NA, 23L, 25L, 26L, 27L, 28L, 30L, 30L, NA, NA, 23L, NA, 26L, 28L, 29L, 26L, NA, 25L, 25L, NA, NA, 26L, 26L ), fertility.adol = c(113L, 16L, 7L, 138L, 57L, 30L, 14L, 12L, 29L, 53L, 17L, 124L, 42L, 22L, 7L, 79L, 120L, 37L, 78L, 20L, 52L, 89L, 27L, 40L, 126L, 55L, 42L, 118L, 14L, 83L, 115L, 164L, 60L, 8L, 65L, 49L, 115L, 222L, 71L, 107L, 13L, 47L, 8L, 11L, 6L, 23L, 108L, 83L, 39L, 81L, 123L, 72L, 21L, 94L, 32L, 9L, 7L, 82L, 104L, 30L, 9L, 55L, 9L, 107L, 149L, 188L, 63L, 46L, 93L, 19L, 15L, 62L, 40L, 20L, 37L, 16L, 14L, 6L, 78L, 3L, 25L, 31L, 104L, 1L, 4L, 13L, 31L, 72L, 14L, 25L, 73L, 219L, 3L, 18L, 10L, 21L, 133L, 135L, 13L, 23L, 179L, 12L, 85L, 41L, 65L, 32L, 45L, 19L, 149L, 16L, 59L, 115L, 5L, 30L, 22L, 113L, 196L, 126L, 8L, 79L, 10L, 36L, 83L, 51L, 72L, 60L, 47L, 13L, 13L, 17L, 32L, 28L, 40L, 60L, 27L, 66L, 28L, 87L, 24L, 160L, 5L, 20L, 7L, 41L, 66L, 61L, 9L, 25L, 57L, 39L, 33L, 4L, 4L, 35L, 28L, 121L, 42L, 89L, 18L, 35L, 7L, 37L, 16L, 152L, 28L, 18L, 24L, 42L, 61L, 34L, 44L, 90L, 17L, 71L, 125L, 59L), fertility.total = c(7.1, 2.1, 2.4, 6.4, 2.3, 1.4, 1.8, 1.4, 1.8, 2, 2.3, 2.8, 1.5, 1.2, 1.6, 2.9, 5.4, 2.2, 3.5, 1.2, 2.9, 2.2, 2.3, 1.3, 6, 6.8, 3.2, 4.3, 1.5, 3.4, 4.6, 6.2, 1.9, 1.7, 2.2, 4.3, 4.5, 6.7, 2.1, 4.5, 1.3, 1.5, 1.6, 1.2, 1.8, 3.9, 2.8, 2.6, 2.9, 2.7, 5.4, 5, 1.5, 5.3, 2.8, 1.8, 1.9, 3.1, 4.7, 1.4, 1.4, 3.8, 1.3, 4.2, 5.4, 7.1, 2.3, 3.5, 3.3, 1.3, 2.1, 2.8, 2.2, 2, 4.3, 2, 2.8, 1.4, 2.4, 1.3, 3.1, 2.3, 5, 1.9, 1.2, 2.2, 2.5, 3.2, 1.3, 2.2, 3.4, 6.8, 2.7, 1.3, 1.7, 1.4, 4.8, 5.6, 2.6, 2.6, 6.5, 1.4, 4.4, 1.9, 2.2, 1.4, 1.9, 2.4, 5.1, 2.1, 3.2, 3.3, 1.7, 1.9, 2, 2.8, 7.2, 5.3, 1.8, 5.1, 3, 3.5, 2.6, 3.8, 3.1, 2.5, 3.2, 1.2, 1.5, 2.7, 1.3, 1.3, 5.9, 2.2, 3.9, 3.9, 3.4, 4.7, 1.8, 6.5, 1.3, 1.3, 1.3, 3.9, 6, 2.6, 1.4, 1.9, 4.2, 2.4, 3.4, 1.8, 1.4, 3.1, 3.3, 5.2, 1.9, 4.8, 3.8, 1.6, 1.9, 2.1, 2.5, 6.5, 1.2, 2.3, 1.8, 2.1, 2.1, 2.5, 3.7, 2.5, 2.1, 5.5, 5.2, 3.2), maternaldeaths = c(1900L, 55L, 140L, 1700L, 82L, 55L, 8L, 4L, 94L, 60L, 28L, 380L, 95L, 35L, 10L, 140L, 850L, 420L, 420L, 31L, 100L, 260L, 37L, 32L, 1000L, 1000L, 450L, 730L, 6L, 150L, 1100L, 1100L, 31L, 56L, 130L, 480L, 510L, 990L, 43L, 690L, 8L, 33L, 47L, 9L, 5L, 730L, 150L, 130L, 84L, 150L, 880L, 630L, 63L, 850L, 75L, 6L, 17L, 420L, 540L, 32L, 8L, 540L, 9L, 240L, 740L, 1100L, 170L, 680L, 110L, 16L, 0L, 540L, 230L, 76L, 250L, 5L, 17L, 5L, 87L, 10L, 41L, 210L, 1000L, 67L, 20L, 5L, 110L, 650L, 42L, 150L, 550L, 760L, 97L, 19L, 28L, 23L, 550L, 1800L, 41L, 110L, 1200L, 21L, 1000L, 24L, 83L, 36L, 110L, 220L, 1000L, 360L, 300L, 740L, 16L, NA, 7L, 230L, 1600L, 800L, 16L, 100L, 87L, 500L, 160L, 300L, 170L, 410L, 200L, 13L, 5L, 7L, 49L, 67L, 1400L, NA, NA, NA, 23L, 690L, 11L, 2000L, 30L, 3L, 17L, 130L, 1100L, 230L, 4L, 92L, 590L, 110L, 370L, 2L, 7L, 160L, 100L, 1500L, 44L, 570L, NA, 160L, 120L, 70L, 31L, 880L, 35L, 54L, 13L, 17L, 27L, 24L, NA, 96L, 130L, 570L, 750L, 1100L ), secondary.f = c(16L, 48L, 51L, NA, 51L, 50L, 48L, 47L, 48L, 50L, 50L, 50L, 50L, 49L, 48L, 50L, 32L, NA, NA, NA, 51L, 52L, 49L, 48L, 40L, 43L, 40L, 41L, 48L, 52L, NA, 25L, 49L, 47L, 52L, 43L, 46L, NA, 50L, NA, NA, 49L, 49L, 49L, 50L, 40L, 54L, 49L, 47L, 50L, NA, 36L, 49L, 38L, 50L, 50L, 49L, NA, 45L, 49L, 48L, 45L, 48L, 47L, 31L, NA, 51L, NA, 55L, 49L, 50L, 43L, 49L, 47L, 39L, 51L, 49L, 48L, 50L, 49L, 49L, 49L, 48L, NA, 47L, 50L, 50L, 42L, 49L, 51L, 56L, NA, NA, 49L, 50L, 48L, NA, 45L, 52L, 52L, 37L, 47L, 45L, 49L, 51L, 50L, 53L, 45L, 41L, 48L, 50L, NA, 