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[inequality] Update exercise 3 (#498)
* [inequality] Update exercise 3 Hi Matt @mmcky , I have updated the exercise 3 of the inequality lecture using your code in #410 and add the simulation part below your solution. What do you think about this version of the solution? Best, Longye * Update inequality.md Hi Matt, I have updated the solution and in the main text by adding ` %%time`. What do you think about this comparison? * Update inequality.md add labels to the main text gini coefficient code. * Update inequality.md * add data.ipynb and delete to csv Hi Matt, I have added the data.ipynb to the folder and I think it contains sufficient code to save the data. I have also modified the contain to deal with the saving and call issues related to the csv. What do you think about these changes? Best, Longye * remove skip-execution code as it is not compatible with google collab * test the problem This commit is to test whether the problem is due to this code. * Revert "test the problem" This reverts commit 395657e. * test google colab RAM this commit is to test whether the crash is led by the * change link to notebook on github * update_inequality_exercise Hi Matt, This commit select 3000 random sample from the original dataset. Best, Longye * update year in the text update year in the text --------- Co-authored-by: mmcky <mamckay@gmail.com>
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‎lectures/_static/lecture_specific/inequality/data.ipynb‎

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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Requirement already satisfied: wbgapi in /Users/longye/anaconda3/lib/python3.10/site-packages (1.0.12)\n",
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]
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}
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],
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"source": [
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"!pip install wbgapi plotly\n",
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"\n",
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"import pandas as pd\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"import random as rd\n",
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"import wbgapi as wb\n",
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"import plotly.express as px\n",
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"\n",
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"url = 'https://media.githubusercontent.com/media/QuantEcon/high_dim_data/main/SCF_plus/SCF_plus_mini.csv'\n",
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"df = pd.read_csv(url)\n",
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"df_income_wealth = df.dropna()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "9630a07a-fce5-474e-92af-104e67e82be5",
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"metadata": {},
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"outputs": [
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Requirement already satisfied: quantecon in /Users/longye/anaconda3/lib/python3.10/site-packages (0.7.1)\n",
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]
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}
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],
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"source": [
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"!pip install quantecon\n",
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"import quantecon as qe\n",
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"\n",
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"varlist = ['n_wealth', # net wealth \n",
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" 't_income', # total income\n",
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" 'l_income'] # labor income\n",
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"\n",
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"df = df_income_wealth\n",
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"years = df.year.unique()\n",
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"\n",
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"# create lists to store Gini for each inequality measure\n",
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"results = {}\n",
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"\n",
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"for var in varlist:\n",
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" # create lists to store Gini\n",
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" gini_yr = []\n",
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" for year in years:\n",
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" # repeat the observations according to their weights\n",
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" counts = list(round(df[df['year'] == year]['weights'] ))\n",
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" y = df[df['year'] == year][var].repeat(counts)\n",
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" y = np.asarray(y)\n",
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" \n",
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" rd.shuffle(y) # shuffle the sequence\n",
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" \n",
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" # calculate and store Gini\n",
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" gini = qe.gini_coefficient(y)\n",
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" gini_yr.append(gini)\n",
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" \n",
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" results[var] = gini_yr\n",
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"\n",
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"# Convert to DataFrame\n",
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"results = pd.DataFrame(results, index=years)\n",
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"results.to_csv(\"usa-gini-nwealth-tincome-lincome.csv\", index_label='year')"
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]
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"nbconvert_exporter": "python",
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"version": "3.10.12"
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‎lectures/_static/lecture_specific/inequality/usa-gini-nwealth-tincome-lincome.csv‎

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year,n_wealth,t_income,l_income
