Southeast Asia
Group 7
Group’s member:
Hien Khon Tran, ID: 10622016
Nguyen Duy Khang, ID: 10622047
Nguyen Thanh Nhan, ID: 10622056
Nguyen Cao Anh Tuan, ID: 10622043
Nguyen Thanh Tung, ID: 10622044
Dau Duc Thang, ID: 10622037
I. INTRODUCTION.
An economical and financial research was conducted on 5 different Southeast-Asian countries (Indonesia, Thailand, Vietnam, Cambodia and Philippines) throughout the span of 10 years, from 2013 to 2022. By analyzing and understanding past key demographics, economists are able to forecast future scenarios.This aids in budget planning, predicting government revenues, and is also a vital tool for each country to understand their economic pulse, make well-informed policy decisions, and adapt to the ever- evolving global economic landscape. For someone focused on finance, this dataset offers a rich source of information to enhance your analytical skills, evaluate economic performance, and make informed decisions in areas like investment, taxation, and debt management.
II. THE SAMPLE.
The dataset is subordinate and was collected from www.databank.worldbank. This dataset was chosen due to its comprehensive coverage of the important economic indexes, which are essential for a widespread financial and economic analysis. The range of metrics, spanning from foundational measures like GDP, its growth rates, and the complex details of foreign investment, trade dynamics, and income indicators such as GNI per capita, offers an all-around view of a nation’s economic landscape.These metrics form the base for in-depth economic inquiries, enabling a distincted knowledge of economic stability, growth prospects, and policy implications. Each of the series’ names has their own measurements (% GDP, BoP, current US dollar, annual %, current international dollar, % of exports of goods, services and primary income).This dataset aligns with the foundational principles of economic analysis, providing a well-bonded framework for further academic studies, strategic financial planning, and informed decision-making in the range of finance and economics.
NOTE: The dataset contains a scientific term “𝒙E+11” or “𝒙E+12”, which both represent very large numbers. For instance, “9.13E+11” stands for 9.13 multiplied by 10 raised to the power of 11. In numerical terms it can be expanded to its full value: “9.13 x 10^11 = 913,000,000,000 So 9.13E+11 is equivalent to 913 billion in standard numerical