48L, NA, 50L, 53L, 39L, 44L, 49L, 50L, 48L, 41L, 51L, NA, 50L, 50L, 52L, 49L, 51L, 49L, 49L, 49L, 48L, 53L, 51L, 50L, 48L, 42L, NA, NA, 49L, 49L, 49L, 44L, NA, 52L, 50L, 49L, 47L, NA, 49L, 49L, 47L, 47L, 45L, NA, 51L, 33L, 49L, 51L, NA, 42L, NA, 44L, 48L, 49L, 49L, 49L, 53L, 49L, 45L, 52L, 48L, 31L, 44L, NA ), laborforce.f = c(29.4, 42, 31.1, 45.8, 43.3, 49.2, 45.2, 44.9, 47.9, 49.1, 18.6, 36.7, 47.4, 49.4, 44.1, 34.8, 38.4, 34.4, 43.6, 48.5, 40.4, 43, 34.1, 45.6, 47.2, 51.7, 50.8, 39.6, 46.5, 33.7, 46.1, 46.8, 35.5, 44.3, 44.9, 40, 40.1, 41.4, 35.8, 29.3, 45.2, 37.6, 45.6, 45.3, 46.9, 39.4, 36.6, 42.9, 21.8, 40.8, 37.1, 41.1, 49.3, 44.9, 38.8, 47.9, 46.1, 42.8, 40.9, 43, 45.5, 47.9, 41.2, 31.3, 47.6, 40.9, 33.2, 41.4, 39.5, 45.3, 46.6, 28.1, 38, 34.5, 20.4, 43.4, 47.4, 40.4, 43.6, 41.2, 25.5, 50, 44.2, 39.3, 40.9, 25.8, 44.1, 41.1, 48.3, 31.1, 43.6, 39.7, 27.8, 49.4, 42.4, 39.3, 48.4, 49.9, 36.1, 39.8, 49.3, 34.9, 39.2, 35.9, 35.3, 47.1, 40.4, 26.2, 53.4, 45.1, 43.8, 40.6, 44.8, 46.6, 46.6, 30.1, 42.5, 35.4, 47.5, 13.2, 17.4, 27.4, 39.4, 48.6, 43.4, 42.7, 40.2, 46, 46.6, 14, 46.3, 49, 51.6, 41.8, 32.3, 29.8, 14.3, 41.3, 43.2, 38.5, 40.1, 45.2, 46.2, 38.2, 39.2, 38, 41.2, 32.4, 24.9, 35, 32.3, 47.4, 46.6, 30.7, 43.9, 49.6, 47, 36.7, 37.8, 39.2, 28.1, 26.5, 46.7, 48.5, 48.4, 14.6, 46, 46.3, 44.7, 44.8, 46.9, 41.5, 48.4, 28.2, 42.6, 43.7), pop = c(26088L, 3172L, 33351L, 16557L, 39134L, 3010L, 20530L, 8327L, 8406L, 327L, 739L, 155991L, 293L, 9742L, 10430L, 282L, 8760L, 649L, 9354L, 3926L, 1858L, 189323L, 382L, 7693L, 14359L, 8173L, 14197L, 18175L, 32577L, 519L, 4265L, 10468L, 16465L, 1328474L, 45558L, 818L, 3689L, 60644L, 4399L, 18914L, 4556L, 11267L, 846L, 10189L, 5430L, 819L, 9615L, 13202L, 74166L, 6762L, 496L, 4692L, 1340L, 81021L, 833L, 5261L, 61330L, 1311L, 1663L, 4433L, 82641L, 23008L, 11123L, 13029L, 9181L, 1646L, 739L, 9446L, 6969L, 10058L, 298L, 1151751L, 228864L, 70270L, 28506L, 4221L, 6810L, 58779L, 2699L, 127953L, 5729L, 15314L, 36553L, 23708L, 48050L, 2779L, 5259L, 5759L, 2289L, 4055L, 1995L, 3579L, 6039L, 3408L, 461L, 2036L, 19159L, 13571L, 26114L, 300L, 11968L, 405L, 3044L, 1252L, 105342L, 3833L, 2605L, 30853L, 20971L, 48379L, 2047L, 27641L, 16379L, NA, 4140L, 5532L, 13737L, 144720L, 4669L, NA, 2546L, 160943L, 3288L, 6202L, 6016L, 27589L, 86264L, 38140L, 10579L, 821L, 21532L, 143221L, 9464L, 163L, 185L, 155L, 24175L, 12072L, 9851L, 5743L, 4382L, 5388L, 2001L, 484L, 8445L, 48282L, 43887L, 19207L, 37707L, 455L, 1134L, 9078L, 7455L, 19408L, 6640L, 39459L, 63444L, 6410L, 100L, 1328L, 10215L, 73922L, 4899L, 29899L, 46557L, 4248L, 60512L, 302841L, 3331L, 26981L, 221L, 27191L, 86206L, 21732L, 11696L, 13228L), exports = c(14, 22.3, 47.6, 79.3, 25.1, 27.3, 18.7, 54.3, 62.9, NA, 72.9, 16.6, 54.3, 59.8, 86.3, 54.6, 13.5, 35.4, 35.5, 33, 48.7, 15.1, 70.2, 60.2, 10, 11.4, 64.2, 20.5, 37.9, 17, 12.6, 55.1, 41.3, 37.3, 21.5, 12.5, 84.8, 31.6, 48.7, 51.1, 47.1, NA, NA, 72.1, 48.8, 40.6, 34, 30.9, 30.3, 26.5, 96.8, 8.8, 78.9, 15.1, 55, 41.8, 26, 67.4, 44.8, 33.7, 40.7, 36.1, 18.4, 25, 28.4, 37.7, 84.5, 14, 59, 66.2, 31.8, 19.9, 33.9, 38.3, NA, 81.3, 43.9, 26.1, 41.1, 14.3, 52.6, 53.5, 26.7, NA, 42.3, 61.7, 38.3, 27.1, 47.8, 21.2, 49.7, 37.9, NA, 58.1, 159.3, 44.7, 26.9, 21.4, 117.6, NA, 25.6, NA, 35.9, 56.5, 30, 51.1, 64.3, 31.6, 32.9, NA, 47.6, 14.6, 69.9, NA, 27.8, 29.1, 15.4, 46.5, 44.5, NA, 63.3, 15.7, 74.8, 73.3, 46.9, 24.7, 47.6, 37.1, 28.5, 68.3, 32.9, 35.2, 9.6, NA, NA, NA, 