job-market-in-low-income-countries.ipynb

{"cells":[{"cell_type":"markdown","metadata":{},"source":["## Questions\n","\n","the focus is to obtain some specific data on skill migration among low income countries, and to predict the skill migration trends in each country for 2020 \n","\n","- list of countries classified as low income by the world bank\n","- which skill group category had positive migration in 2019 \n","- which industry and country had the most positive migration in 2019\n","- list of countries with positive skill migration in 2019\n","- skill migration in countries with more the 1k per 10k in 2019 vs 2015\n","- predict skill migration per 10k for 2020\n","- which country will have the most skill migration in skills that had positive migration in 2019 for 2020"]},{"cell_type":"code","execution_count":264,"metadata":{"trusted":true},"outputs":[],"source":["import pandas as pd\n","import matplotlib.pyplot as plt\n","import numpy as np\n","from statsmodels.tsa.api import Holt\n","\n","# supress annoying warning\n","import warnings\n","from statsmodels.tools.sm_exceptions import ConvergenceWarning\n","warnings.simplefilter('ignore', ConvergenceWarning)\n","\n","# set the size for all figures\n","plt.rcParams['figure.figsize'] = [14, 7]"]},{"cell_type":"code","execution_count":149,"metadata":{"_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","trusted":true},"outputs":[],"source":["# load in the dataset\n","talent_mg_skill = pd.read_excel(\"../input/linkedin-digital-data/public_use-talent-migration.xlsx\", sheet_name=\"Skill Migration\")"]},{"cell_type":"code","execution_count":93,"metadata":{"trusted":true},"outputs":[],"source":["# get countries with low income\n","countries_low = talent_mg_skill[talent_mg_skill[\"wb_income\"] == \"Low income\"]"]},{"cell_type":"code","execution_count":94,"metadata":{"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["list of countries classified as low income:\n","Afghanistan\n","Benin\n","Burkina Faso\n","Congo, Dem. Rep.\n","Ethiopia\n","Haiti\n","Madagascar\n","Malawi\n","Mali\n","Mozambique\n","Nepal\n","Rwanda\n","Senegal\n","Syrian Arab Republic\n","Tanzania\n","Togo\n","Uganda\n","Yemen, Rep.\n","Zimbabwe\n"]}],"source":["print(\"list of countries classified as low income:\")\n","for country in countries_low[\"country_name\"].unique():\n","    print(country)"]},{"cell_type":"code","execution_count":74,"metadata":{"trusted":true},"outputs":[],"source":["# get industries which had positive migration in 2019\n","pos_2019 = countries_low[countries_low[\"net_per_10K_2019\"] > 0]"]},{"cell_type":"code","execution_count":90,"metadata":{"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["list of skill group with positive migration in 2019:\n","Business Skills\n","Specialized Industry Skills\n","Tech Skills\n","Disruptive Tech Skills\n","Soft Skills\n"]}],"source":["print(\"list of skill group with positive migration in 2019:\")\n","for group in pos_2019[\"skill_group_category\"].unique():\n","    print(group)\n","    "]},{"cell_type":"code","execution_count":86,"metadata":{"trusted":true},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>country_code</th>\n","      <th>country_name</th>\n","      <th>wb_income</th>\n","      <th>wb_region</th>\n","      <th>skill_group_id</th>\n","      <th>skill_group_category</th>\n","      <th>skill_group_name</th>\n","      <th>net_per_10K_2015</th>\n","      <th>net_per_10K_2016</th>\n","      <th>net_per_10K_2017</th>\n","      <th>net_per_10K_2018</th>\n","      <th>net_per_10K_2019</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>107</th>\n","      <td>af</td>\n","      <td>Afghanistan</td>\n","      <td>Low income</td>\n","      <td>South Asia</td>\n","      <td>827</td>\n","      <td>Specialized Industry Skills</td>\n","      <td>Automotive</td>\n","      <td>-726.39</td>\n","      <td>-99.97</td>\n","      <td>-479.23</td>\n","      <td>-126.45</td>\n","      <td>518.73</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["    country_code country_name   wb_income   wb_region  skill_group_id  \\\n","107           af  Afghanistan  Low income  South Asia             827   \n","\n","            skill_group_category skill_group_name  net_per_10K_2015  \\\n","107  Specialized Industry Skills       Automotive           -726.39   \n","\n","     net_per_10K_2016  net_per_10K_2017  net_per_10K_2018  net_per_10K_2019  \n","107            -99.97           -479.23           -126.45            518.73  "]},"execution_count":86,"metadata":{},"output_type":"execute_result"}],"source":["pos_2019[pos_2019[\"net_per_10K_2019\"] == pos_2019[\"net_per_10K_2019\"].max()]"]},{"cell_type":"markdown","metadata":{},"source":["the automotive industry in afghanstan had the most growth in 2019 compared to other skill groups, from this we can also infer that the Specialized Industry Skills category had the most growth of all groups in 2019"]},{"cell_type":"code","execution_count":268,"metadata":{"trusted":true},"outputs":[],"source":["# group rows by country\n","country_mg_2019 = pos_2019.groupby(\"country_name\").sum()"]},{"cell_type":"code","execution_count":269,"metadata":{"trusted":true},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>skill_group_id</th>\n","      <th>net_per_10K_2015</th>\n","      <th>net_per_10K_2016</th>\n","      <th>net_per_10K_2017</th>\n","      <th>net_per_10K_2018</th>\n","      <th>net_per_10K_2019</th>\n","    </tr>\n","    <tr>\n","      <th>country_name</th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>Afghanistan</th>\n","      <td>10549</td>\n","      <td>-2487.43</td>\n","      <td>-1295.07</td>\n","      <td>-1406.04</td>\n","      <td>284.92</td>\n","      <td>585.78</td>\n","    </tr>\n","    <tr>\n","      <th>Burkina Faso</th>\n","      <td>4349</td>\n","      <td>-303.68</td>\n","      <td>475.66</td>\n","      <td>-63.77</td>\n","      <td>-96.72</td>\n","      <td>140.30</td>\n","    </tr>\n","    <tr>\n","      <th>Congo, Dem. Rep.</th>\n","      <td>267110</td>\n","      <td>15921.11</td>\n","      <td>5191.17</td>\n","      <td>-608.96</td>\n","      <td>569.27</td>\n","      <td>4348.39</td>\n","    </tr>\n","    <tr>\n","      <th>Ethiopia</th>\n","      <td>43143</td>\n","      <td>653.31</td>\n","      <td>-207.12</td>\n","      <td>-1726.96</td>\n","      <td>-1796.52</td>\n","      <td>554.37</td>\n","    </tr>\n","    <tr>\n","      <th>Madagascar</th>\n","      <td>34054</td>\n","      <td>790.39</td>\n","      <td>-220.53</td>\n","      <td>-1227.28</td>\n","      <td>-703.36</td>\n","      <td>322.82</td>\n","    </tr>\n","    <tr>\n","      <th>Malawi</th>\n","      <td>112564</td>\n","      <td>3260.79</td>\n","      <td>4743.21</td>\n","      <td>-208.35</td>\n","      <td>-597.27</td>\n","      <td>618.25</td>\n","    </tr>\n","    <tr>\n","      <th>Mali</th>\n","      <td>142676</td>\n","      <td>9017.98</td>\n","      <td>5482.09</td>\n","      <td>2333.82</td>\n","      <td>532.32</td>\n","      <td>2081.85</td>\n","    </tr>\n","    <tr>\n","      <th>Mozambique</th>\n","      <td>237709</td>\n","      <td>5390.04</td>\n","      <td>-5692.43</td>\n","      <td>-6644.60</td>\n","      <td>-2357.83</td>\n","      <td>4048.02</td>\n","    </tr>\n","    <tr>\n","      <th>Nepal</th>\n","      <td>36235</td>\n","      <td>417.14</td>\n","      <td>288.91</td>\n","      <td>-178.06</td>\n","      <td>-101.40</td>\n","      <td>597.68</td>\n","    </tr>\n","    <tr>\n","      <th>Rwanda</th>\n","      <td>142192</td>\n","      <td>1884.73</td>\n","      <td>1484.01</td>\n","      <td>919.05</td>\n","      <td>-2263.96</td>\n","      <td>3187.22</td>\n","    </tr>\n","    <tr>\n","      <th>Senegal</th>\n","      <td>203091</td>\n","      <td>3569.81</td>\n","      <td>3784.49</td>\n","      <td>-181.22</td>\n","      <td>224.01</td>\n","      <td>5335.45</td>\n","    </tr>\n","    <tr>\n","      <th>Syrian Arab Republic</th>\n","      <td>133</td>\n","      <td>-513.09</td>\n","      <td>-759.56</td>\n","      <td>-457.79</td>\n","      <td>-243.59</td>\n","      <td>9.12</td>\n","    </tr>\n","    <tr>\n","      <th>Tanzania</th>\n","      <td>83596</td>\n","      <td>453.32</td>\n","      <td>556.95</td>\n","      <td>-365.45</td>\n","      <td>-671.36</td>\n","      <td>284.23</td>\n","    </tr>\n","    <tr>\n","      <th>Togo</th>\n","      <td>559</td>\n","      <td>-113.94</td>\n","      <td>190.67</td>\n","      <td>-234.12</td>\n","      <td>-283.07</td>\n","      <td>9.87</td>\n","    </tr>\n","    <tr>\n","      <th>Uganda</th>\n","      <td>9268</td>\n","      <td>107.40</td>\n","      <td>125.65</td>\n","      <td>-252.66</td>\n","      <td>6.44</td>\n","      <td>261.38</td>\n","    </tr>\n","    <tr>\n","      <th>Yemen, Rep.