## Country.Name Country.Code
## 1 Indonesia IDN
## 2 Indonesia IDN
## 3 Indonesia IDN
## 4 Indonesia IDN
## 5 Indonesia IDN
## 6 Indonesia IDN
## 7 Indonesia IDN
## 8 Indonesia IDN
## 9 Indonesia IDN
## 10 Indonesia IDN
## 11 Indonesia IDN
## 12 Indonesia IDN
## 13 Indonesia IDN
## 14 Indonesia IDN
## 15 Indonesia IDN
## 16 Indonesia IDN
## 17 Indonesia IDN
## 18 Viet Nam VNM
## 19 Viet Nam VNM
## 20 Viet Nam VNM
## 21 Viet Nam VNM
## 22 Viet Nam VNM
## 23 Viet Nam VNM
## 24 Viet Nam VNM
## 25 Viet Nam VNM
## 26 Viet Nam VNM
## 27 Viet Nam VNM
## 28 Viet Nam VNM
## 29 Viet Nam VNM
## 30 Viet Nam VNM
## 31 Viet Nam VNM
## 32 Viet Nam VNM
## 33 Viet Nam VNM
## 34 Viet Nam VNM
## 35 Thailand THA
## 36 Thailand THA
## 37 Thailand THA
## 38 Thailand THA
## 39 Thailand THA
## 40 Thailand THA
## 41 Thailand THA
## 42 Thailand THA
## 43 Thailand THA
## 44 Thailand THA
## 45 Thailand THA
## 46 Thailand THA
## 47 Thailand THA
## 48 Thailand THA
## 49 Thailand THA
## 50 Thailand THA
## 51 Thailand THA
## 52 Philippines PHL
## 53 Philippines PHL
## 54 Philippines PHL
## 55 Philippines PHL
## 56 Philippines PHL
## 57 Philippines PHL
## 58 Philippines PHL
## 59 Philippines PHL
## 60 Philippines PHL
## 61 Philippines PHL
## 62 Philippines PHL
## 63 Philippines PHL
## 64 Philippines PHL
## 65 Philippines PHL
## 66 Philippines PHL
## 67 Philippines PHL
## 68 Philippines PHL
## 69 Cambodia KHM
## 70 Cambodia KHM
## 71 Cambodia KHM
## 72 Cambodia KHM
## 73 Cambodia KHM
## 74 Cambodia KHM
## 75 Cambodia KHM
## 76 Cambodia KHM
## 77 Cambodia KHM
## 78 Cambodia KHM
## 79 Cambodia KHM
## 80 Cambodia KHM
## 81 Cambodia KHM
## 82 Cambodia KHM
## 83 Cambodia KHM
## 84 Cambodia KHM
## 85 Cambodia KHM
## 86
## 87
## 88
## 89 Data from database: World Development Indicators
## 90 Last Updated: 10/26/2023
## Series.Name
## 1 Agriculture, forestry, and fishing, value added (% of GDP)
## 2 Foreign direct investment, net inflows (BoP, current US$)
## 3 GDP (current US$)
## 4 GDP growth (annual %)
## 5 GNI per capita, Atlas method (current US$)
## 6 GNI per capita, PPP (current international $)
## 7 GNI, Atlas method (current US$)
## 8 GNI, PPP (current international $)
## 9 Gross capital formation (% of GDP)
## 10 Imports of goods and services (% of GDP)
## 11 Exports of goods and services (% of GDP)
## 12 Inflation, GDP deflator (annual %)
## 13 Merchandise trade (% of GDP)
## 14 Net barter terms of trade index (2015 = 100)
## 15 Revenue, excluding grants (% of GDP)
## 16 Tax revenue (% of GDP)
## 17 Total debt service (% of exports of goods, services and primary income)
## 18 Agriculture, forestry, and fishing, value added (% of GDP)
## 19 Foreign direct investment, net inflows (BoP, current US$)
## 20 GDP (current US$)
## 21 GDP growth (annual %)
## 22 GNI per capita, Atlas method (current US$)
## 23 GNI per capita, PPP (current international $)
## 24 GNI, Atlas method (current US$)
## 25 GNI, PPP (current international $)
## 26 Gross capital formation (% of GDP)
## 27 Imports of goods and services (% of GDP)
## 28 Exports of goods and services (% of GDP)
## 29 Inflation, GDP deflator (annual %)
## 30 Merchandise trade (% of GDP)
## 31 Net barter terms of trade index (2015 = 100)
## 32 Revenue, excluding grants (% of GDP)
## 33 Tax revenue (% of GDP)
## 34 Total debt service (% of exports of goods, services and primary income)
## 35 Agriculture, forestry, and fishing, value added (% of GDP)
## 36 Foreign direct investment, net inflows (BoP, current US$)
## 37 GDP (current US$)
## 38 GDP growth (annual %)
## 39 GNI per capita, Atlas method (current US$)
## 40 GNI per capita, PPP (current international $)
## 41 GNI, Atlas method (current US$)
## 42 GNI, PPP (current international $)
## 43 Gross capital formation (% of GDP)
## 44 Imports of goods and services (% of GDP)
## 45 Exports of goods and services (% of GDP)
## 46 Inflation, GDP deflator (annual %)
## 47 Merchandise trade (% of GDP)
## 48 Net barter terms of trade index (2015 = 100)
## 49 Revenue, excluding grants (% of GDP)
## 50 Tax revenue (% of GDP)
## 51 Total debt service (% of exports of goods, services and primary income)
## 52 Agriculture, forestry, and fishing, value added (% of GDP)
## 53 Foreign direct investment, net inflows (BoP, current US$)
## 54 GDP (current US$)
## 55 GDP growth (annual %)
## 56 GNI per capita, Atlas method (current US$)
## 57 GNI per capita, PPP (current international $)
## 58 GNI, Atlas method (current US$)
## 59 GNI, PPP (current international $)
## 60 Gross capital formation (% of GDP)
## 61 Imports of goods and services (% of GDP)
## 62 Exports of goods and services (% of GDP)
## 63 Inflation, GDP deflator (annual %)
## 64 Merchandise trade (% of GDP)
## 65 Net barter terms of trade index (2015 = 100)
## 66 Revenue, excluding grants (% of GDP)
## 67 Tax revenue (% of GDP)
## 68 Total debt service (% of exports of goods, services and primary income)
## 69 Agriculture, forestry, and fishing, value added (% of GDP)
## 70 Foreign direct investment, net inflows (BoP, current US$)
## 71 GDP (current US$)
## 72 GDP growth (annual %)
## 73 GNI per capita, Atlas method (current US$)
## 74 GNI per capita, PPP (current international $)
## 75 GNI, Atlas method (current US$)
## 76 GNI, PPP (current international $)
## 77 Gross capital formation (% of GDP)
## 78 Imports of goods and services (% of GDP)
## 79 Exports of goods and services (% of GDP)
## 80 Inflation, GDP deflator (annual %)
## 81 Merchandise trade (% of GDP)
## 82 Net barter terms of trade index (2015 = 100)
## 83 Revenue, excluding grants (% of GDP)
## 84 Tax revenue (% of GDP)
## 85 Total debt service (% of exports of goods, services and primary income)
## 86
## 87
## 88
## 89
## 90
## Series.Code X2013 X2014 X2015 X2016
## 1 NV.AGR.TOTL.ZS 13.3567 13.33676 13.49264 13.47875
## 2 BX.KLT.DINV.CD.WD 23281742362 25120732060 19779127977 4541713739
## 3 NY.GDP.MKTP.CD 9.13E+11 8.91E+11 8.61E+11 9.32E+11
## 4 NY.GDP.MKTP.KD.ZG 5.557263689 5.006668426 4.8763223 5.033069183
## 5 NY.GNP.PCAP.CD 3710 3600 3420 3400
## 6 NY.GNP.PCAP.PP.CD 9710 9890 9880 10150
## 7 NY.GNP.ATLS.CD 9.40E+11 9.23E+11 8.87E+11 8.90E+11
## 8 NY.GNP.MKTP.PP.CD 2.46E+12 2.53E+12 2.56E+12 2.66E+12
## 9 NE.GDI.TOTL.ZS 33.83135679 34.60034391 34.06279218 33.8587393
## 10 NE.IMP.GNFS.ZS 24.7138 24.41419 20.77746 18.33235
## 11 NE.EXP.GNFS.ZS 23.92358 23.66598 21.16018 19.08899
## 12 NY.GDP.DEFL.KD.ZG 4.965990291 5.443174549 3.98024266 2.438924087
## 13 TG.VAL.TOTL.GD.ZS 40.45712164 39.7918869 34.04304572 30.0622175
## 14 TT.PRI.MRCH.XD.WD 104.9038 102.5022 100 101.5419
## 15 GC.REV.XGRT.GD.ZS 15.00494556 14.61652911 12.96669934 12.47382327
## 16 GC.TAX.TOTL.GD.ZS 11.28530146 10.83552375 10.75348778 10.3363487
## 17 DT.TDS.DECT.EX.ZS 19.96524081 29.91739054 34.60619961 37.47857528
## 18 NV.AGR.TOTL.ZS 15.21560889 14.88035727 14.47472745 13.81825836
## 19 BX.KLT.DINV.CD.WD 8900000000 9200000000 11800000000 12600000000
## 20 NY.GDP.MKTP.CD 2.14E+11 2.33E+11 2.39E+11 2.57E+11
## 21 NY.GDP.MKTP.KD.ZG 5.553500245 6.422246656 6.987166724 6.690009213
## 22 NY.GNP.PCAP.CD 2200 2400 2480 2580
## 23 NY.GNP.PCAP.PP.CD 6490 6970 7210 7820
## 24 NY.GNP.ATLS.CD 1.99E+11 2.19E+11 2.28E+11 2.40E+11
## 25 NY.GNP.MKTP.PP.CD 5.86E+11 6.36E+11 6.65E+11 7.28E+11
## 26 NE.GDI.TOTL.ZS 30.21290081 30.28902486 32.10891422 31.72453901
## 27 NE.IMP.GNFS.ZS 64.0459034 65.81194126 71.99137253 71.30222164
## 28 NE.EXP.GNFS.ZS 66.80044 69.59857 72.92285 74.10729
## 29 NY.GDP.DEFL.KD.ZG 4.03855076 3.698131973 -1.716524374 1.819530359
## 30 TG.VAL.TOTL.GD.ZS 123.5634 127.6779 136.9545 136.6746
## 31 TT.PRI.MRCH.XD.WD 95.80829562 97.89003217 100 103.8457475
## 32 GC.REV.XGRT.GD.ZS .. .. .. ..
## 33 GC.TAX.TOTL.GD.ZS .. .. .. ..