60.9, 26.9, 25.2, 22.1, 238, 77.3, 63, NA, NA, 27.5, 25.5, 32.3, 18.2, 30.7, 72.6, 48.7, 47.9, 41.5, 26, 21, 73.7, 34.5, 19.9, 64.5, 47.5, 21.9, 65, 13.2, 51.5, 94.3, 26.6, 10.5, 31.1, 37.9, 45.3, 39.5, 69.4, NA, 33.8, 56.8), industry = c(24.5, 21.5, 61.5, 72.6, 35.6, 42.5, 26.8, 29.7, 63.6, NA, NA, 27.2, 18, 41.8, 24.1, 17.4, 13.4, 37.2, 31.5, 25.1, 53.8, 30.3, 71.6, 29.4, 22.7, 20, 25.1, 30.5, NA, 16.7, 15.3, 53.8, 42.4, 47.5, 34.3, 11, 68.6, 26.9, 29.3, 25.8, 31.6, NA, NA, 38.3, 25.5, 16.6, 25.5, 33.2, 36.3, 29.7, 94.2, 22.6, 28.5, 13, 21.9, 31.4, 20.7, 61.4, 13.1, 26.8, 29.3, 25.1, 19.4, 27.2, 35.3, 11.7, 23.4, NA, 28.7, 30.2, 23.7, 28.8, 46.8, 44.6, NA, 36.1, NA, 26.6, 33.1, 29.9, 28.9, 40.1, 18.5, NA, 40.3, NA, 22.4, 30, 21.6, 22.8, 42.4, 15.7, NA, 33.1, 16.2, 29.6, 15.5, 20.5, 49.7, NA, 24.2, NA, 29.3, 28.2, 26, 16.3, 34.4, 29, 25.3, NA, 28.1, 17.7, 24.2, NA, NA, 29.9, NA, 43.5, 43, NA, NA, 27.1, 18.3, 41.2, 19.3, 34.7, 31.9, 30.8, 25.1, NA, 35, 39.1, 14, 19, 28, 20.5, 63.2, 23.8, 25.5, 24.7, 32.7, 31.7, 33.2, 9.1, NA, 30.7, 29.3, 30.2, 27.8, 38.3, 45.2, 28.2, 28.4, 35.4, 31.3, 16.9, 44, 23.5, 14.5, 59.1, 28.4, 28.5, NA, 18.3, 32.3, 55.7, 24.3, 22.8, 31.5, 23.2, 8.8, NA, 41, NA, 29.9, 23.9), manufact = c(14.9, 0, 5.6, 3.5, 23.2, 20.9, 11.7, 19.4, 7, NA, NA, 16.5, 7.1, 33.7, 17.1, 9.1, 7.5, 7.3, 14, 11.3, 3.7, 18.4, 12.3, 17.8, 14.6, 8.8, 17.8, 17.7, NA, 7.5, 7.3, 4.7, 15.7, 33.5, 16.4, 4.4, 5.3, 6.6, 21.8, 19.3, 20.9, NA, NA, 26.5, 14.2, 2.6, 15.1, 8.9, 17.3, 22.9, 7.7, 8.2, 16.8, 4.8, 13.2, 23.1, 13, 4.1, 5.2, 13.7, 23.2, 8.7, 9.5, 18.7, 3.8, 8.7, 7.7, NA, 20.9, 22.2, 10.1, 15.9, 27.7, 11.8, NA, 24.5, NA, 18.2, 13.6, 21, 18.2, 12.8, 11.5, NA, 28.4, NA, 14.4, 20.6, 12.6, 14.1, 17.6, 12.4, NA, 20.9, 8.8, 18.3, 14, 13.9, 29.8, NA, 3.2, NA, 5, 20.2, 17.8, 15.9, 4.1, 17.2, 15.5, NA, 11.4, 8.2, 14.1, NA, NA, 18.7, NA, 2.8, 9.6, NA, NA, 18.6, 8, 6.4, 12.4, 16.3, 23.3, 18.6, 0, NA, 23.5, 19, 6.2, 5.5, 14.9, 6.4, 9.4, 15.2, 0, 0, 27.1, 20.7, 24, 3.8, NA, 18.6, 15.5, 19.5, 6.8, 19.1, 39.4, 0, 0, 8.5, 23.7, 6.8, 34.8, 9.9, 3.9, 6.5, 17.4, 19.9, NA, 9.3, 19.6, 14.1, 13.6, 14.4, 22.5, 9.1, 3.6, NA, 20.6, NA, 11.3, 13.5), services = c(39.4, 55.7, 30.1, 19.8, 55, 36.7, 69.9, 68.6, 26.5, NA, NA, 52.6, 78.3, 48.5, 74.8, 67.3, 54.4, 39.2, 54.4, 64.4, 44.2, 64, 27.5, 61.2, 43.2, 45.1, 44.2, 50, NA, 74.1, 29, 24.9, 53.3, 39.9, 53.3, 38, 26.8, 27.6, 62.1, 51.4, 60.8, NA, NA, 58.7, 73, 79.9, 62.1, 60.2, 48.8, 59.8, 2.9, 54.8, 67.8, 40.3, 63.8, 65.7, 77, 33.8, 54.2, 56.5, 69.7, 37.4, 76.6, 60.5, 44.5, 28.1, 47.1, NA, 57.6, 65.5, 70.5, 52.4, 40.2, 45, NA, 61.9, NA, 71.2, 61.2, 68.6, 68, 53.1, 54.4, NA, 56.3, NA, 45.7, 25.7, 74.5, 70.7, 40.6, 18.4, NA, 61.3, 83.4, 57.6, 56.3, 46.6, 41.9, NA, 39.3, NA, 47, 65.7, 70.2, 64.2, 40.9, 57.6, 47.8, NA, 59.8, 46, 73.7, NA, NA, 51, NA, 23.7, 55.4, NA, NA, 51.4, 73.6, 20, 58.6, 58.1, 53.7, 64.6, 72.2, NA, 54.9, 55.3, 47.2, 77.1, 58.5, 62.7, 33.5, 59.5, 61.8, 29.5, 67.2, 64.5, 64.2, 55.2, NA, 66.5, 67.5, 58, 38.6, 56.2, 45.9, 70.6, 70.3, 45, 44.7, 37, 45.8, 31.7, 57.2, 40.3, 60.1, 60.7, NA, 48.9, 57.3, 42, 74.8, 76, 59.3, 48.9, 77.1, NA, 38, NA, 48, 57), farming = c(36.1, 22.8, 8.5, 7.7, 9.4, 20.8, 3.3, 1.6, 9.9, NA, NA, 20.1, 3.7, 9.8, 1.1, 15.3, 32.2, 23.6, 14, 10.5, 2, 5.6, 0.9, 9.4, 34.1, 34.8, 30.8, 19.5, NA, 9.2, 55.6, 21.4, 4.4, 12.5, 12.4, 51, 4.6, 45.5, 8.7, 22.8, 7.6, NA, NA, 2.9, 1.5, 3.5, 12.4, 6.6, 14.9, 10.5, 2.9, 22.6, 3.7, 46.7, 14.3, 2.9, 