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1950,0.8257332034366338,0.44248654139458626,0.5342948198773412
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1953,0.8059487586599329,0.4264544060935945,0.5158978980963702
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1956,0.8121790488050616,0.44426942873399283,0.5349293526208142
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1959,0.795206874163792,0.43749348077061573,0.5213985948309416
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1962,0.8086945076579359,0.4435843103853645,0.5345127915054341
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1965,0.7904149225687935,0.43763715466663444,0.7487860020887753
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1968,0.7982885066993497,0.4208620794438902,0.5242396427381545
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1971,0.7911574835420259,0.4233344246090255,0.5576454812313466
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1977,0.7571418922185215,0.46187678800902543,0.5704448110072049
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1983,0.7494335400643013,0.439345618464469,0.5662220844385915
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1989,0.7715705301674302,0.5115249581654197,0.601399568747142
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1992,0.7508126614055308,0.4740650672076798,0.5983592657979563
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1995,0.7569492388110265,0.48965523558400603,0.5969779516716903
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1998,0.7603291991801185,0.49117441585168614,0.5774462841723305
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2001,0.7816118750507056,0.5239092994681135,0.6042739644967272
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2004,0.7700355469522361,0.4884350383903255,0.5981432201792727
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2007,0.7821413776486978,0.5197156312086187,0.626345219575322
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2010,0.8250825295193438,0.5195972120145615,0.6453653328291903
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2013,0.8227698931835303,0.531400174984336,0.6498682917772644
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2016,0.8342975903562234,0.5541400068900825,0.6706846793375284
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1950,0.8257332034366366,0.44248654139458743,0.534294819877344
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1953,0.805948758659935,0.4264544060935942,0.5158978980963682
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1956,0.8121790488050612,0.44426942873399367,0.5349293526208106
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1959,0.7952068741637912,0.43749348077061534,0.5213985948309414
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1962,0.8086945076579386,0.44358431038536356,0.5345127915054446
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1965,0.7904149225687949,0.4376371546666344,0.7487860020887701
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1968,0.7982885066993503,0.4208620794438885,0.5242396427381534
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1971,0.7911574835420282,0.4233344246090255,0.5576454812313462
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1977,0.7571418922185215,0.46187678800902554,0.57044481100722
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1983,0.749433540064301,0.4393456184644682,0.5662220844385925
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1989,0.7715705301674285,0.5115249581654115,0.6013995687471289
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1992,0.7508126614055305,0.4740650672076754,0.5983592657979544
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1995,0.7569492388110274,0.4896552355840001,0.5969779516717039
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1998,0.7603291991801172,0.49117441585168525,0.5774462841723346
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2001,0.781611875050703,0.523909299468113,0.6042739644967232
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2004,0.7700355469522372,0.48843503839032354,0.5981432201792916
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2007,0.782141377648698,0.5197156312086207,0.6263452195753227
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2010,0.825082529519342,0.5195972120145641,0.6453653328291843
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2013,0.8227698931835299,0.5314001749843426,0.6498682917772886
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2016,0.8342975903562537,0.55414000689009,0.6706846793375292

‎lectures/inequality.md‎

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which is derived from the [Survey of Consumer Finances](https://en.wikipedia.org/wiki/Survey_of_Consumer_Finances) (SCF).
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```{code-cell} ipython3
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url = 'https://media.githubusercontent.com/media/QuantEcon/high_dim_data/main/SCF_plus/SCF_plus_mini.csv'
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url = 'https://github.com/QuantEcon/high_dim_data/raw/main/SCF_plus/SCF_plus_mini.csv'
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df = pd.read_csv(url)
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df_income_wealth = df.dropna()
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```
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The code below computes the Gini coefficient from a sample.
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(code:gini-coefficient)=
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```{code-cell} ipython3
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def gini_coefficient(y):
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distribution.
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```{code-cell} ipython3
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%%time
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k = 5
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σ_vals = np.linspace(0.2, 4, k)
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n = 2_000
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df_income_wealth.year.describe()
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```
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This code can be used to compute this information over the full dataset.
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[This notebook](https://github.com/QuantEcon/lecture-python-intro/tree/main/lectures/_static/lecture_specific/inequality/data.ipynb) can be used to compute this information over the full dataset.