## 34 DT.TDS.DECT.EX.ZS 3.167323664 4.172907151 3.817995398 3.868510133
## 35 NV.AGR.TOTL.ZS 11.32222 10.08892 8.872724 8.478077
## 36 BX.KLT.DINV.CD.WD 15935960665 4975455660 8927579182 3486184390
## 37 NY.GDP.MKTP.CD 4.20E+11 4.07E+11 4.01E+11 4.13E+11
## 38 NY.GDP.MKTP.KD.ZG 2.687495563 0.984468864 3.134047249 3.435157717
## 39 NY.GNP.PCAP.CD 5610 5640 5580 5570
## 40 NY.GNP.PCAP.PP.CD 14130 14360 14670 15470
## 41 NY.GNP.ATLS.CD 3.90E+11 3.94E+11 3.92E+11 3.93E+11
## 42 NY.GNP.MKTP.PP.CD 9.83E+11 1.00E+12 1.03E+12 1.09E+12
## 43 NE.GDI.TOTL.ZS 27.45710118 23.91901955 22.35564064 21.1054892
## 44 NE.IMP.GNFS.ZS 65.29113 62.51136 57.20297 53.50434
## 45 NE.EXP.GNFS.ZS 67.17114 68.39414 67.63669 67.07088
## 46 NY.GDP.DEFL.KD.ZG 1.778745892 1.441465365 0.722113573 2.63616762
## 47 TG.VAL.TOTL.GD.ZS 113.9361528 111.7523634 103.904038 99.08547324
## 48 TT.PRI.MRCH.XD.WD 93.93023771 93.84403179 100 102.4681571
## 49 GC.REV.XGRT.GD.ZS 20.60910785 19.68960637 20.54471087 19.81237354
## 50 GC.TAX.TOTL.GD.ZS 17.0125248 15.80833907 16.14061659 15.36208784
## 51 DT.TDS.DECT.EX.ZS 4.851886592 8.950814545 6.734372232 5.066037157
## 52 NV.AGR.TOTL.ZS 12.47343 12.27168 10.9965 10.20513
## 53 BX.KLT.DINV.CD.WD 3737371740 5739574024 5639155962 8279548275
## 54 NY.GDP.MKTP.CD 2.84E+11 2.97E+11 3.06E+11 3.19E+11
## 55 NY.GDP.MKTP.KD.ZG 6.750531301 6.347987483 6.348309717 7.14945675
## 56 NY.GNP.PCAP.CD 3140 3300 3350 3410
## 57 NY.GNP.PCAP.PP.CD 7330 7700 7940 8460
## 58 NY.GNP.ATLS.CD 3.13E+11 3.34E+11 3.46E+11 3.58E+11
## 59 NY.GNP.MKTP.PP.CD 7.31E+11 7.80E+11 8.18E+11 8.87E+11
## 60 NE.GDI.TOTL.ZS 20.64222411 20.92397044 21.34094787 24.6185031
## 61 NE.IMP.GNFS.ZS 29.64736 30.11367 31.93353 35.10306
## 62 NE.EXP.GNFS.ZS 26.17742 27.3545 27.20806 26.673
## 63 NY.GDP.DEFL.KD.ZG 2.061063359 3.053055301 -0.71968279 1.280311744
## 64 TG.VAL.TOTL.GD.ZS 43.12637536 43.9708342 43.58910084 44.64405033
## 65 TT.PRI.MRCH.XD.WD 89.47649309 93.41070151 100 104.2720178
## 66 GC.REV.XGRT.GD.ZS 14.21555059 14.4354569 14.67705028 14.50973202
## 67 GC.TAX.TOTL.GD.ZS 12.74382206 13.01551718 13.01961059 13.08710069
## 68 DT.TDS.DECT.EX.ZS 7.715905391 8.962418562 12.93609855 13.1673522
## 69 NV.AGR.TOTL.ZS 31.59506 28.8713 26.58036 24.74266
## 70 BX.KLT.DINV.CD.WD 2068470774 1853471158 1822804151 2475915854
## 71 NY.GDP.MKTP.CD 15227991395 16702610842 18049954289 20016747858
## 72 NY.GDP.MKTP.KD.ZG 7.356665149 7.142571101 6.965797814 6.933313973
## 73 NY.GNP.PCAP.CD 960 1020 1070 1160
## 74 NY.GNP.PCAP.PP.CD 2890 3020 3220 3510
## 75 NY.GNP.ATLS.CD 14367117134 15519758891 16563942899 18078663605
## 76 NY.GNP.MKTP.PP.CD 43284451677 45941913407 49695125106 54905400965
## 77 NE.GDI.TOTL.ZS 20.00891695 22.09450019 22.4529982 22.70583265
## 78 NE.IMP.GNFS.ZS 67.65855 67.00876 66.14564 65.6685
## 79 NE.EXP.GNFS.ZS 62.38794 62.60347 61.71842 61.28152
## 80 NY.GDP.DEFL.KD.ZG 0.781387552 2.632195879 1.786112459 3.475254586
## 81 TG.VAL.TOTL.GD.ZS 106.5209428 105.0015484 120.7925497 112.1061231
## 82 TT.PRI.MRCH.XD.WD 92.98515339 92.15309512 100 102.1056471
## 83 GC.REV.XGRT.GD.ZS 13.74470323 16.57897293 16.57847147 17.36610756
## 84 GC.TAX.TOTL.GD.ZS 12.07885296 14.62663089 14.5828781 14.82589055
## 85 DT.TDS.DECT.EX.ZS 5.717661106 5.365805927 5.064635161 5.148399034
## 86
## 87
## 88
## 89
## 90
## X2017 X2018 X2019 X2020 X2021 X2022
## 1 13.15663 12.8085 12.7126 13.69841 13.28022 12.39966
## 2 20510310832 18909826044 24993551748 19175077748 21213080330 21428338422
## 3 1.02E+12 1.04E+12 1.12E+12 1.06E+12 1.19E+12 1.32E+12
## 4 5.069785901 5.17429154 5.01928768 -2.065511829 3.703055357 5.308595005
## 5 3530 3850 4070 3900 4170 4580
## 6 10600 11320 11980 11830 12730 14250
## 7 9.34E+11 1.03E+12 1.10E+12 1.06E+12 1.14E+12 1.26E+12
## 8 2.80E+12 3.02E+12 3.23E+12 3.22E+12 3.49E+12 3.93E+12
## 9 33.7105948 34.57058583 33.78014238 32.34341205 31.44898475 29.74533551
## 10 19.17819 22.07156 19.03625 15.64101 18.78963 20.90086
## 11 20.1773 21.00275 18.59153 17.33117 21.40812 24.49245
## 12 4.292678122 3.818323569 1.5984885 -0.401651435 6.003421337 9.567844361
## 13 32.07266525 35.38732358 30.28853891 28.79246548 36.04669477 40.13538864
## 14 101.2076 100.5609 100.9991 98.48086 100.7706 ..
## 15 12.17664757 12.99328466 12.34853846 10.53699207 11.81548514 ..
## 16 9.87729308 10.23014302 9.751961808 8.310105242 9.094042441 ..
## 17 29.43508718 25.07242782 39.41267951 36.70143926 28.78405449 ..
## 18 12.92987422 12.30667472 11.78452616 12.65539719 12.56036351 11.87710793
## 19 14100000000 15500000000 16120000000 15800000000 15660000000 17900000000
## 20 2.81E+11 3.10E+11 3.34E+11 3.47E+11 3.66E+11 4.09E+11
## 21 6.940187782 7.464991257 7.359281 2.865411946 2.561551142 8.019798458
## 22 2720 3060 3340 3450 3590 4010
## 23 8500 9360 10150 10560 11130 12810
## 24 2.55E+11 2.91E+11 3.20E+11 3.34E+11 3.50E+11 3.94E+11
## 25 8.00E+11 8.89E+11 9.72E+11 1.02E+12 1.08E+12 1.26E+12
## 26 32.30529854 32.01950352 31.97996185 31.91568729 33.46748664 ..
## 27 79.21755571 80.24048222 79.54663042 78.86426261 93.17653158 ..
## 28 81.76252 84.42346 85.15759 84.38159 93.29165 ..
## 29 4.362930138 3.62665301 2.423207531 1.467486618 2.778272945 3.860465836
## 30 152.0979 154.9665 154.819 157.3298 182.5633 178.677
## 31 104.2102251 102.5841634 105.0521391 104.2807071 101.6808563 ..
## 32 .. .. .. .. .. ..
## 33 .. .. .. .. .. ..
## 34 5.922366271 6.995912792 5.802447836 5.608666116 5.8525034 ..
## 35 8.406413 8.201819 8.128568 8.702767 8.708376 8.822149
## 36 8285169820 13747219811 5518708214 -4947474467 14640873082 10196091866
## 37 4.56E+11 5.07E+11 5.44E+11 5.00E+11 5.06E+11 4.95E+11
## 38 4.177681032 4.222870287 2.114557796 -6.066925969 1.492095235 2.594733433
## 39 5820 6450 7080 6920 7090 7230
## 40 16250 17220 18070 17420 18180 20070
## 41 4.13E+11 4.59E+11 5.05E+11 4.94E+11 5.07E+11 5.19E+11
## 42 1.15E+12 1.22E+12 1.29E+12 1.25E+12 1.30E+12 1.44E+12
## 43 22.93429569 25.21959016 23.81475257 23.73853101 28.6274347 27.87048772
## 44 54.21859 56.00377 50.17062 46.30479 58.60544 68.12841
## 45 66.67283 64.8381 59.51889 51.4949 58.63799 65.78502
## 46 1.89994499 1.428586164 1.014423406 -1.281944318 1.709812993 4.710172864
## 47 100.3938119 98.8956752 88.7039838 87.47799935 106.590595 119.1622509
## 48 100.7010709 98.53646981 98.59484416 101.7556296 101.6576833 ..
## 49 19.11911048 19.50624038 19.26291288 19.33064074 18.47655305 ..
## 50 14.77967697 14.91490941 14.65538378 14.45787411 14.32414197 ..
## 51 4.722824298 5.523083949 8.087898152 7.736973025 5.864883757 ..
## 52 10.18295 9.65014 8.820324 10.18531 10.06917 9.54935
## 53 10256442399 9948598824 8671365874 6822133291 11983363327 9199942906
## 54 3.28E+11 3.47E+11 3.77E+11 3.62E+11 3.94E+11 4.04E+11
## 55 6.930988326 6.341485572 6.118525662 -9.51829474 5.714733132 7.570332488
## 56 3480 3640 3770 3350 3550 3950
## 57 8880 9480 10010 8830 9250 10730
## 58 3.71E+11 3.95E+11 4.16E+11 3.76E+11 4.04E+11 4.57E+11
## 59 9.48E+11 1.03E+12 1.11E+12 9.91E+11 1.05E+12 1.24E+12
## 60 25.55877386 27.15058204 26.40180846 17.43337903 21.14073715 24.69818262
## 61 38.61608 41.94979 40.45892 32.96672 37.73258 44.03158
## 62 29.55229 30.21361 28.38292 25.20284 25.75203 28.38511
## 63 2.320259946 3.74065383 0.697076297 1.650490187 2.282478462 5.481077739
## 64 51.94064071 52.64848383 48.6283489 42.74485429 50.49261133 55.60368899
## 65 99.06786181 96.33749267 98.13147586 100.489053 95.83385449 ..
## 66 14.93213807 15.51845144 16.06915835 15.9052545 15.48051244 ..
## 67 13.59379375 14.04753328 14.48847302 13.95098571 14.13000393 ..
## 68 11.52756385 8.607486991 9.816799961 10.22529233 12.2404301 ..
## 69 23.36144 22.01296 20.71187 22.6964 22.8475 21.86926
## 70 2788084322 3212633447 3663032999 3624644990 3483461606 3578831296
## 71 22177200589 24571753582 27089390033 25872797892 26961061152 29956769529
## 72 6.996903699 7.469169207 7.054106932 -3.096006731 3.026389363 5.16246
## 73 1260 1420 1560 1530 1580 1700
## 74 3770 4090 4380 4330 4560 5080
## 75 19964016385 22796925306 25211727181 25138725038 26266782636 28579549460
## 76 59657647126 65478977943 71070686302 71047705347 75564500513 85207610441
## 77 22.89196799 23.44825618 24.23348873 24.87956023 26.57745549 31.76832791
## 78 64.10582 63.30289 62.46523 62.46119 67.61144 84.83111
## 79 60.68196 61.59573 61.0913 61.04182 64.60246 77.58178
## 80 3.341042159 3.111821368 3.235371769 -0.671856544 1.291945004 5.742208773
## 81 115.2580097 122.8605842 129.5857897 142.3502791 177.6265397 174.5081356
## 82 97.76001822 92.60951834 94.78870193 99.50823832 88.25546057 ..
## 83 18.55837505 19.91953384 22.79315983 19.94648292 18.1466895 ..
## 84 15.78871893 17.05208455 19.73205523 17.88517278 16.3649333 ..
## 85 6.241011942 6.732936165 6.957101352 7.424588519 10.66148128 ..
## 86
## 87
## 88
## 89
## 90The dataset consists of 4 variables:
Country Name (Indonesia, Vietnam, Thailand, Philippines, Cambodia): The country’s name in which was done the research in.
Country Code (IDN, VNM, THA, PHL, KHM): The code name of the countries in which was done the research in .