2.3, 4.9, 32.6, 16.7, 1, 37.5, 4, 12.4, 20.2, 60.3, 29.6, NA, 13.7, 4.3, 5.8, 18.8, 13.1, 10.4, NA, 2.1, NA, 2.2, 5.7, 1.5, 3.1, 6.8, 27, NA, 3.4, NA, 31.9, 44.3, 4, 6.4, 17, 65.8, NA, 5.6, 0.4, 12.8, 28.2, 32.9, 8.3, NA, 36.6, NA, 23.7, 6.1, 3.8, 19.5, 24.7, 13.3, 26.9, NA, 12.1, 36.3, 2.2, NA, NA, 19.1, NA, 32.8, 1.6, NA, NA, 21.5, 8, 38.8, 22.1, 7.2, 14.3, 4.6, 2.7, NA, 10.1, 5.5, 38.9, 3.9, 13.5, 16.8, 3.2, 16.7, 12.7, 45.8, 0.1, 3.9, 2.5, 35.7, NA, 2.7, 3.3, 11.8, 33.6, 5.5, 8.9, 1.2, 1.3, 19.7, 24, 46.1, 10.2, 42.7, 28.3, 0.7, 11.5, 10.8, NA, 32.8, 10.4, 2.3, 0.9, 1.2, 9.2, 28, 14, NA, 21, NA, 22.1, 19.1), gdppc = c(NA, 5463L, 7176L, 3729L, 10815L, 4162L, 31656L, 34075L, 4496L, NA, 33451L, 1068L, 16957L, 8541L, 31699L, 6253L, 1213L, 3649L, 3758L, 6159L, 12088L, 8474L, 46991L, 9328L, 1026L, 319L, 1440L, 1959L, 34972L, 2538L, 644L, 1468L, 12173L, 4088L, 5867L, 1127L, 3309L, 267L, 9008L, 1614L, 13231L, NA, 24534L, 20280L, 33645L, 1850L, 5415L, 6737L, 4574L, 5167L, 28536L, 526L, 16677L, 628L, 4282L, 30462L, 30591L, 13821L, 1078L, 3520L, 30445L, 1160L, 29261L, 4075L, 1081L, 458L, 2563L, 1068L, 3298L, 17014L, 35465L, 2230L, 3209L, 9314L, NA, 37887L, 22886L, 27750L, 6112L, 30290L, 4342L, 8699L, 1346L, NA, 21273L, 45198L, 1728L, 1814L, 13215L, 9561L, 1311L, 313L, 12560L, 14084L, 69776L, 7394L, 834L, 648L, 11678L, 3995L, 1004L, 20483L, 1684L, 9975L, 11387L, 2190L, 2609L, 3554L, 677L, 854L, 4599L, 960L, 34492L, NA, 24566L, 2311L, 584L, 1731L, 47538L, NA, 20350L, 2184L, 9197L, 1882L, 3824L, 6460L, 2956L, 13571L, 19956L, 70716L, 9368L, 11861L, 772L, NA, 3657L, 1416L, 21220L, 1547L, 8644L, 585L, 43334L, 15881L, 23010L, 1485L, NA, 8478L, 27180L, 3546L, 1679L, 6702L, 4462L, 32016L, 35182L, 4002L, 1477L, 1049L, 7069L, 758L, 3531L, 18818L, 6445L, 10370L, 4677L, 846L, 5583L, 47600L, 31371L, 41813L, 9266L, 2001L, 3226L, 9924L, 2143L, 2189L, 1183L, NA), working.age = c(50.8, 65.3, 65.8, 51.2, 63.4, 67.1, 67.4, 68, 67.6, 66.2, 70.7, 61.3, 71.9, 69.9, 65.7, 58.2, 53.1, 62.4, 57.4, 68.7, 61.1, 66, 67.3, 69, 50.7, 52.3, 59.3, 54.7, 69.2, 56.2, 53.5, 50.8, 67, 70.7, 64.6, 55.4, 54.9, 50.2, 65.8, 55.1, 67.3, 69.6, 68, 71.1, 66.1, 58.6, 60.9, 61.5, 61.8, 60.4, 53.5, 54.6, 68.2, 52.7, 62.9, 66.7, 65.3, 59.4, 55.1, 66.8, 66.9, 57.4, 67.4, 52.6, 53.6, 49.5, 63.2, 57.9, 55.9, 69, 66.2, 62, 66.1, 66.8, 55.7, 68.2, 62, 66.3, 60.8, 66.4, 59.6, 67.8, 54.7, 67.3, 71.9, 74.5, 63.1, 56.7, 69, 64.1, 54.9, 50.9, 65.9, 67.9, 67.3, 69.2, 53, 49.9, 64.3, 62.2, 48.7, 69.4, 56.1, 69, 63.4, 68.9, 67.2, 64.5, 52.6, 67.1, 57.4, 57.4, 67.4, 67.7, 66.4, 58.2, 48.9, 52.7, 65.7, 51.1, 63.6, 58.9, 63.6, 57, 59.4, 62.7, 60, 70.4, 67.4, 76.9, 69.6, 71.1, 54.1, 64.9, 54.7, 54, 62.7, 53.6, NA, 53.9, 72, 71.5, 70.3, 56.6, 53.3, 63.6, 68.8, 69.3, 55.8, 63.9, 57, 65.4, 67.9, 60.2, 56.8, NA, 70.5, 53.7, 56.1, 71.3, 67.7, 66.1, 63.6, 48.2, 69.3, 79, 66, 66.9, 62.7, 62.1, 56.9, 63.7, 64.8, 51.8, 51.4, 57 ), prop.islam = c(0.99, 0.7, 0.99, 0.007, 0.015, NA, 0.017, 0.042, 0.96, NA, 0.812, 0.897, NA, 0.001, 0.04, 0.0058, 0.244, 0.005, 0.0001, 0.45, 0.002, 0.0002, 0.64, 0.122, 0.55, 0.1, 0.035, 0.22, 0.02, NA, 0.2, 0.51, 0.0002, 0.015, 0.0002, 0.98, 0.1, 0.02, 0.001, 0.4, 0.013, 0.0001, 0.18, 0.001, 0.037, 0.99, 0.0002, 0, 0.9, 0.0003, NA, 0.6, 0.004, 0.45, 0.07, 0.004, 0.1, 0.01, 0.95, 0.099, 0.039, 0.156, 0.013, 0.0001, 0.85, 0.45, 0.072, 0.0002, 0.0004, 0.0003, 0.001, 0.134, 0.882, 0.98, 0.97, 0.0049, 0.16, 0.014, 0.002, 0.001, 0.95, 