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  1. COND <-structure(list(Country_Name_i = c("Afghanistan", "Afghanistan",
  2. "Afghanistan", "Afghanistan", "Afghanistan", "Albania", "Albania",
  3. "Albania", "Albania", "Albania", "Albania", "Albania", "Albania",
  4. "Albania", "Albania", "Albania", "Albania", "Albania", "Albania",
  5. "Albania"), Region = structure(c(2L, 2L, 2L, 2L, 2L, 1L, 1L,
  6. 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("Eastern Europe",
  7. "South Asia"), class = "factor"), year_t = c(2009, 2010, 2011,
  8. 2012, 2013, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001,
  9. 2002, 2003, 2004, 2005, 2006, 2012), `2018EconType` = structure(c(1L,
  10. 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
  11. 2L, 2L, 2L), .Label = c("Low income", "Upper middle income"), class = "factor"),
  12. Africa = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
  13. 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = "Non African", class = "factor"),
  14. Asia = structure(c(2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L,
  15. 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("Non Asian",
  16. "Asian"), class = "factor"), Mus20 = structure(c(1L, 1L,
  17. 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
  18. 1L, 1L, 1L), .Label = "Muslim", class = "factor"), Nfithteen = structure(c(1L,
  19. 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
  20. 1L, 1L, 1L, 1L), .Label = "Non Emerging", class = "factor"),
  21. GDP_USD_Cons = c(14697331941, 15936800636, 16911126453, 19352203806,
  22. 20107051005, 4584597705, 4965119313, 5625480182, 6137398879,
  23. 5472236442, 5965222154, 6770597139, 7221967156, 7795415283,
  24. 8125268227, 8594379821, 9085080801, 9604821915, 10126461017,
  25. 12404787541), GDP_Mill = c(14697.331941, 15936.800636, 16911.126453,
  26. 19352.203806, 20107.051005, 4584.597705, 4965.119313, 5625.480182,
  27. 6137.398879, 5472.236442, 5965.222154, 6770.597139, 7221.967156,
  28. 7795.415283, 8125.268227, 8594.379821, 9085.080801, 9604.821915,
  29. 10126.461017, 12404.787541), `Age_dep%` = c(100.740812582094,
  30. 100.406411520766, 98.7307589652135, 96.5285898367748, 94.0017341230592,
  31. 63.5404188127221, 63.874370693716, 64.2040739586526, 63.1592934163748,
  32. 62.2426341564471, 61.4126164277618, 60.5583843646593, 59.585813978959,
  33. 58.5679635169215, 57.4458862172086, 56.2423892890536, 55.0221373205264,
  34. 53.8213141197539, 52.6144730816267, 46.8200639844493), `Urb_Pop%` = c(24.313,
  35. 24.689, 25.074, 25.468, 25.871, 37.799, 38.354, 38.911, 39.473,
  36. 40.035, 40.601, 41.169, 41.741, 42.435, 43.501, 44.573, 45.651,
  37. 46.731, 47.815, 54.33), `GDP%_Ed` = c(NA, 3.461960077, 3.437849998,
  38. 2.524410009, 3.471709967, NA, 3.265189886, 3.764709949, 2.976300001,
  39. 3.234280109, 3.326420069, 3.379709959, 3.286920071, 3.340600014,
  40. 3.05663991, 3.06427002, 3.170079947, 3.23871994, 3.157190084,
  41. NA), `Ed-USD-GDP/CAP2` = c(NA, 19.1550351243671, 19.5693900083966,
  42. 15.9145727023306, 21.9987822204941, NA, 50.5436489714445,
  43. 66.435809978775, 57.6595644317399, 56.2171720911352, 63.4254256437027,
  44. 73.6065894047131, 76.8462975460548, 85.0976869778082, 81.4026146770806,
  45. 86.6408797189043, 95.147052724579, 103.295575431072, 106.835957158119,
  46. NA), GDP_Cap_2010_Con = c(524.823533211573, 553.300289383064,
  47. 569.233387721432, 630.427412567377, 633.658411261349, 1420.57328795871,
  48. 1547.95435292012, 1764.69929632752, 1937.29007198088, 1738.16646043428,
  49. 1906.71726144227, 2177.89663307357, 2337.94238637131, 2547.37731608619,
  50. 2663.14047692587, 2827.45577750698, 3001.40861793159, 3189.39511117692,
  51. 3383.89372561197, 4276.92155019231), FDI = c(1.58175399279445,
  52. 0.340096812635776, 0.321361264147956, 0.229964644562762,
  53. 0.185738818947298, 4.72285382625773, 2.66911917005927, 2.88719441567244,
  54. 2.71803211049261, 2.01279450123347, 1.66265024388059, 1.20652663604229,
  55. 3.93717707227928, 5.10495722596557, 3.04391445388559, 3.09793068135411,
  56. 4.66563777108359, 3.21722676118428, 3.61551129678645, 7.45397250503158