Series name (Agriculture, forestry, and fishing, value added (% of GDP,Foreign direct investment, net inflows (BoP, current US dollar), GDP (current US dollar, GDP growth (annual %), GNI per capita, Atlas method (current US dollar), GNI per capita, PPP (current international dollar), GNI, Atlas method (current US dollar), GNI, PPP (current international dollar), Gross capital formation (% of GDP), Imports of goods and services (% of GDP), Exports of goods and services (% of GDP), Inflation, GDP deflator (annual %), Merchandise trade (% of GDP), Net barter terms of trade index (2015 = 100), Revenue, excluding grants (% of GDP), Tax revenue (% of GDP), Total debt service (% of exports of goods, services and primary income)): The terms indicating the values in economic and financial analysis to assess the economic health and performance of a country.
Years (2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021 2022) (numbers): The years in which the data was taken in.
III. DATA ANALYSIS.
1. GDP of VietNam ( 2013 – 2022 ) ( current US $)
library(ggplot2)
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
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## ✔ lubridate 1.9.2 ✔ tibble 3.2.1
## ✔ purrr 1.0.1 ✔ tidyr 1.3.0
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## ℹ Use the ]8;;http://conflicted.r-lib.org/conflicted package]8;; to force all conflicts to become errorslibrary(tidyr)
# Make a value for data
gdp_values <- as.numeric(SoutheastAsia[20, 5:14])
years <- colnames(SoutheastAsia)[5:14]
# Create a data frame for graphing
plot_data <- data.frame(year = as.factor(years), gdp = gdp_values)
# Calculate the mean
mean_gdp <- mean(gdp_values)
# Graphing
ggplot(plot_data, aes(x = year, y = gdp, group = 1)) +
geom_line(color = "blue", linewidth = 1.5) +
geom_hline(yintercept = mean_gdp, linetype = "dashed", color = "red", linewidth = 1) +
# Add mean line
labs(title = "GDP of Vietnam (2013-2022)",
x = "Year",
y = "GDP (Current US$)",
subtitle = "Values in hundred dollars",
caption = "Source: Group 7") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
scale_y_continuous(labels = scales::dollar_format(scale = 1e-2))
The line graph illustrates the GDP values of Vietnam in US dollars over a span of 10 years, from 2013 to 2022. The GDP value reflects the size of the Vietnamese market and the overall economic status of Vietnam. This graph describes a consistent upward trend, indicating sustained economic growth, with a mean GDP value of 299 billion dollars.
In general, the most rapid increase in GDP occurred in 2021, presenting a strong economic expansion. However, the slowest GDP growth was observed in 2014. The growth rate of Vietnam’s GDP demonstrates variability, ranging from 2.5% to 11.7% annually. This fluctuation suggests that the continuous growth of Vietnam’s GDP is characterized by dynamic economic conditions and varying growth rates across the years.
2. Compare GDP Growth between Vietnam and Thailand (annual % ) (2013- 2022)
# Extract GDP growth rates for Vietnam and Thailand
vietnam_growth <- as.numeric(SoutheastAsia[21, 5:14])
thailand_growth <- as.numeric(SoutheastAsia[38, 5:14])
years <- colnames(SoutheastAsia)[5:14]
# Create a data frame
gdp_data <- data.frame(Year = years, Vietnam_GDP_Growth = vietnam_growth, Thailand_GDP_Growth = thailand_growth)
# Reshape the data to long format
gdp_data_long <- tidyr::gather(gdp_data, Country, GDP_Growth, -Year)
gdp_data_long$Country <- gsub("_", " ", gdp_data_long$Country)
# Create the bar chart
ggplot(gdp_data_long, aes(x = Year, y = GDP_Growth, fill = Country)) +
geom_bar(stat = "identity", position = "dodge", width = 0.7) +
labs(title = "GDP Growth Comparison between Vietnam and Thailand",
x = "Year",
y = "GDP Growth (Annual %)",
fill = "Country") +
theme_minimal()
Based on the provided data, the graph comparing GDP growth between Vietnam and Thailand (annual %) from 2013 to 2022 reveals interesting insights. Both countries experienced fluctuating GDP growth rates over the years.
For Vietnam, the GDP growth exhibited a generally positive trend, with notable peaks in 2016, 2017, and 2019. However, in 2020, there was a significant dip, likely attributed to the global economic challenges, as indicated by the negative growth rate.
Thailand’s GDP growth, on the other hand, shows a more varied pattern. The country faced a sharp decline in 2020, echoing the global economic downturn. In the subsequent years, there was a rebound, indicating efforts towards economic recovery.
Comparatively, Vietnam demonstrated more resilience during challenging periods, maintaining positive growth rates even in the face of global economic uncertainties. A deeper analysis, considering factors such as economic policies, global economic conditions, and domestic influences, would provide a more comprehensive understanding of the observed patterns.
3. Imports and Exports of goods and services of Viet Nam (% of GDP)( 2018 – 2022 )
library(tidyverse)
library(ggplot2)
#Make a value for data
importvn_values <- as.numeric(SoutheastAsia[27, 5:13])
exportvn_values <- as.numeric(SoutheastAsia[28, 5:13])
yearvn <- colnames(SoutheastAsia)[5:13]
#Create a dataframe for graphing
A <- data.frame(year = rep(yearvn, 2), percentage.of.GDP = c(importvn_values, exportvn_values), series.name = factor(rep(c("Imports", "Exports"), each = length(yearvn))))
#Graphing
ggplot(data = A, aes(x = year, y = percentage.of.GDP, fill = series.name)) + geom_col(position = position_dodge(width = 0.5)) + labs(title = "Imports and Exports of goods and services in Vietnam (2013 - 2021)", x = "Year", y = "Percentage of GDP") + scale_y_continuous(breaks = seq(0, 100, 10)) + theme_minimal()
The bar chart illustrates the information about the imports and exports of goods and services in Vietnam during the period of 10 years, from 2013 to 2022, with the values represented by the percentage of GDP, which indicates the degree of openness of a country and its influence on the economy. Besides showing the status of imports and exports, it also examines the balance of trade and the impact of international market fluctuations.
According to the chart, the exports in Vietnam had an upward trend during the period while the imports had a fluctuation.In general, the figure of exports was higher than the imports in 10 years and both exports and imports reached the lowest point in 2013 and the highest point in 2021.
In addition, both exports and imports increased gradually from 2013 to 2017. Then, because of the heavy impact of Covid-19 pandemic on the Vietnamese economy, which took 3 years (2018 - 2020), the percentage of exports fluctuated from 84% to 85% and the imports decreased from 80% to 78%. When the pandemic gradually came to an end, in 2021, the imports and exports situation recovered and grew strongly, which was approximately 93%.
4. Agriculture, forestry, and fishing, value added between Vietnam and 4 different countries (% of GDP) ( 2013 – 2022 )
# Make a value for data
Vietnam_Agri <- as.numeric(SoutheastAsia[18, 5:14])
Indonesia_Agri <- as.numeric(SoutheastAsia[1, 5:14])
Philippines_Agri <-as.numeric(SoutheastAsia[52, 5:14])
Thailand_Agri <- as.numeric(SoutheastAsia[35, 5:14])
Cambodia_Agri <- as.numeric(SoutheastAsia[69, 5:14])
years <- colnames(SoutheastAsia)[5:14]
#Create a dataframe for graphing
A <- data.frame(year = rep(years, 5), percentage.of.GDP = c(Vietnam_Agri, Indonesia_Agri, Thailand_Agri, Philippines_Agri, Cambodia_Agri), country = rep(c("Vietnam", "Indonesia", "Thailand", "Philippines", "Cambodia"), each = length(years)), series.name = factor(rep("Agricultural", each = length(years))))
#Graphing
ggplot(data = A, mapping = aes(x = as.factor(year), y = percentage.of.GDP, color = country, group = country)) + geom_line() +
geom_point(size = 2, alpha = 0.3) + labs(title = "Agriculture, Forestry, and Fishing Value Added (% of GDP)",
x = "Year",
y = "Percentage of GDP", color = "Country")+ theme_minimal()
The dataset provided summarizes the complex fluctuations in Agricultural Value Added as a percentage of GDP across Vietnam and four other countries, spanning from 2013 to 2022. The line graph vividly illustrates the distinctive trajectories of these countries, providing valuable insights into their development of agricultural fields.
Upon close examination of the chart, it is clear that Cambodia has experienced a distinct downturn in Agricultural Value Added, which decreased significantly from 2016 to 2019. During this period, the amount of agriculture to Cambodia’s GDP dropped significantly, signaling a potential economic shift or external factors impacting the country’s agricultural landscape. This deterioration is especially evident when contrasting Cambodia’s data points with those of its partners.
In contrast, Vietnam, Indonesia, Thailand, and the Philippines exhibit varying degrees of stability and volatility in their agricultural contributions over the same period. While each country experienced its own trajectory, the decline observed in Cambodia stands out as a distinct feature in the data set.
In summary, the line chart effectively captures the various trends in Vietnam and its partners’ Agricultural Value Added, providing convincing information about Cambodia’s significant decline in specific years. This visualization serves as a valuable source of information for understanding the economic dynamics of these countries, especially the notable challenges Cambodia faces in maintaining its agricultural contribution. to its GDP.