</th>\n","      <td>9590</td>\n","      <td>-1586.55</td>\n","      <td>-1486.05</td>\n","      <td>-522.68</td>\n","      <td>164.03</td>\n","      <td>324.38</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                      skill_group_id  net_per_10K_2015  net_per_10K_2016  \\\n","country_name                                                               \n","Afghanistan                    10549          -2487.43          -1295.07   \n","Burkina Faso                    4349           -303.68            475.66   \n","Congo, Dem. Rep.              267110          15921.11           5191.17   \n","Ethiopia                       43143            653.31           -207.12   \n","Madagascar                     34054            790.39           -220.53   \n","Malawi                        112564           3260.79           4743.21   \n","Mali                          142676           9017.98           5482.09   \n","Mozambique                    237709           5390.04          -5692.43   \n","Nepal                          36235            417.14            288.91   \n","Rwanda                        142192           1884.73           1484.01   \n","Senegal                       203091           3569.81           3784.49   \n","Syrian Arab Republic             133           -513.09           -759.56   \n","Tanzania                       83596            453.32            556.95   \n","Togo                             559           -113.94            190.67   \n","Uganda                          9268            107.40            125.65   \n","Yemen, Rep.                     9590          -1586.55          -1486.05   \n","\n","                      net_per_10K_2017  net_per_10K_2018  net_per_10K_2019  \n","country_name                                                                \n","Afghanistan                   -1406.04            284.92            585.78  \n","Burkina Faso                    -63.77            -96.72            140.30  \n","Congo, Dem. Rep.               -608.96            569.27           4348.39  \n","Ethiopia                      -1726.96          -1796.52            554.37  \n","Madagascar                    -1227.28           -703.36            322.82  \n","Malawi                         -208.35           -597.27            618.25  \n","Mali                           2333.82            532.32           2081.85  \n","Mozambique                    -6644.60          -2357.83           4048.02  \n","Nepal                          -178.06           -101.40            597.68  \n","Rwanda                          919.05          -2263.96           3187.22  \n","Senegal                        -181.22            224.01           5335.45  \n","Syrian Arab Republic           -457.79           -243.59              9.12  \n","Tanzania                       -365.45           -671.36            284.23  \n","Togo                           -234.12           -283.07              9.87  \n","Uganda                         -252.66              6.44            261.38  \n","Yemen, Rep.                    -522.68            164.03            324.38  "]},"execution_count":269,"metadata":{},"output_type":"execute_result"}],"source":["# lets take a look at each country in numbers\n","country_mg_2019"]},{"cell_type":"code","execution_count":270,"metadata":{"trusted":true},"outputs":[{"data":{"text/plain":["country_name\n","Afghanistan              585.78\n","Burkina Faso             140.30\n","Congo, Dem. Rep.        4348.39\n","Ethiopia                 554.37\n","Madagascar               322.82\n","Malawi                   618.25\n","Mali                    2081.85\n","Mozambique              4048.02\n","Nepal                    597.68\n","Rwanda                  3187.22\n","Senegal                 5335.45\n","Syrian Arab Republic       9.12\n","Tanzania                 284.23\n","Togo                       9.87\n","Uganda                   261.38\n","Yemen, Rep.              324.38\n","Name: net_per_10K_2019, dtype: float64"]},"execution_count":270,"metadata":{},"output_type":"execute_result"}],"source":["country_mg_2019[\"net_per_10K_2019\"]"]},{"cell_type":"markdown","metadata":{},"source":["lets plot countries which have more than 1000 migration on every 10k"]},{"cell_type":"code","execution_count":271,"metadata":{"trusted":true},"outputs":[{"data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7fde87f1d490>"]},"execution_count":271,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 1008x504 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["country_mg_2019[country_mg_2019[\"net_per_10K_2019\"] > 1000].plot(y=[\"net_per_10K_2019\"], style=\".