0.47, 0.1, NA, 0.0007, 0.8, 0.8, 0.0001, 0.0002, 0.55, 0.01, 0.2, 0.97, 0.0008, 0.02, 0.32, 0.07, 0.2, 0.604, 0.9941, 0.9, NA, 0.999, 0.17, 0.0026, 0.0007, 0.06, 0.999, 0.2, 0.04, 0.01, 0.04, 0.06, NA, 0.0058, 0.0001, 0.95, 0.5, 0.018, 0.8374, 0.9266, 0.97, 0.003, 0.0004, 0.0001, 0, 0.05, 0.0001, 0.0033, 0.775, 0.002, 0.14, 0.046, NA, NA, NA, 0.9, 0.95, 0.032, 0.6, 0.15, 0.0006, 0.024, 0.0004, 0.999, 0.015, 0.023, 0.07, 0.7, 0.196, 0.1, 0.03, 0.043, 0.9, 0.9, 0.4, 0.046, 0.2, NA, 0.058, 0.98, 0.99, 0.89, 0.121, 0.011, 0.76, 0.027, 0.01, 0.0001, 0.88, 0.001, 0.0035, 0.0008, 0.99, 0.01, 0.01), arab = structure(c(1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L), .Label = c("0", "1"), class = "factor"), gov.exp = c(664358367.6, 909713813.6, 11816927186, 7899213187, 21820517744, 521821749.3, 100000000000, 55301428891, 1380443210, 875881965, 2172074468, 3615600079, 647753643.9, 6279261962, 85514756444, 159500000, 522939638.6, 179399194.7, 1525430035, 2268793139, 3247248514, 200000000000, 1754609983, 4948099272, 1211652458, 189445567.4, 256133414.8, 1755366675, 200000000000, 182108415.5, 139137737.6, 1223195821, 12953433365, 300000000000, 23084234341, 48115131.14, 588976461.7, 473772688.3, 2755084920, 2654632765, 7569172422, 17867200000, 3060957286, 27678177071, 67030731128, 204405688.8, 2845867341, 4133613000, 11254581979, 1634500000, 222383098.6, 391666886.3, 2391832192, 1562959808, 454569646.1, 43331841019, 500000000000, 731797749.8, 41308944.86, 803759637, 500000000000, 1651572313, 40407712292, 2368800528, 143904885.8, 54790038.78, 222007859.9, 275677686.7, 1145157643, 24926946585, 3923447440, 91725902339, 23182533624, 24310519746, 9975129280, 31766738702, 34928020812, 400000000000, 1473248984, 800000000000, 2475050688, 6425572697, 3208421424, NA, 100000000000, 12455479452, 430785558.5, 195803001.3, 2799209043, 3305326342, 247959331.3, 59089380, 6767772553, 4282894206, 6211549794, 1095486375, 423908661.5, 349135198.4, 16920900163, 285898437.5, 929713540, 1229114481, 420841421.2, 927847284.5, 88716086535, 491211047.9, 285019443.7, 11345731015, 868470893.7, 10389076078, 1513845045, 762616036.7, 200000000000, 775363128.5, 20098415125, 876336039.3, 551830482.9, 7641277503, 60149837409, 1260883992, 6421430429, 9793113356, 2033800000, 695119434.8, 781866218.9, 7979145438, 9543166160, 56049121976, 39069544211, 4881593407, 19290147723, 100000000000, 273522422.4, 161796296.3, 93219107.42, 29299783.35, 71657333482, 1127865271, 5041801265, 225558194.6, 12546942662, 8776575081, 6722289520, 119380459.9, 200596305, 47385192933, 200000000000, 3063392721, 4304318951, 84255074.68, 752636846.5, 96902845350, 41728198964, 3520375752, 337647197.2, 944757630.1, 20941453835, 176544509.3, 27893285.07, 1886640201, 4449403370, 47375719318, 770200587.6, 1300330489, 15713611393, 14698162015, 500000000000, 2000000000000, 1834377783, 2255994919, 79499069.47, 15359465544, 3256969003, 1926988104, 974914478, 713946019.1), tech.prop = c(NA, 4.966892833, 1.484232772, NA, 6.570459541, 0.601591548, 12.74235358, 13.52817624, 0.809377616, NA, 0.039004364, NA, 18.38128887, 2.595134425, 8.660860191, 0.00992531, 0.100970584, NA, 8.8875731, 3.570062969, NA, 12.83707324, NA, 4.68032592, NA, 4.192767898, NA, 2.147640052, 14.4278349, 0.003011483, 0.046000498, NA, 6.488974681, 30.60326374, 4.915933928, NA, NA, NA, 37.9792964, 45.14462745, 11.51353427, NA, 18.2175708, 12.7509748, 21.98282057, NA, NA, 7.641701089, 0.371840308, 