5. Gross capital formation Viet Nam (% of GDP) ( 2013 – 2021 )
# Make a value for data
Vietnam_values <- as.numeric(SoutheastAsia[26,5:13])
years <- colnames(SoutheastAsia)[5:13]
# Create a data frame for graphing
plot_data <- data.frame(Years = as.factor(years), Gross_Capital_Formation = Vietnam_values)
# Graphing
ggplot(plot_data, aes(x = as.factor(years), y = Gross_Capital_Formation, group = 1)) +
geom_line(color = "blue", linewidth = 1.0) +
geom_point(size = 2, alpha = 0.3, color = "red") +
labs(title = "Gross capital formation (% of GDP)",
x = "Years",
y = "Percentage of GDP")+ theme_minimal()
From 2013 to 2021, Vietnam’s Gross Capital Formation (GCF) demonstrated a positive trend, rising from 30.21% to 33.47% of GDP. Noteworthy fluctuations, such as a peak in 2015 and slight declines in 2019 and 2020, marked the trajectory.
The data signifies Vietnam’s consistent commitment to enhancing fixed assets, with 2021 reflecting a substantial boost in investment. However, a deeper analysis is needed to understand the nuanced factors driving these trends, encompassing economic policies and global influences.
In essence, Vietnam’s GCF underscores the nation’s sustained efforts in fortifying its economic infrastructure over the examined period.
6. Inflation, GDP deflator of Vietnam, Indonesia, Philippines (annual %) (2018- 2022)
# Creating value for graphing
vietnam_data <- as.numeric(SoutheastAsia[29, 10:14])
indonesia_data <- as.numeric(SoutheastAsia[12, 10:14])
philippines_data <- as.numeric(SoutheastAsia[63, 10:14])
# Creating a data frame for plotting
plot_data <- data.frame(
Country = rep(c("Vietnam", "Indonesia", "Philippines"), each = 5),
Inflation = c(rep(vietnam_data, each = 1), rep(indonesia_data, each = 1), rep(philippines_data, each = 1)),
Year = rep(as.factor(colnames(SoutheastAsia)[10:14]), times = 3)
)
# Calculate mean values for each country
mean_values <- aggregate(Inflation ~ Country, data = plot_data, mean)
# Graphing
ggplot(plot_data, aes(x = Year, y = Inflation, fill = Country)) +
geom_bar(stat = "identity", position = "dodge", width = 0.7, color = "white") +
geom_hline(data = mean_values, aes(yintercept = Inflation, color = Country), linetype = "dashed", linewidth = 1.5) + # Add mean lines
labs(
title = "Inflation and GDP Deflator (Annual %)",
x = "Year",
y = "Percentage (%)",
fill = "Country"
) +
scale_fill_manual(values = c("Vietnam" = "#1f78b4", "Indonesia" = "#33a02c", "Philippines" = "#e31a1c")) +
scale_color_manual(values = c("Vietnam" = "#1f78b4", "Indonesia" = "#33a02c", "Philippines" = "#e31a1c")) +
theme_minimal() +
theme(
plot.title = element_text(hjust = 0.5, size = 16, face = "bold"),
axis.text.x = element_text(angle = 45, hjust = 1),
legend.position = "top",
legend.title = element_text(size = 12),
legend.text = element_text(size = 10),
axis.text.y = element_text(size = 10),
axis.title.y = element_text(size = 12)
) +
scale_y_continuous(breaks = seq(0, 20, by = 2), labels = paste0(seq(0, 20, by = 2), "%"))
The inflation and GDP deflator of Indonesia, the Philippines, and Vietnam reflect the inflation rate of the entire market cap in each country. Indonesia exhibits the largest fluctuation in the inflation rate, with a mean around 4.1 percent, compared to Vietnam and the Philippines. In 2022, both the inflation rate and GDP deflator reached their peaks, while in 2020, both indices were at their lowest, and Indonesia experienced deflation. In general, this index is not stable throughout the five years due to various economic and health-related challenges.
7. GNI per capita, Atlas method (current US$) and GNI per capita, PPP (current international $) Viet Nam ( 2013 – 2022 )
# Extract GNI per capita data for Vietnam
gni_atlas <- as.numeric(SoutheastAsia[22, 5:14])
gni_ppp <- as.numeric(SoutheastAsia[23, 5:14])
years <- colnames(SoutheastAsia)[5:14]
# Create a data frame
gni_data <- data.frame(Year = years, GNI_Atlas = gni_atlas, GNI_PPP = gni_ppp)
# Reshape the data to long format
gni_data_long <- tidyr::gather(gni_data, Measure, GNI, -Year)
gni_data_long$Measure <- gsub("_", " ", gni_data_long$Measure)
# Create the bar chart
ggplot(gni_data_long, aes(x = Year, y = GNI, fill = Measure)) +
geom_bar(stat = "identity", position = "dodge", width = 0.7) +
labs(title = "GNI per Capita Comparison for Vietnam (2013 - 2022)",
x = "Year",
y = "GNI per Capita (Atlas [Current US$] / PPP [Current International $])",
fill = "Measure") +
theme_minimal()
Based on the GNI per capita data for Vietnam from 2013 to 2022, measured by both the Atlas method (current US$) and PPP (current international $), several observations can be made regarding the purchasing power and economic well-being of the population.
The upward trends in both GNI per capita measurements for Vietnam signify a positive economic outlook and an improvement in the standard of living. The consistent growth in GNI per capita, whether measured by the Atlas method or PPP, suggests that the Vietnamese population has experienced economic development and increasing purchasing power. This is indicative of a thriving economy, potentially leading to improved living standards, greater access to goods and services, and an overall enhancement of the quality of life for the population.
8.GNI, Atlas method (current US$) between VietNam and Indonesia (2018 – 2022)
# Creating value for data
Vietnam_values <- as.numeric(SoutheastAsia[24, 10:14])
Indonesia_values <- as.numeric(SoutheastAsia[7, 10:14])
years <- colnames(SoutheastAsia)[10:14]
#Create a dataframe for graph
data <- data.frame(Year = rep(years, 2), GNI = c(Vietnam_values, Indonesia_values), Country = factor(rep(c("Vietnam", "Indonesia"), each = length(years))))
#Calculate mean GNI value for each country
mean_GNI_vietnam <- mean(subset(data, Country == "Vietnam")$GNI)
mean_GNI_indonesia <- mean(subset(data, Country == "Indonesia")$GNI)
#Graphing
ggplot(data, aes(x = Year, y = GNI, fill = Country)) +
geom_bar(stat = "identity", position = "dodge", width = 0.7, color = "black") +
geom_hline(yintercept =c(mean_GNI_vietnam, mean_GNI_indonesia),
linetype = "dashed", linewidth = 1, color = "green") +
labs(x = "Year", y = "GNI (Atlas method, current US$)",
title = "Compare GNI between Vietnam and Indonesia",
subtitle = "Including Overall Mean GNI") + scale_fill_manual(values = c("blue", "orange")) +
scale_y_continuous(labels = scales::dollar_format(scale = 1e-2),
breaks = seq(0, max(data$GNI), by = 2e11)) +
theme_minimal() +
theme(plot.title = element_text(hjust = 0.5, size = 16, face = "bold"),axis.text.x = element_text(angle = 45, hjust = 1),legend.position = "top", legend.title = element_text(size = 12),legend.text = element_text(size = 10), axis.text.y = element_text(size = 10),axis.title.y = element_text(size = 12))
The comparison between the GNI of Vietnam and Indonesia from 2018 to 2022 is shown through a bar chart. Between 2018 and 2020, Indonesia had an impressive growth in GNI, from $1.06 trillion to $1.26 trillion, in just 3 years. At the same time, Vietnam also recorded a significant growth, however at a lower rate than Indonesia, from $291 billion to $334 billion. In general, In 2021 to 2022 Indonesia’s GNI continues to increase by $1.14 trillion to $1.26 trillion, which shows the stability and strength of the economy.While Vietnam continues to show growth with GNI increasing from $350 billion to $394 billion, representing a steady increase in the economy.
In addition, Indonesia’s overall GNI growth is stronger than Vietnam’s. Shows that Indonesia’s economic strength is superior and more stable than that of Vietnam
9. GNI, PPP (current international $) between Indonesia and Philippines ( 2018 – 2022 )
#Make a value for data
indonesia_values <- as.numeric(SoutheastAsia[8, 5:14])
philippines_values <-as.numeric(SoutheastAsia[59, 5:14])
years <- colnames(SoutheastAsia)[5:14]
#Create a dataframe for graphing
B <- data.frame(year = rep(years, 2), GNI.PPP = c(indonesia_values, philippines_values), country = factor(rep(c("Indonesia", "Philippines"), each = length(years))))
#Graphing
ggplot(data = B, aes(x = year, y = GNI.PPP, fill = country)) +
geom_col(position = position_dodge(width = 0.5)) + labs(title = "GNI, PPP between Indonesia and Philippines (2018 - 2022)", x = "Year", y = "Current international $") + scale_y_continuous(labels = scales::dollar_format(scale = 1e-1)) + theme_minimal() + geom_line(aes(group = country, color = country), linewidth = 1) + scale_color_manual(values = c("purple","darkgreen")) + geom_point(aes(group = country, color = country), size = 3)
The given bar chart provides the information about the comparison of Gross National Income (GNI), Purchasing Power Parity (PPP) between Indonesia and Philippines from 2013 to 2022., which is represented by the current international $ and provides important information about their size and economic performance.