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```{code-cell} ipython3
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:tags: [skip-execution, hide-input, hide-output]
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!pip install quantecon
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import quantecon as qe
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varlist = ['n_wealth', # net wealth
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't_income', # total income
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'l_income'] # labor income
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df = df_income_wealth
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# create lists to store Gini for each inequality measure
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results = {}
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for var in varlist:
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# create lists to store Gini
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gini_yr = []
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for year in years:
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# repeat the observations according to their weights
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counts = list(round(df[df['year'] == year]['weights'] ))
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y = df[df['year'] == year][var].repeat(counts)
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y = np.asarray(y)
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rd.shuffle(y) # shuffle the sequence
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# calculate and store Gini
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gini = qe.gini_coefficient(y)
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gini_yr.append(gini)
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results[var] = gini_yr
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# Convert to DataFrame
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results = pd.DataFrame(results, index=years)
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results.to_csv("_static/lecture_specific/inequality/usa-gini-nwealth-tincome-lincome.csv", index_label='year')
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```
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However, to speed up execution we will import a pre-computed dataset from the lecture repository.
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<!-- TODO: update from csv to github location -->
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```{code-cell} ipython3
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ginis = pd.read_csv("_static/lecture_specific/inequality/usa-gini-nwealth-tincome-lincome.csv", index_col='year')
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data_url = 'https://github.com/QuantEcon/lecture-python-intro/raw/main/lectures/_static/lecture_specific/inequality/usa-gini-nwealth-tincome-lincome.csv'
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ginis = pd.read_csv(data_url, index_col='year')
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ginis.head(n=5)
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```
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However, we will see below that not all advanced economies experienced similar growth of inequality.
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### Cross-country comparisons of income inequality
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Earlier in this lecture we used `wbgapi` to get Gini data across many countries
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```{solution-end}
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```
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```{exercise}
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:label: inequality_ex3
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The {ref}`code to compute the Gini coefficient is listed in the lecture above <code:gini-coefficient>`.
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This code uses loops to calculate the coefficient based on income or wealth data.
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This function can be re-written using vectorization which will greatly improve the computational efficiency when using `python`.
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Re-write the function `gini_coefficient` using `numpy` and vectorized code.
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You can compare the output of this new function with the one above, and note the speed differences.
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```
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```{solution-start} inequality_ex3
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:class: dropdown
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```
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Let's take a look at some raw data for the US that is stored in `df_income_wealth`
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```{code-cell} ipython3
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df_income_wealth.describe()
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```
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```{code-cell} ipython3
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df_income_wealth.head(n=4)
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```
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We will focus on wealth variable `n_wealth` to compute a Gini coefficient for the year 2016.
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```{code-cell} ipython3
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data = df_income_wealth[df_income_wealth.year == 2016].sample(3000, random_state=1)
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```
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```{code-cell} ipython3
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data.head(n=2)
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```
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We can first compute the Gini coefficient using the function defined in the lecture above.
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```{code-cell} ipython3
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gini_coefficient(data.n_wealth.values)
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```
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Now we can write a vectorized version using `numpy`
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```{code-cell} ipython3
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def gini(y):
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n = len(y)
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y_1 = np.reshape(y, (n, 1))
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y_2 = np.reshape(y, (1, n))
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g_sum = np.sum(np.abs(y_1 - y_2))
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return g_sum / (2 * n * np.sum(y))
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```
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```{code-cell} ipython3
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gini(data.n_wealth.values)
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```
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Let's simulate five populations by drawing from a lognormal distribution as before
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```{code-cell} ipython3
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k = 5
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σ_vals = np.linspace(0.2, 4, k)
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n = 2_000
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σ_vals = σ_vals.reshape((k,1))
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μ_vals = -σ_vals**2/2
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y_vals = np.exp(μ_vals + σ_vals*np.random.randn(n))
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```
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We can compute the Gini coefficient for these five populations using the vectorized function, the computation time is shown below:
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```{code-cell} ipython3
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%%time
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gini_coefficients =[]
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for i in range(k):
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gini_coefficients.append(gini(y_vals[i]))
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```
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This shows the vectorized function is much faster.
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This gives us the Gini coefficients for these five households.
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```{code-cell} ipython3
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gini_coefficients
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```
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```{solution-end}
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```
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