3.643039045, NA, NA, 17.56545248, NA, 3.093242427, 25.21499601, 20.02304914, 10.81305784, 5.949520903, 25.17718953, 16.95434329, 0.166966643, 10.18768401, 3.225636575, NA, NA, 0.188499249, NA, 6.239017207, 25.69330705, 27.07662387, 4.828710863, 16.30101605, 2.407363222, NA, 34.52559845, 13.9334541, 7.76681698, 0.0881991, 22.47445725, 1.361049446, 11.33784809, NA, NA, 32.32920037, NA, 1.82904003, NA, 5.290733634, NA, NA, NA, NA, 6.121145728, 11.80608125, 1.073653313, 0.769552259, 7.479991616, 54.70596977, 0, NA, 52.11861771, NA, 1.574120709, 19.55204284, 3.854278693, 0.094157995, 9.626851384, 7.506681665, NA, 11.90242575, NA, 30.11629943, NA, 10.36987445, 5.173293034, 11.41736386, NA, 17.31031834, NA, 0.275470054, 1.386954864, 0.851205845, NA, 7.141396274, 2.608113797, 70.72587909, 3.849953033, 8.985987849, 0.000992603, 3.434966169, 8.094152597, NA, 8.487715004, NA, 1.171646923, 0.638809457, 11.7303558, 4.128825151, NA, 56.58171504, 7.283342838, 4.611709137, NA, NA, 6.578110683, 7.051519537, 2.290827299, 0.578272625, NA, 0.225506809, 16.67461425, 21.92788365, 2.071807456, NA, 0.826299691, 26.58437692, 0.089462905, NA, 1.344543402, 4.382527297, NA, NA, 2.344079975, 3.656358738, NA, 28.01603678, 29.92186126, 2.198945102, NA, NA, 2.578442687, 5.337705005, 0.239386987, 1.092557675, 1.71179254), f.per.100m.rural = c(NA, 91.8, NA, NA, 90.3, 101.4, 91.6, NA, 102.4, NA, NA, NA, NA, 113.4, NA, 93.4, NA, NA, NA, NA, 106.9, NA, NA, 102.6, NA, NA, 107.1, NA, NA, NA, NA, NA, 87.6, NA, NA, NA, NA, NA, 96.7, NA, NA, 89.2, NA, 100.9, NA, NA, NA, 95.3, NA, 97.3, NA, NA, 106.1, 99.2, NA, 98.7, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 105.5, 82.4, 94.8, NA, 99.4, NA, NA, 95.1, NA, 96.4, NA, 93.8, 100, 111.2, NA, NA, NA, 98.8, NA, 106.5, NA, NA, NA, NA, 106.5, NA, NA, NA, NA, NA, 98.7, NA, NA, NA, 101.3, NA, NA, 100.7, 101.6, NA, NA, NA, NA, 99.8, NA, NA, 91.6, NA, NA, NA, NA, NA, NA, NA, NA, NA, 96.8, NA, 100.8, NA, NA, 100.9, 110, NA, NA, NA, NA, NA, NA, 101, NA, NA, 103.4, 102.4, NA, NA, NA, NA, NA, NA, 95.5, NA, NA, 100.5, 97, NA, NA, 101.1, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 77.3, NA, NA, NA, NA, NA, NA, NA), f.per.100m.urban = c(NA, 112.7, NA, NA, 105.8, 111.1, 102.6, NA, 104.7, NA, NA, NA, NA, 113.9, NA, 102.9, NA, NA, NA, NA, 105.7, NA, NA, 107.3, NA, NA, 106.1, NA, NA, NA, NA, NA, 104.4, NA, NA, NA, NA, NA, 106.9, NA, NA, 103.3, NA, 106.9, NA, NA, NA, 101.8, NA, 107.9, NA, NA, 122.4, 101.1, NA, 108.1, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 113.5, 100.9, 90.4, NA, 94.9, NA, NA, 103.3, NA, 109.5, NA, 94.5, 113.9, 68.6, NA, NA, NA, 109.2, NA, 122.4, NA, NA, NA, NA, 118.4, NA, NA, NA, NA, NA, 92.1, NA, NA, NA, 103.3, NA, NA, 107.1, 103.7, NA, NA, NA, NA, 103.3, NA, NA, 107.8, NA, NA, NA, NA, NA, NA, NA, NA, NA, 99.7, NA, 110.4, NA, NA, 108.5, 117.3, NA, NA, NA, NA, NA, NA, 109.3, NA, NA, 108.2, 110.9, NA, NA, NA, NA, NA, NA, 99.8, NA, NA, 105.8, 93.9, NA, NA, 108.4, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 110.2, NA, NA, NA, NA, NA, NA, NA), ratio.urb2rur.fp100m = c(NA, 1.227668845, NA, NA, 1.171650055, 1.09566075, 1.120087336, NA, 1.022460938, NA, NA, NA, NA, 1.004409171, NA, 1.101713062, NA, NA, NA, NA, 0.988774556, NA, NA, 1.045808967, NA, NA, 0.990662932, NA, NA, NA, NA, NA, 1.191780822, NA, NA, NA, NA, NA, 1.105480869, NA, NA, 1.158071749, NA, 1.059464817, NA, NA, NA, 1.068205666, NA, 1.108941418, NA, NA, 1.153628652, 1.019153226, NA, 