Overall, it can be seen that the GNI, PPP growth rate in Indonesia is quite higher than the Philippines during this period. Furthermore, both countries had an upward trend and 2022 was the year when each country got its own highest number.
Both GNI and PPP of each country increased gradually until 2019, when Indonesia got $3.23 trillion and Philippines got $948 billion. They started to fall due to Covid-19 Pandemic and then, they climbed again. In 2022, Indonesia got $3.93 trillion and Philippines got $1.24 trillion.
10. Revenue, excluding grants (% of GDP) and Tax revenue (% of GDP) from 4 countries: Philippines, Cambodia , Indonesia, Thailand (2019 - 2021)
# Assuming SoutheastAsia is your data frame
Indonesia_Rev <- as.numeric(SoutheastAsia[15, 11:13])
Philippines_Rev <- as.numeric(SoutheastAsia[66, 11:13])
Thailand_Rev <- as.numeric(SoutheastAsia[49, 11:13])
Cambodia_Rev <- as.numeric(SoutheastAsia[83, 11:13])
years <- colnames(SoutheastAsia)[11:13]
Indonesia_Tax <- as.numeric(SoutheastAsia[16, 11:13])
Philippines_Tax <- as.numeric(SoutheastAsia[67, 11:13])
Thailand_Tax <- as.numeric(SoutheastAsia[50, 11:13])
Cambodia_Tax <- as.numeric(SoutheastAsia[84, 11:13])
# Create a data frame
df <- data.frame(
Country = rep(c("Indonesia", "Philippines", "Thailand", "Cambodia"), each = 3),
Year = rep(years, times = 4),
Revenue = c(Indonesia_Rev, Philippines_Rev, Thailand_Rev, Cambodia_Rev),
Tax = c(Indonesia_Tax, Philippines_Tax, Thailand_Tax, Cambodia_Tax)
)
# Reshape the data
df_long <- gather(df, key = "Variable", value = "Value", -Country, -Year)
# Calculate mean values
mean_values <- df_long %>%
group_by(Country, Variable) %>%
summarize(Mean = mean(Value, na.rm = TRUE))## `summarise()` has grouped output by 'Country'. You can override using the
## `.groups` argument.# Create a line graph with different colors for each country and each variable
ggplot(df_long, aes(x = Year, y = Value, color = Country, group = interaction(Country, Variable))) +
geom_line(size = 1.5) +
geom_hline(data = mean_values, aes(yintercept = Mean, color = Variable),
linetype = "dashed", size = 1) +
labs(x = "Year", y = "Amount",
title = "Comparison of Revenue and Tax by Country",
subtitle = "Line graph for Indonesia, Philippines, Thailand, and Cambodia with Mean Lines") +
scale_color_manual(values = c("Indonesia" = "blue", "Philippines" = "red", "Thailand" = "green", "Cambodia" = "purple")) +
facet_wrap(~ Variable, scales = "free_y") + # Separate lines for Tax and Revenue
theme_minimal()## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
For four Southeast Asian countries—Indonesia, Thailand, the Philippines, and Cambodia—the dataset shows revenue and tax figures as a percentage of GDP from 2013 to 2021. Thailand has consistently maintained the highest percentage of GDP (excluding grants) in terms of revenue, ranging from roughly 18.5% to 20.6%. This represents a significant portion of economic output, as measured by the comparison between 84 billion US dollars in 2013 and 93.5 billion US dollars in 2021. Following with numbers ranging from 10.5% to 15.5% were Indonesia and the Philippines, while Cambodia demonstrated a significant growth from roughly 13.7% in 2013 to over 22.8% in 2021.
Over the 10 years course, there was a progressive reduction observed in tax income as a percentage of GDP in Indonesia, Thailand, and the Philippines. The Philippines and Indonesia had tax revenues ranging from 8.3% to 14.5%, while Thailand had the highest percentage of tax revenue, averaging between 14.3% and 17%. From a base of 12.1% in 2013, Cambodia saw a steady increase, peaking at roughly 19.7% in 2021. The differences in revenue and tax percentages across the four countries during the given period demonstrate different taxes and economic conditions.
11. Total debt service (% of exports of goods, services and primary income) of Cambodia (2013 - 2021)
# Creating value for data
debt_values <- as.numeric(SoutheastAsia[85,5:13])
year <- colnames(SoutheastAsia)[5:13]
#Create a dataframe for graph
plot_data <- data.frame(Year = as.factor(year), debt = debt_values)
#Calculate mean of total debt of Cambodia
mean_debt <- mean(debt_values)
#Graphing
ggplot(plot_data, aes(x = Year, y = debt_values, group = 1)) +
geom_line(color = "blue", size = 1.5) +
geom_hline(yintercept = mean_debt, linetype = "dashed", color = "red", size = 1) +
labs(title = "Total debt service of Cambodia from (2013 - 2021)",
x = "Year",
y = "Total debt (%)") +
theme_minimal() + theme(axis.text.x = element_text(angle = 45, hjust = 1)) + scale_y_continuous(labels = scales::dollar_format(scale = 1e-2))
Cambodia’s total debt service as a percentage of exports of goods and service and primary income is provided by line graph from 2013 to 2021. Beginning at 5.7177% in 2013, the figures remained relatively.However, there was a noticeable shift following that, with a steady but consistent increase noted each year. This increase peaked in 2021, reaching 10.6615%, indicating a large increase in the proportion of export profits committed to servicing external debt. Such a high increase may indicate increased borrowing or probable difficulties in meeting debt commitments in relation to the country’s export revenue. It raises worries about Cambodia’s debt sustainability and emphasizes the significance of strong debt management techniques in ensuring economic stability and long-term growth.
12. Foreign direct investment, net inflows (BoP, current US$) of Viet Nam ( 2013 – 2022 )
#Creating value for the data
vietnam_data_frame <- as.numeric(SoutheastAsia[19, 5:14])
years <- colnames(SoutheastAsia[5:14])
#Create a data frame for the graph
vietnam_data_frame <- data.frame(
Country = "Vietnam",
Code = "VNM",
Indicator = "Foreign direct investment, net inflows (BoP, current US$)",
Year = as.factor(years),
Value = vietnam_data_frame
)
#Create a mean total of the Foreign direct investment, net inflows if Viet Nam
mean_foreign_investment <- mean(vietnam_data_frame$Value)
#Graphing
custom_colors <- c("#1f78b4", "#33a02c", "#e31a1c", "#ff7f00", "#6a3d9a", "#a6cee3", "#b2df8a", "#fb9a99", "#fdbf6f", "#cab2d6")
ggplot(vietnam_data_frame, aes(x = Year, y = Value, fill = Year)) +
geom_bar(stat = "identity", color = "black") +
geom_hline(yintercept = mean_foreign_investment, linetype = "dashed", color = "red", size = 1) +
labs(x = "Year", y = "Foreign direct investment, net inflows (BoP, current US$)",
title = "Foreign Direct Investment Net Inflows for Vietnam (2013-2022)",
subtitle = "By Year with Mean Highlighted") +
theme_minimal() +
scale_fill_manual(values = custom_colors) + scale_y_continuous(labels = scales::dollar_format(scale = 1e-0))
Measured in current US dollars ($), the dataset offered Vietnam’s Foreign Direct Investment (FDI) data from 2013 to 2022. Over the given time, net FDI inflows have shown a constant rising trend, according to the numbers. $8.9 billion in foreign investments were made in the nation in 2013, and by 2022, that amount had risen to $17.9 billion. The growth pattern points to a successful economic climate that has drawn increasing numbers of foreign investments. These investments are essential to promoting corporate expansion, economic development, and the creation of jobs. With $17.9 billion in net FDI, the high in 2022 reflects a significant increase in investor confidence and interests in Vietnam’s economic potential.
IV. FURTHER ANALYSIS
1. Formula and Method:
We apply the T-test method and the null hypothesis to examine differences in economic indices among five Southeast Asian countries. By using R Studio, we perform statistical calculations and draw boxplots to provide a comprehensive overview of the null hypothesis. This approach facilitates a thorough comparison of the economic characteristics of the five countries.