  57. ), `Sch_Enroll_Prim&Sec_GPI` = c(0.613699972629547, 0.630020022392273,
  58. 0.668110013008118, 0.674070000648499, 0.660059988498688,
  59. 1.03437995910645, 0.995090007781982, 0.971069991588593, 0.975199997425079,
  60. 0.9582399725914, NA, 0.955540001392365, 0.962960004806519,
  61. 0.973630011081696, NA, NA, 0.966390013694763, 0.964280009269714,
  62. 0.970939993858337, 0.96969997882843), HardFP = c(0, 1, 0,
  63. 4, 2, 3, 1, 1, 0, 0, 1, 1, 0, 0, 2, 1, 2, 0, 1, 0), FP_PROG = structure(c(2L,
  64. 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 2L,
  65. 2L, 2L, 2L, 1L), .Label = c("No FP ", "IMF FP"), class = "factor"),
  66. IMF_PROG = c(1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1,
  67. 1, 1, 1, 1, 0), FP_IB = c(4, 0, 1, 0, 0, 0, 2, 0, 0, 0, 0,
  68. 0, 0, 0, 0, 0, 0, 0, 0, 0), FP_PA = c(0, 0, 0, 0, 0, 0, 0,
  69. 1, 0, 0, 1, 1, 0, 0, 2, 1, 2, 0, 1, 0), FP_QPC = c(0, 1,
  70. 0, 4, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), FP_SB = c(0,
  71. 0, 0, 0, 0, 3, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 2, 3, 4, 0),
  72. FP_SPC = c(0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
  73. 0, 0, 0, 0), HardFP2 = c(0, 1, 0, 4, 2, 3, 1, 1, 0, 0, 1,
  74. 1, 0, 0, 2, 1, 2, 0, 1, 0), FP_PROG2 = c(1, 1, 1, 1, 1, 1,
  75. 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0), All_IMF_PROG = c(1,
  76. 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0),
  77. `GDP%_Ed_LG` = c(3.461960077, 3.437849998, 2.524410009, 3.471709967,
  78. 3.777590036, 3.265189886, 3.764709949, 2.976300001, 3.234280109,
  79. 3.326420069, 3.379709959, 3.286920071, 3.340600014, 3.05663991,
  80. 3.06427002, 3.170079947, 3.23871994, 3.157190084, 3.268680096,
  81. 3.539299965), `Ed-USD-GDP/CAP2_LG` = c(19.1550351243671,
  82. 19.5693900083966, 15.9145727023306, 21.9987822204941, 23.810906044697,
  83. 50.5436489714445, 66.435809978775, 57.6595644317399, 56.2171720911352,
  84. 63.4254256437027, 73.6065894047131, 76.8462975460548, 85.0976869778082,
  85. 81.4026146770806, 86.6408797189043, 95.147052724579, 103.295575431072,
  86. 106.835957158119, 118.023132868789, 153.16832091186), GDP_Cap_2010_Con_LG = c(553.300289383064,
  87. 569.233387721432, 630.427412567377, 633.658411261349, 630.320014024333,
  88. 1547.95435292012, 1764.69929632752, 1937.29007198088, 1738.16646043428,
  89. 1906.71726144227, 2177.89663307357, 2337.94238637131, 2547.37731608619,
  90. 2663.14047692587, 2827.45577750698, 3001.40861793159, 3189.39511117692,
  91. 3383.89372561197, 3610.72755370642, 4327.64451802716), FDI_LG = c(0.340096812635776,
  92. 0.321361264147956, 0.229964644562762, 0.185738818947298,
  93. 0.211049413952205, 2.66911917005927, 2.88719441567244, 2.71803211049261,
  94. 2.01279450123347, 1.66265024388059, 1.20652663604229, 3.93717707227928,
  95. 5.10495722596557, 3.04391445388559, 3.09793068135411, 4.66563777108359,
  96. 3.21722676118428, 3.61551129678645, 6.09540486724282, 9.81721374254509
  97. ), `Sch_Enroll_Prim&SecGross_GPI_LG` = c(0.630020022392273,
  98. 0.668110013008118, 0.674070000648499, 0.660059988498688,
  99. 0.658649981021881, 0.995090007781982, 0.971069991588593,
  100. 0.975199997425079, 0.9582399725914, NA, 0.955540001392365,
  101. 0.962960004806519, 0.973630011081696, NA, NA, 0.966390013694763,
  102. 0.964280009269714, 0.970939993858337, 0.982150018215179,
  103. 0.957469999790192)), .Names = c("Country_Name_i", "Region",
  104. "year_t", "2018EconType", "Africa", "Asia", "Mus20", "Nfithteen",
  105. "GDP_USD_Cons", "GDP_Mill", "Age_dep%", "Urb_Pop%", "GDP%_Ed",
  106. "Ed-USD-GDP/CAP2", "GDP_Cap_2010_Con", "FDI", "Sch_Enroll_Prim&Sec_GPI",
  107. "HardFP", "FP_PROG", "IMF_PROG", "FP_IB", "FP_PA", "FP_QPC",
  108. "FP_SB", "FP_SPC", "HardFP2", "FP_PROG2", "All_IMF_PROG", "GDP%_Ed_LG",
  109. "Ed-USD-GDP/CAP2_LG", "GDP_Cap_2010_Con_LG", "FDI_LG", "Sch_Enroll_Prim&SecGross_GPI_LG"
  110. ), row.names = c(30L, 31L, 32L, 33L, 34L, 49L, 50L, 51L, 52L,
  111. 53L, 54L, 55L, 56L, 57L, 58L, 59L, 60L, 61L, 62L, 68L), class = c("tbl_df",
  112. "tbl", "data.frame"))
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