1.095238095, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1.075829384, 1.224514563, 0.953586498, NA, 0.95472837, NA, NA, 1.086225026, NA, 1.135892116, NA, 1.007462687, 1.139, 0.616906475, NA, NA, NA, 1.105263158, NA, 1.149295775, NA, NA, NA, NA, 1.111737089, NA, NA, NA, NA, NA, 0.933130699, NA, NA, NA, 1.019743337, NA, NA, 1.063555114, 1.020669291, NA, NA, NA, NA, 1.03507014, NA, NA, 1.176855895, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1.029958678, NA, 1.095238095, NA, NA, 1.075322101, 1.066363636, NA, NA, NA, NA, NA, NA, 1.082178218, NA, NA, 1.046421663, 1.083007813, NA, NA, NA, NA, NA, NA, 1.045026178, NA, NA, 1.052736318, 0.968041237, NA, NA, 1.072205737, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1.425614489, NA, NA, NA, NA, NA, NA, NA), ratio.fprop2mprop.industry = c(NA, NA, NA, NA, 0.336391437, NA, 0.307189542, 0.325757576, 0.572368421, 0.172297297, NA, NA, NA, NA, 0.322857143, 0.472477064, NA, NA, NA, NA, NA, NA, NA, 0.742930591, NA, NA, NA, NA, 0.330218069, NA, NA, NA, 0.395189003, NA, 0.781553398, NA, NA, NA, 0.477272727, NA, 0.48655914, NA, 0.308139535, 0.536437247, 0.367952522, NA, 0.580769231, 0.441605839, NA, NA, NA, NA, 0.55, 1.615384615, NA, 0.328912467, 0.352601156, NA, NA, 0.321167883, 0.393643032, NA, 0.331125828, NA, NA, NA, NA, NA, 1.182741117, 0.507177033, 0.346153846, NA, 0.742424242, 0.919093851, NA, 0.312977099, 0.344051447, 0.447570332, 0.192592593, 0.498583569, NA, NA, NA, NA, 0.486803519, NA, NA, NA, 0.464183381, NA, NA, NA, NA, 0.562162162, NA, 0.884615385, NA, NA, NA, NA, NA, 0.494252874, NA, 0.842105263, 0.641891892, 0.550239234, 0.783068783, 0.764423077, NA, NA, NA, NA, 0.280405405, NA, 0.329153605, NA, NA, NA, 0.252336449, NA, NA, 0.700934579, 0.417431193, NA, NA, 0.428115016, 0.692307692, 0.438461538, 0.455882353, NA, 0.710601719, 0.556430446, NA, NA, NA, NA, NA, NA, NA, NA, 0.569444444, 0.508064516, 0.534883721, NA, NA, NA, 0.300970874, NA, NA, NA, NA, 0.275964392, 0.348765432, NA, NA, NA, 0.865740741, NA, NA, 0.384428224, NA, 0.537366548, NA, NA, NA, NA, 0.284848485, 0.317880795, 0.439862543, NA, NA, NA, NA, NA, NA, NA), percent.notagr = c(NA, NA, NA, NA, 0.451, NA, 0.489, 0.466, 0.491, 0.5, 0.11, NA, NA, 0.529, 0.453, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0.494, NA, NA, NA, 0.379, NA, NA, NA, NA, NA, 0.396, NA, NA, NA, 0.477, 0.466, 0.488, NA, 0.383, 0.42, NA, NA, NA, NA, 0.526, NA, NA, 0.509, 0.477, NA, NA, 0.486, 0.466, NA, 0.41, NA, NA, NA, NA, NA, 0.453, 0.487, 0.516, NA, NA, NA, NA, 0.479, 0.493, 0.427, 0.474, 0.413, NA, NA, NA, NA, 0.418, NA, 0.519, NA, 0.532, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0.369, 0.391, 0.549, 0.531, 0.222, NA, NA, NA, NA, 0.468, 0.474, NA, NA, NA, 0.211, 0.49, NA, NA, 0.097, 0.434, NA, NA, 0.375, 0.419, 0.467, 0.473, NA, 0.462, 0.509, NA, NA, NA, 0.372, NA, NA, NA, NA, NA, 0.514, 0.474, NA, NA, 0.429, 0.423, NA, NA, NA, NA, 0.505, 0.471, NA, NA, NA, 0.479, NA, NA, 0.436, NA, 0.203, NA, NA, 0.549, NA, NA, 0.484, 0.48, NA, NA, NA, NA, NA, NA, NA), netinflow.FDI = c(NA, 262479012.6, 1081300000, -1303860000, 5265201000, 258233400, 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0.96, 1.24, 0.93, 0.94, 0.87, 0.96, 1.03, 1, 1.1, 0.98, 0.97, 1.01, 0.98, 1, 0.92, 0.99, 0.99, 0.96, 1.01, 0.98, 0.94, 0.98, 0.92, 0.98, 1.06, 0.98, 0.98, 0.99, 0.99, 1.01, 1.03, 0.93, 0.99, 1.04, 0.95, 0.98, 0.86, 1.03, 0.99, 1.03, 0.93, 0.99, 0.98, 0.84, 0.97, 1, 0.96, 0.96, 