- The standard error is:
\[ s.e.(\bar{x}-\bar{y}) = \sqrt{\frac{s_x^2}{n}+\frac{s_y^2}{m}} \]
- The degree of freedom “v” of the t-distribution is given by:
\[ v=\frac{(\frac{s_x^2}{n}+\frac{s_y^2}{m})^2}{\frac{s_x^4}{n^2(n-1)}+\frac{s_y^4}{m^2(m-1)}} \]
- Two-sided hypothesis testing:
\[ H_0 = \mu_A - \mu_B = \delta~versus~H_A: \mu_A - \mu_B \neq 0 \]
\[ p-value = 2*P(X>|t|), X \sim t_v \]
- For some fixed value \(\delta\) ( usally \(\delta\) = 0)
\[ |t| \leq t_\alpha/2,v \]
and reject \(H_0\) if:
\[ |t| > t_\alpha/2,v \]
2. Hypothesis of Economics.
(1). Null hypothesis 1:
GDP of Vietnam and Thailand from 2013 to 2022
Motivation: Researching the difference in GDP between Vietnam and Thailand from 2013 to 2022 is an important step in understanding the economic trends of the two countries over a long period.Comparing and analyzing variations in GDP can provide valuable information about economic developments for each country.
Null hypothesis (H0): There is no relation between GDP of Vietnam and Thailand from 2013 to 2022.
Alternative hypothesis (H1): There is a relation between the GDP of Vietnam and Thailand from 2013 to 2022.
# Cut the data for Vietnam and Thailand
Thailand_values <-as.numeric(SoutheastAsia[37, 5:14])
Vietnam_values <- as.numeric(SoutheastAsia[20, 5:14])
# Perform t-test
test_result <- t.test(Thailand_values, Vietnam_values, alternative = "two.sided")
# Create a dataframe for the results
result_df <- data.frame(Variable = c("Degrees of Freedom", "p-value", "Mean Thailand", "Mean Vietnam"),
Value = c(
test_result$parameter,
test_result$p.value,
test_result$estimate[1],
test_result$estimate[2]
)
)
# Add Hypothesis column
result_df$Hypothesis <- c(
"Equality of Means",
"Alternative Hypothesis",
"",
"" )
# Print the results using kable and kableExtra
result_table <- knitr::kable(result_df, align = c("l", "c", "c"),
caption = "T-test Results: Thailand vs Vietnam") %>%
kableExtra::kable_styling(full_width = FALSE)
result_table| Variable | Value | Hypothesis | |
|---|---|---|---|
| df | Degrees of Freedom | 1.718329e+01 | Equality of Means |
| p-value | 7.000000e-06 | Alternative Hypothesis | |
| mean of x | Mean Thailand | 4.649000e+11 | |
| mean of y | Mean Vietnam | 2.990000e+11 |
The code for computation as follow:
# Create a data for graphing
data <- data.frame(Vietnam = Vietnam_values, Thailand = Thailand_values)
# Create a boxplot
ggplot(data, aes(x = NULL, y = NULL)) +
geom_boxplot(aes(x = "Vietnam", y = Vietnam, fill = "Vietnam"), width = 0.5, alpha = 0.7) +
geom_boxplot(aes(x = "Thailand", y = Thailand, fill = "Thailand"), width = 0.5, alpha = 0.7) +
scale_fill_manual(values = c("#FF5733", "#3498DB")) + scale_y_continuous(labels = function(x) format(x, scientific = FALSE)) +
theme_minimal() +
labs(title = "Box Plot of GDP for Vietnam and Thailand",
x = "Country",
y = "GDP (current US$)")
Based on calculated P value of 0.000000006781887 much smaller than 0.05 (statistical significance level). We can conclude that there is strong enough statistical evidence to reject the null hypothesis (H0) which is “There is no correlation between GDP of Vietnam and Indonesia from 2013 to 2022”. So , we can accept the alternative hypothesis (H1), i.e. there is a correlation between the GDP of these two countries between 2013 and 2022.
(2). Null hypothesis 2:
Merchandise trade (% of GDP) between Philippines and Indonesia
Motivation: In analyzing the dynamic interplay of economic forces between the Philippines and Indonesia, the null hypothesis posits a symmetrical equilibrium: there exists no significant difference in the average “Merchandise trade (% of GDP)” between the two nations from 2013 to 2022. Indonesia and the Philippines are both developing industrial countries. By adopting a 95% Confidence Interval, this statistical analysis strives to elucidate certain facts in trade patterns, providing a more comprehensive perspective on commerce.
Null hypothesis (H0): There is no difference in the average “Merchandise trade (% of GDP)” between the Philippines and Indonesia
Alternative hypothesis (HA): The average “Merchandise trade (% of GDP)” in the Philippines is not equal to the average in Indonesia
We choose the Confidence Interval is 95%
By using Rstudio, we have a code:
library(broom)
# Cut the data for Philippines and Indonesia
philippines_data <- as.numeric(SoutheastAsia[13, 5:14])
indonesia_data <- as.numeric(SoutheastAsia[30, 5:14])
# Perform t-test
test_result <- t.test(philippines_data, indonesia_data)
# Extract the test results
result_df <- data.frame(
Variable = c("Degrees of Freedom", "p-value", "Mean Philippines", "Mean Indonesia"),
Value = c(
test_result$parameter,
sprintf("%.15f", test_result$p.value), # Formatting p-value as an exact number
test_result$estimate[1],
test_result$estimate[2]
),
Hypothesis = c(
"Equality of Means",
"Alternative Hypothesis",
"",
""
)
)
# Print the table
result_table <- knitr::kable(result_df, align = c("l", "c", "c"),
caption = "T-test Results: Philippines vs Indonesia") %>%
kableExtra::kable_styling(full_width = FALSE)
result_table| Variable | Value | Hypothesis | |
|---|---|---|---|
| df | Degrees of Freedom | 9.88541830281311 | Equality of Means |
| p-value | 0.000000006781887 | Alternative Hypothesis | |
| mean of x | Mean Philippines | 34.707734839 | |
| mean of y | Mean Indonesia | 150.53239 |
The code for computation as follows:
# Combine the data into a list
data_list <- list(Philippines = philippines_data, Indonesia = indonesia_data)
# Create a boxplot
boxplot(data_list,
col = c("skyblue", "lightgreen"), # Set colors for each box
names = c("Philippines", "Indonesia"),
main = "Boxplot of Merchandise Trade (% of GDP)",
ylab = "Merchandise Trade (% of GDP)")
# Add a legend
legend("topright", legend = c("Philippines", "Indonesia"), fill = c("skyblue", "lightgreen"))
In conclusion, we can reject the null hypothesis (H0) because the p-value is below the common significance level (6.782e-09 < 0.05). The negative t-value and the confidence interval being entirely below 0 indicate that the mean for the Philippines is significantly lower than the mean for Indonesia (34.7 < 150.5). Finally, the average “Merchandise trade (% of GDP)” in the Philippines is not equal to the average in Indonesia.
(3). Null Hypothesis 3:
Compare Indonesia, Vietnam and Cambodia in Agriculture, forestry, and fishing, value added (% of GDP) from 2013 to 2022
Motivation: The relative balance between Indonesia and Vietnam in GDP percentage from 2013 to 2022, along with Cambodia’s prominence despite a decline, serves as the motivation for this study. We aim to gain deeper insights into the changes in agricultural value added, particularly understanding the factors influencing Cambodia’s sustained superiority within the regional context.
Null Hypothesis (H0): There is no significant difference in the contribution of agriculture, forestry, and fishing to GDP across Indonesia, Vietnam and Cambodia from 2013 to 2022.
Alternative Hypothesis (H1): There is a significant difference in the contribution of agriculture, forestry, and fishing to GDP across Indonesia, Vietnam and Cambodia from 2013 to 2022.