\")"]},{"cell_type":"markdown","metadata":{},"source":["we can see that sengal had the most skill migration in 2019 compared to other countries\n","now lets compare these numbers to 2015 for example"]},{"cell_type":"code","execution_count":148,"metadata":{"trusted":true},"outputs":[{"data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7fde8f0aef10>"]},"execution_count":148,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 1008x504 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["country_mg_2019[country_mg_2019[\"net_per_10K_2019\"] > 1000].plot(y=[\"net_per_10K_2019\", \"net_per_10K_2015\"], style=\".\")"]},{"cell_type":"markdown","metadata":{},"source":["skill migration in countries with more than 1000 per 10k skill migration has drastically changes compared to 2015, for example in the Congo and Mali people are far less likely to migrate industries in 2019 compared to 2015 this could indicate a stability in the job market and that people are now settling to a specific field, in contrast more people are migrating to other industries in 2019 compared to 2015 in Senegal indicating a shift in the job market"]},{"cell_type":"code","execution_count":218,"metadata":{"trusted":true},"outputs":[],"source":["''' \n","Holt's (Method) Exponential Smoothing for predicting next value based on previous years values,\n","also known as forecasting\n","'''\n","\n","def hes_predict(train):\n","    model = Holt(train)\n","    model_fit = model.fit()\n","    fcast = model_fit.forecast()\n","    return fcast[0]"]},{"cell_type":"code","execution_count":255,"metadata":{"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Afghanistan skill migration per 10k for 2020 = 566.64\n","Burkina Faso skill migration per 10k for 2020 = 69.35\n","Congo, Dem. Rep. skill migration per 10k for 2020 = 5297.78\n","Ethiopia skill migration per 10k for 2020 = 2904.68\n","Madagascar skill migration per 10k for 2020 = 1349.0\n","Malawi skill migration per 10k for 2020 = 1700.94\n","Mali skill migration per 10k for 2020 = -893.72\n","Mozambique skill migration per 10k for 2020 = 9463.79\n","Nepal skill migration per 10k for 2020 = 359.15\n","Rwanda skill migration per 10k for 2020 = 1042.21\n","Senegal skill migration per 10k for 2020 = 2587.35\n","Syrian Arab Republic skill migration per 10k for 2020 = 281.75\n","Tanzania skill migration per 10k for 2020 = 51.54\n","Togo skill migration per 10k for 2020 = 0.1\n","Uganda skill migration per 10k for 2020 = 101.82\n","Yemen, Rep. skill migration per 10k for 2020 = 389.26\n"]}],"source":["countries_2020 = pd.DataFrame(columns = [\"net_per_10k_2020\"], index = country_mg_2019.index)\n","\n","for country in country_mg_2019.index:\n","    \n","    # take previous numbers for country as model input\n","    train = country_mg_2019.drop(\"skill_group_id\", axis=1)[country_mg_2019.index == f\"{country}\"].to_numpy()\n","    \n","    # get model prediciton and round to 2 decimal places\n","    result = round(hes_predict(train[0]), 2)\n","    \n","    # save model prediction to dataframe \n","    countries_2020[\"net_per_10k_2020\"][f\"{country}\"] = result\n","    \n","    # print prediction results\n","    print(f\"{country} skill migration per 10k for 2020 = {result}\\n\")\n","    "]},{"cell_type":"code","execution_count":262,"metadata":{"trusted":true},"outputs":[{"data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7fde87fa2210>"]},"execution_count":262,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 1008x504 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["# again plot countries where its more than 1k\n","countries_2020[countries_2020[\"net_per_10k_2020\"] > 1000].plot(style=\".\")"]},{"cell_type":"markdown","metadata":{},"source":["from the model predicitions we can see that the job market will have a major shift in 2020 in Mozambique, with almost all 10k shifting between skills, previous year's leading to a very unstable job market high, last years highest migration was in Senegal with over 5k migrants per 10k, for 2020 the market seems to be stabilizing and people are settling into jobs leading to less than 3k migrants."]},{"cell_type":"markdown","metadata":{},"source":["in conclusion. the market has changed significantly for the past years in low income countries, some of the countries had positive skill migration meaning an unstable market where workers do not settle for a specific field, while other countries had negative migration compared to previous years leading to a more stable market which can be interpreted as a good measure of market stability and quality of work for those workers"]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.7.6"}},"nbformat":4,"nbformat_minor":4}