0.99, 1, 0.91, 0.97, 1, 0.96, 0.97, 1, 0.95, 1, 0.98, 1.01, 0.91, 1, 1.08, 1, 1.02, 1.02, 0.99, 1, 0.96, 0.98, 0.95, 1.1, 0.93, 1, 0.95, 1, 1.54, 0.96, 0.98, 0.86, 0.97, 0.96, 0.99, 1.05, 0.89, 0.97, 1, 0.99, 1, 1.01, 1.05, 0.99, 0.99, 0.93, 0.97, 0.96, 0.91, 1, 0.99, 0.97, NA, 1.01, 0.96, 0.98, 0.93, 0.99, 1, 1, 1.04, 0.98, 1.04, 1.22, 1.09, 1.02, 1.04, 1.01, 1.01, 1, 0.94, 0.95, 2, 0.95, 0.86, 0.99, 0.96, 1.06, 0.98, 1.18, 0.99, 0.95, 0.94, 0.95, 0.94, 0.95, 1.02, 1, 0.99, 0.96, 0.97, 1.03, 0.99, 0.95, 0.98, 0.97, 1.05, 0.99, 0.98, 0.98, 0.97, 0.99, 1.02, 1.01, 1.02, 0.98, 1, 0.86, 2.19, 0.98, 0.97, 0.95, 0.99, 1.04, 0.98, 0.98, 1.03, 1, 0.91), pop.f = c(13300, 1617, 16758, 8360, 19269, 1417, 10265, 4057, 4139, 160, 409, 75167, 142, 4532, 5109, 143, 4380, 340, 4630, 1933, 934, 93705, 191, 3686, 7143, 4066, 6954, 9133, 16124, 251, 2111, 5016, 8149, 683584, 22549, 405, 1835, 30170, 2210, 9597, 2195, 5605, 431, 4964, 2688, 379, 4879, 6568, 37631, 3258, 247, 2322, 612, 39894, 416, 2577, 30039, 652, 832, 2112, 40691, 11504, 5448, 6415, 4590, 802, 370, 4675, 3502, 4792, 149, 598025, 114432, 35483, 14394, 2100, 3405, 28790, 1336, 62336, 3001, 7379, 18276, 11550, 24025, 1685, 2576, 2850, 1058, 1997, 977, 1781, 3093, 1605, 227, 1018, 9531, 6786, 13122, 154, 5954, 201, 1467, 616, 51596, 1826, 1302, 15349, 10326, NA, 1029, 13538, 8107, NA, 2060, 2766, 6868, 73779, 2311, NA, 1399, 83937, 1660, 3162, 3023, 13863, 43132, 18480, 5154, 547, 10490, 66220, 4708, 80, 95, 77, 13086, 6006, 4799, 2783, 2135, 2611, 975, 244, 4222, 24020, 21496, 9457, 19132, 226, 552, 4493, 3671, 9941, 3303, 19530, 31402, 3156, 50, 671, 5133, 37327, 2425, 14950, 21526, 2916, 29950, 149115, 1623, 13423, 113, 13458, 42668, 11027, 5848, 6302), pop.m = c(12788, 1555, 16593, 8197, 19865, 1593, 10265, 4270, 4267, 167, 330, 80824, 151, 5210, 5321, 139, 4380, 309, 4724, 1993, 924, 95618, 191, 4007, 7216, 4107, 7243, 9042, 16453, 268, 2154, 5452, 8316, 644890, 23009, 413, 1854, 30474, 2189, 9317, 2361, 5662, 415, 5225, 2742, 440, 4736, 6634, 36535, 3504, 249, 2370, 728, 41127, 417, 2684, 31291, 659, 831, 2321, 41950, 11504, 5675, 6614, 4591, 844, 369, 4771, 3467, 5266, 149, 553726, 114432, 34787, 14112, 2121, 3405, 29989, 1363, 65617, 2728, 7935, 18277, 12158, 24025, 1094, 2683, 2909, 1231, 2058, 1018, 1798, 2946, 1803, 234, 1018, 9628, 6785, 12992, 146, 6014, 204, 1577, 636, 53746, 2007, 1303, 15504, 10645, NA, 1018, 14103, 8272, NA, 2080, 2766, 6869, 70941, 2358, NA, 1147, 77006, 1628, 3040, 2993, 13726, 43132, 19660, 5425, 274, 11042, 77001, 4756, 83, 90, 78, 11089, 6066, 5052, 2960, 2247, 2777, 1026, 240, 4223, 24262, 22391, 9750, 18575, 229, 582, 4585, 3784, 9467, 3337, 19929, 32042, 3254, 50, 657, 5082, 36595, 2474, 14949, 25031, 1332, 30562, 153726, 1708, 13558, 108, 13733, 43538, 10705, 5848, 6926)), .Names = c("code", "country", "europe", "americas", "asia", "africa", "income.f", "income.m", "inc.parity", "lifexp.f", "lifexp.m", "literacy.f", "literacy.m", "marr1519.f", "mean.marr.f", "mean.marr.m", "fertility.adol", "fertility.total", "maternaldeaths", "secondary.f", "laborforce.f", "pop", "exports", "industry", "manufact", "services", "farming", "gdppc", "working.age", "prop.islam", "arab", "gov.exp", "tech.prop", "f.per.100m.rural", "f.per.100m.urban", "ratio.urb2rur.fp100m", "ratio.fprop2mprop.industry", "percent.notagr", "netinflow.FDI", "ratio.f", "pop.f", "pop.m" ), row.names = c(NA, -176L), class = "data.frame")