By using R studio, we have a code:
# Cut the data for Vietnam and Indonesia
Indonesia_Agricultural <- as.numeric(SoutheastAsia[1, 5:14])
Vietnam_Agricultural <- as.numeric(SoutheastAsia[18, 5:14])
Cambodia_Agricultural <- as.numeric(SoutheastAsia[69, 5:14])
# Create a data frame
data <- data.frame(
Country = rep(c("Indonesia", "Vietnam", "Cambodia"), each = 10),
Value_Added_Percentage = c(Indonesia_Agricultural, Vietnam_Agricultural, Cambodia_Agricultural))
# Perform ANOVA
tryCatch({
anova_result <- aov(Value_Added_Percentage ~ Country, data = data)
# Create a dataframe for the results
result_df <- summary(anova_result)[[1]]
# Print the results using kable and kableExtra
result_table <- knitr::kable(result_df, align = c("l", "c", "c"),
caption = "ANOVA
Analysis of Variance for Agricultural Values in Indonesia, Vietnam, Cambodia") %>%
kableExtra::kable_styling(full_width = FALSE)
result_table
}, error = function(e) {
# Print an error message if an error occurs
cat("Error:", conditionMessage(e), "\n")})| Df | Sum Sq | Mean Sq | F value | Pr(>F) | |
|---|---|---|---|---|---|
| Country | 2 | 853.9650 | 426.982517 | 92.78633 | 0 |
| Residuals | 27 | 124.2481 | 4.601782 | NA | NA |
The code for computation as follows:
# Extract p-value from the ANOVA results
p_value <- result_df$"Pr(>F)"[1]
cat("## Hypothesis Test and Decision:\n\n")## ## Hypothesis Test and Decision:# Print the p-value
cat("The p-value for the ANOVA test is:", p_value, "\n\n")## The p-value for the ANOVA test is: 7.981062e-13#The p-value for the ANOVA test is: 7.981062e-13
# Set the significance level
alpha <- 0.05
# Make a decision based on the p-value
if (p_value < alpha) {
cat("### Decision: Reject Null Hypothesis (H0)\n\n")
cat("There is sufficient evidence to conclude that there is a significant difference in agricultural values among Indonesia, Vietnam, and Cambodia.\n")
} else {
cat("### Decision: Fail to Reject Null Hypothesis (H0)\n\n")
cat("There is not enough evidence to conclude that there is a significant difference in agricultural values among Indonesia, Vietnam, and Cambodia.\n")}## ### Decision: Reject Null Hypothesis (H0)
##
## There is sufficient evidence to conclude that there is a significant difference in agricultural values among Indonesia, Vietnam, and Cambodia.### Decision: Reject Null Hypothesis (H0)
###There is sufficient evidence to conclude that there is a significant difference in agricultural values among Indonesia, Vietnam, and Cambodia.
# Print the table
print(result_table)## <table class="table" style="width: auto !important; margin-left: auto; margin-right: auto;">
## <caption>ANOVA
## Analysis of Variance for Agricultural Values in Indonesia, Vietnam, Cambodia</caption>
## <thead>
## <tr>
## <th style="text-align:left;"> </th>
## <th style="text-align:left;"> Df </th>
## <th style="text-align:center;"> Sum Sq </th>
## <th style="text-align:center;"> Mean Sq </th>
## <th style="text-align:left;"> F value </th>
## <th style="text-align:center;"> Pr(>F) </th>
## </tr>
## </thead>
## <tbody>
## <tr>
## <td style="text-align:left;"> Country </td>
## <td style="text-align:left;"> 2 </td>
## <td style="text-align:center;"> 853.9650 </td>
## <td style="text-align:center;"> 426.982517 </td>
## <td style="text-align:left;"> 92.78633 </td>
## <td style="text-align:center;"> 0 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> Residuals </td>
## <td style="text-align:left;"> 27 </td>
## <td style="text-align:center;"> 124.2481 </td>
## <td style="text-align:center;"> 4.601782 </td>
## <td style="text-align:left;"> NA </td>
## <td style="text-align:center;"> NA </td>
## </tr>
## </tbody>
## </table>The code for drawing image :
data <- data.frame(
Country = rep(c("Indonesia", "Vietnam", "Cambodia"), each = 10),
Value_Added_Percentage = c(Indonesia_Agricultural, Vietnam_Agricultural, Cambodia_Agricultural))
# Calculate summary statistics
summary_stats <- data %>%
group_by(Country) %>%
summarize(Median = median(Value_Added_Percentage),
Mean = mean(Value_Added_Percentage),
SD = sd(Value_Added_Percentage))
# Create a customized theme
custom_theme <- theme_minimal() +
theme(
plot.title = element_text(size = 16, face = "bold"),
axis.title = element_text(size = 14),
axis.text = element_text(size = 12),
axis.text.x = element_text(angle = 45, hjust = 1) )
# Create a boxplot with labels and customization
boxplot_plot <- ggplot(data, aes(x = Country, y = Value_Added_Percentage)) +
geom_boxplot(fill = "lightblue", color = "darkblue", alpha = 0.7) +
geom_text(data = summary_stats, aes(x = Country, y = Mean, label = paste("Mean: ", round(Mean, 2))),
vjust = -0.7, size = 4, color = "darkred") +
geom_text(data = summary_stats, aes(x = Country, y = Median, label = paste("Median: ", round(Median, 2))),
vjust = 1.2, size = 4, color = "darkgreen") +
labs(title = "Boxplot of Agricultural Value Added by Country",
x = "Country", y = "Value Added Percentage") +
custom_theme
# Print the boxplot
print(boxplot_plot)
In conclusion:
Based on the ANOVA analysis of the agricultural value-added data in Indonesia, Vietnam, and Cambodia, the results indicate significant differences in the mean values among these countries. The obtained low p-value (p < 0.05) from the ANOVA suggests sufficient evidence to reject the null hypothesis, indicating that there is a statistically significant difference in agricultural values among at least two of the three countries.
Decision:
- Reject Null Hypothesis (H0):There is sufficient evidence to conclude that there is a significant difference in mean agricultural values among Indonesia, Vietnam, and Cambodia.
Observations:
- The results suggest that agricultural value-added varies significantly among the countries, providing a basis for further detailed investigations to understand the factors contributing to this difference.
3. Cambodia debt analysis
1. Methods
Cambodia is a country that’s growing and getting more investments from the foreign. However, if we look at the total debt over the last 10 years, we see it’s been going up. In the further analysis section, we’ll use some statistical tools like variance, quantiles, and percentiles to understand more about this situation. By doing this, we want to figure out important things about how Cambodia’s money situation has been changing.
The Variance formulas:
(a) Calculate the average value (mean) of the data
\[ \bar x = \frac1n \sum_{i=1}^{x} x_i \]
n: amount of data (years)
\(x_i\): data value in year i
(b) Variance formular:
\[ \sigma^2 = \frac1n \sum_{i=1}^{n} (x_i - \bar x)^2 \]
\(\sigma^2\): a Variance
\(x_i\): data value in year i
\(\bar x\): the average value that we calculate in the first formula
(c) The Percentiles formulas:
Absolute percentiles:
\[ L - S \]
L: the maximum
S: the minimum
Percentile coefficient:
\[ \frac{L-S}{L+S} \] Quartiles:
- The first quartile formula:
\[ Q1 = 25 * \frac{n+1}{100} \]
- The second quartile (the median value ) formula:
\[ Q2 = \frac{n+1}{2} \]
- The third quartile formula:
\[ Q3 = 75 * \frac{n+1}{100} \]
2. Analysis.
We use the Variance to calculate the total debt service of Cambodia from 2013 to 2021.
We apply the formulas:
| Country name | Country code | Series name | Series code | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 | 2022 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 68 | Philippines | PHL | Total debt service (% of exports of goods, services and primary income) | DT.TDS.DECT.EX.ZS | 7.715905391 | 8.962418562 | 12.93609855 | 13.1673522 | 11.52756385 | 8.607486991 | 9.816799961 | 10.22529233 | 12.2404301 | .. |
- First calculate the average value of Cambodia’s total debt service:
\[ \bar{x} = \frac19 (5.71+5.36+5.06+5.14+6.24+6.73+6.95+7.42+10.66)=6.58 \] - Move to calculate the variance
\[ \sigma^2 = \frac19 [(5.71-x)^2 +(5.36-x)^2 +(5.06-x)^2 +(5.14-x)^2 +(6.24-x)^2 +(6.73-x)^2 +(6.95-x)^2 +(7.42-x)^2 +(10.66-x)^2] = 6.58 \]
- Move to calculate the Percentile coefficient:
First, we determine the maximum value (L) and the minimum value (S) in the data. In Cambodia’s data, we got L is 10.66 adn S is 5.06
We apply the formula:
\[ Percentile~coefficient= \frac{10.66-5.06}{10.66+5.06}=0.35 \]
- Move to calculate Quartile:
\[ Q1 = 25*\frac{n+1}{100} = 25*\frac{9+1}{100}=\frac52=2.5 \]
So, the first quartile position is at the second observation. Since this is not a whole number, you would take the averag of the second and third observations:
\[ Q1 = \frac{5.14+5.36}2 = 5.25 \]
\[ Q2 = \frac{n+1}2 = \frac{9+1}2 = 5 \]
So, the second quartile position is at h seventh observation, which is the median value in this case:
\[ Q2 = \frac{6.24 + 6.73}2 = 6.48 \]
\[ Q3 = 75*\frac{n+1}{100} = 75*\frac{9+1}{100} = \frac{15}2 = 7.5 \]
So, the third quartile position is at the seventh observation. Since this is not a whole number, you would take the average of the seventh and eighth observations:
\[ Q3 = \frac{6.95+7.42}2 = 7.19 \]
Interpret the quartiles
Q1 (5.31): 25% of the observations fall below this value. It represents the lower end of the distribution of the debt service percentage. For example, 25% of the years had a debt service percentage below 5.31%.
Q2 (6.48): 50% of the observations fall below this value. This is the median and represents the middle point of the distribution. In this case, it’s the median debt service percentage for the specified period.
Q3 (7.19): 75% of the observations fall below this value. It represents the upper end of the distribution of the debt service percentage. For instance, 75% of the years had a debt service percentage below 7.19%.